mirror of https://github.com/hpcaitech/ColossalAI
876 lines
42 KiB
Python
876 lines
42 KiB
Python
import logging
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import random
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from functools import partial
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from types import MethodType
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from typing import Callable, Dict, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from torch import Tensor, nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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)
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from transformers.models.opt.modeling_opt import (
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OPTForCausalLM,
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OPTForQuestionAnswering,
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OPTForSequenceClassification,
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OPTModel,
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)
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from colossalai.pipeline.stage_manager import PipelineStageManager
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from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
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from .._utils import getattr_, setattr_
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = [
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'OPTPolicy', 'OPTModelPolicy', 'OPTForCausalLMPolicy', 'OPTForSequenceClassificationPolicy',
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'OPTForQuestionAnsweringPolicy'
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]
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class OPTPolicy(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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# reshape the embedding layer
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r"""
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Reshape the Embedding layer to make the embedding dimension divisible by world_size
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"""
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if self.shard_config.enable_tensor_parallelism:
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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):
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from transformers.models.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer
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policy = {}
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if self.shard_config.enable_tensor_parallelism:
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policy[OPTDecoder] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=VocabParallelEmbedding1D,
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)
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])
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policy[OPTDecoderLayer] = ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="fc1",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="fc2",
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target_module=Linear1D_Row,
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)
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])
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policy[OPTAttention] = ModulePolicyDescription(attribute_replacement={
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"embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"num_heads": self.model.config.num_attention_heads // 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="q_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="k_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="v_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="out_proj",
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target_module=Linear1D_Row,
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),
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])
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# optimization configuration
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if self.shard_config.enable_fused_normalization:
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="final_layer_norm", target_module=FusedLayerNorm, ignore_if_not_exist=True),
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policy=policy,
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target_key=OPTDecoder)
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(suffix="self_attn_layer_norm",
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target_module=FusedLayerNorm,
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ignore_if_not_exist=True),
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SubModuleReplacementDescription(suffix="final_layer_norm",
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target_module=FusedLayerNorm,
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ignore_if_not_exist=True)
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],
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policy=policy,
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target_key=OPTDecoderLayer)
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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__ == 'OPTModel':
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module = self.model.decoder
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else:
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module = self.model.model.decoder
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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(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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held_layers.append(module.embed_positions)
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held_layers.append(module.project_in)
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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.final_layer_norm)
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held_layers.append(module.project_out)
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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 self.pipeline_stage_manager:
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == 'OPTModel':
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module = self.model.decoder
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else:
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module = self.model.model.decoder
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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 = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
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self.append_or_create_method_replacement(description=method_replacement,
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policy=policy,
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target_key=model_cls)
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class OPTModelPolicy(OPTPolicy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTModel
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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(model_cls=OPTModel,
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new_forward=OPTPipelineForwards.opt_model_forward,
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policy=policy)
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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 OPTModel."""
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return []
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class OPTForCausalLMPolicy(OPTPolicy):
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTForCausalLM
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)),
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policy=policy,
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target_key=OPTForCausalLM)
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if self.pipeline_stage_manager:
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self.set_pipeline_forward(model_cls=OPTForCausalLM,
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new_forward=OPTPipelineForwards.opt_for_causal_lm_forward,
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policy=policy)
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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.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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opt_model = self.model
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if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
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num_stages = self.pipeline_stage_manager.num_stages
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if id(opt_model.model.decoder.embed_tokens.weight) == id(opt_model.lm_head.weight):
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return [{0: opt_model.model.decoder.embed_tokens.weight, num_stages - 1: opt_model.lm_head.weight}]
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def postprocess(self):
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if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None:
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binding_map = {
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'model.decoder.embed_tokens': 'lm_head',
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}
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for k, v in binding_map.items():
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src_mod = getattr_(self.model, k)
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dst_mod = getattr_(self.model, v)
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dst_mod.weight = src_mod.weight
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return self.model
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class OPTForSequenceClassificationPolicy(OPTPolicy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTForSequenceClassification
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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(model_cls=OPTForSequenceClassification,
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new_forward=OPTPipelineForwards.opt_for_sequence_classification_forward,
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policy=policy)
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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.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 OPTForSequenceClassification"
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return []
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class OPTForQuestionAnsweringPolicy(OPTPolicy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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from transformers.models.opt.modeling_opt import OPTForQuestionAnswering
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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(model_cls=OPTForQuestionAnswering,
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new_forward=OPTPipelineForwards.opt_for_question_answering_forward,
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policy=policy)
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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.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 OPTForSequenceClassification"
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return []
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class OPTPipelineForwards:
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'''
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This class serves as a micro library for forward function substitution of OPT models
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under pipeline setting.
