# Copyright (c) 2023 PaddlePaddle Authors. 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.
import math
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.distributed.fleet.utils import recompute
from paddle.nn import CrossEntropyLoss
from paddlenlp.utils.log import logger
from ...utils.initializer import normal_, ones_, zeros_
from ..activations import ACT2FN
from ..chatglm.configuration import ChatGLMConfig
from ..chatglm.modeling import ChatGLMForCausalLM
from ..model_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPooling,
BaseModelOutputWithPoolingAndCrossAttentions,
ModelOutput,
)
from ..model_utils import (
PretrainedModel,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
VisualGLM_PRETRAINED_MODEL_ARCHIVE_LIST = []
from .configuration import (
VisualGLMConfig,
VisualGLMQFormerConfig,
VisualGLMVisionConfig,
)
__all__ = [
"VisualGLMModel",
"VisualGLMPretrainedModel",
"VisualGLMQFormerModel",
"VisualGLMVisionModel",
"VisualGLMForConditionalGeneration",
]
def Parameter(tensor, dtype="float16"):
tensor = paddle.cast(tensor, dtype)
return paddle.create_parameter(tensor.shape, dtype=tensor.dtype, default_initializer=nn.initializer.Assign(tensor))
@dataclass
class VisualGLMForConditionalGenerationModelOutput(ModelOutput):
"""
Class defining the outputs of [`VisualGLMForConditionalGeneration`].
Args:
loss (`paddle.Tensor`, *optional*, returned when `labels` is provided, `paddle.Tensor` of shape `(1,)`):
Language modeling loss from the language model.
logits (`paddle.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head of the language model.
vision_outputs (`BaseModelOutputWithPooling`):
Outputs of the vision encoder.
qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`):
Outputs of the Q-Former (Querying Transformer).
language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`):
Outputs of the language model.
"""
loss: Optional[Tuple[paddle.Tensor]] = None
logits: Optional[Tuple[paddle.Tensor]] = None
vision_outputs: Optional[paddle.Tensor] = None
qformer_outputs: Optional[Tuple[paddle.Tensor]] = None
language_model_outputs: Optional[Tuple[paddle.Tensor]] = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k]
if k not in ["vision_outputs", "qformer_outputs", "language_model_outputs"]
else getattr(self, k).to_tuple()
for k in self.keys()
)
class VisualGLMPretrainedModel(PretrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VisualGLMConfig
base_model_prefix = "visualglm"
supports_gradient_checkpointing = True
_keys_to_ignore_on_load_missing = [
r"position_ids",
]
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_range
if isinstance(module, nn.Conv2D) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear):
normal_(module.weight, mean=0.0, std=factor)
if hasattr(module, "bias") and module.bias is not None:
zeros_(module.bias)
if isinstance(module, VisualGLMVisionEmbeddings):
if hasattr(self.config, "vision_config"):
factor = self.config.vision_config.initializer_range
trunc_normal_ = nn.initializer.TruncatedNormal(mean=0.0, std=factor)
trunc_normal_(module.position_embedding)
trunc_normal_(
module.class_embedding,
)
elif isinstance(module, nn.LayerNorm):
zeros_(module.bias)
ones_(module.weight)
elif isinstance(module, nn.Linear) and module.bias is not None:
zeros_(module.bias)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, VisualGLMEncoder):
module.gradient_checkpointing = value
class VisualGLMVisionEmbeddings(nn.Layer):
def __init__(self, config: VisualGLMVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.in_channels = config.num_channels
self.patch_embedding = nn.Conv2D(
in_channels=self.in_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.class_embedding = Parameter(paddle.randn([1, 1, self.embed_dim]), dtype=self.patch_embedding.weight.dtype)
self.position_embedding = Parameter(
paddle.randn([1, self.num_positions, self.embed_dim]), dtype=self.patch_embedding.weight.dtype
)
def forward(self, pixel_values: paddle.Tensor) -> paddle.Tensor:
batch_size = pixel_values.shape[0]
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose([0, 2, 1])
class_embeds = self.class_embedding.expand([batch_size, 1, -1]).cast(target_dtype)
embeddings = paddle.concat([class_embeds, patch_embeds], axis=1)
embeddings = embeddings + self.position_embedding[:, : embeddings.shape[1], :].cast(target_dtype)
return embeddings
class VisualGLMAttention(nn.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = nn.Dropout(config.attention_dropout)
# small tweak here compared to CLIP, no bias here
self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias_attr=False)
if config.qkv_bias:
q_bias = Parameter(paddle.zeros([self.embed_dim], dtype=self.qkv.weight.dtype))
v_bias = Parameter(paddle.zeros([self.embed_dim], dtype=self.qkv.weight.dtype))
else:
q_bias = None
v_bias = None
if q_bias is not None:
qkv_bias = paddle.concat((q_bias, paddle.zeros_like(v_bias), v_bias))
self.qkv.bias = Parameter(qkv_bias, dtype=self.qkv.weight.dtype)
self.projection = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: paddle.Tensor, seq_len: int, bsz: int):
return tensor.reshape([bsz, seq_len, self.num_heads, self.head_dim]).transpose([0, 2, 1, 3])
def forward(
self,
hidden_states: paddle.Tensor,
head_mask: Optional[paddle.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[paddle.Tensor, Optional[paddle.Tensor], Optional[Tuple[paddle.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.shape
mixed_qkv = self.qkv(hidden_states)
mixed_qkv = mixed_qkv.reshape([bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads]).transpose(
[2, 0, 3, 1, 4]
)
query_states, key_states, value_states = (
mixed_qkv[0],
mixed_qkv[1],
mixed_qkv[2],
)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = paddle.matmul(query_states, key_states, transpose_y=True)
