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LlamaGen_sample_ddp.py
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# Modified from:
# ./LlamaGen/autoregressive/sample/sample_c2i_ddp.py
# Include LlamaGen repo as a library
import sys
sys.path.append("./LlamaGen")
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import torch.nn.functional as F
import torch.distributed as dist
from tqdm import tqdm
import os
from PIL import Image
import numpy as np
import math
import argparse
from tokenizer.tokenizer_image.vq_model import VQ_models
from autoregressive.models.gpt import GPT_models
from autoregressive.models.generate import generate
def create_npz_from_sample_folder(sample_dir, num=50_000):
"""
Builds a single .npz file from a folder of .png samples.
"""
samples = []
for i in tqdm(range(num), desc="Building .npz file from samples"):
sample_pil = Image.open(f"{sample_dir}/{i:06d}.png")
sample_np = np.asarray(sample_pil).astype(np.uint8)
samples.append(sample_np)
samples = np.stack(samples)
assert samples.shape == (num, samples.shape[1], samples.shape[2], 3)
npz_path = f"{sample_dir}.npz"
np.savez(npz_path, arr_0=samples)
print(f"Saved .npz file to {npz_path} [shape={samples.shape}].")
return npz_path
def main(args):
# Setup PyTorch:
assert torch.cuda.is_available(), "Sampling with DDP requires at least one GPU. sample.py supports CPU-only usage"
torch.set_grad_enabled(False)
# Setup DDP:
dist.init_process_group("nccl")
rank = dist.get_rank()
device = rank % torch.cuda.device_count()
seed = args.global_seed * dist.get_world_size() + rank
torch.manual_seed(seed)
torch.cuda.set_device(device)
print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
# create and load model
vq_model = VQ_models[args.vq_model](
codebook_size=args.codebook_size,
codebook_embed_dim=args.codebook_embed_dim)
vq_model.to(device)
vq_model.eval()
checkpoint = torch.load(args.vq_ckpt, map_location="cpu")
vq_model.load_state_dict(checkpoint["model"])
del checkpoint
# create and load gpt model
precision = {'none': torch.float32, 'bf16': torch.bfloat16, 'fp16': torch.float16}[args.precision]
latent_size = args.image_size // args.downsample_size
gpt_model = GPT_models[args.gpt_model](
vocab_size=args.codebook_size,
block_size=latent_size ** 2,
num_classes=args.num_classes,
cls_token_num=args.cls_token_num,
model_type=args.gpt_type,
).to(device=device, dtype=precision)
checkpoint = torch.load(args.gpt_ckpt, map_location="cpu")
if args.from_fsdp: # fsdp
model_weight = checkpoint
elif "model" in checkpoint: # ddp
model_weight = checkpoint["model"]
elif "module" in checkpoint: # deepspeed
model_weight = checkpoint["module"]
elif "state_dict" in checkpoint:
model_weight = checkpoint["state_dict"]
else:
model_weight = checkpoint
# raise Exception("please check model weight, maybe add --from-fsdp to run command")
# if 'freqs_cis' in model_weight:
# model_weight.pop('freqs_cis')
gpt_model.load_state_dict(model_weight, strict=False)
gpt_model.eval()
del checkpoint
if args.compile:
print(f"compiling the model...")
