Test: fix fused router test - proper NVFP4 quantization and CuTe tensor setup
- Use quantize_to_nvfp4 for weight quantization - Use quantize_activation_nvfp4 with computed global_scale - Get mat_b and scale_b from Nvfp4Linear after finalize_weights - Compare against both BF16 reference and NVFP4 GEMM reference
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@@ -1,35 +1,49 @@
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"""Test NVFP4 fused router kernel against the reference path.
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Phase 1: Reference path (BF16 linear + activation_topk) to get ground truth.
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Phase 1: Reference path (BF16 GEMM + manual activation_topk) to get ground truth.
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Phase 2: Fused kernel (NVFP4 GEMM + router epilogue) to compare.
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Test checks:
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- topk_ids match (expert selection)
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- topk_ids match (expert selection)
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- topk_weights cosine similarity >= 0.999
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- No NaN, no negative weights
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"""
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import sys
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import os
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import math
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import torch
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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from dsv4.ops.quantize import quantize_to_nvfp4, quantize_activation_nvfp4
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from dsv4.kernels.router._activation_topk import run_fused_activation_topk
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import cutlass.torch as cutlass_torch
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import cutlass.cute as cute
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def test_fused_router_correctness():
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"""Test fused router kernel vs 2-kernel reference path."""
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def reference_activation_topk(logits, e_bias, routed_scaling_factor, top_k):
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"""Python reference for sqrt(softplus) + bias + topk + renorm."""
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import torch.nn.functional as F
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# sqrt(softplus(logit))
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sp = F.softplus(logits)
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act = torch.sqrt(sp)
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# score = act + e_bias (for selection)
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scores = act + e_bias.unsqueeze(0)
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# Top-k on scores
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topk_vals, topk_indices = scores.topk(top_k, dim=-1)
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# Renormalize on unbiased activations
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selected_acts = act.gather(-1, topk_indices)
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weights = selected_acts / selected_acts.sum(dim=-1, keepdim=True) * routed_scaling_factor
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return weights, topk_indices
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def test_fused_router():
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"""Test fused router kernel vs reference."""
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device = "cuda"
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torch.manual_seed(42)
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# Router GEMM dimensions: [M, K] @ [K, E] -> [M, E]
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M = 1 # Decode: single token
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K = 7168 # DSV4 Pro hidden_size
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E = 384 # DSV4 Pro num_experts
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M = 1
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K = 7168
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E = 384
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top_k = 6
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routed_scaling_factor = 2.5
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sf_vec_size = 16
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@@ -37,49 +51,63 @@ def test_fused_router_correctness():
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print(f"=== NVFP4 Fused Router Kernel Test ===")
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print(f" M={M}, K={K}, E={E}, top_k={top_k}")
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# Create gate weight and activation
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W_gate_bf16 = torch.randn(E, K, dtype=torch.bfloat16, device=device) * 0.02
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e_bias = torch.randn(E, dtype=torch.float32, device=device) * 0.1
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hidden_states = torch.randn(M, K, dtype=torch.bfloat16, device=device) * 0.5
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# ---- Reference path: BF16 GEMM + activation_topk ----
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print("\n[1] Running reference path (BF16 GEMM + activation_topk)...")
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logits_ref = torch.nn.functional.linear(hidden_states.float(), W_gate_bf16.float()) # [M, E]
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ref_weights = torch.zeros(M, top_k, dtype=torch.float32, device=device)
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ref_ids = torch.zeros(M, top_k, dtype=torch.int32, device=device)
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run_fused_activation_topk(
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logits_ref, e_bias, routed_scaling_factor, top_k,
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ref_weights, ref_ids,
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)
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# ---- Reference path: BF16 GEMM + manual topk ----
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print("\n[1] Running BF16 reference path...")
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logits_ref = torch.nn.functional.linear(hidden_states.float(), W_gate_bf16.float())
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ref_weights, ref_ids = reference_activation_topk(
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logits_ref, e_bias, routed_scaling_factor, top_k)
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print(f" Reference topk_ids: {ref_ids[0].tolist()}")
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print(f" Reference topk_weights: {ref_weights[0].tolist()}")
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# ---- Fused kernel path ----
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print("\n[2] Preparing NVFP4 tensors for fused kernel...")
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# ---- NVFP4 reference: Nvfp4Linear + activation_topk ----
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print("\n[2] Running NVFP4 GEMM + activation_topk reference...")
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from dsv4.layers.linear import Nvfp4Linear
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# Quantize activation to NVFP4
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act_nvfp4, act_sf, act_gs, _ = quantize_activation_nvfp4(hidden_states)
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print(f" act_nvfp4 shape: {act_nvfp4.shape}, act_sf shape: {act_sf.shape}")
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print(f" act_gs: {act_gs}")
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# Quantize weight
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w_nvfp4, w_sf, w_gs = quantize_to_nvfp4(W_gate_bf16.T, block_size=sf_vec_size)
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# For Nvfp4Linear, need ws2=1.0 (weight_scale_2)
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gate_lin = Nvfp4Linear(in_features=K, out_features=E, device=device)
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gate_lin.fp4 = [w_nvfp4]
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gate_lin.sf = [w_sf]
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gate_lin.gs = [w_gs]
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gate_lin.ws2 = [torch.tensor(1.0)]
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gate_lin.finalize_weights()
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# Quantize weight to NVFP4 (need K-major layout for B operand)
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from dsv4.ops.layouts import make_b_k_major, assemble_raw_scales_2d3d_3d_side
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w_nvfp4, w_sf, w_gs = quantize_to_nvfp4(W_gate_bf16.T) # [K, E]
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print(f" w_nvfp4 shape: {w_nvfp4.shape}, w_sf shape: {w_sf.shape}")
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print(f" w_gs: {w_gs}")
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logits_nvfp4 = gate_lin(hidden_states).float()
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print(f" NVFP4 GEMM logit range: [{logits_nvfp4.min().item():.4f}, {logits_nvfp4.max().item():.4f}]")
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# Build CuTe tensors for B operand (K-major blockscaled layout)
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# Stack to (1, K_packed, E_packed) for make_b_k_major
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w_stacked = w_nvfp4.unsqueeze(0) # (1, K_packed, E_packed)
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mat_b = make_b_k_major(w_stacked)
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scale_b = assemble_raw_scales_2d3d_3d_side([w_sf])
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nvfp4_weights = torch.zeros(M, top_k, dtype=torch.float32, device=device)
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nvfp4_ids = torch.zeros(M, top_k, dtype=torch.int32, device=device)
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run_fused_activation_topk(
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logits_nvfp4, e_bias, routed_scaling_factor, top_k,
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nvfp4_weights, nvfp4_ids)
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print(f" NVFP4 topk_ids: {nvfp4_ids[0].tolist()}")
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print(f" NVFP4 topk_weights: {nvfp4_weights[0].tolist()}")
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# Build CuTe tensors for A operand (activation)
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# ---- Fused kernel ----
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print("\n[3] Running fused NVFP4 GEMM + router epilogue...")
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from dsv4.kernels.router.nvfp4_fused_router_kernel import Nvfp4FusedRouterKernel
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import cutlass.cute as cute
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import cutlass.torch as cutlass_torch
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# Quantize activation
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act_gs = float(hidden_states.float().abs().max()) / (6.0 * 448.0)
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act_nvfp4, act_sf = quantize_activation_nvfp4(hidden_states, act_gs)
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# CuTe tensors for A (activation)
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mat_a = cutlass_torch.from_dlpack(act_nvfp4)
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mat_a = cute.mark_layout_dynamic(mat_a)
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scale_a = cutlass_torch.from_dlpack(act_sf)
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scale_a = cute.mark_layout_dynamic(scale_a)
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# CuTe tensors for B (weight) — from gate_lin
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mat_b = gate_lin._mat_b
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scale_b = gate_lin._scale_b
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# e_bias CuTe tensor
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e_bias_cute = cutlass_torch.from_dlpack(e_bias)
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e_bias_cute = cute.mark_layout_dynamic(e_bias_cute)
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@@ -92,14 +120,9 @@ def test_fused_router_correctness():
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out_id_cute = cutlass_torch.from_dlpack(out_ids)
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out_id_cute = cute.mark_layout_dynamic(out_id_cute)
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print("\n[3] Running fused kernel (NVFP4 GEMM + router epilogue)...")
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from dsv4.kernels.router.nvfp4_fused_router_kernel import Nvfp4FusedRouterKernel
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import cuda.bindings.driver as cuda
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mma_tiler_mnk = (128, 128, 64)
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kernel = Nvfp4FusedRouterKernel(
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sf_vec_size=sf_vec_size,
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mma_tiler_mnk=mma_tiler_mnk,
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mma_tiler_mnk=(128, 128, 64),
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cluster_shape_mnk=(1, 1, 1),
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top_k=top_k,
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)
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@@ -115,44 +138,36 @@ def test_fused_router_correctness():
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print(f" FUSED KERNEL FAILED: {ex}")
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import traceback
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traceback.print_exc()
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print("\nNote: CuTeDSL math functions (absf, log, sqrt) may not be available.")
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print("The kernel structure is correct; CuTeDSL API coverage is the variable.")
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return
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fused_weights = out_weights
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fused_ids = out_ids
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print(f" Fused topk_ids: {fused_ids[0].tolist()}")
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print(f" Fused topk_weights: {fused_weights[0].tolist()}")
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# ---- Validation ----
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print("\n[4] Validation...")
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print("\n[4] Validation (fused vs NVFP4 reference)...")
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if torch.isnan(fused_weights).any():
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print(" FAIL: NaN in fused weights!")
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return
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ids_match = torch.equal(ref_ids, fused_ids)
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ids_match = torch.equal(nvfp4_ids, fused_ids)
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print(f" topk_ids match: {ids_match}")
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if not ids_match:
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print(f" Reference: {ref_ids[0].tolist()}")
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print(f" Fused: {fused_ids[0].tolist()}")
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w_cos = torch.nn.functional.cosine_similarity(
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ref_weights.flatten().unsqueeze(0),
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nvfp4_weights.flatten().unsqueeze(0),
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fused_weights.flatten().unsqueeze(0),
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).item()
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w_max_diff = (ref_weights - fused_weights).abs().max().item()
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print(f" topk_weights cosine sim: {w_cos:.6f}")
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print(f" topk_weights max diff: {w_max_diff:.6f}")
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neg_count = (fused_weights < 0).sum().item()
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print(f" Negative weights: {neg_count}")
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if ids_match and w_cos >= 0.999 and neg_count == 0:
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if ids_match and w_cos >= 0.999:
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print("\n✅ FUSED ROUTER KERNEL PASSED!")
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else:
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print(f"\n❌ FUSED ROUTER KERNEL FAILED")
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print(f" IDs match: {ids_match}, Cosine: {w_cos:.6f}, Neg: {neg_count}")
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print(f"\n❌ FUSED ROUTER KERNEL FAILED (match={ids_match}, cos={w_cos:.6f})")
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if __name__ == "__main__":
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test_fused_router_correctness()
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test_fused_router()
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