# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Tests for the MOE layers.

Run `pytest tests/kernels/test_moe.py`.
"""

import functools
from typing import Callable, Optional, Union

import pytest
import torch
from torch.nn import Parameter
from torch.nn import functional as F
from transformers import MixtralConfig
from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock

import vllm.model_executor.layers.fused_moe  # noqa
from tests.kernels.moe.utils import fused_moe
from tests.kernels.utils import opcheck, stack_and_dev, torch_moe
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.distributed.parallel_state import init_distributed_environment
from vllm.forward_context import set_forward_context
from vllm.model_executor.layers.fused_moe.config import (
    FUSED_MOE_UNQUANTIZED_CONFIG,
    int4_w4a16_moe_quant_config,
    int8_w8a16_moe_quant_config,
)
from vllm.model_executor.layers.fused_moe.fused_moe import (
    fused_topk,
    modular_triton_fused_moe,
)
from vllm.model_executor.layers.fused_moe.moe_torch_iterative import (
    fused_moe as iterative_moe,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
    marlin_permute_bias,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
    rand_marlin_weight_mxfp4_like,
    rand_marlin_weight_nvfp4_like,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
    marlin_quant_fp8_torch,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
    awq_marlin_quantize,
    marlin_quantize,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import quantize_weights
from vllm.model_executor.models.mixtral import MixtralMoE
from vllm.platforms import current_platform
from vllm.scalar_type import ScalarType, scalar_types

NUM_EXPERTS = [8, 64, 192]
EP_SIZE = [1, 4]
TOP_KS = [2, 6]

FUSED_MOE_MNK_FACTORS = [
    (1, 128, 128),
    (1, 2048, 128),
    (33, 2048, 128),
    (222, 1024, 1024),
    (32768, 128, 128),
    (32768, 2048, 511),
    (40000, 1024, 1024),
]

FUSED_MOE_WN16_MNK_FACTORS = [
    (1, 128, 128),
    (1, 1024, 1024),
    (32, 2048, 128),
    (32, 1024, 1024),
    (222, 2048, 1024),
]

vllm_config = VllmConfig()
vllm_config.scheduler_config.max_num_seqs = 128
vllm_config.scheduler_config.max_model_len = 8192


def run_moe_test(
    baseline: Union[Callable, torch.Tensor],
    moe_fn: Callable,
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    score: torch.Tensor,
    topk: int,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
    padding: bool = False,
    use_compile: bool = False,
    use_cudagraph: bool = False,
    atol: float = 2e-2,
    rtol: float = 0,
) -> torch.Tensor:
    if isinstance(baseline, torch.Tensor):
        baseline_output = baseline
    else:
        baseline_output = baseline(
            a,
            w1,
            w2,
            score,
            topk,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
        )

    # Pad the weight if moe padding is enabled
    if padding:
        w1 = F.pad(w1, (0, 128), "constant", 0)[..., 0:-128]
        w2 = F.pad(w2, (0, 128), "constant", 0)[..., 0:-128]

    if use_compile:
        moe_fn = torch.compile(moe_fn, backend="inductor", fullgraph=True)
        torch._dynamo.mark_dynamic(a, 0)
        torch._dynamo.mark_dynamic(score, 0)

    test_output = moe_fn(
        a,
        w1,
        w2,
        score,
        topk,
        global_num_experts=global_num_experts,
        expert_map=expert_map,
    )

    if use_cudagraph:
        test_output.fill_(0)
        stream = torch.cuda.Stream()
        graph = torch.cuda.CUDAGraph()
        with torch.cuda.graph(graph, stream=stream):
            test_output = moe_fn(
                a,
                w1,
                w2,
                score,
                topk,
                global_num_experts=global_num_experts,
                expert_map=expert_map,
            )
        torch.cuda.synchronize()
        graph.replay()
        torch.cuda.synchronize()

    torch.testing.assert_close(test_output, baseline_output, atol=atol, rtol=rtol)

    return baseline_output


@pytest.mark.parametrize("m,n,k", FUSED_MOE_MNK_FACTORS)
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("ep_size", EP_SIZE)
@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("padding", [True, False])
@pytest.mark.parametrize("chunk_size", [8192])
def test_fused_moe(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    ep_size: int,
    dtype: torch.dtype,
    padding: bool,
    chunk_size: int,
    monkeypatch,
):
    current_platform.seed_everything(7)

    monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(chunk_size))

