mirror of
https://github.com/huggingface/transformers.git
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565 lines
27 KiB
Python
565 lines
27 KiB
Python
# coding=utf-8
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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 XLNet model.
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"""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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from __future__ import absolute_import, division, print_function, unicode_literals
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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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from torch import nn
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from torch.nn import CrossEntropyLoss
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from .file_utils import cached_path, WEIGHTS_NAME, CONFIG_NAME
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logger = logging.getLogger(__name__)
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PRETRAINED_MODEL_ARCHIVE_MAP = {
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'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-pytorch_model.bin",
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}
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PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
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}
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XLNET_CONFIG_NAME = 'xlnet_config.json'
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TF_WEIGHTS_NAME = 'model.ckpt'
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def load_tf_weights_in_xlnet(model, tf_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(tf_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)
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for name, array in zip(names, arrays):
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name = name.split('/')
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# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
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# which are not required for using pretrained model
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if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
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print("Skipping {}".format("/".join(name)))
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continue
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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] == 'kernel' or l[0] == 'gamma':
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pointer = getattr(pointer, 'weight')
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elif l[0] == 'output_bias' or l[0] == 'beta':
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pointer = getattr(pointer, 'bias')
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elif l[0] == 'output_weights':
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pointer = getattr(pointer, 'weight')
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elif l[0] == 'squad':
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pointer = getattr(pointer, 'classifier')
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else:
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try:
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pointer = getattr(pointer, l[0])
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except AttributeError:
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print("Skipping {}".format("/".join(name)))
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continue
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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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if m_name[-11:] == '_embeddings':
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pointer = getattr(pointer, 'weight')
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elif m_name == 'kernel':
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array = np.transpose(array)
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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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"""Implementation of the gelu activation function.
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For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
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0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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Also see https://arxiv.org/abs/1606.08415
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"""
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return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
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def swish(x):
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return x * torch.sigmoid(x)
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ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
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class XLNetBaseConfig(object):
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@classmethod
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def from_dict(cls, json_object):
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"""Constructs a `XLNetConfig` from a Python dictionary of parameters."""
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config = XLNetConfig(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 `XLNetConfig` 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 XLNetConfig(XLNetBaseConfig):
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"""Configuration class to store the configuration of a `XLNetModel`.
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"""
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def __init__(self,
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vocab_size_or_config_json_file,
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d_model=768,
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n_layer=12,
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n_head=12,
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d_inner=3072,
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ff_activation="gelu",
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untie_r=True,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12):
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"""Constructs XLNetConfig.
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Args:
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vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLNetModel`.
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d_model: Size of the encoder layers and the pooler layer.
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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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d_inner: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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ff_activation: The non-linear activation function (function or string) in the
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encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
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untie_r: untie relative position biases
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dropout: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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dropatt: The dropout ratio for the attention
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probabilities.
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max_position_embeddings: The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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type_vocab_size: The vocabulary size of the `token_type_ids` passed into
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`XLNetModel`.
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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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layer_norm_eps: The epsilon used by LayerNorm.
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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.d_model = d_model
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self.n_layer = n_layer
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self.n_head = n_head
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self.ff_activation = ff_activation
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self.d_inner = d_inner
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self.untie_r = untie_r
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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else:
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raise ValueError("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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class XLNetRunConfig(XLNetBaseConfig):
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"""XLNetRunConfig contains hyperparameters that could be different
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between pretraining and finetuning.
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These hyperparameters can also be changed from run to run.
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We store them separately from XLNetConfig for flexibility.
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"""
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def __init__(self,
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dropout,
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dropatt,
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init="normal",
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init_range=0.1,
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init_std=0.02,
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mem_len=None,
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reuse_len=None,
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bi_data=False,
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clamp_len=-1,
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same_length=False):
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"""
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Args:
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dropout: float, dropout rate.
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dropatt: float, dropout rate on attention probabilities.
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init: str, the initialization scheme, either "normal" or "uniform".
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init_range: float, initialize the parameters with a uniform distribution
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in [-init_range, init_range]. Only effective when init="uniform".
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init_std: float, initialize the parameters with a normal distribution
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with mean 0 and stddev init_std. Only effective when init="normal".
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mem_len: int, the number of tokens to cache.
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reuse_len: int, the number of tokens in the currect batch to be cached
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and reused in the future.
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bi_data: bool, whether to use bidirectional input pipeline.
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Usually set to True during pretraining and False during finetuning.
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clamp_len: int, clamp all relative distances larger than clamp_len.
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-1 means no clamping.
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same_length: bool, whether to use the same attention length for each token.
