python_code
stringlengths 0
992k
| repo_name
stringlengths 8
46
| file_path
stringlengths 5
162
|
|---|---|---|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
def quant_noise(module, p, block_size):
"""
Wraps modules and applies quantization noise to the weights for
subsequent quantization with Iterative Product Quantization as
described in "Training with Quantization Noise for Extreme Model Compression"
Args:
- module: nn.Module
- p: amount of Quantization Noise
- block_size: size of the blocks for subsequent quantization with iPQ
Remarks:
- Module weights must have the right sizes wrt the block size
- Only Linear, Embedding and Conv2d modules are supported for the moment
- For more detail on how to quantize by blocks with convolutional weights,
see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
- We implement the simplest form of noise here as stated in the paper
which consists in randomly dropping blocks
"""
# if no quantization noise, don't register hook
if p <= 0:
return module
# supported modules
assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
# test whether module.weight has the right sizes wrt block_size
is_conv = module.weight.ndim == 4
# 2D matrix
if not is_conv:
assert (
module.weight.size(1) % block_size == 0
), "Input features must be a multiple of block sizes"
# 4D matrix
else:
# 1x1 convolutions
if module.kernel_size == (1, 1):
assert (
module.in_channels % block_size == 0
), "Input channels must be a multiple of block sizes"
# regular convolutions
else:
k = module.kernel_size[0] * module.kernel_size[1]
assert k % block_size == 0, "Kernel size must be a multiple of block size"
def _forward_pre_hook(mod, input):
# no noise for evaluation
if mod.training:
if not is_conv:
# gather weight and sizes
weight = mod.weight
in_features = weight.size(1)
out_features = weight.size(0)
# split weight matrix into blocks and randomly drop selected blocks
mask = torch.zeros(
in_features // block_size * out_features, device=weight.device
)
mask.bernoulli_(p)
mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
else:
# gather weight and sizes
weight = mod.weight
in_channels = mod.in_channels
out_channels = mod.out_channels
# split weight matrix into blocks and randomly drop selected blocks
if mod.kernel_size == (1, 1):
mask = torch.zeros(
int(in_channels // block_size * out_channels),
device=weight.device,
)
mask.bernoulli_(p)
mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
else:
mask = torch.zeros(
weight.size(0), weight.size(1), device=weight.device
)
mask.bernoulli_(p)
mask = (
mask.unsqueeze(2)
.unsqueeze(3)
.repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
)
# scale weights and apply mask
mask = mask.to(
torch.bool
) # x.bool() is not currently supported in TorchScript
s = 1 / (1 - p)
mod.weight.data = s * weight.masked_fill(mask, 0)
module.register_forward_pre_hook(_forward_pre_hook)
return module
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quant_noise.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
import torch
import torch.nn.functional as F
class LocationAttention(nn.Module):
"""
Attention-Based Models for Speech Recognition
https://arxiv.org/pdf/1506.07503.pdf
:param int encoder_dim: # projection-units of encoder
:param int decoder_dim: # units of decoder
:param int attn_dim: attention dimension
:param int conv_dim: # channels of attention convolution
:param int conv_kernel_size: filter size of attention convolution
"""
def __init__(
self,
attn_dim,
encoder_dim,
decoder_dim,
attn_state_kernel_size,
conv_dim,
conv_kernel_size,
scaling=2.0,
):
super(LocationAttention, self).__init__()
self.attn_dim = attn_dim
self.decoder_dim = decoder_dim
self.scaling = scaling
self.proj_enc = nn.Linear(encoder_dim, attn_dim)
self.proj_dec = nn.Linear(decoder_dim, attn_dim, bias=False)
self.proj_attn = nn.Linear(conv_dim, attn_dim, bias=False)
self.conv = nn.Conv1d(
attn_state_kernel_size,
conv_dim,
2 * conv_kernel_size + 1,
padding=conv_kernel_size,
bias=False,
)
self.proj_out = nn.Sequential(nn.Tanh(), nn.Linear(attn_dim, 1))
self.proj_enc_out = None # cache
def clear_cache(self):
self.proj_enc_out = None
def forward(self, encoder_out, encoder_padding_mask, decoder_h, attn_state):
"""
:param torch.Tensor encoder_out: padded encoder hidden state B x T x D
:param torch.Tensor encoder_padding_mask: encoder padding mask
:param torch.Tensor decoder_h: decoder hidden state B x D
:param torch.Tensor attn_prev: previous attention weight B x K x T
:return: attention weighted encoder state (B, D)
:rtype: torch.Tensor
:return: previous attention weights (B x T)
:rtype: torch.Tensor
"""
bsz, seq_len, _ = encoder_out.size()
if self.proj_enc_out is None:
self.proj_enc_out = self.proj_enc(encoder_out)
# B x K x T -> B x C x T
attn = self.conv(attn_state)
# B x C x T -> B x T x C -> B x T x D
attn = self.proj_attn(attn.transpose(1, 2))
if decoder_h is None:
decoder_h = encoder_out.new_zeros(bsz, self.decoder_dim)
dec_h = self.proj_dec(decoder_h).view(bsz, 1, self.attn_dim)
out = self.proj_out(attn + self.proj_enc_out + dec_h).squeeze(2)
out.masked_fill_(encoder_padding_mask, -float("inf"))
w = F.softmax(self.scaling * out, dim=1)
c = torch.sum(encoder_out * w.view(bsz, seq_len, 1), dim=1)
return c, w
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/location_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Layer norm done in fp32 (for fp16 training)
"""
import torch.nn as nn
import torch.nn.functional as F
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
output = F.group_norm(
input.float(),
self.num_groups,
self.weight.float() if self.weight is not None else None,
self.bias.float() if self.bias is not None else None,
self.eps,
)
return output.type_as(input)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/fp32_group_norm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.modules.fairseq_dropout import FairseqDropout
from .unfold import unfold1d
def DynamicConv(
input_size,
kernel_size=1,
padding_l=None,
num_heads=1,
weight_dropout=0.0,
weight_softmax=False,
renorm_padding=False,
bias=False,
conv_bias=False,
query_size=None,
in_proj=False,
):
if torch.cuda.is_available():
try:
from fairseq.modules.dynamicconv_layer import DynamicconvLayer
return DynamicconvLayer(
input_size,
kernel_size=kernel_size,
padding_l=padding_l,
num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax,
renorm_padding=renorm_padding,
bias=bias,
conv_bias=conv_bias,
query_size=query_size,
)
except ImportError as e:
print(e)
return DynamicConv1dTBC(
input_size,
kernel_size=kernel_size,
padding_l=padding_l,
num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax,
renorm_padding=renorm_padding,
bias=bias,
conv_bias=conv_bias,
query_size=query_size,
)
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
@with_incremental_state
class DynamicConv1dTBC(nn.Module):
"""Dynamic lightweight convolution taking T x B x C inputs
Args:
input_size: # of channels of the input
kernel_size: convolution channels
padding_l: padding to the left when using "same" padding
num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size)
weight_dropout: the drop rate of the DropConnect to drop the weight
weight_softmax: normalize the weight with softmax before the convolution
renorm_padding: re-normalize the filters to ignore the padded part (only the non-padding parts sum up to 1)
bias: use bias
conv_bias: bias of the convolution
query_size: specified when feeding a different input as the query
in_proj: project the input and generate the filter together
Shape:
Input: TxBxC, i.e. (timesteps, batch_size, input_size)
Output: TxBxC, i.e. (timesteps, batch_size, input_size)
Attributes:
weight: the learnable weights of the module of shape
`(num_heads, 1, kernel_size)`
bias: the learnable bias of the module of shape `(input_size)`
"""
def __init__(
self,
input_size,
kernel_size=1,
padding_l=None,
num_heads=1,
weight_dropout=0.0,
weight_softmax=False,
renorm_padding=False,
bias=False,
conv_bias=False,
query_size=None,
in_proj=False,
):
super().__init__()
self.input_size = input_size
self.query_size = input_size if query_size is None else query_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_dropout_module = FairseqDropout(
weight_dropout, module_name=self.__class__.__name__
)
self.weight_softmax = weight_softmax
self.renorm_padding = renorm_padding
if in_proj:
self.weight_linear = Linear(
self.input_size, self.input_size + num_heads * kernel_size * 1
)
else:
self.weight_linear = Linear(
self.query_size, num_heads * kernel_size * 1, bias=bias
)
if conv_bias:
self.conv_bias = nn.Parameter(torch.Tensor(input_size))
else:
self.conv_bias = None
self.reset_parameters()
@property
def in_proj(self):
return (
self.weight_linear.out_features
== self.input_size + self.num_heads * self.kernel_size
)
def reset_parameters(self):
self.weight_linear.reset_parameters()
if self.conv_bias is not None:
nn.init.constant_(self.conv_bias, 0.0)
def forward(self, x, incremental_state=None, query=None, unfold=None):
"""Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
args:
x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
incremental_state: A dict to keep the state
unfold: unfold the input or not. If not, we use the matrix trick instead
query: use the specified query to predict the conv filters
"""
unfold = (
x.size(0) > 512 if unfold is None else unfold
) # use unfold mode as default for long sequence to save memory
unfold = unfold or (incremental_state is not None)
assert query is None or not self.in_proj
if query is None:
query = x
if unfold:
output = self._forward_unfolded(x, incremental_state, query)
else:
output = self._forward_expanded(x, incremental_state, query)
if self.conv_bias is not None:
output = output + self.conv_bias.view(1, 1, -1)
return output
def _forward_unfolded(self, x, incremental_state, query):
"""The conventional implementation of convolutions.
Unfolding the input by having a window shifting to the right."""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
if self.in_proj:
proj = self.weight_linear(x)
x = proj.narrow(2, 0, self.input_size).contiguous()
weight = (
proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1)
)
else:
weight = self.weight_linear(query).view(T * B * H, -1)
# renorm_padding is only implemented in _forward_expanded
assert not self.renorm_padding or incremental_state is not None
if incremental_state is not None:
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(
incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :]
)
x_unfold = x_unfold.view(T * B * H, R, -1)
else:
padding_l = self.padding_l
if K > T and padding_l == K - 1:
weight = weight.narrow(1, K - T, T)
K, padding_l = T, T - 1
# unfold the input: T x B x C --> T' x B x C x K
x_unfold = unfold1d(x, K, padding_l, 0)
x_unfold = x_unfold.view(T * B * H, R, K)
if self.weight_softmax and not self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = weight.narrow(1, 0, K)
if incremental_state is not None:
weight = weight[:, -x_unfold.size(2) :]
K = weight.size(1)
if self.weight_softmax and self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = self.weight_dropout_module(weight, inplace=False)
output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1
output = output.view(T, B, C)
return output
def _forward_expanded(self, x, incremental_stat, query):
"""Turn the convolution filters into band matrices and do matrix multiplication.
This is faster when the sequence is short, but less memory efficient.
This is not used in the decoder during inference.
"""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
if self.in_proj:
proj = self.weight_linear(x)
x = proj.narrow(2, 0, self.input_size).contiguous()
weight = (
proj.narrow(2, self.input_size, H * K).contiguous().view(T * B * H, -1)
)
else:
weight = self.weight_linear(query).view(T * B * H, -1)
if not self.renorm_padding:
if self.weight_softmax:
weight = F.softmax(weight, dim=1)
weight = self.weight_dropout_module(weight, inplace=False)
weight = weight.narrow(1, 0, K).contiguous()
weight = weight.view(T, B * H, K).transpose(0, 1)
x = x.view(T, B * H, R).transpose(0, 1)
if self.weight_softmax and self.renorm_padding:
# turn the convolution filters into band matrices
weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf"))
weight_expanded.as_strided(
(B * H, T, K), (T * (T + K - 1), T + K, 1)
).copy_(weight)
weight_expanded = weight_expanded.narrow(2, self.padding_l, T)
# normalize the weight over valid positions like self-attention
weight_expanded = F.softmax(weight_expanded, dim=2)
weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False)
else:
P = self.padding_l
# For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length
if K > T and P == K - 1:
weight = weight.narrow(2, K - T, T)
K, P = T, T - 1
# turn the convolution filters into band matrices
weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False)
weight_expanded.as_strided(
(B * H, T, K), (T * (T + K - 1), T + K, 1)
).copy_(weight)
weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T
output = torch.bmm(weight_expanded, x)
output = output.transpose(0, 1).contiguous().view(T, B, C)
return output
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, "input_buffer")
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(
self, incremental_state, "input_buffer", new_buffer
)
def extra_repr(self):
s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}".format(
self.input_size,
self.kernel_size,
self.padding_l,
self.num_heads,
self.weight_softmax,
self.conv_bias is not None,
self.renorm_padding,
self.in_proj,
)
if self.query_size != self.input_size:
s += ", query_size={}".format(self.query_size)
if self.weight_dropout_module.p > 0.0:
s += ", weight_dropout={}".format(self.weight_dropout_module.p)
return s
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/dynamic_convolution.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import List, Optional
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
class FairseqDropout(nn.Module):
def __init__(self, p, module_name=None):
super().__init__()
self.p = p
self.module_name = module_name
self.apply_during_inference = False
def forward(self, x, inplace: bool = False):
if self.p > 0 and (self.training or self.apply_during_inference):
return F.dropout(x, p=self.p, training=True, inplace=inplace)
else:
return x
def make_generation_fast_(
self,
name: str,
retain_dropout: bool = False,
retain_dropout_modules: Optional[List[str]] = None,
**kwargs
):
if retain_dropout:
if retain_dropout_modules is not None and self.module_name is None:
logger.warning(
"Cannot enable dropout during inference for module {} "
"because module_name was not set".format(name)
)
elif (
retain_dropout_modules is None # if None, apply to all modules
or self.module_name in retain_dropout_modules
):
logger.info(
"Enabling dropout during inference for module: {}".format(name)
)
self.apply_during_inference = True
else:
logger.info("Disabling dropout for module: {}".format(name))
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/fairseq_dropout.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
class GumbelVectorQuantizer(nn.Module):
def __init__(
self,
dim,
num_vars,
temp,
groups,
combine_groups,
vq_dim,
time_first,
activation=nn.GELU(),
weight_proj_depth=1,
weight_proj_factor=1,
):
"""Vector quantization using gumbel softmax
Args:
dim: input dimension (channels)
num_vars: number of quantized vectors per group
temp: temperature for training. this should be a tuple of 3 elements: (start, stop, decay factor)
groups: number of groups for vector quantization
combine_groups: whether to use the vectors for all groups
vq_dim: dimensionality of the resulting quantized vector
time_first: if true, expect input in BxTxC format, otherwise in BxCxT
activation: what activation to use (should be a module). this is only used if weight_proj_depth is > 1
weight_proj_depth: number of layers (with activation in between) to project input before computing logits
weight_proj_factor: this is used only if weight_proj_depth is > 1. scales the inner dimensionality of
projections by this factor
"""
super().__init__()
self.groups = groups
self.combine_groups = combine_groups
self.input_dim = dim
self.num_vars = num_vars
self.time_first = time_first
assert (
vq_dim % groups == 0
), f"dim {vq_dim} must be divisible by groups {groups} for concatenation"
var_dim = vq_dim // groups
num_groups = groups if not combine_groups else 1
self.vars = nn.Parameter(torch.FloatTensor(1, num_groups * num_vars, var_dim))
nn.init.uniform_(self.vars)
if weight_proj_depth > 1:
def block(input_dim, output_dim):
return nn.Sequential(nn.Linear(input_dim, output_dim), activation)
inner_dim = self.input_dim * weight_proj_factor
self.weight_proj = nn.Sequential(
*[
block(self.input_dim if i == 0 else inner_dim, inner_dim)
for i in range(weight_proj_depth - 1)
],
nn.Linear(inner_dim, groups * num_vars),
)
else:
self.weight_proj = nn.Linear(self.input_dim, groups * num_vars)
nn.init.normal_(self.weight_proj.weight, mean=0, std=1)
nn.init.zeros_(self.weight_proj.bias)
if isinstance(temp, str):
import ast
temp = ast.literal_eval(temp)
assert len(temp) == 3, f"{temp}, {len(temp)}"
self.max_temp, self.min_temp, self.temp_decay = temp
self.curr_temp = self.max_temp
self.codebook_indices = None
def set_num_updates(self, num_updates):
self.curr_temp = max(
self.max_temp * self.temp_decay ** num_updates, self.min_temp
)
def get_codebook_indices(self):
if self.codebook_indices is None:
from itertools import product
p = [range(self.num_vars)] * self.groups
inds = list(product(*p))
self.codebook_indices = torch.tensor(
inds, dtype=torch.long, device=self.vars.device
).flatten()
if not self.combine_groups:
self.codebook_indices = self.codebook_indices.view(
self.num_vars ** self.groups, -1
)
for b in range(1, self.groups):
self.codebook_indices[:, b] += self.num_vars * b
self.codebook_indices = self.codebook_indices.flatten()
return self.codebook_indices
def codebook(self):
indices = self.get_codebook_indices()
return (
self.vars.squeeze(0)
.index_select(0, indices)
.view(self.num_vars ** self.groups, -1)
)
def sample_from_codebook(self, b, n):
indices = self.get_codebook_indices()
indices = indices.view(-1, self.groups)
cb_size = indices.size(0)
assert (
n < cb_size
), f"sample size {n} is greater than size of codebook {cb_size}"
sample_idx = torch.randint(low=0, high=cb_size, size=(b * n,))
indices = indices[sample_idx]
z = self.vars.squeeze(0).index_select(0, indices.flatten()).view(b, n, -1)
return z
def to_codebook_index(self, indices):
res = indices.new_full(indices.shape[:-1], 0)
for i in range(self.groups):
exponent = self.groups - i - 1
res += indices[..., i] * (self.num_vars ** exponent)
return res
def forward_idx(self, x):
res = self.forward(x, produce_targets=True)
return res["x"], res["targets"]
def forward(self, x, produce_targets=False):
result = {"num_vars": self.num_vars * self.groups}
if not self.time_first:
x = x.transpose(1, 2)
bsz, tsz, fsz = x.shape
x = x.reshape(-1, fsz)
x = self.weight_proj(x)
x = x.view(bsz * tsz * self.groups, -1)
_, k = x.max(-1)
hard_x = (
x.new_zeros(*x.shape)
.scatter_(-1, k.view(-1, 1), 1.0)
.view(bsz * tsz, self.groups, -1)
)
hard_probs = torch.mean(hard_x.float(), dim=0)
result["code_perplexity"] = torch.exp(
-torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1)
).sum()
avg_probs = torch.softmax(
x.view(bsz * tsz, self.groups, -1).float(), dim=-1
).mean(dim=0)
result["prob_perplexity"] = torch.exp(
-torch.sum(avg_probs * torch.log(avg_probs + 1e-7), dim=-1)
).sum()
result["temp"] = self.curr_temp
if self.training:
x = F.gumbel_softmax(x.float(), tau=self.curr_temp, hard=True).type_as(x)
else:
x = hard_x
x = x.view(bsz * tsz, -1)
vars = self.vars
if self.combine_groups:
vars = vars.repeat(1, self.groups, 1)
if produce_targets:
result["targets"] = (
x.view(bsz * tsz * self.groups, -1)
.argmax(dim=-1)
.view(bsz, tsz, self.groups)
.detach()
)
x = x.unsqueeze(-1) * vars
x = x.view(bsz * tsz, self.groups, self.num_vars, -1)
x = x.sum(-2)
x = x.view(bsz, tsz, -1)
if not self.time_first:
x = x.transpose(1, 2) # BTC -> BCT
result["x"] = x
return result
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/gumbel_vector_quantizer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from fairseq.modules import Fp32GroupNorm
class KmeansVectorQuantizer(nn.Module):
def __init__(
self, dim, num_vars, groups, combine_groups, vq_dim, time_first, gamma=0.25
):
"""Vector quantization using straight pass-through estimator (i.e. kmeans)
Args:
dim: input dimension (channels)
num_vars: number of quantized vectors per group
groups: number of groups for vector quantization
combine_groups: whether to use the vectors for all groups
vq_dim: dimensionality of the resulting quantized vector
time_first: if true, expect input in BxTxC format, otherwise in BxCxT
gamma: commitment loss coefficient
"""
super().__init__()
self.groups = groups
self.combine_groups = combine_groups
self.input_dim = dim
self.num_vars = num_vars
self.vq_dim = vq_dim
self.time_first = time_first
assert (
vq_dim % groups == 0
), f"dim {vq_dim} must be divisible by groups {groups} for concatenation"
self.var_dim = vq_dim // groups
num_groups = groups if not combine_groups else 1
self.embedding = nn.Parameter(
0.01 * torch.randn(num_vars, num_groups, self.var_dim)
)
self.projection = nn.Sequential(
nn.Conv1d(dim, dim, kernel_size=1, groups=groups, bias=False),
Fp32GroupNorm(groups, dim),
)
self.gamma = gamma
self.mse_mean = nn.MSELoss(reduction="mean")
def _pass_grad(self, x, y):
"""Manually set gradient for backward pass.
for y = f(x), ensure that during the backward pass,
dL/dy = dL/dx regardless of f(x).
Returns:
y, with the gradient forced to be dL/dy = dL/dx.
"""
return y.detach() + (x - x.detach())
@property
def expand_embedding(self):
if self.combine_groups:
return self.embedding.expand(self.num_vars, self.groups, self.var_dim)
return self.embedding
def forward_idx(self, x):
res = self.forward(x, produce_targets=True)
return res["x"], res["targets"]
def forward(self, x, produce_targets=False):
result = {"num_vars": self.num_vars}
if self.time_first:
x = x.transpose(1, 2)
bsz, fsz, tsz = x.shape
ze = self.projection(x)
ze_ = ze.view(bsz, self.groups, self.var_dim, tsz).permute(0, 3, 1, 2)
d = (
(ze_.unsqueeze(0) - self.expand_embedding.unsqueeze(1).unsqueeze(1))
.view(self.num_vars, bsz, tsz, self.groups, -1)
.norm(dim=-1, p=2)
)
idx = d.argmin(dim=0)
zq = (
torch.stack(
[
self.expand_embedding[idx[..., group], group]
for group in range(self.groups)
],
dim=-2,
)
.view(bsz, tsz, self.groups * self.var_dim)
.permute(0, 2, 1)
)
assert ze.shape == zq.shape, (ze.shape, zq.shape)
x = self._pass_grad(ze, zq)
hard_x = (
idx.new_zeros(bsz * tsz * self.groups, self.num_vars)
.scatter_(-1, idx.view(-1, 1), 1.0)
.view(bsz * tsz, self.groups, -1)
)
hard_probs = torch.mean(hard_x.float(), dim=0)
result["code_perplexity"] = torch.exp(
-torch.sum(hard_probs * torch.log(hard_probs + 1e-7), dim=-1)
).sum()
if produce_targets:
result["targets"] = idx
if self.time_first:
x = x.transpose(1, 2) # BCT -> BTC
result["x"] = x
ze = ze.float()
zq = zq.float()
latent_loss = self.mse_mean(zq, ze.detach())
commitment_loss = self.mse_mean(ze, zq.detach())
result["kmeans_loss"] = latent_loss + self.gamma * commitment_loss
return result
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/kmeans_vector_quantizer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
class LSTMCellWithZoneOut(nn.Module):
"""
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
https://arxiv.org/abs/1606.01305
"""
def __init__(
self, prob: float, input_size: int, hidden_size: int, bias: bool = True
):
super(LSTMCellWithZoneOut, self).__init__()
self.lstm_cell = nn.LSTMCell(input_size, hidden_size, bias=bias)
self.prob = prob
if prob > 1.0 or prob < 0.0:
raise ValueError(
"zoneout probability must be in the range from " "0.0 to 1.0."
)
def zoneout(self, h, next_h, prob):
if isinstance(h, tuple):
return tuple([self.zoneout(h[i], next_h[i], prob) for i in range(len(h))])
if self.training:
mask = h.new_zeros(*h.size()).bernoulli_(prob)
return mask * h + (1 - mask) * next_h
return prob * h + (1 - prob) * next_h
def forward(self, x, h):
return self.zoneout(h, self.lstm_cell(x, h), self.prob)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/lstm_cell_with_zoneout.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import List
import torch
from fairseq.modules.quant_noise import quant_noise
from torch import nn
class AdaptiveInput(nn.Module):
def __init__(
self,
vocab_size: int,
padding_idx: int,
initial_dim: int,
factor: float,
output_dim: int,
cutoff: List[int],
q_noise: float = 0,
qn_block_size: int = 8,
):
super().__init__()
if vocab_size > cutoff[-1]:
cutoff = cutoff + [vocab_size]
else:
assert (
vocab_size == cutoff[-1]
), "cannot specify cutoff larger than vocab size"
self.cutoff = cutoff
self.embedding_dim = output_dim
self.padding_idx = padding_idx
self.embeddings = nn.ModuleList()
for i in range(len(self.cutoff)):
prev = self.cutoff[i - 1] if i > 0 else 0
size = self.cutoff[i] - prev
dim = int(initial_dim // (factor ** i))
seq = nn.Sequential(
nn.Embedding(size, dim, self.padding_idx),
quant_noise(
nn.Linear(dim, output_dim, bias=False), q_noise, qn_block_size
),
)
self.embeddings.append(seq)
self.padding_idx = None
self.padding_idx = padding_idx
def init_weights(m):
if isinstance(m, nn.Embedding):
nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
elif hasattr(m, "weight"):
nn.init.xavier_uniform_(m.weight)
self.apply(init_weights)
self.register_buffer("_float_tensor", torch.FloatTensor(1))
def weights_for_band(self, band: int):
return self.embeddings[band][0].weight, self.embeddings[band][1].weight
def forward(self, input: torch.Tensor):
result = self._float_tensor.new(input.shape + (self.embedding_dim,))
for i in range(len(self.cutoff)):
mask = input.lt(self.cutoff[i])
if i > 0:
mask.mul_(input.ge(self.cutoff[i - 1]))
chunk_input = input[mask] - self.cutoff[i - 1]
else:
chunk_input = input[mask]
if mask.any():
result[mask] = self.embeddings[i](chunk_input)
return result
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/adaptive_input.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.unfold import unfold1d
def LightweightConv(
input_size,
kernel_size=1,
padding_l=None,
num_heads=1,
weight_dropout=0.0,
weight_softmax=False,
bias=False,
):
if torch.cuda.is_available():
try:
from fairseq.modules.lightconv_layer import LightconvLayer
return LightconvLayer(
input_size,
kernel_size=kernel_size,
padding_l=padding_l,
num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax,
bias=bias,
)
except ImportError as e:
print(e)
return LightweightConv1dTBC(
input_size,
kernel_size=kernel_size,
padding_l=padding_l,
num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax,
bias=bias,
)
class LightweightConv1d(nn.Module):
"""Lightweight Convolution assuming the input is BxCxT
This is just an example that explains LightConv clearer than the TBC version.
We don't use this module in the model.
Args:
input_size: # of channels of the input and output
kernel_size: convolution channels
padding: padding
num_heads: number of heads used. The weight is of shape
`(num_heads, 1, kernel_size)`
weight_softmax: normalize the weight with softmax before the convolution
Shape:
Input: BxCxT, i.e. (batch_size, input_size, timesteps)
Output: BxCxT, i.e. (batch_size, input_size, timesteps)
Attributes:
weight: the learnable weights of the module of shape
`(num_heads, 1, kernel_size)`
bias: the learnable bias of the module of shape `(input_size)`
"""
def __init__(
self,
input_size,
kernel_size=1,
padding=0,
num_heads=1,
weight_softmax=False,
bias=False,
weight_dropout=0.0,
):
super().__init__()
self.input_size = input_size
self.kernel_size = kernel_size
self.num_heads = num_heads
self.padding = padding
self.weight_softmax = weight_softmax
self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(input_size))
else:
self.bias = None
self.weight_dropout_module = FairseqDropout(
weight_dropout, module_name=self.__class__.__name__
)
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight)
if self.bias is not None:
nn.init.constant_(self.bias, 0.0)
def forward(self, input):
"""
input size: B x C x T
output size: B x C x T
"""
B, C, T = input.size()
H = self.num_heads
weight = self.weight
if self.weight_softmax:
weight = F.softmax(weight, dim=-1)
weight = self.weight_dropout_module(weight)
# Merge every C/H entries into the batch dimension (C = self.input_size)
# B x C x T -> (B * C/H) x H x T
# One can also expand the weight to C x 1 x K by a factor of C/H
# and do not reshape the input instead, which is slow though
input = input.view(-1, H, T)
output = F.conv1d(input, weight, padding=self.padding, groups=self.num_heads)
output = output.view(B, C, T)
if self.bias is not None:
output = output + self.bias.view(1, -1, 1)
return output
@with_incremental_state
class LightweightConv1dTBC(nn.Module):
"""Lightweight Convolution assuming the input is TxBxC
Args:
input_size: # of channels of the input
kernel_size: convolution channels
padding_l: padding to the left when using "same" padding
num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size)
weight_dropout: the drop rate of the DropConnect to drop the weight
weight_softmax: normalize the weight with softmax before the convolution
bias: use bias
Shape:
Input: TxBxC, i.e. (timesteps, batch_size, input_size)
Output: TxBxC, i.e. (timesteps, batch_size, input_size)
Attributes:
weight: the learnable weights of the module of shape
`(num_heads, 1, kernel_size)`
bias: the learnable bias of the module of shape `(input_size)`
"""
def __init__(
self,
input_size,
kernel_size=1,
padding_l=None,
num_heads=1,
weight_dropout=0.0,
weight_softmax=False,
bias=False,
):
super().__init__()
self.input_size = input_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_dropout_module = FairseqDropout(
weight_dropout, module_name=self.__class__.__name__
)
self.weight_softmax = weight_softmax
self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(input_size))
else:
self.bias = None
self.reset_parameters()
self.onnx_trace = False
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight)
if self.bias is not None:
nn.init.constant_(self.bias, 0.0)
def forward(self, x, incremental_state=None, unfold=False):
"""Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
args:
x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
incremental_state: A dict to keep the state
unfold: unfold the input or not. If not, we use the matrix trick instead
"""
unfold = unfold or (incremental_state is not None)
if unfold:
output = self._forward_unfolded(x, incremental_state)
else:
output = self._forward_expanded(x, incremental_state)
if self.bias is not None:
output = output + self.bias.view(1, 1, -1)
return output
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def _forward_unfolded(self, x, incremental_state):
"""The conventional implementation of convolutions.
Unfolding the input by having a window shifting to the right."""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight.view(H, K)
if incremental_state is not None:
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(
incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :]
)
x_unfold = x_unfold.view(T * B * H, R, -1)
else:
# unfold the input: T x B x C --> T' x B x C x K
x_unfold = unfold1d(x, self.kernel_size, self.padding_l, 0)
x_unfold = x_unfold.view(T * B * H, R, K)
if self.weight_softmax:
weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as(
weight
)
if incremental_state is not None:
weight = weight[:, -x_unfold.size(2) :]
K = weight.size(1)
weight = (
weight.view(1, H, K).expand(T * B, H, K).contiguous().view(T * B * H, K, 1)
)
weight = self.weight_dropout_module(weight)
output = torch.bmm(x_unfold, weight) # T*B*H x R x 1
output = output.view(T, B, C)
return output
def _forward_expanded(self, x, incremental_state):
"""Turn the convolution filters into band matrices and do matrix multiplication.
This is faster when the sequence is short, but less memory efficient.
This is not used in the decoder during inference.
"""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight.view(H, K)
if self.weight_softmax:
weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as(
weight
)
weight = weight.view(1, H, K).expand(T * B, H, K).contiguous()
weight = weight.view(T, B * H, K).transpose(0, 1)
x = x.view(T, B * H, R).transpose(0, 1)
P = self.padding_l
if K > T and P == K - 1:
weight = weight.narrow(2, K - T, T)
K, P = T, T - 1
# turn the convolution filters into band matrices
weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False)
weight_expanded.as_strided((B * H, T, K), (T * (T + K - 1), T + K, 1)).copy_(
weight
)
weight_expanded = weight_expanded.narrow(2, P, T)
weight_expanded = self.weight_dropout_module(weight_expanded)
output = torch.bmm(weight_expanded, x)
output = output.transpose(0, 1).contiguous().view(T, B, C)
return output
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, "input_buffer")
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(
self, incremental_state, "input_buffer", new_buffer
)
def extra_repr(self):
s = "{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, bias={}".format(
self.input_size,
self.kernel_size,
self.padding_l,
self.num_heads,
self.weight_softmax,
self.bias is not None,
)
if self.weight_dropout_module.p > 0.0:
s += ", weight_dropout={}".format(self.weight_dropout_module.p)
return s
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/lightweight_convolution.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
class ScalarBias(torch.autograd.Function):
"""
Adds a vector of scalars, used in self-attention mechanism to allow
the model to optionally attend to this vector instead of the past
"""
@staticmethod
def forward(ctx, input, dim, bias_init):
size = list(input.size())
size[dim] += 1
output = input.new(*size).fill_(bias_init)
output.narrow(dim, 1, size[dim] - 1).copy_(input)
ctx.dim = dim
return output
@staticmethod
def backward(ctx, grad):
return grad.narrow(ctx.dim, 1, grad.size(ctx.dim) - 1), None, None
def scalar_bias(input, dim, bias_init=0):
return ScalarBias.apply(input, dim, bias_init)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/scalar_bias.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from .learned_positional_embedding import LearnedPositionalEmbedding
from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
def PositionalEmbedding(
num_embeddings: int,
embedding_dim: int,
padding_idx: int,
learned: bool = False,
):
if learned:
# if padding_idx is specified then offset the embedding ids by
# this index and adjust num_embeddings appropriately
# TODO: The right place for this offset would be inside
# LearnedPositionalEmbedding. Move this there for a cleaner implementation.
if padding_idx is not None:
num_embeddings = num_embeddings + padding_idx + 1
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
if padding_idx is not None:
nn.init.constant_(m.weight[padding_idx], 0)
else:
m = SinusoidalPositionalEmbedding(
embedding_dim,
padding_idx,
init_size=num_embeddings + padding_idx + 1,
)
return m
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/positional_embedding.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
from .multihead_attention import MultiheadAttention
class SparseMultiheadAttention(MultiheadAttention):
"""Sparse Multi-Headed Attention.
"Generating Long Sequences with Sparse Transformers". Implements
fixed factorized self attention, where l=stride and c=expressivity.
A(1) includes all words in the stride window and A(2) takes a summary of c
words from the end of each stride window.
If is_bidirectional=False, we do not include any words past the current word,
as in the paper.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
stride=32,
expressivity=8,
is_bidirectional=True,
):
super().__init__(
embed_dim,
num_heads,
kdim,
vdim,
dropout,
bias,
add_bias_kv,
add_zero_attn,
self_attention,
encoder_decoder_attention,
)
self.is_bidirectional = is_bidirectional
self.stride = stride
self.expressivity = expressivity
assert self.stride > 0 and self.stride >= self.expressivity
# Used for Ai(2) calculations - beginning of [l-c, l] range
def compute_checkpoint(self, word_index):
if word_index % self.stride == 0 and word_index != 0:
checkpoint_index = word_index - self.expressivity
else:
checkpoint_index = (
math.floor(word_index / self.stride) * self.stride
+ self.stride
- self.expressivity
)
return checkpoint_index
# Computes Ai(2)
def compute_subset_summaries(self, absolute_max):
checkpoint_index = self.compute_checkpoint(0)
subset_two = set()
while checkpoint_index <= absolute_max - 1:
summary = set(
range(
checkpoint_index,
min(checkpoint_index + self.expressivity + 1, absolute_max),
)
)
subset_two = subset_two.union(summary)
checkpoint_index = self.compute_checkpoint(checkpoint_index + self.stride)
return subset_two
# Sparse Transformer Fixed Attention Pattern: https://arxiv.org/pdf/1904.10509.pdf
def compute_fixed_attention_subset(self, word_index, tgt_len):
# +1s account for range function; [min, max) -> [min, max]
if not self.is_bidirectional:
absolute_max = word_index + 1
else:
absolute_max = tgt_len
# Subset 1 - whole window
rounded_index = (
math.floor((word_index + self.stride) / self.stride) * self.stride
)
if word_index % self.stride == 0 and word_index != 0:
subset_one = set(
range(word_index - self.stride, min(absolute_max, word_index + 1))
)
else:
subset_one = set(
range(
max(0, rounded_index - self.stride),
min(absolute_max, rounded_index + 1),
)
)
# Subset 2 - summary per window
# If bidirectional, subset 2 is the same for every index
subset_two = set()
if not self.is_bidirectional:
subset_two = self.compute_subset_summaries(absolute_max)
return subset_one.union(subset_two)
# Compute sparse mask - if bidirectional, can pre-compute and store
def buffered_sparse_mask(self, tensor, tgt_len, src_len):
assert tgt_len > self.stride
sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float("-inf"))
# If bidirectional, subset 2 is the same for every index
subset_summaries = set()
if self.is_bidirectional:
subset_summaries = self.compute_subset_summaries(tgt_len)
for i in range(tgt_len):
fixed_attention_subset = self.compute_fixed_attention_subset(i, tgt_len)
fixed_attention_subset = fixed_attention_subset.union(subset_summaries)
included_word_indices = torch.LongTensor(list(fixed_attention_subset))
sparse_mask[i].index_fill_(0, included_word_indices, 0)
return sparse_mask.type_as(tensor)
def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
sparse_mask = self.buffered_sparse_mask(attn_weights, tgt_len, src_len)
sparse_mask = sparse_mask.unsqueeze(0).expand(
bsz * self.num_heads, tgt_len, src_len
)
attn_weights += sparse_mask
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/sparse_multihead_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
import math
import torch
class PositionalEncoding(nn.Module):
"""Positional encoding.
Args:
d_model: Embedding dimension.
dropout_rate: Dropout rate.
max_len: Maximum input length.
reverse: Whether to reverse the input position.
"""
def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.reverse = reverse
self.xscale = math.sqrt(self.d_model)
self.dropout = nn.Dropout(p=dropout_rate)
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
if self.pe.size(1) >= x.size(1):
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = torch.zeros(x.size(1), self.d_model)
if self.reverse:
position = torch.arange(
x.size(1) - 1, -1, -1.0, dtype=torch.float32
).unsqueeze(1)
else:
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.float32)
* -(math.log(10000.0) / self.d_model)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor):
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor B X T X C
Returns:
torch.Tensor: Encoded tensor B X T X C
"""
self.extend_pe(x)
x = x * self.xscale + self.pe[:, : x.size(1)]
return self.dropout(x)
class RelPositionalEncoding(nn.Module):
"""Relative positional encoding module (new implementation).
Args:
d_model: Embedding dimension.
dropout_rate: Dropout rate.
max_len: Maximum input length.
"""
def __init__(self, max_len, d_model):
"""Construct an PositionalEncoding object."""
super(RelPositionalEncoding, self).__init__()
self.d_model = d_model
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
# self.pe contains both positive and negative parts
# the length of self.pe is 2 * input_len - 1
if self.pe.size(1) >= x.size(1) * 2 - 1:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
# Suppose `i` means to the position of query vecotr and `j` means the
# position of key vector. We use position relative positions when keys
# are to the left (i>j) and negative relative positions otherwise (i<j).
pe_positive = torch.zeros(x.size(1), self.d_model)
pe_negative = torch.zeros(x.size(1), self.d_model)
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.float32)
* -(math.log(10000.0) / self.d_model)
)
pe_positive[:, 0::2] = torch.sin(position * div_term)
pe_positive[:, 1::2] = torch.cos(position * div_term)
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
# Reserve the order of positive indices and concat both positive and
# negative indices. This is used to support the shifting trick
# as in https://arxiv.org/abs/1901.02860
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
pe_negative = pe_negative[1:].unsqueeze(0)
pe = torch.cat([pe_positive, pe_negative], dim=1)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor):
"""Add positional encoding.
Args:
x : Input tensor T X B X C.
Returns:
torch.Tensor: Encoded tensor T X B X C.
"""
x = x.transpose(0, 1) # Change TBC to BTC
self.extend_pe(x)
pos_emb = self.pe[
:,
self.pe.size(1) // 2 - x.size(1) + 1 : self.pe.size(1) // 2 + x.size(1),
]
pos_emb = pos_emb.transpose(0, 1) # change to TBC
return pos_emb
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/positional_encoding.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from typing import Optional
from fairseq.modules import (
LayerNorm,
MultiheadAttention,
ESPNETMultiHeadedAttention,
RelPositionMultiHeadedAttention,
RotaryPositionMultiHeadedAttention,
)
from fairseq.utils import get_activation_fn
class ConvolutionModule(torch.nn.Module):
"""Convolution block used in the conformer block"""
def __init__(
self,
embed_dim,
channels,
depthwise_kernel_size,
dropout,
activation_fn="swish",
bias=False,
export=False,
):
"""
Args:
embed_dim: Embedding dimension
channels: Number of channels in depthwise conv layers
depthwise_kernel_size: Depthwise conv layer kernel size
dropout: dropout value
activation_fn: Activation function to use after depthwise convolution kernel
bias: If bias should be added to conv layers
export: If layernorm should be exported to jit
"""
super(ConvolutionModule, self).__init__()
assert (
depthwise_kernel_size - 1
) % 2 == 0, "kernel_size should be a odd number for 'SAME' padding"
self.layer_norm = LayerNorm(embed_dim, export=export)
self.pointwise_conv1 = torch.nn.Conv1d(
embed_dim,
2 * channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
self.glu = torch.nn.GLU(dim=1)
self.depthwise_conv = torch.nn.Conv1d(
channels,
channels,
depthwise_kernel_size,
stride=1,
padding=(depthwise_kernel_size - 1) // 2,
groups=channels,
bias=bias,
)
self.batch_norm = torch.nn.BatchNorm1d(channels)
self.activation = get_activation_fn(activation_fn)(channels)
self.pointwise_conv2 = torch.nn.Conv1d(
channels,
embed_dim,
kernel_size=1,
stride=1,
padding=0,
bias=bias,
)
self.dropout = torch.nn.Dropout(dropout)
def forward(self, x):
"""
Args:
x: Input of shape B X T X C
Returns:
Tensor of shape B X T X C
"""
x = self.layer_norm(x)
# exchange the temporal dimension and the feature dimension
x = x.transpose(1, 2)
# GLU mechanism
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
x = self.glu(x) # (batch, channel, dim)
# 1D Depthwise Conv
x = self.depthwise_conv(x)
x = self.batch_norm(x)
x = self.activation(x)
x = self.pointwise_conv2(x)
x = self.dropout(x)
return x.transpose(1, 2)
class FeedForwardModule(torch.nn.Module):
"""Positionwise feed forward layer used in conformer"""
def __init__(
self,
input_feat,
hidden_units,
dropout1,
dropout2,
activation_fn="swish",
bias=True,
):
"""
Args:
input_feat: Input feature dimension
hidden_units: Hidden unit dimension
dropout1: dropout value for layer1
dropout2: dropout value for layer2
activation_fn: Name of activation function
bias: If linear layers should have bias
"""
super(FeedForwardModule, self).__init__()
self.layer_norm = LayerNorm(input_feat)
self.w_1 = torch.nn.Linear(input_feat, hidden_units, bias=bias)
self.w_2 = torch.nn.Linear(hidden_units, input_feat, bias=bias)
self.dropout1 = torch.nn.Dropout(dropout1)
self.dropout2 = torch.nn.Dropout(dropout2)
self.activation = get_activation_fn(activation_fn)(hidden_units)
def forward(self, x):
"""
Args:
x: Input Tensor of shape T X B X C
Returns:
Tensor of shape T X B X C
"""
x = self.layer_norm(x)
x = self.w_1(x)
x = self.activation(x)
x = self.dropout1(x)
x = self.w_2(x)
return self.dropout2(x)
class ConformerEncoderLayer(torch.nn.Module):
"""Conformer block based on https://arxiv.org/abs/2005.08100. We currently don't support relative positional encoding in MHA"""
def __init__(
self,
embed_dim,
ffn_embed_dim,
attention_heads,
dropout,
use_fp16,
depthwise_conv_kernel_size=31,
activation_fn="swish",
attn_type=None,
pos_enc_type="abs",
):
"""
Args:
embed_dim: Input embedding dimension
ffn_embed_dim: FFN layer dimension
attention_heads: Number of attention heads in MHA
dropout: dropout value
depthwise_conv_kernel_size: Size of kernel in depthwise conv layer in convolution module
activation_fn: Activation function name to use in convulation block and feed forward block
attn_type: MHA implementation from ESPNET vs fairseq
pos_enc_type: Positional encoding type - abs, rope, rel_pos
"""
self.pos_enc_type = pos_enc_type
super(ConformerEncoderLayer, self).__init__()
self.ffn1 = FeedForwardModule(
embed_dim,
ffn_embed_dim,
dropout,
dropout,
)
self.self_attn_layer_norm = LayerNorm(embed_dim, export=False)
self.self_attn_dropout = torch.nn.Dropout(dropout)
if attn_type == "espnet":
if self.pos_enc_type == "rel_pos":
self.self_attn = RelPositionMultiHeadedAttention(
embed_dim,
attention_heads,
dropout=dropout,
)
elif self.pos_enc_type == "rope":
self.self_attn = RotaryPositionMultiHeadedAttention(
embed_dim, attention_heads, dropout=dropout, precision=use_fp16
)
elif self.pos_enc_type == "abs":
self.self_attn = ESPNETMultiHeadedAttention(
embed_dim,
attention_heads,
dropout=dropout,
)
else:
raise Exception(f"Unsupported attention type {self.pos_enc_type}")
else:
# Default to fairseq MHA
self.self_attn = MultiheadAttention(
embed_dim,
attention_heads,
dropout=dropout,
)
self.conv_module = ConvolutionModule(
embed_dim=embed_dim,
channels=embed_dim,
depthwise_kernel_size=depthwise_conv_kernel_size,
dropout=dropout,
activation_fn=activation_fn,
)
self.ffn2 = FeedForwardModule(
embed_dim,
ffn_embed_dim,
dropout,
dropout,
activation_fn=activation_fn,
)
self.final_layer_norm = LayerNorm(embed_dim, export=False)
def forward(
self,
x,
encoder_padding_mask: Optional[torch.Tensor],
position_emb: Optional[torch.Tensor] = None,
):
"""
Args:
x: Tensor of shape T X B X C
encoder_padding_mask: Optional mask tensor
positions:
Returns:
Tensor of shape T X B X C
"""
residual = x
x = self.ffn1(x)
x = x * 0.5 + residual
residual = x
x = self.self_attn_layer_norm(x)
if self.pos_enc_type == "rel_pos":
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
pos_emb=position_emb,
need_weights=False,
)
else:
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=encoder_padding_mask,
need_weights=False,
)
x = self.self_attn_dropout(x)
x = x + residual
residual = x
# TBC to BTC
x = x.transpose(0, 1)
x = self.conv_module(x)
# BTC to TBC
x = x.transpose(0, 1)
x = residual + x
residual = x
x = self.ffn2(x)
x = x * 0.5 + residual
x = self.final_layer_norm(x)
return x, attn
class ConformerWav2Vec2EncoderLayer(ConformerEncoderLayer):
"""Encoder layer for Wav2vec2 encoder"""
def forward(
self,
x: torch.Tensor,
self_attn_mask: torch.Tensor = None,
self_attn_padding_mask: torch.Tensor = None,
need_weights: bool = False,
att_args=None,
position_emb=None,
):
return super().forward(x, self_attn_padding_mask, position_emb)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/conformer_layer.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Multi-Head Attention layer definition."""
import math
import torch
from torch import nn
from fairseq.modules.rotary_positional_embedding import (
RotaryPositionalEmbedding,
apply_rotary_pos_emb,
)
class ESPNETMultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head: The number of heads.
n_feat: The number of features.
dropout: Dropout rate.
