1359 lines
61 KiB
Python
1359 lines
61 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 functional as F
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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 build_tf_xlnet_to_pytorch_map(model, config):
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""" A map of modules from TF to PyTorch.
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I use a map to keep the PyTorch model as
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identical to the original PyTorch model as possible.
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"""
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tf_to_pt_map = {}
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if hasattr(model, 'transformer'):
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# We are loading pre-trained weights in a XLNetLMHeadModel => we will load also the output bias
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tf_to_pt_map['model/lm_loss/bias'] = model.lm_loss.bias
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# Now load the rest of the transformer
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model = model.transformer
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# Embeddings and output
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tf_to_pt_map.update({'model/transformer/word_embedding/lookup_table': model.word_embedding.weight,
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'model/transformer/mask_emb/mask_emb': model.mask_emb})
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# Transformer blocks
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for i, b in enumerate(model.layer):
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layer_str = "model/transformer/layer_%d/" % i
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tf_to_pt_map.update({
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layer_str + "rel_attn/LayerNorm/gamma": b.rel_attn.layer_norm.weight,
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layer_str + "rel_attn/LayerNorm/beta": b.rel_attn.layer_norm.bias,
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layer_str + "rel_attn/o/kernel": b.rel_attn.o,
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layer_str + "rel_attn/q/kernel": b.rel_attn.q,
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layer_str + "rel_attn/k/kernel": b.rel_attn.k,
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layer_str + "rel_attn/r/kernel": b.rel_attn.r,
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layer_str + "rel_attn/v/kernel": b.rel_attn.v,
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layer_str + "ff/LayerNorm/gamma": b.ff.layer_norm.weight,
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layer_str + "ff/LayerNorm/beta": b.ff.layer_norm.bias,
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layer_str + "ff/layer_1/kernel": b.ff.layer_1.weight,
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layer_str + "ff/layer_1/bias": b.ff.layer_1.bias,
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layer_str + "ff/layer_2/kernel": b.ff.layer_2.weight,
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layer_str + "ff/layer_2/bias": b.ff.layer_2.bias,
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})
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# Relative positioning biases
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if config.untie_r:
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r_r_list = []
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r_w_list = []
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r_s_list = []
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seg_embed_list = []
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for b in model.layer:
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r_r_list.append(b.rel_attn.r_r_bias)
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r_w_list.append(b.rel_attn.r_w_bias)
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r_s_list.append(b.rel_attn.r_s_bias)
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seg_embed_list.append(b.rel_attn.seg_embed)
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else:
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r_r_list = [model.r_r_bias]
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r_w_list = [model.r_w_bias]
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r_s_list = [model.r_s_bias]
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seg_embed_list = [model.seg_embed]
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tf_to_pt_map.update({
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'model/transformer/r_r_bias': r_r_list,
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'model/transformer/r_w_bias': r_w_list,
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'model/transformer/r_s_bias': r_s_list,
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'model/transformer/seg_embed': seg_embed_list})
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return tf_to_pt_map
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def load_tf_weights_in_xlnet(model, config, tf_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 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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# Build TF to PyTorch weights loading map
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tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config)
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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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tf_weights = {}
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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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tf_weights[name] = array
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for name, pointer in tf_to_pt_map.items():
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print("Importing {}".format(name))
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assert name in tf_weights
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array = tf_weights[name]
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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 'kernel' in name and 'ff' in name:
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print("Transposing")
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array = np.transpose(array)
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if isinstance(pointer, list):
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# Here we will split the TF weigths
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assert len(pointer) == array.shape[0]
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for i, p_i in enumerate(pointer):
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arr_i = array[i, ...]
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try:
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assert p_i.shape == arr_i.shape
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except AssertionError as e:
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e.args += (p_i.shape, arr_i.shape)
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raise
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print("Initialize PyTorch weight {} for layer {}".format(name, i))
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p_i.data = torch.from_numpy(arr_i)
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else:
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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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tf_weights.pop(name, None)
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tf_weights.pop(name + '/Adam', None)
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tf_weights.pop(name + '/Adam_1', None)
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print("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
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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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XLNet is using OpenAI GPT's gelu (not exactly the same as BERT)
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Also see https://arxiv.org/abs/1606.08415
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"""
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cdf = 0.5 * (1.0 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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return x * cdf
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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 `XLNetBaseConfig` from a Python dictionary of parameters."""
