test/test_fp4_moe.py

# SPDX-License-Identifier: Apache-2.0
from typing import Callable

import pytest
import torch
from flashinfer import fp4_quantize
from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
from sgl_kernel import scaled_fp4_grouped_quant, scaled_fp4_quant
from torch.nn import functional as F

from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.moe.cutlass_moe import cutlass_moe_fp4
from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType
from sglang.srt.layers.moe.flashinfer_cutedsl_moe import flashinfer_cutedsl_moe_masked
from sglang.srt.layers.moe.topk import TopKConfig, select_experts

if torch.cuda.get_device_capability() < (10, 0):
    pytest.skip(
        reason="Nvfp4 Requires compute capability of 10 or above.",
        allow_module_level=True,
    )

kE2M1ToFloat = torch.tensor(
    [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], dtype=torch.float32
)

FLOAT8_E4M3_MAX = 448.0
FLOAT4_E2M1_MAX = 6.0


def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
    m_tiles = (m + 128 - 1) // 128
    f = block_size * 4
    k_tiles = (k + f - 1) // f
    tmp = torch.reshape(a_sf_swizzled, (1, m_tiles, k_tiles, 32, 4, 4))
    tmp = torch.permute(tmp, (0, 1, 4, 3, 2, 5))
    out = tmp.reshape(m_tiles * 128, k_tiles * f // block_size)
    return out[0:m, 0:k]


def dequantize_nvfp4_to_dtype(
    tensor_fp4, tensor_sf, global_scale, dtype, device, block_size=16
):
    """Dequantize the fp4 tensor back to high precision."""
    # Two fp4 values are packed into one uint8.
    assert tensor_fp4.dtype == torch.uint8
    m, packed_k = tensor_fp4.shape
    k = packed_k * 2
    tensor_f32 = break_fp4_bytes(tensor_fp4, dtype)
    tensor_f32 = tensor_f32.reshape(m, k // block_size, block_size)
    tensor_sf = tensor_sf.view(torch.float8_e4m3fn)
    tensor_sf = convert_swizzled_to_linear(tensor_sf, m, k, block_size)
    tensor_sf_dtype = tensor_sf.to(torch.float32) / global_scale

    # scale the tensor
    out = (tensor_f32 * tensor_sf_dtype.unsqueeze(-1)).reshape(m, k)
    return out.to(dtype=dtype)


def break_fp4_bytes(a, dtype):
    assert a.dtype == torch.uint8
    m, n = a.shape

    # Vectorized nibble processing
    a_flat = a.flatten()
    high = (a_flat & 0xF0) >> 4  # Upper nibbles
    low = a_flat & 0x0F  # Lower nibbles

    # Combine nibbles for batch processing
    combined = torch.stack((low, high), dim=1).flatten()

    # Vectorized sign and magnitude extraction
    signs = (combined & 0x08).to(torch.bool)  # Sign bits
    abs_vals = (combined & 0x07).to(torch.long)  # Magnitude indices

    # Device-aware lookup and sign application
    kE2M1 = kE2M1ToFloat.to(device=a.device)
    values = kE2M1[abs_vals] * torch.where(signs, -1.0, 1.0)

    # Reshape to final form
    return values.reshape(m, n * 2).to(dtype=dtype)


def compute_routing(router_logits: torch.Tensor, top_k: int):
    routing_weights = torch.softmax(router_logits, dim=1, dtype=torch.float)
    routing_weights, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
    routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
    routing_weights = routing_weights.float()
    return routing_weights, selected_experts


def prepare_inputs(
    hidden_states: torch.Tensor,
    router_logits: torch.Tensor,
    num_experts: int,
    topk: int,
):
    routing_weights, topk_idx = compute_routing(router_logits, topk)

    masked_m = []
    for i in range(num_experts):
        mask = topk_idx.view(-1) == i
        masked_m.append(mask.sum())

    masked_m = torch.tensor(masked_m, dtype=torch.int32)
    hidden_states_3d = torch.empty(
        (num_experts, max(masked_m), hidden_states.shape[1]), dtype=hidden_states.dtype
    )
    for i in range(num_experts):
        hidden_states_3d[i, : masked_m[i], :] = hidden_states[topk_idx.view(-1) == i]

    return hidden_states_3d, masked_m, topk_idx, routing_weights


MNK_FACTORS = [
    (2, 1024, 1024),
    (2, 1024, 1536),
    (2, 3072, 1024),
    (2, 3072, 1536),
    (64, 1024, 1024),
    (64, 1024, 1536),
    (64, 3072, 1024),
    (64, 2048, 1024),
    (224, 1024, 1024),
    (224, 1024, 1536),
]


