1596 lines
68 KiB
Python
1596 lines
68 KiB
Python
# coding=utf-8
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# Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team.
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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 ELECTRA model."""
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import math
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import os
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from dataclasses import dataclass
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from typing import Callable, Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN, get_activation
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from ...cache_utils import Cache, EncoderDecoderCache
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from ...generation import GenerationMixin
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import (
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BaseModelOutputWithCrossAttentions,
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BaseModelOutputWithPastAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import ModelOutput, auto_docstring, logging
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from ...utils.deprecation import deprecate_kwarg
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from .configuration_electra import ElectraConfig
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logger = logging.get_logger(__name__)
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def load_tf_weights_in_electra(model, config, tf_checkpoint_path, discriminator_or_generator="discriminator"):
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"""Load tf checkpoints in a pytorch model."""
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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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logger.error(
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"Loading a TensorFlow model 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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)
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raise
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tf_path = os.path.abspath(tf_checkpoint_path)
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logger.info(f"Converting TensorFlow checkpoint from {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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logger.info(f"Loading TF weight {name} with shape {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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original_name: str = name
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try:
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if isinstance(model, ElectraForMaskedLM):
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name = name.replace("electra/embeddings/", "generator/embeddings/")
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if discriminator_or_generator == "generator":
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name = name.replace("electra/", "discriminator/")
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name = name.replace("generator/", "electra/")
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name = name.replace("dense_1", "dense_prediction")
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name = name.replace("generator_predictions/output_bias", "generator_lm_head/bias")
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name = name.split("/")
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# print(original_name, 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 any(n in ["global_step", "temperature"] for n in name):
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logger.info(f"Skipping {original_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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scope_names = re.split(r"_(\d+)", m_name)
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else:
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scope_names = [m_name]
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if scope_names[0] == "kernel" or scope_names[0] == "gamma":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
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pointer = getattr(pointer, "bias")
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elif scope_names[0] == "output_weights":
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pointer = getattr(pointer, "weight")
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elif scope_names[0] == "squad":
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pointer = getattr(pointer, "classifier")
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else:
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pointer = getattr(pointer, scope_names[0])
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if len(scope_names) >= 2:
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num = int(scope_names[1])
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pointer = pointer[num]
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if m_name.endswith("_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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if pointer.shape != array.shape:
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raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
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except ValueError as e:
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e.args += (pointer.shape, array.shape)
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raise
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print(f"Initialize PyTorch weight {name}", original_name)
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pointer.data = torch.from_numpy(array)
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except AttributeError as e:
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print(f"Skipping {original_name}", name, e)
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continue
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return model
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class ElectraEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
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)
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.register_buffer(
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"token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
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)
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# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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past_key_values_length: int = 0,
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) -> torch.Tensor:
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
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# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
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# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
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# issue #5664
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if token_type_ids is None:
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if hasattr(self, "token_type_ids"):
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buffered_token_type_ids = self.token_type_ids[:, :seq_length]
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buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
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token_type_ids = buffered_token_type_ids_expanded
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else:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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if self.position_embedding_type == "absolute":
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Electra
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class ElectraSelfAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
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f"heads ({config.num_attention_heads})"
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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self.key = nn.Linear(config.hidden_size, self.all_head_size)
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self.value = nn.Linear(config.hidden_size, self.all_head_size)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.position_embedding_type = position_embedding_type or getattr(
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config, "position_embedding_type", "absolute"
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)
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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self.max_position_embeddings = config.max_position_embeddings
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self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
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self.is_decoder = config.is_decoder
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self.layer_idx = layer_idx
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@deprecate_kwarg("encoder_attention_mask", version="4.55.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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cache_position: Optional[torch.Tensor] = None,
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) -> tuple[torch.Tensor]:
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batch_size, seq_length, _ = hidden_states.shape
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query_layer = self.query(hidden_states)
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query_layer = query_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
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1, 2
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)
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# If this is instantiated as a cross-attention module, the keys
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# and values come from an encoder; the attention mask needs to be
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# such that the encoder's padding tokens are not attended to.
