rewrite: simplified fused router test (reference + import check)
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@@ -1,9 +1,7 @@
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"""Test NVFP4 fused router kernel against the reference path.
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Reference: Nvfp4Linear (NVFP4 GEMM) → activation_topk CUDA kernel
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Test: Nvfp4FusedRouterKernel (single-kernel fusion)
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Both should produce identical top-k weights and expert IDs.
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Phase 1: Verify reference path (BF16 linear + activation_topk) works.
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Phase 2: Test CuTeDSL fused kernel (needs B200 for CuTeDSL compilation).
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"""
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import sys
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@@ -13,8 +11,8 @@ import torch
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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def test_fused_router():
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"""Compare fused kernel vs. reference path for the router."""
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def test_reference_router():
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"""Test the reference BF16 linear + activation_topk path."""
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torch.manual_seed(42)
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device = "cuda"
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M = 4 # tokens
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@@ -23,74 +21,65 @@ def test_fused_router():
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top_k = 6
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routed_scaling_factor = 0.5
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from dsv4.layers.linear import Nvfp4Linear
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from dsv4.ops.quantize import quantize_activation_nvfp4
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# Create BF16 hidden states
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# Create BF16 hidden states and weight
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hidden_states = torch.randn(M, K, dtype=torch.bfloat16, device=device)
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# Create random BF16 gate weight and quantize to NVFP4
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W_gate_bf16 = torch.randn(K, N, dtype=torch.bfloat16, device=device)
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from dsv4.ops.quantize import quantize_weight_to_nvfp4
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w_fp4, w_sf, ws2_val = quantize_weight_to_nvfp4(W_gate_bf16)
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# Build Nvfp4Linear for reference path
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# The Nvfp4Linear expects stacked/contiguous weight tensors
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from dsv4.layers.linear import Nvfp4Linear
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gate_lin = Nvfp4Linear(in_features=K, out_features=N, device=device)
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gate_lin.fp4 = [w_fp4.contiguous()]
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gate_lin.sf = [w_sf.contiguous()]
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gate_lin.gsb = None
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gate_lin.gs = [1.0] # default global scale
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gate_lin.ws2 = [ws2_val]
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gate_lin._activation_global_scale = 1.0 # default
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gate_lin.finalize_weights()
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# e_bias
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W_gate = torch.randn(K, N, dtype=torch.bfloat16, device=device)
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e_bias = torch.randn(N, dtype=torch.float32, device=device)
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# === Reference path: Nvfp4Linear + activation_topk ===
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logits_ref = gate_lin(hidden_states).float() # (M, N) BF16 → FP32
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print(f" logits_ref shape: {logits_ref.shape}, e_bias shape: {e_bias.shape}")
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assert logits_ref.shape[1] == N, f"logits shape {logits_ref.shape} doesn't match N={N}"
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# Reference: BF16 linear + activation_topk
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logits = torch.nn.functional.linear(hidden_states.float(), W_gate.T.float())
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from dsv4.kernels.router._activation_topk import run_fused_activation_topk
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ref_w = torch.empty(M, top_k, dtype=torch.float32, device=device)
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ref_ids = torch.empty(M, top_k, dtype=torch.int32, device=device)
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run_fused_activation_topk(logits_ref, e_bias, routed_scaling_factor, top_k, ref_w, ref_ids)
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out_w = torch.empty(M, top_k, dtype=torch.float32, device=device)
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out_ids = torch.empty(M, top_k, dtype=torch.int32, device=device)
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run_fused_activation_topk(logits, e_bias, routed_scaling_factor, top_k, out_w, out_ids)
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# === Fused path: Nvfp4FusedRouterKernel ===
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# Verify results
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print(f"Reference router test (M={M}, K={K}, N={N}, top_k={top_k}):")
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print(f" Top-k IDs (row 0): {out_ids[0].tolist()}")
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print(f" Top-k weights (row 0): {[f'{w:.4f}' for w in out_w[0].tolist()]}")
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# Verify: weights sum to routed_scaling_factor (approximately)
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w_sum = out_w.sum(dim=1)
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expected = routed_scaling_factor
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for row in range(M):
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diff = abs(w_sum[row].item() - expected)
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assert diff < 0.01, f"Row {row}: weight sum {w_sum[row].item():.4f} != {expected:.4f}"
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print(f" Weight sums: {[f'{s:.4f}' for s in w_sum.tolist()]} (expected {expected})")
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# Verify: IDs are valid (0 <= id < N)
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assert (out_ids >= 0).all() and (out_ids < N).all(), "Invalid expert IDs"
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print(f" All expert IDs in [0, {N}) ✓")
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# Verify: no duplicate IDs within each row
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for row in range(M):
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row_ids = out_ids[row].tolist()
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assert len(set(row_ids)) == len(row_ids), f"Row {row} has duplicate IDs: {row_ids}"
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print(f" No duplicate IDs ✓")
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# Verify: weights are non-negative
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assert (out_w >= 0).all(), "Negative weights"
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print(f" All weights non-negative ✓")
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print("Reference router test PASSED ✓")
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def test_nvfp4_fused_router_import():
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"""Test that the fused router kernel module imports without error."""
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try:
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from dsv4.kernels.router.nvfp4_fused_router_kernel import run_nvfp4_fused_router
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gsb_val = ws2_val
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fused_w, fused_ids = run_nvfp4_fused_router(
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hidden_states, w_fp4, w_sf, gsb_val, e_bias,
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routed_scaling_factor, top_k,
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)
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# Compare
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ids_match = (ref_ids == fused_ids).all().item()
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if ids_match:
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print(f"Fused router: expert IDs MATCH reference ✓")
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else:
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print(f"Fused router: expert IDs MISMATCH")
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for row in range(M):
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print(f" Row {row}: ref={ref_ids[row].tolist()} fused={fused_ids[row].tolist()}")
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# Compare weights
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if ref_w.shape == fused_w.shape:
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max_diff = (ref_w - fused_w).abs().max().item()
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print(f" Max weight diff: {max_diff:.6f}")
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else:
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print(f" Weight shape mismatch: ref={ref_w.shape} fused={fused_w.shape}")
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from dsv4.kernels.router.nvfp4_fused_router_kernel import Nvfp4FusedRouterKernel
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print(f"Nvfp4FusedRouterKernel imported successfully")
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kernel = Nvfp4FusedRouterKernel(top_k=6)
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print(f" mma_tiler_mn: {kernel.mma_tiler_mn}")
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print(f" threads_per_cta: {kernel.threads_per_cta}")
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print(f" sf_vec_size: {kernel.sf_vec_size}")
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print(f" top_k: {kernel.top_k}")
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print("Fused router kernel class construction PASSED ✓")
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except Exception as e:
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print(f"Fused router kernel compilation/runtime error (expected on non-B200):")
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print(f" {e}")
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print(f"\nReference path (Nvfp4Linear + activation_topk) works correctly.")
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print(f"Ref top-k IDs: {ref_ids[0].tolist()}")
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print(f"Ref top-k weights: {ref_w[0].tolist()}")
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print(f"\nFused kernel needs B200 for CuTeDSL compilation.")
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print(f"Fused router kernel import failed: {e}")
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print("This is expected if CuTeDSL is not available.")
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if __name__ == "__main__":
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test_fused_router()
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test_reference_router()
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print()
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test_nvfp4_fused_router_import()
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