mirror of
https://github.com/huggingface/transformers.git
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950 lines
44 KiB
Python
950 lines
44 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-2 model."""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import collections
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import copy
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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 sys
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from io import open
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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 torch.nn.parameter import Parameter
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from .file_utils import cached_path, CONFIG_NAME, WEIGHTS_NAME
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from .modeling import BertLayerNorm as LayerNorm
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logger = logging.getLogger(__name__)
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PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
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"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin"}
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PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
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"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json"}
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def prune_conv1d_layer(layer, index, dim=1):
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""" Prune a Conv1D layer (a model parameters) to keep only entries in index.
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A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.
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Return the pruned layer as a new layer with requires_grad=True.
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Used to remove heads.
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"""
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index = index.to(layer.weight.device)
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W = layer.weight.index_select(dim, index).clone().detach()
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if dim == 0:
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b = layer.bias.clone().detach()
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else:
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b = layer.bias[index].clone().detach()
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new_size = list(layer.weight.size())
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new_size[dim] = len(index)
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new_layer = Conv1D(new_size[1], new_size[0]).to(layer.weight.device)
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new_layer.weight.requires_grad = False
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new_layer.weight.copy_(W.contiguous())
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new_layer.weight.requires_grad = True
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new_layer.bias.requires_grad = False
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new_layer.bias.copy_(b.contiguous())
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new_layer.bias.requires_grad = True
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return new_layer
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def load_tf_weights_in_gpt2(model, gpt2_checkpoint_path):
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""" Load tf checkpoints in a pytorch model
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"""
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try:
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import re
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import numpy as np
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import tensorflow as tf
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except ImportError:
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print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
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"https://www.tensorflow.org/install/ for installation instructions.")
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raise
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tf_path = os.path.abspath(gpt2_checkpoint_path)
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print("Converting TensorFlow checkpoint from {}".format(tf_path))
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# Load weights from TF model
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init_vars = tf.train.list_variables(tf_path)
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names = []
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arrays = []
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for name, shape in init_vars:
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print("Loading TF weight {} with shape {}".format(name, shape))
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array = tf.train.load_variable(tf_path, name)
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names.append(name)
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arrays.append(array.squeeze())
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for name, array in zip(names, arrays):
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name = name[6:] # skip "model/"
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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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l = re.split(r'(\d+)', m_name)
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else:
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l = [m_name]
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if l[0] == 'w' or l[0] == 'g':
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pointer = getattr(pointer, 'weight')
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elif l[0] == 'b':
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pointer = getattr(pointer, 'bias')
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elif l[0] == 'wpe' or l[0] == 'wte':
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pointer = getattr(pointer, l[0])
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pointer = getattr(pointer, 'weight')
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else:
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pointer = getattr(pointer, l[0])
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if len(l) >= 2:
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num = int(l[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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print("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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class GPT2Config(object):
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"""Configuration class to store the configuration of a `GPT2Model`.
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"""
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def __init__(
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self,
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vocab_size_or_config_json_file=50257,
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n_special=0,
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n_positions=1024,
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n_ctx=1024,
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n_embd=768,
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n_layer=12,
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n_head=12,
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resid_pdrop=0.1,
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embd_pdrop=0.1,
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attn_pdrop=0.1,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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predict_special_tokens=True
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):
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"""Constructs GPT2Config.
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Args:
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vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
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n_special: The number of special tokens to learn during fine-tuning ('[SEP]', '[CLF]', ...)
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n_positions: Number of positional embeddings.
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n_ctx: Size of the causal mask (usually same as n_positions).
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n_embd: Dimensionality of the embeddings and hidden states.
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n_layer: Number of hidden layers in the Transformer encoder.
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n_head: Number of attention heads for each attention layer in
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the Transformer encoder.
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layer_norm_epsilon: epsilon to use in the layer norm layers
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resid_pdrop: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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attn_pdrop: The dropout ratio for the attention
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probabilities.