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"""
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self.init = init
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self.init_range = init_range
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self.init_std = init_std
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self.dropout = dropout
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self.dropatt = dropatt
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self.mem_len = mem_len
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self.reuse_len = reuse_len
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self.bi_data = bi_data
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self.clamp_len = clamp_len
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self.same_length = same_length
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try:
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from apex.normalization.fused_layer_norm import FusedLayerNorm as XLNetLayerNorm
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except ImportError:
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logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
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class XLNetLayerNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-12):
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"""Construct a layernorm module in the TF style (epsilon inside the square root).
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"""
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super(XLNetLayerNorm, self).__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.bias = nn.Parameter(torch.zeros(hidden_size))
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self.variance_epsilon = eps
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def forward(self, x):
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u = x.mean(-1, keepdim=True)
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s = (x - u).pow(2).mean(-1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.variance_epsilon)
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return self.weight * x + self.bias
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class XLNetPreTrainedModel(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):
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super(XLNetPreTrainedModel, self).__init__()
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if not isinstance(config, XLNetConfig):
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raise ValueError(
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"Parameter config in `{}(config)` should be an instance of class `XLNetConfig`. "
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"To create a model from a Google pretrained model use "
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"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
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self.__class__.__name__, self.__class__.__name__
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))
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self.config = config
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def init_xlnet_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)):
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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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elif isinstance(module, XLNetLayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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if isinstance(module, nn.Linear) and module.bias is not None:
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module.bias.data.zero_()
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
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"""
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Instantiate a XLNetPreTrainedModel from a pre-trained model file or a pytorch state dict.
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Download and cache the pre-trained model file if needed.
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Params:
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pretrained_model_name_or_path: either:
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- a str with the name of a pre-trained model to load selected in the list of:
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. `xlnet-large-cased`
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- a path or url to a pretrained model archive containing:
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. `xlnet_config.json` a configuration file for the model
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. `pytorch_model.bin` a PyTorch dump of a XLNetForPreTraining instance
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- a path or url to a pretrained model archive containing:
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. `xlnet_config.json` a configuration file for the model
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. `model.chkpt` a TensorFlow checkpoint
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from_tf: should we load the weights from a locally saved TensorFlow checkpoint
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cache_dir: an optional path to a folder in which the pre-trained models will be cached.
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state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
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*inputs, **kwargs: additional input for the specific XLNet class
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(ex: num_labels for XLNetForSequenceClassification)
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"""
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state_dict = kwargs.get('state_dict', None)
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kwargs.pop('state_dict', None)
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cache_dir = kwargs.get('cache_dir', None)
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kwargs.pop('cache_dir', None)
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from_tf = kwargs.get('from_tf', False)
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kwargs.pop('from_tf', None)
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if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
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archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
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config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
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else:
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if from_tf:
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# Directly load from a TensorFlow checkpoint
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archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME)
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config_file = os.path.join(pretrained_model_name_or_path, XLNET_CONFIG_NAME)
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else:
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archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
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config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
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# redirect to the cache, if necessary
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try:
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resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
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except EnvironmentError:
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if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
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logger.error(
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"Couldn't reach server at '{}' to download pretrained weights.".format(
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archive_file))
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else:
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logger.error(
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"Model name '{}' was not found in model name list ({}). "
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"We assumed '{}' was a path or url but couldn't find any file "
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"associated to this path or url.".format(
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pretrained_model_name_or_path,
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', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
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archive_file))
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return None
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try:
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resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
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except EnvironmentError:
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if pretrained_model_name_or_path in PRETRAINED_CONFIG_ARCHIVE_MAP:
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logger.error(
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"Couldn't reach server at '{}' to download pretrained model configuration file.".format(
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config_file))
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else:
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logger.error(
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"Model name '{}' was not found in model name list ({}). "
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"We assumed '{}' was a path or url but couldn't find any file "
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"associated to this path or url.".format(
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pretrained_model_name_or_path,
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', '.join(PRETRAINED_CONFIG_ARCHIVE_MAP.keys()),
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config_file))
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return None
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if resolved_archive_file == archive_file and resolved_config_file == config_file:
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logger.info("loading weights file {}".format(archive_file))
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logger.info("loading configuration file {}".format(config_file))
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else:
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logger.info("loading weights file {} from cache at {}".format(
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archive_file, resolved_archive_file))
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logger.info("loading configuration file {} from cache at {}".format(
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config_file, resolved_config_file))
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# Load config
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config = XLNetConfig.from_json_file(resolved_config_file)
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logger.info("Model config {}".format(config))
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# Instantiate model.
