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chaojie-ComfyUI_StreamingT2V/thirdparty/modelscope/models/multi_modal/mplug/modeling_mplug.py
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fles@qq.com fefbc85621 init
2024-04-08 06:24:30 +08:00

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# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch MPLUG model. """
import math
import os
from typing import Tuple
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from torch import Tensor, device, nn
from torch.nn import CrossEntropyLoss
from transformers import BertConfig, BertTokenizer
from transformers.activations import ACT2FN
from transformers.file_utils import (add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings)
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions)
from transformers.modeling_utils import (PreTrainedModel,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer)
from transformers.utils import logging
from modelscope.models.multi_modal.mplug.configuration_mplug import (
HiTeAConfig, MPlugConfig)
from modelscope.models.multi_modal.mplug.mvit import MViTv2, MViTv2_Base_config
from modelscope.models.multi_modal.mplug.predictor import TextGenerator
from modelscope.utils.constant import ModelFile
transformers.logging.set_verbosity_error()
logger = logging.get_logger()
CONFIG_NAME = 'config.yaml'
_CONFIG_FOR_DOC = 'BertConfig'
_TOKENIZER_FOR_DOC = 'BertTokenizer'
def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.'
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info('Converting TensorFlow checkpoint from {}'.format(tf_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info('Loading TF weight {} with shape {}'.format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split('/')
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(n in [
'adam_v', 'adam_m', 'AdamWeightDecayOptimizer',
'AdamWeightDecayOptimizer_1', 'global_step'
] for n in name):
logger.info('Skipping {}'.format('/'.join(name)))
continue
pointer = model
for m_name in name:
if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
scope_names = re.split(r'_(\d+)', m_name)
else:
scope_names = [m_name]
if scope_names[0] == 'kernel' or scope_names[0] == 'gamma':
pointer = getattr(pointer, 'weight')
elif scope_names[0] == 'output_bias' or scope_names[0] == 'beta':
pointer = getattr(pointer, 'bias')
elif scope_names[0] == 'output_weights':
pointer = getattr(pointer, 'weight')
elif scope_names[0] == 'squad':
pointer = getattr(pointer, 'classifier')
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info('Skipping {}'.format('/'.join(name)))
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == '_embeddings':
pointer = getattr(pointer, 'weight')
elif m_name == 'kernel':
array = np.transpose(array)
try:
assert (
pointer.shape == array.shape
), f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched'
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info('Initialize PyTorch weight {}'.format(name))
pointer.data = torch.from_numpy(array)
return model
def clamp_inf(tensor):
if tensor.dtype == torch.float16 and torch.isinf(tensor).any():
clamp_value = torch.finfo(tensor.dtype).max - 1000
tensor = torch.clamp(tensor, min=-clamp_value, max=clamp_value)
return tensor
class BertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(
config.vocab_size,
config.hidden_size,
padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings,
config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
'position_ids',
torch.arange(config.max_position_embeddings).expand((1, -1)))
self.position_embedding_type = getattr(config,
'position_embedding_type',
'absolute')
self.config = config
def forward(self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
past_key_values_length=0):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:,
past_key_values_length:seq_length
+ past_key_values_length]
if token_type_ids is None:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == 'absolute':
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class BertSelfAttention(nn.Module):
def __init__(self, config, is_cross_attention):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
config, 'embedding_size'):
raise ValueError(
'The hidden size (%d) is not a multiple of the number of attention '
'heads (%d)' %
(config.hidden_size, config.num_attention_heads))
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size
/ config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
if is_cross_attention:
self.key = nn.Linear(config.encoder_width, self.all_head_size)
self.value = nn.Linear(config.encoder_width, self.all_head_size)
else:
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config,
'position_embedding_type',
'absolute')
if self.position_embedding_type == 'relative_key' or self.position_embedding_type == 'relative_key_query':
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(
2 * config.max_position_embeddings - 1,
self.attention_head_size)
self.save_attention = False
def save_attn_gradients(self, attn_gradients):
self.attn_gradients = attn_gradients
def get_attn_gradients(self):
return self.attn_gradients
def save_attention_map(self, attention_map):
self.attention_map = attention_map
def get_attention_map(self):
return self.attention_map
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads,
self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention:
key_layer = self.transpose_for_scores(
self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(
self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer,
key_layer.transpose(-1, -2))
attention_scores = clamp_inf(attention_scores)
if self.position_embedding_type == 'relative_key' or self.position_embedding_type == 'relative_key_query':
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(
seq_length, dtype=torch.long,
device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(
seq_length, dtype=torch.long,
device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(
distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(
dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == 'relative_key':
relative_position_scores = torch.einsum(
'bhld,lrd->bhlr', query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == 'relative_key_query':
relative_position_scores_query = torch.einsum(
'bhld,lrd->bhlr', query_layer, positional_embedding)
relative_position_scores_key = torch.einsum(
'bhrd,lrd->bhlr', key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(
self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
if is_cross_attention and self.save_attention:
self.save_attention_map(attention_probs)
attention_probs.register_hook(self.save_attn_gradients)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs_dropped = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs_dropped = attention_probs_dropped * head_mask
context_layer = torch.matmul(attention_probs_dropped, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (
self.all_head_size, )
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer,
attention_probs) if output_attentions else (context_layer, )
outputs = outputs + (past_key_value, )
return outputs
class BertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BertAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.self = BertSelfAttention(config, is_cross_attention)
self.output = BertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads,
self.self.attention_head_size, self.pruned_heads)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(
heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,
) + self_outputs[1:] # add attentions if we output them
return outputs
class BertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class BertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = clamp_inf(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = clamp_inf(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class FusionLayer(nn.Module):
def __init__(self, config, layer_num):
super().__init__()
self.config = config
self.stride_layer = getattr(self.config, 'stride_layer', 100)
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = BertAttention(config)
self.crossattention = BertAttention(config, is_cross_attention=True)
self.intermediate = BertIntermediate(config)
self.output = BertOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
layer_nums=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:
2] if past_key_value is not None else None
if layer_nums == 0 or layer_nums % self.stride_layer != 0:
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
assert encoder_hidden_states is not None, 'encoder_hidden_states must be given for cross-attention layers'
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions=output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[
1:-1] # add cross attentions if we output attention weights
elif layer_nums != 0 and layer_nums % self.stride_layer == 0:
self_attention_outputs = self.attention(
torch.cat([encoder_hidden_states, hidden_states], 1),
torch.cat([encoder_attention_mask, attention_mask], 3),
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
layer_output = apply_chunking_to_forward(self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output)
outputs = (layer_output, ) + outputs
outputs = outputs + (present_key_value[0], present_key_value[1])
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class BertLayer(nn.Module):
def __init__(self, config, layer_num):
super().__init__()
self.config = config
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = BertAttention(config)
self.has_cross_attention = getattr(self.config, 'add_cross_attention',
False)
if self.has_cross_attention:
self.crossattention = BertAttention(
config, is_cross_attention=True)
self.intermediate = BertIntermediate(config)
self.output = BertOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:
2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
if self.has_cross_attention:
assert encoder_hidden_states is not None, 'encoder_hidden_states must be given for cross-attention layers'
if type(encoder_hidden_states) == list:
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states[(self.layer_num
- self.config.fusion_layer)
% len(encoder_hidden_states)],
