models/gen_pipeline.py

import re
import copy
from typing import Literal

from PIL import Image
from tqdm.auto import tqdm

import torch
import torch.nn as nn
import torchvision.transforms as transforms

from transformers import AutoTokenizer
from transformers.cache_utils import Cache, StaticCache

from models.nextstep_model import NextStep
from vae.nextstep_ae import AutoencoderKL
from utils.image_utils import to_pil
from utils.model_utils import layer_norm
from utils.compile_utils import compile_manager
from utils.misc import set_seed

DEFAULT_IMAGE_AREA_TOKEN = "<|image_area|>"

def hw2str(h: int, w: int) -> str:
    return f"{h}*{w}"


class NextStepPipeline:
    def __init__(
        self,
        model_name_or_path: str | None = None,
        vae_name_or_path: str | None = None,
        tokenizer: AutoTokenizer | None = None,
        model: nn.Module | None = None,
        vae: AutoencoderKL | None = None,
    ):
        if model is not None:
            self.tokenizer = copy.deepcopy(tokenizer)
            self.tokenizer.padding_side = "left"
            self.model = model
        elif model_name_or_path is not None:
            self.tokenizer = AutoTokenizer.from_pretrained(
                model_name_or_path,
                local_files_only=True,
                padding_side="left",
                use_fast=True,
            )
            self.model: NextStep = NextStep.from_pretrained(model_name_or_path, local_files_only=True)
        else:
            raise ValueError("model or model_name_or_path is required")

        self.tokenizer.add_eos_token = False
        if vae_name_or_path is None:
            vae_name_or_path = getattr(self.model.config, "vae_name_or_path", None)
        if vae is not None:
            self.vae = vae
        elif vae_name_or_path is not None:
            self.vae = AutoencoderKL.from_pretrained(vae_name_or_path)
        else:
            raise ValueError("vae or vae_name_or_path is required")

        self.model.eval()
        self.vae.eval()

        vae_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.down_factor = vae_factor * self.model.config.latent_patch_size
        self.shift_factor = getattr(self.vae.config, "shift_factor", 0.0)
        self.scaling_factor = getattr(self.vae.config, "scaling_factor", 1.0)

        self.boi = self.model.config.boi
        self.eoi = self.model.config.eoi

        self.image_placeholder_id = self.model.config.image_placeholder_id
        self.pil2tensor = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
            ]
        )
        self.__device = self.model.device
        self.__dtype = self.model.dtype
        self.to(self.device, self.dtype)

    @property
    def device(self):
        return self.__device

    @property
    def device_type(self):
        if isinstance(self.__device, str):
            return self.__device
        return self.__device.type

    @property
    def dtype(self):
        return self.__dtype

    def to(self, device: str | None = None, dtype: torch.dtype | None = None):
        if device is not None:
            self.__device = device
        if dtype is not None:
            self.__dtype = dtype
        self.model.to(self.__device, dtype=self.__dtype)
        self.vae.to(self.__device, dtype=self.__dtype)
        return self

    def _image_str(self, hw: tuple[int, int] = (256, 256)):
        latent_hw = (hw[0] // self.down_factor, hw[1] // self.down_factor)
        image_ids = [self.boi] + [self.image_placeholder_id] * (latent_hw[0] * latent_hw[1]) + [self.eoi]
        image_str = DEFAULT_IMAGE_AREA_TOKEN + hw2str(*latent_hw) + self.tokenizer.decode(image_ids)
        return image_str

    def _check_input(
        self, captions: str | list[str], images: Image.Image | list[Image.Image] | None
    ) -> tuple[list[str], list[Image.Image] | None]:
        if not isinstance(captions, list):
            captions = [captions]
        if images is not None:
            if not isinstance(images, list):
                images = [images]
            # Validate image count matches <image> tokens in captions
            image_token_count = 0
            for caption in captions:
                num_image_token = len(re.findall(r"<image>", caption))
                assert num_image_token == 1, f"Caption `{caption}` has {num_image_token} image tokens, but only 1 is allowed."
                image_token_count += num_image_token
            if image_token_count != len(images):
                raise ValueError(
                    f"Number of images ({len(images)}) does not match number of image tokens ({image_token_count}).\n"
                    f"Captions: {captions}"
                )
            hws = [(image.size[1], image.size[0]) for image in images]
            # Replace <image> tokens sequentially with corresponding image_str based on hw
            processed_captions = []
            image_idx = 0
            for caption in captions:
                # Process each caption
                processed_caption = caption
                num_image_tokens = processed_caption.count("<image>")
                # Replace each <image> token in order
                for _ in range(num_image_tokens):
                    processed_caption = processed_caption.replace("<image>", self._image_str(hws[image_idx]), 1)
                    image_idx += 1
                processed_captions.append(processed_caption)
            captions = processed_captions
        return captions, images

    def _build_captions(
        self,
        captions: str | list[str],
        images: list[Image.Image] | None = None,
        num_images_per_caption: int = 1,
        positive_prompt: str | None = None,
        negative_prompt: str | None = None,
        cfg: float = 1.0,
        cfg_img: float = 1.0,
    ):
        # 1. repeat captions and images
        if not isinstance(captions, list):
            captions = [captions]

        captions = [caption for caption in captions for _ in range(num_images_per_caption)]
        if images is not None:
            images = [image for image in images for _ in range(num_images_per_caption)]

        # 2. add positive prompt
        if positive_prompt is not None and positive_prompt != "":
            captions = [f"{caption} {positive_prompt}" for caption in captions]

        # 3. add negative prompt
        if negative_prompt is None:
            negative_prompt = ""

        num_samples = len(captions)
        if cfg != 1.0 and cfg_img != 1.0:  # use both image and text CFG
            w, h = images[0].size
            captions = (
                captions + [self._image_str((h, w)) + negative_prompt] * num_samples
            )
            images = images + images
            captions = captions + [negative_prompt] * num_samples
        elif cfg != 1.0 and cfg_img == 1.0:  # use text CFG
            captions = captions + [negative_prompt] * num_samples
        elif cfg == 1.0 and cfg_img == 1.0:
            pass

        return captions, images

    def _add_prefix_ids(self, hw: tuple[int, int], input_ids: torch.Tensor, attention_mask: torch.Tensor):
        prefix_str = DEFAULT_IMAGE_AREA_TOKEN + hw2str(hw[0] // self.down_factor, hw[1] // self.down_factor)
        prefix_output = self.tokenizer(
            prefix_str, 
            truncation=False, 
            add_special_tokens=True, 
            return_tensors="pt"
        )
        prefix_input_ids = prefix_output.input_ids.to(input_ids.device, dtype=input_ids.dtype)
        prefix_attention_mask = prefix_output.attention_mask.to(attention_mask.device, dtype=attention_mask.dtype)
        # remove bos token
        if self.tokenizer.bos_token is not None:
            prefix_input_ids = prefix_input_ids[:, 1:]
            prefix_attention_mask = prefix_attention_mask[:, 1:]
        # add boi token
        prefix_input_ids = torch.cat(
            [
                prefix_input_ids,
                prefix_input_ids.new_tensor([self.model.config.boi]).unsqueeze(0),
            ],
            dim=1,
        )
        prefix_attention_mask = torch.cat(
            [
                prefix_attention_mask,
                prefix_attention_mask.new_ones((prefix_attention_mask.shape[0], 1)),
            ],
            dim=1,
        )
        bsz = input_ids.shape[0]
        input_ids = torch.cat([input_ids, prefix_input_ids.expand(bsz, -1)], dim=1)
        attention_mask = torch.cat([attention_mask, prefix_attention_mask.expand(bsz, -1)], dim=1)

        return input_ids, attention_mask

    @torch.no_grad()
    def decoding(
        self,
        c: torch.Tensor,
        attention_mask: torch.Tensor,
        past_key_values: Cache,
        max_new_len: int,
        num_images_per_caption: int,
        use_norm: bool = False,
        cfg: float = 1.0,
        cfg_img: float = 1.0,
        cfg_schedule: Literal["linear", "constant"] = "constant",
        timesteps_shift: float = 1.0,
        num_sampling_steps: int = 20,
        progress: bool = True,
        hw: tuple[int, int] = (256, 256),
        step: int = 0,
    ):
        indices = list(range(max_new_len))
        indices = tqdm(indices, unit="tokens") if progress else indices
        tokens = None
        for step in indices:
            # cfg schedule follow Muse
            if cfg_schedule == "linear":
                tokens_len = 0 if tokens is None else tokens.shape[1]
                cfg_iter = max(cfg / 2, 1 + (cfg - 1) * tokens_len / max_new_len)
                cfg_img_iter = max(cfg_img / 2, 1 + (cfg_img - 1) * tokens_len / max_new_len)
            elif cfg_schedule == "constant":
                cfg_iter = cfg
                cfg_img_iter = cfg_img
            else:
                raise NotImplementedError

            c = self.model.image_out_projector(c)
            token_sampled = self.model.image_head.sample(
                c=c.squeeze(1),
                cfg=cfg_iter,
                cfg_img=cfg_img_iter,
                timesteps_shift=timesteps_shift,
                num_sampling_steps=num_sampling_steps,
                noise_repeat=num_images_per_caption,
            )

            if use_norm:
                token_sampled = layer_norm(token_sampled, normalized_shape=token_sampled.size()[1:])
            if tokens is not None:
                tokens = torch.cat([tokens, token_sampled.unsqueeze(1)], dim=1)
            else:
                tokens = token_sampled.unsqueeze(1)

            cur_inputs_embeds = self.model.image_in_projector(tokens[:, -1:])
            if cfg != 1.0 and cfg_img == 1.0:
                cur_inputs_embeds = torch.cat([cur_inputs_embeds, cur_inputs_embeds], dim=0)
            elif cfg != 1.0 and cfg_img != 1.0:
                cur_inputs_embeds = torch.cat([cur_inputs_embeds, cur_inputs_embeds, cur_inputs_embeds], dim=0)

            attention_mask = torch.cat([attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1)
            outputs = self.model.forward_model(
                inputs_embeds=cur_inputs_embeds,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                use_cache=True,
            )
            past_key_values = outputs.past_key_values
            c = outputs.last_hidden_state[:, -1:]
            if self.model.config.use_gen_pos_embed:
                c = c + self.model.gen_pos_embed_with_ar(hw[0], hw[1])[:, step + 1 : step + 2, :]

        return tokens

    @torch.no_grad()
    def generate_image(
        self,
        captions: str | list[str],
        images: list[Image.Image] | None = None,
        num_images_per_caption: int = 1,
        positive_prompt: str | None = None,
        negative_prompt: str | None = None,
        hw: tuple[int, int] = (256, 256),
        use_norm: bool = False,
        cfg: float = 1.0,
        cfg_img: float = 1.0,
        cfg_schedule: Literal["linear", "constant"] = "constant",
        num_sampling_steps: int = 20,
        timesteps_shift: float = 1.0,
        seed: int = 42,
        progress: bool = True,
    ) -> list[Image.Image]:
        # 0. set seed
        if seed is not None:
            set_seed(seed)

        # 1. check input
        captions, images = self._check_input(captions, images)

        # 2. build captions
        captions, images = self._build_captions(
            captions, images, num_images_per_caption, positive_prompt, negative_prompt, cfg, cfg_img
        )

        # 3. encode images
        # `images` must be processed by `process_images` before calling this function
        latents = None
        if images is not None:
            pixel_values = [self.pil2tensor(image) for image in images]
            pixel_values = torch.stack(pixel_values).to(self.device)
            with compile_manager.compile_disabled():
                posterior = self.vae.encode(pixel_values.to(self.vae.dtype)).latent_dist
            latents = (posterior.sample() - self.shift_factor) * self.scaling_factor
        captions = [self.tokenizer.bos_token + caption if self.tokenizer.bos_token is not None else caption for caption in captions]

        # 4. tokenize caption & add prefix ids
        output = self.tokenizer(
            captions, 
            padding="longest", 
            truncation=False, 
            add_special_tokens=True, 
            return_tensors="pt", 
            padding_side="left"
        )
        input_ids = output.input_ids.to(self.device)
        attention_mask = output.attention_mask.to(self.device)
        input_ids, attention_mask = self._add_prefix_ids(hw, input_ids, attention_mask)

        # 5. LLM prefill
        max_new_len = (hw[0] // self.down_factor) * (hw[1] // self.down_factor)
        max_cache_len = input_ids.shape[1] + max_new_len
        past_key_values = StaticCache(
            config=self.model.config,
            max_batch_size=input_ids.shape[0],
            max_cache_len=max_cache_len,
            device=self.device,
            dtype=self.dtype,
        )
        inputs_embeds = self.model.prepare_inputs_embeds(input_ids, latents)
        with compile_manager.compile_disabled():
            outputs = self.model.forward_model(
                inputs_embeds=inputs_embeds,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                use_cache=True,
            )
        past_key_values = outputs.past_key_values
        c = outputs.last_hidden_state[:, -1:]
        if self.model.config.use_gen_pos_embed:
            c = c + self.model.gen_pos_embed_with_ar(hw[0], hw[1])[:, 0:1, :]

        # 6. decoding
        tokens = self.decoding(
            c=c,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            max_new_len=max_new_len,
            num_images_per_caption=num_images_per_caption,
            use_norm=use_norm,
            cfg=cfg,
            cfg_img=cfg_img,
            cfg_schedule=cfg_schedule,
            timesteps_shift=timesteps_shift,
            num_sampling_steps=num_sampling_steps,
            progress=progress,
            hw=hw,
        )

        # 7. unpatchify
        latents = self.model.unpatchify(tokens)
        latents = (latents / self.scaling_factor) + self.shift_factor

        # 8. decode latents
        with compile_manager.compile_disabled():
            sampled_images = self.vae.decode(latents.to(self.vae.dtype)).sample
        sampled_images = sampled_images.detach().cpu().to(torch.float32)
        pil_images = [to_pil(img) for img in sampled_images]

        return pil_images