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[shardformer]: support gpt-j, falcon, Mistral and add interleaved pipeline for bert (#5088)

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* [shardformer] implement policy for all GPT-J models and test

* [shardformer] support interleaved pipeline parallel for bert finetune

* [shardformer] shardformer support falcon (#4883)

* [shardformer]: fix interleaved pipeline for bert model (#5048)

* [hotfix]: disable seq parallel for gptj and falcon, and polish code (#5093)

* Add Mistral support for Shardformer (#5103)

* [shardformer] add tests to mistral (#5105)

---------

Co-authored-by: Pengtai Xu <henryxu880@gmail.com>
Co-authored-by: ppt0011 <143150326+ppt0011@users.noreply.github.com>
Co-authored-by: flybird11111 <1829166702@qq.com>
Co-authored-by: eric8607242 <e0928021388@gmail.com>
This commit is contained in:
Wenhao Chen
2023-11-28 16:54:42 +08:00
committed by GitHub
parent 126cf180bc
commit 7172459e74
39 changed files with 4007 additions and 234 deletions
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12
examples/language/bert/data.py
View File

@@ -88,20 +88,24 @@ class GLUEDataBuilder:
)
def val_dataloader(self):
# TODO: drop_last is set to True for now to avoid error when using PP
# as the last batch may not be divisible by the number of microbatches
if len(self.eval_splits) == 1:
return self.plugin.prepare_dataloader(self.dataset["validation"], batch_size=self.eval_batch_size)
return self.plugin.prepare_dataloader(
self.dataset["validation"], batch_size=self.eval_batch_size, drop_last=True
)
elif len(self.eval_splits) > 1:
return [
self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size)
self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size, drop_last=True)
for x in self.eval_splits
]
def test_dataloader(self):
if len(self.eval_splits) == 1:
return self.plugin.prepare_dataloader(self.dataset["test"], batch_size=self.eval_batch_size)
return self.plugin.prepare_dataloader(self.dataset["test"], batch_size=self.eval_batch_size, drop_last=True)
elif len(self.eval_splits) > 1:
return [
self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size)
self.plugin.prepare_dataloader(self.dataset[x], batch_size=self.eval_batch_size, drop_last=True)
for x in self.eval_splits
]

19
examples/language/bert/finetune.py
View File

@@ -57,7 +57,9 @@ def evaluate_model(
def evaluate_subset(dataloader: DataLoader):
use_pipeline = isinstance(booster.plugin, HybridParallelPlugin) and booster.plugin.pp_size > 1
is_pp_last_stage = use_pipeline and booster.plugin.stage_manager.is_last_stage()
is_pp_last_device = use_pipeline and booster.plugin.stage_manager.is_last_stage(
None if not booster.plugin.stage_manager.is_interleave else -1
)
accum_loss = torch.zeros(1, device=get_current_device())
for batch in dataloader:
@@ -69,9 +71,10 @@ def evaluate_model(
current_pp_group_ranks = pg_mesh.get_ranks_in_group(pp_group)
current_rank = dist.get_rank()
batch = iter([batch])
outputs = booster.execute_pipeline(batch, model, criterion, return_loss=True, return_outputs=True)
if is_pp_last_stage:
if is_pp_last_device:
logits = outputs["outputs"]["logits"]
val_loss = outputs["loss"]
accum_loss.add_(val_loss)
@@ -133,8 +136,10 @@ def train_epoch(
coordinator: DistCoordinator,
):
use_pipeline = isinstance(booster.plugin, HybridParallelPlugin) and booster.plugin.pp_size > 1
is_pp_last_stage = use_pipeline and booster.plugin.stage_manager.is_last_stage()
print_flag = (not use_pipeline and coordinator.is_master()) or (use_pipeline and is_pp_last_stage)
is_pp_last_device = use_pipeline and booster.plugin.stage_manager.is_last_stage(
None if not booster.plugin.stage_manager.is_interleave else -1
)
print_flag = (not use_pipeline and coordinator.is_master()) or (use_pipeline and is_pp_last_device)
total_step = len(train_dataloader)
model.train()
@@ -148,7 +153,7 @@ def train_epoch(
train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
)
# Backward and optimize
if is_pp_last_stage:
if is_pp_last_device:
loss = outputs["loss"]
pbar.set_postfix({"loss": loss.item()})
else:
@@ -222,7 +227,9 @@ def main():
tp_size=1,
pp_size=2,
num_microbatches=None,
microbatch_size=1,
pp_style="interleaved",
num_model_chunks=2,
microbatch_size=16,
enable_all_optimization=True,
zero_stage=1,
precision="fp16",