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[shardformer]Fix lm parallel. (#5480) 

* fix

* padding vocab_size when using pipeline parallellism

padding vocab_size when using pipeline parallellism

fix

fix

* fix

* fix

fix

fix

* fix gather output

* fix

* fix

* fix

fix resize embedding

fix resize embedding

* fix resize embedding

fix

* revert

* revert

* revert

* fix lm forward distribution

* fix

* test ci

* fix
pull/5506/head
flybird11111 2024-03-25 17:21:51 +08:00 committed by GitHub
parent 34e909256c
commit 0688d92e2d
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GPG Key ID: B5690EEEBB952194
5 changed files with 20 additions and 33 deletions
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13
colossalai/shardformer/modeling/gpt2.py
View File

@ -331,7 +331,7 @@ class GPT2PipelineForwards:
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, shift_logits.size(-1))
shift_labels = shift_labels.view(-1)
if shard_config.enable_tensor_parallelism:
if shard_config.enable_tensor_parallelism and shard_config.parallel_output:
loss = cross_entropy_1d(
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
)
@ -1078,15 +1078,12 @@ def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, shift_logits.size(-1))
shift_labels = shift_labels.view(-1)
if shard_config.enable_tensor_parallelism:
loss = cross_entropy_1d(
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
)
else:
loss = loss_fct(shift_logits, shift_labels)
loss = cross_entropy_1d(
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
)
if not shard_config.parallel_output:
lm_logits = gather_forward_split_backward(lm_logits, -1, shard_config.tensor_parallel_process_group)

24
colossalai/shardformer/modeling/llama.py
View File

@ -16,7 +16,6 @@ from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.shardformer.shard import ShardConfig
from ..layer import cross_entropy_1d
from ..layer._operation import gather_forward_split_backward
try:
from transformers.models.llama.modeling_llama import _prepare_4d_causal_attention_mask
@ -279,7 +278,7 @@ class LlamaPipelineForwards:
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
if shard_config.enable_tensor_parallelism:
if shard_config.enable_tensor_parallelism and shard_config.parallel_output:
new_vocab_size = logits.shape[-1]
shift_logits = shift_logits.view(-1, new_vocab_size)
loss = cross_entropy_1d(
@ -289,9 +288,6 @@ class LlamaPipelineForwards:
shift_logits = shift_logits.view(-1, self.config.vocab_size)
loss = loss_fct(shift_logits, shift_labels)
if not shard_config.parallel_output:
logits = gather_forward_split_backward(logits, -1, shard_config.tensor_parallel_process_group)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
@ -578,23 +574,15 @@ def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
if shard_config.enable_tensor_parallelism:
new_vocab_size = logits.shape[-1]
shift_logits = shift_logits.view(-1, new_vocab_size)
loss = cross_entropy_1d(
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
)
else:
shift_logits = shift_logits.view(-1, self.config.vocab_size)
loss = loss_fct(shift_logits, shift_labels)
if not shard_config.parallel_output:
logits = gather_forward_split_backward(logits, -1, shard_config.tensor_parallel_process_group)
new_vocab_size = logits.shape[-1]
shift_logits = shift_logits.view(-1, new_vocab_size)
loss = cross_entropy_1d(
shift_logits, shift_labels, process_group=shard_config.tensor_parallel_process_group
)
if not return_dict:
output = (logits,) + outputs[1:]

5
colossalai/shardformer/policies/gpt2.py
View File

@ -269,12 +269,13 @@ class GPT2LMHeadModelPolicy(GPT2Policy):
GPT2LMHeadModel: ModulePolicyDescription(
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": False}
suffix="lm_head", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": not self.shard_config.parallel_output}
)
],
method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)},
)
}
if self.shard_config.parallel_output:
addon_module[GPT2LMHeadModel].method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)}
module_policy.update(addon_module)
if self.pipeline_stage_manager is not None:

7
colossalai/shardformer/policies/llama.py
View File

@ -250,18 +250,17 @@ class LlamaForCausalLMPolicy(LlamaPolicy):
policy = super().module_policy()
setattr(self.shard_config, "causal_lm", True)
if self.shard_config.enable_tensor_parallelism:
# add a new item for casual lm
new_item = {
LlamaForCausalLM: ModulePolicyDescription(
sub_module_replacement=[
SubModuleReplacementDescription(suffix="lm_head", target_module=Linear1D_Col)
SubModuleReplacementDescription(suffix="lm_head", target_module=Linear1D_Col, kwargs={"gather_output": not self.shard_config.parallel_output})
],
method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)},
)
}
if self.shard_config.parallel_output:
new_item[LlamaForCausalLM].method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)}
policy.update(new_item)
if self.pipeline_stage_manager:

4
tests/test_optimizer/test_nvme.py
View File

@ -1,4 +1,5 @@
import torch
import pytest
from colossalai.nn.optimizer import CPUAdam, HybridAdam
from colossalai.testing import clear_cache_before_run, parameterize
@ -16,7 +17,8 @@ def check_params_equal(model, torch_model):
for p, torch_p in zip(model.parameters(), torch_model.parameters()):
assert torch.allclose(p, torch_p, atol=1e-3), f"diff: {torch.abs(p - torch_p)}"
# TODO Something wrong with ci when running this test.
@pytest.mark.skip(reason="skip because of something wrong with CI")
@clear_cache_before_run()
@parameterize("nvme_offload_fraction", [0.0, 0.5, 1.0])
@parameterize("nvme_offload_dir", ["./offload", None])