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config = cls(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 `XLNetBaseConfig` 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 update(self, other):
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dict_b = other.to_dict()
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for key, value in dict_b.items():
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self.__dict__[key] = value
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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=1024,
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n_layer=24,
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n_head=16,
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d_inner=4096,
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ff_activation="gelu",
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untie_r=True,
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attn_type="bi",
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max_position_embeddings=512,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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dropout=0.1,
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dropatt=0.1,
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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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"""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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attn_type: 'bi' for XLNet, 'uni' for Transformer-XL
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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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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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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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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.n_token = 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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assert d_model % n_head == 0
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self.d_head = d_model // 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.attn_type = attn_type
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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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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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=0.1,
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dropatt=0.1,
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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, d_model, 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(d_model))
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self.bias = nn.Parameter(torch.zeros(d_model))
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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 XLNetRelativeAttention(nn.Module):
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def __init__(self, config, output_attentions=False, keep_multihead_output=False):
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super(XLNetRelativeAttention, self).__init__()
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self.output_attentions = output_attentions
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if config.d_model % config.n_head != 0:
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.d_model, config.n_head))
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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.n_head = config.n_head
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self.d_head = config.d_head
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self.d_model = config.d_model
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self.scale = 1 / (config.d_head ** 0.5)
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self.q = nn.Parameter(torch.Tensor(config.d_model, self.n_head, self.d_head))
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self.k = nn.Parameter(torch.Tensor(config.d_model, self.n_head, self.d_head))
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self.v = nn.Parameter(torch.Tensor(config.d_model, self.n_head, self.d_head))
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self.o = nn.Parameter(torch.Tensor(config.d_model, self.n_head, self.d_head))
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self.r = nn.Parameter(torch.Tensor(config.d_model, self.n_head, self.d_head))
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self.r_r_bias = nn.Parameter(torch.Tensor(self.n_head, self.d_head))
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self.r_s_bias = nn.Parameter(torch.Tensor(self.n_head, self.d_head))
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self.r_w_bias = nn.Parameter(torch.Tensor(self.n_head, self.d_head))
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self.seg_embed = nn.Parameter(torch.Tensor(2, self.n_head, self.d_head))
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self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.dropout)
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def prune_heads(self, heads):
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raise NotImplementedError
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@staticmethod
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def rel_shift(x, klen=-1):
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"""perform relative shift to form the relative attention score."""
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x_size = x.shape
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x = x.reshape(x_size[1], x_size[0], x_size[2], x_size[3])
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x = x[1:, ...]
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x = x.reshape(x_size[0], x_size[1] - 1, x_size[2], x_size[3])
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x = x[:, 0:klen, :, :]
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return x
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def rel_attn_core(self, q_head, k_head_h, v_head_h, k_head_r, seg_mat=None, attn_mask=None):
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"""Core relative positional attention operations."""
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|
|
# content based attention score
|
|
ac = torch.einsum('ibnd,jbnd->ijbn', q_head + self.r_w_bias, k_head_h)
|
|
|
|
# position based attention score
|
|
bd = torch.einsum('ibnd,jbnd->ijbn', q_head + self.r_r_bias, k_head_r)
|
|
bd = self.rel_shift(bd, klen=ac.shape[1])
|
|
|
|
# segment based attention score
|
|
if seg_mat is None:
|
|
ef = 0
|
|
else:
|
|
ef = torch.einsum('ibnd,snd->ibns', q_head + self.r_s_bias, self.seg_embed)
|
|
ef = torch.einsum('ijbs,ibns->ijbn', seg_mat, ef)
|
|
|
|
# merge attention scores and perform masking
|
|
attn_score = (ac + bd + ef) * self.scale
|
|
if attn_mask is not None:
|
|
# attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
|
|
attn_score = attn_score - 1e30 * attn_mask
|
|
|
|
# attention probability
|
|
attn_prob = F.softmax(attn_score, dim=1)
|
|
attn_prob = self.dropout(attn_prob)
|
|
|
|
# attention output
|
|
attn_vec = torch.einsum('ijbn,jbnd->ibnd', attn_prob, v_head_h)
|
|
|
|
return attn_vec
|
|
|
|
def post_attention(self, h, attn_vec, residual=True):
|
|
"""Post-attention processing."""
|
|
# post-attention projection (back to `d_model`)
|
|
attn_out = torch.einsum('ibnd,hnd->ibh', attn_vec, self.o)
|
|
|
|
attn_out = self.dropout(attn_out)
|
|
if residual:
|
|
attn_out = attn_out + h
|
|
output = self.layer_norm(attn_out)
|
|
|
|
return output
|
|
|
|
def forward(self, h, g,
|
|
attn_mask_h, attn_mask_g,
|
|
r, seg_mat,
|
|
mems=None, target_mapping=None, head_mask=None):
|
|
if g is not None:
