library/strategy_sd3.py

import os
import glob
from typing import Any, List, Optional, Tuple, Union
import torch
import numpy as np
from transformers import CLIPTokenizer, T5TokenizerFast, CLIPTextModel, CLIPTextModelWithProjection, T5EncoderModel

from . import train_util
from .strategy_base import LatentsCachingStrategy, TextEncodingStrategy, TokenizeStrategy, TextEncoderOutputsCachingStrategy

from .utils import setup_logging

setup_logging()
import logging

logger = logging.getLogger(__name__)


CLIP_L_TOKENIZER_ID = "openai/clip-vit-large-patch14"
CLIP_G_TOKENIZER_ID = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
T5_XXL_TOKENIZER_ID = "google/t5-v1_1-xxl"


class Sd3TokenizeStrategy(TokenizeStrategy):
    def __init__(self, t5xxl_max_length: int = 256, tokenizer_cache_dir: Optional[str] = None) -> None:
        self.t5xxl_max_length = t5xxl_max_length
        self.clip_l = self._load_tokenizer(CLIPTokenizer, CLIP_L_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
        self.clip_g = self._load_tokenizer(CLIPTokenizer, CLIP_G_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
        self.t5xxl = self._load_tokenizer(T5TokenizerFast, T5_XXL_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
        self.clip_g.pad_token_id = 0  # use 0 as pad token for clip_g

    def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]:
        text = [text] if isinstance(text, str) else text

        l_tokens = self.clip_l(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
        g_tokens = self.clip_g(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
        t5_tokens = self.t5xxl(text, max_length=self.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt")

        l_attn_mask = l_tokens["attention_mask"]
        g_attn_mask = g_tokens["attention_mask"]
        t5_attn_mask = t5_tokens["attention_mask"]
        l_tokens = l_tokens["input_ids"]
        g_tokens = g_tokens["input_ids"]
        t5_tokens = t5_tokens["input_ids"]

        return [l_tokens, g_tokens, t5_tokens, l_attn_mask, g_attn_mask, t5_attn_mask]


class Sd3TextEncodingStrategy(TextEncodingStrategy):
    def __init__(
        self,
        apply_lg_attn_mask: Optional[bool] = None,
        apply_t5_attn_mask: Optional[bool] = None,
        l_dropout_rate: float = 0.0,
        g_dropout_rate: float = 0.0,
        t5_dropout_rate: float = 0.0,
    ) -> None:
        """
        Args:
            apply_t5_attn_mask: Default value for apply_t5_attn_mask.
        """
        self.apply_lg_attn_mask = apply_lg_attn_mask
        self.apply_t5_attn_mask = apply_t5_attn_mask
        self.l_dropout_rate = l_dropout_rate
        self.g_dropout_rate = g_dropout_rate
        self.t5_dropout_rate = t5_dropout_rate

    def encode_tokens(
        self,
        tokenize_strategy: TokenizeStrategy,
        models: List[Any],
        tokens: List[torch.Tensor],
        apply_lg_attn_mask: Optional[bool] = False,
        apply_t5_attn_mask: Optional[bool] = False,
        enable_dropout: bool = True,
    ) -> List[torch.Tensor]:
        """
        returned embeddings are not masked
        """
        clip_l, clip_g, t5xxl = models
        clip_l: Optional[CLIPTextModel]
        clip_g: Optional[CLIPTextModelWithProjection]
        t5xxl: Optional[T5EncoderModel]

        if apply_lg_attn_mask is None:
            apply_lg_attn_mask = self.apply_lg_attn_mask
        if apply_t5_attn_mask is None:
            apply_t5_attn_mask = self.apply_t5_attn_mask

        l_tokens, g_tokens, t5_tokens, l_attn_mask, g_attn_mask, t5_attn_mask = tokens

        # dropout: if enable_dropout is False, dropout is not applied. dropout means zeroing out embeddings

        if l_tokens is None or clip_l is None:
            assert g_tokens is None, "g_tokens must be None if l_tokens is None"
            lg_out = None
            lg_pooled = None
            l_attn_mask = None
            g_attn_mask = None
        else:
            assert g_tokens is not None, "g_tokens must not be None if l_tokens is not None"

