mirror of https://github.com/hpcaitech/ColossalAI
337 lines
13 KiB
Python
337 lines
13 KiB
Python
import warnings
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from functools import partial
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from typing import Callable, Dict, List, Union
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import torch.nn as nn
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from torch import Tensor
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from torch.nn import Module
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from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, RMSNorm, VocabParallelEmbedding1D
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from ..modeling.qwen2 import (
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Qwen2PipelineForwards,
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get_lm_forward_with_dist_cross_entropy,
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get_qwen2_flash_attention_forward,
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)
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ["Qwen2Policy", "Qwen2ForCausalLMPolicy", "Qwen2ForSequenceClassificationPolicy"]
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class Qwen2Policy(Policy):
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def config_sanity_check(self):
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pass
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def preprocess(self):
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if self.shard_config.enable_tensor_parallelism:
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# Resize embedding
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vocab_size = self.model.config.vocab_size
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world_size = self.shard_config.tensor_parallel_size
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if vocab_size % world_size != 0:
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new_vocab_size = vocab_size + world_size - vocab_size % world_size
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self.model.resize_token_embeddings(new_vocab_size)
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return self.model
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def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
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from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention, Qwen2DecoderLayer, Qwen2Model
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policy = {}
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norm_cls = FusedRMSNorm if self.shard_config.enable_fused_normalization else RMSNorm
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if self.shard_config.enable_sequence_parallelism:
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self.shard_config.enable_sequence_parallelism = False
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warnings.warn("Qwen2 doesn't support sequence parallelism now, will ignore the sequence parallelism flag.")
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if self.shard_config.enable_tensor_parallelism:
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decoder_attribute_replacement = {
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"self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
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}
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if getattr(self.model.config, "num_key_value_heads", False):
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decoder_attribute_replacement["self_attn.num_key_value_heads"] = (
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self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size
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)
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policy[Qwen2DecoderLayer] = ModulePolicyDescription(
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attribute_replacement=decoder_attribute_replacement,
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="self_attn.q_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.k_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.v_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.o_proj",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="mlp.gate_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="mlp.up_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="mlp.down_proj",
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target_module=Linear1D_Row,
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),
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],
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)
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=VocabParallelEmbedding1D,
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),
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policy=policy,
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target_key=Qwen2Model,
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)
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# optimization configuration
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self.append_or_create_submodule_replacement(
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description=[
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SubModuleReplacementDescription(
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suffix="input_layernorm",
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target_module=norm_cls,
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),
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SubModuleReplacementDescription(
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suffix="post_attention_layernorm",
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target_module=norm_cls,
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),
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],
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policy=policy,
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target_key=Qwen2DecoderLayer,
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)
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="norm",
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target_module=norm_cls,
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),
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policy=policy,
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target_key=Qwen2Model,
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)
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# use flash attention
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if self.shard_config.enable_flash_attention:
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self.append_or_create_method_replacement(
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description={
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"forward": get_qwen2_flash_attention_forward(self.shard_config),
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},
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policy=policy,
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target_key=Qwen2Attention,
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)
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return policy
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def postprocess(self):
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return self.model
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def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
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"""If under pipeline parallel setting, replacing the original forward method of huggingface
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to customized forward method, and add this changing to policy."""
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if self.pipeline_stage_manager is None:
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return
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == "Qwen2Model":
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module = self.model
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else:
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module = self.model.model
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if stage_manager.is_interleave:
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layers_per_stage = self.distribute_layers(
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len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
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)
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stage_manager.stage_indices = Policy.get_stage_index(
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layers_per_stage,
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stage_manager.stage,
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num_model_chunks=stage_manager.num_model_chunks,
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num_stages=stage_manager.num_stages,
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)
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method_replacement = {
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"forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config)
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}
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else:
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layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
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stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
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method_replacement = {
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"forward": partial(
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new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
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)
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}
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self.append_or_create_method_replacement(
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description=method_replacement, policy=policy, target_key=model_cls
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)
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self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls)
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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assert self.pipeline_stage_manager is not None
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if self.model.__class__.__name__ == "Qwen2Model":
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module = self.model
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else:
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module = self.model.model
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stage_manager = self.pipeline_stage_manager
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held_layers = []
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if stage_manager.is_interleave:
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assert stage_manager.num_model_chunks is not None
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layers_per_stage = self.distribute_layers(
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len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
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)
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stage_indices = Policy.get_stage_index(
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layers_per_stage,
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stage_manager.stage,
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num_model_chunks=stage_manager.num_model_chunks,
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num_stages=stage_manager.num_stages,
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)
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if stage_manager.is_first_stage(ignore_chunk=True):
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held_layers.append(module.embed_tokens)
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for start_idx, end_idx in stage_indices:
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage(ignore_chunk=True):
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held_layers.append(module.norm)
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else:
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layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
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if stage_manager.is_first_stage():
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held_layers.append(module.embed_tokens)
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start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.norm)
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return held_layers
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class Qwen2ModelPolicy(Qwen2Policy):
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def module_policy(self):
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policy = super().module_policy()
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from transformers.models.qwen2.modeling_qwen2 import Qwen2Model
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=Qwen2Model, new_forward=Qwen2PipelineForwards.qwen2_model_forward, policy=policy
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)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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held_layers = super().get_held_layers()
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in Qwen2 model"""
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return []
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class Qwen2ForCausalLMPolicy(Qwen2Policy):
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def module_policy(self):
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from transformers import Qwen2ForCausalLM
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policy = super().module_policy()
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setattr(self.shard_config, "causal_lm", True)
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for casual lm
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new_item = {
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Qwen2ForCausalLM: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(suffix="lm_head", target_module=Linear1D_Col)
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],
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method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)},
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)
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}
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policy.update(new_item)
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=Qwen2ForCausalLM, new_forward=Qwen2PipelineForwards.qwen2_for_causal_lm_forward, policy=policy
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)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage(ignore_chunk=True):
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held_layers.append(self.model.lm_head)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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qwen2_model = self.model.model
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if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
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if (
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id(qwen2_model.embed_tokens.weight) == id(self.model.lm_head.weight)
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and self.pipeline_stage_manager.num_stages > 1
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):
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# tie weights
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return [
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{
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0: qwen2_model.embed_tokens.weight,
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self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
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}
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]
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return []
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class Qwen2ForSequenceClassificationPolicy(Qwen2Policy):
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def module_policy(self):
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from transformers import Qwen2ForSequenceClassification
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for sequence classification
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new_item = {
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Qwen2ForSequenceClassification: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True)
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)
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]
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)
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}
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policy.update(new_item)
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# to be confirmed
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=Qwen2ForSequenceClassification,
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new_forward=Qwen2PipelineForwards.qwen2_for_sequence_classification_forward,
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policy=policy,
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)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage(ignore_chunk=True):
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held_layers.append(self.model.score)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in Qwen2 for sequence classification model"""
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return []
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