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embd_pdrop: The dropout ratio for the embeddings.
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initializer_range: The sttdev of the truncated_normal_initializer for
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initializing all weight matrices.
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predict_special_tokens: should we predict special tokens (when the model has a LM head)
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"""
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if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
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and isinstance(vocab_size_or_config_json_file, unicode)):
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with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size_or_config_json_file, int):
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self.vocab_size = vocab_size_or_config_json_file
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self.n_special = n_special
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self.n_ctx = n_ctx
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attn_pdrop = attn_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.predict_special_tokens = predict_special_tokens
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else:
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raise ValueError(
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"First argument must be either a vocabulary size (int)"
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"or the path to a pretrained model config file (str)"
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)
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@property
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def total_tokens_embeddings(self):
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return self.vocab_size + self.n_special
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@classmethod
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def from_dict(cls, json_object):
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"""Constructs a `GPT2Config` from a Python dictionary of parameters."""
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config = GPT2Config(vocab_size_or_config_json_file=-1)
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for key, value in json_object.items():
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config.__dict__[key] = value
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return config
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@classmethod
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def from_json_file(cls, json_file):
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"""Constructs a `GPT2Config` from a json file of parameters."""
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with open(json_file, "r", encoding="utf-8") as reader:
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text = reader.read()
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return cls.from_dict(json.loads(text))
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def __repr__(self):
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return str(self.to_json_string())
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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def to_json_file(self, json_file_path):
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""" Save this instance to a json file."""
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with open(json_file_path, "w", encoding='utf-8') as writer:
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writer.write(self.to_json_string())
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class Conv1D(nn.Module):
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def __init__(self, nf, nx):
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super(Conv1D, self).__init__()
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self.nf = nf
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w = torch.empty(nx, nf)
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nn.init.normal_(w, std=0.02)
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self.weight = Parameter(w)
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self.bias = Parameter(torch.zeros(nf))
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def forward(self, x):
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size_out = x.size()[:-1] + (self.nf,)
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x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
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x = x.view(*size_out)
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return x
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class Attention(nn.Module):
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def __init__(self, nx, n_ctx, config, scale=False, output_attentions=False, keep_multihead_output=False):
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super(Attention, self).__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 = output_attentions
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self.keep_multihead_output = keep_multihead_output
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self.multihead_output = None
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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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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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for head in 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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def _attn(self, q, k, v, 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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nd, ns = w.size(-2), w.size(-1)
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b = self.bias[:, :, ns-nd:ns, :ns]
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w = w * b - 1e4 * (1 - b)
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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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if self.output_attentions:
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return w, torch.matmul(w, v)
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return torch.matmul(w, v)
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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) # (batch, head, head_features, seq_length)
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else:
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return x.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
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def forward(self, x, layer_past=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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if layer_past is not None:
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past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1] # transpose back cf below
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key = torch.cat((past_key, key), dim=-1)
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value = torch.cat((past_value, value), dim=-2)
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present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking
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a = self._attn(query, key, value, head_mask)
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if self.keep_multihead_output:
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self.multihead_output = a
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self.multihead_output.retain_grad()
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if self.output_attentions:
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attentions, a = a
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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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if self.output_attentions:
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return attentions, a, present
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return a, present
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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(MLP, self).__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 = gelu
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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, output_attentions=False, keep_multihead_output=False):
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super(Block, self).__init__()
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nx = config.n_embd
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self.output_attentions = output_attentions
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self.ln_1 = LayerNorm(nx, eps=config.layer_norm_epsilon)
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self.attn = Attention(nx, n_ctx, config, scale, output_attentions, keep_multihead_output)
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self.ln_2 = LayerNorm(nx, eps=config.layer_norm_epsilon)
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self.mlp = MLP(4 * nx, config)
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def forward(self, x, layer_past=None, head_mask=None):
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output_attn = self.attn(self.ln_1(x), layer_past=layer_past, head_mask=head_mask)
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if self.output_attentions:
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attentions, a, present = output_attn
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else:
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a, present = output_attn
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x = x + a
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m = self.mlp(self.ln_2(x))
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x = x + m
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if self.output_attentions:
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return attentions, x, present
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return x, present
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class GPT2LMHead(nn.Module):
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""" Language Model Head for the transformer """
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def __init__(self, model_embeddings_weights, config):
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super(GPT2LMHead, self).__init__()
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self.n_embd = config.n_embd
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self.vocab_size = config.vocab_size
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self.predict_special_tokens = config.predict_special_tokens
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embed_shape = model_embeddings_weights.shape
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self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
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self.set_embeddings_weights(model_embeddings_weights)
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def set_embeddings_weights(self, model_embeddings_weights, predict_special_tokens=True):
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self.predict_special_tokens = predict_special_tokens
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self.decoder.weight = model_embeddings_weights # Tied weights
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def forward(self, hidden_state):
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lm_logits = self.decoder(hidden_state)
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if not self.predict_special_tokens:
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lm_logits = lm_logits[..., :self.vocab_size]
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return lm_logits
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class GPT2MultipleChoiceHead(nn.Module):
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""" Classifier Head for the transformer """
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def __init__(self, config):
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super(GPT2MultipleChoiceHead, self).__init__()
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self.n_embd = config.n_embd
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self.dropout = nn.Dropout2d(config.resid_pdrop) # To reproduce the noise_shape parameter of TF implementation
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self.linear = nn.Linear(config.n_embd, 1)
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nn.init.normal_(self.linear.weight, std=0.02)
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nn.init.normal_(self.linear.bias, 0)
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def forward(self, hidden_states, mc_token_ids):
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# Classification logits
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# hidden_state (bsz, num_choices, seq_length, hidden_size)
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# mc_token_ids (bsz, num_choices)
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mc_token_ids = mc_token_ids.unsqueeze(-1).unsqueeze(-1).expand(-1, -1, -1, hidden_states.size(-1))
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# (bsz, num_choices, 1, hidden_size)
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multiple_choice_h = hidden_states.gather(2, mc_token_ids).squeeze(2)
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# (bsz, num_choices, hidden_size)
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multiple_choice_h = self.dropout(multiple_choice_h.transpose(1, 2)).transpose(1, 2)
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multiple_choice_logits = self.linear(multiple_choice_h).squeeze(-1)
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# (bsz, num_choices)
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return multiple_choice_logits
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class GPT2PreTrainedModel(nn.Module):
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""" An abstract class to handle weights initialization and
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a simple interface for dowloading and loading pretrained models.
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"""
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def __init__(self, config, *inputs, **kwargs):
|
|
super(GPT2PreTrainedModel, self).__init__()
|
|
if not isinstance(config, GPT2Config):
|
|
raise ValueError(
|
|
"Parameter config in `{}(config)` should be an instance of class `GPT2Config`. "
|
|
"To create a model from a pretrained model use "
|
|
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
|
|
self.__class__.__name__, self.__class__.__name__
|
|
)
|
|
)
|
|
self.config = config
|
|
|
|
def init_weights(self, module):
|
|
""" Initialize the weights.
|
|
"""
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
|
# 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)
|
|
elif isinstance(module, LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
|
|
"""
|
|
Instantiate a GPT2PreTrainedModel from a pre-trained model file or a pytorch state dict.
|
|
Download and cache the pre-trained model file if needed.
|
|
|
|
Params:
|
|
pretrained_model_name_or_path: either:
|
|
- a str with the name of a pre-trained model to load selected in the list of:
|
|
. `gpt2`
|
|
- a path or url to a pretrained model archive containing:
|
|
. `gpt2_config.json` a configuration file for the model
|
|
. `pytorch_model.bin` a PyTorch dump of a GPT2Model instance
|
|
- a path or url to a pretrained model archive containing:
|
|
. `gpt2_config.json` a configuration file for the model
|
|
. a TensorFlow checkpoint with trained weights
|
|
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
|
|
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
|
|
state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
|
|
*inputs, **kwargs: additional input for the specific GPT2 class
|
|
"""
|
|
state_dict = kwargs.get('state_dict', None)
|
|
kwargs.pop('state_dict', None)
|
|
cache_dir = kwargs.get('cache_dir', None)
|
|
kwargs.pop('cache_dir', None)
|
|
from_tf = kwargs.get('from_tf', False)
|
|
kwargs.pop('from_tf', None)
|
|
num_special_tokens = kwargs.get('num_special_tokens', None)
|
|
kwargs.pop('num_special_tokens', None)
|
|
|
|
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
|
|
archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
|
|
config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
|
|
else:
|
|
archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
|
|
config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
|
|
# redirect to the cache, if necessary
|
|
try:
|
|
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
|
|
except EnvironmentError:
|
|
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
|
|
logger.error(
|
|
"Couldn't reach server at '{}' to download pretrained weights.".format(
|
|
archive_file))
|
|
else:
|
|
logger.error(
|
|
"Model name '{}' was not found in model name list ({}). "
|
|
"We assumed '{}' was a path or url but couldn't find file {} "
|
|
"at this path or url.".format(
|
|
pretrained_model_name_or_path, ", ".join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()), pretrained_model_name_or_path,
|
|
archive_file
|
|
)
|
|
)
|
|
return None
|
|
try:
|
|
resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
|
|
except EnvironmentError:
|
|
if pretrained_model_name_or_path in PRETRAINED_CONFIG_ARCHIVE_MAP:
|
|
logger.error(
|
|
"Couldn't reach server at '{}' to download pretrained model configuration file.".format(
|
|
config_file))
|
|
else:
|
|
logger.error(
|
|
"Model name '{}' was not found in model name list ({}). "
|
|
"We assumed '{}' was a path or url but couldn't find file {} "
|
|
"at this path or url.".format(
|
|
pretrained_model_name_or_path, ", ".join(PRETRAINED_CONFIG_ARCHIVE_MAP.keys()), pretrained_model_name_or_path,
|
|
config_file
|
|
)
|
|
)
|
|
return None
|
|
if resolved_archive_file == archive_file and resolved_config_file == config_file:
|
|
logger.info("loading weights file {}".format(archive_file))
|
|
logger.info("loading configuration file {}".format(config_file))
|
|
else:
|
|
logger.info("loading weights file {} from cache at {}".format(
|
|
archive_file, resolved_archive_file))
|
|
logger.info("loading configuration file {} from cache at {}".format(
|
|
config_file, resolved_config_file))
|
|
# Load config
|
|
config = GPT2Config.from_json_file(resolved_config_file)
|
|
logger.info("Model config {}".format(config))
|
|
# Instantiate model.
|
|
model = cls(config, *inputs, **kwargs)
|
|
if state_dict is None and not from_tf:
|
|
state_dict = torch.load(resolved_archive_file, map_location='cpu')
|
|
if from_tf:
|
|
# Directly load from a TensorFlow checkpoint (stored as NumPy array)
|
|
return load_tf_weights_in_gpt2(model, resolved_archive_file)
|
|
|
|
old_keys = []
|
|
new_keys = []
|
|
for key in state_dict.keys():
|
|
new_key = None
|
|
if key.endswith(".g"):
|
|
new_key = key[:-2] + ".weight"
|
|
elif key.endswith(".b"):
|
|
new_key = key[:-2] + ".bias"
|
|
elif key.endswith(".w"):
|
|
new_key = key[:-2] + ".weight"
|
|
if new_key:
|
|
old_keys.append(key)
|
|
new_keys.append(new_key)
|
|
for old_key, new_key in zip(old_keys, new_keys):
|
|
state_dict[new_key] = state_dict.pop(old_key)
|
|
|
|
missing_keys = []
|
|
unexpected_keys = []
|
|
error_msgs = []
|
|
# copy state_dict so _load_from_state_dict can modify it
|
|
metadata = getattr(state_dict, "_metadata", None)
|
|
state_dict = state_dict.copy()
|
|
if metadata is not None:
|
|
state_dict._metadata = metadata
|
|
|
|
def load(module, prefix=""):
|
|
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
|
|
module._load_from_state_dict(
|
|
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs
|
|
)
|
|
for name, child in module._modules.items():
|
|
if child is not None:
|
|
load(child, prefix + name + ".")