encoder_attention_mask[(self.layer_num
- self.config.fusion_layer)
% len(encoder_hidden_states)],
output_attentions=output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1]
else:
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions=output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[
1:
-1] # add cross attentions if we output attention weights
layer_output = apply_chunking_to_forward(self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output)
outputs = (layer_output, ) + outputs
outputs = outputs + (present_key_value[0], present_key_value[1])
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class FusionEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[FusionLayer(config, i) for i in range(config.num_hidden_layers)])
self.start_layer = max(0,
config.num_hidden_layers - config.fusion_layers)
def forward(self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
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
next_decoder_cache = () if use_cache else None
self.stride_layer = getattr(self.config, 'stride_layer', 100)
image_length = encoder_hidden_states.shape[1]
text_length = hidden_states.shape[1]
for i in range(self.start_layer, len(self.layer)):
layer_module = self.layer[i]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states, )
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[
i] if past_key_values is not None else None
if getattr(self.config, 'gradient_checkpointing',
False) and self.training:
if use_cache:
logger.warning(
'`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting '
'`use_cache=False`...')
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
return tuple(
module(*inputs, past_key_value, output_attentions))
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
i - self.start_layer,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
i - self.start_layer,
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 hidden_states.shape[1] == (image_length + text_length):
encoder_hidden_states_new, hidden_states = torch.split(
hidden_states, (image_length, text_length), 1)
encoder_hidden_states += encoder_hidden_states_new
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states, )
return [encoder_hidden_states, hidden_states]
class BertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[BertLayer(config, i) for i in range(config.num_hidden_layers)])
def forward(self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
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.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i in range(len(self.layer)):
layer_module = self.layer[i]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states, )
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[
i] if past_key_values is not None else None
if getattr(self.config, 'gradient_checkpointing',
False) and self.training:
if use_cache:
logger.warning(
'`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting '
'`use_cache=False`...')
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
return tuple(
module(*inputs, past_key_value, output_attentions))
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
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 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,
)
class BertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class BertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class BertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(
config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
class BertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BertLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class BertOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
class BertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class BertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = 'bert'
_keys_to_ignore_on_load_missing = [r'position_ids']
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
module.weight.data.normal_(
mean=0.0, std=self.config.initializer_range)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
BERT_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
pruning heads etc.)
This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
general usage and behavior.
Parameters:
config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
BERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.BertTokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `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**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`_
position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`_
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`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 (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""
@add_start_docstrings(
'The bare Bert Model transformer outputting raw hidden-states without any specific head on top.',
BERT_START_DOCSTRING,
)
class BertModel(BertPreTrainedModel):
"""
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 :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
input to the forward pass.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder(config)
self.pooler = BertPooler(config) if add_pooling_layer else None
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(
BERT_INPUTS_DOCSTRING.format('batch_size, sequence_length'))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint='bert-base-uncased',
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def get_extended_attention_mask(self, attention_mask: Tensor,
input_shape: Tuple[int], device: device,
is_decoder: bool) -> Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
attention_mask (:obj:`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (:obj:`Tuple[int]`):
The shape of the input to the model.
device: (:obj:`torch.device`):
The device of the input to the model.
Returns:
:obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
"""
# 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.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 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 = torch.arange(seq_length, device=device)
causal_mask = seq_ids[None, None, :].repeat(
batch_size, seq_length, 1) <= seq_ids[None, :, None]
# in case past_key_values are used we need to add a prefix ones mask to the causal mask
# causal and attention masks must have same type with pytorch version < 1.3
causal_mask = causal_mask.to(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 = torch.cat(
[
torch.ones(
(batch_size, seq_length, prefix_seq_len),
device=device,
dtype=causal_mask.dtype),
causal_mask,
],
axis=-1,
)
extended_attention_mask = causal_mask[:,
None, :, :] * attention_mask[:,
None,
None, :]
else:
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(
'Wrong shape for input_ids (shape {}) or attention_mask (shape {})'
.format(input_shape, attention_mask.shape))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(
dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=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"""
encoder_hidden_states
(:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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
(:obj:`tuple(tuple(torch.FloatTensor))` of length
:obj:`config.n_layers` with each tuple having 4 tensors of shape
:obj:`(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 :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
"""
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.size()
batch_size, seq_length = input_shape
device = input_ids.device
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
device = inputs_embeds.device
elif encoder_embeds is not None:
input_shape = encoder_embeds.size()[:-1]
batch_size, seq_length = input_shape
device = encoder_embeds.device
else:
raise ValueError(
'You have to specify either input_ids or inputs_embeds or encoder_embeds'
)
# past_key_values_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 = torch.ones(
((batch_size, seq_length + past_key_values_length)),
device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=device)
# 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: torch.Tensor = self.get_extended_attention_mask(
attention_mask, input_shape, device, 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 type(encoder_hidden_states) == list:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[
0].size()
else:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size(
)
encoder_hidden_shape = (encoder_batch_size,
encoder_sequence_length)
if type(encoder_attention_mask) == list:
encoder_extended_attention_mask = [
self.invert_attention_mask(mask)
for mask in encoder_attention_mask
]
elif encoder_attention_mask is None:
encoder_attention_mask = torch.ones(
encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(
encoder_attention_mask)
else:
encoder_extended_attention_mask = self.invert_attention_mask(
encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask,
self.config.num_hidden_layers)
if encoder_embeds is None:
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
else:
embedding_output = encoder_embeds
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(
sequence_output) if self.pooler is not None else None
if not return_dict:
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,
)
class FusionModel(BertPreTrainedModel):
"""
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 :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
input to the forward pass.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.encoder = FusionEncoder(config)
self.pooler = BertPooler(config) if add_pooling_layer else None
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(
BERT_INPUTS_DOCSTRING.format('batch_size, sequence_length'))
@add_code_sample_docstrings(
# tokenizer_class=_TOKENIZER_FOR_DOC,
processor_class=_TOKENIZER_FOR_DOC,
checkpoint='bert-base-uncased',
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def get_extended_attention_mask(self, attention_mask: Tensor,
input_shape: Tuple[int], device: device,
is_decoder: bool) -> Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
attention_mask (:obj:`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (:obj:`Tuple[int]`):
The shape of the input to the model.
