Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- # 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-2 model."""
- import logging
- import math
- import os
- import torch
- import torch.nn as nn
- from torch.nn import CrossEntropyLoss
- import torch.utils
- import torch.utils.checkpoint
- from .activations import gelu_new
- from .configuration_gpt2 import GPT2Config
- from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
- from .modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, prune_conv1d_layer
- logger = logging.getLogger(__name__)
- GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {
- "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
- "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin",
- "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-pytorch_model.bin",
- "gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-pytorch_model.bin",
- "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-pytorch_model.bin",
- }
- def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
- """ Load tf checkpoints in a pytorch model
- """
- try:
- import re
- import tensorflow as tf
- except ImportError:
- logger.error(
- "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
- "https://www.tensorflow.org/install/ for installation instructions."
- )
- raise
- tf_path = os.path.abspath(gpt2_checkpoint_path)
- logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
- # Load weights from TF model
- init_vars = tf.train.list_variables(tf_path)
- names = []
- arrays = []
- for name, shape in init_vars:
- logger.info("Loading TF weight {} with shape {}".format(name, shape))
- array = tf.train.load_variable(tf_path, name)
- names.append(name)
- arrays.append(array.squeeze())
- for name, array in zip(names, arrays):
- name = name[6:] # skip "model/"
- name = name.split("/")
- pointer = model
- for m_name in name:
- if re.fullmatch(r"[A-Za-z]+\d+", m_name):
- scope_names = re.split(r"(\d+)", m_name)
- else:
- scope_names = [m_name]
- if scope_names[0] == "w" or scope_names[0] == "g":
- pointer = getattr(pointer, "weight")
- elif scope_names[0] == "b":
- pointer = getattr(pointer, "bias")
- elif scope_names[0] == "wpe" or scope_names[0] == "wte":
- pointer = getattr(pointer, scope_names[0])
- pointer = getattr(pointer, "weight")
- else:
- pointer = getattr(pointer, scope_names[0])
- if len(scope_names) >= 2:
- num = int(scope_names[1])
- pointer = pointer[num]
- 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
- class Attention(nn.Module):
- def __init__(self, nx, n_ctx, config, scale=False):
- super().__init__()
- self.output_attentions = config.output_attentions
- 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.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 # Convert to set and emove already pruned heads
- for head in heads:
- # Compute how many pruned heads are before the head and move the index accordingly
- head = 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, attention_mask=None, head_mask=None):
- w = torch.matmul(q, k)
- if self.scale:
- w = w / math.sqrt(v.size(-1))
- nd, ns = w.size(-2), w.size(-1)
- b = self.bias[:, :, ns - nd : ns, :ns]
- w = w * b - 1e4 * (1 - b)
- if attention_mask is not None:
- # Apply the attention mask
- w = w + attention_mask
- 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) # (batch, head, head_features, seq_length)
- else:
- return x.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
- def forward(self, x, layer_past=None, attention_mask=None, 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)
- if layer_past is not None:
- past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1] # transpose back cf below
- key = torch.cat((past_key, key), dim=-1)
- value = torch.cat((past_value, value), dim=-2)
- present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking
- attn_outputs = self._attn(query, key, value, attention_mask, head_mask)
- a = attn_outputs[0]
- a = self.merge_heads(a)
- a = self.c_proj(a)
- a = self.resid_dropout(a)
- outputs = [a, present] + attn_outputs[1:]
- return outputs # a, present, (attentions)
- class MLP(nn.Module):
- def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
- super().__init__()
- nx = config.n_embd
- self.c_fc = Conv1D(n_state, nx)
- self.c_proj = Conv1D(nx, n_state)
- self.act = gelu_new
- 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().__init__()
- nx = config.n_embd
- self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
- self.attn = Attention(nx, n_ctx, config, scale)
- self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
- self.mlp = MLP(4 * nx, config)
- def forward(self, x, layer_past=None, attention_mask=None, head_mask=None):
- output_attn = self.attn(
- self.ln_1(x), layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask
- )
- a = output_attn[0] # output_attn: a, present, (attentions)
- x = x + a
- m = self.mlp(self.ln_2(x))
- x = x + m
- return x, output_attn[1]
- # outputs = [x] + output_attn[1:]
- # return outputs # x, present, (attentions)
- class GPT2PreTrainedModel(PreTrainedModel):
- """ An abstract class to handle weights initialization and
- a simple interface for downloading and loading pretrained models.
- """
- config_class = GPT2Config
- pretrained_model_archive_map = GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
- load_tf_weights = load_tf_weights_in_gpt2
- base_model_prefix = "transformer"
- def __init__(self, *inputs, **kwargs):
- super().__init__(*inputs, **kwargs)
- 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)
- GPT2_START_DOCSTRING = r"""
- This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#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.
- Parameters:
- config (:class:`~transformers.GPT2Config`): 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:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
- """
- GPT2_INPUTS_DOCSTRING = r"""
- Args:
- input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
- Indices of input sequence tokens in the vocabulary.