|
|
###### Two-stream attention with relative positional encoding.
|
|
# content based attention score
|
|
if mems is not None and mems.dim() > 1:
|
|
cat = torch.cat([mems, h], dim=0)
|
|
else:
|
|
cat = h
|
|
|
|
# content-based key head
|
|
k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)
|
|
|
|
# content-based value head
|
|
v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)
|
|
|
|
# position-based key head
|
|
k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)
|
|
|
|
##### h-stream
|
|
# content-stream query head
|
|
q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)
|
|
|
|
# core attention ops
|
|
attn_vec_h = self.rel_attn_core(
|
|
q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h)
|
|
|
|
# post processing
|
|
output_h = self.post_attention(h, attn_vec_h)
|
|
|
|
##### g-stream
|
|
# query-stream query head
|
|
q_head_g = torch.einsum('ibh,hnd->ibnd', g, self.q)
|
|
|
|
# core attention ops
|
|
if target_mapping is not None:
|
|
q_head_g = torch.einsum('mbnd,mlb->lbnd', q_head_g, target_mapping)
|
|
attn_vec_g = self.rel_attn_core(
|
|
q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g)
|
|
attn_vec_g = torch.einsum('lbnd,mlb->mbnd', attn_vec_g, target_mapping)
|
|
else:
|
|
attn_vec_g = self.rel_attn_core(
|
|
q_head_g, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_g)
|
|
|
|
# post processing
|
|
output_g = self.post_attention(g, attn_vec_g)
|
|
else:
|
|
###### Multi-head attention with relative positional encoding
|
|
if mems is not None and mems.dim() > 1:
|
|
cat = torch.cat([mems, h], dim=0)
|
|
else:
|
|
cat = h
|
|
|
|
# content heads
|
|
q_head_h = torch.einsum('ibh,hnd->ibnd', h, self.q)
|
|
k_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.k)
|
|
v_head_h = torch.einsum('ibh,hnd->ibnd', cat, self.v)
|
|
|
|
# positional heads
|
|
k_head_r = torch.einsum('ibh,hnd->ibnd', r, self.r)
|
|
|
|
# core attention ops
|
|
attn_vec = self.rel_attn_core(
|
|
q_head_h, k_head_h, v_head_h, k_head_r, seg_mat=seg_mat, attn_mask=attn_mask_h)
|
|
|
|
# post processing
|
|
output_h = self.post_attention(h, attn_vec)
|
|
output_g = None
|
|
|
|
|
|
# Mask heads if we want to
|
|
# if head_mask is not None:
|
|
# attention_probs = attention_probs * head_mask
|
|
|
|
# context_layer = torch.matmul(attention_probs, value_layer)
|
|
# if self.keep_multihead_output:
|
|
# self.multihead_output = context_layer
|
|
# self.multihead_output.retain_grad()
|
|
|
|
# context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
# new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
# context_layer = context_layer.view(*new_context_layer_shape)
|
|
|
|
# if self.output_attentions:
|
|
# attentions, self_output = self_output
|
|
# if self.output_attentions:
|
|
# return attentions, attention_output
|
|
return output_h, output_g
|
|
|
|
class XLNetFeedForward(nn.Module):
|
|
def __init__(self, config):
|
|
super(XLNetFeedForward, self).__init__()
|
|
self.layer_norm = XLNetLayerNorm(config.d_model, eps=config.layer_norm_eps)
|
|
self.layer_1 = nn.Linear(config.d_model, config.d_inner)
|
|
self.layer_2 = nn.Linear(config.d_inner, config.d_model)
|
|
self.dropout = nn.Dropout(config.dropout)
|
|
if isinstance(config.ff_activation, str) or (sys.version_info[0] == 2 and isinstance(config.ff_activation, unicode)):
|
|
self.activation_function = ACT2FN[config.ff_activation]
|
|
else:
|
|
self.activation_function = config.ff_activation
|
|
|
|
def forward(self, inp):
|
|
output = inp
|
|
output = self.layer_1(output)
|
|
output = self.activation_function(output)
|
|
output = self.dropout(output)
|
|
output = self.layer_2(output)
|
|
output = self.dropout(output)
|
|
output = self.layer_norm(output + inp)
|
|
return output
|
|
|
|
class XLNetLayer(nn.Module):
|
|
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
|
super(XLNetLayer, self).__init__()
|
|
self.output_attentions = output_attentions
|
|
self.rel_attn = XLNetRelativeAttention(config, output_attentions=output_attentions,
|
|
keep_multihead_output=keep_multihead_output)
|
|
self.ff = XLNetFeedForward(config)
|
|
self.dropout = nn.Dropout(config.dropout)
|
|
|
|
def forward(self, output_h, output_g,
|
|
attn_mask_h, attn_mask_g,
|
|
r, seg_mat,
|
|
mems=None, target_mapping=None, head_mask=None):
|
|
output_h, output_g = self.rel_attn(output_h, output_g,
|
|
attn_mask_h, attn_mask_g,
|
|
r, seg_mat,
|
|
mems=mems, target_mapping=target_mapping, head_mask=head_mask)
|
|
if output_g is not None:
|
|
output_g = self.ff(output_g)
|
|
output_h = self.ff(output_h)
|
|
|
|
# if self.output_attentions:
|
|
# return attentions, layer_output
|
|
return output_h, output_g
|
|
|
|
class XLNetPreTrainedModel(nn.Module):
|
|
""" An abstract class to handle weights initialization and
|
|
a simple interface for dowloading and loading pretrained models.
