D1.3: Add SMEM-P coordinate diagnostic test

dsv4/kernels/attention/fmha.py

@@ -353,10 +353,8 @@ class FmhaKernel:
                 else:
                     # SMEM-P: write P to sP using coordinate-indexed store.
                     # tTMEM_LOADcS contains (m, k) coordinates from identity tensor.
-                    # Shape: ((32,1),4,1,1) — indexed with 4 indices.
                     # Each element is an (m, k) coordinate pair.
-                    # Extract m with .load()[0] and k with .load()[1],
-                    # or use indexing tTMEM_LOADcS[...].value[0/1].
+                    # rP_bf16 has the same shape/layout as tTMEM_LOADcS (BF16 view of FP32 registers).
                     for j0 in range(32):
                         for j1 in range(4):
                             coord = tTMEM_LOADcS[(j0, 0), j1, 0, 0]

tests/unit/test_d1_3_smem_diag.py

@@ -0,0 +1,70 @@
+"""
+D1.3 SMEM-P coordinate mapping diagnostic.
+Verifies that tTMEM_LOADcS coordinates and rP_bf16 values
+correctly map to sP indices for the SMEM-P path.
+Runs a minimal kernel that writes P to sP and reads it back.
+"""
+import torch, math
+import cutlass, cutlass.cute as cute, cutlass.utils as utils
+from cutlass.cute.nvgpu import tcgen05
+from cutlass import Float32, BFloat16
+from cutlass.utils import LayoutEnum
+import cutlass.torch as ct
+import cuda.bindings.driver as cuda
+
+# Test: write known P values to sP using coordinate indexing, then read back
+# via the PV MMA's A-operand fragment and verify.
+
+
+def test_smem_p_coords():
+    print("=== SMEM-P Coordinate Diagnostic ===\n")
+    hd = 256; m = 128; s_k = 128; pv_n_tile = 256
+
+    q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
+    k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
+    v = torch.randn(s_k, pv_n_tile, dtype=torch.bfloat16, device='cuda')
+    c = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
+
+    # Simple reference: just compute Q@K^T softmax @ V
+    qf = q[:, :, 0].float()
+    kf = k[:, :, 0].float()
+    vf = v.float()
+    scale = 1.0 / math.sqrt(hd)
+    attn = qf @ kf.T * scale
+    ref = torch.softmax(attn, dim=-1) @ vf  # (128, 256)
+
+    # Run the kernel with use_smem_p=True
+    from dsv4.kernels.attention.fmha import FmhaKernel
+    kern = FmhaKernel(head_dim=hd, s_k=s_k)
+
+    stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
+    v_tile = v.unsqueeze(-1)  # (128, 256, 1)
+    mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
+    mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
+    mV = ct.from_dlpack(v_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
+    mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
+
+    print('Compiling...', flush=True)
+    compiled = cute.compile(kern, mQ, mK, mV, mC, stream)
+    compiled(mQ, mK, mV, mC, stream)
+    torch.cuda.synchronize()
+
+    out = c[:, :, 0].float()
+    cos = torch.nn.functional.cosine_similarity(
+        out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)
+    ).item()
+    max_abs = (out - ref).abs().max().item()
+
+    print(f'hd=256 SMEM-P: cos {cos:.6f}  max_abs {max_abs:.4f}')
+    print(f'  out[0,:4]={out[0,:4].tolist()}')
+    print(f'  ref[0,:4]={ref[0,:4].tolist()}')
+
+    # Also check: are the output values in a reasonable range?
+    print(f'  out range: [{out.min().item():.4f}, {out.max().item():.4f}]')
+    print(f'  ref range: [{ref.min().item():.4f}, {ref.max().item():.4f}]')
+    print(f'  out has NaN: {torch.isnan(out).any().item()}')
+    print(f'  out has inf: {torch.isinf(out).any().item()}')
+
+
+if __name__ == '__main__':
+    test_smem_p_coords()