vllm/benchmarks/kernels/benchmark_mixtral_moe.py

import json
import os
import sys

os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from vllm.model_executor.layers.fused_moe import fused_moe
import torch
import torch.nn.functional as F
import triton


def main():
    method = fused_moe
    for bs in [
            1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
            2048, 3072, 4096
    ]:
        run_grid(bs, method=method)


def run_grid(bs, method):
    d_model = 4096
    num_total_experts = 8
    top_k = 2
    tp_size = 2
    model_intermediate_size = 14336
    num_layers = 32
    num_calls = 100

    num_warmup_trials = 1
    num_trials = 1

    configs = []
    if bs <= 16:
        BLOCK_SIZES_M = [16]
    elif bs <= 32:
        BLOCK_SIZES_M = [16, 32]
    elif bs <= 64:
        BLOCK_SIZES_M = [16, 32, 64]
    elif bs <= 128:
        BLOCK_SIZES_M = [16, 32, 64, 128]
    else:
        BLOCK_SIZES_M = [16, 32, 64, 128, 256]

    for block_size_n in [32, 64, 128, 256]:
        for block_size_m in BLOCK_SIZES_M:
            for block_size_k in [64, 128, 256]:
                for group_size_m in [1, 16, 32, 64]:
                    for num_warps in [4, 8]:
                        configs.append({
                            "BLOCK_SIZE_M": block_size_m,
                            "BLOCK_SIZE_N": block_size_n,
                            "BLOCK_SIZE_K": block_size_k,
                            "GROUP_SIZE_M": group_size_m,
                            "num_warps": num_warps,
                            "num_stages": 4,
                        })

    best_config = None
    best_time_us = 1e20

    for config in configs:
        print(f'{tp_size=} {bs=}')
        print(f'{config}')
        # warmup
        print(f'warming up')
        try:
            for _ in range(num_warmup_trials):
                run_timing(
                    num_calls=num_calls,
                    bs=bs,
                    d_model=d_model,
                    num_total_experts=num_total_experts,
                    top_k=top_k,
                    tp_size=tp_size,
                    model_intermediate_size=model_intermediate_size,
                    method=method,
                    config=config,
                )
        except triton.runtime.autotuner.OutOfResources:
            continue

        # trial
        print(f'benchmarking')
        for _ in range(num_trials):
            kernel_dur_ms = run_timing(
                num_calls=num_calls,
                bs=bs,
                d_model=d_model,
                num_total_experts=num_total_experts,
                top_k=top_k,
                tp_size=tp_size,
                model_intermediate_size=model_intermediate_size,
                method=method,
                config=config,
            )

            kernel_dur_us = 1000 * kernel_dur_ms
            model_dur_ms = kernel_dur_ms * num_layers

            if kernel_dur_us < best_time_us:
                best_config = config
                best_time_us = kernel_dur_us

            print(
                f'{kernel_dur_us=:.1f} {model_dur_ms=:.1f} {bs=} {tp_size=} {top_k=} {num_total_experts=} {d_model=} {model_intermediate_size=} {num_layers=}'
            )

    print("best_time_us", best_time_us)
    print("best_config", best_config)

    filename = "/tmp/config.jsonl"
    print(f"writing config to file {filename}")
    with open(filename, "a") as f:
        f.write(json.dumps({str(bs): best_config}) + "\n")


def run_timing(num_calls: int, bs: int, d_model: int, num_total_experts: int,
               top_k: int, tp_size: int, model_intermediate_size: int, method,
               config) -> float:
    shard_intermediate_size = model_intermediate_size // tp_size

    hidden_states = torch.rand(
        (bs, d_model),
        device="cuda:0",
        dtype=torch.bfloat16,
    )

    ws = torch.rand(
        (num_total_experts, 2 * shard_intermediate_size, d_model),
        device=hidden_states.device,
        dtype=hidden_states.dtype,
    )

    w2s = torch.rand(
        (num_total_experts, d_model, shard_intermediate_size),
        device=hidden_states.device,
        dtype=hidden_states.dtype,
    )

    gating_output = F.softmax(torch.rand(
        (num_calls, bs, num_total_experts),
        device=hidden_states.device,
        dtype=torch.float32,
    ),
                              dim=-1)

    start_event = torch.cuda.Event(enable_timing=True)
    end_event = torch.cuda.Event(enable_timing=True)

    start_event.record()
    for i in range(num_calls):
        hidden_states = method(
            hidden_states=hidden_states,
            w1=ws,
            w2=w2s,
            gating_output=gating_output[i],
            topk=2,
            renormalize=True,
            inplace=True,
            override_config=config,
        )
    end_event.record()
    end_event.synchronize()

    dur_ms = start_event.elapsed_time(end_event) / num_calls
    return dur_ms


if __name__ == "__main__":
    sys.exit(main())