mirror of https://github.com/hpcaitech/ColossalAI
1328 lines
58 KiB
Python
1328 lines
58 KiB
Python
from typing import Dict, List, Optional, Tuple, Union
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import torch
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.modeling_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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from transformers.models.gpt2.modeling_gpt2 import (
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GPT2DoubleHeadsModel,
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GPT2DoubleHeadsModelOutput,
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GPT2ForQuestionAnswering,
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GPT2ForSequenceClassification,
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GPT2ForTokenClassification,
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GPT2LMHeadModel,
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GPT2Model,
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)
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from transformers.utils import logging
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from colossalai.pipeline.stage_manager import PipelineStageManager
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from colossalai.shardformer.layer import ColoAttention
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from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
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from colossalai.shardformer.shard import ShardConfig
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from ..layer import cross_entropy_1d
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logger = logging.get_logger(__name__)
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def _get_attention_mask(
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self: GPT2Model,
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shard_config: ShardConfig,
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hidden_states: torch.Tensor,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]],
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attention_mask: Optional[torch.FloatTensor],
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encoder_hidden_states: Optional[torch.Tensor],
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encoder_attention_mask: Optional[torch.FloatTensor],
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) -> Tuple[Optional[Union[torch.Tensor, dict]], Optional[Union[torch.Tensor, dict]]]:
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batch_size, seq_len = hidden_states.shape[:2]
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# If a 2D or 3D attention mask is provided for the cross-attention
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# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
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if self.config.add_cross_attention and encoder_hidden_states is not None:
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encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
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if shard_config.enable_flash_attention:
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encoder_attention_mask = ColoAttention.prepare_attn_kwargs(
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(encoder_batch_size, 1, seq_len, encoder_sequence_length),
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dtype=hidden_states.dtype,
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dtype2=encoder_hidden_states.dtype,
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q_padding_mask=attention_mask,
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kv_padding_mask=encoder_attention_mask,
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)
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else:
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encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
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if encoder_attention_mask is None:
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encoder_attention_mask = torch.ones(encoder_hidden_shape, device=encoder_hidden_states.device)
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encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
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else:
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if shard_config.enable_flash_attention:
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encoder_attention_mask = {"attention_mask": None}
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else:
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encoder_attention_mask = None
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# GPT2Attention mask.
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past_key_values_length = 0
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if past_key_values is not None and past_key_values[0] is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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if shard_config.enable_flash_attention:
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if attention_mask is not None:
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attention_mask = attention_mask.view(batch_size, -1)
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attention_mask = ColoAttention.prepare_attn_kwargs(
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(batch_size, 1, seq_len, seq_len + past_key_values_length),
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hidden_states.dtype,
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hidden_states.device,
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attention_mask,
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is_causal=True,
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)
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elif attention_mask is not None:
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if batch_size <= 0:
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raise ValueError("batch_size has to be defined and > 0")
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attention_mask = attention_mask.view(batch_size, -1)
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, to_seq_length]
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# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
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# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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attention_mask = attention_mask[:, None, None, :]
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and the dtype's smallest value for masked positions.
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# Since we are adding it to the raw scores before the softmax, this is
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# effectively the same as removing these entirely.
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attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
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attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
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return attention_mask, encoder_attention_mask
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class GPT2PipelineForwards:
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"""
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This class serves as a micro library for forward function substitution of GPT2 models
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under pipeline setting.
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"""
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@staticmethod
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def gpt2_model_forward(
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self: GPT2Model,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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encoder_attention_mask: 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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shard_config: ShardConfig = None,
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) -> Union[Dict, Tuple, BaseModelOutputWithPastAndCrossAttentions]:
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# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2Model.forward.
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# Please refer to original code of transformers for more details.
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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logger = logging.get_logger(__name__)
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# Preprocess passed in arguments
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# TODO(baizhou): 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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if stage_manager.is_first_stage():
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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 input_ids and 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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input_ids.shape[0]
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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inputs_embeds.shape[0]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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if token_type_ids is not None:
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token_type_ids = token_type_ids.view(-1, input_shape[-1])
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else:
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if hidden_states is None:
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raise ValueError("hidden_states shouldn't be None for stages other than the first stage.")
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input_shape = hidden_states.size()[:-1]
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device = hidden_states.device
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hidden_states = hidden_states.view((-1,) + hidden_states.shape[-2:])
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hidden_states.shape[0]
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# Prepare head mask if needed
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# 1.0 in head_mask indicate we keep the head
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# attention_probs has shape bsz x n_heads x N x N
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# head_mask has shape n_layer x batch x n_heads x N x N
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head_mask = self.get_head_mask(head_mask, self.config.n_layer)
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if stage_manager.is_first_stage():
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if position_ids is None:
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position_ids = torch.arange(0, input_shape[-1], dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0)
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if inputs_embeds is None:
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inputs_embeds = self.wte(input_ids)
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position_embeds = self.wpe(position_ids)
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hidden_states = inputs_embeds + position_embeds
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if token_type_ids is not None:
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token_type_embeds = self.wte(token_type_ids)
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hidden_states = hidden_states + token_type_embeds
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hidden_states = self.drop(hidden_states)
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output_shape = input_shape + (hidden_states.size(-1),)
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attention_mask, encoder_attention_mask = _get_attention_mask(
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self,
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shard_config,
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hidden_states,
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past_key_values,
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attention_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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if self.gradient_checkpointing and self.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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)
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use_cache = False
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presents = () if use_cache else None
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all_self_attentions = () if output_attentions else None
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all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
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all_hidden_states = () if output_hidden_states else None
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# split the input tensor along sequence dimension
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# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
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if shard_config and shard_config.enable_sequence_parallelism:
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if shard_config.sequence_parallelism_mode == "split_gather":
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hidden_states = split_forward_gather_backward(
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hidden_states,
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dim=1,
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process_group=shard_config.tensor_parallel_process_group,
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)
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# Going through held blocks.
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start_idx, end_idx = stage_index[0], stage_index[1]
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for i in range(start_idx, end_idx):
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block = self.h[i]
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torch.cuda.set_device(hidden_states.device)
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# Ensure that attention_mask is always on the same device as hidden_states
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if torch.is_tensor(attention_mask):
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attention_mask = attention_mask.to(hidden_states.device)
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if isinstance(head_mask, torch.Tensor):
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head_mask = head_mask.to(hidden_states.device)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if self.gradient_checkpointing and self.training:
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outputs = self._gradient_checkpointing_func(
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block.__call__,
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hidden_states,
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None,
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attention_mask,
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head_mask[i],
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encoder_hidden_states,
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encoder_attention_mask,
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use_cache,
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output_attentions,
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)
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else:
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outputs = block(
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hidden_states,
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layer_past=None,
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attention_mask=attention_mask,
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head_mask=head_mask[i],
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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)
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hidden_states = outputs[0]
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if use_cache is True:
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presents = presents + (outputs[1],)
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if output_attentions:
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all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
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if self.config.add_cross_attention:
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all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
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# When sequence parallelism done, gather the output tensor in forward and split it in backward
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if shard_config and shard_config.enable_sequence_parallelism:
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if shard_config.sequence_parallelism_mode == "split_gather":
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hidden_states = gather_forward_split_backward(
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hidden_states,
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dim=1,
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process_group=shard_config.tensor_parallel_process_group,
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)
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if stage_manager.is_last_stage():
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hidden_states = self.ln_f(hidden_states)
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hidden_states = hidden_states.view(output_shape)
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# Add last hidden state
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if stage_manager.is_last_stage():
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if not return_dict:
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return tuple(
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v
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for v in [
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hidden_states,
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presents,
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all_hidden_states,
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all_self_attentions,
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all_cross_attentions,
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]
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if v is not None
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)
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return BaseModelOutputWithPastAndCrossAttentions(
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last_hidden_state=hidden_states,
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past_key_values=presents,
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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cross_attentions=all_cross_attentions,
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)
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else:
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# always return dict for intermediate stage
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return {"hidden_states": hidden_states}
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@staticmethod
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def gpt2_lmhead_model_forward(
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self: GPT2LMHeadModel,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = 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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shard_config: ShardConfig = None,
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) -> Union[Dict, Tuple, CausalLMOutputWithCrossAttentions]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
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`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
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are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
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This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel.forward.