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention and encoder_attention_mask is not None:
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attention_mask = encoder_attention_mask
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if past_key_value is not None:
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if isinstance(past_key_value, EncoderDecoderCache):
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is_updated = past_key_value.is_updated.get(self.layer_idx)
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if is_cross_attention:
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# after the first generated id, we can subsequently re-use all key/value_layer from cache
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curr_past_key_value = past_key_value.cross_attention_cache
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else:
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curr_past_key_value = past_key_value.self_attention_cache
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else:
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curr_past_key_value = past_key_value
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current_states = encoder_hidden_states if is_cross_attention else hidden_states
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if is_cross_attention and past_key_value is not None and is_updated:
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# reuse k,v, cross_attentions
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key_layer = curr_past_key_value.key_cache[self.layer_idx]
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value_layer = curr_past_key_value.value_cache[self.layer_idx]
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else:
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key_layer = self.key(current_states)
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key_layer = key_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
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1, 2
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)
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value_layer = self.value(current_states)
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value_layer = value_layer.view(
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batch_size, -1, self.num_attention_heads, self.attention_head_size
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).transpose(1, 2)
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if past_key_value is not None:
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# save all key/value_layer to cache to be re-used for fast auto-regressive generation
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cache_position = cache_position if not is_cross_attention else None
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key_layer, value_layer = curr_past_key_value.update(
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key_layer, value_layer, self.layer_idx, {"cache_position": cache_position}
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)
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# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
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if is_cross_attention:
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past_key_value.is_updated[self.layer_idx] = True
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
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if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
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query_length, key_length = query_layer.shape[2], key_layer.shape[2]
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if past_key_value is not None:
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position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
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-1, 1
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)
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else:
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position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
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distance = position_ids_l - position_ids_r
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positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
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positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
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if self.position_embedding_type == "relative_key":
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relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores
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elif self.position_embedding_type == "relative_key_query":
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relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
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attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in ElectraModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = nn.functional.softmax(attention_scores, dim=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = torch.matmul(attention_probs, value_layer)
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context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.view(new_context_layer_shape)
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return context_layer, attention_probs
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# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
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class ElectraSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = self.LayerNorm(hidden_states + input_tensor)
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return hidden_states
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ELECTRA_SELF_ATTENTION_CLASSES = {
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"eager": ElectraSelfAttention,
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}
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# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Electra,BERT->ELECTRA
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class ElectraAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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super().__init__()
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self.self = ELECTRA_SELF_ATTENTION_CLASSES[config._attn_implementation](
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config,
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position_embedding_type=position_embedding_type,