# Reference implementation of torch_moe
def torch_moe(a, w1, w2, score, topk, expert_map):
    B, D = a.shape
    a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
    out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
    score = torch.softmax(score, dim=-1, dtype=torch.float32)
    topk_weight, topk_ids = torch.topk(score, topk)
    topk_weight = topk_weight.view(-1)
    topk_ids = topk_ids.view(-1)
    if expert_map is not None:
        topk_ids = expert_map[topk_ids]
    for i in range(w1.shape[0]):
        mask = topk_ids == i
        if mask.sum():
            out[mask] = SiluAndMul()(a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(
                0, 1
            )
    return (
        out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
    ).sum(dim=1)


def torch_moe_nvfp4(a, w1, w2, topk, topk_weight, topk_ids):
    B, D = a.shape
    a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
    out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)

    topk_weight = topk_weight.view(-1)
    topk_ids = topk_ids.view(-1)

    for i in range(w1.shape[0]):
        mask = topk_ids == i
        if mask.sum():
            m = w1[i].shape[0]
            assert m % 2 == 0
            # Note: w1 and w3 are swapped!
            w3_expert, w1_expert = w1[i][m // 2 :, :], w1[i][: m // 2, :]
            inter = F.silu(a[mask] @ w1_expert.t()) * (a[mask] @ w3_expert.t())
            inter_gs = torch.tensor(1.0).cuda()
            inter_q, inter_blockscale = fp4_quantize(inter, inter_gs)
            inter = dequantize_nvfp4_to_dtype(
                inter_q,
                inter_blockscale,
                inter_gs,
                dtype=inter.dtype,
                device=inter.device,
                block_size=16,
            ).cuda()
            out[mask] = inter @ w2[i].transpose(0, 1)
    return (
        out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
    ).sum(dim=1)


def flashinfer_cutedsl_grouped_gemm_nt_masked(
    hidden_states: torch.Tensor,  # 3d
    input_global_scale: torch.Tensor,  # (l,)
    weights: torch.Tensor,
    w_global_scale: torch.Tensor,  # (l,)
    masked_m: torch.Tensor,
):
    from flashinfer.cute_dsl.blockscaled_gemm import grouped_gemm_nt_masked

    # hidden_states: [l, m, k]
    # weights: [l, n, k]
    aq, aq_sf = scaled_fp4_grouped_quant(
        hidden_states,
        input_global_scale,
        masked_m.to(hidden_states.device),
    )
    num_experts, n, k = weights.shape
    bq, bq_sf = scaled_fp4_grouped_quant(
        weights,
        w_global_scale,
        torch.ones(num_experts, device=weights.device, dtype=torch.int32) * n,
    )

    out = torch.zeros(
        (num_experts, max(masked_m), n), dtype=weights.dtype, device=aq.device
    )
    out = out.permute(1, 2, 0)  # requirement of kernel
    sf_vec_size = 16
    ab_dtype = "float4_e2m1fn"
    sf_dtype = "float8_e4m3fn"
    c_dtype = "bfloat16"
    alpha = 1.0 / (input_global_scale * w_global_scale).to(out.dtype).view(
        1, 1, num_experts
    )

    def get_cute_dtype(input: torch.Tensor) -> str:
        if input.dtype == torch.bfloat16:
            return "bfloat16"
        elif input.dtype == torch.float16:
            return "float16"
        elif input.dtype == torch.float32:
            return "float32"
        else:
            raise ValueError(f"Unsupported cute dtype {input.dtype}")

    grouped_gemm_nt_masked(
        (aq, aq_sf),
        (bq, bq_sf),
        out,
        masked_m.to(aq.device),
        ab_dtype=ab_dtype,
        sf_dtype=sf_dtype,
        c_dtype=c_dtype,
        sf_vec_size=sf_vec_size,
        alpha=alpha,
        alpha_dtype=get_cute_dtype(alpha),
    )

    return out


def check_moe(
    m: int,
    n: int,
    k: int,
    e: int,
    topk: int,
    dtype: torch.dtype,
    moe_impl: Callable,
    flip_w13: bool,
):
    torch.manual_seed(7)
    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
    w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
    quant_blocksize = 16
    round_up = lambda x, y: (x + y - 1) // y * y
    sf_w1_2n = round_up(2 * n, 128)
    sf_w1_k = round_up(k // quant_blocksize, 4)
    w1_blockscale = torch.empty(
        (e, sf_w1_2n, sf_w1_k), device="cuda", dtype=torch.float8_e4m3fn
    )

    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
    sf_w2_k = round_up(k, 128)
    sf_w2_n = round_up(n // quant_blocksize, 4)
    w2_blockscale = torch.empty(
        (e, sf_w2_k, sf_w2_n), device="cuda", dtype=torch.float8_e4m3fn
    )

    w1_q = torch.empty((e, 2 * n, k // 2), device="cuda", dtype=torch.uint8)
    w2_q = torch.empty((e, k, n // 2), device="cuda", dtype=torch.uint8)
    w1_gs = torch.empty((e,), device="cuda", dtype=torch.float32)
    w2_gs = torch.empty((e,), device="cuda", dtype=torch.float32)

    for expert in range(e):
        w1_amax = torch.abs(w1).max().to(torch.float32)
        w2_amax = torch.abs(w2).max().to(torch.float32)
        w1_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
        w2_gs[expert] = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax

        w1_q[expert], w1_blockscale[expert] = scaled_fp4_quant(
            w1[expert], w1_gs[expert]
        )

        w2_q[expert], w2_blockscale[expert] = scaled_fp4_quant(
            w2[expert], w2_gs[expert]
        )

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

    topk_output = select_experts(
        hidden_states=a,
        router_logits=score,
        topk_config=TopKConfig(top_k=topk, renormalize=False),
    )
    topk_weights, topk_ids, _ = topk_output

    a1_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
    a2_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
    test_output = moe_impl(
        a=a,
        topk_weights=topk_weights,
        topk_ids=topk_ids,
        w1_q=w1_q,
        w2_q=w2_q,
        a1_gs=a1_gs,
        w1_blockscale=w1_blockscale,
        w1_alphas=(1 / w1_gs),
        a2_gs=a2_gs,
        w2_blockscale=w2_blockscale,
        w2_alphas=(1 / w2_gs),
    )

    # Reference check:
    a_global_scale = (
        (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(a.flatten(), dim=-1)
    ).to(torch.float32)
    a_fp4, a_scale_interleaved = scaled_fp4_quant(a, a_global_scale)
    _, m_k = a_fp4.shape
    a_in_dtype = dequantize_nvfp4_to_dtype(
        a_fp4,
        a_scale_interleaved,
        a_global_scale,
        dtype=a.dtype,
        device=a.device,
        block_size=quant_blocksize,
    )

    w1_d = torch.empty((e, 2 * n, k), device="cuda", dtype=dtype)
    w2_d = torch.empty((e, k, n), device="cuda", dtype=dtype)

    for idx in range(0, e):
        w1_d[idx] = dequantize_nvfp4_to_dtype(
            w1_q[idx],
            w1_blockscale[idx],
            w1_gs[idx],
            dtype=w1.dtype,
            device=w1.device,
            block_size=quant_blocksize,
        )
        w2_d[idx] = dequantize_nvfp4_to_dtype(
            w2_q[idx],
            w2_blockscale[idx],
            w2_gs[idx],
            dtype=w2.dtype,
            device=w2.device,
            block_size=quant_blocksize,
        )

    if flip_w13:
        dim = -2
        size = w1_d.size(dim)
        assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
        half = size // 2
        # Reorder weight
        w1, w3 = w1_d.split(half, dim=dim)
        w1_d = torch.cat([w3, w1], dim=dim).contiguous()

    torch_output = torch_moe(a_in_dtype, w1_d, w2_d, score, topk, None)

    torch.testing.assert_close(torch_output, test_output, atol=1e-1, rtol=1e-1)


