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[example] llama2 add fine-tune example (#4673)

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* [shardformer] update shardformer readme

[shardformer] update shardformer readme

[shardformer] update shardformer readme

* [shardformer] update llama2/opt finetune example and shardformer update to llama2

* [shardformer] update llama2/opt finetune example and shardformer update to llama2

* [shardformer] update llama2/opt finetune example and shardformer update to llama2

* [shardformer] change dataset

* [shardformer] change dataset

* [shardformer] fix CI

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[example] update opt example

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* [example] llama2 add finetune example

* [example] llama2 add finetune example

* [example] llama2 add finetune example

* [example] llama2 add finetune example

* fix

* update llama2 example

* update llama2 example

* fix

* update llama2 example

* update llama2 example

* update llama2 example

* update llama2 example

* update llama2 example

* update llama2 example

* Update requirements.txt

* update llama2 example

* update llama2 example

* update llama2 example
This commit is contained in:
flybird11111
2023-09-15 18:45:44 +08:00
committed by GitHub
parent ac2797996b
commit 4c4482f3ad
8 changed files with 402 additions and 35 deletions
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7
examples/language/bert/finetune.py
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@@ -129,14 +129,13 @@ def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, _criterion:
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)
total_step = len(train_dataloader)
model.train()
optimizer.zero_grad()
train_dataloader_iter = iter(train_dataloader)
with tqdm(range(total_step),
desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]',
disable=not (coordinator.is_master() or is_pp_last_stage)) as pbar:
with tqdm(range(total_step), desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not print_flag) as pbar:
# Forward pass
for _ in pbar:
if use_pipeline:
@@ -192,13 +191,13 @@ def main():
model_name = "albert-xxlarge-v2"
else:
raise RuntimeError
# ==============================
# Launch Distributed Environment
# ==============================
colossalai.launch_from_torch(config={}, seed=42)
coordinator = DistCoordinator()
# local_batch_size = BATCH_SIZE // coordinator.world_size
lr = LEARNING_RATE * coordinator.world_size
# ==============================