"""
def __init__(self, n_feat, n_head, dropout):
"""Construct an MultiHeadedAttention object."""
super(ESPNETMultiHeadedAttention, self).__init__()
assert n_feat % n_head == 0
# We assume d_v always equals d_k
self.d_k = n_feat // n_head
self.h = n_head
self.linear_q = nn.Linear(n_feat, n_feat)
self.linear_k = nn.Linear(n_feat, n_feat)
self.linear_v = nn.Linear(n_feat, n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
def forward_qkv(self, query, key, value, **kwargs):
"""Transform query, key and value.
Args:
query: Query tensor B X T1 X C
key: Key tensor B X T2 X C
value: Value tensor B X T2 X C
Returns:
torch.Tensor: Transformed query tensor B X n_head X T1 X d_k
torch.Tensor: Transformed key tensor B X n_head X T2 X d_k
torch.Tensor: Transformed value tensor B X n_head X T2 X d_k
"""
n_batch = query.size(0)
q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
q = q.transpose(1, 2) # (batch, head, time1, d_k)
k = k.transpose(1, 2) # (batch, head, time2, d_k)
v = v.transpose(1, 2) # (batch, head, time2, d_k)
return q, k, v
def forward_attention(self, value, scores, mask):
"""Compute attention context vector.
Args:
value: Transformed value B X n_head X T2 X d_k.
scores: Attention score B X n_head X T1 X T2
mask: Mask T2 X B
Returns:
torch.Tensor: Transformed value B X T1 X d_model
weighted by the attention score B X T1 X T2
"""
n_batch = value.size(0)
if mask is not None:
scores = scores.masked_fill(
mask.unsqueeze(1).unsqueeze(2).to(bool),
float("-inf"), # (batch, head, time1, time2)
)
self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
else:
self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
p_attn = self.dropout(self.attn)
x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
x = (
x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
) # (batch, time1, d_model)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, query, key, value, key_padding_mask=None, **kwargs):
"""Compute scaled dot product attention.
Args:
query (torch.Tensor): Query tensor T X B X C
key (torch.Tensor): Key tensor T X B X C
value (torch.Tensor): Value tensor T X B X C
mask (torch.Tensor): Mask tensor T X B
Returns:
torch.Tensor: Output tensor T X B X D.
"""
query = query.transpose(0, 1)
key = key.transpose(0, 1)
value = value.transpose(0, 1)
q, k, v = self.forward_qkv(query, key, value)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
scores = self.forward_attention(v, scores, key_padding_mask)
scores = scores.transpose(0, 1)
return scores, None
class RelPositionMultiHeadedAttention(ESPNETMultiHeadedAttention):
"""Multi-Head Attention layer with relative position encoding.
Paper: https://arxiv.org/abs/1901.02860
Args:
n_head: The number of heads.
n_feat: The number of features.
dropout: Dropout rate.
zero_triu: Whether to zero the upper triangular part of attention matrix.
"""
def __init__(self, n_feat, n_head, dropout, zero_triu=False):
"""Construct an RelPositionMultiHeadedAttention object."""
super().__init__(n_feat, n_head, dropout)
self.zero_triu = zero_triu
# linear transformation for positional encoding
self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
# these two learnable bias are used in matrix c and matrix d
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
torch.nn.init.xavier_uniform_(self.pos_bias_u)
torch.nn.init.xavier_uniform_(self.pos_bias_v)
def rel_shift(self, x):
"""Compute relative positional encoding.
Args:
x: Input tensor B X n_head X T X 2T-1
Returns:
torch.Tensor: Output tensor.
"""
zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
x_padded = torch.cat([zero_pad, x], dim=-1)
x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
x = x_padded[:, :, 1:].view_as(x)[
:, :, :, : x.size(-1) // 2 + 1
] # only keep the positions from 0 to time2
if self.zero_triu:
ones = torch.ones((x.size(2), x.size(3)), device=x.device)
x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
return x
def forward(self, query, key, value, pos_emb, key_padding_mask=None, **kwargs):
"""Compute scaled dot product attention.
Args:
query: Query tensor T X B X C
key: Key tensor T X B X C
value: Value tensor T X B X C
pos_emb: Positional embedding tensor B X 2T-1 X C
key_padding_mask: Mask tensor T X B
Returns:
torch.Tensor: Output tensor T X B X C.
"""
query = query.transpose(0, 1)
key = key.transpose(0, 1)
value = value.transpose(0, 1)
pos_emb = pos_emb.transpose(0, 1)
q, k, v = self.forward_qkv(query, key, value)
q = q.transpose(1, 2) # (batch, time1, head, d_k)
n_batch_pos = pos_emb.size(0)
p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k)
# (batch, head, time1, d_k)
q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
# (batch, head, time1, d_k)
q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
# compute attention score
# first compute matrix a and matrix c
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
# (batch, head, time1, time2)
matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
# compute matrix b and matrix d
# (batch, head, time1, 2*time1-1)
matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
matrix_bd = self.rel_shift(matrix_bd)
scores = (matrix_ac + matrix_bd) / math.sqrt(
self.d_k
) # (batch, head, time1, time2)
scores = self.forward_attention(v, scores, key_padding_mask)
scores = scores.transpose(0, 1)
return scores, None
class RotaryPositionMultiHeadedAttention(ESPNETMultiHeadedAttention):
def __init__(
self,
n_feat,
n_head,
dropout,
precision,
rotary_emd_base=10000,
):
"""Construct an RotaryPositionMultiHeadedAttention object."""
super().__init__(n_feat, n_head, dropout)
precision = torch.float
self.rotary_ndims = self.d_k # also try self.d_k//2
if precision == "fp16":
precision = torch.half
self.rotary_emb = RotaryPositionalEmbedding(
self.rotary_ndims, base=rotary_emd_base, precision=precision
)
def forward(self, query, key, value, key_padding_mask=None, **kwargs):
"""Compute rotary position attention.
Args:
query: Query tensor T X B X C
key: Key tensor T X B X C
value: Value tensor T X B X C
key_padding_mask: Mask tensor T X B
Returns:
torch.Tensor: Output tensor T X B X D.
Notes:
Assumes self attn
"""
T, B, C = value.size()
query = query.view(T, B, self.h, self.d_k)
key = key.view(T, B, self.h, self.d_k)
value = value.view(T, B, self.h, self.d_k)
cos, sin = self.rotary_emb(value, seq_len=T)
query, key = apply_rotary_pos_emb(
query, key, cos, sin, offset=0
) # offset is based on layer_past
query = query.view(T, B, self.h * self.d_k)
key = key.view(T, B, self.h * self.d_k)
value = value.view(T, B, self.h * self.d_k)
# TBD to BTD
query = query.transpose(0, 1)
key = key.transpose(0, 1)
value = value.transpose(0, 1)
q, k, v = self.forward_qkv(query, key, value)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
scores = self.forward_attention(v, scores, key_padding_mask)
scores = scores.transpose(0, 1)
return scores, None
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/espnet_multihead_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
transpose last 2 dimensions of the input
"""
import torch.nn as nn
class TransposeLast(nn.Module):
def __init__(self, deconstruct_idx=None):
super().__init__()
self.deconstruct_idx = deconstruct_idx
def forward(self, x):
if self.deconstruct_idx is not None:
x = x[self.deconstruct_idx]
return x.transpose(-2, -1)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/transpose_last.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Any, Optional
import torch
import torch.onnx.operators
from fairseq import utils
from torch import Tensor, nn
class SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length.
Padding symbols are ignored.
"""
def __init__(self, embedding_dim, padding_idx, init_size=1024):
super().__init__()
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx if padding_idx is not None else 0
self.weights = SinusoidalPositionalEmbedding.get_embedding(
init_size, embedding_dim, padding_idx
)
self.onnx_trace = False
self.register_buffer("_float_tensor", torch.FloatTensor(1))
self.max_positions = int(1e5)
def prepare_for_onnx_export_(self):
self.onnx_trace = True
@staticmethod
def get_embedding(
num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
):
"""Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(
1
) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(
num_embeddings, -1
)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
def forward(
self,
input,
incremental_state: Optional[Any] = None,
timestep: Optional[Tensor] = None,
positions: Optional[Any] = None,
):
"""Input is expected to be of size [bsz x seqlen]."""
bspair = torch.onnx.operators.shape_as_tensor(input)
bsz, seq_len = bspair[0], bspair[1]
max_pos = self.padding_idx + 1 + seq_len
if self.weights is None or max_pos > self.weights.size(0):
# recompute/expand embeddings if needed
self.weights = SinusoidalPositionalEmbedding.get_embedding(
max_pos, self.embedding_dim, self.padding_idx
)
self.weights = self.weights.to(self._float_tensor)
if incremental_state is not None and len(incremental_state) > 0:
# positions is the same for every token when decoding a single step
pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
if self.onnx_trace:
return (
self.weights.index_select(index=self.padding_idx + pos, dim=0)
.unsqueeze(1)
.repeat(bsz, 1, 1)
)
return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
positions = utils.make_positions(
input, self.padding_idx, onnx_trace=self.onnx_trace
)
if self.onnx_trace:
flat_embeddings = self.weights.detach().index_select(0, positions.view(-1))
embedding_shape = torch.cat(
(bsz.view(1), seq_len.view(1), torch.tensor([-1], dtype=torch.long))
)
embeddings = torch.onnx.operators.reshape_from_tensor_shape(
flat_embeddings, embedding_shape
)
return embeddings
return (
self.weights.index_select(0, positions.view(-1))
.view(bsz, seq_len, -1)
.detach()
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/sinusoidal_positional_embedding.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from apex.normalization import FusedLayerNorm as _FusedLayerNorm
has_fused_layernorm = True
class FusedLayerNorm(_FusedLayerNorm):
@torch.jit.unused
def forward(self, x):
if not x.is_cuda:
return super().forward(x)
else:
with torch.cuda.device(x.device):
return super().forward(x)
except ImportError:
has_fused_layernorm = False
def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
if torch.jit.is_scripting() or torch.jit.is_tracing():
export = True
if not export and torch.cuda.is_available() and has_fused_layernorm:
return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
output = F.layer_norm(
input.float(),
self.normalized_shape,
self.weight.float() if self.weight is not None else None,
self.bias.float() if self.bias is not None else None,
self.eps,
)
return output.type_as(input)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/layer_norm.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from inspect import isfunction
from operator import mul
from functools import reduce, wraps
from aml.multimodal_video.utils.einops.lib import rearrange, repeat
from aml.multimodal_video.utils.einops.lib.layers.torch import Rearrange
from fairseq.modules.local_attention import LocalAttention
# constants
TOKEN_SELF_ATTN_VALUE = -5e4
KMEAN_INIT_ITERS = 10
# helper functions
def exists(val):
return val is not None
def identity(x, *args, **kwargs):
return x
def default(x, d):
if not exists(x):
return d if not isfunction(d) else d()
return x
def cast_tuple(x):
return x if isinstance(x, tuple) else (x,)
def cache_fn(f):
cache = None
@wraps(f)
def cached_fn(*args, **kwargs):
nonlocal cache
if exists(cache):
return cache
cache = f(*args, **kwargs)
return cache
return cached_fn
def to(t):
return {"device": t.device, "dtype": t.dtype}
def find_modules(nn_module, type):
return [module for module in nn_module.modules() if isinstance(module, type)]
def is_empty(t):
return t.nelement() == 0
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max
def batched_index_select(values, indices):
last_dim = values.shape[-1]
return values.gather(2, expand_dim(indices, -1, last_dim))
def merge_dims(ind_from, ind_to, tensor):
shape = list(tensor.shape)
arr_slice = slice(ind_from, ind_to + 1)
shape[arr_slice] = [reduce(mul, shape[arr_slice])]
return tensor.reshape(*shape)
def expand_dim(t, dim, k):
t = t.unsqueeze(dim)
expand_shape = [-1] * len(t.shape)
expand_shape[dim] = k
return t.expand(*expand_shape)
def scatter_mean(src, t, index, dim, eps=1e-5):
numer = src.scatter_add(dim, index, t)
denom = src.scatter_add(dim, index, torch.ones_like(t))
return numer / (denom + eps)
def split_at_index(dim, index, t):
pre_slices = (slice(None),) * dim
l = (*pre_slices, slice(None, index))
r = (*pre_slices, slice(index, None))
return t[l], t[r]
def reshape_dim(t, dim, split_dims):
shape = list(t.shape)
num_dims = len(shape)
dim = (dim + num_dims) % num_dims
shape[dim : dim + 1] = split_dims
return t.reshape(shape)
def ema(old, new, decay):
if not exists(old):
return new
return old * decay + new * (1 - decay)
def ema_inplace(moving_avg, new, decay):
if is_empty(moving_avg):
moving_avg.data.copy_(new)
return
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
# helper classes
def map_first_tuple_or_el(x, fn):
if isinstance(x, tuple):
return (fn(x[0]),) + x[1:]
return fn(x)
class Chunk(nn.Module):
def __init__(self, chunks, fn, along_dim=-1):
super().__init__()
self.dim = along_dim
self.chunks = chunks
self.fn = fn
def forward(self, x, **kwargs):
if self.chunks <= 1:
return self.fn(x, **kwargs)
chunks = x.chunk(self.chunks, dim=self.dim)
return torch.cat([self.fn(c, **kwargs) for c in chunks], dim=self.dim)
class PreNorm(nn.ModuleList):
def __init__(self, norm_class, dim, fn):
super().__init__()
self.norm = norm_class(dim)
self.fn = fn
def forward(self, x, **kwargs):
x = self.norm(x)
return self.fn(x, **kwargs)
class ReZero(nn.Module):
def __init__(self, fn):
super().__init__()
self.residual_weight = nn.Parameter(torch.zeros(1))
self.fn = fn
def forward(self, x, **kwargs):
x = self.fn(x, **kwargs)
return map_first_tuple_or_el(x, lambda t: t * self.residual_weight)
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.g = nn.Parameter(torch.ones(1))
self.eps = eps
def forward(self, x):
def norm(t):
n = torch.norm(t, dim=-1, keepdim=True).clamp(min=self.eps)
return t / n * self.g
return map_first_tuple_or_el(x, norm)
class ProjectInOut(nn.Module):
def __init__(self, fn, dim_in, dim_out, project_out=True):
super().__init__()
self.fn = fn
self.project_in = nn.Linear(dim_in, dim_out)
self.project_out = nn.Linear(dim_out, dim_in) if project_out else identity
def forward(self, x, **kwargs):
x = self.project_in(x)
x, loss = self.fn(x, **kwargs)
x = self.project_out(x)
return x, loss
class MatrixMultiply(nn.Module):
def __init__(self, tensor, transpose=False):
super().__init__()
self.tensor = tensor
self.transpose = transpose
def forward(self, x):
tensor = self.tensor
if self.transpose:
tensor = tensor.t()
return x @ tensor
# positional embeddings
class DepthWiseConv1d(nn.Module):
def __init__(self, dim_in, dim_out, kernel_size, stride=1, bias=True, causal=False):
super().__init__()
self.padding = (
((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2)
)
self.net = nn.Sequential(
nn.Conv1d(
dim_in,
dim_in,
kernel_size=kernel_size,
groups=dim_in,
stride=stride,
bias=bias,
),
nn.Conv1d(dim_in, dim_out, 1, bias=bias),
)
def forward(self, x):
x = F.pad(x, self.padding, value=0.0)
return self.net(x)
class FixedPositionalEmbedding(nn.Module):
def __init__(self, dim, max_seq_len):
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
position = torch.arange(0, max_seq_len, dtype=torch.float)
sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq)
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
self.register_buffer("emb", emb)
def forward(self, x):
return self.emb[None, : x.shape[1], :].to(x)
def rotate_every_two(x):
x = rearrange(x, "... (d j) -> ... d j", j=2)
x1, x2 = x.unbind(dim=-1)
x = torch.stack((-x2, x1), dim=-1)
return rearrange(x, "... d j -> ... (d j)")
def apply_rotary_pos_emb(q, k, sinu_pos):
sinu_pos = rearrange(sinu_pos, "() n (j d) -> n j d", j=2)
sin, cos = sinu_pos.unbind(dim=-2)
sin, cos = map(lambda t: repeat(t, "b n -> b (n j)", j=2), (sin, cos))
q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
return q, k
# kmeans related function and class
def update_kmeans_on_backwards(module):
module.kmean_modules = find_modules(module, Kmeans)
def hook(_, grad_in, grad_out):
for m in module.kmean_modules:
m.update()
return module.register_backward_hook(hook)
def similarity(x, means):
return torch.einsum("bhld,hcd->bhlc", x, means)
def dists_and_buckets(x, means):
dists = similarity(x, means)
_, buckets = torch.max(dists, dim=-1)
return dists, buckets
def batched_bincount(index, num_classes, dim=-1):
shape = list(index.shape)
shape[dim] = num_classes
out = index.new_zeros(shape)
out.scatter_add_(dim, index, torch.ones_like(index, dtype=index.dtype))
return out
def kmeans_iter(x, means, buckets=None):
b, h, _, d, dtype, num_clusters = *x.shape, x.dtype, means.shape[1]
if not exists(buckets):
_, buckets = dists_and_buckets(x, means)
bins = batched_bincount(buckets, num_clusters).sum(0, keepdim=True)
zero_mask = bins.long() == 0
means_ = buckets.new_zeros(b, h, num_clusters, d, dtype=dtype)
means_.scatter_add_(-2, expand_dim(buckets, -1, d), x)
means_ = F.normalize(means_.sum(0, keepdim=True), dim=-1).type(dtype)
means = torch.where(zero_mask.unsqueeze(-1), means, means_)
means = means.squeeze(0)
return means
def distribution(dists, window_size):
_, topk_indices = dists.topk(k=window_size, dim=-2)
indices = topk_indices.transpose(-2, -1)
return indices.reshape(*indices.size()[:2], -1)
class Kmeans(nn.Module):
def __init__(
self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4
):
super().__init__()
self.commitment = commitment
self.ema_decay = ema_decay
self.register_buffer("means", torch.randn(num_heads, num_clusters, head_dim))
self.register_buffer("initted", torch.tensor(False))
self.num_new_means = 0
self.new_means = None
@torch.no_grad()
def init(self, x):
if self.initted:
return
_, h, _, d, device, _ = *x.shape, x.device, x.dtype
num_clusters = self.means.shape[1]
means = x.transpose(0, 1).contiguous().view(h, -1, d)
num_samples = means.shape[1]
if num_samples >= num_clusters:
indices = torch.randperm(num_samples, device=device)[:num_clusters]
else:
indices = torch.randint(0, num_samples, (num_clusters,), device=device)
means = means[:, indices]
for _ in range(KMEAN_INIT_ITERS):
means = kmeans_iter(x, means)
self.num_new_means = 0
self.means.data.copy_(means)
self.initted.data.copy_(torch.tensor(True))
@torch.no_grad()
def update(self, new_means=None):
new_means = default(new_means, self.new_means)
assert exists(new_means), "new kmeans has not been supplied"
ema_inplace(self.means, new_means, self.ema_decay)
del self.new_means
self.new_means = None
self.num_new_means = 0
def forward(self, x, update_means=False):
self.init(x)
b, dtype = x.shape[0], x.dtype
means = self.means.type(dtype)
x = F.normalize(x, 2, dim=-1).type(dtype)
with torch.no_grad():
dists, buckets = dists_and_buckets(x, means)
routed_means = batched_index_select(expand_dim(means, 0, b), buckets)
loss = F.mse_loss(x, routed_means) * self.commitment
if update_means:
with torch.no_grad():
means = kmeans_iter(x, means, buckets)
self.new_means = ema(
self.new_means, means, self.num_new_means / (self.num_new_means + 1)
)
self.num_new_means += 1
return dists, loss
# kmeans attention class
class KmeansAttention(nn.Module):
def __init__(
self,
num_clusters,
window_size,
num_heads,
head_dim,
causal=False,
dropout=0.0,
ema_decay=0.999,
commitment=1e-4,
context_window_size=None,
receives_context=False,
num_mem_kv=0,
shared_qk=False,
):
super().__init__()
self.num_heads = num_heads
self.num_clusters = num_clusters
self.head_dim = head_dim
self.window_size = window_size
self.context_window_size = default(context_window_size, window_size)
self.causal = causal
self.shared_qk = shared_qk
self.receives_context = receives_context
self.kmeans = Kmeans(num_heads, head_dim, num_clusters, ema_decay, commitment)
self.dropout = nn.Dropout(dropout)
self.num_mem_kv = max(num_mem_kv, 1 if causal and not shared_qk else 0)
self.mem_key = nn.Parameter(
torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)
)
self.mem_value = nn.Parameter(
torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)
)
def forward(self, q, k, v, query_mask=None, key_mask=None, **kwargs):
b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = (
*q.shape,
k.shape[2],
self.window_size,
self.context_window_size,
self.num_clusters,
q.device,
q.dtype,
)
is_reverse = kwargs.pop("_reverse", False)
out = torch.zeros_like(q, dtype=dtype)
update_kmeans = self.training and not is_reverse
key_mask = (
default(key_mask, query_mask) if not self.receives_context else key_mask
)
kv_wsz = wsz if not self.receives_context else c_wsz
wsz = min(wsz, t)
kv_wsz = min(kv_wsz, kv_t)
if not self.shared_qk or self.receives_context:
dists, aux_loss = self.kmeans(torch.cat((q, k), dim=2), update_kmeans)
q_dists, k_dists = split_at_index(2, t, dists)
indices = distribution(q_dists, wsz)
kv_indices = distribution(k_dists, kv_wsz)
else:
dists, aux_loss = self.kmeans(q, update_kmeans)
k = F.normalize(k, dim=-1).to(q)
indices = distribution(dists, wsz)
kv_indices = indices
q = batched_index_select(q, indices)
k = batched_index_select(k, kv_indices)
v = batched_index_select(v, kv_indices)
def reshape_with_window(x):
return x.reshape(b, h, nc, -1, d)
q, k, v = map(reshape_with_window, (q, k, v))
m_k, m_v = map(
lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value)
)
k, v = map(lambda x: torch.cat(x, dim=3), ((m_k, k), (m_v, v)))
dots = torch.einsum("bhnid,bhnjd->bhnij", q, k) * (d ** -0.5)
mask_value = max_neg_value(dots)
if exists(query_mask) or exists(key_mask):
query_mask = default(
query_mask, lambda: torch.ones((b, t), device=device).bool()
)
key_mask = default(
key_mask, lambda: torch.ones((b, kv_t), device=device).bool()
)
q_mask = expand_dim(query_mask, 1, h).gather(2, indices)
kv_mask = expand_dim(key_mask, 1, h).gather(2, kv_indices)
q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (q_mask, kv_mask))
mask = q_mask[:, :, :, :, None] * kv_mask[:, :, :, None, :]
mask = F.pad(mask, (self.num_mem_kv, 0), value=1)
dots.masked_fill_(~mask, mask_value)
del mask
if self.causal:
q_mask, kv_mask = map(
lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)
)
mask = q_mask[:, :, :, :, None] >= kv_mask[:, :, :, None, :]
mask = F.pad(mask, (self.num_mem_kv, 0), value=1)
dots.masked_fill_(~mask, mask_value)
del mask
if self.shared_qk:
q_mask, kv_mask = map(
lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)
)
mask = q_mask[:, :, :, :, None] == kv_mask[:, :, :, None, :]
mask = F.pad(mask, (self.num_mem_kv, 0), value=0)
dots.masked_fill_(mask, TOKEN_SELF_ATTN_VALUE)
del mask
dots = dots.softmax(dim=-1)
dots = self.dropout(dots)
bo = torch.einsum("bhcij,bhcjd->bhcid", dots, v)
so = torch.reshape(bo, (b, h, -1, bo.shape[-1])).type(dtype)
out = scatter_mean(out, so, indices.unsqueeze(-1).expand_as(so), -2)
return out, aux_loss
# feedforward
class GELU_(nn.Module):
def forward(self, x):
return (
0.5
* x
* (
1
+ torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))
)
)
GELU = nn.GELU if hasattr(nn, "GELU") else GELU_
class FeedForward(nn.Module):
def __init__(self, dim, mult=4, dropout=0.0, activation=None, glu=False):
super().__init__()
activation = default(activation, GELU)
self.glu = glu
self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1))
self.act = activation()
self.dropout = nn.Dropout(dropout)
self.w2 = nn.Linear(dim * mult, dim)
def forward(self, x, **kwargs):
if not self.glu:
x = self.w1(x)
x = self.act(x)
else:
x, v = self.w1(x).chunk(2, dim=-1)
x = self.act(x) * v
x = self.dropout(x)
x = self.w2(x)
return x
# self attention
class SelfAttention(nn.Module):
def __init__(
self,
dim,
max_seq_len,
heads,
local_attn_heads,
window_size,
dim_head=None,
local_attn_window_size=None,
local_attn_radius_blocks=1,
causal=False,
attn_dropout=0.0,
dropout=0.0,
kmeans_ema_decay=0.999,
commitment_factor=1e-4,
receives_context=False,
context_window_size=None,
rel_pos_emb=True,
num_mem_kv=0,
shared_qk=False,
conv_query_kernel=9,
):
super().__init__()
assert (
dim_head or (dim % heads) == 0
), "hidden dimension must be divisible by number of heads"
assert (
max_seq_len % window_size
) == 0, "maximum sequence length must be divisible by the target window size"
assert (
local_attn_heads <= heads
), "number of local attention heads must be less than total heads"
assert not (
receives_context and local_attn_heads > 0
), "local attention cannot be used for self attention with context"
assert not (
receives_context and causal
), "contextual attention layer cannot be causal"
local_attn_window_size = default(local_attn_window_size, window_size)
context_window_size = default(context_window_size, window_size)
self.shared_qk = shared_qk
self.receives_context = receives_context
self.heads = heads
self.local_attn_heads = local_attn_heads
self.global_attn_heads = heads - local_attn_heads
self.causal = causal
self.window_size = window_size
dim_head = default(dim_head, dim // heads)
dim_heads = dim_head * heads
self.dim_head = dim_head
num_clusters = max_seq_len // window_size
# local
local_dim_heads = dim_head * self.local_attn_heads
if self.local_attn_heads > 0:
rel_pos_emb_config = (dim_head, local_attn_heads) if rel_pos_emb else None
self.local_attn = LocalAttention(
dim=dim_head,
window_size=local_attn_window_size,
causal=causal,
dropout=attn_dropout,
rel_pos_emb_config=rel_pos_emb_config,
look_backward=local_attn_radius_blocks,
look_forward=0 if causal else local_attn_radius_blocks,
)
self.local_to_qkv = nn.Linear(dim, 3 * local_dim_heads)
# global
global_dim_heads = dim_head * self.global_attn_heads
if self.global_attn_heads > 0:
self.global_attn = KmeansAttention(
num_clusters,
window_size,
self.global_attn_heads,
dim_head,
causal=causal,
dropout=attn_dropout,
ema_decay=kmeans_ema_decay,
commitment=commitment_factor,
receives_context=receives_context,
num_mem_kv=num_mem_kv,
shared_qk=shared_qk,
)
self.to_q = nn.Sequential(
Rearrange("b n c -> b c n"),
DepthWiseConv1d(dim, global_dim_heads, conv_query_kernel, causal=causal),
Rearrange("b c n -> b n c"),
)
self.to_v = nn.Linear(dim, global_dim_heads, bias=False)
if not self.shared_qk:
self.to_k = nn.Linear(dim, global_dim_heads, bias=False)
# out
self.to_out = nn.Linear(dim_heads, dim, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(
self,
query,
key,
value,
context=None,
key_padding_mask=None,
context_mask=None,
pos_emb=None,
**kwargs
):
assert not (
self.receives_context and not exists(context)
), "context must be passed if self attention is set to receive context"
input_mask = key_padding_mask
x = query.transpose(0, 1)
b, t, _, h, dh = *x.shape, self.heads, self.dim_head
has_local, has_global = map(
lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads)
)
def split_heads(v):
return reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous()
if has_local:
local_qkv = self.local_to_qkv(x).chunk(3, dim=-1)
lq, lk, lv = map(split_heads, local_qkv)
if has_global:
kv_input = x if not self.receives_context else context
q, v = self.to_q(x), self.to_v(kv_input)
if not self.shared_qk:
k = self.to_k(kv_input)
else:
k = self.to_q(kv_input) if self.receives_context else q
q, k, v = map(split_heads, (q, k, v))
out = []
total_loss = torch.tensor(0.0, requires_grad=True, **to(x))
if has_local:
local_out = self.local_attn(lq, lk, lv, input_mask=input_mask)
out.append(local_out)
if has_global:
if not self.receives_context and exists(pos_emb):
q, k = apply_rotary_pos_emb(q, k, pos_emb)
global_out, loss = self.global_attn(
q, k, v, query_mask=input_mask, key_mask=context_mask
)
total_loss = total_loss + loss
out.append(global_out)
out = torch.cat(out, dim=1)
out = out.reshape(b, h, t, -1).transpose(1, 2).reshape(b, t, -1)
out = self.dropout(out.transpose(0, 1))
# out = self.to_out(out)
return out, total_loss
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/kmeans_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch import nn
class SamePad(nn.Module):
def __init__(self, kernel_size, causal=False):
super().__init__()
if causal:
self.remove = kernel_size - 1
else:
self.remove = 1 if kernel_size % 2 == 0 else 0
def forward(self, x):
if self.remove > 0:
x = x[:, :, : -self.remove]
return x
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/same_pad.py |
KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/__init__.py |
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def parse_config_yaml(yaml_data):
# Initialize to default options.
quantization_options = {
"n_centroids": {
"Linear": ["in_features", {"*": 256}],
"Embedding": ["embedding_dim", {"*": 256}],
},
"block_sizes": {
"Linear": ["fuzzy_name", {"fc": 8, "attn": 4, "emb": 4}],
"Embedding": ["fuzzy_name", {"emb": 8}],
},
"layers_to_quantize": [
"decoder\\.layers\\.\\d+\\.fc[12]",
"decoder\\.embed_tokens\\.embeddings\\.[012]\\.[01]",
"decoder\\.layers\\.\\d+\\.self_attn\\.(k_proj|v_proj|q_proj|out_proj)",
],
}
if "n_centroids" in yaml_data:
quantization_options["n_centroids"] = {
layer: convert_yaml_to_tuple(layer_data)
for layer, layer_data in yaml_data["n_centroids"].items()
}
if "block_sizes" in yaml_data:
quantization_options["block_sizes"] = {
layer: convert_yaml_to_tuple(layer_data)
for layer, layer_data in yaml_data["block_sizes"].items()
}
if "layers_to_quantize" in yaml_data:
quantization_options["layers_to_quantize"] = yaml_data["layers_to_quantize"]
return quantization_options
def convert_yaml_to_tuple(yaml_dictionary):
"""Converts a yaml dictionary with two keys: `key` and `value` into a two
argument tuple of those values."""
return (yaml_dictionary["key"], yaml_dictionary["value"])
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/quantization_options.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .utils import SizeTracker, get_param, attrsetter, quantize_model_ # NOQA
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .em import EM, EmptyClusterResolveError
class PQ(EM):
"""
Quantizes the layer weights W with the standard Product Quantization
technique. This learns a codebook of codewords or centroids of size
block_size from W. For further reference on using PQ to quantize
neural networks, see "And the Bit Goes Down: Revisiting the Quantization
of Neural Networks", Stock et al., ICLR 2020.
PQ is performed in two steps:
(1) The matrix W (weights or fully-connected or convolutional layer)
is reshaped to (block_size, -1).
- If W is fully-connected (2D), its columns are split into
blocks of size block_size.
- If W is convolutional (4D), its filters are split along the
spatial dimension.
(2) We apply the standard EM/k-means algorithm to the resulting reshaped matrix.
Args:
- W: weight matrix to quantize of size (in_features x out_features)
- block_size: size of the blocks (subvectors)
- n_centroids: number of centroids
- n_iter: number of k-means iterations
- eps: for cluster reassignment when an empty cluster is found
- max_tentatives for cluster reassignment when an empty cluster is found
- verbose: print information after each iteration
Remarks:
- block_size be compatible with the shape of W
"""
def __init__(
self,
W,
block_size,
n_centroids=256,
n_iter=20,
eps=1e-6,
max_tentatives=30,
verbose=True,
):
self.block_size = block_size
W_reshaped = self._reshape(W)
super(PQ, self).__init__(
W_reshaped,
n_centroids=n_centroids,
n_iter=n_iter,
eps=eps,
max_tentatives=max_tentatives,
verbose=verbose,
)
def _reshape(self, W):
"""
Reshapes the matrix W as expained in step (1).
"""
# fully connected: by convention the weight has size out_features x in_features
if len(W.size()) == 2:
self.out_features, self.in_features = W.size()
assert (
self.in_features % self.block_size == 0
), "Linear: n_blocks must be a multiple of in_features"
return (
W.reshape(self.out_features, -1, self.block_size)
.permute(2, 1, 0)
.flatten(1, 2)
)
# convolutional: we reshape along the spatial dimension
elif len(W.size()) == 4:
self.out_channels, self.in_channels, self.k_h, self.k_w = W.size()
assert (
self.in_channels * self.k_h * self.k_w
) % self.block_size == 0, (
"Conv2d: n_blocks must be a multiple of in_channels * k_h * k_w"
)
return (
W.reshape(self.out_channels, -1, self.block_size)
.permute(2, 1, 0)
.flatten(1, 2)
)
# not implemented
else:
raise NotImplementedError(W.size())
def encode(self):
"""
Performs self.n_iter EM steps.
"""
self.initialize_centroids()
for i in range(self.n_iter):
try:
self.step(i)
except EmptyClusterResolveError:
break
def decode(self):
"""
Returns the encoded full weight matrix. Must be called after
the encode function.
"""
# fully connected case
if "k_h" not in self.__dict__:
return (
self.centroids[self.assignments]
.reshape(-1, self.out_features, self.block_size)
.permute(1, 0, 2)
.flatten(1, 2)
)
# convolutional case
else:
return (
self.centroids[self.assignments]
.reshape(-1, self.out_channels, self.block_size)
.permute(1, 0, 2)
.reshape(self.out_channels, self.in_channels, self.k_h, self.k_w)
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/pq.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import random
from collections import Counter
import torch
class EM:
"""
EM algorithm used to quantize the columns of W to minimize
||W - W_hat||^2
Args:
- W: weight matrix of size (in_features x out_features)
- n_iter: number of k-means iterations
- n_centroids: number of centroids (size of codebook)
- eps: for cluster reassignment when an empty cluster is found
- max_tentatives for cluster reassignment when an empty cluster is found
- verbose: print error after each iteration
Remarks:
- If one cluster is empty, the most populated cluster is split into
two clusters
- All the relevant dimensions are specified in the code
"""
def __init__(
self, W, n_centroids=256, n_iter=20, eps=1e-6, max_tentatives=30, verbose=True
):
self.W = W
self.n_centroids = n_centroids
self.n_iter = n_iter
self.eps = eps
self.max_tentatives = max_tentatives
self.verbose = verbose
self.centroids = torch.Tensor()
self.assignments = torch.Tensor()
self.objective = []
def initialize_centroids(self):
"""
Initializes the centroids by sampling random columns from W.
"""
in_features, out_features = self.W.size()
indices = torch.randint(
low=0, high=out_features, size=(self.n_centroids,)
).long()
self.centroids = self.W[:, indices].t() # (n_centroids x in_features)
def step(self, i):
"""
There are two standard steps for each iteration: expectation (E) and
minimization (M). The E-step (assignment) is performed with an exhaustive
search and the M-step (centroid computation) is performed with
the exact solution.
Args:
- i: step number
Remarks:
- The E-step heavily uses PyTorch broadcasting to speed up computations
and reduce the memory overhead
"""
# assignments (E-step)
distances = self.compute_distances() # (n_centroids x out_features)
self.assignments = torch.argmin(distances, dim=0) # (out_features)
n_empty_clusters = self.resolve_empty_clusters()
# centroids (M-step)
for k in range(self.n_centroids):
W_k = self.W[:, self.assignments == k] # (in_features x size_of_cluster_k)
self.centroids[k] = W_k.mean(dim=1) # (in_features)
# book-keeping
obj = (self.centroids[self.assignments].t() - self.W).norm(p=2).item()
self.objective.append(obj)
if self.verbose:
logging.info(
f"Iteration: {i},\t"
f"objective: {obj:.6f},\t"
f"resolved empty clusters: {n_empty_clusters}"
)
def resolve_empty_clusters(self):
"""
If one cluster is empty, the most populated cluster is split into
two clusters by shifting the respective centroids. This is done
iteratively for a fixed number of tentatives.
"""
# empty clusters
counts = Counter(map(lambda x: x.item(), self.assignments))
empty_clusters = set(range(self.n_centroids)) - set(counts.keys())
n_empty_clusters = len(empty_clusters)
tentatives = 0
while len(empty_clusters) > 0:
# given an empty cluster, find most populated cluster and split it into two
k = random.choice(list(empty_clusters))
m = counts.most_common(1)[0][0]
e = torch.randn_like(self.centroids[m]) * self.eps
self.centroids[k] = self.centroids[m].clone()
self.centroids[k] += e
self.centroids[m] -= e
# recompute assignments
distances = self.compute_distances() # (n_centroids x out_features)
self.assignments = torch.argmin(distances, dim=0) # (out_features)
# check for empty clusters
counts = Counter(map(lambda x: x.item(), self.assignments))
empty_clusters = set(range(self.n_centroids)) - set(counts.keys())
# increment tentatives
if tentatives == self.max_tentatives:
logging.info(
f"Could not resolve all empty clusters, {len(empty_clusters)} remaining"
)
raise EmptyClusterResolveError
tentatives += 1
return n_empty_clusters
def compute_distances(self):
"""
For every centroid m, computes
||M - m[None, :]||_2
Remarks:
- We rely on PyTorch's broadcasting to speed up computations
and reduce the memory overhead
- Without chunking, the sizes in the broadcasting are modified as:
(n_centroids x n_samples x out_features) -> (n_centroids x out_features)
- The broadcasting computation is automatically chunked so that
the tensors fit into the memory of the GPU
"""
nb_centroids_chunks = 1
while True:
try:
return torch.cat(
[
(self.W[None, :, :] - centroids_c[:, :, None]).norm(p=2, dim=1)
for centroids_c in self.centroids.chunk(
nb_centroids_chunks, dim=0
)
],
dim=0,
)
except RuntimeError:
nb_centroids_chunks *= 2
def assign(self):
"""
Assigns each column of W to its closest centroid, thus essentially
performing the E-step in train().
Remarks:
- The function must be called after train() or after loading
centroids using self.load(), otherwise it will return empty tensors
"""
distances = self.compute_distances() # (n_centroids x out_features)
self.assignments = torch.argmin(distances, dim=0) # (out_features)
def save(self, path, layer):
"""
Saves centroids and assignments.
Args:
- path: folder used to save centroids and assignments
"""
torch.save(self.centroids, os.path.join(path, "{}_centroids.pth".format(layer)))
torch.save(
self.assignments, os.path.join(path, "{}_assignments.pth".format(layer))
)
torch.save(self.objective, os.path.join(path, "{}_objective.pth".format(layer)))
def load(self, path, layer):
"""
Loads centroids and assignments from a given path
Args:
- path: folder use to load centroids and assignments
"""
self.centroids = torch.load(
os.path.join(path, "{}_centroids.pth".format(layer))
)
self.assignments = torch.load(
os.path.join(path, "{}_assignments.pth".format(layer))
)
self.objective = torch.load(
os.path.join(path, "{}_objective.pth".format(layer))
)
class EmptyClusterResolveError(Exception):
pass
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/em.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import re
from operator import attrgetter, itemgetter
import torch
import numpy as np
import torch.distributed as dist
import torch.nn as nn
from .modules import PQConv2d, PQEmbedding, PQLinear
from .pq import PQ
def quantize_model_(
model,
size_tracker,
layers_to_quantize,
block_sizes_config,
n_centroids_config,
step=0,
n_iter=15,
eps=1e-6,
max_tentatives=100,
remove_weights=False,
verbose=True,
state_dict=None,
):
"""
Quantize a model in-place by stages. All the targeted
layers are replaced by their quantized counterpart,
and the model is ready for the finetuning of the
centroids in a standard training loop (no modifications
required). Note that we do not quantize biases.
Args:
- model: a nn.Module
- size_tracker: useful for tracking quatization statistics
- layers_to_quantize: a list containing regexps for
filtering the layers to quantize at each stage according
to their name (as in model.named_parameters())
- block_sizes_config: dict like
{
'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
'Linear': ('in_features', {'*': 8})
}
For instance, all conv2d layers with kernel size 3x3 have
a block size of 9 and all Linear layers are quantized with
a block size of 8, irrespective of their size.
