From c33185ca0a21311b46eea5c984f311170ed31bb0 Mon Sep 17 00:00:00 2001
From: biondizzle <biondizzle@gmail.com>
Date: Sun, 24 May 2026 22:18:12 +0000
Subject: [PATCH] D1: add rescale diagnostic

---
 tests/unit/test_d1_rescale_diag.py | 103 +++++++++++++++++++++++++++++
 1 file changed, 103 insertions(+)
 create mode 100644 tests/unit/test_d1_rescale_diag.py

diff --git a/tests/unit/test_d1_rescale_diag.py b/tests/unit/test_d1_rescale_diag.py
new file mode 100644
index 00000000..787dcdc9
--- /dev/null
+++ b/tests/unit/test_d1_rescale_diag.py
@@ -0,0 +1,103 @@
+"""
+D1: Minimal TMEM round-trip test.
+
+Strategy: Run the s_k=256 kernel but SKIP the O rescale (force acc_scale=1.0).
+This tells us whether the O rescale atoms themselves corrupt data,
+or whether the issue is with the acc_scale computation.
+
+If cos with acc_scale=1.0 ≈ 0.8 (same as before), the round-trip is broken.
+If cos with acc_scale=1.0 ≈ 0.999, the round-trip works but acc_scale is wrong.
+"""
+import torch, math
+import cutlass.cute as cute
+import cutlass.torch as ct
+import cuda.bindings.driver as cuda
+from dsv4.kernels.attention.fmha import FmhaKernel
+
+
+def test():
+    # Test s_k=256 with the kernel — this exercises O rescale
+    hd = 64
+    s_k = 256
+    m = 128
+    torch.manual_seed(42)
+
+    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, hd, dtype=torch.bfloat16, device='cuda')
+    c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
+
+    # FP32 reference
+    qf = q[:, :, 0].float()
+    kf = k[:, :, 0].float()
+    scale = 1.0 / math.sqrt(hd)
+    attn_max = (qf @ kf.T * scale).max(dim=-1, keepdim=True)[0]
+    attn_exp = torch.exp(qf @ kf.T * scale - attn_max)
+    attn_sum = attn_exp.sum(dim=-1, keepdim=True)
+    ref_unnorm = attn_exp @ v.float()
+
+    lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
+
+    kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=False, normalize=False)
+    pv_n_tile = kernel.pv_n_tile
+
+    stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
+
+    v_tile = v[:, 0:pv_n_tile].contiguous()
+    v_kernel = v_tile.unsqueeze(-1)
+    c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
+
+    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_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
+    mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
+    mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
+
+    print(f'Compiling s_k={s_k}...', flush=True)
+    compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
+    compiled(mQ, mK, mV, mC, stream, mLSE)
+    torch.cuda.synchronize()
+
+    out = c_tile[:, :, 0].float()
+    cos_unnorm = torch.nn.functional.cosine_similarity(
+        out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
+    ).item()
+
+    # Also compare per-row to see pattern
+    n_bad = 0
+    for i in range(m):
+        rc = torch.nn.functional.cosine_similarity(
+            out[i].unsqueeze(0), ref_unnorm[i].unsqueeze(0)
+        ).item()
+        if rc < 0.95:
+            n_bad += 1
+            if n_bad <= 3:
+                print(f'  Row {i}: cos={rc:.6f}  out[:4]={out[i,:4].tolist()}  ref[:4]={ref_unnorm[i,:4].tolist()}')
+
+    print(f'  cos_unnorm={cos_unnorm:.6f}  {n_bad}/{m} bad rows (cos<0.95)')
+
+    # Now test: does a 1-KV-tile kernel produce perfect output?
+    kernel1 = FmhaKernel(head_dim=hd, s_k=128, use_smem_p=False, normalize=False)
+    k1 = k[:128]
+    c1 = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
+
+    mK1 = ct.from_dlpack(k1).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k1))
+    mC1 = ct.from_dlpack(c1).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c1))
+
+    ref1_unnorm = (torch.exp(qf @ k1[:, :, 0].float().T * scale -
+                  (qf @ k1[:, :, 0].float().T * scale).max(dim=-1, keepdim=True)[0]) @ v[:128].float())
+
+    print(f'Compiling s_k=128...', flush=True)
+    compiled1 = cute.compile(kernel1, mQ, mK1, mV, mC1, stream, mLSE)
+    compiled1(mQ, mK1, mV, mC1, stream, mLSE)
+    torch.cuda.synchronize()
+
+    out1 = c1[:, :, 0].float()
+    cos1 = torch.nn.functional.cosine_similarity(
+        out1.flatten().unsqueeze(0), ref1_unnorm.flatten().unsqueeze(0)
+    ).item()
+    print(f'  s_k=128: cos_unnorm={cos1:.6f}')
+
+
+if __name__ == '__main__':
+    test()