701 lines
32 KiB
Python
701 lines
32 KiB
Python
# coding=utf-8
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# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch OpenAI GPT model."""
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import json
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import logging
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import math
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import os
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import torch
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import torch.nn as nn
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from torch.nn import CrossEntropyLoss
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from .configuration_openai import OpenAIGPTConfig
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from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
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from .modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, prune_conv1d_layer
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logger = logging.getLogger(__name__)
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP = {
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"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-pytorch_model.bin"
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}
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def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
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""" Load tf pre-trained weights in a pytorch model (from NumPy arrays here)
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"""
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import re
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import numpy as np
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if ".ckpt" in openai_checkpoint_folder_path:
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openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)
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logger.info("Loading weights from {}".format(openai_checkpoint_folder_path))
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with open(openai_checkpoint_folder_path + "/parameters_names.json", "r", encoding="utf-8") as names_handle:
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names = json.load(names_handle)
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with open(openai_checkpoint_folder_path + "/params_shapes.json", "r", encoding="utf-8") as shapes_handle:
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shapes = json.load(shapes_handle)
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offsets = np.cumsum([np.prod(shape) for shape in shapes])
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init_params = [np.load(openai_checkpoint_folder_path + "/params_{}.npy".format(n)) for n in range(10)]
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init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
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init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
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# This was used when we had a single embedding matrix for positions and tokens
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# init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
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# del init_params[1]
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init_params = [arr.squeeze() for arr in init_params]
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try:
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assert model.tokens_embed.weight.shape == init_params[1].shape
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assert model.positions_embed.weight.shape == init_params[0].shape
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except AssertionError as e:
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e.args += (model.tokens_embed.weight.shape, init_params[1].shape)
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e.args += (model.positions_embed.weight.shape, init_params[0].shape)
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raise
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model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
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model.positions_embed.weight.data = torch.from_numpy(init_params[0])
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names.pop(0)
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# Pop position and token embedding arrays
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init_params.pop(0)
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init_params.pop(0)
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for name, array in zip(names, init_params): # names[1:n_transfer], init_params[1:n_transfer]):
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name = name[6:] # skip "model/"
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assert name[-2:] == ":0"
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name = name[:-2]
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name = name.split("/")
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pointer = model
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for m_name in name:
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if re.fullmatch(r"[A-Za-z]+\d+", m_name):
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scope_names = re.split(r"(\d+)", m_name)
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else:
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scope_names = [m_name]
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if scope_names[0] == "g":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "b":
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pointer = getattr(pointer, "bias")
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elif scope_names[0] == "w":
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pointer = getattr(pointer, "weight")
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else:
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pointer = getattr(pointer, scope_names[0])
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if len(scope_names) >= 2:
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num = int(scope_names[1])
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pointer = pointer[num]
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try:
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assert pointer.shape == array.shape
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except AssertionError as e:
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e.args += (pointer.shape, array.shape)
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raise
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try:
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assert pointer.shape == array.shape
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except AssertionError as e:
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e.args += (pointer.shape, array.shape)
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raise
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logger.info("Initialize PyTorch weight {}".format(name))
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pointer.data = torch.from_numpy(array)
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return model
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def gelu(x):
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return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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def swish(x):
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return x * torch.sigmoid(x)
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ACT_FNS = {"relu": nn.ReLU, "swish": swish, "gelu": gelu}
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class Attention(nn.Module):
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def __init__(self, nx, n_ctx, config, scale=False):
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super().__init__()
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n_state = nx # in Attention: n_state=768 (nx=n_embd)
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# [switch nx => n_state from Block to Attention to keep identical to TF implem]
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assert n_state % config.n_head == 0
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self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
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self.n_head = config.n_head
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self.split_size = n_state
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self.scale = scale
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self.output_attentions = config.output_attentions
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self.c_attn = Conv1D(n_state * 3, nx)