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Please refer to original code of transformers for more details.
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = GPT2PipelineForwards.gpt2_model_forward(
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self.transformer,
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input_ids,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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stage_manager=stage_manager,
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hidden_states=hidden_states,
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stage_index=stage_index,
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shard_config=shard_config,
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)
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# If not at the last stage, return hidden_states as in GPT2Model
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if not stage_manager.is_last_stage():
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return {"hidden_states": outputs["hidden_states"]}
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hidden_states = outputs[0]
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lm_logits = self.lm_head(hidden_states)
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loss = None
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if labels is not None:
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# move labels to correct device to enable model parallelism
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labels = labels.to(lm_logits.device)
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# Shift so that tokens < n predict n
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shift_logits = lm_logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, shift_logits.size(-1))
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shift_labels = shift_labels.view(-1)
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if shard_config.enable_tensor_parallelism and shard_config.parallel_output:
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loss = cross_entropy_1d(
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shift_logits,
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shift_labels,
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process_group=shard_config.tensor_parallel_process_group,
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vocab_size=self.lm_head.out_features,
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)
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else:
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loss = loss_fct(shift_logits, shift_labels)
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if not return_dict:
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output = (lm_logits,) + outputs[1:]
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return ((loss,) + output) if loss is not None else output
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return CausalLMOutputWithCrossAttentions(
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loss=loss,
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logits=lm_logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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cross_attentions=outputs.cross_attentions,
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)
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@staticmethod
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def gpt2_double_heads_model_forward(
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self: GPT2DoubleHeadsModel,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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mc_token_ids: Optional[torch.LongTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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mc_labels: Optional[torch.LongTensor] = 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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shard_config: ShardConfig = None,
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) -> Union[Dict, Tuple, GPT2DoubleHeadsModelOutput]:
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r"""
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mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
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Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
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1]`.
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids`. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to
|
|
`-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size - 1]`
|
|
mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
|
|
where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)
|
|
|
|
This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel.forward.
|
|
Please refer to original code of transformers for more details.
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(
|
|
self.transformer,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
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,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {"hidden_states": outputs["hidden_states"]}
|
|
|
|
hidden_states = outputs[0]
|
|
lm_logits = self.lm_head(hidden_states)
|
|
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
|
|
|
|
mc_loss = None
|
|
if mc_labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
|
|
lm_loss = None
|
|
if labels is not None:
|
|
labels = labels.to(lm_logits.device)
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
loss_fct = CrossEntropyLoss()
|
|
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (lm_logits, mc_logits) + outputs[1:]
|
|
if mc_loss is not None:
|
|
output = (mc_loss,) + output
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
return GPT2DoubleHeadsModelOutput(
|
|
loss=lm_loss,
|
|
mc_loss=mc_loss,
|
|
logits=lm_logits,
|
|
mc_logits=mc_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def gpt2_for_question_answering_forward(
|
|
self: GPT2ForQuestionAnswering,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = 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,
|
|
shard_config: ShardConfig = None,
|
|
) -> Union[Dict, 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.
|
|
|
|
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForQuestionAnswering.forward.