- Indices can be obtained using :class:`transformers.GPT2Tokenizer`.
- See :func:`transformers.PreTrainedTokenizer.encode` and
- :func:`transformers.PreTrainedTokenizer.encode_plus` for details.
- `What are input IDs? <../glossary.html#input-ids>`__
- past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
- Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
- (see `past` output below). Can be used to speed up sequential decoding. The token ids which have their past given to this model
- should not be passed as input ids as they have already been computed.
- attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
- Mask to avoid performing attention on padding token indices.
- Mask values selected in ``[0, 1]``:
- ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
- `What are attention masks? <../glossary.html#attention-mask>`__
- token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
- Segment token indices to indicate first and second portions of the inputs.
- Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
- corresponds to a `sentence B` token
- `What are token type IDs? <../glossary.html#token-type-ids>`_
- position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
- Indices of positions of each input sequence tokens in the position embeddings.
- Selected in the range ``[0, config.max_position_embeddings - 1]``.
- `What are position IDs? <../glossary.html#position-ids>`_
- head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
- Mask to nullify selected heads of the self-attention modules.
- Mask values selected in ``[0, 1]``:
- :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
- input_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
- Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
- This is useful if you want more control over how to convert `input_ids` indices into associated vectors
- than the model's internal embedding lookup matrix.
- """
- @add_start_docstrings(
- "The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
- GPT2_START_DOCSTRING,
- )
- class GPT2Model(GPT2PreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.output_hidden_states = config.output_hidden_states
- self.output_attentions = config.output_attentions
- self.output_past = config.output_past
- self.wte = nn.Embedding(config.vocab_size, config.n_embd)
- self.wpe = 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.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
- self.init_weights()
- def get_input_embeddings(self):
- return self.wte
- def set_input_embeddings(self, new_embeddings):
- self.wte = new_embeddings
- 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)
- @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids=None,
- past=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- ):
- r"""
- Return:
- :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
- last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
- Sequence of hidden-states at the last layer of the model.
- past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
- Contains pre-computed hidden-states (key and values in the attention blocks).
- Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
- should not be passed as input ids as they have already been computed.
- hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
- Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
- of shape :obj:`(batch_size, sequence_length, hidden_size)`.
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
- Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
- :obj:`(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::
- from transformers import GPT2Tokenizer, GPT2Model
- import torch
- tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
- model = GPT2Model.from_pretrained('gpt2')
- input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).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
- """
- if input_ids is not None and inputs_embeds is not None:
- raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
- elif input_ids is not None:
- input_shape = input_ids.size()
- input_ids = input_ids.view(-1, input_shape[-1])
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- else:
- raise ValueError("You have to specify either input_ids or inputs_embeds")
- if token_type_ids is not None:
- token_type_ids = token_type_ids.view(-1, input_shape[-1])
- if position_ids is not None:
- position_ids = position_ids.view(-1, input_shape[-1])
- if past is None:
- past_length = 0
- past = [None] * len(self.h)
- else:
- past_length = past[0][0].size(-2)
- if position_ids is None:
- device = input_ids.device if input_ids is not None else inputs_embeds.device
- position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
- position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
- # Attention mask.
- if attention_mask is not None:
- attention_mask = attention_mask.view(-1, input_shape[-1])
- # We create a 3D attention mask from a 2D tensor mask.
- # Sizes are [batch_size, 1, 1, to_seq_length]
- # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
- # this attention mask is more simple than the triangular masking of causal attention
- # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
- attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
- # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
- # masked positions, this operation will create a tensor which is 0.0 for
- # positions we want to attend and -10000.0 for masked positions.
- # Since we are adding it to the raw scores before the softmax, this is
- # effectively the same as removing these entirely.
- attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
- attention_mask = (1.0 - attention_mask) * -10000.0
- # 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
- if inputs_embeds is None:
- inputs_embeds = torch.utils.checkpoint.checkpoint(self.wte, input_ids)
- # inputs_embeds = self.wte(input_ids)
- # position_embeds = self.wpe(position_ids)
- position_embeds = torch.utils.checkpoint.checkpoint(self.wpe, position_ids)
- if token_type_ids is not None:
- # token_type_embeds = self.wte(token_type_ids)
- token_type_embeds = torch.utils.checkpoint.checkpoint(self.wte, 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),)
- presents = ()
- all_attentions = []
- all_hidden_states = ()
- for i, (block, layer_past) in enumerate(zip(self.h, past)):
- if self.output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),)
- # outputs = block(
- # hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i]
- # )
- outputs = torch.utils.checkpoint.checkpoint(block, hidden_states, layer_past, attention_mask, head_mask[i])
- hidden_states, present = outputs[:2]
- if self.output_past:
- presents = presents + (present,)
- if self.output_attentions:
- all_attentions.append(outputs[2])
- hidden_states = torch.utils.checkpoint.checkpoint(self.ln_f, hidden_states)
- # hidden_states = self.ln_f(hidden_states)
- hidden_states = hidden_states.view(*output_shape)
- # Add last hidden state
- if self.output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
- outputs = (hidden_states,)
- if self.output_past:
- outputs = outputs + (presents,)
- if self.output_hidden_states:
- outputs = outputs + (all_hidden_states,)
- if self.output_attentions:
- # let the number of heads free (-1) so we can extract attention even after head pruning
- attention_output_shape = input_shape[:-1] + (-1,) + all_attentions[0].shape[-2:]
- all_attentions = tuple(t.view(*attention_output_shape) for t in all_attentions)
- outputs = outputs + (all_attentions,)
- return outputs # last hidden state, (presents), (all hidden_states), (attentions)
- @add_start_docstrings(
- """The GPT2 Model transformer with a language modeling head on top
- (linear layer with weights tied to the input embeddings). """,
- GPT2_START_DOCSTRING,
- )
- class GPT2LMHeadModel(GPT2PreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.transformer = GPT2Model(config)
- self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
- self.init_weights()
- def get_output_embeddings(self):
- return self.lm_head
- def prepare_inputs_for_generation(self, input_ids, **kwargs):
- # only last token for inputs_ids if past is defined in kwargs
- if "past" in kwargs and kwargs["past"]:
- input_ids = input_ids[:, -1].unsqueeze(-1)
- inputs = {"input_ids": input_ids}
- inputs.update(kwargs)
- return inputs
- @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids=None,
- past=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- labels=None,
- ):
- r"""
- labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
- 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 ``[-100, 0, ..., config.vocab_size]``
- All labels set to ``-100`` are ignored (masked), the loss is only
- computed for labels in ``[0, ..., config.vocab_size]``
- Return:
- :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
- loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided)
- Language modeling loss.
- prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
- Contains pre-computed hidden-states (key and values in the attention blocks).
- Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
- should not be passed as input ids as they have already been computed.
- hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
- Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
- of shape :obj:`(batch_size, sequence_length, hidden_size)`.
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
- Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
- :obj:`(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::
- import torch
- from transformers import GPT2Tokenizer, GPT2LMHeadModel
- tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
- model = GPT2LMHeadModel.from_pretrained('gpt2')
- input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
- outputs = model(input_ids, labels=input_ids)
- loss, logits = outputs[:2]
- """
- transformer_outputs = self.transformer(
- input_ids,
- past=past,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- )
- 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()
- loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
- outputs = (loss,) + outputs
- return outputs # (loss), lm_logits, presents, (all hidden_states), (attentions)
- @add_start_docstrings(
- """The GPT2 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).
- """,
- GPT2_START_DOCSTRING,
- )
- class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- config.num_labels = 1
- self.transformer = GPT2Model(config)
- self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
- self.multiple_choice_head = SequenceSummary(config)
- self.init_weights()
- def get_output_embeddings(self):
- return self.lm_head
- @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)
- def forward(
- self,
- input_ids=None,
- past=None,
- attention_mask=None,
- token_type_ids=None,
- position_ids=None,
- head_mask=None,
- inputs_embeds=None,
- mc_token_ids=None,
- lm_labels=None,
- mc_labels=None,
- ):
- r"""
- mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
- Index of the classification token in each input sequence.
- Selected in the range ``[0, input_ids.size(-1) - 1[``.
- lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`)
- 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 ``-100`` are ignored (masked), the loss is only
- computed for labels in ``[0, ..., config.vocab_size]``
- mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`, defaults to :obj:`None`)
- 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)
- Return:
- :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.GPT2Config`) and inputs:
- lm_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when ``lm_labels`` is provided):
- Language modeling loss.
- mc_loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`multiple_choice_labels` is provided):
- Multiple choice classification loss.
- lm_prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
- Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
- past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`):
- Contains pre-computed hidden-states (key and values in the attention blocks).
- Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model
- should not be passed as input ids as they have already been computed.
- hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
- Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
- of shape :obj:`(batch_size, sequence_length, hidden_size)`.
- Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
- Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
- :obj:`(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::
- import torch
- from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel
- tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
- model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
- # Add a [CLS] to the vocabulary (we should train it also!)
- tokenizer.add_special_tokens({'cls_token': '[CLS]'})
- model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings with the new vocabulary size
- print(tokenizer.cls_token_id, len(tokenizer)) # The newly token the last token of the vocabulary
- choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
- encoded_choices = [tokenizer.encode(s) for s in choices]
- cls_token_location = [tokens.index(tokenizer.cls_token_id) for tokens in encoded_choices]
- input_ids = torch.tensor(encoded_choices).unsqueeze(0) # Batch size: 1, number of choices: 2
- mc_token_ids = torch.tensor([cls_token_location]) # Batch size: 1
- outputs = model(input_ids, mc_token_ids=mc_token_ids)
- lm_prediction_scores, mc_prediction_scores = outputs[:2]
- """
- transformer_outputs = self.transformer(
- input_ids,
- past=past,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- )
- 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()
- 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, presents, (all hidden_states), (attentions)
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement