72 lines
3.3 KiB
Python
72 lines
3.3 KiB
Python
"""D1: Test TMEM-P at all head dims (force use_smem_p=False)."""
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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_tmem_p(hd, 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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c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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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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ref_lse = torch.log(attn_sum.squeeze(-1)) + attn_max.squeeze(-1)
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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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n_pv_tiles = kernel.n_pv_tiles
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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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print(f'hd={hd} TMEM-P: Compiling...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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lse_val = None
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for nt in range(n_pv_tiles):
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vs, ve = nt * pv_n_tile, (nt + 1) * pv_n_tile
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v_t = v[:, vs:ve].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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lse_tensor.zero_()
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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_t).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_t))
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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(mQ, mK, mV, mC, stream, mLSE)
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torch.cuda.synchronize()
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c[:, vs:ve, :] = c_tile
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if nt == 0:
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lse_val = lse_tensor[0, 0, 0].item()
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out = c[:, :, 0].float()
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cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
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ref_lse_val = ref_lse[0].item()
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lse_err = abs(lse_val - ref_lse_val) if lse_val is not None else float('inf')
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print(f'hd={hd} TMEM-P: cos {cos:.6f} lse_err {lse_err:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
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return cos
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if __name__ == '__main__':
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print("=== TMEM-P at various head dims ===\n")
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for hd in [64, 128, 256]:
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test_tmem_p(hd)
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