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Removed Bert dependency from BertGeneration code base. (#19370)
* Copied all the code required from transformers.models.bert.modeling_bert to here * Fixed styling issues * Reformatted copied names with Model specific name. * Reverted BertEncoder part as there is already a class called BertGenerationEncoder * Added prefixes in missing places. Co-authored-by: vishwaspai <vishwas.pai@emplay.net>
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Vishwas
GitHub
parent
34e0cc6d86
commit
de4d71ea07
@ -14,6 +14,7 @@
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# limitations under the License.
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"""PyTorch BERT model specific for generation."""
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import math
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from typing import Optional, Tuple, Union
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import torch
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@ -21,8 +22,10 @@ import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from ...activations import ACT2FN
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from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
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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 (
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add_code_sample_docstrings,
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add_start_docstrings,
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@ -30,7 +33,6 @@ from ...utils import (
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logging,
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replace_return_docstrings,
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)
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from ..bert.modeling_bert import BertEncoder
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from .configuration_bert_generation import BertGenerationConfig
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@ -41,6 +43,415 @@ _CONFIG_FOR_DOC = "BertGenerationConfig"
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_TOKENIZER_FOR_DOC = "BertGenerationTokenizer"
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# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->BertGeneration
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class BertGenerationSelfOutput(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.BertSelfAttention with Bert->BertGeneration
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class BertGenerationSelfAttention(nn.Module):
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def __init__(self, config, position_embedding_type=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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def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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mixed_query_layer = self.query(hidden_states)
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# If this is instantiated as a cross-attention module, the keys
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# and values come from an encoder; the attention mask needs to be
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# such that the encoder's padding tokens are not attended to.
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is_cross_attention = encoder_hidden_states is not None
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if is_cross_attention and past_key_value is not None:
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# reuse k,v, cross_attentions
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key_layer = past_key_value[0]
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value_layer = past_key_value[1]
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attention_mask = encoder_attention_mask
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elif is_cross_attention:
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key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
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value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
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attention_mask = encoder_attention_mask
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elif past_key_value is not None:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
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value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
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else:
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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if self.is_decoder:
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# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
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# Further calls to cross_attention layer can then reuse all cross-attention
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# key/value_states (first "if" case)
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# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
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# all previous decoder key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
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# if encoder bi-directional self-attention `past_key_value` is always `None`
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past_key_value = (key_layer, value_layer)
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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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seq_length = hidden_states.size()[1]
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position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
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position_ids_r = torch.arange(seq_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 BertGenerationModel 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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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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if self.is_decoder:
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outputs = outputs + (past_key_value,)
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->BertGeneration
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class BertGenerationAttention(nn.Module):
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def __init__(self, config, position_embedding_type=None):
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super().__init__()
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self.self = BertGenerationSelfAttention(config, position_embedding_type=position_embedding_type)
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self.output = BertGenerationSelfOutput(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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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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self_outputs = self.self(
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hidden_states,
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attention_mask,
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head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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past_key_value,
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output_attentions,
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)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->BertGeneration
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class BertGenerationIntermediate(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 with Bert->BertGeneration
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class BertGenerationOutput(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->BertGeneration
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class BertGenerationLayer(nn.Module):
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def __init__(self, config):
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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 = BertGenerationAttention(config)
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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 = BertGenerationAttention(config, position_embedding_type="absolute")
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self.intermediate = BertGenerationIntermediate(config)
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self.output = BertGenerationOutput(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: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor]:
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# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
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self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
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self_attention_outputs = self.attention(
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hidden_states,
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attention_mask,
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head_mask,
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output_attentions=output_attentions,
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past_key_value=self_attn_past_key_value,
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)
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attention_output = self_attention_outputs[0]
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# if decoder, the last output is tuple of self-attn cache
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if self.is_decoder:
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outputs = self_attention_outputs[1:-1]
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present_key_value = self_attention_outputs[-1]
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else:
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outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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cross_attn_present_key_value = None
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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_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
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cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
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cross_attention_outputs = self.crossattention(
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attention_output,
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attention_mask,
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head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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cross_attn_past_key_value,
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output_attentions,
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)
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attention_output = cross_attention_outputs[0]
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outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
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# add cross-attn cache to positions 3,4 of present_key_value tuple
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cross_attn_present_key_value = cross_attention_outputs[-1]
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present_key_value = present_key_value + cross_attn_present_key_value
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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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outputs = (layer_output,) + outputs
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# if decoder, return the attn key/values as the last output
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if self.is_decoder:
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outputs = outputs + (present_key_value,)
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return outputs
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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->BertGeneration
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class BertEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([BertGenerationLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.FloatTensor] = None,
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head_mask: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = False,
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output_hidden_states: Optional[bool] = False,
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return_dict: Optional[bool] = True,
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) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
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all_hidden_states = () if output_hidden_states else None
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all_self_attentions = () if output_attentions else None
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all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
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next_decoder_cache = () if use_cache else None
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for i, layer_module in enumerate(self.layer):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_head_mask = head_mask[i] if head_mask is not None else None
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past_key_value = past_key_values[i] if past_key_values is not None else None
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs, past_key_value, output_attentions)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(layer_module),
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
layer_head_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
)
|
||||
else:
|
||||
layer_outputs = layer_module(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
layer_head_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
past_key_value,
|
||||
output_attentions,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
if use_cache:
|
||||
next_decoder_cache += (layer_outputs[-1],)
|
||||
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,
|
||||
next_decoder_cache,
|
||||
all_hidden_states,
|
||||
all_self_attentions,
|
||||
all_cross_attentions,
|
||||
]
|
||||
if v is not None
|
||||
)
|
||||
return BaseModelOutputWithPastAndCrossAttentions(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_decoder_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attentions,
|
||||
cross_attentions=all_cross_attentions,
|
||||
)
|
||||
|
||||
|
||||
def load_tf_weights_in_bert_generation(
|
||||
model, tf_hub_path, model_class, is_encoder_named_decoder=False, is_encoder=False
|
||||
):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user