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# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import tempfile
import unittest
from transformers import ProphetNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
ProphetNetDecoder,
ProphetNetEncoder,
ProphetNetForCausalLM,
ProphetNetForConditionalGeneration,
ProphetNetModel,
ProphetNetTokenizer,
)
from transformers.modeling_outputs import BaseModelOutput
class ProphetNetModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
hidden_size=16,
encoder_seq_length=7,
decoder_seq_length=9,
# For common tests
is_training=True,
use_attention_mask=True,
use_labels=True,
decoder_start_token_id=0,
encoder_ffn_dim=32,
num_encoder_layers=2,
num_encoder_attention_heads=4,
decoder_ffn_dim=32,
num_decoder_layers=2,
num_decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
ngram=2,
num_buckets=32,
relative_max_distance=128,
disable_ngram_loss=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_decoder_layers
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_ffn_dim = encoder_ffn_dim
self.num_attention_heads = num_decoder_attention_heads
self.num_encoder_attention_heads = num_encoder_attention_heads
self.num_decoder_attention_heads = num_decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.ngram = ngram
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.disable_ngram_loss = disable_ngram_loss
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 7
self.num_hidden_states_types = 3 # encoder, decoder_main, decoder_ngram
self.decoder_attention_idx = 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
decoder_attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = self.get_config()
return (
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
)
def get_config(self):
return ProphetNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_encoder_layers=self.num_encoder_layers,
num_decoder_layers=self.num_decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_ffn_dim=self.encoder_ffn_dim,
num_encoder_attention_heads=self.num_encoder_attention_heads,
num_decoder_attention_heads=self.num_decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
ngram=self.ngram,
num_buckets=self.num_buckets,
relative_max_distance=self.relative_max_distance,
disable_ngram_loss=self.disable_ngram_loss,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
return (
config,
decoder_input_ids,
decoder_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
lm_labels,
)
def check_prepare_lm_labels_via_shift_left(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetModel(config=config)
model.to(torch_device)
model.eval()
# make sure that lm_labels are correctly padded from the right
lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
# add casaul pad token mask
triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
decoder_input_ids = model._shift_right(lm_labels)
for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
# first item
self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
if i < decoder_input_ids_slice.shape[-1]:
if i < decoder_input_ids.shape[-1] - 1:
# items before diagonal
self.parent.assertListEqual(
decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
)
# pad items after diagonal
if i < decoder_input_ids.shape[-1] - 2:
self.parent.assertListEqual(
decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
)
else:
# all items after square
self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
def create_and_check_model(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
decoder_output = result.last_hidden_state
decoder_past = result.past_key_values
encoder_output = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size))
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(decoder_past), config.num_decoder_layers)
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0]), 4) # cross-attention + uni-directional self-attention
def create_and_check_with_lm_head(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval()
outputs = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
labels=lm_labels,
)
self.parent.assertEqual(len(outputs), 5)
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
self.parent.assertEqual(outputs["loss"].size(), ())
def create_and_check_causal_lm_decoder(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForCausalLM(config=config).to(torch_device).eval()
outputs = model(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
labels=lm_labels,
)
self.parent.assertEqual(len(outputs), 4)
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
self.parent.assertEqual(outputs["loss"].size(), ())
def create_and_check_generate_with_past_key_value_states(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval()
torch.manual_seed(0)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
)
torch.manual_seed(0)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
def create_and_check_decoder_generate_with_past_key_value_states(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForCausalLM(config=config).to(torch_device).eval()
torch.manual_seed(0)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=10, do_sample=True, use_cache=False
)
torch.manual_seed(0)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=10, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
def create_and_check_model_fp16_forward(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetModel(config=config).to(torch_device).half().eval()
output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_encoder_decoder_shared_weights(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
for model_class in [ProphetNetModel, ProphetNetForConditionalGeneration]:
torch.manual_seed(0)
model = model_class(config=config).to(torch_device).eval()
# load state dict copies weights but does not tie them
if model_class == ProphetNetForConditionalGeneration:
model.prophetnet.encoder.load_state_dict(model.prophetnet.decoder.state_dict(), strict=False)
else:
model.encoder.load_state_dict(model.decoder.state_dict(), strict=False)
torch.manual_seed(0)
tied_config = copy.deepcopy(config)
tied_config.tie_encoder_decoder = True
tied_model = model_class(config=tied_config).to(torch_device).eval()
model_result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
tied_model_result = tied_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
# check that models has less parameters
self.parent.assertLess(
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
)
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
# check that outputs are equal
self.parent.assertTrue(
torch.allclose(
model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4
)
)
# check that outputs after saving and loading are equal
with tempfile.TemporaryDirectory() as tmpdirname:
tied_model.save_pretrained(tmpdirname)
tied_model = model_class.from_pretrained(tmpdirname)
tied_model.to(torch_device)
tied_model.eval()
# check that models has less parameters
self.parent.assertLess(
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
)
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
tied_model_result = tied_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
# check that outputs are equal
self.parent.assertTrue(
torch.allclose(
model_result[0][0, :, random_slice_idx],
tied_model_result[0][0, :, random_slice_idx],
atol=1e-4,
)
)
def check_fast_integration(
self,
config,
*args,
):
input_ids = torch.tensor([[7, 4, 78, 0, 24, 52, 43]], device=torch_device, dtype=torch.long)
decoder_input_ids = torch.tensor([[12, 62, 25, 11, 47, 15, 14]], device=torch_device, dtype=torch.long)
attention_mask = torch.tensor([[1, 1, 1, 0, 1, 0, 0]], device=torch_device, dtype=torch.long)
decoder_attention_mask = torch.tensor([[1, 1, 1, 0, 0, 1, 0]], device=torch_device, dtype=torch.long)
lm_labels = torch.tensor([[62, 25, 11, 47, 15, 14, 24]], device=torch_device, dtype=torch.long)
torch.manual_seed(0)
config.ngram = 4
model = ProphetNetForConditionalGeneration(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
labels=lm_labels,
)
self.parent.assertTrue(torch.allclose(result.loss, torch.tensor(4.5892, device=torch_device), atol=1e-3))
expected_logit_slice = torch.tensor(
[-0.0184, 0.0758, -0.0543, -0.0093, 0.0050, -0.0660, -0.1453], device=torch_device
)
self.parent.assertTrue(torch.allclose(result.logits[0, :, 1], expected_logit_slice, atol=1e-3))
def check_model_with_attn_mask(self, config, input_ids, decoder_input_ids, *args):
model = ProphetNetModel(config=config)
model.to(torch_device)
model.eval()
outputs_no_mask = model(input_ids=input_ids[:, :5], decoder_input_ids=decoder_input_ids[:, :5])
attention_mask = torch.ones_like(input_ids)
decoder_attention_mask = torch.ones_like(decoder_input_ids)
attention_mask[:, 5:] = 0
outputs_with_mask = model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
)
# check encoder
self.parent.assertTrue(
torch.allclose(
outputs_no_mask.encoder_last_hidden_state[0, :, 0],
outputs_with_mask.encoder_last_hidden_state[0, :5, 0],
atol=1e-3,
)
)
# check decoder
# main stream
self.parent.assertTrue(
torch.allclose(
outputs_no_mask.last_hidden_state[0, :, 0], outputs_with_mask.last_hidden_state[0, :5, 0], atol=1e-3
)
)
# predict stream
self.parent.assertTrue(
torch.allclose(
outputs_no_mask.last_hidden_state_ngram[0, :5, 0],
outputs_with_mask.last_hidden_state_ngram[0, :5, 0],
atol=1e-2,
)
)
def check_causal_lm_from_pretrained(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, *args
):
model = ProphetNetForConditionalGeneration(config).to(torch_device).eval()
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
decoder = ProphetNetForCausalLM.from_pretrained(tmp_dirname).to(torch_device)
encoder_hidden_states = model.prophetnet.encoder(input_ids).last_hidden_state
model_outputs = model(
encoder_outputs=BaseModelOutput(last_hidden_state=encoder_hidden_states),
decoder_input_ids=decoder_input_ids,
)
dec_outputs = decoder(encoder_hidden_states=encoder_hidden_states, input_ids=decoder_input_ids)
self.parent.assertTrue(
torch.allclose(
model_outputs.logits[0, :5],
dec_outputs.logits[0, :5],
atol=1e-3,
)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"use_cache": False,
}
return config, inputs_dict
class ProphetNetStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
hidden_size=16,
encoder_seq_length=7,
decoder_seq_length=7,
# For common tests
is_training=True,
is_decoder=True,
use_attention_mask=True,
add_cross_attention=False,
use_cache=False,
use_labels=True,
decoder_start_token_id=0,
encoder_ffn_dim=32,
num_encoder_layers=2,
num_encoder_attention_heads=4,
decoder_ffn_dim=32,
num_decoder_layers=2,
num_decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
ngram=2,
num_buckets=32,
relative_max_distance=128,
disable_ngram_loss=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_decoder_layers
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_ffn_dim = encoder_ffn_dim
self.num_attention_heads = num_decoder_attention_heads
self.num_encoder_attention_heads = num_encoder_attention_heads
self.num_decoder_attention_heads = num_decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.ngram = ngram
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.use_cache = use_cache
self.disable_ngram_loss = disable_ngram_loss
self.max_position_embeddings = max_position_embeddings
self.add_cross_attention = add_cross_attention
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.num_hidden_states_types = 2 # decoder_main, decoder_ngram
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
config = ProphetNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_encoder_layers=self.num_encoder_layers,
num_decoder_layers=self.num_decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_ffn_dim=self.encoder_ffn_dim,
num_encoder_attention_heads=self.num_encoder_attention_heads,
num_decoder_attention_heads=self.num_decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
ngram=self.ngram,
num_buckets=self.num_buckets,
relative_max_distance=self.relative_max_distance,
disable_ngram_loss=self.disable_ngram_loss,
max_position_embeddings=self.max_position_embeddings,
add_cross_attention=self.add_cross_attention,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
attention_mask,
lm_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = ProphetNetDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = ProphetNetDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
class ProphetNetStandaloneEncoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
hidden_size=16,
encoder_seq_length=7,
decoder_seq_length=7,
# For common tests
is_training=True,
is_decoder=False,
use_attention_mask=True,
add_cross_attention=False,
use_cache=False,
use_labels=True,
decoder_start_token_id=0,
encoder_ffn_dim=32,
num_encoder_layers=2,
num_encoder_attention_heads=4,
decoder_ffn_dim=32,
num_decoder_layers=2,
num_decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
num_buckets=32,
relative_max_distance=128,
disable_ngram_loss=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_decoder_layers
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_ffn_dim = encoder_ffn_dim
self.num_attention_heads = num_decoder_attention_heads
self.num_encoder_attention_heads = num_encoder_attention_heads
self.num_decoder_attention_heads = num_decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.use_cache = use_cache
self.disable_ngram_loss = disable_ngram_loss
self.max_position_embeddings = max_position_embeddings
self.add_cross_attention = add_cross_attention
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 1
self.num_hidden_states_types = 1
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
config = ProphetNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_encoder_layers=self.num_encoder_layers,
num_decoder_layers=self.num_decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_ffn_dim=self.encoder_ffn_dim,
num_encoder_attention_heads=self.num_encoder_attention_heads,
num_decoder_attention_heads=self.num_decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
num_buckets=self.num_buckets,
relative_max_distance=self.relative_max_distance,
disable_ngram_loss=self.disable_ngram_loss,
max_position_embeddings=self.max_position_embeddings,
add_cross_attention=self.add_cross_attention,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (ProphetNetModel, ProphetNetForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (ProphetNetForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": ProphetNetForConditionalGeneration,
"feature-extraction": ProphetNetModel,
"summarization": ProphetNetForConditionalGeneration,
"text-generation": ProphetNetForCausalLM,
"text2text-generation": ProphetNetForConditionalGeneration,
"translation": ProphetNetForConditionalGeneration,
}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
is_encoder_decoder = True
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `ProphetNetConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def setUp(self):
self.model_tester = ProphetNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_lm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_with_lm_head(*config_and_inputs)
def test_only_decoder_causal_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_decoder(*config_and_inputs)
def test_fast_integration(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_fast_integration(*config_and_inputs)
def test_shared_weights(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs)
def test_shift_labels_via_shift_left(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)
@unittest.skip("Flaky test with no simple resolution. TODO Fix me @patrickvonplaten")
def test_decoder_model_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_generate_with_past_key_value_states(*config_and_inputs)
def test_encoder_decoder_model_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_generate_with_past_key_value_states(*config_and_inputs)
def test_attn_mask_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_model_with_attn_mask(*config_and_inputs)
def test_config_save(self):
config = self.model_tester.prepare_config_and_inputs()[0]
config.add_cross_attention = False
with tempfile.TemporaryDirectory() as tmp_dirname:
config.save_pretrained(tmp_dirname)
config = ProphetNetConfig.from_pretrained(tmp_dirname)
self.assertFalse(config.add_cross_attention)
def test_causal_lm_from_pretrained(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_causal_lm_from_pretrained(*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_fp16_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
# methods overwrite method in `test_modeling_common.py`
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 7
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
(self.model_tester.ngram + 1) * decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(self_attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
def test_generate_with_head_masking(self):
"""Generating with head_masking has not been implemented for ProphetNet models yet."""
pass
@require_torch
class ProphetNetStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (ProphetNetDecoder, ProphetNetForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (ProphetNetForCausalLM,) if is_torch_available() else ()
test_pruning = False
test_resize_embeddings = False
is_encoder_decoder = False
def setUp(self):
self.model_tester = ProphetNetStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
@require_torch
class ProphetNetStandaloneEncoderModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (ProphetNetEncoder,) if is_torch_available() else ()
test_pruning = False
test_resize_embeddings = False
is_encoder_decoder = False
def setUp(self):
self.model_tester = ProphetNetStandaloneEncoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)
def test_config(self):
self.config_tester.run_common_tests()
@require_torch
class ProphetNetModelIntegrationTest(unittest.TestCase):
@slow
def test_pretrained_checkpoint_hidden_states(self):
model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased")
model.to(torch_device)
# encoder-decoder outputs
encoder_ids = torch.tensor(
[
[
2871,
102,
2048,
3176,
2780,
1997,
2871,
26727,
2169,
2097,
12673,
1996,
8457,
2006,
2049,
8240,
2859,
2799,
1012,
2023,
6512,
2038,
2174,
13977,
2195,
25962,
1012,
102,
]
]
).to(torch_device)
decoder_prev_ids = torch.tensor([[102, 2129, 2116, 2372, 2024, 2006, 2169, 1997, 2122, 2048, 2780, 1029]]).to(
torch_device
)
output = model(
input_ids=encoder_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=decoder_prev_ids,
)
output_predited_logits = output[0]
expected_shape = torch.Size((1, 12, 30522))
self.assertEqual(output_predited_logits.shape, expected_shape)
expected_slice = torch.tensor(
[[[-7.7729, -8.0343, -8.26001], [-7.74213, -7.8629, -8.6000], [-7.7328, -7.8269, -8.5264]]]
).to(torch_device)
# self.assertTrue(torch.allclose(output_predited_logits[:, :3, :3], expected_slice, atol=1e-4))
assert torch.allclose(output_predited_logits[:, :3, :3], expected_slice, atol=1e-4)
# encoder outputs
encoder_outputs = model.prophetnet.encoder(encoder_ids)[0]
expected_encoder_outputs_slice = torch.tensor(
[[[-0.2526, -0.1951, -0.2185], [-0.8923, 0.2992, -0.4623], [-0.4585, 0.0165, -0.6652]]]
).to(torch_device)
expected_shape_encoder = torch.Size((1, 28, 1024))
self.assertEqual(encoder_outputs.shape, expected_shape_encoder)
# self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4))
assert torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4)
# decoder outputs
decoder_outputs = model.prophetnet.decoder(decoder_prev_ids, encoder_hidden_states=encoder_outputs)
predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 12, -1)
predicting_streams_logits = model.lm_head(predicting_streams)
next_first_stream_logits = predicting_streams_logits[:, 0]
# self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4))
assert torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4)
@slow
def test_cnndm_inference(self):
model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-cnndm")
model.config.max_length = 512
model.to(torch_device)
tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-cnndm")
ARTICLE_TO_SUMMARIZE = (
"USTC was founded in Beijing by the Chinese Academy of Sciences (CAS) in September 1958. The Director of"
" CAS, Mr. Guo Moruo was appointed the first president of USTC. USTC's founding mission was to develop a"
" high-level science and technology workforce, as deemed critical for development of China's economy,"
' defense, and science and technology education. The establishment was hailed as "A Major Event in the'
' History of Chinese Education and Science." CAS has supported USTC by combining most of its institutes'
" with the departments of the university. USTC is listed in the top 16 national key universities, becoming"
" the youngest national key university.".lower()
)
input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=511, return_tensors="pt").input_ids
input_ids = input_ids.to(torch_device)
summary_ids = model.generate(
input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
)
EXPECTED_SUMMARIZE_512 = (
"us ##tc was founded by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc is listed in the"
" top 16 national key universities ."
)
generated_titles = [
" ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids
]
self.assertListEqual(
[EXPECTED_SUMMARIZE_512],
generated_titles,
)
input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=99, return_tensors="pt").input_ids
input_ids = input_ids.to(torch_device)
# actually 98 tokens are used. max_length=100 contains bos and eos.
summary_ids = model.generate(
input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
)
EXPECTED_SUMMARIZE_100 = (
r"us ##tc was founded in beijing by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc "
"'"
" s founding mission was to develop a high - level science and technology workforce . [X_SEP]"
' establishment hailed as " a major event in the history of chinese education and science "'
)
generated_titles = [
" ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids
]
self.assertListEqual(
[EXPECTED_SUMMARIZE_100],
generated_titles,
)
@slow
def test_question_gen_inference(self):
model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg")
model.to(torch_device)
tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg")
INPUTS = [
"Bill Gates [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
"1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
"April 4, 1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
]
input_ids = tokenizer(INPUTS, truncation=True, padding=True, return_tensors="pt").input_ids
input_ids = input_ids.to(torch_device)
gen_output = model.generate(input_ids, num_beams=5, early_stopping=True)
generated_questions = tokenizer.batch_decode(gen_output, skip_special_tokens=True)
EXPECTED_QUESTIONS = [
"along with paul allen, who founded microsoft?",
"what year was microsoft founded?",
"when was microsoft founded?",
]
self.assertListEqual(
EXPECTED_QUESTIONS,
generated_questions,
)
| transformers-main | tests/models/prophetnet/test_modeling_prophetnet.py |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Falcon model. """
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class FalconModelTester:
def __init__(
self,
parent,
batch_size=3,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return FalconConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
pad_token_id=1,
new_decoder_architecture=True,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = FalconModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = FalconModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = FalconForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = FalconForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class FalconModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (FalconForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": FalconModel,
"question-answering": FalconForQuestionAnswering,
"text-classification": FalconForSequenceClassification,
"text-generation": FalconForCausalLM,
"token-classification": FalconForTokenClassification,
"zero-shot": FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def setUp(self):
self.model_tester = FalconModelTester(self)
self.config_tester = ConfigTester(self, config_class=FalconConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_position_embedding_types(self):
config, *inputs = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
config.alibi = alibi
self.model_tester.create_and_check_model(config, *inputs)
def test_falcon_sequence_classification_model(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = FalconForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_falcon_sequence_classification_model_for_single_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "single_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = FalconForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_cache_conversions(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = input_dict["input_ids"]
model = FalconForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, use_cache=True)
batch_size = input_ids.shape[0]
rw_cache = model._convert_to_rw_cache(result.past_key_values)
standard_cache = model._convert_cache_to_standard_format(rw_cache, batch_size)
for layer in range(len(rw_cache)):
for tensor_idx in range(2):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx])
)
def test_falcon_sequence_classification_model_for_multi_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "multi_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor(
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
).to(torch.float)
model = FalconForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_past_key_values_format(self):
# Falcon can have different numbers of KV-heads than the number of query heads, so we need
# to override this test to use the right head counts.
for model_class in self.all_generative_model_classes:
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(config, "use_cache"):
return
model = model_class(config).to(torch_device)
if "use_cache" not in inputs:
inputs["use_cache"] = True
outputs = model(**inputs)
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
num_hidden_layers = (
getattr(config, "decoder_layers", None)
or getattr(config, "num_decoder_layers", None)
or config.num_hidden_layers
)
num_attention_heads = getattr(config, "num_kv_heads", config.num_attention_heads)
embed_dim = getattr(config, "d_model", config.hidden_size)
per_head_embed_dim = embed_dim // num_attention_heads
past_kv = outputs["past_key_values"]
self.assertEqual(len(past_kv), num_hidden_layers)
batch_size, seq_length = inputs["input_ids"].shape
for i in range(num_hidden_layers):
if config.new_decoder_architecture:
num_attention_heads = config.num_attention_heads
elif config.multi_query:
num_attention_heads = 1
self.assertEqual(len(past_kv[0]), 2) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
self.assertEqual(
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
@require_torch
class FalconLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_falcon(self):
tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b")
model = FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b")
model.eval()
model.to(torch_device)
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
EXPECTED_OUTPUT = (
"My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
)
output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=19)
output_str = tokenizer.batch_decode(output_ids)[0]
self.assertEqual(output_str, EXPECTED_OUTPUT)
@slow
def test_lm_generation_big_models(self):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
tokenizer = AutoTokenizer.from_pretrained(repo)
model = FalconForCausalLM.from_pretrained(repo)
model.eval()
model.to(torch_device)
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**inputs, do_sample=False, max_new_tokens=4)
model.generate(**inputs, do_sample=True, max_new_tokens=4)
model.generate(**inputs, num_beams=2, max_new_tokens=4)
@slow
def test_lm_generation_use_cache(self):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
tokenizer = AutoTokenizer.from_pretrained(repo)
model = FalconForCausalLM.from_pretrained(repo)
model.eval()
model.to(device=torch_device)
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
# Test results are the same with and without cache
outputs_no_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=False)
outputs_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=True)
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)
| transformers-main | tests/models/falcon/test_modeling_falcon.py |
transformers-main | tests/models/falcon/__init__.py |
|
transformers-main | tests/models/blenderbot_small/__init__.py |
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeq2SeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class TFBlenderbotSmallModelTester:
config_cls = BlenderbotSmallConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
input_ids = tf.concat([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
**self.config_updates,
)
inputs_dict = prepare_blenderbot_small_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = TFBlenderbotSmallModel(config=config).get_decoder()
input_ids = inputs_dict["input_ids"]
input_ids = input_ids[:1, :]
attention_mask = inputs_dict["attention_mask"][:1, :]
head_mask = inputs_dict["head_mask"]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def prepare_blenderbot_small_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
if decoder_attention_mask is None:
decoder_attention_mask = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
],
axis=-1,
)
if head_mask is None:
head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class TFBlenderbotSmallModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
all_generative_model_classes = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
pipeline_model_mapping = (
{
"conversational": TFBlenderbotSmallForConditionalGeneration,
"feature-extraction": TFBlenderbotSmallModel,
"summarization": TFBlenderbotSmallForConditionalGeneration,
"text2text-generation": TFBlenderbotSmallForConditionalGeneration,
"translation": TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
is_encoder_decoder = True
test_pruning = False
test_onnx = False
def setUp(self):
self.model_tester = TFBlenderbotSmallModelTester(self)
self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
@require_tokenizers
@require_tf
class TFBlenderbot90MIntegrationTests(unittest.TestCase):
src_text = [
"Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
" i'm going to throw up.\nand why is that?"
]
model_name = "facebook/blenderbot_small-90M"
@cached_property
def tokenizer(self):
# use "old" tokenizer here because of bug when downloading new tokenizer
return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
@cached_property
def model(self):
model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name)
return model
@slow
def test_90_generation_from_long_input(self):
model_inputs = self.tokenizer(self.src_text, return_tensors="tf")
generated_ids = self.model.generate(
model_inputs.input_ids,
attention_mask=model_inputs.attention_mask,
num_beams=2,
use_cache=True,
)
generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| transformers-main | tests/models/blenderbot_small/test_modeling_tf_blenderbot_small.py |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the Blenderbot small tokenizer."""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class BlenderbotSmallTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BlenderbotSmallTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
vocab = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
self.special_tokens_map = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "adapt act apte"
output_text = "adapt act apte"
return input_text, output_text
def test_full_blenderbot_small_tokenizer(self):
tokenizer = BlenderbotSmallTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "adapt act apte"
bpe_tokens = ["adapt", "act", "ap@@", "te"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
input_bpe_tokens = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_special_tokens_small_tok(self):
tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
assert tok("sam").input_ids == [1384]
src_text = "I am a small frog."
encoded = tok([src_text], padding=False, truncation=False)["input_ids"]
decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def test_empty_word_small_tok(self):
tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
src_text = "I am a small frog ."
src_text_dot = "."
encoded = tok(src_text)["input_ids"]
encoded_dot = tok(src_text_dot)["input_ids"]
assert encoded[-1] == encoded_dot[0]
| transformers-main | tests/models/blenderbot_small/test_tokenization_blenderbot_small.py |
# coding=utf-8
# Copyright 2021, The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch BlenderbotSmall model. """
import tempfile
import unittest
from transformers import BlenderbotSmallConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallTokenizer
from transformers.models.blenderbot_small.modeling_blenderbot_small import (
BlenderbotSmallDecoder,
BlenderbotSmallEncoder,
BlenderbotSmallForCausalLM,
)
def prepare_blenderbot_small_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class BlenderbotSmallModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
# forcing a certain token to be generated, sets all other tokens to -inf
# if however the token to be generated is already at -inf then it can lead token
# `nan` values and thus break generation
self.forced_bos_token_id = None
self.forced_eos_token_id = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
inputs_dict = prepare_blenderbot_small_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def get_config(self):
return BlenderbotSmallConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
forced_bos_token_id=self.forced_bos_token_id,
forced_eos_token_id=self.forced_eos_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = BlenderbotSmallModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = BlenderbotSmallModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = BlenderbotSmallEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = BlenderbotSmallDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class BlenderbotSmallModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (BlenderbotSmallModel, BlenderbotSmallForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (BlenderbotSmallForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": BlenderbotSmallForConditionalGeneration,
"feature-extraction": BlenderbotSmallModel,
"summarization": BlenderbotSmallForConditionalGeneration,
"text-generation": BlenderbotSmallForCausalLM,
"text2text-generation": BlenderbotSmallForConditionalGeneration,
"translation": BlenderbotSmallForConditionalGeneration,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = True
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests when this model gets more usage
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def setUp(self):
self.model_tester = BlenderbotSmallModelTester(self)
self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = BlenderbotSmallForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
@require_torch
class Blenderbot90MIntegrationTests(unittest.TestCase):
ckpt = "facebook/blenderbot-90M"
@cached_property
def model(self):
model = BlenderbotSmallForConditionalGeneration.from_pretrained(self.ckpt).to(torch_device)
if torch_device == "cuda":
model = model.half()
return model
@cached_property
def tokenizer(self):
return BlenderbotSmallTokenizer.from_pretrained(self.ckpt)
@slow
def test_90_generation_from_long_input(self):
src_text = [
"Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel"
" like i'm going to throw up.\nand why is that?"
]
model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device)
assert isinstance(self.tokenizer, BlenderbotSmallTokenizer)
generated_ids = self.model.generate(**model_inputs)[0]
reply = self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
assert reply in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
)
@slow
def test_90_generation_from_short_input(self):
model_inputs = self.tokenizer(["sam"], return_tensors="pt").to(torch_device)
generated_utterances = self.model.generate(**model_inputs)
clean_txt = self.tokenizer.decode(
generated_utterances[0], skip_special_tokens=True, clean_up_tokenization_spaces=True
)
assert clean_txt in (
"have you ever been to a sam club? it's a great club in the south.",
"have you ever heard of sam harris? he's an american singer, songwriter, and actor.",
)
class BlenderbotSmallStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=2,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = BlenderbotSmallConfig(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = BlenderbotSmallDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = BlenderbotSmallDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class BlenderbotSmallStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (BlenderbotSmallDecoder, BlenderbotSmallForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (BlenderbotSmallForCausalLM,) if is_torch_available() else ()
test_pruning = False
is_encoder_decoder = False
def setUp(
self,
):
self.model_tester = BlenderbotSmallStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
| transformers-main | tests/models/blenderbot_small/test_modeling_blenderbot_small.py |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def prepare_blenderbot_inputs_dict(
config,
input_ids,
decoder_input_ids=None,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
if decoder_attention_mask is None:
decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
if head_mask is None:
head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class FlaxBlenderbotSmallModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=32,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.initializer_range = initializer_range
def prepare_config_and_inputs(self):
input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
decoder_input_ids = shift_tokens_right(input_ids, 1, 2)
config = BlenderbotSmallConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
initializer_range=self.initializer_range,
use_cache=False,
)
inputs_dict = prepare_blenderbot_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def check_use_cache_forward(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=outputs_cache.past_key_values,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
decoder_attention_mask_cache = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask_cache,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
past_key_values=outputs_cache.past_key_values,
decoder_attention_mask=decoder_attention_mask_cache,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
@require_flax
class BlenderbotHeadTests(unittest.TestCase):
vocab_size = 99
def _get_config_and_data(self):
input_ids = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
],
dtype=np.int64,
)
batch_size = input_ids.shape[0]
config = BlenderbotSmallConfig(
vocab_size=self.vocab_size,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
)
return config, input_ids, batch_size
# @timeout_decorator.timeout(1) # not working with the decorator so far
def test_lm_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
lm_model = FlaxBlenderbotSmallForConditionalGeneration(config)
outputs = lm_model(input_ids=input_ids)
expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_lm_uneven_forward(self):
config = BlenderbotSmallConfig(
vocab_size=self.vocab_size,
d_model=14,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=8,
decoder_ffn_dim=8,
max_position_embeddings=48,
)
lm_model = FlaxBlenderbotSmallForConditionalGeneration(config)
context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64)
summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64)
outputs = lm_model(input_ids=context, decoder_input_ids=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_shift_tokens_right(self):
input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64)
shifted = shift_tokens_right(input_ids, 1, 2)
n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum()
n_pad_after = np.equal(shifted, 1).astype(np.float32).sum()
self.assertEqual(shifted.shape, input_ids.shape)
self.assertEqual(n_pad_after, n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0], 2).all())
@require_flax
class FlaxBlenderbotSmallModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
is_encoder_decoder = True
all_model_classes = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
all_generative_model_classes = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def setUp(self):
self.model_tester = FlaxBlenderbotSmallModelTester(self)
def test_use_cache_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
def test_use_cache_forward_with_attn_mask(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
def test_encode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def encode_jitted(input_ids, attention_mask=None, **kwargs):
return model.encode(input_ids=input_ids, attention_mask=attention_mask)
with self.subTest("JIT Enabled"):
jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_decode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
model = model_class(config)
encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
prepared_inputs_dict = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
return model.decode(
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
)
with self.subTest("JIT Enabled"):
jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("facebook/blenderbot_small-90M")
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
input_ids = np.ones((1, 1)) * model.config.eos_token_id
outputs = model(input_ids)
self.assertIsNotNone(outputs)
| transformers-main | tests/models/blenderbot_small/test_modeling_flax_blenderbot_small.py |
transformers-main | tests/models/megatron_bert/__init__.py |
|
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
# Copyright 2021 NVIDIA Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MegatronBERT model. """
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class MegatronBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=64,
embedding_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.embedding_size = embedding_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return MegatronBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
embedding_size=self.embedding_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def create_and_check_megatron_bert_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_megatron_bert_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_causal_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_megatron_bert_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_megatron_bert_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_megatron_bert_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_megatron_bert_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MegatronBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_megatron_bert_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MegatronBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_megatron_bert_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = MegatronBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class MegatronBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
# test_resize_embeddings = False
test_head_masking = False
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = MegatronBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=MegatronBertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_megatron_bert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*config_and_inputs)
def _long_tensor(tok_lst):
return torch.tensor(
tok_lst,
dtype=torch.long,
device=torch_device,
)
TOLERANCE = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class MegatronBertModelIntegrationTests(unittest.TestCase):
@slow
@unittest.skip("Model is not available.")
def test_inference_no_head(self):
directory = "nvidia/megatron-bert-uncased-345m"
if "MYDIR" in os.environ:
directory = os.path.join(os.environ["MYDIR"], directory)
model = MegatronBertModel.from_pretrained(directory)
model.to(torch_device)
model.half()
input_ids = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 9, 1024))
self.assertEqual(output.shape, expected_shape)
expected = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3):
for jj in range(3):
a = output[0, ii, jj]
b = expected[3 * ii + jj]
msg = "ii={} jj={} a={} b={}".format(ii, jj, a, b)
self.assertTrue(math.isclose(a, b, rel_tol=TOLERANCE, abs_tol=TOLERANCE), msg=msg)
| transformers-main | tests/models/megatron_bert/test_modeling_megatron_bert.py |
transformers-main | tests/models/gpt_bigcode/__init__.py |
|
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import unittest
from parameterized import parameterized
from transformers import GPTBigCodeConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPT2TokenizerFast,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
)
from transformers.models.gpt_bigcode.modeling_gpt_bigcode import GPTBigCodeAttention
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_12
else:
is_torch_greater_or_equal_than_1_12 = False
class GPTBigCodeModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="relu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
multi_query=True,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = None
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 2
self.pad_token_id = vocab_size - 3
self.multi_query = multi_query
def get_large_model_config(self):
return GPTBigCodeConfig.from_pretrained("bigcode/gpt_bigcode-santacoder")
def prepare_config_and_inputs(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config(
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def get_config(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
return GPTBigCodeConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
n_inner=self.intermediate_size,
activation_function=self.hidden_act,
resid_pdrop=self.hidden_dropout_prob,
attn_pdrop=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
attention_softmax_in_fp32=False,
scale_attention_softmax_in_fp32=False,
multi_query=self.multi_query,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_gpt_bigcode_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTBigCodeModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_gpt_bigcode_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTBigCodeModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_gpt_bigcode_model_attention_mask_past(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = GPTBigCodeModel(config=config)
model.to(torch_device)
model.eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = self.seq_length // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_gpt_bigcode_model_past_large_inputs(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = GPTBigCodeModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
)["last_hidden_state"]
output_from_past = model(
next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
)["last_hidden_state"]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTBigCodeForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_forward_and_backwards(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False
):
model = GPTBigCodeForCausalLM(config)
model.to(torch_device)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def create_and_check_gpt_bigcode_for_sequence_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
):
config.num_labels = self.num_labels
model = GPTBigCodeForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_gpt_bigcode_for_token_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
):
config.num_labels = self.num_labels
model = GPTBigCodeForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_gpt_bigcode_weight_initialization(self, config, *args):
model = GPTBigCodeModel(config)
model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer)
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class GPTBigCodeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
# TODO: Update the tests to use valid pretrained models.
all_model_classes = (
(
GPTBigCodeModel,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (GPTBigCodeForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": GPTBigCodeModel,
"text-classification": GPTBigCodeForSequenceClassification,
"text-generation": GPTBigCodeForCausalLM,
"token-classification": GPTBigCodeForTokenClassification,
"zero-shot": GPTBigCodeForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = False
test_missing_keys = False
test_pruning = False
test_torchscript = False
multi_query = True
# special case for DoubleHeads model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
return inputs_dict
def setUp(self):
self.model_tester = GPTBigCodeModelTester(self, multi_query=self.multi_query)
self.config_tester = ConfigTester(self, config_class=GPTBigCodeConfig, n_embd=37)
def tearDown(self):
import gc
gc.collect()
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip("MQA models does not support retain_grad")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip("Contrastive search not supported due to non-standard caching mechanism")
def test_contrastive_generate(self):
pass
@unittest.skip("Contrastive search not supported due to non-standard caching mechanism")
def test_contrastive_generate_dict_outputs_use_cache(self):
pass
@unittest.skip("CPU offload seems to be broken for some reason - tiny models keep hitting corner cases")
def test_cpu_offload(self):
pass
@unittest.skip("Disk offload seems to be broken for some reason - tiny models keep hitting corner cases")
def test_disk_offload(self):
pass
@unittest.skip("BigCodeGPT has a non-standard KV cache format.")