|
|
"""
|
|
def __init__(self, config, *inputs, **kwargs):
|
|
super(XLNetPreTrainedModel, self).__init__()
|
|
if not isinstance(config, XLNetBaseConfig):
|
|
raise ValueError(
|
|
"Parameter config in `{}(config)` should be an instance of class `XLNetBaseConfig`. "
|
|
"To create a model from a Google pretrained model use "
|
|
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
|
|
self.__class__.__name__, self.__class__.__name__
|
|
))
|
|
self.config = config
|
|
|
|
def init_xlnet_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, XLNetLayerNorm):
|
|
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 XLNetPreTrainedModel 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:
|
|
. `xlnet-large-cased`
|
|
- a path or url to a pretrained model archive containing:
|
|
. `config.json` a configuration file for the model
|
|
. `pytorch_model.bin` a PyTorch dump of a XLNetForPreTraining instance
|
|
- a path or url to a pretrained model archive containing:
|
|
. `xlnet_config.json` a configuration file for the model
|
|
. `model.chkpt` a TensorFlow checkpoint
|
|
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 dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
|
|
*inputs, **kwargs: additional input for the specific XLNet class
|
|
(ex: num_labels for XLNetForSequenceClassification)
|
|
"""
|
|
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)
|
|
|
|
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:
|
|
if from_tf:
|
|
# Directly load from a TensorFlow checkpoint
|
|
archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME)
|
|
config_file = os.path.join(pretrained_model_name_or_path, XLNET_CONFIG_NAME)
|
|
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 any file "
|
|
"associated to this path or url.".format(
|
|
pretrained_model_name_or_path,
|
|
', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
|
|
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 any file "
|
|
"associated to this path or url.".format(
|
|
pretrained_model_name_or_path,
|
|
', '.join(PRETRAINED_CONFIG_ARCHIVE_MAP.keys()),
|
|
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 = XLNetConfig.from_json_file(resolved_config_file)
|
|
|
|
# Update config with kwargs if needed
|
|
to_remove = []
|
|
for key, value in kwargs.items():
|
|
if hasattr(config, key):
|
|
setattr(config, key, value)
|
|
to_remove.append(key)
|
|
for key in to_remove:
|
|
kwargs.pop(key, None)
|
|
|
|
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
|
|
return load_tf_weights_in_xlnet(model, config, resolved_archive_file)
|
|
|
|
# Load from a PyTorch state_dict
|
|
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_prefix = ''
|
|
if not hasattr(model, 'transformer') and any(s.startswith('transformer') for s in state_dict.keys()):
|
|
start_prefix = 'transformer.'
|
|
load(model, prefix=start_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)))
|
|
if isinstance(model, XLNetLMHeadModel):
|
|
model.tie_weights() # make sure word embedding weights are still tied
|
|
return model
|
|
|
|
|
|
class XLNetModel(XLNetPreTrainedModel):
|
|
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
|
super(XLNetModel, self).__init__(config)
|
|
self.output_attentions = output_attentions
|
|
self.mem_len = config.mem_len
|
|
self.reuse_len = config.reuse_len
|
|
self.d_model = config.d_model
|
|
self.same_length = config.same_length
|
|
self.attn_type = config.attn_type
|
|
self.bi_data = config.bi_data
|
|
self.clamp_len = config.clamp_len
|
|
|
|
self.word_embedding = nn.Embedding(config.n_token, config.d_model)
|
|
self.mask_emb = nn.Parameter(torch.Tensor(1, 1, config.d_model))
|
|
layer = XLNetLayer(config, output_attentions=output_attentions,
|
|
keep_multihead_output=keep_multihead_output)
|
|
self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.n_layer)])
|
|
self.dropout = nn.Dropout(config.dropout)
|
|
|
|
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.layer[layer].attention.prune_heads(heads)
|
|
|
|
def get_multihead_outputs(self):
|
|
""" Gather all multi-head outputs.
|
|
Return: list (layers) of multihead module outputs with gradients
|
|
"""
|
|
return [layer.attention.self.multihead_output for layer in self.layer]
|
|
|
|
def create_mask(self, qlen, mlen):
|
|
""" create causal attention mask.
|
|
float mask where 1.0 indicate masked, 0.0 indicated not-masked.
|
|
same_length=False: same_length=True:
|
|
<mlen > < qlen > <mlen > < qlen >
|
|
^ [0 0 0 0 0 1 1 1 1] [0 0 0 0 0 1 1 1 1]
|
|
[0 0 0 0 0 0 1 1 1] [1 0 0 0 0 0 1 1 1]
|
|
qlen [0 0 0 0 0 0 0 1 1] [1 1 0 0 0 0 0 1 1]
|
|
[0 0 0 0 0 0 0 0 1] [1 1 1 0 0 0 0 0 1]
|
|
v [0 0 0 0 0 0 0 0 0] [1 1 1 1 0 0 0 0 0]
|
|
"""
|
|
attn_mask = torch.ones([qlen, qlen])
|
|
mask_up = torch.triu(attn_mask, diagonal=1)
|
|
attn_mask_pad = torch.zeros([qlen, mlen])
|
|
ret = torch.cat([attn_mask_pad, mask_up], dim=1)
|
|
if self.same_length:
|
|
mask_lo = torch.tril(attn_mask, diagonal=-1)
|
|
ret = torch.cat([ret[:, :qlen] + mask_lo, ret[:, qlen:]], dim=1)
|
|
|
|
ret = ret.to(next(self.parameters()))
|
|
return ret
|
|
|
|
def cache_mem(self, curr_out, prev_mem):
|
|
"""cache hidden states into memory."""