device: (:obj:`torch.device`):
The device of the input to the model.
Returns:
:obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
"""
# 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.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 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 = torch.arange(seq_length, device=device)
causal_mask = seq_ids[None, None, :].repeat(
batch_size, seq_length, 1) <= seq_ids[None, :, None]
# in case past_key_values are used we need to add a prefix ones mask to the causal mask
# causal and attention masks must have same type with pytorch version < 1.3
causal_mask = causal_mask.to(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 = torch.cat(
[
torch.ones(
(batch_size, seq_length, prefix_seq_len),
device=device,
dtype=causal_mask.dtype),
causal_mask,
],
axis=-1,
)
extended_attention_mask = causal_mask[:,
None, :, :] * attention_mask[:,
None,
None, :]
else:
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(
'Wrong shape for input_ids (shape {}) or attention_mask (shape {})'
.format(input_shape, attention_mask.shape))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(
dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def forward(self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=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"""
encoder_hidden_states
(:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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
(:obj:`tuple(tuple(torch.FloatTensor))` of length
:obj:`config.n_layers` with each tuple having 4 tensors of shape
:obj:`(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 :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
"""
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.size()
batch_size, seq_length = input_shape
device = input_ids.device
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
device = inputs_embeds.device
elif encoder_embeds is not None:
input_shape = encoder_embeds.size()[:-1]
batch_size, seq_length = input_shape
device = encoder_embeds.device
else:
raise ValueError(
'You have to specify either input_ids or inputs_embeds or encoder_embeds'
)
# past_key_values_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 = torch.ones(
((batch_size, seq_length + past_key_values_length)),
device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(
input_shape, dtype=torch.long, device=device)
# 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: torch.Tensor = self.get_extended_attention_mask(
attention_mask, input_shape, device, 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 type(encoder_hidden_states) == list:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[
0].size()
else:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size(
)
encoder_hidden_shape = (encoder_batch_size,
encoder_sequence_length)
if type(encoder_attention_mask) == list:
encoder_extended_attention_mask = [
self.invert_attention_mask(mask)
for mask in encoder_attention_mask
]
elif encoder_attention_mask is None:
encoder_attention_mask = torch.ones(
encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(
encoder_attention_mask)
else:
encoder_extended_attention_mask = self.invert_attention_mask(
encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask,
self.config.num_hidden_layers)
if encoder_embeds is None:
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
else:
embedding_output = encoder_embeds
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
encoder_hidden_states, sequence_output = encoder_outputs
pooled_output = self.pooler(
sequence_output) if self.pooler is not None else None
if not return_dict:
return [encoder_hidden_states, sequence_output]
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,
)
@add_start_docstrings(
"""Bert Model with a `language modeling` head on top for CLM fine-tuning. """,
BERT_START_DOCSTRING)
class BertLMHeadModel(BertPreTrainedModel):
_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):
super().__init__(config)
self.bert = BertModel(config, add_pooling_layer=False)
self.cls = BertOnlyMLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(
BERT_INPUTS_DOCSTRING.format('batch_size, sequence_length'))
@replace_return_docstrings(
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=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,
is_decoder=True,
reduction='mean',
soft_labels=None,
alpha=0,
return_logits=False,
):
r"""
encoder_hidden_states
(:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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**.
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
past_key_values
(:obj:`tuple(tuple(torch.FloatTensor))` of length
:obj:`config.n_layers` with each tuple having 4 tensors of shape
:obj:`(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 :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
Returns:
Example:
>>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
>>> import torch
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
>>> config = BertConfig.from_pretrained("bert-base-cased")
>>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
is_decoder=is_decoder,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
if return_logits:
return prediction_scores[:, :-1, :].contiguous()
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, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss(reduction=reduction)
lm_loss = loss_fct(
shifted_prediction_scores.view(-1, self.config.vocab_size),
labels.view(-1))
lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)
if soft_labels is not None:
loss_distill = -torch.sum(
F.log_softmax(shifted_prediction_scores, dim=1) * soft_labels,
dim=-1)
loss_distill = (loss_distill * (labels != -100)).sum(1)
lm_loss = (1 - alpha) * lm_loss + alpha * loss_distill
if not return_dict:
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=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 = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past is used
if past is not None:
input_ids = input_ids[:, -1:]
return {
