103 lines
4.2 KiB
Python
103 lines
4.2 KiB
Python
"""
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D1: Test TMEM round-trip on O in isolation.
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Runs the kernel with s_k=128 (1 KV tile, no rescale needed).
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Then manually does a load-modify-store round-trip on O in TMEM
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using the correction_rescale atoms.
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If the round-trip corrupts data, we know the atoms are broken.
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If it preserves data, the bug is elsewhere.
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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():
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hd = 64
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s_k = 128 # 1 KV tile, no rescale needed
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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(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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c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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# FP32 reference
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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_max = (qf @ kf.T * scale).max(dim=-1, keepdim=True)[0]
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attn_exp = torch.exp(qf @ kf.T * scale - 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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lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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# Test 1: s_k=128 baseline (no rescale) — should be PASS
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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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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'Test 1: s_k=128 baseline (no rescale)', 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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out1 = c_tile[:, :, 0].float()
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cos1 = torch.nn.functional.cosine_similarity(
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out1.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
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).item()
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print(f' cos_unnorm={cos1:.6f} {"PASS" if cos1 >= 0.99 else "FAIL"}')
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# Test 2: s_k=256 with rescale — this is the failing test
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s_k2 = 256
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k2 = torch.randn(s_k2, hd, 1, dtype=torch.bfloat16, device='cuda')
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v2 = torch.randn(s_k2, hd, dtype=torch.bfloat16, device='cuda')
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c2 = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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kf2 = k2[:, :, 0].float()
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attn_max2 = (qf @ kf2.T * scale).max(dim=-1, keepdim=True)[0]
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attn_exp2 = torch.exp(qf @ kf2.T * scale - attn_max2)
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attn_sum2 = attn_exp2.sum(dim=-1, keepdim=True)
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ref_unnorm2 = attn_exp2 @ v2.float()
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kernel2 = FmhaKernel(head_dim=hd, s_k=s_k2, use_smem_p=False, normalize=False)
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lse_tensor2 = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
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v_tile2 = v2[:, 0:pv_n_tile].contiguous()
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v_kernel2 = v_tile2.unsqueeze(-1)
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c_tile2 = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
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mK2 = ct.from_dlpack(k2).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k2))
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mV2 = ct.from_dlpack(v_kernel2).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel2))
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mC2 = ct.from_dlpack(c_tile2).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile2))
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mLSE2 = ct.from_dlpack(lse_tensor2).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor2))
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print(f'Test 2: s_k=256 with O rescale', flush=True)
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compiled2 = cute.compile(kernel2, mQ, mK2, mV2, mC2, stream, mLSE2)
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compiled2(mQ, mK2, mV2, mC2, stream, mLSE2)
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torch.cuda.synchronize()
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out2 = c_tile2[:, :, 0].float()
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cos2 = torch.nn.functional.cosine_similarity(
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out2.flatten().unsqueeze(0), ref_unnorm2.flatten().unsqueeze(0)
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).item()
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print(f' cos_unnorm={cos2:.6f} {"PASS" if cos2 >= 0.99 else "FAIL"}')
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if __name__ == '__main__':
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test()
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