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

Author: danielvegamyhre

benchmarks/prototype/moe_training/mxfp8/bench_triton_mx_block_rearrange_2d_K_groups.py

@@ -0,0 +1,185 @@
+# 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
+from dataclasses import dataclass
+from typing import List
+
+import torch
+from tabulate import tabulate
+from tqdm import tqdm
+
+from benchmarks.utils import benchmark_cuda_function_in_microseconds
+from torchao.prototype.moe_training.kernels.mxfp8 import (
+    torch_to_blocked_2d_K_groups,
+    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
+
+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:
+    torch_time_us: float
+    triton_time_us: float
+    torch_mem_bw_gbps: float
+    triton_mem_bw_gbps: float
+
+
+@dataclass(frozen=True)
+class Experiment:
+    config: ExperimentConfig
+    result: ExperimentResult
+
+
+def get_configs() -> List[ExperimentConfig]:
+    # Llama4 shapes. Input activations are scaled along K dim.
+    block_size = 32
+    input_shapes = [
+        (5120 // block_size, 16640),
+        (5120 // block_size, 131072),
+    ]
+    num_groups = [16]
+    versions = ["naive", "parallel"]
+    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)
+
+    # bench torch
+    compiled_run_torch = torch.compile(torch_to_blocked_2d_K_groups)
+    torch_out_scales, torch_group_offs = compiled_run_torch(
+        input_tensor,
+        input_group_offsets,
+        block_size=block_size,
+    )
+    torch_time_us = benchmark_cuda_function_in_microseconds(
+        compiled_run_torch,
+        input_tensor,
+        input_group_offsets,
+        block_size=block_size,
+    )
+
+    # bench triton (naive or parallel based on config)
+    if version == "naive":
+        triton_fn = triton_mx_block_rearrange_2d_K_groups_naive
+    else:
+        triton_fn = triton_mx_block_rearrange_2d_K_groups
+
+    triton_out_scales = triton_fn(
+        input_tensor,
+        input_group_offsets,
+    )
+    triton_time_us = benchmark_cuda_function_in_microseconds(
+        triton_fn,
+        input_tensor,
+        input_group_offsets,
+    )
+
+    # mem bw calculations
+    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 = triton_out_scales.numel() * bytes_per_output_el
+
+    torch_mem_bw_gbps = ((read_bytes + write_bytes) / 1e9) / (torch_time_us / 1e6)
+    triton_mem_bw_gbps = ((read_bytes + write_bytes) / 1e9) / (triton_time_us / 1e6)
+
+    return ExperimentResult(
+        torch_time_us=torch_time_us,
+        triton_time_us=triton_time_us,
+        torch_mem_bw_gbps=torch_mem_bw_gbps,
+        triton_mem_bw_gbps=triton_mem_bw_gbps,
+    )
+
+
+def print_results(experiments: List[Experiment]):
+    headers = [
+        "version",
+        "input_shape",
+        "torch_time_us",
+        "triton_time_us",
+        "torch_mem_bw_gbps",
+        "triton_mem_bw_gbps",
+        "triton_speedup",
+    ]
+    rows = []
+    for experiment in experiments:
+        input_shape = (
+            f"({experiment.config.input_shape[0]}, {experiment.config.input_shape[1]})"
+        )
+        rows.append(
+            [
+                experiment.config.version,
+                input_shape,
+                experiment.result.torch_time_us,
+                experiment.result.triton_time_us,
+                round(experiment.result.torch_mem_bw_gbps, 3),
+                round(experiment.result.triton_mem_bw_gbps, 3),
+                f"{experiment.result.torch_time_us / experiment.result.triton_time_us:.2f}x",
+            ]
+        )
+    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()