Add support for bf16 x bf16 cutlass fused MoE (#10275)

Co-authored-by: Sam Li <lsam@nvidia.com>
Co-authored-by: jackeyhua <jackeyhuasjtu@gmail.com>

python/sglang/test/test_cutlass_w16a16_moe.py

@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+import pytest
+import torch
+from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
+
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.moe.topk import TopKConfig, select_experts
+from sglang.srt.server_args import ServerArgs, set_global_server_args_for_scheduler
+
+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 for unquantized weights
+def torch_moe_reference(a, w13, w2, score, topk):
+    B, D = a.shape
+
+    set_global_server_args_for_scheduler(ServerArgs(model_path="dummy"))
+
+    # Flip w13 layout
+    dim = -2
+    size = w13.size(dim)
+    assert size % 2 == 0, f"Expected even size in dim {dim}, got {size}"
+    half = size // 2
+    # Reorder weight
+    w1, w3 = w13.split(half, dim=dim)
+    w13 = torch.cat([w3, w1], dim=dim).contiguous()
+
+    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)
+
+    for i in range(w13.shape[0]):
+        mask = topk_ids == i
+        if mask.sum():
+            out[mask] = SiluAndMul()(a[mask] @ w13[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)
+
+
+@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
+@pytest.mark.parametrize("e", [40, 64, 256])
+@pytest.mark.parametrize("topk", [1, 6, 8])
+@torch.inference_mode()
+def test_flashinfer_bf16_cutlass_moe(m: int, n: int, k: int, e: int, topk: int):
+    """
+    Test the bf16 cutlass moe API.
+
+    Args:
+        m: number of tokens
+        n: intermediate size
+        k: hidden size
+        e: number of experts
+        topk: top-k experts per token
+    """
+    torch.manual_seed(7)
+
+    dtype = torch.bfloat16
+
+    # Create unquantized weights
+    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
+
+    # w13: fused gate_up projection [num_experts, 2*intermediate, hidden]
+    # FlashInfer CUTLASS expects [up, gate] layout
+    w13 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
+
+    # w2: down projection [num_experts, hidden, intermediate]
+    w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
+
+    # Generate router scores
+    score = torch.randn((m, e), device="cuda", dtype=dtype)
+
+    # Get topk routing
+    topk_output = select_experts(
+        hidden_states=a,
+        router_logits=score,
+        topk_config=TopKConfig(top_k=topk, renormalize=False),
+    )
+    topk_weights, topk_ids, _ = topk_output
+
+    # Test: Call FlashInfer CUTLASS fused_moe (unquantized version)
+    test_output = flashinfer_cutlass_fused_moe(
+        input=a,
+        token_selected_experts=topk_ids,
+        token_final_scales=topk_weights,
+        fc1_expert_weights=w13,
+        fc2_expert_weights=w2,
+        output_dtype=dtype,
+        quant_scales=None,
+    )[0]
+
+    # Reference: Torch implementation
+    torch_output = torch_moe_reference(a, w13, w2, score, topk)
+
+    # Compare outputs
+    torch.testing.assert_close(torch_output, test_output, rtol=1e-2, atol=1e-2)
+
+
+if __name__ == "__main__":
+    # Run a simple test case
+    test_flashinfer_bf16_cutlass_moe(224, 1024, 1024, 8, 2)