inferencer.py

# Copyright 2025 Bytedance Ltd. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0

from copy import deepcopy
from typing import List, Optional, Union, Any

from PIL import Image
import torch

from data.data_utils import pil_img2rgb
from modeling.bagel.qwen2_navit import NaiveCache



VLM_THINK_SYSTEM_PROMPT = '''You should first think about the reasoning process in the mind and then provide the user with the answer. 
The reasoning process is enclosed within <think> </think> tags, i.e. <think> reasoning process here </think> answer here'''

GEN_THINK_SYSTEM_PROMPT = '''You should first think about the planning process in the mind and then generate the image. 
The planning process is enclosed within <think> </think> tags, i.e. <think> planning process here </think> image here'''


class InterleaveInferencer:
    def __init__(self, model, vae_model, tokenizer, vae_transform, vit_transform, new_token_ids):
        self.model = model
        self.vae_model = vae_model
        self.tokenizer = tokenizer
        self.vae_transform = vae_transform
        self.vit_transform = vit_transform
        self.new_token_ids = new_token_ids
        
    def init_gen_context(self): 
        gen_context = {
            'kv_lens': [0],
            'ropes': [0],
            'past_key_values': NaiveCache(self.model.config.llm_config.num_hidden_layers),
        }
        return gen_context

    @torch.no_grad()
    def update_context_text(self, text, gen_context):
        # used for interleave data, currently only support 1 data inference, 

        past_key_values = gen_context['past_key_values']
        kv_lens = gen_context['kv_lens']
        ropes = gen_context['ropes']
        generation_input, kv_lens, ropes = self.model.prepare_prompts(
            curr_kvlens=kv_lens,
            curr_rope=ropes, 
            prompts=[text],
            tokenizer=self.tokenizer, 
            new_token_ids=self.new_token_ids,
        )

        past_key_values = self.model.forward_cache_update_text(past_key_values, **generation_input)        
        gen_context['kv_lens'] = kv_lens
        gen_context['ropes'] = ropes
        gen_context['past_key_values'] = past_key_values
        
        return gen_context

    @torch.no_grad()
    def update_context_image(self, image, gen_context, vae=True, vit=True):
        # used for interleave data, currently only support 1 data inference, 

        assert vae or vit
        past_key_values = gen_context['past_key_values']
        kv_lens = gen_context['kv_lens']
        ropes =  gen_context['ropes']

        if vae:
            ## update vae
            generation_input, kv_lens, ropes = self.model.prepare_vae_images(
                curr_kvlens=kv_lens,
                curr_rope=ropes, 
                images=[image],
                transforms=self.vae_transform, 
                new_token_ids=self.new_token_ids,
            )
            past_key_values = self.model.forward_cache_update_vae(self.vae_model, past_key_values, **generation_input)
        
        if vit:
            ## update vit
            generation_input, kv_lens, ropes = self.model.prepare_vit_images(
                curr_kvlens=kv_lens,
                curr_rope=ropes, 
                images=[image],
                transforms=self.vit_transform, 
                new_token_ids=self.new_token_ids,
            )
            past_key_values = self.model.forward_cache_update_vit(past_key_values, **generation_input)

        gen_context['kv_lens'] = kv_lens
        gen_context['ropes'] = ropes
        gen_context['past_key_values'] = past_key_values
        
        return gen_context

    @torch.no_grad()
    def gen_image(
        self, 
        image_shape, 
        gen_context, 
        cfg_text_scale=4.0,
        cfg_img_scale=1.5,

        cfg_text_precontext=None, 
        cfg_img_precontext=None, 
        cfg_interval=(0.4, 1.0),
        cfg_renorm_min=0.0,
        cfg_renorm_type="global",
        
        num_timesteps=50, 
        timestep_shift=3.0
    ):
        # print(cfg_renorm_type)
        past_key_values = gen_context['past_key_values']
        kv_lens = gen_context['kv_lens']
        ropes = gen_context['ropes']
        generation_input = self.model.prepare_vae_latent(
            curr_kvlens=kv_lens,
            curr_rope=ropes, 
            image_sizes=[image_shape], 
            new_token_ids=self.new_token_ids,
        ) 
        
        # text cfg
        cfg_text_past_key_values = cfg_text_precontext['past_key_values']
        kv_lens_cfg = cfg_text_precontext['kv_lens']
        ropes_cfg = cfg_text_precontext['ropes']
        generation_input_cfg_text = self.model.prepare_vae_latent_cfg(
            curr_kvlens=kv_lens_cfg,
            curr_rope=ropes_cfg, 
            image_sizes=[image_shape], 
        )

        # img cfg
        cfg_img_past_key_values = cfg_img_precontext['past_key_values']
        kv_lens_cfg = cfg_img_precontext['kv_lens']
        ropes_cfg = cfg_img_precontext['ropes']
        generation_input_cfg_img = self.model.prepare_vae_latent_cfg(
            curr_kvlens=kv_lens_cfg,
            curr_rope=ropes_cfg, 
            image_sizes=[image_shape], 
        )

        unpacked_latent = self.model.generate_image(
            past_key_values=past_key_values,
            cfg_text_past_key_values=cfg_text_past_key_values,
            cfg_img_past_key_values=cfg_img_past_key_values,
            num_timesteps=num_timesteps,
            cfg_text_scale=cfg_text_scale,
            cfg_img_scale=cfg_img_scale,
            cfg_interval=cfg_interval,
            cfg_renorm_min=cfg_renorm_min,
            cfg_renorm_type=cfg_renorm_type,
            timestep_shift=timestep_shift,
            **generation_input,
            cfg_text_packed_position_ids=generation_input_cfg_text['cfg_packed_position_ids'],
            cfg_text_packed_query_indexes=generation_input_cfg_text['cfg_packed_query_indexes'],
            cfg_text_key_values_lens=generation_input_cfg_text['cfg_key_values_lens'],
            cfg_text_packed_key_value_indexes=generation_input_cfg_text['cfg_packed_key_value_indexes'],
            cfg_img_packed_position_ids=generation_input_cfg_img['cfg_packed_position_ids'],
            cfg_img_packed_query_indexes=generation_input_cfg_img['cfg_packed_query_indexes'],
            cfg_img_key_values_lens=generation_input_cfg_img['cfg_key_values_lens'],
            cfg_img_packed_key_value_indexes=generation_input_cfg_img['cfg_packed_key_value_indexes'],
        )

