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1193 lines
56 KiB
1193 lines
56 KiB
import warnings
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from typing import 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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BaseModelOutputWithPoolingAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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NextSentencePredictorOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from transformers.models.bert.modeling_bert import (
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BertForMaskedLM,
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BertForMultipleChoice,
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BertForNextSentencePrediction,
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BertForPreTraining,
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BertForPreTrainingOutput,
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BertForQuestionAnswering,
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BertForSequenceClassification,
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BertForTokenClassification,
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BertLMHeadModel,
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BertModel,
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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 import ShardConfig
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from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
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class BertPipelineForwards:
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"""
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This class serves as a micro library for forward function substitution of Bert models
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under pipeline setting.
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"""
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@staticmethod
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def bert_model_forward(
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self: BertModel,
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input_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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token_type_ids: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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encoder_attention_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[List[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, # this is from the previous stage
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stage_index: Optional[List[int]] = None,
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shard_config: ShardConfig = None,
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):
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# TODO(jianghai): add explaination of the output here.
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r"""
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encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
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the model is configured as a decoder.
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encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
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the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
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Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
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If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
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don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
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`decoder_input_ids` of shape `(batch_size, sequence_length)`.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
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`past_key_values`).
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"""
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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 = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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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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if self.config.is_decoder:
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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else:
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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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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 input_ids or inputs_embeds")
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batch_size, seq_length = input_shape
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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 None:
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if hasattr(self.embeddings, "token_type_ids"):
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buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
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buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
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token_type_ids = buffered_token_type_ids_expanded
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else:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
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else:
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input_shape = hidden_states.size()[:-1]
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batch_size, seq_length = input_shape
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device = hidden_states.device
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# TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
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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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# past_key_values_length
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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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if attention_mask is None:
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attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
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# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
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# ourselves in which case we just need to make it broadcastable to all heads.
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extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
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attention_mask = extended_attention_mask
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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.is_decoder 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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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=device)
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encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
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else:
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encoder_extended_attention_mask = None
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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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# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
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# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
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head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
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hidden_states = hidden_states if hidden_states is not None else None
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if stage_manager.is_first_stage():
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hidden_states = self.embeddings(
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input_ids=input_ids,
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position_ids=position_ids,
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token_type_ids=token_type_ids,
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inputs_embeds=inputs_embeds,
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past_key_values_length=past_key_values_length,
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)
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# inherit from bert_layer,this should be changed when we add the feature to record hidden_states
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all_hidden_states = () if output_hidden_states 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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if self.encoder.gradient_checkpointing and self.encoder.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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next_decoder_cache = () if use_cache else None
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start_idx, end_idx = stage_index[0], stage_index[1]
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# layer_outputs
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layer_outputs = hidden_states if hidden_states is not None 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 is not None 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, dim=1, process_group=shard_config.tensor_parallel_process_group
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)
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if encoder_hidden_states is not None:
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encoder_hidden_states = split_forward_gather_backward(
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encoder_hidden_states, dim=1, process_group=shard_config.tensor_parallel_process_group
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)
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for idx, encoder_layer in enumerate(self.encoder.layer[start_idx:end_idx], start=start_idx):
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if stage_manager.is_first_stage() and idx == 0:
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encoder_attention_mask = encoder_extended_attention_mask
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_head_mask = head_mask[idx] if head_mask is not None else None
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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 self.encoder.gradient_checkpointing and self.encoder.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs, past_key_value, output_attentions)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(encoder_layer),
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hidden_states,
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attention_mask,
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layer_head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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else:
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layer_outputs = encoder_layer(
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hidden_states,
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attention_mask,
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layer_head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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past_key_value,
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output_attentions,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache += (layer_outputs[-1],)
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if output_attentions:
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all_self_attentions = all_self_attentions + (layer_outputs[1],)
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if self.config.add_cross_attention:
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all_cross_attentions = all_cross_attentions + (layer_outputs[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 is not None 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, dim=1, process_group=shard_config.tensor_parallel_process_group
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)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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# end of a stage loop
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sequence_output = hidden_states if hidden_states is not None else None
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if stage_manager.is_last_stage():
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pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
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if not return_dict:
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return (sequence_output, pooled_output) + layer_outputs[1:]
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# return dict is not supported at this moment
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else:
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return BaseModelOutputWithPoolingAndCrossAttentions(
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last_hidden_state=sequence_output,
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pooler_output=pooled_output,
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past_key_values=next_decoder_cache,
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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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# output of non-first and non-last stages: must be a dict
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else:
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# intermediate stage always return dict
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return {
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"hidden_states": hidden_states,
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}
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@staticmethod
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def bert_for_pretraining_forward(
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self: BertForPreTraining,
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input_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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token_type_ids: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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labels: Optional[torch.Tensor] = None,
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next_sentence_label: Optional[torch.Tensor] = 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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hidden_states: Optional[torch.FloatTensor] = None,
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stage_manager: Optional[PipelineStageManager] = None,
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stage_index: Optional[List[int]] = None,
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shard_config: ShardConfig = None,
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):
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logger = logging.get_logger(__name__)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# TODO(jianghai) left the recording kv-value tensors as () or None type, this feature may be added in the future.