|
|
# Please refer to original code of transformers for more details.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(
|
|
self.transformer,
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
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,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {"hidden_states": outputs["hidden_states"]}
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
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).to(start_logits.device)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
|
# 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) + 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=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def gpt2_for_token_classification_forward(
|
|
self: GPT2ForTokenClassification,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[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,
|
|
shard_config: ShardConfig = None,
|
|
) -> Union[Dict, Tuple, TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *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).
|
|
|
|
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification.forward.
|
|
# Please refer to original code of transformers for more details.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(
|
|
self.transformer,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
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,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {"hidden_states": outputs["hidden_states"]}
|
|
|
|
hidden_states = outputs[0]
|
|
hidden_states = self.dropout(hidden_states)
|
|
logits = self.classifier(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
labels = labels.to(logits.device)
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
@staticmethod
|
|
def gpt2_for_sequence_classification_forward(
|
|
self: GPT2ForSequenceClassification,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[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,
|
|
shard_config: ShardConfig = None,
|
|
) -> Union[Dict, 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).
|
|
|
|
# This function is modified on the basis of transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification.forward.
|
|
# Please refer to original code of transformers for more details.
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
if input_ids is not None:
|
|
batch_size, _ = input_ids.shape[:2]
|
|
else:
|
|
batch_size, _ = hidden_states.shape[:2]
|
|
assert (
|
|
self.config.pad_token_id is not None or batch_size == 1
|
|
), "Cannot handle batch sizes > 1 if no padding token is defined."
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = GPT2PipelineForwards.gpt2_model_forward(
|
|
self.transformer,
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
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,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
# If not at the last stage, return hidden_states as in GPT2Model
|
|
if not stage_manager.is_last_stage():
|
|
return {"hidden_states": outputs["hidden_states"]}
|
|
|
|
hidden_states = outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
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_once(
|
|
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,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
def get_gpt2_flash_attention_forward():
|
|
from transformers.models.gpt2.modeling_gpt2 import GPT2Attention
|
|
|
|
def forward(
|
|
self: GPT2Attention,
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
|
layer_past: Optional[Tuple[torch.Tensor]] = None,
|
|
attention_mask: Optional[dict] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[dict] = None,
|
|
use_cache: Optional[bool] = False,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
|
|
assert head_mask is None, "FlashAttention does not support head_mask"
|
|
if encoder_hidden_states is not None:
|
|
if not hasattr(self, "q_attn"):
|
|
raise ValueError(
|
|
"If class is used as cross attention, the weights `q_attn` have to be defined. "
|
|
"Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
|
|
)
|
|
|
|
query = self.q_attn(hidden_states)
|
|
key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
|
|
attention_mask = encoder_attention_mask
|
|
else:
|
|
query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
|
|
query = self._split_heads(query, self.num_heads, self.head_dim)
|
|
key = self._split_heads(key, self.num_heads, self.head_dim)
|
|
value = self._split_heads(value, self.num_heads, self.head_dim)
|
|
|
|
if layer_past is not None:
|
|
past_key, past_value = layer_past
|
|
key = torch.cat((past_key, key), dim=1)
|
|
value = torch.cat((past_value, value), dim=1)
|
|
|
|
if use_cache is True:
|
|
present = (key, value)
|
|
else:
|
|
present = None
|
|
|
|
scale = 1.0
|
|
if self.scale_attn_weights:
|
|
scale /= value.size(-1) ** 0.5
|
|
if self.scale_attn_by_inverse_layer_idx:
|
|
scale /= float(self.layer_idx + 1)
|
|
dropout_p = self.attn_dropout.p if self.training else 0.0
|
|
attn_output = ColoAttention.attention(query, key, value, **attention_mask, dropout_p=dropout_p, scale=scale)
|
|
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
|
|
attn_output = self.c_proj(attn_output)
|
|
attn_output = self.resid_dropout(attn_output)
|
|
outputs = (attn_output, present, None)
|
|
|
|
return outputs
|
|
|
|
return forward
|
|
|
|
|
|
def get_gpt_model_forward_for_flash_attn(shard_config: ShardConfig):
|
|
def forward(
|
|
self: GPT2Model,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
|
|
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
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
input_ids.shape[0]
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
inputs_embeds.shape[0]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if token_type_ids is not None:
|
|
token_type_ids = token_type_ids.view(-1, input_shape[-1])
|
|
if position_ids is not None:
|
|
position_ids = position_ids.view(-1, input_shape[-1])
|
|
|
|
if past_key_values is None:
|
|
past_length = 0
|
|
past_key_values = tuple([None] * len(self.h))
|
|
else:
|
|
past_length = past_key_values[0][0].size(-2)
|
|
if position_ids is None:
|
|
position_ids = torch.arange(
|
|
past_length,
|
|
input_shape[-1] + past_length,
|
|
dtype=torch.long,
|
|
device=device,
|
|
)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# head_mask has shape n_layer x batch x n_heads x N x N
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.wte(input_ids)
|
|
position_embeds = self.wpe(position_ids)
|
|
hidden_states = inputs_embeds + position_embeds
|
|
|
|
if token_type_ids is not None:
|
|
token_type_embeds = self.wte(token_type_ids)
|
|
hidden_states = hidden_states + token_type_embeds
|
|
|
|
hidden_states = self.drop(hidden_states)
|
|
|
|
output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),)
|
|
|
|
attention_mask, encoder_attention_mask = _get_attention_mask(
|
|
self,
|
|
shard_config,
|
|
hidden_states,
|
|
past_key_values,
|
|
attention_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
presents = () if use_cache else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
|
# Model parallel
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(hidden_states.device)
|
|
# Ensure layer_past is on same device as hidden_states (might not be correct)
|
|
if layer_past is not None:
|
|
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
|
|
# Ensure that attention_mask is always on the same device as hidden_states
|
|
if torch.is_tensor(attention_mask):
|
|
attention_mask = attention_mask.to(hidden_states.device)
|
|
if isinstance(head_mask, torch.Tensor):
|
|
head_mask = head_mask.to(hidden_states.device)
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, use_cache, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
hidden_states,
|
|
None,
|
|
attention_mask,
|
|
head_mask[i],
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
else:
|
|
outputs = block(
|
|
hidden_states,
|
|
layer_past=layer_past,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask[i],
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if use_cache is True:
|
|
presents = presents + (outputs[1],)
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
|
|
|
|
# Model Parallel: If it's the last layer for that device, put things on the next device
|
|
if self.model_parallel:
|
|
for k, v in self.device_map.items():
|
|
if i == v[-1] and "cuda:" + str(k) != self.last_device:
|
|
hidden_states = hidden_states.to("cuda:" + str(k + 1))
|
|
|
|
hidden_states = self.ln_f(hidden_states)
|
|
|
|
hidden_states = hidden_states.view(output_shape)
|
|
# Add last hidden state
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
presents,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=presents,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
return forward
|
|
|
|
|
|
def gpt2_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
|
|
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
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
input_ids.shape[0]
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
inputs_embeds.shape[0]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if token_type_ids is not None:
|
|
token_type_ids = token_type_ids.view(-1, input_shape[-1])
|
|
if position_ids is not None:
|
|
position_ids = position_ids.view(-1, input_shape[-1])
|
|
|
|
if past_key_values is None:
|
|
past_length = 0
|
|
past_key_values = tuple([None] * len(self.h))
|
|
else:
|
|
past_length = past_key_values[0][0].size(-2)
|
|
if position_ids is None:
|
|
