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create_embeds.py
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create_embeds.py
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import os
import glob
import time
import torch
import argparse
from tqdm import tqdm
from utils import loader_utils, embed_utils
print_filler = "--------------------------------------------------"
def get_paths(dataset_path, out_path, model_type, text_ext):
print(print_filler)
print(f"Discovering {text_ext} files")
file_list = glob.glob(f"{dataset_path}/**/*{text_ext}", recursive=True)
print(f"Found {len(file_list)} {text_ext} files")
paths = []
texts = []
for text_file in file_list:
embed_path = os.path.splitext(text_file[len(dataset_path)+1:])[0] + "_" + model_type + "_embed.pt"
embed_path = os.path.join(out_path, embed_path)
if not os.path.exists(embed_path):
paths.append(embed_path)
with open(text_file, "r") as file:
text = file.readlines()[0].replace("\n","")
texts.append(text)
print(f"Found {len(paths)} {text_ext} files to encode")
return texts, paths
def get_batches(batch_size, dataset_path, out_path, model_type, text_ext):
texts, paths = get_paths(dataset_path, out_path, model_type, text_ext)
path_batches = []
path_batch = []
text_batches = []
text_batch = []
for i in range(len(paths)):
path_batch.append(paths[i])
text_batch.append(texts[i])
if len(path_batch) >= batch_size:
path_batches.append(path_batch)
text_batches.append(text_batch)
path_batch = []
text_batch = []
if len(path_batch) != 0:
path_batches.append(path_batch)
text_batches.append(text_batch)
return text_batches, path_batches
def write_embeds(embed_encoder, device, model_type, cache_backend, text_batch, path_batch):
embeds = embed_utils.encode_embeds(embed_encoder, device, model_type, text_batch)
getattr(torch, device.type).synchronize(device)
for i in range(len(text_batch)):
cache_backend.save(embeds[i], path_batch[i])
if __name__ == '__main__':
print("\n" + print_filler)
parser = argparse.ArgumentParser(description='Create embed cache')
parser.add_argument('model_path', type=str)
parser.add_argument('dataset_path', type=str)
parser.add_argument('out_path', type=str)
parser.add_argument('--model_type', default="sd3", type=str)
parser.add_argument('--device', default="cuda", type=str)
parser.add_argument('--dtype', default="bfloat16", type=str)
parser.add_argument('--dynamo_backend', default="inductor", type=str)
parser.add_argument('--batch_size', default=1, type=int)
parser.add_argument('--max_save_workers', default=8, type=int)
parser.add_argument('--text_ext', default=".txt", type=str)
parser.add_argument('--disable_tunableop', default=False, action='store_true')
args = parser.parse_args()
if torch.version.hip:
try:
# don't use this for training models, only for inference with latent encoder and embed encoder
# https://github.com/huggingface/diffusers/discussions/7172
from functools import wraps
from flash_attn import flash_attn_func
backup_sdpa = torch.nn.functional.scaled_dot_product_attention
@wraps(torch.nn.functional.scaled_dot_product_attention)
def sdpa_hijack(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
if query.shape[3] <= 128 and attn_mask is None and query.dtype != torch.float32:
return flash_attn_func(q=query.transpose(1, 2), k=key.transpose(1, 2), v=value.transpose(1, 2), dropout_p=dropout_p, causal=is_causal, softmax_scale=scale).transpose(1, 2)
else:
return backup_sdpa(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale)
torch.nn.functional.scaled_dot_product_attention = sdpa_hijack
except Exception as e:
print(f"Failed to enable Flash Atten for ROCm: {e}")
if not args.disable_tunableop:
torch.cuda.tunable.enable(val=True)
dtype = getattr(torch, args.dtype)
device = torch.device(args.device)
print(f"Loading embed encoder models with dtype {dtype} to device {device}")
print(print_filler)
embed_encoder = embed_utils.get_embed_encoder(args.model_type, args.model_path, device, dtype, args.dynamo_backend)
cache_backend = loader_utils.SaveBackend(args.model_type, max_save_workers=args.max_save_workers)
text_batches, path_batches = get_batches(args.batch_size, args.dataset_path, args.out_path, args.model_type, args.text_ext)
epoch_len = len(text_batches)
print(f"Starting to encode {epoch_len} batches with batch size {args.batch_size}")
for _ in tqdm(range(epoch_len)):
write_embeds(embed_encoder, device, args.model_type, cache_backend, text_batches.pop(0), path_batches.pop(0))
while not cache_backend.save_queue.empty():
print(f"Waiting for the remaining writes: {cache_backend.save_queue.qsize()}")
time.sleep(1)
cache_backend.keep_saving = False
cache_backend.save_thread.shutdown(wait=True)
del cache_backend