attention_scores = attention_scores * self.scale
# Normalize the attention scores to probabilities.
attention_probs = F.softmax(attention_scores, axis=-1)
# 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 = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = paddle.matmul(attention_probs, value_states).transpose([0, 2, 1, 3])
new_context_layer_shape = context_layer.shape[:-2] + [
self.embed_dim,
]
context_layer = context_layer.reshape(new_context_layer_shape)
output = self.projection(context_layer)
outputs = (output, attention_probs) if output_attentions else (output, None)
return outputs
class VisualGLMMLP(nn.Layer):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class VisualGLMEncoderLayer(nn.Layer):
def __init__(self, config: VisualGLMConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = VisualGLMAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_eps)
self.mlp = VisualGLMMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_eps)
def forward(
self,
hidden_states: paddle.Tensor,
attention_mask: paddle.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[paddle.Tensor]:
"""
Args:
hidden_states (`paddle.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`paddle.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
head_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = hidden_states + residual
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = hidden_states + residual
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class VisualGLMEncoder(nn.Layer):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`VisualGLMEncoderLayer`].
Args:
config (`VisualGLMConfig`):
The corresponding vision configuration for the `VisualGLMEncoder`.
"""
def __init__(self, config: VisualGLMConfig):
super().__init__()
self.config = config
self.layers = nn.LayerList([VisualGLMEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[paddle.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
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.
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**.
[What are attention masks?](../glossary#attention-mask)
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 [`~utils.ModelOutput`] instead of a plain tuple.
"""
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
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = recompute(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class VisualGLMVisionModel(VisualGLMPretrainedModel):
main_input_name = "pixel_values"
config_class = VisualGLMVisionConfig
def __init__(self, config: VisualGLMVisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embeddings = VisualGLMVisionEmbeddings(config)
self.encoder = VisualGLMEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, epsilon=config.layer_norm_eps)
def forward(
self,
pixel_values: Optional[paddle.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
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 pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.post_layernorm(last_hidden_state)
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def get_input_embeddings(self):
return self.embeddings
class VisualGLMQFormerMultiHeadAttention(nn.Layer):
def __init__(self, config, is_cross_attention=False):
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_hidden_size, self.all_head_size)
self.value = nn.Linear(config.encoder_hidden_size, 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.shape[:-1] + [self.num_attention_heads, self.attention_head_size]
x = x.reshape(new_x_shape)
return x.transpose([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,
):
# 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 = paddle.concat([past_key_value[0], key_layer], axis=2)
value_layer = paddle.concat([past_key_value[1], value_layer], axis=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
mixed_query_layer = self.query(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 = paddle.matmul(query_layer, key_layer, transpose_y=True)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.shape[1]
position_ids_l = paddle.arange(seq_length, dtype="int64").reshape([-1, 1])
position_ids_r = paddle.arange(seq_length, dtype="int64").reshape([1, -1])
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.cast(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = paddle.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 = paddle.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = paddle.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(axis=-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 = paddle.matmul(attention_probs_dropped, value_layer)
context_layer = context_layer.transpose([0, 2, 1, 3])
new_context_layer_shape = context_layer.shape[:-2] + [
self.all_head_size,
]
context_layer = context_layer.reshape(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
outputs = outputs + (past_key_value,)
return outputs
class VisualGLMQFormerSelfOutput(nn.Layer):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.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 VisualGLMQFormerAttention(nn.Layer):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.attention = VisualGLMQFormerMultiHeadAttention(config, is_cross_attention)
self.output = VisualGLMQFormerSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, axis=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: paddle.Tensor,
attention_mask: Optional[paddle.Tensor] = None,
