From 66aaadadbfa618d0d41f019ff2a354b21fc7ca86 Mon Sep 17 00:00:00 2001 From: biondizzle Date: Sun, 24 May 2026 03:23:16 +0000 Subject: [PATCH] D1: LSE diagnostic at various hd --- tests/unit/test_d1_lse.py | 60 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 tests/unit/test_d1_lse.py diff --git a/tests/unit/test_d1_lse.py b/tests/unit/test_d1_lse.py new file mode 100644 index 00000000..891597c2 --- /dev/null +++ b/tests/unit/test_d1_lse.py @@ -0,0 +1,60 @@ +"""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)