- n_centroids_config: dict like
{
'Conv2d': ('kernel_size', {'*': 256}),
'Linear': ('in_features', {'*': 256})
}
For instance, all conv2d layers are quantized with 256 centroids
- step: the layers to quantize inplace corresponding
to layers_to_quantize[step]
"""
quantized_layers = get_layers(
model, layers_to_quantize[step], remove_weights=remove_weights
)
for layer in quantized_layers:
# book-keeping
is_master_process = (not dist.is_initialized()) or (
dist.is_initialized() and dist.get_rank() == 0
)
verbose = verbose and is_master_process
# get block size and centroids
module = attrgetter(layer)(model)
block_size = get_param(module, layer, block_sizes_config)
n_centroids = get_param(module, layer, n_centroids_config)
if verbose:
logging.info(
f"Quantizing layer {layer} with block size {block_size} and {n_centroids} centroids"
)
# quantize layer
weight = module.weight.data.clone()
is_bias = "bias" in [x[0] for x in module.named_parameters()]
bias = module.bias.data.clone() if is_bias else None
quantizer = PQ(
weight,
block_size,
n_centroids=n_centroids,
n_iter=n_iter,
eps=eps,
max_tentatives=max_tentatives,
verbose=verbose,
)
# quantization performed on all GPUs with same seed
quantizer.encode()
centroids = quantizer.centroids.contiguous()
assignments = quantizer.assignments.contiguous()
# If n_iter = 0 and state_dict is provided, then
# we initialize random assignments and centroids to
# random values of the appropriate dimensions
# because the quantized model parameters will
# overwritten by the state_dict later on.
if n_iter == 0 and state_dict:
# Initialize random centroids of the correct size
centroids = torch.rand(centroids.size())
centroids.cuda()
# Get counts and assignment keys from layer in loaded checkpoint.
counts_key = layer + "." + "counts"
assignment_key = layer + "." + "assignments"
# Get number of different bins to include.
counts = list(state_dict[counts_key].shape)[0]
print(layer)
print(state_dict[counts_key])
print(counts)
# Initialize random assignments of the correct size
# with an appropriate number of bins.
num_assignments = list(state_dict[assignment_key].shape)[0]
num_extra = num_assignments - counts
print(num_assignments)
print(num_extra)
assignments_bins = torch.arange(counts)
assignments_rand = torch.randint(0, counts - 1, (num_extra,))
assignments = torch.cat((assignments_bins, assignments_rand), 0)
# assignments = assignments.type(torch.IntTensor)
assignments.cuda()
print("assignments")
print(assignments)
# broadcast results to make sure weights are up-to-date
if dist.is_initialized():
dist.broadcast(centroids, 0)
dist.broadcast(assignments, 0)
# instantiate the quantized counterpart
if isinstance(module, nn.Linear):
out_features, in_features = map(
lambda k: module.__dict__[k], ["out_features", "in_features"]
)
quantized_module = PQLinear(
centroids, assignments, bias, in_features, out_features
)
elif isinstance(module, nn.Embedding):
num_embeddings, embedding_dim = map(
lambda k: module.__dict__[k], ["num_embeddings", "embedding_dim"]
)
quantized_module = PQEmbedding(
centroids, assignments, num_embeddings, embedding_dim
)
elif isinstance(module, nn.Conv2d):
out_channels, in_channels, kernel_size = map(
lambda k: module.__dict__[k],
["out_channels", "in_channels", "kernel_size"],
)
stride, padding, dilation, groups, padding_mode = map(
lambda k: module.__dict__[k],
["stride", "padding", "dilation", "groups", "padding_mode"],
)
quantized_module = PQConv2d(
centroids,
assignments,
bias,
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
padding_mode=padding_mode,
)
else:
raise ValueError(f"Module {module} not yet supported for quantization")
# replace layer by its quantized counterpart
attrsetter(layer)(model, quantized_module)
# update statistics
size_tracker.update(weight, block_size, n_centroids)
# return name of quantized layers
return quantized_layers
def get_layers(model, filter_regexp, remove_weights=False):
"""
Filters out the layers according to a regexp. Note that
we omit biases.
Args:
- model: a nn.Module
- filter_regexp: a regexp to filter the layers to keep
according to their name in model.named_parameters().
For instance, the regexp:
down_layers\\.[123456]\\.(conv[12]|identity\\.conv))
is keeping blocks down_layers from 1 to 6, and inside
each block is keeping conv1, conv2 and identity.conv.
Remarks:
- We add (module\\.)? at the beginning of the regexp to
account for the possible use of nn.parallel.DataParallel
"""
# get all parameter names
all_layers = map(itemgetter(0), model.named_parameters())
# remove biases
all_layers = filter(lambda x: "bias" not in x, all_layers)
# remove .weight in all other names (or .weight_orig is spectral norm)
all_layers = map(lambda x: x.replace(".weight_orig", ""), all_layers)
# remove weights indicates whether the weights extension should be removed, in addition to
# weight_orig and weight extension on names
if remove_weights:
all_layers = map(lambda x: x.replace(".weights", ""), all_layers)
all_layers = map(lambda x: x.replace(".weight", ""), all_layers)
# return filtered layers
filter_regexp = "(module\\.)?" + "(" + filter_regexp + ")"
r = re.compile(filter_regexp)
return list(filter(r.match, all_layers))
def get_param(module, layer_name, param_config):
"""
Given a quantization configuration, get the right parameter
for the module to be quantized.
Args:
- module: a nn.Module
- layer_name: the name of the layer
- param_config: a dict like
{
'Conv2d': ('kernel_size', {'(3, 3)': 9, '(1, 1)': 4}),
'Linear': ('in_features', {'*': 8})
}
For instance, all conv2d layers with kernel size 3x3 have
a block size of 9 and all Linear layers are quantized with
a block size of 8, irrespective of their size.
Remarks:
- if 'fuzzy_name' is passed as a parameter, layers whose layer_name
include 'fuzzy_name' will be assigned the given parameter.
In the following example, conv.expand layers will have a block
size of 9 while conv.reduce will have a block size of 4 and all
other layers will have a block size of 2.
{
'Conv2d': ('fuzzy_name', {'expand': 9, 'reduce': 4, '*': 2}),
'Linear': ('fuzzy_name', {'classifier': 8, 'projection': 4})
}
"""
layer_type = module.__class__.__name__
if layer_type not in param_config:
raise KeyError(f"Layer type {layer_type} not in config for layer {module}")
feature, params = param_config[module.__class__.__name__]
if feature != "fuzzy_name":
feature_value = str(getattr(module, feature))
if feature_value not in params:
if "*" in params:
feature_value = "*"
else:
raise KeyError(
f"{feature}={feature_value} not in config for layer {module}"
)
else:
feature_values = [name for name in params if name in layer_name]
if len(feature_values) == 0:
if "*" in params:
feature_value = "*"
else:
raise KeyError(f"name={layer_name} not in config for {module}")
else:
feature_value = feature_values[0]
return params[feature_value]
class SizeTracker(object):
"""
Class to keep track of the compressed network size with iPQ.
Args:
- model: a nn.Module
Remarks:
- The compressed size is the sum of three components
for each layer in the network:
(1) Storing the centroids given by iPQ in fp16
(2) Storing the assignments of the blocks in int8
(3) Storing all non-compressed elements such as biases
- This cost in only valid if we use 256 centroids (then
indexing can indeed by done with int8).
"""
def __init__(self, model):
self.model = model
self.size_non_compressed_model = self.compute_size()
self.size_non_quantized = self.size_non_compressed_model
self.size_index = 0
self.size_centroids = 0
self.n_quantized_layers = 0
def compute_size(self):
"""
Computes the size of the model (in MB).
"""
res = 0
for _, p in self.model.named_parameters():
res += p.numel()
return res * 4 / 1024 / 1024
def update(self, W, block_size, n_centroids):
"""
Updates the running statistics when quantizing a new layer.
"""
# bits per weights
bits_per_weight = np.log2(n_centroids) / block_size
self.n_quantized_layers += 1
# size of indexing the subvectors of size block_size (in MB)
size_index_layer = bits_per_weight * W.numel() / 8 / 1024 / 1024
self.size_index += size_index_layer
# size of the centroids stored in float16 (in MB)
size_centroids_layer = n_centroids * block_size * 2 / 1024 / 1024
self.size_centroids += size_centroids_layer
# size of non-compressed layers, e.g. LayerNorms or biases (in MB)
size_uncompressed_layer = W.numel() * 4 / 1024 / 1024
self.size_non_quantized -= size_uncompressed_layer
def __repr__(self):
size_compressed = (
self.size_index + self.size_centroids + self.size_non_quantized
)
compression_ratio = self.size_non_compressed_model / size_compressed # NOQA
return (
f"Non-compressed model size: {self.size_non_compressed_model:.2f} MB. "
f"After quantizing {self.n_quantized_layers} layers, size "
f"(indexing + centroids + other): {self.size_index:.2f} MB + "
f"{self.size_centroids:.2f} MB + {self.size_non_quantized:.2f} MB = "
f"{size_compressed:.2f} MB, compression ratio: {compression_ratio:.2f}x"
)
def attrsetter(*items):
def resolve_attr(obj, attr):
attrs = attr.split(".")
head = attrs[:-1]
tail = attrs[-1]
for name in head:
obj = getattr(obj, name)
return obj, tail
def g(obj, val):
for attr in items:
resolved_obj, resolved_attr = resolve_attr(obj, attr)
setattr(resolved_obj, resolved_attr, val)
return g
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
class PQLinear(nn.Module):
"""
Quantized counterpart of nn.Linear module. Stores the centroid, the assignments
and the non-quantized biases. The full weight is re-instantiated at each forward
pass.
Args:
- centroids: centroids of size n_centroids x block_size
- assignments: assignments of the centroids to the subvectors
of size self.out_features x n_blocks
- bias: the non-quantized bias
Remarks:
- We refer the reader to the official documentation of the nn.Linear module
for the other arguments and the behavior of the module
- Performance tests on GPU show that this implementation is 15% slower than
the non-quantized nn.Linear module for a standard training loop.
"""
def __init__(self, centroids, assignments, bias, in_features, out_features):
super(PQLinear, self).__init__()
self.block_size = centroids.size(1)
self.n_centroids = centroids.size(0)
self.in_features = in_features
self.out_features = out_features
# check compatibility
if self.in_features % self.block_size != 0:
raise ValueError("Wrong PQ sizes")
if len(assignments) % self.out_features != 0:
raise ValueError("Wrong PQ sizes")
# define parameters
self.centroids = nn.Parameter(centroids, requires_grad=True)
self.register_buffer("assignments", assignments)
self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
if bias is not None:
self.bias = nn.Parameter(bias)
else:
self.register_parameter("bias", None)
@property
def weight(self):
return (
self.centroids[self.assignments]
.reshape(-1, self.out_features, self.block_size)
.permute(1, 0, 2)
.flatten(1, 2)
)
def forward(self, x):
return F.linear(
x,
self.weight,
self.bias,
)
def extra_repr(self):
return f"in_features={self.in_features},\
out_features={self.out_features},\
n_centroids={self.n_centroids},\
block_size={self.block_size},\
bias={self.bias is not None}"
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class PQConv2d(nn.Module):
"""
Quantized counterpart of nn.Conv2d module. Stores the centroid, the assignments
and the non-quantized biases. The full weight is re-instantiated at each forward
pass and autograd automatically computes the gradients with respect to the
centroids.
Args:
- centroids: centroids of size n_centroids x block_size
- assignments: assignments of the centroids to the subvectors
of size self.out_channels x n_blocks
- bias: the non-quantized bias, must be either torch.Tensor or None
Remarks:
- We refer the reader to the official documentation of the nn.Conv2d module
for the other arguments and the behavior of the module.
- Performance tests on GPU show that this implementation is 10% slower than
the non-quantized nn.Conv2d module for a standard training loop.
- During the backward, the gradients are averaged by cluster and not summed.
This explains the hook registered to the centroids.
"""
def __init__(
self,
centroids,
assignments,
bias,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
padding_mode="zeros",
):
super(PQConv2d, self).__init__()
self.block_size = centroids.size(1)
self.n_centroids = centroids.size(0)
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _pair(kernel_size)
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.padding_mode = padding_mode
# check compatibility
if in_channels // groups * np.prod(self.kernel_size) % self.block_size != 0:
raise ValueError("Wrong PQ sizes")
if len(assignments) % out_channels != 0:
raise ValueError("Wrong PQ sizes")
if in_channels % groups != 0:
raise ValueError("in_channels must be divisible by groups")
if out_channels % groups != 0:
raise ValueError("out_channels must be divisible by groups")
# define parameters
self.centroids = nn.Parameter(centroids, requires_grad=True)
self.register_buffer("assignments", assignments)
self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
if bias is not None:
self.bias = nn.Parameter(bias)
else:
self.register_parameter("bias", None)
# register hook for averaging gradients per centroids instead of summing
self.centroids.register_hook(lambda x: x / self.counts[:, None])
@property
def weight(self):
return (
self.centroids[self.assignments]
.reshape(-1, self.out_channels, self.block_size)
.permute(1, 0, 2)
.reshape(
self.out_channels, self.in_channels // self.groups, *self.kernel_size
)
)
def forward(self, x):
return F.conv2d(
x,
self.weight,
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
def extra_repr(self):
s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}"
if self.padding != (0,) * len(self.padding):
s += ", padding={padding}"
if self.dilation != (1,) * len(self.dilation):
s += ", dilation={dilation}"
if self.groups != 1:
s += ", groups={groups}"
if self.bias is None:
s += ", bias=False"
if self.padding_mode != "zeros":
s += ", padding_mode={padding_mode}"
s += ", n_centroids={n_centroids}, block_size={block_size}"
return s.format(**self.__dict__)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/modules/qconv.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .qconv import PQConv2d # NOQA
from .qemb import PQEmbedding # NOQA
from .qlinear import PQLinear # NOQA
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/modules/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
class PQEmbedding(nn.Module):
"""
Quantized counterpart of nn.Embedding module. Stores the centroids and
the assignments. The full weight is re-instantiated at each forward
pass.
Args:
- centroids: centroids of size n_centroids x block_size
- assignments: assignments of the centroids to the subvectors
of size self.out_features x n_blocks
- bias: the non-quantized bias
Remarks:
- We refer the reader to the official documentation of the nn.Embedding module
for the other arguments and the behavior of the module
- Performance tests on GPU show that this implementation is 10% slower than
the non-quantized nn.Embedding module for a standard training loop.
"""
def __init__(
self,
centroids,
assignments,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
):
super(PQEmbedding, self).__init__()
self.block_size = centroids.size(1)
self.n_centroids = centroids.size(0)
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
if padding_idx is not None:
if padding_idx > 0:
assert (
padding_idx < self.num_embeddings
), "Padding_idx must be within num_embeddings"
elif padding_idx < 0:
assert (
padding_idx >= -self.num_embeddings
), "Padding_idx must be within num_embeddings"
padding_idx = self.num_embeddings + padding_idx
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
self.sparse = sparse
# check compatibility
if self.embedding_dim % self.block_size != 0:
raise ValueError("Wrong PQ sizes")
if len(assignments) % self.num_embeddings != 0:
raise ValueError("Wrong PQ sizes")
# define parameters
self.centroids = nn.Parameter(centroids, requires_grad=True)
self.register_buffer("assignments", assignments)
self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
@property
def weight(self):
return (
self.centroids[self.assignments]
.reshape(-1, self.num_embeddings, self.block_size)
.permute(1, 0, 2)
.flatten(1, 2)
)
def forward(self, input):
return F.embedding(
input,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
def extra_repr(self):
s = "{num_embeddings}, {embedding_dim}"
if self.padding_idx is not None:
s += ", padding_idx={padding_idx}"
if self.max_norm is not None:
s += ", max_norm={max_norm}"
if self.norm_type != 2:
s += ", norm_type={norm_type}"
if self.scale_grad_by_freq is not False:
s += ", scale_grad_by_freq={scale_grad_by_freq}"
if self.sparse is not False:
s += ", sparse=True"
s += ", n_centroids={n_centroids}, block_size={block_size}"
return s.format(**self.__dict__)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/pq/modules/qemb.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .utils import quantize_model_ # NOQA
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
try:
import torch.ao.quantization as quantization
except ImportError:
import torch.quantization as quantization
def emulate_int(w, bits, method, scale=None, zero_point=None):
q = globals()[f"emulate_int8_{method}"]
return q(w, scale=scale, zero_point=zero_point, bits=bits)
def quantize(w, scale, zero_point, bits=8):
# In the default behavior, max_val = 255.
max_val = 2 ** bits - 1
return (
torch.clamp(torch.round(w / scale + zero_point), 0, max_val) - zero_point
) * scale
def emulate_int8_histogram(w, scale=None, zero_point=None, bits=8):
if scale is None:
obs = quantization.observer.HistogramObserver()
obs.to(device=w.device)
_ = obs(w.float())
scale, zero_point = obs.calculate_qparams()
scale = scale.cuda().type_as(w)
zero_point = zero_point.cuda().type_as(w)
return quantize(w, scale, zero_point, bits=bits), scale, zero_point
def emulate_int8_channel(w, scale=None, zero_point=None, bits=8):
if scale is None:
obs = quantization.observer.PerChannelMinMaxObserver(
ch_axis=-1, qscheme=torch.per_channel_symmetric
)
obs.to(device=w.device)
_ = obs(w)
scale, zero_point, ch_axis = obs.get_qparams()
scale = scale.cuda().type_as(w)
zero_point = zero_point.cuda().type_as(w)
return quantize(w, scale, zero_point, bits=bits), scale, zero_point
def emulate_int8_tensor(w, scale=None, zero_point=None, bits=8):
if scale is None:
obs = quantization.observer.MinMaxObserver()
obs.to(device=w.device)
_ = obs(w)
scale, zero_point = obs.calculate_qparams()
scale = scale.cuda().type_as(w)
zero_point = zero_point.cuda().type_as(w)
return quantize(w, scale, zero_point, bits=bits), scale, zero_point
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/ops.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from operator import attrgetter
import torch.distributed as dist
import torch.nn as nn
from ..pq.utils import attrsetter, get_layers
from .modules import ActivationQuantizer, IntConv2d, IntEmbedding, IntLinear
MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d}
def quantize_model_(
model, p=0.2, bits=8, update_step=3000, method="histogram", remove_weights=False
):
"""
Replaces all modules with their scalar quantized counterpart and
registers hooks to quantize the post-ativations of those modules.
Args:
- model: a nn.Module
- p: amount of noise (0 for no noise, 1 to quantize all the weights/activations)
- bits: number of bits
- update_step: update quantization parameters every update_step steps
"""
# quantize all layers
# remove weights indicates whether the weights extension should be removed, in addition to
# weight_orig and weight extension on names
quantized_layers = get_layers(model, "(.*?)", remove_weights=remove_weights)
for layer in quantized_layers:
# book-keeping
is_master_process = (not dist.is_initialized()) or (
dist.is_initialized() and dist.get_rank() == 0
)
# recover module
module = attrgetter(layer)(model)
if is_master_process:
logging.info(
f"Quantizing layer {layer} with bits={bits} and QuantNoise={p}"
)
# quantization params
q_params = {
"p": p,
"update_step": update_step,
"bits": bits,
"method": method,
"counter": 0,
}
# instantiate the quantized counterpart
if isinstance(module, tuple(MAPPING.keys())):
QuantizedModule = MAPPING[module.__class__]
quantized_module = QuantizedModule.__new__(QuantizedModule)
params = module.__dict__
params.update(q_params)
quantized_module.__dict__.update(params)
else:
if is_master_process:
logging.info(f"Module {module} not yet supported for quantization")
continue
# activation quantization
ActivationQuantizer(quantized_module, p=0, bits=bits, method=method)
# replace layer by its quantized counterpart
attrsetter(layer)(model, quantized_module)
# return name of quantized layers
return quantized_layers
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..ops import emulate_int
class IntLinear(nn.Module):
"""
Quantized counterpart of the nn.Linear module that applies QuantNoise during training.
Args:
- in_features: input features
- out_features: output features
- bias: bias or not
- p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights)
- bits: number of bits
- method: choose among {"tensor", "histogram", "channel"}
- update_step: recompute scale and zero_point every update_steps iterations
Remarks:
- We use the straight-through estimator so that the gradients
back-propagate nicely in the network, this is implemented with
the detach() trick.
- Parameters scale and zero_point are recomputed every update_step
forward pass to reduce the overhead
- At test time, the weights are fully quantized
"""
def __init__(
self,
in_features,
out_features,
bias=True,
p=0,
update_step=3000,
bits=8,
method="histogram",
):
super(IntLinear, self).__init__()
self.in_features = int(in_features)
self.out_features = int(out_features)
self.weight = torch.nn.Parameter(torch.Tensor(out_features, in_features))
self.chosen_bias = bias
if self.chosen_bias:
self.bias = torch.nn.Parameter(torch.Tensor(out_features))
else:
self.register_parameter("bias", None)
self.reset_parameters()
# quantization parameters
self.p = p
self.bits = bits
self.method = method
self.update_step = update_step
self.counter = 0
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight)
if self.chosen_bias:
nn.init.constant_(self.bias, 0.0)
return
def forward(self, input):
# train with QuantNoise and evaluate the fully quantized network
p = self.p if self.training else 1
# update parameters every 100 iterations
if self.counter % self.update_step == 0:
self.scale = None
self.zero_point = None
self.counter += 1
# quantize weight
weight_quantized, self.scale, self.zero_point = emulate_int(
self.weight.detach(),
bits=self.bits,
method=self.method,
scale=self.scale,
zero_point=self.zero_point,
)
# mask to apply noise
mask = torch.zeros_like(self.weight)
mask.bernoulli_(1 - p)
noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0)
# using straight-through estimator (STE)
clamp_low = -self.scale * self.zero_point
clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
weight = (
torch.clamp(self.weight, clamp_low.item(), clamp_high.item())
+ noise.detach()
)
# return output
output = F.linear(input, weight, self.bias)
return output
def extra_repr(self):
return "in_features={}, out_features={}, bias={}, quant_noise={}, bits={}, method={}".format(
self.in_features,
self.out_features,
self.bias is not None,
self.p,
self.bits,
self.method,
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/modules/qlinear.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd
from torch.nn.modules.utils import _pair
from ..ops import emulate_int
class IntConv2d(_ConvNd):
"""
Quantized counterpart of the nn.Conv2d module that applies QuantNoise during training.
Args:
- standard nn.Conv2d parameters
- p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights)
- bits: number of bits
- method: choose among {"tensor", "histogram", "channel"}
- update_step: recompute scale and zero_point every update_steps iterations
Remarks:
- We use the straight-thgourh estimator so that the gradients
back-propagate nicely in the network, this is implemented with
the detach() trick
- Parameters scale and zero_point are recomputed every update_step
forward pass to reduce the overhead
- At test time, the weights are fully quantized
"""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode="zeros",
p=0,
bits=8,
method="histogram",
update_step=1000,
):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(IntConv2d, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
False,
_pair(0),
groups,
bias,
padding_mode,
)
# quantization parameters
self.p = p
self.bits = bits
self.method = method
self.update_step = update_step
self.counter = 0
def _conv_forward(self, input, weight):
if self.padding_mode != "zeros":
return F.conv2d(
F.pad(input, self._padding_repeated_twice, mode=self.padding_mode),
weight,
self.bias,
self.stride,
_pair(0),
self.dilation,
self.groups,
)
return F.conv2d(
input,
weight,
self.bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
def forward(self, input):
# train with QuantNoise and evaluate the fully quantized network
p = self.p if self.training else 1
# update parameters every 100 iterations
if self.counter % self.update_step == 0:
self.scale = None
self.zero_point = None
self.counter += 1
# quantize weight
weight_quantized, self.scale, self.zero_point = emulate_int(
self.weight.detach(),
bits=self.bits,
method=self.method,
scale=self.scale,
zero_point=self.zero_point,
)
# mask to apply noise
mask = torch.zeros_like(self.weight)
mask.bernoulli_(1 - p)
noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0)
# using straight-through estimator (STE)
clamp_low = -self.scale * self.zero_point
clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
weight = (
torch.clamp(self.weight, clamp_low.item(), clamp_high.item())
+ noise.detach()
)
# return output
output = self._conv_forward(input, weight)
return output
def extra_repr(self):
return (
"in_channels={}, out_channels={}, kernel_size={}, stride={}, "
"padding={}, dilation={}, groups={}, bias={}, quant_noise={}, "
"bits={}, method={}".format(
self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.padding,
self.dilation,
self.groups,
self.bias is not None,
self.p,
self.bits,
self.method,
)
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .qact import ActivationQuantizer # NOQA
from .qconv import IntConv2d # NOQA
from .qemb import IntEmbedding # NOQA
from .qlinear import IntLinear # NOQA
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/modules/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..ops import emulate_int
class IntEmbedding(nn.Module):
"""
Quantized counterpart of the nn.Embedding module that applies QuantNoise during training.
Args:
- num_embeddings: number of tokens
- embedding_dim: embedding dimension
- p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights)
- bits: number of bits
- method: choose among {"tensor", "histogram", "channel"}
- update_step: recompute scale and zero_point every update_steps iterations
Remarks:
- We use the straight-through estimator so that the gradients
back-propagate nicely in the network, this is implemented with
the detach() trick
- Parameters scale and zero_point are recomputed every update_step
forward pass to reduce the overhead
- At test time, the weights are fully quantized
"""
def __init__(
self,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
p=0,
update_step=1000,
bits=8,
method="histogram",
):
super(IntEmbedding, self).__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
if padding_idx is not None:
if padding_idx > 0:
assert (
padding_idx < self.num_embeddings
), "Padding_idx must be within num_embeddings"
elif padding_idx < 0:
assert (
padding_idx >= -self.num_embeddings
), "Padding_idx must be within num_embeddings"
padding_idx = self.num_embeddings + padding_idx
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
if _weight is None:
self.weight = nn.Parameter(torch.Tensor(num_embeddings, embedding_dim))
self.reset_parameters()
else:
assert list(_weight.shape) == [
num_embeddings,
embedding_dim,
], "Shape of weight does not match num_embeddings and embedding_dim"
self.weight = nn.Parameter(_weight)
self.sparse = sparse
# quantization parameters
self.p = p
self.bits = bits
self.method = method
self.update_step = update_step
self.counter = 0
def reset_parameters(self):
nn.init.normal_(self.weight)
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def forward(self, input):
# train with QuantNoise and evaluate the fully quantized network
p = self.p if self.training else 1
# update parameters every 1000 iterations
if self.counter % self.update_step == 0:
self.scale = None
self.zero_point = None
self.counter += 1
# quantize weight
weight_quantized, self.scale, self.zero_point = emulate_int(
self.weight.detach(),
bits=self.bits,
method=self.method,
scale=self.scale,
zero_point=self.zero_point,
)
# mask to apply noise
mask = torch.zeros_like(self.weight)
mask.bernoulli_(1 - p)
noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0)
# using straight-through estimator (STE)
clamp_low = -self.scale * self.zero_point
clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
weight = (
torch.clamp(self.weight, clamp_low.item(), clamp_high.item())
+ noise.detach()
)
# return output
output = F.embedding(
input,
weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
return output
def extra_repr(self):
s = "{num_embeddings}, {embedding_dim}"
if self.padding_idx is not None:
s += ", padding_idx={padding_idx}"
if self.max_norm is not None:
s += ", max_norm={max_norm}"
if self.norm_type != 2:
s += ", norm_type={norm_type}"
if self.scale_grad_by_freq is not False:
s += ", scale_grad_by_freq={scale_grad_by_freq}"
if self.sparse is not False:
s += ", sparse=True"
s += "quant_noise={p}, bits={bits}, method={method}"
return s.format(**self.__dict__)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/modules/qemb.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from ..ops import emulate_int
class ActivationQuantizer:
"""
Fake scalar quantization of the activations using a forward hook.
Args:
- module. a nn.Module for which we quantize the *post-activations*
- p: proportion of activations to quantize, set by default to 1
- update_step: to recompute quantization parameters
- bits: number of bits for quantization
- method: choose among {"tensor", "histogram", "channel"}
- clamp_threshold: to prevent gradients overflow
Remarks:
- Parameters scale and zero_point are recomputed every update_step
forward pass to reduce the overhead
- For the list of quantization methods and number of bits, see ops.py
- To remove the hook from the module, simply call self.handle.remove()
- At test time, the activations are fully quantized
- We use the straight-through estimator so that the gradients
back-propagate nicely in the network, this is implemented with
the detach() trick
- The activations are hard-clamped in [-clamp_threshold, clamp_threshold]
to prevent overflow during the backward pass
"""
def __init__(
self,
module,
p=1,
update_step=1000,
bits=8,
method="histogram",
clamp_threshold=5,
):
self.module = module
self.p = p
self.update_step = update_step
self.counter = 0
self.bits = bits
self.method = method
self.clamp_threshold = clamp_threshold
self.handle = None
self.register_hook()
def register_hook(self):
# forward hook
def quantize_hook(module, x, y):
# update parameters every 1000 iterations
if self.counter % self.update_step == 0:
self.scale = None
self.zero_point = None
self.counter += 1
# train with QuantNoise and evaluate the fully quantized network
p = self.p if self.module.training else 1
# quantize activations
y_q, self.scale, self.zero_point = emulate_int(
y.detach(),
bits=self.bits,
method=self.method,
scale=self.scale,
zero_point=self.zero_point,
)
# mask to apply noise
mask = torch.zeros_like(y)
mask.bernoulli_(1 - p)
noise = (y_q - y).masked_fill(mask.bool(), 0)
# using straight-through estimator (STE)
clamp_low = -self.scale * self.zero_point
clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
return torch.clamp(y, clamp_low.item(), clamp_high.item()) + noise.detach()
# register hook
self.handle = self.module.register_forward_hook(quantize_hook)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/quantization/scalar/modules/qact.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def gen_forward():
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "lightconv_cuda.cuh"
std::vector<at::Tensor> lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l) {
at::DeviceGuard g(input.device());
const auto minibatch = input.size(0);
const auto numFeatures = input.size(1);
const auto sequenceLength = input.size(2);
const auto numHeads = filters.size(0);
const auto filterSize = filters.size(1);
const auto numFiltersInBlock = numFeatures / numHeads;
const dim3 blocks(minibatch, numFeatures);
auto output = at::zeros_like(input);
auto stream = at::cuda::getCurrentCUDAStream();
"""
sequence_if = """
if (sequenceLength <= {seq}) {{
switch(filterSize) {{
"""
case_k = """
case {k}:
"""
main_block = """
if (padding_l == {pad}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_forward", ([&] {{
lightconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t>
<<<blocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
filters.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
output.data<scalar_t>());
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl;
}
break;
"""
bad_filter = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl;
}
"""
con_else = """
} else
"""
final_else = """
{
switch(filterSize) {
"""
final_return = """
}
return {output};
}
"""
with open("lightconv_cuda_forward.cu", "w") as forward:
forward.write(head)
for seq in seqs:
forward.write(sequence_if.format(seq=seq))
for k in kernels:
forward.write(case_k.format(k=k))
for pad in [k // 2, k - 1]:
forward.write(main_block.format(k=k, b_size=seq, pad=pad))
forward.write(bad_padding)
forward.write(bad_filter)
forward.write(con_else)
forward.write(final_else)
for k in kernels:
forward.write(case_k.format(k=k))
for pad in [k // 2, k - 1]:
forward.write(main_block.format(k=k, b_size=seq, pad=pad))
forward.write(bad_padding)
forward.write(bad_filter)
forward.write(final_return)
def gen_backward():
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "lightconv_cuda.cuh"
std::vector<at::Tensor> lightconv_cuda_backward(
at::Tensor gradOutput,
int padding_l,
at::Tensor input,
at::Tensor filters) {
// gradWrtInput
const int minibatch = input.size(0);
const int numFeatures = input.size(1);
const int sequenceLength = input.size(2);
const int numHeads = filters.size(0);
const int filterSize = filters.size(1);
const dim3 gradBlocks(minibatch, numFeatures);
const dim3 weightGradFirstpassShortBlocks(minibatch, numHeads);
const dim3 weightGradSecondpassBlocks(numHeads, filterSize);
const int numFiltersInBlock = numFeatures / numHeads;
auto gradInput = at::zeros_like(input);
auto gradFilters = at::zeros_like(filters);
at::DeviceGuard g(input.device());
auto stream = at::cuda::getCurrentCUDAStream();
switch(filterSize) {
"""
sequence_if = """
if (sequenceLength <= {seq}) {{
"""
case_k = """
case {k}:
"""
main_block = """
if (padding_l == {p}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_backward", ([&] {{
lightconv_grad_wrt_input_kernel<{k}, {b_size}, {p}, scalar_t>
<<<gradBlocks, {b_size}, 0, stream>>>(
gradOutput.data<scalar_t>(),
filters.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
gradInput.data<scalar_t>());
"""
weight_grad_short = """
at::Tensor tempSumGradFilters = at::zeros({{minibatch, numHeads, filterSize}}, input.options().dtype(at::kFloat));
lightconv_grad_wrt_weights_firstpass_short_kernel<{k}, {b_size}, {p}, scalar_t>
<<<weightGradFirstpassShortBlocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
gradOutput.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
numHeads,
tempSumGradFilters.data<float>()
);
lightconv_grad_wrt_weights_secondpass_short_kernel<{k}, {b_size}, scalar_t>
<<<weightGradSecondpassBlocks, {b_size}, 0, stream>>>(
tempSumGradFilters.data<float>(),
minibatch,
numFiltersInBlock,
gradFilters.data<scalar_t>()
);
}}));
}} else
"""
weight_grad = """
at::Tensor tempSumGradFilters = at::zeros({{minibatch, numFeatures, filterSize}}, input.options().dtype(at::kFloat));
lightconv_grad_wrt_weights_firstpass_kernel<{k}, {b_size}, {p}, scalar_t>
<<<gradBlocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
gradOutput.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
tempSumGradFilters.data<float>()
);
lightconv_grad_wrt_weights_secondpass_kernel<{k}, {b_size}, scalar_t>
<<<weightGradSecondpassBlocks, {b_size}, 0, stream>>>(
tempSumGradFilters.data<float>(),
minibatch,
numFiltersInBlock,
gradFilters.data<scalar_t>()
);
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl;
}
"""
breakout = """
break;
"""
bad_filter = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl;
"""
con_else = """
} else
"""
last_return = """
}
return {gradInput, gradFilters};
}
"""
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
thresh = [32, 32, 64, 128, 256, -1, -1, -1]
max_mem = [-1, -1, -1, -1, -1, 192, 96, 64]
with open("lightconv_cuda_backward.cu", "w") as backward:
backward.write(head)
for (k, t, mem) in zip(kernels, thresh, max_mem):
backward.write(case_k.format(k=k))
for seq in seqs:
if (t == -1 or seq <= t) and (mem == -1 or seq < mem):
backward.write(sequence_if.format(seq=seq))
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=seq, p=p))
backward.write(weight_grad_short.format(k=k, b_size=seq, p=p))
backward.write(bad_padding)
else:
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=32, p=p))
backward.write(weight_grad.format(k=k, b_size=32, p=p))
backward.write(bad_padding)
backward.write(breakout)
break
backward.write(con_else)
backward.write(bad_filter)
backward.write(last_return)
if __name__ == "__main__":
gen_forward()
gen_backward()
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/lightconv_layer/cuda_function_gen.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .lightconv_layer import LightconvLayer # noqa
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/lightconv_layer/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import lightconv_cuda
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.modules.fairseq_dropout import FairseqDropout
from torch import nn
from torch.autograd import Function
class lightconvFunction(Function):
@staticmethod
def forward(ctx, x, weights, padding_l):
ctx.padding_l = padding_l
outputs = lightconv_cuda.forward(x, weights, padding_l)
variables = [x, weights]
ctx.save_for_backward(*variables)
return outputs[0]
@staticmethod
def backward(ctx, grad_output):
outputs = lightconv_cuda.backward(
grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors
)
grad_input, grad_weights = outputs
return grad_input, grad_weights, None
@with_incremental_state
class LightconvLayer(nn.Module):
def __init__(
self,
input_size,
kernel_size=1,
padding_l=None,
weight_softmax=False,
num_heads=1,
weight_dropout=0.0,
bias=False,
):
super(LightconvLayer, self).__init__()
self.input_size = input_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_softmax = weight_softmax
self.weight_dropout_module = FairseqDropout(
weight_dropout, module_name=self.__class__.__name__
)
self.weight = nn.Parameter(torch.Tensor(num_heads, kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(input_size))
else:
self.bias = None
self.reset_parameters()
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + "." if name != "" else ""
for k, v in state_dict.items():
if k.endswith(prefix + "weight"):
if v.dim() == 3 and v.size(1) == 1:
state_dict[k] = v.squeeze(1)
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight)
if self.bias is not None:
nn.init.constant_(self.bias, 0.0)
def forward(self, x, incremental_state=None):
# during inference time, incremental BMM is faster
if incremental_state is not None:
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(
incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :]
)
x_unfold = x_unfold.view(T * B * H, R, -1)
weight = self.weight
if self.weight_softmax:
weight = F.softmax(weight.float(), dim=1).type_as(weight)
weight = weight[:, -x_unfold.size(2) :]
K = weight.size(1)
weight = (
weight.view(1, H, K)
.expand(T * B, H, K)
.contiguous()
.view(T * B * H, K, 1)
)
weight = self.weight_dropout_module(weight)
output = torch.bmm(x_unfold, weight) # T*B*H x R x 1
output = output.view(T, B, C)
return output
# during training time, use CUDA kernel
else:
x = x.permute(1, 2, 0).contiguous()
weight = self.weight
if self.weight_softmax:
weight = F.softmax(self.weight, -1)
if self.weight_dropout_module.p:
weight = self.weight_dropout_module(weight)
return lightconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1)
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, "input_buffer")
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(
self, incremental_state, "input_buffer", new_buffer
)
def half(self):
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name="lightconv_layer",
ext_modules=[
CUDAExtension(
"lightconv_cuda",
[
"lightconv_cuda.cpp",
"lightconv_cuda_kernel.cu",
],
),
],
cmdclass={"build_ext": BuildExtension},
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/lightconv_layer/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def gen_forward():
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
blocks = [32, 64, 128, 256]
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "dynamicconv_cuda.cuh"
std::vector<at::Tensor> dynamicconv_cuda_forward(at::Tensor input, at::Tensor weight, int padding_l) {
at::DeviceGuard g(input.device());
const auto minibatch = input.size(0);
const auto numFeatures = input.size(1);
const auto sequenceLength = input.size(2);
const auto numHeads = weight.size(1);
const auto filterSize = weight.size(2);
const auto numFiltersInBlock = numFeatures / numHeads;
const dim3 blocks(minibatch, numFeatures);
auto output = at::zeros_like(input);
auto stream = at::cuda::getCurrentCUDAStream();
"""
switch = """
switch(filterSize) {
"""
case_k = """
case {k}:
"""
main_block = """
if (padding_l == {pad}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "dynamicconv_forward", ([&] {{
dynamicconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t>
<<<blocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
weight.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
numHeads,
output.data<scalar_t>());
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl;
}
break;\n
"""
end = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl;
}
return {output};
}
"""
with open("dynamicconv_cuda_forward.cu", "w") as forward:
forward.write(head)
forward.write(switch)
for k in kernels:
b_size = 32
for b in blocks:
if b > k:
b_size = b
break
forward.write(case_k.format(k=k))
for pad in [k // 2, k - 1]:
forward.write(main_block.format(k=k, b_size=b_size, pad=pad))
forward.write(bad_padding)
forward.write(end)
def gen_backward():
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
thresh = [512, 512, 512, 512, 512, 380, 256, 256]
min_block = [64, 64, 64, 64, 64, 64, 128, 256]
seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "dynamicconv_cuda.cuh"
std::vector<at::Tensor> dynamicconv_cuda_backward(at::Tensor gradOutput, int padding_l, at::Tensor input, at::Tensor weight) {
at::DeviceGuard g(input.device());
const auto minibatch = input.size(0);
const auto numFeatures = input.size(1);
const auto sequenceLength = input.size(2);
const auto numHeads = weight.size(1);
const auto filterSize = weight.size(2);
const auto numFiltersInBlock = numFeatures / numHeads;
auto numChunks = 1;
auto gradInput = at::zeros_like(input);
auto gradWeight = at::zeros_like(weight);
auto stream = at::cuda::getCurrentCUDAStream();
dim3 blocks(minibatch, numHeads, numChunks);
"""
sequence_if = """
if (sequenceLength < {seq}) {{
switch(filterSize) {{
"""
case_k = """
case {k}:
"""
chunks_reset = """
numChunks = int(ceilf(sequenceLength/float({b_size})));
blocks = dim3(minibatch, numHeads, numChunks);
"""
main_block = """
if (padding_l == {p}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(gradOutput.scalar_type(), "dynamicconv_backward", ([&] {{
dynamicconv_backward_kernel<{k}, {b_size}, {p}, scalar_t>
<<<blocks, {b_size}, 0, stream>>>(
gradOutput.data<scalar_t>(),
input.data<scalar_t>(),
weight.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
numHeads,
gradWeight.data<scalar_t>(),
gradInput.data<scalar_t>());
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl;
}
break;\n
"""
bad_filter = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl;
}
"""
con_else = """
} else
"""
final_else = """
{
switch(filterSize) {
"""
last_return = """
}
return {gradInput, gradWeight};
}
"""
with open("dynamicconv_cuda_backward.cu", "w") as backward:
backward.write(head)
for seq in seqs:
backward.write(sequence_if.format(seq=seq))
for k, t, m in zip(kernels, thresh, min_block):
backward.write(case_k.format(k=k))
if seq <= t:
b_size = seq
else:
b_size = m
backward.write(chunks_reset.format(b_size=b_size))
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=b_size, p=p))
backward.write(bad_padding)
backward.write(bad_filter)
backward.write(con_else)
backward.write(final_else)
for k, m in zip(kernels, min_block):
backward.write(case_k.format(k=k))
backward.write(chunks_reset.format(b_size=m))
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=m, p=p))
backward.write(bad_padding)
backward.write(bad_filter)
backward.write(last_return)
if __name__ == "__main__":
gen_forward()
gen_backward()
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .dynamicconv_layer import DynamicconvLayer # noqa
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/dynamicconv_layer/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import dynamicconv_cuda
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.modules.fairseq_dropout import FairseqDropout
from fairseq.modules.unfold import unfold1d
from torch import nn
from torch.autograd import Function
class dynamicconvFunction(Function):
@staticmethod
def forward(ctx, x, weights, padding_l):
ctx.padding_l = padding_l
outputs = dynamicconv_cuda.forward(x, weights, padding_l)
variables = [x, weights]
ctx.save_for_backward(*variables)
return outputs[0]
@staticmethod
def backward(ctx, grad_output):
outputs = dynamicconv_cuda.backward(
grad_output.contiguous(), ctx.padding_l, *ctx.saved_tensors
)
grad_input, grad_weights = outputs
return grad_input, grad_weights, None
@with_incremental_state
class DynamicconvLayer(nn.Module):
def __init__(
self,
input_size,
kernel_size=1,
padding_l=None,
weight_softmax=False,
num_heads=1,
weight_dropout=0.0,
bias=False,
renorm_padding=False,
conv_bias=False,
query_size=None,
):
super(DynamicconvLayer, self).__init__()
self.input_size = input_size
self.query_size = input_size if query_size is None else query_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_softmax = weight_softmax
self.weight_dropout_module = FairseqDropout(
weight_dropout, module_name=self.__class__.__name__
)
self.renorm_padding = renorm_padding
self.bias = bias
self.weight_linear = nn.Linear(input_size, num_heads * kernel_size, bias)
if conv_bias:
self.conv_bias = nn.Parameter(torch.Tensor(input_size))
else:
self.conv_bias = None
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight_linear.weight)
if self.conv_bias is not None:
nn.init.constant_(self.conv_bias, 0.0)
nn.init.constant_(self.weight_linaer.bias, 0.0)
def forward(self, x, incremental_state=None, query=None, unfold=None):
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
# R = C // H
# during inference time, incremental BMM is faster
if incremental_state is not None:
unfold = (
x.size(0) > 512 if unfold is None else unfold
) # use unfold mode as default for long sequence to save memory
unfold = unfold or (incremental_state is not None)
assert query is None
if query is None:
query = x
if unfold:
output = self._forward_unfolded(x, incremental_state, query)
else:
output = self._forward_expanded(x, incremental_state, query)
if self.conv_bias is not None:
output = output + self.conv_bias.view(1, 1, -1)
return output
# during training time, use CUDA kernel
else:
weight = self.weight_linear(x).view(T, B, H, K)
if self.weight_softmax:
weight = F.softmax(weight, dim=-1)
if self.weight_dropout_module.p:
weight = self.weight_dropout_module(weight)
weight = weight.permute(1, 2, 3, 0).contiguous()
self.filters = weight
x = x.permute(1, 2, 0).contiguous()
output = dynamicconvFunction.apply(x, weight, self.padding_l).permute(
2, 0, 1
)
if self.conv_bias is not None:
output = output + self.conv_bias.view(1, 1, -1)
return output
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, "input_buffer")
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(
self, incremental_state, "input_buffer", new_buffer
)
def _forward_unfolded(self, x, incremental_state, query):
"""The conventional implementation of convolutions.
Unfolding the input by having a window shifting to the right."""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight_linear(query).view(T * B * H, -1)
# renorm_padding is only implemented in _forward_expanded
assert not self.renorm_padding or incremental_state is not None
if incremental_state is not None:
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(
incremental_state, x_unfold[:, :, :, -self.kernel_size + 1 :]
)
x_unfold = x_unfold.view(T * B * H, R, -1)
else:
padding_l = self.padding_l
if K > T and padding_l == K - 1:
weight = weight.narrow(1, K - T, T)
K, padding_l = T, T - 1
# unfold the input: T x B x C --> T' x B x C x K
x_unfold = unfold1d(x, K, padding_l, 0)
x_unfold = x_unfold.view(T * B * H, R, K)
if self.weight_softmax and not self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = weight.narrow(1, 0, K)
if incremental_state is not None:
weight = weight[:, -x_unfold.size(2) :]
K = weight.size(1)
if self.weight_softmax and self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = self.weight_dropout_module(weight, inplace=False)
output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1
output = output.view(T, B, C)
return output
def _forward_expanded(self, x, incremental_stat, query):
"""Turn the convolution filters into band matrices and do matrix multiplication.
