[Triton/Gluon] [OPUS] [FlyDSL] Gfx1250/microbench

.github/scripts/aiter_test.sh

@@ -84,9 +84,9 @@ for file in "${sharded_files[@]}"; do
     # batch gate so they exercise the persistent kernel at every batch size.
     test_cmd=(timeout 60m python3 "$file")
     case "$file" in
-        op_tests/multigpu_tests/test_mega_moe_gfx1250.py)
+        op_tests/multigpu_tests/bench_mega_moe.py)
             {
-                echo "Running gfx1250 MegaMoE fused-scatter accuracy on 8 GPUs when supported"
+                echo "Running MegaMoE fused-scatter accuracy on 8 GPUs when supported"
             } | tee -a latest_test.log
             test_cmd=(
                 timeout 60m

aiter/benchmark_data_init.py

@@ -0,0 +1,348 @@
+# SPDX-License-Identifier: MIT
+# Copyright (C) 2024-2026, Advanced Micro Devices, Inc. All rights reserved.
+"""Shared benchmark data and scale initialization helpers."""
+
+import torch
+
+# --------------------------------------------------------------------------- #
+# DATA / SCALE init.
+#   gen = make_generator(seed)
+#   x  = fill(shape, dist, gen, dtype=...)            # bf16 / fp32 / float8
+#   s  = fill_scale(shape, dist, gen)                 # float32 block scale
+#   xq = fill_fp4(shape, dist, gen)                   # MXFP4 packed e2m1
+#   x8 = fill_fp8(shape, dist, gen)                   # MXFP8 e4m3
+#   s8 = fill_scale_e8m0(shape, dist, gen)            # MX E8M0 on-wire
+#   s4 = fill_scale_e4m3(shape, dist, gen)            # NVFP4 E4M3 on-wire
+#
+# OCP (Open Compute Project) published the MX microscaling formats: e2m1/e4m3
+# data plus a tiny E8M0 (or E4M3) scale per block. fill_fp* emit those on-wire
+# buffers. Large 2-D tensors are filled in row chunks (~1 GiB f32 staging).
+# --------------------------------------------------------------------------- #
+DATA_DISTS = ("zero", "constant", "uniform", "norm")
+SCALE_DISTS = DATA_DISTS
+SCALE_UNIFORM = (0.5, 2.0)
+SCALE_NORM_MEAN, SCALE_NORM_STD = 1.0, 0.25
+FP8_E4M3 = torch.float8_e4m3fn
+FP4_UNIFORM = (-3.0, 3.0)  # e2m1 max is 6.0; keep headroom
+FP8_UNIFORM = (-6.0, 6.0)
+E8M0_BIAS = 127
+E8M0_NEUTRAL = 0x7F  # 2^0 = 1.0
+E4M3_NEUTRAL = 0x38  # e4m3 exp bias -> 1.0
+E4M3_SCALE_MEAN, E4M3_SCALE_STD = 0.34375, 0.08
+POW2_BINOMIAL_N = 10
+E8M0_SCALE_DISTS = ("zero", "constant", "uniform", "norm", "auto", "pow2_binomial")
+E4M3_SCALE_DISTS = ("zero", "constant", "uniform", "norm", "auto")
+_STAGE_ELEMS = 1 << 28  # 256M f32 = 1 GiB per chunk
+
+
+def make_generator(seed, device="cuda"):
+    """Seeded ``torch.Generator`` -- same seed => bit-identical buffers."""
+    return torch.Generator(device=device).manual_seed(int(seed))
+
+
+def add_data_init_args(
+    parser,
+    *,
+    default_dist="uniform",
+    default_scale="constant",
+    default_seed=0,
+    include_scale=True,
+):
+    """Attach shared data initialization arguments to a benchmark parser."""
