mirror of https://github.com/hpcaitech/ColossalAI
258 lines
10 KiB
Python
258 lines
10 KiB
Python
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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import colossalai.shardformer.layer as col_nn
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from colossalai.shardformer.modeling.chatglm2 import ChatGLMPipelineForwards
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from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
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from ..modeling.chatglm2 import (
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get_chatglm_sequence_parallel_forward_fn,
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get_flash_core_attention_forward,
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get_jit_fused_glm_block_forward,
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)
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from ..modeling.jit import get_jit_fused_dropout_add_func
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ["ChatGLMPolicy", "ChatGLMModelPolicy", "ChatGLMForConditionalGenerationPolicy"]
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class ChatGLMPolicy(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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# Resize embedding
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if self.shard_config.enable_tensor_parallelism:
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vocab_size = self.model.config.padded_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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if self.pipeline_stage_manager is not None:
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# the batch_size_dim is bounded to Model
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bsz_dim = 1
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setattr(self.model, "batch_size_dim", bsz_dim)
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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 colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMModel, CoreAttention, GLMBlock
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policy = {}
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if self.shard_config.enable_fused_normalization:
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if self.model.config.rmsnorm:
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norm_cls = col_nn.FusedRMSNorm
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else:
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norm_cls = col_nn.FusedLayerNorm
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else:
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if self.model.config.rmsnorm:
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norm_cls = col_nn.RMSNorm
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else:
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norm_cls = col_nn.LayerNorm
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use_sequence_parallel = self.shard_config.enable_sequence_parallelism
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overlap = self.shard_config.enable_sequence_overlap
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if self.shard_config.enable_tensor_parallelism:
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policy[ChatGLMModel] = ModulePolicyDescription(
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attribute_replacement={},
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="embedding.word_embeddings",
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target_module=col_nn.VocabParallelEmbedding1D,
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)
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],
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)
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policy[GLMBlock] = ModulePolicyDescription(
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attribute_replacement={
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"self_attention.num_attention_heads_per_partition": self.model.config.num_attention_heads
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// self.shard_config.tensor_parallel_size,
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"self_attention.projection_size": (
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self.model.config.kv_channels * self.model.config.num_attention_heads
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)
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// self.shard_config.tensor_parallel_size,
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"self_attention.qkv_hidden_size": (
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self.model.config.kv_channels * self.model.config.num_attention_heads * 3
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)
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// self.shard_config.tensor_parallel_size,
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"self_attention.core_attention.num_attention_heads_per_partition": self.model.config.num_attention_heads
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// self.shard_config.tensor_parallel_size,
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"self_attention.core_attention.hidden_size_per_partition": self.model.config.kv_channels
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* self.model.config.num_attention_heads
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// self.shard_config.tensor_parallel_size,
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},
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param_replacement=[],
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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": use_sequence_parallel, "seq_parallel_dim": 0, "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": use_sequence_parallel, "seq_parallel_dim": 0},
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),
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SubModuleReplacementDescription(
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suffix="self_attention.core_attention.attention_dropout",
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target_module=col_nn.DropoutForParallelInput,
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),
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],
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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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kwargs={"sp_partial_derived": use_sequence_parallel},
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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": use_sequence_parallel},
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),
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],
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policy=policy,
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target_key=GLMBlock,
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)
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if self.model.config.post_layer_norm:
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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="encoder.final_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=ChatGLMModel,
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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_flash_core_attention_forward(),
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},
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policy=policy,
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target_key=CoreAttention,
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)
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# use sequence parallel
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if use_sequence_parallel:
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self.append_or_create_method_replacement(
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description={"forward": get_chatglm_sequence_parallel_forward_fn(self.shard_config)},
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policy=policy,
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target_key=ChatGLMModel,
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)
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# use 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_glm_block_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=GLMBlock,
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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 get_held_layers(self) -> List[nn.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__ == "ChatGLMModel":
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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 = self.distribute_layers(module.num_layers, stage_manager.num_stages)
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if stage_manager.is_first_stage():
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held_layers.append(module.embedding)
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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.encoder.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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if module.encoder.post_layer_norm:
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held_layers.append(module.encoder.final_layernorm)
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# rotary_pos_emb is needed for all stages
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held_layers.append(module.rotary_pos_emb)
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return held_layers
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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 not self.pipeline_stage_manager:
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raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == "ChatGLMModel":
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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 = Policy.distribute_layers(module.num_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(description=method_replacement, policy=policy, target_key=model_cls)
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class ChatGLMModelPolicy(ChatGLMPolicy):
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def module_policy(self):
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pass
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policy = super().module_policy()
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(
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model_cls=ChatGLMModel, new_forward=ChatGLMPipelineForwards.chatglm_model_forward, policy=policy
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)
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return policy
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def get_held_layers(self) -> List[nn.Module]:
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return super().get_held_layers()
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in ChatGLMModel."""
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return []
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class ChatGLMForConditionalGenerationPolicy(ChatGLMModelPolicy):
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def module_policy(self):
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policy = super().module_policy()
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if self.pipeline_stage_manager is not None:
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self.set_pipeline_forward(
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model_cls=ChatGLMForConditionalGeneration,
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new_forward=ChatGLMPipelineForwards.chatglm_for_conditional_generation_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[nn.Module]:
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held_layers = super().get_held_layers()
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if self.pipeline_stage_manager.is_last_stage():
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held_layers.append(self.model.transformer.output_layer)
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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 ChatGLMForConditionalGenerationModel."""
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return []
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