This is faster when the sequence is short, but less memory efficient.
This is not used in the decoder during inference.
"""
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight_linear(query).view(T * B * H, -1)
if not self.renorm_padding:
if self.weight_softmax:
weight = F.softmax(weight, dim=1)
weight = self.weight_dropout_module(weight, inplace=False)
weight = weight.narrow(1, 0, K).contiguous()
weight = weight.view(T, B * H, K).transpose(0, 1)
x = x.view(T, B * H, R).transpose(0, 1)
if self.weight_softmax and self.renorm_padding:
# turn the convolution filters into band matrices
weight_expanded = weight.new(B * H, T, T + K - 1).fill_(float("-inf"))
weight_expanded.as_strided(
(B * H, T, K), (T * (T + K - 1), T + K, 1)
).copy_(weight)
weight_expanded = weight_expanded.narrow(2, self.padding_l, T)
# normalize the weight over valid positions like self-attention
weight_expanded = F.softmax(weight_expanded, dim=2)
weight_expanded = self.weight_dropout_module(weight_expanded, inplace=False)
else:
P = self.padding_l
# For efficiency, we cut the kernel size and reduce the padding when the kernel is larger than the length
if K > T and P == K - 1:
weight = weight.narrow(2, K - T, T)
K, P = T, T - 1
# turn the convolution filters into band matrices
weight_expanded = weight.new_zeros(B * H, T, T + K - 1, requires_grad=False)
weight_expanded.as_strided(
(B * H, T, K), (T * (T + K - 1), T + K, 1)
).copy_(weight)
weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T
output = torch.bmm(weight_expanded, x)
output = output.transpose(0, 1).contiguous().view(T, B, C)
return output
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name="dynamicconv_layer",
ext_modules=[
CUDAExtension(
name="dynamicconv_cuda",
sources=[
"dynamicconv_cuda.cpp",
"dynamicconv_cuda_kernel.cu",
],
),
],
cmdclass={"build_ext": BuildExtension},
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/modules/dynamicconv_layer/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class OffsetTokensDataset(BaseWrapperDataset):
def __init__(self, dataset, offset):
super().__init__(dataset)
self.offset = offset
def __getitem__(self, idx):
return self.dataset[idx] + self.offset
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/offset_tokens_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
import torch
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
def _flatten(dico, prefix=None):
"""Flatten a nested dictionary."""
new_dico = OrderedDict()
if isinstance(dico, dict):
prefix = prefix + "." if prefix is not None else ""
for k, v in dico.items():
if v is None:
continue
new_dico.update(_flatten(v, prefix + k))
elif isinstance(dico, list):
for i, v in enumerate(dico):
new_dico.update(_flatten(v, prefix + ".[" + str(i) + "]"))
else:
new_dico = OrderedDict({prefix: dico})
return new_dico
def _unflatten(dico):
"""Unflatten a flattened dictionary into a nested dictionary."""
new_dico = OrderedDict()
for full_k, v in dico.items():
full_k = full_k.split(".")
node = new_dico
for k in full_k[:-1]:
if k.startswith("[") and k.endswith("]"):
k = int(k[1:-1])
if k not in node:
node[k] = OrderedDict()
node = node[k]
node[full_k[-1]] = v
return new_dico
class NestedDictionaryDataset(FairseqDataset):
def __init__(self, defn, sizes=None):
super().__init__()
self.defn = _flatten(defn)
self.sizes = [sizes] if not isinstance(sizes, (list, tuple)) else sizes
first = None
for v in self.defn.values():
if not isinstance(
v,
(
FairseqDataset,
torch.utils.data.Dataset,
),
):
raise ValueError("Expected Dataset but found: {}".format(v.__class__))
first = first or v
if len(v) > 0:
assert len(v) == len(first), "dataset lengths must match"
self._len = len(first)
def __getitem__(self, index):
return OrderedDict((k, ds[index]) for k, ds in self.defn.items())
def __len__(self):
return self._len
def collater(self, samples):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch suitable for forwarding with a Model
"""
if len(samples) == 0:
return {}
sample = OrderedDict()
for k, ds in self.defn.items():
try:
sample[k] = ds.collater([s[k] for s in samples])
except NotImplementedError:
sample[k] = default_collate([s[k] for s in samples])
return _unflatten(sample)
def num_tokens(self, index):
"""Return the number of tokens in a sample. This value is used to
enforce ``--max-tokens`` during batching."""
return max(s[index] for s in self.sizes)
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
if len(self.sizes) == 1:
return self.sizes[0][index]
else:
return (s[index] for s in self.sizes)
@property
def supports_prefetch(self):
"""Whether this dataset supports prefetching."""
return any(ds.supports_prefetch for ds in self.defn.values())
def prefetch(self, indices):
"""Prefetch the data required for this epoch."""
for ds in self.defn.values():
if getattr(ds, "supports_prefetch", False):
ds.prefetch(indices)
@property
def can_reuse_epoch_itr_across_epochs(self):
return all(ds.can_reuse_epoch_itr_across_epochs for ds in self.defn.values())
def set_epoch(self, epoch):
super().set_epoch(epoch)
for ds in self.defn.values():
ds.set_epoch(epoch)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/nested_dictionary_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import time
from collections import OrderedDict
from typing import Dict, List, Optional
import numpy as np
from fairseq.data import data_utils
from . import FairseqDataset
logger = logging.getLogger(__name__)
class MultiCorpusDataset(FairseqDataset):
"""
Stores multiple instances of FairseqDataset together.
Unless batch_sample=True, requires each instance
to be the same dataset, as the collate method needs to work on batches with
samples from each dataset.
Allows specifying a distribution over the datasets to use. Note that unlike
MultiCorpusSampledDataset, this distribution allows sampling for each item,
rather than on a batch level. Note that datasets with sampling probabilty
of 0 will be skipped.
Each time ordered_indices() is called, a new sample is generated with
the specified distribution.
Args:
datasets: a OrderedDict of FairseqDataset instances.
distribution: a List containing the probability of getting an utterance from
corresponding dataset
seed: random seed for sampling the datsets
sort_indices: if true, will sort the ordered indices by size
batch_sample: if true, will ensure each batch is from a single dataset
"""
def __init__(
self,
datasets: Dict[str, FairseqDataset],
distribution: List[float],
seed: int,
sort_indices: bool = False,
batch_sample: bool = False,
distributed_rank: Optional[int] = None,
):
super().__init__()
assert isinstance(datasets, OrderedDict)
assert len(datasets) == len(distribution)
assert sum(distribution) == 1
self.datasets = datasets
self.distribution = distribution
self.seed = seed
self.sort_indices = sort_indices
self.batch_sample = batch_sample
self.distributed_rank = distributed_rank
# Avoid repeated conversions to list later
self.dataset_list = list(datasets.values())
self.total_num_instances = 0
first_dataset = self.dataset_list[0]
self.num_instances_per_dataset = []
self.dataset_offsets = []
for i, dataset in enumerate(self.dataset_list):
assert isinstance(dataset, FairseqDataset)
assert type(dataset) is type(first_dataset)
self.num_instances_per_dataset.append(
0 if self.distribution[i] == 0 else len(dataset)
)
self.dataset_offsets.append(self.total_num_instances)
self.total_num_instances += self.num_instances_per_dataset[i]
def ordered_indices(self):
start = time.time()
with data_utils.numpy_seed(self.seed, self.epoch):
logger.info(
f"sampling new dataset with seed {self.seed} epoch {self.epoch}"
)
sampled_indices = []
num_selected_instances = 0
# For each dataset i, sample self.distribution[i] * self.total_num_instances
for i, key in enumerate(self.datasets):
if self.distribution[i] == 0:
# skip dataset if sampling probability is 0
continue
if i < len(self.datasets) - 1:
num_instances = int(self.distribution[i] * self.total_num_instances)
high = self.dataset_offsets[i + 1]
else:
num_instances = self.total_num_instances - num_selected_instances
high = self.total_num_instances
logger.info(f"sampling {num_instances} from {key} dataset")
num_selected_instances += num_instances
# First, add k copies of the dataset where k = num_instances // len(dataset).
# This ensures an equal distribution of the data points as much as possible.
# For the remaining entries randomly sample them
dataset_size = len(self.datasets[key])
num_copies = num_instances // dataset_size
dataset_indices = (
np.random.permutation(high - self.dataset_offsets[i])
+ self.dataset_offsets[i]
)[: num_instances - num_copies * dataset_size]
if num_copies > 0:
sampled_indices += list(
np.concatenate(
(
np.repeat(
np.arange(self.dataset_offsets[i], high), num_copies
),
dataset_indices,
)
)
)
else:
sampled_indices += list(dataset_indices)
assert (
len(sampled_indices) == self.total_num_instances
), f"{len(sampled_indices)} vs {self.total_num_instances}"
np.random.shuffle(sampled_indices)
if self.sort_indices:
sampled_indices.sort(key=lambda i: self.num_tokens(i))
logger.info(
"multi_corpus_dataset ordered_indices took {}s".format(
time.time() - start
)
)
return np.array(sampled_indices, dtype=np.int64)
def _map_index(self, index: int):
"""
If dataset A has length N and dataset B has length M
then index 1 maps to index 1 of dataset A, and index N + 1
maps to index 1 of B.
"""
counter = 0
for num_instances, key in zip(self.num_instances_per_dataset, self.datasets):
if index < counter + num_instances:
return index - counter, key
counter += num_instances
raise ValueError(
"Invalid index: {}, max: {}".format(index, self.total_num_instances)
)
def __len__(self):
"""
Length of this dataset is the sum of individual datasets
"""
return self.total_num_instances
def __getitem__(self, index):
new_index, key = self._map_index(index)
try:
item = self.datasets[key][new_index]
item["full_id"] = index
return item
except Exception as e:
e.args = (f"Error from {key} dataset", *e.args)
raise
def collater(self, samples):
"""
If we are doing batch sampling, then pick the right collater to use.
Otherwise we assume all collaters are the same.
"""
if len(samples) == 0:
return None
if "full_id" in samples[0]:
_, key = self._map_index(samples[0]["full_id"])
try:
batch = self.datasets[key].collater(samples)
except Exception:
print(f"Collating failed for key {key}", flush=True)
raise
return batch
else:
# Subclasses may override __getitem__ to not specify full_id
return list(self.datasets.values())[0].collater(samples)
def num_tokens(self, index: int):
index, key = self._map_index(index)
return self.datasets[key].num_tokens(index)
def size(self, index: int):
index, key = self._map_index(index)
return self.datasets[key].size(index)
@property
def can_reuse_epoch_itr_across_epochs(self):
return False
def set_epoch(self, epoch, **unused):
super().set_epoch(epoch)
logger.info(f"setting epoch of multi_corpus_dataset to {epoch}")
self.epoch = epoch
@property
def supports_prefetch(self):
return False
@property
def supports_fetch_outside_dataloader(self):
return all(
self.datasets[key].supports_fetch_outside_dataloader
for key in self.datasets
)
def batch_by_size(
self,
indices,
max_tokens=None,
max_sentences=None,
required_batch_size_multiple=1,
):
if not self.batch_sample:
return super().batch_by_size(
indices, max_tokens, max_sentences, required_batch_size_multiple
)
dataset_indices = {key: [] for key in self.datasets}
for i in indices:
_, key = self._map_index(i)
dataset_indices[key].append(i)
batches = []
for key in dataset_indices:
cur_batches = super().batch_by_size(
np.array(dataset_indices[key], dtype=np.int64),
max_tokens,
max_sentences,
required_batch_size_multiple,
)
logger.info(f"Created {len(cur_batches)} batches for dataset {key}")
batches += cur_batches
# If this dataset is used in a distributed training setup,
# then shuffle such that the order is seeded by the distributed rank
# as well
if self.distributed_rank is not None:
with data_utils.numpy_seed(self.seed, self.epoch, self.distributed_rank):
np.random.shuffle(batches)
return batches
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/multi_corpus_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import bisect
import numpy as np
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class ConcatDataset(FairseqDataset):
@staticmethod
def cumsum(sequence, sample_ratios):
r, s = [], 0
for e, ratio in zip(sequence, sample_ratios):
curr_len = int(ratio * len(e))
r.append(curr_len + s)
s += curr_len
return r
def __init__(self, datasets, sample_ratios=1):
super(ConcatDataset, self).__init__()
assert len(datasets) > 0, "datasets should not be an empty iterable"
self.datasets = list(datasets)
if isinstance(sample_ratios, int):
sample_ratios = [sample_ratios] * len(self.datasets)
self.sample_ratios = sample_ratios
self.cumulative_sizes = self.cumsum(self.datasets, sample_ratios)
self.real_sizes = [len(d) for d in self.datasets]
def __len__(self):
return self.cumulative_sizes[-1]
def __getitem__(self, idx):
dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
return self.datasets[dataset_idx][sample_idx]
def _get_dataset_and_sample_index(self, idx: int):
dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
if dataset_idx == 0:
sample_idx = idx
else:
sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
sample_idx = sample_idx % self.real_sizes[dataset_idx]
return dataset_idx, sample_idx
def collater(self, samples, **extra_args):
# For now only supports datasets with same underlying collater implementations
if hasattr(self.datasets[0], "collater"):
return self.datasets[0].collater(samples, **extra_args)
else:
return default_collate(samples, **extra_args)
def size(self, idx: int):
"""
Return an example's size as a float or tuple.
"""
dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
return self.datasets[dataset_idx].size(sample_idx)
def num_tokens(self, index: int):
return np.max(self.size(index))
def attr(self, attr: str, index: int):
dataset_idx = bisect.bisect_right(self.cumulative_sizes, index)
return getattr(self.datasets[dataset_idx], attr, None)
@property
def sizes(self):
_dataset_sizes = []
for ds, sr in zip(self.datasets, self.sample_ratios):
if isinstance(ds.sizes, np.ndarray):
_dataset_sizes.append(np.tile(ds.sizes, sr))
else:
# Only support underlying dataset with single size array.
assert isinstance(ds.sizes, list)
_dataset_sizes.append(np.tile(ds.sizes[0], sr))
return np.concatenate(_dataset_sizes)
@property
def supports_prefetch(self):
return all(d.supports_prefetch for d in self.datasets)
def ordered_indices(self):
"""
Returns indices sorted by length. So less padding is needed.
"""
if isinstance(self.sizes, np.ndarray) and len(self.sizes.shape) > 1:
# special handling for concatenating lang_pair_datasets
indices = np.arange(len(self))
sizes = self.sizes
tgt_sizes = (
sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
)
src_sizes = (
sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
)
# sort by target length, then source length
if tgt_sizes is not None:
indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
return indices[np.argsort(src_sizes[indices], kind="mergesort")]
else:
return np.argsort(self.sizes)
def prefetch(self, indices):
frm = 0
for to, ds in zip(self.cumulative_sizes, self.datasets):
real_size = len(ds)
if getattr(ds, "supports_prefetch", False):
ds.prefetch([(i - frm) % real_size for i in indices if frm <= i < to])
frm = to
@property
def can_reuse_epoch_itr_across_epochs(self):
return all(d.can_reuse_epoch_itr_across_epochs for d in self.datasets)
def set_epoch(self, epoch):
super().set_epoch(epoch)
for ds in self.datasets:
if hasattr(ds, "set_epoch"):
ds.set_epoch(epoch)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/concat_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class ReplaceDataset(BaseWrapperDataset):
"""Replaces tokens found in the dataset by a specified replacement token
Args:
dataset (~torch.utils.data.Dataset): dataset to replace tokens in
replace_map(Dictionary[int,int]): map of token to replace -> replacement token
offsets (List[int]): do not replace tokens before (from left if pos, right if neg) this offset. should be
as many as the number of objects returned by the underlying dataset __getitem__ method.
"""
def __init__(self, dataset, replace_map, offsets):
super().__init__(dataset)
assert len(replace_map) > 0
self.replace_map = replace_map
self.offsets = offsets
def __getitem__(self, index):
item = self.dataset[index]
is_tuple = isinstance(item, tuple)
srcs = item if is_tuple else [item]
for offset, src in zip(self.offsets, srcs):
for k, v in self.replace_map.items():
src_off = src[offset:] if offset >= 0 else src[:offset]
src_off.masked_fill_(src_off == k, v)
item = srcs if is_tuple else srcs[0]
return item
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/replace_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import utils
from . import FairseqDataset
def backtranslate_samples(samples, collate_fn, generate_fn, cuda=True):
"""Backtranslate a list of samples.
Given an input (*samples*) of the form:
[{'id': 1, 'source': 'hallo welt'}]
this will return:
[{'id': 1, 'source': 'hello world', 'target': 'hallo welt'}]
Args:
samples (List[dict]): samples to backtranslate. Individual samples are
expected to have a 'source' key, which will become the 'target'
after backtranslation.
collate_fn (callable): function to collate samples into a mini-batch
generate_fn (callable): function to generate backtranslations
cuda (bool): use GPU for generation (default: ``True``)
Returns:
List[dict]: an updated list of samples with a backtranslated source
"""
collated_samples = collate_fn(samples)
s = utils.move_to_cuda(collated_samples) if cuda else collated_samples
generated_sources = generate_fn(s)
id_to_src = {sample["id"]: sample["source"] for sample in samples}
# Go through each tgt sentence in batch and its corresponding best
# generated hypothesis and create a backtranslation data pair
# {id: id, source: generated backtranslation, target: original tgt}
return [
{
"id": id.item(),
"target": id_to_src[id.item()],
"source": hypos[0]["tokens"].cpu(),
}
for id, hypos in zip(collated_samples["id"], generated_sources)
]
class BacktranslationDataset(FairseqDataset):
"""
Sets up a backtranslation dataset which takes a tgt batch, generates
a src using a tgt-src backtranslation function (*backtranslation_fn*),
and returns the corresponding `{generated src, input tgt}` batch.
Args:
tgt_dataset (~fairseq.data.FairseqDataset): the dataset to be
backtranslated. Only the source side of this dataset will be used.
After backtranslation, the source sentences in this dataset will be
returned as the targets.
src_dict (~fairseq.data.Dictionary): the dictionary of backtranslated
sentences.
tgt_dict (~fairseq.data.Dictionary, optional): the dictionary of
sentences to be backtranslated.
backtranslation_fn (callable, optional): function to call to generate
backtranslations. This is typically the `generate` method of a
:class:`~fairseq.sequence_generator.SequenceGenerator` object.
Pass in None when it is not available at initialization time, and
use set_backtranslation_fn function to set it when available.
output_collater (callable, optional): function to call on the
backtranslated samples to create the final batch
(default: ``tgt_dataset.collater``).
cuda: use GPU for generation
"""
def __init__(
self,
tgt_dataset,
src_dict,
tgt_dict=None,
backtranslation_fn=None,
output_collater=None,
cuda=True,
**kwargs
):
self.tgt_dataset = tgt_dataset
self.backtranslation_fn = backtranslation_fn
self.output_collater = (
output_collater if output_collater is not None else tgt_dataset.collater
)
self.cuda = cuda if torch.cuda.is_available() else False
self.src_dict = src_dict
self.tgt_dict = tgt_dict
def __getitem__(self, index):
"""
Returns a single sample from *tgt_dataset*. Note that backtranslation is
not applied in this step; use :func:`collater` instead to backtranslate
a batch of samples.
"""
return self.tgt_dataset[index]
def __len__(self):
return len(self.tgt_dataset)
def set_backtranslation_fn(self, backtranslation_fn):
self.backtranslation_fn = backtranslation_fn
def collater(self, samples):
"""Merge and backtranslate a list of samples to form a mini-batch.
Using the samples from *tgt_dataset*, load a collated target sample to
feed to the backtranslation model. Then take the backtranslation with
the best score as the source and the original input as the target.
Note: we expect *tgt_dataset* to provide a function `collater()` that
will collate samples into the format expected by *backtranslation_fn*.
After backtranslation, we will feed the new list of samples (i.e., the
`(backtranslated source, original source)` pairs) to *output_collater*
and return the result.
Args:
samples (List[dict]): samples to backtranslate and collate
Returns:
dict: a mini-batch with keys coming from *output_collater*
"""
if samples[0].get("is_dummy", False):
return samples
samples = backtranslate_samples(
samples=samples,
collate_fn=self.tgt_dataset.collater,
generate_fn=(lambda net_input: self.backtranslation_fn(net_input)),
cuda=self.cuda,
)
return self.output_collater(samples)
def num_tokens(self, index):
"""Just use the tgt dataset num_tokens"""
return self.tgt_dataset.num_tokens(index)
def ordered_indices(self):
"""Just use the tgt dataset ordered_indices"""
return self.tgt_dataset.ordered_indices()
def size(self, index):
"""Return an example's size as a float or tuple. This value is used
when filtering a dataset with ``--max-positions``.
Note: we use *tgt_dataset* to approximate the length of the source
sentence, since we do not know the actual length until after
backtranslation.
"""
tgt_size = self.tgt_dataset.size(index)[0]
return (tgt_size, tgt_size)
@property
def supports_prefetch(self):
return getattr(self.tgt_dataset, "supports_prefetch", False)
def prefetch(self, indices):
return self.tgt_dataset.prefetch(indices)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/backtranslation_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class IdDataset(FairseqDataset):
def __getitem__(self, index):
return index
def __len__(self):
return 0
def collater(self, samples):
return torch.tensor(samples)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/id_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class PrependDataset(BaseWrapperDataset):
def __init__(self, dataset, prepend_getter, ensure_first_token_is=None):
super().__init__(dataset)
self.prepend_getter = prepend_getter
self.ensure_first_token = ensure_first_token_is
def __getitem__(self, idx):
item = self.dataset[idx]
is_tuple = isinstance(item, tuple)
src = item[0] if is_tuple else item
assert self.ensure_first_token is None or src[0] == self.ensure_first_token
prepend_idx = self.prepend_getter(self.dataset, idx)
assert isinstance(prepend_idx, int)
src[0] = prepend_idx
item = tuple((src,) + item[1:]) if is_tuple else src
return item
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/prepend_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
from typing import Callable, Dict, List
import numpy as np
from . import FairseqDataset
def uniform_sampler(x):
# Sample from uniform distribution
return np.random.choice(x, 1).item()
class MultiCorpusSampledDataset(FairseqDataset):
"""
Stores multiple instances of FairseqDataset together and in every iteration
creates a batch by first sampling a dataset according to a specified
probability distribution and then getting instances from that dataset.
Args:
datasets: an OrderedDict of FairseqDataset instances.
sampling_func: A function for sampling over list of dataset keys.
The default strategy is to sample uniformly.
"""
def __init__(
self,
datasets: Dict[str, FairseqDataset],
sampling_func: Callable[[List], int] = None,
):
super().__init__()
assert isinstance(datasets, OrderedDict)
self.datasets = datasets
if sampling_func is None:
sampling_func = uniform_sampler
self.sampling_func = sampling_func
self.total_num_instances = 0
for _, dataset in datasets.items():
assert isinstance(dataset, FairseqDataset)
self.total_num_instances += len(dataset)
self._ordered_indices = None
def __len__(self):
"""
Length of this dataset is the sum of individual datasets
"""
return self.total_num_instances
def ordered_indices(self):
"""
Ordered indices for batching. Here we call the underlying
dataset's ordered_indices() so that we get the same random ordering
as we would have from using the underlying dataset directly.
"""
if self._ordered_indices is None:
self._ordered_indices = OrderedDict(
[
(key, dataset.ordered_indices())
for key, dataset in self.datasets.items()
]
)
return np.arange(len(self))
def _map_index_to_dataset(self, key: int, index: int):
"""
Different underlying datasets have different lengths. In order to ensure
we are not accessing an index outside the range of the current dataset
size, we wrap around. This function should be called after we have
created an ordering for this and all underlying datasets.
"""
assert (
self._ordered_indices is not None
), "Must call MultiCorpusSampledDataset.ordered_indices() first"
mapped_index = index % len(self.datasets[key])
return self._ordered_indices[key][mapped_index]
def __getitem__(self, index: int):
"""
Get the item associated with index from each underlying dataset.
Since index is in the range of [0, TotalNumInstances], we need to
map the index to the dataset before retrieving the item.
"""
return OrderedDict(
[
(key, dataset[self._map_index_to_dataset(key, index)])
for key, dataset in self.datasets.items()
]
)
def collater(self, samples: List[Dict]):
"""
Generate a mini-batch for this dataset.
To convert this into a regular mini-batch we use the following
logic:
1. Select a dataset using the specified probability distribution.
2. Call the collater function of the selected dataset.
"""
if len(samples) == 0:
return None
selected_key = self.sampling_func(list(self.datasets.keys()))
selected_samples = [sample[selected_key] for sample in samples]
return self.datasets[selected_key].collater(selected_samples)
def num_tokens(self, index: int):
"""
Return an example's length (number of tokens), used for batching. Here
we return the max across all examples at index across all underlying
datasets.
"""
return max(
dataset.num_tokens(self._map_index_to_dataset(key, index))
for key, dataset in self.datasets.items()
)
def size(self, index: int):
"""
Return an example's size as a float or tuple. Here we return the max
across all underlying datasets. This value is used when filtering a
dataset with max-positions.
"""
return max(
dataset.size(self._map_index_to_dataset(key, index))
for key, dataset in self.datasets.items()
)
@property
def supports_prefetch(self):
return all(
getattr(dataset, "supports_prefetch", False)
for dataset in self.datasets.values()
)
def prefetch(self, indices):
for key, dataset in self.datasets.items():
dataset.prefetch(
[self._map_index_to_dataset(key, index) for index in indices]
)
@property
def supports_fetch_outside_dataloader(self):
return all(
self.datasets[key].supports_fetch_outside_dataloader
for key in self.datasets
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/multi_corpus_sampled_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqDataset
class NumSamplesDataset(FairseqDataset):
def __getitem__(self, index):
return 1
def __len__(self):
return 0
def collater(self, samples):
return sum(samples)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/num_samples_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from fairseq.data import data_utils
class WordNoising(object):
"""Generate a noisy version of a sentence, without changing words themselves."""
def __init__(self, dictionary, bpe_cont_marker="@@", bpe_end_marker=None):
self.dictionary = dictionary
self.bpe_end = None
if bpe_cont_marker:
self.bpe_end = np.array(
[
not self.dictionary[i].endswith(bpe_cont_marker)
for i in range(len(self.dictionary))
]
)
elif bpe_end_marker:
self.bpe_end = np.array(
[
self.dictionary[i].endswith(bpe_end_marker)
for i in range(len(self.dictionary))
]
)
self.get_word_idx = (
self._get_bpe_word_idx if self.bpe_end is not None else self._get_token_idx
)
def noising(self, x, lengths, noising_prob=0.0):
raise NotImplementedError()
def _get_bpe_word_idx(self, x):
"""
Given a list of BPE tokens, for every index in the tokens list,
return the index of the word grouping that it belongs to.
For example, for input x corresponding to ["how", "are", "y@@", "ou"],
return [[0], [1], [2], [2]].
"""
# x: (T x B)
bpe_end = self.bpe_end[x]
if x.size(0) == 1 and x.size(1) == 1:
# Special case when we only have one word in x. If x = [[N]],
# bpe_end is a scalar (bool) instead of a 2-dim array of bools,
# which makes the sum operation below fail.
return np.array([[0]])
# do a reduce front sum to generate word ids
word_idx = bpe_end[::-1].cumsum(0)[::-1]
word_idx = word_idx.max(0)[None, :] - word_idx
return word_idx
def _get_token_idx(self, x):
"""
This is to extend noising functions to be able to apply to non-bpe
tokens, e.g. word or characters.
"""
x = torch.t(x)
word_idx = np.array([range(len(x_i)) for x_i in x])
return np.transpose(word_idx)
class WordDropout(WordNoising):
"""Randomly drop input words. If not passing blank_idx (default is None),
then dropped words will be removed. Otherwise, it will be replaced by the
blank_idx."""
def __init__(
self,
dictionary,
default_dropout_prob=0.1,
bpe_cont_marker="@@",
bpe_end_marker=None,
):
super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
self.default_dropout_prob = default_dropout_prob
def noising(self, x, lengths, dropout_prob=None, blank_idx=None):
if dropout_prob is None:
dropout_prob = self.default_dropout_prob
# x: (T x B), lengths: B
if dropout_prob == 0:
return x, lengths
assert 0 < dropout_prob < 1
# be sure to drop entire words
word_idx = self.get_word_idx(x)
sentences = []
modified_lengths = []
for i in range(lengths.size(0)):
# Since dropout probabilities need to apply over non-pad tokens,
# it is not trivial to generate the keep mask without consider
# input lengths; otherwise, this could be done outside the loop
# We want to drop whole words based on word_idx grouping
num_words = max(word_idx[:, i]) + 1
# ith example: [x0, x1, ..., eos, pad, ..., pad]
# We should only generate keep probs for non-EOS tokens. Thus if the
# input sentence ends in EOS, the last word idx is not included in
# the dropout mask generation and we append True to always keep EOS.
# Otherwise, just generate the dropout mask for all word idx
# positions.
has_eos = x[lengths[i] - 1, i] == self.dictionary.eos()
if has_eos: # has eos?
keep = np.random.rand(num_words - 1) >= dropout_prob
keep = np.append(keep, [True]) # keep EOS symbol
else:
keep = np.random.rand(num_words) >= dropout_prob
words = x[: lengths[i], i].tolist()
# TODO: speed up the following loop
# drop words from the input according to keep
new_s = [
w if keep[word_idx[j, i]] else blank_idx for j, w in enumerate(words)
]
new_s = [w for w in new_s if w is not None]
# we need to have at least one word in the sentence (more than the
# start / end sentence symbols)
if len(new_s) <= 1:
# insert at beginning in case the only token left is EOS
# EOS should be at end of list.
new_s.insert(0, words[np.random.randint(0, len(words))])
assert len(new_s) >= 1 and (
not has_eos # Either don't have EOS at end or last token is EOS
or (len(new_s) >= 2 and new_s[-1] == self.dictionary.eos())
), "New sentence is invalid."
sentences.append(new_s)
modified_lengths.append(len(new_s))
# re-construct input
modified_lengths = torch.LongTensor(modified_lengths)
modified_x = torch.LongTensor(
modified_lengths.max(), modified_lengths.size(0)
).fill_(self.dictionary.pad())
for i in range(modified_lengths.size(0)):
modified_x[: modified_lengths[i], i].copy_(torch.LongTensor(sentences[i]))
return modified_x, modified_lengths
class WordShuffle(WordNoising):
"""Shuffle words by no more than k positions."""
def __init__(
self,
dictionary,
default_max_shuffle_distance=3,
bpe_cont_marker="@@",
bpe_end_marker=None,
):
super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
self.default_max_shuffle_distance = 3
def noising(self, x, lengths, max_shuffle_distance=None):
if max_shuffle_distance is None:
max_shuffle_distance = self.default_max_shuffle_distance
# x: (T x B), lengths: B
if max_shuffle_distance == 0:
return x, lengths
# max_shuffle_distance < 1 will return the same sequence
assert max_shuffle_distance > 1
# define noise word scores
noise = np.random.uniform(
0,
max_shuffle_distance,
size=(x.size(0), x.size(1)),
)
noise[0] = -1 # do not move start sentence symbol
# be sure to shuffle entire words
word_idx = self.get_word_idx(x)
x2 = x.clone()
for i in range(lengths.size(0)):
length_no_eos = lengths[i]
if x[lengths[i] - 1, i] == self.dictionary.eos():
length_no_eos = lengths[i] - 1
# generate a random permutation
scores = word_idx[:length_no_eos, i] + noise[word_idx[:length_no_eos, i], i]
# ensure no reordering inside a word
scores += 1e-6 * np.arange(length_no_eos.item())
permutation = scores.argsort()
# shuffle words
x2[:length_no_eos, i].copy_(
x2[:length_no_eos, i][torch.from_numpy(permutation)]
)
return x2, lengths
class UnsupervisedMTNoising(WordNoising):
"""
Implements the default configuration for noising in UnsupervisedMT
(github.com/facebookresearch/UnsupervisedMT)
"""
def __init__(
self,
dictionary,
max_word_shuffle_distance,
word_dropout_prob,
word_blanking_prob,
bpe_cont_marker="@@",
bpe_end_marker=None,
):
super().__init__(dictionary)
self.max_word_shuffle_distance = max_word_shuffle_distance
self.word_dropout_prob = word_dropout_prob
self.word_blanking_prob = word_blanking_prob
self.word_dropout = WordDropout(
dictionary=dictionary,
bpe_cont_marker=bpe_cont_marker,
bpe_end_marker=bpe_end_marker,
)
self.word_shuffle = WordShuffle(
dictionary=dictionary,
bpe_cont_marker=bpe_cont_marker,
bpe_end_marker=bpe_end_marker,
)
def noising(self, x, lengths):
# 1. Word Shuffle
noisy_src_tokens, noisy_src_lengths = self.word_shuffle.noising(
x=x,
lengths=lengths,
max_shuffle_distance=self.max_word_shuffle_distance,
)
# 2. Word Dropout
noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
x=noisy_src_tokens,
lengths=noisy_src_lengths,
dropout_prob=self.word_dropout_prob,
)
# 3. Word Blanking
noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
x=noisy_src_tokens,
lengths=noisy_src_lengths,
dropout_prob=self.word_blanking_prob,
blank_idx=self.dictionary.unk(),
)
return noisy_src_tokens
class NoisingDataset(torch.utils.data.Dataset):
def __init__(
self,
src_dataset,
src_dict,
seed,
noiser=None,
noising_class=UnsupervisedMTNoising,
**kwargs
):
"""
Wrap a :class:`~torch.utils.data.Dataset` and apply noise to the
samples based on the supplied noising configuration.
Args:
src_dataset (~torch.utils.data.Dataset): dataset to wrap.
to build self.src_dataset --
a LanguagePairDataset with src dataset as the source dataset and
None as the target dataset. Should NOT have padding so that
src_lengths are accurately calculated by language_pair_dataset
collate function.
We use language_pair_dataset here to encapsulate the tgt_dataset
so we can re-use the LanguagePairDataset collater to format the
batches in the structure that SequenceGenerator expects.
src_dict (~fairseq.data.Dictionary): source dictionary
seed (int): seed to use when generating random noise
noiser (WordNoising): a pre-initialized :class:`WordNoising`
instance. If this is None, a new instance will be created using
*noising_class* and *kwargs*.
noising_class (class, optional): class to use to initialize a
default :class:`WordNoising` instance.
kwargs (dict, optional): arguments to initialize the default
:class:`WordNoising` instance given by *noiser*.
"""
self.src_dataset = src_dataset
self.src_dict = src_dict
self.seed = seed
self.noiser = (
noiser
if noiser is not None
else noising_class(
dictionary=src_dict,
**kwargs,
)
)
self.sizes = src_dataset.sizes
def __getitem__(self, index):
"""
Returns a single noisy sample. Multiple samples are fed to the collater
create a noising dataset batch.
"""
src_tokens = self.src_dataset[index]
src_lengths = torch.LongTensor([len(src_tokens)])
src_tokens = src_tokens.unsqueeze(0)
# Transpose src tokens to fit expected shape of x in noising function
# (batch size, sequence length) -> (sequence length, batch size)
src_tokens_t = torch.t(src_tokens)
with data_utils.numpy_seed(self.seed + index):
noisy_src_tokens = self.noiser.noising(src_tokens_t, src_lengths)
# Transpose back to expected src_tokens format
# (sequence length, 1) -> (1, sequence length)
noisy_src_tokens = torch.t(noisy_src_tokens)
return noisy_src_tokens[0]
def __len__(self):
"""
The length of the noising dataset is the length of src.
"""
return len(self.src_dataset)
@property
def supports_prefetch(self):
return self.src_dataset.supports_prefetch
def prefetch(self, indices):
if self.src_dataset.supports_prefetch:
self.src_dataset.prefetch(indices)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/noising.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from fairseq.data import data_utils
from . import BaseWrapperDataset
class TruncateDataset(BaseWrapperDataset):
"""Truncate a sequence by returning the first truncation_length tokens"""
def __init__(self, dataset, truncation_length):
super().__init__(dataset)
assert truncation_length is not None
self.truncation_length = truncation_length
self.dataset = dataset
def __getitem__(self, index):
item = self.dataset[index]
item_len = item.size(0)
if item_len > self.truncation_length:
item = item[: self.truncation_length]
return item
@property
def sizes(self):
return np.minimum(self.dataset.sizes, self.truncation_length)
def __len__(self):
return len(self.dataset)
class RandomCropDataset(TruncateDataset):
"""Truncate a sequence by returning a random crop of truncation_length tokens"""
def __init__(self, dataset, truncation_length, seed=1):
super().__init__(dataset, truncation_length)
self.seed = seed
self.epoch = 0
@property
def can_reuse_epoch_itr_across_epochs(self):
return True # only the crop changes, not item sizes
def set_epoch(self, epoch, **unused):
super().set_epoch(epoch)
self.epoch = epoch
def __getitem__(self, index):
with data_utils.numpy_seed(self.seed, self.epoch, index):
item = self.dataset[index]
item_len = item.size(0)
excess = item_len - self.truncation_length
if excess > 0:
start_idx = np.random.randint(0, excess)
item = item[start_idx : start_idx + self.truncation_length]
return item
def maybe_shorten_dataset(
dataset,
split,
shorten_data_split_list,
shorten_method,
tokens_per_sample,
seed,
):
truncate_split = (
split in shorten_data_split_list.split(",") or len(shorten_data_split_list) == 0
)
if shorten_method == "truncate" and truncate_split:
dataset = TruncateDataset(dataset, tokens_per_sample)
elif shorten_method == "random_crop" and truncate_split:
dataset = RandomCropDataset(dataset, tokens_per_sample, seed)
return dataset
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/shorten_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import numpy as np
from . import BaseWrapperDataset
logger = logging.getLogger(__name__)
class SubsampleDataset(BaseWrapperDataset):
"""Subsamples a given dataset by a specified ratio. Subsampling is done on the number of examples
Args:
dataset (~torch.utils.data.Dataset): dataset to subsample
size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive)
"""
def __init__(self, dataset, size_ratio, shuffle=False):
super().__init__(dataset)
assert size_ratio < 1
self.actual_size = np.ceil(len(dataset) * size_ratio).astype(int)
self.indices = np.random.choice(
list(range(len(self.dataset))), self.actual_size, replace=False
)
self.shuffle = shuffle
logger.info(
"subsampled dataset from {} to {} (ratio={})".format(
len(self.dataset), self.actual_size, size_ratio
)
)
def __getitem__(self, index):
return self.dataset[self.indices[index]]
def __len__(self):
return self.actual_size
def collater(self, samples):
return self.dataset.collater(samples)
@property
def sizes(self):
return self.dataset.sizes[self.indices]
@property
def name(self):
return self.dataset.name
def num_tokens(self, index):
return self.dataset.num_tokens(self.indices[index])
def size(self, index):
return self.dataset.size(self.indices[index])
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
if self.shuffle:
order = [np.random.permutation(len(self))]
else:
order = [np.arange(len(self))]
order.append(self.sizes)
return np.lexsort(order)
def prefetch(self, indices):
self.dataset.prefetch(self.indices[indices])
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/subsample_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import BaseWrapperDataset
class SortDataset(BaseWrapperDataset):
def __init__(self, dataset, sort_order):
super().__init__(dataset)
if not isinstance(sort_order, (list, tuple)):
sort_order = [sort_order]
self.sort_order = sort_order
assert all(len(so) == len(dataset) for so in sort_order)
def ordered_indices(self):
return np.lexsort(self.sort_order)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/sort_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from enum import Enum
class TextCompressionLevel(Enum):
none = 0
low = 1
high = 2
class TextCompressor(object):
def __init__(
self, level: TextCompressionLevel, max_input_byte_length: int = 2 ** 16
):
self.level = level
self.max_input_length = max_input_byte_length
def compress(self, text: str) -> bytes:
if self.level == TextCompressionLevel.low:
import zlib
# zlib: built-in, fast
return zlib.compress(text.encode(), level=0)
elif self.level == TextCompressionLevel.high:
try:
import unishox2
# unishox2: optimized for short text but slower
except ImportError:
raise ImportError(
"Please install unishox2 for the text compression feature: "
"pip install unishox2-py3"
)
assert len(text.encode()) <= self.max_input_length
return unishox2.compress(text)[0]
else:
return text.encode()
def decompress(self, compressed: bytes) -> str:
if self.level == TextCompressionLevel.low:
import zlib
return zlib.decompress(compressed).decode()
elif self.level == TextCompressionLevel.high:
try:
import unishox2
except ImportError:
raise ImportError(
"Please install unishox2 for the text compression feature: "
"pip install unishox2-py3"
)
return unishox2.decompress(compressed, self.max_input_length)
else:
return compressed.decode()
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/text_compressor.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import FairseqDataset, data_utils
def collate(samples, pad_idx, eos_idx, fixed_pad_length=None, pad_to_bsz=None):
if len(samples) == 0:
return {}
def merge(key, is_list=False):
if is_list:
res = []
for i in range(len(samples[0][key])):
res.append(
data_utils.collate_tokens(
[s[key][i] for s in samples],
pad_idx,
eos_idx,
left_pad=False,
pad_to_length=fixed_pad_length,
pad_to_bsz=pad_to_bsz,
)
)
return res
else:
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx,
eos_idx,
left_pad=False,
pad_to_length=fixed_pad_length,
pad_to_bsz=pad_to_bsz,
)
src_tokens = merge("source")
if samples[0]["target"] is not None:
is_target_list = isinstance(samples[0]["target"], list)
target = merge("target", is_target_list)
else:
target = src_tokens
return {
"id": torch.LongTensor([s["id"] for s in samples]),
"nsentences": len(samples),
"ntokens": sum(len(s["source"]) for s in samples),
"net_input": {
"src_tokens": src_tokens,
"src_lengths": torch.LongTensor([s["source"].numel() for s in samples]),
},
"target": target,
}
class MonolingualDataset(FairseqDataset):
"""
A wrapper around torch.utils.data.Dataset for monolingual data.
Args:
dataset (torch.utils.data.Dataset): dataset to wrap
sizes (List[int]): sentence lengths
vocab (~fairseq.data.Dictionary): vocabulary
shuffle (bool, optional): shuffle the elements before batching
(default: True).
"""
def __init__(
self,
dataset,
sizes,
src_vocab,
tgt_vocab=None,
add_eos_for_other_targets=False,
shuffle=False,
targets=None,
add_bos_token=False,
fixed_pad_length=None,
pad_to_bsz=None,
src_lang_idx=None,
tgt_lang_idx=None,
):
self.dataset = dataset
self.sizes = np.array(sizes)
self.vocab = src_vocab
self.tgt_vocab = tgt_vocab or src_vocab
self.add_eos_for_other_targets = add_eos_for_other_targets
self.shuffle = shuffle
self.add_bos_token = add_bos_token
self.fixed_pad_length = fixed_pad_length
self.pad_to_bsz = pad_to_bsz
self.src_lang_idx = src_lang_idx
self.tgt_lang_idx = tgt_lang_idx
assert targets is None or all(
t in {"self", "future", "past"} for t in targets
), "targets must be none or one of 'self', 'future', 'past'"
if targets is not None and len(targets) == 0:
targets = None
self.targets = targets
def __getitem__(self, index):
if self.targets is not None:
# *future_target* is the original sentence
# *source* is shifted right by 1 (maybe left-padded with eos)
# *past_target* is shifted right by 2 (left-padded as needed)
#
# Left-to-right language models should condition on *source* and
# predict *future_target*.
# Right-to-left language models should condition on *source* and
# predict *past_target*.
source, future_target, past_target = self.dataset[index]
source, target = self._make_source_target(
source, future_target, past_target
)
else:
source = self.dataset[index]
target = None
source, target = self._maybe_add_bos(source, target)
return {"id": index, "source": source, "target": target}
def __len__(self):
return len(self.dataset)
def _make_source_target(self, source, future_target, past_target):
if self.targets is not None:
target = []
if (
self.add_eos_for_other_targets
and (("self" in self.targets) or ("past" in self.targets))
and source[-1] != self.vocab.eos()
):
# append eos at the end of source
source = torch.cat([source, source.new([self.vocab.eos()])])
if "future" in self.targets:
future_target = torch.cat(
[future_target, future_target.new([self.vocab.pad()])]
)
if "past" in self.targets:
# first token is before the start of sentence which is only used in "none" break mode when
# add_eos_for_other_targets is False
past_target = torch.cat(
[
past_target.new([self.vocab.pad()]),
past_target[1:],
source[-2, None],
]
)
for t in self.targets:
if t == "self":
target.append(source)
elif t == "future":
target.append(future_target)
elif t == "past":
target.append(past_target)
else:
raise Exception("invalid target " + t)
if len(target) == 1:
target = target[0]
else:
target = future_target
return source, self._filter_vocab(target)
def _maybe_add_bos(self, source, target):
if self.add_bos_token:
source = torch.cat([source.new([self.vocab.bos()]), source])
if target is not None:
target = torch.cat([target.new([self.tgt_vocab.bos()]), target])
return source, target
def num_tokens_vec(self, indices):
"""Return the number of tokens for a set of positions defined by indices.