position_ids = torch.arange(
|
|
past_length,
|
|
input_shape[-1] + past_length,
|
|
dtype=torch.long,
|
|
device=device,
|
|
)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# head_mask has shape n_layer x batch x n_heads x N x N
|
|
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.wte(input_ids)
|
|
position_embeds = self.wpe(position_ids)
|
|
hidden_states = inputs_embeds + position_embeds
|
|
|
|
if token_type_ids is not None:
|
|
token_type_embeds = self.wte(token_type_ids)
|
|
hidden_states = hidden_states + token_type_embeds
|
|
|
|
hidden_states = self.drop(hidden_states)
|
|
|
|
output_shape = input_shape + (hidden_states.size(-1),)
|
|
attention_mask, encoder_attention_mask = _get_attention_mask(
|
|
self,
|
|
shard_config,
|
|
hidden_states,
|
|
past_key_values,
|
|
attention_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger = logging.get_logger(__name__)
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
presents = () if use_cache else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
# split the input tensor along sequence dimension
|
|
# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
|
|
hidden_states = split_forward_gather_backward(
|
|
hidden_states,
|
|
dim=1,
|
|
process_group=shard_config.sequence_parallel_process_group,
|
|
)
|
|
|
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
|
# Model parallel
|
|
if self.model_parallel:
|
|
torch.cuda.set_device(hidden_states.device)
|
|
# Ensure layer_past is on same device as hidden_states (might not be correct)
|
|
if layer_past is not None:
|
|
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
|
|
# Ensure that attention_mask is always on the same device as hidden_states
|
|
if torch.is_tensor(attention_mask):
|
|
attention_mask = attention_mask.to(hidden_states.device)
|
|
if isinstance(head_mask, torch.Tensor):
|
|
head_mask = head_mask.to(hidden_states.device)
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, use_cache, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
hidden_states,
|
|
None,
|
|
attention_mask,
|
|
head_mask[i],
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
else:
|
|
outputs = block(
|
|
hidden_states,
|
|
layer_past=layer_past,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask[i],
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if use_cache is True:
|
|
presents = presents + (outputs[1],)
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
|
|
|
|
# Model Parallel: If it's the last layer for that device, put things on the next device
|
|
if self.model_parallel:
|
|
for k, v in self.device_map.items():
|
|
if i == v[-1] and "cuda:" + str(k) != self.last_device:
|
|
hidden_states = hidden_states.to("cuda:" + str(k + 1))
|
|
|
|
# When sequence parallelism done, gather the output tensor in forward and split it in backward
|
|
hidden_states = gather_forward_split_backward(
|
|
hidden_states,
|
|
dim=1,
|
|
process_group=shard_config.sequence_parallel_process_group,
|
|
)
|
|
|
|
hidden_states = self.ln_f(hidden_states)
|
|
hidden_states = hidden_states.view(output_shape)
|
|
# Add last hidden state
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
presents,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=presents,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
return forward
|
|
|
|
|
|
def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
|
|
from transformers import GPT2LMHeadModel
|
|
|
|
def forward(
|
|
self: GPT2LMHeadModel,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: 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,
|
|
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
lm_logits = self.lm_head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(lm_logits.device)
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
shift_logits = shift_logits.view(-1, shift_logits.size(-1))
|
|
shift_labels = shift_labels.view(-1)
|
|
loss = cross_entropy_1d(
|
|
shift_logits,
|
|
shift_labels,
|
|
process_group=shard_config.tensor_parallel_process_group,
|
|
vocab_size=self.lm_head.out_features,
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=lm_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
cross_attentions=transformer_outputs.cross_attentions,
|
|
)
|
|
|
|
return forward
|
|
|
|
|
|
def get_jit_fused_gpt2_mlp_forward():
|
|
from transformers.models.gpt2.modeling_gpt2 import GPT2MLP
|
|
|
|
from colossalai.kernel.jit.bias_gelu import GeLUFunction as JitGeLUFunction
|
|
|
|
def forward(self: GPT2MLP, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
|
|
hidden_states, bias = self.c_fc(hidden_states)
|
|
hidden_states = JitGeLUFunction.apply(hidden_states, bias)
|
|
hidden_states = self.c_proj(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
return hidden_states
|
|
|
|
return forward
|