benchmark_moe.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import argparse
import gc
import json
import os
import time
from contextlib import nullcontext
from datetime import datetime
from itertools import product
from typing import Any, TypedDict

import ray
import torch
from ray.experimental.tqdm_ray import tqdm

from vllm.model_executor.layers.fused_moe import fused_topk
from vllm.model_executor.layers.fused_moe.activation import MoEActivation
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEConfig,
    FusedMoEParallelConfig,
    FusedMoEQuantConfig,
    RoutingMethodType,
    _get_config_dtype_str,
)
from vllm.model_executor.layers.fused_moe.fused_moe import *
from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
    TritonOrDeepGemmExperts,
)
from vllm.transformers_utils.config import get_config
from vllm.triton_utils import triton
from vllm.utils.argparse_utils import FlexibleArgumentParser
from vllm.utils.torch_utils import set_random_seed

FP8_DTYPE = current_platform.fp8_dtype()

# Default interval for clearing Triton JIT cache during tuning
# Set to 0 to disable automatic cache clearing
_CACHE_CLEAR_INTERVAL_ENV = "VLLM_MOE_TUNE_CACHE_CLEAR_INTERVAL"
TRITON_CACHE_CLEAR_INTERVAL = int(os.environ.get(_CACHE_CLEAR_INTERVAL_ENV, "50"))


def clear_triton_cache():
    """Clear Triton JIT compilation cache and Python/CUDA memory.

    This helps prevent OOM during tuning with large models (many experts).
    """
    # Force Python garbage collection
    gc.collect()

    # Clear CUDA memory cache
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

    # Try to clear Triton's runtime cache
    try:
        if (
            hasattr(triton, "runtime")
            and hasattr(triton.runtime, "cache")
            and hasattr(triton.runtime.cache, "clear")
        ):
            triton.runtime.cache.clear()
    except ImportError:
        # Triton not installed, skip cache clearing
        pass
    except AttributeError:
        # Triton version doesn't have expected cache API
        pass
    except Exception as e:
        print(f"Warning: Failed to clear Triton cache: {e}")

    # Additional garbage collection after clearing caches
    gc.collect()


def ensure_divisibility(numerator, denominator, text):
    """Ensure that numerator is divisible by the denominator."""
    assert numerator % denominator == 0, "{} {} is not divisible by tp {}.".format(
        text, numerator, denominator
    )


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


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,
    use_int4_w4a16: bool = False,
    num_iters: int = 100,
    block_quant_shape: list[int] = None,
    use_deep_gemm: bool = False,
) -> float:
    init_dtype = torch.float16 if use_fp8_w8a8 else dtype
    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
    if use_int4_w4a16:
        # Int4 packed weights: 2 int4 values per uint8 byte
        # K dimension is packed (halved)
        intermediate_size = shard_intermediate_size // 2  # after silu_and_mul
        w1 = torch.randint(
            0,
            255,
            (
                num_experts,
                shard_intermediate_size,
                hidden_size // 2,  # int4 packing
            ),
            dtype=torch.uint8,
        )
        w2 = torch.randint(
            0,
            255,
            (
                num_experts,
                hidden_size,
                intermediate_size // 2,  # int4 packing
            ),
            dtype=torch.uint8,
        )
    elif use_int8_w8a16:
        w1 = torch.randint(
            -127,
            127,
            (
                num_experts,
                shard_intermediate_size,
                hidden_size,
            ),
            dtype=torch.int8,
        )
        w2 = torch.randint(
            -127,
            127,
            (
                num_experts,
                hidden_size,
                shard_intermediate_size // 2,
            ),
            dtype=torch.int8,
        )
    else:
        w1 = torch.randn(
            num_experts, shard_intermediate_size, hidden_size, dtype=init_dtype
        )
        w2 = torch.randn(
            num_experts, hidden_size, shard_intermediate_size // 2, dtype=init_dtype
        )
    gating_output = torch.randn(num_iters, num_tokens, num_experts, dtype=torch.float32)

