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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import copy
import torch
from deepspeed.ops.transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
def module_inject(layer_obj, model, config, micro_batch_size, max_seq_length, seed, preln, fp16=True):
for name, child in model.named_children():
if isinstance(child, layer_obj):
print('REPLACING BertLayer')
cuda_config = DeepSpeedTransformerConfig(batch_size=micro_batch_size,
max_seq_length=max_seq_length,
hidden_size=config.hidden_size,
heads=config.num_attention_heads,
attn_dropout_ratio=config.attention_probs_dropout_prob,
hidden_dropout_ratio=config.hidden_dropout_prob,
num_hidden_layers=config.num_hidden_layers,
initializer_range=config.initializer_range,
seed=seed,
fp16=fp16,
pre_layer_norm=preln)
new_module = DeepSpeedTransformerLayer(cuda_config)
# copy relevant state from child -> new module
qw = child.attention.self.query.weight
qb = child.attention.self.query.bias
kw = child.attention.self.key.weight
kb = child.attention.self.key.bias
vw = child.attention.self.value.weight
vb = child.attention.self.value.bias
qkvw = torch.cat((qw, kw, vw), 0)
qkvb = torch.cat((qb, kb, vb), 0)
new_module.attn_qkvw.data = qkvw
new_module.attn_qkvb.data = qkvb
new_module.attn_ow.data = child.attention.output.dense.weight
new_module.attn_ob.data = child.attention.output.dense.bias
if preln:
attention_layerNorm = child.PostAttentionLayerNorm
else:
attention_layerNorm = child.attention.output.LayerNorm
new_module.attn_nw.data = attention_layerNorm.weight
new_module.attn_nb.data = attention_layerNorm.bias
if preln:
intermediate_FF = child.intermediate.dense_act
else:
intermediate_FF = child.intermediate.dense
new_module.inter_w.data = intermediate_FF.weight
new_module.inter_b.data = intermediate_FF.bias
new_module.output_w.data = child.output.dense.weight
new_module.output_b.data = child.output.dense.bias
if preln:
transformer_LayerNorm = child.PreAttentionLayerNorm
else:
transformer_LayerNorm = child.output.LayerNorm
new_module.norm_w.data = transformer_LayerNorm.weight
new_module.norm_b.data = transformer_LayerNorm.bias
setattr(model, name, copy.deepcopy(new_module))
else:
module_inject(layer_obj, child, config, micro_batch_size, max_seq_length, seed, preln, fp16)
return model
def test_hi():
from turing.nvidia_modelingpreln import BertConfig as BertConfigPreLN
from turing.nvidia_modelingpreln import BertForQuestionAnswering as BertForQuestionAnsweringPreLN
from turing.nvidia_modelingpreln import BertLayer
bert_model_config = {
"vocab_size_or_config_json_file": 119547,
"hidden_size": 1024,
"num_hidden_layers": 1,
"num_attention_heads": 16,
"intermediate_size": 4096,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"type_vocab_size": 2,
"initializer_range": 0.02
}
bert_config = BertConfigPreLN(**bert_model_config)
base_model = BertForQuestionAnsweringPreLN(bert_config, args=None)
#base_model = LinearStack()
test_model = copy.deepcopy(base_model)
test_model = module_inject(BertLayer, test_model, bert_config, 4, 384, 1234)
print('BASE', base_model)
print('TEST', test_model)
#base_model.eval()
#test_model.eval()
#test_input = torch.rand(1, base_model.input_dim)
#base_output = base_model(test_input)
#test_output = test_model(test_input)
#
#assert torch.allclose(base_output, test_output, atol=3e-8)
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