            # drop some members of the batch: we do not call clip_l and clip_g for dropped members
            batch_size, l_seq_len = l_tokens.shape
            g_seq_len = g_tokens.shape[1]

            non_drop_l_indices = []
            non_drop_g_indices = []
            for i in range(l_tokens.shape[0]):
                drop_l = enable_dropout and (self.l_dropout_rate > 0.0 and random.random() < self.l_dropout_rate)
                drop_g = enable_dropout and (self.g_dropout_rate > 0.0 and random.random() < self.g_dropout_rate)
                if not drop_l:
                    non_drop_l_indices.append(i)
                if not drop_g:
                    non_drop_g_indices.append(i)

            # filter out dropped members
            if len(non_drop_l_indices) > 0 and len(non_drop_l_indices) < batch_size:
                l_tokens = l_tokens[non_drop_l_indices]
                l_attn_mask = l_attn_mask[non_drop_l_indices]
            if len(non_drop_g_indices) > 0 and len(non_drop_g_indices) < batch_size:
                g_tokens = g_tokens[non_drop_g_indices]
                g_attn_mask = g_attn_mask[non_drop_g_indices]

            # call clip_l for non-dropped members
            if len(non_drop_l_indices) > 0:
                nd_l_attn_mask = l_attn_mask.to(clip_l.device)
                prompt_embeds = clip_l(
                    l_tokens.to(clip_l.device), nd_l_attn_mask if apply_lg_attn_mask else None, output_hidden_states=True
                )
                nd_l_pooled = prompt_embeds[0]
                nd_l_out = prompt_embeds.hidden_states[-2]
            if len(non_drop_g_indices) > 0:
                nd_g_attn_mask = g_attn_mask.to(clip_g.device)
                prompt_embeds = clip_g(
                    g_tokens.to(clip_g.device), nd_g_attn_mask if apply_lg_attn_mask else None, output_hidden_states=True
                )
                nd_g_pooled = prompt_embeds[0]
                nd_g_out = prompt_embeds.hidden_states[-2]

            # fill in the dropped members
            if len(non_drop_l_indices) == batch_size:
                l_pooled = nd_l_pooled
                l_out = nd_l_out
            else:
                # model output is always float32 because of the models are wrapped with Accelerator
                l_pooled = torch.zeros((batch_size, 768), device=clip_l.device, dtype=torch.float32)
                l_out = torch.zeros((batch_size, l_seq_len, 768), device=clip_l.device, dtype=torch.float32)
                l_attn_mask = torch.zeros((batch_size, l_seq_len), device=clip_l.device, dtype=l_attn_mask.dtype)
                if len(non_drop_l_indices) > 0:
                    l_pooled[non_drop_l_indices] = nd_l_pooled
                    l_out[non_drop_l_indices] = nd_l_out
                    l_attn_mask[non_drop_l_indices] = nd_l_attn_mask

            if len(non_drop_g_indices) == batch_size:
                g_pooled = nd_g_pooled
                g_out = nd_g_out
            else:
                g_pooled = torch.zeros((batch_size, 1280), device=clip_g.device, dtype=torch.float32)
                g_out = torch.zeros((batch_size, g_seq_len, 1280), device=clip_g.device, dtype=torch.float32)
                g_attn_mask = torch.zeros((batch_size, g_seq_len), device=clip_g.device, dtype=g_attn_mask.dtype)
                if len(non_drop_g_indices) > 0:
                    g_pooled[non_drop_g_indices] = nd_g_pooled
                    g_out[non_drop_g_indices] = nd_g_out
                    g_attn_mask[non_drop_g_indices] = nd_g_attn_mask

            lg_pooled = torch.cat((l_pooled, g_pooled), dim=-1)
            lg_out = torch.cat([l_out, g_out], dim=-1)

        if t5xxl is None or t5_tokens is None:
            t5_out = None
            t5_attn_mask = None
        else:
            # drop some members of the batch: we do not call t5xxl for dropped members
            batch_size, t5_seq_len = t5_tokens.shape
            non_drop_t5_indices = []
            for i in range(t5_tokens.shape[0]):
                drop_t5 = enable_dropout and (self.t5_dropout_rate > 0.0 and random.random() < self.t5_dropout_rate)
                if not drop_t5:
                    non_drop_t5_indices.append(i)