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self.c_proj = Conv1D(n_state, nx)
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self.attn_dropout = nn.Dropout(config.attn_pdrop)
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self.resid_dropout = nn.Dropout(config.resid_pdrop)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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mask = torch.ones(self.n_head, self.split_size // self.n_head)
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heads = set(heads) - self.pruned_heads
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for head in heads:
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head -= sum(1 if h < head else 0 for h in self.pruned_heads)
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mask[head] = 0
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mask = mask.view(-1).contiguous().eq(1)
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index = torch.arange(len(mask))[mask].long()
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index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
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# Prune conv1d layers
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self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
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self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
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# Update hyper params
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self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
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self.n_head = self.n_head - len(heads)
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self.pruned_heads = self.pruned_heads.union(heads)
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def _attn(self, q, k, v, attention_mask=None, head_mask=None):
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w = torch.matmul(q, k)
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if self.scale:
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w = w / math.sqrt(v.size(-1))
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# w = w * self.bias + -1e9 * (1 - self.bias) # TF implem method: mask_attn_weights
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# XD: self.b may be larger than w, so we need to crop it
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b = self.bias[:, :, : w.size(-2), : w.size(-1)]
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w = w * b + -1e4 * (1 - b)
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if attention_mask is not None:
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# Apply the attention mask
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w = w + attention_mask
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w = nn.Softmax(dim=-1)(w)
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w = self.attn_dropout(w)
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# Mask heads if we want to
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if head_mask is not None:
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w = w * head_mask
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outputs = [torch.matmul(w, v)]
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if self.output_attentions:
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outputs.append(w)
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return outputs
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def merge_heads(self, x):
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x = x.permute(0, 2, 1, 3).contiguous()
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new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
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return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states
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def split_heads(self, x, k=False):
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new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
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x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
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if k:
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return x.permute(0, 2, 3, 1)
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else:
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return x.permute(0, 2, 1, 3)
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def forward(self, x, attention_mask=None, head_mask=None):
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x = self.c_attn(x)
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query, key, value = x.split(self.split_size, dim=2)
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query = self.split_heads(query)
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key = self.split_heads(key, k=True)
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value = self.split_heads(value)
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attn_outputs = self._attn(query, key, value, attention_mask, head_mask)
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a = attn_outputs[0]
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a = self.merge_heads(a)
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a = self.c_proj(a)
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a = self.resid_dropout(a)
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outputs = [a] + attn_outputs[1:]
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return outputs # a, (attentions)
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class MLP(nn.Module):
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def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
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super().__init__()
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nx = config.n_embd
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self.c_fc = Conv1D(n_state, nx)
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self.c_proj = Conv1D(nx, n_state)
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self.act = ACT_FNS[config.afn]
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self.dropout = nn.Dropout(config.resid_pdrop)
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def forward(self, x):
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h = self.act(self.c_fc(x))
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h2 = self.c_proj(h)
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return self.dropout(h2)
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class Block(nn.Module):
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def __init__(self, n_ctx, config, scale=False):
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super().__init__()
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nx = config.n_embd
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self.attn = Attention(nx, n_ctx, config, scale)
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self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
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self.mlp = MLP(4 * nx, config)
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self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
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def forward(self, x, attention_mask=None, head_mask=None):
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attn_outputs = self.attn(x, attention_mask=attention_mask, head_mask=head_mask)
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a = attn_outputs[0]
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n = self.ln_1(x + a)
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m = self.mlp(n)
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h = self.ln_2(n + m)
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outputs = [h] + attn_outputs[1:]
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return outputs
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class OpenAIGPTPreTrainedModel(PreTrainedModel):
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""" An abstract class to handle weights initialization and
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a simple interface for downloading and loading pretrained models.
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"""
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config_class = OpenAIGPTConfig
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pretrained_model_archive_map = OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
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load_tf_weights = load_tf_weights_in_openai_gpt
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base_model_prefix = "transformer"
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def _init_weights(self, module):
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""" Initialize the weights.