'input_ids':
input_ids,
'attention_mask':
attention_mask,
'past_key_values':
past,
'encoder_hidden_states':
model_kwargs.get('encoder_hidden_states', None),
'encoder_attention_mask':
model_kwargs.get('encoder_attention_mask', None),
'is_decoder':
True,
}
def _reorder_cache(self, past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(
past_state.index_select(0, beam_idx)
for past_state in layer_past), )
return reordered_past
class BertPrefixModel(BertPreTrainedModel):
_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):
super().__init__(config)
self.bert = BertModel(config, add_pooling_layer=False)
self.cls = BertOnlyMLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(
BERT_INPUTS_DOCSTRING.format('batch_size, sequence_length'))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint='bert-base-uncased',
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=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,
is_decoder=True,
reduction='mean',
soft_labels=None,
alpha=0,
return_logits=False,
):
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,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
is_decoder=is_decoder,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
if return_logits:
return prediction_scores[:, :-1, :].contiguous()
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, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(
shifted_prediction_scores.view(-1, self.config.vocab_size),
labels.view(-1))
if soft_labels is not None:
loss_distill = -torch.sum(
F.log_softmax(shifted_prediction_scores, dim=1) * soft_labels,
dim=-1)
loss_distill = loss_distill[labels != -100].mean()
lm_loss = (1 - alpha) * lm_loss + alpha * loss_distill
if not return_dict:
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,
)
class MPlug(PreTrainedModel):
config_class = MPlugConfig
def __init__(self, config):
super().__init__(config)
self.config = config
self.tokenizer = BertTokenizer.from_pretrained(
os.path.join(config.model_dir, ModelFile.VOCAB_FILE))
self.module_setting(config)
self.visual_encoder = self._initialize_clip(config)
self.text_encoder = BertModel(
self.config_encoder, add_pooling_layer=False)
self.fusion_encoder = FusionModel(
self.config_fusion, add_pooling_layer=False)
@classmethod
def from_pretrained(cls, model_dir, task=None, load_checkpoint=True):
from modelscope.utils.constant import Tasks
task_mapping = {
Tasks.visual_question_answering: MPlugForVisualQuestionAnswering,
Tasks.image_captioning: MPlugForImageCaption,
Tasks.image_text_retrieval: MPlugForImageTextRetrieval,
}
config = cls.config_class.from_yaml_file(
os.path.join(model_dir, CONFIG_NAME))
config.model_dir = model_dir
if task is None:
task = config.task
model = task_mapping[task](config)
if load_checkpoint:
checkpoint_path = os.path.join(model_dir,
ModelFile.TORCH_MODEL_BIN_FILE)
checkpoint = torch.load(checkpoint_path, map_location='cpu')
if 'model' in checkpoint:
checkpoint = checkpoint['model']
if 'module' in checkpoint:
checkpoint = checkpoint['module']
checkpoint = {
k.replace('model.', ''): v
for k, v in checkpoint.items()
}
msg = model.load_state_dict(checkpoint, strict=False)
print('load checkpoint from %s' % checkpoint_path)
print(msg)
return model
@staticmethod
def _initialize_clip(config, num_patches=240):
def resize_pos_embed(posemb, posemb_new):
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
ntok_new = posemb_new.shape[1]
posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
ntok_new -= 1
gs_old = int(math.sqrt(len(posemb_grid)))
gs_new = int(math.sqrt(ntok_new))
# _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old,
-1).permute(0, 3, 1, 2)
orig = posemb_grid.dtype
posemb_grid = F.interpolate(
posemb_grid.float(), size=(gs_new, gs_new), mode='bilinear')
posemb_grid = posemb_grid.to(orig)
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(
1, gs_new * gs_new, -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb
from .clip import clip
clip_model = clip.load_from_config(config)
if 'ViT-B-16' in config.clip_name:
num_patches = int(config.image_res * config.image_res / (16 * 16))
pos_embed = nn.Parameter(torch.zeros(num_patches + 1, 768).float())
else:
num_patches = int(config.image_res * config.image_res / (14 * 14))
pos_embed = nn.Parameter(
torch.zeros(num_patches + 1, 1024).float())
pos_embed.weight = resize_pos_embed(
clip_model.visual.positional_embedding.unsqueeze(0),
pos_embed.unsqueeze(0))
clip_model.visual.positional_embedding = pos_embed
return clip_model
def init_distill(self, config):
self.distill = config.distill
if self.distill:
self.visual_encoder_m = self._initialize_clip(config)
self.text_encoder_m = BertModel(
self.config_encoder, add_pooling_layer=False)
self.fusion_encoder_m = FusionModel(
self.config_fusion, add_pooling_layer=False)
self.text_decoder_m = BertLMHeadModel(self.config_decoder)
self.model_pairs = [
[self.visual_encoder, self.visual_encoder_m],
[self.text_encoder, self.text_encoder_m],
[self.text_decoder, self.text_decoder_m],
]
if self.config_encoder.hidden_size != config.vision_width:
self.visn_fc_m = nn.Linear(config.vision_width,
self.config_encoder.hidden_size)
self.visn_layer_norm_m = nn.LayerNorm(
self.config_encoder.hidden_size, eps=1e-12)
self.dropout_m = nn.Dropout(
self.config_encoder.hidden_dropout_prob)
self.model_pairs.extend(
[[self.visn_fc, self.visn_fc_m],
[self.visn_layer_norm, self.visn_layer_norm_m]])
self.copy_params()
self.momentum = 0.995
def forward(self, *args, **kwargs):
raise NotImplementedError
def module_setting(self, config):
bert_config_path = os.path.join(config.model_dir, config.bert_config)
self.config_encoder = BertConfig.from_json_file(bert_config_path)
self.config_encoder.num_hidden_layers = self.config_encoder.text_encoder_layers
self.config_fusion = BertConfig.from_json_file(bert_config_path)
self.config_decoder = BertConfig.from_json_file(bert_config_path)
self.config_decoder.add_cross_attention = True
self.config_decoder.num_hidden_layers = self.config_decoder.text_decode_layers
self.large = False
if self.config_encoder.hidden_size != config.vision_width:
self.visn_fc = nn.Linear(config.vision_width,
self.config_encoder.hidden_size)
self.visn_layer_norm = nn.LayerNorm(
self.config_encoder.hidden_size, eps=1e-12)
self.dropout = nn.Dropout(self.config_encoder.hidden_dropout_prob)
self.large = True
@torch.no_grad()
def copy_params(self):
for model_pair in self.model_pairs:
for param, param_m in zip(model_pair[0].parameters(),
model_pair[1].parameters()):
param_m.data.copy_(param.data) # initialize
param_m.requires_grad = False # not update by gradient
@torch.no_grad()
def _momentum_update(self):
for model_pair in self.model_pairs:
for param, param_m in zip(model_pair[0].parameters(),
model_pair[1].parameters()):
param_m.data = param_m.data * self.momentum + param.data * (
1. - self.momentum)
def generation(self, question_states, question_atts, out_size=1):
encoder_inputs = [question_states, question_atts]
topk_ids, topk_scores = self.beam_generator.translate_batch(
encoder_inputs, out_size=out_size)
return topk_ids, topk_scores
@staticmethod
def _tile(x, dim, n_tile):
import numpy as np
init_dim = x.size(dim)
repeat_idx = [1] * x.dim()
repeat_idx[dim] = n_tile
x = x.repeat(*(repeat_idx))
order_index = torch.LongTensor(
np.concatenate(
[init_dim * np.arange(n_tile) + i for i in range(init_dim)]))
return torch.index_select(x, dim, order_index.to(x.device))
class MPlugForVisualQuestionAnswering(MPlug):
def __init__(self, config):
super().__init__(config)
self.text_decoder = BertLMHeadModel(self.config_decoder)
self.beam_generator = TextGenerator(config, self.text_decoder)
self.init_distill(config)
def forward(self,
image,
question,
answer=None,
alpha=0,
k=None,
weights=None,
train=True):
image = image.to(dtype=next(self.parameters()).dtype)
image_embeds = self.visual_encoder.visual(image, skip_last_layer=True)
if self.large:
image_embeds = self.dropout(
self.visn_layer_norm(self.visn_fc(image_embeds)))
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(image.device)
if train:
'''
k: number of answers for each question
weights: weight for each answer
'''
answer_targets = answer.input_ids.masked_fill(
answer.input_ids == self.tokenizer.pad_token_id, -100)
text_output = self.text_encoder(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds = text_output.last_hidden_state
fusion_output = self.fusion_encoder(
encoder_embeds=text_embeds,
attention_mask=question.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False)
image_output, question_output = fusion_output
question_output = torch.cat([image_output, question_output], 1)
merge_text_attention = torch.cat(
[image_atts, question.attention_mask], 1)
if k is None:
k = [1] * question_output.shape[0]
question_states = []
question_atts = []
for b, n in enumerate(k):
question_states += [question_output[b]] * n
question_atts += [merge_text_attention[b]] * n
question_states = torch.stack(question_states, 0)
question_atts = torch.stack(question_atts, 0)
if self.distill:
with torch.no_grad():
self._momentum_update()
image_embeds_m = self.visual_encoder_m.visual(
image, skip_last_layer=True)
if self.large:
image_embeds_m = self.dropout_m(
self.visn_layer_norm_m(
self.visn_fc_m(image_embeds_m)))
text_output_m = self.text_encoder_m(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds_m = text_output_m.last_hidden_state
fusion_output_m = self.fusion_encoder_m(
encoder_embeds=text_embeds_m,
attention_mask=question.attention_mask,
encoder_hidden_states=image_embeds_m,
encoder_attention_mask=image_atts,
return_dict=False)
image_output_m, question_output_m = fusion_output_m
question_output_m = torch.cat(
[image_output_m, question_output_m], 1)
question_states_m = []
for b, n in enumerate(k):
question_states_m += [question_output_m[b]] * n
question_states_m = torch.stack(question_states_m, 0)
logits_m = self.text_decoder_m(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=question_states_m,
encoder_attention_mask=question_atts,
return_logits=True,
)
answer_output = self.text_decoder(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=question_states,
encoder_attention_mask=question_atts,
labels=answer_targets,
return_dict=True,
soft_labels=F.softmax(logits_m, dim=-1),
reduction='none',
)
else:
answer_output = self.text_decoder(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=question_states,
encoder_attention_mask=question_atts,
labels=answer_targets,
return_dict=True,
reduction='none',
)
if weights is None:
weights = 1
loss = weights * answer_output.loss
loss = loss.sum() / image.size(0)
return loss
else:
text_output = self.text_encoder(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds = text_output.last_hidden_state
fusion_output = self.fusion_encoder(
encoder_embeds=text_embeds,
attention_mask=question.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False)
image_output, question_output = fusion_output
question_output = torch.cat([image_output, question_output], 1)
merge_text_attention = torch.cat(
[image_atts, question.attention_mask], 1)
topk_ids, topk_probs = self.generation(question_output,
merge_text_attention)
return topk_ids, topk_probs
class MPlugForImageCaption(MPlug):
def __init__(self, config):
super().__init__(config)
self.text_decoder = BertPrefixModel(self.config_decoder)
self.beam_generator = TextGenerator(config, self.text_decoder)
def beam_search(self,
image,
question,
answer=None,
train=True,
out_size=5):
image_embeds = self.visual_encoder.visual(image, skip_last_layer=True)
if self.large:
image_embeds = self.dropout(
self.visn_layer_norm(self.visn_fc(image_embeds)))
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(image.device)
text_output = self.text_encoder(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds = text_output.last_hidden_state
fusion_output = self.fusion_encoder(
encoder_embeds=text_embeds,
attention_mask=question.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False)
image_output, question_output = fusion_output
question_output = torch.cat([image_output, question_output], 1)
