Add debug script for wo_a

tests/debug_wo_a.py

@@ -0,0 +1,41 @@
+"""Minimal debug: verify wo_a grouped matmul reference is correct."""
+import torch
+import torch.nn.functional as F
+
+torch.cuda.set_device(0)
+torch.manual_seed(42)
+
+# Small dimensions for debugging
+G, HPG, HD, OR = 2, 4, 128, 64
+GI = HPG * HD
+T = 4
+DEVICE = "cuda:0"
+
+o = torch.randn(T, G*HPG, HD, dtype=torch.bfloat16, device=DEVICE) * 2.0
+w = torch.randn(G*OR, GI, dtype=torch.bfloat16, device=DEVICE) * 0.1
+
+# Reference: per-group matmul
+o_g = o.reshape(T, G, GI)
+z_ref = torch.empty(T, G, OR, dtype=torch.bfloat16, device=DEVICE)
+for g in range(G):
+    z_ref[:, g, :] = o_g[:, g, :] @ w[g*OR:(g+1)*OR, :].T
+
+print(f"z_ref shape={z_ref.shape} amax={z_ref.amax():.4f}")
+
+# Now test the CuTeDSL runner
+from cutedsl.wo_a_grouped_linear import CuTeDSLNvfp4WoA
+
+runner = CuTeDSLNvfp4WoA(
+    n_local_groups=G, heads_per_group=HPG, head_dim=HD,
+    o_lora_rank=OR, max_num_tokens=8, device=DEVICE,
+)
+runner.set_bf16_weight(w)
+runner.finalize_weights()
+runner._ensure_initialized()
+runner.compute_activation_global_scale(o)
+
+with torch.no_grad():
+    z_out = runner.run(o)
+
+cos = F.cosine_similarity(z_ref.flatten().unsqueeze(0).float(), z_out.flatten().unsqueeze(0).float()).item()
+print(f"cosine={cos:.6f} amax_ref={z_ref.amax():.4f} amax_out={z_out.amax():.4f}")