mirror of
https://github.com/hpcaitech/ColossalAI.git
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[pipeline] reformat for unified design (#4283)
* bert_reformat * reformat * reformat * fix a typo * format * format * fix bug
This commit is contained in:
Jianghai
Hongxin Liu
@@ -1,6 +1,27 @@
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.distributed as dist
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from torch.distributed import ProcessGroup
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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.bloom.modeling_bloom import (
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BloomForCausalLM,
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BloomForQuestionAnswering,
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BloomForSequenceClassification,
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BloomForTokenClassification,
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BloomModel,
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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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def build_bloom_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor:
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@@ -67,3 +88,602 @@ def build_bloom_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor:
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return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
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return build_bloom_alibi_tensor
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class BloomPipelineForwards:
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'''
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This class serves as a micro library for bloom pipeline forwards.
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'''
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@staticmethod
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def bloom_model_forward(
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self: BloomModel,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.LongTensor] = None,
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inputs_embeds: 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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**deprecated_arguments,
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) -> Union[Tuple[torch.Tensor, ...], 'BaseModelOutputWithPastAndCrossAttentions']:
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logger = logging.get_logger(__name__)
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if deprecated_arguments.pop("position_ids", False) is not False:
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# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
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warnings.warn(
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"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
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" passing `position_ids`.",
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FutureWarning,
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (output_hidden_states
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if output_hidden_states is not None else self.config.output_hidden_states)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# add warnings here
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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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# 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 batch_size x num_heads x N x N
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# head_mask has shape n_layer x batch x num_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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# case: First stage of training
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if stage_manager.is_first_stage():
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# check input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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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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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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hidden_states = self.word_embeddings_layernorm(inputs_embeds)
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# initialize in the first stage and then pass to the next stage
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else:
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input_shape = hidden_states.shape[:-1]
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batch_size, seq_length = input_shape
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# extra recording tensor should be generated in the first stage
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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_hidden_states = () if output_hidden_states else None
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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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use_cache = False
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if past_key_values is None:
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past_key_values = tuple([None] * len(self.h))
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# Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
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seq_length_with_past = seq_length
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past_key_values_length = 0
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if past_key_values[0] is not None:
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past_key_values_length = past_key_values[0][0].shape[2] # source_len
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seq_length_with_past = seq_length_with_past + past_key_values_length
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if attention_mask is None:
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attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
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else:
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attention_mask = attention_mask.to(hidden_states.device)
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alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
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# causal_mask is constructed every stage and its input is passed through different stages
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causal_mask = self._prepare_attn_mask(
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attention_mask,
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input_shape=(batch_size, seq_length),
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past_key_values_length=past_key_values_length,
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)
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start_idx, end_idx = stage_index[0], stage_index[1]
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for i, (block, layer_past) in enumerate(zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]),
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start=start_idx):
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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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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
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return custom_forward
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outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(block),
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hidden_states,
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alibi,
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causal_mask,
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layer_past,
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head_mask[i],
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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=layer_past,
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attention_mask=causal_mask,
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head_mask=head_mask[i],
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use_cache=use_cache,
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output_attentions=output_attentions,
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alibi=alibi,
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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 + \
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(outputs[2 if use_cache else 1],)
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if stage_manager.is_last_stage():
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# Add last hidden state
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hidden_states = self.ln_f(hidden_states)
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# TODO: deal with all_hidden_states, all_self_attentions, presents
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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 for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
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# attention_mask is not returned ; presents = past_key_values
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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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)
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else:
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# always return dict for imediate stage
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return {'hidden_states': hidden_states}
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@staticmethod
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def bloom_for_causal_lm_forward(self: BloomForCausalLM,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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labels: Optional[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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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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**deprecated_arguments):
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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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"""
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logger = logging.get_logger(__name__)
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if deprecated_arguments.pop("position_ids", False) is not False:
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# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
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warnings.warn(
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"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
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" passing `position_ids`.",
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FutureWarning,
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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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: 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 return_dict:
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logger.warning_once('return_dict is not supported for pipeline models at the moment')
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return_dict = False
