D2: add simple n_h=1 regression test

tests/unit/test_d2_regression.py

@@ -0,0 +1,60 @@
+"""
+D2: n_h=1 regression test. Identical to D1 but with the D2 test infrastructure.
+"""
+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():
+    hd = 64
+    s_k = 128
+    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_norm = (attn_exp / attn_sum) @ 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...', 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 = torch.nn.functional.cosine_similarity(
+        out.flatten().unsqueeze(0), ref_norm.flatten().unsqueeze(0)
+    ).item()
+    print(f'hd={hd}, s_k={s_k}: cos_norm {cos:.6f}  {"PASS" if cos >= 0.99 else "FAIL"}')
+
+
+if __name__ == '__main__':
+    test()