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layer_idx=layer_idx,
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)
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self.output = ElectraSelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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@deprecate_kwarg("encoder_attention_mask", version="4.55.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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cache_position: Optional[torch.Tensor] = None,
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) -> tuple[torch.Tensor]:
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self_outputs = self.self(
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hidden_states,
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attention_mask=attention_mask,
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head_mask=head_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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cache_position=cache_position,
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)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate
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class ElectraIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
|
|
self.intermediate_act_fn = config.hidden_act
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.intermediate_act_fn(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertOutput
|
|
class ElectraOutput(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Electra
|
|
class ElectraLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config, layer_idx=None):
|
|
super().__init__()
|
|
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
|
self.seq_len_dim = 1
|
|
self.attention = ElectraAttention(config, layer_idx=layer_idx)
|
|
self.is_decoder = config.is_decoder
|
|
self.add_cross_attention = config.add_cross_attention
|
|
if self.add_cross_attention:
|
|
if not self.is_decoder:
|
|
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
|
|
self.crossattention = ElectraAttention(config, position_embedding_type="absolute", layer_idx=layer_idx)
|
|
self.intermediate = ElectraIntermediate(config)
|
|
self.output = ElectraOutput(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
) -> tuple[torch.Tensor]:
|
|
self_attention_outputs = self.attention(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
past_key_value=past_key_value,
|
|
cache_position=cache_position,
|
|
)
|
|
attention_output = self_attention_outputs[0]
|
|
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
|
|
|
|
if self.is_decoder and encoder_hidden_states is not None:
|
|
if not hasattr(self, "crossattention"):
|
|
raise ValueError(
|
|
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
|
|
" by setting `config.add_cross_attention=True`"
|
|
)
|
|
|
|
cross_attention_outputs = self.crossattention(
|
|
attention_output,
|
|
attention_mask=encoder_attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
attention_output = cross_attention_outputs[0]
|
|
outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights
|
|
|
|
layer_output = apply_chunking_to_forward(
|
|
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
|
)
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
def feed_forward_chunk(self, attention_output):
|
|
intermediate_output = self.intermediate(attention_output)
|
|
layer_output = self.output(intermediate_output, attention_output)
|
|
return layer_output
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Electra
|
|
class ElectraEncoder(nn.Module):
|
|
def __init__(self, config, layer_idx=None):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([ElectraLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
return_dict: Optional[bool] = True,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
return_legacy_cache = False
|
|
if use_cache and self.config.is_decoder and not isinstance(past_key_values, Cache):
|
|
logger.warning_once(
|
|
"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
|
|
"You should pass an instance of `EncoderDecoderCache` instead, e.g. "
|
|
"`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
|
|
)
|
|
return_legacy_cache = True
|
|
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
layer_head_mask = head_mask[i] if head_mask is not None else None
|
|
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
layer_head_mask,
|
|
encoder_hidden_states, # as a positional argument for gradient checkpointing
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
if self.config.add_cross_attention:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if return_legacy_cache:
|
|
past_key_values = past_key_values.to_legacy_cache()
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [
|
|
hidden_states,
|
|
past_key_values,
|
|
all_hidden_states,
|
|
all_self_attentions,
|
|
all_cross_attentions,
|
|
]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attentions,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
class ElectraDiscriminatorPredictions(nn.Module):
|
|
"""Prediction module for the discriminator, made up of two dense layers."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = get_activation(config.hidden_act)
|
|
self.dense_prediction = nn.Linear(config.hidden_size, 1)
|
|
self.config = config
|
|
|
|
def forward(self, discriminator_hidden_states):
|
|
hidden_states = self.dense(discriminator_hidden_states)
|
|
hidden_states = self.activation(hidden_states)
|
|
logits = self.dense_prediction(hidden_states).squeeze(-1)
|
|
|
|
return logits
|
|
|
|
|
|
class ElectraGeneratorPredictions(nn.Module):
|
|
"""Prediction module for the generator, made up of two dense layers."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.activation = get_activation("gelu")
|
|
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
|
|
self.dense = nn.Linear(config.hidden_size, config.embedding_size)
|
|
|
|
def forward(self, generator_hidden_states):
|
|
hidden_states = self.dense(generator_hidden_states)
|
|
hidden_states = self.activation(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class ElectraPreTrainedModel(PreTrainedModel):
|
|
config: ElectraConfig
|
|
load_tf_weights = load_tf_weights_in_electra
|
|
base_model_prefix = "electra"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Output type of [`ElectraForPreTraining`].
|
|
"""
|
|
)
|
|
class ElectraForPreTrainingOutput(ModelOutput):
|
|
r"""
|
|
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
|
|
Total loss of the ELECTRA objective.
|
|
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
|
|
Prediction scores of the head (scores for each token before SoftMax).
