[feat] add MXFP8 support on H3 (#1796)

2026-09-05 17:08:58 -07:00
parent e19913f6e9
commit 4e52f47d1e
15 changed files with 1350 additions and 49 deletions
@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Generate 5-second FastH3 videos with a supported Preview adapter."""
+
+from __future__ import annotations
+
+import argparse
+import os
+import statistics
+import sys
+import time
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+
+FASTVIDEO_ROOT = Path(__file__).resolve().parents[3]
+sys.path.insert(0, str(FASTVIDEO_ROOT))
+
+from fastvideo import VideoGenerator  # noqa: E402
+from fastvideo.api import (  # noqa: E402
+    CompileConfig,
+    ComponentConfig,
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    OffloadConfig,
+    OutputConfig,
+    ParallelismConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
+
+# FastVideo exposes these three backend switches only through environment variables.
+os.environ.update({
+    "FASTVIDEO_FA4": "1",
+    "FASTVIDEO_MINIMAX_H3_FUSIONS": "all",
+    "FASTVIDEO_VSA_SM100A": "0",
+})
+os.environ.pop("FASTVIDEO_INFERENCE_TORCH_COMPILE", None)
+
+VARIANT_BACKENDS = {
+    "dense-datafree": "FLASH_ATTN",
+    "vsa-datafree": "VIDEO_SPARSE_ATTN_H3",
+    "vsa-synthetic-step1300": "VIDEO_SPARSE_ATTN_H3",
+    "vsa-synthetic-step1900": "VIDEO_SPARSE_ATTN_H3",
+}
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("variant", choices=VARIANT_BACKENDS)
+    parser.add_argument("--prompt", required=True)
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Run one compile warmup and three measured generations with one fixed recipe."""
+    args = parse_args()
+
+    attention_backend = VARIANT_BACKENDS[args.variant]
+    experimental = {
+        "attention_backend": attention_backend,
+        "inference_torch_compile": attention_backend == "FLASH_ATTN",
+        "vae_parallel_decode": True,
+        "vae_parallel_decode_strategy": "gather",
+    }
+    if attention_backend == "VIDEO_SPARSE_ATTN_H3":
+        experimental.update({
+            "VSA_sparsity": 0.9,
+            "VSA_tile_size": 64,
+        })
+
+    adapter_path = hf_hub_download(
+        repo_id="FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA",
+        filename=f"{args.variant}/adapter_model.safetensors",
+    )
+    output_dir = FASTVIDEO_ROOT / "outputs/fasth3_lora_preview" / args.variant
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    generator = VideoGenerator.from_config(
+        GeneratorConfig(
+            model_path="MiniMaxAI/MiniMax-H3",
+            pipeline=PipelineSelection(
+                components=ComponentConfig(lora_path=adapter_path, lora_strength=1.0),
+                experimental=experimental,
+            ),
+            engine=EngineConfig(
+                num_gpus=4,
+                parallelism=ParallelismConfig(tp_size=1, sp_size=4),
+                offload=OffloadConfig(dit=False, dit_layerwise=False),
+                compile=CompileConfig(vae_enabled=True),
+            ),
+        )
+    )
+
+    generation_count = 4
+    warmup_count = 1
+    measured_count = generation_count - warmup_count
+    measured_seconds: list[float] = []
+
+    print(f"Variant: {args.variant} ({attention_backend})")
+    print(f"Output directory: {output_dir}")
+    try:
+        for generation_index in range(generation_count):
+            measured = generation_index >= warmup_count
+            if measured:
+                measured_index = generation_index - warmup_count + 1
+                label = f"measured {measured_index}/{measured_count}"
+                output_path = output_dir / f"fasth3_all_run_{measured_index:02d}.mp4"
+            else:
+                warmup_index = generation_index + 1
+                label = f"warmup {warmup_index}/{warmup_count}"
+                output_path = output_dir / f"_fasth3_warmup_{warmup_index:02d}.mp4"
+
+            request = GenerationRequest(
+                prompt=args.prompt,
+                negative_prompt="",
+                sampling=SamplingConfig(
+                    height=768,
+                    width=1344,
+                    num_frames=345, # <-- Change video length: 5sec: 124; 10sec: 243; 15sec: 345.
+                    fps=24,
+                    num_inference_steps=5,
+                    guidance_scale=1.0,
+                    batch_cfg=False,
+                    seed=1000,
+                ),
+                output=OutputConfig(
+                    output_path=str(output_path),
+                    save_video=True,
+                    return_frames=False,
+                ),
+            )
+            started = time.perf_counter()
+            result = generator.generate(request)
+            elapsed = time.perf_counter() - started
+            if measured:
+                measured_seconds.append(elapsed)
+            suffix = "" if measured else " (excluded from median)"
+            print(f"[{label}] {result.video_path or output_path}: {elapsed:.3f}s{suffix}")
+    finally:
+        generator.shutdown()
+
+    print(f"Median E2E wall time: {statistics.median(measured_seconds):.3f}s")
+
+
+if __name__ == "__main__":
+    main()