|
import math |
|
from typing import List, Optional |
|
|
|
import timm |
|
import torch |
|
import torchvision |
|
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD |
|
from torchvision import transforms |
|
from transformers import LlamaTokenizer |
|
|
|
from .configuration_minicpm import MiniCPMVConfig |
|
from .modeling_minicpm import MiniCPMPreTrainedModel, MiniCPMForCausalLM |
|
from .resampler import Resampler |
|
|
|
|
|
class MiniCPMVPreTrainedModel(MiniCPMPreTrainedModel): |
|
config_class = MiniCPMVConfig |
|
|
|
|
|
class MiniCPMV(MiniCPMVPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
self.llm = MiniCPMForCausalLM(config) |
|
self.vpm = self.init_vision_module() |
|
self.vision_dim = self.vpm.embed_dim |
|
self.embed_dim = self.llm.config.hidden_size |
|
self.resampler = self.init_resampler(self.embed_dim ,self.vision_dim) |
|
self.transform = self.init_transform() |
|
|
|
|
|
def init_vision_module(self): |
|
model = timm.create_model( |
|
self.config.vision_encoder, |
|
pretrained=False, |
|
num_classes=0, |
|
dynamic_img_size=True, |
|
dynamic_img_pad=True |
|
) |
|
|
|
if isinstance(model, timm.models.VisionTransformer): |
|
if model.attn_pool is not None: |
|
model.attn_pool = torch.nn.Identity() |
|
|
|
if self.config.drop_vision_last_layer: |
|
model.blocks = model.blocks[:-1] |
|
|
|
return model |
|
|
|
def init_resampler(self, embed_dim, vision_dim): |
|
return Resampler( |
|
grid_size=int(math.sqrt(self.config.query_num)), |
|
embed_dim=embed_dim, |
|
num_heads=embed_dim // 128, |
|
kv_dim=vision_dim, |
|
) |
|
|
|
def init_transform(self): |
|
return transforms.Compose([ |
|
transforms.Resize( |
|
(self.config.image_size, self.config.image_size), |
|
interpolation=torchvision.transforms.InterpolationMode.BICUBIC |
|
), |
|
transforms.ToTensor(), |
|
transforms.Normalize(mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD) |
|
]) |
|
|
|
|
|
|
|
def get_vision_embedding(self, pixel_values): |
|
res = [] |
|
dtype = self.vpm.pos_embed.data.dtype |
|
for pixel_value in pixel_values: |
|
vision_embedding = self.vpm.forward_features(pixel_value.unsqueeze(0).type(dtype)) |
|
if hasattr(self.vpm, 'num_prefix_tokens') and self.vpm.num_prefix_tokens > 0: |
|
vision_embedding = vision_embedding[:, self.vpm.num_prefix_tokens:] |
|
res.append(self.resampler(vision_embedding)) |
|
return torch.vstack(res) |
|
|
|
def get_vllm_embedding(self, data): |
|
if 'vision_hidden_states' not in data: |
|
pixel_values_list = data['pixel_values'] |
|
vision_hidden_states = [] |
|
for pixel_values in pixel_values_list: |
|
if len(pixel_values) > 0: |
|
vision_hidden_states.append(self.get_vision_embedding(pixel_values)) |
|
elif self.training: |
|
dtype = self.vpm.pos_embed.data.dtype |
|
device = self.vpm.pos_embed.data.device |
|
dummy_image = torch.zeros( |
|
(1, 3, 224, 224), |
|
device=device, dtype=dtype |
|
) |
|
vision_hidden_states.append(self.get_vision_embedding(dummy_image)) |
|
else: |
|
vision_hidden_states.append([]) |
|
|
|
else: |
|
vision_hidden_states = data['vision_hidden_states'] |
|
|
|
vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb |
|
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])) |
|
elif self.training: |
|
cur_vllm_emb += cur_vs_hs[0].mean() * 0 |
|
|
|
return vllm_embedding, vision_hidden_states |
|
|
|
def forward(self, data, **kwargs): |
|
vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data) |
|
position_ids = data["position_ids"] |
|
if position_ids.dtype != torch.int64: |
|
position_ids = position_ids.long() |