This value is used to enforce ``--max-tokens`` during batching."""
return self.sizes[indices]
def _filter_vocab(self, target):
if len(self.tgt_vocab) != len(self.vocab):
def _filter(target):
mask = target.ge(len(self.tgt_vocab))
if mask.any():
target[mask] = self.tgt_vocab.unk()
return target
if isinstance(target, list):
return [_filter(t) for t in target]
return _filter(target)
return target
def collater(self, samples):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch with the following keys:
- `id` (LongTensor): example IDs in the original input order
- `ntokens` (int): total number of tokens in the batch
- `net_input` (dict): the input to the Model, containing keys:
- `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
the source sentence of shape `(bsz, src_len)`. Padding will
appear on the right.
- `target` (LongTensor): a padded 2D Tensor of tokens in the
target sentence of shape `(bsz, tgt_len)`. Padding will appear
on the right.
"""
return collate(
samples,
self.vocab.pad(),
self.vocab.eos(),
self.fixed_pad_length,
self.pad_to_bsz,
)
def num_tokens(self, index):
"""Return the number of tokens in a sample. This value is used to
enforce ``--max-tokens`` during batching."""
return self.sizes[index]
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return self.sizes[index]
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
if self.shuffle:
order = [np.random.permutation(len(self))]
else:
order = [np.arange(len(self))]
order.append(self.sizes)
return np.lexsort(order)
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
self.dataset.prefetch(indices)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/monolingual_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import subprocess
import threading
from pathlib import Path
import numpy as np
import torch
def fasta_file_path(prefix_path):
return prefix_path + ".fasta"
class FastaDataset(torch.utils.data.Dataset):
"""
For loading protein sequence datasets in the common FASTA data format
"""
def __init__(self, path: str, cache_indices=False):
self.fn = fasta_file_path(path)
self.threadlocal = threading.local()
self.cache = Path(f"{path}.fasta.idx.npy")
if cache_indices:
if self.cache.exists():
self.offsets, self.sizes = np.load(self.cache)
else:
self.offsets, self.sizes = self._build_index(path)
np.save(self.cache, np.stack([self.offsets, self.sizes]))
else:
self.offsets, self.sizes = self._build_index(path)
def _get_file(self):
if not hasattr(self.threadlocal, "f"):
self.threadlocal.f = open(self.fn, "r")
return self.threadlocal.f
def __getitem__(self, idx):
f = self._get_file()
f.seek(self.offsets[idx])
desc = f.readline().strip()
line = f.readline()
seq = ""
while line != "" and line[0] != ">":
seq += line.strip()
line = f.readline()
return desc, seq
def __len__(self):
return self.offsets.size
def _build_index(self, path: str):
# Use grep and awk to get 100M/s on local SSD.
# Should process your enormous 100G fasta in ~10 min single core...
path = fasta_file_path(path)
bytes_offsets = subprocess.check_output(
f"cat {path} | tqdm --bytes --total $(wc -c < {path})"
"| grep --byte-offset '^>' -o | cut -d: -f1",
shell=True,
)
fasta_lengths = subprocess.check_output(
f"cat {path} | tqdm --bytes --total $(wc -c < {path})"
"| awk '/^>/ {print \"\";next;} { printf(\"%s\",$0);}' | tail -n+2 | awk '{print length($1)}'",
shell=True,
)
bytes_np = np.fromstring(bytes_offsets, dtype=np.int64, sep=" ")
sizes_np = np.fromstring(fasta_lengths, dtype=np.int64, sep=" ")
return bytes_np, sizes_np
def __setstate__(self, state):
self.__dict__ = state
self.threadlocal = threading.local()
def __getstate__(self):
d = {}
for i, v in self.__dict__.items():
if i != "threadlocal":
d[i] = v
return d
def __del__(self):
if hasattr(self.threadlocal, "f"):
self.threadlocal.f.close()
del self.threadlocal.f
@staticmethod
def exists(path):
return os.path.exists(fasta_file_path(path))
class EncodedFastaDataset(FastaDataset):
"""
The FastaDataset returns raw sequences - this allows us to return
indices with a dictionary instead.
"""
def __init__(self, path, dictionary):
super().__init__(path, cache_indices=True)
self.dictionary = dictionary
def __getitem__(self, idx):
desc, seq = super().__getitem__(idx)
return self.dictionary.encode_line(seq, line_tokenizer=list).long()
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/fasta_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class BaseWrapperDataset(FairseqDataset):
def __init__(self, dataset):
super().__init__()
self.dataset = dataset
def __getitem__(self, index):
return self.dataset[index]
def __len__(self):
return len(self.dataset)
def collater(self, samples):
if hasattr(self.dataset, "collater"):
return self.dataset.collater(samples)
else:
return default_collate(samples)
@property
def sizes(self):
return self.dataset.sizes
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
return self.dataset.size(index)
def ordered_indices(self):
return self.dataset.ordered_indices()
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def attr(self, attr: str, index: int):
return self.dataset.attr(attr, index)
def prefetch(self, indices):
self.dataset.prefetch(indices)
def get_batch_shapes(self):
return self.dataset.get_batch_shapes()
def batch_by_size(
self,
indices,
max_tokens=None,
max_sentences=None,
required_batch_size_multiple=1,
):
return self.dataset.batch_by_size(
indices,
max_tokens=max_tokens,
max_sentences=max_sentences,
required_batch_size_multiple=required_batch_size_multiple,
)
def filter_indices_by_size(self, indices, max_sizes):
return self.dataset.filter_indices_by_size(indices, max_sizes)
@property
def can_reuse_epoch_itr_across_epochs(self):
return self.dataset.can_reuse_epoch_itr_across_epochs
def set_epoch(self, epoch):
super().set_epoch(epoch)
if hasattr(self.dataset, "set_epoch"):
self.dataset.set_epoch(epoch)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/base_wrapper_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class NumelDataset(BaseWrapperDataset):
def __init__(self, dataset, reduce=False):
super().__init__(dataset)
self.reduce = reduce
def __getitem__(self, index):
item = self.dataset[index]
if torch.is_tensor(item):
return torch.numel(item)
else:
return np.size(item)
def __len__(self):
return len(self.dataset)
def collater(self, samples):
if self.reduce:
return sum(samples)
else:
return torch.tensor(samples)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/numel_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""isort:skip_file"""
from .dictionary import Dictionary, TruncatedDictionary
from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
from .base_wrapper_dataset import BaseWrapperDataset
from .add_target_dataset import AddTargetDataset
from .append_token_dataset import AppendTokenDataset
from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
from .audio.hubert_dataset import HubertDataset
from .backtranslation_dataset import BacktranslationDataset
from .bucket_pad_length_dataset import BucketPadLengthDataset
from .colorize_dataset import ColorizeDataset
from .concat_dataset import ConcatDataset
from .concat_sentences_dataset import ConcatSentencesDataset
from .denoising_dataset import DenoisingDataset
from .id_dataset import IdDataset
from .indexed_dataset import (
IndexedCachedDataset,
IndexedDataset,
IndexedRawTextDataset,
MMapIndexedDataset,
)
from .language_pair_dataset import LanguagePairDataset
from .list_dataset import ListDataset
from .lm_context_window_dataset import LMContextWindowDataset
from .lru_cache_dataset import LRUCacheDataset
from .mask_tokens_dataset import MaskTokensDataset
from .monolingual_dataset import MonolingualDataset
from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
from .nested_dictionary_dataset import NestedDictionaryDataset
from .noising import NoisingDataset
from .numel_dataset import NumelDataset
from .num_samples_dataset import NumSamplesDataset
from .offset_tokens_dataset import OffsetTokensDataset
from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
from .prepend_dataset import PrependDataset
from .prepend_token_dataset import PrependTokenDataset
from .raw_label_dataset import RawLabelDataset
from .replace_dataset import ReplaceDataset
from .resampling_dataset import ResamplingDataset
from .roll_dataset import RollDataset
from .round_robin_zip_datasets import RoundRobinZipDatasets
from .sort_dataset import SortDataset
from .strip_token_dataset import StripTokenDataset
from .subsample_dataset import SubsampleDataset
from .token_block_dataset import TokenBlockDataset
from .transform_eos_dataset import TransformEosDataset
from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
from .shorten_dataset import TruncateDataset, RandomCropDataset
from .multilingual.sampled_multi_dataset import SampledMultiDataset
from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
from .fasta_dataset import FastaDataset, EncodedFastaDataset
from .transform_eos_concat_langpair_dataset import TransformEosConcatLangPairDataset
from .iterators import (
CountingIterator,
EpochBatchIterator,
GroupedIterator,
ShardedIterator,
)
__all__ = [
"AddTargetDataset",
"AppendTokenDataset",
"BacktranslationDataset",
"BaseWrapperDataset",
"BinarizedAudioDataset",
"BucketPadLengthDataset",
"ColorizeDataset",
"ConcatDataset",
"ConcatSentencesDataset",
"CountingIterator",
"DenoisingDataset",
"Dictionary",
"EncodedFastaDataset",
"EpochBatchIterator",
"FairseqDataset",
"FairseqIterableDataset",
"FastaDataset",
"FileAudioDataset",
"GroupedIterator",
"HubertDataset",
"IdDataset",
"IndexedCachedDataset",
"IndexedDataset",
"IndexedRawTextDataset",
"LanguagePairDataset",
"LeftPadDataset",
"ListDataset",
"LMContextWindowDataset",
"LRUCacheDataset",
"MaskTokensDataset",
"MMapIndexedDataset",
"MonolingualDataset",
"MultiCorpusSampledDataset",
"NestedDictionaryDataset",
"NoisingDataset",
"NumelDataset",
"NumSamplesDataset",
"OffsetTokensDataset",
"PadDataset",
"PrependDataset",
"PrependTokenDataset",
"RandomCropDataset",
"RawLabelDataset",
"ResamplingDataset",
"ReplaceDataset",
"RightPadDataset",
"RollDataset",
"RoundRobinZipDatasets",
"SampledMultiDataset",
"SampledMultiEpochDataset",
"ShardedIterator",
"SortDataset",
"StripTokenDataset",
"SubsampleDataset",
"TokenBlockDataset",
"TransformEosDataset",
"TransformEosLangPairDataset",
"TransformEosConcatLangPairDataset",
"TruncateDataset",
"TruncatedDictionary",
]
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class ConcatSentencesDataset(FairseqDataset):
def __init__(self, *datasets):
super().__init__()
self.datasets = datasets
assert all(
len(ds) == len(datasets[0]) for ds in datasets
), "datasets must have the same length"
def __getitem__(self, index):
return torch.cat([ds[index] for ds in self.datasets])
def __len__(self):
return len(self.datasets[0])
def collater(self, samples):
return self.datasets[0].collater(samples)
@property
def sizes(self):
return sum(ds.sizes for ds in self.datasets)
def num_tokens(self, index):
return sum(ds.num_tokens(index) for ds in self.datasets)
def size(self, index):
return sum(ds.size(index) for ds in self.datasets)
def ordered_indices(self):
return self.datasets[0].ordered_indices()
@property
def supports_prefetch(self):
return any(getattr(ds, "supports_prefetch", False) for ds in self.datasets)
def prefetch(self, indices):
for ds in self.datasets:
if getattr(ds, "supports_prefetch", False):
ds.prefetch(indices)
def set_epoch(self, epoch):
super().set_epoch(epoch)
for ds in self.datasets:
if hasattr(ds, "set_epoch"):
ds.set_epoch(epoch)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/concat_sentences_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from functools import lru_cache
import numpy as np
import torch
from fairseq.data import Dictionary, data_utils
from . import BaseWrapperDataset, LRUCacheDataset
class MaskTokensDataset(BaseWrapperDataset):
"""
A wrapper Dataset for masked language modeling.
Input items are masked according to the specified masking probability.
Args:
dataset: Dataset to wrap.
sizes: Sentence lengths
vocab: Dictionary with the vocabulary and special tokens.
pad_idx: Id of pad token in vocab
mask_idx: Id of mask token in vocab
return_masked_tokens: controls whether to return the non-masked tokens
(the default) or to return a tensor with the original masked token
IDs (and *pad_idx* elsewhere). The latter is useful as targets for
masked LM training.
seed: Seed for random number generator for reproducibility.
mask_prob: probability of replacing a token with *mask_idx*.
leave_unmasked_prob: probability that a masked token is unmasked.
random_token_prob: probability of replacing a masked token with a
random token from the vocabulary.
freq_weighted_replacement: sample random replacement words based on
word frequencies in the vocab.
mask_whole_words: only mask whole words. This should be a byte mask
over vocab indices, indicating whether it is the beginning of a
word. We will extend any mask to encompass the whole word.
bpe: BPE to use for whole-word masking.
mask_multiple_length : repeat each mask index multiple times. Default
value is 1.
mask_stdev : standard deviation of masks distribution in case of
multiple masking. Default value is 0.
"""
@classmethod
def apply_mask(cls, dataset: torch.utils.data.Dataset, *args, **kwargs):
"""Return the source and target datasets for masked LM training."""
dataset = LRUCacheDataset(dataset)
return (
LRUCacheDataset(cls(dataset, *args, **kwargs, return_masked_tokens=False)),
LRUCacheDataset(cls(dataset, *args, **kwargs, return_masked_tokens=True)),
)
def __init__(
self,
dataset: torch.utils.data.Dataset,
vocab: Dictionary,
pad_idx: int,
mask_idx: int,
return_masked_tokens: bool = False,
seed: int = 1,
mask_prob: float = 0.15,
leave_unmasked_prob: float = 0.1,
random_token_prob: float = 0.1,
freq_weighted_replacement: bool = False,
mask_whole_words: torch.Tensor = None,
mask_multiple_length: int = 1,
mask_stdev: float = 0.0,
):
assert 0.0 < mask_prob < 1.0
assert 0.0 <= random_token_prob <= 1.0
assert 0.0 <= leave_unmasked_prob <= 1.0
assert random_token_prob + leave_unmasked_prob <= 1.0
assert mask_multiple_length >= 1
assert mask_stdev >= 0.0
self.dataset = dataset
self.vocab = vocab
self.pad_idx = pad_idx
self.mask_idx = mask_idx
self.return_masked_tokens = return_masked_tokens
self.seed = seed
self.mask_prob = mask_prob
self.leave_unmasked_prob = leave_unmasked_prob
self.random_token_prob = random_token_prob
self.mask_whole_words = mask_whole_words
self.mask_multiple_length = mask_multiple_length
self.mask_stdev = mask_stdev
if random_token_prob > 0.0:
if freq_weighted_replacement:
weights = np.array(self.vocab.count)
else:
weights = np.ones(len(self.vocab))
weights[: self.vocab.nspecial] = 0
self.weights = weights / weights.sum()
self.epoch = 0
@property
def can_reuse_epoch_itr_across_epochs(self):
return True # only the noise changes, not item sizes
def set_epoch(self, epoch, **unused):
super().set_epoch(epoch)
self.epoch = epoch
def __getitem__(self, index: int):
return self.__getitem_cached__(self.seed, self.epoch, index)
@lru_cache(maxsize=8)
def __getitem_cached__(self, seed: int, epoch: int, index: int):
with data_utils.numpy_seed(self.seed, self.epoch, index):
item = self.dataset[index]
sz = len(item)
assert (
self.mask_idx not in item
), "Dataset contains mask_idx (={}), this is not expected!".format(
self.mask_idx,
)
if self.mask_whole_words is not None:
word_begins_mask = self.mask_whole_words.gather(0, item)
word_begins_idx = word_begins_mask.nonzero().view(-1)
sz = len(word_begins_idx)
words = np.split(word_begins_mask, word_begins_idx)[1:]
assert len(words) == sz
word_lens = list(map(len, words))
# decide elements to mask
mask = np.full(sz, False)
num_mask = int(
# add a random number for probabilistic rounding
self.mask_prob * sz / float(self.mask_multiple_length)
+ np.random.rand()
)
# multiple masking as described in the vq-wav2vec paper (https://arxiv.org/abs/1910.05453)
mask_idc = np.random.choice(sz, num_mask, replace=False)
if self.mask_stdev > 0.0:
lengths = np.random.normal(
self.mask_multiple_length, self.mask_stdev, size=num_mask
)
lengths = [max(0, int(round(x))) for x in lengths]
mask_idc = np.asarray(
[
mask_idc[j] + offset
for j in range(len(mask_idc))
for offset in range(lengths[j])
],
dtype=np.int64,
)
else:
mask_idc = np.concatenate(
[mask_idc + i for i in range(self.mask_multiple_length)]
)
mask_idc = mask_idc[mask_idc < len(mask)]
try:
mask[mask_idc] = True
except: # something wrong
print(
"Assigning mask indexes {} to mask {} failed!".format(
mask_idc, mask
)
)
raise
if self.return_masked_tokens:
# exit early if we're just returning the masked tokens
# (i.e., the targets for masked LM training)
if self.mask_whole_words is not None:
mask = np.repeat(mask, word_lens)
new_item = np.full(len(mask), self.pad_idx)
new_item[mask] = item[torch.from_numpy(mask.astype(np.uint8)) == 1]
return torch.from_numpy(new_item)
# decide unmasking and random replacement
rand_or_unmask_prob = self.random_token_prob + self.leave_unmasked_prob
if rand_or_unmask_prob > 0.0:
rand_or_unmask = mask & (np.random.rand(sz) < rand_or_unmask_prob)
if self.random_token_prob == 0.0:
unmask = rand_or_unmask
rand_mask = None
elif self.leave_unmasked_prob == 0.0:
unmask = None
rand_mask = rand_or_unmask
else:
unmask_prob = self.leave_unmasked_prob / rand_or_unmask_prob
decision = np.random.rand(sz) < unmask_prob
unmask = rand_or_unmask & decision
rand_mask = rand_or_unmask & (~decision)
else:
unmask = rand_mask = None
if unmask is not None:
mask = mask ^ unmask
if self.mask_whole_words is not None:
mask = np.repeat(mask, word_lens)
new_item = np.copy(item)
new_item[mask] = self.mask_idx
if rand_mask is not None:
num_rand = rand_mask.sum()
if num_rand > 0:
if self.mask_whole_words is not None:
rand_mask = np.repeat(rand_mask, word_lens)
num_rand = rand_mask.sum()
new_item[rand_mask] = np.random.choice(
len(self.vocab),
num_rand,
p=self.weights,
)
return torch.from_numpy(new_item)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/mask_tokens_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from functools import lru_cache
from . import BaseWrapperDataset
class LRUCacheDataset(BaseWrapperDataset):
def __init__(self, dataset, token=None):
super().__init__(dataset)
@lru_cache(maxsize=8)
def __getitem__(self, index):
return self.dataset[index]
@lru_cache(maxsize=8)
def collater(self, samples):
return self.dataset.collater(samples)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/lru_cache_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import numpy as np
import torch
from . import FairseqDataset, data_utils
def collate(
samples,
pad_idx,
eos_idx,
vocab,
left_pad_source=False,
left_pad_target=False,
input_feeding=True,
pad_to_length=None,
):
assert input_feeding
if len(samples) == 0:
return {}
def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx,
eos_idx=None, # use eos_idx of each sample instead of vocab.eos()
left_pad=left_pad,
move_eos_to_beginning=move_eos_to_beginning,
pad_to_length=pad_to_length,
)
id = torch.LongTensor([s["id"] for s in samples])
src_tokens = merge(
"source",
left_pad=left_pad_source,
pad_to_length=pad_to_length["source"] if pad_to_length is not None else None,
)
# sort by descending source length
src_lengths = torch.LongTensor([s["source"].numel() for s in samples])
src_lengths, sort_order = src_lengths.sort(descending=True)
id = id.index_select(0, sort_order)
src_tokens = src_tokens.index_select(0, sort_order)
prev_output_tokens = None
target = None
if samples[0].get("target", None) is not None:
target = merge(
"target",
left_pad=left_pad_target,
pad_to_length=pad_to_length["target"]
if pad_to_length is not None
else None,
)
target = target.index_select(0, sort_order)
ntokens = sum(len(s["target"]) for s in samples)
if input_feeding:
# we create a shifted version of targets for feeding the
# previous output token(s) into the next decoder step
prev_output_tokens = merge(
"target",
left_pad=left_pad_target,
move_eos_to_beginning=True,
pad_to_length=pad_to_length["target"]
if pad_to_length is not None
else None,
)
prev_output_tokens = prev_output_tokens.index_select(0, sort_order)
else:
ntokens = sum(len(s["source"]) for s in samples)
batch = {
"id": id,
"ntokens": ntokens,
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
},
"target": target,
"nsentences": samples[0]["source"].size(0),
"sort_order": sort_order,
}
if prev_output_tokens is not None:
batch["net_input"]["prev_output_tokens"] = prev_output_tokens
return batch
class DenoisingDataset(FairseqDataset):
"""
A wrapper around TokenBlockDataset for BART dataset.
Args:
dataset (TokenBlockDataset): dataset to wrap
sizes (List[int]): sentence lengths
vocab (~fairseq.data.Dictionary): vocabulary
mask_idx (int): dictionary index used for masked token
mask_whole_words: only mask whole words. This should be a byte mask
over vocab indices, indicating whether it is the beginning of a
word. We will extend any mask to encompass the whole word.
shuffle (bool, optional): shuffle the elements before batching.
Default: ``True``
seed: Seed for random number generator for reproducibility.
args: argparse arguments.
"""
def __init__(
self,
dataset,
sizes,
vocab,
mask_idx,
mask_whole_words,
shuffle,
seed,
args,
eos=None,
item_transform_func=None,
):
self.dataset = dataset
self.sizes = sizes
self.vocab = vocab
self.shuffle = shuffle
self.seed = seed
self.mask_idx = mask_idx
self.mask_whole_word = mask_whole_words
self.mask_ratio = args.mask
self.random_ratio = args.mask_random
self.insert_ratio = args.insert
self.rotate_ratio = args.rotate
self.permute_sentence_ratio = args.permute_sentences
self.eos = eos if eos is not None else vocab.eos()
self.item_transform_func = item_transform_func
if args.bpe != "gpt2":
self.full_stop_index = self.vocab.eos()
else:
assert args.bpe == "gpt2"
self.full_stop_index = self.vocab.index("13")
self.replace_length = args.replace_length
if self.replace_length not in [-1, 0, 1]:
raise ValueError(f"invalid arg: replace_length={self.replace_length}")
if args.mask_length not in ["subword", "word", "span-poisson"]:
raise ValueError(f"invalid arg: mask-length={args.mask_length}")
if args.mask_length == "subword" and args.replace_length not in [0, 1]:
raise ValueError("if using subwords, use replace-length=1 or 0")
self.mask_span_distribution = None
if args.mask_length == "span-poisson":
_lambda = args.poisson_lambda
lambda_to_the_k = 1
e_to_the_minus_lambda = math.exp(-_lambda)
k_factorial = 1
ps = []
for k in range(0, 128):
ps.append(e_to_the_minus_lambda * lambda_to_the_k / k_factorial)
lambda_to_the_k *= _lambda
k_factorial *= k + 1
if ps[-1] < 0.0000001:
break
ps = torch.FloatTensor(ps)
self.mask_span_distribution = torch.distributions.Categorical(ps)
self.epoch = 0
@property
def can_reuse_epoch_itr_across_epochs(self):
return True # only the noise changes, not item sizes
def set_epoch(self, epoch, **unused):
self.epoch = epoch
def __getitem__(self, index):
with data_utils.numpy_seed(self.seed, self.epoch, index):
tokens = self.dataset[index]
assert tokens[-1] == self.eos
source, target = tokens, tokens.clone()
if self.permute_sentence_ratio > 0.0:
source = self.permute_sentences(source, self.permute_sentence_ratio)
if self.mask_ratio > 0:
source = self.add_whole_word_mask(source, self.mask_ratio)
if self.insert_ratio > 0:
source = self.add_insertion_noise(source, self.insert_ratio)
if self.rotate_ratio > 0.0 and np.random.random() < self.rotate_ratio:
source = self.add_rolling_noise(source)
# there can additional changes to make:
if self.item_transform_func is not None:
source, target = self.item_transform_func(source, target)
assert (source >= 0).all()
assert (source[1:-1] >= 1).all()
assert (source <= len(self.vocab)).all()
assert source[0] == self.vocab.bos()
assert source[-1] == self.eos
return {
"id": index,
"source": source,
"target": target,
}
def __len__(self):
return len(self.dataset)
def permute_sentences(self, source, p=1.0):
full_stops = source == self.full_stop_index
# Pretend it ends with a full stop so last span is a sentence
full_stops[-2] = 1
# Tokens that are full stops, where the previous token is not
sentence_ends = (full_stops[1:] * ~full_stops[:-1]).nonzero(as_tuple=False) + 2
result = source.clone()
num_sentences = sentence_ends.size(0)
num_to_permute = math.ceil((num_sentences * 2 * p) / 2.0)
substitutions = torch.randperm(num_sentences)[:num_to_permute]
ordering = torch.arange(0, num_sentences)
ordering[substitutions] = substitutions[torch.randperm(num_to_permute)]
# Ignore <bos> at start
index = 1
for i in ordering:
sentence = source[(sentence_ends[i - 1] if i > 0 else 1) : sentence_ends[i]]
result[index : index + sentence.size(0)] = sentence
index += sentence.size(0)
return result
def word_starts(self, source):
if self.mask_whole_word is not None:
is_word_start = self.mask_whole_word.gather(0, source)
else:
is_word_start = torch.ones(source.size())
is_word_start[0] = 0
is_word_start[-1] = 0
return is_word_start
def add_whole_word_mask(self, source, p):
is_word_start = self.word_starts(source)
num_to_mask = int(math.ceil(is_word_start.float().sum() * p))
num_inserts = 0
if num_to_mask == 0:
return source
if self.mask_span_distribution is not None:
lengths = self.mask_span_distribution.sample(sample_shape=(num_to_mask,))
# Make sure we have enough to mask
cum_length = torch.cumsum(lengths, 0)
while cum_length[-1] < num_to_mask:
lengths = torch.cat(
[
lengths,
self.mask_span_distribution.sample(sample_shape=(num_to_mask,)),
],
dim=0,
)
cum_length = torch.cumsum(lengths, 0)
# Trim to masking budget
i = 0
while cum_length[i] < num_to_mask:
i += 1
lengths[i] = num_to_mask - (0 if i == 0 else cum_length[i - 1])
num_to_mask = i + 1
lengths = lengths[:num_to_mask]
# Handle 0-length mask (inserts) separately
lengths = lengths[lengths > 0]
num_inserts = num_to_mask - lengths.size(0)
num_to_mask -= num_inserts
if num_to_mask == 0:
return self.add_insertion_noise(source, num_inserts / source.size(0))
assert (lengths > 0).all()
else:
lengths = torch.ones((num_to_mask,)).long()
assert is_word_start[-1] == 0
word_starts = is_word_start.nonzero(as_tuple=False)
indices = word_starts[
torch.randperm(word_starts.size(0))[:num_to_mask]
].squeeze(1)
mask_random = torch.FloatTensor(num_to_mask).uniform_() < self.random_ratio
source_length = source.size(0)
assert source_length - 1 not in indices
to_keep = torch.ones(source_length, dtype=torch.bool)
is_word_start[
-1
] = 255 # acts as a long length, so spans don't go over the end of doc
if self.replace_length == 0:
to_keep[indices] = 0
else:
# keep index, but replace it with [MASK]
source[indices] = self.mask_idx
source[indices[mask_random]] = torch.randint(
1, len(self.vocab), size=(mask_random.sum(),)
)
if self.mask_span_distribution is not None:
assert len(lengths.size()) == 1
assert lengths.size() == indices.size()
lengths -= 1
while indices.size(0) > 0:
assert lengths.size() == indices.size()
lengths -= is_word_start[indices + 1].long()
uncompleted = lengths >= 0
indices = indices[uncompleted] + 1
mask_random = mask_random[uncompleted]
lengths = lengths[uncompleted]
if self.replace_length != -1:
# delete token
to_keep[indices] = 0
else:
# keep index, but replace it with [MASK]
source[indices] = self.mask_idx
source[indices[mask_random]] = torch.randint(
1, len(self.vocab), size=(mask_random.sum(),)
)
else:
# A bit faster when all lengths are 1
while indices.size(0) > 0:
uncompleted = is_word_start[indices + 1] == 0
indices = indices[uncompleted] + 1
mask_random = mask_random[uncompleted]
if self.replace_length != -1:
# delete token
to_keep[indices] = 0
else:
# keep index, but replace it with [MASK]
source[indices] = self.mask_idx
source[indices[mask_random]] = torch.randint(
1, len(self.vocab), size=(mask_random.sum(),)
)
assert source_length - 1 not in indices
source = source[to_keep]
if num_inserts > 0:
source = self.add_insertion_noise(source, num_inserts / source.size(0))
return source
def add_permuted_noise(self, tokens, p):
num_words = len(tokens)
num_to_permute = math.ceil(((num_words * 2) * p) / 2.0)
substitutions = torch.randperm(num_words - 2)[:num_to_permute] + 1
tokens[substitutions] = tokens[substitutions[torch.randperm(num_to_permute)]]
return tokens
def add_rolling_noise(self, tokens):
offset = np.random.randint(1, max(1, tokens.size(-1) - 1) + 1)
tokens = torch.cat(
(tokens[0:1], tokens[offset:-1], tokens[1:offset], tokens[-1:]),
dim=0,
)
return tokens
def add_insertion_noise(self, tokens, p):
if p == 0.0:
return tokens
num_tokens = len(tokens)
n = int(math.ceil(num_tokens * p))
noise_indices = torch.randperm(num_tokens + n - 2)[:n] + 1
noise_mask = torch.zeros(size=(num_tokens + n,), dtype=torch.bool)
noise_mask[noise_indices] = 1
result = torch.LongTensor(n + len(tokens)).fill_(-1)
num_random = int(math.ceil(n * self.random_ratio))
result[noise_indices[num_random:]] = self.mask_idx
result[noise_indices[:num_random]] = torch.randint(
low=1, high=len(self.vocab), size=(num_random,)
)
result[~noise_mask] = tokens
assert (result >= 0).all()
return result
def collater(self, samples, pad_to_length=None):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch of data
"""
return collate(
samples, self.vocab.pad(), self.eos, self.vocab, pad_to_length=pad_to_length
)
def num_tokens(self, index):
"""Return the number of tokens in a sample. This value is used to
enforce ``--max-tokens`` during batching."""
return self.sizes[index]
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return self.sizes[index]
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
if self.shuffle:
indices = np.random.permutation(len(self))
else:
indices = np.arange(len(self))
return indices[np.argsort(self.sizes[indices], kind="mergesort")]
def prefetch(self, indices):
self.src.prefetch(indices)
self.tgt.prefetch(indices)
@property
def supports_prefetch(self):
return (
hasattr(self.src, "supports_prefetch")
and self.src.supports_prefetch
and hasattr(self.tgt, "supports_prefetch")
and self.tgt.supports_prefetch
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/denoising_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class StripTokenDataset(BaseWrapperDataset):
def __init__(self, dataset, id_to_strip):
super().__init__(dataset)
self.id_to_strip = id_to_strip
def __getitem__(self, index):
item = self.dataset[index]
while len(item) > 0 and item[-1] == self.id_to_strip:
item = item[:-1]
while len(item) > 0 and item[0] == self.id_to_strip:
item = item[1:]
return item
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/strip_token_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
try:
from collections.abc import Iterable
except ImportError:
from collections import Iterable
import contextlib
import itertools
import logging
import re
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
from fairseq.file_io import PathManager
from fairseq import utils
import os
logger = logging.getLogger(__name__)
def infer_language_pair(path):
"""Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
src, dst = None, None
for filename in PathManager.ls(path):
parts = filename.split(".")
if len(parts) >= 3 and len(parts[1].split("-")) == 2:
return parts[1].split("-")
return src, dst
def collate_tokens(
values,
pad_idx,
eos_idx=None,
left_pad=False,
move_eos_to_beginning=False,
pad_to_length=None,
pad_to_multiple=1,
pad_to_bsz=None,
):
"""Convert a list of 1d tensors into a padded 2d tensor."""
size = max(v.size(0) for v in values)
size = size if pad_to_length is None else max(size, pad_to_length)
if pad_to_multiple != 1 and size % pad_to_multiple != 0:
size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
batch_size = len(values) if pad_to_bsz is None else max(len(values), pad_to_bsz)
res = values[0].new(batch_size, size).fill_(pad_idx)
def copy_tensor(src, dst):
assert dst.numel() == src.numel()
if move_eos_to_beginning:
if eos_idx is None:
# if no eos_idx is specified, then use the last token in src
dst[0] = src[-1]
else:
dst[0] = eos_idx
dst[1:] = src[:-1]
else:
dst.copy_(src)
for i, v in enumerate(values):
copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
return res
def load_indexed_dataset(
path, dictionary=None, dataset_impl=None, combine=False, default="cached"
):
"""A helper function for loading indexed datasets.
Args:
path (str): path to indexed dataset (e.g., 'data-bin/train')
dictionary (~fairseq.data.Dictionary): data dictionary
dataset_impl (str, optional): which dataset implementation to use. If
not provided, it will be inferred automatically. For legacy indexed
data we use the 'cached' implementation by default.
combine (bool, optional): automatically load and combine multiple
datasets. For example, if *path* is 'data-bin/train', then we will
combine 'data-bin/train', 'data-bin/train1', ... and return a
single ConcatDataset instance.
"""
import fairseq.data.indexed_dataset as indexed_dataset
from fairseq.data.concat_dataset import ConcatDataset
datasets = []
for k in itertools.count():
path_k = path + (str(k) if k > 0 else "")
try:
path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
except Exception as e:
if "StorageException: [404] Path not found" in str(e):
logger.warning(f"path_k: {e} not found")
else:
raise e
dataset_impl_k = dataset_impl
if dataset_impl_k is None:
dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
dataset = indexed_dataset.make_dataset(
path_k,
impl=dataset_impl_k or default,
fix_lua_indexing=True,
dictionary=dictionary,
)
if dataset is None:
break
logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
datasets.append(dataset)
if not combine:
break
if len(datasets) == 0:
return None
elif len(datasets) == 1:
return datasets[0]
else:
return ConcatDataset(datasets)
@contextlib.contextmanager
def numpy_seed(seed, *addl_seeds):
"""Context manager which seeds the NumPy PRNG with the specified seed and
restores the state afterward"""
if seed is None:
yield
return
if len(addl_seeds) > 0:
seed = int(hash((seed, *addl_seeds)) % 1e6)
state = np.random.get_state()
np.random.seed(seed)
try:
yield
finally:
np.random.set_state(state)
def collect_filtered(function, iterable, filtered):
"""
Similar to :func:`filter` but collects filtered elements in ``filtered``.
Args:
function (callable): function that returns ``False`` for elements that
should be filtered
iterable (iterable): iterable to filter
filtered (list): list to store filtered elements
"""
for el in iterable:
if function(el):
yield el
else:
filtered.append(el)
def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
def compare_leq(a, b):
return a <= b if not isinstance(a, tuple) else max(a) <= b
def check_size(idx):
if isinstance(max_positions, float) or isinstance(max_positions, int):
return size_fn(idx) <= max_positions
elif isinstance(max_positions, dict):
idx_size = size_fn(idx)
assert isinstance(idx_size, dict)
intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
return all(
all(
a is None or b is None or a <= b
for a, b in zip(idx_size[key], max_positions[key])
)
for key in intersect_keys
)
else:
# For MultiCorpusSampledDataset, will generalize it later
if not isinstance(size_fn(idx), Iterable):
return all(size_fn(idx) <= b for b in max_positions)
return all(
a is None or b is None or a <= b
for a, b in zip(size_fn(idx), max_positions)
)
ignored = []
itr = collect_filtered(check_size, indices, ignored)
indices = np.fromiter(itr, dtype=np.int64, count=-1)
return indices, ignored
def filter_by_size(indices, dataset, max_positions, raise_exception=False):
"""
[deprecated] Filter indices based on their size.
Use `FairseqDataset::filter_indices_by_size` instead.
Args:
indices (List[int]): ordered list of dataset indices
dataset (FairseqDataset): fairseq dataset instance
max_positions (tuple): filter elements larger than this size.
Comparisons are done component-wise.
raise_exception (bool, optional): if ``True``, raise an exception if
any elements are filtered (default: False).
"""
warnings.warn(
"data_utils.filter_by_size is deprecated. "
"Use `FairseqDataset::filter_indices_by_size` instead.",
stacklevel=2,
)
if isinstance(max_positions, float) or isinstance(max_positions, int):
if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
ignored = indices[dataset.sizes[indices] > max_positions].tolist()
indices = indices[dataset.sizes[indices] <= max_positions]
elif (
hasattr(dataset, "sizes")
and isinstance(dataset.sizes, list)
and len(dataset.sizes) == 1
):
ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
indices = indices[dataset.sizes[0][indices] <= max_positions]
else:
indices, ignored = _filter_by_size_dynamic(
indices, dataset.size, max_positions
)
else:
indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
if len(ignored) > 0 and raise_exception:
raise Exception(
(
"Size of sample #{} is invalid (={}) since max_positions={}, "
"skip this example with --skip-invalid-size-inputs-valid-test"
).format(ignored[0], dataset.size(ignored[0]), max_positions)
)
if len(ignored) > 0:
logger.warning(
(
"{} samples have invalid sizes and will be skipped, "
"max_positions={}, first few sample ids={}"
).format(len(ignored), max_positions, ignored[:10])
)
return indices
def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
"""Filter a list of sample indices. Remove those that are longer
than specified in max_sizes.
Args:
indices (np.array): original array of sample indices
max_sizes (int or list[int] or tuple[int]): max sample size,
can be defined separately for src and tgt (then list or tuple)
Returns:
np.array: filtered sample array
list: list of removed indices
"""
if max_sizes is None:
return indices, []
if type(max_sizes) in (int, float):
max_src_size, max_tgt_size = max_sizes, max_sizes
else:
max_src_size, max_tgt_size = max_sizes
if tgt_sizes is None:
ignored = indices[src_sizes[indices] > max_src_size]
else:
ignored = indices[
(src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
]
if len(ignored) > 0:
if tgt_sizes is None:
indices = indices[src_sizes[indices] <= max_src_size]
else:
indices = indices[
(src_sizes[indices] <= max_src_size)
& (tgt_sizes[indices] <= max_tgt_size)
]
return indices, ignored.tolist()
def batch_by_size(
indices,
num_tokens_fn,
num_tokens_vec=None,
max_tokens=None,
max_sentences=None,
required_batch_size_multiple=1,
fixed_shapes=None,
):
"""
Yield mini-batches of indices bucketed by size. Batches may contain
sequences of different lengths.
Args:
indices (List[int]): ordered list of dataset indices
num_tokens_fn (callable): function that returns the number of tokens at
a given index
num_tokens_vec (List[int], optional): precomputed vector of the number
of tokens for each index in indices (to enable faster batch generation)
max_tokens (int, optional): max number of tokens in each batch
(default: None).
max_sentences (int, optional): max number of sentences in each
batch (default: None).
required_batch_size_multiple (int, optional): require batch size to
be less than N or a multiple of N (default: 1).
fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
only be created with the given shapes. *max_sentences* and
*required_batch_size_multiple* will be ignored (default: None).
"""
try:
from fairseq.data.data_utils_fast import (
batch_by_size_fn,
batch_by_size_vec,
batch_fixed_shapes_fast,
)
except ImportError:
raise ImportError(
"Please build Cython components with: "
"`python setup.py build_ext --inplace`"
)
except ValueError:
raise ValueError(
"Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`."
)
# added int() to avoid TypeError: an integer is required
max_tokens = int(max_tokens) if max_tokens is not None else -1
max_sentences = max_sentences if max_sentences is not None else -1
bsz_mult = required_batch_size_multiple
if not isinstance(indices, np.ndarray):
indices = np.fromiter(indices, dtype=np.int64, count=-1)
if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)
if fixed_shapes is None:
if num_tokens_vec is None:
return batch_by_size_fn(
indices,
num_tokens_fn,
max_tokens,
max_sentences,
bsz_mult,
)
else:
return batch_by_size_vec(
indices,
num_tokens_vec,
max_tokens,
max_sentences,
bsz_mult,
)
else:
fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
sort_order = np.lexsort(
[
fixed_shapes[:, 1].argsort(), # length
fixed_shapes[:, 0].argsort(), # bsz
]
)
fixed_shapes_sorted = fixed_shapes[sort_order]
return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)
def post_process(sentence: str, symbol: str):
if symbol == "sentencepiece":
sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
elif symbol == "wordpiece":
sentence = sentence.replace(" ", "").replace("_", " ").strip()
elif symbol == "letter":
sentence = sentence.replace(" ", "").replace("|", " ").strip()
elif symbol == "silence":
import re
sentence = sentence.replace("<SIL>", "")
sentence = re.sub(" +", " ", sentence).strip()
elif symbol == "_EOW":
sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
elif symbol in {"subword_nmt", "@@ ", "@@"}:
if symbol == "subword_nmt":
symbol = "@@ "
sentence = (sentence + " ").replace(symbol, "").rstrip()
elif symbol == "none":
pass
elif symbol is not None:
raise NotImplementedError(f"Unknown post_process option: {symbol}")
return sentence
def compute_mask_indices(
shape: Tuple[int, int],
padding_mask: Optional[torch.Tensor],
mask_prob: float,
mask_length: int,
mask_type: str = "static",
mask_other: float = 0.0,
min_masks: int = 0,
no_overlap: bool = False,
min_space: int = 0,
require_same_masks: bool = True,
pct_holes: float = 0.0,
) -> np.ndarray:
"""
Computes random mask spans for a given shape
Args:
shape: the the shape for which to compute masks.
should be of size 2 where first element is batch size and 2nd is timesteps
padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
however due to overlaps, the actual number will be smaller (unless no_overlap is True)
mask_type: how to compute mask lengths
static = fixed size
uniform = sample from uniform distribution [mask_other, mask_length*2]
normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
poisson = sample from possion distribution with lambda = mask length
min_masks: minimum number of masked spans
no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
"""
bsz, all_sz = shape
mask = np.full((bsz, all_sz), False)
all_num_mask = int(
# add a random number for probabilistic rounding
mask_prob * all_sz / float(mask_length)
+ np.random.rand()
)
all_num_mask = max(min_masks, all_num_mask)
mask_idcs = []
for i in range(bsz):
if padding_mask is not None:
sz = all_sz - padding_mask[i].long().sum().item()
num_mask = int(
# add a random number for probabilistic rounding
mask_prob * sz / float(mask_length)
+ np.random.rand()
)
num_mask = max(min_masks, num_mask)
else:
sz = all_sz
num_mask = all_num_mask
if mask_type == "static":
lengths = np.full(num_mask, mask_length)
elif mask_type == "uniform":
lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
elif mask_type == "normal":
lengths = np.random.normal(mask_length, mask_other, size=num_mask)
lengths = [max(1, int(round(x))) for x in lengths]
elif mask_type == "poisson":
lengths = np.random.poisson(mask_length, size=num_mask)
lengths = [int(round(x)) for x in lengths]
else:
raise Exception("unknown mask selection " + mask_type)
if sum(lengths) == 0:
lengths[0] = min(mask_length, sz - 1)
if no_overlap:
mask_idc = []
def arrange(s, e, length, keep_length):
span_start = np.random.randint(s, e - length)
mask_idc.extend(span_start + i for i in range(length))
new_parts = []
if span_start - s - min_space >= keep_length:
new_parts.append((s, span_start - min_space + 1))
if e - span_start - keep_length - min_space > keep_length:
new_parts.append((span_start + length + min_space, e))
return new_parts
parts = [(0, sz)]
min_length = min(lengths)
for length in sorted(lengths, reverse=True):
lens = np.fromiter(
(e - s if e - s >= length + min_space else 0 for s, e in parts),
np.int,
)
l_sum = np.sum(lens)
if l_sum == 0:
break
probs = lens / np.sum(lens)
c = np.random.choice(len(parts), p=probs)
s, e = parts.pop(c)
parts.extend(arrange(s, e, length, min_length))
mask_idc = np.asarray(mask_idc)
else:
min_len = min(lengths)
if sz - min_len <= num_mask:
min_len = sz - num_mask - 1
mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
mask_idc = np.asarray(
[
mask_idc[j] + offset
for j in range(len(mask_idc))
for offset in range(lengths[j])
]
)
mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
min_len = min([len(m) for m in mask_idcs])
for i, mask_idc in enumerate(mask_idcs):
if len(mask_idc) > min_len and require_same_masks:
mask_idc = np.random.choice(mask_idc, min_len, replace=False)
if pct_holes > 0:
num_holes = np.rint(len(mask_idc) * pct_holes).astype(int)
mask_idc = np.random.choice(
mask_idc, len(mask_idc) - num_holes, replace=False
)
mask[i, mask_idc] = True
return mask
def get_mem_usage():
try:
import psutil
mb = 1024 * 1024
return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
except ImportError:
return "N/A"
# lens: torch.LongTensor
# returns: torch.BoolTensor
def lengths_to_padding_mask(lens):
bsz, max_lens = lens.size(0), torch.max(lens).item()
mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
return mask
# lens: torch.LongTensor
# returns: torch.BoolTensor
def lengths_to_mask(lens):
return ~lengths_to_padding_mask(lens)
def get_buckets(sizes, num_buckets):
buckets = np.unique(
np.percentile(
sizes,
np.linspace(0, 100, num_buckets + 1),
interpolation="lower",
)[1:]
)
return buckets
def get_bucketed_sizes(orig_sizes, buckets):
sizes = np.copy(orig_sizes)
assert np.min(sizes) >= 0
start_val = -1
for end_val in buckets:
mask = (sizes > start_val) & (sizes <= end_val)
sizes[mask] = end_val
start_val = end_val
return sizes
def _find_extra_valid_paths(dataset_path: str) -> set:
paths = utils.split_paths(dataset_path)
all_valid_paths = set()
for sub_dir in paths:
contents = PathManager.ls(sub_dir)
valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None]
all_valid_paths |= {os.path.basename(p) for p in valid_paths}
# Remove .bin, .idx etc
roots = {os.path.splitext(p)[0] for p in all_valid_paths}
return roots
def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
"""Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
if (
train_cfg.dataset.ignore_unused_valid_subsets
or train_cfg.dataset.combine_valid_subsets
or train_cfg.dataset.disable_validation
or not hasattr(train_cfg.task, "data")
):
return
other_paths = _find_extra_valid_paths(train_cfg.task.data)
specified_subsets = train_cfg.dataset.valid_subset.split(",")
ignored_paths = [p for p in other_paths if p not in specified_subsets]
if ignored_paths:
advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them."
msg = f"Valid paths {ignored_paths} will be ignored. {advice}"
raise ValueError(msg)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/data_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class PrependTokenDataset(BaseWrapperDataset):
def __init__(self, dataset, token=None):
super().__init__(dataset)
self.token = token
if token is not None:
self._sizes = np.array(dataset.sizes) + 1
else:
self._sizes = dataset.sizes
def __getitem__(self, idx):
item = self.dataset[idx]
if self.token is not None:
item = torch.cat([item.new([self.token]), item])
return item
@property
def sizes(self):
return self._sizes
def num_tokens(self, index):
n = self.dataset.num_tokens(index)
if self.token is not None:
n += 1
return n
def size(self, index):
n = self.dataset.size(index)
if self.token is not None:
n += 1
return n
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/prepend_token_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class TransformEosDataset(FairseqDataset):
"""A :class:`~fairseq.data.FairseqDataset` wrapper that appends/prepends/strips EOS.