+    parser.add_argument(
+        "--data-init",
+        dest="data_init",
+        nargs="+",
+        choices=list(DATA_DISTS),
+        default=[default_dist],
+        help="DATA init: zero | constant | uniform | norm (N(0,1)). "
+        "e.g.: --data-init uniform norm",
+    )
+    if include_scale:
+        parser.add_argument(
+            "--scale-init",
+            dest="scale_init",
+            nargs="+",
+            choices=list(SCALE_DISTS),
+            default=[default_scale],
+            help="SCALE init (non-negative float): zero | constant(=1) | "
+            "uniform U(0.5,2) | norm N(1,0.25). Independent of --data-init.",
+        )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=default_seed,
+        help="RNG seed; same seed -> bit-identical uniform/norm buffers",
+    )
+    return parser
+
+
+def _row_chunks(rows, cols):
+    """Row slices whose f32 staging stays around _STAGE_ELEMS elements."""
+    step = max(_STAGE_ELEMS // max(cols, 1), 1)
+    for start in range(0, rows, step):
+        yield start, min(start + step, rows)
+
+
+def _canon_dist(dist, allowed):
+    if dist == "gaussian":
+        dist = "norm"
+    if dist not in allowed:
+        raise ValueError(f"dist {dist!r}; choose from {allowed}")
+    return dist
+
+
+def _sample_data_f32(shape, dist, gen, *, lo, hi, device):
+    if dist == "uniform":
+        return torch.empty(shape, dtype=torch.float32, device=device).uniform_(
+            lo, hi, generator=gen
+        )
+    if dist == "norm":
+        return torch.empty(shape, dtype=torch.float32, device=device).normal_(
+            0.0, 1.0, generator=gen
+        )
+    raise ValueError(f"data dist {dist!r} is not continuous; use fill dispatch")
+
+
+def _sample_scale_f32(shape, dist, gen, *, lo, hi, device):
+    if dist == "uniform":
+        v = torch.empty(shape, dtype=torch.float32, device=device).uniform_(
+            lo, hi, generator=gen
+        )
+    elif dist == "norm":
+        v = torch.empty(shape, dtype=torch.float32, device=device).normal_(
+            SCALE_NORM_MEAN, SCALE_NORM_STD, generator=gen
+        )
+    else:
+        raise ValueError(f"scale dist {dist!r} is not continuous; use fill_scale")
+    v.clamp_(min=0.0)
+    return v
+
+
+def _fill_sampled(shape, dist, gen, *, dtype, device, uniform, constant, sample_fn):
+    if dist == "zero":
+        return torch.zeros(shape, dtype=dtype, device=device)
+    if dist == "constant":
+        return torch.full(shape, constant, dtype=dtype, device=device)
+    lo, hi = uniform
+    if len(shape) != 2:
+        return sample_fn(shape, dist, gen, lo=lo, hi=hi, device=device).to(dtype)
+    rows, cols = shape
+    out = torch.empty(shape, dtype=dtype, device=device)
+    for r0, r1 in _row_chunks(rows, cols):
+        v = sample_fn((r1 - r0, cols), dist, gen, lo=lo, hi=hi, device=device)
+        out[r0:r1] = v.to(dtype)
+        del v
+    return out
+
+
+def fill(
+    shape,
+    dist,
+    gen,
+    *,
+    dtype=torch.float32,
+    device="cuda",
+    uniform=(-1.0, 1.0),
+    constant=1.0,
+):
+    """Return a ``dtype`` DATA tensor of ``shape``.
+
+    ``dist`` in {zero, constant, uniform, norm}. ``uniform`` is U(lo, hi);
+    ``norm`` / ``gaussian`` is N(0, 1). ``zero`` / ``constant`` ignore ``gen``.
+    """
+    dist = _canon_dist(dist, DATA_DISTS)
+    return _fill_sampled(
+        shape,
+        dist,
+        gen,
+        dtype=dtype,
+        device=device,
+        uniform=uniform,
+        constant=constant,
+        sample_fn=_sample_data_f32,
+    )
+
+
+def fill_scale(
+    shape,
+    dist,
+    gen,
+    *,
+    dtype=torch.float32,
+    device="cuda",
+    uniform=SCALE_UNIFORM,
+    constant=1.0,
+):
+    """Return a non-negative float SCALE tensor of ``shape``.
+
+    Same dist names as ``fill``, sampled independently. ``constant`` defaults
+    to 1.0 (neutral). ``norm`` is N(1, 0.25) clamped >= 0 -- not DATA's N(0,1).
+    For MX on-wire scales use ``fill_scale_e8m0`` / ``fill_scale_e4m3``.
+    """
+    dist = _canon_dist(dist, SCALE_DISTS)
+    return _fill_sampled(
+        shape,
+        dist,
+        gen,
+        dtype=dtype,
+        device=device,
+        uniform=uniform,
+        constant=constant,
+        sample_fn=_sample_scale_f32,
+    )
+
+
+def _f32_to_e8m0(v: torch.Tensor) -> torch.Tensor:
+    """Round positive floats to the nearest E8M0 on-wire byte (bias 127)."""
+    e = torch.zeros_like(v, dtype=torch.int32)
+    pos = v > 0
+    e[pos] = v[pos].log2().round().to(torch.int32) + E8M0_BIAS
+    return e.clamp_(0, 255).to(torch.uint8)
+
+
+def _popcount64(x: torch.Tensor) -> torch.Tensor:
+    """Population count for a non-negative int64 tensor (SWAR bit-hack)."""
+    x = x - ((x >> 1) & 0x5555555555555555)
+    x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)
+    x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F
+    return (x * 0x0101010101010101) >> 56
+
+
+def fill_fp4(shape, dist, gen, *, uniform=FP4_UNIFORM, device="cuda", constant=0):
+    """MXFP4 on-wire: packed e2m1 ``uint8`` of shape ``(rows, cols // 2)``.
+
+    Samples with the same DATA dists as ``fill``, then round-to-nearest e2m1.
+    ``shape`` is the logical ``(rows, cols)``; ``cols`` must be even.
+    """
+    dist = _canon_dist(dist, DATA_DISTS)
+    rows, cols = shape
+    assert cols % 2 == 0, f"FP4 needs even columns, got {cols}"
+    packed = (rows, cols // 2)
+    if dist == "zero":
+        return torch.zeros(packed, dtype=torch.uint8, device=device)
+    if dist == "constant":
+        return torch.full(packed, int(constant), dtype=torch.uint8, device=device)
+
+    from aiter.utility import fp4_utils  # local: fp4_utils pulls in triton
+
+    out = torch.empty(packed, dtype=torch.uint8, device=device)
+    for r0, r1 in _row_chunks(rows, cols):
+        v = _sample_data_f32(
+            (r1 - r0, cols),
+            dist,
+            gen,
+            lo=uniform[0],
+            hi=uniform[1],
+            device=device,
+        )
+        out[r0:r1] = fp4_utils.f32_to_mxfp4(v).view(torch.uint8)
+        del v
+    return out
+
+
+def fill_fp8(
+    shape,
+    dist,
+    gen,
+    *,
+    dtype=FP8_E4M3,
+    uniform=FP8_UNIFORM,
+    device="cuda",
+    constant=0.5,
+):
+    """MXFP8 on-wire: e4m3 tensor of ``shape``."""
+    dist = _canon_dist(dist, DATA_DISTS)
+    if dist == "zero":
+        return torch.zeros(shape, dtype=dtype, device=device)
+    if dist == "constant":
+        return torch.full(
+            shape, float(constant), dtype=torch.float32, device=device
+        ).to(dtype)
+    if len(shape) != 2:
+        v = _sample_data_f32(
+            shape,
+            dist,
+            gen,
+            lo=uniform[0],
+            hi=uniform[1],
+            device=device,
+        )
+        return v.to(dtype)
+    rows, cols = shape
+    out = torch.empty(shape, dtype=dtype, device=device)
+    for r0, r1 in _row_chunks(rows, cols):
+        v = _sample_data_f32(
+            (r1 - r0, cols),
+            dist,
+            gen,
+            lo=uniform[0],
+            hi=uniform[1],
+            device=device,
+        )
+        out[r0:r1] = v.to(dtype)
+        del v
+    return out
+
+
+def fill_scale_e8m0(
+    shape,
+    dist="auto",
+    gen=None,
+    *,
+    device="cuda",
+    n=POW2_BINOMIAL_N,
+    constant=E8M0_NEUTRAL,
+):
+    """MX E8M0 on-wire ``uint8`` (biased exponent, bias 127).