merge_text_attention = torch.cat([image_atts, question.attention_mask],
1)
topk_ids, topk_probs = self.generation(
question_output, merge_text_attention, out_size=out_size)
return topk_ids, topk_probs
def forward(self,
image,
question,
answer=None,
train=True,
out_size=5,
scst=False):
if (scst):
return self.beam_search(
image, question, answer, train=True, out_size=out_size)
image = image.to(dtype=next(self.parameters()).dtype)
image_embeds = self.visual_encoder.visual(image, skip_last_layer=True)
if self.large:
image_embeds = self.dropout(
self.visn_layer_norm(self.visn_fc(image_embeds)))
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(image.device)
if train:
answer_targets = answer.input_ids.masked_fill(
answer.input_ids == self.tokenizer.pad_token_id, -100)
answer_output = self.text_decoder(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
labels=answer_targets,
return_dict=True,
reduction='none')
loss = answer_output.loss
return loss
else:
topk_ids, topk_probs = self.generation(image_embeds, image_atts)
return topk_ids, topk_probs
class MPlugForImageTextRetrieval(MPlug):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.embed_dim
self.temp = nn.Parameter(torch.ones([]) * config.temp)
self.queue_size = config.queue_size
self.momentum = config.momentum
self.alpha = config.alpha
self.queue_size = config.queue_size
self.text_width = self.config_encoder.hidden_size
self.embed_dim = config.embed_dim
self.vision_proj = nn.Linear(self.text_width, self.embed_dim)
self.text_proj = nn.Linear(self.text_width, self.embed_dim)
self.itm_head = nn.Linear(self.text_width, 2)
self.register_buffer('image_queue',
torch.randn(self.embed_dim, self.queue_size))
self.register_buffer('text_queue',
torch.randn(self.embed_dim, self.queue_size))
self.register_buffer('idx_queue', torch.full((1, self.queue_size),
-100))
self.register_buffer('queue_ptr', torch.zeros(1, dtype=torch.long))
self.image_queue = F.normalize(self.image_queue, dim=0)
self.text_queue = F.normalize(self.text_queue, dim=0)
self.init_distill(config)
def init_distill(self, config):
self.distill = config.distill
if self.distill:
self.visual_encoder_m = self._initialize_clip(config)
self.text_encoder_m = BertModel(
self.config_encoder, add_pooling_layer=False)
self.fusion_encoder_m = FusionModel(
self.config_fusion, add_pooling_layer=False)
self.vision_proj_m = nn.Linear(self.text_width, self.embed_dim)
self.text_proj_m = nn.Linear(self.text_width, self.embed_dim)
self.model_pairs = [
[self.visual_encoder, self.visual_encoder_m],
[self.text_encoder, self.text_encoder_m],
[self.text_proj, self.text_proj_m],
[self.vision_proj, self.vision_proj_m],
]
if self.config_encoder.hidden_size != config.vision_width:
self.visn_fc_m = nn.Linear(config.vision_width,
self.config_encoder.hidden_size)
self.visn_layer_norm_m = nn.LayerNorm(
self.config_encoder.hidden_size, eps=1e-12)
self.dropout_m = nn.Dropout(
self.config_encoder.hidden_dropout_prob)
self.model_pairs.extend(
[[self.visn_fc, self.visn_fc_m],
[self.visn_layer_norm, self.visn_layer_norm_m]])
self.copy_params()
self.momentum = 0.995
@torch.no_grad()
def _dequeue_and_enqueue(self, image_feat, text_feat, idx):
def concat_all_gather(tensor):
"""
Performs all_gather operation on the provided tensors.
*** Warning ***: torch.distributed.all_gather has no gradient.
"""
if not torch.distributed.is_initialized():
return tensor
tensors_gather = [
torch.ones_like(tensor)
for _ in range(torch.distributed.get_world_size())
]
torch.distributed.all_gather(
tensors_gather, tensor, async_op=False)
output = torch.cat(tensors_gather, dim=0)
return output
# gather keys before updating queue
image_feats = concat_all_gather(image_feat)
text_feats = concat_all_gather(text_feat)
idxs = concat_all_gather(idx)
batch_size = image_feats.shape[0]
ptr = int(self.queue_ptr)
# assert self.queue_size % batch_size == 0 # for simplicity
# replace the keys at ptr (dequeue and enqueue)
self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
self.idx_queue[:, ptr:ptr + batch_size] = idxs.T
ptr = (ptr + batch_size) % self.queue_size # move pointer
self.queue_ptr[0] = ptr
def forward(self, image, text, idx=None, train=True):
if train:
image_embeds = self.visual_encoder.visual(
image, skip_last_layer=True)
if self.large:
image_embeds = self.dropout(
self.visn_layer_norm(self.visn_fc(image_embeds)))
image_atts = torch.ones(
image_embeds.size()[:-1], dtype=torch.long).to(image.device)
image_feat = F.normalize(
self.vision_proj(image_embeds[:, 0, :]), dim=-1)
text_output = self.text_encoder(
text.input_ids,
attention_mask=text.attention_mask,
return_dict=True)
text_embeds = text_output.last_hidden_state
text_feat = F.normalize(
self.text_proj(text_embeds[:, 0, :]), dim=-1)
idx = idx.view(-1, 1)
idx_all = torch.cat(
[idx.t(), self.idx_queue.clone().detach()], dim=1)
pos_idx = torch.eq(idx, idx_all).float()
sim_targets = pos_idx / pos_idx.sum(1, keepdim=True)
with torch.no_grad():
self._momentum_update()
image_embeds_m = self.visual_encoder_m.visual(
image, skip_last_layer=True)
if self.large:
image_embeds_m = self.dropout_m(
self.visn_layer_norm_m(self.visn_fc_m(image_embeds_m)))
image_feat_m = F.normalize(
self.vision_proj_m(image_embeds_m[:, 0, :]), dim=-1)
image_feat_all = torch.cat(
[image_feat_m.t(),
self.image_queue.clone().detach()],
dim=1)
text_output_m = self.text_encoder_m(
text.input_ids,
attention_mask=text.attention_mask,
return_dict=True)
text_feat_m = F.normalize(
self.text_proj_m(text_output_m.last_hidden_state[:, 0, :]),
dim=-1)
text_feat_all = torch.cat(
[text_feat_m.t(),
self.text_queue.clone().detach()], dim=1)
if self.distill:
sim_i2t_m = image_feat_m @ text_feat_all / self.temp
sim_t2i_m = text_feat_m @ image_feat_all / self.temp
sim_i2t_targets = self.alpha * F.softmax(
sim_i2t_m, dim=1) + (1 - self.alpha) * sim_targets
sim_t2i_targets = self.alpha * F.softmax(
sim_t2i_m, dim=1) + (1 - self.alpha) * sim_targets
sim_i2t = image_feat @ text_feat_all / self.temp
sim_t2i = text_feat @ image_feat_all / self.temp
if self.distill:
loss_i2t = -torch.sum(
F.log_softmax(sim_i2t, dim=1) * sim_i2t_targets,
dim=1).mean()
loss_t2i = -torch.sum(
F.log_softmax(sim_t2i, dim=1) * sim_t2i_targets,
dim=1).mean()
else:
loss_i2t = -torch.sum(
F.log_softmax(sim_i2t, dim=1) * sim_targets, dim=1).mean()
loss_t2i = -torch.sum(
F.log_softmax(sim_t2i, dim=1) * sim_targets, dim=1).mean()
loss_ita = (loss_i2t + loss_t2i) / 2
self._dequeue_and_enqueue(image_feat_m, text_feat_m, idx)
# forward the positve image-text pair
_, output_pos = self.fusion_encoder(
encoder_embeds=text_embeds,
attention_mask=text.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=False,
)
with torch.no_grad():
bs = image.size(0)
weights_i2t = F.softmax(sim_i2t[:, :bs], dim=1)
weights_t2i = F.softmax(sim_t2i[:, :bs], dim=1)
mask = torch.eq(idx, idx.T)
weights_i2t.masked_fill_(mask, 0)
weights_t2i.masked_fill_(mask, 0)
# select a negative image for each text
image_embeds_neg = []
for b in range(bs):
neg_idx = torch.multinomial(weights_t2i[b], 1).item()
image_embeds_neg.append(image_embeds[neg_idx])
image_embeds_neg = torch.stack(image_embeds_neg, dim=0)
# select a negative text for each image
text_embeds_neg = []
text_atts_neg = []
for b in range(bs):
neg_idx = torch.multinomial(weights_i2t[b], 1).item()
text_embeds_neg.append(text_embeds[neg_idx])
text_atts_neg.append(text.attention_mask[neg_idx])
text_embeds_neg = torch.stack(text_embeds_neg, dim=0)
text_atts_neg = torch.stack(text_atts_neg, dim=0)
text_embeds_all = torch.cat([text_embeds, text_embeds_neg], dim=0)
text_atts_all = torch.cat([text.attention_mask, text_atts_neg],
dim=0)
image_embeds_all = torch.cat([image_embeds_neg, image_embeds],
dim=0)
image_atts_all = torch.cat([image_atts, image_atts], dim=0)
_, output_neg = self.fusion_encoder(
encoder_embeds=text_embeds_all,
attention_mask=text_atts_all,
encoder_hidden_states=image_embeds_all,
encoder_attention_mask=image_atts_all,
return_dict=False,
)
vl_embeddings = torch.cat(
[output_pos[:, 0, :], output_neg[:, 0, :]], dim=0)
vl_output = self.itm_head(vl_embeddings)
ones_tmp = torch.ones(bs, dtype=torch.long)
zeros_tmp = torch.zeros(2 * bs, dtype=torch.long)
itm_labels = torch.cat([ones_tmp, zeros_tmp],
dim=0).to(image.device)
loss_itm = F.cross_entropy(vl_output, itm_labels)
return loss_ita + loss_itm
else:
text_output = self.text_encoder(
text.input_ids, attention_mask=text.attention_mask)
text_feat = text_output.last_hidden_state
image_feat = self.visual_encoder.visual(
image, skip_last_layer=True)
image_feat = self.visn_layer_norm(self.visn_fc(image_feat))
image_att = torch.ones(
image_feat.size()[:-1],
dtype=torch.long,
device=image_feat.device)
_, output = self.fusion_encoder(
encoder_embeds=text_feat,
attention_mask=text.attention_mask,
encoder_hidden_states=image_feat,
encoder_attention_mask=image_att,
return_dict=False,
)
scores = self.itm_head(output[:, 0, :])
scores = F.softmax(scores, dim=-1)
return scores
class HiTeA(PreTrainedModel):
config_class = HiTeAConfig
def __init__(self, config):
super().__init__(config)
self.config = config
self.tokenizer = BertTokenizer.from_pretrained(
os.path.join(config.model_dir, ModelFile.VOCAB_FILE))
self.module_setting(config)
self.visual_encoder = MViTv2(
img_size=config.image_res,
config=MViTv2_Base_config,
num_frames=config.num_frames)
self.text_encoder = BertModel(
self.config_encoder, add_pooling_layer=False)
self.fusion_encoder = FusionModel(
self.config_fusion, add_pooling_layer=False)
@classmethod
def from_pretrained(cls, model_dir, load_checkpoint=True):
from modelscope.utils.constant import Tasks
task_mapping = {
Tasks.video_question_answering: HiTeAForVideoQuestionAnswering,
Tasks.video_captioning: HiTeAForVideoCaption,
}
config = cls.config_class.from_yaml_file(
os.path.join(model_dir, CONFIG_NAME))
config.model_dir = model_dir
model = task_mapping[config.task](config)
if load_checkpoint:
checkpoint_path = os.path.join(model_dir,
ModelFile.TORCH_MODEL_BIN_FILE)
checkpoint = torch.load(checkpoint_path, map_location='cpu')
if 'model' in checkpoint:
checkpoint = checkpoint['model']
if 'module' in checkpoint:
checkpoint = checkpoint['module']
checkpoint = {
k.replace('model.', ''): v
for k, v in checkpoint.items()
}
model.load_state_dict(checkpoint, strict=False)
return model
def init_distill(self, config):
self.distill = config.distill
if self.distill:
self.visual_encoder_m = MViTv2(
img_size=config.image_res,
config=MViTv2_Base_config,
num_frames=config.num_frames)
self.text_encoder_m = BertModel(
self.config_encoder, add_pooling_layer=False)
self.fusion_encoder_m = FusionModel(
self.config_fusion, add_pooling_layer=False)
self.text_decoder_m = BertLMHeadModel(self.config_decoder)
self.model_pairs = [
[self.visual_encoder, self.visual_encoder_m],
[self.text_encoder, self.text_encoder_m],
[self.text_decoder, self.text_decoder_m],
]
self.copy_params()
self.momentum = 0.995
def forward(self, *args, **kwargs):
raise NotImplementedError
def module_setting(self, config):
bert_config_path = os.path.join(config.model_dir, config.bert_config)
self.config_encoder = BertConfig.from_json_file(bert_config_path)
self.config_encoder.num_hidden_layers = self.config_encoder.text_encoder_layers
self.config_fusion = BertConfig.from_json_file(bert_config_path)
self.config_decoder = BertConfig.from_json_file(bert_config_path)
self.config_decoder.add_cross_attention = True
self.config_decoder.num_hidden_layers = self.config_decoder.text_decode_layers
@torch.no_grad()
def copy_params(self):
for model_pair in self.model_pairs:
for param, param_m in zip(model_pair[0].parameters(),
model_pair[1].parameters()):
param_m.data.copy_(param.data) # initialize
param_m.requires_grad = False # not update by gradient
@torch.no_grad()
def _momentum_update(self):
for model_pair in self.model_pairs:
for param, param_m in zip(model_pair[0].parameters(),
model_pair[1].parameters()):
param_m.data = param_m.data * self.momentum + param.data * (
1. - self.momentum)
def generation(self, question_states, question_atts, out_size=1):
encoder_inputs = [question_states, question_atts]