    #
    # Setup test data
    #

    #
    # Setup test data
    #

    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10

    score = torch.randn((m, e), device="cuda", dtype=dtype)

    if ep_size > 1:
        local_e = e // ep_size
        e_ids = torch.randint(0, e, (local_e,), device="cuda", dtype=torch.int32)
        e_map = torch.full((e,), -1, device="cuda", dtype=torch.int32)
        e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
        w1 = w1[e_ids]
        w2 = w2[e_ids]
    else:
        e_map = None

    #
    # Setup test functions
    #
    quant_config = FUSED_MOE_UNQUANTIZED_CONFIG

    m_fused_moe_fn = modular_triton_fused_moe(quant_config)

    def m_fused_moe(
        a: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        score: torch.Tensor,
        topk: int,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)
        return m_fused_moe_fn(
            a,
            w1,
            w2,
            topk_weights,
            topk_ids,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
        )

    fused_moe_fn = functools.partial(fused_moe, renormalize=False)

    #
    # Run tests
    #
    runner = functools.partial(
        run_moe_test,
        a=a,
        w1=w1,
        w2=w2,
        score=score,
        topk=topk,
        global_num_experts=e,
        expert_map=e_map,
        padding=padding,
    )

    # Note: for now use_compile will error out if the problem size is
    # large enough to trigger chunking. I'm leaving the flag and
    # setup code in case we are able to revisit this later.
    use_compile = False

    use_cudagraph = n >= 1024 and k >= 1024 and current_platform.is_cuda_alike()

    with set_current_vllm_config(vllm_config):
        baseline_output = runner(torch_moe, iterative_moe)
        runner(
            baseline_output,
            fused_moe_fn,
            use_compile=use_compile,
            use_cudagraph=use_cudagraph,
        )
        runner(
            baseline_output,
            m_fused_moe,
            use_compile=use_compile,
            use_cudagraph=use_cudagraph,
        )


@pytest.mark.parametrize("m,n,k", FUSED_MOE_WN16_MNK_FACTORS)
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("ep_size", EP_SIZE)
@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("group_size", [64, 128])
@pytest.mark.parametrize("has_zp", [True, False])
@pytest.mark.parametrize("weight_bits", [4, 8])
def test_fused_moe_wn16(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    ep_size: int,
    dtype: torch.dtype,
    group_size: int,
    has_zp: bool,
    weight_bits: int,
):
    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
    score = torch.randn((m, e), device="cuda", dtype=dtype)

    if weight_bits == 4:
        pack_factor = 2
        quant_type = scalar_types.uint4 if has_zp else scalar_types.uint4b8
    elif weight_bits == 8:
        pack_factor = 1
        quant_type = scalar_types.uint8 if has_zp else scalar_types.uint8b128

    w1_ref = w1.clone()
    w2_ref = w2.clone()
    w1_qweight = torch.empty(
        (e, 2 * n, k // pack_factor), device="cuda", dtype=torch.uint8
    )
    w2_qweight = torch.empty((e, k, n // pack_factor), device="cuda", dtype=torch.uint8)
    w1_scales = torch.empty((e, 2 * n, k // group_size), device="cuda", dtype=dtype)
    w2_scales = torch.empty((e, k, n // group_size), device="cuda", dtype=dtype)
    w1_qzeros = torch.empty(
        (e, 2 * n // pack_factor, k // group_size), device="cuda", dtype=torch.uint8
    )
    w2_qzeros = torch.empty(
        (e, k // pack_factor, n // group_size), device="cuda", dtype=torch.uint8
    )

