mirror of https://github.com/hpcaitech/ColossalAI
435 lines
17 KiB
Python
435 lines
17 KiB
Python
import warnings
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from functools import partial
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from typing import Callable, Dict, List
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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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import colossalai.shardformer.layer as col_nn
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from ..modeling.bloom import (
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BloomPipelineForwards,
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build_bloom_alibi_tensor_fn,
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get_bloom_sequence_parallel_forward_fn,
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get_jit_fused_bloom_attention_forward,
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get_jit_fused_bloom_gelu_forward,
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get_jit_fused_bloom_mlp_forward,
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get_lm_forward_with_dist_cross_entropy,
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)
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from ..modeling.jit import get_jit_fused_dropout_add_func, get_jit_fused_gelu_forward_func
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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class BloomPolicy(Policy):
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def __init__(self) -> None:
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super().__init__()
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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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self.tie_weight = self.tie_weight_check()
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return self.model
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def module_policy(self):
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from transformers.models.bloom.modeling_bloom import BloomAttention, BloomBlock, BloomGelu, BloomMLP, BloomModel
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policy = {}
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embedding_cls = None
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if self.shard_config.enable_tensor_parallelism:
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embedding_cls = col_nn.VocabParallelEmbedding1D
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else:
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if self.tie_weight:
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embedding_cls = col_nn.PaddingEmbedding
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if self.shard_config.enable_fused_normalization:
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norm_cls = col_nn.FusedLayerNorm
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else:
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norm_cls = col_nn.LayerNorm
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sp_mode = self.shard_config.sequence_parallelism_mode or None
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assert sp_mode != "all_to_all", "all_to_all sequence parallelism is not supported for BLOOM"
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if sp_mode == "ring":
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warnings.warn(
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f"For BLOOM, sequence parallelism is currently not support mode {sp_mode}, will set to be split_gather"
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)
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sp_mode = "split_gather"
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overlap = self.shard_config.enable_sequence_overlap
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sp_partial_derived = sp_mode == "split_gather"
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if self.shard_config.enable_tensor_parallelism:
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assert (
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self.model.config.n_head % self.shard_config.tensor_parallel_size == 0
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), f"The number of attention heads must be divisible by tensor parallel size."
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policy[BloomBlock] = ModulePolicyDescription(
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attribute_replacement={
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"self_attention.hidden_size": self.model.config.hidden_size
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// self.shard_config.tensor_parallel_size,
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"self_attention.split_size": self.model.config.hidden_size
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// self.shard_config.tensor_parallel_size,
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"self_attention.num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size,
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},
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="self_attention.query_key_value",
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target_module=col_nn.Linear1D_Col,
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kwargs={"seq_parallel_mode": sp_mode, "overlap": overlap},
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),
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SubModuleReplacementDescription(
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suffix="self_attention.dense",
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target_module=col_nn.Linear1D_Row,
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kwargs={"seq_parallel_mode": sp_mode},
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),
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SubModuleReplacementDescription(
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suffix="self_attention.attention_dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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SubModuleReplacementDescription(
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suffix="mlp.dense_h_to_4h",
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target_module=col_nn.Linear1D_Col,
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kwargs={"seq_parallel_mode": sp_mode, "overlap": overlap},
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),
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SubModuleReplacementDescription(
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suffix="mlp.dense_4h_to_h",
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target_module=col_nn.Linear1D_Row,
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kwargs={"seq_parallel_mode": sp_mode},
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),
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],
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)
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policy[BloomModel] = ModulePolicyDescription(
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attribute_replacement={
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"num_heads": self.model.config.n_head // self.shard_config.tensor_parallel_size,
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},
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method_replacement={
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"build_alibi_tensor": build_bloom_alibi_tensor_fn(self.shard_config.tensor_parallel_process_group)
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},
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)
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if embedding_cls is not None:
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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="word_embeddings",
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target_module=embedding_cls,
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kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by},
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),
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],
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policy=policy,
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target_key=BloomModel,
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)
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# optimization configuration
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# handle bloom model
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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="ln_f",
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target_module=norm_cls,
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),
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SubModuleReplacementDescription(
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suffix="word_embeddings_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=BloomModel,
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)
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# handle bloom block
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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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kwargs={"sp_partial_derived": sp_partial_derived},
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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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kwargs={"sp_partial_derived": sp_partial_derived},
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),
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],
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policy=policy,
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target_key=BloomBlock,
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)
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if sp_mode == "split_gather":
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self.append_or_create_method_replacement(
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description={"forward": get_bloom_sequence_parallel_forward_fn(self.shard_config)},
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policy=policy,
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target_key=BloomModel,
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)
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# enable jit fused operator
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if self.shard_config.enable_jit_fused:
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self.append_or_create_method_replacement(
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description={
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"forward": get_jit_fused_bloom_attention_forward(),
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"dropout_add": get_jit_fused_dropout_add_func(),
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},
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policy=policy,
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target_key=BloomAttention,
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)
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self.append_or_create_method_replacement(
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description={
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"forward": get_jit_fused_bloom_mlp_forward(),
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"dropout_add": get_jit_fused_dropout_add_func(),
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},
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policy=policy,
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target_key=BloomMLP,
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)
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self.append_or_create_method_replacement(
