add benchmark_moe.py to perf

vllm/perf/benchmark_moe.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import argparse
+import json
+import time
+from contextlib import nullcontext
+from datetime import datetime
+from itertools import product
+from typing import Any, TypedDict, Optional
+
+import ray
+import torch
+from ray.experimental.tqdm_ray import tqdm
+
+from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.transformers_utils.config import get_config
+from vllm.triton_utils import triton
+from vllm.utils import FlexibleArgumentParser
+
+# 移除全局的 current_platform 导入，改为在需要时局部导入
+# FP8_DTYPE = current_platform.fp8_dtype()
+
+
+class BenchmarkConfig(TypedDict):
+    BLOCK_SIZE_M: int
+    BLOCK_SIZE_N: int
+    BLOCK_SIZE_K: int
+    GROUP_SIZE_M: int
+    num_warps: int
+    num_stages: int
+    num_ldmatrixes: Optional[int]
+
+
+def benchmark_config(
+    config: BenchmarkConfig,
+    num_tokens: int,
+    num_experts: int,
+    shard_intermediate_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a16: bool,
+    num_iters: int = 100,
+    block_quant_shape: list[int] = None,
+    use_deep_gemm: bool = False,
+    nn_moe: Optional[bool] = False
+) -> float:
+   
+    from vllm.platforms import current_platform
+    device = torch.cuda.current_device()
+    
+    init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    x = torch.randn(num_tokens, hidden_size, dtype=dtype, device=device)
+    if use_int8_w8a16:
+        if not nn_moe:
+            w1 = torch.randint(
+                -127,
+                127,
+                (
+                    num_experts,
+                    shard_intermediate_size,
+                    hidden_size,
+                ),
+                dtype=torch.int8,
+                device=device,
+            )
+            w2 = torch.randint(
+                -127,
+                127,
+                (
+                    num_experts,
+                    hidden_size,
+                    shard_intermediate_size // 2,
+                ),
+                dtype=torch.int8,
+                device=device,
+            )
+        else:
+            w1 = torch.randint(
+                -127,
+                127,
+                (
+                    num_experts,
+                    hidden_size,
+                    shard_intermediate_size,
+                ),
+                dtype=torch.int8,
+                device=device,
+            )
+            w2 = torch.randint(
+                -127,
+                127,
+                (
+                    num_experts,
+                    shard_intermediate_size // 2,
+                    hidden_size,
+                ),
+                dtype=torch.int8,
+                device=device,
+            )
+    else:
+        if not nn_moe:
+            w1 = torch.randn(
+                num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype, device=device
+            )
+            w2 = torch.randn(
+                num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype, device=device
+            )
+        else:
+            w1 = torch.randn(
+                num_experts, hidden_size, shard_intermediate_size, dtype=init_dtype, device=device
+            )
+            w2 = torch.randn(
+                num_experts, shard_intermediate_size // 2, hidden_size, dtype=init_dtype, device=device
+            )
+    gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32, device=device)
+
+    w1_scale = None
+    w2_scale = None
+    a1_scale = None
+    a2_scale = None
+    if use_int8_w8a16:
+        w1_scale = torch.randn(
+            (num_experts, 2 * shard_intermediate_size), dtype=torch.float32, device=device
+        )
+        w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32, device=device)
+    if use_fp8_w8a8:
+        if block_quant_shape:
+            block_n, block_k = block_quant_shape[0], block_quant_shape[1]
+            E = num_experts
+            N = shard_intermediate_size // 2
+            K = hidden_size
+            factor_for_scale = 1e-2
+            n_tiles_w1 = (2 * N + block_n - 1) // block_n
+            n_tiles_w2 = (K + block_n - 1) // block_n
+            k_tiles_w1 = (K + block_k - 1) // block_k
+            k_tiles_w2 = (N + block_k - 1) // block_k
+            w1_scale = (
+                torch.rand((E, n_tiles_w1, k_tiles_w1), dtype=torch.float32, device=device)
+                * factor_for_scale
+            )
+            w2_scale = (
+                torch.rand((E, n_tiles_w2, k_tiles_w2), dtype=torch.float32, device=device)
+                * factor_for_scale
+            )
+        else:
+            w1_scale = torch.randn(num_experts, dtype=torch.float32, device=device)
+            w2_scale = torch.randn(num_experts, dtype=torch.float32, device=device)
+
+        a1_scale = torch.randn(1, dtype=torch.float32, device=device)
+        a2_scale = torch.randn(1, dtype=torch.float32, device=device)
+
+        # 获取 FP8_DTYPE
+        FP8_DTYPE = current_platform.fp8_dtype()
+        w1 = w1.to(FP8_DTYPE)
+        w2 = w2.to(FP8_DTYPE)
+
+    input_gating = torch.empty(num_tokens, num_experts, dtype=torch.float32, device=device)
+
+    def prepare(i: int):
+        input_gating.copy_(gating_output[i])
+
+    def run():
+        from vllm.model_executor.layers.fused_moe import override_config
+
+        with override_config(config):
+            if use_deep_gemm:
+                topk_weights, topk_ids, token_expert_indices = fused_topk(
+                    x, input_gating, topk, False
+                )
+                return fused_experts(
+                    x,
+                    w1,
+                    w2,
+                    topk_weights,
+                    topk_ids,
+                    inplace=True,
+                    use_fp8_w8a8=use_fp8_w8a8,
+                    w1_scale=w1_scale,
+                    w2_scale=w2_scale,
+                    a1_scale=a1_scale,
+                    a2_scale=a2_scale,
+                    block_shape=block_quant_shape,
+                    allow_deep_gemm=True,
+                    use_nn_moe=nn_moe,
+                )
+            else:
+                fused_moe(
+                    x,
+                    w1,
+                    w2,
+                    input_gating,
+                    topk,
+                    renormalize=True,
+                    inplace=True,
+                    use_fp8_w8a8=use_fp8_w8a8,
+                    use_int8_w8a16=use_int8_w8a16,
+                    w1_scale=w1_scale,
+                    w2_scale=w2_scale,
+                    a1_scale=a1_scale,
+                    a2_scale=a2_scale,
+                    block_shape=block_quant_shape,
+                    use_nn_moe=nn_moe,
+                )
+
+    # JIT compilation & warmup
+    run()
+    torch.cuda.synchronize()
+
+    # Capture 10 invocations with CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        for _ in range(10):
+            run()
+    torch.cuda.synchronize()
+
+    # Warmup
+    for _ in range(5):
+        graph.replay()
+        # run()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        prepare(i)
+        torch.cuda.synchronize()
+
+        start_event.record()
+        graph.replay()
+        # run()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    graph.reset()
+    return avg
+
+
+def get_rocm_tuning_space(use_fp16, nn_moe: Optional[bool] = False):
+    block_m_range = [16, 32, 64, 128, 256]
+    block_n_range = [32, 64, 128, 256]
+    block_k_range = [32, 64, 128, 256]
+    if not use_fp16:
+        block_k_range.remove(16)  # BLOCK_K=16 not supported for fp8
+    num_warps_range = [2, 4, 8]
+    group_m_range = [1, 16, 32, 64]
+    num_stage_range = [2, 3, 4, 5]
+    # waves_per_eu_range = [0]
+    # matrix_instr_nonkdim_range = [16, 32] if use_fp16 else []
+    # kpack_range = [1, 2] if use_fp16 else []
+
+    param_ranges = {
+        "BLOCK_SIZE_M": block_m_range,
+        "BLOCK_SIZE_N": block_n_range,
+        "BLOCK_SIZE_K": block_k_range,
+        "GROUP_SIZE_M": group_m_range,
+        "num_warps": num_warps_range,
+        "num_stages": num_stage_range,
+        # "waves_per_eu": waves_per_eu_range,
+    }
+    if nn_moe:
+        param_ranges["num_ldmatrixes"] = [1]
+    
+    # DCU currently does not support the following parameters
+    # if use_fp16:
+    #     param_ranges["matrix_instr_nonkdim"] = matrix_instr_nonkdim_range
+    #     param_ranges["kpack"] = kpack_range
+
+    return param_ranges
+
+
+def get_configs_compute_bound(use_fp16, block_quant_shape, nn_moe: Optional[bool] = False) -> list[dict[str, int]]:
+    configs: list[BenchmarkConfig] = []
+    
+    # 局部导入 current_platform
+    from vllm.platforms import current_platform
+
+    if current_platform.is_rocm():
+        param_ranges = get_rocm_tuning_space(use_fp16, nn_moe)
+    else:
+        # Reduced search space for faster tuning.
+        # TODO(woosuk): Increase the search space and use a performance model to
+        # prune the search space.
+        block_m_range = [16, 32, 64, 128, 256]
+        block_n_range = [32, 64, 128, 256]
+        block_k_range = [64, 128, 256]
+        num_warps_range = [4, 8]
+        group_m_range = [1, 16, 32, 64]
+        num_stage_range = [2, 3, 4, 5]
+
+        param_ranges = {
+            "BLOCK_SIZE_M": block_m_range,
+            "BLOCK_SIZE_N": block_n_range,
+            "BLOCK_SIZE_K": block_k_range,
+            "GROUP_SIZE_M": group_m_range,
+            "num_warps": num_warps_range,
+            "num_stages": num_stage_range,
+        }
+
+    keys, values = zip(*param_ranges.items())
+    for config_values in product(*values):
+        config = dict(zip(keys, config_values))
+        configs.append(config)
+
+    # Remove configs that are not compatible with fp8 block quantization
+    # BLOCK_SIZE_K must be a multiple of block_k
+    # BLOCK_SIZE_N must be a multiple of block_n
+    if block_quant_shape is not None and not use_fp16:
+        block_n, block_k = block_quant_shape[0], block_quant_shape[1]
+        for config in configs[:]:
+            if (
+                config["BLOCK_SIZE_K"] % block_k != 0
+                or config["BLOCK_SIZE_N"] % block_n != 0
+            ):
+                configs.remove(config)
+    return configs
+
+
+def prune_rocm_search_space(
+    num_tokens, shard_intermediate_size, hidden_size, search_space, is_fp16, topk
+):
+    N1, K1 = shard_intermediate_size, hidden_size
+    N2, K2 = hidden_size, shard_intermediate_size // 2
+    pruned_space_1 = prune_rocm_configs(
+        num_tokens * topk, N1, K1, search_space, is_fp16
+    )
+    pruned_space_2 = prune_rocm_configs(
+        num_tokens * topk, N2, K2, search_space, is_fp16
+    )
+    search_space = merge_unique_dicts(pruned_space_1, pruned_space_2)
+    return search_space
+
+
+# The following code is inspired by ROCm/Triton GEMM tuning script:
+# https://github.com/ROCm/triton/blob/triton-mlir/scripts/amd/gemm/tune_gemm.py#L89
+def prune_rocm_configs(M, N, K, configs, is_fp16=True):
+    pruned_configs = []
+    elemBytes_a = 2 if is_fp16 else 1
+    elemBytes_b = 2 if is_fp16 else 1
+
+    mfma = 16 if M < 32 or N < 32 else 32
+
+    # TODO (zhanglx): figure out the boundary between large and small gemms
+    large_gemm = False
+    if M >= 2048 and N >= 2048:
+        large_gemm = True
+
+    for config in configs:
+        BLOCK_SIZE_M = config.get("BLOCK_SIZE_M")
+        BLOCK_SIZE_N = config.get("BLOCK_SIZE_N")
+        BLOCK_SIZE_K = config.get("BLOCK_SIZE_K")
+        num_warps = config.get("num_warps")
+
+        # DCU currently does not support matrix_instr_nonkdim param
+        # if is_fp16:
+        #     matrix_instr_nonkdim = config.get("matrix_instr_nonkdim")
+        #     if matrix_instr_nonkdim > mfma:
+        #         continue
+        if mfma == 4 and BLOCK_SIZE_K < 64:
+            continue
+        # some layouts could not work properly in case
+        # number elements per thread is less 1
+        if BLOCK_SIZE_M * BLOCK_SIZE_N < 64:
+            continue
+        SPLIT_K = config.get("SPLIT_K", 1)
+        GROUP_M = config.get("GROUP_SIZE_M")
+
+        # DCU currently does not support matrix_instr_nonkdim param
+        # if is_fp16:
+        #     if (
+        #         matrix_instr_nonkdim > BLOCK_SIZE_M
+        #         or matrix_instr_nonkdim > BLOCK_SIZE_N
+        #     ):
+        #         continue
+        #     if matrix_instr_nonkdim >= M and matrix_instr_nonkdim != BLOCK_SIZE_M:
+        #         continue
+        #     if matrix_instr_nonkdim >= N and matrix_instr_nonkdim != BLOCK_SIZE_N:
+        #         continue
+        
+        # Skip BLOCK_SIZE that is too large compare to M/N
+        # unless BLOCK_SIZE is already small enough
+        if M * 2 < BLOCK_SIZE_M and BLOCK_SIZE_M != 16:
+            continue
+        if N * 2 < BLOCK_SIZE_N and BLOCK_SIZE_N != 16:
+            continue
+        # skip large split_k when not necessary
+        if SPLIT_K != 1 and not need_split_k(M, N, K):
+            continue
+        # skip split_k that leads to EVEN_K = false
+        leap = SPLIT_K * BLOCK_SIZE_K
+        modv = K % leap
+        if modv != 0:
+            continue
+        # skip large GROUP_M
+        if GROUP_M * BLOCK_SIZE_M > M and GROUP_M != 1:
+            continue
+        # out of shared memory resource
+        # TODO (zhanglx): This does not consider the LDS usage in the epilogue
+        LDS = (
+            BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a
+            + BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b
+        )
+        if LDS > 65536:
+            continue
+        # Skip small block sizes and num_warps for large gemm
+        # For fp16 and f8, we want to only use BLOCK_SIZE >= 64