head_mask: Optional[paddle.Tensor] = None,
encoder_hidden_states: Optional[paddle.Tensor] = None,
encoder_attention_mask: Optional[paddle.Tensor] = None,
past_key_value: Optional[Tuple[Tuple[paddle.Tensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[paddle.Tensor]:
self_outputs = self.attention(
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 VisualGLMQFormerIntermediate(nn.Layer):
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: paddle.Tensor) -> paddle.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class VisualGLMQFormerOutput(nn.Layer):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
# self.LayerNorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
# hidden_states = self.LayerNorm()
return hidden_states
class VisualGLMQFormerLayer(nn.Layer):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.input_layernorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
self.attention = VisualGLMQFormerAttention(config)
self.layer_idx = layer_idx
if layer_idx % config.cross_attention_frequency == 0:
self.crossattention = VisualGLMQFormerAttention(config, is_cross_attention=True)
self.has_cross_attention = True
else:
self.has_cross_attention = False
self.intermediate_query = VisualGLMQFormerIntermediate(config)
self.output_query = VisualGLMQFormerOutput(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,
query_length=0,
):
# 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
hidden_states = self.input_layernorm(hidden_states)
self_attention_outputs = self.attention(
hidden_states, # 1, 32, 768
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 query_length > 0:
query_attention_output = attention_output[:, :query_length, :]
if self.has_cross_attention:
if encoder_hidden_states is None:
raise ValueError("encoder_hidden_states must be given for cross-attention layers")
cross_attention_outputs = self.crossattention(
query_attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions=output_attentions,
)
query_attention_output = cross_attention_outputs[0]
# add cross attentions if we output attention weights
outputs = outputs + cross_attention_outputs[1:-1]
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk_query,
self.chunk_size_feed_forward,
self.seq_len_dim,
query_attention_output,
)
if attention_output.shape[1] > query_length:
layer_output_text = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output[:, query_length:, :],
)
layer_output = paddle.concat([layer_output, layer_output_text], axis=1)
else:
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,)
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
def feed_forward_chunk_query(self, attention_output):
intermediate_output = self.intermediate_query(attention_output)
layer_output = self.output_query(intermediate_output, attention_output)
return layer_output
class VisualGLMQFormerEncoder(nn.Layer):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.LayerList(
[VisualGLMQFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
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,
query_length=0,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions 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,)
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 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, query_length)
return custom_forward
layer_outputs = recompute(
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,
query_length,
)
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 layer_module.has_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
class VisualGLMQFormerModel(VisualGLMPretrainedModel):
"""
Querying Transformer (Q-Former), used in VisualGLM.
"""
def __init__(self, config: VisualGLMQFormerConfig):
super().__init__(config)
self.config = config
self.final_layernorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.encoder = VisualGLMQFormerEncoder(config)
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)
def get_extended_attention_mask(
self,
attention_mask: paddle.Tensor,
input_shape: Tuple[int],
has_query: bool = False,
) -> paddle.Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
attention_mask (`paddle.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (`Tuple[int]`):
The shape of the input to the model.
Returns:
`paddle.Tensor` The extended attention mask, with a the same dtype as `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]
# - the model is an encoder, so make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
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.cast(dtype=self.config.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def invert_attention_mask(self, encoder_attention_mask: paddle.Tensor) -> paddle.Tensor:
"""
Invert an attention mask (e.g., switches 0. and 1.).
Args:
encoder_attention_mask (`paddle.Tensor`): An attention mask.
Returns:
`paddle.Tensor`: The inverted attention mask.
"""
if encoder_attention_mask.ndim == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.ndim == 2:
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
encoder_extended_attention_mask = encoder_extended_attention_mask.cast(
dtype=self.config.dtype
) # fp16 compatibility
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e4
return encoder_extended_attention_mask
def get_head_mask(
self, head_mask: Optional[paddle.Tensor], num_hidden_layers: int, is_attention_chunked: bool = False
) -> paddle.Tensor:
"""
Prepare the head mask if needed.