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"""
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if isinstance(module, (nn.Linear, nn.Embedding, Conv1D)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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OPENAI_GPT_START_DOCSTRING = r"""
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This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
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usage and behavior.
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Parameters:
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config (:class:`~transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the configuration.
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Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
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"""
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OPENAI_GPT_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary.
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Indices can be obtained using :class:`transformers.OpenAIGPTTokenizer`.
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See :func:`transformers.PreTrainedTokenizer.encode` and
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:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
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`What are input IDs? <../glossary.html#input-ids>`__
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attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
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Mask to avoid performing attention on padding token indices.
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Mask values selected in ``[0, 1]``:
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``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
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`What are attention masks? <../glossary.html#attention-mask>`__
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token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
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Segment token indices to indicate first and second portions of the inputs.
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Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
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corresponds to a `sentence B` token
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`What are token type IDs? <../glossary.html#token-type-ids>`_
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position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
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Indices of positions of each input sequence tokens in the position embeddings.
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Selected in the range ``[0, config.max_position_embeddings - 1]``.
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`What are position IDs? <../glossary.html#position-ids>`_
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head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
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Mask to nullify selected heads of the self-attention modules.
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Mask values selected in ``[0, 1]``:
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:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
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input_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
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Optionally, instead of passing :obj:`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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"""
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@add_start_docstrings(
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"The bare OpenAI GPT transformer model outputting raw hidden-states without any specific head on top.",
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OPENAI_GPT_START_DOCSTRING,
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)
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class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.output_attentions = config.output_attentions
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self.output_hidden_states = config.output_hidden_states
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self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
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self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
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self.drop = nn.Dropout(config.embd_pdrop)
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self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
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self.init_weights()
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def get_input_embeddings(self):
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return self.tokens_embed
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def set_input_embeddings(self, new_embeddings):
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self.tokens_embed = new_embeddings
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def _prune_heads(self, heads_to_prune):
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""" Prunes heads of the model.
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heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
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"""
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for layer, heads in heads_to_prune.items():
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self.h[layer].attn.prune_heads(heads)
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@add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
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def forward(
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self,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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):
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r"""
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Return:
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:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.OpenAIGPTConfig`) and inputs:
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last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the last layer of the model.
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hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
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Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
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of shape :obj:`(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
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Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
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:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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Examples::
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from transformers import OpenAIGPTTokenizer, OpenAIGPTModel
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import torch
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tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
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model = OpenAIGPTModel.from_pretrained('openai-gpt')
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input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
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outputs = model(input_ids)
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last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
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"""
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = input_ids.size()
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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 input_ids or inputs_embeds")
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if position_ids is None:
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# Code is different from when we had a single embedding matrice from position and token embeddings
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
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# Attention mask.
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if attention_mask is not None:
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# We create a 3D attention mask from a 2D tensor mask.
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# Sizes are [batch_size, 1, 1, to_seq_length]
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# 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 = 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, attention_mask, 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,
|
|
)
|
|
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.transformer = OpenAIGPTModel(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
|
|
|
|
@add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
|
|
def forward(
|
|
self,
|
|
input_ids=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.OpenAIGPTConfig`) 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::
|
|
|
|
from transformers import OpenAIGPTTokenizer, OpenAIGPTLMHeadModel
|
|
import torch
|
|
|
|
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
|
|
model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
|
|
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,
|
|
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, (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):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
config.num_labels = 1
|
|
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()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
@add_start_docstrings_to_callable(OPENAI_GPT_INPUTS_DOCSTRING)
|
|
def forward(
|
|
self,
|
|
input_ids=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.OpenAIGPTConfig`) 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::
|
|
|
|
from transformers import OpenAIGPTTokenizer, OpenAIGPTDoubleHeadsModel
|
|
import torch
|
|
|
|
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!)
|
|
model.resize_token_embeddings(len(tokenizer))
|
|
|
|
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)-1, input_ids.size(-1)-1]).unsqueeze(0) # 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,
|
|
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, (all hidden_states), (attentions)
|