            # filter out dropped members
            if len(non_drop_t5_indices) > 0 and len(non_drop_t5_indices) < batch_size:
                t5_tokens = t5_tokens[non_drop_t5_indices]
                t5_attn_mask = t5_attn_mask[non_drop_t5_indices]

            # call t5xxl for non-dropped members
            if len(non_drop_t5_indices) > 0:
                nd_t5_attn_mask = t5_attn_mask.to(t5xxl.device)
                nd_t5_out, _ = t5xxl(
                    t5_tokens.to(t5xxl.device),
                    nd_t5_attn_mask if apply_t5_attn_mask else None,
                    return_dict=False,
                    output_hidden_states=True,
                )

            # fill in the dropped members
            if len(non_drop_t5_indices) == batch_size:
                t5_out = nd_t5_out
            else:
                t5_out = torch.zeros((batch_size, t5_seq_len, 4096), device=t5xxl.device, dtype=torch.float32)
                t5_attn_mask = torch.zeros((batch_size, t5_seq_len), device=t5xxl.device, dtype=t5_attn_mask.dtype)
                if len(non_drop_t5_indices) > 0:
                    t5_out[non_drop_t5_indices] = nd_t5_out
                    t5_attn_mask[non_drop_t5_indices] = nd_t5_attn_mask

        # masks are used for attention masking in transformer
        return [lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask]

    def drop_cached_text_encoder_outputs(
        self,
        lg_out: torch.Tensor,
        t5_out: torch.Tensor,
        lg_pooled: torch.Tensor,
        l_attn_mask: torch.Tensor,
        g_attn_mask: torch.Tensor,
        t5_attn_mask: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        # dropout: if enable_dropout is True, dropout is not applied. dropout means zeroing out embeddings
        if lg_out is not None:
            for i in range(lg_out.shape[0]):
                drop_l = self.l_dropout_rate > 0.0 and random.random() < self.l_dropout_rate
                if drop_l:
                    lg_out[i, :, :768] = torch.zeros_like(lg_out[i, :, :768])
                    lg_pooled[i, :768] = torch.zeros_like(lg_pooled[i, :768])
                    if l_attn_mask is not None:
                        l_attn_mask[i] = torch.zeros_like(l_attn_mask[i])
                drop_g = self.g_dropout_rate > 0.0 and random.random() < self.g_dropout_rate
                if drop_g:
                    lg_out[i, :, 768:] = torch.zeros_like(lg_out[i, :, 768:])
                    lg_pooled[i, 768:] = torch.zeros_like(lg_pooled[i, 768:])
                    if g_attn_mask is not None:
                        g_attn_mask[i] = torch.zeros_like(g_attn_mask[i])

        if t5_out is not None:
            for i in range(t5_out.shape[0]):
                drop_t5 = self.t5_dropout_rate > 0.0 and random.random() < self.t5_dropout_rate
                if drop_t5:
                    t5_out[i] = torch.zeros_like(t5_out[i])
                    if t5_attn_mask is not None:
                        t5_attn_mask[i] = torch.zeros_like(t5_attn_mask[i])

        return [lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask]

    def concat_encodings(
        self, lg_out: torch.Tensor, t5_out: Optional[torch.Tensor], lg_pooled: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
        if t5_out is None:
            t5_out = torch.zeros((lg_out.shape[0], 77, 4096), device=lg_out.device, dtype=lg_out.dtype)
        return torch.cat([lg_out, t5_out], dim=-2), lg_pooled


class Sd3TextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy):
    SD3_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_sd3_te.npz"

    def __init__(
        self,
        cache_to_disk: bool,
        batch_size: int,
        skip_disk_cache_validity_check: bool,
        is_partial: bool = False,
        apply_lg_attn_mask: bool = False,
        apply_t5_attn_mask: bool = False,
    ) -> None:
        super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial)
        self.apply_lg_attn_mask = apply_lg_attn_mask
        self.apply_t5_attn_mask = apply_t5_attn_mask

    def get_outputs_npz_path(self, image_abs_path: str) -> str:
        return os.path.splitext(image_abs_path)[0] + Sd3TextEncoderOutputsCachingStrategy.SD3_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX

    def is_disk_cached_outputs_expected(self, npz_path: str):
        if not self.cache_to_disk:
            return False
        if not os.path.exists(npz_path):
            return False
        if self.skip_disk_cache_validity_check:
            return True

        try:
            npz = np.load(npz_path)
            if "lg_out" not in npz:
                return False
            if "lg_pooled" not in npz:
                return False
            if "clip_l_attn_mask" not in npz or "clip_g_attn_mask" not in npz:  # necessary even if not used
                return False
            if "apply_lg_attn_mask" not in npz:
                return False
            if "t5_out" not in npz:
                return False
            if "t5_attn_mask" not in npz:
                return False
            npz_apply_lg_attn_mask = npz["apply_lg_attn_mask"]
            if npz_apply_lg_attn_mask != self.apply_lg_attn_mask:
                return False
            if "apply_t5_attn_mask" not in npz:
                return False
            npz_apply_t5_attn_mask = npz["apply_t5_attn_mask"]
            if npz_apply_t5_attn_mask != self.apply_t5_attn_mask:
                return False
        except Exception as e:
            logger.error(f"Error loading file: {npz_path}")
            raise e

        return True

    def load_outputs_npz(self, npz_path: str) -> List[np.ndarray]:
        data = np.load(npz_path)
        lg_out = data["lg_out"]
        lg_pooled = data["lg_pooled"]
        t5_out = data["t5_out"]

        l_attn_mask = data["clip_l_attn_mask"]
        g_attn_mask = data["clip_g_attn_mask"]
        t5_attn_mask = data["t5_attn_mask"]

        # apply_t5_attn_mask and apply_lg_attn_mask are same as self.apply_t5_attn_mask and self.apply_lg_attn_mask
        return [lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask]

    def cache_batch_outputs(
        self, tokenize_strategy: TokenizeStrategy, models: List[Any], text_encoding_strategy: TextEncodingStrategy, infos: List
    ):
        sd3_text_encoding_strategy: Sd3TextEncodingStrategy = text_encoding_strategy
        captions = [info.caption for info in infos]

        tokens_and_masks = tokenize_strategy.tokenize(captions)
        with torch.no_grad():
            # always disable dropout during caching
            lg_out, t5_out, lg_pooled, l_attn_mask, g_attn_mask, t5_attn_mask = sd3_text_encoding_strategy.encode_tokens(
                tokenize_strategy,
                models,
                tokens_and_masks,
                apply_lg_attn_mask=self.apply_lg_attn_mask,
                apply_t5_attn_mask=self.apply_t5_attn_mask,
                enable_dropout=False,
            )

        if lg_out.dtype == torch.bfloat16:
            lg_out = lg_out.float()
        if lg_pooled.dtype == torch.bfloat16:
            lg_pooled = lg_pooled.float()
        if t5_out.dtype == torch.bfloat16:
            t5_out = t5_out.float()

        lg_out = lg_out.cpu().numpy()
        lg_pooled = lg_pooled.cpu().numpy()
        t5_out = t5_out.cpu().numpy()

        l_attn_mask = tokens_and_masks[3].cpu().numpy()
        g_attn_mask = tokens_and_masks[4].cpu().numpy()
        t5_attn_mask = tokens_and_masks[5].cpu().numpy()

        for i, info in enumerate(infos):
            lg_out_i = lg_out[i]
            t5_out_i = t5_out[i]
            lg_pooled_i = lg_pooled[i]
            l_attn_mask_i = l_attn_mask[i]
            g_attn_mask_i = g_attn_mask[i]
            t5_attn_mask_i = t5_attn_mask[i]
            apply_lg_attn_mask = self.apply_lg_attn_mask
            apply_t5_attn_mask = self.apply_t5_attn_mask

            if self.cache_to_disk:
                np.savez(
                    info.text_encoder_outputs_npz,
                    lg_out=lg_out_i,
                    lg_pooled=lg_pooled_i,
                    t5_out=t5_out_i,
                    clip_l_attn_mask=l_attn_mask_i,
                    clip_g_attn_mask=g_attn_mask_i,
                    t5_attn_mask=t5_attn_mask_i,
                    apply_lg_attn_mask=apply_lg_attn_mask,
                    apply_t5_attn_mask=apply_t5_attn_mask,
                )
            else:
                # it's fine that attn mask is not None. it's overwritten before calling the model if necessary
                info.text_encoder_outputs = (lg_out_i, t5_out_i, lg_pooled_i, l_attn_mask_i, g_attn_mask_i, t5_attn_mask_i)


class Sd3LatentsCachingStrategy(LatentsCachingStrategy):
    SD3_LATENTS_NPZ_SUFFIX = "_sd3.npz"

    def __init__(self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool) -> None:
        super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check)

    @property
    def cache_suffix(self) -> str:
        return Sd3LatentsCachingStrategy.SD3_LATENTS_NPZ_SUFFIX

    def get_latents_npz_path(self, absolute_path: str, image_size: Tuple[int, int]) -> str:
        return (
            os.path.splitext(absolute_path)[0]
            + f"_{image_size[0]:04d}x{image_size[1]:04d}"
            + Sd3LatentsCachingStrategy.SD3_LATENTS_NPZ_SUFFIX
        )

    def is_disk_cached_latents_expected(self, bucket_reso: Tuple[int, int], npz_path: str, flip_aug: bool, alpha_mask: bool):
        return self._default_is_disk_cached_latents_expected(8, bucket_reso, npz_path, flip_aug, alpha_mask, multi_resolution=True)

    def load_latents_from_disk(
        self, npz_path: str, bucket_reso: Tuple[int, int]
    ) -> Tuple[Optional[np.ndarray], Optional[List[int]], Optional[List[int]], Optional[np.ndarray], Optional[np.ndarray]]:
        return self._default_load_latents_from_disk(8, npz_path, bucket_reso)  # support multi-resolution

    # TODO remove circular dependency for ImageInfo
    def cache_batch_latents(self, vae, image_infos: List, flip_aug: bool, alpha_mask: bool, random_crop: bool):
        encode_by_vae = lambda img_tensor: vae.encode(img_tensor).to("cpu")
        vae_device = vae.device
        vae_dtype = vae.dtype

        self._default_cache_batch_latents(
            encode_by_vae, vae_device, vae_dtype, image_infos, flip_aug, alpha_mask, random_crop, multi_resolution=True
        )

        if not train_util.HIGH_VRAM:
            train_util.clean_memory_on_device(vae.device)


if __name__ == "__main__":
    # test code for Sd3TokenizeStrategy
    # tokenizer = sd3_models.SD3Tokenizer()
    strategy = Sd3TokenizeStrategy(256)
    text = "hello world"

    l_tokens, g_tokens, t5_tokens = strategy.tokenize(text)
    # print(l_tokens.shape)
    print(l_tokens)
    print(g_tokens)
    print(t5_tokens)

    texts = ["hello world", "the quick brown fox jumps over the lazy dog"]
    l_tokens_2 = strategy.clip_l(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
    g_tokens_2 = strategy.clip_g(texts, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
    t5_tokens_2 = strategy.t5xxl(
        texts, max_length=strategy.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt"
    )
    print(l_tokens_2)
    print(g_tokens_2)
    print(t5_tokens_2)

    # compare
    print(torch.allclose(l_tokens, l_tokens_2["input_ids"][0]))
    print(torch.allclose(g_tokens, g_tokens_2["input_ids"][0]))
    print(torch.allclose(t5_tokens, t5_tokens_2["input_ids"][0]))

    text = ",".join(["hello world! this is long text"] * 50)
    l_tokens, g_tokens, t5_tokens = strategy.tokenize(text)
    print(l_tokens)
    print(g_tokens)
    print(t5_tokens)

    print(f"model max length l: {strategy.clip_l.model_max_length}")
    print(f"model max length g: {strategy.clip_g.model_max_length}")
    print(f"model max length t5: {strategy.t5xxl.model_max_length}")