Note that the transformation is applied in :func:`collater`.
Args:
dataset (~fairseq.data.FairseqDataset): dataset to wrap
eos (int): index of the end-of-sentence symbol
append_eos_to_src (bool, optional): append EOS to the end of src
remove_eos_from_src (bool, optional): remove EOS from the end of src
append_eos_to_tgt (bool, optional): append EOS to the end of tgt
remove_eos_from_tgt (bool, optional): remove EOS from the end of tgt
"""
def __init__(
self,
dataset,
eos,
append_eos_to_src=False,
remove_eos_from_src=False,
append_eos_to_tgt=False,
remove_eos_from_tgt=False,
has_target=True,
):
if not isinstance(dataset, FairseqDataset):
raise ValueError("dataset must be an instance of FairseqDataset")
if append_eos_to_src and remove_eos_from_src:
raise ValueError("cannot combine append_eos_to_src and remove_eos_from_src")
if append_eos_to_tgt and remove_eos_from_tgt:
raise ValueError("cannot combine append_eos_to_tgt and remove_eos_from_tgt")
self.dataset = dataset
self.eos = torch.LongTensor([eos])
self.append_eos_to_src = append_eos_to_src
self.remove_eos_from_src = remove_eos_from_src
self.append_eos_to_tgt = append_eos_to_tgt
self.remove_eos_from_tgt = remove_eos_from_tgt
self.has_target = has_target
# precompute how we should adjust the reported sizes
self._src_delta = 0
self._src_delta += 1 if append_eos_to_src else 0
self._src_delta -= 1 if remove_eos_from_src else 0
self._tgt_delta = 0
self._tgt_delta += 1 if append_eos_to_tgt else 0
self._tgt_delta -= 1 if remove_eos_from_tgt else 0
self._checked_src = False
self._checked_tgt = False
def _check_src(self, src, expect_eos):
if not self._checked_src:
assert (src[-1] == self.eos[0]) == expect_eos
self._checked_src = True
def _check_tgt(self, tgt, expect_eos):
if self.has_target and not self._checked_tgt:
assert (tgt[-1] == self.eos[0]) == expect_eos
self._checked_tgt = True
def __getitem__(self, index):
return self.dataset[index]
def __len__(self):
return len(self.dataset)
def collater(self, samples):
def transform(item):
if self.append_eos_to_src:
self.eos = self.eos.to(device=item["source"].device)
self._check_src(item["source"], expect_eos=False)
item["source"] = torch.cat([item["source"], self.eos])
if self.remove_eos_from_src:
self.eos = self.eos.to(device=item["source"].device)
self._check_src(item["source"], expect_eos=True)
item["source"] = item["source"][:-1]
if self.append_eos_to_tgt:
self.eos = self.eos.to(device=item["target"].device)
self._check_tgt(item["target"], expect_eos=False)
item["target"] = torch.cat([item["target"], self.eos])
if self.remove_eos_from_tgt:
self.eos = self.eos.to(device=item["target"].device)
self._check_tgt(item["target"], expect_eos=True)
item["target"] = item["target"][:-1]
return item
samples = list(map(transform, samples))
return self.dataset.collater(samples)
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
if self.has_target:
src_len, tgt_len = self.dataset.size(index)
return (src_len + self._src_delta, tgt_len + self._tgt_delta)
else:
return self.dataset.size(index)
def ordered_indices(self):
# NOTE: we assume that the ordering does not change based on the
# addition or removal of eos
return self.dataset.ordered_indices()
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
return self.dataset.prefetch(indices)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/transform_eos_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset
class ColorizeDataset(BaseWrapperDataset):
"""Adds 'colors' property to net input that is obtained from the provided color getter for use by models"""
def __init__(self, dataset, color_getter):
super().__init__(dataset)
self.color_getter = color_getter
def collater(self, samples):
base_collate = super().collater(samples)
if len(base_collate) > 0:
base_collate["net_input"]["colors"] = torch.tensor(
list(self.color_getter(self.dataset, s["id"]) for s in samples),
dtype=torch.long,
)
return base_collate
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/colorize_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class RawLabelDataset(FairseqDataset):
def __init__(self, labels):
super().__init__()
self.labels = labels
def __getitem__(self, index):
return self.labels[index]
def __len__(self):
return len(self.labels)
def collater(self, samples):
return torch.tensor(samples)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/raw_label_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class ListDataset(BaseWrapperDataset):
def __init__(self, dataset, sizes=None):
super().__init__(dataset)
self._sizes = sizes
def __iter__(self):
for x in self.dataset:
yield x
def collater(self, samples):
return samples
@property
def sizes(self):
return self._sizes
def num_tokens(self, index):
return self.sizes[index]
def size(self, index):
return self.sizes[index]
def set_epoch(self, epoch):
pass
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/list_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from collections import OrderedDict
from typing import Dict, Sequence
import numpy as np
from . import FairseqDataset, LanguagePairDataset
logger = logging.getLogger(__name__)
class RoundRobinZipDatasets(FairseqDataset):
"""Zip multiple :class:`~fairseq.data.FairseqDataset` instances together.
Shorter datasets are repeated in a round-robin fashion to match the length
of the longest one.
Args:
datasets (Dict[~fairseq.data.FairseqDataset]): a dictionary of
:class:`~fairseq.data.FairseqDataset` instances.
eval_key (str, optional): a key used at evaluation time that causes
this instance to pass-through batches from *datasets[eval_key]*.
"""
def __init__(self, datasets, eval_key=None):
super().__init__()
if isinstance(datasets, dict):
datasets = OrderedDict(datasets)
assert isinstance(datasets, OrderedDict)
assert datasets, "Can't make a RoundRobinZipDatasets out of nothing"
for dataset in datasets.values():
assert isinstance(dataset, FairseqDataset)
self.datasets = datasets
self.eval_key = eval_key
self.longest_dataset_key = max(datasets, key=lambda k: len(datasets[k]))
self.longest_dataset = datasets[self.longest_dataset_key]
self._ordered_indices: Dict[str, Sequence[int]] = None
def _map_index(self, key, index):
assert (
self._ordered_indices is not None
), "Must call RoundRobinZipDatasets.ordered_indices() first"
o = self._ordered_indices[key]
return o[index % len(o)]
def __getitem__(self, index):
if self.eval_key is None:
return OrderedDict(
[
(key, dataset[self._map_index(key, index)])
for key, dataset in self.datasets.items()
]
)
else:
# at evaluation time it's useful to pass-through batches from a single key
return self.datasets[self.eval_key][self._map_index(self.eval_key, index)]
def __len__(self):
if self._ordered_indices is not None:
return len(self._ordered_indices[self.longest_dataset_key])
return len(self.longest_dataset)
def collater(self, samples):
"""Merge a list of samples to form a mini-batch."""
if len(samples) == 0:
return None
if self.eval_key is None:
return OrderedDict(
[
(key, dataset.collater([sample[key] for sample in samples]))
for key, dataset in self.datasets.items()
]
)
else:
# at evaluation time it's useful to pass-through batches from a single key
return self.datasets[self.eval_key].collater(samples)
def num_tokens(self, index):
"""Return an example's length (number of tokens), used for batching."""
# TODO make it configurable whether to use max() or sum() here
return max(
dataset.num_tokens(self._map_index(key, index))
for key, dataset in self.datasets.items()
)
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return {
key: dataset.size(self._map_index(key, index))
for key, dataset in self.datasets.items()
}
def ordered_indices(self):
"""Ordered indices for batching."""
if self._ordered_indices is None:
# Call the underlying dataset's ordered_indices() here, so that we
# get the same random ordering as we would have from using the
# underlying sub-datasets directly.
self._ordered_indices = OrderedDict(
[
(key, dataset.ordered_indices())
for key, dataset in self.datasets.items()
]
)
return np.arange(len(self))
def filter_indices_by_size(self, indices, max_positions=None):
"""
Filter each sub-dataset independently, then update the round robin to work
on the filtered sub-datasets.
"""
def _deep_until_language_pair(dataset):
if isinstance(dataset, LanguagePairDataset):
return dataset
if hasattr(dataset, "tgt_dataset"):
return _deep_until_language_pair(dataset.tgt_dataset)
if hasattr(dataset, "dataset"):
return _deep_until_language_pair(dataset.dataset)
raise Exception(f"Don't know how to unwrap this dataset: {dataset}")
if not isinstance(max_positions, dict):
max_positions = {k: max_positions for k in self.datasets.keys()}
ignored_some = False
for key, dataset in self.datasets.items():
dataset = _deep_until_language_pair(dataset)
self._ordered_indices[key], ignored = dataset.filter_indices_by_size(
self._ordered_indices[key], max_positions[key]
)
if len(ignored) > 0:
ignored_some = True
logger.warning(
f"{len(ignored)} samples from {key} have invalid sizes and will be skipped, "
f"max_positions={max_positions[key]}, first few sample ids={ignored[:10]}"
)
# Since we are modifying in place the _ordered_indices,
# it's not possible anymore to return valid ignored indices.
# Hopefully the extra debug information print above should be enough to debug.
# Ideally we would receive ignore_invalid_inputs so that we could have
# a proper error message.
return (np.arange(len(self)), [0] if ignored_some else [])
@property
def supports_prefetch(self):
return all(
getattr(dataset, "supports_prefetch", False)
for dataset in self.datasets.values()
)
def prefetch(self, indices):
for key, dataset in self.datasets.items():
dataset.prefetch([self._map_index(key, index) for index in indices])
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/round_robin_zip_datasets.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import logging
import math
import operator
import os
import queue
import time
from threading import Thread
import numpy as np
import torch
from fairseq.data import data_utils
logger = logging.getLogger(__name__)
# Object used by _background_consumer to signal the source is exhausted
# to the main thread.
_sentinel = object()
class CountingIterator(object):
"""Wrapper around an iterable that maintains the iteration count.
Args:
iterable (iterable): iterable to wrap
start (int): starting iteration count. Note that this doesn't
actually advance the iterator.
total (int): override the iterator length returned by ``__len``.
This can be used to truncate *iterator*.
Attributes:
n (int): number of elements consumed from this iterator
"""
def __init__(self, iterable, start=None, total=None):
self._itr = iter(iterable)
self.n = start or getattr(iterable, "n", 0)
self.total = total if total is not None else self.n + len(iterable)
def __len__(self):
return self.total
def __iter__(self):
return self
def __next__(self):
if not self.has_next():
raise StopIteration
try:
x = next(self._itr)
except StopIteration:
raise IndexError(
f"Iterator expected to have length {self.total}, "
"but exhausted at position {self.n}."
)
self.n += 1
return x
def has_next(self):
"""Whether the iterator has been exhausted."""
return self.n < self.total
def skip(self, n):
"""Fast-forward the iterator by skipping n elements."""
for _ in range(n):
next(self)
return self
def take(self, n):
"""Truncate the iterator to n elements at most."""
self.total = min(self.total, n)
# Propagate this change to the underlying iterator
if hasattr(self._itr, "take"):
self._itr.take(max(n - self.n, 0))
return self
class EpochBatchIterating(object):
def __len__(self) -> int:
raise NotImplementedError
@property
def next_epoch_idx(self):
raise NotImplementedError
def next_epoch_itr(
self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True
):
"""Return a new iterator over the dataset.
Args:
shuffle (bool, optional): shuffle batches before returning the
iterator (default: True).
fix_batches_to_gpus (bool, optional): ensure that batches are always
allocated to the same shards across epochs. Requires
that :attr:`dataset` supports prefetching (default: False).
set_dataset_epoch (bool, optional): update the wrapped Dataset with
the new epoch number (default: True).
"""
raise NotImplementedError
def end_of_epoch(self) -> bool:
"""Returns whether the most recent epoch iterator has been exhausted"""
raise NotImplementedError
@property
def iterations_in_epoch(self) -> int:
"""The number of consumed batches in the current epoch."""
raise NotImplementedError
def state_dict(self):
"""Returns a dictionary containing a whole state of the iterator."""
raise NotImplementedError
def load_state_dict(self, state_dict):
"""Copies the state of the iterator from the given *state_dict*."""
raise NotImplementedError
@property
def first_batch(self):
return "DUMMY"
class StreamingEpochBatchIterator(EpochBatchIterating):
"""A steaming-style iterator over a :class:`torch.utils.data.IterableDataset`.
Args:
dataset (~torch.utils.data.Dataset): dataset from which to load the data
max_sentences: batch size
collate_fn (callable): merges a list of samples to form a mini-batch
num_workers (int, optional): how many subprocesses to use for data
loading. 0 means the data will be loaded in the main process
(default: 0).
epoch (int, optional): the epoch to start the iterator from
(default: 1).
buffer_size (int, optional): the number of batches to keep ready in the
queue. Helps speeding up dataloading. When buffer_size is zero, the
default torch.utils.data.DataLoader preloading is used.
timeout (int, optional): if positive, the timeout value for collecting a batch
from workers. Should always be non-negative (default: ``0``).
"""
def __init__(
self,
dataset,
max_sentences=1,
collate_fn=None,
epoch=1,
num_workers=0,
buffer_size=0,
timeout=0,
):
assert isinstance(dataset, torch.utils.data.IterableDataset)
self.dataset = dataset
self.max_sentences = max_sentences
self.collate_fn = collate_fn
self.epoch = max(epoch, 1) # we use 1-based indexing for epochs
self.num_workers = num_workers
# This upper limit here is to prevent people from abusing this feature
# in a shared computing environment.
self.buffer_size = min(buffer_size, 20)
self.timeout = timeout
self._current_epoch_iterator = None
@property
def next_epoch_idx(self):
"""Return the epoch index after *next_epoch_itr* is called."""
if self._current_epoch_iterator is not None and self.end_of_epoch():
return self.epoch + 1
else:
return self.epoch
def next_epoch_itr(
self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True
):
self.epoch = self.next_epoch_idx
if set_dataset_epoch and hasattr(self.dataset, "set_epoch"):
self.dataset.set_epoch(self.epoch)
self._current_epoch_iterator = self._get_iterator_for_epoch(self.epoch, shuffle)
return self._current_epoch_iterator
def end_of_epoch(self) -> bool:
return not self._current_epoch_iterator.has_next()
@property
def iterations_in_epoch(self) -> int:
if self._current_epoch_iterator is not None:
return self._current_epoch_iterator.n
return 0
def state_dict(self):
return {
"epoch": self.epoch,
}
def load_state_dict(self, state_dict):
self.epoch = state_dict["epoch"]
def _get_iterator_for_epoch(self, epoch, shuffle, offset=0):
if self.num_workers > 0:
os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning"
# Create data loader
worker_init_fn = getattr(self.dataset, "worker_init_fn", None)
itr = torch.utils.data.DataLoader(
self.dataset,
batch_size=self.max_sentences,
collate_fn=self.collate_fn,
num_workers=self.num_workers,
timeout=self.timeout,
worker_init_fn=worker_init_fn,
pin_memory=True,
)
# Wrap with a BufferedIterator if needed
if self.buffer_size > 0:
itr = BufferedIterator(self.buffer_size, itr)
# Wrap with CountingIterator
itr = CountingIterator(itr, start=offset)
return itr
class EpochBatchIterator(EpochBatchIterating):
"""A multi-epoch iterator over a :class:`torch.utils.data.Dataset`.
Compared to :class:`torch.utils.data.DataLoader`, this iterator:
- can be reused across multiple epochs with the :func:`next_epoch_itr`
method (optionally shuffled between epochs)
- can be serialized/deserialized with the :func:`state_dict` and
:func:`load_state_dict` methods
- supports sharding with the *num_shards* and *shard_id* arguments
Args:
dataset (~torch.utils.data.Dataset): dataset from which to load the data
collate_fn (callable): merges a list of samples to form a mini-batch
batch_sampler (~torch.utils.data.Sampler or a callable): an iterator over batches of
indices, or a callable to create such an iterator (~torch.utils.data.Sampler).
A callable batch_sampler will be called for each epoch to enable per epoch dynamic
batch iterators defined by this callable batch_sampler.
seed (int, optional): seed for random number generator for
reproducibility (default: 1).
num_shards (int, optional): shard the data iterator into N
shards (default: 1).
shard_id (int, optional): which shard of the data iterator to
return (default: 0).
num_workers (int, optional): how many subprocesses to use for data
loading. 0 means the data will be loaded in the main process
(default: 0).
epoch (int, optional): the epoch to start the iterator from
(default: 1).
buffer_size (int, optional): the number of batches to keep ready in the
queue. Helps speeding up dataloading. When buffer_size is zero, the
default torch.utils.data.DataLoader preloading is used.
timeout (int, optional): if positive, the timeout value for collecting a batch
from workers. Should always be non-negative (default: ``0``).
disable_shuffling (bool, optional): force disable shuffling
(default: ``False``).
skip_remainder_batch (bool, optional): if set, discard the last batch in an epoch
for the sake of training stability, as the last batch is usually smaller than
local_batch_size * distributed_word_size (default: ``False``).
grouped_shuffling (bool, optional): enable shuffling batches in groups
of num_shards. Ensures that each GPU receives similar length sequences when
batches are sorted by length.
"""
def __init__(
self,
dataset,
collate_fn,
batch_sampler,
seed=1,
num_shards=1,
shard_id=0,
num_workers=0,
epoch=1,
buffer_size=0,
timeout=0,
disable_shuffling=False,
skip_remainder_batch=False,
grouped_shuffling=False,
):
assert isinstance(dataset, torch.utils.data.Dataset)
self.dataset = dataset
self.collate_fn = collate_fn
self.batch_sampler = batch_sampler
self._frozen_batches = (
tuple(batch_sampler) if not callable(batch_sampler) else None
)
self.seed = seed
self.num_shards = num_shards
self.shard_id = shard_id
self.num_workers = num_workers
# This upper limit here is to prevent people from abusing this feature
# in a shared computing environment.
self.buffer_size = min(buffer_size, 20)
self.timeout = timeout
self.disable_shuffling = disable_shuffling
self.skip_remainder_batch = skip_remainder_batch
self.grouped_shuffling = grouped_shuffling
self.epoch = max(epoch, 1) # we use 1-based indexing for epochs
self.shuffle = not disable_shuffling
self._cur_epoch_itr = None
self._next_epoch_itr = None
self._supports_prefetch = getattr(dataset, "supports_prefetch", False)
@property
def frozen_batches(self):
if self._frozen_batches is None:
self._frozen_batches = tuple(self.batch_sampler(self.dataset, self.epoch))
return self._frozen_batches
@property
def first_batch(self):
if len(self.frozen_batches) == 0:
raise Exception(
"The dataset is empty. This could indicate "
"that all elements in the dataset have been skipped. "
"Try increasing the max number of allowed tokens or using "
"a larger dataset."
)
if getattr(self.dataset, "supports_fetch_outside_dataloader", True):
return self.collate_fn([self.dataset[i] for i in self.frozen_batches[0]])
else:
return "DUMMY"
def __len__(self):
return int(math.ceil(len(self.frozen_batches) / float(self.num_shards)))
@property
def n(self):
return self.iterations_in_epoch
@property
def next_epoch_idx(self):
"""Return the epoch index after *next_epoch_itr* is called."""
if self._next_epoch_itr is not None:
return self.epoch
elif self._cur_epoch_itr is not None and self.end_of_epoch():
return self.epoch + 1
else:
return self.epoch
def next_epoch_itr(
self, shuffle=True, fix_batches_to_gpus=False, set_dataset_epoch=True
):
"""Return a new iterator over the dataset.
Args:
shuffle (bool, optional): shuffle batches before returning the
iterator (default: True).
fix_batches_to_gpus (bool, optional): ensure that batches are always
allocated to the same shards across epochs. Requires
that :attr:`dataset` supports prefetching (default: False).
set_dataset_epoch (bool, optional): update the wrapped Dataset with
the new epoch number (default: True).
"""
if self.disable_shuffling:
shuffle = False
prev_epoch = self.epoch
self.epoch = self.next_epoch_idx
if set_dataset_epoch and hasattr(self.dataset, "set_epoch"):
self.dataset.set_epoch(self.epoch)
if self._next_epoch_itr is not None:
self._cur_epoch_itr = self._next_epoch_itr
self._next_epoch_itr = None
else:
if callable(self.batch_sampler) and prev_epoch != self.epoch:
# reset _frozen_batches to refresh the next epoch
self._frozen_batches = None
self._cur_epoch_itr = self._get_iterator_for_epoch(
self.epoch,
shuffle,
fix_batches_to_gpus=fix_batches_to_gpus,
)
self.shuffle = shuffle
return self._cur_epoch_itr
def end_of_epoch(self) -> bool:
"""Returns whether the most recent epoch iterator has been exhausted"""
return not self._cur_epoch_itr.has_next()
@property
def iterations_in_epoch(self):
"""The number of consumed batches in the current epoch."""
if self._cur_epoch_itr is not None:
return self._cur_epoch_itr.n
elif self._next_epoch_itr is not None:
return self._next_epoch_itr.n
return 0
def state_dict(self):
"""Returns a dictionary containing a whole state of the iterator."""
if self.end_of_epoch():
epoch = self.epoch + 1
iter_in_epoch = 0
else:
epoch = self.epoch
iter_in_epoch = self.iterations_in_epoch
return {
"version": 2,
"epoch": epoch,
"iterations_in_epoch": iter_in_epoch,
"shuffle": self.shuffle,
}
def load_state_dict(self, state_dict):
"""Copies the state of the iterator from the given *state_dict*."""
self.epoch = state_dict["epoch"]
itr_pos = state_dict.get("iterations_in_epoch", 0)
version = state_dict.get("version", 1)
if itr_pos > 0:
# fast-forward epoch iterator
self._next_epoch_itr = self._get_iterator_for_epoch(
self.epoch,
shuffle=state_dict.get("shuffle", True),
offset=itr_pos,
)
if self._next_epoch_itr is None:
if version == 1:
# legacy behavior: we finished the epoch, increment epoch counter
self.epoch += 1
else:
raise RuntimeError(
"Cannot resume training due to dataloader mismatch, please "
"report this to the fairseq developers. You can relaunch "
"training with `--reset-dataloader` and it should work."
)
else:
self._next_epoch_itr = None
def _get_iterator_for_epoch(
self, epoch, shuffle, fix_batches_to_gpus=False, offset=0
):
def shuffle_batches(batches, seed):
with data_utils.numpy_seed(seed):
if self.grouped_shuffling:
grouped_batches = [
batches[(i * self.num_shards) : ((i + 1) * self.num_shards)]
for i in range((len(batches) // self.num_shards))
]
np.random.shuffle(grouped_batches)
batches = list(itertools.chain(*grouped_batches))
else:
np.random.shuffle(batches)
return batches
if self._supports_prefetch:
batches = self.frozen_batches
if shuffle and not fix_batches_to_gpus:
batches = shuffle_batches(list(batches), self.seed + epoch)
batches = list(
ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[])
)
self.dataset.prefetch([i for s in batches for i in s])
if shuffle and fix_batches_to_gpus:
batches = shuffle_batches(batches, self.seed + epoch + self.shard_id)
else:
if shuffle:
batches = shuffle_batches(list(self.frozen_batches), self.seed + epoch)
else:
batches = self.frozen_batches
batches = list(
ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[])
)
if offset > 0 and offset >= len(batches):
return None
if self.num_workers > 0:
os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning"
# Create data loader
itr = torch.utils.data.DataLoader(
self.dataset,
collate_fn=self.collate_fn,
batch_sampler=batches[offset:],
num_workers=self.num_workers,
timeout=self.timeout,
pin_memory=True,
)
# Wrap with a BufferedIterator if needed
if self.buffer_size > 0:
itr = BufferedIterator(self.buffer_size, itr)
# Wrap with CountingIterator
itr = CountingIterator(itr, start=offset)
if self.skip_remainder_batch:
# TODO: Below is a lazy implementation which discard the final batch regardless
# of whether it is a full batch or not.
total_num_itrs = len(batches) - 1
itr.take(total_num_itrs)
logger.info(f"skip final residual batch, total_num_itrs = {total_num_itrs}")
return itr
class GroupedIterator(CountingIterator):
"""Wrapper around an iterable that returns groups (chunks) of items.
Args:
iterable (iterable): iterable to wrap
chunk_size (int): size of each chunk
skip_remainder_batch (bool, optional): if set, discard the last grouped batch in
each training epoch, as the last grouped batch is usually smaller than
local_batch_size * distributed_word_size * chunk_size (default: ``False``).
Attributes:
n (int): number of elements consumed from this iterator
"""
def __init__(self, iterable, chunk_size, skip_remainder_batch=False):
if skip_remainder_batch:
total_num_itrs = int(math.floor(len(iterable) / float(chunk_size)))
logger.info(
f"skip final residual batch, grouped total_num_itrs = {total_num_itrs}"
)
else:
total_num_itrs = int(math.ceil(len(iterable) / float(chunk_size)))
logger.info(f"grouped total_num_itrs = {total_num_itrs}")
itr = _chunk_iterator(iterable, chunk_size, skip_remainder_batch)
super().__init__(
itr,
start=int(math.ceil(getattr(iterable, "n", 0) / float(chunk_size))),
total=total_num_itrs,
)
self.chunk_size = chunk_size
if skip_remainder_batch:
self.take(total_num_itrs)
# TODO: [Hack] Here the grouped iterator modifies the base iterator size so that
# training can move into the next epoch once the grouped iterator is exhausted.
# Double-check this implementation in case unexpected behavior occurs.
iterable.take(total_num_itrs * chunk_size)
def _chunk_iterator(itr, chunk_size, skip_remainder_batch=False):
chunk = []
for x in itr:
chunk.append(x)
if len(chunk) == chunk_size:
yield chunk
chunk = []
if not skip_remainder_batch and len(chunk) > 0:
yield chunk
class ShardedIterator(CountingIterator):
"""A sharded wrapper around an iterable, padded to length.
Args:
iterable (iterable): iterable to wrap
num_shards (int): number of shards to split the iterable into
shard_id (int): which shard to iterator over
fill_value (Any, optional): padding value when the iterable doesn't
evenly divide *num_shards* (default: None).
Attributes:
n (int): number of elements consumed from this iterator
"""
def __init__(
self, iterable, num_shards, shard_id, fill_value=None, skip_remainder_batch=None
):
"""
Args:
skip_remainder_batch: ignored"""
if shard_id < 0 or shard_id >= num_shards:
raise ValueError("shard_id must be between 0 and num_shards")
sharded_len = int(math.ceil(len(iterable) / float(num_shards)))
itr = map(
operator.itemgetter(1),
itertools.zip_longest(
range(sharded_len),
itertools.islice(iterable, shard_id, len(iterable), num_shards),
fillvalue=fill_value,
),
)
super().__init__(
itr,
start=int(math.ceil(getattr(iterable, "n", 0) / float(num_shards))),
total=sharded_len,
)
class BackgroundConsumer(Thread):
def __init__(self, queue, source, max_len, cuda_device):
Thread.__init__(self)
self._queue = queue
self._source = source
self._max_len = max_len
self.count = 0
self.cuda_device = cuda_device
def run(self):
# set_device to avoid creation of GPU0 context when using pin_memory
if self.cuda_device is not None:
torch.cuda.set_device(self.cuda_device)
try:
for item in self._source:
self._queue.put(item)
# Stop if we reached the maximum length
self.count += 1
if self._max_len is not None and self.count >= self._max_len:
break
# Signal the consumer we are done.
self._queue.put(_sentinel)
except Exception as e:
self._queue.put(e)
class BufferedIterator(object):
def __init__(self, size, iterable):
self._queue = queue.Queue(size)
self._iterable = iterable
self._consumer = None
self.start_time = time.time()
self.warning_time = None
self.total = len(iterable)
def _create_consumer(self):
self._consumer = BackgroundConsumer(
self._queue,
self._iterable,
self.total,
torch.cuda.current_device() if torch.cuda.is_available() else None,
)
self._consumer.daemon = True
self._consumer.start()
def __iter__(self):
return self
def __len__(self):
return self.total
def take(self, n):
self.total = min(self.total, n)
# Propagate this change to the underlying iterator
if hasattr(self._iterable, "take"):
self._iterable.take(n)
return self
def __next__(self):
# Create consumer if not created yet
if self._consumer is None:
self._create_consumer()
# Notify the user if there is a data loading bottleneck
if self._queue.qsize() < min(2, max(1, self._queue.maxsize // 2)):
if time.time() - self.start_time > 5 * 60:
if (
self.warning_time is None
or time.time() - self.warning_time > 15 * 60
):
logger.debug(
"Data loading buffer is empty or nearly empty. This may "
"indicate a data loading bottleneck, and increasing the "
"number of workers (--num-workers) may help."
)
self.warning_time = time.time()
# Get next example
item = self._queue.get(True)
if isinstance(item, Exception):
raise item
if item is _sentinel:
raise StopIteration()
return item
class GroupedEpochBatchIterator(EpochBatchIterator):
"""Grouped version of EpochBatchIterator
It takes several samplers from different datasets.
Each epoch shuffle the dataset wise sampler individually with different
random seed. The those sub samplers are combined with into
one big samplers with deterministic permutation to mix batches from
different datasets. It will act like EpochBatchIterator but make sure
1) data from one data set each time
2) for different workers, they use the same order to fetch the data
so they will use data from the same dataset everytime
mult_rate is used for update_freq > 1 case where we want to make sure update_freq
mini-batches come from same source
"""
def __init__(
self,
dataset,
collate_fn,
batch_samplers,
seed=1,
num_shards=1,
shard_id=0,
num_workers=0,
epoch=0,
mult_rate=1,
buffer_size=0,
skip_remainder_batch=False,
):
super().__init__(
dataset,
collate_fn,
batch_samplers,
seed,
num_shards,
shard_id,
num_workers,
epoch,
buffer_size,
skip_remainder_batch=skip_remainder_batch,
)
# level 0: sub-samplers 1: batch_idx 2: batches
self._frozen_batches = tuple([tuple(sub_batch) for sub_batch in batch_samplers])
self.step_size = mult_rate * num_shards
self.lengths = [
(len(x) // self.step_size) * self.step_size for x in self.frozen_batches
]
def __len__(self):
return sum(self.lengths)
@property
def first_batch(self):
if len(self.frozen_batches) == 0:
raise Exception(
"The dataset is empty. This could indicate "
"that all elements in the dataset have been skipped. "
"Try increasing the max number of allowed tokens or using "
"a larger dataset."
)
if self.dataset.supports_fetch_outside_dataloader:
return self.collate_fn([self.dataset[i] for i in self.frozen_batches[0][0]])
else:
return "DUMMY"
def _get_iterator_for_epoch(
self, epoch, shuffle, fix_batches_to_gpus=False, offset=0
):
def shuffle_batches(batches, seed):
with data_utils.numpy_seed(seed):
np.random.shuffle(batches)
return batches
def return_full_batches(batch_sets, seed, shuffle):
if shuffle:
batch_sets = [shuffle_batches(list(x), seed) for x in batch_sets]
batch_sets = [
batch_sets[i][: self.lengths[i]] for i in range(len(batch_sets))
]
batches = list(itertools.chain.from_iterable(batch_sets))
if shuffle:
with data_utils.numpy_seed(seed):
idx = np.random.permutation(len(batches) // self.step_size)
if len(idx) * self.step_size != len(batches):
raise ValueError(
"ERROR: %d %d %d %d"
% (len(idx), self.step_size, len(batches), self.shard_id),
":".join(["%d" % x for x in self.lengths]),
)
mini_shards = [
batches[i * self.step_size : (i + 1) * self.step_size]
for i in idx
]
batches = list(itertools.chain.from_iterable(mini_shards))
return batches
if self._supports_prefetch:
raise NotImplementedError("To be implemented")
else:
batches = return_full_batches(
self.frozen_batches, self.seed + epoch, shuffle
)
batches = list(
ShardedIterator(batches, self.num_shards, self.shard_id, fill_value=[])
)
if offset > 0 and offset >= len(batches):
return None
if self.num_workers > 0:
os.environ["PYTHONWARNINGS"] = "ignore:semaphore_tracker:UserWarning"
itr = torch.utils.data.DataLoader(
self.dataset,
collate_fn=self.collate_fn,
batch_sampler=batches[offset:],
num_workers=self.num_workers,
)
if self.buffer_size > 0:
itr = BufferedIterator(self.buffer_size, itr)
return CountingIterator(itr, start=offset)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/iterators.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import subprocess
import json
import tempfile
import hashlib
from typing import Hashable
try:
import pyarrow.plasma as plasma
PYARROW_AVAILABLE = True
except ImportError:
plasma = None
PYARROW_AVAILABLE = False
class PlasmaArray:
"""
Wrapper around numpy arrays that automatically moves the data to shared
memory upon serialization. This is particularly helpful when passing numpy
arrays through multiprocessing, so that data is not unnecessarily
duplicated or pickled.
"""
def __init__(self, array):
super().__init__()
self.array = array
self.disable = array.nbytes < 134217728 # disable for arrays <128MB
self.object_id = None
self.path = None
# variables with underscores shouldn't be pickled
self._client = None
self._server = None
self._server_tmp = None
self._plasma = None
@property
def plasma(self):
if self._plasma is None and not self.disable:
self._plasma = plasma
return self._plasma
def start_server(self):
if self.plasma is None or self._server is not None:
return
assert self.object_id is None
assert self.path is None
self._server_tmp = tempfile.NamedTemporaryFile()
self.path = self._server_tmp.name
self._server = subprocess.Popen(
["plasma_store", "-m", str(int(1.05 * self.array.nbytes)), "-s", self.path]
)
@property
def client(self):
if self._client is None:
assert self.path is not None
self._client = self.plasma.connect(self.path, num_retries=200)
return self._client
def __getstate__(self):
"""Called on pickle load"""
if self.plasma is None:
return self.__dict__
if self.object_id is None:
self.start_server()
self.object_id = self.client.put(self.array)
state = self.__dict__.copy()
del state["array"]
state["_client"] = None
state["_server"] = None
state["_server_tmp"] = None
state["_plasma"] = None
return state
def __setstate__(self, state):
"""Called on pickle save"""
self.__dict__.update(state)
if self.plasma is None:
return
self.array = self.client.get(self.object_id)
def __del__(self):
if self._server is not None:
self._server.kill()
self._server = None
self._server_tmp.close()
self._server_tmp = None
DEFAULT_PLASMA_PATH = "/tmp/plasma"
class PlasmaView:
"""Interface to write and read from shared memory. Whereas PlasmaArray writes to plasma on serialization,
PlasmaView writes to shared memory on instantiation."""
def __init__(self, array, split_path: str, hash_data: Hashable, plasma_path=None):
"""
Args:
array: numpy array to store. This can be read with ``PlasmaView().array``
split_path: the path whence the data was read, used for hashing
hash_data: other metadata about the array that can be used to create a unique key.
as of writing, the 3 callers in ``TokenBlockDataset`` use::
hash_data = ((block_size, document_sep_len, str(break_mode), len(dataset)), 0|1|2)
"""
assert PYARROW_AVAILABLE
assert split_path is not None
if plasma_path is None:
plasma_path = DEFAULT_PLASMA_PATH
self.path = plasma_path
self.split_path = split_path
self._client = None # Initialize lazily for pickle. plasma clients should not be deep copied or serialized.
self._n = None
self.object_id = self.get_object_id(self.split_path, hash_data)
try:
self.client.put(array, object_id=self.object_id)
except plasma.PlasmaObjectExists:
pass
@property
def client(self):
if self._client is None:
self._client = plasma.connect(self.path, num_retries=200)
return self._client
@property
def array(self):
"""Fetch a read only view of an np.array, stored in plasma."""
ret = self.client.get(self.object_id)
return ret
@staticmethod
def get_object_id(split_path: str, hash_data: Hashable):
"""Returns plasma.ObjectID from hashing split_path and object_num."""
hash = hashlib.blake2b(bytes(split_path, "utf-8"), digest_size=20)
harg = json.dumps(hash_data).encode("utf-8")
hash.update(harg)
return plasma.ObjectID(hash.digest())
def __getstate__(self):
"""Called on pickle save"""
self.disconnect()
state = self.__dict__.copy()
assert state["_client"] is None
assert "object_id" in state
return state
def __setstate__(self, state):
"""Called on pickle load"""
self.__dict__.update(state)
def __del__(self):
self.disconnect()
def disconnect(self):
if self._client is not None:
self._client.disconnect()
self._client = None
def __len__(self):
"""Save reads by caching len"""
if self._n is None:
self._n = len(self.array)
return self._n
GB100 = (1024 ** 3) * 100
class PlasmaStore:
def __init__(self, path=DEFAULT_PLASMA_PATH, nbytes: int = GB100):
self.server = self.start(path, nbytes)
def __del__(self):
self.server.kill()
@staticmethod
def start(path=DEFAULT_PLASMA_PATH, nbytes: int = GB100) -> subprocess.Popen:
if not PYARROW_AVAILABLE:
raise ImportError("please run pip install pyarrow to use --use_plasma_view")
# best practice is to allocate more space than we need. The limitation seems to be the size of /dev/shm
_server = subprocess.Popen(["plasma_store", "-m", str(nbytes), "-s", path])
plasma.connect(path, num_retries=200) # If we can't connect we fail immediately
return _server
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/plasma_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import numpy as np
from fairseq.data import BaseWrapperDataset, plasma_utils
logger = logging.getLogger(__name__)
class ResamplingDataset(BaseWrapperDataset):
"""Randomly samples from a given dataset at each epoch.
Sampling is done with or without replacement, depending on the "replace"
parameter.
Optionally, the epoch size can be rescaled. This is potentially desirable
to increase per-epoch coverage of the base dataset (since sampling with
replacement means that many items in the dataset will be left out). In the
case of sampling without replacement, size_ratio should be strictly less
than 1.
Args:
dataset (~torch.utils.data.Dataset): dataset on which to sample.
weights (List[float]): list of probability weights
(default: None, which corresponds to uniform sampling).
replace (bool): sampling mode; True for "with replacement", or False
for "without replacement" (default: True)
size_ratio (float): the ratio to subsample to; must be positive
(default: 1.0).
batch_by_size (bool): whether or not to batch by sequence length
(default: True).
seed (int): RNG seed to use (default: 0).
epoch (int): starting epoch number (default: 1).
"""
def __init__(
self,
dataset,
weights=None,
replace=True,
size_ratio=1.0,
batch_by_size=True,
seed=0,
epoch=1,
):
super().__init__(dataset)
if weights is None:
self.weights = None
else:
assert len(weights) == len(dataset)
weights_arr = np.array(weights, dtype=np.float64)
weights_arr /= weights_arr.sum()
self.weights = plasma_utils.PlasmaArray(weights_arr)
self.replace = replace
assert size_ratio > 0.0
if not self.replace:
assert size_ratio < 1.0
self.size_ratio = float(size_ratio)
self.actual_size = np.ceil(len(dataset) * self.size_ratio).astype(int)
self.batch_by_size = batch_by_size
self.seed = seed
self._cur_epoch = None
self._cur_indices = None
self.set_epoch(epoch)
def __getitem__(self, index):
return self.dataset[self._cur_indices.array[index]]
def __len__(self):
return self.actual_size
@property
def sizes(self):
if isinstance(self.dataset.sizes, list):
return [s[self._cur_indices.array] for s in self.dataset.sizes]
return self.dataset.sizes[self._cur_indices.array]
def num_tokens(self, index):
return self.dataset.num_tokens(self._cur_indices.array[index])
def size(self, index):
return self.dataset.size(self._cur_indices.array[index])
def ordered_indices(self):
if self.batch_by_size:
order = [
np.arange(len(self)),
self.sizes,
] # No need to handle `self.shuffle == True`
return np.lexsort(order)
else:
return np.arange(len(self))
def prefetch(self, indices):
self.dataset.prefetch(self._cur_indices.array[indices])
@property
def can_reuse_epoch_itr_across_epochs(self):
return False
def set_epoch(self, epoch):
logger.debug("ResamplingDataset.set_epoch: {}".format(epoch))
super().set_epoch(epoch)
if epoch == self._cur_epoch:
return
self._cur_epoch = epoch
# Generate a weighted sample of indices as a function of the
# random seed and the current epoch.
rng = np.random.RandomState(
[
42, # magic number
self.seed % (2 ** 32), # global seed
self._cur_epoch, # epoch index
]
)
self._cur_indices = plasma_utils.PlasmaArray(
rng.choice(
len(self.dataset),
self.actual_size,
replace=self.replace,
p=(None if self.weights is None else self.weights.array),
)
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/resampling_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import numpy as np
import torch
from fairseq.data import FairseqDataset, data_utils
logger = logging.getLogger(__name__)
def collate(
samples,
pad_idx,
eos_idx,
left_pad_source=True,
left_pad_target=False,
input_feeding=True,
pad_to_length=None,
pad_to_multiple=1,
):
if len(samples) == 0:
return {}
def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
return data_utils.collate_tokens(
[s[key] for s in samples],
pad_idx,
eos_idx,
left_pad,
move_eos_to_beginning,
pad_to_length=pad_to_length,
pad_to_multiple=pad_to_multiple,
)
def check_alignment(alignment, src_len, tgt_len):
if alignment is None or len(alignment) == 0:
return False
if (
alignment[:, 0].max().item() >= src_len - 1
or alignment[:, 1].max().item() >= tgt_len - 1
):
logger.warning("alignment size mismatch found, skipping alignment!")
return False
return True
def compute_alignment_weights(alignments):
"""
Given a tensor of shape [:, 2] containing the source-target indices
corresponding to the alignments, a weight vector containing the
inverse frequency of each target index is computed.
