modeling_minicpmv.py

import math
from typing import List, Optional
import json
import torch
import torchvision

from threading import Thread
from copy import deepcopy
from PIL import Image
from transformers import AutoProcessor, Qwen2PreTrainedModel, Qwen2ForCausalLM, TextIteratorStreamer

from .configuration_minicpm import MiniCPMVConfig
from .modeling_navit_siglip import SiglipVisionTransformer
from .resampler import Resampler



class MiniCPMVPreTrainedModel(Qwen2PreTrainedModel):
    config_class = MiniCPMVConfig


class MiniCPMV(MiniCPMVPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.llm = Qwen2ForCausalLM(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.processor = None

        self.terminators = ['<|im_end|>', '<|endoftext|>']

    def init_vision_module(self):
        # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
        if self.config._attn_implementation == 'flash_attention_2':
            self.config.vision_config._attn_implementation = 'flash_attention_2'
        else:
            # not suport sdpa
            self.config.vision_config._attn_implementation = 'eager'
        model = SiglipVisionTransformer(self.config.vision_config)
        if self.config.drop_vision_last_layer:
            model.encoder.layers = model.encoder.layers[:-1]

        setattr(model, 'embed_dim', model.embeddings.embed_dim)
        setattr(model, 'patch_size', model.embeddings.patch_size)

        return model

    def init_resampler(self, embed_dim, vision_dim):
        return Resampler(
            num_queries=self.config.query_num,
            embed_dim=embed_dim,
            num_heads=embed_dim // 128,
            kv_dim=vision_dim,
            adaptive=True
        )

    def get_input_embeddings(self):
        return self.llm.get_input_embeddings()

    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 prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        cache_position=None,
        position_ids=None,
        use_cache=True,
        **kwargs,
    ):
        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
        # Exception 1: when passing input_embeds, input_ids may be missing entries
        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
        if past_key_values is not None:
            if inputs_embeds is not None:  # Exception 1
                input_ids = input_ids[:, -cache_position.shape[0] :]
            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
                input_ids = input_ids[:, cache_position]

        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                position_ids = position_ids.clone(memory_format=torch.contiguous_format)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and cache_position[0] == 0:
            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
        else:
            # The clone here is for the same reason as for `position_ids`.
            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
    

        model_inputs.update(
            {
                "position_ids": position_ids,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": use_cache,
                "attention_mask": attention_mask,
            }
        )
        return model_inputs
    def get_decoder(self):
        return self.llm

    def get_vllm_embedding(self, data):
        if 'vision_hidden_states' not in data:
            dtype = self.llm.model.embed_tokens.weight.dtype
            device = self.llm.model.embed_tokens.weight.device
            tgt_sizes = data['tgt_sizes']
            pixel_values_list = data['pixel_values']
            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])

            # exist image
            if all_pixel_values:
                tgt_sizes = [tgt_size for tgt_size in tgt_sizes if isinstance(tgt_size, torch.Tensor)]
                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)
                B, L, _ = all_pixel_values.shape
                all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)

                patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device)
                for i in range(B):
                    patch_attn_mask[i, 0, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True

                vision_batch_size = self.config.vision_batch_size
                all_pixel_values = all_pixel_values.type(dtype)
                if B > vision_batch_size:
                    hs = []
                    for i in range(0, B, vision_batch_size):
                        start_idx = i
                        end_idx = i + vision_batch_size
                        tmp_hs = self.vpm(all_pixel_values[start_idx:end_idx], patch_attention_mask=patch_attn_mask[start_idx:end_idx], tgt_sizes=tgt_sizes[start_idx:end_idx]).last_hidden_state
                        hs.append(tmp_hs)
                    vision_embedding = torch.cat(hs, dim=0)
                else:
                    vision_embedding = self.vpm(all_pixel_values, patch_attention_mask=patch_attn_mask, tgt_sizes=tgt_sizes).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
                    )
                    tgt_sizes = torch.Tensor([[(224 // self.config.patch_size), math.ceil(224 / self.config.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]))
                elif self.training:
                    cur_vllm_emb += cur_vs_hs[0].mean() * 0

        return vllm_embedding, vision_hidden_states

    def forward(self, data, **kwargs):
        if isinstance(data, torch.Tensor):
            return self.llm(
            input_ids=data,
            **kwargs
        )
        else:
            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(self, inputs_embeds, tokenizer, attention_mask, decode_text=False, **kwargs):
        terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
        output = self.llm.generate(
            inputs_embeds=inputs_embeds,
            pad_token_id=0,
            eos_token_id=terminators,
            attention_mask=attention_mask,
            **kwargs
        )
        if decode_text:
            return self._decode_text(output, tokenizer)
        return output

    def _decode_stream(self, inputs_embeds, tokenizer, **kwargs):
        terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
        streamer = TextIteratorStreamer(tokenizer=tokenizer)
        generation_kwargs = {
            'inputs_embeds': inputs_embeds,
            'pad_token_id': 0,
            'eos_token_id': terminators,
            'streamer': streamer
        }
        generation_kwargs.update(kwargs)

