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- # coding=utf-8
- # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
- # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- """PyTorch OpenAI GPT model."""
-
- from __future__ import absolute_import, division, print_function, unicode_literals
-
- import collections
- import json
- import logging
- import math
- import os
- import sys
- from io import open
-
- import torch
- import torch.nn as nn
- from torch.nn import CrossEntropyLoss
- from torch.nn.parameter import Parameter
-
- from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
- from .configuration_openai import OpenAIGPTConfig
- from .file_utils import add_start_docstrings
-
- logger = logging.getLogger(__name__)
-
- OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-pytorch_model.bin"}
-
-
- def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
- """ Load tf pre-trained weights in a pytorch model (from NumPy arrays here)
- """
- import re
- import numpy as np
-
- if '.ckpt' in openai_checkpoint_folder_path:
- openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)
-
- logger.info("Loading weights from {}".format(openai_checkpoint_folder_path))
-
- names = json.load(open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8'))
- shapes = json.load(open(openai_checkpoint_folder_path + '/params_shapes.json', "r", encoding='utf-8'))
- offsets = np.cumsum([np.prod(shape) for shape in shapes])
- init_params = [np.load(openai_checkpoint_folder_path + '/params_{}.npy'.format(n)) for n in range(10)]
- init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
- init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
-
- # This was used when we had a single embedding matrix for positions and tokens
- # init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
- # del init_params[1]
- init_params = [arr.squeeze() for arr in init_params]
-
- try:
- assert model.tokens_embed.weight.shape == init_params[1].shape
- assert model.positions_embed.weight.shape == init_params[0].shape
- except AssertionError as e:
- e.args += (model.tokens_embed.weight.shape, init_params[1].shape)
- e.args += (model.positions_embed.weight.shape, init_params[0].shape)
- raise
-
- model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
- model.positions_embed.weight.data = torch.from_numpy(init_params[0])
- names.pop(0)
- # Pop position and token embedding arrays
- init_params.pop(0)
- init_params.pop(0)
-
- for name, array in zip(names, init_params): # names[1:n_transfer], init_params[1:n_transfer]):
- name = name[6:] # skip "model/"
- assert name[-2:] == ":0"
- name = name[:-2]
- name = name.split('/')
- pointer = model
- for m_name in name:
- if re.fullmatch(r'[A-Za-z]+\d+', m_name):
- l = re.split(r'(\d+)', m_name)
- else:
- l = [m_name]
- if l[0] == 'g':
- pointer = getattr(pointer, 'weight')
- elif l[0] == 'b':
- pointer = getattr(pointer, 'bias')
- elif l[0] == 'w':
- pointer = getattr(pointer, 'weight')
- else:
- pointer = getattr(pointer, l[0])
- if len(l) >= 2:
- num = int(l[1])
- pointer = pointer[num]
- try:
- assert pointer.shape == array.shape
- except AssertionError as e:
- e.args += (pointer.shape, array.shape)
- raise
- try:
- assert pointer.shape == array.shape
- except AssertionError as e:
- e.args += (pointer.shape, array.shape)
- raise
- logger.info("Initialize PyTorch weight {}".format(name))
- pointer.data = torch.from_numpy(array)
- return model
-
-
- def gelu(x):
- return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
-
-
- def swish(x):
- return x * torch.sigmoid(x)
-
-
- ACT_FNS = {"relu": nn.ReLU, "swish": swish, "gelu": gelu}
-
-
- class Attention(nn.Module):
- def __init__(self, nx, n_ctx, config, scale=False):
- super(Attention, self).__init__()
- n_state = nx # in Attention: n_state=768 (nx=n_embd)
- # [switch nx => n_state from Block to Attention to keep identical to TF implem]
- assert n_state % config.n_head == 0
- self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
- self.n_head = config.n_head
- self.split_size = n_state
- self.scale = scale
-
- self.output_attentions = config.output_attentions
-
- self.c_attn = Conv1D(n_state * 3, nx)
- self.c_proj = Conv1D(n_state, nx)
- self.attn_dropout = nn.Dropout(config.attn_pdrop)
- self.resid_dropout = nn.Dropout(config.resid_pdrop)
- self.pruned_heads = set()
-
- def prune_heads(self, heads):
- if len(heads) == 0:
- return
- mask = torch.ones(self.n_head, self.split_size // self.n_head)
- heads = set(heads) - self.pruned_heads
- for head in heads:
- head -= sum(1 if h < head else 0 for h in self.pruned_heads)
- mask[head] = 0
- mask = mask.view(-1).contiguous().eq(1)
- index = torch.arange(len(mask))[mask].long()
- index_attn = torch.cat([index, index + self.split_size, index + (2*self.split_size)])
- # Prune conv1d layers
- self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
- self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
- # Update hyper params
- self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
- self.n_head = self.n_head - len(heads)
- self.pruned_heads = self.pruned_heads.union(heads)
-
- def _attn(self, q, k, v, head_mask=None):
- w = torch.matmul(q, k)
- if self.scale:
- w = w / math.sqrt(v.size(-1))
- # w = w * self.bias + -1e9 * (1 - self.bias) # TF implem method: mask_attn_weights
- # XD: self.b may be larger than w, so we need to crop it
- b = self.bias[:, :, : w.size(-2), : w.size(-1)]
- w = w * b + -1e9 * (1 - b)
-
- w = nn.Softmax(dim=-1)(w)
- w = self.attn_dropout(w)
-
- # Mask heads if we want to
- if head_mask is not None:
- w = w * head_mask
-
- outputs = [torch.matmul(w, v)]
- if self.output_attentions:
- outputs.append(w)
- return outputs
-
- def merge_heads(self, x):
- x = x.permute(0, 2, 1, 3).contiguous()
- new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
- return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states
-
- def split_heads(self, x, k=False):
- new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
- x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
- if k:
- return x.permute(0, 2, 3, 1)
- else:
- return x.permute(0, 2, 1, 3)
-
- def forward(self, x, head_mask=None):
- x = self.c_attn(x)
- query, key, value = x.split(self.split_size, dim=2)
- query = self.split_heads(query)
- key = self.split_heads(key, k=True)
- value = self.split_heads(value)
-
- attn_outputs = self._attn(query, key, value, head_mask)
- a = attn_outputs[0]
-
- a = self.merge_heads(a)
- a = self.c_proj(a)
- a = self.resid_dropout(a)
-
- outputs = [a] + attn_outputs[1:]
- return outputs # a, (attentions)
-
-
- class MLP(nn.Module):
- def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
- super(MLP, self).__init__()
- nx = config.n_embd
- self.c_fc = Conv1D(n_state, nx)
- self.c_proj = Conv1D(nx, n_state)
- self.act = ACT_FNS[config.afn]
- self.dropout = nn.Dropout(config.resid_pdrop)
-
- def forward(self, x):
- h = self.act(self.c_fc(x))
- h2 = self.c_proj(h)
- return self.dropout(h2)
-
-
- class Block(nn.Module):
- def __init__(self, n_ctx, config, scale=False):
- super(Block, self).__init__()
- nx = config.n_embd
- self.attn = Attention(nx, n_ctx, config, scale)
- self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
- self.mlp = MLP(4 * nx, config)
- self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
-
- def forward(self, x, head_mask=None):
- attn_outputs = self.attn(x, head_mask=head_mask)
- a = attn_outputs[0]
-
- n = self.ln_1(x + a)
- m = self.mlp(n)
- h = self.ln_2(n + m)
-
- outputs = [h] + attn_outputs[1:]
- return outputs
-
-
- class OpenAIGPTPreTrainedModel(PreTrainedModel):
- """ An abstract class to handle weights initialization and
- a simple interface for dowloading and loading pretrained models.