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model = cls(config, *inputs, **kwargs)
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if state_dict is None and not from_tf:
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state_dict = torch.load(resolved_archive_file, map_location='cpu')
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if from_tf:
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# Directly load from a TensorFlow checkpoint
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return load_tf_weights_in_xlnet(model, resolved_archive_file)
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# Load from a PyTorch state_dict
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missing_keys = []
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unexpected_keys = []
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error_msgs = []
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# copy state_dict so _load_from_state_dict can modify it
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metadata = getattr(state_dict, '_metadata', None)
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state_dict = state_dict.copy()
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if metadata is not None:
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state_dict._metadata = metadata
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def load(module, prefix=''):
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local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
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module._load_from_state_dict(
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state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
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for name, child in module._modules.items():
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if child is not None:
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load(child, prefix + name + '.')
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start_prefix = ''
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if not hasattr(model, 'xlnet') and any(s.startswith('xlnet.') for s in state_dict.keys()):
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start_prefix = 'xlnet.'
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load(model, prefix=start_prefix)
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if len(missing_keys) > 0:
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logger.info("Weights of {} not initialized from pretrained model: {}".format(
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model.__class__.__name__, missing_keys))
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if len(unexpected_keys) > 0:
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logger.info("Weights from pretrained model not used in {}: {}".format(
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model.__class__.__name__, unexpected_keys))
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if len(error_msgs) > 0:
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raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
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model.__class__.__name__, "\n\t".join(error_msgs)))
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return model
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class XLNetModel(XLNetPreTrainedModel):
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"""XLNet model ("XLNet: Generalized Autoregressive Pretraining for Language Understanding").
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Params:
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`config`: a XLNetConfig 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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Inputs:
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`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
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with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
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`extract_features.py`, `run_classifier.py` and `run_squad.py`)
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`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
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types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
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a `sentence B` token (see XLNet paper for more details).
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`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
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selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
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input sequence length in the current batch. It's the mask that we typically use for attention when
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a batch has varying length sentences.
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`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
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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: Tuple of (encoded_layers, pooled_output)
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`encoded_layers`: controled by `output_all_encoded_layers` argument:
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- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
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of each attention block (i.e. 12 full sequences for XLNet-base, 24 for XLNet-large), each
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encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
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- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
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to the last attention block of shape [batch_size, sequence_length, hidden_size],
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`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
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classifier pretrained on top of the hidden state associated to the first character of the
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input (`CLS`) to train on the Next-Sentence task (see XLNet's paper).
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Example usage:
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```python
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# Already been converted into WordPiece token ids
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input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
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input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
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token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
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config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
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num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
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model = modeling.XLNetModel(config=config)
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all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
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```
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"""
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def __init__(self, config, output_attentions=False, keep_multihead_output=False):
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super(XLNetModel, self).__init__(config)
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self.output_attentions = output_attentions
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self.apply(self.init_xlnet_weights)
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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.encoder.layer[layer].attention.prune_heads(heads)
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def get_multihead_outputs(self):
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""" Gather all multi-head outputs.
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Return: list (layers) of multihead module outputs with gradients
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"""
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return [layer.attention.self.multihead_output for layer in self.encoder.layer]
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def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True, head_mask=None):
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if attention_mask is None:
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attention_mask = torch.ones_like(input_ids)
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if token_type_ids is None:
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token_type_ids = torch.zeros_like(input_ids)
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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]
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|
# this attention mask is more simple than the triangular masking of causal attention
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# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
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|
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
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|
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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# positions we want to attend and -10000.0 for masked positions.
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|
# Since we are adding it to the raw scores before the softmax, this is
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|
# effectively the same as removing these entirely.
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|
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
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|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
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|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
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|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
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.num_hidden_layers, -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.num_hidden_layers
|
|
|
|
embedding_output = self.embeddings(input_ids, token_type_ids)
|
|
encoded_layers = self.encoder(embedding_output,
|
|
extended_attention_mask,
|
|
output_all_encoded_layers=output_all_encoded_layers,
|
|
head_mask=head_mask)
|
|
if self.output_attentions:
|
|
all_attentions, encoded_layers = encoded_layers
|
|
sequence_output = encoded_layers[-1]
|
|
pooled_output = self.pooler(sequence_output)
|
|
if not output_all_encoded_layers:
|
|
encoded_layers = encoded_layers[-1]
|
|
if self.output_attentions:
|
|
return all_attentions, encoded_layers, pooled_output
|
|
return encoded_layers, pooled_output
|
|
|