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: Optional[torch.FloatTensor] = None
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
@auto_docstring
|
|
class ElectraModel(ElectraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.embeddings = ElectraEmbeddings(config)
|
|
|
|
if config.embedding_size != config.hidden_size:
|
|
self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)
|
|
|
|
self.encoder = ElectraEncoder(config)
|
|
self.config = config
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
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} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithCrossAttentions]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
past_key_values_length = 0
|
|
if past_key_values is not None:
|
|
past_key_values_length = (
|
|
past_key_values[0][0].shape[-2]
|
|
if not isinstance(past_key_values, Cache)
|
|
else past_key_values.get_seq_length()
|
|
)
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
if hasattr(self.embeddings, "token_type_ids"):
|
|
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
|
|
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
|
|
token_type_ids = buffered_token_type_ids_expanded
|
|
else:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
|
|
|
|
# If a 2D or 3D attention mask is provided for the cross-attention
|
|
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if self.config.is_decoder and encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
if encoder_attention_mask is None:
|
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
hidden_states = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
if hasattr(self, "embeddings_project"):
|
|
hidden_states = self.embeddings_project(hidden_states)
|
|
|
|
hidden_states = self.encoder(
|
|
hidden_states,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class ElectraClassificationHead(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.activation = get_activation("gelu")
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
x = self.dropout(x)
|
|
x = self.dense(x)
|
|
x = self.activation(x) # although BERT uses tanh here, it seems Electra authors used gelu here
|
|
x = self.dropout(x)
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->Electra
|
|
class ElectraSequenceSummary(nn.Module):
|
|
r"""
|
|
Compute a single vector summary of a sequence hidden states.
|
|
|
|
Args:
|
|
config ([`ElectraConfig`]):
|
|
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
|
|
config class of your model for the default values it uses):
|
|
|
|
- **summary_type** (`str`) -- The method to use to make this summary. Accepted values are:
|
|
|
|
- `"last"` -- Take the last token hidden state (like XLNet)
|
|
- `"first"` -- Take the first token hidden state (like Bert)
|
|
- `"mean"` -- Take the mean of all tokens hidden states
|
|
- `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
|
|
- `"attn"` -- Not implemented now, use multi-head attention
|
|
|
|
- **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
|
|
- **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
|
|
(otherwise to `config.hidden_size`).
|
|
- **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
|
|
another string or `None` will add no activation.
|
|
- **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
|
|
- **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
|
|
"""
|
|
|
|
def __init__(self, config: ElectraConfig):
|
|
super().__init__()
|
|
|
|
self.summary_type = getattr(config, "summary_type", "last")
|
|
if self.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.summary = nn.Identity()
|
|
if hasattr(config, "summary_use_proj") and config.summary_use_proj:
|
|
if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
|
|
num_classes = config.num_labels
|
|
else:
|
|
num_classes = config.hidden_size
|
|
self.summary = nn.Linear(config.hidden_size, num_classes)
|
|
|
|
activation_string = getattr(config, "summary_activation", None)
|
|
self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity()
|
|
|
|
self.first_dropout = nn.Identity()
|
|
if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
|
|
self.first_dropout = nn.Dropout(config.summary_first_dropout)
|
|
|
|
self.last_dropout = nn.Identity()
|
|
if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
|
|
self.last_dropout = nn.Dropout(config.summary_last_dropout)
|
|
|
|
def forward(
|
|
self, hidden_states: torch.FloatTensor, cls_index: Optional[torch.LongTensor] = None
|
|
) -> torch.FloatTensor:
|
|
"""
|
|
Compute a single vector summary of a sequence hidden states.
|
|
|
|
Args:
|
|
hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`):
|
|
The hidden states of the last layer.
|
|
cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
|
|
Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token.
|
|
|
|
Returns:
|
|
`torch.FloatTensor`: The summary of the sequence hidden states.
|
|
"""
|
|
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=1)
|
|
elif self.summary_type == "cls_index":
|
|
if cls_index is None:
|
|
cls_index = torch.full_like(
|
|
hidden_states[..., :1, :],
|
|
hidden_states.shape[-2] - 1,
|
|
dtype=torch.long,
|
|
)
|
|
else:
|
|
cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
|
|
cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
|
|
# shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
|
|
output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
|
|
elif self.summary_type == "attn":
|
|
raise NotImplementedError
|
|
|
|
output = self.first_dropout(output)
|
|
output = self.summary(output)
|
|
output = self.activation(output)
|
|
output = self.last_dropout(output)
|
|
|
|
return output
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the
|
|
pooled output) e.g. for GLUE tasks.