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'''
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@staticmethod
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def _prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, device, past_key_values_length):
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# create causal mask
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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from transformers.models.opt.modeling_opt import _make_causal_mask
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combined_attention_mask = None
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if input_shape[-1] > 1:
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combined_attention_mask = _make_causal_mask(
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input_shape,
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_dtype,
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device,
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past_key_values_length=past_key_values_length,
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)
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if attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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expanded_attn_mask = OPTPipelineForwards._expand_mask(attention_mask, _dtype,
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tgt_len=input_shape[-1]).to(device)
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combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask +
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combined_attention_mask)
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return combined_attention_mask
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@staticmethod
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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@staticmethod
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def opt_model_forward(
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self: OPTModel,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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stage_manager: Optional[PipelineStageManager] = None,
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hidden_states: Optional[torch.FloatTensor] = None,
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stage_index: Optional[List[int]] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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'''
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This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward
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'''
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (output_hidden_states
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if output_hidden_states is not None else self.config.output_hidden_states)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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decoder = self.decoder
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if stage_manager.is_first_stage():
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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else:
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raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
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batch_size, seq_length = input_shape
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if inputs_embeds is None:
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inputs_embeds = decoder.embed_tokens(input_ids)
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if decoder.project_in is not None:
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inputs_embeds = decoder.project_in(inputs_embeds)
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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_dtype = inputs_embeds.dtype
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else:
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if hidden_states is None:
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raise ValueError("hidden_states shouln't be None for intermediate stages.")
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input_shape = hidden_states.size()[:-1]
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batch_size, seq_length = input_shape[0], input_shape[1]
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device = hidden_states.device
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_dtype = hidden_states.dtype
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past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
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# required mask seq length can be calculated via length of past
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mask_seq_length = past_key_values_length + seq_length
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# embed positions
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if attention_mask is None:
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attention_mask = torch.ones(batch_size, mask_seq_length, device=device)
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elif attention_mask.shape[1] != mask_seq_length:
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raise ValueError(
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f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
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f"{mask_seq_length} (sum of the lengths of current and past inputs)")
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causal_attention_mask = OPTPipelineForwards._prepare_decoder_attention_mask(attention_mask, input_shape, _dtype,
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device, past_key_values_length)
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if stage_manager.is_first_stage():
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pos_embeds = decoder.embed_positions(attention_mask, past_key_values_length)
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hidden_states = inputs_embeds + pos_embeds
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if decoder.gradient_checkpointing and decoder.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
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use_cache = False
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# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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if past_key_values:
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logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
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past_key_values = None
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if output_attentions:
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logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
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output_attentions = False
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if output_hidden_states:
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logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
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output_hidden_states = False
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if use_cache:
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logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
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use_cache = False
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = () if use_cache else None
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# check if head_mask has a correct number of layers specified if desired
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for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
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if attn_mask is not None:
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if attn_mask.size()[0] != (len(decoder.layers)):
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raise ValueError(
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f"The `{mask_name}` should be specified for {len(decoder.layers)} layers, but it is for"
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f" {head_mask.size()[0]}.")
