mirror of
https://github.com/hpcaitech/ColossalAI.git
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[shardformer] rewrite tests for opt/bloom/llama/vit/chatglm (#4395)
* rewrite opt tests * rewrite llama tests * rewrite bloom & vit tests * rewrite chatglm tests * fix LinearCol for classfiers * add judge for other tp layers, fix lazy init in util
This commit is contained in:
Baizhou Zhang
committed by
Hongxin Liu
Hongxin Liu
parent
21e0a42fd1
commit
7711bd524a
@@ -1,32 +1,14 @@
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import logging
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import random
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from functools import partial
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from types import MethodType
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from typing import Callable, Dict, List, Optional, Tuple, Union
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from typing import Callable, Dict, List
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import torch
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import torch.nn as nn
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from torch import Tensor, nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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)
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from transformers.models.opt.modeling_opt import (
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OPTForCausalLM,
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OPTForQuestionAnswering,
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OPTForSequenceClassification,
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OPTModel,
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)
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from colossalai.pipeline.stage_manager import PipelineStageManager
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from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
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from .._utils import getattr_, setattr_
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from .._utils import getattr_
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from ..modeling.jit import get_jit_fused_dropout_add_func
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from ..modeling.opt import get_jit_fused_opt_decoder_layer_forward, get_opt_flash_attention_forward
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from ..modeling.opt import OPTPipelineForwards, get_jit_fused_opt_decoder_layer_forward, get_opt_flash_attention_forward
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = [
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@@ -228,6 +210,7 @@ class OPTForCausalLMPolicy(OPTPolicy):
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num_stages = self.pipeline_stage_manager.num_stages
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if id(opt_model.model.decoder.embed_tokens.weight) == id(opt_model.lm_head.weight):
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return [{0: opt_model.model.decoder.embed_tokens.weight, num_stages - 1: opt_model.lm_head.weight}]
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return []
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def postprocess(self):
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if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None:
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@@ -295,594 +278,3 @@ class OPTForQuestionAnsweringPolicy(OPTPolicy):
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"no shared params in OPTForSequenceClassification"
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return []
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class OPTPipelineForwards:
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'''
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This class serves as a micro library for forward function substitution of OPT models
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under pipeline setting.
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'''
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@staticmethod
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def _prepare_decoder_attention_mask(attention_mask, input_shape, _dtype, device, past_key_values_length):
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# create causal mask
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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from transformers.models.opt.modeling_opt import _make_causal_mask
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combined_attention_mask = None
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if input_shape[-1] > 1:
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combined_attention_mask = _make_causal_mask(
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input_shape,
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_dtype,
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device,
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past_key_values_length=past_key_values_length,
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)
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if attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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expanded_attn_mask = OPTPipelineForwards._expand_mask(attention_mask, _dtype,
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tgt_len=input_shape[-1]).to(device)
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combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask +
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combined_attention_mask)
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return combined_attention_mask
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@staticmethod
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
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@staticmethod
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def opt_model_forward(
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self: OPTModel,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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stage_manager: Optional[PipelineStageManager] = None,
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hidden_states: Optional[torch.FloatTensor] = None,
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stage_index: Optional[List[int]] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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'''
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This forward method is modified based on transformers.models.opt.modeling_opt.OPTModel.forward
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'''
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (output_hidden_states
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if output_hidden_states is not None else self.config.output_hidden_states)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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decoder = self.decoder
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if stage_manager.is_first_stage():
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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else:
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raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
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batch_size, seq_length = input_shape
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if inputs_embeds is None:
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inputs_embeds = decoder.embed_tokens(input_ids)
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if decoder.project_in is not None:
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inputs_embeds = decoder.project_in(inputs_embeds)
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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_dtype = inputs_embeds.dtype
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else:
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if hidden_states is None:
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raise ValueError("hidden_states shouln't be None for intermediate stages.")
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input_shape = hidden_states.size()[:-1]
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batch_size, seq_length = input_shape[0], input_shape[1]
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device = hidden_states.device
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_dtype = hidden_states.dtype
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past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
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# required mask seq length can be calculated via length of past
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mask_seq_length = past_key_values_length + seq_length
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# embed positions
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if attention_mask is None:
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attention_mask = torch.ones(batch_size, mask_seq_length, device=device)
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elif attention_mask.shape[1] != mask_seq_length:
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raise ValueError(
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f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
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f"{mask_seq_length} (sum of the lengths of current and past inputs)")
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causal_attention_mask = OPTPipelineForwards._prepare_decoder_attention_mask(attention_mask, input_shape, _dtype,
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device, past_key_values_length)
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if stage_manager.is_first_stage():
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pos_embeds = decoder.embed_positions(attention_mask, past_key_values_length)
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hidden_states = inputs_embeds + pos_embeds
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if decoder.gradient_checkpointing and decoder.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
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use_cache = False
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# TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future.