@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("e", [40, 64, 256])
@pytest.mark.parametrize("topk", [1, 6, 8])
@pytest.mark.parametrize("dtype", [torch.half, torch.bfloat16])
@torch.inference_mode()
def test_cutlass_fp4_moe_no_graph(
    m: int, n: int, k: int, e: int, topk: int, dtype: torch.dtype
):
    def cutlass_moe_impl(
        a,
        topk_weights,
        topk_ids,
        w1_q,
        w2_q,
        a1_gs,
        w1_blockscale,
        w1_alphas,
        a2_gs,
        w2_blockscale,
        w2_alphas,
    ):
        params = CutlassMoEParams(
            CutlassMoEType.BlockscaledFP4,
            device=a.device,
            num_experts=e,
            intermediate_size_per_partition=n,  # n
            hidden_size=k,
        )  # k
        return cutlass_moe_fp4(
            a=a,
            a1_gscale=a1_gs,
            w1_fp4=w1_q,
            w1_blockscale=w1_blockscale,
            w1_alphas=w1_alphas,
            a2_gscale=a2_gs,
            w2_fp4=w2_q,
            w2_blockscale=w2_blockscale,
            w2_alphas=w2_alphas,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            params=params,
            apply_router_weight_on_input=False,
        )

    check_moe(m, n, k, e, topk, dtype, cutlass_moe_impl, flip_w13=False)


@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
@pytest.mark.parametrize("e", [40, 64, 256])
@pytest.mark.parametrize("topk", [1, 6, 8])
@pytest.mark.parametrize("dtype", [torch.half, torch.bfloat16])
@torch.inference_mode()
def test_flashinfer_fp4_moe_no_graph(
    m: int, n: int, k: int, e: int, topk: int, dtype: torch.dtype
):
    def flashinfer_moe_impl(
        a,
        topk_weights,
        topk_ids,
        w1_q,
        w2_q,
        a1_gs,
        w1_blockscale,
        w1_alphas,
        a2_gs,
        w2_blockscale,
        w2_alphas,
    ):
        return flashinfer_cutlass_fused_moe(
            a,
            topk_ids.to(torch.int),
            topk_weights,
            w1_q.view(torch.long),
            w2_q.view(torch.long),
            a.dtype,
            quant_scales=[
                a1_gs,
                w1_blockscale.view(torch.int32),
                w1_alphas,
                a2_gs,
                w2_blockscale.view(torch.int32),
                w2_alphas,
            ],
        )[0]

    check_moe(m, n, k, e, topk, dtype, flashinfer_moe_impl, flip_w13=True)


@pytest.mark.parametrize("bs, hidden_dim, inter_dim", [(2, 128, 256), (16, 128, 512)])
@pytest.mark.parametrize("topk", [1, 2, 4])
@torch.inference_mode()
def test_flashinfer_cutedsl_moe_masked(
    bs: int, hidden_dim: int, inter_dim: int, topk: int
):
    torch.manual_seed(42)
    device = "cuda"
    dtype = torch.bfloat16
    num_experts = 8
    hidden_states = (
        torch.randn(bs, hidden_dim, dtype=torch.bfloat16, device=device) / 5.0
    )
    w1 = (
        torch.randn(
            num_experts, 2 * inter_dim, hidden_dim, dtype=torch.bfloat16, device=device
        )
        / 10.0
    )
    w2 = (
        torch.randn(
            num_experts, hidden_dim, inter_dim, dtype=torch.bfloat16, device=device
        )
        / 10.0
    )
    router_logits = torch.randn(bs, num_experts, dtype=torch.float32)

