[mxfp8 moe training] parallelize along col blocks in scale blocked format kernel for groups along K

stack-info: PR: #3416, branch: danielvegamyhre/stack/85

Author: danielvegamyhre

benchmarks/prototype/moe_training/mxfp8/bench_triton_mx_block_rearrange_2d_K_groups.py

@@ -0,0 +1,229 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+import itertools
+import os
+from dataclasses import dataclass
+from typing import List
+
+import torch
+from tabulate import tabulate
+from torch.utils.cpp_extension import load
+from tqdm import tqdm
+
+from benchmarks.utils import benchmark_cuda_function_in_microseconds
+from torchao.prototype.moe_training.kernels.mxfp8 import (
+    triton_mx_block_rearrange_2d_K_groups,
+)
+from torchao.prototype.moe_training.kernels.mxfp8.quant import (
+    triton_mx_block_rearrange_2d_K_groups_naive,
+)
+from torchao.prototype.moe_training.utils import generate_jagged_offs
+
+# Build CUDA kernel directly using torch.utils.cpp_extension.load
+mxfp8_cuda = None
+try:
+    # Get the kernel source directory
+    KERNEL_DIR = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)),
+        "..",
+        "..",
+        "..",
+        "..",
+        "torchao",
+        "csrc",
+        "cuda",
+        "mx_kernels",
+    )
+    KERNEL_DIR = os.path.normpath(KERNEL_DIR)
+
+    print("Compiling CUDA kernel...")
+    mxfp8_cuda = load(
+        name="mxfp8_cuda",
+        sources=[
+            os.path.join(KERNEL_DIR, "mxfp8_extension.cpp"),
+            os.path.join(KERNEL_DIR, "mxfp8_cuda.cu"),
+            os.path.join(KERNEL_DIR, "mx_block_rearrange_2d_K_groups.cu"),
+        ],
+        extra_cuda_cflags=[
+            "-O3",
+            "--use_fast_math",
+            "-std=c++17",
+            "-gencode=arch=compute_90,code=sm_90",
+        ],
+        extra_cflags=["-O3", "-std=c++17"],
+        verbose=True,
+    )
+    print("✓ CUDA kernel compilation successful!")
+except (ImportError, RuntimeError) as e:
+    print(f"⚠ CUDA kernel not available: {e}")
+    print("The benchmark will only run 'naive' and 'parallel' Triton versions.\n")
+
+device = torch.device("cuda")
+
+# Needed since changing args to function causes recompiles
+torch._dynamo.config.cache_size_limit = 1000
+
+
+@dataclass(frozen=True)
+class ExperimentConfig:
+    input_shape: tuple[int]
+    num_groups: int
+    version: str  # "naive" or "parallel"
+
+
+@dataclass(frozen=True)
+class ExperimentResult:
+    time_us: float
+    mem_bw_gbps: float
+
+
+@dataclass(frozen=True)
+class Experiment:
+    config: ExperimentConfig
+    result: ExperimentResult
+
+
+def get_configs() -> List[ExperimentConfig]:
+    # Llama4 and DSV3 671b shapes. Input activations are scaled along the total_M dim, which contains all the token groups.
+    block_size = 32
+    input_shapes = [
+        (5120, 16384 // block_size),
+        (5120, 131072 // block_size),
+        (8192, 16384 // block_size),
+        (8192, 131072 // block_size),
+        (7168, 16384 // block_size),
+        (7168, 131072 // block_size),
+        (2048, 16384 // block_size),
+        (2048, 131072 // block_size),
+    ]
+    num_groups = [8]
+    versions = ["naive", "parallel", "cuda"]
+
+    configs = []
+    for shape, groups, version in itertools.product(
+        input_shapes,
+        num_groups,
+        versions,
+    ):
+        configs.append(
+            ExperimentConfig(
+                input_shape=shape,
+                num_groups=groups,
+                version=version,
+            )
+        )
+    return configs
+
+
+def run_experiment(config: ExperimentConfig) -> ExperimentResult:
+    input_shape, num_groups, version = (
+        config.input_shape,
+        config.num_groups,
+        config.version,
+    )
+    input_tensor = torch.randint(
+        low=0,
+        high=256,
+        size=input_shape,
+        dtype=torch.uint8,
+        device=device,
+    )
+
+    M, Kg = input_shape
+    block_size = 32
+    input_group_offsets = generate_jagged_offs(num_groups, Kg, multiple_of=block_size)
+
+    # Select which kernel to benchmark based on version
+    if version == "naive":
+        kernel_fn = triton_mx_block_rearrange_2d_K_groups_naive
+    elif version == "parallel":
+        kernel_fn = triton_mx_block_rearrange_2d_K_groups
+    elif version == "cuda":
+        kernel_fn = mxfp8_cuda.mx_block_rearrange_2d_K_groups
+    else:
+        raise ValueError(f"Unknown version: {version}")
+
+    # Run kernel to get output shape
+    out_scales = kernel_fn(
+        input_tensor,
+        input_group_offsets,
+    )
+
+    # Benchmark the kernel
+    assert input_tensor.is_contiguous()
+    time_us = benchmark_cuda_function_in_microseconds(
+        kernel_fn,
+        input_tensor,
+        input_group_offsets,
+    )
+
+    # Calculate memory bandwidth
+    bytes_per_input_el = torch.finfo(torch.float8_e8m0fnu).bits / 8
+    bytes_per_output_el = torch.finfo(torch.float8_e4m3fn).bits / 8
+
+    read_bytes = input_tensor.numel() * bytes_per_input_el
+    write_bytes = out_scales.numel() * bytes_per_output_el
+
+    mem_bw_gbps = ((read_bytes + write_bytes) / 1e9) / (time_us / 1e6)
+
+    return ExperimentResult(
+        time_us=time_us,
+        mem_bw_gbps=mem_bw_gbps,
+    )
+
+
+def print_results(experiments: List[Experiment]):
+    # Group experiments by input shape
+    shapes_dict = {}
+    for exp in experiments:
+        shape_key = exp.config.input_shape
+        if shape_key not in shapes_dict:
+            shapes_dict[shape_key] = {}
+        shapes_dict[shape_key][exp.config.version] = exp.result
+
+    headers = [
+        "kernel_version",
+        "input_shape",
+        "time_us",
+        "mem_bw_gbps",
+        "fastest_version",
+    ]
+
+    rows = []
+    for shape, versions in shapes_dict.items():
+        # Find fastest version for this shape
+        fastest_version = min(versions.items(), key=lambda x: x[1].time_us)[0]
+
+        # Add rows for each version
+        for version, result in versions.items():
+            rows.append(
+                [
+                    version,
+                    f"({shape[0]}, {shape[1]})",
+                    f"{result.time_us:.2f}",
+                    round(result.mem_bw_gbps, 3),
+                    fastest_version,
+                ]
+            )
+
+    print(tabulate(rows, headers=headers))
+
+
+def main():
+    torch.random.manual_seed(123)
+    configs = get_configs()
+    results = []
+    for config in tqdm(configs):
+        result = run_experiment(config)
+        results.append(Experiment(config=config, result=result))
+
+    # Use Tabulate to print results
+    print_results(results)
+
+
+if __name__ == "__main__":
+    main()