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if past_key_values:
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logger.warning_once('Non-empty past_key_values is not supported for pipeline models at the moment.')
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past_key_values = None
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if output_attentions:
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logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
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output_attentions = False
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if output_hidden_states:
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logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
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output_hidden_states = False
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if use_cache:
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logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
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use_cache = False
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = () if use_cache else None
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# check if head_mask has a correct number of layers specified if desired
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for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
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if attn_mask is not None:
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if attn_mask.size()[0] != (len(decoder.layers)):
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raise ValueError(
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f"The `{mask_name}` should be specified for {len(decoder.layers)} layers, but it is for"
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f" {head_mask.size()[0]}.")
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start_idx, end_idx = stage_index[0], stage_index[1]
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torch.cuda.set_device(device)
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for idx in range(start_idx, end_idx):
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# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
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decoder_layer = decoder.layers[idx]
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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dropout_probability = random.uniform(0, 1)
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if decoder.training and (dropout_probability < decoder.layerdrop):
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continue
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past_key_value = past_key_values[idx] if past_key_values is not None else None
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if decoder.gradient_checkpointing and decoder.training:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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# None for past_key_value
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return module(*inputs, output_attentions, None)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(decoder_layer),
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hidden_states,
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causal_attention_mask,
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head_mask[idx] if head_mask is not None else None,
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None,
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)
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else:
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_attention_mask,
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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_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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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[2 if output_attentions else 1],)
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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if stage_manager.is_last_stage():
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if decoder.final_layer_norm is not None:
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hidden_states = decoder.final_layer_norm(hidden_states)
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if decoder.project_out is not None:
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hidden_states = decoder.project_out(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if stage_manager.is_last_stage():
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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else:
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return {'hidden_states': hidden_states}
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@staticmethod
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def opt_for_causal_lm_forward(
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self: OPTForCausalLM,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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stage_manager: Optional[PipelineStageManager] = None,
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hidden_states: Optional[torch.FloatTensor] = None,
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stage_index: Optional[List[int]] = None,
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) -> Union[Tuple, CausalLMOutputWithPast]:
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r"""
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
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provide it.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. 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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[What are attention masks?](../glossary#attention-mask)
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head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
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Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
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shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
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shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
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tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
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Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
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cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
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If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
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that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
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all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert `input_ids` indices into associated vectors
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than the model's internal embedding lookup matrix.
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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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
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(see `past_key_values`).
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
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for more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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Returns:
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Example:
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```python
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>>> from transformers import AutoTokenizer, OPTForCausalLM
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>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
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>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
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>>> prompt = "Hey, are you consciours? Can you talk to me?"
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
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```"""
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from transformers.modeling_outputs import CausalLMOutputWithPast