    hidden_states_expanded = (
        hidden_states.view(bs, -1, hidden_dim)
        .repeat(1, topk, 1)
        .reshape(-1, hidden_dim)
    )
    hidden_states_3d, masked_m, topk_idx, routing_weights = prepare_inputs(
        hidden_states_expanded, router_logits, num_experts, topk
    )

    w1_amax = w1.abs().amax(dim=(1, 2)).to(torch.float32).to(w1.device)
    w2_amax = w2.abs().amax(dim=(1, 2)).to(torch.float32).to(w2.device)
    input_global_scale = torch.ones(
        (num_experts,), dtype=torch.float32, device=hidden_states.device
    )

    w1_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
    w2_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
    a2_global_scale = torch.ones(
        (num_experts,), dtype=torch.float32, device=hidden_states.device
    )  # assume intermediate scale is 1.0

    w1_fp4, w1_blockscale = scaled_fp4_grouped_quant(
        w1,
        w1_global_scale,
        torch.ones(num_experts, dtype=torch.int32, device=w1.device) * 2 * inter_dim,
    )
    w2_fp4, w2_blockscale = scaled_fp4_grouped_quant(
        w2,
        w2_global_scale,
        torch.ones(num_experts, dtype=torch.int32, device=w2.device) * hidden_dim,
    )

    w1_alpha = 1.0 / (input_global_scale * w1_global_scale)
    w2_alpha = 1.0 / (a2_global_scale * w2_global_scale)

    out = flashinfer_cutedsl_moe_masked(
        hidden_states_3d.to(hidden_states.device),
        input_global_scale,
        w1_fp4.permute(2, 0, 1),
        w1_blockscale,
        w1_alpha,
        w2_fp4.permute(2, 0, 1),
        a2_global_scale,
        w2_blockscale,
        w2_alpha,
        masked_m.to(hidden_states.device),
    )

    # reference
    a_fp4, a_scale_interleaved = fp4_quantize(hidden_states, input_global_scale)
    a_in_dtype = dequantize_nvfp4_to_dtype(
        a_fp4,
        a_scale_interleaved,
        input_global_scale,
        dtype=hidden_states.dtype,
        device=hidden_states.device,
        block_size=16,
    )
    w1_d = torch.empty(
        (num_experts, 2 * inter_dim, hidden_dim), device=w1.device, dtype=w1.dtype
    )
    w2_d = torch.empty(
        (num_experts, hidden_dim, inter_dim), device=w2.device, dtype=w2.dtype
    )

    for idx in range(0, num_experts):
        w1_fp4_sliced, w1_blockscale_sliced = fp4_quantize(
            w1[idx], w1_global_scale[idx]
        )
        w2_fp4_sliced, w2_blockscale_sliced = fp4_quantize(
            w2[idx], w2_global_scale[idx]
        )
        w1_d[idx] = dequantize_nvfp4_to_dtype(
            w1_fp4_sliced,
            w1_blockscale_sliced,
            w1_global_scale[idx],
            dtype=w1.dtype,
            device=w1.device,
            block_size=16,
        )
        w2_d[idx] = dequantize_nvfp4_to_dtype(
            w2_fp4_sliced,
            w2_blockscale_sliced,
            w2_global_scale[idx],
            dtype=w2.dtype,
            device=w2.device,
            block_size=16,
        )

    ref_output = torch_moe_nvfp4(
        a_in_dtype,
        w1_d,
        w2_d,
        topk,
        routing_weights.to(a_in_dtype.device),
        topk_idx.to(a_in_dtype.device),
    )
    out_weighted = torch.zeros_like(ref_output, device=out.device, dtype=out.dtype)

    positions = torch.nonzero(masked_m[topk_idx], as_tuple=False)
    rows, cols = positions[:, 0], positions[:, 1]
    experts = topk_idx[rows, cols]
    for i in range(num_experts):
        mask = experts == i
        if mask.any():
            idx = torch.nonzero(mask, as_tuple=False).squeeze(-1)
            r, c = rows[idx], cols[idx]
            out_weighted[r] += out[i, : len(r), :] * routing_weights[r, c].to(
                out.device
            ).unsqueeze(-1)
    torch.testing.assert_close(
        out_weighted.cpu(), ref_output.cpu(), atol=5e-2, rtol=5e-2
    )


