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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright @2023 AI, ZHIHU Inc. (zhihu.com)
#
# @author: wangchongyi <[email protected]>
# @date: 2023/9/1
#
# coding=utf-8
# Copyright 2024 RhapsodyAI. 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 torch
from torch import nn
import math
from dataclasses import dataclass
from typing import Optional, Tuple
from transformers.utils import ModelOutput
from transformers.modeling_utils import PreTrainedModel
from .configuration_siglip import SiglipVisionConfig
from .configuration_minicpm import MiniCPMConfig
from .configuration_minicpmv import MiniCPMVConfig
from .resampler import Resampler
from .modeling_minicpm import MiniCPMForCausalLM
from .modeling_siglip import SiglipVisionModel
from transformers import LlamaTokenizer # for text processing
@dataclass
class CausalVLMOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
class MiniCPMVForCausalLM(PreTrainedModel):
model_type = "minicpm"
_supports_flash_attn_2 = True
def __init__(self, config: MiniCPMVConfig, adaptive=False):
super().__init__(config)
llm_config = config.llm_config
vpm_config = config.vpm_config
self.query_num = config.query_num
self.patch_size = vpm_config.patch_size
self.adaptive = adaptive
self.slice_mode = config.slice_mode
self.max_slice_nums = config.max_slice_nums
self.mm_use_im_start_end = config.mm_use_im_start_end
drop_vision_last_layer = config.drop_vision_last_layer
# should assert vpm_config is SiglipVisionConfig
vpm = SiglipVisionModel(vpm_config).vision_model
if drop_vision_last_layer: # drop last vision layer
vpm.encoder.layers = nn.ModuleList(vpm.encoder.layers[:-1])
self.vpm = vpm
# should assert llm_config is minicpmconfig
self.llm = MiniCPMForCausalLM(llm_config)
embed_dim = llm_config.hidden_size
self.resampler = Resampler(
num_queries=config.query_num,
embed_dim=embed_dim,
num_heads=embed_dim // 128,
kv_dim=vpm_config.hidden_size,
adaptive=adaptive
)
return
def vpm_forward(self, data):
if 'vision_hidden_states' not in data:
dtype = self.vpm.embeddings.position_embedding.weight.dtype
device = self.vpm.embeddings.position_embedding.weight.device
pixel_values_list = data['pixel_values']
tgt_sizes = data['tgt_sizes']
vision_hidden_states = []
all_pixel_values = []
img_cnt = []
for pixel_values in pixel_values_list:
img_cnt.append(len(pixel_values))
all_pixel_values.extend([i.flatten(end_dim=1).permute(1, 0) for i in pixel_values]) # 42 * L
# exist image
if all_pixel_values:
tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32)
max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1])
all_pixel_values = torch.nn.utils.rnn.pad_sequence(all_pixel_values, batch_first=True, padding_value=0.0)
all_pixel_values = all_pixel_values.to(device) # here we finally could put `all_pixel_values` to device.
B, L, _ = all_pixel_values.shape
all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L) # B, 3, 14, L
patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device)
for i in range(B):
patch_attn_mask[i, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True
vision_embedding = self.vpm(all_pixel_values.type(dtype), patch_attention_mask=patch_attn_mask).last_hidden_state
vision_embedding = self.resampler(vision_embedding, tgt_sizes)
start = 0
for pixel_values in pixel_values_list:
img_cnt = len(pixel_values)
if img_cnt > 0:
vision_hidden_states.append(vision_embedding[start: start + img_cnt])
start += img_cnt
else:
vision_hidden_states.append([])
else: # no image
if self.training:
dummy_image = torch.zeros(
(1, 3, 224, 224),
device=device, dtype=dtype
)
# 这是一个 dummy feature
tgt_sizes = torch.Tensor([[(224 // self.patch_size), math.ceil(224 / self.patch_size)]]).type(torch.int32)
dummy_feature = self.resampler(self.vpm(dummy_image).last_hidden_state, tgt_sizes)
else:
dummy_feature = []
for _ in range(len(pixel_values_list)):
vision_hidden_states.append(dummy_feature)
else:
vision_hidden_states = data['vision_hidden_states']
if hasattr(self.llm.config, 'scale_emb'):
vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb
else:
vllm_embedding = self.llm.model.embed_tokens(data['input_ids'])
vision_hidden_states = [i.type(vllm_embedding.dtype) if isinstance(
i, torch.Tensor) else i for i in vision_hidden_states]
bs = len(data['input_ids'])
for i in range(bs):
cur_vs_hs = vision_hidden_states[i]
if len(cur_vs_hs) > 0:
cur_vllm_emb = vllm_embedding[i]
cur_image_bound = data['image_bound'][i]
if len(cur_image_bound) > 0:
image_indices = torch.stack(
[torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound]
).to(vllm_embedding.device)
cur_vllm_emb.scatter_(
0,
image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]),
cur_vs_hs.view(-1, cur_vs_hs.shape[-1])
)
return vllm_embedding, vision_hidden_states
def forward(self, data, **kwargs):
vllm_embedding, vision_hidden_states = self.vpm_forward(data)
output = self.llm(
inputs_embeds=vllm_embedding,
attention_mask=data["attention_mask"],
return_dict=True
)
return CausalVLMOutput(
logits=output.logits,
hidden_states=output.hidden_states,
vision_hidden_states=vision_hidden_states
)
def generate(self, data, **kwargs):
vllm_embedding, vision_hidden_states = self.vpm_forward(data)
# position_ids = torch.arange(data["input_ids"].size(1), dtype=torch.long).to(data["input_ids"].device)
# position_ids = position_ids.unsqueeze(0).expand_as(data["input_ids"])
# 使用attention_mask将填充位置的position_ids设置为0
# position_ids = position_ids * data["attention_mask"]
output = self.llm.generate(
inputs_embeds=vllm_embedding,
# position_ids=position_ids,
attention_mask=data["attention_mask"],
**kwargs
)
return output
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