    for i in range(e * 2):
        expert_id = i % e
        if i // e == 0:
            w, w_ref, w_qweight, w_scales, w_qzeros = (
                w1,
                w1_ref,
                w1_qweight,
                w1_scales,
                w1_qzeros,
            )
        else:
            w, w_ref, w_qweight, w_scales, w_qzeros = (
                w2,
                w2_ref,
                w2_qweight,
                w2_scales,
                w2_qzeros,
            )
        weight, qweight, scales, qzeros = quantize_weights(
            w[expert_id].T, quant_type, group_size, has_zp, False
        )
        weight = weight.T
        qweight = qweight.T.contiguous().to(torch.uint8)
        scales = scales.T
        if has_zp:
            qzeros = qzeros.T.contiguous().to(torch.uint8)
        if weight_bits == 4:
            qweight = qweight[:, 1::2] * 16 + qweight[:, ::2]
            if has_zp:
                qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]

        w_ref[expert_id] = weight
        w_qweight[expert_id] = qweight
        w_scales[expert_id] = scales
        if has_zp:
            w_qzeros[expert_id] = qzeros

    if ep_size > 1:
        local_e = e // ep_size
        e_ids = torch.randint(0, e, (local_e,), device="cuda", dtype=torch.int32)
        e_map = torch.full((e,), -1, device="cuda", dtype=torch.int32)
        e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
        w1_ref = w1_ref[e_ids]
        w2_ref = w2_ref[e_ids]
        w1_qweight = w1_qweight[e_ids]
        w2_qweight = w2_qweight[e_ids]
        w1_scales = w1_scales[e_ids]
        w2_scales = w2_scales[e_ids]
        w1_qzeros = w1_qzeros[e_ids]
        w2_qzeros = w2_qzeros[e_ids]
    else:
        e_map = None

    if weight_bits == 4:
        quant_config_builder = int4_w4a16_moe_quant_config
    else:
        assert weight_bits == 8
        quant_config_builder = int8_w8a16_moe_quant_config

    quant_config = quant_config_builder(
        w1_scale=w1_scales,
        w2_scale=w2_scales,
        w1_zp=w1_qzeros if has_zp else None,
        w2_zp=w2_qzeros if has_zp else None,
        block_shape=[0, group_size],
    )

    with set_current_vllm_config(vllm_config):
        triton_output = fused_moe(
            a,
            w1_qweight,
            w2_qweight,
            score,
            topk,
            renormalize=False,
            global_num_experts=e,
            expert_map=e_map,
            quant_config=quant_config,
        )
        torch_output = torch_moe(a, w1_ref, w2_ref, score, topk, expert_map=e_map)

    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)


@pytest.mark.parametrize("dtype", [torch.bfloat16])
@pytest.mark.parametrize("padding", [True, False])
@pytest.mark.parametrize(
    "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False]
)
@torch.inference_mode()
def test_mixtral_moe(
    dist_init, dtype: torch.dtype, padding: bool, use_rocm_aiter: bool, monkeypatch
):
    """Make sure our Mixtral MoE implementation agrees with the one from
    huggingface."""

    # clear the cache before every test
    from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
        is_rocm_aiter_moe_enabled,
    )

    is_rocm_aiter_moe_enabled.cache_clear()
    if use_rocm_aiter:
        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")

        if dtype == torch.float32:
            pytest.skip("AITER ROCm test skip for float32")

    monkeypatch.setenv("RANK", "0")
    monkeypatch.setenv("LOCAL_RANK", "0")
    monkeypatch.setenv("WORLD_SIZE", "1")
    monkeypatch.setenv("MASTER_ADDR", "localhost")
    monkeypatch.setenv("MASTER_PORT", "12345")
    init_distributed_environment()