|
|
if self.mem_len is None or self.mem_len == 0:
|
|
return None
|
|
else:
|
|
if self.reuse_len is not None and self.reuse_len > 0:
|
|
curr_out = curr_out[:self.reuse_len]
|
|
|
|
if prev_mem is None:
|
|
new_mem = curr_out[-self.mem_len:]
|
|
else:
|
|
new_mem = torch.cat([prev_mem, curr_out], dim=0)[-self.mem_len:]
|
|
|
|
return new_mem.detach()
|
|
|
|
@staticmethod
|
|
def positional_embedding(pos_seq, inv_freq, bsz=None):
|
|
sinusoid_inp = torch.einsum('i,d->id', pos_seq, inv_freq)
|
|
pos_emb = torch.cat([torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)], dim=-1)
|
|
pos_emb = pos_emb[:, None, :]
|
|
|
|
if bsz is not None:
|
|
pos_emb = pos_emb.expand(-1, bsz, -1)
|
|
|
|
return pos_emb
|
|
|
|
def relative_positional_encoding(self, qlen, klen, bsz=None):
|
|
"""create relative positional encoding."""
|
|
freq_seq = torch.arange(0, self.d_model, 2.0, dtype=torch.float)
|
|
inv_freq = 1 / (10000 ** (freq_seq / self.d_model))
|
|
|
|
if self.attn_type == 'bi':
|
|
# beg, end = klen - 1, -qlen
|
|
beg, end = klen, -qlen
|
|
elif self.attn_type == 'uni':
|
|
# beg, end = klen - 1, -1
|
|
beg, end = klen, -1
|
|
else:
|
|
raise ValueError('Unknown `attn_type` {}.'.format(self.attn_type))
|
|
|
|
if self.bi_data:
|
|
fwd_pos_seq = torch.arange(beg, end, -1.0, dtype=torch.float)
|
|
bwd_pos_seq = torch.arange(-beg, -end, 1.0, dtype=torch.float)
|
|
|
|
if self.clamp_len > 0:
|
|
fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
|
|
bwd_pos_seq = bwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
|
|
|
|
if bsz is not None:
|
|
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz//2)
|
|
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz//2)
|
|
else:
|
|
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
|
|
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)
|
|
|
|
pos_emb = torch.cat([fwd_pos_emb, bwd_pos_emb], dim=1)
|
|
else:
|
|
fwd_pos_seq = torch.arange(beg, end, -1.0)
|
|
if self.clamp_len > 0:
|
|
fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
|
|
pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)
|
|
|
|
pos_emb = pos_emb.to(next(self.parameters()))
|
|
return pos_emb
|
|
|
|
def forward(self, inp_k, seg_id=None, input_mask=None,
|
|
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
|
output_all_encoded_layers=True, head_mask=None):
|
|
"""
|
|
Args:
|
|
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
|
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
|
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
|
|
0 for real tokens and 1 for padding.
|
|
mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
|
from previous batches. The length of the list equals n_layer.
|
|
If None, no memory is used.
|
|
perm_mask: [optional] float32 Tensor in shape [bsz, len, len].
|
|
If perm_mask[k, i, j] = 0, i attend to j in batch k;
|
|
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
|
|
If None, each position attends to all the others.
|
|
target_mapping: [optional] float32 Tensor in shape [bsz, num_predict, len].
|
|
If target_mapping[k, i, j] = 1, the i-th predict in batch k is
|
|
on the j-th token.
|
|
Only used during pretraining for partial prediction.
|
|
Set to None during finetuning.
|
|
inp_q: [optional] float32 Tensor in shape [bsz, len].
|
|
1 for tokens with losses and 0 for tokens without losses.
|
|
Only used during pretraining for two-stream attention.
|
|
Set to None during finetuning.
|
|
|
|
mem_len: int, the number of tokens to cache.
|
|
reuse_len: int, the number of tokens in the currect batch to be cached
|
|
and reused in the future.
|
|
bi_data: bool, whether to use bidirectional input pipeline.
|
|
Usually set to True during pretraining and False during finetuning.
|
|
clamp_len: int, clamp all relative distances larger than clamp_len.
|
|
-1 means no clamping.
|
|
same_length: bool, whether to use the same attention length for each token.
|
|
summary_type: str, "last", "first", "mean", or "attn". The method
|
|
to pool the input to get a vector representation.