+
+    ``zero`` / ``constant`` / ``uniform`` / ``norm`` map from our SCALE dists
+    (float then round to nearest power-of-two byte). ``auto`` /
+    ``pow2_binomial`` match the MX GEMM default: 2^(Binomial(21,0.5)-11).
+    """
+    if dist == "gaussian":
+        dist = "norm"
+    if dist not in E8M0_SCALE_DISTS:
+        raise ValueError(f"E8M0 scale dist {dist!r}; choose from {E8M0_SCALE_DISTS}")
+    if dist == "zero":
+        return torch.zeros(shape, dtype=torch.uint8, device=device)
+    if dist == "constant":
+        return torch.full(shape, int(constant), dtype=torch.uint8, device=device)
+    if dist in ("uniform", "norm"):
+        v = fill_scale(shape, dist, gen, device=device)
+        return _f32_to_e8m0(v)
+    # auto / pow2_binomial: Binomial(k, 0.5) == popcount of a uniform k-bit int
+    trials = 2 * n + 1
+    assert trials <= 24, "pow2_binomial popcount path assumes <= 24 trials"
+    bits = torch.randint(
+        0, 1 << trials, shape, dtype=torch.int64, device=device, generator=gen
+    )
+    e = _popcount64(bits).to(torch.int32) - (n + 1)
+    return (e + E8M0_BIAS).clamp_(0, 255).to(torch.uint8)
+
+
+def fill_scale_e4m3(
+    shape, dist="auto", gen=None, *, device="cuda", constant=E4M3_NEUTRAL
+):
+    """NVFP4 / E4M3 on-wire ``uint8``.
+
+    ``auto`` -> N(0.34375, 0.08) clamped >= 0, then cast e4m3 (MX GEMM default).
+    ``uniform`` / ``norm`` use ``fill_scale`` then cast. ``constant`` is 0x38
+    (1.0).
+    """
+    if dist == "gaussian":
+        dist = "auto"
+    if dist not in E4M3_SCALE_DISTS:
+        raise ValueError(f"E4M3 scale dist {dist!r}; choose from {E4M3_SCALE_DISTS}")
+    if dist == "zero":
+        return torch.zeros(shape, dtype=torch.uint8, device=device)
+    if dist == "constant":
+        return torch.full(shape, int(constant), dtype=torch.uint8, device=device)
+    if dist in ("uniform", "norm"):
+        v = fill_scale(shape, dist, gen, device=device)
+        return v.to(FP8_E4M3).view(torch.uint8)
+    v = torch.empty(shape, dtype=torch.float32, device=device).normal_(
+        E4M3_SCALE_MEAN, E4M3_SCALE_STD, generator=gen
+    )
+    v.clamp_(min=0.0)
+    return v.to(FP8_E4M3).view(torch.uint8)

aiter/benchmark_reporting.py

@@ -0,0 +1,36 @@
+# SPDX-License-Identifier: MIT
+# Copyright (C) 2024-2026, Advanced Micro Devices, Inc. All rights reserved.
+"""Structured output helpers for benchmark drivers."""
+
+import json
+
+import pandas as pd
+
+
+def _json_default(value):
+    """Render non-native JSON scalars without expanding their attributes."""
+    return str(value).removeprefix("torch.")
+
+
+def print_json_table(name, rows, keep=None):
+    """Print benchmark rows as one record-oriented JSON object.
+
+    A single-line object is intentional: parent benchmark drivers can validate
+    and forward it without parsing pandas' human-readable table formats.
+    """
+    if isinstance(rows, pd.DataFrame):
+        df = rows.copy()
+    else:
+        df = pd.DataFrame([row for row in rows if row is not None])
+    if not df.empty:
+        df = df.replace("", pd.NA).dropna(axis=1, how="all")
+        if keep is not None:
+            cols = [column for column in keep if column in df.columns]
+            cols += [
+                column
+                for column in df.columns
+                if "err_msg" in column and column not in cols
+            ]
+            df = df[cols]
+    records = json.loads(df.to_json(orient="records", default_handler=_json_default))
+    print(json.dumps({"name": name, "rows": records}), flush=True)