|
|
|
return self.llm( |
|
input_ids=None, |
|
position_ids=position_ids, |
|
inputs_embeds=vllm_embedding, |
|
**kwargs |
|
) |
|
|
|
|
|
def _convert_to_tensors(self, tokenizer, input_str, max_inp_length: Optional[int] = None): |
|
if tokenizer.add_bos_token: |
|
input_ids = tokenizer.encode(input_str) |
|
else: |
|
input_ids = [tokenizer.bos_id] + tokenizer.encode(input_str) |
|
if max_inp_length is not None: |
|
input_ids = input_ids[: max_inp_length] |
|
input_ids = torch.tensor(input_ids, dtype=torch.int32) |
|
|
|
image_start_tokens = torch.where(input_ids == tokenizer.im_start_id)[0] |
|
|
|
image_start_tokens += 1 |
|
image_end_tokens = torch.where(input_ids == tokenizer.im_end_id)[0] |
|
valid_image_nums = max(len(image_start_tokens), len(image_end_tokens)) |
|
image_bound = torch.hstack( |
|
[image_start_tokens[: valid_image_nums].unsqueeze(-1), |
|
image_end_tokens[:valid_image_nums].unsqueeze(-1)] |
|
) |
|
|
|
model_input = {} |
|
model_input["input_ids"] = input_ids.unsqueeze(0).to(self.device) |
|
model_input["image_bound"] = image_bound |
|
|
|
return model_input |
|
|
|
|
|
def _process_list(self, tokenizer, data_list: List[str], max_inp_length: Optional[int] = None): |
|
pad_keys = ['input_ids'] |
|
input_tensors = [] |
|
for data in data_list: |
|
input_tensors.append(self._convert_to_tensors(tokenizer, data, max_inp_length)) |
|
padded = {} |
|
for key in pad_keys: |
|
padded[key] = pad(input_tensors, key, padding_side="left").to(self.device) |
|
padded['image_bound'] = [i['image_bound'] for i in input_tensors] |
|
return padded |
|
|
|
def _decode(self, inputs_embeds, tokenizer, **kwargs): |
|
output = self.llm.generate( |
|
inputs_embeds=inputs_embeds, |
|
pad_token_id=0, |
|
eos_token_id=tokenizer.eos_token_id, |
|
**kwargs |
|
) |
|
return self._decode_text(output, tokenizer) |
|
|
|
def _decode_text(self, result_ids, tokenizer): |
|
result_text = [] |
|
for result in result_ids: |
|
result = result[result != 0] |
|
if result[0] == tokenizer.bos_id: |
|
result = result[1:] |
|
if result[-1] == tokenizer.eos_id: |
|
result = result[:-1] |
|
result_text.append(tokenizer.decode(result).strip()) |
|
return result_text |
|
|
|
def generate( |
|
self, |
|
data_list=None, |
|
img_list=None, |
|
tokenizer=None, |
|
max_inp_length: Optional[int] = None, |
|
vision_hidden_states=None, |
|
return_vision_hidden_states=False, |
|
**kwargs |
|
): |
|
|
|
assert data_list is not None |
|
bs = len(data_list) |
|
if img_list == None: |
|
img_list = [[] for i in range(bs)] |
|
assert bs == len(img_list) |
|
|
|
model_inputs = self._process_list(tokenizer, data_list, max_inp_length) |
|
|
|
if vision_hidden_states is None: |
|
pixel_values = [] |
|
for i in range(bs): |
|
img_inps = [] |
|
for img in img_list[i]: |
|
img_inps.append(self.transform(img)) |
|
if img_inps: |
|
pixel_values.append(torch.stack(img_inps).to(self.device)) |
|
else: |
|
pixel_values.append([]) |
|
model_inputs['pixel_values'] = pixel_values |
|
else: |
|
model_inputs['vision_hidden_states'] = vision_hidden_states |
|
|
|
with torch.inference_mode(): |
|
model_inputs['inputs_embeds'], vision_hidden_states = self.get_vllm_embedding(model_inputs) |
|
|
|
result = self._decode(model_inputs['inputs_embeds'], tokenizer, **kwargs) |
|
|
|
if return_vision_hidden_states: |
|
return result, vision_hidden_states |
|
|
|
return result |
|
|
|
|
|
def chat(self, image, msgs, context, tokenizer, vision_hidden_states=None, max_new_tokens=2048, sampling=False, **kwargs): |
|