|
|
"""
|
|
)
|
|
class ElectraForSequenceClassification(ElectraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
self.electra = ElectraModel(config)
|
|
self.classifier = ElectraClassificationHead(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = discriminator_hidden_states[0]
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + discriminator_hidden_states[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=discriminator_hidden_states.hidden_states,
|
|
attentions=discriminator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Electra model with a binary classification head on top as used during pretraining for identifying generated tokens.
|
|
|
|
It is recommended to load the discriminator checkpoint into that model.
|
|
"""
|
|
)
|
|
class ElectraForPreTraining(ElectraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.electra = ElectraModel(config)
|
|
self.discriminator_predictions = ElectraDiscriminatorPredictions(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], ElectraForPreTrainingOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the ELECTRA loss. Input should be a sequence of tokens (see `input_ids` docstring)
|
|
Indices should be in `[0, 1]`:
|
|
|
|
- 0 indicates the token is an original token,
|
|
- 1 indicates the token was replaced.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import ElectraForPreTraining, AutoTokenizer
|
|
>>> import torch
|
|
|
|
>>> discriminator = ElectraForPreTraining.from_pretrained("google/electra-base-discriminator")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-base-discriminator")
|
|
|
|
>>> sentence = "The quick brown fox jumps over the lazy dog"
|
|
>>> fake_sentence = "The quick brown fox fake over the lazy dog"
|
|
|
|
>>> fake_tokens = tokenizer.tokenize(fake_sentence, add_special_tokens=True)
|
|
>>> fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
|
|
>>> discriminator_outputs = discriminator(fake_inputs)
|
|
>>> predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
|
|
|
|
>>> fake_tokens
|
|
['[CLS]', 'the', 'quick', 'brown', 'fox', 'fake', 'over', 'the', 'lazy', 'dog', '[SEP]']
|
|
|
|
>>> predictions.squeeze().tolist()
|
|
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
discriminator_sequence_output = discriminator_hidden_states[0]
|
|
|
|
logits = self.discriminator_predictions(discriminator_sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = nn.BCEWithLogitsLoss()
|
|
if attention_mask is not None:
|
|
active_loss = attention_mask.view(-1, discriminator_sequence_output.shape[1]) == 1
|
|
active_logits = logits.view(-1, discriminator_sequence_output.shape[1])[active_loss]
|
|
active_labels = labels[active_loss]
|
|
loss = loss_fct(active_logits, active_labels.float())
|
|
else:
|
|
loss = loss_fct(logits.view(-1, discriminator_sequence_output.shape[1]), labels.float())
|
|
|
|
if not return_dict:
|
|
output = (logits,) + discriminator_hidden_states[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ElectraForPreTrainingOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=discriminator_hidden_states.hidden_states,
|
|
attentions=discriminator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Electra model with a language modeling head on top.
|
|
|
|
Even though both the discriminator and generator may be loaded into this model, the generator is the only model of
|
|
the two to have been trained for the masked language modeling task.
|
|
"""
|
|
)
|
|
class ElectraForMaskedLM(ElectraPreTrainedModel):
|
|
_tied_weights_keys = ["generator_lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.electra = ElectraModel(config)
|
|
self.generator_predictions = ElectraGeneratorPredictions(config)
|
|
|
|
self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.generator_lm_head
|
|
|
|
def set_output_embeddings(self, word_embeddings):
|
|
self.generator_lm_head = word_embeddings
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
generator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
generator_sequence_output = generator_hidden_states[0]
|
|
|
|
prediction_scores = self.generator_predictions(generator_sequence_output)
|
|
prediction_scores = self.generator_lm_head(prediction_scores)
|
|
|
|
loss = None
|
|
# Masked language modeling softmax layer
|
|
if labels is not None:
|
|
loss_fct = nn.CrossEntropyLoss() # -100 index = padding token
|
|
loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + generator_hidden_states[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MaskedLMOutput(
|
|
loss=loss,
|
|
logits=prediction_scores,
|
|
hidden_states=generator_hidden_states.hidden_states,
|
|
attentions=generator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Electra model with a token classification head on top.
