model.py

# This code is adapted from https://github.com/THUDM/CogView2/blob/4e55cce981eb94b9c8c1f19ba9f632fd3ee42ba8/cogview2_text2image.py

from __future__ import annotations

import argparse
import functools
import logging
import os
import pathlib
import subprocess
import sys
import time
import zipfile
from typing import Any

if os.getenv('SYSTEM') == 'spaces':
    subprocess.run('pip install icetk==0.0.3'.split())
    subprocess.run('pip install SwissArmyTransformer==0.2.4'.split())
    subprocess.run(
        'pip install git+https://github.com/Sleepychord/Image-Local-Attention@43fee31'
        .split())
    subprocess.run('git clone https://github.com/NVIDIA/apex'.split())
    subprocess.run('git checkout 1403c21'.split(), cwd='apex')
    subprocess.run(
        'pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./'
        .split(),
        cwd='apex')
    subprocess.run('rm -rf apex'.split())
    with open('patch') as f:
        subprocess.run('patch -p1'.split(), cwd='CogView2', stdin=f)

    from huggingface_hub import hf_hub_download

    def download_and_extract_icetk_models() -> None:
        icetk_model_dir = pathlib.Path('/home/user/.icetk_models')
        icetk_model_dir.mkdir()
        path = hf_hub_download('THUDM/icetk',
                               'models.zip',
                               use_auth_token=os.getenv('HF_TOKEN'))
        with zipfile.ZipFile(path) as f:
            f.extractall(path=icetk_model_dir.as_posix())

    def download_and_extract_cogview2_models(name: str) -> None:
        path = hf_hub_download('THUDM/CogView2',
                               name,
                               use_auth_token=os.getenv('HF_TOKEN'))
        with zipfile.ZipFile(path) as f:
            f.extractall()
        os.remove(path)

    download_and_extract_icetk_models()
    names = [
        'coglm.zip',
        'cogview2-dsr.zip',
        #'cogview2-itersr.zip',
    ]
    for name in names:
        download_and_extract_cogview2_models(name)

    os.environ['SAT_HOME'] = '/home/user/app/sharefs/cogview-new'

import gradio as gr
import numpy as np
import torch
from icetk import icetk as tokenizer
from SwissArmyTransformer import get_args
from SwissArmyTransformer.arguments import set_random_seed
from SwissArmyTransformer.generation.autoregressive_sampling import \
    filling_sequence
from SwissArmyTransformer.model import CachedAutoregressiveModel

app_dir = pathlib.Path(__file__).parent
submodule_dir = app_dir / 'CogView2'
sys.path.insert(0, submodule_dir.as_posix())

from coglm_strategy import CoglmStrategy
from sr_pipeline import SRGroup

formatter = logging.Formatter(
    '[%(asctime)s] %(name)s %(levelname)s: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S')
stream_handler = logging.StreamHandler(stream=sys.stdout)
stream_handler.setLevel(logging.DEBUG)
stream_handler.setFormatter(formatter)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.propagate = False
logger.addHandler(stream_handler)

tokenizer.add_special_tokens(
    ['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])


def get_masks_and_position_ids_coglm(
        seq: torch.Tensor, context_length: int
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    tokens = seq.unsqueeze(0)

    attention_mask = torch.ones((1, len(seq), len(seq)), device=tokens.device)
    attention_mask.tril_()
    attention_mask[..., :context_length] = 1
    attention_mask.unsqueeze_(1)

    position_ids = torch.zeros(len(seq),
                               device=tokens.device,
                               dtype=torch.long)
    torch.arange(0, context_length, out=position_ids[:context_length])
    torch.arange(512,
                 512 + len(seq) - context_length,
                 out=position_ids[context_length:])

    position_ids = position_ids.unsqueeze(0)
    return tokens, attention_mask, position_ids


class InferenceModel(CachedAutoregressiveModel):
    def final_forward(self, logits, **kwargs):
        logits_parallel = logits
        logits_parallel = torch.nn.functional.linear(
            logits_parallel.float(),
            self.transformer.word_embeddings.weight[:20000].float())
        return logits_parallel


def get_recipe(name: str) -> dict[str, Any]:
    r = {
        'attn_plus': 1.4,
        'temp_all_gen': 1.15,
        'topk_gen': 16,
        'temp_cluster_gen': 1.,
        'temp_all_dsr': 1.5,
        'topk_dsr': 100,
        'temp_cluster_dsr': 0.89,
        'temp_all_itersr': 1.3,
        'topk_itersr': 16,
        'query_template': '{}<start_of_image>',
    }
    if name == 'none':
        pass
    elif name == 'mainbody':
        r['query_template'] = '{} 高清摄影 隔绝<start_of_image>'