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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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outputs = BertPipelineForwards.bert_model_forward(
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self.bert,
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input_ids,
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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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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 if hidden_states is not None else None,
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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 stage_manager.is_last_stage():
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sequence_output, pooled_output = outputs[:2]
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prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
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# the last stage for pretraining model
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total_loss = None
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if labels is not None and next_sentence_label is not None:
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loss_fct = CrossEntropyLoss()
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masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
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next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
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total_loss = masked_lm_loss + next_sentence_loss
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if not return_dict:
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output = (prediction_scores, seq_relationship_score) + outputs[2:]
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return ((total_loss,) + output) if total_loss is not None else output
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return BertForPreTrainingOutput(
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loss=total_loss,
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prediction_logits=prediction_scores,
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seq_relationship_logits=seq_relationship_score,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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else:
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hidden_states = outputs.get("hidden_states")
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# intermediate stage always return dict
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return {
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"hidden_states": hidden_states,
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}
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@staticmethod
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def bert_lm_head_model_forward(
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self: BertLMHeadModel,
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input_ids: Optional[torch.Tensor] = None,
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|
attention_mask: Optional[torch.Tensor] = None,
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|
token_type_ids: Optional[torch.Tensor] = None,
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|
position_ids: Optional[torch.Tensor] = None,
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|
head_mask: Optional[torch.Tensor] = None,
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|
inputs_embeds: Optional[torch.Tensor] = None,
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|
encoder_hidden_states: Optional[torch.Tensor] = None,
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|
encoder_attention_mask: Optional[torch.Tensor] = None,
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|
labels: Optional[torch.Tensor] = None,
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|
past_key_values: Optional[List[torch.Tensor]] = 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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|
hidden_states: Optional[torch.FloatTensor] = None,
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|
stage_manager: Optional[PipelineStageManager] = None,
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|
stage_index: Optional[List[int]] = None,
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|
shard_config: ShardConfig = None,
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|
):
|
|
r"""
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
|
the model is configured as a decoder.
|
|
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
|
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
|
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
|
|
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
|
|
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`).