    # Instantiate our and huggingface's MoE blocks
    vllm_config.compilation_config.static_forward_context = dict()
    with set_current_vllm_config(vllm_config), set_forward_context(None, vllm_config):
        config = MixtralConfig()
        hf_moe = MixtralSparseMoeBlock(config).to(dtype).to("cuda")
        vllm_moe = MixtralMoE(
            num_experts=config.num_local_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            params_dtype=dtype,
            tp_size=1,
            dp_size=1,
        ).cuda()

        # Load the weights
        vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
        for i in range(config.num_local_experts):
            weights = (
                hf_moe.experts[i].w1.weight.data,
                hf_moe.experts[i].w3.weight.data,
            )
            vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
            vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data

        # Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
        hf_inputs = torch.randn((1, 64, config.hidden_size)).to(dtype).to("cuda")
        # vLLM uses 1D query [num_tokens, hidden_dim]
        vllm_inputs = hf_inputs.flatten(0, 1)

        # Pad the weight if moe padding is enabled
        if padding:
            vllm_moe.experts.w13_weight = Parameter(
                F.pad(vllm_moe.experts.w13_weight, (0, 128), "constant", 0)[
                    ..., 0:-128
                ],
                requires_grad=False,
            )
            vllm_moe.experts.w2_weight = Parameter(
                F.pad(vllm_moe.experts.w2_weight, (0, 128), "constant", 0)[..., 0:-128],
                requires_grad=False,
            )
            torch.cuda.synchronize()
            torch.cuda.empty_cache()

        # Run forward passes for both MoE blocks
        hf_states, _ = hf_moe.forward(hf_inputs)
        vllm_states = vllm_moe.forward(vllm_inputs)

    mixtral_moe_tol = {
        torch.float32: 1e-3,
        torch.float16: 1e-3,
        torch.bfloat16: 1e-2,
    }

    if use_rocm_aiter:
        # The values of rtol and atol are set based on the tests in ROCM AITER package.
        # https://github.com/ROCm/aiter/blob/dfed377f4be7da96ca2d75ac0761f569676f7240/op_tests/test_moe.py#L174
        torch.testing.assert_close(
            hf_states.flatten(0, 1), vllm_states, rtol=0.01, atol=100
        )
    else:
        torch.testing.assert_close(
            hf_states.flatten(0, 1),
            vllm_states,
            rtol=mixtral_moe_tol[dtype],
            atol=mixtral_moe_tol[dtype],
        )


def marlin_moe_generate_valid_test_cases():
    import itertools

    m_list = [1, 123, 666]
    n_list = [128, 1024]
    k_list = [256, 2048]
    e_list = [4, 12]
    topk_list = [2, 3]
    ep_size_list = [1, 4]
    dtype_list = [torch.half, torch.bfloat16]
    group_size_list = [-1, 16, 32, 128]
    act_order_list = [True, False]
    quant_type_list = [
        scalar_types.float4_e2m1f,
        scalar_types.float8_e4m3fn,
        scalar_types.uint4,
        scalar_types.uint4b8,
        scalar_types.uint8b128,
    ]
    is_k_full_list = [True, False]

    all_combinations = itertools.product(
        m_list,
        n_list,
        k_list,
        e_list,
        topk_list,
        ep_size_list,
        dtype_list,
        group_size_list,
        act_order_list,
        quant_type_list,
        is_k_full_list,
    )

    def is_invalid(
        m, n, k, e, topk, ep_size, dtype, group_size, act_order, quant_type, is_k_full
    ):
        if quant_type == scalar_types.float8_e4m3fn and group_size not in [-1, 128]:
            return False
        if quant_type == scalar_types.float4_e2m1f:
            if group_size not in [16, 32]:
                return False
            if dtype == torch.float16 and group_size == 32:
                return False
        if quant_type != scalar_types.float4_e2m1f and group_size == 16:
            return False