    w1_scale = None
    w2_scale = None
    a1_scale = None
    a2_scale = None
    if use_int4_w4a16:
        if block_quant_shape is None:
            raise ValueError("block_quant_shape is required for int4_w4a16")
        group_size = block_quant_shape[1]
        # Scales shape: (E, N, K // group_size) in fp16
        w1_scale = torch.rand(
            (num_experts, shard_intermediate_size, hidden_size // group_size),
            dtype=dtype,
        )
        w2_scale = torch.rand(
            (num_experts, hidden_size, intermediate_size // group_size),
            dtype=dtype,
        )
    elif use_int8_w8a16:
        w1_scale = torch.randn(
            (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
        )
        w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
    if use_deep_gemm:
        # we use the default block shape for deepgemm
        block_quant_shape = [128, 128]
    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)
                * factor_for_scale
            )
            w2_scale = (
                torch.rand((E, n_tiles_w2, k_tiles_w2), dtype=torch.float32)
                * factor_for_scale
            )
        else:
            w1_scale = torch.randn(num_experts, dtype=torch.float32)
            w2_scale = torch.randn(num_experts, dtype=torch.float32)

        a1_scale = torch.randn(1, dtype=torch.float32)
        a2_scale = torch.randn(1, dtype=torch.float32)

        w1 = w1.to(FP8_DTYPE)
        w2 = w2.to(FP8_DTYPE)

    input_gating = torch.empty(num_tokens, num_experts, dtype=torch.float32)

    def prepare(i: int):
        input_gating.copy_(gating_output[i])

    def run():
        from vllm.model_executor.layers.fused_moe import override_config

        if use_fp8_w8a8:
            quant_dtype = torch.float8_e4m3fn
        elif use_int8_w8a16:
            quant_dtype = torch.int8
        else:
            quant_dtype = None

        quant_config = FusedMoEQuantConfig.make(
            quant_dtype=quant_dtype,
            w1_scale=w1_scale,
            w2_scale=w2_scale,
            a1_scale=a1_scale,
            a2_scale=a2_scale,
            block_shape=block_quant_shape,
            weight_dtype="int4" if use_int4_w4a16 else None,
        )

        deep_gemm_experts = None
        if use_deep_gemm:
            deep_gemm_experts = mk.FusedMoEModularKernel(
                prepare_finalize=MoEPrepareAndFinalizeNoEP(),
                fused_experts=TritonOrDeepGemmExperts(
                    moe_config=FusedMoEConfig(
                        num_experts=num_experts,
                        experts_per_token=topk,
                        hidden_dim=hidden_size,
                        intermediate_size_per_partition=shard_intermediate_size,
                        num_local_experts=num_experts,
                        num_logical_experts=num_experts,
                        activation=MoEActivation.SILU,
                        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
                        in_dtype=init_dtype,
                        routing_method=RoutingMethodType.TopK,
                        device="cuda",
                    ),
                    quant_config=quant_config,
                ),
            )

        with override_config(config):
            topk_weights, topk_ids, token_expert_indices = fused_topk(
                x, input_gating, topk, renormalize=not use_deep_gemm
            )

            inplace = not disable_inplace()
            if use_deep_gemm:
                return deep_gemm_experts(
                    x, w1, w2, topk_weights, topk_ids, inplace=inplace
                )
            return fused_experts(
                x,
                w1,
                w2,
                topk_weights,
                topk_ids,
                inplace=inplace,
                quant_config=quant_config,
            )

    # 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()
    torch.cuda.synchronize()

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

    latencies: list[float] = []
    for i in range(num_iters):
        prepare(i)
        torch.cuda.synchronize()

        start_event.record()
        graph.replay()
        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):
    block_mn_range = [16, 32, 64, 128, 256]
    block_k_range = [16, 32, 64, 128, 256]
    if not use_fp16:
        block_k_range.remove(16)  # BLOCK_K=16 not supported for fp8
    num_warps_range = [1, 2, 4, 8]
    group_m_range = [1, 4, 8, 16, 32]
    num_stage_range = [2]
    waves_per_eu_range = [0, 1, 2, 4]
    matrix_instr_nonkdim_range = [16, 32] if use_fp16 else []
    kpack_range = [1, 2] if use_fp16 else []

    param_ranges = {
        "BLOCK_SIZE_M": block_mn_range,
        "BLOCK_SIZE_N": block_mn_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 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) -> list[dict[str, int]]:
    configs: list[BenchmarkConfig] = []

    if current_platform.is_rocm():
        param_ranges = get_rocm_tuning_space(use_fp16)
    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")

