[diffusion] Default NVFP4 to CUTLASS and add all-model shape benchmarks (#22091)

python/sglang/jit_kernel/benchmark/diffusion/bench_diffusion_nvfp4_scaled_mm.py

@@ -0,0 +1,363 @@
+import argparse
+import csv
+import json
+import os
+import re
+import statistics
+from pathlib import Path
+from typing import Any, Callable
+
+import flashinfer
+import sgl_kernel
+import torch
+
+from sglang.jit_kernel.benchmark.utils import DEFAULT_DTYPE
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.utils import is_in_ci
+
+register_cuda_ci(
+    est_time=120,
+    suite="stage-b-kernel-benchmark-1-gpu-large",
+    disabled="standalone diffusion NVFP4 benchmark",
+)
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+REPO_ROOT = (
+    Path(os.environ["SGLANG_NVFP4_REPO_ROOT"])
+    if os.environ.get("SGLANG_NVFP4_REPO_ROOT")
+    else Path(__file__).resolve().parents[5]
+)
+DEFAULT_OUTPUT_DIR = REPO_ROOT / "outputs" / "nvfp4_benchmarks"
+DEFAULT_SHAPE_LIBRARY = SCRIPT_DIR / "diffusion_nvfp4_shapes.json"
+DTYPE = DEFAULT_DTYPE
+WARMUP = 8
+ITERS = 20
+FLOAT4_E2M1_MAX = 6.0
+FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
+METHODS = ("cutlass", "flashinfer_auto", "flashinfer_cudnn")
+
+
+def benchmark_provider(
+    fn: Callable[[], torch.Tensor],
+    warmup: int = WARMUP,
+    iters: int = ITERS,
+) -> tuple[float, float, float]:
+    for _ in range(warmup):
+        y = fn()
+        del y
+    torch.cuda.synchronize()
+
+    times_ms: list[float] = []
+    for _ in range(iters):
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        start.record()
+        y = fn()
+        end.record()
+        end.synchronize()
+        times_ms.append(start.elapsed_time(end))
+        del y
+    return statistics.median(times_ms), max(times_ms), min(times_ms)
+
+
+def make_global_scale(x: torch.Tensor) -> torch.Tensor:
+    max_abs = torch.amax(x.abs()).clamp_min_(1e-6)
+    return (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / max_abs).to(torch.float32)
+
+
+def build_quantized_inputs(
+    m: int,
+    n: int,
+    k: int,
+    device: torch.device,
+    seed: int,
+) -> dict[str, Any]:
+    assert k % 16 == 0, f"NVFP4 requires k % 16 == 0, got k={k}"
+
+    gen = torch.Generator(device=device)
+    gen.manual_seed(seed)
+    x = torch.randn((m, k), device=device, dtype=DTYPE, generator=gen)
+    w = torch.randn((n, k), device=device, dtype=DTYPE, generator=gen)
+
+    x_global_scale = make_global_scale(x)
+    w_global_scale = make_global_scale(w)
+    alpha = (1.0 / (x_global_scale * w_global_scale)).to(torch.float32)
+
+    x_fp4, x_sf = flashinfer.fp4_quantize(x, x_global_scale)
+    w_fp4, w_sf = flashinfer.fp4_quantize(w, w_global_scale)
+    if x_sf.dtype == torch.uint8:
+        x_sf = x_sf.view(torch.float8_e4m3fn)
+    if w_sf.dtype == torch.uint8:
+        w_sf = w_sf.view(torch.float8_e4m3fn)
+
+    return {
+        "x_fp4": x_fp4,
+        "w_fp4": w_fp4,
+        "x_sf": x_sf,
+        "w_sf": w_sf,
+        "alpha": alpha,
+    }
+
+
+def make_shape_id(
+    model: str, shape_kind: str, prefix: str, m: int, n: int, k: int
+) -> str:
+    prefix_slug = re.sub(r"[^a-zA-Z0-9]+", "_", prefix).strip("_")
+    return f"{model}_{shape_kind}_{prefix_slug}_{m}x{n}x{k}"
+
+
+def load_shape_cases(shape_library: Path) -> list[dict[str, Any]]:
+    payload = json.loads(shape_library.read_text(encoding="utf-8"))
+    if not isinstance(payload, dict) or not payload:
+        raise RuntimeError(
+            f"Expected a non-empty model->shape list mapping in {shape_library}."
+        )
+
+    rows: list[dict[str, Any]] = []
+    for model, shapes in payload.items():
+        if not isinstance(shapes, list):
+            raise RuntimeError(
+                f"Expected {model} to map to a list of shapes in {shape_library}."
+            )
+        for shape in shapes:
+            m, n, k = (int(x) for x in shape["shape"])
+            count = int(shape["count"])
+            shape_kind = str(shape.get("kind", "actual_runtime_linear"))