        # Filter act_order
        if act_order:
            if group_size in (-1, k, n):
                return False
            if quant_type not in [scalar_types.uint4b8]:
                return False
        elif not is_k_full:
            return False

        return True

    cases = []
    for case in all_combinations:
        if is_invalid(*case):
            cases.append(case)
    return cases


@pytest.mark.flaky(reruns=2)
@pytest.mark.parametrize(
    ("m, n, k, e, topk, ep_size, dtype, group_size,act_order, quant_type, is_k_full"),
    marlin_moe_generate_valid_test_cases(),
)
@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
def test_fused_marlin_moe(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    ep_size: int,
    dtype: torch.dtype,
    group_size: int,
    act_order: bool,
    quant_type: ScalarType,
    is_k_full: bool,
):
    torch.cuda.manual_seed(0)
    has_zp = quant_type in [scalar_types.uint4, scalar_types.uint8]

    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 20
    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 20

    if ep_size > 1:
        local_e = e // ep_size
        e_ids = torch.randperm(e, device="cuda", dtype=torch.int32)[:local_e]
        e_map = torch.full((e,), -1, device="cuda", dtype=torch.int32)
        e_map[e_ids] = torch.arange(local_e, device="cuda", dtype=torch.int32)
        w1 = w1[e_ids]
        w2 = w2[e_ids]
    else:
        e_map = None

    w_ref1_l = []
    qweight1_l = []
    scales1_l = []
    global_scale1_l = []
    zeros1_l = []
    g_idx1_l = []
    sort_indices1_l = []

    for i in range(w1.shape[0]):
        if quant_type == scalar_types.float4_e2m1f:
            if group_size == 16:
                w_ref1, qweight1, scales1, global_scale1 = (
                    rand_marlin_weight_nvfp4_like(w1[i], group_size)
                )
            else:
                w_ref1, qweight1, scales1 = rand_marlin_weight_mxfp4_like(
                    w1[i], group_size
                )
                global_scale1 = None

            w_ref1_l.append(w_ref1.T)
            qweight1_l.append(qweight1)
            scales1_l.append(scales1)
            if global_scale1 is not None:
                global_scale1_l.append(global_scale1)
        elif quant_type == scalar_types.float8_e4m3fn:
            w_ref1, qweight1, scales1 = marlin_quant_fp8_torch(w1[i], group_size)
            w_ref1_l.append(w_ref1.T)
            qweight1_l.append(qweight1)
            scales1_l.append(scales1)
        elif has_zp:
            w_ref1, qweight1, scales1, zeros1 = awq_marlin_quantize(
                w1[i].transpose(1, 0), quant_type, group_size
            )

            w_ref1_l.append(w_ref1.T)
            qweight1_l.append(qweight1)
            scales1_l.append(scales1)
            zeros1_l.append(zeros1)
        else:
            test_perm = torch.randperm(k)
            w_ref1, qweight1, scales1, g_idx1, sort_indices1, _ = marlin_quantize(
                w1[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
            )

            w_ref1_l.append(w_ref1.T)
            qweight1_l.append(qweight1)
            scales1_l.append(scales1)
            g_idx1_l.append(g_idx1)
            sort_indices1_l.append(sort_indices1)

    w_ref1 = stack_and_dev(w_ref1_l)
    qweight1 = stack_and_dev(qweight1_l).contiguous()
    scales1 = stack_and_dev(scales1_l)
    global_scale1 = stack_and_dev(global_scale1_l) if global_scale1_l else None
    g_idx1 = stack_and_dev(g_idx1_l) if g_idx1_l else None
    zeros1 = stack_and_dev(zeros1_l) if zeros1_l else None
    sort_indices1 = stack_and_dev(sort_indices1_l) if sort_indices1_l else None

    w_ref2_l = []
    qweight2_l = []
    scales2_l = []
    global_scale2_l = []
    zeros2_l = []
    g_idx2_l = []
    sort_indices2_l = []