def test_past_key_values_format(self):
pass
def test_gpt_bigcode_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_bigcode_model(*config_and_inputs)
def test_gpt_bigcode_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_bigcode_model_past(*config_and_inputs)
def test_gpt_bigcode_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_bigcode_model_attention_mask_past(*config_and_inputs)
def test_gpt_bigcode_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_bigcode_model_past_large_inputs(*config_and_inputs)
def test_gpt_bigcode_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_gpt_bigcode_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_bigcode_for_sequence_classification(*config_and_inputs)
def test_gpt_bigcode_token_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_bigcode_for_token_classification(*config_and_inputs)
def test_gpt_bigcode_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
def test_gpt_bigcode_scale_attn_by_inverse_layer_idx(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True)
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
def test_gpt_bigcode_reorder_and_upcast_attn(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True)
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
def test_gpt_bigcode_weight_initialization(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt_bigcode_weight_initialization(*config_and_inputs)
@require_torch
class GPTBigCodeMHAModelTest(GPTBigCodeModelTest):
# `parameterized_class` breaks with mixins, so we use inheritance instead
multi_query = False
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_12,
reason="`GPTBigCode` checkpoints use `PytorchGELUTanh` which requires `torch>=1.12.0`.",
)
@slow
@require_torch
class GPTBigCodeModelLanguageGenerationTest(unittest.TestCase):
def test_generate_simple(self):
model = GPTBigCodeForCausalLM.from_pretrained("bigcode/gpt_bigcode-santacoder").to(torch_device)
tokenizer = GPT2TokenizerFast.from_pretrained("bigcode/gpt_bigcode-santacoder")
input_ids = tokenizer("def print_hello_world():", return_tensors="pt").input_ids.to(torch_device)
output_sequence = model.generate(input_ids)
output_sentence = tokenizer.decode(output_sequence[0], skip_special_tokens=True)
expected_output = """def print_hello_world():\n print("Hello World!")\n\n\ndef print_hello_"""
self.assertEqual(output_sentence, expected_output)
def test_generate_batched(self):
tokenizer = GPT2TokenizerFast.from_pretrained("bigcode/gpt_bigcode-santacoder")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
model = GPTBigCodeForCausalLM.from_pretrained("bigcode/gpt_bigcode-santacoder").to(torch_device)
inputs = tokenizer(["def print_hello_world():", "def say_hello():"], return_tensors="pt", padding=True).to(
torch_device
)
outputs = model.generate(**inputs)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
expected_output = [
'def print_hello_world():\n print("Hello World!")\n\n\ndef print_hello_',
'def say_hello():\n print("Hello, World!")\n\n\nsay_hello()',
]
self.assertListEqual(outputs, expected_output)
@require_torch
class GPTBigCodeMQATest(unittest.TestCase):
def get_attention(self, multi_query):
config = GPTBigCodeConfig.from_pretrained(
"bigcode/gpt_bigcode-santacoder",
multi_query=multi_query,
attn_pdrop=0,
resid_pdrop=0,
)
return GPTBigCodeAttention(config)
@parameterized.expand([(seed, is_train_mode) for seed in range(5) for is_train_mode in [True, False]])
def test_mqa_reduces_to_mha(self, seed, is_train_mode=True):
torch.manual_seed(seed)
# CREATE MQA AND MHA ATTENTIONS
attention_mqa = self.get_attention(True)
attention_mha = self.get_attention(False)
# ENFORCE MATCHING WEIGHTS
num_heads = attention_mqa.num_heads
embed_dim = attention_mqa.embed_dim
head_dim = attention_mqa.head_dim
with torch.no_grad():
mqa_q_weight = attention_mqa.c_attn.weight[:embed_dim, :].view(num_heads, 1, head_dim, embed_dim)
mqa_kv_weight = attention_mqa.c_attn.weight[embed_dim:, :].view(1, 2, head_dim, embed_dim)
mha_c_weight = torch.cat(
[mqa_q_weight, mqa_kv_weight.expand(num_heads, 2, head_dim, embed_dim)], dim=1
).view(3 * num_heads * head_dim, embed_dim)
mqa_q_bias = attention_mqa.c_attn.bias[:embed_dim].view(num_heads, 1, head_dim)
mqa_kv_bias = attention_mqa.c_attn.bias[embed_dim:].view(1, 2, head_dim)
mha_c_bias = torch.cat([mqa_q_bias, mqa_kv_bias.expand(num_heads, 2, head_dim)], dim=1).view(
3 * num_heads * head_dim
)
attention_mha.c_attn.weight.copy_(mha_c_weight)
attention_mha.c_attn.bias.copy_(mha_c_bias)
attention_mha.c_proj.weight.copy_(attention_mqa.c_proj.weight)
attention_mha.c_proj.bias.copy_(attention_mqa.c_proj.bias)
# PUT THE MODEL INTO THE CORRECT MODE
attention_mha.train(is_train_mode)
attention_mqa.train(is_train_mode)
# RUN AN INPUT THROUGH THE MODELS
num_tokens = 5
hidden_states = torch.randn(1, num_tokens, embed_dim)
attention_mha_result = attention_mha(hidden_states)[0]
attention_mqa_result = attention_mqa(hidden_states)[0]
# CHECK THAT ALL OUTPUTS ARE THE SAME
self.assertTrue(torch.allclose(attention_mha_result, attention_mqa_result, atol=1e-5))
| transformers-main | tests/models/gpt_bigcode/test_modeling_gpt_bigcode.py |
transformers-main | tests/models/funnel/__init__.py |
|
# coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class FunnelTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = FunnelTokenizer
rust_tokenizer_class = FunnelTokenizerFast
test_rust_tokenizer = True
space_between_special_tokens = True
def setUp(self):
super().setUp()
vocab_tokens = [
"<unk>",
"<cls>",
"<sep>",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_tokenizer(self, **kwargs):
return FunnelTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs):
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "UNwant\u00E9d,running"
output_text = "unwanted, running"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
def test_token_type_ids(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
inputs = tokenizer("UNwant\u00E9d,running")
sentence_len = len(inputs["input_ids"]) - 1
self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len)
inputs = tokenizer("UNwant\u00E9d,running", "UNwant\u00E9d,running")
self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len + [1] * sentence_len)
| transformers-main | tests/models/funnel/test_tokenization_funnel.py |
# coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
)
class FunnelModelTester:
"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
block_sizes=[1, 1, 2],
num_decoder_layers=1,
d_model=32,
n_head=4,
d_head=8,
d_inner=37,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=3,
initializer_std=0.02, # Set to a smaller value, so we can keep the small error threshold (1e-5) in the test
num_labels=3,
num_choices=4,
scope=None,
base=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.block_sizes = block_sizes
self.num_decoder_layers = num_decoder_layers
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = 2
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.initializer_std = initializer_std
# Used in the tests to check the size of the first attention layer
self.num_attention_heads = n_head
# Used in the tests to check the size of the first hidden state
self.hidden_size = self.d_model
# Used in the tests to check the number of output hidden states/attentions
self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
self.expected_num_hidden_layers = self.num_hidden_layers + 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
)
def get_config(self):
return FunnelConfig(
vocab_size=self.vocab_size,
block_sizes=self.block_sizes,
num_decoder_layers=self.num_decoder_layers,
d_model=self.d_model,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_inner,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
activation_dropout=self.activation_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_std=self.initializer_std,
)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
def create_and_check_base_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelBaseModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_choices = self.num_choices
model = FunnelForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class FunnelModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(
FunnelModel,
FunnelForMaskedLM,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": (FunnelBaseModel, FunnelModel),
"fill-mask": FunnelForMaskedLM,
"question-answering": FunnelForQuestionAnswering,
"text-classification": FunnelForSequenceClassification,
"token-classification": FunnelForTokenClassification,
"zero-shot": FunnelForSequenceClassification,
}
if is_torch_available()
else {}
)
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = FunnelModelTester(self)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
)
def setUp(self):
self.model_tester = FunnelModelTester(self, base=True)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_base_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
# overwrite from test_modeling_common
def test_training(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class.__name__ == "FunnelBaseModel":
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
@require_sentencepiece
@require_tokenizers
class FunnelModelIntegrationTest(unittest.TestCase):
def test_inference_tiny_model(self):
batch_size = 13
sequence_length = 7
input_ids = torch.arange(0, batch_size * sequence_length).long().reshape(batch_size, sequence_length)
lengths = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
token_type_ids = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])
model = FunnelModel.from_pretrained("sgugger/funnel-random-tiny")
output = model(input_ids, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2344.8352)
expected_output_mean = torch.tensor(0.8052)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
attention_mask = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2343.8425)
expected_output_mean = torch.tensor(0.8049)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
@slow
def test_inference_model(self):
tokenizer = FunnelTokenizer.from_pretrained("huggingface/funnel-small")
model = FunnelModel.from_pretrained("huggingface/funnel-small")
inputs = tokenizer("Hello! I am the Funnel Transformer model.", return_tensors="pt")
output = model(**inputs)[0]
expected_output_sum = torch.tensor(235.7246)
expected_output_mean = torch.tensor(0.0256)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
| transformers-main | tests/models/funnel/test_modeling_funnel.py |
# coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class TFFunnelModelTester:
"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
block_sizes=[1, 1, 2],
num_decoder_layers=1,
d_model=32,
n_head=4,
d_head=8,
d_inner=37,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=3,
initializer_std=0.02, # Set to a smaller value, so we can keep the small error threshold (1e-5) in the test
num_labels=3,
num_choices=4,
scope=None,
base=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.block_sizes = block_sizes
self.num_decoder_layers = num_decoder_layers
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = 2
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.initializer_std = initializer_std
# Used in the tests to check the size of the first attention layer
self.num_attention_heads = n_head
# Used in the tests to check the size of the first hidden state
self.hidden_size = self.d_model
# Used in the tests to check the number of output hidden states/attentions
self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
self.expected_num_hidden_layers = self.num_hidden_layers + 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = FunnelConfig(
vocab_size=self.vocab_size,
block_sizes=self.block_sizes,
num_decoder_layers=self.num_decoder_layers,
d_model=self.d_model,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_inner,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
activation_dropout=self.activation_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_std=self.initializer_std,
)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
config.truncate_seq = False
model = TFFunnelModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
config.separate_cls = False
model = TFFunnelModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
def create_and_check_base_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelBaseModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
config.truncate_seq = False
model = TFFunnelBaseModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
config.separate_cls = False
model = TFFunnelBaseModel(config=config)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelForPreTraining(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelForMaskedLM(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.num_labels = self.num_labels
model = TFFunnelForSequenceClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.num_choices = self.num_choices
model = TFFunnelForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.num_labels = self.num_labels
model = TFFunnelForTokenClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = TFFunnelForQuestionAnswering(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFFunnelModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": (TFFunnelBaseModel, TFFunnelModel),
"fill-mask": TFFunnelForMaskedLM,
"question-answering": TFFunnelForQuestionAnswering,
"text-classification": TFFunnelForSequenceClassification,
"token-classification": TFFunnelForTokenClassification,
"zero-shot": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFFunnelModelTester(self)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@require_tf
class TFFunnelBaseModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFFunnelModelTester(self, base=True)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_base_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
| transformers-main | tests/models/funnel/test_modeling_tf_funnel.py |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BertJapaneseTokenizer
test_rust_tokenizer = False
space_between_special_tokens = True
def setUp(self):
super().setUp()
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"こんにちは",
"こん",
"にちは",
"ばんは",
"##こん",
"##にちは",
"##ばんは",
"世界",
"##世界",
"、",
"##、",
"。",
"##。",
]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_input_output_texts(self, tokenizer):
input_text = "こんにちは、世界。 \nこんばんは、世界。"
output_text = "こんにちは 、 世界 。 こんばんは 、 世界 。"
return input_text, output_text
def get_clean_sequence(self, tokenizer):
input_text, output_text = self.get_input_output_texts(tokenizer)
ids = tokenizer.encode(output_text, add_special_tokens=False)
text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
return text, ids
def test_pretokenized_inputs(self):
pass # TODO add if relevant
def test_maximum_encoding_length_pair_input(self):
pass # TODO add if relevant
def test_maximum_encoding_length_single_input(self):
pass # TODO add if relevant
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。")
self.assertListEqual(tokens, ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [3, 12, 10, 14, 4, 9, 12, 10, 14])
def test_pickle_mecab_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file, word_tokenizer_type="mecab")
self.assertIsNotNone(tokenizer)
text = "こんにちは、世界。\nこんばんは、世界。"
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [3, 12, 10, 14, 4, 9, 12, 10, 14])
filename = os.path.join(self.tmpdirname, "tokenizer.bin")
with open(filename, "wb") as handle:
pickle.dump(tokenizer, handle)
with open(filename, "rb") as handle:
tokenizer_new = pickle.load(handle)
tokens_loaded = tokenizer_new.tokenize(text)
self.assertListEqual(tokens, tokens_loaded)
def test_mecab_tokenizer_ipadic(self):
tokenizer = MecabTokenizer(mecab_dic="ipadic")
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"],
)
def test_mecab_tokenizer_unidic_lite(self):
try:
tokenizer = MecabTokenizer(mecab_dic="unidic_lite")
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"],
)
def test_mecab_tokenizer_unidic(self):
try:
tokenizer = MecabTokenizer(mecab_dic="unidic")
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"],
)
def test_mecab_tokenizer_lower(self):
tokenizer = MecabTokenizer(do_lower_case=True, mecab_dic="ipadic")
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["アップルストア", "で", "iphone", "8", "が", "発売", "さ", "れ", "た", "。"],
)
def test_mecab_tokenizer_with_option(self):
try:
tokenizer = MecabTokenizer(
do_lower_case=True, normalize_text=False, mecab_option="-d /usr/local/lib/mecab/dic/jumandic"
)
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れた", "\u3000", "。"],
)
def test_mecab_tokenizer_no_normalize(self):
tokenizer = MecabTokenizer(normalize_text=False, mecab_dic="ipadic")
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れ", "た", " ", "。"],
)
@require_sudachi
def test_pickle_sudachi_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file, word_tokenizer_type="sudachi")
self.assertIsNotNone(tokenizer)
text = "こんにちは、世界。\nこんばんは、世界。"
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [3, 12, 10, 14, 4, 9, 12, 10, 14])
filename = os.path.join(self.tmpdirname, "tokenizer.bin")
with open(filename, "wb") as handle:
pickle.dump(tokenizer, handle)
with open(filename, "rb") as handle:
tokenizer_new = pickle.load(handle)
tokens_loaded = tokenizer_new.tokenize(text)
self.assertListEqual(tokens, tokens_loaded)
@require_sudachi
def test_sudachi_tokenizer_core(self):
tokenizer = SudachiTokenizer(sudachi_dict_type="core")
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
# fmt: off
[" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "],
# fmt: on
)
@require_sudachi
def test_sudachi_tokenizer_split_mode_A(self):
tokenizer = SudachiTokenizer(sudachi_dict_type="core", sudachi_split_mode="A")
self.assertListEqual(tokenizer.tokenize("外国人参政権"), ["外国", "人", "参政", "権"])
@require_sudachi
def test_sudachi_tokenizer_split_mode_B(self):
tokenizer = SudachiTokenizer(sudachi_dict_type="core", sudachi_split_mode="B")
self.assertListEqual(tokenizer.tokenize("外国人参政権"), ["外国人", "参政権"])
@require_sudachi
def test_sudachi_tokenizer_split_mode_C(self):
tokenizer = SudachiTokenizer(sudachi_dict_type="core", sudachi_split_mode="C")
self.assertListEqual(tokenizer.tokenize("外国人参政権"), ["外国人参政権"])
@require_sudachi
def test_sudachi_tokenizer_lower(self):
tokenizer = SudachiTokenizer(do_lower_case=True, sudachi_dict_type="core")
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
# fmt: off
[" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "],
# fmt: on
)
@require_sudachi
def test_sudachi_tokenizer_no_normalize(self):
tokenizer = SudachiTokenizer(normalize_text=False, sudachi_dict_type="core")
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
# fmt: off
[" ", "\t", "ｱｯﾌﾟﾙ", "ストア", "で", "iPhone", "８", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "],
# fmt: on
)
@require_sudachi
def test_sudachi_tokenizer_trim_whitespace(self):
tokenizer = SudachiTokenizer(trim_whitespace=True, sudachi_dict_type="core")
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"],
)
@require_jumanpp
def test_pickle_jumanpp_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file, word_tokenizer_type="jumanpp")
self.assertIsNotNone(tokenizer)
text = "こんにちは、世界。\nこんばんは、世界。"
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [3, 12, 10, 14, 4, 9, 12, 10, 14])
filename = os.path.join(self.tmpdirname, "tokenizer.bin")
with open(filename, "wb") as handle:
pickle.dump(tokenizer, handle)
with open(filename, "rb") as handle:
tokenizer_new = pickle.load(handle)
tokens_loaded = tokenizer_new.tokenize(text)
self.assertListEqual(tokens, tokens_loaded)
@require_jumanpp
def test_jumanpp_tokenizer(self):
tokenizer = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
# fmt: off
["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"],
# fmt: on
)
@require_jumanpp
def test_jumanpp_tokenizer_lower(self):
tokenizer = JumanppTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
# fmt: off
["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"],
# fmt: on
)
@require_jumanpp
def test_jumanpp_tokenizer_no_normalize(self):
tokenizer = JumanppTokenizer(normalize_text=False)
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
# fmt: off
["ｱ", "ｯ", "ﾌ", "ﾟ", "ﾙ", "ストア", "で", "iPhone", "８", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"],
# fmt: on
)
@require_jumanpp
def test_jumanpp_tokenizer_trim_whitespace(self):
tokenizer = JumanppTokenizer(trim_whitespace=True)
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れた", "。"],
)
@require_jumanpp
def test_jumanpp_tokenizer_ext(self):
tokenizer = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("ありがとうございますm(_ _)ｍ見つけるのが大変です。"),
["ありがとう", "ございます", "m(_ _)m", "見つける", "の", "が", "大変です", "。"],
)
def test_wordpiece_tokenizer(self):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"]
vocab = {}
for i, token in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize(""), [])
self.assertListEqual(tokenizer.tokenize("こんにちは"), ["こんにちは"])
self.assertListEqual(tokenizer.tokenize("こんばんは"), ["こん", "##ばんは"])
self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは"), ["こん", "##ばんは", "[UNK]", "こんにちは"])
def test_sentencepiece_tokenizer(self):
tokenizer = BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp")
subword_tokenizer = tokenizer.subword_tokenizer
tokens = subword_tokenizer.tokenize("国境 の 長い トンネル を 抜ける と 雪国 であった 。")
self.assertListEqual(tokens, ["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"])
tokens = subword_tokenizer.tokenize("こんばんは こんばん にち は こんにちは")
self.assertListEqual(tokens, ["▁こん", "ばん", "は", "▁こん", "ばん", "▁に", "ち", "▁は", "▁こんにちは"])
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese")
text = tokenizer.encode("ありがとう。", add_special_tokens=False)
text_2 = tokenizer.encode("どういたしまして。", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_2 + [3]
@custom_tokenizers
class BertJapaneseCharacterTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BertJapaneseTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_tokenizer(self, **kwargs):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname, subword_tokenizer_type="character", **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "こんにちは、世界。 \nこんばんは、世界。"
output_text = "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
return input_text, output_text
def test_pretokenized_inputs(self):
pass # TODO add if relevant
def test_maximum_encoding_length_pair_input(self):
pass # TODO add if relevant
def test_maximum_encoding_length_single_input(self):
pass # TODO add if relevant
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file, subword_tokenizer_type="character")
tokens = tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。")
self.assertListEqual(
tokens, ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"]
)
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens), [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12]
)
def test_character_tokenizer(self):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
vocab = {}
for i, token in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = CharacterTokenizer(vocab=vocab, unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize(""), [])
self.assertListEqual(tokenizer.tokenize("こんにちは"), ["こ", "ん", "に", "ち", "は"])
self.assertListEqual(tokenizer.tokenize("こんにちほ"), ["こ", "ん", "に", "ち", "[UNK]"])
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char")
text = tokenizer.encode("ありがとう。", add_special_tokens=False)
text_2 = tokenizer.encode("どういたしまして。", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_2 + [3]
@custom_tokenizers
class AutoTokenizerCustomTest(unittest.TestCase):
def test_tokenizer_bert_japanese(self):
EXAMPLE_BERT_JAPANESE_ID = "cl-tohoku/bert-base-japanese"
tokenizer = AutoTokenizer.from_pretrained(EXAMPLE_BERT_JAPANESE_ID)
self.assertIsInstance(tokenizer, BertJapaneseTokenizer)
class BertTokenizerMismatchTest(unittest.TestCase):
def test_tokenizer_mismatch_warning(self):
EXAMPLE_BERT_JAPANESE_ID = "cl-tohoku/bert-base-japanese"
with self.assertLogs("transformers", level="WARNING") as cm:
BertTokenizer.from_pretrained(EXAMPLE_BERT_JAPANESE_ID)
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from."
)
)
EXAMPLE_BERT_ID = "bert-base-cased"
with self.assertLogs("transformers", level="WARNING") as cm:
BertJapaneseTokenizer.from_pretrained(EXAMPLE_BERT_ID)
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from."
)
)
| transformers-main | tests/models/bert_japanese/test_tokenization_bert_japanese.py |
transformers-main | tests/models/bert_japanese/__init__.py |
|
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
# using a different tiny model than the one used for default params defined in init to ensure proper testing
FSMT_TINY2 = "stas/tiny-wmt19-en-ru"
class FSMTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = FSMTTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
self.langs = ["en", "ru"]
config = {
"langs": self.langs,
"src_vocab_size": 10,
"tgt_vocab_size": 20,
}
self.src_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["src_vocab_file"])
self.tgt_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["tgt_vocab_file"])
config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.src_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.tgt_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
with open(config_file, "w") as fp:
fp.write(json.dumps(config))
@cached_property
def tokenizer_ru_en(self):
return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en")
@cached_property
def tokenizer_en_ru(self):
return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru")
def test_online_tokenizer_config(self):
"""this just tests that the online tokenizer files get correctly fetched and
loaded via its tokenizer_config.json and it's not slow so it's run by normal CI
"""
tokenizer = FSMTTokenizer.from_pretrained(FSMT_TINY2)
self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang], ["en", "ru"])
self.assertEqual(tokenizer.src_vocab_size, 21)
self.assertEqual(tokenizer.tgt_vocab_size, 21)
def test_full_tokenizer(self):
"""Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt"""
tokenizer = FSMTTokenizer(self.langs, self.src_vocab_file, self.tgt_vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_ru_en
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == text + [2]
assert encoded_pair == text + [2] + text_2 + [2]
@slow
def test_match_encode_decode(self):
tokenizer_enc = self.tokenizer_en_ru
tokenizer_dec = self.tokenizer_ru_en
targets = [
[
"Here's a little song I wrote. Don't worry, be happy.",
[2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
],
["This is it. No more. I'm done!", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
]
# if data needs to be recreated or added, run:
# import torch
# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")
for src_text, tgt_input_ids in targets:
encoded_ids = tokenizer_enc.encode(src_text, return_tensors=None)
self.assertListEqual(encoded_ids, tgt_input_ids)
# and decode backward, using the reversed languages model
decoded_text = tokenizer_dec.decode(encoded_ids, skip_special_tokens=True)
self.assertEqual(decoded_text, src_text)
@slow
def test_tokenizer_lower(self):
tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en", do_lower_case=True)
tokens = tokenizer.tokenize("USA is United States of America")
expected = ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
self.assertListEqual(tokens, expected)
@unittest.skip("FSMTConfig.__init__ requires non-optional args")
def test_torch_encode_plus_sent_to_model(self):
pass
@unittest.skip("FSMTConfig.__init__ requires non-optional args")
def test_np_encode_plus_sent_to_model(self):
pass
| transformers-main | tests/models/fsmt/test_tokenization_fsmt.py |
transformers-main | tests/models/fsmt/__init__.py |
|
# coding=utf-8
# Copyright 2020 Huggingface
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import timeout_decorator # noqa
from parameterized import parameterized
from transformers import FSMTConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import FSMTForConditionalGeneration, FSMTModel, FSMTTokenizer
from transformers.models.fsmt.modeling_fsmt import (
SinusoidalPositionalEmbedding,
_prepare_fsmt_decoder_inputs,
invert_mask,
shift_tokens_right,
)
from transformers.pipelines import TranslationPipeline
class FSMTModelTester:
def __init__(
self,
parent,
src_vocab_size=99,
tgt_vocab_size=99,
langs=["ru", "en"],
batch_size=13,
seq_length=7,
is_training=False,
use_labels=False,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="relu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
bos_token_id=0,
pad_token_id=1,
eos_token_id=2,
):
self.parent = parent
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.langs = langs
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.eos_token_id = eos_token_id
torch.manual_seed(0)
# hack needed for modeling_common tests - despite not really having this attribute in this model
self.vocab_size = self.src_vocab_size
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.src_vocab_size).clamp(
3,
)
input_ids[:, -1] = 2 # Eos Token
config = self.get_config()
inputs_dict = prepare_fsmt_inputs_dict(config, input_ids)
return config, inputs_dict
def get_config(self):
return FSMTConfig(
vocab_size=self.src_vocab_size, # hack needed for common tests
src_vocab_size=self.src_vocab_size,
tgt_vocab_size=self.tgt_vocab_size,
langs=self.langs,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"]
inputs_dict["decoder_attention_mask"] = inputs_dict["attention_mask"]
inputs_dict["use_cache"] = False
return config, inputs_dict
def prepare_fsmt_inputs_dict(
config,
input_ids,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_torch
class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (FSMTModel, FSMTForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (FSMTForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": FSMTForConditionalGeneration,
"feature-extraction": FSMTModel,
"summarization": FSMTForConditionalGeneration,
"text2text-generation": FSMTForConditionalGeneration,
"translation": FSMTForConditionalGeneration,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
test_pruning = False
test_missing_keys = False
def setUp(self):
self.model_tester = FSMTModelTester(self)
self.langs = ["en", "ru"]
config = {
"langs": self.langs,
"src_vocab_size": 10,
"tgt_vocab_size": 20,
}
# XXX: hack to appease to all other models requiring `vocab_size`
config["vocab_size"] = 99 # no such thing in FSMT
self.config_tester = ConfigTester(self, config_class=FSMTConfig, **config)
def test_config(self):
self.config_tester.run_common_tests()
# XXX: override test_model_common_attributes / different Embedding type
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Embedding))
model.set_input_embeddings(nn.Embedding(10, 10))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.modules.sparse.Embedding))
def test_initialization_more(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
model = FSMTModel(config)
model.to(torch_device)
model.eval()
# test init
# self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item())
def _check_var(module):
"""Check that we initialized various parameters from N(0, config.init_std)."""
self.assertAlmostEqual(torch.std(module.weight).item(), config.init_std, 2)
_check_var(model.encoder.embed_tokens)
_check_var(model.encoder.layers[0].self_attn.k_proj)
_check_var(model.encoder.layers[0].fc1)
# XXX: different std for fairseq version of SinusoidalPositionalEmbedding
# self.assertAlmostEqual(torch.std(model.encoder.embed_positions.weights).item(), config.init_std, 2)
def test_advanced_inputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
config.use_cache = False
inputs_dict["input_ids"][:, -2:] = config.pad_token_id
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
config, inputs_dict["input_ids"]
)
model = FSMTModel(config).to(torch_device).eval()
decoder_features_with_created_mask = model(**inputs_dict)[0]
decoder_features_with_passed_mask = model(
decoder_attention_mask=invert_mask(decoder_attn_mask), decoder_input_ids=decoder_input_ids, **inputs_dict
)[0]
_assert_tensors_equal(decoder_features_with_passed_mask, decoder_features_with_created_mask)
useless_mask = torch.zeros_like(decoder_attn_mask)
decoder_features = model(decoder_attention_mask=useless_mask, **inputs_dict)[0]
self.assertTrue(isinstance(decoder_features, torch.Tensor)) # no hidden states or attentions
self.assertEqual(
decoder_features.size(),
(self.model_tester.batch_size, self.model_tester.seq_length, config.tgt_vocab_size),
)
if decoder_attn_mask.min().item() < -1e3: # some tokens were masked
self.assertFalse((decoder_features_with_created_mask == decoder_features).all().item())
# Test different encoder attention masks
decoder_features_with_long_encoder_mask = model(
inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"].long()
)[0]
_assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask)
def test_save_load_missing_keys(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
@unittest.skip("Test has a segmentation fault on torch 1.8.0")
def test_export_to_onnx(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
model = FSMTModel(config).to(torch_device)
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
model,
(inputs_dict["input_ids"], inputs_dict["attention_mask"]),
f"{tmpdirname}/fsmt_test.onnx",
export_params=True,
opset_version=12,
input_names=["input_ids", "attention_mask"],
)
@unittest.skip("can't be implemented for FSMT due to dual vocab.")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip("Passing inputs_embeds not implemented for FSMT.")
def test_inputs_embeds(self):
pass
@unittest.skip("model weights aren't tied in FSMT.")
def test_tie_model_weights(self):
pass
@unittest.skip("TODO: Decoder embeddings cannot be resized at the moment")
def test_resize_embeddings_untied(self):
pass
@require_torch
class FSMTHeadTests(unittest.TestCase):
src_vocab_size = 99
tgt_vocab_size = 99
langs = ["ru", "en"]
def _get_config(self):
return FSMTConfig(
src_vocab_size=self.src_vocab_size,
tgt_vocab_size=self.tgt_vocab_size,
langs=self.langs,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
)
def _get_config_and_data(self):
input_ids = torch.tensor(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
],
dtype=torch.long,
device=torch_device,
)
batch_size = input_ids.shape[0]
config = self._get_config()
return config, input_ids, batch_size
def test_generate_beam_search(self):
input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], dtype=torch.long, device=torch_device)
config = self._get_config()
lm_model = FSMTForConditionalGeneration(config).to(torch_device)
lm_model.eval()
max_length = 5
new_input_ids = lm_model.generate(
input_ids.clone(),
do_sample=True,
num_return_sequences=1,
num_beams=2,
no_repeat_ngram_size=3,
max_length=max_length,
)
self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length))
def test_shift_tokens_right(self):
input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long)
shifted = shift_tokens_right(input_ids, 1)
n_pad_before = input_ids.eq(1).float().sum()
n_pad_after = shifted.eq(1).float().sum()
self.assertEqual(shifted.shape, input_ids.shape)
self.assertEqual(n_pad_after, n_pad_before - 1)
self.assertTrue(torch.eq(shifted[:, 0], 2).all())
def test_generate_fp16(self):
config, input_ids, batch_size = self._get_config_and_data()
attention_mask = input_ids.ne(1).to(torch_device)
model = FSMTForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_dummy_inputs(self):
config, *_ = self._get_config_and_data()
model = FSMTForConditionalGeneration(config).eval().to(torch_device)
model(**model.dummy_inputs)
def test_prepare_fsmt_decoder_inputs(self):
config, *_ = self._get_config_and_data()
input_ids = _long_tensor(([4, 4, 2]))
decoder_input_ids = _long_tensor([[26388, 2, config.pad_token_id]])
causal_mask_dtype = torch.float32
ignore = torch.finfo(causal_mask_dtype).min
decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
config, input_ids, decoder_input_ids, causal_mask_dtype=causal_mask_dtype
)
expected_causal_mask = torch.tensor(
[[0, ignore, ignore], [0, 0, ignore], [0, 0, 0]] # never attend to the final token, because its pad
).to(input_ids.device)
self.assertEqual(decoder_attn_mask.size(), decoder_input_ids.size())
self.assertTrue(torch.eq(expected_causal_mask, causal_mask).all())
def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
"""If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
if len(prefix) > 0:
prefix = f"{prefix}: "
raise AssertionError(f"{prefix}{a} != {b}")
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
TOLERANCE = 1e-4
pairs = [
["en-ru"],
["ru-en"],
["en-de"],
["de-en"],
]
@require_torch
@require_sentencepiece
@require_tokenizers
class FSMTModelIntegrationTests(unittest.TestCase):
tokenizers_cache = {}
models_cache = {}
default_mname = "facebook/wmt19-en-ru"
@cached_property
def default_tokenizer(self):
return self.get_tokenizer(self.default_mname)
@cached_property
def default_model(self):
return self.get_model(self.default_mname)
def get_tokenizer(self, mname):
if mname not in self.tokenizers_cache:
self.tokenizers_cache[mname] = FSMTTokenizer.from_pretrained(mname)
return self.tokenizers_cache[mname]
def get_model(self, mname):
if mname not in self.models_cache:
self.models_cache[mname] = FSMTForConditionalGeneration.from_pretrained(mname).to(torch_device)
if torch_device == "cuda":
self.models_cache[mname].half()
return self.models_cache[mname]
@slow
def test_inference_no_head(self):
tokenizer = self.default_tokenizer
model = FSMTModel.from_pretrained(self.default_mname).to(torch_device)
src_text = "My friend computer will translate this for me"
input_ids = tokenizer([src_text], return_tensors="pt")["input_ids"]
input_ids = _long_tensor(input_ids).to(torch_device)
inputs_dict = prepare_fsmt_inputs_dict(model.config, input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 10, model.config.tgt_vocab_size))
self.assertEqual(output.shape, expected_shape)
# expected numbers were generated when en-ru model, using just fairseq's model4.pt
# may have to adjust if switched to a different checkpoint
expected_slice = torch.tensor(
[[-1.5753, -1.5753, 2.8975], [-0.9540, -0.9540, 1.0299], [-3.3131, -3.3131, 0.5219]]
).to(torch_device)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def translation_setup(self, pair):
text = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
src, tgt = pair.split("-")
print(f"Testing {src} -> {tgt}")
mname = f"facebook/wmt19-{pair}"
src_text = text[src]
tgt_text = text[tgt]
tokenizer = self.get_tokenizer(mname)
model = self.get_model(mname)
return tokenizer, model, src_text, tgt_text
@parameterized.expand(pairs)
@slow
def test_translation_direct(self, pair):
tokenizer, model, src_text, tgt_text = self.translation_setup(pair)
input_ids = tokenizer.encode(src_text, return_tensors="pt").to(torch_device)
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
assert decoded == tgt_text, f"\n\ngot: {decoded}\nexp: {tgt_text}\n"
@parameterized.expand(pairs)
@slow
def test_translation_pipeline(self, pair):
tokenizer, model, src_text, tgt_text = self.translation_setup(pair)
device = 0 if torch_device == "cuda" else -1
pipeline = TranslationPipeline(model, tokenizer, framework="pt", device=device)
output = pipeline([src_text])
self.assertEqual([tgt_text], [x["translation_text"] for x in output])
@require_torch
class TestSinusoidalPositionalEmbeddings(unittest.TestCase):
padding_idx = 1
tolerance = 1e-4
def test_basic(self):
input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device)
emb1 = SinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6, padding_idx=self.padding_idx).to(
torch_device
)
emb = emb1(input_ids)
desired_weights = torch.tensor(
[
[9.0930e-01, 1.9999e-02, 2.0000e-04, -4.1615e-01, 9.9980e-01, 1.0000e00],
[1.4112e-01, 2.9995e-02, 3.0000e-04, -9.8999e-01, 9.9955e-01, 1.0000e00],
]
).to(torch_device)
self.assertTrue(
torch.allclose(emb[0], desired_weights, atol=self.tolerance),
msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n",
)
def test_odd_embed_dim(self):
# odd embedding_dim is allowed
SinusoidalPositionalEmbedding(num_positions=4, embedding_dim=5, padding_idx=self.padding_idx).to(torch_device)
# odd num_embeddings is allowed
SinusoidalPositionalEmbedding(num_positions=5, embedding_dim=4, padding_idx=self.padding_idx).to(torch_device)
@unittest.skip("different from marian (needs more research)")
def test_positional_emb_weights_against_marian(self):
desired_weights = torch.tensor(
[
[0, 0, 0, 0, 0],
[0.84147096, 0.82177866, 0.80180490, 0.78165019, 0.76140374],
[0.90929741, 0.93651021, 0.95829457, 0.97505713, 0.98720258],
]
)
emb1 = SinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512, padding_idx=self.padding_idx).to(
torch_device
)
weights = emb1.weights.data[:3, :5]
# XXX: only the 1st and 3rd lines match - this is testing against
# verbatim copy of SinusoidalPositionalEmbedding from fairseq
self.assertTrue(
torch.allclose(weights, desired_weights, atol=self.tolerance),
msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n",
)
# test that forward pass is just a lookup, there is no ignore padding logic
input_ids = torch.tensor(
[[4, 10, self.padding_idx, self.padding_idx, self.padding_idx]], dtype=torch.long, device=torch_device
)
no_cache_pad_zero = emb1(input_ids)[0]
# XXX: only the 1st line matches the 3rd
self.assertTrue(
torch.allclose(torch.tensor(desired_weights, device=torch_device), no_cache_pad_zero[:3, :5], atol=1e-3)
)
| transformers-main | tests/models/fsmt/test_modeling_fsmt.py |
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import XLMRobertaTokenizer, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XmodConfig,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
)
from transformers.models.xmod.modeling_xmod import XmodEmbeddings, create_position_ids_from_input_ids
class XmodModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return XmodConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
default_language="en_XX",
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = XmodModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = XmodModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = XmodForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = XmodForCausalLM(config=config).to(torch_device).eval()
# make sure that ids don't start with pad token
mask = input_ids.ne(config.pad_token_id).long()
input_ids = input_ids * mask
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
# make sure that ids don't start with pad token
mask = next_tokens.ne(config.pad_token_id).long()
next_tokens = next_tokens * mask
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = XmodForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = XmodForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = XmodForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = XmodForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class XmodModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
XmodForCausalLM,
XmodForMaskedLM,
XmodModel,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodForMultipleChoice,
XmodForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (XmodForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": XmodModel,
"fill-mask": XmodForMaskedLM,
"question-answering": XmodForQuestionAnswering,
"text-classification": XmodForSequenceClassification,
"text-generation": XmodForCausalLM,
"token-classification": XmodForTokenClassification,
"zero-shot": XmodForSequenceClassification,
}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
def setUp(self):
self.model_tester = XmodModelTester(self)
self.config_tester = ConfigTester(self, config_class=XmodConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs_relative_pos_emb(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
config_and_inputs[0].position_embedding_type = "relative_key"
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_create_position_ids_respects_padding_index(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XmodEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
model = XmodEmbeddings(config=config)
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
expected_positions = torch.as_tensor(
[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
)
position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_create_position_ids_from_inputs_embeds(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is XmodEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
embeddings = XmodEmbeddings(config=config)
inputs_embeds = torch.empty(2, 4, 30)
expected_single_positions = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_set_default_language(self):
config = self.model_tester.prepare_config_and_inputs()[0]
model = XmodForMaskedLM(config=config)
model.set_default_language("en_XX")
self.assertEqual(model.config.default_language, "en_XX")
with self.assertRaises(ValueError):
model.set_default_language("xx_XX")
def test_freeze_embeddings_and_language_adapters(self):
config = self.model_tester.prepare_config_and_inputs()[0]
model = XmodForMaskedLM(config=config)
num_trainable_params_before = sum(p.numel() for p in model.parameters() if p.requires_grad)
model.freeze_embeddings_and_language_adapters()
num_trainable_params_after = sum(p.numel() for p in model.parameters() if p.requires_grad)
self.assertLess(num_trainable_params_after, num_trainable_params_before)
@require_sentencepiece
@require_tokenizers
@require_torch
class XmodModelIntegrationTest(unittest.TestCase):
@slow
def test_xmod_base(self):
model = XmodModel.from_pretrained("facebook/xmod-base")
# language en_XX
model.set_default_language("en_XX")
input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
expected_output_shape = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
expected_output_values_last_dim = torch.tensor(
[[-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724]]
)
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
# language de_DE
model.set_default_language("de_DE")
input_ids = torch.tensor([[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2315, 58761, 18391, 5, 2]])
# Der Hund ist niedlich und wohnt in einem Gartenhaus.
expected_output_shape = torch.Size((1, 16, 768)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor(
[[0.0162, 0.0075, -0.1882, 0.2335, -0.0952, -0.3994, -0.0317, -0.1174, 0.0177, 0.4280, -0.0240, -0.2138,
0.0785, -0.1045, -0.2811, -0.3220]]
)
# fmt: on
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
@slow
def test_xmod_large_prenorm(self):
model = XmodModel.from_pretrained("facebook/xmod-large-prenorm")
# language en_XX
model.set_default_language("en_XX")
input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
expected_output_shape = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor(
[[-0.0121, -0.0194, -0.0240, -0.0160, -0.0205, -0.0159, -0.0243, -0.0206, -0.0161, -0.0335, -0.0196,
-0.0141]]
)
# fmt: on
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
# language de_DE
model.set_default_language("de_DE")
input_ids = torch.tensor([[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2315, 58761, 18391, 5, 2]])
# Der Hund ist niedlich und wohnt in einem Gartenhaus.
expected_output_shape = torch.Size((1, 16, 1024)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor(
[[-0.0120, -0.0262, -0.0253, -0.0112, -0.0128, -0.0164, -0.0080, -0.0081, -0.0192, -0.0117, -0.0170,
-0.0120, -0.0210, -0.0173, -0.0078, -0.0122]]
)
# fmt: on
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
@slow
def test_multilingual_batch(self):
model = XmodModel.from_pretrained("facebook/xmod-base")
# fmt: off
input_ids = torch.tensor([
[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2],
[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2],
[0, 1310, 49083, 443, 269, 71, 5486, 165, 60429, 660, 23, 2],
[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2],
])
# fmt: on
lang_ids = torch.LongTensor([0, 8, 8, 0])
expected_output_shape = torch.Size((4, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
# fmt: off
expected_output_values_last_dim = torch.tensor([
[-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724],
[-0.2668, -0.0235, -0.1739, 0.2266, -0.0901, -0.3482, 0.0105, -0.1915, 0.0397, 0.3822, 0.1836, -0.3407],
[-0.2668, -0.0235, -0.1739, 0.2266, -0.0901, -0.3482, 0.0105, -0.1915, 0.0397, 0.3822, 0.1836, -0.3407],
[-0.2394, -0.0036, 0.1252, -0.0087, 0.1325, 0.0580, -0.2049, -0.1978, -0.1223, 0.0648, -0.2599, -0.3724],
])
# fmt: on
output = model(input_ids, lang_ids=lang_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
@slow
def test_end_to_end_mask_fill(self):
tokenizer = XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")
model = XmodForMaskedLM.from_pretrained("facebook/xmod-base", default_language="en_XX")
model.to(torch_device)
sentences = [
"Hello, my dog is a little <mask>.",
"Hi <mask>!",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
outputs = model(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
)
probs = outputs.logits.softmax(dim=-1)
_, predictions = probs.topk(1)
predictions = predictions.squeeze(-1)
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model(input_ids=inputs_non_padded)
probs_non_padded = output_non_padded.logits.softmax(dim=-1)
_, predictions_non_padded = probs_non_padded.topk(1)
predictions_non_padded = predictions_non_padded.squeeze(-1)
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model(input_ids=inputs_padded)
probs_padded = output_padded.logits.softmax(dim=-1)
_, predictions_padded = probs_padded.topk(1)
predictions_padded = predictions_padded.squeeze(-1)
batch_out_sentence = tokenizer.batch_decode(predictions, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(predictions_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(predictions_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little girl.",
"Hi everyone!",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])
| transformers-main | tests/models/xmod/test_modeling_xmod.py |
transformers-main | tests/models/xmod/__init__.py |
|
transformers-main | tests/models/gpt_neox_japanese/__init__.py |
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch GPTNeoXJapanese model. """
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class GPTNeoXJapaneseModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_multiple_size=4,
hidden_act="gelu",
hidden_dropout=0.0,
attention_dropout=0.1,
weight_tying=True,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_multiple_size = intermediate_multiple_size
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.weight_tying = weight_tying
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_labels = None
if self.use_labels:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = self.get_config()
return config, input_ids, input_mask, token_labels
def get_config(self):
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_multiple_size=self.intermediate_multiple_size,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
weight_tying=self.weight_tying,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
config.is_decoder = True
return config, input_ids, input_mask, token_labels
def create_and_check_model(self, config, input_ids, input_mask):
model = GPTNeoXJapaneseModel(config=config)
model.to(torch_device)
model.eval()
_ = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(self, config, input_ids, input_mask):
config.add_cross_attention = True
model = GPTNeoXJapaneseModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(self, config, input_ids, input_mask, token_labels):
model = GPTNeoXJapaneseForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(self, config, input_ids, input_mask):
config.is_decoder = True
model = GPTNeoXJapaneseForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True)
output_from_no_past = output_from_no_past["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, input_mask, token_labels = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class GPTNeoXModelJapaneseTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
test_pruning = False
test_missing_keys = False
test_model_parallel = False
test_head_masking = False
def setUp(self):
self.model_tester = GPTNeoXJapaneseModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPTNeoXJapaneseConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(config, input_ids, input_mask)
def test_model_as_decoder(self):
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
def test_decoder_model_past_large_inputs(self):
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(config, input_ids, input_mask)
def test_model_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
@slow
def test_generation(self):
model_id = "abeja/gpt-neox-japanese-2.7b"
prompts = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]
EXPECTED_OUTPUTS = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained(model_id)
model = GPTNeoXJapaneseForCausalLM.from_pretrained(model_id)
predicted_outputs = []
for prompt in prompts:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=50)
generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
predicted_outputs += generated_string
self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
| transformers-main | tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
VOCAB_FILES_NAMES,
GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class GPTNeoXJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = GPTNeoXJapaneseTokenizer
test_rust_tokenizer = False
from_pretrained_kwargs = {"do_clean_text": False, "add_prefix_space": False}
def setUp(self):
super().setUp()
vocab_tokens = [
"こん",
"こんに",
"にちは",
"ばんは",
"世界,㔺界",
"、",
"。",
"<BR>",
"<SP>",
"<TAB>",
"<URL>",
"<EMAIL>",
"<TEL>",
"<DATE>",
"<PRICE>",
"<BLOCK>",
"<KIGOU>",
"<U2000U2BFF>",
"<|emoji1|>",
"<unk>",
"<|startoftext|>",
"<|endoftext|>",
]
emoji_tokens = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}} # 😀
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.emoji_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["emoji_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
with open(self.emoji_file, "w") as emoji_writer:
emoji_writer.write(json.dumps(emoji_tokens))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "こんにちは、世界。 \nこんばんは、㔺界。😀"
output_text = "こんにちは、世界。 \nこんばんは、世界。😀"
return input_text, output_text
def get_clean_sequence(self, tokenizer):
input_text, output_text = self.get_input_output_texts(tokenizer)
ids = tokenizer.encode(output_text, add_special_tokens=False)
text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
return text, ids
def test_pretokenized_inputs(self):
pass # TODO add if relevant
def test_maximum_encoding_length_pair_input(self):
pass # TODO add if relevant
def test_maximum_encoding_length_single_input(self):
pass # TODO add if relevant
def test_full_tokenizer(self):
tokenizer = self.get_tokenizer()
# Testing tokenization
input_text = "こんにちは、世界。 こんばんは、㔺界。"
expected_token = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
tokens = tokenizer.tokenize(input_text)
self.assertListEqual(tokens, expected_token)
# Testing conversion to ids without special tokens
expected_ids = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
input_ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(input_ids, expected_ids)
# Testing conversion to ids with special tokens
input_tokens = tokens + [tokenizer.unk_token]
expected_ids = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
self.assertListEqual(input_ids, expected_ids)
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")
ids_1 = tokenizer.encode("ありがとう。", add_special_tokens=False)
ids_2 = tokenizer.encode("どういたしまして。", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(ids_1)
encoded_pair = tokenizer.build_inputs_with_special_tokens(ids_1, ids_2)
assert encoded_sentence == ids_1
assert encoded_pair == ids_1 + ids_2
def test_conversion_reversible(self):
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def test_padding_different_model_input_name(self):
# tokenizer has no padding token
pass
| transformers-main | tests/models/gpt_neox_japanese/test_tokenization_gpt_neox_japanese.py |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Speech2Text model. """
import unittest
from transformers import Speech2Text2Config
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.speech_to_text_2.modeling_speech_to_text_2 import (
Speech2Text2Decoder,
Speech2Text2ForCausalLM,
)
@require_torch
class Speech2Text2StandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=2,
decoder_attention_heads=4,
max_position_embeddings=30,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = Speech2Text2Config(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = Speech2Text2Decoder(config=config).to(torch_device).eval()
input_ids = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((2, 1), config.vocab_size - 1) + 1
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
print(next_input_ids)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class Speech2Text2StandaloneDecoderModelTest(
ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
):
all_model_classes = (Speech2Text2Decoder, Speech2Text2ForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (Speech2Text2ForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = {"text-generation": Speech2Text2ForCausalLM} if is_torch_available() else {}
fx_compatible = True
test_pruning = False
def setUp(
self,
):
self.model_tester = Speech2Text2StandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=Speech2Text2Config)
# not implemented currently
def test_inputs_embeds(self):
pass
# speech2text2 has no base model
def test_save_load_fast_init_from_base(self):
pass
# speech2text2 has no base model
def test_save_load_fast_init_to_base(self):
pass
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
# decoder cannot keep gradients
def test_retain_grad_hidden_states_attentions(self):
return
| transformers-main | tests/models/speech_to_text_2/test_modeling_speech_to_text_2.py |
transformers-main | tests/models/speech_to_text_2/__init__.py |
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import json
import os
import tempfile
import unittest
from transformers.models.speech_to_text_2 import Speech2Text2Tokenizer
from transformers.models.speech_to_text_2.tokenization_speech_to_text_2 import VOCAB_FILES_NAMES
from ...test_tokenization_common import TokenizerTesterMixin
class SpeechToTextTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = Speech2Text2Tokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
vocab = "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" ")
merges = ["he re</w> 123", "here a 1456"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
self.tmpdirname = tempfile.mkdtemp()
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<s>")
self.assertEqual(vocab_keys[1], "<pad>")
self.assertEqual(vocab_keys[-1], "vocab")
self.assertEqual(len(vocab_keys), 14)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 14)
def test_tokenizer_decode(self):
tokenizer = Speech2Text2Tokenizer.from_pretrained(self.tmpdirname)
# make sure @@ is correctly concatenated
token_ids = [4, 6, 8, 7, 10] # ["here@@", "couple", "words", "of@@", "the"]
output_string = tokenizer.decode(token_ids)
self.assertTrue(output_string == "herecouple words ofthe")
def test_load_no_merges_file(self):
tokenizer = Speech2Text2Tokenizer.from_pretrained(self.tmpdirname)
with tempfile.TemporaryDirectory() as tmp_dirname:
tokenizer.save_pretrained(tmp_dirname)
os.remove(os.path.join(tmp_dirname, "merges.txt"))
# load tokenizer without merges file should not throw an error
tokenizer = Speech2Text2Tokenizer.from_pretrained(tmp_dirname)
with tempfile.TemporaryDirectory() as tmp_dirname:
# save tokenizer and load again
tokenizer.save_pretrained(tmp_dirname)
tokenizer = Speech2Text2Tokenizer.from_pretrained(tmp_dirname)
self.assertIsNotNone(tokenizer)
# overwrite since merges_file is optional
def test_tokenizer_slow_store_full_signature(self):
if not self.test_slow_tokenizer:
return
signature = inspect.signature(self.tokenizer_class.__init__)
tokenizer = self.get_tokenizer()
for parameter_name, parameter in signature.parameters.items():
if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
self.assertIn(parameter_name, tokenizer.init_kwargs)
| transformers-main | tests/models/speech_to_text_2/test_tokenization_speech_to_text_2.py |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class LayoutLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = LayoutLMTokenizer
rust_tokenizer_class = LayoutLMTokenizerFast
test_rust_tokenizer = True
space_between_special_tokens = True
def setUp(self):
super().setUp()
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_tokenizer(self, **kwargs):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "UNwant\u00E9d,running"
output_text = "unwanted, running"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
def test_special_tokens_as_you_expect(self):
"""If you are training a seq2seq model that expects a decoder_prefix token make sure it is prepended to decoder_input_ids"""
pass
| transformers-main | tests/models/layoutlm/test_tokenization_layoutlm.py |
transformers-main | tests/models/layoutlm/__init__.py |
|
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LayoutLMForMaskedLM,
LayoutLMForQuestionAnswering,
LayoutLMForSequenceClassification,
LayoutLMForTokenClassification,
LayoutLMModel,
)
class LayoutLMModelTester:
"""You can also import this e.g from .test_modeling_layoutlm import LayoutLMModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
range_bbox=1000,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.range_bbox = range_bbox
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
t = bbox[i, j, 3]
bbox[i, j, 3] = bbox[i, j, 1]
bbox[i, j, 1] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
t = bbox[i, j, 2]
bbox[i, j, 2] = bbox[i, j, 0]
bbox[i, j, 0] = t
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return LayoutLMConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
def create_and_check_model(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LayoutLMModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, bbox, token_type_ids=token_type_ids)
result = model(input_ids, bbox)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LayoutLMForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = LayoutLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(
input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = LayoutLMForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LayoutLMForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class LayoutLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
LayoutLMModel,
LayoutLMForMaskedLM,
LayoutLMForSequenceClassification,
LayoutLMForTokenClassification,
LayoutLMForQuestionAnswering,
)
if is_torch_available()
else None
)
pipeline_model_mapping = (
{
"document-question-answering": LayoutLMForQuestionAnswering,
"feature-extraction": LayoutLMModel,
"fill-mask": LayoutLMForMaskedLM,
"text-classification": LayoutLMForSequenceClassification,
"token-classification": LayoutLMForTokenClassification,
"zero-shot": LayoutLMForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
def setUp(self):
self.model_tester = LayoutLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def prepare_layoutlm_batch_inputs():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],device=torch_device) # noqa: E231
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],device=torch_device) # noqa: E231
bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],device=torch_device) # noqa: E231
token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],device=torch_device) # noqa: E231
# these are sequence labels (i.e. at the token level)
labels = torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],device=torch_device) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_torch
class LayoutLMModelIntegrationTest(unittest.TestCase):
@slow
def test_forward_pass_no_head(self):
model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the sequence output on [0, :3, :3]
expected_slice = torch.tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
# test the pooled output on [1, :3]
expected_slice = torch.tensor([-0.6580, -0.0214, 0.8552], device=torch_device)
self.assertTrue(torch.allclose(outputs.pooler_output[1, :3], expected_slice, atol=1e-3))
@slow
def test_forward_pass_sequence_classification(self):
# initialize model with randomly initialized sequence classification head
model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2).to(
torch_device
)
input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
labels=torch.tensor([1, 1], device=torch_device),
)
# test whether we get a loss as a scalar
loss = outputs.loss
expected_shape = torch.Size([])
self.assertEqual(loss.shape, expected_shape)
# test the shape of the logits
logits = outputs.logits
expected_shape = torch.Size((2, 2))
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_token_classification(self):
# initialize model with randomly initialized token classification head
model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13).to(
torch_device
)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
)
# test the loss calculation to be around 2.65
# expected_loss = torch.tensor(2.65, device=torch_device)
# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))
# test the shape of the logits
logits = outputs.logits
expected_shape = torch.Size((2, 25, 13))
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_question_answering(self):
# initialize model with randomly initialized token classification head
model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the shape of the logits
expected_shape = torch.Size((2, 25))
self.assertEqual(outputs.start_logits.shape, expected_shape)
self.assertEqual(outputs.end_logits.shape, expected_shape)
| transformers-main | tests/models/layoutlm/test_modeling_layoutlm.py |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class TFLayoutLMModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
range_bbox=1000,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.range_bbox = range_bbox
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
# convert bbox to numpy since TF does not support item assignment
bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
t = bbox[i, j, 3]
bbox[i, j, 3] = bbox[i, j, 1]
bbox[i, j, 1] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
t = bbox[i, j, 2]
bbox[i, j, 2] = bbox[i, j, 0]
bbox[i, j, 0] = t
bbox = tf.convert_to_tensor(bbox)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = LayoutLMConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_model(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFLayoutLMModel(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, bbox, token_type_ids=token_type_ids)
result = model(input_ids, bbox)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFLayoutLMForMaskedLM(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFLayoutLMForSequenceClassification(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFLayoutLMForTokenClassification(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFLayoutLMForQuestionAnswering(config=config)
result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class TFLayoutLMModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": TFLayoutLMModel,
"fill-mask": TFLayoutLMForMaskedLM,
"text-classification": TFLayoutLMForSequenceClassification,
"token-classification": TFLayoutLMForTokenClassification,
"zero-shot": TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = True
onnx_min_opset = 10
def setUp(self):
self.model_tester = TFLayoutLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFLayoutLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# TODO (Joao): fix me
@unittest.skip("Onnx compliancy broke with TF 2.10")
def test_onnx_compliancy(self):
pass
def prepare_layoutlm_batch_inputs():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
input_ids = tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231
attention_mask = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231
bbox = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231
token_type_ids = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231
# these are sequence labels (i.e. at the token level)
labels = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class TFLayoutLMModelIntegrationTest(unittest.TestCase):
@slow
def test_forward_pass_no_head(self):
model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the sequence output on [0, :3, :3]
expected_slice = tf.convert_to_tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
)
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
# test the pooled output on [1, :3]
expected_slice = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])
self.assertTrue(np.allclose(outputs.pooler_output[1, :3], expected_slice, atol=1e-3))
@slow
def test_forward_pass_sequence_classification(self):
# initialize model with randomly initialized sequence classification head
model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2)
input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
labels=tf.convert_to_tensor([1, 1]),
)
# test whether we get a loss as a scalar
loss = outputs.loss
expected_shape = (2,)
self.assertEqual(loss.shape, expected_shape)
# test the shape of the logits
logits = outputs.logits
expected_shape = (2, 2)
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_token_classification(self):
# initialize model with randomly initialized token classification head
model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13)
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(
input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
)
# test the shape of the logits
logits = outputs.logits
expected_shape = tf.convert_to_tensor((2, 25, 13))
self.assertEqual(logits.shape, expected_shape)
@slow
def test_forward_pass_question_answering(self):
# initialize model with randomly initialized token classification head
model = TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased")
input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
# forward pass
outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
# test the shape of the logits
expected_shape = tf.convert_to_tensor((2, 25))
self.assertEqual(outputs.start_logits.shape, expected_shape)
self.assertEqual(outputs.end_logits.shape, expected_shape)
| transformers-main | tests/models/layoutlm/test_modeling_tf_layoutlm.py |
transformers-main | tests/models/efficientformer/__init__.py |
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch EfficientFormer model. """
import inspect
import unittest
import warnings
from typing import List
from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
)
from transformers.models.efficientformer.modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class EfficientFormerModelTester:
def __init__(
self,
parent,
batch_size: int = 13,
image_size: int = 64,
patch_size: int = 2,
embed_dim: int = 3,
num_channels: int = 3,
is_training: bool = True,
use_labels: bool = True,
hidden_size: int = 128,
hidden_sizes=[16, 32, 64, 128],
num_hidden_layers: int = 7,
num_attention_heads: int = 4,
intermediate_size: int = 37,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.1,
attention_probs_dropout_prob: float = 0.1,
type_sequence_label_size: int = 10,
initializer_range: float = 0.02,
encoder_stride: int = 2,
num_attention_outputs: int = 1,
dim: int = 128,
depths: List[int] = [2, 2, 2, 2],
resolution: int = 2,
mlp_expansion_ratio: int = 2,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.encoder_stride = encoder_stride
self.num_attention_outputs = num_attention_outputs
self.embed_dim = embed_dim
self.seq_length = embed_dim + 1
self.resolution = resolution
self.depths = depths
self.hidden_sizes = hidden_sizes
self.dim = dim
self.mlp_expansion_ratio = mlp_expansion_ratio
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return EfficientFormerConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
encoder_stride=self.encoder_stride,
resolution=self.resolution,
depths=self.depths,
hidden_sizes=self.hidden_sizes,
dim=self.dim,
mlp_expansion_ratio=self.mlp_expansion_ratio,
)
def create_and_check_model(self, config, pixel_values, labels):
model = EfficientFormerModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = EfficientFormerForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
config.num_channels = 1
model = EfficientFormerForImageClassification(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
pixel_values,
labels,
) = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class EfficientFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as EfficientFormer does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(
EfficientFormerModel,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerForImageClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": EfficientFormerModel,
"image-classification": (
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
),
}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = EfficientFormerModelTester(self)
self.config_tester = ConfigTester(
self, config_class=EfficientFormerConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="EfficientFormer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="EfficientFormer does not support input and output embeddings")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
seq_length = seq_length * self.model_tester.chunk_length
else:
seq_length = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[-1].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet")
def test_for_masked_image_modeling(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
# special case for EfficientFormerForImageClassificationWithTeacher model
def test_training(self):
if not self.model_tester.is_training:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
# EfficientFormerForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(MODEL_MAPPING)
or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
):
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_problem_types(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
problem_types = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
]
or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
config.problem_type = problem_type["title"]
config.num_labels = problem_type["num_labels"]
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
if problem_type["num_labels"] > 1:
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=True) as warning_list:
loss = model(**inputs).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}"
)
loss.backward()
@slow
def test_model_from_pretrained(self):
for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = EfficientFormerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_attention_outputs)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_attention_outputs)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class EfficientFormerModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
if is_vision_available()
else None
)
@slow
def test_inference_image_classification_head(self):
model = EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300").to(
torch_device
)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = (1, 1000)
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.0555, 0.4825, -0.0852]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0][:3], expected_slice, atol=1e-4))
@slow
def test_inference_image_classification_head_with_teacher(self):
model = EfficientFormerForImageClassificationWithTeacher.from_pretrained(
"snap-research/efficientformer-l1-300"
).to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = (1, 1000)
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.1312, 0.4353, -1.0499]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0][:3], expected_slice, atol=1e-4))
| transformers-main | tests/models/efficientformer/test_modeling_efficientformer.py |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow EfficientFormer model. """
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class TFEfficientFormerModelTester:
def __init__(
self,
parent,
batch_size: int = 13,
image_size: int = 64,
patch_size: int = 2,
embed_dim: int = 3,
num_channels: int = 3,
is_training: bool = True,
use_labels: bool = True,
hidden_size: int = 128,
hidden_sizes=[16, 32, 64, 128],
num_hidden_layers: int = 7,
num_attention_heads: int = 4,
intermediate_size: int = 37,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.1,
attention_probs_dropout_prob: float = 0.1,
type_sequence_label_size: int = 10,
initializer_range: float = 0.02,
encoder_stride: int = 2,
num_attention_outputs: int = 1,
dim: int = 128,
depths: List[int] = [2, 2, 2, 2],
resolution: int = 2,
mlp_expansion_ratio: int = 2,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.encoder_stride = encoder_stride
self.num_attention_outputs = num_attention_outputs
self.embed_dim = embed_dim
self.seq_length = embed_dim + 1
self.resolution = resolution
self.depths = depths
self.hidden_sizes = hidden_sizes
self.dim = dim
self.mlp_expansion_ratio = mlp_expansion_ratio
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return EfficientFormerConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
encoder_stride=self.encoder_stride,
resolution=self.resolution,
depths=self.depths,
hidden_sizes=self.hidden_sizes,
dim=self.dim,
mlp_expansion_ratio=self.mlp_expansion_ratio,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFEfficientFormerModel(config=config)
result = model(pixel_values, training=False)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = TFEfficientFormerForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
config.num_channels = 1
model = TFEfficientFormerForImageClassification(config)
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFEfficientFormerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_tf_common.py, as EfficientFormer does not use input_ids,
inputs_embeds, attention_mask and seq_length.
