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1406 lines
57 KiB
1406 lines
57 KiB
# 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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from collections.abc import Callable
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from dataclasses import dataclass
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import torch
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ... import initialization as init
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from ...activations import ACT2FN, get_activation
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from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
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from ...generation import GenerationMixin
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from ...masking_utils import create_bidirectional_mask, create_causal_mask
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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 ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...pytorch_utils import apply_chunking_to_forward
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from ...utils import (
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ModelOutput,
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TransformersKwargs,
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auto_docstring,
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logging,
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)
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from ...utils.generic import can_return_tuple, check_model_inputs
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from .configuration_electra import ElectraConfig
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logger = logging.get_logger(__name__)
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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 = 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.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: torch.LongTensor | None = None,
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token_type_ids: torch.LongTensor | None = None,
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position_ids: torch.LongTensor | None = None,
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inputs_embeds: torch.FloatTensor | None = 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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batch_size, seq_length = input_shape
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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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# NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
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buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
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buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
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token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
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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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position_embeddings = self.position_embeddings(position_ids)
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embeddings = 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.eager_attention_forward
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float | None = None,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None:
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attention_mask = attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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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, is_causal=False, 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.config = config
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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.scaling = self.attention_head_size**-0.5
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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.is_decoder = config.is_decoder
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self.is_causal = is_causal
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self.layer_idx = layer_idx
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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: torch.FloatTensor | None = None,
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past_key_values: Cache | None = None,
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cache_position: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.attention_head_size)
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# get all proj
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query_layer = self.query(hidden_states).view(*hidden_shape).transpose(1, 2)
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key_layer = self.key(hidden_states).view(*hidden_shape).transpose(1, 2)
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value_layer = self.value(hidden_states).view(*hidden_shape).transpose(1, 2)
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if past_key_values is not None:
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# decoder-only bert can have a simple dynamic cache for example
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current_past_key_values = past_key_values
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if isinstance(past_key_values, EncoderDecoderCache):
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current_past_key_values = past_key_values.self_attention_cache
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# save all key/value_layer to cache to be re-used for fast auto-regressive generation
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key_layer, value_layer = current_past_key_values.update(
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key_layer,
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value_layer,
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self.layer_idx,
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{"cache_position": cache_position},
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)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout.p,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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return attn_output, attn_weights
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# Copied from transformers.models.bert.modeling_bert.BertCrossAttention with Bert->Electra
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class ElectraCrossAttention(nn.Module):
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def __init__(self, config, is_causal=False, 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.config = config
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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.scaling = self.attention_head_size**-0.5
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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.is_causal = is_causal
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self.layer_idx = layer_idx
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.FloatTensor | None = None,
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attention_mask: torch.FloatTensor | None = None,
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past_key_values: EncoderDecoderCache | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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# determine input shapes
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bsz, tgt_len = hidden_states.shape[:-1]
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src_len = encoder_hidden_states.shape[1]
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q_input_shape = (bsz, tgt_len, -1, self.attention_head_size)
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kv_input_shape = (bsz, src_len, -1, self.attention_head_size)
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# get query proj
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query_layer = self.query(hidden_states).view(*q_input_shape).transpose(1, 2)
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is_updated = past_key_values.is_updated.get(self.layer_idx) if past_key_values is not None else False
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if past_key_values is not None and is_updated:
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# reuse k,v, cross_attentions
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key_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].keys
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value_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].values
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else:
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key_layer = self.key(encoder_hidden_states).view(*kv_input_shape).transpose(1, 2)
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value_layer = self.value(encoder_hidden_states).view(*kv_input_shape).transpose(1, 2)
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if past_key_values is not None:
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# save all states to the cache
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key_layer, value_layer = past_key_values.cross_attention_cache.update(
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key_layer, value_layer, self.layer_idx
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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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past_key_values.is_updated[self.layer_idx] = True
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout.p,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
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return attn_output, attn_weights
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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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# 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, is_causal=False, layer_idx=None, is_cross_attention=False):
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super().__init__()
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self.is_cross_attention = is_cross_attention
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attention_class = ElectraCrossAttention if is_cross_attention else ElectraSelfAttention
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self.self = attention_class(config, is_causal=is_causal, layer_idx=layer_idx)
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self.output = ElectraSelfOutput(config)
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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: torch.FloatTensor | None = None,
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encoder_hidden_states: torch.FloatTensor | None = None,
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encoder_attention_mask: torch.FloatTensor | None = None,
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past_key_values: Cache | None = None,
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cache_position: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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attention_mask = attention_mask if not self.is_cross_attention else encoder_attention_mask
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attention_output, attn_weights = self.self(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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cache_position=cache_position,
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**kwargs,
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)
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attention_output = self.output(attention_output, hidden_states)
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return attention_output, attn_weights
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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:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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# Copied from transformers.models.bert.modeling_bert.BertOutput
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class ElectraOutput(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.intermediate_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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# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Electra
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class ElectraLayer(GradientCheckpointingLayer):
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def __init__(self, config, layer_idx=None):
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = ElectraAttention(config, is_causal=config.is_decoder, layer_idx=layer_idx)
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self.is_decoder = config.is_decoder
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self.add_cross_attention = config.add_cross_attention
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if self.add_cross_attention:
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if not self.is_decoder:
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raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
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self.crossattention = ElectraAttention(
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config,
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is_causal=False,
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layer_idx=layer_idx,
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is_cross_attention=True,
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)
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self.intermediate = ElectraIntermediate(config)
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self.output = ElectraOutput(config)
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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: torch.FloatTensor | None = None,
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encoder_hidden_states: torch.FloatTensor | None = None,
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encoder_attention_mask: torch.FloatTensor | None = None,
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past_key_values: Cache | None = None,
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cache_position: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor]:
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self_attention_output, _ = self.attention(
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hidden_states,
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attention_mask,
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past_key_values=past_key_values,
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cache_position=cache_position,
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**kwargs,
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)
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attention_output = self_attention_output
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if self.is_decoder and encoder_hidden_states is not None:
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if not hasattr(self, "crossattention"):
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raise ValueError(
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f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
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" by setting `config.add_cross_attention=True`"
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)
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cross_attention_output, _ = self.crossattention(
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self_attention_output,
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None, # attention_mask
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encoder_hidden_states,
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encoder_attention_mask,
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past_key_values=past_key_values,
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**kwargs,
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)
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attention_output = cross_attention_output
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layer_output = apply_chunking_to_forward(
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self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
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)