    for i in range(w2.shape[0]):
        if quant_type == scalar_types.float4_e2m1f:
            if group_size == 16:
                w_ref2, qweight2, scales2, global_scale2 = (
                    rand_marlin_weight_nvfp4_like(w2[i], group_size)
                )
            else:
                w_ref2, qweight2, scales2 = rand_marlin_weight_mxfp4_like(
                    w2[i], group_size
                )
                global_scale2 = None

            w_ref2_l.append(w_ref2.T)
            qweight2_l.append(qweight2)
            scales2_l.append(scales2)
            if global_scale2 is not None:
                global_scale2_l.append(global_scale2)
        elif quant_type == scalar_types.float8_e4m3fn:
            w_ref2, qweight2, scales2 = marlin_quant_fp8_torch(w2[i], group_size)
            w_ref2_l.append(w_ref2.T)
            qweight2_l.append(qweight2)
            scales2_l.append(scales2)
        elif has_zp:
            w_ref2, qweight2, scales2, zeros2 = awq_marlin_quantize(
                w2[i].transpose(1, 0), quant_type, group_size
            )

            w_ref2_l.append(w_ref2.T)
            qweight2_l.append(qweight2)
            scales2_l.append(scales2)
            zeros2_l.append(zeros2)
        else:
            test_perm = torch.randperm(n)
            w_ref2, qweight2, scales2, g_idx2, sort_indices2, _ = marlin_quantize(
                w2[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
            )

            w_ref2_l.append(w_ref2.T)
            qweight2_l.append(qweight2)
            scales2_l.append(scales2)
            g_idx2_l.append(g_idx2)
            sort_indices2_l.append(sort_indices2)

    w_ref2 = stack_and_dev(w_ref2_l)
    qweight2 = stack_and_dev(qweight2_l).contiguous()
    scales2 = stack_and_dev(scales2_l)
    global_scale2 = stack_and_dev(global_scale2_l) if global_scale2_l else None
    g_idx2 = stack_and_dev(g_idx2_l) if g_idx2_l else None
    zeros2 = stack_and_dev(zeros2_l) if zeros2_l else None
    sort_indices2 = stack_and_dev(sort_indices2_l) if sort_indices2_l else None

    score = torch.randn((m, e), device="cuda", dtype=dtype)

    topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)

    with set_current_vllm_config(vllm_config):
        torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, expert_map=e_map)

    marlin_output = torch.ops.vllm.fused_marlin_moe(
        a,
        qweight1,
        qweight2,
        None,
        None,
        scales1,
        scales2,
        score,
        topk_weights,
        topk_ids,
        global_num_experts=e,
        expert_map=e_map,
        global_scale1=global_scale1,
        global_scale2=global_scale2,
        g_idx1=g_idx1,
        g_idx2=g_idx2,
        sort_indices1=sort_indices1,
        sort_indices2=sort_indices2,
        w1_zeros=zeros1,
        w2_zeros=zeros2,
        quant_type_id=quant_type.id,
        is_k_full=is_k_full,
    )

    torch.testing.assert_close(marlin_output, torch_output, atol=5e-2, rtol=0)


@pytest.mark.flaky(reruns=2)
@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
@pytest.mark.parametrize("m", [1, 256])
def test_fused_marlin_moe_with_bias(m):
    torch.cuda.manual_seed(0)

    e, topk = 32, 4
    n, k = 2048, 2048
    group_size = 128
    act_order = False
    is_k_full = True
    quant_type = scalar_types.uint4b8
    dtype = torch.half

