test_nvfp4_moe.py

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

import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from tests.kernels.moe.utils import make_dummy_moe_config, make_test_weights
from tests.kernels.quantization.nvfp4_utils import (
    FLOAT4_E2M1_MAX,
    FLOAT8_E4M3_MAX,
    dequantize_nvfp4_to_dtype,
)
from tests.kernels.utils import torch_moe
from vllm import _custom_ops as ops
from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
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.all2all_utils import (
    maybe_make_prepare_finalize,
)
from vllm.model_executor.layers.fused_moe.config import nvfp4_moe_quant_config
from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
    CutlassExpertsFp4,
)
from vllm.model_executor.layers.fused_moe.prepare_finalize import (
    make_moe_prepare_and_finalize_no_dp_ep,
)
from vllm.platforms import current_platform
from vllm.utils.torch_utils import set_random_seed

if not current_platform.has_device_capability(100):
    pytest.skip(
        "Nvfp4 Requires compute capability of 10 or above.", allow_module_level=True
    )

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


@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.bfloat16])
@torch.inference_mode()
def test_cutlass_fp4_moe_no_graph(
    m: int, n: int, k: int, e: int, topk: int, dtype: torch.dtype, workspace_init
):
    set_random_seed(7)
    with set_current_vllm_config(
        VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
    ):
        quant_blocksize = 16

        a = torch.randn((m, k), device="cuda", dtype=dtype) / 10

        (_, w1_q, w1_blockscale, w1_gs), (_, w2_q, w2_blockscale, w2_gs) = (
            make_test_weights(
                e,
                n,
                k,
                in_dtype=dtype,
                quant_dtype="nvfp4",
                block_shape=None,  # use quant_blocksize?
                per_out_ch_quant=False,
            )
        )

        score = torch.randn((m, e), device="cuda", dtype=dtype)
        topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=False)

        a1_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
        a2_gs = torch.ones((e,), device="cuda", dtype=torch.float32)

        assert w1_gs is not None
        assert w2_gs is not None
        assert w1_blockscale is not None
        assert w2_blockscale is not None

        quant_config = nvfp4_moe_quant_config(
            g1_alphas=(1 / w1_gs),
            g2_alphas=(1 / w2_gs),
            a1_gscale=a1_gs,
            a2_gscale=a2_gs,
            w1_scale=w1_blockscale,
            w2_scale=w2_blockscale,
        )
        moe_config = make_dummy_moe_config()

        kernel = mk.FusedMoEKernel(
            maybe_make_prepare_finalize(
                moe=moe_config,
                quant_config=quant_config,
                allow_new_interface=True,
                use_monolithic=False,
            ),
            CutlassExpertsFp4(
                moe_config=moe_config,
                quant_config=quant_config,
            ),
            inplace=False,
        )

        cutlass_output = kernel.apply(
            hidden_states=a,
            w1=w1_q,
            w2=w2_q,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            global_num_experts=e,
            activation=mk.MoEActivation.SILU,
            apply_router_weight_on_input=False,
            expert_map=None,
        )

        # 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 = ops.scaled_fp4_quant(a, a_global_scale)

        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=dtype,
                device=w1_q.device,
                block_size=quant_blocksize,
            )
            w2_d[idx] = dequantize_nvfp4_to_dtype(
                w2_q[idx],
                w2_blockscale[idx],
                w2_gs[idx],
                dtype=dtype,
                device=w2_q.device,
                block_size=quant_blocksize,
            )

        torch_output = torch_moe(a_in_dtype, w1_d, w2_d, score, topk)

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


# step3.5-flash uses swiglustep activation (clipped SwiGLU with limit=7.0)
# for MoE layers 43-44. This tests the non-fused activation fallback path
# in run_cutlass_moe_fp4 (apply_moe_activation + separate fp4 quantization).
# Model dims: e=288, topk=8, n=1280 (moe_intermediate_size), k=4096 (hidden)
SWIGLUSTEP_MNK_FACTORS = [
    (2, 1280, 4096),
    (64, 1280, 4096),
    (224, 1280, 4096),
]


@pytest.mark.parametrize("m,n,k", SWIGLUSTEP_MNK_FACTORS)
@pytest.mark.parametrize("e", [64, 288])
@pytest.mark.parametrize("topk", [1, 8])
@pytest.mark.parametrize("dtype", [torch.bfloat16])
@torch.inference_mode()
def test_cutlass_fp4_moe_swiglustep(
    m: int, n: int, k: int, e: int, topk: int, dtype: torch.dtype, workspace_init
):
    set_random_seed(7)
    with set_current_vllm_config(
        VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
    ):
        quant_blocksize = 16

        a = torch.randn((m, k), device="cuda", dtype=dtype) / 10

        (_, w1_q, w1_blockscale, w1_gs), (_, w2_q, w2_blockscale, w2_gs) = (
            make_test_weights(
                e,
                n,
                k,
                in_dtype=dtype,
                quant_dtype="nvfp4",
                block_shape=None,
                per_out_ch_quant=False,
            )
        )

        score = torch.randn((m, e), device="cuda", dtype=dtype)
        topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=False)

        a1_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
        a2_gs = torch.ones((e,), device="cuda", dtype=torch.float32)

        assert w1_gs is not None
        assert w2_gs is not None
        assert w1_blockscale is not None
        assert w2_blockscale is not None

        quant_config = nvfp4_moe_quant_config(
            g1_alphas=(1 / w1_gs),
            g2_alphas=(1 / w2_gs),
            a1_gscale=a1_gs,
            a2_gscale=a2_gs,
            w1_scale=w1_blockscale,
            w2_scale=w2_blockscale,
        )

        kernel = mk.FusedMoEKernel(
            make_moe_prepare_and_finalize_no_dp_ep(use_monolithic=False),
            CutlassExpertsFp4(
                moe_config=make_dummy_moe_config(),
                quant_config=quant_config,
            ),
            inplace=False,
        )

        cutlass_output = kernel.apply(
            hidden_states=a,
            w1=w1_q,
            w2=w2_q,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            activation=MoEActivation.SWIGLUSTEP,
            global_num_experts=e,
            expert_map=None,
            apply_router_weight_on_input=False,
        )

        # Reference: dequantize everything and run torch_moe with swiglustep
        a_global_scale = (
            (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(a.flatten(), dim=-1)
        ).to(torch.float32)
        a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(a, a_global_scale)

        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=dtype,
                device=w1_q.device,
                block_size=quant_blocksize,
            )
            w2_d[idx] = dequantize_nvfp4_to_dtype(
                w2_q[idx],
                w2_blockscale[idx],
                w2_gs[idx],
                dtype=dtype,
                device=w2_q.device,
                block_size=quant_blocksize,
            )

        torch_output = torch_moe(
            a_in_dtype,
            w1_d,
            w2_d,
            score,
            topk,
            activation=MoEActivation.SWIGLUSTEP,
        )

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


if __name__ == "__main__":
    test_cutlass_fp4_moe_no_graph((2, 1024, 1024), 40, 1, torch.half)