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start_idx, end_idx = stage_index[0], stage_index[1]
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torch.cuda.set_device(device)
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for idx in range(start_idx, end_idx):
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# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
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decoder_layer = decoder.layers[idx]
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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dropout_probability = random.uniform(0, 1)
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if decoder.training and (dropout_probability < decoder.layerdrop):
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continue
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past_key_value = past_key_values[idx] if past_key_values is not None else None
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if decoder.gradient_checkpointing and decoder.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, output_attentions, None)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(decoder_layer),
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hidden_states,
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causal_attention_mask,
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head_mask[idx] if head_mask is not None else None,
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None,
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)
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else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_attention_mask,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
if stage_manager.is_last_stage():
|
|
if decoder.final_layer_norm is not None:
|
|
hidden_states = decoder.final_layer_norm(hidden_states)
|
|
if decoder.project_out is not None:
|
|
hidden_states = decoder.project_out(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
|
|
if stage_manager.is_last_stage():
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
else:
|
|
return {'hidden_states': hidden_states}
|
|
|
|
@staticmethod
|
|
def opt_for_causal_lm_forward(
|
|
self: OPTForCausalLM,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
|
provide it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
|
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
|
|
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
|
|
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
|
|
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
|
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
|
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, OPTForCausalLM
|
|
|
|
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
|
|
|
>>> prompt = "Hey, are you consciours? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
|
|
```"""
|
|
from transformers.modeling_outputs import CausalLMOutputWithPast
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (output_hidden_states
|
|
if output_hidden_states is not None else self.config.output_hidden_states)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs = OPTPipelineForwards.opt_model_forward(
|
|
self.model,
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index,
|
|
)
|
|
if stage_manager.is_last_stage():
|
|
logits = self.lm_head(outputs[0]).contiguous()
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get('hidden_states')
|
|
return {'hidden_states': hidden_states}
|
|
|
|
@staticmethod
|
|
def opt_for_sequence_classification_forward(
|
|
self: OPTForSequenceClassification,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
|
|
from transformers.utils import logging
|
|
logger = logging.get_logger(__name__)
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index)
|
|
|
|
if stage_manager.is_last_stage():
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
batch_size = input_ids.shape[0] if input_ids is not None else hidden_states.shape[0]
|
|
|
|
if self.config.pad_token_id is None:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
|
|
else:
|
|
sequence_lengths = -1
|
|
logger.warning(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`")
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
else:
|
|
hidden_states = transformer_outputs.get('hidden_states')
|
|
return {'hidden_states': hidden_states}
|
|
|
|
@staticmethod
|
|
def opt_for_question_answering_forward(
|
|
self: OPTForQuestionAnswering,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
hidden_states: Optional[torch.FloatTensor] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, OPTForQuestionAnswering
|
|
>>> import torch
|
|
|
|
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
|
|
|
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
|
|
>>> # so the head will be randomly initialized, hence the predictions will be random
|
|
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
|
|
|
|
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
|
|
>>> inputs = tokenizer(question, text, return_tensors="pt")
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs)
|
|
|
|
>>> answer_start_index = outputs.start_logits.argmax()
|
|
>>> answer_end_index = outputs.end_logits.argmax()
|
|
|
|
>>> answer_offset = len(tokenizer(question)[0])
|
|
|
|
>>> predict_answer_tokens = inputs.input_ids[
|
|
... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1
|
|
... ]
|
|
>>> predicted = tokenizer.decode(predict_answer_tokens)
|
|
>>> predicted
|
|
' a nice puppet'
|
|
```"""
|
|
from transformers.modeling_outputs import QuestionAnsweringModelOutput
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
stage_manager=stage_manager,
|
|
hidden_states=hidden_states,
|
|
stage_index=stage_index)
|
|
if stage_manager.is_last_stage():
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
logits = self.qa_outputs(hidden_states)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + transformer_outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
else:
|
|
hidden_states = transformer_outputs.get('hidden_states')
|
|
return {'hidden_states': hidden_states}
|