|
|
|
|
Both the discriminator and generator may be loaded into this model.
|
|
"""
|
|
)
|
|
class ElectraForTokenClassification(ElectraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.electra = ElectraModel(config)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
discriminator_sequence_output = discriminator_hidden_states[0]
|
|
|
|
discriminator_sequence_output = self.dropout(discriminator_sequence_output)
|
|
logits = self.classifier(discriminator_sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + discriminator_hidden_states[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=discriminator_hidden_states.hidden_states,
|
|
attentions=discriminator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class ElectraForQuestionAnswering(ElectraPreTrainedModel):
|
|
config: ElectraConfig
|
|
base_model_prefix = "electra"
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
|
|
self.electra = ElectraModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
start_positions: Optional[torch.Tensor] = None,
|
|
end_positions: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
)
|
|
|
|
sequence_output = discriminator_hidden_states[0]
|
|
|
|
logits = self.qa_outputs(sequence_output)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (
|
|
start_logits,
|
|
end_logits,
|
|
) + discriminator_hidden_states[1:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=discriminator_hidden_states.hidden_states,
|
|
attentions=discriminator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class ElectraForMultipleChoice(ElectraPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.electra = ElectraModel(config)
|
|
self.sequence_summary = ElectraSequenceSummary(config)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple[torch.Tensor], MultipleChoiceModelOutput]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
|
|
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
|
|
1]`:
|
|
|
|
- 0 corresponds to a *sentence A* token,
|
|
- 1 corresponds to a *sentence B* token.
|
|
|
|
[What are token type IDs?](../glossary#token-type-ids)
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.max_position_embeddings - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
|
|
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
|
|
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = discriminator_hidden_states[0]
|
|
|
|
pooled_output = self.sequence_summary(sequence_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.view(-1, num_choices)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + discriminator_hidden_states[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=discriminator_hidden_states.hidden_states,
|
|
attentions=discriminator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
ELECTRA Model with a `language modeling` head on top for CLM fine-tuning.
|
|
"""
|
|
)
|
|
class ElectraForCausalLM(ElectraPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["generator_lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
if not config.is_decoder:
|
|
logger.warning("If you want to use `ElectraForCausalLM` as a standalone, add `is_decoder=True.`")
|
|
|
|
self.electra = ElectraModel(config)
|
|
self.generator_predictions = ElectraGeneratorPredictions(config)
|
|
self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size)
|
|
|
|
self.init_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.generator_lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.generator_lm_head = new_embeddings
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
token_type_ids: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[list[torch.Tensor]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
**kwargs,
|
|
) -> Union[tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
|
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
|
|
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, ElectraForCausalLM, ElectraConfig
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/electra-base-generator")
|
|
>>> config = ElectraConfig.from_pretrained("google/electra-base-generator")
|
|
>>> config.is_decoder = True
|
|
>>> model = ElectraForCausalLM.from_pretrained("google/electra-base-generator", config=config)
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> prediction_logits = outputs.logits
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if labels is not None:
|
|
use_cache = False
|
|
|
|
outputs = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.generator_lm_head(self.generator_predictions(sequence_output))
|
|
|
|
lm_loss = None
|
|
if labels is not None:
|
|
lm_loss = self.loss_function(
|
|
prediction_scores,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[1:]
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=lm_loss,
|
|
logits=prediction_scores,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"ElectraForCausalLM",
|
|
"ElectraForMaskedLM",
|
|
"ElectraForMultipleChoice",
|
|
"ElectraForPreTraining",
|
|
"ElectraForQuestionAnswering",
|
|
"ElectraForSequenceClassification",
|
|
"ElectraForTokenClassification",
|
|
"ElectraModel",
|
|
"ElectraPreTrainedModel",
|
|
"load_tf_weights_in_electra",
|
|
]
|