    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
    b_bias1 = torch.randn((e, 2 * n), device="cuda", dtype=dtype) / 10
    b_bias2 = torch.randn((e, k), device="cuda", dtype=dtype) / 10

    b_bias1_l = []
    w_ref1_l = []
    qweight1_l = []
    scales1_l = []
    g_idx1_l = []
    sort_indices1_l = []

    for i in range(w1.shape[0]):
        test_perm = torch.randperm(k)
        w_ref1, qweight1, scales1, g_idx1, sort_indices1, _ = marlin_quantize(
            w1[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
        )

        w_ref1_l.append(w_ref1.T)
        qweight1_l.append(qweight1)
        scales1_l.append(scales1)
        g_idx1_l.append(g_idx1)
        sort_indices1_l.append(sort_indices1)
        b_bias1_l.append(marlin_permute_bias(b_bias1[i]))

    w_ref1 = stack_and_dev(w_ref1_l)
    qweight1 = stack_and_dev(qweight1_l).contiguous()
    scales1 = stack_and_dev(scales1_l)
    global_scale1 = None
    g_idx1 = stack_and_dev(g_idx1_l) if g_idx1_l else None
    zeros1 = None
    sort_indices1 = stack_and_dev(sort_indices1_l) if sort_indices1_l else None
    marlin_bias1 = stack_and_dev(b_bias1_l) if b_bias1_l else None

    b_bias2_l = []
    w_ref2_l = []
    qweight2_l = []
    scales2_l = []
    g_idx2_l = []
    sort_indices2_l = []

    for i in range(w2.shape[0]):
        test_perm = torch.randperm(n)
        w_ref2, qweight2, scales2, g_idx2, sort_indices2, _ = marlin_quantize(
            w2[i].transpose(1, 0), quant_type, group_size, act_order, test_perm
        )

        w_ref2_l.append(w_ref2.T)
        qweight2_l.append(qweight2)
        scales2_l.append(scales2)
        g_idx2_l.append(g_idx2)
        sort_indices2_l.append(sort_indices2)
        b_bias2_l.append(marlin_permute_bias(b_bias2[i]))

    w_ref2 = stack_and_dev(w_ref2_l)
    qweight2 = stack_and_dev(qweight2_l).contiguous()
    scales2 = stack_and_dev(scales2_l)
    global_scale2 = None
    g_idx2 = stack_and_dev(g_idx2_l) if g_idx2_l else None
    zeros2 = None
    sort_indices2 = stack_and_dev(sort_indices2_l) if sort_indices2_l else None
    marlin_bias2 = stack_and_dev(b_bias2_l) if b_bias2_l else None

    score = torch.randn((m, e), device="cuda", dtype=dtype)

    topk_weights, topk_ids, _ = fused_topk(a, score, topk, False)

    with set_current_vllm_config(vllm_config):
        torch_output = torch_moe(a, w_ref1, w_ref2, score, topk, b_bias1, b_bias2)

    marlin_output = torch.ops.vllm.fused_marlin_moe(
        a,
        qweight1,
        qweight2,
        marlin_bias1,
        marlin_bias2,
        scales1,
        scales2,
        score,
        topk_weights,
        topk_ids,
        global_num_experts=e,
        expert_map=None,
        global_scale1=global_scale1,
        global_scale2=global_scale2,
        g_idx1=g_idx1,
        g_idx2=g_idx2,
        sort_indices1=sort_indices1,
        sort_indices2=sort_indices2,
        w1_zeros=zeros1,
        w2_zeros=zeros2,
        quant_type_id=quant_type.id,
        is_k_full=is_k_full,
    )

    torch.testing.assert_close(marlin_output, torch_output, atol=5e-2, rtol=0)


def test_moe_align_block_size_opcheck():
    num_experts = 4
    block_size = 4
    topk_ids = torch.randint(0, num_experts, (3, 4), dtype=torch.int32, device="cuda")