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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|
|
|
if labels is not None:
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use_cache = False
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if output_attentions:
|
|
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:
|
|
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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outputs = BertPipelineForwards.bert_model_forward(
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self.bert,
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input_ids,
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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,
|
|
past_key_values=past_key_values,
|
|
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 if hidden_states is not None else None,
|
|
stage_index=stage_index,
|
|
shard_config=shard_config,
|
|
)
|
|
past_key_values = None
|
|
|
|
if stage_manager.is_last_stage():
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
lm_loss = None
|
|
if labels is not None:
|
|
# we are doing next-token prediction; shift prediction scores and input ids by one
|
|
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
|
|
labels = labels[:, 1:].contiguous()
|
|
loss_fct = CrossEntropyLoss()
|
|
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=lm_loss,
|
|
logits=prediction_scores,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get("hidden_states")
|
|
# intermediate stage always return dict
|
|
return {"hidden_states": hidden_states}
|
|
|
|
@staticmethod
|
|
def bert_for_masked_lm_forward(
|
|
self: BertForMaskedLM,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
hidden_states: Optional[torch.Tensor] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
shard_config: ShardConfig = None,
|
|
):
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size]` (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]`
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if output_attentions:
|
|
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
|
|
output_attentions = False
|
|
if output_hidden_states:
|
|
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
|
|
output_hidden_states = False
|
|
|
|
outputs = BertPipelineForwards.bert_model_forward(
|
|
self.bert,
|
|
input_ids,
|
|
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,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
hidden_states=hidden_states,
|
|
stage_manager=stage_manager,
|
|
stage_index=stage_index,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
if stage_manager.is_last_stage():
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get("hidden_states")
|
|
return {"hidden_states": hidden_states}
|
|
|
|
@staticmethod
|
|
def bert_for_next_sentence_prediction_forward(
|
|
self: BertForNextSentencePrediction,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
hidden_states: Optional[torch.Tensor] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
shard_config: ShardConfig = None,
|
|
**kwargs,
|
|
):
|
|
# -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
|
|
(see `input_ids` docstring). Indices should be in `[0, 1]`:
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
- 1 indicates sequence B is a random sequence.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, BertForNextSentencePrediction
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
|
>>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased")
|
|
|
|
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
|
|
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
|
|
>>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
|
|
|
|
>>> outputs = model(**encoding, labels=torch.LongTensor([1]))
|
|
>>> logits = outputs.logits
|
|
>>> assert logits[0, 0] < logits[0, 1] # next sentence was random
|
|
```
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
if "next_sentence_label" in kwargs:
|
|
warnings.warn(
|
|
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
|
|
" `labels` instead.",
|
|
FutureWarning,
|
|
)
|
|
labels = kwargs.pop("next_sentence_label")
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if output_attentions:
|
|
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
|
|
output_attentions = False
|
|
if output_hidden_states:
|
|
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
|
|
output_hidden_states = False
|
|
|
|
outputs = BertPipelineForwards.bert_model_forward(
|
|
self.bert,
|
|
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,
|
|
hidden_states=hidden_states,
|
|
stage_manager=stage_manager,
|
|
stage_index=stage_index,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
if stage_manager.is_last_stage():
|
|
pooled_output = outputs[1]
|
|
seq_relationship_scores = self.cls(pooled_output)
|
|
|
|
next_sentence_loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (seq_relationship_scores,) + outputs[2:]
|
|
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
|
|
|
|
return NextSentencePredictorOutput(
|
|
loss=next_sentence_loss,
|
|
logits=seq_relationship_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get("hidden_states")
|
|
# intermediate stage always return dict
|
|
return {"hidden_states": hidden_states}
|
|
|
|
@staticmethod
|
|
def bert_for_sequence_classification_forward(
|
|
self: BertForSequenceClassification,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
hidden_states: Optional[torch.Tensor] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
shard_config: ShardConfig = None,
|
|
):
|
|
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).
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if output_attentions:
|
|
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
|
|
output_attentions = False
|
|
if output_hidden_states:
|
|
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
|
|
output_hidden_states = False
|
|
|
|
outputs = BertPipelineForwards.bert_model_forward(
|
|
self.bert,
|
|
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,
|
|
hidden_states=hidden_states,
|
|
stage_manager=stage_manager,
|
|
stage_index=stage_index,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
if stage_manager.is_last_stage():
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
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(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get("hidden_states")
|
|
return {"hidden_states": hidden_states}
|
|
|
|
@staticmethod
|
|
def bert_for_token_classification_forward(
|
|
self: BertForTokenClassification,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