+            prefix = str(shape.get("prefix", ""))
+            rows.append(
+                {
+                    "shape_id": make_shape_id(model, shape_kind, prefix, m, n, k),
+                    "source_model": model,
+                    "shape_kind": shape_kind,
+                    "runtime_prefix": prefix,
+                    "m": m,
+                    "n": n,
+                    "k": k,
+                    "count": count,
+                    "approx_flops": 2 * m * n * k * count,
+                }
+            )
+
+    if not rows:
+        raise RuntimeError(f"No shapes found in {shape_library}.")
+    return rows
+
+
+def split_csv_arg(text: str | None) -> set[str]:
+    if text is None or not text.strip():
+        return set()
+    return {item.strip() for item in text.split(",") if item.strip()}
+
+
+def select_shape_cases(
+    rows: list[dict[str, Any]],
+    *,
+    models: set[str],
+    shape_kinds: set[str],
+    top_k: int,
+    rank_by: str,
+) -> list[dict[str, Any]]:
+    filtered = [
+        row
+        for row in rows
+        if (not models or row["source_model"] in models)
+        and (not shape_kinds or row["shape_kind"] in shape_kinds)
+    ]
+    key = "approx_flops" if rank_by == "flops" else "count"
+    return sorted(filtered, key=lambda row: int(row[key]), reverse=True)[:top_k]
+
+
+def write_csv(rows: list[dict[str, Any]], output_path: Path) -> None:
+    with output_path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(
+            f,
+            fieldnames=[
+                "shape_id",
+                "source_model",
+                "shape_kind",
+                "runtime_prefix",
+                "m",
+                "n",
+                "k",
+                "count",
+                "approx_flops",
+                "method",
+                "median_ms",
+                "min_ms",
+                "max_ms",
+                "tflops",
+            ],
+        )
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def write_markdown(rows: list[dict[str, Any]], output_path: Path) -> None:
+    shape_rows = []
+    seen_shape_ids = set()
+    for row in rows:
+        if row["shape_id"] in seen_shape_ids:
+            continue
+        seen_shape_ids.add(row["shape_id"])
+        shape_rows.append(row)
+
+    lines: list[str] = []
+    lines.append("# Diffusion NVFP4 Scaled MM Benchmark")
+    lines.append("")
+    lines.append("## Shape Cases")
+    lines.append("")
+    lines.append("| Shape ID | Model | Shape Kind | Calls | Shape `(M,N,K)` | Prefix |")
+    lines.append("|---|---|---|---:|---|---|")
+    for row in shape_rows:
+        lines.append(
+            f"| {row['shape_id']} | {row['source_model']} | {row['shape_kind']} | {row['count']} | `({row['m']}, {row['n']}, {row['k']})` | `{row['runtime_prefix']}` |"
+        )
+    lines.append("")
+
+    for shape_row in shape_rows:
+        shape_id = shape_row["shape_id"]
+        scoped = [row for row in rows if row["shape_id"] == shape_id]
+        lines.append(f"## {shape_id}")
+        lines.append("")
+        lines.append("| Method | Median ms | TFLOPS |")
+        lines.append("|---|---:|---:|")
+        for row in sorted(scoped, key=lambda item: float(item["median_ms"])):
+            lines.append(
+                f"| {row['method']} | {float(row['median_ms']):.4f} | {float(row['tflops']):.1f} |"
+            )
+        lines.append("")
+
+    output_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def run_shape_suite(shape_cases: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    device = torch.device("cuda")
+    rows: list[dict[str, Any]] = []
+    for idx, shape in enumerate(shape_cases):
+        m = int(shape["m"])
+        n = int(shape["n"])
+        k = int(shape["k"])
+        quantized = build_quantized_inputs(m, n, k, device, seed=idx)
+
+        metadata = {
+            "shape_id": str(shape["shape_id"]),
+            "source_model": str(shape["source_model"]),