"""
all_model_classes = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": TFEfficientFormerModel,
"image-classification": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFEfficientFormerModelTester(self)
self.config_tester = ConfigTester(
self, config_class=EfficientFormerConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="EfficientFormer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="EfficientFormer does not support input and output embeddings")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
seq_length = seq_length * self.model_tester.chunk_length
else:
seq_length = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.asseretIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[-1].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet")
def test_for_masked_image_modeling(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFEfficientFormerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_attention_outputs)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_attention_outputs)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_compile_tf_model(self):
# We use a simplified version of this test for EfficientFormer because it requires training=False
# and Keras refuses to let us force that during functional construction
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
model = model_class(config)
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
functional_inputs = {
key: tf.keras.Input(shape=val.shape[1:], dtype=val.dtype, name=key)
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
outputs_dict = model(functional_inputs)
self.assertTrue(outputs_dict is not None)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class EfficientFormerModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
if is_vision_available()
else None
)
@slow
def test_inference_image_classification_head(self):
model = TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300")
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs, training=False)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-0.0555, 0.4825, -0.0852])
self.assertTrue(np.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@slow
def test_inference_image_classification_head_with_teacher(self):
model = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"snap-research/efficientformer-l1-300"
)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs, training=False)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-0.1312, 0.4353, -1.0499])
self.assertTrue(np.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
| transformers-main | tests/models/efficientformer/test_modeling_tf_efficientformer.py |
# coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_vision_available():
from transformers import ViTImageProcessor
class EfficientFormerImageProcessorTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
num_channels=3,
image_size=224,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class EfficientFormerImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ViTImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = EfficientFormerImageProcessorTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_proc_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
| transformers-main | tests/models/efficientformer/test_image_processing_efficientformer.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import GitConfig, GitProcessor, GitVisionConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, GitForCausalLM, GitModel, GitVisionModel
from transformers.models.git.modeling_git import GIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class GitVisionModelTester:
def __init__(
self,
parent,
batch_size=12,
image_size=32,
patch_size=16,
num_channels=3,
is_training=True,
hidden_size=32,
projection_dim=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.hidden_size = hidden_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.scope = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
return GitVisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values):
model = GitVisionModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
image_size = (self.image_size, self.image_size)
patch_size = (self.patch_size, self.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class GitVisionModelTest(ModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as GIT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (GitVisionModel,) if is_torch_available() else ()
fx_compatible = True
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = GitVisionModelTester(self)
self.config_tester = ConfigTester(self, config_class=GitVisionConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="GIT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="GitVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="GitVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in GIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GitVisionModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class GitModelTester:
def __init__(
self,
parent,
num_channels=3,
image_size=32,
patch_size=16,
batch_size=13,
text_seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
initializer_range=0.02,
num_labels=3,
scope=None,
):
self.parent = parent
self.num_channels = num_channels
self.image_size = image_size
self.patch_size = patch_size
self.batch_size = batch_size
self.text_seq_length = text_seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
# make sure the BOS, EOS and PAD tokens are within the vocab
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
# for GIT, the sequence length is the sum of the text and patch tokens, + 1 due to the CLS token
self.seq_length = self.text_seq_length + int((self.image_size / self.patch_size) ** 2) + 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.text_seq_length])
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, input_ids, input_mask, pixel_values
def get_config(self):
"""
Returns a tiny configuration by default.
"""
return GitConfig(
vision_config={
"num_channels": self.num_channels,
"image_size": self.image_size,
"patch_size": self.patch_size,
"hidden_size": self.hidden_size,
"projection_dim": 32,
"num_hidden_layers": self.num_hidden_layers,
"num_attention_heads": self.num_attention_heads,
},
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
)
def create_and_check_model(self, config, input_ids, input_mask, pixel_values):
model = GitModel(config=config)
model.to(torch_device)
model.eval()
# inference with pixel values
result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
# inference without pixel values
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size)
)
def create_and_check_for_causal_lm(self, config, input_ids, input_mask, pixel_values):
model = GitForCausalLM(config=config)
model.to(torch_device)
model.eval()
# inference with pixel values
result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
# inference without pixel values
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.vocab_size))
# training
result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertTrue(result.loss.item() > 0)
def _test_beam_search_generate(self, config, input_ids, input_mask, pixel_values):
model = GitForCausalLM(config=config)
model.to(torch_device)
model.eval()
# generate
generated_ids = model.generate(
input_ids,
attention_mask=input_mask,
pixel_values=pixel_values,
do_sample=False,
max_length=20,
num_beams=2,
num_return_sequences=2,
)
self.parent.assertEqual(generated_ids.shape, (self.batch_size * 2, 20))
def _test_batched_generate_captioning(self, config, input_ids, input_mask, pixel_values):
model = GitForCausalLM(config=config)
model.to(torch_device)
model.eval()
# generate
generated_ids = model.generate(
input_ids=None, # captioning -> no input_ids
attention_mask=None,
pixel_values=pixel_values,
do_sample=False,
max_length=20,
num_beams=2,
num_return_sequences=2,
)
self.parent.assertEqual(generated_ids.shape, (self.batch_size * 2, 20))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
pixel_values,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"pixel_values": pixel_values,
}
return config, inputs_dict
@require_torch
class GitModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GitModel, GitForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (GitForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": GitModel, "image-to-text": GitForCausalLM, "text-generation": GitForCausalLM}
if is_torch_available()
else {}
)
fx_compatible = False
test_torchscript = False
# special case for GitForCausalLM model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_CAUSAL_LM_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length),
dtype=torch.long,
device=torch_device,
)
return inputs_dict
def setUp(self):
self.model_tester = GitModelTester(self)
self.config_tester = ConfigTester(self, config_class=GitConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_beam_search_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester._test_beam_search_generate(*config_and_inputs)
def test_batched_generate_captioning(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester._test_batched_generate_captioning(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in GIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GitModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip(reason="GIT has pixel values as additional input")
def test_beam_search_generate_dict_outputs_use_cache(self):
pass
@unittest.skip(reason="GIT has pixel values as additional input")
def test_contrastive_generate(self):
pass
@unittest.skip(reason="GIT has pixel values as additional input")
def test_contrastive_generate_dict_outputs_use_cache(self):
pass
@unittest.skip(reason="GIT has pixel values as additional input")
def test_greedy_generate_dict_outputs_use_cache(self):
pass
@require_torch
@require_vision
@slow
class GitModelIntegrationTest(unittest.TestCase):
def test_forward_pass(self):
processor = GitProcessor.from_pretrained("microsoft/git-base")
model = GitForCausalLM.from_pretrained("microsoft/git-base")
model.to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, text="hello world", return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
expected_shape = torch.Size((1, 201, 30522))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor(
[[-0.9514, -0.9512, -0.9507], [-0.5454, -0.5453, -0.5453], [-0.8862, -0.8857, -0.8848]],
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))
def test_inference_image_captioning(self):
processor = GitProcessor.from_pretrained("microsoft/git-base")
model = GitForCausalLM.from_pretrained("microsoft/git-base")
model.to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
outputs = model.generate(
pixel_values=pixel_values, max_length=20, output_scores=True, return_dict_in_generate=True
)
generated_caption = processor.batch_decode(outputs.sequences, skip_special_tokens=True)[0]
expected_shape = torch.Size((1, 9))
self.assertEqual(outputs.sequences.shape, expected_shape)
self.assertEquals(generated_caption, "two cats laying on a pink blanket")
self.assertTrue(outputs.scores[-1].shape, expected_shape)
expected_slice = torch.tensor([[-0.8805, -0.8803, -0.8799]], device=torch_device)
self.assertTrue(torch.allclose(outputs.scores[-1][0, :3], expected_slice, atol=1e-4))
def test_visual_question_answering(self):
processor = GitProcessor.from_pretrained("microsoft/git-base-textvqa")
model = GitForCausalLM.from_pretrained("microsoft/git-base-textvqa")
model.to(torch_device)
# prepare image
file_path = hf_hub_download(repo_id="nielsr/textvqa-sample", filename="bus.png", repo_type="dataset")
image = Image.open(file_path).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# prepare question
question = "what does the front of the bus say at the top?"
input_ids = processor(text=question, add_special_tokens=False).input_ids
input_ids = [processor.tokenizer.cls_token_id] + input_ids
input_ids = torch.tensor(input_ids).unsqueeze(0).to(torch_device)
generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=20)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
expected_shape = torch.Size((1, 15))
self.assertEqual(generated_ids.shape, expected_shape)
self.assertEquals(generated_caption, "what does the front of the bus say at the top? special")
def test_batched_generation(self):
processor = GitProcessor.from_pretrained("microsoft/git-base-coco")
model = GitForCausalLM.from_pretrained("microsoft/git-base-coco")
model.to(torch_device)
# create batch of size 2
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=[image, image], return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# we have to prepare `input_ids` with the same batch size as `pixel_values`
start_token_id = model.config.bos_token_id
input_ids = torch.tensor([[start_token_id], [start_token_id]], device=torch_device)
generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
generated_captions = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEquals(generated_captions, ["two cats sleeping on a pink blanket next to remotes."] * 2)
| transformers-main | tests/models/git/test_modeling_git.py |
transformers-main | tests/models/git/__init__.py |
|
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class GitProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
image_processor = CLIPImageProcessor()
tokenizer = BertTokenizer.from_pretrained(
"hf-internal-testing/tiny-random-BertModel", model_input_names=["input_ids", "attention_mask"]
)
processor = GitProcessor(image_processor, tokenizer)
processor.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
def test_save_load_pretrained_additional_features(self):
processor = GitProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
processor = GitProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, CLIPImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_feat_extract = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str, return_token_type_ids=False)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
| transformers-main | tests/models/git/test_processor_git.py |
# coding=utf-8
# Copyright 2019 Hugging Face inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import FNetTokenizer, FNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow, tooslow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class FNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = FNetTokenizer
rust_tokenizer_class = FNetTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
test_sentencepiece_ignore_case = True
test_seq2seq = False
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = FNetTokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
def get_input_output_texts(self, tokenizer):
input_text = "this is a test"
output_text = "this is a test"
return input_text, output_text
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<pad>"
token_id = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<pad>")
self.assertEqual(vocab_keys[1], "<unk>")
self.assertEqual(vocab_keys[-1], "▁eloquent")
self.assertEqual(len(vocab_keys), 30_000)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 30_000)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_full_tokenizer(self):
tokenizer = FNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁", "T", "his", "▁is", "▁a", "▁test"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [13, 1, 4398, 25, 21, 1289])
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
"▁",
"<unk>",
"▁was",
"▁born",
"▁in",
"▁9",
"2000",
",",
"▁and",
"▁this",
"▁is",
"▁fal",
"s",
"<unk>",
".",
],
)
def test_sequence_builders(self):
tokenizer = FNetTokenizer(SAMPLE_VOCAB)
text = tokenizer.encode("sequence builders")
text_2 = tokenizer.encode("multi-sequence build")
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [
tokenizer.sep_token_id
]
# Overriden Tests - loading the fast tokenizer from slow just takes too long
def test_special_tokens_initialization(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
r_output = tokenizer_r.encode("Hey this is a <special> token")
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
self.assertTrue(special_token_id in r_output)
if self.test_slow_tokenizer:
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
p_output = tokenizer_p.encode("Hey this is a <special> token")
cr_output = tokenizer_r.encode("Hey this is a <special> token")
self.assertEqual(p_output, r_output)
self.assertEqual(cr_output, r_output)
self.assertTrue(special_token_id in p_output)
self.assertTrue(special_token_id in cr_output)
@tooslow
def test_special_tokens_initialization_from_slow(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
)
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
p_output = tokenizer_p.encode("Hey this is a <special> token")
cr_output = tokenizer_r.encode("Hey this is a <special> token")
self.assertEqual(p_output, cr_output)
self.assertTrue(special_token_id in p_output)
self.assertTrue(special_token_id in cr_output)
# Overriden Tests
def test_padding(self, max_length=50):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
pad_token_id = tokenizer_p.pad_token_id
# Encode - Simple input
input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode("This is a simple input", padding="longest")
input_p = tokenizer_p.encode("This is a simple input", padding=True)
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# Encode - Pair input
input_r = tokenizer_r.encode(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# Encode_plus - Simple input
input_r = tokenizer_r.encode_plus(
"This is a simple input", max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
input_r = tokenizer_r.encode_plus(
"This is a simple input", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
# Encode_plus - Pair input
input_r = tokenizer_r.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
input_r = tokenizer_r.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
# Batch_encode_plus - Simple input
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
pad_to_max_length=True,
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
pad_to_max_length=True,
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding="max_length",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding="longest",
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"],
max_length=max_length,
padding=True,
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], padding="longest"
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], padding=True
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Batch_encode_plus - Pair input
input_r = tokenizer_r.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=max_length,
truncation=True,
padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=max_length,
truncation=True,
padding="max_length",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
padding=True,
)
input_p = tokenizer_p.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
padding="longest",
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Using pad on single examples after tokenization
input_r = tokenizer_r.encode_plus("This is a input 1")
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_r.encode_plus("This is a input 1")
input_p = tokenizer_r.pad(input_p)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
# Using pad on single examples after tokenization
input_r = tokenizer_r.encode_plus("This is a input 1")
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_r.encode_plus("This is a input 1")
input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
# Using pad after tokenization
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_r.pad(input_p)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Using pad after tokenization
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
@slow
def test_save_pretrained(self):
super().test_save_pretrained()
@slow
def test_save_slow_from_fast_and_reload_fast(self):
super().test_save_slow_from_fast_and_reload_fast()
def assert_batch_padded_input_match(
self,
input_r: dict,
input_p: dict,
max_length: int,
pad_token_id: int,
model_main_input_name: str = "input_ids",
):
for i_r in input_r.values():
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
len(i_r[1]), max_length
)
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
len(i_r[1]), max_length
)
for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]):
self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id)
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[4, 4616, 107, 163, 328, 14, 63, 1726, 106, 11954, 16659, 23, 83, 16688, 11427, 328, 107, 36, 11954, 16659, 23, 83, 16688, 6153, 82, 961, 16688, 3474, 16710, 1696, 2306, 16688, 10854, 2524, 3827, 561, 163, 3474, 16680, 62, 226, 2092, 16680, 379, 3474, 16660, 16680, 2436, 16667, 16671, 16680, 999, 87, 3474, 16680, 2436, 16667, 5208, 800, 16710, 68, 2018, 2959, 3037, 163, 16663, 11617, 16710, 36, 2018, 2959, 4737, 163, 16663, 16667, 16674, 16710, 91, 372, 5087, 16745, 2205, 82, 961, 3608, 38, 1770, 16745, 7984, 36, 2565, 751, 9017, 1204, 864, 218, 1244, 16680, 11954, 16659, 23, 83, 36, 14686, 23, 7619, 16678, 5], [4, 28, 532, 65, 1929, 33, 391, 16688, 3979, 9, 2565, 7849, 299, 225, 34, 2040, 305, 167, 289, 16667, 16078, 32, 1966, 181, 4626, 63, 10575, 71, 851, 1491, 36, 624, 4757, 38, 208, 8038, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 13, 1467, 5187, 26, 2521, 4567, 16664, 372, 13, 16209, 3314, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="google/fnet-base",
revision="34219a71ca20e280cc6000b89673a169c65d605c",
)
| transformers-main | tests/models/fnet/test_tokenization_fnet.py |
transformers-main | tests/models/fnet/__init__.py |
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch FNet model. """
import unittest
from typing import Dict, List, Tuple
from transformers import FNetConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetModel,
FNetTokenizerFast,
)
from transformers.models.fnet.modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetBasicFourierTransform,
is_scipy_available,
)
# Override ConfigTester
class FNetConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
if self.has_text_modality:
self.parent.assertTrue(hasattr(config, "vocab_size"))
self.parent.assertTrue(hasattr(config, "hidden_size"))
self.parent.assertTrue(hasattr(config, "num_hidden_layers"))
class FNetModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
def get_config(self):
return FNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
tpu_short_seq_length=self.seq_length,
)
@require_torch
def create_and_check_fourier_transform(self, config):
hidden_states = floats_tensor([self.batch_size, self.seq_length, config.hidden_size])
transform = FNetBasicFourierTransform(config)
fftn_output = transform(hidden_states)
config.use_tpu_fourier_optimizations = True
if is_scipy_available():
transform = FNetBasicFourierTransform(config)
dft_output = transform(hidden_states)
config.max_position_embeddings = 4097
transform = FNetBasicFourierTransform(config)
fft_output = transform(hidden_states)
if is_scipy_available():
self.parent.assertTrue(torch.allclose(fftn_output[0][0], dft_output[0][0], atol=1e-4))
self.parent.assertTrue(torch.allclose(fft_output[0][0], dft_output[0][0], atol=1e-4))
self.parent.assertTrue(torch.allclose(fftn_output[0][0], fft_output[0][0], atol=1e-4))
def create_and_check_model(self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels):
model = FNetModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_pretraining(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_next_sentence_prediction(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
token_type_ids=token_type_ids,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = FNetForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = FNetForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = FNetForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids}
return config, inputs_dict
@require_torch
class FNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
FNetModel,
FNetForPreTraining,
FNetForMaskedLM,
FNetForNextSentencePrediction,
FNetForMultipleChoice,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": FNetModel,
"fill-mask": FNetForMaskedLM,
"question-answering": FNetForQuestionAnswering,
"text-classification": FNetForSequenceClassification,
"token-classification": FNetForTokenClassification,
"zero-shot": FNetForSequenceClassification,
}
if is_torch_available()
else {}
)
# Skip Tests
test_pruning = False
test_head_masking = False
test_pruning = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
# Overriden Tests
def test_attention_outputs(self):
pass
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
# tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
# dict_inputs = self._prepare_for_class(inputs_dict, model_class)
# check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
def setUp(self):
self.model_tester = FNetModelTester(self)
self.config_tester = FNetConfigTester(self, config_class=FNetConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in FNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = FNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class FNetModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_for_masked_lm(self):
"""
For comparison:
1. Modify the pre-training model `__call__` to skip computing metrics and return masked_lm_output like so:
```
...
sequence_output, pooled_output = EncoderModel(
self.config, random_seed=self.random_seed, name="encoder")(
input_ids, input_mask, type_ids, deterministic=deterministic)
masked_lm_output = nn.Dense(
self.config.d_emb,
kernel_init=default_kernel_init,
name="predictions_dense")(
sequence_output)
masked_lm_output = nn.gelu(masked_lm_output)
masked_lm_output = nn.LayerNorm(
epsilon=LAYER_NORM_EPSILON, name="predictions_layer_norm")(
masked_lm_output)
masked_lm_logits = layers.OutputProjection(
kernel=self._get_embedding_table(), name="predictions_output")(
masked_lm_output)
next_sentence_logits = layers.OutputProjection(
n_out=2, kernel_init=default_kernel_init, name="classification")(
pooled_output)
return masked_lm_logits
...
```
2. Run the following:
>>> import jax.numpy as jnp
>>> import sentencepiece as spm
>>> from flax.training import checkpoints
>>> from f_net.models import PreTrainingModel
>>> from f_net.configs.pretraining import get_config, ModelArchitecture
>>> pretrained_params = checkpoints.restore_checkpoint('./f_net/f_net_checkpoint', None) # Location of original checkpoint
>>> pretrained_config = get_config()
>>> pretrained_config.model_arch = ModelArchitecture.F_NET
>>> vocab_filepath = "./f_net/c4_bpe_sentencepiece.model" # Location of the sentence piece model
>>> tokenizer = spm.SentencePieceProcessor()
>>> tokenizer.Load(vocab_filepath)
>>> with pretrained_config.unlocked():
>>> pretrained_config.vocab_size = tokenizer.GetPieceSize()
>>> tokens = jnp.array([[0, 1, 2, 3, 4, 5]])
>>> type_ids = jnp.zeros_like(tokens, dtype="i4")
>>> attention_mask = jnp.ones_like(tokens) # Dummy. This gets deleted inside the model.
>>> flax_pretraining_model = PreTrainingModel(pretrained_config)
>>> pretrained_model_params = freeze(pretrained_params['target'])
>>> flax_model_outputs = flax_pretraining_model.apply({"params": pretrained_model_params}, tokens, attention_mask, type_ids, None, None, None, None, deterministic=True)
>>> masked_lm_logits[:, :3, :3]
"""
model = FNetForMaskedLM.from_pretrained("google/fnet-base")
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
with torch.no_grad():
output = model(input_ids)[0]
vocab_size = 32000
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-1.7819, -7.7384, -7.5002], [-3.4746, -8.5943, -7.7762], [-3.2052, -9.0771, -8.3468]]],
device=torch_device,
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
@require_tokenizers
def test_inference_long_sentence(self):
model = FNetForMaskedLM.from_pretrained("google/fnet-base")
model.to(torch_device)
tokenizer = FNetTokenizerFast.from_pretrained("google/fnet-base")
inputs = tokenizer(
"the man worked as a [MASK].",
"this is his [MASK].",
return_tensors="pt",
padding="max_length",
max_length=512,
)
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
logits = model(**inputs).logits
predictions_mask_1 = tokenizer.decode(logits[0, 6].topk(5).indices)
predictions_mask_2 = tokenizer.decode(logits[0, 12].topk(5).indices)
self.assertEqual(predictions_mask_1.split(" "), ["man", "child", "teacher", "woman", "model"])
self.assertEqual(predictions_mask_2.split(" "), ["work", "wife", "job", "story", "name"])
@slow
def test_inference_for_next_sentence_prediction(self):
model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base")
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 2))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[-0.2234, -0.0226]], device=torch_device)
self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
@slow
def test_inference_model(self):
model = FNetModel.from_pretrained("google/fnet-base")
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 6, model.config.hidden_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[4.1541, -0.1051, -0.1667], [-0.9144, 0.2939, -0.0086], [-0.8472, -0.7281, 0.0256]]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| transformers-main | tests/models/fnet/test_modeling_fnet.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch REALM model. """
import copy
import unittest
import numpy as np
from transformers import RealmConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
RealmEmbedder,
RealmForOpenQA,
RealmKnowledgeAugEncoder,
RealmReader,
RealmRetriever,
RealmScorer,
RealmTokenizer,
)
class RealmModelTester:
def __init__(
self,
parent,
batch_size=13,
retriever_proj_size=128,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
span_hidden_size=50,
max_span_width=10,
reader_layer_norm_eps=1e-3,
reader_beam_size=4,
reader_seq_len=288 + 32,
num_block_records=13353718,
searcher_beam_size=8,
searcher_seq_len=64,
num_labels=3,
num_choices=4,
num_candidates=10,
scope=None,
):
# General config
self.parent = parent
self.batch_size = batch_size
self.retriever_proj_size = retriever_proj_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
# Reader config
self.span_hidden_size = span_hidden_size
self.max_span_width = max_span_width
self.reader_layer_norm_eps = reader_layer_norm_eps
self.reader_beam_size = reader_beam_size
self.reader_seq_len = reader_seq_len
# Searcher config
self.num_block_records = num_block_records
self.searcher_beam_size = searcher_beam_size
self.searcher_seq_len = searcher_seq_len
self.num_labels = num_labels
self.num_choices = num_choices
self.num_candidates = num_candidates
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
candiate_input_ids = ids_tensor([self.batch_size, self.num_candidates, self.seq_length], self.vocab_size)
reader_input_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.vocab_size)
input_mask = None
candiate_input_mask = None
reader_input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
candiate_input_mask = random_attention_mask([self.batch_size, self.num_candidates, self.seq_length])
reader_input_mask = random_attention_mask([self.reader_beam_size, self.reader_seq_len])
token_type_ids = None
candidate_token_type_ids = None
reader_token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
candidate_token_type_ids = ids_tensor(
[self.batch_size, self.num_candidates, self.seq_length], self.type_vocab_size
)
reader_token_type_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
# inputs with additional num_candidates axis.
scorer_encoder_inputs = (candiate_input_ids, candiate_input_mask, candidate_token_type_ids)
# reader inputs
reader_inputs = (reader_input_ids, reader_input_mask, reader_token_type_ids)
return (
config,
input_ids,
token_type_ids,
input_mask,
scorer_encoder_inputs,
reader_inputs,
sequence_labels,
token_labels,
choice_labels,
)
def get_config(self):
return RealmConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
retriever_proj_size=self.retriever_proj_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
num_candidates=self.num_candidates,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
def create_and_check_embedder(
self,
config,
input_ids,
token_type_ids,
input_mask,
scorer_encoder_inputs,
reader_inputs,
sequence_labels,
token_labels,
choice_labels,
):
model = RealmEmbedder(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.projected_score.shape, (self.batch_size, self.retriever_proj_size))
def create_and_check_encoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
scorer_encoder_inputs,
reader_inputs,
sequence_labels,
token_labels,
choice_labels,
):
model = RealmKnowledgeAugEncoder(config=config)
model.to(torch_device)
model.eval()
relevance_score = floats_tensor([self.batch_size, self.num_candidates])
result = model(
scorer_encoder_inputs[0],
attention_mask=scorer_encoder_inputs[1],
token_type_ids=scorer_encoder_inputs[2],
relevance_score=relevance_score,
labels=token_labels,
)
self.parent.assertEqual(
result.logits.shape, (self.batch_size * self.num_candidates, self.seq_length, self.vocab_size)
)
def create_and_check_reader(
self,
config,
input_ids,
token_type_ids,
input_mask,
scorer_encoder_inputs,
reader_inputs,
sequence_labels,
token_labels,
choice_labels,
):
model = RealmReader(config=config)
model.to(torch_device)
model.eval()
relevance_score = floats_tensor([self.reader_beam_size])
result = model(
reader_inputs[0],
attention_mask=reader_inputs[1],
token_type_ids=reader_inputs[2],
relevance_score=relevance_score,
)
self.parent.assertEqual(result.block_idx.shape, ())
self.parent.assertEqual(result.candidate.shape, ())
self.parent.assertEqual(result.start_pos.shape, ())
self.parent.assertEqual(result.end_pos.shape, ())
def create_and_check_scorer(
self,
config,
input_ids,
token_type_ids,
input_mask,
scorer_encoder_inputs,
reader_inputs,
sequence_labels,
token_labels,
choice_labels,
):
model = RealmScorer(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
candidate_input_ids=scorer_encoder_inputs[0],
candidate_attention_mask=scorer_encoder_inputs[1],
candidate_token_type_ids=scorer_encoder_inputs[2],
)
self.parent.assertEqual(result.relevance_score.shape, (self.batch_size, self.num_candidates))
self.parent.assertEqual(result.query_score.shape, (self.batch_size, self.retriever_proj_size))
self.parent.assertEqual(
result.candidate_score.shape, (self.batch_size, self.num_candidates, self.retriever_proj_size)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
scorer_encoder_inputs,
reader_inputs,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class RealmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
RealmEmbedder,
RealmKnowledgeAugEncoder,
# RealmScorer is excluded from common tests as it is a container model
# consisting of two RealmEmbedders & a simple inner product calculation.
# RealmScorer
)
if is_torch_available()
else ()
)
all_generative_model_classes = ()
pipeline_model_mapping = {} if is_torch_available() else {}
# disable these tests because there is no base_model in Realm
test_save_load_fast_init_from_base = False
test_save_load_fast_init_to_base = False
def setUp(self):
self.test_pruning = False
self.model_tester = RealmModelTester(self)
self.config_tester = ConfigTester(self, config_class=RealmConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_embedder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_embedder(*config_and_inputs)
def test_encoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_encoder(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_embedder(*config_and_inputs)
self.model_tester.create_and_check_encoder(*config_and_inputs)
def test_scorer(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_scorer(*config_and_inputs)
def test_training(self):
if not self.model_tester.is_training:
return
config, *inputs = self.model_tester.prepare_config_and_inputs()
input_ids, token_type_ids, input_mask, scorer_encoder_inputs = inputs[0:4]
config.return_dict = True
tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa")
# RealmKnowledgeAugEncoder training
model = RealmKnowledgeAugEncoder(config)
model.to(torch_device)
model.train()
inputs_dict = {
"input_ids": scorer_encoder_inputs[0].to(torch_device),
"attention_mask": scorer_encoder_inputs[1].to(torch_device),
"token_type_ids": scorer_encoder_inputs[2].to(torch_device),
"relevance_score": floats_tensor([self.model_tester.batch_size, self.model_tester.num_candidates]),
}
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs = inputs_dict
loss = model(**inputs).loss
loss.backward()
# RealmForOpenQA training
openqa_config = copy.deepcopy(config)
openqa_config.vocab_size = 30522 # the retrieved texts will inevitably have more than 99 vocabs.
openqa_config.num_block_records = 5
openqa_config.searcher_beam_size = 2
block_records = np.array(
[
b"This is the first record.",
b"This is the second record.",
b"This is the third record.",
b"This is the fourth record.",
b"This is the fifth record.",
],
dtype=object,
)
retriever = RealmRetriever(block_records, tokenizer)
model = RealmForOpenQA(openqa_config, retriever)
model.to(torch_device)
model.train()
inputs_dict = {
"input_ids": input_ids[:1].to(torch_device),
"attention_mask": input_mask[:1].to(torch_device),
"token_type_ids": token_type_ids[:1].to(torch_device),
"answer_ids": input_ids[:1].tolist(),
}
inputs = self._prepare_for_class(inputs_dict, RealmForOpenQA)
loss = model(**inputs).reader_output.loss
loss.backward()
# Test model.block_embedding_to
device = torch.device("cpu")
model.block_embedding_to(device)
loss = model(**inputs).reader_output.loss
loss.backward()
self.assertEqual(model.block_emb.device.type, device.type)
@slow
def test_embedder_from_pretrained(self):
model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
self.assertIsNotNone(model)
@slow
def test_encoder_from_pretrained(self):
model = RealmKnowledgeAugEncoder.from_pretrained("google/realm-cc-news-pretrained-encoder")
self.assertIsNotNone(model)
@slow
def test_open_qa_from_pretrained(self):
model = RealmForOpenQA.from_pretrained("google/realm-orqa-nq-openqa")
self.assertIsNotNone(model)
@slow
def test_reader_from_pretrained(self):
model = RealmReader.from_pretrained("google/realm-orqa-nq-reader")
self.assertIsNotNone(model)
@slow
def test_scorer_from_pretrained(self):
model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer")
self.assertIsNotNone(model)
@require_torch
class RealmModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_embedder(self):
retriever_projected_size = 128
model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, retriever_projected_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[-0.0714, -0.0837, -0.1314]])
self.assertTrue(torch.allclose(output[:, :3], expected_slice, atol=1e-4))
@slow
def test_inference_encoder(self):
num_candidates = 2
vocab_size = 30522
model = RealmKnowledgeAugEncoder.from_pretrained(
"google/realm-cc-news-pretrained-encoder", num_candidates=num_candidates
)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
relevance_score = torch.tensor([[0.3, 0.7]], dtype=torch.float32)
output = model(input_ids, relevance_score=relevance_score)[0]
expected_shape = torch.Size((2, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[[-11.0888, -11.2544], [-10.2170, -10.3874]]])
self.assertTrue(torch.allclose(output[1, :2, :2], expected_slice, atol=1e-4))
@slow
def test_inference_open_qa(self):
from transformers.models.realm.retrieval_realm import RealmRetriever
tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa")
retriever = RealmRetriever.from_pretrained("google/realm-orqa-nq-openqa")
model = RealmForOpenQA.from_pretrained(
"google/realm-orqa-nq-openqa",
retriever=retriever,
)
question = "Who is the pioneer in modern computer science?"
question = tokenizer(
[question],
padding=True,
truncation=True,
max_length=model.config.searcher_seq_len,
return_tensors="pt",
).to(model.device)
predicted_answer_ids = model(**question).predicted_answer_ids
predicted_answer = tokenizer.decode(predicted_answer_ids)
self.assertEqual(predicted_answer, "alan mathison turing")
@slow
def test_inference_reader(self):
config = RealmConfig(reader_beam_size=2, max_span_width=3)
model = RealmReader.from_pretrained("google/realm-orqa-nq-reader", config=config)
concat_input_ids = torch.arange(10).view((2, 5))
concat_token_type_ids = torch.tensor([[0, 0, 1, 1, 1], [0, 0, 1, 1, 1]], dtype=torch.int64)
concat_block_mask = torch.tensor([[0, 0, 1, 1, 0], [0, 0, 1, 1, 0]], dtype=torch.int64)
relevance_score = torch.tensor([0.3, 0.7], dtype=torch.float32)
output = model(
concat_input_ids,
token_type_ids=concat_token_type_ids,
relevance_score=relevance_score,
block_mask=concat_block_mask,
return_dict=True,
)
block_idx_expected_shape = torch.Size(())
start_pos_expected_shape = torch.Size((1,))
end_pos_expected_shape = torch.Size((1,))
self.assertEqual(output.block_idx.shape, block_idx_expected_shape)
self.assertEqual(output.start_pos.shape, start_pos_expected_shape)
self.assertEqual(output.end_pos.shape, end_pos_expected_shape)
expected_block_idx = torch.tensor(1)
expected_start_pos = torch.tensor(3)
expected_end_pos = torch.tensor(3)
self.assertTrue(torch.allclose(output.block_idx, expected_block_idx, atol=1e-4))
self.assertTrue(torch.allclose(output.start_pos, expected_start_pos, atol=1e-4))
self.assertTrue(torch.allclose(output.end_pos, expected_end_pos, atol=1e-4))
@slow
def test_inference_scorer(self):
num_candidates = 2
model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer", num_candidates=num_candidates)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
candidate_input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
output = model(input_ids, candidate_input_ids=candidate_input_ids)[0]
expected_shape = torch.Size((1, 2))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[0.7410, 0.7170]])
self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
| transformers-main | tests/models/realm/test_modeling_realm.py |
transformers-main | tests/models/realm/__init__.py |
|
# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class RealmTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = RealmTokenizer
rust_tokenizer_class = RealmTokenizerFast
test_rust_tokenizer = True
space_between_special_tokens = True
from_pretrained_filter = filter_non_english
def setUp(self):
super().setUp()
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_input_output_texts(self, tokenizer):
input_text = "UNwant\u00E9d,running"
output_text = "unwanted, running"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11])
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "UNwant\u00E9d,running"
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
# With lower casing
tokenizer = self.get_tokenizer(do_lower_case=True)
rust_tokenizer = self.get_rust_tokenizer(do_lower_case=True)
sequence = "UNwant\u00E9d,running"
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_chinese(self):
tokenizer = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"), ["ah", "\u535A", "\u63A8", "zz"])
def test_basic_tokenizer_lower(self):
tokenizer = BasicTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["hello", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
def test_basic_tokenizer_lower_strip_accents_false(self):
tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=False)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hällo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["h\u00E9llo"])
def test_basic_tokenizer_lower_strip_accents_true(self):
tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
def test_basic_tokenizer_lower_strip_accents_default(self):
tokenizer = BasicTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["hallo", "!", "how", "are", "you", "?"]
)
self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
def test_basic_tokenizer_no_lower(self):
tokenizer = BasicTokenizer(do_lower_case=False)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? "), ["HeLLo", "!", "how", "Are", "yoU", "?"]
)
def test_basic_tokenizer_no_lower_strip_accents_false(self):
tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=False)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HäLLo", "!", "how", "Are", "yoU", "?"]
)
def test_basic_tokenizer_no_lower_strip_accents_true(self):
tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=True)
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? "), ["HaLLo", "!", "how", "Are", "yoU", "?"]
)
def test_basic_tokenizer_respects_never_split_tokens(self):
tokenizer = BasicTokenizer(do_lower_case=False, never_split=["[UNK]"])
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]"), ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]
)
def test_wordpiece_tokenizer(self):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
vocab = {}
for i, token in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
self.assertListEqual(tokenizer.tokenize(""), [])
self.assertListEqual(tokenizer.tokenize("unwanted running"), ["un", "##want", "##ed", "runn", "##ing"])
self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
def test_is_whitespace(self):
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))
self.assertTrue(_is_whitespace("\r"))
self.assertTrue(_is_whitespace("\n"))
self.assertTrue(_is_whitespace("\u00A0"))
self.assertFalse(_is_whitespace("A"))
self.assertFalse(_is_whitespace("-"))
def test_is_control(self):
self.assertTrue(_is_control("\u0005"))
self.assertFalse(_is_control("A"))
self.assertFalse(_is_control(" "))
self.assertFalse(_is_control("\t"))
self.assertFalse(_is_control("\r"))
def test_is_punctuation(self):
self.assertTrue(_is_punctuation("-"))
self.assertTrue(_is_punctuation("$"))
self.assertTrue(_is_punctuation("`"))
self.assertTrue(_is_punctuation("."))
self.assertFalse(_is_punctuation("A"))
self.assertFalse(_is_punctuation(" "))
def test_clean_text(self):
tokenizer = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]], [["[UNK]"], [], ["[UNK]"]])
if self.test_rust_tokenizer:
rust_tokenizer = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]], [["[UNK]"], [], ["[UNK]"]]
)
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_2 + [102]
def test_offsets_with_special_characters(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
sentence = f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
tokens = tokenizer_r.encode_plus(
sentence,
return_attention_mask=False,
return_token_type_ids=False,
return_offsets_mapping=True,
add_special_tokens=True,
)
do_lower_case = tokenizer_r.do_lower_case if hasattr(tokenizer_r, "do_lower_case") else False
expected_results = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results], tokenizer_r.convert_ids_to_tokens(tokens["input_ids"])
)
self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])
@slow
def test_batch_encode_candidates(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]
encoded_sentence_r = tokenizer_r.batch_encode_candidates(text, max_length=10, return_tensors="np")
encoded_sentence_p = tokenizer_p.batch_encode_candidates(text, max_length=10, return_tensors="np")
expected_shape = (2, 2, 10)
self.assertEqual(encoded_sentence_r["input_ids"].shape, expected_shape)
self.assertEqual(encoded_sentence_r["attention_mask"].shape, expected_shape)
self.assertEqual(encoded_sentence_r["token_type_ids"].shape, expected_shape)
self.assertEqual(encoded_sentence_p["input_ids"].shape, expected_shape)
self.assertEqual(encoded_sentence_p["attention_mask"].shape, expected_shape)
self.assertEqual(encoded_sentence_p["token_type_ids"].shape, expected_shape)
| transformers-main | tests/models/realm/test_tokenization_realm.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class RealmRetrieverTest(TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
self.num_block_records = 5
# Realm tok
vocab_tokens = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"test",
"question",
"this",
"is",
"the",
"first",
"second",
"third",
"fourth",
"fifth",
"record",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
realm_tokenizer_path = os.path.join(self.tmpdirname, "realm_tokenizer")
os.makedirs(realm_tokenizer_path, exist_ok=True)
self.vocab_file = os.path.join(realm_tokenizer_path, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
realm_block_records_path = os.path.join(self.tmpdirname, "realm_block_records")
os.makedirs(realm_block_records_path, exist_ok=True)
def get_tokenizer(self) -> RealmTokenizer:
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname, "realm_tokenizer"))
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def get_config(self):
config = RealmConfig(num_block_records=self.num_block_records)
return config
def get_dummy_dataset(self):
dataset = Dataset.from_dict(
{
"id": ["0", "1"],
"question": ["foo", "bar"],
"answers": [["Foo", "Bar"], ["Bar"]],
}
)
return dataset
def get_dummy_block_records(self):
block_records = np.array(
[
b"This is the first record",
b"This is the second record",
b"This is the third record",
b"This is the fourth record",
b"This is the fifth record",
b"This is a longer longer longer record",
],
dtype=object,
)
return block_records
def get_dummy_retriever(self):
retriever = RealmRetriever(
block_records=self.get_dummy_block_records(),
tokenizer=self.get_tokenizer(),
)
return retriever
def test_retrieve(self):
config = self.get_config()
retriever = self.get_dummy_retriever()
tokenizer = retriever.tokenizer
retrieved_block_ids = np.array([0, 3], dtype="long")
question_input_ids = tokenizer(["Test question"]).input_ids
answer_ids = tokenizer(
["the fourth"],
add_special_tokens=False,
return_token_type_ids=False,
return_attention_mask=False,
).input_ids
max_length = config.reader_seq_len
has_answers, start_pos, end_pos, concat_inputs = retriever(
retrieved_block_ids, question_input_ids, answer_ids=answer_ids, max_length=max_length, return_tensors="np"
)
self.assertEqual(len(has_answers), 2)
self.assertEqual(len(start_pos), 2)
self.assertEqual(len(end_pos), 2)
self.assertEqual(concat_inputs.input_ids.shape, (2, 10))
self.assertEqual(concat_inputs.attention_mask.shape, (2, 10))
self.assertEqual(concat_inputs.token_type_ids.shape, (2, 10))
self.assertEqual(concat_inputs.special_tokens_mask.shape, (2, 10))
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]),
["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"],
)
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]),
["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"],
)
def test_block_has_answer(self):
config = self.get_config()
retriever = self.get_dummy_retriever()
tokenizer = retriever.tokenizer
retrieved_block_ids = np.array([0, 3, 5], dtype="long")
question_input_ids = tokenizer(["Test question"]).input_ids
answer_ids = tokenizer(
["the fourth", "longer longer"],
add_special_tokens=False,
return_token_type_ids=False,
return_attention_mask=False,
).input_ids
max_length = config.reader_seq_len
has_answers, start_pos, end_pos, _ = retriever(
retrieved_block_ids, question_input_ids, answer_ids=answer_ids, max_length=max_length, return_tensors="np"
)
self.assertEqual([False, True, True], has_answers)
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]], start_pos)
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]], end_pos)
def test_save_load_pretrained(self):
retriever = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname, "realm_block_records"))
# Test local path
retriever = retriever.from_pretrained(os.path.join(self.tmpdirname, "realm_block_records"))
self.assertEqual(retriever.block_records[0], b"This is the first record")
# Test mocked remote path
with patch("transformers.models.realm.retrieval_realm.hf_hub_download") as mock_hf_hub_download:
mock_hf_hub_download.return_value = os.path.join(
os.path.join(self.tmpdirname, "realm_block_records"), _REALM_BLOCK_RECORDS_FILENAME
)
retriever = RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa")
self.assertEqual(retriever.block_records[0], b"This is the first record")
| transformers-main | tests/models/realm/test_retrieval_realm.py |
transformers-main | tests/models/canine/__init__.py |
|
# coding=utf-8
# Copyright 2021 Google AI and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class CanineTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = CanineTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
tokenizer = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def canine_tokenizer(self):
return CanineTokenizer.from_pretrained("google/canine-s")
def get_tokenizer(self, **kwargs) -> CanineTokenizer:
tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
tokenizer._unicode_vocab_size = 1024
return tokenizer
@require_torch
def test_prepare_batch_integration(self):
tokenizer = self.canine_tokenizer
src_text = ["Life is like a box of chocolates.", "You never know what you're gonna get."]
# fmt: off
expected_src_tokens = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
# fmt: on
batch = tokenizer(src_text, padding=True, return_tensors="pt")
self.assertIsInstance(batch, BatchEncoding)
result = list(batch.input_ids.numpy()[0])
self.assertListEqual(expected_src_tokens, result)
self.assertEqual((2, 39), batch.input_ids.shape)
self.assertEqual((2, 39), batch.attention_mask.shape)
@require_torch
def test_encoding_keys(self):
tokenizer = self.canine_tokenizer
src_text = ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
batch = tokenizer(src_text, padding=True, return_tensors="pt")
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("input_ids", batch)
self.assertIn("attention_mask", batch)
self.assertIn("token_type_ids", batch)
@require_torch
def test_max_length_integration(self):
tokenizer = self.canine_tokenizer
tgt_text = [
"What's the weater?",
"It's about 25 degrees.",
]
targets = tokenizer(
text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors="pt"
)
self.assertEqual(32, targets["input_ids"].shape[1])
# cannot use default save_and_load_tokenzier test method because tokenzier has no vocab
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
shutil.rmtree(tmpdirname)
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
additional_special_tokens = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
new_additional_special_token = chr(0xE007)
additional_special_tokens.append(new_additional_special_token)
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
self.assertIn(new_additional_special_token, after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
def test_add_special_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input_text, ids = self.get_clean_sequence(tokenizer)
# a special token for Canine can be defined as follows:
SPECIAL_TOKEN = 0xE005
special_token = chr(SPECIAL_TOKEN)
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(len(encoded_special_token), 1)
text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
encoded = tokenizer.encode(text, add_special_tokens=False)
input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(encoded, input_encoded + special_token_id)
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
self.assertTrue(special_token not in decoded)
def test_tokenize_special_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
SPECIAL_TOKEN_1 = chr(0xE005)
SPECIAL_TOKEN_2 = chr(0xE006)
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1], special_tokens=True)
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]})
token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1)
token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2)
self.assertEqual(len(token_1), 1)
self.assertEqual(len(token_2), 1)
self.assertEqual(token_1[0], SPECIAL_TOKEN_1)
self.assertEqual(token_2[0], SPECIAL_TOKEN_2)
@require_tokenizers
def test_added_token_serializable(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# a special token for Canine can be defined as follows:
NEW_TOKEN = 0xE006
new_token = chr(NEW_TOKEN)
new_token = AddedToken(new_token, lstrip=True)
tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(tmp_dir_name)
tokenizer.from_pretrained(tmp_dir_name)
def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
tokenizer_list = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(tmp_dir)
with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
special_tokens_map = json.load(json_file)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
tokenizer_config = json.load(json_file)
# a special token for Canine can be defined as follows:
NEW_TOKEN = 0xE006
new_token_1 = chr(NEW_TOKEN)
special_tokens_map["additional_special_tokens"] = [new_token_1]
tokenizer_config["additional_special_tokens"] = [new_token_1]
with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
json.dump(special_tokens_map, outfile)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
json.dump(tokenizer_config, outfile)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
tokenizer_without_change_in_init = tokenizer_class.from_pretrained(tmp_dir, extra_ids=0)
self.assertIn(new_token_1, tokenizer_without_change_in_init.additional_special_tokens)
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_1],
tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_1])
),
)
NEW_TOKEN = 0xE007
new_token_2 = chr(NEW_TOKEN)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
new_added_tokens = [AddedToken(new_token_2, lstrip=True)]
tokenizer = tokenizer_class.from_pretrained(
tmp_dir, additional_special_tokens=new_added_tokens, extra_ids=0
)
self.assertIn(new_token_2, tokenizer.additional_special_tokens)
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_2], tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_2]))
)
@require_tokenizers
def test_encode_decode_with_spaces(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input = "hello world"
if self.space_between_special_tokens:
output = "[CLS] hello world [SEP]"
else:
output = input
encoded = tokenizer.encode(input, add_special_tokens=False)
decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
self.assertIn(decoded, [output, output.lower()])
# cannot use default `test_tokenizers_common_ids_setters` method because tokenizer has no vocab
def test_tokenizers_common_ids_setters(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
token_to_test_setters = "a"
token_id_to_test_setters = ord(token_to_test_setters)
for attr in attributes_list:
setattr(tokenizer, attr + "_id", None)
self.assertEqual(getattr(tokenizer, attr), None)
self.assertEqual(getattr(tokenizer, attr + "_id"), None)
setattr(tokenizer, attr + "_id", token_id_to_test_setters)
self.assertEqual(getattr(tokenizer, attr), token_to_test_setters)
self.assertEqual(getattr(tokenizer, attr + "_id"), token_id_to_test_setters)
setattr(tokenizer, "additional_special_tokens_ids", [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [])
additional_special_token_id = 0xE006
additional_special_token = chr(additional_special_token_id)
setattr(tokenizer, "additional_special_tokens_ids", [additional_special_token_id])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [additional_special_token])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [additional_special_token_id])
# tokenizer has a fixed vocab_size (namely all possible unicode code points)
def test_add_tokens_tokenizer(self):
pass
# CanineTokenizer does not support do_lower_case = True, as each character has its own Unicode code point
# ("b" and "B" for example have different Unicode code points)
def test_added_tokens_do_lower_case(self):
pass
# CanineModel does not support the get_input_embeddings nor the get_vocab method
def test_np_encode_plus_sent_to_model(self):
pass
# CanineModel does not support the get_input_embeddings nor the get_vocab method
def test_torch_encode_plus_sent_to_model(self):
pass
# tokenizer can be instantiated without any pretrained files, so no need for pretrained tokenizer list
def test_pretrained_model_lists(self):
pass
# tokenizer does not have vocabulary
def test_get_vocab(self):
pass
# inputs cannot be pretokenized since ids depend on whole input string and not just on single characters
def test_pretokenized_inputs(self):
pass
# tests all ids in vocab => vocab doesn't exist so unnecessary to test
def test_conversion_reversible(self):
pass
| transformers-main | tests/models/canine/test_tokenization_canine.py |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch CANINE model. """
import unittest
from typing import List, Tuple
from transformers import CanineConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, global_rng, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineModel,
)
from transformers.models.canine.modeling_canine import CANINE_PRETRAINED_MODEL_ARCHIVE_LIST
class CanineModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
# let's use a vocab size that's way bigger than BERT's one
# NOTE: this is not a model parameter, just an input
vocab_size=100000,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
num_hash_buckets=16,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.num_hash_buckets = num_hash_buckets
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor(input_ids.shape, self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return CanineConfig(
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
num_hash_buckets=self.num_hash_buckets,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = CanineModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = CanineForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = CanineForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = CanineForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = CanineForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class CanineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
CanineModel,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": CanineModel,
"question-answering": CanineForQuestionAnswering,
"text-classification": CanineForSequenceClassification,
"token-classification": CanineForTokenClassification,
"zero-shot": CanineForSequenceClassification,
}
if is_torch_available()
else {}
)
test_mismatched_shapes = False
test_resize_embeddings = False
test_pruning = False
def setUp(self):
self.model_tester = CanineModelTester(self)
# we set has_text_modality to False as the config has no vocab_size attribute
self.config_tester = ConfigTester(self, config_class=CanineConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
# expected_num_layers equals num_hidden_layers of the deep encoder + 1, + 2 for the first shallow encoder, + 2
# for the final shallow encoder
expected_num_layers = self.model_tester.num_hidden_layers + 1 + 2 + 2
self.assertEqual(len(hidden_states), expected_num_layers)
seq_length = self.model_tester.seq_length
for i in range(expected_num_layers):
if (i < 2) or ((expected_num_layers - i) < 3):
# the expected length of the hidden_states of the first and final shallow encoders
# is equal to the seq_length
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
else:
# the expected length of the hidden_states of the deep encoder need to be updated
# for CANINE since the seq length is downsampled
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[seq_length // config.downsampling_rate, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
# we add + 2 due to the 2 shallow encoders
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers + 2)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
# we add + 2 due to the 2 shallow encoders
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers + 2)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers + 2)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
print(model_class)
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
)
def test_headmasking(self):
if not self.test_head_masking:
return
global_rng.seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
global_rng.seed()
inputs_dict["output_attentions"] = True
config.output_hidden_states = True
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(
self.model_tester.num_hidden_layers,
self.model_tester.num_attention_heads,
device=torch_device,
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
inputs["head_mask"] = head_mask
outputs = model(**inputs, return_dict=True)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
def check_attentions_validity(attentions):
# Remove Nan
for t in attentions:
self.assertLess(
torch.sum(torch.isnan(t)), t.numel() / 4
) # Check we don't have more than 25% nans (arbitrary)
attentions = [
t.masked_fill(torch.isnan(t), 0.0) for t in attentions
] # remove them (the test is less complete)
self.assertAlmostEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[1][..., -1, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(attentions[-2][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[-2][..., -1, :, :].flatten().sum().item(), 0.0)
check_attentions_validity(outputs.attentions)
@unittest.skip("CANINE does not have a get_input_embeddings() method.")
def test_inputs_embeds(self):
# ViT does not use inputs_embeds
pass
@unittest.skip("CANINE does not have a get_input_embeddings() method.")
def test_model_common_attributes(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in CANINE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = CanineModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class CanineModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = CanineModel.from_pretrained("google/canine-s")
# this one corresponds to the first example of the TydiQA dev set (in Swahili)
# fmt: off
input_ids = [57344, 57349, 85, 107, 117, 98, 119, 97, 32, 119, 97, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 111, 114, 105, 32, 110, 105, 32, 107, 105, 97, 115, 105, 32, 103, 97, 110, 105, 63, 57345, 57350, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 111, 114, 105, 32, 44, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 97, 114, 117, 115, 105, 32, 97, 117, 32, 105, 110, 103, 46, 32, 65, 108, 112, 104, 97, 32, 67, 101, 110, 116, 97, 117, 114, 105, 32, 40, 112, 105, 97, 58, 32, 84, 111, 108, 105, 109, 97, 110, 32, 97, 117, 32, 82, 105, 103, 105, 108, 32, 75, 101, 110, 116, 97, 117, 114, 117, 115, 41, 32, 110, 105, 32, 110, 121, 111, 116, 97, 32, 105, 110, 97, 121, 111, 110, 103, 39, 97, 97, 32, 115, 97, 110, 97, 32, 107, 97, 116, 105, 107, 97, 32, 97, 110, 103, 97, 32, 121, 97, 32, 107, 117, 115, 105, 110, 105, 32, 107, 119, 101, 110, 121, 101, 32, 107, 117, 110, 100, 105, 110, 121, 111, 116, 97, 32, 121, 97, 32, 75, 97, 110, 116, 97, 114, 117, 115, 105, 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0, 0, 0, 0, 0, 0]
attention_mask = [1 if x != 0 else 0 for x in input_ids]
token_type_ids = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# fmt: on
input_ids = torch.tensor([input_ids])
attention_mask = torch.tensor([attention_mask])
token_type_ids = torch.tensor([token_type_ids])
outputs = model(input_ids, attention_mask, token_type_ids)
# verify sequence output
expected_shape = torch.Size((1, 2048, 768))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[
[-0.161433131, 0.395568609, 0.0407391489],
[-0.108025983, 0.362060368, -0.544592619],
[-0.141537309, 0.180541009, 0.076907],
]
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-2))
# verify pooled output
expected_shape = torch.Size((1, 768))
self.assertEqual(outputs.pooler_output.shape, expected_shape)
expected_slice = torch.tensor([-0.884311497, -0.529064834, 0.723164916])
self.assertTrue(torch.allclose(outputs.pooler_output[0, :3], expected_slice, atol=1e-2))
| transformers-main | tests/models/canine/test_modeling_canine.py |
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class OpenAIGPTModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.pad_token_id = self.vocab_size - 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = OpenAIGPTConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
# intermediate_size=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
pad_token_id=self.pad_token_id,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
model = OpenAIGPTModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
model = OpenAIGPTLMHeadModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
model = OpenAIGPTDoubleHeadsModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_openai_gpt_for_sequence_classification(
self, config, input_ids, head_mask, token_type_ids, *args
):
config.num_labels = self.num_labels
model = OpenAIGPTForSequenceClassification(config)
model.to(torch_device)
model.eval()
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
all_generative_model_classes = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
pipeline_model_mapping = (
{
"feature-extraction": OpenAIGPTModel,
"text-classification": OpenAIGPTForSequenceClassification,
"text-generation": OpenAIGPTLMHeadModel,
"zero-shot": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
# special case for DoubleHeads model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
dtype=torch.long,
device=torch_device,
)
inputs_dict["input_ids"] = inputs_dict["labels"]
inputs_dict["token_type_ids"] = inputs_dict["labels"]
inputs_dict["mc_token_ids"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices),
dtype=torch.long,
device=torch_device,
)
inputs_dict["mc_labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = OpenAIGPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_openai_gpt_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)
def test_openai_gpt_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_openai_gpt_double_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)
def test_openai_gpt_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = OpenAIGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class OPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_openai_gpt(self):
model = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
model.to(torch_device)
input_ids = torch.tensor([[481, 4735, 544]], dtype=torch.long, device=torch_device) # the president is
expected_output_ids = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
| transformers-main | tests/models/openai/test_modeling_openai.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
"""Tests OpenAIGPTTokenizer that uses BERT BasicTokenizer."""
tokenizer_class = OpenAIGPTTokenizer
rust_tokenizer_class = OpenAIGPTTokenizerFast
test_rust_tokenizer = True
test_seq2seq = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(self.merges_file, "w") as fp:
fp.write("\n".join(merges))
def get_input_output_texts(self, tokenizer):
return "lower newer", "lower newer"
def test_full_tokenizer(self):
tokenizer = OpenAIGPTTokenizer(self.vocab_file, self.merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + ["<unk>"]
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
# tokenizer has no padding token
def test_padding_different_model_input_name(self):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class OpenAIGPTTokenizationTestWithSpacy(OpenAIGPTTokenizationTest):
"""Tests OpenAIGPTTokenizer that uses SpaCy and ftfy."""
pass
| transformers-main | tests/models/openai/test_tokenization_openai.py |
transformers-main | tests/models/openai/__init__.py |
|
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.openai.modeling_tf_openai import (
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFOpenAIGPTDoubleHeadsModel,
TFOpenAIGPTForSequenceClassification,
TFOpenAIGPTLMHeadModel,
TFOpenAIGPTModel,
)
class TFOpenAIGPTModelTester:
def __init__(
self,
parent,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_token_type_ids = True
self.use_input_mask = True
self.use_labels = True
self.use_mc_token_ids = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 2
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
self.pad_token_id = self.vocab_size - 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = OpenAIGPTConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
# intermediate_size=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range,
pad_token_id=self.pad_token_id,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFOpenAIGPTModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_openai_gpt_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = TFOpenAIGPTLMHeadModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_openai_gpt_double_head(
self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
):
model = TFOpenAIGPTDoubleHeadsModel(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"mc_token_ids": mc_token_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)
)
self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
def create_and_check_openai_gpt_for_sequence_classification(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
config.num_labels = self.num_labels
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"labels": sequence_labels,
}
model = TFOpenAIGPTForSequenceClassification(config)
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFOpenAIGPTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
if is_tf_available()
else ()
)
all_generative_model_classes = (
(TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
pipeline_model_mapping = (
{
"feature-extraction": TFOpenAIGPTModel,
"text-classification": TFOpenAIGPTForSequenceClassification,
"text-generation": TFOpenAIGPTLMHeadModel,
"zero-shot": TFOpenAIGPTForSequenceClassification,
}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def setUp(self):
self.model_tester = TFOpenAIGPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_openai_gpt_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)
def test_openai_gpt_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_lm_head(*config_and_inputs)
def test_openai_gpt_double_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs)
def test_openai_gpt_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFOpenAIGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_tf
class TFOPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_openai_gpt(self):
model = TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
input_ids = tf.convert_to_tensor([[481, 4735, 544]], dtype=tf.int32) # the president is
expected_output_ids = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
| transformers-main | tests/models/openai/test_modeling_tf_openai.py |
transformers-main | tests/models/dinov2/__init__.py |
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Dinov2 model. """
import inspect
import unittest
from transformers import Dinov2Config
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import Dinov2ForImageClassification, Dinov2Model
from transformers.models.dinov2.modeling_dinov2 import DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class Dinov2ModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
# in Dinov2, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return Dinov2Config(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels):
model = Dinov2Model(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = Dinov2ForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
config.num_channels = 1
model = Dinov2ForImageClassification(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
pixel_values,
labels,
) = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class Dinov2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Dinov2 does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (Dinov2Model, Dinov2ForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": Dinov2Model, "image-classification": Dinov2ForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = Dinov2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=Dinov2Config, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="Dinov2 does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = Dinov2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class Dinov2ModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("facebook/dinov2-base") if is_vision_available() else None
@slow
def test_inference_no_head(self):
model = Dinov2Model.from_pretrained("facebook/dinov2-base").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the last hidden states
expected_shape = torch.Size((1, 257, 768))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[-2.1747, -0.4729, 1.0936], [-3.2780, -0.8269, -0.9210], [-2.9129, 1.1284, -0.7306]],
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
| transformers-main | tests/models/dinov2/test_modeling_dinov2.py |
transformers-main | tests/models/lilt/__init__.py |
|
# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class LiltModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=24,
num_hidden_layers=2,
num_attention_heads=6,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
range_bbox=1000,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.range_bbox = range_bbox
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
t = bbox[i, j, 3]
bbox[i, j, 3] = bbox[i, j, 1]
bbox[i, j, 1] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
t = bbox[i, j, 2]
bbox[i, j, 2] = bbox[i, j, 0]
bbox[i, j, 0] = t
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def get_config(self):
return LiltConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
def create_and_check_model(
self,
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
model = LiltModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, bbox=bbox, token_type_ids=token_type_ids)
result = model(input_ids, bbox=bbox)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_token_classification(
self,
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
config.num_labels = self.num_labels
model = LiltForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids, bbox=bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
model = LiltForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
bbox=bbox,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
bbox,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class LiltModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": LiltModel,
"question-answering": LiltForQuestionAnswering,
"text-classification": LiltForSequenceClassification,
"token-classification": LiltForTokenClassification,
"zero-shot": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
return True
def setUp(self):
self.model_tester = LiltModelTester(self)
self.config_tester = ConfigTester(self, config_class=LiltConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = LiltModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
@slow
class LiltModelIntegrationTest(unittest.TestCase):
def test_inference_no_head(self):
model = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(torch_device)
input_ids = torch.tensor([[1, 2]], device=torch_device)
bbox = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]], device=torch_device)
# forward pass
with torch.no_grad():
outputs = model(input_ids=input_ids, bbox=bbox)
expected_shape = torch.Size([1, 2, 768])
expected_slice = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]],
device=torch_device,
)
self.assertTrue(outputs.last_hidden_state.shape, expected_shape)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3], expected_slice, atol=1e-3))
| transformers-main | tests/models/lilt/test_modeling_lilt.py |
transformers-main | tests/models/llama/__init__.py |
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch LLaMA model. """
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class LlamaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
pad_token_id=0,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.pad_token_id = pad_token_id
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return LlamaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
pad_token_id=self.pad_token_id,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = LlamaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = LlamaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = LlamaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = LlamaForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
all_generative_model_classes = (LlamaForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": LlamaModel,
"text-classification": LlamaForSequenceClassification,
"text-generation": LlamaForCausalLM,
"zero-shot": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def setUp(self):
self.model_tester = LlamaModelTester(self)
self.config_tester = ConfigTester(self, config_class=LlamaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_llama_sequence_classification_model(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = LlamaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_llama_sequence_classification_model_for_single_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "single_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = LlamaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_llama_sequence_classification_model_for_multi_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "multi_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor(
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
).to(torch.float)
model = LlamaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
@unittest.skip("LLaMA buffers include complex numbers, which breaks this test")
def test_save_load_fast_init_from_base(self):
pass
@parameterized.expand([("linear",), ("dynamic",)])
def test_model_rope_scaling(self, scaling_type):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
short_input = ids_tensor([1, 10], config.vocab_size)
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
set_seed(42) # Fixed seed at init time so the two models get the same random weights
original_model = LlamaModel(config)
original_model.to(torch_device)
original_model.eval()
original_short_output = original_model(short_input).last_hidden_state
original_long_output = original_model(long_input).last_hidden_state
set_seed(42) # Fixed seed at init time so the two models get the same random weights
config.rope_scaling = {"type": scaling_type, "factor": 10.0}
scaled_model = LlamaModel(config)
scaled_model.to(torch_device)
scaled_model.eval()
scaled_short_output = scaled_model(short_input).last_hidden_state
scaled_long_output = scaled_model(long_input).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
else:
self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
# The output should be different for long inputs
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
@require_torch
class LlamaIntegrationTest(unittest.TestCase):
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
@slow
def test_model_7b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="auto")
out = model(torch.tensor([input_ids]))
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]])
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
# fmt: off
EXPECTED_SLICE = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,])
# fmt: on
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
@slow
def test_model_13b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", device_map="auto")
out = model(torch.tensor(input_ids))
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]])
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
# fmt: off
EXPECTED_SLICE = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273])
# fmt: on
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
@slow
def test_model_13bf_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf", device_map="auto")
out = model(torch.tensor(input_ids))
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]])
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
# fmt: off
EXPECTED_SLICE = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513])
# fmt: on
torch.testing.assert_close(out.mean(-1), EXPECTED_SLICE, atol=1e-2, rtol=1e-2)
@unittest.skip(
"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"
)
@slow
def test_model_70b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf", device_map="auto")
out = model(torch.tensor(input_ids))
EXPECTED_MEAN = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]], dtype=torch.float32
)
torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# fmt: off
EXPECTED_SLICE = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312])
# fmt: on
torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
@unittest.skip("Model is curently gated")
@slow
def test_model_13b_greedy_generation(self):
EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi"""
prompt = "Simply put, the theory of relativity states that "
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
input_ids = tokenizer.encode(prompt, return_tensors="pt")
model = LlamaForCausalLM.from_pretrained(
"meta-llama/Llama-2-13b-chat-hf", device_map="sequential", use_safetensors=False
)
# greedy generation outputs
generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
| transformers-main | tests/models/llama/test_modeling_llama.py |
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pickle
import shutil
import tempfile
import unittest
from datasets import load_dataset
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
LlamaTokenizer,
LlamaTokenizerFast,
is_torch_available,
)
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
pass
@require_sentencepiece
@require_tokenizers
class LlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = LlamaTokenizer
test_rust_tokenizer = False
test_sentencepiece = True
from_pretrained_kwargs = {}
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.save_pretrained(self.tmpdirname)
def test_full_tokenizer(self):
tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
@unittest.skip("Let's wait for the fast tokenizer!")
def test_save_pretrained(self):
self.tokenizers_list += (self.rust_tokenizer_class, "hf-internal-testing/llama-tokenizer", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
@require_torch
def test_batch_tokenization(self):
if not self.test_seq2seq:
return
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Longer text that will definitely require truncation.
text = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
try:
batch = tokenizer(
text=text,
max_length=3,
max_target_length=10,
return_tensors="pt",
)
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1], 3)
# max_target_length will default to max_length if not specified
batch = tokenizer(text, max_length=3, return_tensors="pt")
self.assertEqual(batch.input_ids.shape[1], 3)
batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt")
self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
self.assertNotIn("decoder_input_ids", batch_encoder_only)
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
def test_save_slow_from_fast_and_reload_fast(self):
pass
def test_special_tokens_initialization(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
r_output = tokenizer_r.encode("Hey this is a <special> token")
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
self.assertTrue(special_token_id in r_output)
if self.test_slow_tokenizer:
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
pretrained_name,
additional_special_tokens=added_tokens,
**kwargs, # , from_slow=True <- unfortunately too slow to convert
)
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
p_output = tokenizer_p.encode("Hey this is a <special> token")
cr_output = tokenizer_cr.encode("Hey this is a <special> token")
self.assertEqual(p_output, r_output)
self.assertEqual(cr_output, r_output)
self.assertTrue(special_token_id in p_output)
self.assertTrue(special_token_id in cr_output)
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="hf-internal-testing/llama-tokenizer",
revision="0984d03108b1a041ed679bd253b6519b7e1a4778",
padding=False,
)
def test_picklable(self):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SAMPLE_VOCAB, f.name)
tokenizer = LlamaTokenizer(f.name, keep_accents=True)
pickled_tokenizer = pickle.dumps(tokenizer)
pickle.loads(pickled_tokenizer)
@require_torch
@require_sentencepiece
@require_tokenizers
class LlamaIntegrationTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
checkpoint_name = "hf-internal-testing/llama-tokenizer"
cls.tokenizer: LlamaTokenizer = LlamaTokenizer.from_pretrained(checkpoint_name)
cls.rust_tokenizer = LlamaTokenizerFast.from_pretrained(checkpoint_name)
return cls
@require_torch
def integration_tests(self):
inputs = self.tokenizer(
["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"],
return_tensors="pt",
)
self.assertEqual(
nested_simplify(inputs),
{
"input_ids": [
[1, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889],
[1, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718],
],
"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
},
)
def test_fast_special_tokens(self):
slow_tokenizer = self.tokenizer
fast_tokenizer = self.rust_tokenizer
slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
assert slow == [1, 319, 4559, 1243]
fast_tokenizer.add_eos_token = False
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
assert fast == [1, 319, 4559, 1243]
fast_tokenizer.add_eos_token = True
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
assert fast == [1, 319, 4559, 1243, 2]
slow_tokenizer.add_eos_token = True
slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
assert slow == [1, 319, 4559, 1243, 2]
fast_tokenizer = LlamaTokenizerFast.from_pretrained(
"hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False
)
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
assert fast == [319, 4559, 1243, 2]
slow_tokenzier = LlamaTokenizer.from_pretrained(
"hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False
)
slow = slow_tokenzier.encode("A sample test", add_special_tokens=True)
assert slow == [319, 4559, 1243, 2]
self.tokenizer.add_eos_token = False
self.rust_tokenizer.add_eos_token = False
@slow
def test_conversion(self):
# This is excruciatingly slow since it has to recreate the entire merge
# list from the original vocabulary in spm
self.rust_tokenizer.save_pretrained("./out")
with tempfile.TemporaryDirectory() as dirname:
self.rust_tokenizer.save_pretrained(dirname)
with open(os.path.join(dirname, "tokenizer.json"), "r") as f:
old_serialized = f.read()
new_tokenizer = convert_slow_tokenizer(self.tokenizer)
with tempfile.NamedTemporaryFile() as f:
new_tokenizer.save(f.name)
# Re-opening since `f` is in bytes.
new_serialized = open(f.name, "r").read()
with open("out_tokenizer.json", "w") as g:
g.write(new_serialized)
self.assertEqual(old_serialized, new_serialized)
def test_simple_encode_decode(self):
pyth_tokenizer = self.tokenizer
rust_tokenizer = self.rust_tokenizer
self.assertEqual(pyth_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
self.assertEqual(rust_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
self.assertEqual(pyth_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
# bytefallback showcase
self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
self.assertEqual(
pyth_tokenizer.decode(
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
),
"生活的真谛是",
)
self.assertEqual(
rust_tokenizer.decode(
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
),
"生活的真谛是",
)
# Inner spaces showcase
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
self.assertEqual(pyth_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
self.assertEqual(rust_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
self.assertEqual(pyth_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
self.assertEqual(rust_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
self.assertEqual(pyth_tokenizer.encode(""), [1])
self.assertEqual(rust_tokenizer.encode(""), [1])
self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
def test_no_differences_showcase(self):
pyth_tokenizer = self.tokenizer
rust_tokenizer = self.rust_tokenizer
self.assertEqual(pyth_tokenizer.encode(""), [1])
self.assertEqual(rust_tokenizer.encode(""), [1])
self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
def test_no_differences_decode(self):
pyth_tokenizer = self.tokenizer
rust_tokenizer = self.rust_tokenizer
self.assertEqual(pyth_tokenizer.decode([869]), ".")
self.assertEqual(rust_tokenizer.decode([869]), ".")
self.assertEqual(pyth_tokenizer.decode([30112, 869]), "ا .")
self.assertEqual(rust_tokenizer.decode([30112, 869]), "ا .")
def test_no_differences_special_tokens(self):
pyth_tokenizer = self.tokenizer
rust_tokenizer = self.rust_tokenizer
self.assertEqual(pyth_tokenizer.encode(""), [1])
self.assertEqual(rust_tokenizer.encode(""), [1])
self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
@unittest.skipIf(
os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
"RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
)
def test_integration_test_xnli(self):
import tqdm
pyth_tokenizer = self.tokenizer
rust_tokenizer = self.rust_tokenizer
dataset = load_dataset("code_x_glue_ct_code_to_text", "go")
for item in tqdm.tqdm(dataset["validation"]):
string = item["code"]
encoded1 = pyth_tokenizer.encode(string)
encoded2 = rust_tokenizer.encode(string)
self.assertEqual(encoded1, encoded2)
decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True)
self.assertEqual(decoded1, decoded2)
dataset = load_dataset("xnli", "all_languages")
for item in tqdm.tqdm(dataset["train"]):
for string in item["premise"].values():
encoded1 = pyth_tokenizer.encode(string)
encoded2 = rust_tokenizer.encode(string)
self.assertEqual(encoded1, encoded2)
decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True)
self.assertEqual(decoded1, decoded2)
@require_sentencepiece
@require_tokenizers
class CommonSpmIntegrationTests(unittest.TestCase):
"""
A class that regroups important test to make sure that we properly handle the special tokens.
"""
@classmethod
def setUpClass(cls):
tokenizer = LlamaTokenizer(SAMPLE_VOCAB, extra_ids=0, add_bos_token=False, legacy=False)
tokenizer.add_special_tokens({"additional_special_tokens": ["<s>"]})
tokenizer._create_trie(tokenizer.all_special_tokens)
# TODO ArthurZ the above is necessary as addedTokens / intialization sucks. Trie is not correctly created
# So the extra ids are split....
cls.tokenizer = tokenizer
return cls
def test_add_dummy_prefix(self):
# make sure `'▁'` is prepended, and outputs match sp_model's
# `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
input_ids = self.tokenizer.encode(". Hello")
self.assertEqual(input_ids, [7, 4, 156, 86, 20])
sp_encode = self.tokenizer.sp_model.encode(". Hello")
self.assertEqual(input_ids, sp_encode)
tokens = self.tokenizer.tokenize(". Hello")
self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
def test_remove_extra_whitespaces(self):
# make sure the extra spaces are eaten. Since the sample vocab does not have
# `______`. sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute is set to False
input_ids = self.tokenizer.encode(" . Hello")
self.assertEqual(input_ids, [7, 4, 156, 86, 20])
sp_encode = self.tokenizer.sp_model.encode(" . Hello")
self.assertEqual(input_ids, sp_encode)
tokens = self.tokenizer.tokenize(" . Hello")
self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
# `'▁'` is also a whitespace
input_ids = self.tokenizer.encode("▁He is not")
self.assertEqual(input_ids, [156, 46, 44])
tokens = self.tokenizer.tokenize("▁He is not")
sp_encode = self.tokenizer.sp_model.encode("▁He is not")
self.assertEqual(input_ids, sp_encode)
self.assertEqual(tokens, ["▁He", "▁is", "▁not"]) # no extra space added
input_ids = self.tokenizer.encode("▁He is not<s> ▁He")
self.assertEqual(input_ids, [156, 46, 44, 1, 156])
tokens = self.tokenizer.tokenize("▁He is not<s> ▁He")
self.assertEqual(tokens, ["▁He", "▁is", "▁not", "<s>", "▁He"]) # spaces are eaten by spm + our strip
# make sure that the output after the extra id is the same as if
# extra_id was not there
input_ids = self.tokenizer.encode("▁He is not ▁He")
self.assertEqual(input_ids, [156, 46, 44, 156])
tokens = self.tokenizer.tokenize("▁He is not ▁He")
self.assertEqual(tokens, ["▁He", "▁is", "▁not", "▁He"]) # spaces are eaten by spm even if not start
def test_character_after_special_token(self):
# Make sure that `tokenizer.tokenize` is similar to
# adding the equivalent special token to the vocab
input_ids = self.tokenizer.encode("Hey <s>I")
self.assertEqual(input_ids, [156, 30, 1, 100])
sp_encode = self.tokenizer.sp_model.encode("Hey .I")
# the last token should be 100
self.assertEqual(input_ids[-1], sp_encode[-1])
tokens = self.tokenizer.tokenize("<s>I")
self.assertEqual(tokens, ["<s>", "I"])
input_ids = self.tokenizer.encode("Hello, <s>,")
self.assertEqual(input_ids, [156, 86, 20, 3, 1, 3])
tokens = self.tokenizer.tokenize("Hello, <s>,")
self.assertEqual(tokens, ["▁He", "ll", "o", ",", "<s>", ","])
def test_special_tokens_strip(self):
input_ids = self.tokenizer.encode(" <s> ,")
self.assertEqual(input_ids, [1, 7, 3])
tokens = self.tokenizer.tokenize(" <s> ,")
# spaces are eaten by rstrip / lstrip + spm sp_model.encode(" ") = []
self.assertEqual(tokens, ["<s>", "▁", ","])
input_ids = self.tokenizer.encode("No <s> ▁He")
self.assertEqual(input_ids, [284, 1, 156])
tokens = self.tokenizer.tokenize("No <s> ▁He")
self.assertEqual(tokens, ["▁No", "<s>", "▁He"]) # spaces are eaten by rstrip / lstrip
| transformers-main | tests/models/llama/test_tokenization_llama.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
import pytest
from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
from .test_feature_extraction_whisper import floats_list
if is_speech_available():
from transformers import WhisperFeatureExtractor, WhisperProcessor
TRANSCRIBE = 50358
NOTIMESTAMPS = 50362
@require_torch
@require_torchaudio
@require_sentencepiece
class WhisperProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "openai/whisper-small.en"
self.tmpdirname = tempfile.mkdtemp()
def get_tokenizer(self, **kwargs):
return WhisperTokenizer.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return WhisperFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = WhisperProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, WhisperTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = WhisperProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, WhisperTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(raw_speech, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names,
feature_extractor.model_input_names,
msg="`processor` and `feature_extractor` model input names do not match",
)
def test_get_decoder_prompt_ids(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
forced_decoder_ids = processor.get_decoder_prompt_ids(task="transcribe", no_timestamps=True)
self.assertIsInstance(forced_decoder_ids, list)
for ids in forced_decoder_ids:
self.assertIsInstance(ids, (list, tuple))
expected_ids = [TRANSCRIBE, NOTIMESTAMPS]
self.assertListEqual([ids[-1] for ids in forced_decoder_ids], expected_ids)
def test_get_prompt_ids(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
prompt_ids = processor.get_prompt_ids("Mr. Quilter")
decoded_prompt = processor.tokenizer.decode(prompt_ids)
self.assertListEqual(prompt_ids.tolist(), [50360, 1770, 13, 2264, 346, 353])
self.assertEqual(decoded_prompt, "<|startofprev|> Mr. Quilter")
def test_empty_get_prompt_ids(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
prompt_ids = processor.get_prompt_ids("")
decoded_prompt = processor.tokenizer.decode(prompt_ids)
self.assertListEqual(prompt_ids.tolist(), [50360, 220])
self.assertEqual(decoded_prompt, "<|startofprev|> ")
def test_get_prompt_ids_with_special_tokens(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
def _test_prompt_error_raised_helper(prompt, special_token):
with pytest.raises(ValueError) as excinfo:
processor.get_prompt_ids(prompt)
expected = f"Encountered text in the prompt corresponding to disallowed special token: {special_token}."
self.assertEqual(expected, str(excinfo.value))
_test_prompt_error_raised_helper("<|startofprev|> test", "<|startofprev|>")
_test_prompt_error_raised_helper("test <|notimestamps|>", "<|notimestamps|>")
_test_prompt_error_raised_helper("test <|zh|> test <|transcribe|>", "<|zh|>")
| transformers-main | tests/models/whisper/test_processor_whisper.py |
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
global_rng = random.Random()
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
@require_torchaudio
class WhisperFeatureExtractionTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=10,
hop_length=160,
chunk_length=8,
padding_value=0.0,
sampling_rate=4_000,
return_attention_mask=False,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
self.feature_size = feature_size
self.chunk_length = chunk_length
self.hop_length = hop_length
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
speech_inputs = [
floats_list((x, self.feature_size))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class WhisperFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = WhisperFeatureExtractor if is_speech_available() else None
def setUp(self):
self.feat_extract_tester = WhisperFeatureExtractionTester(self)
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
mel_1 = feat_extract_first.mel_filters
mel_2 = feat_extract_second.mel_filters
self.assertTrue(np.allclose(mel_1, mel_2))
self.assertEqual(dict_first, dict_second)
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
mel_1 = feat_extract_first.mel_filters
mel_2 = feat_extract_second.mel_filters
self.assertTrue(np.allclose(mel_1, mel_2))
self.assertEqual(dict_first, dict_second)
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test feature size
input_features = feature_extractor(np_speech_inputs, padding="max_length", return_tensors="np").input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames)
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)
# Test not batched input
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test truncation required
speech_inputs = [floats_list((1, x))[0] for x in range(200, (feature_extractor.n_samples + 500), 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
speech_inputs_truncated = [x[: feature_extractor.n_samples] for x in speech_inputs]
np_speech_inputs_truncated = [np.asarray(speech_input) for speech_input in speech_inputs_truncated]
encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs_truncated, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_features.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_features.dtype == torch.float32)
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_integration(self):
# fmt: off
EXPECTED_INPUT_FEATURES = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
]
)
# fmt: on
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
self.assertEqual(input_features.shape, (1, 80, 3000))
self.assertTrue(torch.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, atol=1e-4))
def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
audio = self._load_datasamples(1)[0]
audio = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535 # Rescale to [0, 65535] to show issue
audio = feat_extract.zero_mean_unit_var_norm([audio], attention_mask=None)[0]
self.assertTrue(np.all(np.mean(audio) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(audio) - 1) < 1e-3))
| transformers-main | tests/models/whisper/test_feature_extraction_whisper.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import inspect
import tempfile
import unittest
import transformers
from transformers import WhisperConfig, is_flax_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
from transformers.utils import cached_property
from transformers.utils.import_utils import is_datasets_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_datasets_available():
import datasets
from datasets import load_dataset
if is_flax_available():
import jax
import numpy as np
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import (
FLAX_MODEL_MAPPING,
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
WhisperFeatureExtractor,
WhisperProcessor,
)
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
@require_flax
class FlaxWhisperModelTester:
config_cls = WhisperConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=60,
is_training=True,
use_labels=False,
vocab_size=99,
d_model=16,
decoder_attention_heads=4,
decoder_ffn_dim=16,
decoder_layers=2,
encoder_attention_heads=4,
encoder_ffn_dim=16,
encoder_layers=2,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=70,
max_source_positions=30,
max_target_positions=40,
bos_token_id=98,
eos_token_id=98,
pad_token_id=0,
num_mel_bins=80,
decoder_start_token_id=85,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = encoder_layers
self.num_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_seq_length = seq_length // 2
self.decoder_seq_length = 1
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.decoder_start_token_id = decoder_start_token_id
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
def prepare_config_and_inputs_for_common(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length], self.vocab_size)
decoder_input_ids = np.array(self.batch_size * [[self.decoder_start_token_id]])
config = WhisperConfig(
vocab_size=self.vocab_size,
num_mel_bins=self.num_mel_bins,
decoder_start_token_id=self.decoder_start_token_id,
is_encoder_decoder=True,
activation_function=self.hidden_act,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
pad_token_id=self.pad_token_id,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
tie_word_embeddings=True,
d_model=self.d_model,
decoder_attention_heads=self.decoder_attention_heads,
decoder_ffn_dim=self.decoder_ffn_dim,
decoder_layers=self.decoder_layers,
encoder_attention_heads=self.encoder_attention_heads,
encoder_ffn_dim=self.encoder_ffn_dim,
encoder_layers=self.encoder_layers,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
)
inputs_dict = prepare_whisper_inputs_dict(config, input_features, decoder_input_ids)
return config, inputs_dict
def prepare_whisper_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if decoder_attention_mask is None:
decoder_attention_mask = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.int8),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id).astype(np.int8),
],
axis=-1,
)
return {
"input_features": input_ids,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
def partialclass(cls, *args, **kwargs):
class NewCls(cls):
__init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
return NewCls
def make_partial_class(full_class, *args, **kwargs):
partial_class = partialclass(full_class, *args, **kwargs)
partial_class.__name__ = full_class.__name__
partial_class.__module__ = full_class.__module__
return partial_class
@require_flax
class FlaxWhisperModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (FlaxWhisperForConditionalGeneration, FlaxWhisperModel) if is_flax_available() else ()
all_generative_model_classes = (FlaxWhisperForConditionalGeneration,) if is_flax_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = FlaxWhisperModelTester(self)
_, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self.init_shape = (1,) + inputs_dict["input_features"].shape[1:]
self.all_model_classes = (
make_partial_class(model_class, input_shape=self.init_shape) for model_class in self.all_model_classes
)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
def test_config(self):
self.config_tester.run_common_tests()
# overwrite because of `input_features`
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_features", "decoder_input_ids"]
self.assertListEqual(arg_names[:2], expected_arg_names)
# overwrite because of `input_features`
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_features, decoder_input_ids, **kwargs):
return model(input_features=input_features, decoder_input_ids=decoder_input_ids, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# We override with a slightly higher tol value, as test recently became flaky
super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)
# overwrite because of `input_features`
@is_pt_flax_cross_test
def test_save_load_bf16_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = model_class(config)
model.params = model.to_bf16(model.params)
base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
@is_pt_flax_cross_test
def test_save_load_from_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, base_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
# save pt model
pt_model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname, from_pt=True)
base_param_from_head = flatten_dict(unfreeze(head_model.params[head_model.base_model_prefix]))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
@is_pt_flax_cross_test
def test_save_load_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
def test_save_load_from_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname)
base_param_from_head = flatten_dict(unfreeze(head_model.params[head_model.base_model_prefix]))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
def test_save_load_to_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
@slow
@require_flax
class FlaxWhisperModelIntegrationTest(unittest.TestCase):
@cached_property
def default_processor(self):
return WhisperProcessor.from_pretrained("openai/whisper-base")
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_tiny_logits_librispeech(self):
model = FlaxWhisperModel.from_pretrained("openai/whisper-tiny", from_pt=True)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="np").input_features
logits = model(
input_features,
decoder_input_ids=np.array([[50258, 50259, 50359]]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
)
# fmt: off
EXPECTED_LOGITS = np.array(
[
2.9892, -6.7607, 5.7348, 3.6096, 0.2152, -5.7321, 4.8855, -1.6407,
0.2823, -1.5718, 10.4269, 3.4427, 0.0219, -8.0612, 3.4784, 8.4246,
4.0575, -2.2864, 11.1084, 0.9963, 0.9884, -8.5154, -3.5469, -9.3713,
0.9786, 3.5435, 7.4850, -5.2579, -1.4366, 10.4841
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0][0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
def test_small_en_logits_librispeech(self):
model = FlaxWhisperModel.from_pretrained("openai/whisper-small.en", from_pt=True)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="np").input_features
logits = model(
input_features,
decoder_input_ids=np.array([model.config.decoder_start_token_id]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
)
logits = logits[0] @ model.params["model"]["decoder"]["embed_tokens"]["embedding"].T
# fmt: off
EXPECTED_LOGITS = np.array(
[
-3.6784, -7.7211, -9.5070, -11.9286, -7.6489, -9.7026, -5.6188,
-8.0104, -4.6238, -5.1833, -9.0485, -3.4079, -5.4874, -2.6935,
-6.3479, -7.3398, -6.9558, -7.6867, -7.4748, -8.3463, -9.9781,
-10.8389, -10.3105, -11.7201, -9.7261, -7.1590, -5.9272, -12.4509,
-11.1146, -8.1918
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
def test_large_logits_librispeech(self):
model = FlaxWhisperModel.from_pretrained("openai/whisper-large", from_pt=True)
input_speech = self._load_datasamples(1)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
processed_inputs = processor(
audio=input_speech, text="This part of the speech", add_special_tokens=False, return_tensors="np"
)
input_features = processed_inputs.input_features
decoder_input_ids = processed_inputs.labels
logits = model(
input_features,
decoder_input_ids=decoder_input_ids,
output_hidden_states=False,
output_attentions=False,
return_dict=False,
)
logits = logits[0] @ model.params["model"]["decoder"]["embed_tokens"]["embedding"].T
# fmt: off
EXPECTED_LOGITS = np.array(
[
2.1382, 0.9381, 4.4671, 3.5589, 2.4022, 3.8576, -0.6521, 2.5472,
1.8301, 1.9957, 2.3432, 1.4678, 0.5459, 2.2597, 1.5179, 2.5357,
1.1624, 0.6194, 1.0757, 1.8259, 2.4076, 1.6601, 2.3503, 1.3376,
1.9891, 1.8635, 3.8931, 5.3699, 4.4772, 3.9184
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
def test_tiny_en_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
model.config.decoder_start_token_id = 50257
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(
raw_speech=input_speech, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="jax"
).input_features
generated_ids = model.generate(input_features, num_beams=5, max_length=20).sequences
transcript = processor.tokenizer.decode(generated_ids[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad to"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_tiny_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", from_pt=True)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(
raw_speech=input_speech, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="jax"
).input_features
generated_ids = model.generate(input_features, num_beams=5, max_length=20).sequences
transcript = processor.tokenizer.decode(generated_ids[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_large_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-large", from_pt=True)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(
raw_speech=input_speech, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="jax"
).input_features
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="en", task="transcribe")
generated_ids = model.generate(input_features, num_beams=5, max_length=20).sequences
transcript = processor.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes and we are glad"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_large_generation_multilingual(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-large", from_pt=True)
ds = load_dataset("common_voice", "ja", split="test", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
input_speech = next(iter(ds))["audio"]["array"]
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="np")
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe")
generated_ids = model.generate(input_features, do_sample=False, max_length=20).sequences
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = "木村さんに電話を貸してもらいました"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="en", task="transcribe")
generated_ids = model.generate(
input_features,
do_sample=False,
max_length=20,
).sequences
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Kimura-san called me."
