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[shardformer] shardformer support opt models (#4091) 

* [shardformer] shardformer support opt models

* [shardformer] shardformer support opt models, fix

* [shardformer] shardformer support opt models, fix

* [shardformer] shardformer support opt models, fix
pull/4157/head
jiangmingyan 2023-06-27 17:39:29 +08:00 committed by Frank Lee
parent d33a44e8c3
commit ac80937138
6 changed files with 264 additions and 10 deletions
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10
colossalai/shardformer/policies/autopolicy.py
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@ -68,6 +68,16 @@ _POLICY_LIST = {
PolicyLocation(file_name="gpt2", class_name="GPT2ForTokenClassificationPolicy"),
"transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification":
PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"),
# OPT
"transformers.models.opt.modeling_opt.OPTModel":
PolicyLocation(file_name="opt", class_name="OPTModelPolicy"),
"transformers.models.opt.modeling_opt.OPTForCausalLM":
PolicyLocation(file_name="opt", class_name="OPTForCausalLMPolicy"),
"transformers.models.opt.modeling_opt.OPTForSequenceClassification":
PolicyLocation(file_name="opt", class_name="OPTForSequenceClassificationPolicy"),
"transformers.models.opt.modeling_opt.OPTForQuestionAnswering":
PolicyLocation(file_name="opt", class_name="OPTForQuestionAnsweringPolicy"),
}

133
colossalai/shardformer/policies/opt.py Normal file
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@ -0,0 +1,133 @@
from transformers.models.opt.modeling_opt import (
OPTAttention,
OPTDecoder,
OPTDecoderLayer,
OPTForCausalLM,
OPTForSequenceClassification,
)
from colossalai.shardformer.layer import Embedding1D, FusedLayerNorm, Linear1D_Col, Linear1D_Row
from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
class OPTPolicy(Policy):
def preprocess(self):
# reshape the embedding layer
r"""
Reshape the Embedding layer to make the embedding dimension divisible by world_size
"""
vocab_size = self.model.config.vocab_size
world_size = self.shard_config.tensor_parallel_size
if vocab_size % world_size != 0:
new_vocab_size = vocab_size + world_size - vocab_size % world_size
self.model.resize_token_embeddings(new_vocab_size)
return self.model
def module_policy(self):
base_policy = {
OPTDecoder:
ModulePolicyDescription(attribute_replacement={},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="embed_tokens",
target_module=Embedding1D,
)
]),
OPTDecoderLayer:
ModulePolicyDescription(attribute_replacement={},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="fc1",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="fc2",
target_module=Linear1D_Row,
)
]),
OPTAttention:
ModulePolicyDescription(attribute_replacement={
"embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size
},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="q_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="k_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="v_proj",
target_module=Linear1D_Col,
),
SubModuleReplacementDescription(
suffix="out_proj",
target_module=Linear1D_Row,
),
]),
}
if self.shard_config.fused_layernorm:
base_policy[OPTDecoder].sub_module_replacement.append(
SubModuleReplacementDescription(suffix="final_layer_norm",
target_module=FusedLayerNorm,
ignore_if_not_exist=True))
base_policy[OPTDecoderLayer].sub_module_replacement.extend([
SubModuleReplacementDescription(suffix="self_attn_layer_norm",
target_module=FusedLayerNorm,
ignore_if_not_exist=True),
SubModuleReplacementDescription(suffix="final_layer_norm",
target_module=FusedLayerNorm,
ignore_if_not_exist=True)
])
return base_policy
def new_model_class(self):
return None
def postprocess(self):
return self.model
class OPTModelPolicy(OPTPolicy):
def __init__(self) -> None:
super().__init__()
class OPTForCausalLMPolicy(OPTPolicy):
def module_policy(self):
policy = super().module_policy()
new_item = {
OPTForCausalLM:
ModulePolicyDescription(attribute_replacement={},
param_replacement=[],
sub_module_replacement=[
SubModuleReplacementDescription(suffix="lm_head",
target_module=Linear1D_Col,
kwargs=dict(gather_output=True))
])
}
policy.update(new_item)
return policy
class OPTForSequenceClassificationPolicy(OPTPolicy):
def __init__(self) -> None:
super().__init__()
class OPTForQuestionAnsweringPolicy(OPTPolicy):
def __init__(self) -> None:
super().__init__()