gpt_model = torch.compile(
gpt_model,
mode="reduce-overhead",
fullgraph=True
) # requires PyTorch 2.0 (optional)
else:
print(f"no model compile")
# Create folder to save samples:
model_string_name = args.gpt_model.replace("/", "-")
if args.from_fsdp:
assert False
ckpt_string_name = args.gpt_ckpt.split('/')[-2]
else:
# ckpt_string_name = os.path.basename(args.gpt_ckpt).replace(".pth", "").replace(".pt", "")
path_parts = args.gpt_ckpt.replace(".pth", "").replace(".pt", "").split('/')
ckpt_string_name = f"{path_parts[-3]}-{path_parts[-1]}"
folder_name = f"{model_string_name}-{ckpt_string_name}-size-{args.image_size}-size-{args.image_size_eval}-{args.vq_model}-" \
f"topk-{args.top_k}-topp-{args.top_p}-temperature-{args.temperature}-" \
f"cfg-{args.cfg_scale}-seed-{args.global_seed}"
sample_folder_dir = f"{args.sample_dir}/{folder_name}"
if rank == 0:
os.makedirs(sample_folder_dir, exist_ok=True)
print(f"Saving .png samples at {sample_folder_dir}")
dist.barrier()
# Figure out how many samples we need to generate on each GPU and how many iterations we need to run:
n = args.per_proc_batch_size
global_batch_size = n * dist.get_world_size()
# To make things evenly-divisible, we'll sample a bit more than we need and then discard the extra samples:
total_samples = int(math.ceil(args.num_fid_samples / global_batch_size) * global_batch_size)
if rank == 0:
print(f"Total number of images that will be sampled: {total_samples}")
assert total_samples % dist.get_world_size() == 0, "total_samples must be divisible by world_size"
samples_needed_this_gpu = int(total_samples // dist.get_world_size())
assert samples_needed_this_gpu % n == 0, "samples_needed_this_gpu must be divisible by the per-GPU batch size"
iterations = int(samples_needed_this_gpu // n)
pbar = range(iterations)
pbar = tqdm(pbar) if rank == 0 else pbar
total = 0
for _ in pbar:
# Sample inputs:
c_indices = torch.randint(0, args.num_classes, (n,), device=device)
qzshape = [len(c_indices), args.codebook_embed_dim, latent_size, latent_size]
index_sample = generate(
gpt_model, c_indices, latent_size ** 2,
cfg_scale=args.cfg_scale, cfg_interval=args.cfg_interval,
temperature=args.temperature, top_k=args.top_k,
top_p=args.top_p, sample_logits=True,
)
samples = vq_model.decode_code(index_sample, qzshape) # output value is between [-1, 1]
if args.image_size_eval != args.image_size:
samples = F.interpolate(samples, size=(args.image_size_eval, args.image_size_eval), mode='bicubic')
samples = torch.clamp(127.5 * samples + 128.0, 0, 255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()
# Save samples to disk as individual .png files
for i, sample in enumerate(samples):
index = i * dist.get_world_size() + rank + total
Image.fromarray(sample).save(f"{sample_folder_dir}/{index:06d}.png")
total += global_batch_size
# Make sure all processes have finished saving their samples before attempting to convert to .npz
dist.barrier()
if rank == 0:
create_npz_from_sample_folder(sample_folder_dir, args.num_fid_samples)
print("Done.")
dist.barrier()
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--gpt-model", type=str, choices=list(GPT_models.keys()), default="GPT-B")
parser.add_argument("--gpt-ckpt", type=str, default=None)
parser.add_argument("--gpt-type", type=str, choices=['c2i', 't2i'], default="c2i", help="class-conditional or text-conditional")
parser.add_argument("--from-fsdp", action='store_true')
parser.add_argument("--cls-token-num", type=int, default=1, help="max token number of condition input")
parser.add_argument("--precision", type=str, default='bf16', choices=["none", "fp16", "bf16"])
parser.add_argument("--compile", action='store_true', default=True)
parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16")
parser.add_argument("--vq-ckpt", type=str, default=None, help="ckpt path for vq model")
parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization")
parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization")
parser.add_argument("--image-size", type=int, choices=[256, 384, 512], default=384)
parser.add_argument("--image-size-eval", type=int, choices=[256, 384, 512], default=256)
parser.add_argument("--downsample-size", type=int, choices=[8, 16], default=16)
parser.add_argument("--num-classes", type=int, default=1000)
parser.add_argument("--cfg-scale", type=float, default=1.5)
parser.add_argument("--cfg-interval", type=float, default=-1)
parser.add_argument("--sample-dir", type=str, default="samples")
parser.add_argument("--per-proc-batch-size", type=int, default=32)
parser.add_argument("--num-fid-samples", type=int, default=50000)
parser.add_argument("--global-seed", type=int, default=0)
parser.add_argument("--top-k", type=int, default=0,help="top-k value to sample with")
parser.add_argument("--temperature", type=float, default=1.0, help="temperature value to sample with")
parser.add_argument("--top-p", type=float, default=1.0, help="top-p value to sample with")
args = parser.parse_args()
main(args)