869 lines
40 KiB
Python
869 lines
40 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from examples/modular-transformers/modular_roberta.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_roberta.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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import math
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from typing import Optional, Union
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import torch
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import torch.nn as nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
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from ...modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask_for_sdpa
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions
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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 auto_docstring, logging
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from ...utils.deprecation import deprecate_kwarg
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from .configuration_roberta import RobertaConfig
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logger = logging.get_logger(__name__)
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class RobertaEmbeddings(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.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(
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config.max_position_embeddings, config.hidden_size, config.pad_token_id
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)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_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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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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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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"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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self.pad_token_id = config.pad_token_id
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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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class RobertaSelfAttention(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("past_key_value", new_name="past_key_values", version="4.58")
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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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past_key_values: 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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is_updated = False
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is_cross_attention = encoder_hidden_states is not None
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if past_key_values is not None:
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if isinstance(past_key_values, EncoderDecoderCache):
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is_updated = past_key_values.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_values.cross_attention_cache
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else:
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curr_past_key_value = past_key_values.self_attention_cache
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else:
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curr_past_key_value = past_key_values
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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_values 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.layers[self.layer_idx].keys
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value_layer = curr_past_key_value.layers[self.layer_idx].values
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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_values 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 and isinstance(past_key_values, EncoderDecoderCache):
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past_key_values.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_values 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 RobertaModel 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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class RobertaSdpaSelfAttention(RobertaSelfAttention):
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def __init__(self, config, position_embedding_type=None, layer_idx=None):
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super().__init__(config, position_embedding_type=position_embedding_type, layer_idx=layer_idx)
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self.dropout_prob = config.attention_probs_dropout_prob
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# Adapted from RobertaSelfAttention
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@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
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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.Tensor] = 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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past_key_values: 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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if self.position_embedding_type != "absolute" or output_attentions or head_mask is not None:
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# TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once implemented.
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logger.warning_once(
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"RobertaSdpaSelfAttention is used but `torch.nn.functional.scaled_dot_product_attention` does not support "
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"non-absolute `position_embedding_type` or `output_attentions=True` or `head_mask`. Falling back to "
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"the manual attention implementation, but specifying the manual implementation will be required from "
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"Transformers version v5.0.0 onwards. This warning can be removed using the argument "
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'`attn_implementation="eager"` when loading the model.'
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)
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return super().forward(
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hidden_states,
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attention_mask,
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head_mask,
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encoder_hidden_states,
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past_key_values,
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output_attentions,
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cache_position,
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)
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bsz, tgt_len, _ = hidden_states.size()
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query_layer = (
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self.query(hidden_states).view(bsz, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
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)
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is_updated = False
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is_cross_attention = encoder_hidden_states is not None
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current_states = encoder_hidden_states if is_cross_attention else hidden_states
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if past_key_values is not None:
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if isinstance(past_key_values, EncoderDecoderCache):
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is_updated = past_key_values.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_states from cache
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curr_past_key_value = past_key_values.cross_attention_cache
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else:
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curr_past_key_value = past_key_values.self_attention_cache
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else:
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curr_past_key_value = past_key_values
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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_values 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.layers[self.layer_idx].keys
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value_layer = curr_past_key_value.layers[self.layer_idx].values
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else:
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key_layer = (
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self.key(current_states)
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.view(bsz, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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value_layer = (
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self.value(current_states)
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.view(bsz, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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if past_key_values 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 and isinstance(past_key_values, EncoderDecoderCache):
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past_key_values.is_updated[self.layer_idx] = True
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# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
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# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
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# The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create
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# a causal mask in case tgt_len == 1.
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is_causal = self.is_decoder and not is_cross_attention and attention_mask is None and tgt_len > 1
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query_layer,
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key_layer,
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value_layer,
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attn_mask=attention_mask,
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dropout_p=self.dropout_prob if self.training else 0.0,
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is_causal=is_causal,
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)
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attn_output = attn_output.transpose(1, 2)
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attn_output = attn_output.reshape(bsz, tgt_len, self.all_head_size)
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return attn_output, None
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class RobertaSelfOutput(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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ROBERTA_SELF_ATTENTION_CLASSES = {
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"eager": RobertaSelfAttention,
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"sdpa": RobertaSdpaSelfAttention,
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}
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class RobertaAttention(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 = ROBERTA_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 = RobertaSelfOutput(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("past_key_value", new_name="past_key_values", version="4.58")
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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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past_key_values: 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(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
past_key_values=past_key_values,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class RobertaIntermediate(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
|
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
|
|
|
|
|
|
class RobertaOutput(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
|
|
|
|
|
|
class RobertaLayer(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 = RobertaAttention(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 = RobertaAttention(config, position_embedding_type="absolute", layer_idx=layer_idx)
|
|
self.intermediate = RobertaIntermediate(config)
|
|
self.output = RobertaOutput(config)
|
|
|
|
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
|
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[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_values=past_key_values,
|
|
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_values=past_key_values,
|
|
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
|
|
|
|
|
|
class RobertaEncoder(nn.Module):
|
|
def __init__(self, config, layer_idx=None):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([RobertaLayer(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
|
|
|
|
if use_cache and self.config.is_decoder and past_key_values is None:
|
|
past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
|
|
|
|
if use_cache and self.config.is_decoder and isinstance(past_key_values, tuple):
|
|
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)`."
