D1: LSE diagnostic at various hd

tests/unit/test_d1_lse.py

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+"""Quick LSE diagnostic: is the softmax correct at hd>64?"""
+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_lse(hd, n_kv=128):
+    m = 128
+    torch.manual_seed(42)
+    q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
+    k = torch.randn(n_kv, hd, 1, dtype=torch.bfloat16, device='cuda')
+    v = torch.randn(n_kv, hd, dtype=torch.bfloat16, device='cuda')
+    c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
+
+    # Reference LSE
+    qf = q[:, :, 0].float()
+    kf = k[:, :, 0].float()
+    scale = 1.0 / math.sqrt(hd)
+    attn = qf @ kf.T * scale
+    attn_max = attn.max(dim=-1, keepdim=True)[0]
+    attn_exp = torch.exp(attn - attn_max)
+    attn_sum = attn_exp.sum(dim=-1, keepdim=True)
+    ref_lse = torch.log(attn_sum.squeeze(-1)) + attn_max.squeeze(-1)
+
+    lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
+    kernel = FmhaKernel(head_dim=hd, s_k=n_kv)
+    pv_n_tile = kernel.pv_n_tile
+    stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
+
+    v_tile = v[:, 0:pv_n_tile].contiguous().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_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
+    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'hd={hd}: Compiling...', flush=True)
+    compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
+    compiled(mQ, mK, mV, mC, stream, mLSE)
+    torch.cuda.synchronize()
+
+    kernel_lse = lse_tensor[0, 0, 0].item()
+    ref_lse_val = ref_lse[0].item()
+    lse_err = abs(kernel_lse - ref_lse_val)
+    print(f'hd={hd}: kernel_lse={kernel_lse:.6f}  ref_lse={ref_lse_val:.6f}  err={lse_err:.6f}  {"PASS" if lse_err < 0.01 else "FAIL"}')
+
+    # Also check if P store to TMEM is correct by comparing O directly
+    # Output the raw O (un-normalized) from the kernel
+    out = c[:, :, 0].float()
+    ref_unnorm = attn_exp @ v.float()
+    cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
+    print(f'hd={hd}: cos_unnorm={cos:.6f}')
+
+
+if __name__ == '__main__':
+    for hd in [64, 128, 256]:
+        test_lse(hd)