|
|
|
|
start_model = model
|
|
if hasattr(model, "transformer") and all(not s.startswith('transformer.') for s in state_dict.keys()):
|
|
start_model = model.transformer
|
|
load(start_model, prefix="")
|
|
|
|
if len(missing_keys) > 0:
|
|
logger.info(
|
|
"Weights of {} not initialized from pretrained model: {}".format(model.__class__.__name__, missing_keys)
|
|
)
|
|
if len(unexpected_keys) > 0:
|
|
logger.info(
|
|
"Weights from pretrained model not used in {}: {}".format(model.__class__.__name__, unexpected_keys)
|
|
)
|
|
if len(error_msgs) > 0:
|
|
raise RuntimeError(
|
|
"Error(s) in loading state_dict for {}:\n\t{}".format(model.__class__.__name__, "\n\t".join(error_msgs))
|
|
)
|
|
|
|
# Add additional embeddings for special tokens if needed
|
|
# This step also make sure we are still sharing the output and input embeddings after loading weights
|
|
model.set_num_special_tokens(num_special_tokens if num_special_tokens is not None else config.n_special)
|
|
return model
|
|
|
|
|
|
class GPT2Model(GPT2PreTrainedModel):
|
|
"""OpenAI GPT-2 model ("Language Models are Unsupervised Multitask Learners").
|
|
|
|
GPT-2 use a single embedding matrix to store the word and special embeddings.
|
|
Special tokens embeddings are additional tokens that are not pre-trained: [SEP], [CLS]...
|
|
Special tokens need to be trained during the fine-tuning if you use them.
|
|
The number of special embeddings can be controled using the `set_num_special_tokens(num_special_tokens)` function.
|
|
|
|
The embeddings are ordered as follow in the token embeddings matrice:
|
|
[0, ----------------------
|
|
... -> word embeddings
|
|
config.vocab_size - 1, ______________________
|
|
config.vocab_size,
|
|
... -> special embeddings
|
|
config.vocab_size + config.n_special - 1] ______________________
|
|
|
|
where total_tokens_embeddings can be obtained as config.total_tokens_embeddings and is:
|
|
total_tokens_embeddings = config.vocab_size + config.n_special
|
|
You should use the associate indices to index the embeddings.
|
|
|
|
Params:
|
|
`config`: a GPT2Config class instance with the configuration to build a new model
|
|
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
|
|
`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
|
|
This can be used to compute head importance metrics. Default: False
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] (or more generally [d_1, ..., d_n, sequence_length]
|
|
were d_1 ... d_n are arbitrary dimensions) with the word BPE token indices selected in the range [0, config.vocab_size[
|
|
`position_ids`: an optional torch.LongTensor with the same shape as input_ids
|
|
with the position indices (selected in the range [0, config.n_positions - 1[.
|
|
`token_type_ids`: an optional torch.LongTensor with the same shape as input_ids
|
|
You can use it to add a third type of embedding to each input token in the sequence
|
|
(the previous two being the word and position embeddings).
|
|
The input, position and token_type embeddings are summed inside the Transformer before the first
|
|
self-attention block.
|
|
`past`: an optional list of torch.LongTensor that contains pre-computed hidden-states
|
|
(key and values in the attention blocks) to speed up sequential decoding
|
|
(this is the presents output of the model, cf. below).
|
|
`head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
|
|
It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
|
|
|
|
Outputs a tuple consisting of:
|
|
`hidden_states`: a list of all the encoded-hidden-states in the model (length of the list: number of layers + 1 for the output of the embeddings)
|
|
as torch.FloatTensor of size [batch_size, sequence_length, hidden_size]
|
|
(or more generally [d_1, ..., d_n, hidden_size] were d_1 ... d_n are the dimension of input_ids)
|
|
`presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as
|
|
torch.FloatTensors. They can be reused to speed up sequential decoding.