Args:
head_mask (`paddle.Tensor` with shape `[num_heads]` or `[num_hidden_layers x num_heads]`, *optional*):
The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).
num_hidden_layers (`int`):
The number of hidden layers in the model.
is_attention_chunked: (`bool`, *optional*, defaults to `False`):
Whether or not the attentions scores are computed by chunks or not.
Returns:
`paddle.Tensor` with shape `[num_hidden_layers x batch x num_heads x seq_length x seq_length]` or list with
`[None]` for each layer.
"""
if head_mask is not None:
head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
if is_attention_chunked is True:
head_mask = head_mask.unsqueeze(-1)
else:
head_mask = [None] * num_hidden_layers
return head_mask
def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
"""-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
if head_mask.ndim == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand([num_hidden_layers, -1, -1, -1, -1])
elif head_mask.ndim == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
assert head_mask.ndim == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
head_mask = head_mask.cast(dtype=self.config.dtype) # switch to float if need + fp16 compatibility
return head_mask
def forward(
self,
query_embeds,
attention_mask=None,
head_mask=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,
):
r"""
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 = 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
# past_key_values_length
past_key_values_length = (
past_key_values[0][0].shape[2] - self.config.query_length if past_key_values is not None else 0
)
query_length = query_embeds.shape[1] if query_embeds is not None else 0
embedding_output = self.dropout(query_embeds)
input_shape = embedding_output.shape[:-1]
batch_size, seq_length = input_shape
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 = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_hidden_states is not None:
if type(encoder_hidden_states) == list:
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 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 = 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
# 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)
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,
query_length=query_length,
)
sequence_output = encoder_outputs[0]
sequence_output = self.final_layernorm(sequence_output)
pooled_output = sequence_output[:, 0, :]
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 VisualGLMModel(VisualGLMPretrainedModel):
config_class = VisualGLMConfig
main_input_name = "pixel_values"
def __init__(self, config: VisualGLMConfig):
super().__init__(config)
self.vision_model = VisualGLMVisionModel(config.vision_config)
self.query_tokens = Parameter(
paddle.zeros([1, config.num_query_tokens, config.qformer_config.hidden_size]), dtype=self.config.dtype
)
self.qformer = VisualGLMQFormerModel(config.qformer_config)
self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
self.language_model = ChatGLMForCausalLM(config.text_config)
def get_input_embeddings(self) -> nn.Layer:
return self.vision_model.embeddings.patch_embedding
def get_text_features(
self,
input_ids: Optional[paddle.Tensor] = None,
attention_mask: Optional[paddle.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs
):
r"""
Returns:
text_outputs (`CausalLMOutputWithPast`, or `tuple(paddle.Tensor)` if `return_dict=False`):
The language model outputs. If `return_dict=True`, the output is a [`CausalLMOutputWithPast`] that
contains the language model logits, the past key values and the hidden states if
`output_hidden_states=True`.
Examples:
```python
>>> import paddle
>>> from paddlenlp.transformers import ChatGLMTokenizer, VisualGLMModel
>>> tokenizer = ChatGLMTokenizer.from_pretrained("model_name")
>>> tokenizer.pad_token = tokenizer.eos_token
>>> model = VisualGLMModel.from_pretrained("model_name")
>>> model.eval()
>>> inputs = tokenizer(["a photo of a cat"], padding=True, return_tensors="pd", return_token_type_ids=False)
>>> text_features = model.get_text_features(**inputs)
```"""
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
text_outputs = self.language_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return text_outputs
def get_image_features(
self,
pixel_values: Optional[paddle.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs
):
r"""
Returns:
vision_outputs (`BaseModelOutputWithPooling` or tuple of `paddle.Tensor`):
The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that
contains the image features, the pooled image features and the hidden states if
`output_hidden_states=True`.
Examples:
```python
>>> import paddle
>>> from PIL import Image
>>> import requests
>>> from paddlenlp.transformers import MinitGPT4Processor, VisualGLMModel
>>> processor = MinitGPT4Processor.from_pretrained("model_name")
>>> model = VisualGLMModel.from_pretrained("model_name")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor.process_images(images=image, return_tensors="pd")
>>> image_outputs = model.get_image_features(**inputs)
```"""
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
pixel_values = paddle.cast(pixel_values, self.vision_model.embeddings.patch_embedding.weight.dtype)
vision_outputs = self.vision_model(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return vision_outputs
def get_qformer_features(
self,
pixel_values: Optional[paddle.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs
):
r"""
Returns:
vision_outputs (`BaseModelOutputWithPooling` or tuple of `paddle.Tensor`):
The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that
contains the image features, the pooled image features and the hidden states if
`output_hidden_states=True`.