        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")
        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) -> None:
        torch.set_default_device("cuda")
        set_random_seed(seed)
        self.seed = seed
        # Get the device ID to allocate tensors and kernels
        # on the respective GPU. This is required for Ray to work
        # correctly with multi-GPU tuning on the ROCm platform.
        self.device_id = int(ray.get_gpu_ids()[0])

    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,
        use_int4_w4a16: bool = False,
        block_quant_shape: list[int] = None,
        use_deep_gemm: bool = False,
    ) -> tuple[dict[str, int], float]:
        # local import to allow serialization by ray

        set_random_seed(self.seed)
        dtype_str = _get_config_dtype_str(
            dtype,
            use_int8_w8a16=use_int8_w8a16,
            use_fp8_w8a8=use_fp8_w8a8,
            use_int4_w4a16=use_int4_w4a16,
        )
        # NOTE(woosuk): The current naming convention uses w2.shape[2], which
        # is the intermediate size after silu_and_mul.
        block_n = block_quant_shape[0] if block_quant_shape else None
        block_k = block_quant_shape[1] if block_quant_shape else None
        op_config = get_moe_configs(
            num_experts, shard_intermediate_size // 2, dtype_str, block_n, block_k
        )
        if op_config is None:
            config = get_default_config(
                num_tokens,
                num_experts,
                shard_intermediate_size,
                hidden_size,
                topk,
                dtype_str,
                block_quant_shape,
            )
        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,
            use_int4_w4a16=use_int4_w4a16,
            num_iters=100,
            block_quant_shape=block_quant_shape,
            use_deep_gemm=use_deep_gemm,
        )
        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,
        use_int4_w4a16: bool,
        search_space: list[dict[str, int]],
        block_quant_shape: list[int],
        use_deep_gemm: bool,
    ) -> dict[str, int]:
        # local import to allow serialization by ray
        from vllm.platforms import current_platform

        best_config = None
        best_time = float("inf")
        if current_platform.is_rocm():
            is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16 or use_int4_w4a16)
            search_space = prune_rocm_search_space(
                num_tokens,
                shard_intermediate_size,
                hidden_size,
                search_space,
                is_fp16,
                topk,
            )

        need_device_guard = False
        if current_platform.is_rocm():
            visible_device = os.environ.get("ROCR_VISIBLE_DEVICES", None)
            if visible_device != f"{self.device_id}":
                need_device_guard = True

        with torch.cuda.device(self.device_id) if need_device_guard else nullcontext():
            for idx, config in enumerate(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,
                        use_int4_w4a16,
                        num_iters=20,
                        block_quant_shape=block_quant_shape,
                        use_deep_gemm=use_deep_gemm,
                    )
                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

                # Periodically clear Triton JIT cache to prevent OOM
                # This is especially important for large models with many experts
                if (
                    TRITON_CACHE_CLEAR_INTERVAL > 0
                    and idx > 0
                    and idx % TRITON_CACHE_CLEAR_INTERVAL == 0
                ):
                    clear_triton_cache()

        # Final cleanup after tuning completes
        clear_triton_cache()

        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"],
        **(
            {"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 {}),
        **({"SPLIT_K": config["SPLIT_K"]} if "SPLIT_K" 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,
    use_int4_w4a16: bool,
    block_quant_shape: list[int],
    save_dir: str,
) -> None:
    dtype_str = _get_config_dtype_str(
        dtype,
        use_int8_w8a16=use_int8_w8a16,
        use_fp8_w8a8=use_fp8_w8a8,
        use_int4_w4a16=use_int4_w4a16,
    )