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="translate")
generated_ids = model.generate(input_features, do_sample=False, max_length=20).sequences
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " I borrowed a phone from Kimura san"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_large_batched_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-large", from_pt=True)
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="np").input_features
generated_ids = model.generate(input_features, max_length=20).sequences
# fmt: off
EXPECTED_LOGITS = np.array(
[
[50258, 50358, 50363, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 293, 321, 366, 5404, 281],
[50258, 50358, 50363, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50257, 50257],
[50258, 50358, 50363, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256],
[50258, 50358, 50363, 634, 575, 12525, 22618, 1968, 6144, 35617, 20084, 1756, 311, 589, 307, 534, 10281, 934, 439, 11]
]
)
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all,",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
def test_tiny_en_batched_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="np").input_features
generated_ids = model.generate(input_features, max_length=20).sequences
# fmt: off
EXPECTED_LOGITS = np.array(
[
[50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284],
[50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256],
[50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236],
[50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460]
]
)
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes, and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef looming",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_timestamp_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
input_speech = np.concatenate(self._load_datasamples(4))
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="jax").input_features
generate_fn = jax.jit(functools.partial(model.generate, max_length=448, return_timestamps=True))
generated_ids = generate_fn(input_features)
# fmt: off
EXPECTED_OUTPUT = np.array([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257])
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_OUTPUT))
EXPECTED_TRANSCRIPT = [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is"
" Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season"
" of the year, with Christmas and roast beef looming before us, similarly drawn from eating and"
" its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins'"
" work is really Greek after all, and"
),
"offsets": [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
),
"timestamp": (0.0, 6.5600000000000005),
},
{
"text": " Nor is Mr. Quilter's manner less interesting than his matter.",
"timestamp": (6.5600000000000005, 11.24),
},
{
"text": (
" He tells us that at this festive season of the year, with Christmas and roast beef"
" looming"
),
"timestamp": (11.24, 16.88),
},
{
"text": (
" before us, similarly drawn from eating and its results occur most readily to the mind."
),
"timestamp": (16.88, 23.76),
},
{
"text": (
" He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and"
),
"timestamp": (23.76, 29.44),
},
],
}
]
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True, output_offsets=True)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
class FlaxWhisperEncoderModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=60,
is_training=True,
use_labels=True,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=30,
num_mel_bins=80,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
classifier_proj_size=4,
num_labels=2,
is_encoder_decoder=False,
is_decoder=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
self.classifier_proj_size = classifier_proj_size
self.num_labels = num_labels
self.is_encoder_decoder = is_encoder_decoder
self.is_decoder = is_decoder
def get_config(self):
return WhisperConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
decoder_ffn_dim=self.hidden_size,
encoder_ffn_dim=self.hidden_size,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
classifier_proj_size=self.classifier_proj_size,
num_labels=self.num_labels,
is_encoder_decoder=self.is_encoder_decoder,
is_decoder=self.is_decoder,
)
def prepare_whisper_encoder_inputs_dict(
self,
input_features,
):
return {
"input_features": input_features,
}
def prepare_config_and_inputs(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length])
config = self.get_config()
inputs_dict = self.prepare_whisper_encoder_inputs_dict(
input_features=input_features,
)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
@property
def encoder_seq_length(self):
return self.get_subsampled_output_lengths(self.seq_length)
@require_flax
class WhisperEncoderModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (FlaxWhisperForAudioClassification,) if is_flax_available() else ()
is_encoder_decoder = False
fx_compatible = False
test_pruning = False
test_missing_keys = False
input_name = "input_features"
def setUp(self):
self.model_tester = FlaxWhisperEncoderModelTester(self)
_, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self.init_shape = (1,) + inputs_dict["input_features"].shape[1:]
self.all_model_classes = (
make_partial_class(model_class, input_shape=self.init_shape) for model_class in self.all_model_classes
)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
def test_config(self):
self.config_tester.run_common_tests()
# overwrite because of `input_features`
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_features, **kwargs):
return model(input_features=input_features, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
# overwrite because of `input_features`
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_features", "attention_mask", "output_attentions"]
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_inputs_embeds(self):
pass
# WhisperEncoder has no inputs_embeds and thus the `get_input_embeddings` fn is not implemented
def test_model_common_attributes(self):
pass
# WhisperEncoder cannot resize token embeddings since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# WhisperEncoder does not have any base model
def test_save_load_to_base(self):
pass
# WhisperEncoder does not have any base model
def test_save_load_from_base(self):
pass
# WhisperEncoder does not have any base model
@is_pt_flax_cross_test
def test_save_load_from_base_pt(self):
pass
# WhisperEncoder does not have any base model
@is_pt_flax_cross_test
def test_save_load_to_base_pt(self):
pass
# WhisperEncoder does not have any base model
@is_pt_flax_cross_test
def test_save_load_bf16_to_base_pt(self):
pass
| transformers-main | tests/models/whisper/test_modeling_flax_whisper.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers.models.whisper import WhisperTokenizer, WhisperTokenizerFast
from transformers.models.whisper.tokenization_whisper import _combine_tokens_into_words, _find_longest_common_sequence
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
ES_CODE = 50262
EN_CODE = 50259
END_OF_TRANSCRIPT = 50257
START_OF_TRANSCRIPT = 50258
TRANSLATE = 50358
TRANSCRIBE = 50359
NOTIMESTAMPS = 50363
class WhisperTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = WhisperTokenizer
rust_tokenizer_class = WhisperTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = False
test_seq2seq = False
def setUp(self):
super().setUp()
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny")
tokenizer.pad_token_id = 50256
tokenizer.pad_token = "<|endoftext|>"
tokenizer.save_pretrained(self.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "Where"
token_id = 14436
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "!")
self.assertEqual(vocab_keys[1], '"')
self.assertEqual(vocab_keys[-1], "<|notimestamps|>")
self.assertEqual(len(vocab_keys), 50364)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 50258)
def test_full_tokenizer(self):
tokenizer = WhisperTokenizer.from_pretrained(self.tmpdirname)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["This", "Ġis", "Ġa", "Ġ", "test"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[5723, 307, 257, 220, 31636],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
# fmt: off
['I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġ', 'this', 'Ġis', 'Ġfals', 'é', '.'],
# fmt: on
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [40, 390, 4232, 294, 1722, 25743, 11, 293, 220, 11176, 307, 16720, 526, 13])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
# fmt: off
['I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġ', 'this', 'Ġis', 'Ġfals', 'é', '.'],
# fmt: on
)
def test_tokenizer_slow_store_full_signature(self):
pass
def test_tokenizer_fast_store_full_signature(self):
pass
def test_special_tokens_initialization(self):
# Whisper relies on specific additional special tokens, so we skip this
# general test. In particular, this test loads fast tokenizer from slow
# tokenizer, and the conversion uses prefix_tokens, where we reference
# additional special tokens by specific indices, hence overriding the
# list with less tokens leads to out of index error
pass
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[50257, 50362, 41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13, 50256], [50257, 50362, 13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13, 50256], [50257, 50362, 464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13, 50256]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding, model_name="openai/whisper-tiny.en", padding=False
)
def test_output_offsets(self):
tokenizer = self.get_tokenizer()
previous_sequence = [51492, 406, 3163, 1953, 466, 13, 51612, 51612]
self.assertEqual(
tokenizer.decode(previous_sequence, output_offsets=True),
{
"text": " not worth thinking about.",
"offsets": [{"text": " not worth thinking about.", "timestamp": (22.56, 24.96)}],
},
)
# Merge when the previous sequence is a suffix of the next sequence
# fmt: off
next_sequences_1 = [50364, 295, 6177, 3391, 11, 19817, 3337, 507, 307, 406, 3163, 1953, 466, 13, 50614, 50614, 2812, 9836, 14783, 390, 6263, 538, 257, 1359, 11, 8199, 6327, 1090, 322, 702, 7443, 13, 50834, 50257]
# fmt: on
self.assertEqual(
tokenizer.decode(next_sequences_1, output_offsets=True),
{
"text": (
" of spectators, retrievality is not worth thinking about. His instant panic was followed by a"
" small, sharp blow high on his chest.<|endoftext|>"
),
"offsets": [
{"text": " of spectators, retrievality is not worth thinking about.", "timestamp": (0.0, 5.0)},
{
"text": " His instant panic was followed by a small, sharp blow high on his chest.",
"timestamp": (5.0, 9.4),
},
],
},
)
def test_find_longest_common_subsequence(self):
previous_sequence = [1, 2, 3]
next_sequence = [2, 3, 4, 5]
merge = _find_longest_common_sequence([previous_sequence, next_sequence])
self.assertEqual(merge, [1, 2, 3, 4, 5])
# Now previous is larger than next.
# We merge what we can and remove the extra right side of the left sequence
previous_sequence = [1, 2, 3, 4, 5, 6, 7]
next_sequence = [2, 3, 4, 5]
merge = _find_longest_common_sequence([previous_sequence, next_sequence])
self.assertEqual(merge, [1, 2, 3, 4, 5])
# Nothing in common
previous_sequence = [1, 2, 3]
next_sequence = [4, 5, 6]
merge = _find_longest_common_sequence([previous_sequence, next_sequence])
self.assertEqual(merge, [1, 2, 3, 4, 5, 6])
# Some errors in the overlap.
# We take from previous on the left, from the next on the right of the overlap
previous_sequence = [1, 2, 3, 4, 99]
next_sequence = [2, 98, 4, 5, 6]
merge = _find_longest_common_sequence([previous_sequence, next_sequence])
self.assertEqual(merge, [1, 2, 3, 4, 5, 6])
# We take from previous on the left, from the next on the right of the overlap
previous_sequence = [1, 2, 99, 4, 5]
next_sequence = [2, 3, 4, 98, 6]
merge = _find_longest_common_sequence([previous_sequence, next_sequence])
self.assertEqual(merge, [1, 2, 99, 4, 98, 6])
# This works on 3 sequences
seq1 = [1, 2, 3]
seq2 = [2, 3, 4]
seq3 = [3, 4, 5]
merge = _find_longest_common_sequence([seq1, seq2, seq3])
self.assertEqual(merge, [1, 2, 3, 4, 5])
# This works on 3 sequences with errors
seq1 = [1, 2, 3, 98, 5]
seq2 = [2, 99, 4, 5, 6, 7]
seq3 = [4, 97, 6, 7, 8]
merge = _find_longest_common_sequence([seq1, seq2, seq3])
self.assertEqual(merge, [1, 2, 3, 4, 5, 6, 7, 8])
def test_skip_special_tokens_skips_prompt_ids(self):
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
# fmt: off
encoded_input = [
50361, 2221, 13, 2326, 388, 391, 50258, 50259, 50359,
50363, 1282, 264, 2674, 9156, 295, 1523, 11, 2221, 13,
2326, 388, 391, 13657, 365, 2681, 21296, 17711, 13, 50257,
]
# fmt: on
expected_with_special_tokens = "<|startofprev|> Mr. Quilter<|startoftranscript|><|en|><|transcribe|><|notimestamps|> On the general principles of art, Mr. Quilter writes with equal lucidity.<|endoftext|>"
expected_without_special_tokens = " On the general principles of art, Mr. Quilter writes with equal lucidity."
self.assertEqual(tokenizer.decode(encoded_input, skip_special_tokens=False), expected_with_special_tokens)
self.assertEqual(tokenizer.decode(encoded_input, skip_special_tokens=True), expected_without_special_tokens)
self.assertEqual(rust_tokenizer.decode(encoded_input, skip_special_tokens=False), expected_with_special_tokens)
self.assertEqual(
rust_tokenizer.decode(encoded_input, skip_special_tokens=True), expected_without_special_tokens
)
def test_skip_special_tokens_with_timestamps(self):
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
# fmt: off
encoded_input = [
50258, 50363, 50364, 634, 575, 12525, 22618, 1968, 6144,
35617, 20084, 1756, 311, 589, 307, 534, 10281, 934,
439, 293, 50676, 50676, 393, 4411, 294, 309, 457,
707, 295, 33301, 286, 392, 6628, 13, 50836, 50257,
]
# fmt: on
expected_with_special_tokens = "<|startoftranscript|><|notimestamps|><|0.00|> He has grave doubts whether Sir Frederick Layton's work is really Greek after all and<|6.24|><|6.24|> can discover in it but little of rocky Ithaca.<|9.44|><|endoftext|>"
expected_without_special_tokens = "<|0.00|> He has grave doubts whether Sir Frederick Layton's work is really Greek after all and<|6.24|><|6.24|> can discover in it but little of rocky Ithaca.<|9.44|>"
self.assertEqual(
tokenizer.decode(encoded_input, decode_with_timestamps=True, skip_special_tokens=False),
expected_with_special_tokens,
)
self.assertEqual(
tokenizer.decode(encoded_input, decode_with_timestamps=True, skip_special_tokens=True),
expected_without_special_tokens,
)
self.assertEqual(
rust_tokenizer.decode(encoded_input, decode_with_timestamps=True, skip_special_tokens=False),
expected_with_special_tokens,
)
self.assertEqual(
rust_tokenizer.decode(encoded_input, decode_with_timestamps=True, skip_special_tokens=True),
expected_without_special_tokens,
)
def test_fast_tokenizer_get_prompt_ids(self):
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
prompt = "This is test prompt text."
tokenizer_prompt_ids = tokenizer.get_prompt_ids(prompt)
fast_tokenizer_prompt_ids = rust_tokenizer.get_prompt_ids(prompt)
self.assertListEqual(tokenizer_prompt_ids.tolist(), fast_tokenizer_prompt_ids.tolist())
def test_combine_tokens_into_words(self):
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
# 'whatever "whatever" said someone, clever!?'
encoded_input = [1363, 7969, 503, 1363, 7969, 1, 848, 1580, 11, 13494, 7323]
expected_words = ["whatever", ' "whatever"', " said", " someone,", " clever!?"]
expected_tokens = [[1363, 7969], [503, 1363, 7969, 1], [848], [1580, 11], [13494, 7323]]
expected_indices = [[0, 1], [2, 3, 4, 5], [6], [7, 8], [9, 10]]
output = _combine_tokens_into_words(tokenizer, encoded_input)
self.assertEqual(expected_words, output[0])
self.assertEqual(expected_tokens, output[1])
self.assertEqual(expected_indices, output[2])
output_rust = _combine_tokens_into_words(rust_tokenizer, encoded_input)
self.assertEqual(expected_words, output_rust[0])
self.assertEqual(expected_tokens, output_rust[1])
self.assertEqual(expected_indices, output_rust[2])
class SpeechToTextTokenizerMultilinguialTest(unittest.TestCase):
checkpoint_name = "openai/whisper-small.en"
@classmethod
def setUpClass(cls):
cls.tokenizer: WhisperTokenizer = WhisperTokenizer.from_pretrained(cls.checkpoint_name)
return cls
def test_tokenizer_equivalence(self):
text = "다람쥐 헌 쳇바퀴에 타고파"
multilingual_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="korean")
monolingual_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny.en")
monolingual_tokens = monolingual_tokenizer.encode(text, add_special_tokens=False)
multilingual_tokens = multilingual_tokenizer.encode(text, add_special_tokens=False)
assert monolingual_tokenizer.decode(monolingual_tokens) == text
assert multilingual_tokenizer.decode(multilingual_tokens) == text
assert len(monolingual_tokens) > len(multilingual_tokens)
# fmt: off
EXPECTED_ENG = [
46695, 97, 167, 252, 234, 168, 98, 238, 220, 169,
245, 234, 23821, 111, 229, 167, 108, 242, 169, 222,
112, 168, 245, 238, 220, 169, 225, 222, 166, 111,
254, 169, 234, 234
]
EXPECTED_MULTI = [
9835, 22855, 168, 98, 238, 13431, 234, 43517, 229, 47053,
169, 222, 19086, 19840, 1313, 17974
]
# fmt: on
self.assertListEqual(monolingual_tokens, EXPECTED_ENG)
self.assertListEqual(multilingual_tokens, EXPECTED_MULTI)
def test_tokenizer_special(self):
multilingual_tokenizer = WhisperTokenizer.from_pretrained(
"openai/whisper-tiny", language="english", task="transcribe"
)
text = "Hey! How are you feeling? J'ai l'impression que 郷さん est prêt"
multilingual_tokens = multilingual_tokenizer.encode(text)
# fmt: off
# format: <|startoftranscript|> <|lang-id|> <|task|> <|notimestamps|> ... transcription ids ... <|endoftext|>
EXPECTED_MULTI = [
START_OF_TRANSCRIPT, EN_CODE, TRANSCRIBE, NOTIMESTAMPS, 7057, 0, 1012, 366, 291,
2633, 30, 508, 6, 1301, 287, 6, 36107, 631, 220, 11178,
115, 15567, 871, 44393, END_OF_TRANSCRIPT
]
EXPECTED_SPECIAL_TEXT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|>Hey! How are you feeling? "
"J'ai l'impression que 郷さん est prêt<|endoftext|>"
)
# fmt: on
self.assertListEqual(multilingual_tokens, EXPECTED_MULTI)
special_transcript = multilingual_tokenizer.decode(multilingual_tokens, skip_special_tokens=False)
self.assertEqual(special_transcript, EXPECTED_SPECIAL_TEXT)
transcript = multilingual_tokenizer.decode(multilingual_tokens, skip_special_tokens=True)
self.assertEqual(transcript, text)
def test_vocab_size(self):
self.assertEqual(self.tokenizer.vocab_size, 50257)
# Copied from transformers.tests.speech_to_test.test_tokenization_speech_to_text.py
def test_tokenizer_decode_ignores_language_codes(self):
self.assertIn(ES_CODE, self.tokenizer.all_special_ids)
generated_ids = [ES_CODE, 4, 1601, 47, 7647, 2]
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_spanish = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_spanish)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_batch_encoding(self):
multilingual_tokenizer = WhisperTokenizer.from_pretrained(
"openai/whisper-tiny", language="spanish", task="translate"
)
batch = ["El gato ", "El gato se sentó"]
batch_output = multilingual_tokenizer.batch_encode_plus(batch, padding=True).input_ids
# fmt: off
EXPECTED_MULTI = [
[START_OF_TRANSCRIPT, ES_CODE, TRANSLATE, NOTIMESTAMPS, 17356, 290, 2513, 220,
END_OF_TRANSCRIPT, END_OF_TRANSCRIPT, END_OF_TRANSCRIPT],
[START_OF_TRANSCRIPT, ES_CODE, TRANSLATE, NOTIMESTAMPS, 17356, 290, 2513, 369,
2279, 812, END_OF_TRANSCRIPT]
]
# fmt: on
self.assertListEqual(batch_output, EXPECTED_MULTI)
def test_set_prefix_tokens(self):
multilingual_tokenizer = WhisperTokenizer.from_pretrained(
"openai/whisper-tiny", language="spanish", task="translate"
)
# change the language prefix token from Spanish to English
multilingual_tokenizer.set_prefix_tokens(language="english")
batch = ["the cat", "the cat sat"]
batch_output = multilingual_tokenizer.batch_encode_plus(batch, padding=True).input_ids
# fmt: off
EXPECTED_MULTI = [
[START_OF_TRANSCRIPT, EN_CODE, TRANSLATE, NOTIMESTAMPS, 3322, 3857,
END_OF_TRANSCRIPT, END_OF_TRANSCRIPT],
[START_OF_TRANSCRIPT, EN_CODE, TRANSLATE, NOTIMESTAMPS, 3322, 3857,
3227, END_OF_TRANSCRIPT]
]
# fmt: on
self.assertListEqual(batch_output, EXPECTED_MULTI)
def test_batch_encoding_decoding(self):
multilingual_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="spanish")
batch = ["hola güey", "que onda"]
batch_encoding = multilingual_tokenizer.batch_encode_plus(batch, padding=True).input_ids
transcription = multilingual_tokenizer.batch_decode(batch_encoding, skip_special_tokens=True)
self.assertListEqual(batch, transcription)
def test_offset_decoding(self):
multilingual_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny")
# fmt: off
INPUT_TOKENS = [
50258, 50259, 50359, 50364, 441, 1857, 4174, 11, 5242, 366,
257, 1333, 295, 493, 2794, 2287, 293, 12018, 14880, 11,
293, 25730, 311, 454, 34152, 4496, 904, 50724, 50724, 366,
382, 4048, 382, 257, 361, 18459, 13065, 13, 2221, 13,
7145, 74, 325, 38756, 311, 29822, 7563, 412, 472, 709,
294, 264, 51122, 51122, 912, 636, 300, 2221, 13, 2741,
5767, 1143, 281, 7319, 702, 7798, 13, 400, 2221, 13,
2619, 4004, 811, 2709, 702, 51449, 51449, 50257
]
# fmt: on
output = multilingual_tokenizer.decode(INPUT_TOKENS, output_offsets=True)["offsets"]
self.assertEqual(
output,
[
{
"text": (
" Lennils, pictures are a sort of upguards and atom paintings, and Mason's exquisite idles"
),
"timestamp": (0.0, 7.2),
},
{
"text": (
" are as national as a jingo poem. Mr. Birkut Foster's landscapes smile at one much in the"
),
"timestamp": (7.2, 15.16),
},
{
"text": " same way that Mr. Carker used to flash his teeth. And Mr. John Colier gives his",
"timestamp": (15.16, 21.7),
},
],
)
# test `decode_with_offsets`
output = multilingual_tokenizer.decode(INPUT_TOKENS, decode_with_timestamps=True)
self.assertEqual(
output,
"<|startoftranscript|><|en|><|transcribe|><|0.00|> Lennils, pictures are a sort of upguards and atom"
" paintings, and Mason's exquisite idles<|7.20|><|7.20|> are as national as a jingo poem. Mr. Birkut"
" Foster's landscapes smile at one much in the<|15.16|><|15.16|> same way that Mr. Carker used to flash"
" his teeth. And Mr. John Colier gives his<|21.70|><|21.70|><|endoftext|>",
)
# test a single sequence with timestamps
# fmt: off
INPUT_TOKENS = [
50364, 441, 1857, 4174, 11, 5242, 366,
257, 1333, 295, 493, 2794, 2287, 293, 12018, 14880, 11,
293, 25730, 311, 454, 34152, 4496, 904, 50724
]
# fmt: on
output = multilingual_tokenizer.decode(INPUT_TOKENS, output_offsets=True)["offsets"]
self.assertEqual(
output[0],
{
"text": " Lennils, pictures are a sort of upguards and atom paintings, and Mason's exquisite idles",
"timestamp": (0.0, 7.2),
},
)
# test a sequence without a single timestamps
# fmt: off
INPUT_TOKENS = [
441, 1857, 4174, 11, 5242, 366,
257, 1333, 295, 493, 2794, 2287, 293, 12018, 14880, 11,
293, 25730, 311, 454, 34152, 4496, 904, 50724
]
# fmt: on
output = multilingual_tokenizer.decode(INPUT_TOKENS, output_offsets=True)["offsets"]
self.assertEqual(output, [])
| transformers-main | tests/models/whisper/test_tokenization_whisper.py |
transformers-main | tests/models/whisper/__init__.py |
|
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Whisper model. """
import copy
import inspect
import os
import tempfile
import unittest
import numpy as np
import transformers
from transformers import WhisperConfig
from transformers.testing_utils import is_pt_flax_cross_test, require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_flax_available, is_torch_available
from transformers.utils.import_utils import is_datasets_available
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_datasets_available():
import datasets
from datasets import load_dataset
if is_torch_available():
import torch
from transformers import (
WhisperFeatureExtractor,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperProcessor,
set_seed,
)
from transformers.models.whisper.modeling_whisper import WhisperDecoder, WhisperEncoder
if is_flax_available():
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
def prepare_whisper_inputs_dict(
config,
input_features,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
# "input_ids": input_features,
"input_features": input_features,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_torch
class WhisperModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=60,
is_training=True,
use_labels=False,
vocab_size=200,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=30,
max_target_positions=40,
bos_token_id=98,
eos_token_id=98,
pad_token_id=0,
num_mel_bins=80,
decoder_start_token_id=85,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.decoder_start_token_id = decoder_start_token_id
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
def prepare_config_and_inputs(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length], self.vocab_size)
decoder_input_ids = torch.tensor(self.batch_size * [[self.decoder_start_token_id]], device=torch_device)
config = self.get_config()
inputs_dict = prepare_whisper_inputs_dict(
config,
attention_mask=None,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
)
return config, inputs_dict
def get_config(self):
return WhisperConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_ffn_dim=self.hidden_size,
encoder_ffn_dim=self.hidden_size,
decoder_start_token_id=self.decoder_start_token_id,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
def create_and_check_model_forward(self, config, inputs_dict, freeze_encoder=False):
model = WhisperModel(config=config).to(torch_device).eval()
if freeze_encoder:
model.freeze_encoder()
input_features = inputs_dict["input_features"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
# first forward pass
last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16))
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = WhisperModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["decoder_input_ids"]
attention_mask = inputs_dict["decoder_attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = WhisperModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = WhisperEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_features"])[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = WhisperDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (WhisperModel, WhisperForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (WhisperForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"audio-classification": WhisperForAudioClassification,
"automatic-speech-recognition": WhisperForConditionalGeneration,
"feature-extraction": WhisperModel,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = False
test_pruning = False
test_missing_keys = False
# Needs higher percentages after model tester's vocab_size is changed to 200 (PR #21222)
# `0.5` is for `test_disk_offload` (which also works for `test_model_parallelism`)
model_split_percents = [0.5, 0.8, 0.9]
input_name = "input_features"
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name in [
"AutomaticSpeechRecognitionPipelineTests",
"AudioClassificationPipelineTests",
]:
# RuntimeError: The size of tensor a (1500) must match the size of tensor b (30) at non-singleton
# dimension 1
return True
return False
def setUp(self):
self.model_tester = WhisperModelTester(self)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_model_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs)
def test_model_forward_with_frozen_encoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs, freeze_encoder=True)
def test_requires_grad_with_frozen_encoder(self):
config = self.model_tester.get_config()
for model_class in self.all_model_classes:
model = model_class(config)
model.freeze_encoder()
try:
encoder_grads = [param.requires_grad for param in model.encoder.parameters()]
decoder_grads = [param.requires_grad for param in model.decoder.parameters()]
except AttributeError:
encoder_grads = [param.requires_grad for param in model.model.encoder.parameters()]
decoder_grads = [param.requires_grad for param in model.model.decoder.parameters()]
self.assertFalse(all(encoder_grads))
self.assertTrue(all(decoder_grads))
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
def _get_input_ids_and_config(self, batch_size=3):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict[self.input_name]
# cut to half length & take max batch_size=batch_size
input_ids = input_ids[:batch_size, :, :]
# generate max 3 tokens
max_length = 4
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
config.pad_token_id = config.eos_token_id
return config, input_ids, None, max_length
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
decoder_input_ids = inputs.pop("decoder_input_ids", None)
inputs.pop("decoder_attention_mask", None)
wte = model.get_input_embeddings()
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
# training is not supported yet
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
def test_generate_with_head_masking(self):
pass
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
config.max_target_positions = 400
input_features = input_dict["input_features"]
model = WhisperForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
input_features = input_features.half()
model.half()
model.generate(input_features)
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_generate_language(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_features = input_dict["input_features"]
model = WhisperForConditionalGeneration(config).to(torch_device)
# Hack to keep the test fast and not require downloading a model with a generation_config
model.generation_config.__setattr__("lang_to_id", {"<|en|>": 1})
model.generation_config.__setattr__("task_to_id", {"transcribe": 2})
# test language code
model.generate(input_features, language="en")
# test tokenizer code
model.generate(input_features, language="<|en|>")
# test language name
model.generate(input_features, language="English")
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"input_features",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
else:
seq_length = self.model_tester.seq_length
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[subsampled_seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", 1)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", 1)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
subsampled_encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
def test_resize_tokens_embeddings(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# make sure that decoder_input_ids are resized
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
def test_generate_without_input_ids(self):
pass
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = input_ids[:, :, 0]
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + torch.tensor(
[model._get_decoder_start_token_id()], device=input_ids.device
)
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, mel, seq_length = input_ids.shape
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# Attentions
# encoder
self._check_encoder_attention_for_generate(
output.encoder_attentions, batch_size, config, subsampled_seq_length
)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
input_features = inputs["input_features"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
# prepare `attention_mask` with shape (batch_size, sequence_length)
attention_mask = torch.ones(
input_features.shape[0],
input_features.shape[-1],
device=input_features.device,
dtype=input_features.dtype,
)
traced_model = torch.jit.trace(
model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
)
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# We override with a slightly higher tol value, as test recently became flaky
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# We override with a slightly higher tol value, as test recently became flaky
super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, input_shape=init_shape, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
def test_mask_feature_prob(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.mask_feature_prob = 0.2
config.mask_feature_length = 2
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.train()
# forward pass
encoder_last_hidden_state = model(**input_dict).encoder_last_hidden_state
self.assertTrue(encoder_last_hidden_state.shape, (13, 30, 16))
def test_mask_time_prob(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.mask_time_prob = 0.2
config.mask_time_length = 2
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.train()
# forward pass
encoder_last_hidden_state = model(**input_dict).encoder_last_hidden_state
self.assertTrue(encoder_last_hidden_state.shape, (13, 30, 16))
def test_generate_with_prompt_ids_and_task_and_language(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = WhisperForConditionalGeneration(config).eval().to(torch_device)
input_features = input_dict["input_features"]
prompt_ids = np.arange(5)
language = "<|de|>"
task = "translate"
lang_id = 6
task_id = 7
model.generation_config.__setattr__("lang_to_id", {language: lang_id})
model.generation_config.__setattr__("task_to_id", {task: task_id})
output = model.generate(input_features, max_new_tokens=5, task=task, language=language, prompt_ids=prompt_ids)
expected_output_start = [
*prompt_ids.tolist(),
model.generation_config.decoder_start_token_id,
lang_id,
task_id,
]
for row in output.tolist():
self.assertListEqual(row[: len(expected_output_start)], expected_output_start)
def test_generate_with_prompt_ids_and_forced_decoder_ids(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = WhisperForConditionalGeneration(config).eval().to(torch_device)
input_features = input_dict["input_features"]
prompt_ids = np.asarray(range(5))
forced_decoder_ids = [(1, 6), (2, 7), (3, 8)]
output = model.generate(
input_features, max_new_tokens=5, forced_decoder_ids=forced_decoder_ids, prompt_ids=prompt_ids
)
expected_output_start = [
*prompt_ids.tolist(),
model.generation_config.decoder_start_token_id,
*[token for _rank, token in forced_decoder_ids],
]
for row in output.tolist():
self.assertListEqual(row[: len(expected_output_start)], expected_output_start)
@require_torch
@require_torchaudio
class WhisperModelIntegrationTests(unittest.TestCase):
@cached_property
def default_processor(self):
return WhisperProcessor.from_pretrained("openai/whisper-base")
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@slow
def test_tiny_logits_librispeech(self):
torch_device = "cpu"
set_seed(0)
model = WhisperModel.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
with torch.no_grad():
logits = model(
input_features,
decoder_input_ids=torch.tensor([[50258, 50259, 50359]]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
use_cache=False,
)
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
2.9892, -6.7607, 5.7348, 3.6096, 0.2152, -5.7321, 4.8855, -1.6407,
0.2823, -1.5718, 10.4269, 3.4427, 0.0219, -8.0612, 3.4784, 8.4246,
4.0575, -2.2864, 11.1084, 0.9963, 0.9884, -8.5154, -3.5469, -9.3713,
0.9786, 3.5435, 7.4850, -5.2579, -1.4366, 10.4841
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0][0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
# fmt: off
EXPECTED_GENERATION = torch.tensor(
[
-1.4651, -2.6944, 2.7821, 2.3793, 4.0738, 0.0188, -3.3203, 1.9836,
0.0520, 0.7095, 1.1063, 0.2952, -3.6786, -0.5249, 0.3105, 4.7691,
1.1562, 1.3046, 0.5810, -0.3624, 1.7006, 1.3424, 0.9817, 2.1958,
1.8775, -5.7046, -0.7679, 4.0113, 2.6848, 2.8609
]
)
# fmt: on
head_logits = logits[0] @ model.decoder.embed_tokens.weight.T
self.assertTrue(torch.allclose(head_logits[0, 0, :30].cpu(), EXPECTED_GENERATION, atol=1e-4))
@slow
def test_small_en_logits_librispeech(self):
set_seed(0)
torch_device = "cpu"
model = WhisperModel.from_pretrained("openai/whisper-small.en")
model.to(torch_device)
input_speech = self._load_datasamples(1)
feaure_extractor = WhisperFeatureExtractor()
input_features = feaure_extractor(input_speech, return_tensors="pt").input_features.to(torch_device)
logits = model(
input_features,
decoder_input_ids=torch.tensor([[model.config.decoder_start_token_id]]),
output_hidden_states=False,
output_attentions=False,
use_cache=False,
)
logits = logits.last_hidden_state @ model.decoder.embed_tokens.weight.T
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
-3.6784, -7.7211, -9.5070, -11.9286, -7.6489, -9.7026, -5.6188,
-8.0104, -4.6238, -5.1833, -9.0485, -3.4079, -5.4874, -2.6935,
-6.3479, -7.3398, -6.9558, -7.6867, -7.4748, -8.3463, -9.9781,
-10.8389, -10.3105, -11.7201, -9.7261, -7.1590, -5.9272, -12.4509,
-11.1146, -8.1918
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_large_logits_librispeech(self):
set_seed(0)
torch_device = "cpu"
model = WhisperModel.from_pretrained("openai/whisper-large")
model.to(torch_device)
input_speech = self._load_datasamples(1)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
processed_inputs = processor(
audio=input_speech, text="This part of the speech", add_special_tokens=False, return_tensors="pt"
)
input_features = processed_inputs.input_features.to(torch_device)
decoder_input_ids = processed_inputs.labels.to(torch_device)
logits = model(
input_features,
decoder_input_ids=decoder_input_ids,
output_hidden_states=False,
output_attentions=False,
use_cache=False,
)
logits = logits.last_hidden_state @ model.decoder.embed_tokens.weight.T
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
2.1382, 0.9381, 4.4671, 3.5589, 2.4022, 3.8576, -0.6521, 2.5472,
1.8301, 1.9957, 2.3432, 1.4678, 0.5459, 2.2597, 1.5179, 2.5357,
1.1624, 0.6194, 1.0757, 1.8259, 2.4076, 1.6601, 2.3503, 1.3376,
1.9891, 1.8635, 3.8931, 5.3699, 4.4772, 3.9184
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_tiny_en_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.to(torch_device)
model.config.decoder_start_token_id = 50257
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, num_beams=5, max_length=20)
transcript = processor.tokenizer.batch_decode(generated_ids)[0]
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes, and we are glad to"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, num_beams=5, max_length=20)
transcript = processor.tokenizer.decode(generated_ids[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_large_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes and we are glad"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_large_generation_multilingual(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
model.to(torch_device)
ds = load_dataset("common_voice", "ja", split="test", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
input_speech = next(iter(ds))["audio"]["array"]
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|ja|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = "木村さんに電話を貸してもらいました"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Kimura-san called me."
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|ja|>", task="translate"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " I borrowed a phone from Kimura san"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_large_batched_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features
generated_ids = model.generate(input_features, max_length=20, task="translate")
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
[50258, 50259, 50358, 50363, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 293, 321, 366, 5404],
[50258, 50259, 50358, 50363, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50257],
[50258, 50259, 50358, 50363, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904],
[50258, 50259, 50358, 50363, 634, 575, 12525, 22618, 1968, 6144, 35617, 20084, 1756, 311, 589, 307, 534, 10281, 934, 439]
]
)
# fmt: on
self.assertTrue(torch.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes and we are glad",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_en_batched_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.to(torch_device)
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, max_length=20).to("cpu")
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
[50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284],
[50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256],
[50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236],
[50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460]
]
)
# fmt: on
self.assertTrue(torch.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes, and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef looming",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_timestamp_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = np.concatenate(self._load_datasamples(4))
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, max_length=448, return_timestamps=True).to("cpu")
# fmt: off
EXPECTED_OUTPUT = torch.tensor([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257])
# fmt: on
self.assertTrue(torch.allclose(generated_ids, EXPECTED_OUTPUT))
EXPECTED_TRANSCRIPT = [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is"
" Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season"
" of the year, with Christmas and roast beef looming before us, similarly drawn from eating and"
" its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins'"
" work is really Greek after all, and"
),
"offsets": [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
),
"timestamp": (0.0, 6.5600000000000005),
},
{
"text": " Nor is Mr. Quilter's manner less interesting than his matter.",
"timestamp": (6.5600000000000005, 11.24),
},
{
"text": (
" He tells us that at this festive season of the year, with Christmas and roast beef"
" looming"
),
"timestamp": (11.24, 16.88),
},
{
"text": (
" before us, similarly drawn from eating and its results occur most readily to the mind."
),
"timestamp": (16.88, 23.76),
},
{
"text": (
" He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and"
),
"timestamp": (23.76, 29.44),
},
],
}
]
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True, output_offsets=True)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_token_timestamp_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
model.generation_config.alignment_heads = [[2, 2], [3, 0], [3, 2], [3, 3], [3, 4], [3, 5]]
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generate_outputs = model.generate(
input_features, max_length=448, return_timestamps=True, return_token_timestamps=True
)
self.assertEqual(generate_outputs.sequences.shape, generate_outputs.token_timestamps.shape)
# fmt: off
EXPECTED_OUTPUT = torch.tensor([
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.4800, 0.8200, 0.9600, 1.1200, 1.1200, 1.2200, 1.5000, 1.7200, 2.0000, 2.3400, 2.5000, 2.6600, 3.1800, 3.5600, 3.6800, 3.8000, 4.1000, 4.3000, 4.5800, 4.9400, 5.3800, 12.4200, 12.8400, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9400, 26.9400, 26.9400, 26.9400, 29.8400 ],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.5200, 0.9000, 1.1400, 1.4200, 1.5200, 1.6800, 1.6800, 1.8800, 2.1000, 2.2200, 2.6200, 3.1400, 3.5800, 3.9600, 4.4000, 17.3000, 17.3000, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7400, 26.7400, 26.7400, 26.7400, 26.7400, 26.7400, 28.0000 ],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7600, 1.0000, 1.4200, 1.8000, 1.9400, 2.1800, 2.5200, 3.0200, 3.3200, 3.5400, 3.9400, 4.5600, 4.9200, 5.2800, 5.5600, 5.9000, 6.1600, 6.3000, 6.4800, 6.4800, 6.6400, 7.8200, 7.9600, 8.2200, 8.6000, 8.9200, 9.2200, 9.5200, 9.7200, 10.0600, 10.5400, 10.8800, 11.2600, 11.5400, 11.7400, 12.0800, 15.6800, 15.6800],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7400, 1.0400, 1.3200, 1.6800, 2.1400, 2.4800, 2.7800, 3.0800, 3.1600, 3.4000, 3.6000, 4.0200, 4.2200, 4.8600, 5.2400, 5.7400, 6.3400, 6.6200, 6.7600, 6.7600, 6.8600, 7.2400, 7.4200, 7.6800, 7.9200, 8.4800, 8.7600, 9.2000, 9.2000, 9.4200, 15.8200, 15.8200, 29.6400, 29.6600, 29.6600, 29.6600, 29.6600, 29.7600]
])
# fmt: on
self.assertTrue(torch.allclose(generate_outputs.token_timestamps.to("cpu"), EXPECTED_OUTPUT))
@slow
def test_tiny_specaugment_librispeech(self):
torch_device = "cpu"
set_seed(0)
# Apply SpecAugment
model = WhisperModel.from_pretrained("openai/whisper-tiny", apply_spec_augment=True)
# Set model to training mode to enable SpecAugment
model.train()
model.to(torch_device)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
with torch.no_grad():
logits = model(
input_features,
decoder_input_ids=torch.tensor([[50258, 50259, 50359]]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
use_cache=False,
)
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
0.9362, -4.7105, 5.0879, 3.9642, 1.0013, -6.0096, 4.7285, -3.1847,
-0.8648, 1.9631, 6.2653, 3.6936, 0.3575, -4.5818, 3.0564, 7.8712,
2.9951, 0.6848, 9.9497, -2.6638, 1.1571, -6.8546, -1.4333, -7.7584,
1.1200, 3.9030, 4.4655, -4.4919, -1.1703, 9.6241
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0][0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_generate_with_prompt_ids(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(4)[-1:]
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
output_without_prompt = model.generate(input_features)
prompt_ids = processor.get_prompt_ids("Leighton")
output_with_prompt = model.generate(input_features, prompt_ids=prompt_ids)
expected_without_prompt = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|> He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in it but little of Rocky Ithaca.<|endoftext|>"
expected_with_prompt = "<|startofprev|> Leighton<|startoftranscript|><|en|><|transcribe|><|notimestamps|> He has grave doubts whether Sir Frederick Leighton's work is really Greek after all and can discover in it but little of Rocky Ithaca.<|endoftext|>"
self.assertEqual(processor.decode(output_without_prompt[0]), expected_without_prompt)
self.assertEqual(processor.decode(output_with_prompt[0]), expected_with_prompt)
@slow
def test_generate_with_prompt_ids_and_forced_decoder_ids(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
task = "translate"
language = "de"
expected_tokens = [f"<|{task}|>", f"<|{language}|>"]
prompt = "test prompt"
prompt_ids = processor.get_prompt_ids(prompt)
output = model.generate(input_features, task=task, language=language, prompt_ids=prompt_ids)
text = processor.decode(output[0])
self.assertTrue(prompt in text)
self.assertTrue(all(token in text for token in expected_tokens))
@slow
def test_generate_with_prompt_ids_and_no_non_prompt_forced_decoder_ids(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
prompt = "test prompt"
prompt_ids = processor.get_prompt_ids(prompt)
model.generation_config.forced_decoder_ids = None
model.config.forced_decoder_ids = None
output = model.generate(input_features, prompt_ids=prompt_ids, return_timestamps=True)
text = processor.decode(output[0])
self.assertTrue(prompt in text)
def prepare_whisper_encoder_inputs_dict(config, input_features, head_mask=None):
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
return {"input_features": input_features, "head_mask": head_mask}
@require_torch
class WhisperEncoderModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=60,
is_training=True,
use_labels=True,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=30,
num_mel_bins=80,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
classifier_proj_size=4,
num_labels=2,
is_encoder_decoder=False,
is_decoder=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
self.classifier_proj_size = classifier_proj_size
self.num_labels = num_labels
self.is_encoder_decoder = is_encoder_decoder
self.is_decoder = is_decoder
def get_config(self):
return WhisperConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
decoder_ffn_dim=self.hidden_size,
encoder_ffn_dim=self.hidden_size,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
classifier_proj_size=self.classifier_proj_size,
num_labels=self.num_labels,
is_encoder_decoder=self.is_encoder_decoder,
is_decoder=self.is_decoder,
)
def prepare_config_and_inputs(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length])
config = self.get_config()
inputs_dict = prepare_whisper_encoder_inputs_dict(
config,
input_features=input_features,
)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
@property
def encoder_seq_length(self):
return self.get_subsampled_output_lengths(self.seq_length)
def create_and_check_model_forward(self, config, inputs_dict, freeze_encoder=False):
model = WhisperForAudioClassification(config=config).to(torch_device).eval()
if freeze_encoder:
model.freeze_encoder()
input_features = inputs_dict["input_features"]
# first forward pass
last_hidden_state = model(input_features).logits
self.parent.assertTrue(last_hidden_state.shape, (13, 2))
@require_torch
class WhisperEncoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (WhisperForAudioClassification,) if is_torch_available() else ()
is_encoder_decoder = False
fx_compatible = False
test_pruning = False
test_missing_keys = False
input_name = "input_features"
def setUp(self):
self.model_tester = WhisperEncoderModelTester(self)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_features", "head_mask", "encoder_outputs"]
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
@unittest.skip(reason="Some undefined behavior encountered with tiny versions of this model. Skip for now.")
def test_cpu_offload(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with tiny versions of this model. Skip for now.")
def test_disk_offload(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with tiny versions of this model. Skip for now.")
def test_model_parallelism(self):
pass
# input embeds is meaningless for an encoder-only acoustic model
def test_inputs_embeds(self):
pass
# the equivalent test is passing the encoder outputs directly to the model
def test_encoder_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
with torch.no_grad():
outputs = model(**inputs)[0]
input_ids = inputs["input_features"]
del inputs["input_features"]
encoder = model.encoder
with torch.no_grad():
inputs["encoder_outputs"] = encoder(input_ids)
outputs_embeds = model(**inputs)[0]
self.assertTrue((outputs_embeds == outputs).all())
# Needs to override as the encoder input embedding is a Conv1d
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Conv1d))
model.set_input_embeddings(torch.nn.Conv1d(10, 10, 3))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, torch.nn.Conv1d))
# WhisperEncoder cannot resize token embeddings since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, input_shape=init_shape, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
| transformers-main | tests/models/whisper/test_modeling_whisper.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow Whisper model. """
from __future__ import annotations
import inspect
import tempfile
import traceback
import unittest
import numpy as np
from transformers import WhisperConfig, WhisperFeatureExtractor, WhisperProcessor
from transformers.testing_utils import is_tf_available, require_tf, require_tokenizers, run_test_in_subprocess, slow
from transformers.utils import cached_property
from transformers.utils.import_utils import is_datasets_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_datasets_available():
import datasets
from datasets import load_dataset
if is_tf_available():
import tensorflow as tf
from transformers import TFWhisperForConditionalGeneration, TFWhisperModel, set_seed
from transformers.models.whisper.modeling_tf_whisper import TFWhisperDecoder, TFWhisperEncoder
def prepare_whisper_inputs_dict(
config,
input_features,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if decoder_attention_mask is None:
decoder_attention_mask = tf.where(decoder_input_ids != config.pad_token_id, 1, 0)
if head_mask is None:
head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_features": input_features,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class TFWhisperModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=60,
is_training=True,
use_labels=False,
vocab_size=200,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=30,
max_target_positions=60,
bos_token_id=98,
eos_token_id=98,
pad_token_id=0,
num_mel_bins=80,
decoder_start_token_id=85,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.decoder_start_token_id = decoder_start_token_id
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
def prepare_config_and_inputs(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
inputs_dict = prepare_whisper_inputs_dict(
config,
attention_mask=None,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
)
return config, inputs_dict
def get_config(self):
return WhisperConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_ffn_dim=self.hidden_size,
encoder_ffn_dim=self.hidden_size,
decoder_start_token_id=self.decoder_start_token_id,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
def create_and_check_model_forward(self, config, inputs_dict):
model = TFWhisperModel(config=config)
input_features = inputs_dict["input_features"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
# first forward pass
last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16))
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = TFWhisperModel(config=config).get_decoder()
# take a slice so we're shorter than the seqeuence length and can append later
input_ids = inputs_dict["decoder_input_ids"][:, :-10]
attention_mask = inputs_dict["decoder_attention_mask"][:, :-10]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_token = ids_tensor((self.batch_size, 3), config.vocab_size)
next_tokens = tf.where(next_token <= 2, 2, next_token)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = np.random.randint(0, output_from_past.shape[-1])
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = TFWhisperModel(config=config)
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = TFWhisperEncoder.from_pretrained(tmpdirname)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_features"])[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = TFWhisperDecoder.from_pretrained(tmpdirname)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max() < 1e-3)
@require_tf
class TFWhisperModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (TFWhisperModel, TFWhisperForConditionalGeneration) if is_tf_available() else ()
all_generative_model_classes = (TFWhisperForConditionalGeneration,) if is_tf_available() else ()
pipeline_model_mapping = {"feature-extraction": TFWhisperModel} if is_tf_available() else {}
is_encoder_decoder = True
fx_compatible = False
test_pruning = False
test_missing_keys = False
test_onnx = False
input_name = "input_features"
def setUp(self):
self.model_tester = TFWhisperModelTester(self)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
model.build()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=False)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_model_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def _get_input_ids_and_config(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict[self.input_name]
# cut to half length & take max batch_size 3
max_batch_size = 3
input_ids = input_ids[:max_batch_size, :, :]
# generate max 3 tokens
max_length = 4
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
config.pad_token_id = config.eos_token_id
return config, input_ids, None, max_length
# not implemented currently
def test_inputs_embeds(self):
pass
@unittest.skip("Training is not yet supported")
def test_training(self):
pass
def test_generate_with_head_masking(self):
pass
@unittest.skip("fp16 is not yet supported for TF models")
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
config.max_target_positions = 400
input_features = input_dict["input_features"]
model = TFWhisperForConditionalGeneration(config)
model.generate(input_features)
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"input_features",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["decoder_position_ids", "head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
else:
seq_length = self.model_tester.seq_length
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[subsampled_seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_length)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# We override with a slightly higher tol value, as test recently became flaky
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", encoder_key_length)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
subsampled_encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
def test_generate_without_input_ids(self):
pass
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = input_ids[:, :, 0]
input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + tf.convert_to_tensor(
[model._get_decoder_start_token_id()]
)
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, mel, seq_length = input_ids.shape
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# Attentions
# encoder
self._check_encoder_attention_for_generate(
output.encoder_attentions, batch_size, config, subsampled_seq_length
)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is
# `input_features`
def test_lm_head_model_random_no_beam_search_generate(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_features = inputs_dict.get("input_features", None)
# iterate over all generative models
for model_class in self.all_generative_model_classes:
model = model_class(config)
if config.bos_token_id is None:
# if bos token id is not defined model needs input_features
with self.assertRaises(AssertionError):
model.generate(do_sample=True, max_length=5)
# num_return_sequences = 1
self._check_generated_ids(model.generate(input_features, do_sample=True))
with self.assertRaises(ValueError):
# generating multiple sequences when no beam search generation
# is not allowed as it would always generate the same sequences
model.generate(input_features, do_sample=False, num_return_sequences=2)
# num_return_sequences > 1, sample
self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2))
# check bad words tokens language generation
# create list of 1-seq bad token and list of 2-seq of bad tokens
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
output_tokens = model.generate(
input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2
)
# only count generated tokens
generated_ids = output_tokens[:, input_features.shape[-1] :]
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
# overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is
# `input_features`
def test_lm_head_model_random_beam_search_generate(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_features = inputs_dict.get("input_features", None)
for model_class in self.all_generative_model_classes:
model = model_class(config)
if config.bos_token_id is None:
# if bos token id is not defined model needs input_ids, num_return_sequences = 1
self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2))
with self.assertRaises(ValueError):
# generating more sequences than having beams leads is not possible
model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2)
# num_return_sequences > 1, sample
self._check_generated_ids(
model.generate(
input_features,
do_sample=True,
num_beams=2,
num_return_sequences=2,
)
)
# num_return_sequences > 1, greedy
self._check_generated_ids(
model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2)
)
# check bad words tokens language generation
# create list of 1-seq bad token and list of 2-seq of bad tokens
bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
output_tokens = model.generate(
input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2
)
# only count generated tokens
generated_ids = output_tokens[:, input_features.shape[-1] :]
self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))
def test_generate_with_prompt_ids_and_task_and_language(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = TFWhisperForConditionalGeneration(config)
input_features = input_dict["input_features"]
prompt_ids = np.arange(5)
language = "<|de|>"
task = "translate"
lang_id = 6
task_id = 7
model.generation_config.__setattr__("lang_to_id", {language: lang_id})
model.generation_config.__setattr__("task_to_id", {task: task_id})
output = model.generate(input_features, max_new_tokens=5, task=task, language=language, prompt_ids=prompt_ids)
expected_output_start = [
*prompt_ids.tolist(),
model.generation_config.decoder_start_token_id,
lang_id,
task_id,
]
for row in output.numpy().tolist():
self.assertListEqual(row[: len(expected_output_start)], expected_output_start)
def test_generate_with_prompt_ids_and_forced_decoder_ids(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = TFWhisperForConditionalGeneration(config)
input_features = input_dict["input_features"]
prompt_ids = np.asarray(range(5))
forced_decoder_ids = [(1, 6), (2, 7), (3, 8)]
output = model.generate(
input_features, max_new_tokens=5, forced_decoder_ids=forced_decoder_ids, prompt_ids=prompt_ids
)
expected_output_start = [
*prompt_ids.tolist(),
model.generation_config.decoder_start_token_id,
*[token for _rank, token in forced_decoder_ids],
]
for row in output.numpy().tolist():
self.assertListEqual(row[: len(expected_output_start)], expected_output_start)
def _load_datasamples(num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _test_large_logits_librispeech(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
set_seed(0)
model = TFWhisperModel.from_pretrained("openai/whisper-large")
input_speech = _load_datasamples(1)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
processed_inputs = processor(
audio=input_speech, text="This part of the speech", add_special_tokens=False, return_tensors="tf"
)
input_features = processed_inputs.input_features
decoder_input_ids = processed_inputs.labels
logits = model(
input_features,
decoder_input_ids=decoder_input_ids,
output_hidden_states=False,
output_attentions=False,
use_cache=False,
)
logits = logits.last_hidden_state @ tf.transpose(model.model.decoder.embed_tokens.weights[0])
# fmt: off
EXPECTED_LOGITS = tf.convert_to_tensor(
[
2.1382, 0.9381, 4.4671, 3.5589, 2.4022, 3.8576, -0.6521, 2.5472,
1.8301, 1.9957, 2.3432, 1.4678, 0.5459, 2.2597, 1.5179, 2.5357,
1.1624, 0.6194, 1.0757, 1.8259, 2.4076, 1.6601, 2.3503, 1.3376,
1.9891, 1.8635, 3.8931, 5.3699, 4.4772, 3.9184
]
)
# fmt: on
unittest.TestCase().assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
def _test_large_generation(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
input_speech = _load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes and we are glad"
unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT)
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
def _test_large_generation_multilingual(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
ds = load_dataset("common_voice", "ja", split="test", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
input_speech = next(iter(ds))["audio"]["array"]
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|ja|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = "木村さんに電話を貸してもらいました"
unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Kimura-san called me."
unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|ja|>", task="translate"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " I borrowed a phone from Kimura san"
unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT)
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
def _test_large_batched_generation(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
input_speech = _load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
generated_ids_1 = model.generate(input_features[0:2], max_length=20)
generated_ids_2 = model.generate(input_features[2:4], max_length=20)
generated_ids = np.concatenate([generated_ids_1, generated_ids_2])
# fmt: off
EXPECTED_IDS = [
[50258, 50358, 50363, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 293, 321, 366, 5404, 281],
[50258, 50358, 50363, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50257, 50257],
[50258, 50358, 50363, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256],
[50258, 50358, 50363, 634, 575, 12525, 22618, 1968, 6144, 35617, 20084, 1756, 311, 589, 307, 534, 10281, 934, 439, 11]
]
# fmt: on
unittest.TestCase().assertEqual(generated_ids.tolist(), EXPECTED_IDS)
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all,"
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
unittest.TestCase().assertListEqual(transcript, EXPECTED_TRANSCRIPT)
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
@require_tf
@require_tokenizers
class TFWhisperModelIntegrationTests(unittest.TestCase):
@cached_property
def default_processor(self):
return WhisperProcessor.from_pretrained("openai/whisper-base")
def _load_datasamples(self, num_samples):
return _load_datasamples(num_samples)
@slow
def test_tiny_logits_librispeech(self):
set_seed(0)
model = TFWhisperModel.from_pretrained("openai/whisper-tiny")
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="tf").input_features
logits = model(
input_features,
decoder_input_ids=tf.convert_to_tensor([[50258, 50259, 50359]]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
use_cache=False,
)
# fmt: off
EXPECTED_LOGITS = tf.convert_to_tensor(
[
2.9892, -6.7607, 5.7348, 3.6096, 0.2152, -5.7321, 4.8855, -1.6407,
0.2823, -1.5718, 10.4269, 3.4427, 0.0219, -8.0612, 3.4784, 8.4246,
4.0575, -2.2864, 11.1084, 0.9963, 0.9884, -8.5154, -3.5469, -9.3713,
0.9786, 3.5435, 7.4850, -5.2579, -1.4366, 10.4841
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0][0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
# fmt: off
EXPECTED_GENERATION = tf.convert_to_tensor(
[
-1.4651, -2.6944, 2.7821, 2.3793, 4.0738, 0.0188, -3.3203, 1.9836,
0.0520, 0.7095, 1.1063, 0.2952, -3.6786, -0.5249, 0.3105, 4.7691,
1.1562, 1.3046, 0.5810, -0.3624, 1.7006, 1.3424, 0.9817, 2.1958,
1.8775, -5.7046, -0.7679, 4.0113, 2.6848, 2.8609
]
)
# fmt: on
head_logits = logits[0] @ tf.transpose(model.model.decoder.embed_tokens.weights[0])
self.assertTrue(np.allclose(head_logits[0, 0, :30], EXPECTED_GENERATION, atol=1e-4))
@slow
def test_small_en_logits_librispeech(self):
set_seed(0)
model = TFWhisperModel.from_pretrained("openai/whisper-small.en")
input_speech = self._load_datasamples(1)
feaure_extractor = WhisperFeatureExtractor()
input_features = feaure_extractor(input_speech, return_tensors="tf").input_features
logits = model(
input_features,
decoder_input_ids=tf.convert_to_tensor([[model.config.decoder_start_token_id]]),
output_hidden_states=False,
output_attentions=False,
use_cache=False,
)
logits = logits.last_hidden_state @ tf.transpose(model.model.decoder.embed_tokens.weights[0])
# fmt: off
EXPECTED_LOGITS = tf.convert_to_tensor(
[
-3.6784, -7.7211, -9.5070, -11.9286, -7.6489, -9.7026, -5.6188,
-8.0104, -4.6238, -5.1833, -9.0485, -3.4079, -5.4874, -2.6935,
-6.3479, -7.3398, -6.9558, -7.6867, -7.4748, -8.3463, -9.9781,
-10.8389, -10.3105, -11.7201, -9.7261, -7.1590, -5.9272, -12.4509,
-11.1146, -8.1918
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
@slow
def test_large_logits_librispeech(self):
run_test_in_subprocess(test_case=self, target_func=_test_large_logits_librispeech, inputs=None)
@slow
def test_tiny_en_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.config.decoder_start_token_id = 50257
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
generated_ids = model.generate(input_features, num_beams=5, max_length=20)
transcript = processor.tokenizer.batch_decode(generated_ids)[0]
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes, and we are glad to"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
generated_ids = model.generate(input_features, num_beams=5, max_length=20)
transcript = processor.tokenizer.decode(generated_ids[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_xla_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
xla_generate = tf.function(model.generate, jit_compile=True)
generated_ids = model.generate(input_features, num_beams=5, max_length=20)
generated_ids_xla = xla_generate(input_features, num_beams=5, max_length=20)
transcript = processor.tokenizer.decode(generated_ids[0])
transcript_xla = processor.tokenizer.decode(generated_ids_xla[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
self.assertEqual(transcript_xla, EXPECTED_TRANSCRIPT)
@slow
def test_large_generation(self):
run_test_in_subprocess(test_case=self, target_func=_test_large_generation, inputs=None)
@slow
def test_large_generation_multilingual(self):
run_test_in_subprocess(test_case=self, target_func=_test_large_generation_multilingual, inputs=None)
@slow
def test_large_batched_generation(self):
run_test_in_subprocess(test_case=self, target_func=_test_large_batched_generation, inputs=None)
@slow
def test_tiny_en_batched_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
generated_ids = model.generate(input_features, max_length=20)
# fmt: off
EXPECTED_LOGITS = tf.convert_to_tensor(
[
[50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284],
[50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256],
[50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236],
[50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460]
]
)
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes, and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef looming",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_en_batched_xla_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features
xla_generate = tf.function(model.generate, jit_compile=True)
generated_ids = model.generate(input_features, max_length=20)
generated_ids_xla = xla_generate(input_features, max_length=20)
# fmt: off
EXPECTED_LOGITS = tf.convert_to_tensor(
[
[50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284],
[50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256],
[50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236],
[50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460]
]
)
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS))
self.assertTrue(np.allclose(generated_ids_xla, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes, and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef looming",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
transcript_xla = processor.batch_decode(generated_ids_xla, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
self.assertListEqual(transcript_xla, EXPECTED_TRANSCRIPT)
| transformers-main | tests/models/whisper/test_modeling_tf_whisper.py |
transformers-main | tests/models/rembert/__init__.py |
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import RemBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertModel,
)
class TFRemBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
input_embedding_size=18,
output_embedding_size=43,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = True
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.input_embedding_size = input_embedding_size
self.output_embedding_size = output_embedding_size
self.num_hidden_layers = 2
self.num_attention_heads = 4
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.scope = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RemBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
input_embedding_size=self.input_embedding_size,
output_embedding_size=self.output_embedding_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRemBertModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_causal_lm_base_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TFRemBertModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TFRemBertModel(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
# Also check the case where encoder outputs are not passed
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_causal_lm_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.is_decoder = True
model = TFRemBertForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
prediction_scores = model(inputs)["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_causal_lm_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TFRemBertForCausalLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
prediction_scores = result["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
)
def create_and_check_causal_lm_model_past(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TFRemBertForCausalLM(config=config)
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and attn_mask
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
output_from_past = model(
next_tokens, past_key_values=past_key_values, output_hidden_states=True
).hidden_states[0]
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_causal_lm_model_past_with_attn_mask(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TFRemBertForCausalLM(config=config)
# create attention mask
half_seq_length = self.seq_length // 2
attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
# first forward pass
outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
past_key_values = outputs.past_key_values
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
condition = tf.transpose(
tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
)
input_ids = tf.where(condition, random_other_next_tokens, input_ids)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
attn_mask = tf.concat(
[attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
axis=1,
)
output_from_no_past = model(
next_input_ids,
attention_mask=attn_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
).hidden_states[0]
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
def create_and_check_causal_lm_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
config.is_decoder = True
model = TFRemBertForCausalLM(config=config)
input_ids = input_ids[:1, :]
input_mask = input_mask[:1, :]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
).hidden_states[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TFRemBertForCausalLM(config=config)
input_ids = input_ids[:1, :]
input_mask = input_mask[:1, :]
encoder_hidden_states = encoder_hidden_states[:1, :, :]
encoder_attention_mask = encoder_attention_mask[:1, :]
self.batch_size = 1
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
).hidden_states[0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
).hidden_states[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRemBertForMaskedLM(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRemBertForSequenceClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = TFRemBertForMultipleChoice(config=config)
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
inputs = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = TFRemBertForTokenClassification(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFRemBertForQuestionAnswering(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
result = model(inputs)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFRemBertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFRemBertModel,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertForMultipleChoice,
)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": TFRemBertModel,
"fill-mask": TFRemBertForMaskedLM,
"question-answering": TFRemBertForQuestionAnswering,
"text-classification": TFRemBertForSequenceClassification,
"text-generation": TFRemBertForCausalLM,
"token-classification": TFRemBertForTokenClassification,
"zero-shot": TFRemBertForSequenceClassification,
}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFRemBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=RemBertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
"""Test the base model"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_causal_lm_base_model(self):
"""Test the base model of the causal LM model
is_deocder=True, no cross_attention, no encoder outputs
"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)
def test_model_as_decoder(self):
"""Test the base model as a decoder (of an encoder-decoder architecture)
is_deocder=True + cross_attention + pass encoder outputs
"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm(self):
"""Test the causal LM model"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model(*config_and_inputs)
def test_causal_lm_model_as_decoder(self):
"""Test the causal LM model as a decoder"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)
def test_causal_lm_model_past(self):
"""Test causal LM model with `past_key_values`"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs)
def test_causal_lm_model_past_with_attn_mask(self):
"""Test the causal LM model with `past_key_values` and `attention_mask`"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
def test_causal_lm_model_past_with_large_inputs(self):
"""Test the causal LM model with `past_key_values` and a longer decoder sequence length"""
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
"""Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention"""
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TFRemBertModel.from_pretrained("google/rembert")
self.assertIsNotNone(model)
@require_tf
class TFRemBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_model(self):
model = TFRemBertModel.from_pretrained("google/rembert")
input_ids = tf.constant([[312, 56498, 313, 2125, 313]])
segment_ids = tf.constant([[0, 0, 0, 1, 1]])
output = model(input_ids, token_type_ids=segment_ids, output_hidden_states=True)
hidden_size = 1152
expected_shape = [1, 5, hidden_size]
self.assertEqual(output["last_hidden_state"].shape, expected_shape)
expected_implementation = tf.constant(
[
[
[0.0754, -0.2022, 0.1904],
[-0.3354, -0.3692, -0.4791],
[-0.2314, -0.6729, -0.0749],
[-0.0396, -0.3105, -0.4234],
[-0.1571, -0.0525, 0.5353],
]
]
)
tf.debugging.assert_near(output["last_hidden_state"][:, :, :3], expected_implementation, atol=1e-4)
# Running on the original tf implementation gives slightly different results here.
# Not clear why this variations is present
# TODO: Find reason for discrepancy
# expected_original_implementation = [[
# [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
# [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
# [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
# [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
# [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
# ]]
| transformers-main | tests/models/rembert/test_modeling_tf_rembert.py |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch RemBERT model. """
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
RemBertConfig,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertModel,
)
from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class RemBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
input_embedding_size=18,
output_embedding_size=43,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.input_embedding_size = input_embedding_size
self.output_embedding_size = output_embedding_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RemBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
input_embedding_size=self.input_embedding_size,
output_embedding_size=self.output_embedding_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RemBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = RemBertModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = RemBertForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RemBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = RemBertForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RemBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = RemBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = RemBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = RemBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class RemBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
RemBertModel,
RemBertForMaskedLM,
RemBertForCausalLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (RemBertForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": RemBertModel,
"fill-mask": RemBertForMaskedLM,
"question-answering": RemBertForQuestionAnswering,
"text-classification": RemBertForSequenceClassification,
"text-generation": RemBertForCausalLM,
"token-classification": RemBertForTokenClassification,
"zero-shot": RemBertForSequenceClassification,
}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = RemBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=RemBertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
@slow
def test_model_from_pretrained(self):
for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = RemBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class RemBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_model(self):
# Test exact values at the last hidden layer
model = RemBertModel.from_pretrained("google/rembert")
input_ids = torch.tensor([[312, 56498, 313, 2125, 313]])
segment_ids = torch.tensor([[0, 0, 0, 1, 1]])
with torch.no_grad():
output = model(input_ids, token_type_ids=segment_ids, output_hidden_states=True)
hidden_size = 1152
expected_shape = torch.Size((1, 5, hidden_size))
self.assertEqual(output["last_hidden_state"].shape, expected_shape)
expected_implementation = torch.tensor(
[
[
[0.0754, -0.2022, 0.1904],
[-0.3354, -0.3692, -0.4791],
[-0.2314, -0.6729, -0.0749],
[-0.0396, -0.3105, -0.4234],
[-0.1571, -0.0525, 0.5353],
]
]
)
# Running on the original tf implementation gives slightly different results here.
# Not clear why this variations is present
# TODO: Find reason for discrepancy
# expected_original_implementation = [[
# [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
# [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
# [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
# [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
# [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
# ]]
self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3], expected_implementation, atol=1e-4))
| transformers-main | tests/models/rembert/test_modeling_rembert.py |
# coding=utf-8
# Copyright 2023 Toshiyuki Sakamoto(tanreinama) and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class GPTSanJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = GPTSanJapaneseTokenizer
test_rust_tokenizer = False
from_pretrained_kwargs = {"do_clean_text": False, "add_prefix_space": False}
def setUp(self):
super().setUp()
# fmt: off
vocab_tokens = ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
# fmt: on
emoji_tokens = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}} # 😀
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.emoji_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["emoji_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
with open(self.emoji_file, "w") as emoji_writer:
emoji_writer.write(json.dumps(emoji_tokens))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname, **kwargs)
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.get_input_output_texts
def get_input_output_texts(self, tokenizer):
input_text = "こんにちは、世界。 \nこんばんは、㔺界。😀"
output_text = "こんにちは、世界。 \nこんばんは、世界。😀"
return input_text, output_text
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.get_clean_sequence
def get_clean_sequence(self, tokenizer):
input_text, output_text = self.get_input_output_texts(tokenizer)
ids = tokenizer.encode(output_text, add_special_tokens=False)
text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
return text, ids
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_pretokenized_inputs
def test_pretokenized_inputs(self):
pass # TODO add if relevant
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_maximum_encoding_length_pair_input
def test_maximum_encoding_length_pair_input(self):
pass # TODO add if relevant
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_maximum_encoding_length_single_input
def test_maximum_encoding_length_single_input(self):
pass # TODO add if relevant
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_full_tokenizer
def test_full_tokenizer(self):
tokenizer = self.get_tokenizer()
# Testing tokenization
input_text = "こんにちは、世界。 こんばんは、㔺界。"
expected_token = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
tokens = tokenizer.tokenize(input_text)
self.assertListEqual(tokens, expected_token)
# Testing conversion to ids without special tokens
expected_ids = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
input_ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(input_ids, expected_ids)
# Testing conversion to ids with special tokens
input_tokens = tokens + [tokenizer.unk_token]
expected_ids = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
self.assertListEqual(input_ids, expected_ids)
def test_token_bagging(self):
tokenizer = self.get_tokenizer()
# Testing tokenization
input_text = "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
expected_text = "こんにちは、、、、世界。こんばんは、、、、世界。"
tokens = tokenizer.encode(input_text)
output_text = tokenizer.decode(tokens)
self.assertEqual(output_text, expected_text)
@slow
def test_prefix_input(self):
tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")
# Testing tokenization
prefix_text = "こんにちは、世界。"
input_text = "こんばんは、㔺界。😀"
expected_text = "こんにちは、世界。こんばんは、世界。😀"
tokens_1 = tokenizer.encode(prefix_text + input_text)
tokens_2 = tokenizer.encode("", prefix_text=prefix_text + input_text)
tokens_3 = tokenizer.encode(input_text, prefix_text=prefix_text)
output_text_1 = tokenizer.decode(tokens_1)
output_text_2 = tokenizer.decode(tokens_2)
output_text_3 = tokenizer.decode(tokens_3)
self.assertEqual(output_text_1, expected_text)
self.assertEqual(output_text_2, expected_text)
self.assertEqual(output_text_3, expected_text)
@slow
def test_token_type_ids(self):
tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")
# Testing tokenization
prefix_text = "こんにちは、世界。"
input_text = "こんばんは、㔺界。😀"
len_prefix = len(tokenizer.encode(prefix_text)) - 2
len_text = len(tokenizer.encode(input_text)) - 2
expected_mask_1 = [1] + [0] * (len_prefix + len_text + 1)
expected_mask_2 = [1] * (len_prefix + len_text + 1) + [0]
expected_mask_3 = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
type_id_1 = tokenizer(prefix_text + input_text).token_type_ids
type_id_2 = tokenizer("", prefix_text=prefix_text + input_text).token_type_ids
type_id_3 = tokenizer(input_text, prefix_text=prefix_text).token_type_ids
self.assertListEqual(type_id_1, expected_mask_1)
self.assertListEqual(type_id_2, expected_mask_2)
self.assertListEqual(type_id_3, expected_mask_3)
@slow
def test_prefix_tokens(self):
tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")
x_token_1 = tokenizer.encode("あンいワ")
x_token_2 = tokenizer.encode("", prefix_text="あンいワ")
x_token_3 = tokenizer.encode("いワ", prefix_text="あン")
self.assertEqual(tokenizer.decode(x_token_1), tokenizer.decode(x_token_2))
self.assertEqual(tokenizer.decode(x_token_1), tokenizer.decode(x_token_3))
self.assertNotEqual(x_token_1, x_token_2)
self.assertNotEqual(x_token_1, x_token_3)
self.assertEqual(x_token_1[1], x_token_2[-1]) # SEG token
self.assertEqual(x_token_1[1], x_token_3[3]) # SEG token
@slow
def test_batch_encode(self):
tokenizer = self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese")
input_pairs = [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
x_token = tokenizer(input_pairs, padding=True)
x_token_2 = tokenizer.batch_encode_plus(input_pairs, padding=True)
# fmt: off
expected_outputs = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
expected_typeids = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
expected_attmask = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids, expected_outputs)
self.assertListEqual(x_token.token_type_ids, expected_typeids)
self.assertListEqual(x_token.attention_mask, expected_attmask)
self.assertListEqual(x_token_2.input_ids, expected_outputs)
self.assertListEqual(x_token_2.token_type_ids, expected_typeids)
self.assertListEqual(x_token_2.attention_mask, expected_attmask)
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_conversion_reversible
def test_conversion_reversible(self):
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
# Copied from tests.models.gpt_neox_japanese.test_tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizationTest.test_padding_different_model_input_name
def test_padding_different_model_input_name(self):
# tokenizer has no padding token
pass
| transformers-main | tests/models/gptsan_japanese/test_tokenization_gptsan_japanese.py |
transformers-main | tests/models/gptsan_japanese/__init__.py |
|
# coding=utf-8
# Copyright 2023 Toshiyuki Sakamoto(tanreinama) and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers import (
GPTSanJapaneseConfig,
GPTSanJapaneseForConditionalGeneration,
GPTSanJapaneseModel,
GPTSanJapaneseTokenizer,
is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
class GPTSanJapaneseTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
num_contexts=7,
# For common tests
is_training=True,
hidden_size=32,
ext_size=42,
num_hidden_layers=2,
num_ext_layers=2,
num_attention_heads=4,
num_experts=2,
d_ff=32,
d_ext=80,
d_spout=33,
dropout_rate=0.0,
layer_norm_epsilon=1e-6,
expert_capacity=100,
router_jitter_noise=0.0,
):
self.vocab_size = vocab_size
self.parent = parent
self.batch_size = batch_size
self.num_contexts = num_contexts
# For common tests
self.seq_length = self.num_contexts
self.is_training = is_training
self.hidden_size = hidden_size
self.num_ext_layers = num_ext_layers
self.ext_size = ext_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_experts = num_experts
self.d_ff = d_ff
self.d_ext = d_ext
self.d_spout = d_spout
self.dropout_rate = dropout_rate
self.layer_norm_epsilon = layer_norm_epsilon
self.expert_capacity = expert_capacity
self.router_jitter_noise = router_jitter_noise
def get_large_model_config(self):
return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese")
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
return (config, input_ids)
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
return (config, {"input_ids": input_ids})
def get_config(self):
return GPTSanJapaneseConfig(
vocab_size=self.vocab_size,
num_contexts=self.seq_length,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_ext=self.d_ext,
d_spout=self.d_spout,
num_switch_layers=self.num_hidden_layers - self.num_ext_layers,
num_ext_layers=self.num_ext_layers,
num_heads=self.num_attention_heads,
num_experts=self.num_experts,
expert_capacity=self.expert_capacity,
dropout_rate=self.dropout_rate,
layer_norm_epsilon=self.layer_norm_epsilon,
router_jitter_noise=self.router_jitter_noise,
)
def create_and_check_model(
self,
config,
input_ids,
):
model = GPTSanJapaneseForConditionalGeneration(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
)
self.parent.assertIsNotNone(result)
@require_torch
class GPTSanJapaneseTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GPTSanJapaneseModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": GPTSanJapaneseForConditionalGeneration,
"feature-extraction": GPTSanJapaneseForConditionalGeneration,
"summarization": GPTSanJapaneseForConditionalGeneration,
"text2text-generation": GPTSanJapaneseForConditionalGeneration,
"translation": GPTSanJapaneseForConditionalGeneration,
}
if is_torch_available()
else {}
)
fx_compatible = False
is_encoder_decoder = False
test_pruning = False
test_headmasking = False
test_cpu_offload = False
test_disk_offload = False
test_save_load_fast_init_to_base = False
test_training = False
# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
model_split_percents = [0.8, 0.9]
# TODO: Fix the failed tests when this model gets more usage
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "SummarizationPipelineTests":
# TODO: fix `_reorder_cache` is not implemented for this model
return True
elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
# TODO: check this.
return True
return False
def setUp(self):
self.model_tester = GPTSanJapaneseTester(self)
self.config_tester = ConfigTester(self, config_class=GPTSanJapaneseConfig, d_model=37)
def test_config(self):
GPTSanJapaneseConfig()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(
reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"
)
def test_model_parallelism(self):
super().test_model_parallelism()
@require_torch
class GPTSanJapaneseForConditionalGenerationTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
fx_compatible = False
is_encoder_decoder = False
test_pruning = False
test_headmasking = False
test_cpu_offload = False
test_disk_offload = False
# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
model_split_percents = [0.8, 0.9]
def setUp(self):
self.model_tester = GPTSanJapaneseTester(self)
self.config_tester = ConfigTester(self, config_class=GPTSanJapaneseConfig, d_model=37)
def test_config(self):
GPTSanJapaneseConfig()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(
reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"
)
def test_model_parallelism(self):
super().test_model_parallelism()
@slow
def test_logits(self):
model = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
input_ids = tokenizer.encode("武田信玄は", return_tensors="pt")
outputs = model(input_ids)
output_logits = outputs.logits.detach().cpu().numpy()
# Output of original model created with mesh-tensoflow
target = [
# fmt: off
[-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
-11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
-11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
-12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
[-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
-7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
-6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
-8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
[-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
-11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
-9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
-10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
[-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
-9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
-8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
-11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
[-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
-10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
-11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
-10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
# fmt: on
]
target = np.array(target).flatten()
predict = output_logits[0, :, :20].flatten()
def check(a, b, epsilon=5e-4):
return abs(a - b) < epsilon * max(abs(a), abs(b))
self.assertTrue(np.all([check(target[i], predict[i]) for i in range(len(target))]))
@slow
def test_batch_generation(self):
model = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
model.to(torch_device)
# set deterministically
generation_config = GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese")
generation_config.top_k = 1
# use different length sentences to test batching
sentences = [
"甲斐なら武田と言うほど",
"織田信長は、",
]
tokenizer.padding_side = "left"
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
self.assertNotEqual(inputs["attention_mask"][0].numpy().tolist(), inputs["attention_mask"][1].numpy().tolist())
outputs = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
max_new_tokens=3,
generation_config=generation_config,
)
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model.generate(
input_ids=inputs_non_padded, max_new_tokens=3, generation_config=generation_config
)
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model.generate(input_ids=inputs_padded, max_new_tokens=3, generation_config=generation_config)
self.assertNotEqual(inputs_non_padded.shape, inputs_padded.shape)
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"甲斐なら武田と言うほど甲斐の武田",
"織田信長は、このような",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])
@tooslow
def test_sample(self):
model = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
# Output of original model created with mesh-tensoflow
target = [
("武田信玄は", 35675),
("武田信玄は、", 45),
("武田信玄は、この", 29),
("武田信玄は、このよう", 30642),
("武田信玄は、このような", 35680),
("武田信玄は、このような「", 8640),
("武田信玄は、このような「武田", 31617),
("武田信玄は、このような「武田家", 30646),
("武田信玄は、このような「武田家の", 31617),
("武田信玄は、このような「武田家の家", 31381),
]
for input, output in target:
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model(input_ids)
output_logits = outputs.logits.detach().cpu().numpy()[0]
output_id = np.argmax(output_logits[-1])
self.assertEqual(output_id, output)
@slow
def test_spout_generation(self):
model = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
model.to(torch_device)
# set deterministically
generation_config = GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese")
generation_config.top_k = 1
input_text = "武田信玄は、"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(torch_device)
input_ids_batch = tokenizer([input_text, input_text], return_tensors="pt").input_ids.to(torch_device)
# spout from uniform and one-hot
spouts = [
# fmt: off
[0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
0.04803985, 0.211572 , 0.23188473, 0.37153068, 0.7836377 ,
0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363 ,
0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
0.9552151 , 0.93901511, 0.22489288], # uniform
[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.],
# fmt: on
]
output1 = model.generate(
input_ids=input_ids,
spout=spouts[0],
max_new_tokens=20,
generation_config=generation_config,
)
output2 = model.generate(
input_ids=input_ids,
spout=spouts[1],
max_new_tokens=20,
generation_config=generation_config,
)
output3 = model.generate(
input_ids=input_ids_batch,
spout=spouts,
max_new_tokens=20,
generation_config=generation_config,
)
out1_sentence = tokenizer.decode(output1[0])
out2_sentence = tokenizer.decode(output2[0])
batch_out_sentence = tokenizer.batch_decode(output3)
expected_output_sentence = [
"武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である",
"武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(batch_out_sentence, [out1_sentence, out2_sentence])
@slow
def test_prefix_lm_generation(self):
model = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
model.to(torch_device)
# set deterministically
generation_config = GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese")
generation_config.top_k = 1
prefix_text_1 = "武田信玄"
prefix_text_2 = "織田信長"
input_text_1 = "は、"
input_text_2 = "が、"
input_tok_1 = tokenizer(input_text_1, prefix_text=prefix_text_1, return_tensors="pt")
input_tok_2 = tokenizer(input_text_2, prefix_text=prefix_text_2, return_tensors="pt")
input_tok_3 = tokenizer([[prefix_text_1, input_text_1], [prefix_text_2, input_text_2]], return_tensors="pt")
output1 = model.generate(
input_ids=input_tok_1.input_ids.to(torch_device),
token_type_ids=input_tok_1.token_type_ids.to(torch_device),
max_new_tokens=20,
generation_config=generation_config,
)
output2 = model.generate(
input_ids=input_tok_2.input_ids.to(torch_device),
token_type_ids=input_tok_2.token_type_ids.to(torch_device),
max_new_tokens=20,
generation_config=generation_config,
)
output3 = model.generate(
input_ids=input_tok_3.input_ids.to(torch_device),
token_type_ids=input_tok_3.token_type_ids.to(torch_device),
attention_mask=input_tok_3.attention_mask.to(torch_device),
max_new_tokens=20,
generation_config=generation_config,
)
out1_sentence = tokenizer.decode(output1[0])
out2_sentence = tokenizer.decode(output2[0])
batch_out_sentence = tokenizer.batch_decode(output3)
expected_output_sentence = [
"武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して",
"織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(batch_out_sentence, [out1_sentence, out2_sentence])
| transformers-main | tests/models/gptsan_japanese/test_modeling_gptsan_japanese.py |
# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import Swin2SRImageProcessor
from transformers.image_transforms import get_image_size
class Swin2SRImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_rescale=True,
rescale_factor=1 / 255,
do_pad=True,
pad_size=8,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_pad = do_pad
self.pad_size = pad_size
def prepare_image_processor_dict(self):
return {
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
"pad_size": self.pad_size,
}
def expected_output_image_shape(self, images):
img = images[0]
if isinstance(img, Image.Image):
input_width, input_height = img.size
else:
input_height, input_width = img.shape[-2:]
pad_height = (input_height // self.pad_size + 1) * self.pad_size - input_height
pad_width = (input_width // self.pad_size + 1) * self.pad_size - input_width
return self.num_channels, input_height + pad_height, input_width + pad_width
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class Swin2SRImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = Swin2SRImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = Swin2SRImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_rescale"))
self.assertTrue(hasattr(image_processor, "rescale_factor"))
self.assertTrue(hasattr(image_processor, "do_pad"))
self.assertTrue(hasattr(image_processor, "pad_size"))
def calculate_expected_size(self, image):
old_height, old_width = get_image_size(image)
size = self.image_processor_tester.pad_size
pad_height = (old_height // size + 1) * size - old_height
pad_width = (old_width // size + 1) * size - old_width
return old_height + pad_height, old_width + pad_width
# Swin2SRImageProcessor does not support batched input
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Swin2SRImageProcessor does not support batched input
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Swin2SRImageProcessor does not support batched input
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
| transformers-main | tests/models/swin2sr/test_image_processing_swin2sr.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Swin2SR model. """
import inspect
import unittest
from transformers import Swin2SRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import Swin2SRForImageSuperResolution, Swin2SRModel
from transformers.models.swin2sr.modeling_swin2sr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import Swin2SRImageProcessor
class Swin2SRModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=1,
num_channels=3,
embed_dim=16,
depths=[1, 2, 1],
num_heads=[2, 2, 4],
window_size=2,
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=False,
upscale=2,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.upscale = upscale
# here we set some attributes to make tests pass
self.num_hidden_layers = len(depths)
self.hidden_size = embed_dim
self.seq_length = (image_size // patch_size) ** 2
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return Swin2SRConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
depths=self.depths,
num_heads=self.num_heads,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
use_absolute_embeddings=self.use_absolute_embeddings,
path_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
upscale=self.upscale,
)
def create_and_check_model(self, config, pixel_values, labels):
model = Swin2SRModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.embed_dim, self.image_size, self.image_size)
)
def create_and_check_for_image_super_resolution(self, config, pixel_values, labels):
model = Swin2SRForImageSuperResolution(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_image_size = self.image_size * self.upscale
self.parent.assertEqual(
result.reconstruction.shape, (self.batch_size, self.num_channels, expected_image_size, expected_image_size)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": Swin2SRModel} if is_torch_available() else {}
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torchscript = False
def setUp(self):
self.model_tester = Swin2SRModelTester(self)
self.config_tester = ConfigTester(self, config_class=Swin2SRConfig, embed_dim=37)
def test_config(self):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_for_image_super_resolution(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_super_resolution(*config_and_inputs)
# TODO: check if this works again for PyTorch 2.x.y
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip(reason="Swin2SR does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Swin2SR does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="Swin2SR does not support training yet")
def test_training_gradient_checkpointing(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@slow
def test_model_from_pretrained(self):
for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = Swin2SRModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# overwriting because of `logit_scale` parameter
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "logit_scale" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
expected_num_attentions = len(self.model_tester.depths)
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
window_size_squared = config.window_size**2
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_heads[0], window_size_squared, window_size_squared],
)
@require_vision
@require_torch
@slow
class Swin2SRModelIntegrationTest(unittest.TestCase):
def test_inference_image_super_resolution_head(self):
processor = Swin2SRImageProcessor()
model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64").to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size([1, 3, 976, 1296])
self.assertEqual(outputs.reconstruction.shape, expected_shape)
expected_slice = torch.tensor(
[[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3], expected_slice, atol=1e-4))
| transformers-main | tests/models/swin2sr/test_modeling_swin2sr.py |
transformers-main | tests/models/swin2sr/__init__.py |
|
# coding=utf-8
# Copyright 2021, The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MBART model. """
import copy
import tempfile
import unittest
from transformers import MBartConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
AutoTokenizer,
BatchEncoding,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
)
from transformers.models.mbart.modeling_mbart import MBartDecoder, MBartEncoder
def prepare_mbart_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class MBartModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=100,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
# forcing a certain token to be generated, sets all other tokens to -inf
# if however the token to be generated is already at -inf then it can lead token
# `nan` values and thus break generation
self.forced_bos_token_id = None
self.forced_eos_token_id = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def get_config(self):
return MBartConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
forced_bos_token_id=self.forced_bos_token_id,
forced_eos_token_id=self.forced_eos_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = MBartModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = MBartModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = MBartEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = MBartDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class MBartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(MBartModel, MBartForConditionalGeneration, MBartForSequenceClassification, MBartForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = (MBartForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": MBartForConditionalGeneration,
"feature-extraction": MBartModel,
"fill-mask": MBartForConditionalGeneration,
"question-answering": MBartForQuestionAnswering,
"summarization": MBartForConditionalGeneration,
"text-classification": MBartForSequenceClassification,
"text-generation": MBartForCausalLM,
"text2text-generation": MBartForConditionalGeneration,
"translation": MBartForConditionalGeneration,
"zero-shot": MBartForSequenceClassification,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = False # Fix me Michael
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
def setUp(self):
self.model_tester = MBartModelTester(self)
self.config_tester = ConfigTester(self, config_class=MBartConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# MBartForSequenceClassification does not support inputs_embeds
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MBartModel, MBartForConditionalGeneration, MBartForQuestionAnswering):
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = MBartForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
@require_torch
@require_sentencepiece
@require_tokenizers
class AbstractSeq2SeqIntegrationTest(unittest.TestCase):
maxDiff = 1000 # longer string compare tracebacks
checkpoint_name = None
@classmethod
def setUpClass(cls):
cls.tokenizer = AutoTokenizer.from_pretrained(cls.checkpoint_name, use_fast=False)
return cls
@cached_property
def model(self):
"""Only load the model if needed."""
model = MBartForConditionalGeneration.from_pretrained(self.checkpoint_name).to(torch_device)
if "cuda" in torch_device:
model = model.half()
return model
@require_torch
@require_sentencepiece
@require_tokenizers
class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
checkpoint_name = "facebook/mbart-large-en-ro"
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
" face decât să înrăutăţească violenţa şi mizeria pentru milioane de oameni.",
]
expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, 250004]
@slow
def test_enro_generate_one(self):
batch: BatchEncoding = self.tokenizer(
["UN Chief Says There Is No Military Solution in Syria"], return_tensors="pt"
).to(torch_device)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
self.assertEqual(self.tgt_text[0], decoded[0])
# self.assertEqual(self.tgt_text[1], decoded[1])
@slow
def test_enro_generate_batch(self):
batch: BatchEncoding = self.tokenizer(self.src_text, return_tensors="pt", padding=True, truncation=True).to(
torch_device
)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
assert self.tgt_text == decoded
def test_mbart_enro_config(self):
mbart_models = ["facebook/mbart-large-en-ro"]
expected = {"scale_embedding": True, "output_past": True}
for name in mbart_models:
config = MBartConfig.from_pretrained(name)
for k, v in expected.items():
try:
self.assertEqual(v, getattr(config, k))
except AssertionError as e:
e.args += (name, k)
raise
def test_mbart_fast_forward(self):
config = MBartConfig(
vocab_size=99,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
add_final_layer_norm=True,
)
lm_model = MBartForConditionalGeneration(config).to(torch_device)
context = torch.tensor(
[[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long
)
summary = torch.tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], device=torch_device, dtype=torch.long)
result = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(result.logits.shape, expected_shape)
@require_torch
@require_sentencepiece
@require_tokenizers
class MBartCC25IntegrationTest(AbstractSeq2SeqIntegrationTest):
checkpoint_name = "facebook/mbart-large-cc25"
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
" I ate lunch twice yesterday",
]
tgt_text = ["Şeful ONU declară că nu există o soluţie militară în Siria", "to be padded"]
@unittest.skip("This test is broken, still generates english")
def test_cc25_generate(self):
inputs = self.tokenizer([self.src_text[0]], return_tensors="pt").to(torch_device)
translated_tokens = self.model.generate(
input_ids=inputs["input_ids"].to(torch_device),
decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"],
)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
self.assertEqual(self.tgt_text[0], decoded[0])
@slow
def test_fill_mask(self):
inputs = self.tokenizer(["One of the best <mask> I ever read!"], return_tensors="pt").to(torch_device)
outputs = self.model.generate(
inputs["input_ids"], decoder_start_token_id=self.tokenizer.lang_code_to_id["en_XX"], num_beams=1
)
prediction: str = self.tokenizer.batch_decode(
outputs, clean_up_tokenization_spaces=True, skip_special_tokens=True
)[0]
self.assertEqual(prediction, "of the best books I ever read!")
class MBartStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=2,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = MBartConfig(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = MBartDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = MBartDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=attn_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class MBartStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (MBartDecoder, MBartForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (MBartForCausalLM,) if is_torch_available() else ()
test_pruning = False
is_encoder_decoder = False
def setUp(
self,
):
self.model_tester = MBartStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=MBartConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
| transformers-main | tests/models/mbart/test_modeling_mbart.py |
transformers-main | tests/models/mbart/__init__.py |
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeq2SeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class TFMBartModelTester:
config_cls = MBartConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
input_ids = tf.concat([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
**self.config_updates,
)
inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = TFMBartModel(config=config).get_decoder()
input_ids = inputs_dict["input_ids"]
input_ids = input_ids[:1, :]
attention_mask = inputs_dict["attention_mask"][:1, :]
head_mask = inputs_dict["head_mask"]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
past_key_values = past_key_values[1]
def prepare_mbart_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
if decoder_attention_mask is None:
decoder_attention_mask = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
],
axis=-1,
)
if head_mask is None:
head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class TFMBartModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
all_generative_model_classes = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
pipeline_model_mapping = (
{
"conversational": TFMBartForConditionalGeneration,
"feature-extraction": TFMBartModel,
"summarization": TFMBartForConditionalGeneration,
"text2text-generation": TFMBartForConditionalGeneration,
"translation": TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
is_encoder_decoder = True
test_pruning = False
test_onnx = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def setUp(self):
self.model_tester = TFMBartModelTester(self)
self.config_tester = ConfigTester(self, config_class=MBartConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
@require_sentencepiece
@require_tokenizers
@require_tf
class TFMBartModelIntegrationTest(unittest.TestCase):
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
]
expected_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
]
model_name = "facebook/mbart-large-en-ro"
@cached_property
def tokenizer(self):
return AutoTokenizer.from_pretrained(self.model_name)
@cached_property
def model(self):
model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name)
return model
def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
generated_words = self.translate_src_text(**tokenizer_kwargs)
self.assertListEqual(self.expected_text, generated_words)
def translate_src_text(self, **tokenizer_kwargs):
model_inputs = self.tokenizer(self.src_text, **tokenizer_kwargs, return_tensors="tf")
generated_ids = self.model.generate(
model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2
)
generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
return generated_words
@slow
def test_batch_generation_en_ro(self):
self._assert_generated_batch_equal_expected()
| transformers-main | tests/models/mbart/test_modeling_tf_mbart.py |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import jax
import jax.numpy as jnp
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
shift_tokens_right,
)
def prepare_mbart_inputs_dict(
config,
input_ids,
decoder_input_ids=None,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
if decoder_attention_mask is None:
decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
if head_mask is None:
head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
class FlaxMBartModelTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=32,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
decoder_start_token_id=2,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.decoder_start_token_id = decoder_start_token_id
self.initializer_range = initializer_range
def prepare_config_and_inputs(self):
input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)
decoder_input_ids = shift_tokens_right(input_ids, 1)
config = MBartConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
initializer_range=self.initializer_range,
use_cache=False,
)
inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def check_use_cache_forward(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=outputs_cache.past_key_values,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
decoder_attention_mask_cache = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask_cache,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
past_key_values=outputs_cache.past_key_values,
decoder_attention_mask=decoder_attention_mask_cache,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
@require_flax
class MBartHeadTests(unittest.TestCase):
vocab_size = 99
def _get_config_and_data(self):
input_ids = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
],
dtype=np.int64,
)
batch_size = input_ids.shape[0]
config = MBartConfig(
vocab_size=self.vocab_size,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
)
return config, input_ids, batch_size
def test_sequence_classification_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
model = FlaxMBartForSequenceClassification(config)
outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
expected_shape = (batch_size, config.num_labels)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_question_answering_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
model = FlaxMBartForQuestionAnswering(config)
outputs = model(input_ids=input_ids)
self.assertEqual(outputs["start_logits"].shape, input_ids.shape)
self.assertEqual(outputs["end_logits"].shape, input_ids.shape)
# @timeout_decorator.timeout(1) # not working with the decorator so far
def test_lm_forward(self):
config, input_ids, batch_size = self._get_config_and_data()
lm_model = FlaxMBartForConditionalGeneration(config)
outputs = lm_model(input_ids=input_ids)
expected_shape = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_lm_uneven_forward(self):
config = MBartConfig(
vocab_size=self.vocab_size,
d_model=14,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=8,
decoder_ffn_dim=8,
max_position_embeddings=48,
)
lm_model = FlaxMBartForConditionalGeneration(config)
context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64)
summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64)
outputs = lm_model(input_ids=context, decoder_input_ids=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape, expected_shape)
def test_shift_tokens_right(self):
input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64)
shifted = shift_tokens_right(input_ids, 1)
n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum()
n_pad_after = np.equal(shifted, 1).astype(np.float32).sum()
self.assertEqual(shifted.shape, input_ids.shape)
self.assertEqual(n_pad_after, n_pad_before - 1)
self.assertTrue(np.equal(shifted[:, 0], 2).all())
@require_flax
class FlaxMBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
is_encoder_decoder = True
all_model_classes = (
(
FlaxMBartModel,
FlaxMBartForConditionalGeneration,
FlaxMBartForSequenceClassification,
FlaxMBartForQuestionAnswering,
)
if is_flax_available()
else ()
)
all_generative_model_classes = (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
def setUp(self):
self.model_tester = FlaxMBartModelTester(self)
def test_use_cache_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
def test_use_cache_forward_with_attn_mask(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
def test_encode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def encode_jitted(input_ids, attention_mask=None, **kwargs):
return model.encode(input_ids=input_ids, attention_mask=attention_mask)
with self.subTest("JIT Enabled"):
jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_decode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
model = model_class(config)
encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
prepared_inputs_dict = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
return model.decode(
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
)
with self.subTest("JIT Enabled"):
jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("facebook/mbart-large-cc25", from_pt=True)
# FlaxMBartForSequenceClassification expects eos token in input_ids
input_ids = np.ones((1, 1)) * model.config.eos_token_id
outputs = model(input_ids)
self.assertIsNotNone(outputs)
@require_flax
@require_sentencepiece
@require_tokenizers
class FlaxMBartModelIntegrationTest(unittest.TestCase):
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
]
expected_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
]
model_name = "facebook/mbart-large-en-ro"
@cached_property
def tokenizer(self):
return AutoTokenizer.from_pretrained(self.model_name)
@cached_property
def model(self):
model = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name, from_pt=True)
return model
def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
generated_words = self.translate_src_text(**tokenizer_kwargs)
self.assertListEqual(self.expected_text, generated_words)
def translate_src_text(self, **tokenizer_kwargs):
model_inputs = self.tokenizer(self.src_text, **tokenizer_kwargs, return_tensors="np")
generated_ids = self.model.generate(
model_inputs.input_ids,
attention_mask=model_inputs.attention_mask,
decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"],
early_stopping=True,
num_beams=2,
).sequences
generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
return generated_words
@slow
def test_batch_generation_en_ro(self):
self._assert_generated_batch_equal_expected()
| transformers-main | tests/models/mbart/test_modeling_flax_mbart.py |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
EN_CODE = 250004
RO_CODE = 250020
@require_sentencepiece
@require_tokenizers
class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = MBartTokenizer
rust_tokenizer_class = MBartTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_full_tokenizer(self):
tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
# overwrite from test_tokenization_common to speed up test
def test_save_pretrained(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
@require_torch
@require_sentencepiece
@require_tokenizers
class MBartEnroIntegrationTest(unittest.TestCase):
checkpoint_name = "facebook/mbart-large-en-ro"
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
@classmethod
def setUpClass(cls):
cls.tokenizer: MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO"
)
cls.pad_token_id = 1
return cls
def check_language_codes(self):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020)
def test_enro_tokenizer_batch_encode_plus(self):
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens, ids)
def test_enro_tokenizer_decode_ignores_language_codes(self):
self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_romanian)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_enro_tokenizer_truncation(self):
src_text = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0], str)
desired_max_length = 10
ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
self.assertEqual(ids[-2], 2)
self.assertEqual(ids[-1], EN_CODE)
self.assertEqual(len(ids), desired_max_length)
def test_mask_token(self):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250026, 250001])
def test_special_tokens_unaffacted_by_save_load(self):
tmpdirname = tempfile.mkdtemp()
original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(tmpdirname)
new_tok = MBartTokenizer.from_pretrained(tmpdirname)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
@require_torch
def test_batch_fairseq_parity(self):
batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt")
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def test_enro_tokenizer_prepare_batch(self):
batch = self.tokenizer(
self.src_text,
text_target=self.tgt_text,
padding=True,
truncation=True,
max_length=len(self.expected_src_tokens),
return_tensors="pt",
)
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual((2, 14), batch.input_ids.shape)
self.assertEqual((2, 14), batch.attention_mask.shape)
result = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens, result)
self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens, [])
self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, EN_CODE])
def test_seq2seq_max_length(self):
batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
targets = self.tokenizer(
text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
)
labels = targets["input_ids"]
batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 10)
@require_torch
def test_tokenizer_translation(self):
inputs = self.tokenizer._build_translation_inputs(
"A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR"
)
self.assertEqual(
nested_simplify(inputs),
{
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
},
)
| transformers-main | tests/models/mbart/test_tokenization_mbart.py |
# coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class XGLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = XGLMTokenizer
rust_tokenizer_class = XGLMTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = XGLMTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<pad>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<s>")
self.assertEqual(vocab_keys[1], "<pad>")
self.assertEqual(len(vocab_keys), 1_008)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_008)
def test_full_tokenizer(self):
tokenizer = XGLMTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
@cached_property
def big_tokenizer(self):
return XGLMTokenizer.from_pretrained("facebook/xglm-564M")
def test_picklable_without_disk(self):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SAMPLE_VOCAB, f.name)
tokenizer = XGLMTokenizer(f.name, keep_accents=True)
pickled_tokenizer = pickle.dumps(tokenizer)
pickle.loads(pickled_tokenizer)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [2, 31227, 4447, 35]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenization_base_hard_symbols(self):
symbols = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
" add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
)
# fmt: off
original_tokenizer_encodings = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {
'input_ids': [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="facebook/xglm-564M",
padding=False,
)
| transformers-main | tests/models/xglm/test_tokenization_xglm.py |
transformers-main | tests/models/xglm/__init__.py |
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
import numpy as np
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel
if is_torch_available():
import torch
@require_flax
class FlaxXGLMModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
d_model=32,
num_hidden_layers=2,
num_attention_heads=4,
ffn_dim=37,
activation_function="gelu",
activation_dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=512,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = d_model
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.ffn_dim = ffn_dim
self.activation_function = activation_function
self.activation_dropout = activation_dropout
self.attention_dropout = attention_dropout
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = None
self.bos_token_id = 0
self.eos_token_id = 2
self.pad_token_id = 1
def prepare_config_and_inputs(self):
input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 3, self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
config = XGLMConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
num_layers=self.num_hidden_layers,
attention_heads=self.num_attention_heads,
ffn_dim=self.ffn_dim,
activation_function=self.activation_function,
activation_dropout=self.activation_dropout,
attention_dropout=self.attention_dropout,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
)
return (config, input_ids, input_mask)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def prepare_config_and_inputs_for_decoder(self):
config, input_ids, attention_mask = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask):
max_decoder_length = 20
model = model_class_name(config)
past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4")
position_ids = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
)
outputs_cache = model(
input_ids[:, :-1],
attention_mask=attention_mask,
past_key_values=past_key_values,
position_ids=position_ids,
)
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model(
input_ids[:, -1:],
attention_mask=attention_mask,
past_key_values=outputs_cache.past_key_values,
position_ids=position_ids,
)
outputs = model(input_ids)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, input_ids, attention_mask):
max_decoder_length = 20
model = model_class_name(config)
attention_mask_cache = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))],
axis=-1,
)
past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
position_ids = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
)
outputs_cache = model(
input_ids[:, :-1],
attention_mask=attention_mask_cache,
past_key_values=past_key_values,
position_ids=position_ids,
)
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model(
input_ids[:, -1:],
past_key_values=outputs_cache.past_key_values,
attention_mask=attention_mask_cache,
position_ids=position_ids,
)
outputs = model(input_ids, attention_mask=attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
@require_sentencepiece
@require_flax
class FlaxXGLMModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
all_model_classes = (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
all_generative_model_classes = (FlaxXGLMForCausalLM,) if is_flax_available() else ()
def setUp(self):
self.model_tester = FlaxXGLMModelTester(self)
def test_use_cache_forward(self):
for model_class_name in self.all_model_classes:
config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask)
def test_use_cache_forward_with_attn_mask(self):
for model_class_name in self.all_model_classes:
config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
model_class_name, config, input_ids, attention_mask
)
@slow
def test_batch_generation(self):
tokenizer = XGLMTokenizer.from_pretrained("XGLM", padding_side="left")
inputs = tokenizer(["Hello this is a long string", "Hey"], return_tensors="np", padding=True, truncation=True)
model = FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
model.config.num_beams = 1
model.config.do_sample = False
jit_generate = jax.jit(model.generate)
output_sequences = jit_generate(inputs["input_ids"], attention_mask=inputs["attention_mask"]).sequences
output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
expected_string = [
"Hello this is a long string of questions, but I'm not sure if I'm",
"Hey, I'm a newbie to the forum and I'",
]
self.assertListEqual(output_string, expected_string)
# overwrite from common since `attention_mask` in combination
# with `causal_mask` behaves slighly differently
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
batch_size, seq_length = pt_inputs["input_ids"].shape
rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
pt_inputs["attention_mask"][batch_idx, :start_index] = 0
pt_inputs["attention_mask"][batch_idx, start_index:] = 1
prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0
prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1
pt_model = pt_model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
fx_model = model_class(config, dtype=jnp.float32)
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs).to_tuple()
fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs, pt_outputs):
self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple()
self.assertEqual(
len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
)
for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
self.assert_almost_equals(fx_output_loaded[:, -1], pt_output[:, -1].numpy(), 4e-2)
# overwrite from common since `attention_mask` in combination
# with `causal_mask` behaves slighly differently
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
pt_model = pt_model_class(config).eval()
pt_model.config.use_cache = False
fx_model = model_class(config, dtype=jnp.float32)
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
batch_size, seq_length = pt_inputs["input_ids"].shape
rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
pt_inputs["attention_mask"][batch_idx, :start_index] = 0
pt_inputs["attention_mask"][batch_idx, start_index:] = 1
prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0
prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs).to_tuple()
fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs, pt_outputs):
self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
self.assertEqual(
len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
)
for fx_output, pt_output in zip(fx_outputs, pt_outputs_loaded):
self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("facebook/xglm-564M")
outputs = model(np.ones((1, 1)))
self.assertIsNotNone(outputs)
| transformers-main | tests/models/xglm/test_modeling_flax_xglm.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class TFXGLMModelTester:
config_cls = XGLMConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
d_model=32,
num_hidden_layers=2,
num_attention_heads=4,
ffn_dim=37,
activation_function="gelu",
activation_dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=512,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = d_model
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.ffn_dim = ffn_dim
self.activation_function = activation_function
self.activation_dropout = activation_dropout
self.attention_dropout = attention_dropout
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = None
self.bos_token_id = 0
self.eos_token_id = 2
self.pad_token_id = 1
def get_large_model_config(self):
return XGLMConfig.from_pretrained("facebook/xglm-564M")
def prepare_config_and_inputs(self):
input_ids = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size), clip_value_min=0, clip_value_max=3
)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
head_mask = floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
)
def get_config(self):
return XGLMConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
num_layers=self.num_hidden_layers,
attention_heads=self.num_attention_heads,
ffn_dim=self.ffn_dim,
activation_function=self.activation_function,
activation_dropout=self.activation_dropout,
attention_dropout=self.attention_dropout,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
return_dict=True,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class TFXGLMModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
all_generative_model_classes = (TFXGLMForCausalLM,) if is_tf_available() else ()
pipeline_model_mapping = (
{"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
)
test_onnx = False
test_missing_keys = False
test_pruning = False
def setUp(self):
self.model_tester = TFXGLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XGLMConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
@slow
def test_model_from_pretrained(self):
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFXGLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
def test_resize_token_embeddings(self):
super().test_resize_token_embeddings()
@require_tf
class TFXGLMModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_xglm(self, verify_outputs=True):
model = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
input_ids = tf.convert_to_tensor([[2, 268, 9865]], dtype=tf.int32) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
# fmt: on
output_ids = model.generate(input_ids, do_sample=False, num_beams=1)
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
@slow
def test_xglm_sample(self):
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
model = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
tf.random.set_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="tf")
input_ids = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0"):
output_ids = model.generate(input_ids, do_sample=True, seed=[7, 0])
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
EXPECTED_OUTPUT_STR = (
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
@slow
def test_batch_generation(self):
model = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
tokenizer.padding_side = "left"
# use different length sentences to test batching
sentences = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
inputs = tokenizer(sentences, return_tensors="tf", padding=True)
input_ids = inputs["input_ids"]
outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"], max_new_tokens=12)
inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids
output_non_padded = model.generate(input_ids=inputs_non_padded, max_new_tokens=12)
inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids
output_padded = model.generate(input_ids=inputs_padded, max_new_tokens=12)
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
| transformers-main | tests/models/xglm/test_modeling_tf_xglm.py |
# coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import gc
import math
import unittest
from transformers import XGLMConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMTokenizer
class XGLMModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
d_model=32,
num_hidden_layers=2,
num_attention_heads=4,
ffn_dim=37,
activation_function="gelu",
activation_dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=512,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = d_model
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.ffn_dim = ffn_dim
self.activation_function = activation_function
self.activation_dropout = activation_dropout
self.attention_dropout = attention_dropout
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = None
self.bos_token_id = 0
self.eos_token_id = 2
self.pad_token_id = 1
def get_large_model_config(self):
return XGLMConfig.from_pretrained("facebook/xglm-564M")
def prepare_config_and_inputs(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config(gradient_checkpointing=gradient_checkpointing)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
)
def get_config(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
return XGLMConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
num_layers=self.num_hidden_layers,
attention_heads=self.num_attention_heads,
ffn_dim=self.ffn_dim,
activation_function=self.activation_function,
activation_dropout=self.activation_dropout,
attention_dropout=self.attention_dropout,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
gradient_checkpointing=gradient_checkpointing,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
head_mask,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
input_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_xglm_model(self, config, input_ids, input_mask, head_mask, *args):
model = XGLMModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, head_mask=head_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.past_key_values), config.num_hidden_layers)
def create_and_check_xglm_model_past(self, config, input_ids, input_mask, head_mask, *args):
model = XGLMModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_xglm_model_attention_mask_past(self, config, input_ids, input_mask, head_mask, *args):
model = XGLMModel(config=config)
model.to(torch_device)
model.eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = self.seq_length // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.zeros((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_xglm_model_past_large_inputs(self, config, input_ids, input_mask, head_mask, *args):
model = XGLMModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=1)
# append to next input_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past)[
"last_hidden_state"
]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, *args):
model = XGLMForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_forward_and_backwards(
self, config, input_ids, input_mask, head_mask, *args, gradient_checkpointing=False
):
model = XGLMForCausalLM(config)
model.to(torch_device)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
result = model(input_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def create_and_check_xglm_weight_initialization(self, config, *args):
model = XGLMModel(config)
model_std = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class XGLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (XGLMModel, XGLMForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (XGLMForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": XGLMModel, "text-generation": XGLMForCausalLM} if is_torch_available() else {}
)
fx_compatible = True
test_missing_keys = False
test_pruning = False
def setUp(self):
self.model_tester = XGLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XGLMConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xglm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xglm_model(*config_and_inputs)
def test_xglm_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xglm_model_past(*config_and_inputs)
def test_xglm_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xglm_model_attention_mask_past(*config_and_inputs)
def test_xglm_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xglm_model_past_large_inputs(*config_and_inputs)
def test_xglm_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_xglm_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
def test_xglm_weight_initialization(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xglm_weight_initialization(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XGLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
def test_model_parallelism(self):
super().test_model_parallelism()
@require_torch
class XGLMModelLanguageGenerationTest(unittest.TestCase):
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def _test_lm_generate_xglm_helper(
self,
gradient_checkpointing=False,
verify_outputs=True,
):
model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
if gradient_checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(torch_device)
input_ids = torch.tensor([[2, 268, 9865]], dtype=torch.long, device=torch_device) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
# fmt: on
output_ids = model.generate(input_ids, do_sample=False, num_beams=1)
if verify_outputs:
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@slow
def test_batch_generation(self):
model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
model.to(torch_device)
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
tokenizer.padding_side = "left"
# use different length sentences to test batching
sentences = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
outputs = model.generate(
input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), max_new_tokens=12
)
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model.generate(input_ids=inputs_non_padded, max_new_tokens=12)
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model.generate(input_ids=inputs_padded, max_new_tokens=12)
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
@slow
def test_lm_generate_xglm(self):
self._test_lm_generate_xglm_helper()
@slow
def test_lm_generate_xglm_with_gradient_checkpointing(self):
self._test_lm_generate_xglm_helper(gradient_checkpointing=True)
@slow
def test_xglm_sample(self):
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
torch.manual_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="pt")
input_ids = tokenized.input_ids
output_ids = model.generate(input_ids, do_sample=True, num_beams=1)
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
EXPECTED_OUTPUT_STRS = [
# TODO: remove this once we move to torch 2.0
# torch 1.13.1 + cu116
"Today is a nice day and the sun is shining. A nice day with warm rainy",
# torch 2.0 + cu117
"Today is a nice day and the water is still cold. We just stopped off for some fresh",
]
self.assertIn(output_str, EXPECTED_OUTPUT_STRS)
@slow
def test_xglm_sample_max_time(self):
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
model.to(torch_device)
torch.manual_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="pt")
input_ids = tokenized.input_ids.to(torch_device)
MAX_TIME = 0.15
start = datetime.datetime.now()
model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=1.25 * MAX_TIME))
@require_torch_gpu
def test_batched_nan_fp16(self):
model_name = "facebook/xglm-564M"
tokenizer = XGLMTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left")
model = XGLMForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_cache=True).cuda()
model = model.eval()
batch = tokenizer(["Who are you?", "Joe Biden is the president of"], padding=True, return_tensors="pt")
input_ids = batch["input_ids"].cuda()
attention_mask = batch["attention_mask"].cuda()
with torch.no_grad():
outputs = model(input_ids, attention_mask=attention_mask)
self.assertFalse(
torch.isnan(outputs.logits[0]).any().item()
) # the first logits could contain NaNs if it fails
| transformers-main | tests/models/xglm/test_modeling_xglm.py |
transformers-main | tests/tokenization/__init__.py |
|
# coding=utf-8
# Copyright 2018 HuggingFace Inc..
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
isort:skip_file
"""
import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional
import numpy as np
from transformers import (
BatchEncoding,
BertTokenizer,
BertTokenizerFast,
PreTrainedTokenizer,
PreTrainedTokenizerFast,
TensorType,
TokenSpan,
is_tokenizers_available,
)
from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow
if is_tokenizers_available():
from tokenizers import Tokenizer
from tokenizers.models import WordPiece
class TokenizerUtilsTest(unittest.TestCase):
def check_tokenizer_from_pretrained(self, tokenizer_class):
s3_models = list(tokenizer_class.max_model_input_sizes.keys())
for model_name in s3_models[:1]:
tokenizer = tokenizer_class.from_pretrained(model_name)
self.assertIsNotNone(tokenizer)
self.assertIsInstance(tokenizer, tokenizer_class)
self.assertIsInstance(tokenizer, PreTrainedTokenizer)
for special_tok in tokenizer.all_special_tokens:
self.assertIsInstance(special_tok, str)
special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
self.assertIsInstance(special_tok_id, int)
def assert_dump_and_restore(self, be_original: BatchEncoding, equal_op: Optional[Callable] = None):
batch_encoding_str = pickle.dumps(be_original)
self.assertIsNotNone(batch_encoding_str)
be_restored = pickle.loads(batch_encoding_str)
# Ensure is_fast is correctly restored
self.assertEqual(be_restored.is_fast, be_original.is_fast)
# Ensure encodings are potentially correctly restored
if be_original.is_fast:
self.assertIsNotNone(be_restored.encodings)
else:
self.assertIsNone(be_restored.encodings)
# Ensure the keys are the same
for original_v, restored_v in zip(be_original.values(), be_restored.values()):
if equal_op:
self.assertTrue(equal_op(restored_v, original_v))
else:
self.assertEqual(restored_v, original_v)
@slow
def test_pretrained_tokenizers(self):
self.check_tokenizer_from_pretrained(GPT2Tokenizer)
def test_tensor_type_from_str(self):
self.assertEqual(TensorType("tf"), TensorType.TENSORFLOW)
self.assertEqual(TensorType("pt"), TensorType.PYTORCH)
self.assertEqual(TensorType("np"), TensorType.NUMPY)
@require_tokenizers
def test_batch_encoding_pickle(self):
import numpy as np
tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
# Python no tensor
with self.subTest("BatchEncoding (Python, return_tensors=None)"):
self.assert_dump_and_restore(tokenizer_p("Small example to encode"))
with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"):
self.assert_dump_and_restore(
tokenizer_p("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
)
with self.subTest("BatchEncoding (Rust, return_tensors=None)"):
self.assert_dump_and_restore(tokenizer_r("Small example to encode"))
with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"):
self.assert_dump_and_restore(
tokenizer_r("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
)
@require_tf
@require_tokenizers
def test_batch_encoding_pickle_tf(self):
import tensorflow as tf
def tf_array_equals(t1, t2):
return tf.reduce_all(tf.equal(t1, t2))
tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"):
self.assert_dump_and_restore(
tokenizer_p("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
)
with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"):
self.assert_dump_and_restore(
tokenizer_r("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
)
@require_torch
@require_tokenizers
def test_batch_encoding_pickle_pt(self):
import torch
tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"):
self.assert_dump_and_restore(
tokenizer_p("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
)
with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"):
self.assert_dump_and_restore(
tokenizer_r("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
)
@require_tokenizers
def test_batch_encoding_is_fast(self):
tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
with self.subTest("Python Tokenizer"):
self.assertFalse(tokenizer_p("Small example to_encode").is_fast)
with self.subTest("Rust Tokenizer"):
self.assertTrue(tokenizer_r("Small example to_encode").is_fast)
@require_tokenizers
def test_batch_encoding_word_to_tokens(self):
tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
encoded = tokenizer_r(["Test", "\xad", "test"], is_split_into_words=True)
self.assertEqual(encoded.word_to_tokens(0), TokenSpan(start=1, end=2))
self.assertEqual(encoded.word_to_tokens(1), None)
self.assertEqual(encoded.word_to_tokens(2), TokenSpan(start=2, end=3))
def test_batch_encoding_with_labels(self):
batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
tensor_batch = batch.convert_to_tensors(tensor_type="np")
self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
self.assertEqual(tensor_batch["labels"].shape, (2,))
# test converting the converted
with CaptureStderr() as cs:
tensor_batch = batch.convert_to_tensors(tensor_type="np")
self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
tensor_batch = batch.convert_to_tensors(tensor_type="np", prepend_batch_axis=True)
self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
self.assertEqual(tensor_batch["labels"].shape, (1,))
@require_torch
def test_batch_encoding_with_labels_pt(self):
batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
tensor_batch = batch.convert_to_tensors(tensor_type="pt")
self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
self.assertEqual(tensor_batch["labels"].shape, (2,))
# test converting the converted
with CaptureStderr() as cs:
tensor_batch = batch.convert_to_tensors(tensor_type="pt")
self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
tensor_batch = batch.convert_to_tensors(tensor_type="pt", prepend_batch_axis=True)
self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
self.assertEqual(tensor_batch["labels"].shape, (1,))
@require_tf
def test_batch_encoding_with_labels_tf(self):
batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
tensor_batch = batch.convert_to_tensors(tensor_type="tf")
self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
self.assertEqual(tensor_batch["labels"].shape, (2,))
# test converting the converted
with CaptureStderr() as cs:
tensor_batch = batch.convert_to_tensors(tensor_type="tf")
self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
tensor_batch = batch.convert_to_tensors(tensor_type="tf", prepend_batch_axis=True)
self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
self.assertEqual(tensor_batch["labels"].shape, (1,))
@require_flax
def test_batch_encoding_with_labels_jax(self):
batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
tensor_batch = batch.convert_to_tensors(tensor_type="jax")
self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
self.assertEqual(tensor_batch["labels"].shape, (2,))
# test converting the converted
with CaptureStderr() as cs:
tensor_batch = batch.convert_to_tensors(tensor_type="jax")
self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
tensor_batch = batch.convert_to_tensors(tensor_type="jax", prepend_batch_axis=True)
self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
self.assertEqual(tensor_batch["labels"].shape, (1,))
def test_padding_accepts_tensors(self):
features = [{"input_ids": np.array([0, 1, 2])}, {"input_ids": np.array([0, 1, 2, 3])}]
tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
batch = tokenizer.pad(features, padding=True)
self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
batch = tokenizer.pad(features, padding=True, return_tensors="np")
self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
@require_torch
def test_padding_accepts_tensors_pt(self):
import torch
features = [{"input_ids": torch.tensor([0, 1, 2])}, {"input_ids": torch.tensor([0, 1, 2, 3])}]
tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
batch = tokenizer.pad(features, padding=True)
self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
batch = tokenizer.pad(features, padding=True, return_tensors="pt")
self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
@require_tf
def test_padding_accepts_tensors_tf(self):
import tensorflow as tf
features = [{"input_ids": tf.constant([0, 1, 2])}, {"input_ids": tf.constant([0, 1, 2, 3])}]
tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
batch = tokenizer.pad(features, padding=True)
self.assertTrue(isinstance(batch["input_ids"], tf.Tensor))
self.assertEqual(batch["input_ids"].numpy().tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
batch = tokenizer.pad(features, padding=True, return_tensors="tf")
self.assertTrue(isinstance(batch["input_ids"], tf.Tensor))
self.assertEqual(batch["input_ids"].numpy().tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
@require_tokenizers
def test_instantiation_from_tokenizers(self):
bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
PreTrainedTokenizerFast(tokenizer_object=bert_tokenizer)
@require_tokenizers
def test_instantiation_from_tokenizers_json_file(self):
bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
with tempfile.TemporaryDirectory() as tmpdirname:
bert_tokenizer.save(os.path.join(tmpdirname, "tokenizer.json"))
PreTrainedTokenizerFast(tokenizer_file=os.path.join(tmpdirname, "tokenizer.json"))
| transformers-main | tests/tokenization/test_tokenization_utils.py |
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest
from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers
from ..test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase):
rust_tokenizer_class = PreTrainedTokenizerFast
test_slow_tokenizer = False
test_rust_tokenizer = True
from_pretrained_vocab_key = "tokenizer_file"
def setUp(self):
self.test_rust_tokenizer = False # because we don't have pretrained_vocab_files_map
super().setUp()
self.test_rust_tokenizer = True
model_paths = ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
self.bytelevel_bpe_model_name = "SaulLu/dummy-tokenizer-bytelevel-bpe"
# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
self.tokenizers_list = [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_paths[0])
tokenizer.save_pretrained(self.tmpdirname)
def test_tokenizer_mismatch_warning(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_pretrained_model_lists(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_prepare_for_model(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_rust_tokenizer_signature(self):
# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
pass
def test_training_new_tokenizer(self):
tmpdirname_orig = self.tmpdirname
# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
try:
self.tmpdirname = tempfile.mkdtemp()
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer.save_pretrained(self.tmpdirname)
super().test_training_new_tokenizer()
finally:
# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
# is restored
shutil.rmtree(self.tmpdirname)
self.tmpdirname = tmpdirname_orig
def test_training_new_tokenizer_with_special_tokens_change(self):
tmpdirname_orig = self.tmpdirname
# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
try:
self.tmpdirname = tempfile.mkdtemp()
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer.save_pretrained(self.tmpdirname)
super().test_training_new_tokenizer_with_special_tokens_change()
finally:
# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
# is restored
shutil.rmtree(self.tmpdirname)
self.tmpdirname = tmpdirname_orig
def test_training_new_tokenizer_with_bytelevel(self):
tokenizer = self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name)
toy_text_iterator = ("a" for _ in range(1000))
new_tokenizer = tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
encoding_ids = new_tokenizer.encode("a🤗")
self.assertEqual(encoding_ids, [64, 172, 253, 97, 245])
def test_init_from_tokenizers_model(self):
from tokenizers import Tokenizer
sentences = ["Hello, y'all!", "How are you 😁 ? There should not be any issue right?"]
tokenizer = Tokenizer.from_pretrained("t5-base")
# Enable padding
tokenizer.enable_padding(pad_id=0, pad_token="<pad>", length=512, pad_to_multiple_of=8)
self.assertEqual(
tokenizer.padding,
{
"length": 512,
"pad_to_multiple_of": 8,
"pad_id": 0,
"pad_token": "<pad>",
"pad_type_id": 0,
"direction": "right",
},
)
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
tmpdirname = tempfile.mkdtemp()
fast_tokenizer.save_pretrained(tmpdirname)
fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname)
for tok in [fast_tokenizer, fast_from_saved]:
self.assertEqual(tok.pad_token_id, 0)
self.assertEqual(tok.padding_side, "right")
self.assertEqual(tok.pad_token, "<pad>")
self.assertEqual(tok.init_kwargs["max_length"], 512)
self.assertEqual(tok.init_kwargs["pad_to_multiple_of"], 8)
# fmt: off
self.assertEqual(tok(sentences, padding = True), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1, 0, 0, 0, 0,0, 0, 0, 0],[ 571, 33, 25, 3, 2, 3, 58, 290, 225, 59, 36, 136, 962, 269, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]})
# fmt: on
tokenizer.enable_truncation(8, stride=0, strategy="longest_first", direction="right")
self.assertEqual(
tokenizer.truncation, {"max_length": 8, "stride": 0, "strategy": "longest_first", "direction": "right"}
)
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
tmpdirname = tempfile.mkdtemp()
fast_tokenizer.save_pretrained(tmpdirname)
fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname)
for tok in [fast_tokenizer, fast_from_saved]:
self.assertEqual(tok.truncation_side, "right")
self.assertEqual(tok.init_kwargs["truncation_strategy"], "longest_first")
self.assertEqual(tok.init_kwargs["max_length"], 8)
self.assertEqual(tok.init_kwargs["stride"], 0)
# NOTE even if the model has a default max_length, it is not used...
# thus tok(sentences, truncation = True) does nothing and does not warn either
# fmt: off
self.assertEqual(tok(sentences, truncation = True, max_length = 8), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1],[ 571, 33, 25, 3, 2, 3, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1]]})
# fmt: on
@require_tokenizers
class TokenizerVersioningTest(unittest.TestCase):
def test_local_versioning(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
json_tokenizer["model"]["vocab"]["huggingface"] = len(tokenizer)
with tempfile.TemporaryDirectory() as tmp_dir:
# Hack to save this in the tokenizer_config.json
tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.4.0.0.json"]
tokenizer.save_pretrained(tmp_dir)
json.dump(json_tokenizer, open(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), "w"))
# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
self.assertEqual(len(new_tokenizer), len(tokenizer) + 1)
json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
shutil.move(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), os.path.join(tmp_dir, "tokenizer.42.0.0.json"))
tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.42.0.0.json"]
tokenizer.save_pretrained(tmp_dir)
new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
self.assertEqual(len(new_tokenizer), len(tokenizer))
json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
def test_repo_versioning(self):
# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
repo = "hf-internal-testing/test-two-tokenizers"
# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
tokenizer = AutoTokenizer.from_pretrained(repo)
self.assertEqual(len(tokenizer), 28997)
json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
old_transformers.tokenization_utils_base.__version__ = "3.0.0"
old_tokenizer = old_transformers.models.auto.AutoTokenizer.from_pretrained(repo)
self.assertEqual(len(old_tokenizer), 28996)
json_tokenizer = json.loads(old_tokenizer._tokenizer.to_str())
self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
@require_tokenizers
class ReduceMutableBorrowTests(unittest.TestCase):
def test_async_share_tokenizer(self):
# See https://github.com/huggingface/transformers/pull/12550
# and https://github.com/huggingface/tokenizers/issues/537
tokenizer = PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel")
text = "The Matrix is a 1999 science fiction action film."
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.fetch, tokenizer, text) for i in range(10)]
return_value = [future.result() for future in futures]
self.assertEqual(return_value, [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10)])
def fetch(self, tokenizer, text):
return tokenizer.encode(text, truncation="longest_first", padding="longest")
| transformers-main | tests/tokenization/test_tokenization_fast.py |
# coding=utf-8
# Copyright 2022 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_accelerate_available,
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def get_some_linear_layer(model):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_4h_to_h
if is_accelerate_available():
from accelerate import PartialState
from accelerate.logging import get_logger
logger = get_logger(__name__)
_ = PartialState()
if is_torch_available():
import torch
import torch.nn as nn
class LoRALayer(nn.Module):
"""Wraps a linear layer with LoRA-like adapter - Used for testing purposes only"""
def __init__(self, module: nn.Module, rank: int):
super().__init__()
self.module = module
self.adapter = nn.Sequential(
nn.Linear(module.in_features, rank, bias=False),
nn.Linear(rank, module.out_features, bias=False),
)
small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
nn.init.normal_(self.adapter[0].weight, std=small_std)
nn.init.zeros_(self.adapter[1].weight)
self.adapter.to(module.weight.device)
def forward(self, input, *args, **kwargs):
return self.module(input, *args, **kwargs) + self.adapter(input)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class BaseMixedInt8Test(unittest.TestCase):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
model_name = "bigscience/bloom-1b7"
# Constant values
EXPECTED_RELATIVE_DIFFERENCE = (
1.540025 # This was obtained on a Quadro RTX 8000 so the number might slightly change
)
input_text = "Hello my name is"
EXPECTED_OUTPUT = "Hello my name is John.\nI am a friend of the family.\n"
MAX_NEW_TOKENS = 10
def setUp(self):
# Models and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
class MixedInt8Test(BaseMixedInt8Test):
def setUp(self):
super().setUp()
# Models and tokenizer
self.model_fp16 = AutoModelForCausalLM.from_pretrained(
self.model_name, torch_dtype=torch.float16, device_map="auto"
)
self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
del self.model_fp16
del self.model_8bit
gc.collect()
torch.cuda.empty_cache()
def test_get_keys_to_not_convert(self):
r"""
Test the `get_keys_to_not_convert` function.
"""
from accelerate import init_empty_weights
from transformers import AutoModelForMaskedLM, Blip2ForConditionalGeneration, MptForCausalLM, OPTForCausalLM
from transformers.utils.bitsandbytes import get_keys_to_not_convert
model_id = "mosaicml/mpt-7b"
config = AutoConfig.from_pretrained(
model_id, trust_remote_code=True, revision="72e5f594ce36f9cabfa2a9fd8f58b491eb467ee7"
)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
self.assertEqual(get_keys_to_not_convert(model), ["transformer.wte"])
# without trust_remote_code
config = AutoConfig.from_pretrained(model_id, revision="72e5f594ce36f9cabfa2a9fd8f58b491eb467ee7")
with init_empty_weights():
model = MptForCausalLM(config)
# The order of the keys does not matter, so we sort them before comparing, same for the other tests.
self.assertEqual(get_keys_to_not_convert(model).sort(), ["lm_head", "transformer.wte"].sort())
model_id = "Salesforce/blip2-opt-2.7b"
config = AutoConfig.from_pretrained(model_id, revision="1ef7f63a8f0a144c13fdca8103eb7b4691c74cec")
with init_empty_weights():
model = Blip2ForConditionalGeneration(config)
self.assertEqual(
get_keys_to_not_convert(model).sort(),
["language_model.lm_head", "language_model.model.decoder.embed_tokens"].sort(),
)
model_id = "facebook/opt-350m"
config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5")
with init_empty_weights():
model = OPTForCausalLM(config)
self.assertEqual(get_keys_to_not_convert(model).sort(), ["lm_head", "model.decoder.embed_tokens"].sort())
model_id = "roberta-large"
config = AutoConfig.from_pretrained(model_id, revision="716877d372b884cad6d419d828bac6c85b3b18d9")
with init_empty_weights():
model = AutoModelForMaskedLM.from_config(config)
self.assertEqual(
get_keys_to_not_convert(model).sort(),
["'roberta.embeddings.word_embeddings', 'lm_head', 'lm_head.decoder"].sort(),
)
def test_quantization_config_json_serialization(self):
r"""
A simple test to check if the quantization config is correctly serialized and deserialized
"""
config = self.model_8bit.config
self.assertTrue(hasattr(config, "quantization_config"))
_ = config.to_dict()
_ = config.to_diff_dict()
_ = config.to_json_string()
def test_memory_footprint(self):
r"""
A simple test to check if the model conversion has been done correctly by checking on the
memory footprint of the converted model and the class type of the linear layers of the converted models
"""
from bitsandbytes.nn import Int8Params
mem_fp16 = self.model_fp16.get_memory_footprint()
mem_8bit = self.model_8bit.get_memory_footprint()
self.assertAlmostEqual(mem_fp16 / mem_8bit, self.EXPECTED_RELATIVE_DIFFERENCE)
self.assertTrue(get_some_linear_layer(self.model_8bit).weight.__class__ == Int8Params)
def test_linear_are_8bit(self):
r"""
A simple test to check if the model conversion has been done correctly by checking on the
memory footprint of the converted model and the class type of the linear layers of the converted models
"""
from transformers import T5PreTrainedModel
self.model_fp16.get_memory_footprint()
self.model_8bit.get_memory_footprint()
for name, module in self.model_8bit.named_modules():
if isinstance(module, torch.nn.Linear):
if name not in ["lm_head"] + T5PreTrainedModel._keep_in_fp32_modules:
self.assertTrue(module.weight.dtype == torch.int8)
def test_llm_skip(self):
r"""
A simple test to check if `llm_int8_skip_modules` works as expected
"""
import bitsandbytes as bnb
quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=["classifier"])
seq_classification_model = AutoModelForSequenceClassification.from_pretrained(
"roberta-large-mnli", quantization_config=quantization_config
)
self.assertTrue(seq_classification_model.roberta.encoder.layer[0].output.dense.weight.dtype == torch.int8)
self.assertTrue(
isinstance(seq_classification_model.roberta.encoder.layer[0].output.dense, bnb.nn.Linear8bitLt)
)
self.assertTrue(isinstance(seq_classification_model.classifier.dense, nn.Linear))
self.assertTrue(seq_classification_model.classifier.dense.weight.dtype != torch.int8)
self.assertTrue(isinstance(seq_classification_model.classifier.out_proj, nn.Linear))
self.assertTrue(seq_classification_model.classifier.out_proj != torch.int8)
def test_generate_quality(self):
r"""
Test the generation quality of the quantized model and see that we are matching the expected output.