- """
- config_class = OpenAIGPTConfig
- pretrained_model_archive_map = OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
- load_tf_weights = load_tf_weights_in_openai_gpt
- base_model_prefix = "transformer"
-
- def _init_weights(self, module):
- """ Initialize the weights.
- """
- if isinstance(module, (nn.Linear, nn.Embedding, Conv1D)):
- # Slightly different from the TF version which uses truncated_normal for initialization
- # cf https://github.com/pytorch/pytorch/pull/5617
- module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
- if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
- module.bias.data.zero_()
- elif isinstance(module, nn.LayerNorm):
- module.bias.data.zero_()
- module.weight.data.fill_(1.0)
-
-
- OPENAI_GPT_START_DOCSTRING = r""" OpenAI GPT model was proposed in
- `Improving Language Understanding by Generative Pre-Training`_
- by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
- It's a causal (unidirectional) transformer pre-trained using language modeling on a large
- corpus will long range dependencies, the Toronto Book Corpus.
-
- This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
- refer to the PyTorch documentation for all matter related to general usage and behavior.
-
- .. _`Improving Language Understanding by Generative Pre-Training`:
- https://openai.com/blog/language-unsupervised/
-
- .. _`torch.nn.Module`:
- https://pytorch.org/docs/stable/nn.html#module
-
- Parameters:
- config (:class:`~pytorch_transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
- Initializing with a config file does not load the weights associated with the model, only the configuration.
- Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
- """
-
- OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
- **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
- Indices of input sequence tokens in the vocabulary.
- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
- the right rather than the left.
- Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
- See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
- :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
- **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
- Indices of positions of each input sequence tokens in the position embeddings.
- Selected in the range ``[0, config.max_position_embeddings - 1]``.
- **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
- A parallel sequence of tokens (can be used to indicate various portions of the inputs).
- The embeddings from these tokens will be summed with the respective token embeddings.
- Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices)
- **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
- Mask to nullify selected heads of the self-attention modules.
- Mask values selected in ``[0, 1]``:
- ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
- """
-
- @add_start_docstrings("The bare OpenAI GPT transformer model outputing raw hidden-states without any specific head on top.",
- OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
- class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
- r"""
- Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
- **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
- Sequence of hidden-states at the last layer of the model.
- **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
- list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
- of shape ``(batch_size, sequence_length, hidden_size)``:
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions**: (`optional`, returned when ``config.output_attentions=True``)
- list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
- Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
- Examples::
-
- tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
- model = OpenAIGPTModel.from_pretrained('openai-gpt')
- input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
- outputs = model(input_ids)
- last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
-
- """
- def __init__(self, config):
- super(OpenAIGPTModel, self).__init__(config)
- self.output_attentions = config.output_attentions
- self.output_hidden_states = config.output_hidden_states
-
- self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
- self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
- self.drop = nn.Dropout(config.embd_pdrop)
- self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
-
- self.init_weights()
-
- def _resize_token_embeddings(self, new_num_tokens):
- self.tokens_embed = self._get_resized_embeddings(self.tokens_embed, new_num_tokens)
- return self.tokens_embed
-
- def _prune_heads(self, heads_to_prune):
- """ Prunes heads of the model.
- heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
- """
- for layer, heads in heads_to_prune.items():
- self.h[layer].attn.prune_heads(heads)
-
- def forward(self, input_ids, position_ids=None, token_type_ids=None, head_mask=None):
- if position_ids is None:
- # This was used when we had a single embedding matrice from position and token embeddings
- # start = self.config.vocab_size + self.config.n_special
- # end = start + input_ids.size(-1)
- # position_ids = torch.arange(start, end, dtype=torch.long, device=input_ids.device)
- position_ids = torch.arange(input_ids.size(-1), dtype=torch.long, device=input_ids.device)
- position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
-
- # Prepare head mask if needed
- # 1.0 in head_mask indicate we keep the head
- # attention_probs has shape bsz x n_heads x N x N
- # head_mask has shape n_layer x batch x n_heads x N x N
- if head_mask is not None:
- if head_mask.dim() == 1:
- head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
- head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1)
- elif head_mask.dim() == 2:
- head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
- head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
- else:
- head_mask = [None] * self.config.n_layer
-
- input_shape = input_ids.size()
- input_ids = input_ids.view(-1, input_ids.size(-1))
- position_ids = position_ids.view(-1, position_ids.size(-1))
-
- inputs_embeds = self.tokens_embed(input_ids)
- position_embeds = self.positions_embed(position_ids)
- if token_type_ids is not None:
- token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
- token_type_embeds = self.tokens_embed(token_type_ids)
- else:
- token_type_embeds = 0
- hidden_states = inputs_embeds + position_embeds + token_type_embeds
- hidden_states = self.drop(hidden_states)
-
- output_shape = input_shape + (hidden_states.size(-1),)
-
- all_attentions = ()
- all_hidden_states = ()
- for i, block in enumerate(self.h):
- if self.output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
-
- outputs = block(hidden_states, head_mask[i])
- hidden_states = outputs[0]
- if self.output_attentions:
- all_attentions = all_attentions + (outputs[1],)
-
- # Add last layer
- if self.output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
-
- outputs = (hidden_states.view(*output_shape),)
- if self.output_hidden_states:
- outputs = outputs + (all_hidden_states,)
- if self.output_attentions:
- outputs = outputs + (all_attentions,)
- return outputs # last hidden state, (all hidden states), (all attentions)
-
-
- @add_start_docstrings("""OpenAI GPT Model transformer with a language modeling head on top
- (linear layer with weights tied to the input embeddings). """, OPENAI_GPT_START_DOCSTRING, OPENAI_GPT_INPUTS_DOCSTRING)
- class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
- r"""
- **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
- Labels for language modeling.
- Note that the labels **are shifted** inside the model, i.e. you can set ``labels = input_ids``
- Indices are selected in ``[-1, 0, ..., config.vocab_size]``
- All labels set to ``-1`` are ignored (masked), the loss is only
- computed for labels in ``[0, ..., config.vocab_size]``
-
- Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
- **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
- Language modeling loss.
- **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
- list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
- of shape ``(batch_size, sequence_length, hidden_size)``:
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions**: (`optional`, returned when ``config.output_attentions=True``)
- list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
- Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
- Examples::
-
- tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
- model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
- input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
- outputs = model(input_ids, labels=input_ids)
- loss, logits = outputs[:2]
-
- """
- def __init__(self, config):
- super(OpenAIGPTLMHeadModel, self).__init__(config)
- self.transformer = OpenAIGPTModel(config)
- self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-
- self.init_weights()
- self.tie_weights()
-
- def tie_weights(self):
- """ Make sure we are sharing the input and output embeddings.
- Export to TorchScript can't handle parameter sharing so we are cloning them instead.
- """
- self._tie_or_clone_weights(self.lm_head,
- self.transformer.tokens_embed)
-
- def forward(self, input_ids, position_ids=None, token_type_ids=None, labels=None, head_mask=None):
- transformer_outputs = self.transformer(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
- head_mask=head_mask)
- hidden_states = transformer_outputs[0]
- lm_logits = self.lm_head(hidden_states)
-
- outputs = (lm_logits,) + transformer_outputs[1:]
- if labels is not None:
- # Shift so that tokens < n predict n
- shift_logits = lm_logits[..., :-1, :].contiguous()
- shift_labels = labels[..., 1:].contiguous()
- # Flatten the tokens
- loss_fct = CrossEntropyLoss(ignore_index=-1)
- loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
- shift_labels.view(-1))
- outputs = (loss,) + outputs
-
- return outputs # (loss), lm_logits, (all hidden states), (all attentions)
-
-
- @add_start_docstrings("""OpenAI GPT Model transformer with a language modeling and a multiple-choice classification
- head on top e.g. for RocStories/SWAG tasks. The two heads are two linear layers.
- The language modeling head has its weights tied to the input embeddings,
- the classification head takes as input the input of a specified classification token index in the input sequence).