Examples:
```python
>>> import paddle
>>> from PIL import Image
>>> import requests
>>> from paddlenlp.transformers import MinitGPT4Processor, VisualGLMModel
>>> processor = MinitGPT4Processor.from_pretrained("model_name")
>>> model = VisualGLMModel.from_pretrained("model_name")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor.process_images(images=image, return_tensors="pd")
>>> qformer_outputs = model.get_qformer_features(**inputs)
```"""
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
# step 1: forward the images through the vision encoder,
# to get image embeddings of shape (batch_size, seq_len, hidden_size)
pixel_values = paddle.cast(pixel_values, self.vision_model.embeddings.patch_embedding.weight.dtype)
vision_outputs = self.vision_model(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = vision_outputs[0]
image_attention_mask = paddle.ones(image_embeds.shape[:-1], dtype="int64")
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
query_tokens = self.query_tokens.expand([image_embeds.shape[0], -1, -1])
query_tokens = paddle.cast(query_tokens, self.qformer.layernorm.weight.dtype)
image_embeds = paddle.cast(image_embeds, self.qformer.layernorm.weight.dtype)
query_outputs = self.qformer(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
return query_outputs
def forward(
self,
pixel_values: paddle.Tensor, # processed image
first_input_ids: paddle.Tensor,
second_input_ids: paddle.Tensor,
first_attention_mask: Optional[paddle.Tensor] = None,
second_attention_mask: Optional[paddle.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[paddle.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, VisualGLMForConditionalGenerationModelOutput]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> import paddle
>>> from paddlenlp.transformers import VisualGLMProcessor, VisualGLMModel
>>> processor = VisualGLMProcessor.from_pretrained("model_name")
>>> model = VisualGLMModel.from_pretrained("model_name")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text = "describe this image"
>>> prompt = "###Human: ![]()
###Assistant:"
>>> inputs = processor(images=image, texts=text, prompts=prompt, return_tensors="pd")
>>> outputs = model(**inputs)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# step 1: forward the images through the vision encoder,
# to get image embeddings of shape (batch_size, seq_len, hidden_size)
vision_outputs = self.vision_model(pixel_values, return_dict=True)
image_embeds = vision_outputs.last_hidden_state
image_attention_mask = paddle.ones(image_embeds.shape[:-1], dtype="int64")
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
query_tokens = self.query_tokens.expand([image_embeds.shape[0], -1, -1])
query_tokens = paddle.cast(query_tokens, self.qformer.layernorm.weight.dtype)
image_embeds = paddle.cast(image_embeds, self.qformer.layernorm.weight.dtype)
query_outputs = self.qformer(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
return_dict=True,
)
query_output = query_outputs.last_hidden_state
# step 3: use the language model, conditioned on the text and image
language_model_inputs = self.language_projection(query_output)
language_model_attention_mask = paddle.ones(language_model_inputs.shape[:-1], dtype="int64")
first_embeds = self.language_model.chatglm.transformer.word_embeddings(first_input_ids)
second_embeds = self.language_model.chatglm.word_embeddings(second_input_ids)
language_model_inputs = paddle.cast(language_model_inputs, dtype=first_embeds.dtype)
inputs_embeds = paddle.concat([first_embeds, language_model_inputs, second_embeds], axis=1)
if first_attention_mask is None:
first_attention_mask = paddle.ones_like(first_embeds.shape[:-1], dtype="int64")
if second_attention_mask is None:
second_attention_mask = paddle.ones_like(second_embeds.shape[:-1], dtype="int64")
attention_mask = paddle.concat(
[first_attention_mask, language_model_attention_mask, second_attention_mask], axis=1
)
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = outputs.logits if return_dict else outputs[0]
loss = None
# we compute the loss here since we need to take into account the sequence length of the query embeds
if labels is not None:
logits = logits[:, -labels.shape[1] :, :]
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :]
shift_labels = labels[..., 1:]
# Flatten the tokens
loss_fct = CrossEntropyLoss(reduction="mean")
loss = loss_fct(shift_logits.reshape([-1, self.config.text_config.vocab_size]), shift_labels.reshape([-1]))
if not return_dict:
output = (logits, vision_outputs, query_outputs, outputs)
return ((loss,) + output) if loss is not None else output
return VisualGLMForConditionalGenerationModelOutput(
loss=loss,
logits=logits,