|
|
)
|
|
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_values=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 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 RobertaPooler(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class RobertaPredictionHeadTransform(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
if isinstance(config.hidden_act, str):
|
|
self.transform_act_fn = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.transform_act_fn = config.hidden_act
|
|
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class RobertaLMPredictionHead(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.transform = RobertaPredictionHeadTransform(config)
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
|
|
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
|
self.decoder.bias = self.bias
|
|
|
|
def _tie_weights(self):
|
|
self.decoder.bias = self.bias
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.decoder(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class RobertaPreTrainedModel(PreTrainedModel):
|
|
config: RobertaConfig
|
|
base_model_prefix = "roberta"
|
|
supports_gradient_checkpointing = True
|
|
_supports_sdpa = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, nn.Linear):
|
|
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)
|
|
elif isinstance(module, RobertaLMPredictionHead):
|
|
module.bias.data.zero_()
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
|
|
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
|
|
all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
|
|
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
|
|
|
|
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
|
|
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
|
|
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
|
|
"""
|
|
)
|
|
class RobertaModel(RobertaPreTrainedModel):
|
|
_no_split_modules = ["RobertaEmbeddings", "RobertaLayer"]
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
r"""
|
|
add_pooling_layer (bool, *optional*, defaults to `True`):
|
|
Whether to add a pooling layer
|
|
"""
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = RobertaEmbeddings(config)
|
|
self.encoder = RobertaEncoder(config)
|
|
|
|
self.pooler = RobertaPooler(config) if add_pooling_layer else None
|
|
|
|
self.attn_implementation = config._attn_implementation
|
|
self.position_embedding_type = config.position_embedding_type
|
|
|
|
# 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,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
|
|
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 self.config.is_decoder:
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
else:
|
|
use_cache = False
|
|
|
|
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 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)
|
|
|
|
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 attention_mask is None:
|
|
attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=device)
|
|
|
|
use_sdpa_attention_masks = (
|
|
self.attn_implementation == "sdpa"
|
|
and self.position_embedding_type == "absolute"
|
|
and head_mask is None
|
|
and not output_attentions
|
|
)
|
|
|
|
# Expand the attention mask
|
|
if use_sdpa_attention_masks and attention_mask.dim() == 2:
|
|
# Expand the attention mask for SDPA.
|
|
# [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
|
|
if self.config.is_decoder:
|
|
extended_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
|
attention_mask,
|
|
input_shape,
|
|
embedding_output,
|
|
past_key_values_length,
|
|
)
|
|
else:
|
|
extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
|
|
attention_mask, embedding_output.dtype, tgt_len=seq_length
|
|
)
|
|
else:
|
|
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
|
# ourselves in which case we just need to make it broadcastable to all heads.
|
|
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)
|
|
|
|
if use_sdpa_attention_masks and encoder_attention_mask.dim() == 2:
|
|
# Expand the attention mask for SDPA.
|
|
# [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
|
|
encoder_extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
|
|
encoder_attention_mask, embedding_output.dtype, tgt_len=seq_length
|
|
)
|
|
else:
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
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# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
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head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
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|
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encoder_outputs = self.encoder(
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embedding_output,
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attention_mask=extended_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_extended_attention_mask,
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|
past_key_values=past_key_values,
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|
use_cache=use_cache,
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|
output_attentions=output_attentions,
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|
output_hidden_states=output_hidden_states,
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|
return_dict=return_dict,
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|
cache_position=cache_position,
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|
)
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sequence_output = encoder_outputs[0]
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|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
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|
|
|
if not return_dict:
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|
return (sequence_output, pooled_output) + encoder_outputs[1:]
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|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
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|
last_hidden_state=sequence_output,
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|
pooler_output=pooled_output,
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|
past_key_values=encoder_outputs.past_key_values,
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|
hidden_states=encoder_outputs.hidden_states,
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|
attentions=encoder_outputs.attentions,
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|
cross_attentions=encoder_outputs.cross_attentions,
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|
)
|