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import math
from typing import List, Optional
import json
import timm
import torch
import torchvision
from PIL import Image
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from torchvision import transforms
from .configuration_minicpm import MiniCPMVConfig
from .modeling_minicpm import MiniCPMForCausalLM, MiniCPMPreTrainedModel
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,
adaptive=True
)
def init_transform(self):
return transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD
),
]
)
def get_input_embeddings(self):
return self.llm.embed_tokens
def set_input_embeddings(self, value):
self.llm.embed_tokens = value
def get_output_embeddings(self):
return self.llm.lm_head
def set_output_embeddings(self, new_embeddings):
self.llm.lm_head = new_embeddings
def set_decoder(self, decoder):
self.llm = decoder
def get_decoder(self):
return self.llm
def get_vision_embedding(self, pixel_values):
res = []
dtype = self.vpm.pos_embed.data.dtype
for pixel_value in pixel_values:
H, W = pixel_value.shape[-2:]
tgt_size = (
math.ceil(H / self.vpm.patch_embed.patch_size[0]), math.ceil(W / self.vpm.patch_embed.patch_size[0]))
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, tgt_size))
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_bounds"][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 _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 _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 if tokenizer is not None else kwargs.pop("eos_token_id", 2),
**kwargs
)
return output
def generate(
self,
input_ids,
pixel_values=None,
image_sizes=[],
image_bounds=[],
tgt_sizes=[],
tokenizer=None,
vision_hidden_states=None,
**kwargs
):
bs = len(input_ids)
img_list = pixel_values
if img_list == None:
img_list = [[] for i in range(bs)]
assert bs == len(img_list)
if vision_hidden_states is None:
pixel_values = []
for i in range(bs):
img_inps = []
for img in img_list[i]:
img_inps.append(img.to(self.device, self.dtype))
pixel_values.append(img_inps)
# with torch.inference_mode():
(
input_embeds,
vision_hidden_states,
) = self.get_vllm_embedding({
"input_ids": input_ids,
"pixel_values": pixel_values,
"image_sizes": image_sizes,
"image_bounds": image_bounds,
"tgt_sizes": tgt_sizes
})
result = self._decode(input_embeds, tokenizer, **kwargs)
return result
def chat(
self,
image,
msgs,
context,
tokenizer,
processor,
vision_hidden_states=None,
max_new_tokens=1024,
sampling=True,
max_inp_length=2048,
**kwargs
):
if isinstance(msgs, str):
msgs = json.loads(msgs)
# msgs to prompt
prompt = processor.tokenizer.apply_chat_template(msgs)
inputs = processor(prompt, [image], return_tensors="pt").to(self.device)
if sampling:
generation_config = {
"top_p": 0.8,
"top_k": 100,
"temperature": 0.7,
"do_sample": True,
"repetition_penalty": 1.05
}
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 = self.generate(
**inputs,
tokenizer=tokenizer,
max_new_tokens=max_new_tokens,
vision_hidden_states=vision_hidden_states,
**generation_config,
)
res = self._decode_text(res, tokenizer)
answer = res[0]
context = msgs.copy()
context.append({"role": "assistant", "content": answer})
return answer, context, generation_config
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