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into BPE token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
|
|
config = modeling_gpt2.GPT2Config()
|
|
|
|
model = modeling_gpt2.GPT2Model(config)
|
|
hidden_states, presents = model(input_ids)
|
|
```
|
|
"""
|
|
|
|
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
|
super(GPT2Model, self).__init__(config)
|
|
self.output_attentions = output_attentions
|
|
self.wte = nn.Embedding(config.total_tokens_embeddings, config.n_embd)
|
|
self.wpe = nn.Embedding(config.n_positions, config.n_embd)
|
|
self.drop = nn.Dropout(config.embd_pdrop)
|
|
block = Block(config.n_ctx, config, scale=True, output_attentions=output_attentions,
|
|
keep_multihead_output=keep_multihead_output)
|
|
self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(config.n_layer)])
|
|
self.ln_f = LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
|
|
|
self.apply(self.init_weights)
|
|
|
|
def set_num_special_tokens(self, num_special_tokens):
|
|
" Update input embeddings with new embedding matrice if needed "
|
|
if self.config.n_special == num_special_tokens:
|
|
return
|
|
# Update config
|
|
self.config.n_special = num_special_tokens
|
|
# Build new embeddings and initialize all new embeddings (in particular the special tokens)
|
|
old_embed = self.wte
|
|
self.wte = nn.Embedding(self.config.total_tokens_embeddings, self.config.n_embd)
|
|
self.wte.to(old_embed.weight.device)
|
|
self.init_weights(self.wte)
|
|
# Copy word embeddings from the previous weights
|
|
self.wte.weight.data[:self.config.vocab_size, :] = old_embed.weight.data[:self.config.vocab_size, :]
|
|
|
|
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 get_multihead_outputs(self):
|
|
""" Gather all multi-head outputs.
|
|
Return: list (layers) of multihead module outputs with gradients
|
|
"""
|
|
return [h.attn.multihead_output for h in self.h]
|
|
|
|
def forward(self, input_ids, position_ids=None, token_type_ids=None, past=None, head_mask=None):
|
|
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:
|
|
position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, 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_as(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.wte(input_ids)
|
|
position_embeds = self.wpe(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.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)):
|
|
all_hidden_states.append(hidden_states.view(*output_shape))
|
|
outputs = block(hidden_states, layer_past, head_mask[i])
|
|
if self.output_attentions:
|
|
attentions, hidden_states, present = outputs
|
|
all_attentions.append(attentions)
|
|
else:
|
|
hidden_states, present = outputs
|
|
presents.append(present)
|
|
hidden_states = self.ln_f(hidden_states)
|
|
all_hidden_states.append(hidden_states.view(*output_shape))
|
|
|
|
if self.output_attentions:
|
|
return all_attentions, all_hidden_states, presents
|
|
return all_hidden_states, presents
|
|
|
|
|
|
class GPT2LMHeadModel(GPT2PreTrainedModel):
|
|
"""OpenAI GPT-2 model with a Language Modeling head ("Language Models are Unsupervised Multitask Learners").
|
|
|
|
Params:
|
|
`config`: a GPT2Config class instance with the configuration to build a new model
|
|
`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
|
|
`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
|
|
This can be used to compute head importance metrics. Default: False
|
|
|
|
Inputs:
|
|
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length] (or more generally [d_1, ..., d_n, sequence_length]
|
|
were d_1 ... d_n are arbitrary dimensions) with the word BPE token indices selected in the range [0, config.vocab_size[
|
|
`position_ids`: an optional torch.LongTensor with the same shape as input_ids
|
|
with the position indices (selected in the range [0, config.n_positions - 1[.