57
tests/kit/model_zoo/transformers/opt.py
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@ -11,14 +11,47 @@ SEQ_LENGTH = 16
def data_gen():
input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
input_ids = torch.Tensor([[1, 15043, 29892, 590, 11203, 338, 274, 1082]]).long()
attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1]]).long()
return dict(input_ids=input_ids, attention_mask=attention_mask)
output_transform_fn = lambda x: x
def data_gen_for_causal_lm():
# LM data gen
# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
data = data_gen()
labels = data['input_ids'].clone()
data['labels'] = labels
return data
config = transformers.OPTConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4)
def data_gen_for_sequence_classification():
# LM data gen
# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
data = data_gen()
labels = data['input_ids'].clone()
data['labels'] = torch.tensor([1])
return data
def data_gen_for_question_answering():
# LM data gen
# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
data = data_gen()
data['start_positions'] = torch.tensor([0])
data['end_positions'] = torch.tensor([1])
return data
output_transform_fn = lambda x: x
loss_fn_for_opt_model = lambda x: x.last_hidden_state.mean()
loss_fn_for_lm = lambda x: x.loss
config = transformers.OPTConfig(
hidden_size=128,
num_hidden_layers=2,
num_attention_heads=4,
dropout=0,
)
# register the following models
# transformers.OPTModel,
@ -27,9 +60,23 @@ model_zoo.register(name='transformers_opt',
model_fn=lambda: transformers.OPTModel(config),
data_gen_fn=data_gen,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn_for_opt_model,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_opt_for_causal_lm',
model_fn=lambda: transformers.OPTForCausalLM(config),
data_gen_fn=data_gen,
data_gen_fn=data_gen_for_causal_lm,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn_for_lm,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_opt_for_question_answering',
model_fn=lambda: transformers.OPTForQuestionAnswering(config),
data_gen_fn=data_gen_for_question_answering,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn_for_lm,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(name='transformers_opt_for_sequence_classification',
model_fn=lambda: transformers.OPTForSequenceClassification(config),
data_gen_fn=data_gen_for_sequence_classification,
output_transform_fn=output_transform_fn,
loss_fn=loss_fn_for_lm,
model_attribute=ModelAttribute(has_control_flow=True))

3
tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py
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@ -11,10 +11,9 @@ from tests.kit.model_zoo import model_zoo
@clear_cache_before_run()
def test_opt():
sub_registry = model_zoo.get_sub_registry('transformers_opt')
for name, (model_fn, data_gen_fn, _, _, _) in sub_registry.items():
model = model_fn()
trace_model_and_compare_output(model, data_gen_fn)
trace_model_and_compare_output(model, data_gen_fn, ignore_data=['labels', 'start_positions', 'end_positions'])
if __name__ == '__main__':

4
tests/test_shardformer/test_model/_utils.py
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@ -25,7 +25,6 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn,
# switch to train mode
original_model.train()
sharded_model.train()
# run forward
org_output = original_model(**data)
org_output = output_transform_fn(org_output)
@ -34,5 +33,4 @@ def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn,
shard_output = sharded_model(**data)
shard_output = output_transform_fn(shard_output)
shard_loss = loss_fn(shard_output)
return org_output, org_loss, shard_output, shard_loss
return org_output, org_loss, shard_output, shard_loss

67
tests/test_shardformer/test_model/test_shard_opt.py Normal file
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@ -0,0 +1,67 @@
import copy
import os
import pytest
import torch
import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.testing import (
assert_hf_output_close,
check_state_dict_equal,
clear_cache_before_run,
rerun_if_address_is_in_use,
spawn,
)
from tests.kit.model_zoo import model_zoo
from tests.test_shardformer.test_model._utils import build_model, run_forward
os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
output_transform_fn, loss_fn)
assert_hf_output_close(org_output, shard_output, ignore_keys=['past_key_values'], rtol=1e-4)
# run backward
org_loss.backward()
shard_loss.backward()
# check grad
if hasattr(org_model, 'model'):
opt_model = org_model.model
shard_opt_model = sharded_model.model
else:
opt_model = org_model
shard_opt_model = sharded_model
org_grad = opt_model.decoder.layers[0].self_attn.q_proj.weight.grad
shard_grad = shard_opt_model.decoder.layers[0].self_attn.q_proj.weight.grad
shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(4)]
shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
all_shard_grad = torch.cat(shard_grad_list, dim=0)
assert torch.allclose(org_loss, shard_loss,
atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
assert torch.allclose(org_grad, all_shard_grad,
atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
def check_OPTModel(rank, world_size, port):
disable_existing_loggers()
colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
sub_model_zoo = model_zoo.get_sub_registry('transformers_opt')
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
org_model, sharded_model = build_model(world_size, model_fn)
check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
torch.cuda.empty_cache()
@pytest.mark.dist
@rerun_if_address_is_in_use()
@clear_cache_before_run()
def test_OPTModel():
spawn(check_OPTModel, 4)