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[shardformer, pipeline] add gradient_checkpointing_ratio and heterogenous shard policy for llama (#5508)

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* feat: add `GradientCheckpointConfig` and `PipelineGradientCheckpointConfig`

* feat: apply `GradientCheckpointConfig` to policy and llama_forward

* feat: move `distribute_layer` and `get_stage_index` to PipelineStageManager

* fix: add optional args for `distribute_layer` and `get_stage_index`

* fix: fix changed API calls

* test: update llama tests

* style: polish `GradientCheckpointConfig`

* fix: fix pipeline utils tests
This commit is contained in:
Wenhao Chen
2024-04-01 11:34:58 +08:00
committed by GitHub
parent df5e9c53cf
commit e614aa34f3
28 changed files with 396 additions and 213 deletions
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14
colossalai/shardformer/modeling/llama.py
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@@ -138,13 +138,25 @@ class LlamaPipelineForwards:
next_decoder_cache = () if use_cache else None
start_idx, end_idx = stage_index[0], stage_index[1]
num_ckpt_layers = 0
if self.gradient_checkpointing and self.training:
num_ckpt_layers = end_idx - start_idx
# TODO: We can replace `gradient_checkpointing_enable` fn and initialize a gradient_checkpointing (List[bool]) for each layer
if shard_config.gradient_checkpoint_config is not None:
num_ckpt_layers = shard_config.gradient_checkpoint_config.get_num_ckpt_layers(
stage=stage_manager.stage,
num_layers=end_idx - start_idx,
model_chunk_id=stage_manager.model_chunk_id if stage_manager.is_interleave else 0,
)
assert num_ckpt_layers <= end_idx - start_idx
for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
if idx - start_idx < num_ckpt_layers:
def create_custom_forward(module):
def custom_forward(*inputs):