|
|
"""
|
|
# the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
|
|
# but we want a unified interface in the library with the batch size on the first dimension
|
|
# so we move here the first dimension (batch) to the end
|
|
inp_k = inp_k.transpose(0, 1).contiguous()
|
|
seg_id = seg_id.transpose(0, 1).contiguous() if seg_id is not None else None
|
|
input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
|
|
perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
|
|
target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
|
|
inp_q = inp_q.transpose(0, 1).contiguous() if inp_q is not None else None
|
|
|
|
qlen, bsz = inp_k.shape[0], inp_k.shape[1]
|
|
mlen = mems[0].shape[0] if mems is not None else 0
|
|
klen = mlen + qlen
|
|
|
|
dtype_float = next(self.parameters()).dtype
|
|
device = next(self.parameters()).device
|
|
|
|
##### Attention mask
|
|
# causal attention mask
|
|
if self.attn_type == 'uni':
|
|
attn_mask = self.create_mask(qlen, mlen)
|
|
attn_mask = attn_mask[:, :, None, None]
|
|
elif self.attn_type == 'bi':
|
|
attn_mask = None
|
|
else:
|
|
raise ValueError('Unsupported attention type: {}'.format(self.attn_type))
|
|
|
|
# data mask: input mask & perm mask
|
|
if input_mask is not None and perm_mask is not None:
|
|
data_mask = input_mask[None] + perm_mask
|
|
elif input_mask is not None and perm_mask is None:
|
|
data_mask = input_mask[None]
|
|
elif input_mask is None and perm_mask is not None:
|
|
data_mask = perm_mask
|
|
else:
|
|
data_mask = None
|
|
|
|
if data_mask is not None:
|
|
# all mems can be attended to
|
|
mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
|
|
data_mask = torch.cat([mems_mask, data_mask], dim=1)
|
|
if attn_mask is None:
|
|
attn_mask = data_mask[:, :, :, None]
|
|
else:
|
|
attn_mask += data_mask[:, :, :, None]
|
|
|
|
if attn_mask is not None:
|
|
attn_mask = (attn_mask > 0).to(dtype_float)
|
|
|
|
if attn_mask is not None:
|
|
non_tgt_mask = -torch.eye(qlen).to(attn_mask)
|
|
non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
|
|
non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)
|
|
else:
|
|
non_tgt_mask = None
|
|
|
|
##### Word embeddings and prepare h & g hidden states
|
|
word_emb_k = self.word_embedding(inp_k)
|
|
output_h = self.dropout(word_emb_k)
|
|
if inp_q is not None:
|
|
if target_mapping is not None:
|
|
word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
|
|
else:
|
|
inp_q_ext = inp_q[:, :, None]
|
|
word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
|
|
output_g = self.dropout(word_emb_q)
|
|
else:
|
|
output_g = None
|
|
|
|
##### Segment embedding
|
|
if seg_id is not None:
|
|
# Convert `seg_id` to one-hot `seg_mat`
|
|
mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
|
|
cat_ids = torch.cat([mem_pad, seg_id], dim=0)
|
|
|
|
# `1` indicates not in the same segment [qlen x klen x bsz]
|
|
seg_mat = (seg_id[:, None] != cat_ids[None, :]).long()
|
|
seg_mat = F.one_hot(seg_mat, num_classes=2).to(dtype_float)
|
|
else:
|
|
seg_mat = None
|
|
|
|
##### Positional encoding
|
|
pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
|
|
pos_emb = self.dropout(pos_emb)
|
|
|
|
##### Head mask if needed (for bertology/pruning)
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [n_layer x num_heads]
|
|
# and head_mask is converted to shape [n_layer 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(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
|
|
|
|
new_mems = []
|
|
if mems is None:
|
|
mems = [None] * len(self.layer)
|
|
|
|
hidden_states = []
|
|
for i, layer_module in enumerate(self.layer):
|
|
# cache new mems
|
|
new_mems.append(self.cache_mem(output_h, mems[i]))
|
|
|
|
output_h, output_g = layer_module(output_h, output_g,
|
|
attn_mask_h=non_tgt_mask, attn_mask_g=attn_mask,
|
|
r=pos_emb, seg_mat=seg_mat,
|
|
mems=mems[i], target_mapping=target_mapping,
|
|
head_mask=head_mask)
|
|
hidden_states.append(output_h)
|
|
output = self.dropout(output_g if output_g is not None else output_h)
|
|
|
|
# We transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
|
|
output = output.permute(1, 0, 2).contiguous()
|
|
hidden_states = [hs.permute(1, 0, 2).contiguous() for hs in hidden_states]
|
|
|
|
return output, hidden_states, new_mems
|
|
|
|
|
|
class XLNetLMHeadModel(XLNetPreTrainedModel):
|
|
"""XLNet model ("XLNet: Generalized Autoregressive Pretraining for Language Understanding").
|
|
|
|
Params:
|
|
`config`: a XLNetConfig 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:
|
|
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
|
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
|
input_mask: [optional] float32 Tensor in shape [bsz, len], the input mask.
|
|
0 for real tokens and 1 for padding.
|
|
mems: [optional] a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
|
from previous batches. The length of the list equals n_layer.
|
|
If None, no memory is used.
|
|
perm_mask: [optional] float32 Tensor in shape [bsz, len, len].
|
|
If perm_mask[k, i, j] = 0, i attend to j in batch k;
|
|
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
|
|
If None, each position attends to all the others.
|
|
target_mapping: [optional] float32 Tensor in shape [bsz, num_predict, len].
|
|
If target_mapping[k, i, j] = 1, the i-th predict in batch k is
|
|
on the j-th token.
|
|
Only used during pretraining for partial prediction.
|
|
Set to None during finetuning.