For e.g. if alignments = [[5, 7], [2, 3], [1, 3], [4, 2]], then
a tensor containing [1., 0.5, 0.5, 1] should be returned (since target
index 3 is repeated twice)
"""
align_tgt = alignments[:, 1]
_, align_tgt_i, align_tgt_c = torch.unique(
align_tgt, return_inverse=True, return_counts=True
)
align_weights = align_tgt_c[align_tgt_i[np.arange(len(align_tgt))]]
return 1.0 / align_weights.float()
id = torch.LongTensor([s["id"] for s in samples])
src_tokens = merge(
"source",
left_pad=left_pad_source,
pad_to_length=pad_to_length["source"] if pad_to_length is not None else None,
)
# sort by descending source length
src_lengths = torch.LongTensor(
[s["source"].ne(pad_idx).long().sum() for s in samples]
)
src_lengths, sort_order = src_lengths.sort(descending=True)
id = id.index_select(0, sort_order)
src_tokens = src_tokens.index_select(0, sort_order)
prev_output_tokens = None
target = None
if samples[0].get("target", None) is not None:
target = merge(
"target",
left_pad=left_pad_target,
pad_to_length=pad_to_length["target"]
if pad_to_length is not None
else None,
)
target = target.index_select(0, sort_order)
tgt_lengths = torch.LongTensor(
[s["target"].ne(pad_idx).long().sum() for s in samples]
).index_select(0, sort_order)
ntokens = tgt_lengths.sum().item()
if samples[0].get("prev_output_tokens", None) is not None:
prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target)
elif input_feeding:
# we create a shifted version of targets for feeding the
# previous output token(s) into the next decoder step
prev_output_tokens = merge(
"target",
left_pad=left_pad_target,
move_eos_to_beginning=True,
pad_to_length=pad_to_length["target"]
if pad_to_length is not None
else None,
)
else:
ntokens = src_lengths.sum().item()
batch = {
"id": id,
"nsentences": len(samples),
"ntokens": ntokens,
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
},
"target": target,
}
if prev_output_tokens is not None:
batch["net_input"]["prev_output_tokens"] = prev_output_tokens.index_select(
0, sort_order
)
if samples[0].get("alignment", None) is not None:
bsz, tgt_sz = batch["target"].shape
src_sz = batch["net_input"]["src_tokens"].shape[1]
offsets = torch.zeros((len(sort_order), 2), dtype=torch.long)
offsets[:, 1] += torch.arange(len(sort_order), dtype=torch.long) * tgt_sz
if left_pad_source:
offsets[:, 0] += src_sz - src_lengths
if left_pad_target:
offsets[:, 1] += tgt_sz - tgt_lengths
alignments = [
alignment + offset
for align_idx, offset, src_len, tgt_len in zip(
sort_order, offsets, src_lengths, tgt_lengths
)
for alignment in [samples[align_idx]["alignment"].view(-1, 2)]
if check_alignment(alignment, src_len, tgt_len)
]
if len(alignments) > 0:
alignments = torch.cat(alignments, dim=0)
align_weights = compute_alignment_weights(alignments)
batch["alignments"] = alignments
batch["align_weights"] = align_weights
if samples[0].get("constraints", None) is not None:
# Collate the packed constraints across the samples, padding to
# the length of the longest sample.
lens = [sample.get("constraints").size(0) for sample in samples]
max(lens)
constraints = torch.zeros((len(samples), max(lens))).long()
for i, sample in enumerate(samples):
constraints[i, 0 : lens[i]] = samples[i].get("constraints")
batch["constraints"] = constraints.index_select(0, sort_order)
return batch
class LanguagePairDataset(FairseqDataset):
"""
A pair of torch.utils.data.Datasets.
Args:
src (torch.utils.data.Dataset): source dataset to wrap
src_sizes (List[int]): source sentence lengths
src_dict (~fairseq.data.Dictionary): source vocabulary
tgt (torch.utils.data.Dataset, optional): target dataset to wrap
tgt_sizes (List[int], optional): target sentence lengths
tgt_dict (~fairseq.data.Dictionary, optional): target vocabulary
left_pad_source (bool, optional): pad source tensors on the left side
(default: True).
left_pad_target (bool, optional): pad target tensors on the left side
(default: False).
shuffle (bool, optional): shuffle dataset elements before batching
(default: True).
input_feeding (bool, optional): create a shifted version of the targets
to be passed into the model for teacher forcing (default: True).
remove_eos_from_source (bool, optional): if set, removes eos from end
of source if it's present (default: False).
append_eos_to_target (bool, optional): if set, appends eos to end of
target if it's absent (default: False).
align_dataset (torch.utils.data.Dataset, optional): dataset
containing alignments.
constraints (Tensor, optional): 2d tensor with a concatenated, zero-
delimited list of constraints for each sentence.
append_bos (bool, optional): if set, appends bos to the beginning of
source/target sentence.
num_buckets (int, optional): if set to a value greater than 0, then
batches will be bucketed into the given number of batch shapes.
src_lang_id (int, optional): source language ID, if set, the collated batch
will contain a field 'src_lang_id' in 'net_input' which indicates the
source language of the samples.
tgt_lang_id (int, optional): target language ID, if set, the collated batch
will contain a field 'tgt_lang_id' which indicates the target language
of the samples.
"""
def __init__(
self,
src,
src_sizes,
src_dict,
tgt=None,
tgt_sizes=None,
tgt_dict=None,
left_pad_source=True,
left_pad_target=False,
shuffle=True,
input_feeding=True,
remove_eos_from_source=False,
append_eos_to_target=False,
align_dataset=None,
constraints=None,
append_bos=False,
eos=None,
num_buckets=0,
src_lang_id=None,
tgt_lang_id=None,
pad_to_multiple=1,
):
if tgt_dict is not None:
assert src_dict.pad() == tgt_dict.pad()
assert src_dict.eos() == tgt_dict.eos()
assert src_dict.unk() == tgt_dict.unk()
if tgt is not None:
assert len(src) == len(
tgt
), "Source and target must contain the same number of examples"
self.src = src
self.tgt = tgt
self.src_sizes = np.array(src_sizes)
self.tgt_sizes = np.array(tgt_sizes) if tgt_sizes is not None else None
self.sizes = (
np.vstack((self.src_sizes, self.tgt_sizes)).T
if self.tgt_sizes is not None
else self.src_sizes
)
self.src_dict = src_dict
self.tgt_dict = tgt_dict
self.left_pad_source = left_pad_source
self.left_pad_target = left_pad_target
self.shuffle = shuffle
self.input_feeding = input_feeding
self.remove_eos_from_source = remove_eos_from_source
self.append_eos_to_target = append_eos_to_target
self.align_dataset = align_dataset
if self.align_dataset is not None:
assert (
self.tgt_sizes is not None
), "Both source and target needed when alignments are provided"
self.constraints = constraints
self.append_bos = append_bos
self.eos = eos if eos is not None else src_dict.eos()
self.src_lang_id = src_lang_id
self.tgt_lang_id = tgt_lang_id
if num_buckets > 0:
from fairseq.data import BucketPadLengthDataset
self.src = BucketPadLengthDataset(
self.src,
sizes=self.src_sizes,
num_buckets=num_buckets,
pad_idx=self.src_dict.pad(),
left_pad=self.left_pad_source,
)
self.src_sizes = self.src.sizes
logger.info("bucketing source lengths: {}".format(list(self.src.buckets)))
if self.tgt is not None:
self.tgt = BucketPadLengthDataset(
self.tgt,
sizes=self.tgt_sizes,
num_buckets=num_buckets,
pad_idx=self.tgt_dict.pad(),
left_pad=self.left_pad_target,
)
self.tgt_sizes = self.tgt.sizes
logger.info(
"bucketing target lengths: {}".format(list(self.tgt.buckets))
)
# determine bucket sizes using self.num_tokens, which will return
# the padded lengths (thanks to BucketPadLengthDataset)
num_tokens = np.vectorize(self.num_tokens, otypes=[np.compat.long])
self.bucketed_num_tokens = num_tokens(np.arange(len(self.src)))
self.buckets = [
(None, num_tokens) for num_tokens in np.unique(self.bucketed_num_tokens)
]
else:
self.buckets = None
self.pad_to_multiple = pad_to_multiple
def get_batch_shapes(self):
return self.buckets
def __getitem__(self, index):
tgt_item = self.tgt[index] if self.tgt is not None else None
src_item = self.src[index]
# Append EOS to end of tgt sentence if it does not have an EOS and remove
# EOS from end of src sentence if it exists. This is useful when we use
# use existing datasets for opposite directions i.e., when we want to
# use tgt_dataset as src_dataset and vice versa
if self.append_eos_to_target:
eos = self.tgt_dict.eos() if self.tgt_dict else self.src_dict.eos()
if self.tgt and self.tgt[index][-1] != eos:
tgt_item = torch.cat([self.tgt[index], torch.LongTensor([eos])])
if self.append_bos:
bos = self.tgt_dict.bos() if self.tgt_dict else self.src_dict.bos()
if self.tgt and self.tgt[index][0] != bos:
tgt_item = torch.cat([torch.LongTensor([bos]), self.tgt[index]])
bos = self.src_dict.bos()
if self.src[index][0] != bos:
src_item = torch.cat([torch.LongTensor([bos]), self.src[index]])
if self.remove_eos_from_source:
eos = self.src_dict.eos()
if self.src[index][-1] == eos:
src_item = self.src[index][:-1]
example = {
"id": index,
"source": src_item,
"target": tgt_item,
}
if self.align_dataset is not None:
example["alignment"] = self.align_dataset[index]
if self.constraints is not None:
example["constraints"] = self.constraints[index]
return example
def __len__(self):
return len(self.src)
def collater(self, samples, pad_to_length=None):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
pad_to_length (dict, optional): a dictionary of
{'source': source_pad_to_length, 'target': target_pad_to_length}
to indicate the max length to pad to in source and target respectively.
Returns:
dict: a mini-batch with the following keys:
- `id` (LongTensor): example IDs in the original input order
- `ntokens` (int): total number of tokens in the batch
- `net_input` (dict): the input to the Model, containing keys:
- `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
the source sentence of shape `(bsz, src_len)`. Padding will
appear on the left if *left_pad_source* is ``True``.
- `src_lengths` (LongTensor): 1D Tensor of the unpadded
lengths of each source sentence of shape `(bsz)`
- `prev_output_tokens` (LongTensor): a padded 2D Tensor of
tokens in the target sentence, shifted right by one
position for teacher forcing, of shape `(bsz, tgt_len)`.
This key will not be present if *input_feeding* is
``False``. Padding will appear on the left if
*left_pad_target* is ``True``.
- `src_lang_id` (LongTensor): a long Tensor which contains source
language IDs of each sample in the batch
- `target` (LongTensor): a padded 2D Tensor of tokens in the
target sentence of shape `(bsz, tgt_len)`. Padding will appear
on the left if *left_pad_target* is ``True``.
- `tgt_lang_id` (LongTensor): a long Tensor which contains target language
IDs of each sample in the batch
"""
res = collate(
samples,
pad_idx=self.src_dict.pad(),
eos_idx=self.eos,
left_pad_source=self.left_pad_source,
left_pad_target=self.left_pad_target,
input_feeding=self.input_feeding,
pad_to_length=pad_to_length,
pad_to_multiple=self.pad_to_multiple,
)
if self.src_lang_id is not None or self.tgt_lang_id is not None:
src_tokens = res["net_input"]["src_tokens"]
bsz = src_tokens.size(0)
if self.src_lang_id is not None:
res["net_input"]["src_lang_id"] = (
torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens)
)
if self.tgt_lang_id is not None:
res["tgt_lang_id"] = (
torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens)
)
return res
def num_tokens(self, index):
"""Return the number of tokens in a sample. This value is used to
enforce ``--max-tokens`` during batching."""
return max(
self.src_sizes[index],
self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
)
def num_tokens_vec(self, indices):
"""Return the number of tokens for a set of positions defined by indices.
This value is used to enforce ``--max-tokens`` during batching."""
sizes = self.src_sizes[indices]
if self.tgt_sizes is not None:
sizes = np.maximum(sizes, self.tgt_sizes[indices])
return sizes
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return (
self.src_sizes[index],
self.tgt_sizes[index] if self.tgt_sizes is not None else 0,
)
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
if self.shuffle:
indices = np.random.permutation(len(self)).astype(np.int64)
else:
indices = np.arange(len(self), dtype=np.int64)
if self.buckets is None:
# sort by target length, then source length
if self.tgt_sizes is not None:
indices = indices[np.argsort(self.tgt_sizes[indices], kind="mergesort")]
return indices[np.argsort(self.src_sizes[indices], kind="mergesort")]
else:
# sort by bucketed_num_tokens, which is:
# max(padded_src_len, padded_tgt_len)
return indices[
np.argsort(self.bucketed_num_tokens[indices], kind="mergesort")
]
@property
def supports_prefetch(self):
return getattr(self.src, "supports_prefetch", False) and (
getattr(self.tgt, "supports_prefetch", False) or self.tgt is None
)
def prefetch(self, indices):
self.src.prefetch(indices)
if self.tgt is not None:
self.tgt.prefetch(indices)
if self.align_dataset is not None:
self.align_dataset.prefetch(indices)
def filter_indices_by_size(self, indices, max_sizes):
"""Filter a list of sample indices. Remove those that are longer
than specified in max_sizes.
Args:
indices (np.array): original array of sample indices
max_sizes (int or list[int] or tuple[int]): max sample size,
can be defined separately for src and tgt (then list or tuple)
Returns:
np.array: filtered sample array
list: list of removed indices
"""
return data_utils.filter_paired_dataset_indices_by_size(
self.src_sizes,
self.tgt_sizes,
indices,
max_sizes,
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/language_pair_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class AppendTokenDataset(BaseWrapperDataset):
def __init__(self, dataset, token=None):
super().__init__(dataset)
self.token = token
if token is not None:
self._sizes = np.array(dataset.sizes) + 1
else:
self._sizes = dataset.sizes
def __getitem__(self, idx):
item = self.dataset[idx]
if self.token is not None:
item = torch.cat([item, item.new([self.token])])
return item
@property
def sizes(self):
return self._sizes
def num_tokens(self, index):
n = self.dataset.num_tokens(index)
if self.token is not None:
n += 1
return n
def size(self, index):
n = self.dataset.size(index)
if self.token is not None:
n += 1
return n
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/append_token_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.data import data_utils
from . import BaseWrapperDataset
class PadDataset(BaseWrapperDataset):
def __init__(self, dataset, pad_idx, left_pad, pad_length=None):
super().__init__(dataset)
self.pad_idx = pad_idx
self.left_pad = left_pad
self.pad_length = pad_length
def collater(self, samples):
return data_utils.collate_tokens(samples, self.pad_idx, left_pad=self.left_pad, pad_to_length=self.pad_length)
class LeftPadDataset(PadDataset):
def __init__(self, dataset, pad_idx, pad_length=None):
super().__init__(dataset, pad_idx, left_pad=True, pad_length=pad_length)
class RightPadDataset(PadDataset):
def __init__(self, dataset, pad_idx, pad_length=None):
super().__init__(dataset, pad_idx, left_pad=False, pad_length=pad_length) | KosmosX-API-main | kosmosX/fairseq/fairseq/data/pad_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from fairseq.data.huffman import HuffmanMMapIndexedDataset, HuffmanMMapIndex
from . import FairseqDataset
from typing import Union
def best_fitting_int_dtype(
max_int_to_represent,
) -> Union[np.uint16, np.uint32, np.int64]:
if max_int_to_represent is None:
return np.uint32 # Safe guess
elif max_int_to_represent < 65500:
return np.uint16
elif max_int_to_represent < 4294967295:
return np.uint32
else:
return np.int64
# we avoid np.uint64 because it doesn't save space and its type promotion behaves unexpectedly
# https://github.com/numpy/numpy/issues/5745
def get_available_dataset_impl():
return list(map(str, DATASET_IMPL_CHOICES))
def infer_dataset_impl(path):
if IndexedRawTextDataset.exists(path):
return "raw"
elif IndexedDataset.exists(path):
with open(index_file_path(path), "rb") as f:
magic = f.read(8)
if magic == IndexedDataset._HDR_MAGIC:
return "cached"
elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]:
return "mmap"
elif magic == HuffmanMMapIndex._HDR_MAGIC[:8]:
return "huffman"
else:
return None
elif FastaDataset.exists(path):
return "fasta"
else:
return None
def make_builder(out_file, impl, vocab_size=None):
if impl == "mmap":
return MMapIndexedDatasetBuilder(
out_file, dtype=best_fitting_int_dtype(vocab_size)
)
elif impl == "fasta":
raise NotImplementedError
elif impl == "huffman":
raise ValueError(
"Use HuffmanCodeBuilder directly as it has a different interface."
)
else:
return IndexedDatasetBuilder(out_file)
def make_dataset(path, impl, fix_lua_indexing=False, dictionary=None):
if impl == "raw" and IndexedRawTextDataset.exists(path):
assert dictionary is not None
return IndexedRawTextDataset(path, dictionary)
elif impl == "lazy" and IndexedDataset.exists(path):
return IndexedDataset(path, fix_lua_indexing=fix_lua_indexing)
elif impl == "cached" and IndexedDataset.exists(path):
return IndexedCachedDataset(path, fix_lua_indexing=fix_lua_indexing)
elif impl == "mmap" and MMapIndexedDataset.exists(path):
return MMapIndexedDataset(path)
elif impl == "fasta" and FastaDataset.exists(path):
from fairseq.data.fasta_dataset import EncodedFastaDataset
return EncodedFastaDataset(path, dictionary)
elif impl == "huffman" and HuffmanMMapIndexedDataset.exists(path):
return HuffmanMMapIndexedDataset(path)
return None
def dataset_exists(path, impl):
if impl == "raw":
return IndexedRawTextDataset.exists(path)
elif impl == "mmap":
return MMapIndexedDataset.exists(path)
elif impl == "huffman":
return HuffmanMMapIndexedDataset.exists(path)
else:
return IndexedDataset.exists(path)
def read_longs(f, n):
a = np.empty(n, dtype=np.int64)
f.readinto(a)
return a
def write_longs(f, a):
f.write(np.array(a, dtype=np.int64))
_code_to_dtype = {
1: np.uint8,
2: np.int8,
3: np.int16,
4: np.int32,
5: np.int64,
6: np.float,
7: np.double,
8: np.uint16,
9: np.uint32,
10: np.uint64,
}
def _dtype_header_code(dtype) -> int:
for k in _code_to_dtype.keys():
if _code_to_dtype[k] == dtype:
return k
raise ValueError(dtype)
def index_file_path(prefix_path):
return prefix_path + ".idx"
def data_file_path(prefix_path):
return prefix_path + ".bin"
class IndexedDataset(FairseqDataset):
"""Loader for TorchNet IndexedDataset"""
_HDR_MAGIC = b"TNTIDX\x00\x00"
def __init__(self, path, fix_lua_indexing=False):
super().__init__()
self.path = path
self.fix_lua_indexing = fix_lua_indexing
self.data_file = None
self.read_index(path)
def read_index(self, path):
with open(index_file_path(path), "rb") as f:
magic = f.read(8)
assert magic == self._HDR_MAGIC, (
"Index file doesn't match expected format. "
"Make sure that --dataset-impl is configured properly."
)
version = f.read(8)
assert struct.unpack("<Q", version) == (1,)
code, self.element_size = struct.unpack("<QQ", f.read(16))
self.dtype = _code_to_dtype[code]
self._len, self.s = struct.unpack("<QQ", f.read(16))
self.dim_offsets = read_longs(f, self._len + 1)
self.data_offsets = read_longs(f, self._len + 1)
self.sizes = read_longs(f, self.s)
def read_data(self, path):
self.data_file = open(data_file_path(path), "rb", buffering=0)
def check_index(self, i):
if i < 0 or i >= self._len:
raise IndexError("index out of range")
def __del__(self):
if self.data_file:
self.data_file.close()
@lru_cache(maxsize=8)
def __getitem__(self, i) -> torch.Tensor:
if not self.data_file:
self.read_data(self.path)
self.check_index(i)
tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
a = np.empty(tensor_size, dtype=self.dtype)
self.data_file.seek(self.data_offsets[i] * self.element_size)
self.data_file.readinto(a)
item = torch.from_numpy(a).long()
if self.fix_lua_indexing:
item -= 1 # subtract 1 for 0-based indexing
return item
def __len__(self):
return self._len
def num_tokens(self, index):
return self.sizes[index]
def size(self, index):
return self.sizes[index]
@staticmethod
def exists(path):
return PathManager.exists(index_file_path(path)) and PathManager.exists(
data_file_path(path)
)
@property
def supports_prefetch(self):
return False # avoid prefetching to save memory
class IndexedCachedDataset(IndexedDataset):
def __init__(self, path, fix_lua_indexing=False):
super().__init__(path, fix_lua_indexing=fix_lua_indexing)
self.cache = None
self.cache_index = {}
@property
def supports_prefetch(self):
return True
def prefetch(self, indices):
if all(i in self.cache_index for i in indices):
return
if not self.data_file:
self.read_data(self.path)
indices = sorted(set(indices))
total_size = 0
for i in indices:
total_size += self.data_offsets[i + 1] - self.data_offsets[i]
self.cache = np.empty(total_size, dtype=self.dtype)
ptx = 0
self.cache_index.clear()
for i in indices:
self.cache_index[i] = ptx
size = self.data_offsets[i + 1] - self.data_offsets[i]
a = self.cache[ptx : ptx + size]
self.data_file.seek(self.data_offsets[i] * self.element_size)
self.data_file.readinto(a)
ptx += size
if self.data_file:
# close and delete data file after prefetch so we can pickle
self.data_file.close()
self.data_file = None
@lru_cache(maxsize=8)
def __getitem__(self, i):
self.check_index(i)
tensor_size = self.sizes[self.dim_offsets[i] : self.dim_offsets[i + 1]]
a = np.empty(tensor_size, dtype=self.dtype)
ptx = self.cache_index[i]
np.copyto(a, self.cache[ptx : ptx + a.size])
item = torch.from_numpy(a).long()
if self.fix_lua_indexing:
item -= 1 # subtract 1 for 0-based indexing
return item
class IndexedRawTextDataset(FairseqDataset):
"""Takes a text file as input and binarizes it in memory at instantiation.
Original lines are also kept in memory"""
def __init__(self, path, dictionary, append_eos=True, reverse_order=False):
self.tokens_list = []
self.lines = []
self.sizes = []
self.append_eos = append_eos
self.reverse_order = reverse_order
self.read_data(path, dictionary)
self.size = len(self.tokens_list)
def read_data(self, path, dictionary):
with open(path, "r", encoding="utf-8") as f:
for line in f:
self.lines.append(line.strip("\n"))
tokens = dictionary.encode_line(
line,
add_if_not_exist=False,
append_eos=self.append_eos,
reverse_order=self.reverse_order,
).long()
self.tokens_list.append(tokens)
self.sizes.append(len(tokens))
self.sizes = np.array(self.sizes)
def check_index(self, i):
if i < 0 or i >= self.size:
raise IndexError("index out of range")
@lru_cache(maxsize=8)
def __getitem__(self, i):
self.check_index(i)
return self.tokens_list[i]
def get_original_text(self, i):
self.check_index(i)
return self.lines[i]
def __del__(self):
pass
def __len__(self):
return self.size
def num_tokens(self, index):
return self.sizes[index]
def size(self, index):
return self.sizes[index]
@staticmethod
def exists(path):
return PathManager.exists(path)
class IndexedDatasetBuilder:
element_sizes = {
np.uint8: 1,
np.int8: 1,
np.int16: 2,
np.int32: 4,
np.int64: 8,
np.float: 4,
np.double: 8,
}
def __init__(self, out_file, dtype=np.int32):
self.out_file = open(out_file, "wb")
self.dtype = dtype
self.data_offsets = [0]
self.dim_offsets = [0]
self.sizes = []
self.element_size = self.element_sizes[self.dtype]
def add_item(self, tensor):
# +1 for Lua compatibility
bytes = self.out_file.write(np.array(tensor.numpy() + 1, dtype=self.dtype))
self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size)
for s in tensor.size():
self.sizes.append(s)
self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size()))
def merge_file_(self, another_file):
index = IndexedDataset(another_file)
assert index.dtype == self.dtype
begin = self.data_offsets[-1]
for offset in index.data_offsets[1:]:
self.data_offsets.append(begin + offset)
self.sizes.extend(index.sizes)
begin = self.dim_offsets[-1]
for dim_offset in index.dim_offsets[1:]:
self.dim_offsets.append(begin + dim_offset)
with open(data_file_path(another_file), "rb") as f:
while True:
data = f.read(1024)
if data:
self.out_file.write(data)
else:
break
def finalize(self, index_file):
self.out_file.close()
index = open(index_file, "wb")
index.write(b"TNTIDX\x00\x00")
index.write(struct.pack("<Q", 1))
index.write(
struct.pack("<QQ", _dtype_header_code(self.dtype), self.element_size)
)
index.write(struct.pack("<QQ", len(self.data_offsets) - 1, len(self.sizes)))
write_longs(index, self.dim_offsets)
write_longs(index, self.data_offsets)
write_longs(index, self.sizes)
index.close()
def _warmup_mmap_file(path):
with open(path, "rb") as stream:
while stream.read(100 * 1024 * 1024):
pass
class MMapIndexedDataset(torch.utils.data.Dataset):
class Index:
_HDR_MAGIC = b"MMIDIDX\x00\x00"
@classmethod
def writer(cls, path, dtype):
class _Writer:
def __enter__(self):
self._file = open(path, "wb")
self._file.write(cls._HDR_MAGIC)
self._file.write(struct.pack("<Q", 1))
self._file.write(struct.pack("<B", _dtype_header_code(dtype)))
return self
@staticmethod
def _get_pointers(sizes):
dtype_size = dtype().itemsize
address = 0
pointers = []
for size in sizes:
pointers.append(address)
address += size * dtype_size
return pointers
def write(self, sizes):
pointers = self._get_pointers(sizes)
self._file.write(struct.pack("<Q", len(sizes)))
sizes = np.array(sizes, dtype=np.int32)
self._file.write(sizes.tobytes(order="C"))
del sizes
pointers = np.array(pointers, dtype=np.int64)
self._file.write(pointers.tobytes(order="C"))
del pointers
def __exit__(self, exc_type, exc_val, exc_tb):
self._file.close()
return _Writer()
def __init__(self, path):
with open(path, "rb") as stream:
magic_test = stream.read(9)
assert self._HDR_MAGIC == magic_test, (
"Index file doesn't match expected format. "
"Make sure that --dataset-impl is configured properly."
)
version = struct.unpack("<Q", stream.read(8))
assert (1,) == version
(dtype_code,) = struct.unpack("<B", stream.read(1))
self._dtype = _code_to_dtype[dtype_code]
self._dtype_size = self._dtype().itemsize
self._len = struct.unpack("<Q", stream.read(8))[0]
offset = stream.tell()
_warmup_mmap_file(path)
self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
self._bin_buffer = memoryview(self._bin_buffer_mmap)
self._sizes = np.frombuffer(
self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
)
self._pointers = np.frombuffer(
self._bin_buffer,
dtype=np.int64,
count=self._len,
offset=offset + self._sizes.nbytes,
)
def __del__(self):
self._bin_buffer_mmap._mmap.close()
del self._bin_buffer_mmap
@property
def dtype(self):
return self._dtype
@property
def sizes(self):
return self._sizes
@lru_cache(maxsize=8)
def __getitem__(self, i):
return self._pointers[i], self._sizes[i]
def __len__(self):
return self._len
def __init__(self, path):
super().__init__()
self._path = None
self._index = None
self._bin_buffer = None
self._do_init(path)
def __getstate__(self):
return self._path
def __setstate__(self, state):
self._do_init(state)
def _do_init(self, path):
self._path = path
self._index = self.Index(index_file_path(self._path))
_warmup_mmap_file(data_file_path(self._path))
self._bin_buffer_mmap = np.memmap(
data_file_path(self._path), mode="r", order="C"
)
self._bin_buffer = memoryview(self._bin_buffer_mmap)
def __del__(self):
self._bin_buffer_mmap._mmap.close()
del self._bin_buffer_mmap
del self._index
def __len__(self):
return len(self._index)
@lru_cache(maxsize=8)
def __getitem__(self, i):
ptr, size = self._index[i]
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
)
if self._index.dtype != np.int64:
np_array = np_array.astype(np.int64)
return torch.from_numpy(np_array)
@property
def sizes(self):
return self._index.sizes
@property
def supports_prefetch(self):
return False
@staticmethod
def exists(path):
return PathManager.exists(index_file_path(path)) and PathManager.exists(
data_file_path(path)
)
def get_indexed_dataset_to_local(path) -> str:
local_index_path = PathManager.get_local_path(index_file_path(path))
local_data_path = PathManager.get_local_path(data_file_path(path))
assert local_index_path.endswith(".idx") and local_data_path.endswith(".bin"), (
"PathManager.get_local_path does not return files with expected patterns: "
f"{local_index_path} and {local_data_path}"
)
local_path = local_data_path[:-4] # stripping surfix ".bin"
assert local_path == local_index_path[:-4] # stripping surfix ".idx"
return local_path
class MMapIndexedDatasetBuilder:
def __init__(self, out_file, dtype=np.int64):
self._data_file = open(out_file, "wb")
self._dtype = dtype
self._sizes = []
def add_item(self, tensor):
np_array = np.array(tensor.numpy(), dtype=self._dtype)
self._data_file.write(np_array.tobytes(order="C"))
self._sizes.append(np_array.size)
def merge_file_(self, another_file):
# Concatenate index
index = MMapIndexedDataset.Index(index_file_path(another_file))
assert index.dtype == self._dtype
for size in index.sizes:
self._sizes.append(size)
# Concatenate data
with open(data_file_path(another_file), "rb") as f:
shutil.copyfileobj(f, self._data_file)
def finalize(self, index_file):
self._data_file.close()
with MMapIndexedDataset.Index.writer(index_file, self._dtype) as index:
index.write(self._sizes)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/indexed_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset
class RollDataset(BaseWrapperDataset):
def __init__(self, dataset, shifts):
super().__init__(dataset)
self.shifts = shifts
def __getitem__(self, index):
item = self.dataset[index]
return torch.roll(item, self.shifts)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/roll_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from collections import Counter
from multiprocessing import Pool
import torch
from fairseq import utils
from fairseq.data import data_utils
from fairseq.file_chunker_utils import Chunker, find_offsets
from fairseq.file_io import PathManager
from fairseq.tokenizer import tokenize_line
class Dictionary:
"""A mapping from symbols to consecutive integers"""
def __init__(
self,
*, # begin keyword-only arguments
bos="<s>",
pad="<pad>",
eos="</s>",
unk="<unk>",
extra_special_symbols=None,
):
self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos
self.symbols = []
self.count = []
self.indices = {}
self.bos_index = self.add_symbol(bos)
self.pad_index = self.add_symbol(pad)
self.eos_index = self.add_symbol(eos)
self.unk_index = self.add_symbol(unk)
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(s)
self.nspecial = len(self.symbols)
def __eq__(self, other):
return self.indices == other.indices
def __getitem__(self, idx):
if idx < len(self.symbols):
return self.symbols[idx]
return self.unk_word
def get_count(self, idx):
return self.count[idx]
def __len__(self):
"""Returns the number of symbols in the dictionary"""
return len(self.symbols)
def __contains__(self, sym):
return sym in self.indices
def index(self, sym):
"""Returns the index of the specified symbol"""
assert isinstance(sym, str)
if sym in self.indices:
return self.indices[sym]
return self.unk_index
def string(
self,
tensor,
bpe_symbol=None,
escape_unk=False,
extra_symbols_to_ignore=None,
unk_string=None,
include_eos=False,
separator=" ",
):
"""Helper for converting a tensor of token indices to a string.
Can optionally remove BPE symbols or escape <unk> words.
"""
if torch.is_tensor(tensor) and tensor.dim() == 2:
return "\n".join(
self.string(
t,
bpe_symbol,
escape_unk,
extra_symbols_to_ignore,
include_eos=include_eos,
)
for t in tensor
)
extra_symbols_to_ignore = set(extra_symbols_to_ignore or [])
if not include_eos:
extra_symbols_to_ignore.add(self.eos())
def token_string(i):
if i == self.unk():
if unk_string is not None:
return unk_string
else:
return self.unk_string(escape_unk)
else:
return self[i]
if hasattr(self, "bos_index"):
extra_symbols_to_ignore.add(self.bos())
sent = separator.join(
token_string(i)
for i in tensor
if utils.item(i) not in extra_symbols_to_ignore
)
return data_utils.post_process(sent, bpe_symbol)
def unk_string(self, escape=False):
"""Return unknown string, optionally escaped as: <<unk>>"""
if escape:
return "<{}>".format(self.unk_word)
else:
return self.unk_word
def add_symbol(self, word, n=1, overwrite=False):
"""Adds a word to the dictionary"""
if word in self.indices and not overwrite:
idx = self.indices[word]
self.count[idx] = self.count[idx] + n
return idx
else:
idx = len(self.symbols)
self.indices[word] = idx
self.symbols.append(word)
self.count.append(n)
return idx
def update(self, new_dict):
"""Updates counts from new dictionary."""
for word in new_dict.symbols:
idx2 = new_dict.indices[word]
if word in self.indices:
idx = self.indices[word]
self.count[idx] = self.count[idx] + new_dict.count[idx2]
else:
idx = len(self.symbols)
self.indices[word] = idx
self.symbols.append(word)
self.count.append(new_dict.count[idx2])
def finalize(self, threshold=-1, nwords=-1, padding_factor=8):
"""Sort symbols by frequency in descending order, ignoring special ones.
Args:
- threshold defines the minimum word count
- nwords defines the total number of words in the final dictionary,
including special symbols
- padding_factor can be used to pad the dictionary size to be a
multiple of 8, which is important on some hardware (e.g., Nvidia
Tensor Cores).
"""
if nwords <= 0:
nwords = len(self)
new_indices = dict(zip(self.symbols[: self.nspecial], range(self.nspecial)))
new_symbols = self.symbols[: self.nspecial]
new_count = self.count[: self.nspecial]
c = Counter(
dict(
sorted(zip(self.symbols[self.nspecial :], self.count[self.nspecial :]))
)
)
for symbol, count in c.most_common(nwords - self.nspecial):
if count >= threshold:
new_indices[symbol] = len(new_symbols)
new_symbols.append(symbol)
new_count.append(count)
else:
break
assert len(new_symbols) == len(new_indices)
self.count = list(new_count)
self.symbols = list(new_symbols)
self.indices = new_indices
self.pad_to_multiple_(padding_factor)
def pad_to_multiple_(self, padding_factor):
"""Pad Dictionary size to be a multiple of *padding_factor*."""
if padding_factor > 1:
i = 0
while len(self) % padding_factor != 0:
symbol = "madeupword{:04d}".format(i)
self.add_symbol(symbol, n=0)
i += 1
def bos(self):
"""Helper to get index of beginning-of-sentence symbol"""
return self.bos_index
def pad(self):
"""Helper to get index of pad symbol"""
return self.pad_index
def eos(self):
"""Helper to get index of end-of-sentence symbol"""
return self.eos_index
def unk(self):
"""Helper to get index of unk symbol"""
return self.unk_index
@classmethod
def load(cls, f):
"""Loads the dictionary from a text file with the format:
```
<symbol0> <count0>
<symbol1> <count1>
...
```
"""
d = cls()
d.add_from_file(f)
return d
def add_from_file(self, f):
"""
Loads a pre-existing dictionary from a text file and adds its symbols
to this instance.
"""
if isinstance(f, str):
try:
with open(PathManager.get_local_path(f), "r", encoding="utf-8") as fd:
self.add_from_file(fd)
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(
"Incorrect encoding detected in {}, please "
"rebuild the dataset".format(f)
)
return
lines = f.readlines()
indices_start_line = self._load_meta(lines)
for line in lines[indices_start_line:]:
try:
line, field = line.rstrip().rsplit(" ", 1)
if field == "#fairseq:overwrite":
overwrite = True
line, field = line.rsplit(" ", 1)
else:
overwrite = False
count = int(field)
word = line
if word in self and not overwrite:
raise RuntimeError(
"Duplicate word found when loading Dictionary: '{}'. "
"Duplicate words can overwrite earlier ones by adding the "
"#fairseq:overwrite flag at the end of the corresponding row "
"in the dictionary file. If using the Camembert model, please "
"download an updated copy of the model file.".format(word)
)
self.add_symbol(word, n=count, overwrite=overwrite)
except ValueError:
raise ValueError(
f"Incorrect dictionary format, expected '<token> <cnt> [flags]': \"{line}\""
)
def _save(self, f, kv_iterator):
if isinstance(f, str):
PathManager.mkdirs(os.path.dirname(f))
with PathManager.open(f, "w", encoding="utf-8") as fd:
return self.save(fd)
for k, v in kv_iterator:
print("{} {}".format(k, v), file=f)
def _get_meta(self):
return [], []
def _load_meta(self, lines):
return 0
def save(self, f):
"""Stores dictionary into a text file"""
ex_keys, ex_vals = self._get_meta()
self._save(
f,
zip(
ex_keys + self.symbols[self.nspecial :],
ex_vals + self.count[self.nspecial :],
),
)
def dummy_sentence(self, length):
t = torch.Tensor(length).uniform_(self.nspecial + 1, len(self)).long()
t[-1] = self.eos()
return t
def encode_line(
self,
line,
line_tokenizer=tokenize_line,
add_if_not_exist=True,
consumer=None,
append_eos=True,
reverse_order=False,
) -> torch.IntTensor:
words = line_tokenizer(line)
if reverse_order:
words = list(reversed(words))
nwords = len(words)
ids = torch.IntTensor(nwords + 1 if append_eos else nwords)
for i, word in enumerate(words):
if add_if_not_exist:
idx = self.add_symbol(word)
else:
idx = self.index(word)
if consumer is not None:
consumer(word, idx)
ids[i] = idx
if append_eos:
ids[nwords] = self.eos_index
return ids
@staticmethod
def _add_file_to_dictionary_single_worker(
filename,
tokenize,
eos_word,
start_offset,
end_offset,
):
counter = Counter()
with Chunker(filename, start_offset, end_offset) as line_iterator:
for line in line_iterator:
for word in tokenize(line):
counter.update([word])
counter.update([eos_word])
return counter
@staticmethod
def add_file_to_dictionary(filename, dict, tokenize, num_workers):
def merge_result(counter):
for w, c in sorted(counter.items()):
dict.add_symbol(w, c)
local_file = PathManager.get_local_path(filename)
offsets = find_offsets(local_file, num_workers)
if num_workers > 1:
chunks = zip(offsets, offsets[1:])
pool = Pool(processes=num_workers)
results = []
for (start_offset, end_offset) in chunks:
results.append(
pool.apply_async(
Dictionary._add_file_to_dictionary_single_worker,
(
local_file,
tokenize,
dict.eos_word,
start_offset,
end_offset,
),
)
)
pool.close()
pool.join()
for r in results:
merge_result(r.get())
else:
merge_result(
Dictionary._add_file_to_dictionary_single_worker(
local_file, tokenize, dict.eos_word, offsets[0], offsets[1]
)
)
class TruncatedDictionary(object):
def __init__(self, wrapped_dict, length):
self.__class__ = type(
wrapped_dict.__class__.__name__,
(self.__class__, wrapped_dict.__class__),
{},
)
self.__dict__ = wrapped_dict.__dict__
self.wrapped_dict = wrapped_dict
self.length = min(len(self.wrapped_dict), length)
def __len__(self):
return self.length
def __getitem__(self, i):
if i < self.length:
return self.wrapped_dict[i]
return self.wrapped_dict.unk()
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/dictionary.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import numpy as np
import torch.utils.data
from fairseq.data import data_utils
logger = logging.getLogger(__name__)
class EpochListening:
"""Mixin for receiving updates whenever the epoch increments."""
@property
def can_reuse_epoch_itr_across_epochs(self):
"""
Whether we can reuse the :class:`fairseq.data.EpochBatchIterator` for
this dataset across epochs.
This needs to return ``False`` if the sample sizes can change across
epochs, in which case we may need to regenerate batches at each epoch.
If your dataset relies in ``set_epoch`` then you should consider setting
this to ``False``.
"""
return True
def set_epoch(self, epoch):
"""Will receive the updated epoch number at the beginning of the epoch."""
pass
class FairseqDataset(torch.utils.data.Dataset, EpochListening):
"""A dataset that provides helpers for batching."""
def __getitem__(self, index):
raise NotImplementedError
def __len__(self):
raise NotImplementedError
def collater(self, samples):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch suitable for forwarding with a Model
"""
raise NotImplementedError
def num_tokens(self, index):
"""Return the number of tokens in a sample. This value is used to
enforce ``--max-tokens`` during batching."""
raise NotImplementedError
def num_tokens_vec(self, indices):
"""Return the number of tokens for a set of positions defined by indices.
This value is used to enforce ``--max-tokens`` during batching."""
raise NotImplementedError
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
raise NotImplementedError
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
return np.arange(len(self), dtype=np.int64)
@property
def supports_prefetch(self):
"""Whether this dataset supports prefetching."""
return False
def attr(self, attr: str, index: int):
return getattr(self, attr, None)
def prefetch(self, indices):
"""Prefetch the data required for this epoch."""
raise NotImplementedError
def get_batch_shapes(self):
"""
Return a list of valid batch shapes, for example::
[(8, 512), (16, 256), (32, 128)]
The first dimension of each tuple is the batch size and can be ``None``
to automatically infer the max batch size based on ``--max-tokens``.
The second dimension of each tuple is the max supported length as given
by :func:`fairseq.data.FairseqDataset.num_tokens`.
This will be used by :func:`fairseq.data.FairseqDataset.batch_by_size`
to restrict batch shapes. This is useful on TPUs to avoid too many
dynamic shapes (and recompilations).
"""
return None
def batch_by_size(
self,
indices,
max_tokens=None,
max_sentences=None,
required_batch_size_multiple=1,
):
"""
Given an ordered set of indices, return batches according to
*max_tokens*, *max_sentences* and *required_batch_size_multiple*.
"""
from fairseq.data import data_utils
fixed_shapes = self.get_batch_shapes()
if fixed_shapes is not None:
def adjust_bsz(bsz, num_tokens):
if bsz is None:
assert max_tokens is not None, "Must specify --max-tokens"
bsz = max_tokens // num_tokens
if max_sentences is not None:
bsz = min(bsz, max_sentences)
elif (
bsz >= required_batch_size_multiple
and bsz % required_batch_size_multiple != 0
):
bsz -= bsz % required_batch_size_multiple
return bsz
fixed_shapes = np.array(
[
[adjust_bsz(bsz, num_tokens), num_tokens]
for (bsz, num_tokens) in fixed_shapes
]
)
try:
num_tokens_vec = self.num_tokens_vec(indices).astype("int64")
except NotImplementedError:
num_tokens_vec = None
return data_utils.batch_by_size(
indices,
num_tokens_fn=self.num_tokens,
num_tokens_vec=num_tokens_vec,
max_tokens=max_tokens,
max_sentences=max_sentences,
required_batch_size_multiple=required_batch_size_multiple,
fixed_shapes=fixed_shapes,
)
def filter_indices_by_size(self, indices, max_sizes):
"""
Filter a list of sample indices. Remove those that are longer than
specified in *max_sizes*.
WARNING: don't update, override method in child classes
Args:
indices (np.array): original array of sample indices
max_sizes (int or list[int] or tuple[int]): max sample size,
can be defined separately for src and tgt (then list or tuple)
Returns:
np.array: filtered sample array
list: list of removed indices
"""
if isinstance(max_sizes, float) or isinstance(max_sizes, int):
if hasattr(self, "sizes") and isinstance(self.sizes, np.ndarray):
ignored = indices[self.sizes[indices] > max_sizes].tolist()
indices = indices[self.sizes[indices] <= max_sizes]
elif (
hasattr(self, "sizes")
and isinstance(self.sizes, list)
and len(self.sizes) == 1
):
ignored = indices[self.sizes[0][indices] > max_sizes].tolist()
indices = indices[self.sizes[0][indices] <= max_sizes]
else:
indices, ignored = data_utils._filter_by_size_dynamic(
indices, self.size, max_sizes
)
else:
indices, ignored = data_utils._filter_by_size_dynamic(
indices, self.size, max_sizes
)
return indices, ignored
@property
def supports_fetch_outside_dataloader(self):
"""Whether this dataset supports fetching outside the workers of the dataloader."""
return True
class FairseqIterableDataset(torch.utils.data.IterableDataset, EpochListening):
"""
For datasets that need to be read sequentially, usually because the data is
being streamed or otherwise can't be manipulated on a single machine.
"""
def __iter__(self):
raise NotImplementedError
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/fairseq_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from typing import Dict
from fairseq.data.monolingual_dataset import MonolingualDataset
from . import FairseqDataset
class LMContextWindowDataset(FairseqDataset):
"""
Wraps a MonolingualDataset and provides more context for evaluation.
Each item in the new dataset will have a maximum size of
``tokens_per_sample + context_window``.