hidden_states: Optional[torch.Tensor] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
shard_config: ShardConfig = None,
|
|
):
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if output_attentions:
|
|
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
|
|
output_attentions = False
|
|
if output_hidden_states:
|
|
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
|
|
output_hidden_states = False
|
|
|
|
outputs = BertPipelineForwards.bert_model_forward(
|
|
self.bert,
|
|
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,
|
|
hidden_states=hidden_states,
|
|
stage_manager=stage_manager,
|
|
stage_index=stage_index,
|
|
shard_config=shard_config,
|
|
)
|
|
|
|
if stage_manager.is_last_stage():
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
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,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get("hidden_states")
|
|
return {"hidden_states": hidden_states}
|
|
|
|
@staticmethod
|
|
def bert_for_multiple_choice_forward(
|
|
self: BertForMultipleChoice,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
hidden_states: Optional[torch.Tensor] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
shard_config: ShardConfig = None,
|
|
):
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if output_attentions:
|
|
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
|
|
output_attentions = False
|
|
if output_hidden_states:
|
|
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
|
|
output_hidden_states = False
|
|
|
|
# in our pipeline design,input ids are copied for every stage and shouldn't be none
|
|
# the input_ids for multiple choice model is [batch_size, num_choices, sequence_length]
|
|
if stage_manager.is_last_stage():
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
outputs = BertPipelineForwards.bert_model_forward(
|
|
self.bert,
|
|
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,
|
|
hidden_states=hidden_states,
|
|
stage_manager=stage_manager,
|
|
stage_index=stage_index,
|
|
shard_config=shard_config,
|
|
)
|
|
if stage_manager.is_last_stage():
|
|
pooled_output = outputs[1]
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get("hidden_states")
|
|
return {"hidden_states": hidden_states}
|
|
|
|
@staticmethod
|
|
def bert_for_question_answering_forward(
|
|
self: BertForQuestionAnswering,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
hidden_states: Optional[torch.Tensor] = None,
|
|
stage_manager: Optional[PipelineStageManager] = None,
|
|
stage_index: Optional[List[int]] = None,
|
|
shard_config: ShardConfig = None,
|
|
):
|
|
# NOTE: the arg start_position and end_position are used only for the last stage
|
|
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.
|
|
"""
|
|
logger = logging.get_logger(__name__)
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if output_attentions:
|
|
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
|
|
output_attentions = False
|
|
if output_hidden_states:
|
|
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
|
|
output_hidden_states = False
|
|
|
|
outputs = BertPipelineForwards.bert_model_forward(
|
|
self.bert,
|
|
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,
|
|
hidden_states=hidden_states,
|
|
stage_manager=stage_manager,
|
|
stage_index=stage_index,
|
|
shard_config=shard_config,
|
|
)
|
|
if stage_manager.is_last_stage():
|
|
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)
|
|
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) + 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,
|
|
)
|
|
else:
|
|
hidden_states = outputs.get("hidden_states")
|
|
return {"hidden_states": hidden_states}
|
|
|
|
|
|
def get_jit_fused_bert_self_output_forward():
|
|
from transformers.models.bert.modeling_bert import BertSelfOutput
|
|
|
|
def forward(self: BertSelfOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
return forward
|
|
|
|
|
|
def get_jit_fused_bert_output_forward():
|
|
from transformers.models.bert.modeling_bert import BertOutput
|
|
|
|
def forward(self: BertOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
return forward
|
|
|
|
|
|
def bert_sequence_parallel_forward_fn(shard_config: ShardConfig):
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[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[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
r"""
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
|
the model is configured as a decoder.
|
|
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
|
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`.
|
|
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 = 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
|
|
|
|
if self.config.is_decoder:
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
else:
|
|
use_cache = False
|
|
|
|
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()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
# past_key_values_length
|
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
|
|
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if self.config.is_decoder and encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
if encoder_attention_mask is None:
|
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
# 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
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
# split the input tensor along sequence dimension
|
|
# [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
|
|
embedding_output = split_forward_gather_backward(
|
|
embedding_output, dim=1, process_group=shard_config.tensor_parallel_process_group
|
|
)
|
|
if encoder_hidden_states is not None:
|
|
encoder_hidden_states = split_forward_gather_backward(
|
|
encoder_hidden_states, dim=1, process_group=shard_config.tensor_parallel_process_group
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
# When sequence parallelism done, gather the output tensor in forward and split it in backward
|
|
sequence_output = gather_forward_split_backward(
|
|
sequence_output, dim=1, process_group=shard_config.tensor_parallel_process_group
|
|
)
|
|
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
return forward
|
|
|
|
|
|
def get_jit_fused_bert_intermediate_forward():
|
|
from transformers.models.bert.modeling_bert import BertIntermediate
|
|
|
|
from colossalai.kernel.jit.bias_gelu import GeLUFunction as JitGeLUFunction
|
|
|
|
def forward(self: BertIntermediate, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states, bias = self.dense(hidden_states)
|
|
hidden_states = JitGeLUFunction.apply(hidden_states, bias)
|
|
return hidden_states
|
|
|
|
return forward
|