+        if large_gemm:
+            if BLOCK_SIZE_M < 64 or BLOCK_SIZE_N < 64:
+                continue
+            if BLOCK_SIZE_K < 64:
+                continue
+            if num_warps < 4:
+                continue
+
+        pruned_configs.append(config)
+
+    return pruned_configs
+
+
+def need_split_k(SIZE_M, SIZE_N, SIZE_K):
+    return (SIZE_M < 64 or SIZE_N < 64) and SIZE_K > 1024
+
+
+def merge_unique_dicts(list1, list2):
+    result = []
+    combined_list = list1.copy()
+    combined_list.extend(list2)
+    for dictionary in combined_list:
+        if dictionary not in result:
+            result.append(dictionary)
+    return result
+
+
+@ray.remote(num_gpus=1)
+class BenchmarkWorker:
+    def __init__(self, seed: int, device_id: int) -> None:
+        from vllm.platforms import current_platform
+        import os
+        
+        if current_platform.is_rocm():
+            # In ROCm environment with Ray, let Ray handle device assignment
+            # Don't manually set default device as it may conflict with Ray's device mapping
+            pass
+        else:
+            torch.set_default_device("cuda:"+ str(device_id))
+        current_platform.seed_everything(seed)
+        self.seed = seed
+        # Store the logical device ID for Ray
+        self.device_id = device_id
+
+    def benchmark(
+        self,
+        num_tokens: int,
+        num_experts: int,
+        shard_intermediate_size: int,
+        hidden_size: int,
+        topk: int,
+        dtype: torch.dtype,
+        use_fp8_w8a8: bool,
+        use_int8_w8a16: bool,
+        block_quant_shape: list[int] = None,
+        use_deep_gemm: bool = False,
+        nn_moe: Optional[bool] = False,
+    ) -> tuple[dict[str, int], float]:
+        # 局部导入 current_platform
+        from vllm.platforms import current_platform
+        current_platform.seed_everything(self.seed)
+        
+        from vllm.model_executor.layers.fused_moe.fused_moe import (
+            get_config_dtype_str, get_moe_configs, get_default_config
+        )
+        dtype_str = get_config_dtype_str(
+            dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+        )
+        # NOTE(woosuk): The current naming convention uses w2.shape[2], which
+        # is the intermediate size after silu_and_mul.
+        op_config = get_moe_configs(
+            num_experts, shard_intermediate_size // 2, dtype_str, use_nn_moe=nn_moe
+        )
+        if op_config is None:
+            config = get_default_config(
+                num_tokens,
+                num_experts,
+                shard_intermediate_size,
+                hidden_size,
+                topk,
+                dtype_str,
+                is_marlin=False,
+                use_nn_moe=nn_moe
+            )
+        else:
+            config = op_config[min(op_config.keys(), key=lambda x: abs(x - num_tokens))]
+        kernel_time = benchmark_config(
+            config,
+            num_tokens,
+            num_experts,
+            shard_intermediate_size,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            block_quant_shape=block_quant_shape,
+            use_deep_gemm=use_deep_gemm,
+            use_nn_moe=nn_moe
+        )
+        return config, kernel_time
+
+    def tune(
+        self,
+        num_tokens: int,
+        num_experts: int,
+        shard_intermediate_size: int,
+        hidden_size: int,
+        topk: int,
+        dtype: torch.dtype,
+        use_fp8_w8a8: bool,
+        use_int8_w8a16: bool,
+        search_space: list[dict[str, int]],
+        block_quant_shape: list[int],
+        use_deep_gemm: bool,
+        nn_moe: Optional[bool] = False,
+    ) -> dict[str, int]:
+        from vllm.platforms import current_platform
+        import os
+        
+        best_config = None
+        best_time = float("inf")
+        if current_platform.is_rocm():
+            is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
+            search_space = prune_rocm_search_space(
+                num_tokens,
+                shard_intermediate_size,
+                hidden_size,
+                search_space,
+                is_fp16,
+                topk,
+            )
+
+        # In ROCm environments with Ray, device context is already handled by Ray
+        # Using torch.cuda.device() may cause device ordinal conflicts
+        need_device_guard = False
+        if current_platform.is_rocm():
+            # For ROCm with Ray, skip additional device context management
+            need_device_guard = False
+        else:
+            # For other platforms, use device guard if needed
+            visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", None)
+            if visible_devices is not None and len(visible_devices.split(',')) > 1:
+                need_device_guard = True
+
+        with torch.cuda.device(self.device_id) if need_device_guard else nullcontext():
+            for config in tqdm(search_space):
+                try:
+                    kernel_time = benchmark_config(
+                        config,
+                        num_tokens,
+                        num_experts,
+                        shard_intermediate_size,
+                        hidden_size,
+                        topk,
+                        dtype,
+                        use_fp8_w8a8,
+                        use_int8_w8a16,
+                        num_iters=20,
+                        block_quant_shape=block_quant_shape,
+                        use_deep_gemm=use_deep_gemm,
+                        nn_moe=nn_moe)
+                except triton.runtime.autotuner.OutOfResources:
+                    # Some configurations may be invalid and fail to compile.
+                    continue
+
+                if kernel_time < best_time:
+                    best_time = kernel_time
+                    best_config = config
+        now = datetime.now()
+        print(f"{now.ctime()}] Completed tuning for batch_size={num_tokens}")
+        assert best_config is not None
+        return best_config
+
+
+def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
+
+    return {
+        "BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
+        "BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
+        "BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
+        "GROUP_SIZE_M": config["GROUP_SIZE_M"],
+        "num_warps": config["num_warps"],
+        "num_stages": config["num_stages"],
+        **(
+            {"num_ldmatrixes": config["num_ldmatrixes"]} if "num_ldmatrixes" in config else {}
+        ),
+        **(
+            {"waves_per_eu": config["waves_per_eu"]} if "waves_per_eu" in config else {}
+        ),
+        **(
+            {"matrix_instr_nonkdim": config["matrix_instr_nonkdim"]}
+            if "matrix_instr_nonkdim" in config
+            else {}
+        ),
+        **({"kpack": config["kpack"]} if "kpack" in config else {}),
+    }
+
+
+def save_configs(
+    configs: dict[int, BenchmarkConfig],
+    num_experts: int,
+    shard_intermediate_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a16: bool,
+    block_quant_shape: list[int],
+    use_nn_moe: Optional[bool] = False,
+) -> None:
+    from vllm.model_executor.layers.fused_moe.fused_moe import (
+        get_config_dtype_str, get_config_file_name
+    )
+    
+    dtype_str = get_config_dtype_str(
+        dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
+    )
+
+    # NOTE(woosuk): The current naming convention uses w2.shape[2], which
+    # is the intermediate size after silu_and_mul.
+    filename = get_config_file_name(
+        num_experts, shard_intermediate_size // 2, dtype_str, block_quant_shape, use_nn_moe=use_nn_moe
+    )
+
+    print(f"Writing best config to {filename}...")
+    with open(filename, "w") as f:
+        json.dump(configs, f, indent=4)
+        f.write("\n")
+
+
+def get_weight_block_size_safety(config, default_value=None):
+    quantization_config = getattr(config, "quantization_config", {})
+    if isinstance(quantization_config, dict):
+        return quantization_config.get("weight_block_size", default_value)
+    return default_value
+
+
+def main(args: argparse.Namespace):
+    import os
+    import logging
+
+    from vllm.platforms import current_platform
+    
+    logger = logging.getLogger(__name__)
+    print(args)
+    
+    tp_size = args.tp_size
+    config = get_config(model=args.model, trust_remote_code=args.trust_remote_code)
+    if args.model_prefix:
+        config = getattr(config, args.model_prefix)
+
+    if config.architectures[0] == "DbrxForCausalLM":
+        E = config.ffn_config.moe_num_experts
+        topk = config.ffn_config.moe_top_k
+        intermediate_size = config.ffn_config.ffn_hidden_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] == "JambaForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in ("DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM", "Glm4MoeForCausalLM"):
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in ("Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"):
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    elif config.architectures[0] in ("Step3VLForConditionalGeneration"):
+        E = config.text_config.moe_num_experts
+        topk = config.text_config.moe_top_k
+        intermediate_size = config.text_config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+    else:
+        # Support for llama4
+        config = config.get_text_config()
+        # Default: Mixtral.