if isinstance(msgs, str): |
|
msgs = json.loads(msgs) |
|
|
|
prompt = '' |
|
for i, msg in enumerate(msgs): |
|
role = msg['role'] |
|
content = msg['content'] |
|
assert role in ['user', 'assistant'] |
|
if i == 0: |
|
assert role == 'user', 'The role of first msg should be user' |
|
content = tokenizer.im_start + tokenizer.unk_token * self.config.query_num + tokenizer.im_end + '\n' + content |
|
prompt += '<用户>' if role=='user' else '<AI>' |
|
prompt += content |
|
prompt += '<AI>' |
|
final_input = prompt |
|
|
|
if sampling: |
|
generation_config = { |
|
'top_p': 0.8, |
|
'top_k': 100, |
|
'temperature':0.6, |
|
'do_sample': True |
|
} |
|
else: |
|
generation_config = { |
|
'num_beams': 3, |
|
'repetition_penalty': 1.2, |
|
} |
|
|
|
generation_config.update((k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()) |
|
|
|
with torch.inference_mode(): |
|
res, vision_hidden_states = self.generate( |
|
data_list=[final_input], |
|
max_inp_length=2048, |
|
img_list=[[image]], |
|
tokenizer=tokenizer, |
|
max_new_tokens=max_new_tokens, |
|
vision_hidden_states=vision_hidden_states, |
|
return_vision_hidden_states=True, |
|
**generation_config |
|
) |
|
answer = res[0] |
|
context = msgs |
|
context.append({'role':'assistant', 'content': answer}) |
|
|
|
return answer, context, generation_config |
|
|
|
|
|
class LlamaTokenizerWrapper(LlamaTokenizer): |
|
def __init__(self, **kwargs): |
|
super().__init__(**kwargs) |
|
self.im_start = "<image>" |
|
self.im_end = "</image>" |
|
self.ref_start = "<ref>" |
|
self.ref_end = "</ref>" |
|
self.box_start = "<box>" |
|
self.box_end = "</box>" |
|
self.quad_start = "<quad>" |
|
self.quad_end = "</quad>" |
|
|
|
@property |
|
def eos_id(self): |
|
return self.sp_model.eos_id() |
|
|
|
@property |
|
def bos_id(self): |
|
return self.sp_model.bos_id() |
|
|
|
@property |
|
def unk_id(self): |
|
return self.sp_model.unk_id() |
|
|
|
@property |
|
def im_start_id(self): |
|
return self._convert_token_to_id(self.im_start) |
|
|
|
@property |
|
def im_end_id(self): |
|
return self._convert_token_to_id(self.im_end) |
|
|
|
|
|
def pad(orig_items, key, max_length=None, padding_value=0, padding_side="left"): |
|
items = [] |
|
if isinstance(orig_items[0][key], list): |
|
assert isinstance(orig_items[0][key][0], torch.Tensor) |
|
for it in orig_items: |
|
for tr in it[key]: |
|
items.append({key: tr}) |
|
else: |
|
assert isinstance(orig_items[0][key], torch.Tensor) |
|
items = orig_items |
|
|
|
batch_size = len(items) |
|
shape = items[0][key].shape |
|
dim = len(shape) |
|
assert dim <= 3 |
|
if max_length is None: |
|
max_length = 0 |
|
max_length = max(max_length, max(item[key].shape[-1] for item in items)) |
|
min_length = min(item[key].shape[-1] for item in items) |
|
dtype = items[0][key].dtype |
|
|
|
if dim == 1: |
|
return torch.cat([item[key] for item in items], dim=0) |
|
elif dim == 2: |
|
if max_length == min_length: |
|
return torch.cat([item[key] for item in items], dim=0) |
|
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value |
|
else: |
|
tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value |
|
|
|
for i, item in enumerate(items): |
|
if dim == 2: |
|
if padding_side == "left": |
|
tensor[i, -len(item[key][0]):] = item[key][0].clone() |
|
else: |
|
tensor[i, : len(item[key][0])] = item[key][0].clone() |
|
elif dim == 3: |
|
if padding_side == "left": |
|
tensor[i, -len(item[key][0]):, :] = item[key][0].clone() |
|
else: |
|
tensor[i, : len(item[key][0]), :] = item[key][0].clone() |
|
|
|
return tensor |
|
|
