[mxfp8 moe training] integrate triton kernels for converting scales to blocked format

benchmarks/prototype/moe_training/benchmark_2d_blocked_swizzle_scale_kernels.py

@@ -0,0 +1,171 @@
+# 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.
+# this benchmarking script is a modified version of the original script from: https://github.com/drisspg/transformer_nuggets/blob/main/transformer_nuggets/utils/benchmark.py
+
+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_blocked_scales import (
+    compute_per_group_blocked_scale_offsets,
+    torch_to_blocked_per_group_2d,
+    triton_mx_block_rearrange_per_group_2d,
+)
+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
+
+
+@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 = [
+        (16640, 5120 // block_size),
+    ]
+    num_groups = [16]
+    configs = []
+    for shape, groups in itertools.product(
+        input_shapes,
+        num_groups,
+    ):
+        configs.append(
+            ExperimentConfig(
+                input_shape=shape,
+                num_groups=groups,
+            )
+        )
+    return configs
+
+
+def run_experiment(config: ExperimentConfig) -> ExperimentResult:
+    input_shape, num_groups = config.input_shape, config.num_groups
+    input_tensor = torch.randint(
+        low=0,
+        high=256,
+        size=input_shape,
+        dtype=torch.uint8,
+        device=device,
+    )
+
+    Mg, K = input_shape
+    input_group_offsets = generate_jagged_offs(num_groups, Mg, multiple_of=32)
+
+    # bench torch
+    compiled_run_torch = torch.compile(torch_to_blocked_per_group_2d)
+    torch_out_scales, torch_group_offs = compiled_run_torch(
+        input_tensor, input_group_offsets, Mg, K
+    )
+    torch_time_us = benchmark_cuda_function_in_microseconds(
+        compiled_run_torch,
+        input_tensor,
+        input_group_offsets,
+        Mg,
+        K,
+    )
+
+    # bench triton
+    _, output_group_offsets = compute_per_group_blocked_scale_offsets(
+        input_group_offsets
+    )
+    triton_out_scales = triton_mx_block_rearrange_per_group_2d(
+        input_tensor,
+        input_group_offsets,
+        output_group_offsets,
+    )
+    triton_time_us = benchmark_cuda_function_in_microseconds(
+        triton_mx_block_rearrange_per_group_2d,
+        input_tensor,
+        input_group_offsets,
+        output_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 = [
+        "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(
+            [
+                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()

benchmarks/prototype/moe_training/benchmark_3d_blocked_swizzle_scale_kernels.py

@@ -0,0 +1,160 @@
+# 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.
+# this benchmarking script is a modified version of the original script from: https://github.com/drisspg/transformer_nuggets/blob/main/transformer_nuggets/utils/benchmark.py
+
+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_blocked_scales import (
+    torch_to_blocked_per_group_3d,
+    triton_mx_block_rearrange_per_group_3d,
+)
+
+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]
+
+
+@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 = [
+        # w1, w3 scaled along K (fwd)
+        (1, 8192, 5120 // block_size),
+        (2, 8192, 5120 // block_size),
+        (4, 8192, 5120 // block_size),
+        (8, 8192, 5120 // block_size),
+        (16, 8192, 5120 // block_size),
+        # w2 scaled along K (fwd)
+        (1, 5120, 8192 // block_size),
+        (2, 5120, 8192 // block_size),
+        (4, 5120, 8192 // block_size),
+        (8, 5120, 8192 // block_size),
+        (16, 5120, 8192 // block_size),
+    ]
+    configs = []
+    for shape in input_shapes:
+        configs.append(
+            ExperimentConfig(
+                input_shape=shape,
+            )
+        )
+    return configs
+
+
+def run_experiment(config: ExperimentConfig) -> ExperimentResult:
+    input_tensor = torch.randint(
+        low=0,
+        high=256,
+        size=config.input_shape,
+        dtype=torch.uint8,
+        device=device,
+    )
+
+    def warmup(fn, *args, **kwargs):
+        for _ in range(5):
+            fn(*args, **kwargs)
+
+    E, N, K = config.input_shape
+
+    # bench torch
+    compiled_run_torch = torch.compile(torch_to_blocked_per_group_3d)
+    warmup(compiled_run_torch, input_tensor)
+    torch_time_us = benchmark_cuda_function_in_microseconds(
+        compiled_run_torch,
+        input_tensor,
+    )
+
+    # bench triton
+    triton_out_scales = triton_mx_block_rearrange_per_group_3d(input_tensor)
+    warmup(triton_mx_block_rearrange_per_group_3d, input_tensor)
+    triton_time_us = benchmark_cuda_function_in_microseconds(
+        triton_mx_block_rearrange_per_group_3d,
+        input_tensor,
+    )
+
+    # 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 = [
+        "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]}, {experiment.config.input_shape[2]})"
+        rows.append(
+            [
+                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()