Given that we are operating on small numbers + the testing model is relatively small, we might not get
the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
"""
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = self.model_8bit.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertEqual(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
def test_generate_quality_config(self):
r"""
Test that loading the model with the config is equivalent
"""
bnb_config = BitsAndBytesConfig()
bnb_config.load_in_8bit = True
model_8bit_from_config = AutoModelForCausalLM.from_pretrained(
self.model_name, quantization_config=bnb_config, device_map="auto"
)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = model_8bit_from_config.generate(
input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
)
self.assertEqual(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
def test_warns_save_pretrained(self):
r"""
Test whether trying to save a model after converting it in 8-bit will throw a warning.
"""
with self.assertWarns(UserWarning), tempfile.TemporaryDirectory() as tmpdirname:
self.model_8bit.save_pretrained(tmpdirname)
def test_raise_if_config_and_load_in_8bit(self):
r"""
Test that loading the model with the config and `load_in_8bit` raises an error
"""
bnb_config = BitsAndBytesConfig()
with self.assertRaises(ValueError):
_ = AutoModelForCausalLM.from_pretrained(
self.model_name,
quantization_config=bnb_config,
load_in_8bit=True,
device_map="auto",
llm_int8_enable_fp32_cpu_offload=True,
)
def test_device_and_dtype_assignment(self):
r"""
Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error.
Checks also if other models are casted correctly.
"""
with self.assertRaises(ValueError):
# Tries with `str`
self.model_8bit.to("cpu")
with self.assertRaises(ValueError):
# Tries with a `dtype``
self.model_8bit.to(torch.float16)
with self.assertRaises(ValueError):
# Tries with a `device`
self.model_8bit.to(torch.device("cuda:0"))
with self.assertRaises(ValueError):
# Tries with a `device`
self.model_8bit.float()
with self.assertRaises(ValueError):
# Tries with a `device`
self.model_8bit.half()
# Test if we did not break anything
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
self.model_fp16 = self.model_fp16.to(torch.float32)
_ = self.model_fp16.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
# Check this does not throw an error
_ = self.model_fp16.to("cpu")
# Check this does not throw an error
_ = self.model_fp16.half()
# Check this does not throw an error
_ = self.model_fp16.float()
def test_fp32_int8_conversion(self):
r"""
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
"""
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small", load_in_8bit=True, device_map="auto")
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
def test_int8_serialization(self):
r"""
Test whether it is possible to serialize a model in 8-bit.
"""
from bitsandbytes.nn import Int8Params
with tempfile.TemporaryDirectory() as tmpdirname:
self.model_8bit.save_pretrained(tmpdirname)
# check that the file `quantization_config` is present
config = AutoConfig.from_pretrained(tmpdirname)
self.assertTrue(hasattr(config, "quantization_config"))
model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, load_in_8bit=True, device_map="auto")
linear = get_some_linear_layer(model_from_saved)
self.assertTrue(linear.weight.__class__ == Int8Params)
self.assertTrue(hasattr(linear.weight, "SCB"))
# generate
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertEqual(
self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT
)
def test_int8_serialization_sharded(self):
r"""
Test whether it is possible to serialize a model in 8-bit - sharded version.
"""
from bitsandbytes.nn import Int8Params
with tempfile.TemporaryDirectory() as tmpdirname:
self.model_8bit.save_pretrained(tmpdirname, max_shard_size="200MB")
# check that the file `quantization_config` is present
config = AutoConfig.from_pretrained(tmpdirname)
self.assertTrue(hasattr(config, "quantization_config"))
model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname)
linear = get_some_linear_layer(model_from_saved)
self.assertTrue(linear.weight.__class__ == Int8Params)
self.assertTrue(hasattr(linear.weight, "SCB"))
# generate
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = model_from_saved.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertEqual(
self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT
)
def test_int8_from_pretrained(self):
r"""
Test whether loading a 8bit model from the Hub works as expected
"""
from bitsandbytes.nn import Int8Params
model_id = "ybelkada/bloom-1b7-8bit"
model = AutoModelForCausalLM.from_pretrained(model_id)
linear = get_some_linear_layer(model)
self.assertTrue(linear.weight.__class__ == Int8Params)
self.assertTrue(hasattr(linear.weight, "SCB"))
# generate
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertEqual(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class MixedInt8T5Test(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_name = "t5-small"
cls.dense_act_model_name = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.input_text = "Translate in German: Hello, my dog is cute"
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
gc.collect()
torch.cuda.empty_cache()
def test_inference_without_keep_in_fp32(self):
r"""
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
both cases.
"""
from transformers import T5ForConditionalGeneration
modules = T5ForConditionalGeneration._keep_in_fp32_modules
T5ForConditionalGeneration._keep_in_fp32_modules = None
# test with `t5-small`
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
# test with `flan-t5-small`
model = T5ForConditionalGeneration.from_pretrained(
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
T5ForConditionalGeneration._keep_in_fp32_modules = modules
def test_inference_with_keep_in_fp32(self):
r"""
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
both cases.
"""
import bitsandbytes as bnb
from transformers import T5ForConditionalGeneration
# test with `t5-small`
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
# test with `flan-t5-small`
model = T5ForConditionalGeneration.from_pretrained(
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
def test_inference_with_keep_in_fp32_serialized(self):
r"""
Test whether it is possible to mix both `int8` and `fp32` weights when using `keep_in_fp32_modules` correctly on
a serialized model.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
both cases.
"""
import bitsandbytes as bnb
from transformers import T5ForConditionalGeneration
# test with `t5-small`
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
model = T5ForConditionalGeneration.from_pretrained(tmp_dir)
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear8bitLt))
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
# test with `flan-t5-small`
model = T5ForConditionalGeneration.from_pretrained(
self.dense_act_model_name, load_in_8bit=True, device_map="auto"
)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
class MixedInt8ModelClassesTest(BaseMixedInt8Test):
def setUp(self):
super().setUp()
# model_name
self.model_name = "bigscience/bloom-560m"
self.seq_to_seq_name = "t5-small"
# Different types of model
self.base_model = AutoModel.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
# Sequence classification model
self.sequence_model = AutoModelForSequenceClassification.from_pretrained(
self.model_name, load_in_8bit=True, device_map="auto"
)
# CausalLM model
self.model_8bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True, device_map="auto")
# Seq2seq model
self.seq_to_seq_model = AutoModelForSeq2SeqLM.from_pretrained(
self.seq_to_seq_name, load_in_8bit=True, device_map="auto"
)
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
del self.base_model
del self.sequence_model
del self.model_8bit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def test_correct_head_class(self):
r"""
A simple test to check if the last modules for some classes (AutoModelForCausalLM or SequenceClassification)
are kept in their native class.
"""
from bitsandbytes.nn import Int8Params
# last param of a base model should be a linear8bit module
self.assertTrue(self.base_model.h[-1].mlp.dense_4h_to_h.weight.__class__ == Int8Params)
# Other heads should be nn.Parameter
self.assertTrue(self.model_8bit.lm_head.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
class MixedInt8TestPipeline(BaseMixedInt8Test):
def setUp(self):
super().setUp()
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def test_pipeline(self):
r"""
The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
on pipline.
"""
# self._clear_cuda_cache()
self.pipe = pipeline(
"text-generation",
model=self.model_name,
model_kwargs={"device_map": "auto", "load_in_8bit": True},
max_new_tokens=self.MAX_NEW_TOKENS,
)
# Real second forward pass
pipeline_output = self.pipe(self.input_text)
self.assertEqual(pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUT)
@require_torch_multi_gpu
class MixedInt8TestMultiGpu(BaseMixedInt8Test):
def setUp(self):
super().setUp()
def test_multi_gpu_loading(self):
r"""
This tests that the model has been loaded and can be used correctly on a multi-GPU setup.
Let's just try to load a model on 2 GPUs and see if it works. The model we test has ~2GB of total, 3GB should suffice
"""
model_parallel = AutoModelForCausalLM.from_pretrained(
self.model_name, load_in_8bit=True, device_map="balanced"
)
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values()), {0, 1})
# Check that inference pass works on the model
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
# Second real batch
output_parallel = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertEqual(self.tokenizer.decode(output_parallel[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
@require_torch_multi_gpu
class MixedInt8TestCpuGpu(BaseMixedInt8Test):
def setUp(self):
super().setUp()
def check_inference_correctness(self, model):
# Check that inference pass works on the model
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
# Check the exactness of the results
output_parallel = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
# Get the generation
output_text = self.tokenizer.decode(output_parallel[0], skip_special_tokens=True)
self.assertEqual(output_text, self.EXPECTED_OUTPUT)
def test_cpu_gpu_loading_random_device_map(self):
r"""
A test to check is dispatching a model on cpu & gpu works correctly using a random `device_map`.
"""
device_map = {
"transformer.word_embeddings": 0,
"transformer.word_embeddings_layernorm": 0,
"lm_head": 0,
"transformer.h.0": "cpu",
"transformer.h.1": "cpu",
"transformer.h.2": 0,
"transformer.h.3": 0,
"transformer.h.4": 0,
"transformer.h.5": 0,
"transformer.h.6": 0,
"transformer.h.7": 0,
"transformer.h.8": 0,
"transformer.h.9": 1,
"transformer.h.10": 0,
"transformer.h.11": 1,
"transformer.h.12": 0,
"transformer.h.13": 0,
"transformer.h.14": 1,
"transformer.h.15": 0,
"transformer.h.16": 0,
"transformer.h.17": 1,
"transformer.h.18": 1,
"transformer.h.19": 0,
"transformer.h.20": 1,
"transformer.h.21": 1,
"transformer.h.22": 0,
"transformer.h.23": 0,
"transformer.ln_f": 1,
}
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
model_8bit = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=device_map,
quantization_config=bnb_config,
)
# Check that the model has been correctly set on device 0, 1, and `cpu`.
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})
self.check_inference_correctness(model_8bit)
def test_cpu_gpu_loading_custom_device_map(self):
r"""
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
This time the device map is more organized than the test above and uses the abstraction
`transformer.h` to encapsulate all the decoder layers.
"""
device_map = {
"transformer.word_embeddings": "cpu",
"transformer.word_embeddings_layernorm": "cpu",
"lm_head": "cpu",
"transformer.h": 0,
"transformer.ln_f": 1,
}
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
# Load model
model_8bit = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=device_map,
quantization_config=bnb_config,
)
# Check that the model has been correctly set on device 0, 1, and `cpu`.
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu"})
self.check_inference_correctness(model_8bit)
def test_cpu_gpu_disk_loading_custom_device_map(self):
r"""
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
This time we also add `disk` on the device_map.
"""
device_map = {
"transformer.word_embeddings": 0,
"transformer.word_embeddings_layernorm": "cpu",
"lm_head": 0,
"transformer.h": 1,
"transformer.ln_f": "disk",
}
bnb_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
with tempfile.TemporaryDirectory() as tmpdirname:
# Load model
model_8bit = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=device_map,
quantization_config=bnb_config,
offload_folder=tmpdirname,
)
# Check that the model has been correctly set on device 0, 1, and `cpu`.
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})
self.check_inference_correctness(model_8bit)
def test_cpu_gpu_disk_loading_custom_device_map_kwargs(self):
r"""
A test to check is dispatching a model on cpu & gpu works correctly using a custom `device_map`.
This time we also add `disk` on the device_map - using the kwargs directly instead of the quantization config
"""
device_map = {
"transformer.word_embeddings": 0,
"transformer.word_embeddings_layernorm": "cpu",
"lm_head": 0,
"transformer.h": 1,
"transformer.ln_f": "disk",
}
with tempfile.TemporaryDirectory() as tmpdirname:
# Load model
model_8bit = AutoModelForCausalLM.from_pretrained(
self.model_name,
device_map=device_map,
load_in_8bit=True,
llm_int8_enable_fp32_cpu_offload=True,
offload_folder=tmpdirname,
)
# Check that the model has been correctly set on device 0, 1, and `cpu`.
self.assertEqual(set(model_8bit.hf_device_map.values()), {0, 1, "cpu", "disk"})
self.check_inference_correctness(model_8bit)
class MixedInt8TestTraining(BaseMixedInt8Test):
def setUp(self):
self.model_name = "facebook/opt-350m"
super().setUp()
def test_training(self):
if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.37.0"):
return
# Step 1: freeze all parameters
model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_8bit=True)
self.assertEqual(set(model.hf_device_map.values()), {torch.cuda.current_device()})
for param in model.parameters():
param.requires_grad = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
param.data = param.data.to(torch.float32)
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(module)):
module.q_proj = LoRALayer(module.q_proj, rank=16)
module.k_proj = LoRALayer(module.k_proj, rank=16)
module.v_proj = LoRALayer(module.v_proj, rank=16)
# Step 3: dummy batch
batch = self.tokenizer("Test batch ", return_tensors="pt").to(0)
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
out = model.forward(**batch)
out.logits.norm().backward()
for module in model.modules():
if isinstance(module, LoRALayer):
self.assertTrue(module.adapter[1].weight.grad is not None)
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
elif isinstance(module, nn.Embedding):
self.assertTrue(module.weight.grad is None)
class MixedInt8GPT2Test(MixedInt8Test):
model_name = "gpt2-xl"
EXPECTED_RELATIVE_DIFFERENCE = 1.8720077507258357
EXPECTED_OUTPUT = "Hello my name is John Doe, and I'm a big fan of"
def test_int8_from_pretrained(self):
r"""
Test whether loading a 8bit model from the Hub works as expected
"""
from bitsandbytes.nn import Int8Params
model_id = "ybelkada/gpt2-xl-8bit"
model = AutoModelForCausalLM.from_pretrained(model_id)
linear = get_some_linear_layer(model)
self.assertTrue(linear.weight.__class__ == Int8Params)
self.assertTrue(hasattr(linear.weight, "SCB"))
# generate
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertEqual(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
| transformers-main | tests/quantization/bnb/test_mixed_int8.py |
transformers-main | tests/quantization/bnb/__init__.py |
|
# coding=utf-8
# Copyright 2022 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def get_some_linear_layer(model):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_4h_to_h
if is_torch_available():
import torch
import torch.nn as nn
class LoRALayer(nn.Module):
"""Wraps a linear layer with LoRA-like adapter - Used for testing purposes only"""
def __init__(self, module: nn.Module, rank: int):
super().__init__()
self.module = module
self.adapter = nn.Sequential(
nn.Linear(module.in_features, rank, bias=False),
nn.Linear(rank, module.out_features, bias=False),
)
small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5
nn.init.normal_(self.adapter[0].weight, std=small_std)
nn.init.zeros_(self.adapter[1].weight)
self.adapter.to(module.weight.device)
def forward(self, input, *args, **kwargs):
return self.module(input, *args, **kwargs) + self.adapter(input)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class Base4bitTest(unittest.TestCase):
# We keep the constants inside the init function and model loading inside setUp function
# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
# Therefore here we use only bloom-1b3 to test our module
model_name = "bigscience/bloom-1b7"
# Constant values
EXPECTED_RELATIVE_DIFFERENCE = (
2.109659552692574 # This was obtained on a RTX Titan so the number might slightly change
)
input_text = "Hello my name is"
EXPECTED_OUTPUTS = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I")
EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n")
EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University")
MAX_NEW_TOKENS = 10
def setUp(self):
# Models and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
class Bnb4BitTest(Base4bitTest):
def setUp(self):
super().setUp()
# Models and tokenizer
self.model_fp16 = AutoModelForCausalLM.from_pretrained(
self.model_name, torch_dtype=torch.float16, device_map="auto"
)
self.model_4bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
del self.model_fp16
del self.model_4bit
gc.collect()
torch.cuda.empty_cache()
def test_quantization_config_json_serialization(self):
r"""
A simple test to check if the quantization config is correctly serialized and deserialized
"""
config = self.model_4bit.config
self.assertTrue(hasattr(config, "quantization_config"))
_ = config.to_dict()
_ = config.to_diff_dict()
_ = config.to_json_string()
def test_memory_footprint(self):
r"""
A simple test to check if the model conversion has been done correctly by checking on the
memory footprint of the converted model and the class type of the linear layers of the converted models
"""
from bitsandbytes.nn import Params4bit
mem_fp16 = self.model_fp16.get_memory_footprint()
mem_4bit = self.model_4bit.get_memory_footprint()
self.assertAlmostEqual(mem_fp16 / mem_4bit, self.EXPECTED_RELATIVE_DIFFERENCE)
linear = get_some_linear_layer(self.model_4bit)
self.assertTrue(linear.weight.__class__ == Params4bit)
def test_linear_are_4bit(self):
r"""
A simple test to check if the model conversion has been done correctly by checking on the
memory footprint of the converted model and the class type of the linear layers of the converted models
"""
from transformers import T5PreTrainedModel
self.model_fp16.get_memory_footprint()
self.model_4bit.get_memory_footprint()
for name, module in self.model_4bit.named_modules():
if isinstance(module, torch.nn.Linear):
if name not in ["lm_head"] + T5PreTrainedModel._keep_in_fp32_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uint8)
def test_generate_quality(self):
r"""
Test the generation quality of the quantized model and see that we are matching the expected output.
Given that we are operating on small numbers + the testing model is relatively small, we might not get
the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
"""
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = self.model_4bit.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
def test_generate_quality_config(self):
r"""
Test that loading the model with the config is equivalent
"""
bnb_config = BitsAndBytesConfig()
bnb_config.load_in_4bit = True
model_4bit_from_config = AutoModelForCausalLM.from_pretrained(
self.model_name, quantization_config=bnb_config, device_map="auto"
)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
output_sequences = model_4bit_from_config.generate(
input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
)
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
def test_raise_on_save_pretrained(self):
r"""
Test whether trying to save a model after converting it in 8-bit will throw a warning.
"""
with self.assertRaises(NotImplementedError), tempfile.TemporaryDirectory() as tmpdirname:
self.model_4bit.save_pretrained(tmpdirname)
def test_raise_if_config_and_load_in_4bit(self):
r"""
Test that loading the model with the config and `load_in_4bit` raises an error
"""
bnb_config = BitsAndBytesConfig()
with self.assertRaises(ValueError):
_ = AutoModelForCausalLM.from_pretrained(
self.model_name,
quantization_config=bnb_config,
load_in_4bit=True,
device_map="auto",
bnb_4bit_quant_type="nf4",
)
def test_device_and_dtype_assignment(self):
r"""
Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error.
Checks also if other models are casted correctly.
"""
with self.assertRaises(ValueError):
# Tries with `str`
self.model_4bit.to("cpu")
with self.assertRaises(ValueError):
# Tries with a `dtype``
self.model_4bit.to(torch.float16)
with self.assertRaises(ValueError):
# Tries with a `device`
self.model_4bit.to(torch.device("cuda:0"))
with self.assertRaises(ValueError):
# Tries with a `device`
self.model_4bit.float()
with self.assertRaises(ValueError):
# Tries with a `device`
self.model_4bit.half()
# Test if we did not break anything
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
self.model_fp16 = self.model_fp16.to(torch.float32)
_ = self.model_fp16.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
# Check this does not throw an error
_ = self.model_fp16.to("cpu")
# Check this does not throw an error
_ = self.model_fp16.half()
# Check this does not throw an error
_ = self.model_fp16.float()
def test_fp32_4bit_conversion(self):
r"""
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
"""
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small", load_in_4bit=True, device_map="auto")
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.float32)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class Bnb4BitT5Test(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_name = "t5-small"
cls.dense_act_model_name = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.input_text = "Translate in German: Hello, my dog is cute"
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
gc.collect()
torch.cuda.empty_cache()
def test_inference_without_keep_in_fp32(self):
r"""
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
both cases.
"""
from transformers import T5ForConditionalGeneration
modules = T5ForConditionalGeneration._keep_in_fp32_modules
T5ForConditionalGeneration._keep_in_fp32_modules = None
# test with `t5-small`
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
# test with `flan-t5-small`
model = T5ForConditionalGeneration.from_pretrained(
self.dense_act_model_name, load_in_4bit=True, device_map="auto"
)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
T5ForConditionalGeneration._keep_in_fp32_modules = modules
def test_inference_with_keep_in_fp32(self):
r"""
Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
`flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
both cases.
"""
import bitsandbytes as bnb
from transformers import T5ForConditionalGeneration
# test with `t5-small`
model = T5ForConditionalGeneration.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q, bnb.nn.Linear4bit))
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
# test with `flan-t5-small`
model = T5ForConditionalGeneration.from_pretrained(
self.dense_act_model_name, load_in_4bit=True, device_map="auto"
)
encoded_input = self.tokenizer(self.input_text, return_tensors="pt").to(0)
_ = model.generate(**encoded_input)
class Classes4BitModelTest(Base4bitTest):
def setUp(self):
super().setUp()
# model_name
self.model_name = "bigscience/bloom-560m"
self.seq_to_seq_name = "t5-small"
# Different types of model
self.base_model = AutoModel.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
# Sequence classification model
self.sequence_model = AutoModelForSequenceClassification.from_pretrained(
self.model_name, load_in_4bit=True, device_map="auto"
)
# CausalLM model
self.model_4bit = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True, device_map="auto")
# Seq2seq model
self.seq_to_seq_model = AutoModelForSeq2SeqLM.from_pretrained(
self.seq_to_seq_name, load_in_4bit=True, device_map="auto"
)
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
del self.base_model
del self.sequence_model
del self.model_4bit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def test_correct_head_class(self):
r"""
A simple test to check if the last modules for some classes (AutoModelForCausalLM or SequenceClassification)
are kept in their native class.
"""
from bitsandbytes.nn import Params4bit
self.assertTrue(self.base_model.h[-1].mlp.dense_4h_to_h.weight.__class__ == Params4bit)
# Other heads should be nn.Parameter
self.assertTrue(self.model_4bit.lm_head.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
class Pipeline4BitTest(Base4bitTest):
def setUp(self):
super().setUp()
def tearDown(self):
r"""
TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
"""
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def test_pipeline(self):
r"""
The aim of this test is to verify that the mixed 4bit is compatible with `pipeline` from transformers. Since
we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
on pipline.
"""
# self._clear_cuda_cache()
self.pipe = pipeline(
"text-generation",
model=self.model_name,
model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.float16},
max_new_tokens=self.MAX_NEW_TOKENS,
)
# Real second forward pass
pipeline_output = self.pipe(self.input_text)
self.assertIn(pipeline_output[0]["generated_text"], self.EXPECTED_OUTPUTS)
@require_torch_multi_gpu
class Bnb4bitTestMultiGpu(Base4bitTest):
def setUp(self):
super().setUp()
def test_multi_gpu_loading(self):
r"""
This tests that the model has been loaded and can be used correctly on a multi-GPU setup.
Let's just try to load a model on 2 GPUs and see if it works. The model we test has ~2GB of total, 3GB should suffice
"""
model_parallel = AutoModelForCausalLM.from_pretrained(
self.model_name, load_in_4bit=True, device_map="balanced"
)
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values()), {0, 1})
# Check that inference pass works on the model
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
# Second real batch
output_parallel = model_parallel.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
self.assertIn(self.tokenizer.decode(output_parallel[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
class Bnb4BitTestTraining(Base4bitTest):
def setUp(self):
self.model_name = "facebook/opt-350m"
super().setUp()
def test_training(self):
if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.37.0"):
return
# Step 1: freeze all parameters
model = AutoModelForCausalLM.from_pretrained(self.model_name, load_in_4bit=True)
self.assertEqual(set(model.hf_device_map.values()), {torch.cuda.current_device()})
for param in model.parameters():
param.requires_grad = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
param.data = param.data.to(torch.float32)
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(module)):
module.q_proj = LoRALayer(module.q_proj, rank=16)
module.k_proj = LoRALayer(module.k_proj, rank=16)
module.v_proj = LoRALayer(module.v_proj, rank=16)
# Step 3: dummy batch
batch = self.tokenizer("Test batch ", return_tensors="pt").to(0)
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
out = model.forward(**batch)
out.logits.norm().backward()
for module in model.modules():
if isinstance(module, LoRALayer):
self.assertTrue(module.adapter[1].weight.grad is not None)
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
elif isinstance(module, nn.Embedding):
self.assertTrue(module.weight.grad is None)
class Bnb4BitGPT2Test(Bnb4BitTest):
model_name = "gpt2-xl"
EXPECTED_RELATIVE_DIFFERENCE = 3.3191854854152187
| transformers-main | tests/quantization/bnb/test_4bit.py |
transformers-main | tests/quantization/gptq/__init__.py |
|
# coding=utf-8
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import pytest
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_auto_gptq,
require_optimum,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
if is_torch_available():
import torch
class GPTQConfigTest(unittest.TestCase):
def test_bits(self):
with self.assertRaises(ValueError):
GPTQConfig(bits="")
GPTQConfig(bits=1)
GPTQConfig(bits=2)
GPTQConfig(bits=4)
def test_dataset(self):
with self.assertRaises(ValueError):
GPTQConfig(bits=2, dataset="auto_gpt")
GPTQConfig(bits=2, dataset="c4")
GPTQConfig(bits=2, dataset="ptb-new")
def test_damp_percent(self):
with self.assertRaises(ValueError):
GPTQConfig(bits=2, damp_percent=10)
GPTQConfig(bits=2, damp_percent=-1)
GPTQConfig(bits=2, damp_percent="0")
GPTQConfig(bits=2, damp_percent=0.01)
def test_to_dict(self):
quantization_config = GPTQConfig(bits=2)
quantization_config.to_dict()
def test_from_dict(self):
dict = {"bits": 2}
quantization_config = GPTQConfig.from_dict(dict)
self.assertEqual(dict["bits"], quantization_config.bits)
@require_optimum
def test_optimum_config(self):
from optimum.gptq import GPTQQuantizer
config = GPTQConfig(bits=2)
optimum_config = GPTQQuantizer.from_dict(config.to_dict())
self.assertEqual(optimum_config.bits, config.bits)
new_config = GPTQConfig.from_dict(optimum_config.to_dict())
self.assertEqual(optimum_config.bits, new_config.bits)
@slow
@require_optimum
@require_auto_gptq
@require_torch_gpu
class GPTQTest(unittest.TestCase):
model_name = "bigscience/bloom-560m"
input_text = "Hello my name is"
EXPECTED_OUTPUTS = set()
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I")
EXPECTED_OUTPUTS.add("Hello my name is John and I am a very good looking man.")
EXPECTED_OUTPUTS.add("Hello my name is Alyson and I am a professional photographer")
# this seems a little small considering that we are doing 4bit quant but we have a small model and ww don't quantize the embeddings
EXPECTED_RELATIVE_DIFFERENCE = 1.664253062
bits = 4
group_size = 128
desc_act = False
disable_exllama = True
dataset = [
"auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
]
device_map = None
# called only once for all test in this class
@classmethod
def setUpClass(cls):
"""
Setup quantized model
"""
cls.model_fp16 = AutoModelForCausalLM.from_pretrained(
cls.model_name, torch_dtype=torch.float16, device_map=cls.device_map
)
cls.mem_fp16 = cls.model_fp16.get_memory_footprint()
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name, use_fast=True)
quantization_config = GPTQConfig(
bits=cls.bits,
dataset=cls.dataset,
tokenizer=cls.tokenizer,
group_size=cls.group_size,
desc_act=cls.desc_act,
disable_exllama=cls.disable_exllama,
)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name,
torch_dtype=torch.float16,
device_map=cls.device_map,
quantization_config=quantization_config,
)
def test_memory_footprint(self):
r"""
A simple test to check if the model conversion has been done correctly by checking on the
memory footprint of the converted model
"""
mem_quantized = self.quantized_model.get_memory_footprint()
self.assertAlmostEqual(self.mem_fp16 / mem_quantized, self.EXPECTED_RELATIVE_DIFFERENCE)
def test_quantized_layers_class(self):
"""
Simple test to check if the model conversion has been done correctly by checking on
the class type of the linear layers of the converted models
"""
from auto_gptq.utils.import_utils import dynamically_import_QuantLinear
QuantLinear = dynamically_import_QuantLinear(
use_triton=False,
desc_act=self.desc_act,
group_size=self.group_size,
bits=self.bits,
disable_exllama=self.disable_exllama,
)
self.assertTrue(self.quantized_model.transformer.h[0].mlp.dense_4h_to_h.__class__ == QuantLinear)
def check_inference_correctness(self, model):
r"""
Test the generation quality of the quantized model and see that we are matching the expected output.
Given that we are operating on small numbers + the testing model is relatively small, we might not get
the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
"""
# Check that inference pass works on the model
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
# Check the exactness of the results
output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
# Get the generation
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
def test_generate_quality(self):
"""
Simple test to check the quality of the model by comapring the the generated tokens with the expected tokens
"""
if self.device_map is None:
self.check_inference_correctness(self.quantized_model.to(0))
else:
self.check_inference_correctness(self.quantized_model)
def test_serialization(self):
"""
Test the serialization of the model and the loading of the quantized weights works
"""
with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname)
if self.disable_exllama:
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname).to(0)
else:
# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map={"": 0})
self.check_inference_correctness(quantized_model_from_saved)
@require_accelerate
def test_serialization_big_model_inference(self):
"""
Test the serialization of the model and the loading of the quantized weights with big model inference
"""
with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname)
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map="auto")
self.check_inference_correctness(quantized_model_from_saved)
def test_change_loading_attributes(self):
"""
Test the serialization of the model and the loading of the quantized weights works with another config file
"""
with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname)
if self.disable_exllama:
self.assertEqual(self.quantized_model.config.quantization_config.disable_exllama, True)
# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
tmpdirname, quantization_config=GPTQConfig(disable_exllama=False, bits=6), device_map={"": 0}
)
self.assertEqual(quantized_model_from_saved.config.quantization_config.disable_exllama, False)
self.assertEqual(quantized_model_from_saved.config.quantization_config.bits, self.bits)
self.check_inference_correctness(quantized_model_from_saved)
@require_accelerate
@require_torch_multi_gpu
class GPTQTestDeviceMap(GPTQTest):
device_map = "auto"
@require_accelerate
@require_torch_multi_gpu
class GPTQTestDeviceMapExllama(GPTQTest):
device_map = "auto"
disable_exllama = False
# fail when run all together
@pytest.mark.skip
@require_accelerate
@require_torch_multi_gpu
class GPTQTestDeviceMapCPUOffload(GPTQTest):
device_map = {
"transformer.word_embeddings": 0,
"transformer.word_embeddings_layernorm": 0,
"lm_head": 0,
"transformer.h.0": 0,
"transformer.h.1": 0,
"transformer.h.2": 0,
"transformer.h.3": 0,
"transformer.h.4": 0,
"transformer.h.5": 0,
"transformer.h.6": 0,
"transformer.h.7": 0,
"transformer.h.8": 0,
"transformer.h.9": 0,
"transformer.h.10": 1,
"transformer.h.11": 1,
"transformer.h.12": 1,
"transformer.h.13": 1,
"transformer.h.14": 1,
"transformer.h.15": 1,
"transformer.h.16": 1,
"transformer.h.17": 0,
"transformer.h.18": "cpu",
"transformer.h.19": "cpu",
"transformer.h.20": "cpu",
"transformer.h.21": "cpu",
"transformer.h.22": "cpu",
"transformer.h.23": 1,
"transformer.ln_f": 0,
}
| transformers-main | tests/quantization/gptq/test_gptq.py |
transformers-main | tests/bettertransformer/__init__.py |
|
# coding=utf-8
# Copyright 2023 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class BetterTransformerIntegrationTest(unittest.TestCase):
# refer to the full test suite in Optimum library:
# https://github.com/huggingface/optimum/tree/main/tests/bettertransformer
def test_transform_and_reverse(self):
r"""
Classic tests to simply check if the conversion has been successfull.
"""
model_id = "hf-internal-testing/tiny-random-t5"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
inp = tokenizer("This is me", return_tensors="pt")
model = model.to_bettertransformer()
self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()))
output = model.generate(**inp)
model = model.reverse_bettertransformer()
self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()))
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model_reloaded = AutoModelForSeq2SeqLM.from_pretrained(tmpdirname)
self.assertFalse(
any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules())
)
output_from_pretrained = model_reloaded.generate(**inp)
self.assertTrue(torch.allclose(output, output_from_pretrained))
def test_error_save_pretrained(self):
r"""
The save_pretrained method should raise a ValueError if the model is in BetterTransformer mode.
All should be good if the model is reversed.
"""
model_id = "hf-internal-testing/tiny-random-t5"
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
model = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(ValueError):
model.save_pretrained(tmpdirname)
model = model.reverse_bettertransformer()
model.save_pretrained(tmpdirname)
| transformers-main | tests/bettertransformer/test_integration.py |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import unittest
git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
BERT_TEST_FILE = os.path.join("tests", "models", "bert", "test_modeling_bert.py")
BLIP_TEST_FILE = os.path.join("tests", "models", "blip", "test_modeling_blip.py")
class GetTestInfoTester(unittest.TestCase):
def test_get_test_to_tester_mapping(self):
bert_test_tester_mapping = get_test_to_tester_mapping(BERT_TEST_FILE)
blip_test_tester_mapping = get_test_to_tester_mapping(BLIP_TEST_FILE)
EXPECTED_BERT_MAPPING = {"BertModelTest": "BertModelTester"}
EXPECTED_BLIP_MAPPING = {
"BlipModelTest": "BlipModelTester",
"BlipTextImageModelTest": "BlipTextImageModelsModelTester",
"BlipTextModelTest": "BlipTextModelTester",
"BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
"BlipVQAModelTest": "BlipVQAModelTester",
"BlipVisionModelTest": "BlipVisionModelTester",
}
self.assertEqual(get_test_info.to_json(bert_test_tester_mapping), EXPECTED_BERT_MAPPING)
self.assertEqual(get_test_info.to_json(blip_test_tester_mapping), EXPECTED_BLIP_MAPPING)
def test_get_model_to_test_mapping(self):
bert_model_test_mapping = get_model_to_test_mapping(BERT_TEST_FILE)
blip_model_test_mapping = get_model_to_test_mapping(BLIP_TEST_FILE)
EXPECTED_BERT_MAPPING = {
"BertForMaskedLM": ["BertModelTest"],
"BertForMultipleChoice": ["BertModelTest"],
"BertForNextSentencePrediction": ["BertModelTest"],
"BertForPreTraining": ["BertModelTest"],
"BertForQuestionAnswering": ["BertModelTest"],
"BertForSequenceClassification": ["BertModelTest"],
"BertForTokenClassification": ["BertModelTest"],
"BertLMHeadModel": ["BertModelTest"],
"BertModel": ["BertModelTest"],
}
EXPECTED_BLIP_MAPPING = {
"BlipForConditionalGeneration": ["BlipTextImageModelTest"],
"BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
"BlipForQuestionAnswering": ["BlipVQAModelTest"],
"BlipModel": ["BlipModelTest"],
"BlipTextModel": ["BlipTextModelTest"],
"BlipVisionModel": ["BlipVisionModelTest"],
}
self.assertEqual(get_test_info.to_json(bert_model_test_mapping), EXPECTED_BERT_MAPPING)
self.assertEqual(get_test_info.to_json(blip_model_test_mapping), EXPECTED_BLIP_MAPPING)
def test_get_model_to_tester_mapping(self):
bert_model_tester_mapping = get_model_to_tester_mapping(BERT_TEST_FILE)
blip_model_tester_mapping = get_model_to_tester_mapping(BLIP_TEST_FILE)
EXPECTED_BERT_MAPPING = {
"BertForMaskedLM": ["BertModelTester"],
"BertForMultipleChoice": ["BertModelTester"],
"BertForNextSentencePrediction": ["BertModelTester"],
"BertForPreTraining": ["BertModelTester"],
"BertForQuestionAnswering": ["BertModelTester"],
"BertForSequenceClassification": ["BertModelTester"],
"BertForTokenClassification": ["BertModelTester"],
"BertLMHeadModel": ["BertModelTester"],
"BertModel": ["BertModelTester"],
}
EXPECTED_BLIP_MAPPING = {
"BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
"BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
"BlipForQuestionAnswering": ["BlipVQAModelTester"],
"BlipModel": ["BlipModelTester"],
"BlipTextModel": ["BlipTextModelTester"],
"BlipVisionModel": ["BlipVisionModelTester"],
}
self.assertEqual(get_test_info.to_json(bert_model_tester_mapping), EXPECTED_BERT_MAPPING)
self.assertEqual(get_test_info.to_json(blip_model_tester_mapping), EXPECTED_BLIP_MAPPING)
| transformers-main | tests/repo_utils/test_get_test_info.py |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import sys
import tempfile
import unittest
from contextlib import contextmanager
from pathlib import Path
git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
from check_copies import convert_to_localized_md, find_code_in_transformers, is_copy_consistent # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
REFERENCE_CODE = """ def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
"""
MOCK_BERT_CODE = """from ...modeling_utils import PreTrainedModel
def bert_function(x):
return x
class BertAttention(nn.Module):
def __init__(self, config):
super().__init__()
class BertModel(BertPreTrainedModel):
def __init__(self, config):
super().__init__()
self.bert = BertEncoder(config)
@add_docstring(BERT_DOCSTRING)
def forward(self, x):
return self.bert(x)
"""
MOCK_BERT_COPY_CODE = """from ...modeling_utils import PreTrainedModel
# Copied from transformers.models.bert.modeling_bert.bert_function
def bert_copy_function(x):
return x
# Copied from transformers.models.bert.modeling_bert.BertAttention
class BertCopyAttention(nn.Module):
def __init__(self, config):
super().__init__()
# Copied from transformers.models.bert.modeling_bert.BertModel with Bert->BertCopy all-casing
class BertCopyModel(BertCopyPreTrainedModel):
def __init__(self, config):
super().__init__()
self.bertcopy = BertCopyEncoder(config)
@add_docstring(BERTCOPY_DOCSTRING)
def forward(self, x):
return self.bertcopy(x)
"""
def replace_in_file(filename, old, new):
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
content = content.replace(old, new)
with open(filename, "w", encoding="utf-8") as f:
f.write(content)
def create_tmp_repo(tmp_dir):
"""
Creates a mock repository in a temporary folder for testing.
"""
tmp_dir = Path(tmp_dir)
if tmp_dir.exists():
shutil.rmtree(tmp_dir)
tmp_dir.mkdir(exist_ok=True)
model_dir = tmp_dir / "src" / "transformers" / "models"
model_dir.mkdir(parents=True, exist_ok=True)
models = {"bert": MOCK_BERT_CODE, "bertcopy": MOCK_BERT_COPY_CODE}
for model, code in models.items():
model_subdir = model_dir / model
model_subdir.mkdir(exist_ok=True)
with open(model_subdir / f"modeling_{model}.py", "w", encoding="utf-8") as f:
f.write(code)
@contextmanager
def patch_transformer_repo_path(new_folder):
"""
Temporarily patches the variables defines in `check_copies` to use a different location for the repo.
"""
old_repo_path = check_copies.REPO_PATH
old_doc_path = check_copies.PATH_TO_DOCS
old_transformer_path = check_copies.TRANSFORMERS_PATH
repo_path = Path(new_folder).resolve()
check_copies.REPO_PATH = str(repo_path)
check_copies.PATH_TO_DOCS = str(repo_path / "docs" / "source" / "en")
check_copies.TRANSFORMERS_PATH = str(repo_path / "src" / "transformers")
try:
yield
finally:
check_copies.REPO_PATH = old_repo_path
check_copies.PATH_TO_DOCS = old_doc_path
check_copies.TRANSFORMERS_PATH = old_transformer_path
class CopyCheckTester(unittest.TestCase):
def test_find_code_in_transformers(self):
with tempfile.TemporaryDirectory() as tmp_folder:
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
code = find_code_in_transformers("models.bert.modeling_bert.BertAttention")
reference_code = (
"class BertAttention(nn.Module):\n def __init__(self, config):\n super().__init__()\n"
)
self.assertEqual(code, reference_code)
def test_is_copy_consistent(self):
path_to_check = ["src", "transformers", "models", "bertcopy", "modeling_bertcopy.py"]
with tempfile.TemporaryDirectory() as tmp_folder:
# Base check
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
file_to_check = os.path.join(tmp_folder, *path_to_check)
diffs = is_copy_consistent(file_to_check)
self.assertEqual(diffs, [])
# Base check with an inconsistency
create_tmp_repo(tmp_folder)
with patch_transformer_repo_path(tmp_folder):
file_to_check = os.path.join(tmp_folder, *path_to_check)
replace_in_file(file_to_check, "self.bertcopy(x)", "self.bert(x)")
diffs = is_copy_consistent(file_to_check)
self.assertEqual(diffs, [["models.bert.modeling_bert.BertModel", 22]])
diffs = is_copy_consistent(file_to_check, overwrite=True)
with open(file_to_check, "r", encoding="utf-8") as f:
self.assertEqual(f.read(), MOCK_BERT_COPY_CODE)
def test_convert_to_localized_md(self):
localized_readme = check_copies.LOCALIZED_READMES["README_zh-hans.md"]
md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
" released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
" (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
" as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
" Luong, Quoc V. Le, Christopher D. Manning."
)
localized_md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
converted_md_list_sample = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
" [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
" Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
" than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
" Christopher D. Manning 发布。\n"
)
num_models_equal, converted_md_list = convert_to_localized_md(
md_list, localized_md_list, localized_readme["format_model_list"]
)
self.assertFalse(num_models_equal)
self.assertEqual(converted_md_list, converted_md_list_sample)
num_models_equal, converted_md_list = convert_to_localized_md(
md_list, converted_md_list, localized_readme["format_model_list"]
)
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(num_models_equal)
link_changed_md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
)
link_unchanged_md_list = (
"1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
" the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
converted_md_list_sample = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
num_models_equal, converted_md_list = convert_to_localized_md(
link_changed_md_list, link_unchanged_md_list, localized_readme["format_model_list"]
)
# Check if the model link is synchronized.
self.assertEqual(converted_md_list, converted_md_list_sample)
| transformers-main | tests/repo_utils/test_check_copies.py |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import unittest
git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
check_dummies.PATH_TO_TRANSFORMERS = os.path.join(git_repo_path, "src", "transformers")
DUMMY_CONSTANT = """
{0} = None
"""
DUMMY_CLASS = """
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
"""
DUMMY_FUNCTION = """
def {0}(*args, **kwargs):
requires_backends({0}, {1})
"""
class CheckDummiesTester(unittest.TestCase):
def test_find_backend(self):
no_backend = find_backend(' _import_structure["models.albert"].append("AlbertTokenizerFast")')
self.assertIsNone(no_backend)
simple_backend = find_backend(" if not is_tokenizers_available():")
self.assertEqual(simple_backend, "tokenizers")
backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
self.assertEqual(backend_with_underscore, "tensorflow_text")
double_backend = find_backend(" if not (is_sentencepiece_available() and is_tokenizers_available()):")
self.assertEqual(double_backend, "sentencepiece_and_tokenizers")
double_backend_with_underscore = find_backend(
" if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
)
self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
triple_backend = find_backend(
" if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"
)
self.assertEqual(triple_backend, "sentencepiece_and_tokenizers_and_vision")
def test_read_init(self):
objects = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch", objects)
self.assertIn("tensorflow_text", objects)
self.assertIn("sentencepiece_and_tokenizers", objects)
# Likewise, we can't assert on the exact content of a key
self.assertIn("BertModel", objects["torch"])
self.assertIn("TFBertModel", objects["tf"])
self.assertIn("FlaxBertModel", objects["flax"])
self.assertIn("BertModel", objects["torch"])
self.assertIn("TFBertTokenizer", objects["tensorflow_text"])
self.assertIn("convert_slow_tokenizer", objects["sentencepiece_and_tokenizers"])
def test_create_dummy_object(self):
dummy_constant = create_dummy_object("CONSTANT", "'torch'")
self.assertEqual(dummy_constant, "\nCONSTANT = None\n")
dummy_function = create_dummy_object("function", "'torch'")
self.assertEqual(
dummy_function, "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n"
)
expected_dummy_class = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
"""
dummy_class = create_dummy_object("FakeClass", "'torch'")
self.assertEqual(dummy_class, expected_dummy_class)
def test_create_dummy_files(self):
expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
"""
dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file)
| transformers-main | tests/repo_utils/test_check_dummies.py |
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