|
|
`token_type_ids`: an optional torch.LongTensor with the same shape as input_ids
|
|
You can use it to add a third type of embedding to each input token in the sequence
|
|
(the previous two being the word and position embeddings).
|
|
The input, position and token_type embeddings are summed inside the Transformer before the first
|
|
self-attention block.
|
|
`lm_labels`: optional language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
|
|
with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
|
|
is only computed for the labels set in [0, ..., vocab_size]
|
|
`past`: an optional list of torch.LongTensor that contains pre-computed hidden-states
|
|
(key and values in the attention blocks) to speed up sequential decoding
|
|
(this is the presents output of the model, cf. below).
|
|
`head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
|
|
It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
|
|
|
|
Outputs:
|
|
if `lm_labels` is not `None`:
|
|
Outputs the language modeling loss.
|
|
else a tuple:
|
|
`lm_logits`: the language modeling logits as a torch.FloatTensor of size [batch_size, sequence_length, config.vocab_size]
|
|
(or more generally [d_1, ..., d_n, config.vocab_size] were d_1 ... d_n are the dimension of input_ids)
|
|
`presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as
|
|
torch.FloatTensors. They can be reused to speed up sequential decoding.
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into BPE token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
|
|
config = modeling_gpt2.GPT2Config()
|
|
|
|
model = modeling_gpt2.GPT2LMHeadModel(config)
|
|
lm_logits, presents = model(input_ids)
|
|
```
|
|
"""
|
|
|
|
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
|
super(GPT2LMHeadModel, self).__init__(config)
|
|
self.transformer = GPT2Model(config, output_attentions=output_attentions,
|
|
keep_multihead_output=keep_multihead_output)
|
|
self.lm_head = GPT2LMHead(self.transformer.wte.weight, config)
|
|
self.apply(self.init_weights)
|
|
|
|
def set_num_special_tokens(self, num_special_tokens, predict_special_tokens=True):
|
|
""" Update input and output embeddings with new embedding matrice
|
|
Make sure we are sharing the embeddings
|
|
"""
|
|
self.config.predict_special_tokens = self.transformer.config.predict_special_tokens = predict_special_tokens
|
|
self.transformer.set_num_special_tokens(num_special_tokens)
|
|
self.lm_head.set_embeddings_weights(self.transformer.wte.weight, predict_special_tokens=predict_special_tokens)
|
|
|
|
def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None, past=None, head_mask=None):
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transformer_output = self.transformer(input_ids, position_ids, token_type_ids, past, head_mask)
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if self.transformer.output_attentions:
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all_attentions, hidden_states, presents = transformer_output
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else:
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hidden_states, presents = transformer_output
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hidden_states = hidden_states[-1]
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lm_logits = self.lm_head(hidden_states)
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if lm_labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = lm_logits[..., :-1, :].contiguous()
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shift_labels = lm_labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss(ignore_index=-1)
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
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shift_labels.view(-1))
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return loss
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if self.transformer.output_attentions:
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return all_attentions, lm_logits, presents
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return lm_logits, presents
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class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
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"""OpenAI GPT-2 model with a Language Modeling and a Multiple Choice head ("Language Models are Unsupervised Multitask Learners").
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Params:
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`config`: a GPT2Config class instance with the configuration to build a new model
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`output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
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`keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
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This can be used to compute head importance metrics. Default: False
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|
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Inputs:
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`input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length] with the BPE token
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indices selected in the range [0, config.vocab_size[
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`mc_token_ids`: a torch.LongTensor of shape [batch_size, num_choices] with the index of the token from
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which we should take the hidden state to feed the multiple choice classifier (usually last token of the sequence)
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`position_ids`: an optional torch.LongTensor with the same shape as input_ids
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with the position indices (selected in the range [0, config.n_positions - 1[.
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`token_type_ids`: an optional torch.LongTensor with the same shape as input_ids
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You can use it to add a third type of embedding to each input token in the sequence
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(the previous two being the word and position embeddings).