Args:
dataset: dataset to wrap
tokens_per_sample (int): the max number of tokens in each dataset item
context_window (int): the number of accumulated tokens to add to each
dataset item
pad_idx (int): padding symbol
"""
def __init__(
self,
dataset: MonolingualDataset,
tokens_per_sample: int,
context_window: int,
pad_idx: int,
):
assert context_window > 0
self.dataset = dataset
self.tokens_per_sample = tokens_per_sample
self.context_window = context_window
self.pad_idx = pad_idx
self.prev_tokens = np.empty([0])
def __getitem__(self, index):
return self.dataset[index]
def __len__(self):
return len(self.dataset)
def collater(self, samples) -> Dict:
sample = self.dataset.collater(samples)
pad = self.pad_idx
max_sample_len = self.tokens_per_sample + self.context_window
bsz, tsz = sample["net_input"]["src_tokens"].shape
start_idxs = [0] * bsz
toks = sample["net_input"]["src_tokens"]
lengths = sample["net_input"]["src_lengths"]
tgt = sample["target"]
new_toks = np.empty([bsz, tsz + self.context_window], dtype=np.int64)
new_tgt = np.full([bsz, tsz + self.context_window], pad, dtype=np.int64)
sample_lens = toks.ne(pad).long().sum(dim=1).cpu()
for i in range(bsz):
sample_len = sample_lens[i]
extra = len(self.prev_tokens) + sample_len - max_sample_len
if extra > 0:
self.prev_tokens = self.prev_tokens[extra:]
pads = np.full(self.context_window - len(self.prev_tokens), pad)
new_toks[i] = np.concatenate([self.prev_tokens, toks[i].numpy(), pads])
new_tgt[
i, len(self.prev_tokens) : len(self.prev_tokens) + len(tgt[i])
] = tgt[i]
start_idxs[i] = len(self.prev_tokens)
lengths[i] += len(self.prev_tokens)
self.prev_tokens = new_toks[i][new_toks[i] != pad][-self.context_window :]
sample["net_input"]["src_tokens"] = torch.from_numpy(new_toks)
sample["target"] = torch.from_numpy(new_tgt)
sample["start_indices"] = start_idxs
return sample
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
return self.dataset.size(index)
def ordered_indices(self):
# NOTE we don't shuffle the data to retain access to the previous dataset elements
return np.arange(len(self.dataset))
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
return self.dataset.prefetch(indices)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/lm_context_window_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import torch
from torch.utils.data.dataloader import default_collate
from fairseq.data import ConcatDataset
logger = logging.getLogger(__name__)
class TransformEosConcatLangPairDataset(ConcatDataset):
"""
It is a combination of TransformEosLangPairDataset and ConcatDataset for multiple LangPairDataset datasets.
Assume all datasets share the same src_eos, tgt_bos, left_pad_source and left_pad_target
"""
def __init__(
self,
datasets,
src_eos,
tgt_bos,
new_src_eos=None,
new_tgt_bos=None,
):
super().__init__(datasets)
if new_src_eos is not None:
assert len(new_src_eos) == len(datasets)
else:
new_src_eos = []
if new_tgt_bos is not None:
assert len(new_tgt_bos) == len(datasets)
else:
new_tgt_bos = []
self.src_eos = src_eos
self.tgt_bos = tgt_bos
self.new_src_eos = (
torch.LongTensor(new_src_eos).cpu() if len(new_src_eos) > 0 else []
)
self.new_tgt_bos = (
torch.LongTensor(new_tgt_bos).cpu() if len(new_tgt_bos) > 0 else []
)
self.left_pad_source = self.is_left_pad_source(datasets)
self.left_pad_target = self.is_left_pad_target(datasets)
self.pad_idx = self.src_dict_pad()
def src_dict_pad(self):
if hasattr(self.datasets[0], "src_dict"):
return self.datasets[0].src_dict.pad()
if hasattr(self.datasets[0], "dataset"):
return self.datasets[0].dataset.src_dict.pad()
raise NotImplementedError("No src_dict is found")
def __getitem__(self, idx):
dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
return dataset_idx, self.datasets[dataset_idx][sample_idx]
def is_left_pad_source(self, datasets):
def _left_pad_source(ds):
if hasattr(ds, "left_pad_source"):
return ds.left_pad_source
if hasattr(ds, "dataset"):
return _left_pad_source(ds.dataset)
logger.warn(f"{type(ds)} has no left_pad_source, using default True")
return True
left_pad_source = _left_pad_source(datasets[0])
for ds in datasets:
if left_pad_source != _left_pad_source(ds):
raise ValueError("Different left_pad_source setting detected!")
return left_pad_source
def is_left_pad_target(self, datasets):
def _left_pad_target(ds):
if hasattr(ds, "left_pad_target"):
return ds.left_pad_target
if hasattr(ds, "dataset"):
return _left_pad_target(ds.dataset)
logger.warn(f"{type(ds)} has no left_pad_target, using default False")
return False
left_pad_target = _left_pad_target(datasets[0])
for ds in datasets:
if left_pad_target != _left_pad_target(ds):
raise ValueError("Different left_pad_target setting detected!")
return left_pad_target
def collater(self, samples, **extra_args):
if len(samples) == 0:
return samples
dataset_ids = [s[0] for s in samples]
samples = [s[1] for s in samples]
if hasattr(self.datasets[0], "collater"):
samples = self.datasets[0].collater(samples, **extra_args)
else:
samples = default_collate(samples, **extra_args)
if len(self.new_src_eos) > 0:
if self.left_pad_source:
assert (
samples["net_input"]["src_tokens"][:, -1] != self.src_eos
).sum() == 0
samples["net_input"]["src_tokens"][:, -1] = self.new_src_eos[
dataset_ids
]
else:
eos_idx = samples["net_input"]["src_lengths"] - 1
assert (
samples["net_input"]["src_tokens"][
torch.arange(eos_idx.size(0)), eos_idx
]
!= self.src_eos
).sum() == 0
samples["net_input"]["src_tokens"].scatter_(
1, eos_idx.view(-1, 1), self.new_src_eos[dataset_ids].view(-1, 1)
)
if len(self.new_tgt_bos) > 0 and "prev_output_tokens" in samples["net_input"]:
if self.left_pad_target:
# TODO: support different padding direction on target side
raise NotImplementedError(
"TransformEosLangPairDataset does not implement --left-pad-target True option"
)
else:
assert (
samples["net_input"]["prev_output_tokens"][:, 0] != self.tgt_bos
).sum() == 0
samples["net_input"]["prev_output_tokens"][:, 0] = self.new_tgt_bos[
dataset_ids
]
return samples
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/transform_eos_concat_langpair_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn.functional as F
from fairseq.data import BaseWrapperDataset
from fairseq.data.data_utils import get_buckets, get_bucketed_sizes
class BucketPadLengthDataset(BaseWrapperDataset):
"""
Bucket and pad item lengths to the nearest bucket size. This can be used to
reduce the number of unique batch shapes, which is important on TPUs since
each new batch shape requires a recompilation.
Args:
dataset (FairseqDatset): dataset to bucket
sizes (List[int]): all item sizes
num_buckets (int): number of buckets to create
pad_idx (int): padding symbol
left_pad (bool): if True, pad on the left; otherwise right pad
"""
def __init__(
self,
dataset,
sizes,
num_buckets,
pad_idx,
left_pad,
tensor_key=None,
):
super().__init__(dataset)
self.pad_idx = pad_idx
self.left_pad = left_pad
assert num_buckets > 0
self.buckets = get_buckets(sizes, num_buckets)
self._bucketed_sizes = get_bucketed_sizes(sizes, self.buckets)
self._tensor_key = tensor_key
def _set_tensor(self, item, val):
if self._tensor_key is None:
return val
item[self._tensor_key] = val
return item
def _get_tensor(self, item):
if self._tensor_key is None:
return item
return item[self._tensor_key]
def _pad(self, tensor, bucket_size, dim=-1):
num_pad = bucket_size - tensor.size(dim)
return F.pad(
tensor,
(num_pad if self.left_pad else 0, 0 if self.left_pad else num_pad),
value=self.pad_idx,
)
def __getitem__(self, index):
item = self.dataset[index]
bucket_size = self._bucketed_sizes[index]
tensor = self._get_tensor(item)
padded = self._pad(tensor, bucket_size)
return self._set_tensor(item, padded)
@property
def sizes(self):
return self._bucketed_sizes
def num_tokens(self, index):
return self._bucketed_sizes[index]
def size(self, index):
return self._bucketed_sizes[index]
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/bucket_pad_length_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from fairseq.data import FairseqDataset, plasma_utils
from fairseq.data.indexed_dataset import best_fitting_int_dtype
from typing import Tuple
class TokenBlockDataset(FairseqDataset):
"""Break a Dataset of tokens into blocks.
Args:
dataset (~torch.utils.data.Dataset): dataset to break into blocks
sizes (List[int]): sentence lengths (required for 'complete' and 'eos')
block_size (int): maximum block size (ignored in 'eos' break mode)
break_mode (str, optional): Mode used for breaking tokens. Values can
be one of:
- 'none': break tokens into equally sized blocks (up to block_size)
- 'complete': break tokens into blocks (up to block_size) such that
blocks contains complete sentences, although block_size may be
exceeded if some sentences exceed block_size
- 'complete_doc': similar to 'complete' mode, but do not
cross document boundaries
- 'eos': each block contains one sentence (block_size is ignored)
include_targets (bool, optional): return next tokens as targets
(default: False).
document_sep_len (int, optional): document separator size (required for
'complete_doc' break mode). Typically 1 if the sentences have eos
and 0 otherwise.
"""
def __init__(
self,
dataset,
sizes,
block_size,
pad,
eos,
break_mode=None,
include_targets=False,
document_sep_len=1,
use_plasma_view=False,
split_path=None,
plasma_path=None,
):
super().__init__()
self.dataset = dataset
self.pad = pad
self.eos = eos
self.include_targets = include_targets
assert len(dataset) > 0
assert len(dataset) == len(sizes)
_sizes, block_to_dataset_index, slice_indices = self._build_slice_indices(
sizes, break_mode, document_sep_len, block_size
)
if use_plasma_view:
plasma_id = (block_size, document_sep_len, str(break_mode), len(dataset))
self._slice_indices = plasma_utils.PlasmaView(
slice_indices, split_path, (plasma_id, 0), plasma_path=plasma_path
)
self._sizes = plasma_utils.PlasmaView(
_sizes, split_path, (plasma_id, 1), plasma_path=plasma_path
)
self._block_to_dataset_index = plasma_utils.PlasmaView(
block_to_dataset_index,
split_path,
(plasma_id, 2),
plasma_path=plasma_path,
)
else:
self._slice_indices = plasma_utils.PlasmaArray(slice_indices)
self._sizes = plasma_utils.PlasmaArray(_sizes)
self._block_to_dataset_index = plasma_utils.PlasmaArray(
block_to_dataset_index
)
@staticmethod
def _build_slice_indices(
sizes, break_mode, document_sep_len, block_size
) -> Tuple[np.ndarray]:
"""Use token_block_utils_fast to build arrays for indexing into self.dataset"""
try:
from fairseq.data.token_block_utils_fast import (
_get_slice_indices_fast,
_get_block_to_dataset_index_fast,
)
except ImportError:
raise ImportError(
"Please build Cython components with: `pip install --editable .` "
"or `python setup.py build_ext --inplace`"
)
if isinstance(sizes, list):
sizes = np.array(sizes, dtype=np.int64)
else:
if torch.is_tensor(sizes):
sizes = sizes.numpy()
sizes = sizes.astype(np.int64)
break_mode = break_mode if break_mode is not None else "none"
# For "eos" break-mode, block_size is not required parameters.
if break_mode == "eos" and block_size is None:
block_size = 0
slice_indices = _get_slice_indices_fast(
sizes, str(break_mode), block_size, document_sep_len
)
_sizes = slice_indices[:, 1] - slice_indices[:, 0]
# build index mapping block indices to the underlying dataset indices
if break_mode == "eos":
# much faster version for eos break mode
block_to_dataset_index = np.stack(
[
np.arange(len(sizes)), # starting index in dataset
np.zeros(
len(sizes), dtype=np.compat.long
), # starting offset within starting index
np.arange(len(sizes)), # ending index in dataset
],
1,
)
else:
block_to_dataset_index = _get_block_to_dataset_index_fast(
sizes,
slice_indices,
)
size_dtype = np.uint16 if block_size < 65535 else np.uint32
num_tokens = slice_indices[-1].max()
slice_indices_dtype = best_fitting_int_dtype(num_tokens)
slice_indices = slice_indices.astype(slice_indices_dtype)
_sizes = _sizes.astype(size_dtype)
block_to_dataset_index = block_to_dataset_index.astype(slice_indices_dtype)
return _sizes, block_to_dataset_index, slice_indices
@property
def slice_indices(self):
return self._slice_indices.array
@property
def sizes(self):
return self._sizes.array
@property
def block_to_dataset_index(self):
return self._block_to_dataset_index.array
def attr(self, attr: str, index: int):
start_ds_idx, _, _ = self.block_to_dataset_index[index]
return self.dataset.attr(attr, start_ds_idx)
def __getitem__(self, index):
start_ds_idx, start_offset, end_ds_idx = self.block_to_dataset_index[index]
buffer = torch.cat(
[self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)]
)
slice_s, slice_e = self.slice_indices[index]
length = slice_e - slice_s
s, e = start_offset, start_offset + length
item = buffer[s:e]
if self.include_targets:
# *target* is the original sentence (=item)
# *source* is shifted right by 1 (maybe left-padded with eos)
# *past_target* is shifted right by 2 (left-padded as needed)
if s == 0:
source = torch.cat([item.new([self.eos]), buffer[0 : e - 1]])
past_target = torch.cat(
[item.new([self.pad, self.eos]), buffer[0 : e - 2]]
)
else:
source = buffer[s - 1 : e - 1]
if s == 1:
past_target = torch.cat([item.new([self.eos]), buffer[0 : e - 2]])
else:
past_target = buffer[s - 2 : e - 2]
return source, item, past_target
return item
def __len__(self):
return len(self.slice_indices)
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
self.dataset.prefetch(
{
ds_idx
for index in indices
for start_ds_idx, _, end_ds_idx in [self.block_to_dataset_index[index]]
for ds_idx in range(start_ds_idx, end_ds_idx + 1)
}
)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/token_block_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import torch
from . import FairseqDataset
class TransformEosLangPairDataset(FairseqDataset):
"""A :class:`~fairseq.data.FairseqDataset` wrapper that transform bos on
collated samples of language pair dataset.
Note that the transformation is applied in :func:`collater`.
Args:
dataset (~fairseq.data.FairseqDataset): dataset that collates sample into
LanguagePairDataset schema
src_eos (int): original source end-of-sentence symbol index to be replaced
new_src_eos (int, optional): new end-of-sentence symbol index to replace source eos symbol
tgt_bos (int, optional): original target beginning-of-sentence symbol index to be replaced
new_tgt_bos (int, optional): new beginning-of-sentence symbol index to replace at the
beginning of 'prev_output_tokens'
"""
def __init__(
self,
dataset: FairseqDataset,
src_eos: int,
new_src_eos: Optional[int] = None,
tgt_bos: Optional[int] = None,
new_tgt_bos: Optional[int] = None,
):
self.dataset = dataset
self.src_eos = src_eos
self.new_src_eos = new_src_eos
self.tgt_bos = tgt_bos
self.new_tgt_bos = new_tgt_bos
def __getitem__(self, index):
return self.dataset[index]
def __len__(self):
return len(self.dataset)
def collater(self, samples, **extra_args):
samples = self.dataset.collater(samples, **extra_args)
if len(samples) == 0:
return samples
if "net_input" not in samples:
return samples
if self.new_src_eos is not None:
if self.dataset.left_pad_source:
assert (
samples["net_input"]["src_tokens"][:, -1] != self.src_eos
).sum() == 0
samples["net_input"]["src_tokens"][:, -1] = self.new_src_eos
else:
eos_idx = samples["net_input"]["src_lengths"] - 1
assert (
samples["net_input"]["src_tokens"][
torch.arange(eos_idx.size(0)), eos_idx
]
!= self.src_eos
).sum() == 0
eos_idx = eos_idx.resize_(len(samples["net_input"]["src_lengths"]), 1)
samples["net_input"]["src_tokens"].scatter_(
1, eos_idx, self.new_src_eos
)
if (
self.new_tgt_bos is not None
and "prev_output_tokens" in samples["net_input"]
):
if self.dataset.left_pad_target:
# TODO: support different padding direction on target side
raise NotImplementedError(
"TransformEosLangPairDataset does not implement --left-pad-target True option"
)
else:
assert (
samples["net_input"]["prev_output_tokens"][:, 0] != self.tgt_bos
).sum() == 0
samples["net_input"]["prev_output_tokens"][:, 0] = self.new_tgt_bos
return samples
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
return self.dataset.size(index)
@property
def sizes(self):
# dataset.sizes can be a dynamically computed sizes:
return self.dataset.sizes
def ordered_indices(self):
return self.dataset.ordered_indices()
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
return self.dataset.prefetch(indices)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/transform_eos_lang_pair_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset, data_utils
from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
class AddTargetDataset(BaseWrapperDataset):
def __init__(
self,
dataset,
labels,
pad,
eos,
batch_targets,
process_label=None,
label_len_fn=None,
add_to_input=False,
text_compression_level=TextCompressionLevel.none,
):
super().__init__(dataset)
self.labels = labels
self.batch_targets = batch_targets
self.pad = pad
self.eos = eos
self.process_label = process_label
self.label_len_fn = label_len_fn
self.add_to_input = add_to_input
self.text_compressor = TextCompressor(level=text_compression_level)
def get_label(self, index, process_fn=None):
lbl = self.labels[index]
lbl = self.text_compressor.decompress(lbl)
return lbl if process_fn is None else process_fn(lbl)
def __getitem__(self, index):
item = self.dataset[index]
item["label"] = self.get_label(index, process_fn=self.process_label)
return item
def size(self, index):
sz = self.dataset.size(index)
own_sz = self.label_len_fn(self.get_label(index))
return sz, own_sz
def collater(self, samples):
collated = self.dataset.collater(samples)
if len(collated) == 0:
return collated
indices = set(collated["id"].tolist())
target = [s["label"] for s in samples if s["id"] in indices]
if self.batch_targets:
collated["target_lengths"] = torch.LongTensor([len(t) for t in target])
target = data_utils.collate_tokens(target, pad_idx=self.pad, left_pad=False)
collated["ntokens"] = collated["target_lengths"].sum().item()
else:
collated["ntokens"] = sum([len(t) for t in target])
collated["target"] = target
if self.add_to_input:
eos = target.new_full((target.size(0), 1), self.eos)
collated["target"] = torch.cat([target, eos], dim=-1).long()
collated["net_input"]["prev_output_tokens"] = torch.cat(
[eos, target], dim=-1
).long()
collated["ntokens"] += target.size(0)
return collated
def filter_indices_by_size(self, indices, max_sizes):
indices, ignored = data_utils._filter_by_size_dynamic(
indices, self.size, max_sizes
)
return indices, ignored
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/add_target_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.data import Dictionary
class MaskedLMDictionary(Dictionary):
"""
Dictionary for Masked Language Modelling tasks. This extends Dictionary by
adding the mask symbol.
"""
def __init__(
self,
pad="<pad>",
eos="</s>",
unk="<unk>",
mask="<mask>",
):
super().__init__(pad=pad, eos=eos, unk=unk)
self.mask_word = mask
self.mask_index = self.add_symbol(mask)
self.nspecial = len(self.symbols)
def mask(self):
"""Helper to get index of mask symbol"""
return self.mask_index
class BertDictionary(MaskedLMDictionary):
"""
Dictionary for BERT task. This extends MaskedLMDictionary by adding support
for cls and sep symbols.
"""
def __init__(
self,
pad="<pad>",
eos="</s>",
unk="<unk>",
mask="<mask>",
cls="<cls>",
sep="<sep>",
):
super().__init__(pad=pad, eos=eos, unk=unk, mask=mask)
self.cls_word = cls
self.sep_word = sep
self.cls_index = self.add_symbol(cls)
self.sep_index = self.add_symbol(sep)
self.nspecial = len(self.symbols)
def cls(self):
"""Helper to get index of cls symbol"""
return self.cls_index
def sep(self):
"""Helper to get index of sep symbol"""
return self.sep_index
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/legacy/masked_lm_dictionary.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import numpy as np
import torch
from fairseq.data import FairseqDataset
class BlockPairDataset(FairseqDataset):
"""Break a Dataset of tokens into sentence pair blocks for next sentence
prediction as well as masked language model.
High-level logics are:
1. break input tensor to tensor blocks
2. pair the blocks with 50% next sentence and 50% random sentence
3. return paired blocks as well as related segment labels
Args:
dataset (~torch.utils.data.Dataset): dataset to break into blocks
sizes: array of sentence lengths
dictionary: dictionary for the task
block_size: maximum block size
break_mode: mode for breaking copurs into block pairs. currently we support
2 modes
doc: respect document boundaries and each part of the pair should belong to on document
none: don't respect any boundary and cut tokens evenly
short_seq_prob: probability for generating shorter block pairs
doc_break_size: Size for empty line separating documents. Typically 1 if
the sentences have eos, 0 otherwise.
"""
def __init__(
self,
dataset,
dictionary,
sizes,
block_size,
break_mode="doc",
short_seq_prob=0.1,
doc_break_size=1,
):
super().__init__()
self.dataset = dataset
self.pad = dictionary.pad()
self.eos = dictionary.eos()
self.cls = dictionary.cls()
self.mask = dictionary.mask()
self.sep = dictionary.sep()
self.break_mode = break_mode
self.dictionary = dictionary
self.short_seq_prob = short_seq_prob
self.block_indices = []
assert len(dataset) == len(sizes)
if break_mode == "doc":
cur_doc = []
for sent_id, sz in enumerate(sizes):
assert doc_break_size == 0 or sz != 0, (
"when doc_break_size is non-zero, we expect documents to be"
"separated by a blank line with a single eos."
)
# empty line as document separator
if sz == doc_break_size:
if len(cur_doc) == 0:
continue
self.block_indices.append(cur_doc)
cur_doc = []
else:
cur_doc.append(sent_id)
max_num_tokens = block_size - 3 # Account for [CLS], [SEP], [SEP]
self.sent_pairs = []
self.sizes = []
for doc_id, doc in enumerate(self.block_indices):
self._generate_sentence_pair(doc, doc_id, max_num_tokens, sizes)
elif break_mode is None or break_mode == "none":
# each block should have half of the block size since we are constructing block pair
sent_length = (block_size - 3) // 2
total_len = sum(dataset.sizes)
length = math.ceil(total_len / sent_length)
def block_at(i):
start = i * sent_length
end = min(start + sent_length, total_len)
return (start, end)
sent_indices = np.array([block_at(i) for i in range(length)])
sent_sizes = np.array([e - s for s, e in sent_indices])
dataset_index = self._sent_to_dataset_index(sent_sizes)
# pair sentences
self._pair_sentences(dataset_index)
else:
raise ValueError("Invalid break_mode: " + break_mode)
def _pair_sentences(self, dataset_index):
"""
Give a list of evenly cut blocks/sentences, pair these sentences with 50%
consecutive sentences and 50% random sentences.
This is used for none break mode
"""
# pair sentences
for sent_id, sent in enumerate(dataset_index):
next_sent_label = (
1 if np.random.rand() > 0.5 and sent_id != len(dataset_index) - 1 else 0
)
if next_sent_label:
next_sent = dataset_index[sent_id + 1]
else:
next_sent = dataset_index[
self._skip_sampling(len(dataset_index), [sent_id, sent_id + 1])
]
self.sent_pairs.append((sent, next_sent, next_sent_label))
# The current blocks don't include the special tokens but the
# sizes already account for this
self.sizes.append(3 + sent[3] + next_sent[3])
def _sent_to_dataset_index(self, sent_sizes):
"""
Build index mapping block indices to the underlying dataset indices
"""
dataset_index = []
ds_idx, ds_remaining = -1, 0
for to_consume in sent_sizes:
sent_size = to_consume
if ds_remaining == 0:
ds_idx += 1
ds_remaining = sent_sizes[ds_idx]
start_ds_idx = ds_idx
start_offset = sent_sizes[ds_idx] - ds_remaining
while to_consume > ds_remaining:
to_consume -= ds_remaining
ds_idx += 1
ds_remaining = sent_sizes[ds_idx]
ds_remaining -= to_consume
dataset_index.append(
(
start_ds_idx, # starting index in dataset
start_offset, # starting offset within starting index
ds_idx, # ending index in dataset
sent_size, # sentence length
)
)
assert ds_remaining == 0
assert ds_idx == len(self.dataset) - 1
return dataset_index
def _generate_sentence_pair(self, doc, doc_id, max_num_tokens, sizes):
"""
Go through a single document and genrate sentence paris from it
"""
current_chunk = []
current_length = 0
curr = 0
# To provide more randomness, we decrease target seq length for parts of
# samples (10% by default). Note that max_num_tokens is the hard threshold
# for batching and will never be changed.
target_seq_length = max_num_tokens
if np.random.random() < self.short_seq_prob:
target_seq_length = np.random.randint(2, max_num_tokens)
# loop through all sentences in document
while curr < len(doc):
sent_id = doc[curr]
current_chunk.append(sent_id)
current_length = sum(sizes[current_chunk])
# split chunk and generate pair when exceed target_seq_length or
# finish the loop
if curr == len(doc) - 1 or current_length >= target_seq_length:
# split the chunk into 2 parts
a_end = 1
if len(current_chunk) > 2:
a_end = np.random.randint(1, len(current_chunk) - 1)
sent_a = current_chunk[:a_end]
len_a = sum(sizes[sent_a])
# generate next sentence label, note that if there is only 1 sentence
# in current chunk, label is always 0
next_sent_label = (
1 if np.random.rand() > 0.5 and len(current_chunk) != 1 else 0
)
if not next_sent_label:
# if next sentence label is 0, sample sent_b from a random doc
target_b_length = target_seq_length - len_a
rand_doc_id = self._skip_sampling(len(self.block_indices), [doc_id])
random_doc = self.block_indices[rand_doc_id]
random_start = np.random.randint(0, len(random_doc))
sent_b = []
len_b = 0
for j in range(random_start, len(random_doc)):
sent_b.append(random_doc[j])
len_b = sum(sizes[sent_b])
if len_b >= target_b_length:
break
# return the second part of the chunk since it's not used
num_unused_segments = len(current_chunk) - a_end
curr -= num_unused_segments
else:
# if next sentence label is 1, use the second part of chunk as sent_B
sent_b = current_chunk[a_end:]
len_b = sum(sizes[sent_b])
# currently sent_a and sent_B may be longer than max_num_tokens,
# truncate them and return block idx and offsets for them
sent_a, sent_b = self._truncate_sentences(
sent_a, sent_b, max_num_tokens
)
self.sent_pairs.append((sent_a, sent_b, next_sent_label))
self.sizes.append(3 + sent_a[3] + sent_b[3])
current_chunk = []
curr += 1
def _skip_sampling(self, total, skip_ids):
"""
Generate a random integer which is not in skip_ids. Sample range is [0, total)
TODO: ids in skip_ids should be consecutive, we can extend it to more generic version later
"""
rand_id = np.random.randint(total - len(skip_ids))
return rand_id if rand_id < min(skip_ids) else rand_id + len(skip_ids)
def _truncate_sentences(self, sent_a, sent_b, max_num_tokens):
"""
Trancate a pair of sentence to limit total length under max_num_tokens
Logics:
1. Truncate longer sentence
2. Tokens to be truncated could be at the beginning or the end of the sentnce
Returns:
Truncated sentences represented by dataset idx
"""
len_a, len_b = sum(self.dataset.sizes[sent_a]), sum(self.dataset.sizes[sent_b])
front_cut_a = front_cut_b = end_cut_a = end_cut_b = 0
while True:
total_length = (
len_a + len_b - front_cut_a - front_cut_b - end_cut_a - end_cut_b
)
if total_length <= max_num_tokens:
break
if len_a - front_cut_a - end_cut_a > len_b - front_cut_b - end_cut_b:
if np.random.rand() < 0.5:
front_cut_a += 1
else:
end_cut_a += 1
else:
if np.random.rand() < 0.5:
front_cut_b += 1
else:
end_cut_b += 1
# calculate ds indices as well as offsets and return
truncated_sent_a = self._cut_sentence(sent_a, front_cut_a, end_cut_a)
truncated_sent_b = self._cut_sentence(sent_b, front_cut_b, end_cut_b)
return truncated_sent_a, truncated_sent_b
def _cut_sentence(self, sent, front_cut, end_cut):
"""
Cut a sentence based on the numbers of tokens to be cut from beginning and end
Represent the sentence as dataset idx and return
"""
start_ds_idx, end_ds_idx, offset = sent[0], sent[-1], 0
target_len = sum(self.dataset.sizes[sent]) - front_cut - end_cut
while front_cut > 0:
if self.dataset.sizes[start_ds_idx] > front_cut:
offset += front_cut
break
else:
front_cut -= self.dataset.sizes[start_ds_idx]
start_ds_idx += 1
while end_cut > 0:
if self.dataset.sizes[end_ds_idx] > end_cut:
break
else:
end_cut -= self.dataset.sizes[end_ds_idx]
end_ds_idx -= 1
return start_ds_idx, offset, end_ds_idx, target_len
def _fetch_block(self, start_ds_idx, offset, end_ds_idx, length):
"""
Fetch a block of tokens based on its dataset idx
"""
buffer = torch.cat(
[self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)]
)
s, e = offset, offset + length
return buffer[s:e]
def __getitem__(self, index):
block1, block2, next_sent_label = self.sent_pairs[index]
block1 = self._fetch_block(*block1)
block2 = self._fetch_block(*block2)
return block1, block2, next_sent_label
def __len__(self):
return len(self.sizes)
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
prefetch_idx = set()
for index in indices:
for block1, block2, _ in [self.sent_pairs[index]]:
for ds_idx in range(block1[0], block1[2] + 1):
prefetch_idx.add(ds_idx)
for ds_idx in range(block2[0], block2[2] + 1):
prefetch_idx.add(ds_idx)
self.dataset.prefetch(prefetch_idx)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/legacy/block_pair_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .block_pair_dataset import BlockPairDataset
from .masked_lm_dataset import MaskedLMDataset
from .masked_lm_dictionary import BertDictionary, MaskedLMDictionary
__all__ = [
"BertDictionary",
"BlockPairDataset",
"MaskedLMDataset",
"MaskedLMDictionary",
]
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/legacy/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Dict, List, Tuple
import numpy as np
import torch
from fairseq.data import Dictionary, FairseqDataset, data_utils
from fairseq.data.concat_dataset import ConcatDataset
from fairseq.data.legacy.block_pair_dataset import BlockPairDataset
from fairseq.data.token_block_dataset import TokenBlockDataset
class MaskedLMDataset(FairseqDataset):
"""
A wrapper Dataset for masked language modelling. The dataset
wraps around TokenBlockDataset or BlockedPairDataset and creates a batch
where the input blocks are masked according to the specified masking
probability. Additionally the batch can also contain sentence level targets
if this is specified.
Args:
dataset: Dataset which generates blocks of data. Only BlockPairDataset
and TokenBlockDataset are supported.
sizes: Sentence lengths
vocab: Dictionary with the vocabulary and special tokens.
pad_idx: Id of padding token in dictionary
mask_idx: Id of mask token in dictionary
classif_token_idx: Id of classification token in dictionary. This is the
token associated with the sentence embedding (Eg: CLS for BERT)
sep_token_idx: Id of separator token in dictionary
(Eg: SEP in BERT)
seed: Seed for random number generator for reproducibility.
shuffle: Shuffle the elements before batching.
has_pairs: Specifies whether the underlying dataset
generates a pair of blocks along with a sentence_target or not.
Setting it to True assumes that the underlying dataset generates a
label for the pair of sentences which is surfaced as
sentence_target. The default value assumes a single block with no
sentence target.
segment_id: An optional segment id for filling in the segment labels
when we are in the single block setting (Eg: XLM). Default is 0.
masking_ratio: specifies what percentage of the blocks should be masked.
masking_prob: specifies the probability of a given token being
replaced with the "MASK" token.
random_token_prob: specifies the probability of a given token being
replaced by a random token from the vocabulary.
"""
def __init__(
self,
dataset: FairseqDataset,
sizes: np.ndarray,
vocab: Dictionary,
pad_idx: int,
mask_idx: int,
classif_token_idx: int,
sep_token_idx: int,
seed: int = 1,
shuffle: bool = True,
has_pairs: bool = True,
segment_id: int = 0,
masking_ratio: float = 0.15,
masking_prob: float = 0.8,
random_token_prob: float = 0.1,
):
# Make sure the input datasets are the ones supported
assert (
isinstance(dataset, TokenBlockDataset)
or isinstance(dataset, BlockPairDataset)
or isinstance(dataset, ConcatDataset)
), (
"MaskedLMDataset only wraps TokenBlockDataset or BlockPairDataset or "
"ConcatDataset"
)
self.dataset = dataset
self.sizes = np.array(sizes)
self.vocab = vocab
self.pad_idx = pad_idx
self.mask_idx = mask_idx
self.classif_token_idx = classif_token_idx
self.sep_token_idx = sep_token_idx
self.shuffle = shuffle
self.seed = seed
self.has_pairs = has_pairs
self.segment_id = segment_id
self.masking_ratio = masking_ratio
self.masking_prob = masking_prob
self.random_token_prob = random_token_prob
# If we have only one block then sizes needs to be updated to include
# the classification token
if not has_pairs:
self.sizes = self.sizes + 1
def __getitem__(self, index: int):
# if has_pairs, then expect 2 blocks and a sentence target
if self.has_pairs:
(block_one, block_two, sentence_target) = self.dataset[index]
else:
block_one = self.dataset[index]
return {
"id": index,
"block_one": block_one,
"block_two": block_two if self.has_pairs else None,
"sentence_target": sentence_target if self.has_pairs else None,
}
def __len__(self):
return len(self.dataset)
def _mask_block(
self,
sentence: np.ndarray,
mask_idx: int,
pad_idx: int,
dictionary_token_range: Tuple,
):
"""
Mask tokens for Masked Language Model training
Samples mask_ratio tokens that will be predicted by LM.
Note:This function may not be efficient enough since we had multiple
conversions between np and torch, we can replace them with torch
operators later.
Args:
sentence: 1d tensor to be masked
mask_idx: index to use for masking the sentence
pad_idx: index to use for masking the target for tokens we aren't
predicting
dictionary_token_range: range of indices in dictionary which can
be used for random word replacement
(e.g. without special characters)
Return:
masked_sent: masked sentence
target: target with words which we are not predicting replaced
by pad_idx
"""
masked_sent = np.copy(sentence)
sent_length = len(sentence)
mask_num = math.ceil(sent_length * self.masking_ratio)
mask = np.random.choice(sent_length, mask_num, replace=False)
target = np.copy(sentence)
for i in range(sent_length):
if i in mask:
rand = np.random.random()
# replace with mask if probability is less than masking_prob
# (Eg: 0.8)
if rand < self.masking_prob:
masked_sent[i] = mask_idx
# replace with random token if probability is less than
# masking_prob + random_token_prob (Eg: 0.9)
elif rand < (self.masking_prob + self.random_token_prob):
# sample random token from dictionary
masked_sent[i] = np.random.randint(
dictionary_token_range[0], dictionary_token_range[1]
)
else:
target[i] = pad_idx
return masked_sent, target
def _collate(self, samples: List[Dict], pad_idx: int, eos_idx: int):
"""
Does the heavy lifting for creating a batch from the input list of
examples. The logic is as follows:
1. Mask the input blocks. In case has_pair is True then we have 2
blocks to mask.
2. Prepend the first masked block tensor with the special token
used as sentence embedding. Eg: CLS in BERT. This happens
irrespective of the value of has_pair.
3. If has_pair is True, then append the first masked block with the
special separator token (eg: SEP for BERT) and compute segment
label accordingly. In this case, also append the second masked
block with this special separator token and compute its segment
label.
4. For the targets tensor, prepend and append with padding index
accordingly.
5. Concatenate all tensors.
"""
if len(samples) == 0:
return {}
# To ensure determinism, we reset the state of the PRNG after every
# batch based on the seed and the first id of the batch. This ensures
# that across epochs we get the same mask for the same example. This
# is needed for reproducibility and is how BERT does masking
# TODO: Can we add deteminism without this constraint?
with data_utils.numpy_seed(self.seed + samples[0]["id"]):
for s in samples:
# token range is needed for replacing with random token during
# masking
token_range = (self.vocab.nspecial, len(self.vocab))
# mask according to specified probabilities.
masked_blk_one, masked_tgt_one = self._mask_block(
s["block_one"],
self.mask_idx,
self.pad_idx,
token_range,
)
tokens = np.concatenate([[self.classif_token_idx], masked_blk_one])
targets = np.concatenate([[self.pad_idx], masked_tgt_one])
segments = np.ones(len(tokens)) * self.segment_id
# if has_pairs is True then we need to add the SEP token to both
# the blocks after masking and re-compute segments based on the new
# lengths.
if self.has_pairs:
tokens_one = np.concatenate([tokens, [self.sep_token_idx]])
targets_one = np.concatenate([targets, [self.pad_idx]])
masked_blk_two, masked_tgt_two = self._mask_block(
s["block_two"], self.mask_idx, self.pad_idx, token_range
)
tokens_two = np.concatenate([masked_blk_two, [self.sep_token_idx]])
targets_two = np.concatenate([masked_tgt_two, [self.pad_idx]])
# block + 1 sep + 1 special (CLS)
segments_one = np.zeros(len(tokens_one))
# block + 1 sep
segments_two = np.ones(len(tokens_two))
tokens = np.concatenate([tokens_one, tokens_two])
targets = np.concatenate([targets_one, targets_two])
segments = np.concatenate([segments_one, segments_two])
s["source"] = torch.LongTensor(tokens)
s["segment_labels"] = torch.LongTensor(segments)
s["lm_target"] = torch.LongTensor(targets)
def merge(key):
return data_utils.collate_tokens(
[s[key] for s in samples], pad_idx, eos_idx, left_pad=False
)
return {
"id": torch.LongTensor([s["id"] for s in samples]),
"ntokens": sum(len(s["source"]) for s in samples),
"net_input": {
"src_tokens": merge("source"),
"segment_labels": merge("segment_labels"),
},
"lm_target": merge("lm_target"),
"sentence_target": torch.LongTensor([s["sentence_target"] for s in samples])
if self.has_pairs
else None,
"nsentences": len(samples),
}
def collater(self, samples: List[Dict]):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch of data
"""
return self._collate(samples, self.vocab.pad(), self.vocab.eos())
def num_tokens(self, index: int):
"""
Return the number of tokens in a sample. This value is used to
enforce max-tokens during batching.
"""
return self.sizes[index]
def size(self, index: int):
"""
Return an example's size as a float or tuple. This value is used when
filtering a dataset with max-positions.
"""
return self.sizes[index]
def ordered_indices(self):
"""
Return an ordered list of indices. Batches will be constructed based
on this order.
"""
if self.shuffle:
return np.random.permutation(len(self))
else:
order = [np.arange(len(self))]
order.append(self.sizes)
return np.lexsort(order)
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
self.dataset.prefetch(indices)
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/legacy/masked_lm_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hashlib
import logging
import math
import numpy as np
from fairseq.data import SampledMultiDataset
from .sampled_multi_dataset import CollateFormat, default_virtual_size_func
logger = logging.getLogger(__name__)
class SampledMultiEpochDataset(SampledMultiDataset):
"""Samples from multiple sub-datasets according to sampling ratios
using virtual epoch sizes to speed up dataloading.
Args:
datasets (
List[~torch.utils.data.Dataset]
or OrderedDict[str, ~torch.utils.data.Dataset]
): datasets
sampling_ratios (List[float]): list of probability of each dataset to be sampled
(default: None, which corresponds to concating all dataset together).
seed (int): RNG seed to use (default: 2).
epoch (int): starting epoch number (default: 1).
eval_key (str, optional): a key used at evaluation time that causes
this instance to pass-through batches from *datasets[eval_key]*.
collate_format (CollateFormat): collater output format, either CollateFormat.ordered_dict or
CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures
the collater to output batches of data mixed from all sub-datasets,
and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys
of sub-datasets.
Note that not all sub-datasets will present in a single batch in both formats.
virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func).
split (str): the split of the data, e.g. 'train', 'valid' or 'test'.
virtual_epoch_size (int): virtual epoch size, the dataset will go through the data by
this virtual epoch size one by one to speed up data loading, e.g. indicing and filtering
can be performed whenever a virtual epoch is loaded without waiting for the whole dataset to be loaded.
shared_collater (bool): whether or not to all sub-datasets have the same collater.
shard_epoch (int): the real epoch number for shard selection.
shuffle (bool): whether or not to shuffle data (default: True).
"""
def __init__(
self,
datasets,
sampling_ratios=None,
seed=2,
epoch=1,
eval_key=None,
collate_format=CollateFormat.single,
virtual_size=default_virtual_size_func,
split="",
virtual_epoch_size=None,
shared_collater=False,
shard_epoch=1,
shuffle=True,
):
self.virtual_epoch_size = virtual_epoch_size
self._current_epoch_start_index = None
self._random_global_indices = None
self.shard_epoch = shard_epoch if shard_epoch is not None else 1
self.load_next_shard = None
self._epoch_sizes = None
super().__init__(
datasets=datasets,
sampling_ratios=sampling_ratios,
seed=seed,
epoch=epoch,
eval_key=eval_key,
collate_format=collate_format,
virtual_size=virtual_size,
split=split,
shared_collater=shared_collater,
shuffle=shuffle,
)
def _setup(self, epoch):
self.virtual_epoch_size = (
self.virtual_epoch_size
if self.virtual_epoch_size is not None
else self.virtual_size
)
if self.virtual_epoch_size > self.virtual_size:
logger.warning(
f"virtual epoch size {self.virtual_epoch_size} "
f"is greater than virtual dataset size {self.virtual_size}"
)
self.virtual_epoch_size = self.virtual_size
self.num_virtual_epochs = math.ceil(self.virtual_size / self.virtual_epoch_size)
self._current_epoch_start_index = self._get_epoch_start_index(epoch)
logger.info(
f"virtual epoch size {self.virtual_epoch_size}; virtual dataset size {self.virtual_size}"
)
def _map_epoch_index_to_global(self, index):
index = self._current_epoch_start_index + index
# add randomness
return self._random_global_indices[index]
@property
def sizes(self):
if self._epoch_sizes is not None:
return self._epoch_sizes
_sizes = super().sizes
indices = self._random_global_indices[
self._current_epoch_start_index : self._current_epoch_start_index
+ len(self)
]
self._epoch_sizes = _sizes[indices]
# del super()._sizes to save memory
del self._sizes
self._sizes = None
return self._epoch_sizes
def _get_dataset_and_index(self, index):
i = self._map_epoch_index_to_global(index)
return super()._get_dataset_and_index(i)
def __len__(self):
return (
self.virtual_epoch_size
if self._current_epoch_start_index + self.virtual_epoch_size
< self.virtual_size
else self.virtual_size - self._current_epoch_start_index
)
def set_epoch(self, epoch):
if self._current_epoch_start_index is None:
# initializing epoch idnices of a virtual dataset
self._setup(epoch)
self._next_virtual_epoch(epoch)
else:
# working on already intialized epoch indices
if epoch == self._cur_epoch:
# re-enter so return
return
self._next_virtual_epoch(epoch)
def _get_epoch_start_index(self, epoch):
assert epoch >= 1 # fairseq is using 1-based epoch everywhere
return ((epoch - 1) % self.num_virtual_epochs) * self.virtual_epoch_size
def _next_global_indices(self, epoch):
rng = np.random.RandomState(
[
int(
hashlib.sha1(
str(self.__class__.__name__).encode("utf-8")
).hexdigest(),
16,
)
% (2 ** 32),
self.seed % (2 ** 32), # global seed
epoch, # epoch index,
]
)
del self._random_global_indices
self._random_global_indices = rng.choice(
self.virtual_size, self.virtual_size, replace=False
)
if self.load_next_shard is None:
self.load_next_shard = False
else:
# increase shard epoch for next loading
self.shard_epoch += 1
self.load_next_shard = True
logger.info(
"to load next epoch/shard in next load_dataset: "
f"epoch={epoch}/shard_epoch={self.shard_epoch}"
)
def _next_virtual_epoch(self, epoch):
index = self._get_epoch_start_index(epoch)
if index == 0 or self._random_global_indices is None:
# need to start from the beginning,
# so call super().set_epoch(epoch) to establish the global virtual indices
logger.info(
"establishing a new set of global virtual indices for "
f"epoch={epoch}/shard_epoch={self.shard_epoch}"
)
super().set_epoch(epoch)
self._next_global_indices(epoch)
else:
self._cur_epoch = epoch
# reset cache sizes and ordered_indices for the epoch after moving to a new epoch
self._clean_if_not_none(
[
self._epoch_sizes,
]
)
self._epoch_sizes = None
self._current_epoch_start_index = index
| KosmosX-API-main | kosmosX/fairseq/fairseq/data/multilingual/sampled_multi_epoch_dataset.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.