    elif name == 'photo':
        r['query_template'] = '{} 高清摄影<start_of_image>'

    elif name == 'flat':
        r['query_template'] = '{} 平面风格<start_of_image>'
        # r['attn_plus'] = 1.8
        # r['temp_cluster_gen'] = 0.75
        r['temp_all_gen'] = 1.1
        r['topk_dsr'] = 5
        r['temp_cluster_dsr'] = 0.4

        r['temp_all_itersr'] = 1
        r['topk_itersr'] = 5
    elif name == 'comics':
        r['query_template'] = '{} 漫画 隔绝<start_of_image>'
        r['topk_dsr'] = 5
        r['temp_cluster_dsr'] = 0.4
        r['temp_all_gen'] = 1.1
        r['temp_all_itersr'] = 1
        r['topk_itersr'] = 5
    elif name == 'oil':
        r['query_template'] = '{} 油画风格<start_of_image>'
        pass
    elif name == 'sketch':
        r['query_template'] = '{} 素描风格<start_of_image>'
        r['temp_all_gen'] = 1.1
    elif name == 'isometric':
        r['query_template'] = '{} 等距矢量图<start_of_image>'
        r['temp_all_gen'] = 1.1
    elif name == 'chinese':
        r['query_template'] = '{} 水墨国画<start_of_image>'
        r['temp_all_gen'] = 1.12
    elif name == 'watercolor':
        r['query_template'] = '{} 水彩画风格<start_of_image>'
    return r


def get_default_args() -> argparse.Namespace:
    arg_list = ['--mode', 'inference', '--fp16']
    args = get_args(arg_list)
    known = argparse.Namespace(img_size=160,
                               only_first_stage=False,
                               inverse_prompt=False,
                               style='mainbody')
    args = argparse.Namespace(**vars(args), **vars(known),
                              **get_recipe(known.style))
    return args


class Model:
    def __init__(self,
                 max_inference_batch_size: int,
                 only_first_stage: bool = False):
        self.args = get_default_args()
        self.args.only_first_stage = only_first_stage
        self.args.max_inference_batch_size = max_inference_batch_size

        self.model, self.args = self.load_model()
        self.strategy = self.load_strategy()
        self.srg = self.load_srg()

        self.query_template = self.args.query_template
        self.style = self.args.style
        self.device = torch.device(self.args.device)
        self.fp16 = self.args.fp16
        self.max_batch_size = self.args.max_inference_batch_size
        self.only_first_stage = self.args.only_first_stage

    def load_model(self) -> tuple[InferenceModel, argparse.Namespace]:
        logger.info('--- load_model ---')
        start = time.perf_counter()

        model, args = InferenceModel.from_pretrained(self.args, 'coglm')

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('--- done ---')
        return model, args

    def load_strategy(self) -> CoglmStrategy:
        logger.info('--- load_strategy ---')
        start = time.perf_counter()

        invalid_slices = [slice(tokenizer.num_image_tokens, None)]
        strategy = CoglmStrategy(invalid_slices,
                                 temperature=self.args.temp_all_gen,
                                 top_k=self.args.topk_gen,
                                 top_k_cluster=self.args.temp_cluster_gen)

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('--- done ---')
        return strategy

    def load_srg(self) -> SRGroup:
        logger.info('--- load_srg ---')
        start = time.perf_counter()

        srg = None if self.args.only_first_stage else SRGroup(self.args)

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('--- done ---')
        return srg

    def update_style(self, style: str) -> None:
        if style == self.style:
            return
        logger.info('--- update_style ---')
        start = time.perf_counter()

        self.style = style
        self.args = argparse.Namespace(**(vars(self.args) | get_recipe(style)))
        self.query_template = self.args.query_template
        logger.info(f'{self.query_template=}')

        self.strategy.temperature = self.args.temp_all_gen

        if self.srg is not None:
            self.srg.dsr.strategy.temperature = self.args.temp_all_dsr
            self.srg.dsr.strategy.topk = self.args.topk_dsr
            self.srg.dsr.strategy.temperature2 = self.args.temp_cluster_dsr

            self.srg.itersr.strategy.temperature = self.args.temp_all_itersr
            self.srg.itersr.strategy.topk = self.args.topk_itersr

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('--- done ---')

    def run(self, text: str, style: str, seed: int, only_first_stage: bool,
            num: int) -> list[np.ndarray] | None:
        logger.info('==================== run ====================')
        start = time.perf_counter()

        self.update_style(style)
        set_random_seed(seed)
        seq, txt_len = self.preprocess_text(text)
        if seq is None:
            return None
        self.only_first_stage = only_first_stage
        tokens = self.generate_tokens(seq, txt_len, num)
        res = self.generate_images(seq, txt_len, tokens)