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transformer_outputs = BloomPipelineForwards.bloom_model_forward(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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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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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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past_key_values = None
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all_hidden_states = None
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all_self_attentions = None
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all_cross_attentions = None
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if stage_manager.is_last_stage():
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hidden_states = transformer_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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batch_size, seq_length, vocab_size = shift_logits.shape
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size),
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shift_labels.view(batch_size * seq_length))
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if not return_dict:
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output = (lm_logits,) + transformer_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=transformer_outputs.past_key_values,
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hidden_states=transformer_outputs.hidden_states,
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attentions=transformer_outputs.attentions,
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)
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else:
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hidden_states = transformer_outputs.get('hidden_states')
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return {'hidden_states': hidden_states}
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@staticmethod
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def bloom_for_sequence_classification_forward(
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self: BloomForSequenceClassification,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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labels: Optional[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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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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**deprecated_arguments,
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):
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r"""
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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"""
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logger = logging.get_logger(__name__)
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if deprecated_arguments.pop("position_ids", False) is not False:
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# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
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warnings.warn(
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"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
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" passing `position_ids`.",
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FutureWarning,
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)
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if len(deprecated_arguments) > 0:
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raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
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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: 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 return_dict:
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logger.warning_once('return_dict is not supported for pipeline models at the moment')
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return_dict = False
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transformer_outputs = BloomPipelineForwards.bloom_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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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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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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)
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past_key_values = None
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all_hidden_states = None
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all_self_attentions = None
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all_cross_attentions = None
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if stage_manager.is_last_stage():
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batch_size = hidden_states.shape[0]
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#update batch size
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hidden_states = transformer_outputs[0]
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logits = self.score(hidden_states)
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if self.config.pad_token_id is None and batch_size != 1:
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raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
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if self.config.pad_token_id is None:
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sequence_lengths = -1
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else:
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if input_ids is not None:
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sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
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else:
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sequence_lengths = -1
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logger.warning(
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f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
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"unexpected if using padding tokens in conjunction with `inputs_embeds.`")
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pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
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loss = None
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if labels is not None:
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if self.config.problem_type is None:
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if self.num_labels == 1:
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self.config.problem_type = "regression"
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
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self.config.problem_type = "single_label_classification"
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else:
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self.config.problem_type = "multi_label_classification"
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if self.config.problem_type == "regression":
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loss_fct = MSELoss()
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if self.num_labels == 1:
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loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
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else:
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loss = loss_fct(pooled_logits, labels)
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elif self.config.problem_type == "single_label_classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(pooled_logits, labels)
|
||||
elif self.config.problem_type == "multi_label_classification":
|
||||
loss_fct = BCEWithLogitsLoss()
|
||||
loss = loss_fct(pooled_logits, labels)
|
||||
if not return_dict:
|
||||
output = (pooled_logits,) + transformer_outputs[1:]
|
||||
return ((loss,) + output) if loss is not None else output
|
||||
|
||||
return SequenceClassifierOutputWithPast(
|
||||
loss=loss,
|
||||
logits=pooled_logits,
|
||||
past_key_values=transformer_outputs.past_key_values,
|
||||
hidden_states=transformer_outputs.hidden_states,
|
||||
attentions=transformer_outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = transformer_outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def bloom_for_token_classification_forward(
|
||||
self: BloomForTokenClassification,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
head_mask: Optional[torch.Tensor] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
labels: Optional[torch.Tensor] = 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,
|
||||
**deprecated_arguments,
|
||||
):
|
||||
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__)
|
||||
|
||||
if deprecated_arguments.pop("position_ids", False) is not False:
|
||||
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
||||
warnings.warn(
|
||||
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
||||
" passing `position_ids`.",
|
||||
FutureWarning,
|
||||
)
|
||||
if len(deprecated_arguments) > 0:
|
||||
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
|
||||
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
|
||||
if return_dict:
|
||||
logger.warning_once('return_dict is not supported for pipeline models at the moment')
|
||||
return_dict = False
|
||||
|
||||
transformer_outputs = BloomPipelineForwards.bloom_model_forward(
|
||||
self.transformer,
|
||||
input_ids,
|
||||
past_key_values=past_key_values,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
stage_manager=stage_manager,
|
||||
hidden_states=hidden_states,
|
||||
stage_index=stage_index,
|
||||
)
|
||||
past_key_values = None
|
||||
all_hidden_states = None
|
||||
all_self_attentions = None
|
||||
all_cross_attentions = None
|
||||
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = transformer_outputs[0]
|
||||
hidden_states = self.dropout(hidden_states)
|
||||
logits = self.classifier(hidden_states)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# move labels to correct device to enable model parallelism
|
||||
labels = labels.to(logits.device)
|
||||
batch_size, seq_length = labels.shape
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels),
|
||||
labels.view(batch_size * seq_length))
|
||||
|
||||
if not return_dict:
|
||||
output = (logits,) + transformer_outputs[2:]
|
||||
return ((loss,) + output) if loss is not None else output
|
||||
|
||||
return TokenClassifierOutput(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
hidden_states=transformer_outputs.hidden_states,
|
||||
attentions=transformer_outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = transformer_outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def bloom_for_question_answering_forward(
|
||||
self: BloomForQuestionAnswering,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = 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,
|
||||
):
|
||||
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
|
||||
# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
|
||||
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
|
||||
if return_dict:
|
||||
logger.warning_once('return_dict is not supported for pipeline models at the moment')
|
||||
return_dict = False
|
||||
|
||||
outputs = BloomPipelineForwards.bloom_model_forward(
|
||||
self.transformer,
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
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,
|
||||
)
|
||||
past_key_values = None
|
||||
all_hidden_states = None
|
||||
all_self_attentions = None
|
||||
all_cross_attentions = None
|
||||
|
||||
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}
|
||||
|
||||
Reference in New Issue
Block a user