- """, OPENAI_GPT_START_DOCSTRING)
- class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
- r""" Inputs:
- **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
- Indices of input sequence tokens in the vocabulary.
- The second dimension of the input (`num_choices`) indicates the number of choices to score.
- Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
- See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
- :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
- **mc_token_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices)``:
- Index of the classification token in each input sequence.
- Selected in the range ``[0, input_ids.size(-1) - 1[``.
- **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
- Indices of positions of each input sequence tokens in the position embeddings.
- Selected in the range ``[0, config.max_position_embeddings - 1]``.
- **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
- A parallel sequence of tokens (can be used to indicate various portions of the inputs).
- The embeddings from these tokens will be summed with the respective token embeddings.
- Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
- **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
- Mask to nullify selected heads of the self-attention modules.
- Mask values selected in ``[0, 1]``:
- ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
- **lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
- Labels for language modeling.
- Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
- Indices are selected in ``[-1, 0, ..., config.vocab_size]``
- All labels set to ``-1`` are ignored (masked), the loss is only
- computed for labels in ``[0, ..., config.vocab_size]``
- **mc_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size)``:
- Labels for computing the multiple choice classification loss.
- Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
- of the input tensors. (see `input_ids` above)
-
- `multiple_choice_labels`: optional multiple choice labels: ``torch.LongTensor`` of shape [batch_size]
- with indices selected in [0, ..., num_choices].
-
- Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
- **lm_loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
- Language modeling loss.
- **mc_loss**: (`optional`, returned when ``multiple_choice_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
- Multiple choice classification loss.
- **lm_prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length, config.vocab_size)``
- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **mc_prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)``
- Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax).
- **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
- list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
- of shape ``(batch_size, sequence_length, hidden_size)``:
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions**: (`optional`, returned when ``config.output_attentions=True``)
- list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
- Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
- Examples::
-
- tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
- model = OpenAIGPTDoubleHeadsModel.from_pretrained('openai-gpt')
- tokenizer.add_special_tokens({'cls_token': '[CLS]'}) # Add a [CLS] to the vocabulary (we should train it also!)
- choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
- input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
- mc_token_ids = torch.tensor([input_ids.size(-1), input_ids.size(-1)]).unsqueeze(0) # Batch size 1
- outputs = model(input_ids, mc_token_ids)
- lm_prediction_scores, mc_prediction_scores = outputs[:2]
-
- """
- def __init__(self, config):
- super(OpenAIGPTDoubleHeadsModel, self).__init__(config)
-
- self.transformer = OpenAIGPTModel(config)
- self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
- self.multiple_choice_head = SequenceSummary(config)
-
- self.init_weights()
- self.tie_weights()
-
- def tie_weights(self):
- """ Make sure we are sharing the input and output embeddings.
- Export to TorchScript can't handle parameter sharing so we are cloning them instead.
- """
- self._tie_or_clone_weights(self.lm_head,
- self.transformer.tokens_embed)
-
- def forward(self, input_ids, mc_token_ids=None, lm_labels=None, mc_labels=None, token_type_ids=None,
- position_ids=None, head_mask=None):
- transformer_outputs = self.transformer(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
- head_mask=head_mask)
- hidden_states = transformer_outputs[0]
-
- lm_logits = self.lm_head(hidden_states)
- mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
-
- outputs = (lm_logits, mc_logits) + transformer_outputs[1:]
- if mc_labels is not None:
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)),
- mc_labels.view(-1))
- outputs = (loss,) + outputs
- if lm_labels is not None:
- shift_logits = lm_logits[..., :-1, :].contiguous()
- shift_labels = lm_labels[..., 1:].contiguous()
- loss_fct = CrossEntropyLoss(ignore_index=-1)
- loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
- shift_labels.view(-1))
- outputs = (loss,) + outputs
-
- return outputs # (lm loss), (mc loss), lm logits, mc logits, (all hidden_states), (attentions)
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