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // tp_size
+
+    hidden_size = config.hidden_size
+    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
+    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
+    use_int8_w8a16 = args.dtype == "int8_w8a16"
+    block_quant_shape = get_weight_block_size_safety(config)
+
+    if args.batch_size is None:
+        batch_sizes = [
+            1,
+            2,
+            4,
+            8,
+            16,
+            24,
+            32,
+            48,
+            64,
+            96,
+            128,
+            256,
+            512,
+            1024,
+            1536,
+            2048,
+            3072,
+            4096,
+        ]
+    else:
+        batch_sizes = args.batch_size
+
+    use_deep_gemm = bool(args.use_deep_gemm)
+
+    if current_platform.is_rocm() and "HIP_VISIBLE_DEVICES" in os.environ:
+        # Ray will set ROCR_VISIBLE_DEVICES for device visibility
+        logger.warning(
+            "Ray uses ROCR_VISIBLE_DEVICES to control device accessibility."
+            "Replacing HIP_VISIBLE_DEVICES with ROCR_VISIBLE_DEVICES."
+        )
+        val = os.environ["HIP_VISIBLE_DEVICES"]
+        os.environ["ROCR_VISIBLE_DEVICES"] = val
+        del os.environ["HIP_VISIBLE_DEVICES"]
+
+    ray.init(address=None, ignore_reinit_error=True, num_gpus=args.num_gpus)
+    num_gpus = int(ray.available_resources()["GPU"])
+    workers = [BenchmarkWorker.remote(args.seed, i) for i in range(num_gpus)]
+
+    def _distribute(method: str, inputs: list[Any]) -> list[Any]:
+        outputs = []
+        worker_idx = 0
+        for input_args in inputs:
+            worker = workers[worker_idx]
+            worker_method = getattr(worker, method)
+            output = worker_method.remote(*input_args)
+            outputs.append(output)
+            worker_idx = (worker_idx + 1) % num_gpus
+        return ray.get(outputs)
+
+    if args.tune:
+        is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16)
+        search_space = get_configs_compute_bound(is_fp16, block_quant_shape, args.nn_moe)
+        print(f"Start tuning over {len(search_space)} configurations...")
+
+        start = time.time()
+        configs = _distribute(
+            "tune",
+            [
+                (
+                    batch_size,
+                    E,
+                    shard_intermediate_size,
+                    hidden_size,
+                    topk,
+                    dtype,
+                    use_fp8_w8a8,
+                    use_int8_w8a16,
+                    search_space,
+                    block_quant_shape,
+                    use_deep_gemm,
+                    args.nn_moe,
+                )
+                for batch_size in batch_sizes
+            ],
+        )
+        best_configs = {
+            M: sort_config(config) for M, config in zip(batch_sizes, configs)
+        }
+        save_configs(
+            best_configs,
+            E,
+            shard_intermediate_size,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            block_quant_shape,
+            use_nn_moe=args.nn_moe,
+        )
+        end = time.time()
+        print(f"Tuning took {end - start:.2f} seconds")
+    else:
+        outputs = _distribute(
+            "benchmark",
+            [
+                (
+                    batch_size,
+                    E,
+                    shard_intermediate_size,
+                    hidden_size,
+                    topk,
+                    dtype,
+                    use_fp8_w8a8,
+                    use_int8_w8a16,
+                    block_quant_shape,
+                    use_deep_gemm,
+                    args.nn_moe,
+                )
+                for batch_size in batch_sizes
+            ],
+        )
+
+        for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
+            print(f"Batch size: {batch_size}, config: {config}")
+            print(f"Kernel time: {kernel_time:.2f} us")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser()
+    parser.add_argument(
+        "--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1"
+    )
+    parser.add_argument(
+        "--tp-size", "-tp", "--tensor-parallel-size", type=int, default=2
+    )
+    parser.add_argument(
+        "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
+    )
+    parser.add_argument("--use-deep-gemm", action="store_true")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--batch-size", type=int, nargs="+", required=False)
+    parser.add_argument("--tune", action="store_true")
+    parser.add_argument("--nn-moe", action='store_true', default=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--model-prefix", type=str, required=False)
+    parser.add_argument("--num-gpus", type=int, default=1)
+    args = parser.parse_args()
+
+    main(args)