topk_ids, topk_scores = self.beam_generator.translate_batch(
encoder_inputs, out_size=out_size)
return topk_ids, topk_scores
@staticmethod
def _tile(x, dim, n_tile):
import numpy as np
init_dim = x.size(dim)
repeat_idx = [1] * x.dim()
repeat_idx[dim] = n_tile
x = x.repeat(*(repeat_idx))
order_index = torch.LongTensor(
np.concatenate(
[init_dim * np.arange(n_tile) + i for i in range(init_dim)]))
return torch.index_select(x, dim, order_index.to(x.device))
class HiTeAForVideoQuestionAnswering(HiTeA):
def __init__(self, config):
super().__init__(config)
self.text_decoder = BertLMHeadModel(self.config_decoder)
self.beam_generator = TextGenerator(config, self.text_decoder)
self.init_distill(config)
def forward(self,
video,
question,
answer=None,
alpha=0,
k=None,
weights=None,
train=True):
video = video.to(dtype=next(self.parameters()).dtype)
video_embeds = self.visual_encoder(video)
video_atts = torch.ones(
video_embeds.size()[:-1], dtype=torch.long).to(video.device)
if train:
'''
k: number of answers for each question
weights: weight for each answer
'''
answer_targets = answer.input_ids.masked_fill(
answer.input_ids == self.tokenizer.pad_token_id, -100)
text_output = self.text_encoder(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds = text_output.last_hidden_state
fusion_output = self.fusion_encoder(
encoder_embeds=text_embeds,
attention_mask=question.attention_mask,
encoder_hidden_states=video_embeds,
encoder_attention_mask=video_atts,
return_dict=False)
video_output, question_output = fusion_output
question_output = torch.cat([video_output, question_output], 1)
merge_text_attention = torch.cat(
[video_atts, question.attention_mask], 1)
if k is None:
k = [1] * question_output.shape[0]
question_states = []
question_atts = []
for b, n in enumerate(k):
question_states += [question_output[b]] * n
question_atts += [merge_text_attention[b]] * n
question_states = torch.stack(question_states, 0)
question_atts = torch.stack(question_atts, 0)
if self.distill:
with torch.no_grad():
self._momentum_update()
video_embeds_m = self.visual_encoder_m(video)
text_output_m = self.text_encoder_m(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds_m = text_output_m.last_hidden_state
fusion_output_m = self.fusion_encoder_m(
encoder_embeds=text_embeds_m,
attention_mask=question.attention_mask,
encoder_hidden_states=video_embeds_m,
encoder_attention_mask=video_atts,
return_dict=False)
image_output_m, question_output_m = fusion_output_m
question_output_m = torch.cat(
[image_output_m, question_output_m], 1)
question_states_m = []
for b, n in enumerate(k):
question_states_m += [question_output_m[b]] * n
question_states_m = torch.stack(question_states_m, 0)
logits_m = self.text_decoder_m(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=question_states_m,
encoder_attention_mask=question_atts,
return_logits=True,
)
answer_output = self.text_decoder(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=question_states,
encoder_attention_mask=question_atts,
labels=answer_targets,
return_dict=True,
soft_labels=F.softmax(logits_m, dim=-1),
reduction='none',
)
else:
answer_output = self.text_decoder(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=question_states,
encoder_attention_mask=question_atts,
labels=answer_targets,
return_dict=True,
reduction='none',
)
if weights is None:
weights = 1
loss = weights * answer_output.loss
loss = loss.sum() / video.size(0)
return loss
else:
text_output = self.text_encoder(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds = text_output.last_hidden_state
fusion_output = self.fusion_encoder(
encoder_embeds=text_embeds,
attention_mask=question.attention_mask,
encoder_hidden_states=video_embeds,
encoder_attention_mask=video_atts,
return_dict=False)
video_output, question_output = fusion_output
question_output = torch.cat([video_output, question_output], 1)
merge_text_attention = torch.cat(
[video_atts, question.attention_mask], 1)
topk_ids, topk_probs = self.generation(question_output,
merge_text_attention)
return topk_ids, topk_probs
class HiTeAForVideoCaption(HiTeA):
def __init__(self, config):
super().__init__(config)
self.text_decoder = BertPrefixModel(self.config_decoder)
self.beam_generator = TextGenerator(config, self.text_decoder)
def beam_search(self,
video,
question,
answer=None,
train=True,
out_size=5):
video_embeds = self.visual_encoder(video)
video_atts = torch.ones(
video_embeds.size()[:-1], dtype=torch.long).to(video.device)
text_output = self.text_encoder(
question.input_ids,
attention_mask=question.attention_mask,
return_dict=True)
text_embeds = text_output.last_hidden_state
fusion_output = self.fusion_encoder(
encoder_embeds=text_embeds,
attention_mask=question.attention_mask,
encoder_hidden_states=video_embeds,
encoder_attention_mask=video_atts,
return_dict=False)
video_output, question_output = fusion_output
question_output = torch.cat([video_output, question_output], 1)
merge_text_attention = torch.cat([video_atts, question.attention_mask],
1)
topk_ids, topk_probs = self.generation(
question_output, merge_text_attention, out_size=out_size)
return topk_ids, topk_probs
def forward(self,
video,
question,
answer=None,
train=True,
out_size=5,
scst=False):
if (scst):
return self.beam_search(
video, question, answer, train=True, out_size=out_size)
video = video.to(dtype=next(self.parameters()).dtype)
video_embeds = self.visual_encoder(video)
video_atts = torch.ones(
video_embeds.size()[:-1], dtype=torch.long).to(video.device)
if train:
answer_targets = answer.input_ids.masked_fill(
answer.input_ids == self.tokenizer.pad_token_id, -100)
answer_output = self.text_decoder(
answer.input_ids,
attention_mask=answer.attention_mask,
encoder_hidden_states=video_embeds,
encoder_attention_mask=video_atts,
labels=answer_targets,
return_dict=True,
reduction='none')
loss = answer_output.loss
return loss
else:
topk_ids, topk_probs = self.generation(video_embeds, video_atts)
return topk_ids, topk_probs
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