wehub-resource-sync
1102 lines
49 KiB
Python
1102 lines
49 KiB
Python
# coding=utf-8
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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2022 The Salesforce Team Authors and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the BSD-3-clause license (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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# https://opensource.org/licenses/BSD-3-Clause
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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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import inspect
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import math
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from functools import partial
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from typing import Callable, Optional, Tuple
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.distributed.fleet.utils import recompute
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from ...utils.initializer import normal_, ones_, zeros_
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from ...utils.log import logger
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from ..activations import ACT2FN
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from ..model_outputs import (
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BaseModelOutputWithPastAndCrossAttentions,
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BaseModelOutputWithPoolingAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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)
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from ..model_utils import PretrainedModel
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from .configuration import BlipTextConfig
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__all__ = [
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"BlipTextPretrainedModel",
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"BlipTextModel",
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"BlipTextLMHeadModel",
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]
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def apply_chunking_to_forward(
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forward_fn: Callable[..., paddle.Tensor], chunk_size: int, chunk_dim: int, *input_tensors
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) -> paddle.Tensor:
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"""
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This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension
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`chunk_dim`. It then applies a layer `forward_fn` to each chunk independently to save memory.
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If the `forward_fn` is independent across the `chunk_dim` this function will yield the same result as directly
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applying `forward_fn` to `input_tensors`.
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Args:
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forward_fn (`Callable[..., paddle.Tensor]`):
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The forward function of the model.
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chunk_size (`int`):
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The chunk size of a chunked tensor: `num_chunks = len(input_tensors[0]) / chunk_size`.
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chunk_dim (`int`):
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The dimension over which the `input_tensors` should be chunked.
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input_tensors (`Tuple[paddle.Tensor]`):
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The input tensors of `forward_fn` which will be chunked
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Returns:
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`paddle.Tensor`: A tensor with the same shape as the `forward_fn` would have given if applied`.
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Examples:
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```python
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# rename the usual forward() fn to forward_chunk()
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def forward_chunk(self, hidden_states):
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hidden_states = self.decoder(hidden_states)
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return hidden_states
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# implement a chunked forward function
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def forward(self, hidden_states):
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return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)
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```"""
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assert len(input_tensors) > 0, f"{input_tensors} has to be a tuple/list of tensors"
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# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
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num_args_in_forward_chunk_fn = len(inspect.signature(forward_fn).parameters)
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if num_args_in_forward_chunk_fn != len(input_tensors):
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raise ValueError(
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f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(input_tensors)} input "
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"tensors are given"
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)
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if chunk_size > 0:
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tensor_shape = input_tensors[0].shape[chunk_dim]
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for input_tensor in input_tensors:
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if input_tensor.shape[chunk_dim] != tensor_shape:
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raise ValueError(
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f"All input tenors have to be of the same shape: {tensor_shape}, "
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f"found shape {input_tensor.shape[chunk_dim]}"
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)
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if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
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raise ValueError(
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f"The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk "
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f"size {chunk_size}"
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)
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num_chunks = input_tensors[0].shape[chunk_dim] // chunk_size
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# chunk input tensor into tuples
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input_tensors_chunks = tuple(input_tensor.chunk(num_chunks, axis=chunk_dim) for input_tensor in input_tensors)
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# apply forward fn to every tuple
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output_chunks = tuple(forward_fn(*input_tensors_chunk) for input_tensors_chunk in zip(*input_tensors_chunks))
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# concatenate output at same dimension
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return paddle.concat(output_chunks, axis=chunk_dim)
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return forward_fn(*input_tensors)
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# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L52
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class BlipTextEmbeddings(nn.Layer):
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"""Construct the embeddings from word and position embeddings."""
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def __init__(self, config: BlipTextConfig):
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super().__init__()
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self.word_embeddings = nn.Embedding(
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config.vocab_size, config.hidden_size
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) # , padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=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", paddle.arange(config.max_position_embeddings, dtype="int64").reshape((1, -1))
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)
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self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
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self.config = config
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def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
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if input_ids is not None:
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input_shape = input_ids.shape
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else:
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input_shape = inputs_embeds.shape[:-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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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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embeddings = inputs_embeds
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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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# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L97
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class BlipTextSelfAttention(nn.Layer):
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def __init__(self, config: BlipTextConfig, is_cross_attention: bool = False):
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super().__init__()
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self.config = config
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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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"The hidden size (%d) is not a multiple of the number of attention heads (%d)"
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% (config.hidden_size, 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.scale = math.sqrt(self.attention_head_size)
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self.query = nn.Linear(config.hidden_size, self.all_head_size)
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if is_cross_attention:
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self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size)
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self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size)
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else:
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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 = getattr(config, "position_embedding_type", "absolute")
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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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def save_attn_gradients(self, attn_gradients):
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self.attn_gradients = attn_gradients
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def get_attn_gradients(self):
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return self.attn_gradients
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def save_attention_map(self, attention_map):
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self.attention_map = attention_map
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def get_attention_map(self):
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return self.attention_map
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def transpose_for_scores(self, x):
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new_x_shape = x.shape[:-1] + [self.num_attention_heads, self.attention_head_size]
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x = x.reshape(new_x_shape)
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return x.transpose([0, 2, 1, 3])
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_value=None,
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output_attentions=False,
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):
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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:
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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 = paddle.concat([past_key_value[0], key_layer], axis=2)
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value_layer = paddle.concat([past_key_value[1], value_layer], axis=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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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 = paddle.matmul(query_layer, key_layer, transpose_y=True)
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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.shape[1]
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position_ids_l = paddle.arange(seq_length, dtype=paddle.int64).reshape([-1, 1])
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position_ids_r = paddle.arange(seq_length, dtype=paddle.int64).reshape([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.cast(query_layer.dtype) # fp16 compatibility
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if self.position_embedding_type == "relative_key":
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relative_position_scores = paddle.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 = paddle.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
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relative_position_scores_key = paddle.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 / self.scale
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in BlipTextModel 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 = F.softmax(attention_scores, axis=-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_dropped = self.dropout(attention_probs)
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context_layer = paddle.matmul(attention_probs_dropped, value_layer)
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context_layer = context_layer.transpose([0, 2, 1, 3])
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new_context_layer_shape = context_layer.shape[:-2] + [
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self.all_head_size,
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]
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context_layer = context_layer.reshape(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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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.BertSelfOutput with Bert -> BlipText
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class BlipTextSelfOutput(nn.Layer):
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def __init__(self, config: BlipTextConfig):
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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, epsilon=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: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.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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# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#242
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class BlipTextAttention(nn.Layer):
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def __init__(self, config: BlipTextConfig, is_cross_attention: bool = False):
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super().__init__()
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self.self = BlipTextSelfAttention(config, is_cross_attention)
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self.output = BlipTextSelfOutput(config)
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self.pruned_heads = set()
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def forward(
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self,
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hidden_states: paddle.Tensor,
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attention_mask: Optional[paddle.Tensor] = None,
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encoder_hidden_states: Optional[paddle.Tensor] = None,
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encoder_attention_mask: Optional[paddle.Tensor] = None,
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past_key_value: Optional[Tuple[Tuple[paddle.Tensor]]] = None,
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output_attentions: Optional[bool] = False,
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):
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self_outputs = self.self(
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hidden_states,
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attention_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 -> BlipText
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class BlipTextIntermediate(nn.Layer):
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def __init__(self, config: BlipTextConfig):
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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: paddle.Tensor) -> paddle.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 -> BlipText
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class BlipTextOutput(nn.Layer):
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def __init__(self, config: BlipTextConfig):
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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, epsilon=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: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.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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class BlipTextLayer(nn.Layer):
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def __init__(self, config: BlipTextConfig, layer_num: int):
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super().__init__()
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self.config = config
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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 = BlipTextAttention(config)
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self.layer_num = layer_num
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if self.config.is_decoder:
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self.crossattention = BlipTextAttention(config, is_cross_attention=self.config.is_decoder)
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self.intermediate = BlipTextIntermediate(config)
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self.output = BlipTextOutput(config)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_value=None,
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output_attentions=False,
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):
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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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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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outputs = self_attention_outputs[1:-1]
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present_key_value = self_attention_outputs[-1]
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if encoder_hidden_states is not 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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encoder_hidden_states,
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encoder_attention_mask,
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output_attentions=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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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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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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# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L386
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class BlipTextEncoder(nn.Layer):
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def __init__(self, config: BlipTextConfig):
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super().__init__()
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self.config = config
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self.layer = nn.LayerList([BlipTextLayer(config, i) for i 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,
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attention_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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past_key_values=None,
|
|
use_cache=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=True,
|
|
):
|
|
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.is_decoder else None
|
|
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
for i in range(self.config.num_hidden_layers):
|
|
layer_module = self.layer[i]
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
past_key_value = past_key_values[i] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
if use_cache:
|
|
logger.warning(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, past_key_value, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = recompute(
|
|
create_custom_forward(layer_module),
|
|
hidden_states,
|
|
attention_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(
|
|
hidden_states,
|
|
attention_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 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,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->BlipText
|
|
class BlipTextPooler(nn.Layer):
|
|
def __init__(self, config: BlipTextConfig):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states: paddle.Tensor) -> paddle.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
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->BlipText
|
|
class BlipTextPredictionHeadTransform(nn.Layer):
|
|
def __init__(self, config: BlipTextConfig):
|
|
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, epsilon=config.layer_norm_eps)
|
|
|
|
def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.transform_act_fn(hidden_states)
|
|
hidden_states = self.LayerNorm(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->BlipText
|
|
class BlipTextLMPredictionHead(nn.Layer):
|
|
def __init__(self, config: BlipTextConfig, embedding_weights=None):
|
|
super().__init__()
|
|
self.transform = BlipTextPredictionHeadTransform(config)
|
|
|
|
# The output weights are the same as the input embeddings, but there is
|
|
# an output-only bias for each token.
|
|
self.decoder_weight = (
|
|
self.create_parameter(
|
|
shape=[config.vocab_size, config.hidden_size], dtype=self.transform.weight.dtype, is_bias=False
|
|
)
|
|
if embedding_weights is None
|
|
else embedding_weights
|
|
)
|
|
|
|
self.bias = self.create_parameter(
|
|
shape=[
|
|
config.vocab_size,
|
|
],
|
|
dtype=self.decoder_weight.dtype,
|
|
is_bias=True,
|
|
)
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = paddle.matmul(hidden_states, self.decoder_weight, transpose_y=True) + self.bias
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->BlipText
|
|
class BlipTextOnlyMLMHead(nn.Layer):
|
|
"""
|
|
Perform language modeling task.
|
|
|
|
Args:
|
|
config (:class:`BlipTextConfig`):
|
|
An instance of BlipTextConfig used to construct BlipTextLMHeadModel.
|
|
embedding_weights (Tensor, optional):
|
|
Decoding weights used to map hidden_states to logits of the masked token prediction.
|
|
Its data type should be float32 and its shape is [vocab_size, hidden_size].
|
|
Defaults to `None`, which means use the same weights of the embedding layer.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: BlipTextConfig, embedding_weights=None):
|
|
super().__init__()
|
|
self.predictions = BlipTextLMPredictionHead(config, embedding_weights)
|
|
|
|
def forward(self, sequence_output: paddle.Tensor) -> paddle.Tensor:
|
|
prediction_scores = self.predictions(sequence_output)
|
|
return prediction_scores
|
|
|
|
|
|
# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L548
|
|
class BlipTextPretrainedModel(PretrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = BlipTextConfig
|
|
base_model_prefix = "bert"
|
|
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
normal_(module.weight, mean=0.0, std=self.config.initializer_range)
|
|
elif isinstance(module, nn.LayerNorm):
|
|
zeros_(module.bias)
|
|
ones_(module.weight)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
zeros_(module.bias)
|
|
|
|
def gradient_checkpointing_enable(self):
|
|
"""
|
|
Activates gradient checkpointing for the current model.
|
|
|
|
Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
|
|
activations".
|
|
"""
|
|
if not self.supports_gradient_checkpointing:
|
|
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
|
|
self.apply(partial(self._set_gradient_checkpointing, value=True))
|
|
|
|
def gradient_checkpointing_disable(self):
|
|
"""
|
|
Deactivates gradient checkpointing for the current model.
|
|
|
|
Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
|
|
activations".
|
|
"""
|
|
if self.supports_gradient_checkpointing:
|
|
self.apply(partial(self._set_gradient_checkpointing, value=False))
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
if isinstance(module, BlipTextEncoder):
|
|
module.gradient_checkpointing = value
|
|
|
|
|
|
# Adapted from https://github.com/salesforce/BLIP/blob/3a29b7410476bf5f2ba0955827390eb6ea1f4f9d/models/med.py#L571
|
|
class BlipTextModel(BlipTextPretrainedModel):
|
|
"""
|
|
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://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
|
|
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and `is_decoder` set to `True`; an
|
|
`encoder_hidden_states` is then expected as an input to the forward pass.
|
|
|
|
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
|
|
Refer to the superclass documentation for the generic methods.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`BlipTextConfig`):
|
|
An instance of BlipTextConfig used to construct BlipTextModel.
|
|
"""
|
|
|
|
def __init__(self, config: BlipTextConfig, add_pooling_layer: bool = True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = BlipTextEmbeddings(config)
|
|
self.encoder = BlipTextEncoder(config)
|
|
self.pooler = BlipTextPooler(config) if add_pooling_layer else None
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self.embeddings.word_embeddings.weight.dtype
|
|
|
|
def get_extended_attention_mask(
|
|
self, attention_mask: paddle.Tensor, input_shape: Tuple[int], is_decoder: bool
|
|
) -> paddle.Tensor:
|
|
if attention_mask.ndim == 3:
|
|
extended_attention_mask = attention_mask.unsqueeze(1)
|
|
elif attention_mask.ndim == 2:
|
|
# Provided a padding mask of dimensions [batch_size, seq_length]
|
|
# - if the model is a decoder, apply a causal mask in addition to the padding mask
|
|
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
|
if is_decoder:
|
|
batch_size, seq_length = input_shape
|
|
seq_ids = paddle.arange(seq_length)
|
|
causal_mask = paddle.tile(
|
|
seq_ids.unsqueeze(axis=[0, 1]), [batch_size, seq_length, 1]
|
|
) <= seq_ids.unsqueeze(axis=[0, 2])
|
|
causal_mask = causal_mask.cast(attention_mask.dtype)
|
|
|
|
if causal_mask.shape[1] < attention_mask.shape[1]:
|
|
prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
|
|
causal_mask = paddle.concat(
|
|
[
|
|
paddle.ones(
|
|
[batch_size, seq_length, prefix_seq_len],
|
|
dtype=causal_mask.dtype,
|
|
),
|
|
causal_mask,
|
|
],
|
|
axis=-1,
|
|
)
|
|
|
|
extended_attention_mask = causal_mask.unsqueeze(1) * attention_mask.unsqueeze([1, 2])
|
|
else:
|
|
extended_attention_mask = attention_mask.unsqueeze([1, 2])
|
|
else:
|
|
raise ValueError(
|
|
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})"
|
|
)
|
|
|
|
extended_attention_mask = extended_attention_mask.cast(self.dtype)
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
|
return extended_attention_mask
|
|
|
|
def invert_attention_mask(self, encoder_attention_mask):
|
|
if encoder_attention_mask.ndim == 4:
|
|
encoder_extended_attention_mask = encoder_attention_mask
|
|
elif encoder_attention_mask.ndim == 3:
|
|
encoder_extended_attention_mask = encoder_attention_mask.unsqueeze(1)
|
|
elif encoder_attention_mask.ndim == 2:
|
|
encoder_extended_attention_mask = encoder_attention_mask.unsqueeze([1, 2])
|
|
encoder_extended_attention_mask = encoder_extended_attention_mask.cast(self.dtype) # fp16 compatibility
|
|
|
|
if self.dtype == paddle.float16:
|
|
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
|
|
elif self.dtype == paddle.float32:
|
|
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
|
|
else:
|
|
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
|
|
|
|
return encoder_extended_attention_mask
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
encoder_embeds=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
past_key_values=None,
|
|
use_cache=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
is_decoder=False,
|
|
):
|
|
r"""
|
|
input_ids (`paddle.Tensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`BertTokenizer`]. See [`PretrainedTokenizer.encode`] and
|
|
[`PretrainedTokenizer.__call__`] for details.
|
|
|
|
attention_mask (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
position_ids (`paddle.Tensor` of shape `(batch_size, 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]`.
|
|
|
|
inputs_embeds (`paddle.Tensor` of shape `(batch_size, 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.
|
|
encoder_embeds (`paddle.Tensor` of shape `(batch_size, hidden_size)`, *optional*):
|
|
Optionally, same as inputs_embeds.
|
|
encoder_hidden_states (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
|
the model is configured as a decoder.
|
|
encoder_attention_mask (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
|
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
past_key_values (`tuple(tuple(paddle.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`BaseModelOutputWithPoolingAndCrossAttentions`] instead of a plain tuple.
|
|
is_decoder (`bool`, *optional*, defaults to `False`):
|
|
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
|
|
"""
|
|
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 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:
|
|
input_shape = input_ids.shape
|
|
batch_size, seq_length = input_shape
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.shape[:-1]
|
|
batch_size, seq_length = input_shape
|
|
elif encoder_embeds is not None:
|
|
input_shape = encoder_embeds.shape[:-1]
|
|
batch_size, seq_length = input_shape
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
|
|
|
|
# cache_length
|
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
|
|
|
if attention_mask is None:
|
|
attention_mask = paddle.ones((batch_size, seq_length + past_key_values_length))
|
|
|
|
# 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: paddle.Tensor = self.get_extended_attention_mask(
|
|
attention_mask, input_shape, is_decoder
|
|
)
|
|
|
|
# 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 encoder_hidden_states is not None:
|
|
if isinstance(encoder_hidden_states, (list, tuple)):
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].shape
|
|
else:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.shape
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
|
|
if isinstance(encoder_attention_mask, (list, tuple)):
|
|
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
|
|
elif encoder_attention_mask is None:
|
|
encoder_attention_mask = paddle.ones(encoder_hidden_shape)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
if encoder_embeds is None:
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
else:
|
|
embedding_output = encoder_embeds
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
if pooled_output is None:
|
|
# note: we do not output pooled_output
|
|
return (sequence_output,) + encoder_outputs[1:]
|
|
else:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
cross_attentions=encoder_outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
# Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L811
|
|
class BlipTextLMHeadModel(BlipTextPretrainedModel):
|
|
"""
|
|
Bert Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
config (:class:`BlipTextConfig`):
|
|
An instance of BlipTextConfig used to construct BlipTextLMHeadModel.
|
|
|
|
"""
|
|
|
|
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
|
|
|
|
def __init__(self, config: BlipTextConfig):
|
|
super().__init__(config)
|
|
|
|
self.bert = BlipTextModel(config, add_pooling_layer=False)
|
|
self.cls = BlipTextOnlyMLMHead(config, embedding_weights=self.bert.embeddings.word_embeddings.weight)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
position_ids=None,
|
|
inputs_embeds=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
labels=None,
|
|
past_key_values=None,
|
|
use_cache=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
return_logits=False,
|
|
is_decoder=True,
|
|
reduction="mean",
|
|
):
|
|
r"""
|
|
input_ids (`paddle.Tensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary.
|
|
|
|
Indices can be obtained using [`BertTokenizer`]. See [`PretrainedTokenizer.encode`] and
|
|
[`PretrainedTokenizer.__call__`] for details.
|
|
|
|
attention_mask (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
position_ids (`paddle.Tensor` of shape `(batch_size, 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]`.
|
|
|
|
inputs_embeds (`paddle.Tensor` of shape `(batch_size, 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.
|
|
encoder_hidden_states (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
|
the model is configured as a decoder.
|
|
encoder_attention_mask (`paddle.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
|
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
past_key_values (`tuple(tuple(paddle.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`CausalLMOutputWithCrossAttentions`] instead of a plain tuple.
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if labels is not None:
|
|
use_cache = False
|
|
|
|
outputs = self.bert(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
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,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
is_decoder=is_decoder,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output)
|
|
|
|
if return_logits:
|
|
return prediction_scores[:, :-1, :]
|
|
|
|
lm_loss = None
|
|
if labels is not None:
|
|
# we are doing next-token prediction; shift prediction scores and input ids by one
|
|
shifted_prediction_scores = prediction_scores[:, :-1, :]
|
|
labels = labels[:, 1:]
|
|
loss_fct = nn.CrossEntropyLoss(reduction=reduction) # TODO label_smoothing=0.1
|
|
lm_loss = loss_fct(shifted_prediction_scores.reshape([-1, self.config.vocab_size]), labels.flatten())
|
|
if reduction == "none":
|
|
lm_loss = lm_loss.reshape([prediction_scores.shape[0], -1]).sum(1)
|
|
|
|
if not return_dict:
|
|
# note: we do not output pooler
|
|
if self.bert.pooler is None:
|
|
output = (prediction_scores,) + outputs[1:]
|
|
else:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=lm_loss,
|
|
logits=prediction_scores,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
|
|
input_shape = input_ids.shape
|
|
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
|
if attention_mask is None:
|
|
attention_mask = paddle.ones(input_shape, dtype=input_ids.dtype)
|
|
|
|
# cut decoder_input_ids if past_key_values is used
|
|
if past_key_values is not None:
|
|
input_ids = input_ids[:, -1:]
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"past_key_values": past_key_values,
|
|
"encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
|
|
"encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
|
|
"is_decoder": True,
|
|
# we must set return_dict False
|
|
"return_dict": False,
|
|
}
|
|
|
|
def prepare_attention_mask_for_generation(
|
|
self,
|
|
inputs: paddle.Tensor,
|
|
pad_token_id: Optional[int],
|
|
eos_token_id: Optional[int],
|
|
) -> paddle.Tensor:
|
|
# do not create 4d attention mask
|
|
is_input_ids = len(inputs.shape) == 2 and inputs.dtype in [paddle.int32, paddle.int64]
|
|
is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs.tolist())
|
|
is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (pad_token_id != eos_token_id)
|
|
|
|
# Check if input is input_ids and padded -> only then is attention_mask defined
|
|
if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
|
|
return (inputs != pad_token_id).cast("int64")
|
|
else:
|
|
return paddle.ones(inputs.shape[:2], dtype=paddle.int64)
|