74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
"""D1: Debug hd=128 — check if the QK output is correct."""
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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_hd128_debug():
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hd = 128
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n_kv = 128
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m = 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(n_kv, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(n_kv, hd, dtype=torch.bfloat16, device='cuda')
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# Reference: just the QK @ V attention (un-normalized)
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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, keepdim=True)[0]
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attn_exp = torch.exp(attn - attn_max)
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attn_sum = attn_exp.sum(dim=-1, keepdim=True)
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ref_unnorm = attn_exp @ v.float()
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ref_lse = (torch.log(attn_sum.squeeze(-1)) + attn_max.squeeze(-1))[0].item()
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# Run kernel with TMEM-P (force)
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lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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kernel = FmhaKernel(head_dim=hd, s_k=n_kv, use_smem_p=False)
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pv_n_tile = kernel.pv_n_tile
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print(f'pv_n_tile={pv_n_tile}, n_pv_tiles={kernel.n_pv_tiles}')
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print(f'tmem_o0_offset={kernel.tmem_o0_offset}, tmem_p0_offset={kernel.tmem_p0_offset}')
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print(f'tOrP0_offset={kernel.tOrP0_offset}')
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print(f'num_tmem_alloc_cols={kernel.num_tmem_alloc_cols}')
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print(f'scale_softmax={kernel.scale_softmax}, scale_softmax_log2={kernel.scale_softmax_log2}')
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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().unsqueeze(-1)
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c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, 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_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
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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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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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out = c_tile[:, :, 0].float()
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kernel_lse = lse_tensor[0, 0, 0].item()
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cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
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print(f'\nResults:')
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print(f' cos_unnorm={cos:.6f}')
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print(f' kernel_lse={kernel_lse:.6f} ref_lse={ref_lse:.6f} err={abs(kernel_lse - ref_lse):.6f}')
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print(f' out[0,:4]={out[0,:4].tolist()}')
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print(f' ref[0,:4]={ref_unnorm[0,:4].tolist()}')
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# Check: is the output roughly the right magnitude?
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print(f' out.abs().max()={out.abs().max().item():.4f}')
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print(f' ref.abs().max()={ref_unnorm.abs().max().item():.4f}')
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# Check row-by-row: is O[0] proportional to ref[0]?
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if cos < 0.99:
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row_cos = torch.nn.functional.cosine_similarity(out[0].unsqueeze(0), ref_unnorm[0].unsqueeze(0)).item()
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print(f' row0_cos={row_cos:.6f}')
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if __name__ == '__main__':
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test_hd128_debug()
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