|
|
inp_q: [optional] float32 Tensor in shape [bsz, len].
|
|
1 for tokens with losses and 0 for tokens without losses.
|
|
Only used during pretraining for two-stream attention.
|
|
Set to None during finetuning.
|
|
|
|
|
|
Outputs: Tuple of (encoded_layers, pooled_output)
|
|
`encoded_layers`: controled by `output_all_encoded_layers` argument:
|
|
- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
|
|
of each attention block (i.e. 12 full sequences for XLNet-base, 24 for XLNet-large), each
|
|
encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, d_model],
|
|
- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
|
|
to the last attention block of shape [batch_size, sequence_length, d_model],
|
|
`pooled_output`: a torch.FloatTensor of size [batch_size, d_model] which is the output of a
|
|
classifier pretrained on top of the hidden state associated to the first character of the
|
|
input (`CLS`) to train on the Next-Sentence task (see XLNet's paper).
|
|
|
|
Example usage:
|
|
```python
|
|
# Already been converted into WordPiece token ids
|
|
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
|
|
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
|
|
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
|
|
|
|
config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
|
|
n_layer=12, num_attention_heads=12, intermediate_size=3072)
|
|
|
|
model = modeling.XLNetModel(config=config)
|
|
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
|
|
```
|
|
"""
|
|
def __init__(self, config, output_attentions=False, keep_multihead_output=False):
|
|
super(XLNetLMHeadModel, self).__init__(config)
|
|
self.output_attentions = output_attentions
|
|
self.attn_type = config.attn_type
|
|
self.same_length = config.same_length
|
|
|
|
self.transformer = XLNetModel(config, output_attentions=output_attentions,
|
|
keep_multihead_output=keep_multihead_output)
|
|
self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)
|
|
|
|
# Tie weights
|
|
|
|
self.apply(self.init_xlnet_weights)
|
|
self.tie_weights()
|
|
|
|
def tie_weights(self):
|
|
""" Make sure we are sharing the embeddings
|
|
"""
|
|
self.lm_loss.weight = self.transformer.word_embedding.weight
|
|
|
|
def forward(self, inp_k, seg_id=None, input_mask=None,
|
|
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
|
target=None, output_all_encoded_layers=True, head_mask=None):
|
|
"""
|
|
Args:
|
|
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
|
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
|
input_mask: float32 Tensor in shape [bsz, len], the input mask.
|
|
0 for real tokens and 1 for padding.
|
|
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
|
from previous batches. The length of the list equals n_layer.
|
|
If None, no memory is used.
|
|
perm_mask: float32 Tensor in shape [bsz, len, len].
|
|
If perm_mask[k, i, j] = 0, i attend to j in batch k;
|
|
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
|
|
If None, each position attends to all the others.
|
|
target_mapping: float32 Tensor in shape [bsz, num_predict, len].
|
|
If target_mapping[k, i, j] = 1, the i-th predict in batch k is
|
|
on the j-th token.
|
|
Only used during pretraining for partial prediction.
|
|
Set to None during finetuning.
|
|
inp_q: float32 Tensor in shape [bsz, len].
|
|
1 for tokens with losses and 0 for tokens without losses.
|
|
Only used during pretraining for two-stream attention.
|
|
Set to None during finetuning.
|
|
|
|
summary_type: str, "last", "first", "mean", or "attn". The method
|
|
to pool the input to get a vector representation.
|
|
"""
|
|
output, hidden_states, new_mems = self.transformer(inp_k, seg_id, input_mask,
|
|
mems, perm_mask, target_mapping, inp_q,
|
|
output_all_encoded_layers, head_mask)
|
|
|
|
logits = self.lm_loss(output)
|
|
|
|
if target is not None:
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
|
loss = loss_fct(logits.view(-1, logits.size(-1)),
|
|
target.view(-1))
|
|
return loss, new_mems
|
|
|
|
# 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 logits, new_mems
|
|
# return all_attentions, encoded_layers, pooled_output
|
|
|
|
class XLNetSequenceSummary(nn.Module):
|
|
def __init__(self, config, summary_type="last", use_proj=True,
|
|
output_attentions=False, keep_multihead_output=False):
|
|
super(XLNetSequenceSummary, self).__init__()
|
|
self.summary_type = summary_type
|
|
if use_proj:
|
|
self.summary = nn.Linear(config.hidden_size, num_labels)
|
|
else:
|
|
self.summary = None
|
|
if summary_type == 'attn':
|
|
# We should use a standard multi-head attention module with absolute positional embedding for that.
|
|
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
|
|
# We can probably just use the multi-head attention module of PyTorch >=1.1.0
|
|
raise NotImplementedError
|
|
self.dropout = nn.Dropout(config.dropout)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states, input_mask=None):
|
|
if self.summary_type == 'last':
|
|
output = hidden_states[-1]
|
|
elif self.summary_type == 'first':
|
|
output = hidden_states[0]
|
|
elif self.summary_type == 'mean':
|
|
output = hidden_states.mean(dim=0)
|
|
elif summary_type == 'attn':
|
|
raise NotImplementedError
|
|
|
|
output = self.summary(output)
|
|
output = self.dropout(output)
|
|
output = self.activation(output)
|
|
return output
|
|
|
|
|
|
class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
|
"""XLNet model ("XLNet: Generalized Autoregressive Pretraining for Language Understanding").
