Add unit test for flashinfer fp4 moe (#8330)

Co-authored-by: Yineng Zhang <me@zhyncs.com>

python/sglang/test/test_fp4_moe.py

@@ -1,6 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
+from typing import Callable
+
 import pytest
 import torch
+from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
 from sgl_kernel import scaled_fp4_quant
 
 from sglang.srt.layers.activation import SiluAndMul
@@ -111,15 +114,16 @@ def torch_moe(a, w1, w2, score, topk, expert_map):
     ).sum(dim=1)
 
 
-@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
+def check_moe(
-@pytest.mark.parametrize("e", [40, 64, 256])
+    m: int,
-@pytest.mark.parametrize("topk", [1, 6, 8])
+    n: int,
-@pytest.mark.parametrize("dtype", [torch.half, torch.bfloat16])
+    k: int,
-@torch.inference_mode()
+    e: int,
-def test_cutlass_fp4_moe_no_graph(
+    topk: int,
-    m: int, n: int, k: int, e: int, topk: int, dtype: torch.dtype
+    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
@@ -167,38 +171,18 @@ def test_cutlass_fp4_moe_no_graph(
 
     a1_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
     a2_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
-    # strides for the cutlass moe_fp4 kernel
+    test_output = moe_impl(
-    ab_strides_13 = torch.full(
-        (e,), w1_q.shape[2] * 2, dtype=torch.int64, device=w1_q.device
-    )
-    c_strides_13 = torch.full(
-        (e,), w1_q.shape[1], dtype=torch.int64, device=w1_q.device
-    )
-    ab_strides_2 = torch.full(
-        (e,), w2_q.shape[2] * 2, dtype=torch.int64, device=w2_q.device
-    )
-    c_strides_2 = torch.full((e,), w2_q.shape[1], dtype=torch.int64, device=w2_q.device)
-    params = CutlassMoEParams(
-        CutlassMoEType.BlockscaledFP4,
-        device=a.device,
-        num_experts=e,
-        intermediate_size_per_partition=n,  # n
-        hidden_size=k,
-    )  # k
-    cutlass_output = cutlass_moe_fp4(
         a=a,
-        a1_gscale=a1_gs,
-        w1_fp4=w1_q,
-        w1_blockscale=w1_blockscale,
-        w1_alphas=(1 / w1_gs),
-        a2_gscale=a2_gs,
-        w2_fp4=w2_q,
-        w2_blockscale=w2_blockscale,
-        w2_alphas=(1 / w2_gs),
         topk_weights=topk_weights,
         topk_ids=topk_ids,
-        params=params,
+        w1_q=w1_q,
-        apply_router_weight_on_input=False,
+        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:
@@ -237,10 +221,108 @@ def test_cutlass_fp4_moe_no_graph(
             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, cutlass_output, atol=1e-1, rtol=1e-1)
+    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)
 
 
 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)