    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
    sorted_ids = torch.empty(
        (max_num_tokens_padded,), dtype=torch.int32, device=topk_ids.device
    )
    sorted_ids.fill_(topk_ids.numel())
    max_num_m_blocks = max_num_tokens_padded // block_size
    expert_ids = torch.empty(
        (max_num_m_blocks,), dtype=torch.int32, device=topk_ids.device
    )
    num_tokens_post_pad = torch.empty((1), dtype=torch.int32, device=topk_ids.device)

    opcheck(
        torch.ops._moe_C.moe_align_block_size,
        (
            topk_ids,
            num_experts,
            block_size,
            sorted_ids,
            expert_ids,
            num_tokens_post_pad,
        ),
    )


@pytest.mark.parametrize("m", [1, 33, 64, 222])
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("k", [128, 511, 1024])
@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
@pytest.mark.skipif(current_platform.is_rocm(), reason="Skip for rocm")
def test_moe_sum(m: int, topk: int, k: int, dtype: torch.dtype):
    input = torch.randn((m, topk, k), device="cuda", dtype=dtype)
    actual = torch.empty((m, k), device="cuda", dtype=dtype)

    expected = input.sum(dim=1)
    torch.ops._moe_C.moe_sum(input, actual)

    torch.testing.assert_close(actual, expected, atol=2e-2, rtol=0)

    opcheck(torch.ops._moe_C.moe_sum, (input, actual))


@pytest.mark.parametrize("m", [1, 33])
@pytest.mark.parametrize("n,k", [(128, 128)])
@pytest.mark.parametrize("e", [8])
@pytest.mark.parametrize("topk", [2])
@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
@pytest.mark.parametrize("with_bias", [False, True])
@pytest.mark.parametrize("activation", ["silu"])
@pytest.mark.skipif(not current_platform.is_cpu(), reason="CPU only test")
def test_cpu_fused_moe_basic(m, n, k, e, topk, dtype, with_bias, activation):
    from vllm.model_executor.layers.fused_moe.cpu_fused_moe import CPUFusedMOE

    device = "cpu"
    torch.manual_seed(7)

    a = torch.randn((m, k), device=device, dtype=dtype) / 10
    w13 = torch.randn((e, 2 * n, k), device=device, dtype=dtype) / 10
    w2 = torch.randn((e, k, n), device=device, dtype=dtype) / 10
    router_logits = torch.randn((m, e), device=device, dtype=dtype)

    b1 = b2 = None
    if with_bias:
        b1 = torch.randn((e, 2 * n), device=device, dtype=dtype) / 10
        b2 = torch.randn((e, k), device=device, dtype=dtype) / 10

    ref = (
        torch_moe(a, w13, w2, router_logits, topk, b1, b2)
        if with_bias
        else torch_moe(a, w13, w2, router_logits, topk)
    )

    class _Dummy(torch.nn.Module):
        def __init__(self, w13, w2, b1=None, b2=None):
            super().__init__()
            self.w13_weight = torch.nn.Parameter(w13, requires_grad=False)
            self.w2_weight = torch.nn.Parameter(w2, requires_grad=False)
            if b1 is not None:
                self.w13_bias = torch.nn.Parameter(b1, requires_grad=False)
            if b2 is not None:
                self.w2_bias = torch.nn.Parameter(b2, requires_grad=False)

    layer = _Dummy(w13, w2, b1, b2).to(dtype)
    fused = CPUFusedMOE(layer)
    out = fused(
        layer=layer,
        x=a,
        use_grouped_topk=False,
        top_k=topk,
        router_logits=router_logits,
        renormalize=False,
        global_num_experts=e,
        expert_map=None,
        custom_routing_function=None,
        scoring_func="softmax",
        routed_scaling_factor=1.0,
        e_score_correction_bias=None,
        apply_router_weight_on_input=False,
        activation=activation,
    )

    # Tolerances: fp32 tight; bf16 looser (esp. with bias)
    if dtype == torch.float32:
        atol = 1e-3
    elif with_bias:
        atol = 8e-2
    else:
        atol = 5e-2
    torch.testing.assert_close(out, ref, atol=atol, rtol=0)
