89 lines
3.8 KiB
Python
89 lines
3.8 KiB
Python
"""D1 test: HEAD_DIM=512 only (faster iteration on compilation issues)."""
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import torch, math, sys
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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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torch.manual_seed(42)
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hd, n = 512, 128
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m = 128
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(n, hd, 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))[0].item()
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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, 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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print(f'hd={hd}, pv_n_tile={pv_n_tile}, n_pv_tiles={n_pv_tiles}, n_k_sub_tiles={kernel.n_k_sub_tiles}, k_tile={kernel.k_tile}', flush=True)
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print(f'Compiling first PV tile...', flush=True)
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# Only compile the first PV tile to isolate compilation issues
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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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import time
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t0 = time.time()
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from cutlass.base_dsl.compiler import PtxasOptions, OptLevel
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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t1 = time.time()
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print(f'Compilation took {t1-t0:.1f}s', flush=True)
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# Run all PV tiles
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lse_val = None
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c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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for nt in range(n_pv_tiles):
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v_start = nt * pv_n_tile
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v_end = v_start + pv_n_tile
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v_tile = v[:, v_start:v_end].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.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_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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compiled(mQ, mK, mV, mC, stream, mLSE)
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torch.cuda.synchronize()
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print(f' PV tile {nt}: done', flush=True)
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c[:, v_start:v_end, :] = 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_unnorm = c[:, :, 0].float()
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cos_unnorm = torch.nn.functional.cosine_similarity(
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out_unnorm.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
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).item()
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lse_err = abs(lse_val - ref_lse) if lse_val is not None else float('inf')
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status = "PASS" if cos_unnorm >= 0.99 else "FAIL"
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print(f'hd={hd}: cos_unnorm {cos_unnorm:.6f} lse_err {lse_err:.6f} {status}')
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if __name__ == '__main__':
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test()
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