116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
"""
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D1: Minimal O rescale test with just s_k=256 at hd=64.
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Tests the exact same thing as test_d1_multi_kv but simpler.
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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 = 256
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m = 128
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n_kv_tiles = 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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c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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# FP32 reference (full attention)
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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_norm = (attn_exp / attn_sum) @ v.float()
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# Per-tile references for debugging
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# Tile 0 only
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kf0 = k[:128, :, 0].float()
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attn0 = qf @ kf0.T * scale
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attn_max0 = attn0.max(dim=-1, keepdim=True)[0]
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attn_exp0 = torch.exp(attn0 - attn_max0)
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ref0 = attn_exp0 @ v[:128].float()
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# Tile 1 only (with rescale from tile 0's max)
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kf1 = k[128:, :, 0].float()
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attn1 = qf @ kf1.T * scale
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new_max = torch.max(attn_max0, (qf @ kf1.T * scale).max(dim=-1, keepdim=True)[0])
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acc_scale = torch.exp(attn_max0 - new_max)
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attn_exp1 = torch.exp(attn1 - new_max)
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ref_rescaled = acc_scale * ref0 + attn_exp1 @ v[128:].float()
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print(f" Tile-0 only O[0,:4] = {ref0[0,:4].tolist()}")
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print(f" Rescaled O[0,:4] = {ref_rescaled[0,:4].tolist()}")
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print(f" Full ref O[0,:4] = {ref_unnorm[0,:4].tolist()}")
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print(f" acc_scale range = [{acc_scale.min().item():.4f}, {acc_scale.max().item():.4f}]")
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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=s_k, use_smem_p=False, normalize=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()
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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' Compiling (n_kv_tiles={n_kv_tiles})...', 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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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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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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out_norm = out_unnorm / attn_sum
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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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cos_norm = torch.nn.functional.cosine_similarity(
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out_norm.flatten().unsqueeze(0), ref_norm.flatten().unsqueeze(0)
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).item()
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print(f" cos_unnorm={cos_unnorm:.6f} cos_norm={cos_norm:.6f}")
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print(f" out[0,:4]={out_unnorm[0,:4].tolist()}")
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print(f" lse_val={lse_val}")
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print(f" {'PASS' if cos_unnorm >= 0.99 else 'FAIL'}")
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if __name__ == '__main__':
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test()
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