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('==================== done ====================')
        return res

    @torch.inference_mode()
    def preprocess_text(
            self, text: str) -> tuple[torch.Tensor, int] | tuple[None, None]:
        logger.info('--- preprocess_text ---')
        start = time.perf_counter()

        text = self.query_template.format(text)
        logger.info(f'{text=}')
        seq = tokenizer.encode(text)
        logger.info(f'{len(seq)=}')
        if len(seq) > 110:
            logger.info('The input text is too long.')
            return None, None
        txt_len = len(seq) - 1
        seq = torch.tensor(seq + [-1] * 400, device=self.device)

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('--- done ---')
        return seq, txt_len

    @torch.inference_mode()
    def generate_tokens(self,
                        seq: torch.Tensor,
                        txt_len: int,
                        num: int = 8) -> torch.Tensor:
        logger.info('--- generate_tokens ---')
        start = time.perf_counter()

        # calibrate text length
        log_attention_weights = torch.zeros(
            len(seq),
            len(seq),
            device=self.device,
            dtype=torch.half if self.fp16 else torch.float32)
        log_attention_weights[:, :txt_len] = self.args.attn_plus
        get_func = functools.partial(get_masks_and_position_ids_coglm,
                                     context_length=txt_len)

        output_list = []
        remaining = num
        for _ in range((num + self.max_batch_size - 1) // self.max_batch_size):
            self.strategy.start_pos = txt_len + 1
            coarse_samples = filling_sequence(
                self.model,
                seq.clone(),
                batch_size=min(remaining, self.max_batch_size),
                strategy=self.strategy,
                log_attention_weights=log_attention_weights,
                get_masks_and_position_ids=get_func)[0]
            output_list.append(coarse_samples)
            remaining -= self.max_batch_size
        output_tokens = torch.cat(output_list, dim=0)
        logger.info(f'{output_tokens.shape=}')

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('--- done ---')
        return output_tokens

    @staticmethod
    def postprocess(tensor: torch.Tensor) -> np.ndarray:
        return tensor.cpu().mul(255).add_(0.5).clamp_(0, 255).permute(
            1, 2, 0).to(torch.uint8).numpy()

    @torch.inference_mode()
    def generate_images(self, seq: torch.Tensor, txt_len: int,
                        tokens: torch.Tensor) -> list[np.ndarray]:
        logger.info('--- generate_images ---')
        start = time.perf_counter()

        logger.info(f'{self.only_first_stage=}')
        res = []
        if self.only_first_stage:
            for i in range(len(tokens)):
                seq = tokens[i]
                decoded_img = tokenizer.decode(image_ids=seq[-400:])
                decoded_img = torch.nn.functional.interpolate(decoded_img,
                                                              size=(480, 480))
                decoded_img = self.postprocess(decoded_img[0])
                res.append(decoded_img)  # only the last image (target)
        else:  # sr
            iter_tokens = self.srg.sr_base(tokens[:, -400:], seq[:txt_len])
            for seq in iter_tokens:
                decoded_img = tokenizer.decode(image_ids=seq[-3600:])
                decoded_img = torch.nn.functional.interpolate(decoded_img,
                                                              size=(480, 480))
                decoded_img = self.postprocess(decoded_img[0])
                res.append(decoded_img)  # only the last image (target)

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed}')
        logger.info('--- done ---')
        return res


class AppModel(Model):
    def __init__(self, max_inference_batch_size: int, only_first_stage: bool):
        super().__init__(max_inference_batch_size, only_first_stage)
        self.translator = gr.Interface.load(
            'spaces/chinhon/translation_eng2ch')

    def make_grid(self, images: list[np.ndarray] | None) -> np.ndarray | None:
        if images is None or len(images) == 0:
            return None
        ncols = 1
        while True:
            if ncols**2 >= len(images):
                break
            ncols += 1
        nrows = (len(images) + ncols - 1) // ncols
        h, w = images[0].shape[:2]
        grid = np.zeros((h * nrows, w * ncols, 3), dtype=np.uint8)
        for i in range(nrows):
            for j in range(ncols):
                index = ncols * i + j
                if index >= len(images):
                    break
                grid[h * i:h * (i + 1), w * j:w * (j + 1)] = images[index]
        return grid

    def run_with_translation(
        self, text: str, translate: bool, style: str, seed: int,
        only_first_stage: bool, num: int
    ) -> tuple[str | None, np.ndarray | None, list[np.ndarray] | None]:
        if translate:
            text = translated_text = self.translator(text)
        else:
            translated_text = None
        results = self.run(text, style, seed, only_first_stage, num)
        grid_image = self.make_grid(results)
        return translated_text, grid_image, results