test_moe_fused_gate.py

import pytest
import torch
from sgl_kernel import moe_fused_gate

from sglang.srt.layers.moe.topk import biased_grouped_topk


@pytest.mark.parametrize(
    "seq_length",
    list(range(1, 10))
    + [16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536],
)
@pytest.mark.parametrize("dtype", [torch.float16, torch.float32, torch.bfloat16])
@pytest.mark.parametrize(
    "params",
    [
        (128, 4, 2, 4),
        (256, 8, 4, 8),  # deepseek v3
        (512, 16, 8, 16),
    ],
)
def test_moe_fused_gate_combined(seq_length, dtype, params):
    num_experts, num_expert_group, topk_group, topk = params

    torch.manual_seed(seq_length)
    tensor = torch.rand((seq_length, num_experts)).to(dtype).cuda()
    scores = tensor.clone()
    bias = torch.rand(num_experts).to(dtype).cuda()

    output, indices = moe_fused_gate(
        tensor,
        bias,
        num_expert_group=num_expert_group,
        topk_group=topk_group,
        topk=topk,
    )
    ref_output, ref_indices = biased_grouped_topk(
        scores,
        scores,
        bias,
        topk=topk,
        renormalize=True,
        num_expert_group=num_expert_group,
        topk_group=topk_group,
        compiled=False,
    )

    idx_check = torch.allclose(
        ref_indices.sort()[0].to(torch.int32),
        indices.sort()[0].to(torch.int32),
        rtol=1e-04,
        atol=1e-05,
    )
    output_check = torch.allclose(
        ref_output.sort()[0].to(torch.float32),
        output.sort()[0].to(torch.float32),
        rtol=1e-04,
        atol=1e-05,
    )

    assert idx_check, (
        f"Indices mismatch at seq_length {seq_length}, dtype {dtype}, "
        f"params {params}"
    )
    assert output_check, (
        f"Output mismatch at seq_length {seq_length}, dtype {dtype}, "
        f"params {params}"
    )


if __name__ == "__main__":
    pytest.main([__file__])