torch_compile/flux/test_vae_decoder.py

#!/usr/bin/env python3
"""
Flux VAE decoder (16-ch latent → RGB image) on Neuron.
Checkpoint: black-forest-labs/FLUX.1-dev/vae
"""
import argparse
import logging
import time
from pathlib import Path

import torch
from diffusers import AutoencoderKL
import torch_neuronx  # noqa: F401  guarantees Neuron backend
from PIL import Image

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def main():
    parser = argparse.ArgumentParser(
        description="Flux VAE decoder (latent → image) with torch.compile on Neuron"
    )
    parser.add_argument(
        "--model",
        type=str,
        # default="black-forest-labs/FLUX.1-dev/vae",
        default="/workspace/flux_weight/",
        help="Flux VAE checkpoint on Hugging Face Hub",
    )
    parser.add_argument("--latent-ch", type=int, default=16, help="Latent channels (Flux=16)")
    parser.add_argument("--scale", type=int, default=32, help="Latent spatial size (256 px / 8)")
    parser.add_argument("--output", type=str, default="flux_vae_out.png", help="Output image path")
    args = parser.parse_args()

    torch.set_default_dtype(torch.float32)
    torch.manual_seed(42)

    # Load Flux VAE decoder
    vae = AutoencoderKL.from_pretrained(args.model, subfolder="vae", torch_dtype=torch.float32).eval()

    # Create dummy latent (bfloat16, N(0,1)) - shape: [B, 16, H/8, W/8]
    latent = torch.randn(1, args.latent_ch, args.scale, args.scale, dtype=torch.float32)

    # Pre-run once to freeze shapes before compilation
    with torch.no_grad():
        _ = vae.decode(latent).sample

    # Compile decode function (allow graph breaks for big kernels)
    decode_fn = torch.compile(vae.decode, backend="neuron", fullgraph=True)

    # Warmup
    warmup_start = time.time()
    with torch.no_grad():
        _ = decode_fn(latent)
    warmup_time = time.time() - warmup_start

    # Actual run
    run_start = time.time()
    with torch.no_grad():
        image = decode_fn(latent).sample
    run_time = time.time() - run_start

    logger.info("Warmup: %.2f s, Run: %.4f s", warmup_time, run_time)
    logger.info("VAE output shape: %s", image.shape)  # [1, 3, H, W]

    # Convert to PIL and save
    image = (image / 2 + 0.5).clamp(0, 1)  # scale to [0,1]
    image = image.cpu().float()
    Image.fromarray((image[0].permute(1, 2, 0).numpy() * 255).astype("uint8")).save(args.output)
    logger.info("Saved decoded image to %s", Path(args.output).resolve())


if __name__ == "__main__":
    main()

"""
The compilation process took more than 2 hours.
/usr/local/lib/python3.10/site-packages/torch_mlir/dialects/stablehlo/__init__.py:24: UserWarning: Could not import StableHLO C++ extension: libStablehloUnifiedPythonCAPI.so.22.0git: cannot open shared object file: No such file or directory
  warnings.warn(f"Could not import StableHLO C++ extension: {e}")
INFO:__main__:Warmup: 4010.52 s, Run: 22.5420 s
INFO:__main__:VAE output shape: torch.Size([1, 3, 256, 256])
INFO:__main__:Saved decoded image to /workspace/torch_neuron_samples/torch-neuron-samples/scripts/torch_compile/flux/flux_vae_out.png
"""