@pytest.mark.parametrize(
    "bs, hidden_dim, inter_dim, topk", [(2, 128, 256, 2), (16, 128, 512, 5)]
)
@torch.inference_mode()
def test_grouped_gemm_nt_masked(
    bs: int, hidden_dim: int, inter_dim: int, topk: int
) -> None:
    torch.manual_seed(42)
    B = bs
    D = hidden_dim
    N = inter_dim
    num_experts = 8
    hidden_states = torch.randn(B, D, dtype=torch.bfloat16, device="cuda")
    weights = torch.randn(num_experts, N, D, dtype=torch.bfloat16, device="cuda")
    router_logits = torch.randn(B, num_experts, dtype=torch.float32)

    hidden_states_expanded = (
        hidden_states.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
    )
    hidden_states_3d, masked_m, topk_idx, _ = prepare_inputs(
        hidden_states_expanded, router_logits, num_experts, topk
    )

    # reference
    out = torch.zeros(
        (B * topk, weights.shape[1]), dtype=weights.dtype, device=weights.device
    )
    for i in range(num_experts):
        mask = topk_idx.view(-1) == i
        if mask.sum():
            lhs = hidden_states_expanded[mask]
            rhs = weights[i]
            a_amax = lhs.abs().max().to(torch.float32).to(hidden_states.device)
            b_amax = rhs.abs().amax().to(torch.float32).to(weights.device)
            a_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
            b_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax

            lhsq, lhsq_sf = fp4_quantize(
                lhs,
                a_gs,
            )
            rhsq, rhsq_sf = fp4_quantize(
                rhs,
                b_gs,
            )

            lhs_in_dtype = dequantize_nvfp4_to_dtype(
                lhsq,
                lhsq_sf,
                a_gs,
                dtype=hidden_states.dtype,
                device=hidden_states.device,
                block_size=16,
            )

            rhs_in_dtype = dequantize_nvfp4_to_dtype(
                rhsq,
                rhsq_sf,
                b_gs,
                dtype=hidden_states.dtype,
                device=hidden_states.device,
                block_size=16,
            )
            out[mask] = lhs_in_dtype @ rhs_in_dtype.t()

    a_amax = (
        hidden_states_3d.abs()
        .amax(dim=(1, 2))
        .to(torch.float32)
        .to(hidden_states.device)
    )
    b_amax = weights.abs().amax(dim=(1, 2)).to(torch.float32).to(weights.device)
    a_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
    b_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
    out_flashinfer = flashinfer_cutedsl_grouped_gemm_nt_masked(
        hidden_states_3d.to(hidden_states.device), a_gs, weights, b_gs, masked_m
    )

    # re-pack out into [num_experts, max_m, n]
    out_ref = torch.zeros(
        (num_experts, max(masked_m), weights.shape[1]), dtype=out.dtype
    )
    expert_slot = [0] * num_experts
    for i, expert_id in enumerate(topk_idx.view(-1).tolist()):
        out_ref[expert_id, expert_slot[expert_id], :] = out[i]
        expert_slot[expert_id] += 1

    # Note: just to compare the masked position due to cutedsl may write nan
    # into unmasked position.
    for i in range(num_experts):
        torch.testing.assert_close(
            out_flashinfer.permute(2, 0, 1)[i, : masked_m[i]],
            out_ref.to(out_flashinfer.device)[i, : masked_m[i]],
            atol=1e-1,
            rtol=5e-2,
        )


if __name__ == "__main__":
    test_cutlass_fp4_moe_no_graph(224, 1024, 1024, 256, 8, torch.half)
    test_flashinfer_fp4_moe_no_graph(224, 1024, 1024, 256, 8, torch.half)
    test_flashinfer_cutedsl_moe_masked(16, 128, 512, 4)
    test_grouped_gemm_nt_masked(16, 128, 512, 4)