        image = self.decode_image(unpacked_latent[0], image_shape)
        return image

        
    def decode_image(self, latent, image_shape):
        H, W = image_shape
        h, w = H // self.model.latent_downsample, W // self.model.latent_downsample

        latent = latent.reshape(1, h, w, self.model.latent_patch_size, self.model.latent_patch_size, self.model.latent_channel)
        latent = torch.einsum("nhwpqc->nchpwq", latent)
        latent = latent.reshape(1, self.model.latent_channel, h * self.model.latent_patch_size, w * self.model.latent_patch_size)
        image = self.vae_model.decode(latent)
        image = (image * 0.5 + 0.5).clamp(0, 1)[0].permute(1, 2, 0) * 255
        image = Image.fromarray((image).to(torch.uint8).cpu().numpy())

        return image

    @torch.no_grad()
    def gen_text(self, gen_context, max_length: int = 500, do_sample: bool = True, temperature: float = 1.0):
        gen_context = deepcopy(gen_context)
        past_key_values = gen_context['past_key_values']
        kv_lens = gen_context['kv_lens']
        ropes = gen_context['ropes']

        generation_input = self.model.prepare_start_tokens(kv_lens, ropes, self.new_token_ids)
        for unpacked_latent in self.model.generate_text(
            past_key_values=past_key_values,
            max_length=max_length,
            do_sample=do_sample,
            temperature=temperature,
            end_token_id=self.new_token_ids['eos_token_id'],
            **generation_input,
        ):
            output = self.tokenizer.decode(unpacked_latent)
            if output != "<|im_end|>":
                yield output
        
    @torch.no_grad()
    def interleave_inference(
        self,
        input_lists: List[Union[str, Image.Image]],
        think=False,
        understanding_output=False,
        # for gen_text
        max_think_token_n=1000,
        do_sample=False,
        text_temperature=0.3,
        # for gen_image
        cfg_text_scale=3.0,
        cfg_img_scale=1.5,
        cfg_interval=[0.4, 1.0],
        timestep_shift=3.0,
        num_timesteps=50,
        cfg_renorm_min=0.0,
        cfg_renorm_type="global",
        image_shapes=(1024, 1024), # Default, can be overridden by actual input image
    ):
        gen_context = self.init_gen_context()
        cfg_text_context = deepcopy(gen_context)
        cfg_img_context = deepcopy(gen_context)

        with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):

            if think:
                system_prompt = VLM_THINK_SYSTEM_PROMPT if understanding_output else GEN_THINK_SYSTEM_PROMPT
                gen_context = self.update_context_text(system_prompt, gen_context)
                cfg_text_context = self.update_context_text(system_prompt, cfg_text_context)
                cfg_img_context = self.update_context_text(system_prompt, cfg_img_context)
    
            for input_term in input_lists:
                if isinstance(input_term, str):
                    cfg_text_context = deepcopy(gen_context)
                    gen_context = self.update_context_text(input_term, gen_context)
                    cfg_img_context = self.update_context_text(input_term, cfg_img_context)

                elif isinstance(input_term, Image.Image):
                    input_term = self.vae_transform.resize_transform(pil_img2rgb(input_term))
                    gen_context = self.update_context_image(input_term, gen_context, vae=not understanding_output)
                    image_shapes = input_term.size[::-1]
                    cfg_text_context = deepcopy(gen_context)

                else:
                    raise ValueError(f"Unsupported input type: {type(input_term)}")
    
            if understanding_output: # Generate text
                yield from self.gen_text(gen_context, max_length=max_think_token_n, do_sample=do_sample, temperature=text_temperature)
            else: # Generate image
                if think:
                    thought_text_parts = []
                    for part in self.gen_text(gen_context, max_length=max_think_token_n, do_sample=do_sample, temperature=text_temperature):
                        yield part # Stream the thought
                        thought_text_parts.append(part)
                    full_thought_text = "".join(thought_text_parts)
                    if full_thought_text: # Only update if thought was generated
                        gen_context = self.update_context_text(full_thought_text, gen_context)
                
                img = self.gen_image(
                    image_shape=image_shapes, 
                    gen_context=gen_context, 
                    cfg_text_precontext=cfg_text_context, 
                    cfg_img_precontext=cfg_img_context,
                    cfg_text_scale=cfg_text_scale, 
                    cfg_img_scale=cfg_img_scale, 
                    cfg_interval=cfg_interval, 
                    timestep_shift=timestep_shift, 
                    num_timesteps=num_timesteps,
                    cfg_renorm_min=cfg_renorm_min,
                    cfg_renorm_type=cfg_renorm_type,
                )
                yield img

    def __call__(
        self, 
        image: Optional[Image.Image] = None, 
        text: Optional[str] = None, 
        **kargs 
    ) -> Any:
        input_list = []
        if image is not None:
            input_list.append(image)
        if text is not None:
            input_list.append(text)
        
        if not input_list and not kargs.get('force_empty_input', False): # allow forcing for special cases if needed
            return

        yield from self.interleave_inference(input_list, **kargs)