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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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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = OPTPipelineForwards.opt_model_forward(
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self.model,
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input_ids=input_ids,
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attention_mask=attention_mask,
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head_mask=head_mask,
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past_key_values=past_key_values,
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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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if stage_manager.is_last_stage():
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logits = self.lm_head(outputs[0]).contiguous()
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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(logits.device)
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# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
return (loss,) + output if loss is not None else output
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def opt_for_sequence_classification_forward(
|
||||
self: OPTForSequenceClassification,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
head_mask: Optional[torch.FloatTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
stage_manager: Optional[PipelineStageManager] = None,
|
||||
hidden_states: Optional[torch.FloatTensor] = None,
|
||||
stage_index: Optional[List[int]] = None,
|
||||
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
||||
r"""
|
||||
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||||
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
||||
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||||
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||||
"""
|
||||
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
|
||||
from transformers.utils import logging
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model,
|
||||
input_ids,
|
||||
past_key_values=past_key_values,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
stage_manager=stage_manager,
|
||||
hidden_states=hidden_states,
|
||||
stage_index=stage_index)
|
||||
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = transformer_outputs[0]
|
||||
logits = self.score(hidden_states)
|
||||
|
||||
batch_size = input_ids.shape[0] if input_ids is not None else hidden_states.shape[0]
|
||||
|
||||
if self.config.pad_token_id is None:
|
||||
sequence_lengths = -1
|
||||
else:
|
||||
if input_ids is not None:
|
||||
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
|
||||
else:
|
||||
sequence_lengths = -1
|
||||
logger.warning(
|
||||
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
||||
"unexpected if using padding tokens in conjunction with `inputs_embeds.`")
|
||||
|
||||
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
if self.config.problem_type is None:
|
||||
if self.num_labels == 1:
|
||||
self.config.problem_type = "regression"
|
||||
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||||
self.config.problem_type = "single_label_classification"
|
||||
else:
|
||||
self.config.problem_type = "multi_label_classification"
|
||||
|
||||
if self.config.problem_type == "regression":
|
||||
loss_fct = MSELoss()
|
||||
if self.num_labels == 1:
|
||||
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
||||
else:
|
||||
loss = loss_fct(pooled_logits, labels)
|
||||
elif self.config.problem_type == "single_label_classification":
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
||||
elif self.config.problem_type == "multi_label_classification":
|
||||
loss_fct = BCEWithLogitsLoss()
|
||||
loss = loss_fct(pooled_logits, labels)
|
||||
|
||||
if not return_dict:
|
||||
output = (pooled_logits,) + transformer_outputs[1:]
|
||||
return ((loss,) + output) if loss is not None else output
|
||||
|
||||
return SequenceClassifierOutputWithPast(
|
||||
loss=loss,
|
||||
logits=pooled_logits,
|
||||
past_key_values=transformer_outputs.past_key_values,
|
||||
hidden_states=transformer_outputs.hidden_states,
|
||||
attentions=transformer_outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = transformer_outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
@staticmethod
|
||||
def opt_for_question_answering_forward(
|
||||
self: OPTForQuestionAnswering,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
head_mask: Optional[torch.FloatTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
start_positions: Optional[torch.LongTensor] = None,
|
||||
end_positions: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
stage_manager: Optional[PipelineStageManager] = None,
|
||||
hidden_states: Optional[torch.FloatTensor] = None,
|
||||
stage_index: Optional[List[int]] = None,
|
||||
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
||||
r"""
|
||||
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||||
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
||||
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
||||
are not taken into account for computing the loss.
|
||||
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||||
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
||||
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
||||
are not taken into account for computing the loss.
|
||||
|
||||
Returns:
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, OPTForQuestionAnswering
|
||||
>>> import torch
|
||||
|
||||
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
||||
|
||||
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
|
||||
>>> # so the head will be randomly initialized, hence the predictions will be random
|
||||
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
|
||||
|
||||
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
||||
|
||||
>>> inputs = tokenizer(question, text, return_tensors="pt")
|
||||
>>> with torch.no_grad():
|
||||
... outputs = model(**inputs)
|
||||
|
||||
>>> answer_start_index = outputs.start_logits.argmax()
|
||||
>>> answer_end_index = outputs.end_logits.argmax()
|
||||
|
||||
>>> answer_offset = len(tokenizer(question)[0])
|
||||
|
||||
>>> predict_answer_tokens = inputs.input_ids[
|
||||
... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1
|
||||
... ]
|
||||
>>> predicted = tokenizer.decode(predict_answer_tokens)
|
||||
>>> predicted
|
||||
' a nice puppet'
|
||||
```"""
|
||||
from transformers.modeling_outputs import QuestionAnsweringModelOutput
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
transformer_outputs = OPTPipelineForwards.opt_model_forward(self.model,
|
||||
input_ids,
|
||||
past_key_values=past_key_values,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
stage_manager=stage_manager,
|
||||
hidden_states=hidden_states,
|
||||
stage_index=stage_index)
|
||||
if stage_manager.is_last_stage():
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
logits = self.qa_outputs(hidden_states)
|
||||
start_logits, end_logits = logits.split(1, dim=-1)
|
||||
start_logits = start_logits.squeeze(-1).contiguous()
|
||||
end_logits = end_logits.squeeze(-1).contiguous()
|
||||
|
||||
total_loss = None
|
||||
if start_positions is not None and end_positions is not None:
|
||||
# If we are on multi-GPU, split add a dimension
|
||||
if len(start_positions.size()) > 1:
|
||||
start_positions = start_positions.squeeze(-1)
|
||||
if len(end_positions.size()) > 1:
|
||||
end_positions = end_positions.squeeze(-1)
|
||||
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
||||
ignored_index = start_logits.size(1)
|
||||
start_positions = start_positions.clamp(0, ignored_index)
|
||||
end_positions = end_positions.clamp(0, ignored_index)
|
||||
|
||||
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
||||
start_loss = loss_fct(start_logits, start_positions)
|
||||
end_loss = loss_fct(end_logits, end_positions)
|
||||
total_loss = (start_loss + end_loss) / 2
|
||||
|
||||
if not return_dict:
|
||||
output = (start_logits, end_logits) + transformer_outputs[2:]
|
||||
return ((total_loss,) + output) if total_loss is not None else output
|
||||
|
||||
return QuestionAnsweringModelOutput(
|
||||
loss=total_loss,
|
||||
start_logits=start_logits,
|
||||
end_logits=end_logits,
|
||||
hidden_states=transformer_outputs.hidden_states,
|
||||
attentions=transformer_outputs.attentions,
|
||||
)
|
||||
else:
|
||||
hidden_states = transformer_outputs.get('hidden_states')
|
||||
return {'hidden_states': hidden_states}
|
||||
|
||||
Reference in New Issue
Block a user