    # 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
    )
    os.makedirs(save_dir, exist_ok=True)
    filename = os.path.join(save_dir, filename)
    print(f"Writing best config to {filename}...")
    with open(filename, "w") as f:
        json.dump({"triton_version": triton.__version__, **configs}, f, indent=4)
        f.write("\n")


def get_compressed_tensors_block_structure(config, default_value=None):
    config_groups = config.get("config_groups", {})
    if len(config_groups) != 1:
        return default_value
    group = next(iter(config_groups.values()))
    weights = group.get("weights", {})
    block_structure = weights.get("block_structure", default_value)
    return block_structure


def get_weight_block_size_safety(config, default_value=None):
    quantization_config = getattr(config, "quantization_config", {})
    if isinstance(quantization_config, dict):
        if "weight_block_size" in quantization_config:
            return quantization_config["weight_block_size"]
        return get_compressed_tensors_block_structure(
            quantization_config, default_value
        )
    return default_value


def get_model_params(config):
    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
        hidden_size = config.hidden_size
    elif config.architectures[0] == "JambaForCausalLM":
        E = config.num_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.intermediate_size
        hidden_size = config.hidden_size
    elif config.architectures[0] in (
        "DeepseekV2ForCausalLM",
        "DeepseekV3ForCausalLM",
        "DeepseekV32ForCausalLM",
        "GlmMoeDsaForCausalLM",
        "Glm4MoeForCausalLM",
        "Glm4MoeLiteForCausalLM",
        "NemotronHForCausalLM",
        "MistralLarge3ForCausalLM",
    ):
        E = config.n_routed_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.moe_intermediate_size
        hidden_size = config.hidden_size
    elif config.architectures[0] in (
        "Qwen2MoeForCausalLM",
        "Qwen3MoeForCausalLM",
        "Qwen3NextForCausalLM",
    ):
        E = config.num_experts
        topk = config.num_experts_per_tok
        intermediate_size = config.moe_intermediate_size
        hidden_size = config.hidden_size
    elif config.architectures[0] == "Qwen3VLMoeForConditionalGeneration":
        text_config = config.get_text_config()
        E = text_config.num_experts
        topk = text_config.num_experts_per_tok
        intermediate_size = text_config.moe_intermediate_size
        hidden_size = text_config.hidden_size
    elif config.architectures[0] == "HunYuanMoEV1ForCausalLM":
        E = config.num_experts
        topk = config.moe_topk[0]
        intermediate_size = config.moe_intermediate_size[0]
        hidden_size = config.hidden_size
    elif config.architectures[0] == "Qwen3OmniMoeForConditionalGeneration":
        E = config.thinker_config.text_config.num_experts
        topk = config.thinker_config.text_config.num_experts_per_tok
        intermediate_size = config.thinker_config.text_config.moe_intermediate_size
        hidden_size = config.thinker_config.text_config.hidden_size
    elif config.architectures[0] == "PixtralForConditionalGeneration":
        # Pixtral can contain different LLM architectures,
        # recurse to get their parameters
        return get_model_params(config.get_text_config())
    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
        hidden_size = config.hidden_size
    return E, topk, intermediate_size, hidden_size


def get_quantization_group_size(config) -> int | None:
    """Extract the quantization group size from the HF model config.

    This reads directly from the HuggingFace config object (as returned by
    ``get_config()``), not from vLLM's quantization config classes.

    Supports AWQ/GPTQ-style configs (direct 'group_size' key) and
    compressed-tensors configs (nested inside 'config_groups').
    """
    quantization_config = getattr(config, "quantization_config", {})
    if not isinstance(quantization_config, dict):
        return None
    # AWQ / GPTQ style: group_size is a top-level key
    gs = quantization_config.get("group_size")
    if gs is not None:
        return gs
    # compressed-tensors style: group_size is nested in config_groups
    config_groups = quantization_config.get("config_groups", {})
    if not isinstance(config_groups, dict):
        return None
    for group_cfg in config_groups.values():
        if not isinstance(group_cfg, dict):
            continue
        weights = group_cfg.get("weights", {})
        if not isinstance(weights, dict):
            continue
        gs = weights.get("group_size")
        if gs is not None:
            return gs
    return None


def main(args: argparse.Namespace):
    print(args)

    config = get_config(model=args.model, trust_remote_code=args.trust_remote_code)
    if args.model_prefix:
        config = getattr(config, args.model_prefix)
    E, topk, intermediate_size, hidden_size = get_model_params(config)
    enable_ep = bool(args.enable_expert_parallel)
    if enable_ep:
        ensure_divisibility(E, args.tp_size, "Number of experts")
        E = E // args.tp_size
        shard_intermediate_size = 2 * intermediate_size
    else:
        ensure_divisibility(intermediate_size, args.tp_size, "intermediate_size")
        shard_intermediate_size = 2 * intermediate_size // args.tp_size
    dtype = torch.float16 if current_platform.is_rocm() else config.dtype
    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
    use_int8_w8a16 = args.dtype == "int8_w8a16"
    use_int4_w4a16 = args.dtype == "int4_w4a16"
    block_quant_shape = get_weight_block_size_safety(config)
    if use_int4_w4a16:
        group_size = get_quantization_group_size(config)
        if group_size is None:
            raise ValueError(
                "Could not determine group_size from model config. "
                "The model's quantization_config must contain a 'group_size' "
                "field (AWQ/GPTQ) or 'config_groups.*.weights.group_size' "
                "(compressed-tensors)."
            )
        # For int4_w4a16, block_shape = [0, group_size]
        # block_shape[0]=0 means no block quantization on N dimension
        block_quant_shape = [0, group_size]

    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()
    num_gpus = int(ray.available_resources()["GPU"])
    workers = [BenchmarkWorker.remote(args.seed) for _ 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:
        # int4_w4a16 weights are uint8-packed, not fp16; treat like fp8 for
        # search space generation (no matrix_instr_nonkdim/kpack exploration).
        is_fp16 = not (use_fp8_w8a8 or use_int8_w8a16 or use_int4_w4a16)
        # For int4_w4a16, the group_size constraint on BLOCK_SIZE_K does not
        # apply: the gptq_awq kernel handles arbitrary BLOCK_SIZE_K regardless
        # of group_size. Skip block_quant_shape filtering to keep the full
        # search space (e.g. BLOCK_SIZE_K=64 with group_size=128).
        tune_block_quant_shape = None if use_int4_w4a16 else block_quant_shape
        search_space = get_configs_compute_bound(is_fp16, tune_block_quant_shape)
        if use_int4_w4a16:
            # SPLIT_K is a required kernel constexpr for gptq_awq kernel;
            # only SPLIT_K=1 is used at runtime, so fix it during tuning.
            for cfg in search_space:
                cfg["SPLIT_K"] = 1
        print(f"Start tuning over {len(search_space)} configurations...")
        if use_deep_gemm:
            raise ValueError(
                "Tuning with --use-deep-gemm is not supported as it only tunes Triton "
                "kernels. Please remove the flag."
            )
        start = time.time()
        configs = _distribute(
            "tune",
            [
                (
                    batch_size,
                    E,
                    shard_intermediate_size,
                    hidden_size,
                    topk,
                    dtype,
                    use_fp8_w8a8,
                    use_int8_w8a16,
                    use_int4_w4a16,
                    search_space,
                    block_quant_shape,
                    use_deep_gemm,
                )
                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,
            use_int4_w4a16,
            block_quant_shape,
            args.save_dir,
        )
        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,
                    use_int4_w4a16,
                    block_quant_shape,
                    use_deep_gemm,
                )
                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("--enable-expert-parallel", "-enable-ep", action="store_true")
    parser.add_argument(
        "--dtype",
        type=str,
        choices=["auto", "fp8_w8a8", "int8_w8a16", "int4_w4a16"],
        default="auto",
    )
    parser.add_argument("--use-deep-gemm", action="store_true")
    parser.add_argument(
        "--save-dir", type=str, default="./", help="Directory to save tuned results"
    )
    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("--trust-remote-code", action="store_true")
    parser.add_argument("--model-prefix", type=str, required=False)
    args = parser.parse_args()

    main(args)