+            "shape_kind": str(shape["shape_kind"]),
+            "runtime_prefix": str(shape["runtime_prefix"]),
+            "m": m,
+            "n": n,
+            "k": k,
+            "count": int(shape["count"]),
+            "approx_flops": int(shape["approx_flops"]),
+        }
+
+        providers: dict[str, Callable[[], torch.Tensor]] = {
+            "cutlass": lambda: sgl_kernel.cutlass_scaled_fp4_mm(
+                quantized["x_fp4"],
+                quantized["w_fp4"],
+                quantized["x_sf"],
+                quantized["w_sf"],
+                quantized["alpha"],
+                DTYPE,
+            ),
+            "flashinfer_auto": lambda: flashinfer.mm_fp4(
+                quantized["x_fp4"],
+                quantized["w_fp4"].T,
+                quantized["x_sf"],
+                quantized["w_sf"].T,
+                quantized["alpha"],
+                DTYPE,
+                backend="auto",
+            ),
+            "flashinfer_cudnn": lambda: flashinfer.mm_fp4(
+                quantized["x_fp4"],
+                quantized["w_fp4"].T,
+                quantized["x_sf"],
+                quantized["w_sf"].T,
+                quantized["alpha"],
+                DTYPE,
+                backend="cudnn",
+            ),
+        }
+
+        for method in METHODS:
+            median_ms, max_ms, min_ms = benchmark_provider(providers[method])
+            rows.append(
+                {
+                    **metadata,
+                    "method": method,
+                    "median_ms": median_ms,
+                    "min_ms": min_ms,
+                    "max_ms": max_ms,
+                    "tflops": (2 * m * n * k) / (median_ms / 1e3) / 1e12,
+                }
+            )
+    return rows
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Benchmark diffusion NVFP4 GEMM backends on the captured diffusion shape library."
+    )
+    parser.add_argument(
+        "--models",
+        help="Comma-separated source_model filter. Default: all models in the JSON shape library.",
+    )
+    parser.add_argument(
+        "--shape-kinds",
+        help="Comma-separated shape_kind filter. Default: benchmark every shape kind in the JSON shape library.",
+    )
+    parser.add_argument(
+        "--top-k",
+        type=int,
+        default=64,
+        help="Benchmark the top-k shapes after filtering and ranking.",
+    )
+    parser.add_argument(
+        "--rank-by",
+        choices=["flops", "count"],
+        default="flops",
+        help="How to rank shapes before selecting top-k.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default=str(DEFAULT_OUTPUT_DIR),
+        help="Directory for CSV/Markdown outputs.",
+    )
+    args = parser.parse_args()
+
+    if is_in_ci():
+        print("Skipping bench_diffusion_nvfp4_scaled_mm.py in CI")
+        return
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required for NVFP4 scaled mm benchmarks.")
+    if not DEFAULT_SHAPE_LIBRARY.exists():
+        raise RuntimeError(
+            f"Shape library not found at {DEFAULT_SHAPE_LIBRARY}. "
+            "Commit or copy the generated diffusion_nvfp4_shapes.json first."
+        )
+
+    shape_cases = load_shape_cases(DEFAULT_SHAPE_LIBRARY)
+    selected_shapes = select_shape_cases(
+        shape_cases,
+        models=split_csv_arg(args.models),
+        shape_kinds=split_csv_arg(args.shape_kinds),
+        top_k=args.top_k,
+        rank_by=args.rank_by,
+    )
+    if not selected_shapes:
+        raise RuntimeError("No shapes matched the requested filters.")
+    rows = run_shape_suite(selected_shapes)
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    csv_path = output_dir / "diffusion_nvfp4_scaled_mm.csv"
+    md_path = output_dir / "diffusion_nvfp4_scaled_mm_summary.md"
+    write_csv(rows, csv_path)
+    write_markdown(rows, md_path)
+    print(f"Wrote {csv_path}")
+    print(f"Wrote {md_path}")
+
+
+if __name__ == "__main__":
+    main()