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description={
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"forward": get_jit_fused_bloom_gelu_forward(),
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"bloom_gelu_forward": get_jit_fused_gelu_forward_func(),
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},
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policy=policy,
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target_key=BloomGelu,
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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:
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == "BloomModel":
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module = self.model
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else:
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module = self.model.transformer
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layers_per_stage = stage_manager.distribute_layers(len(module.h))
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stage_index = stage_manager.get_stage_index(layers_per_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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return
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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__ == "BloomModel":
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module = self.model
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else:
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module = self.model.transformer
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stage_manager = self.pipeline_stage_manager
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held_layers = []
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layers_per_stage = stage_manager.distribute_layers(len(module.h))
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if stage_manager.is_first_stage():
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held_layers.append(module.word_embeddings)
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held_layers.append(module.word_embeddings_layernorm)
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start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
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held_layers.extend(module.h[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.ln_f)
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return held_layers
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class BloomModelPolicy(BloomPolicy):
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def module_policy(self):
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policy = super().module_policy()
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from transformers.models.bloom.modeling_bloom import BloomModel
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=BloomModel, new_forward=BloomPipelineForwards.bloom_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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"""
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get pipeline layers for current stage
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"""
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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 bloom model"""
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return []
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class BloomForCausalLMPolicy(BloomPolicy):
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def module_policy(self):
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from transformers.models.bloom.modeling_bloom import BloomForCausalLM
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policy = super().module_policy()
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# handle tensor parallelism
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if self.shard_config.enable_tensor_parallelism:
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="lm_head",
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target_module=col_nn.VocabParallelLMHead1D,
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kwargs=dict(
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gather_output=not self.shard_config.parallel_output,
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make_vocab_size_divisible_by=self.shard_config.make_vocab_size_divisible_by,
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),
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),
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policy=policy,
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target_key=BloomForCausalLM,
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)
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if self.shard_config.parallel_output:
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method_replacement = {"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)}
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self.append_or_create_method_replacement(
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description=method_replacement, policy=policy, target_key=BloomForCausalLM
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)
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else:
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="lm_head",
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target_module=col_nn.PaddingLMHead,
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kwargs=dict(make_vocab_size_divisible_by=self.shard_config.make_vocab_size_divisible_by),
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),
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policy=policy,
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target_key=BloomForCausalLM,
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)
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=BloomForCausalLM, new_forward=BloomPipelineForwards.bloom_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():
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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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bloom_model = self.model
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if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
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if id(bloom_model.transformer.word_embeddings.weight) == id(bloom_model.lm_head.weight):
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# tie weights
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return [
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{
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0: bloom_model.transformer.word_embeddings.weight,
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self.pipeline_stage_manager.num_stages - 1: bloom_model.lm_head.weight,
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}
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]
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return []
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class BloomForSequenceClassificationPolicy(BloomPolicy):
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def module_policy(self):
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from transformers.models.bloom.modeling_bloom import BloomForSequenceClassification
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policy = super().module_policy()
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# handle tensor parallelism
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if self.shard_config.enable_tensor_parallelism:
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="score", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)
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),
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policy=policy,
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target_key=BloomForSequenceClassification,
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)
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=BloomForSequenceClassification,
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new_forward=BloomPipelineForwards.bloom_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():
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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 bloom for sequence classification model"""
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return []
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class BloomForTokenClassificationPolicy(BloomPolicy):
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def module_policy(self):
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from transformers.models.bloom.modeling_bloom import BloomForTokenClassification
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policy = super().module_policy()
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# handle tensor parallelism
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if self.shard_config.enable_tensor_parallelism:
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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="classifier", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True)
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),
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SubModuleReplacementDescription(
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suffix="dropout",
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target_module=col_nn.DropoutForReplicatedInput,
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),
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],
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policy=policy,
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target_key=BloomForTokenClassification,
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)
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=BloomForTokenClassification,
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new_forward=BloomPipelineForwards.bloom_for_token_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():
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held_layers.append(self.model.dropout)
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held_layers.append(self.model.classifier)
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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 bloom for token classification model"""
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return []
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class BloomForQuestionAnsweringPolicy(BloomPolicy):
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# No head sharding as the output features is only 2
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def module_policy(self):
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from transformers.models.bloom.modeling_bloom import BloomForQuestionAnswering
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policy = super().module_policy()
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(
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model_cls=BloomForQuestionAnswering,
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new_forward=BloomPipelineForwards.bloom_for_question_answering_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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held_layers = super().get_held_layers()
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stage_manager = self.pipeline_stage_manager
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if stage_manager.is_last_stage():
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held_layers.append(self.model.qa_outputs)
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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 bloom for question answering model"""
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return []
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