|
|
|
|
Params:
|
|
`config`: a XLNetConfig 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
|
|
`summary_type`: str, "last", "first", "mean", or "attn". The method
|
|
to pool the input to get a vector representation. Default: last
|
|
|
|
Inputs:
|
|
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
|
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
|
|
input_mask: float32 Tensor in shape [bsz, len], the input mask.
|
|
0 for real tokens and 1 for padding.
|
|
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
|
from previous batches. The length of the list equals n_layer.
|
|
If None, no memory is used.
|
|
perm_mask: float32 Tensor in shape [bsz, len, len].
|
|
If perm_mask[k, i, j] = 0, i attend to j in batch k;
|
|
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
|
|
If None, each position attends to all the others.
|
|
target_mapping: float32 Tensor in shape [bsz, num_predict, len].
|
|
If target_mapping[k, i, j] = 1, the i-th predict in batch k is
|
|
on the j-th token.
|
|
Only used during pretraining for partial prediction.
|
|
Set to None during finetuning.
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inp_q: float32 Tensor in shape [bsz, len].
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1 for tokens with losses and 0 for tokens without losses.
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Only used during pretraining for two-stream attention.
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Set to None during finetuning.
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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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|
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Outputs: Tuple of (logits or loss, mems)
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`logits or loss`:
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if target is None:
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Token logits with shape [batch_size, sequence_length]
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else:
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CrossEntropy loss with the targets
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`new_mems`: list (num layers) of updated mem states at the entry of each layer
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each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]
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Note that the first two dimensions are transposed in `mems` with regards to `input_ids` and `target`
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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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|
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config = modeling.XLNetConfig(vocab_size_or_config_json_file=32000, d_model=768,
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n_layer=12, num_attention_heads=12, intermediate_size=3072)
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|
|
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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, summary_type="last", use_proj=True, num_labels=2,
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is_regression=False, output_attentions=False, keep_multihead_output=False):
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super(XLNetForSequenceClassification, self).__init__(config)
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self.output_attentions = output_attentions
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self.attn_type = config.attn_type
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self.same_length = config.same_length
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self.summary_type = summary_type
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self.is_regression = is_regression
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|
|
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self.transformer = XLNetModel(config, output_attentions=output_attentions,
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keep_multihead_output=keep_multihead_output)
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|
|
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self.sequence_summary = XLNetSequenceSummary(config, summary_type=summary_type,
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use_proj=use_proj, output_attentions=output_attentions,
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keep_multihead_output=keep_multihead_output)
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|
self.loss_proj = nn.Linear(config.d_model, num_classes if not is_regression else 1)
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self.apply(self.init_bert_weights)
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|
|
|
def forward(self, inp_k, seg_id=None, input_mask=None,
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|
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
|
|
target=None, output_all_encoded_layers=True, head_mask=None):
|
|
"""
|
|
Args:
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|
inp_k: int32 Tensor in shape [bsz, len], the input token IDs.
|
|
seg_id: int32 Tensor in shape [bsz, len], the input segment IDs.
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|
input_mask: float32 Tensor in shape [bsz, len], the input mask.
|
|
0 for real tokens and 1 for padding.
|
|
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
|
|
from previous batches. The length of the list equals n_layer.
|
|
If None, no memory is used.
|
|
perm_mask: float32 Tensor in shape [bsz, len, len].
|
|
If perm_mask[k, i, j] = 0, i attend to j in batch k;
|
|
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
|
|
If None, each position attends to all the others.
|
|
target_mapping: float32 Tensor in shape [bsz, num_predict, len].
|
|
If target_mapping[k, i, j] = 1, the i-th predict in batch k is
|
|
on the j-th token.
|
|
Only used during pretraining for partial prediction.
|
|
Set to None during finetuning.
|
|
inp_q: float32 Tensor in shape [bsz, len].
|
|
1 for tokens with losses and 0 for tokens without losses.
|
|
Only used during pretraining for two-stream attention.
|
|
Set to None during finetuning.
|
|
"""
|
|
output, _, new_mems = self.transformer(inp_k, seg_id, input_mask,
|
|
mems, perm_mask, target_mapping, inp_q,
|
|
output_all_encoded_layers, head_mask)
|
|
|
|
output = self.sequence_summary(output)
|
|
logits = self.loss_proj(output)
|
|
|
|
if target is not None:
|
|
if self.is_regression:
|
|
loss_fct = MSELoss()
|
|
loss = loss_fct(logits.view(-1), target.view(-1))
|
|
else:
|
|
loss_fct = CrossEntropyLoss(ignore_index=-1)
|
|
loss = loss_fct(logits.view(-1, logits.size(-1)), target.view(-1))
|
|
return loss, new_mems
|
|
|
|
# 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 logits, new_mems
|
|
# return all_attentions, encoded_layers, pooled_output
|