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return layer_output
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def feed_forward_chunk(self, attention_output):
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intermediate_output = self.intermediate(attention_output)
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layer_output = self.output(intermediate_output, attention_output)
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return layer_output
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|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Electra
|
|
class ElectraEncoder(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([ElectraLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.FloatTensor | None = None,
|
|
encoder_hidden_states: torch.FloatTensor | None = None,
|
|
encoder_attention_mask: torch.FloatTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | BaseModelOutputWithPastAndCrossAttentions:
|
|
for i, layer_module in enumerate(self.layer):
|
|
hidden_states = layer_module(
|
|
hidden_states,
|
|
attention_mask,
|
|
encoder_hidden_states, # as a positional argument for gradient checkpointing
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values if use_cache else None,
|
|
)
|
|
|
|
|
|
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_class = ElectraConfig
|
|
base_model_prefix = "electra"
|
|
supports_gradient_checkpointing = True
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": ElectraLayer,
|
|
"attentions": ElectraSelfAttention,
|
|
"cross_attentions": ElectraCrossAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, ElectraEmbeddings):
|
|
init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
|
|
init.zeros_(module.token_type_ids)
|
|
|
|
|
|
@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: torch.FloatTensor | None = None
|
|
logits: torch.FloatTensor | None = None
|
|
hidden_states: tuple[torch.FloatTensor] | None = None
|
|
attentions: tuple[torch.FloatTensor] | None = 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
|
|
self.gradient_checkpointing = False
|
|
# 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
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
encoder_hidden_states: torch.Tensor | None = None,
|
|
encoder_attention_mask: torch.Tensor | None = None,
|
|
past_key_values: list[torch.FloatTensor] | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | BaseModelOutputWithCrossAttentions:
|
|
if self.config.is_decoder:
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
else:
|
|
use_cache = False
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = (
|
|
EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
|
|
if encoder_hidden_states is not None or self.config.is_encoder_decoder
|
|
else DynamicCache(config=self.config)
|
|
)
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if input_ids is not None:
|
|
device = input_ids.device
|
|
input_shape = input_ids.shape
|
|
else:
|
|
device = inputs_embeds.device
|
|
input_shape = inputs_embeds.shape[:-1]
|
|
|
|
seq_length = input_shape[1]
|
|
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
if cache_position is None:
|
|
cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=device)
|
|
|
|
embedding_output = 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"):
|
|
embedding_output = self.embeddings_project(embedding_output)
|
|
|
|
attention_mask, encoder_attention_mask = self._create_attention_masks(
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
embedding_output=embedding_output,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
position_ids=position_ids,
|
|
**kwargs,
|
|
)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=encoder_outputs.last_hidden_state,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
)
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertModel._create_attention_masks
|
|
def _create_attention_masks(
|
|
self,
|
|
attention_mask,
|
|
encoder_attention_mask,
|
|
embedding_output,
|
|
encoder_hidden_states,
|
|
cache_position,
|
|
past_key_values,
|
|
):
|
|
if self.config.is_decoder:
|
|
attention_mask = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=embedding_output,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
)
|
|
else:
|
|
attention_mask = create_bidirectional_mask(
|
|
config=self.config,
|
|
input_embeds=embedding_output,
|
|
attention_mask=attention_mask,
|
|
)
|
|
|
|
if encoder_attention_mask is not None:
|
|
encoder_attention_mask = create_bidirectional_mask(
|
|
config=self.config,
|
|
input_embeds=embedding_output,
|
|
attention_mask=encoder_attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
)
|
|
|
|
return attention_mask, encoder_attention_mask
|
|
|
|
|
|
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: torch.LongTensor | None = 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()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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).
|
|
"""
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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)
|
|
|
|
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()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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]
|
|
```"""
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
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())
|
|
|
|
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": "electra.embeddings.word_embeddings.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
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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]`
|
|
"""
|
|
generator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
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))
|
|
|
|
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()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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]`.
|
|
"""
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
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))
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=discriminator_hidden_states.hidden_states,
|
|
attentions=discriminator_hidden_states.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class ElectraForQuestionAnswering(ElectraPreTrainedModel):
|
|
config_class = 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()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
start_positions: torch.Tensor | None = None,
|
|
end_positions: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput:
|
|
discriminator_hidden_states = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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
|
|
|
|
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()
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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)
|
|
"""
|
|
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,
|
|
inputs_embeds=inputs_embeds,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
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)
|
|
|
|
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": "electra.embeddings.word_embeddings.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.post_init()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.generator_lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.generator_lm_head = new_embeddings
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
token_type_ids: torch.Tensor | None = None,
|
|
position_ids: torch.Tensor | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
encoder_hidden_states: torch.Tensor | None = None,
|
|
encoder_attention_mask: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.Tensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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
|
|
```"""
|
|
if labels is not None:
|
|
use_cache = False
|
|
|
|
outputs: BaseModelOutputWithPastAndCrossAttentions = self.electra(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
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,
|
|
cache_position=cache_position,
|
|
return_dict=True,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.generator_lm_head(self.generator_predictions(hidden_states[:, slice_indices, :]))
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=logits,
|
|
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",
|
|
]
|