vision_outputs=vision_outputs,
qformer_outputs=query_outputs,
language_model_outputs=outputs,
)
class ChatGLMForConditionalGenerationWithImage(ChatGLMForCausalLM):
def __init__(self, config: ChatGLMConfig):
super(ChatGLMForConditionalGenerationWithImage, self).__init__(config)
self.config = config
def forward(
self,
image_features: paddle.Tensor,
input_ids: paddle.Tensor,
position_ids: Optional[paddle.Tensor] = None,
attention_mask: Optional[paddle.Tensor] = None,
pre_image_length: Optional[int] = None,
cache: Optional[Tuple[paddle.Tensor]] = None,
inputs_embeds: Optional[paddle.Tensor] = None,
labels: Optional[paddle.Tensor] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if inputs_embeds is None and cache is None and image_features is not None:
pre_ids, pad_ids, post_ids = paddle.split(input_ids, num_or_sections=[pre_image_length, 32, -1], axis=1)
pre_txt_emb = self.chatglm.transformer.word_embeddings(pre_ids)
post_txt_emb = self.chatglm.transformer.word_embeddings(post_ids)
inputs_embeds = paddle.concat([pre_txt_emb, image_features, post_txt_emb], axis=1)
outputs = super().forward(
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
cache=cache,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
return_dict=return_dict,
)
return outputs
class VisualGLMForConditionalGeneration(VisualGLMPretrainedModel):
config_class = VisualGLMConfig
main_input_name = "pixel_values"
def __init__(self, config: VisualGLMConfig):
super().__init__(config)
self.config = config
self.vision_model = VisualGLMVisionModel(config.vision_config)
self.query_tokens = Parameter(
paddle.zeros([1, config.num_query_tokens, config.qformer_config.hidden_size]), dtype=self.config.dtype
)
self.qformer = VisualGLMQFormerModel(config.qformer_config)
self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
self.language_model = ChatGLMForConditionalGenerationWithImage(config.text_config)
def get_input_embeddings(self) -> nn.Layer:
return self.vision_model.embeddings.patch_embedding
def encode_images(
self,
pixel_values: paddle.Tensor, # processed image
):
# step 1: forward the images through the vision encoder,
# to get image embeddings of shape (batch_size, seq_len, hidden_size)
pixel_values = paddle.cast(pixel_values, self.vision_model.embeddings.patch_embedding.weight.dtype)
vision_outputs = self.vision_model(pixel_values, return_dict=True)
image_embeds = vision_outputs.last_hidden_state
image_attention_mask = paddle.ones(image_embeds.shape[:-1], dtype="int64")
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
query_tokens = self.query_tokens.expand([image_embeds.shape[0], -1, -1])
query_tokens = paddle.cast(query_tokens, self.qformer.final_layernorm.weight.dtype)
image_embeds = paddle.cast(image_embeds, self.qformer.final_layernorm.weight.dtype)
query_outputs = self.qformer(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
return_dict=True,
)
query_output = query_outputs.last_hidden_state
# step 3: mapping query_output into language_model space
language_model_inputs = self.language_projection(query_output)
return language_model_inputs
@paddle.no_grad()
def generate(
self,
pixel_values: paddle.Tensor,
input_ids: paddle.Tensor,
pre_image_length: int,
attention_mask: Optional[paddle.Tensor] = None,
**generate_kwargs,
) -> paddle.Tensor:
"""
Overrides `generate` function to be able to use the model as a conditional generator.
Args:
pixel_values (`paddle.Tensor` of shape (batch_size, num_channels, height, width)):
Input images to be processed.
input_ids (`paddle.Tensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt for the generation.
attention_mask (`paddle.Tensor` of shape (batch_size, sequence_length), *optional*):
Mask to avoid performing attention on padding token indices
Returns:
captions (list): A list of strings of length batch_size * num_captions.
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> import paddle
>>> from paddlenlp.transformers import VisualGLMProcessor, VisualGLMForConditionalGeneration
>>> processor = VisualGLMProcessor.from_pretrained("model_name")
>>> model = VisualGLMForConditionalGeneration.from_pretrained("model_name")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text = "describe this image"
>>> prompt = "###Human: ![]()
###Assistant:"
>>> inputs = processor(images=image, texts=text, prompts=prompt, return_tensors="pd")
>>> generated_ids, scores= model.generate(**inputs)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
"""
image_features = self.encode_images(pixel_values)
outputs = self.language_model.generate(
input_ids=input_ids,
image_features=image_features,
pre_image_length=pre_image_length,
attention_mask=attention_mask,
**generate_kwargs,
)
return outputs