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[shardformer] Support customized policy for llamav2 based model with HybridParallelPlugin #4614

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[shardformer] Support customized policy for llamav2 based model with HybridParallelPlugin #4614

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d69f88f
Enable policy assignment in HybridPlugin and enable llama policy for …
eric8607242 Sep 5, 2023
028be03
Remove Policy from Plugin
eric8607242 Sep 5, 2023
226dce8
Merge branch 'hpcaitech:feature/shardformer' into feature/shardformer
eric8607242 Sep 5, 2023
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11 changes: 7 additions & 4 deletions colossalai/booster/plugin/hybrid_parallel_plugin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,13 +38,13 @@ def _convert_floating_point(x, dtype: torch.dtype = torch.float16):
class HybridParallelModule(ModelWrapper):

def __init__(self, module: Module, precision: str, shard_config: ShardConfig, dp_group: ProcessGroup, use_ddp: bool,
ddp_config: dict) -> None:
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@flybird11111 flybird11111 Sep 5, 2023 •
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Thank you for your contribution. Could you please make some changes here? It's best not to expose 'Policy' in the plugin; it's just a component of Shardformer.

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@eric8607242 eric8607242 Sep 5, 2023

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Hi,

Thanks for your review!

I have two candidate approaches currently.

  1. Remove the typing hint
  2. Modify the typing hint from Policy to Object

Which one do you think better?

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@eric8607242 eric8607242 Sep 5, 2023

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Hi,

I modify the code with the second approach (modify from Policy to object)

ddp_config: dict, policy: Optional[object]=None) -> None:

self.stage_manager = shard_config.pipeline_stage_manager
self.dp_group = dp_group

shardformer = ShardFormer(shard_config)
module, self.shared_params = shardformer.optimize(module)
module, self.shared_params = shardformer.optimize(module, policy=policy)

# setting process groups for shared parameters
self.shared_param_process_groups = []
Expand Down Expand Up @@ -268,6 +268,7 @@ class HybridParallelPlugin(PipelinePluginBase):
cpu_offload (bool, optional): Whether to open cpu_offload when using ZeRO. Defaults to False.
communication_dtype (torch.dtype, optional): Communication dtype when using ZeRO. If not specified, the dtype of param will be used. Defaults to None.
overlap_communication (bool, optional): Whether to overlap communication and computation when using ZeRO. Defaults to True.
policy (object, optional): Shardformer policy when using custom model. If not specified, ShardFormer will try to fetch the policy automatically.
"""

def __init__(self,
Expand Down Expand Up @@ -300,7 +301,8 @@ def __init__(self,
zero_bucket_size_in_m: int = 12,
cpu_offload: bool = False,
communication_dtype: Optional[torch.dtype] = None,
overlap_communication: bool = True) -> None:
overlap_communication: bool = True,
policy: Optional[object] = None) -> None:

super().__init__()
assert dist.get_world_size() % (
Expand All @@ -324,6 +326,7 @@ def __init__(self,
self.pg_mesh = ProcessGroupMesh(self.dp_size, self.pp_size, self.tp_size)
self.stage_manager = None
self.schedule = None
self.policy = policy
assert zero_stage in (0, 1, 2)
if self.pp_size > 1:
assert num_microbatches is not None or microbatch_size is not None, 'num_microbatches or microbatch_size must be specified when using pipeline parallelism'
Expand Down Expand Up @@ -403,7 +406,7 @@ def configure(
if not isinstance(model, ModelWrapper):
use_ddp = self.dp_size > 1 and self.pp_size == 1 and self.zero_stage == 0
model = HybridParallelModule(model, self.precision, self.shard_config, self.dp_group, use_ddp,
self.ddp_config)
self.ddp_config, self.policy)
if optimizer is not None and not isinstance(optimizer, OptimizerWrapper):
if self.zero_stage == 0:
if self.precision in ['fp16', 'bf16']:
Expand Down
18 changes: 12 additions & 6 deletions colossalai/shardformer/policies/llama.py
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Expand Up @@ -40,14 +40,20 @@ def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
self.shard_config.enable_sequence_parallelism = False
warnings.warn("Llama dosen't support sequence parallelism now, will ignore the sequence parallelism flag.")


if self.shard_config.enable_tensor_parallelism:
decoder_attribute_replacement = {
"self_attn.hidden_size":
self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"self_attn.num_heads":
self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
}
if getattr(self.model.config, "num_key_value_heads", False):
decoder_attribute_replacement["self_attn.num_key_value_heads"] = \
self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size

policy[LlamaDecoderLayer] = ModulePolicyDescription(
attribute_replacement={
"self_attn.hidden_size":
self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
"self_attn.num_heads":
self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
},
attribute_replacement=decoder_attribute_replacement,
sub_module_replacement=[
SubModuleReplacementDescription(
suffix="self_attn.q_proj",
Expand Down