        thread = Thread(target=self.llm.generate, kwargs=generation_kwargs)
        thread.start()
    
        return streamer

    def _decode_text(self, result_ids, tokenizer):
        terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
        result_text = []
        for result in result_ids:
            result = result[result != 0]
            if result[0] == tokenizer.bos_id:
                result = result[1:]
            if result[-1] in terminators:
                result = result[:-1]
            result_text.append(tokenizer.decode(result).strip())
        return result_text

    def generate(
        self,
        input_ids=None,
        pixel_values=None,
        tgt_sizes=None,
        image_bound=None,
        attention_mask=None,
        tokenizer=None,
        vision_hidden_states=None,
        return_vision_hidden_states=False,
        stream=False,
        decode_text=False,
        **kwargs
    ):
        assert input_ids is not None
        assert len(input_ids) == len(pixel_values)

        model_inputs = {
            "input_ids": input_ids,
            "image_bound": image_bound,
        }

        if vision_hidden_states is None:
            model_inputs["pixel_values"] = pixel_values
            model_inputs['tgt_sizes'] = tgt_sizes
        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)

            if stream:
                result = self._decode_stream(model_inputs["inputs_embeds"], tokenizer, **kwargs)
            else:
                result = self._decode(model_inputs["inputs_embeds"], tokenizer, attention_mask, decode_text=decode_text, **kwargs)

        if return_vision_hidden_states:
            return result, vision_hidden_states
        
        return result

    def chat(
        self,
        image,
        msgs,
        tokenizer,
        processor=None,
        vision_hidden_states=None,
        max_new_tokens=2048,
        min_new_tokens=0,
        sampling=True,
        max_inp_length=8192,
        system_prompt='',
        stream=False,
        max_slice_nums=None,
        use_image_id=None,
        **kwargs
    ):
        if isinstance(msgs[0], list):
            batched = True
        else:
            batched = False
        msgs_list = msgs
        images_list = image
        
        if batched is False:
            images_list, msgs_list = [images_list], [msgs_list]
        else:
            assert images_list is None, "Please integrate image to msgs when using batch inference."
            images_list = [None] * len(msgs_list)
        assert len(images_list) == len(msgs_list), "The batch dim of images_list and msgs_list should be the same."

        if processor is None:
            if self.processor is None:
                self.processor = AutoProcessor.from_pretrained(self.config._name_or_path, trust_remote_code=True)
            processor = self.processor
        
        assert self.config.query_num == processor.image_processor.image_feature_size, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
        assert self.config.patch_size == processor.image_processor.patch_size, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
        assert self.config.use_image_id == processor.image_processor.use_image_id, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
        assert self.config.slice_config.max_slice_nums == processor.image_processor.max_slice_nums, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."
        assert self.config.slice_mode == processor.image_processor.slice_mode, "These two values should be the same. Check `config.json` and `preprocessor_config.json`."

        prompts_lists = []
        input_images_lists = []
        for image, msgs in zip(images_list, msgs_list):
            if isinstance(msgs, str):
                msgs = json.loads(msgs)
            copy_msgs = deepcopy(msgs)

            assert len(msgs) > 0, "msgs is empty"
            assert sampling or not stream, "if use stream mode, make sure sampling=True"

            if image is not None and isinstance(copy_msgs[0]["content"], str):
                copy_msgs[0]["content"] = [image, copy_msgs[0]["content"]]

            images = []
            for i, msg in enumerate(copy_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"
                if isinstance(content, str):
                    content = [content]
                cur_msgs = []
                for c in content:
                    if isinstance(c, Image.Image):
                        images.append(c)
                        cur_msgs.append("(<image>./</image>)")
                    elif isinstance(c, str):
                        cur_msgs.append(c)
                msg["content"] = "\n".join(cur_msgs)

            if system_prompt:
                sys_msg = {'role': 'system', 'content': system_prompt}
                copy_msgs = [sys_msg] + copy_msgs        

            prompts_lists.append(processor.tokenizer.apply_chat_template(copy_msgs, tokenize=False, add_generation_prompt=True))
            input_images_lists.append(images)

        inputs = processor(
            prompts_lists, 
            input_images_lists, 
            max_slice_nums=max_slice_nums,
            use_image_id=use_image_id,
            return_tensors="pt", 
            max_length=max_inp_length
        ).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,
            }
            
        if min_new_tokens > 0:
            generation_config['min_new_tokens'] = min_new_tokens

        generation_config.update(
            (k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
        )

        inputs.pop("image_sizes")
        with torch.inference_mode():
            res = self.generate(
                **inputs,
                tokenizer=tokenizer,
                max_new_tokens=max_new_tokens,
                vision_hidden_states=vision_hidden_states,
                stream=stream,
                decode_text=True,
                **generation_config
            )
        
        if stream:
            def stream_gen():
                for text in res:
                    for term in self.terminators:
                        text = text.replace(term, '')
                    yield text
            return stream_gen()

        else:
            if batched:
                answer = res
            else:
                answer = res[0]
            return answer