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The input, position and token_type embeddings are summed inside the Transformer before the first
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self-attention block.
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`lm_labels`: optional language modeling labels: torch.LongTensor of shape [batch_size, num_choices, sequence_length]
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with indices selected in [-1, 0, ..., config.vocab_size]. All labels set to -1 are ignored (masked), the loss
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is only computed for the labels set in [0, ..., config.vocab_size]
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`multiple_choice_labels`: optional multiple choice labels: torch.LongTensor of shape [batch_size]
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|
with indices selected in [0, ..., num_choices].
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`past`: an optional list of torch.LongTensor that contains pre-computed hidden-states
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|
(key and values in the attention blocks) to speed up sequential decoding
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|
(this is the presents output of the model, cf. below).
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`head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
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It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
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|
|
|
Outputs:
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|
if `lm_labels` and `multiple_choice_labels` are not `None`:
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|
Outputs a tuple of losses with the language modeling loss and the multiple choice loss.
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|
else: a tuple with
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|
`lm_logits`: the language modeling logits as a torch.FloatTensor of size [batch_size, num_choices, sequence_length, config.vocab_size]
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|
`multiple_choice_logits`: the multiple choice logits as a torch.FloatTensor of size [batch_size, num_choices]
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|
`presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as
|
|
torch.FloatTensors. They can be reused to speed up sequential decoding.
|
|
|
|
Example usage:
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|
```python
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# Already been converted into BPE token ids
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input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]]]) # (bsz, number of choice, seq length)
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mc_token_ids = torch.LongTensor([[2], [1]]) # (bsz, number of choice)
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|
|
|
config = modeling_gpt2.GPT2Config()
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|
|
|
model = modeling_gpt2.GPT2DoubleHeadsModel(config)
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|
lm_logits, multiple_choice_logits, presents = model(input_ids, mc_token_ids)
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|
```
|
|
"""
|
|
|
|
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
|
super(GPT2DoubleHeadsModel, self).__init__(config)
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|
self.transformer = GPT2Model(config, output_attentions=output_attentions,
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|
keep_multihead_output=keep_multihead_output)
|
|
self.lm_head = GPT2LMHead(self.transformer.wte.weight, config)
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|
self.multiple_choice_head = GPT2MultipleChoiceHead(config)
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|
self.apply(self.init_weights)
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|
|
|
def set_num_special_tokens(self, num_special_tokens, predict_special_tokens=True):
|
|
""" Update input and output embeddings with new embedding matrice
|
|
Make sure we are sharing the embeddings
|
|
"""
|
|
self.config.predict_special_tokens = self.transformer.config.predict_special_tokens = predict_special_tokens
|
|
self.transformer.set_num_special_tokens(num_special_tokens)
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|
self.lm_head.set_embeddings_weights(self.transformer.wte.weight, predict_special_tokens=predict_special_tokens)
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|
|
|
def forward(self, input_ids, mc_token_ids, lm_labels=None, mc_labels=None, token_type_ids=None,
|
|
position_ids=None, past=None, head_mask=None):
|
|
transformer_output = self.transformer(input_ids, position_ids, token_type_ids, past, head_mask)
|
|
if self.transformer.output_attentions:
|
|
all_attentions, hidden_states, presents = transformer_output
|
|
else:
|
|
hidden_states, presents = transformer_output
|
|
hidden_states = hidden_states[-1]
|
|
|
|
lm_logits = self.lm_head(hidden_states)
|
|
mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
|
|
losses = []
|
|
if lm_labels is not None:
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_labels = lm_labels[..., 1:].contiguous()
|
|
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
|
losses.append(loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)))
|
|
if mc_labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
losses.append(loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)))
|
|
if losses:
|
|
return losses
|
|
if self.transformer.output_attentions:
|
|
return all_attentions, lm_logits, mc_logits, presents
|
|
return lm_logits, mc_logits, presents
|