82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
"""
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D1: Verify per-row LSE correctness.
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"""
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import torch, math
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import cutlass.cute as cute
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import cutlass.torch as ct
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import cuda.bindings.driver as cuda
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from dsv4.kernels.attention.fmha import FmhaKernel
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def test_lse(hd=64):
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m = 128
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s_k = 128
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torch.manual_seed(42)
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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# FP32 reference LSE
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qf = q[:, :, 0].float()
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kf = k[:, :, 0].float()
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scale = 1.0 / math.sqrt(hd)
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attn = qf @ kf.T * scale
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attn_max = attn.max(dim=-1)[0] # (m,)
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attn_exp = torch.exp(attn - attn_max.unsqueeze(-1))
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attn_sum = attn_exp.sum(dim=-1) # (m,)
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ref_lse = torch.log(attn_sum) + attn_max # (m,) natural log domain
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# Our kernel LSE: lse = ln(row_sum) + row_max * ln(2)
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# row_max is in scale_log2 domain: max(S * scale * log2(e))
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# So row_max * ln(2) converts back to natural domain.
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# Thus lse = ln(row_sum) + row_max * ln(2)
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# But row_max = max(S * scale * log2(e)) = max(S * scale) * log2(e)
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# So row_max * ln(2) = max(S * scale) * log2(e) * ln(2) = max(S * scale)
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# Therefore lse = ln(row_sum) + max(S * scale)
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# And our ref: lse = ln(sum(exp(S * scale - max))) + max = ln(sum) + max
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# These should be the same.
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kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=False, normalize=False)
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pv_n_tile = kernel.pv_n_tile
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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v_tile = v[:, 0:pv_n_tile].contiguous()
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v_kernel = v_tile.unsqueeze(-1)
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c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
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lse_tensor = torch.zeros(m, dtype=torch.float32, device='cuda')
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mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
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mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
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mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
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print(f' Compiling...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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compiled(mQ, mK, mV, mC, stream, mLSE)
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torch.cuda.synchronize()
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kernel_lse = lse_tensor.cpu()
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ref_lse_cpu = ref_lse.cpu()
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# Compare
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lse_err = (kernel_lse - ref_lse_cpu).abs()
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print(f' LSE max err: {lse_err.max().item():.6f}')
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print(f' LSE mean err: {lse_err.mean().item():.6f}')
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print(f' Kernel LSE[:8]: {kernel_lse[:8].tolist()}')
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print(f' Ref LSE[:8]: {ref_lse_cpu[:8].tolist()}')
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# Check O too
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ref_unnorm = attn_exp @ v.float()
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out = c_tile[:, :, 0].float()
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cos = torch.nn.functional.cosine_similarity(
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out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
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).item()
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print(f' O cos_unnorm: {cos:.6f}')
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if __name__ == '__main__':
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test_lse()
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