python/sglang/jit_kernel/benchmark/diffusion/bench_norm_impls.py

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import argparse
 import csv
 import functools

python/sglang/multimodal_gen/envs.py

@@ -277,6 +277,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "SGLANG_USE_RUNAI_MODEL_STREAMER": _lazy_bool(
         "SGLANG_USE_RUNAI_MODEL_STREAMER", "true"
     ),
+    # FlashInfer FP4 GEMM backend for the generic diffusion NVFP4 fallback.
     "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND": _lazy_str(
         "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND"
     ),

python/sglang/multimodal_gen/runtime/loader/transformer_load_utils.py

@@ -20,7 +20,6 @@ from sglang.multimodal_gen.runtime.layers.quantization.configs.nunchaku_config i
     _patch_nunchaku_scales,
 )
 from sglang.multimodal_gen.runtime.loader.utils import _list_safetensors_files
-from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import maybe_download_model
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -134,14 +133,6 @@ class _Flux2Nvfp4FallbackAdapter(_TransformerQuantAdapter):
         if quant_name != "modelopt_fp4":
             return
 
-        use_best_perf_kit = getattr(
-            current_platform,
-            "should_use_modelopt_fp4_best_performance_kit",
-            None,
-        )
-        if callable(use_best_perf_kit) and use_best_perf_kit():
-            return
-
         weights_path = os.path.basename(server_args.transformer_weights_path or "")
         if not weights_path.endswith("-mixed.safetensors") or server_args.tp_size <= 1:
             return
@@ -150,10 +141,10 @@ class _Flux2Nvfp4FallbackAdapter(_TransformerQuantAdapter):
             server_args.dit_cpu_offload = False
             server_args.text_encoder_cpu_offload = False
             logger.warning(
-                "FLUX.2 mixed NVFP4 is using the generic ModelOpt FP4 fallback with "
-                "tp_size=%d; disabling dit/text-encoder CPU offload to avoid TP "
-                "all-gather launch failures. Override the offload flags explicitly if "
-                "you need the old behavior.",
+                "FLUX.2 mixed NVFP4 is using the ModelOpt FP4 path with tp_size=%d; "
+                "disabling dit/text-encoder CPU offload to avoid TP all-gather "
+                "launch failures. Override the offload flags explicitly if you need "
+                "the old behavior.",
                 server_args.tp_size,
             )
 

python/sglang/multimodal_gen/runtime/platforms/cuda.py

@@ -120,17 +120,27 @@ class CudaPlatformBase(Platform):
         except ImportError:
             return None
 
+    @classmethod
+    @lru_cache(maxsize=1)
+    def get_modelopt_flashinfer_fp4_backend(cls) -> str:
+        backend = envs.SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND
+        if backend is None:
+            return "cudnn" if cls.is_blackwell() else "auto"
+
+        backend = backend.lower()
+        if backend not in {"auto", "cudnn"}:
+            logger.warning(
+                "Unsupported SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=%r. "
+                "Falling back to %r.",
+                backend,
+                "cudnn" if cls.is_blackwell() else "auto",
+            )
+            return "cudnn" if cls.is_blackwell() else "auto"
+        return backend
+
     @classmethod
     @lru_cache(maxsize=1)
     def get_modelopt_fp4_gemm_op(cls) -> tuple[Callable | None, str | None]:
-        if cls.is_blackwell():
-            try:
-                from flashinfer import mm_fp4 as flashinfer_mm_fp4
-
-                return flashinfer_mm_fp4, "cudnn"
-            except ImportError:
-                pass
-
         try:
             from sgl_kernel import cutlass_scaled_fp4_mm as cutlass_fp4_gemm
 
@@ -141,7 +151,7 @@ class CudaPlatformBase(Platform):
         try:
             from flashinfer import mm_fp4 as flashinfer_mm_fp4
 
-            return flashinfer_mm_fp4, "auto"
+            return flashinfer_mm_fp4, cls.get_modelopt_flashinfer_fp4_backend()
         except ImportError:
             return None, None
 

python/sglang/multimodal_gen/runtime/platforms/interface.py

@@ -208,20 +208,8 @@ class Platform:
         return None, None
 
     @classmethod
-    def has_modelopt_fp4_best_performance_kit(cls) -> bool:
-        return False
-
-    @classmethod
-    def can_use_modelopt_fp4_best_performance_kit(cls) -> bool:
-        return False
-
-    @classmethod
-    def should_use_modelopt_fp4_best_performance_kit(cls) -> bool:
-        return False
-
-    @classmethod
-    def warn_if_modelopt_fp4_best_performance_kit_missing(cls) -> None:
-        pass
+    def get_modelopt_flashinfer_fp4_backend(cls) -> str:
+        return "auto"
 
     @classmethod
     def get_local_torch_device(cls) -> torch.device: