120 lines
4.8 KiB
Python
120 lines
4.8 KiB
Python
"""
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D1: Test multi-KV-tile by running s_k=128 kernel per KV segment and
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merging in Python using log-sum-exp (D5 merge formula).
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This avoids the broken TMEM round-trip O rescale entirely.
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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_multi_kv_merge(hd=64, s_k=256):
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m = 128
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n_kv_segments = 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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# 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_norm = (attn_exp / attn_sum) @ v.float()
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# Run s_k=128 kernel per KV segment and merge using log-sum-exp
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kernel = FmhaKernel(head_dim=hd, s_k=128, 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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# Compile once with segment 0's K
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k_seg = k[:128]
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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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lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, 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_seg).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_seg))
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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 (hd={hd}, s_k=128 per segment, {n_kv_segments} segments)...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
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# Accumulate across KV segments using log-sum-exp merge
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# O_merged = sum_i(exp(lse_i) * O_i) / sum_i(exp(lse_i))
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o_accum = torch.zeros(m, hd, dtype=torch.float32, device='cuda')
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lse_accum = torch.full((m, 1), float('-inf'), dtype=torch.float32, device='cuda')
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for seg in range(n_kv_segments):
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k_start = seg * 128
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k_end = k_start + 128
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k_seg = k[k_start:k_end]
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v_seg = v[k_start:k_end]
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# Per-segment O and LSE
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seg_o = torch.zeros(m, hd, dtype=torch.float32, device='cuda')
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seg_lse = torch.zeros(m, 1, dtype=torch.float32, 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_seg[:, 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_seg).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k_seg))
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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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seg_o[:, v_start:v_end] = c_tile[:, :, 0].float()
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if nt == 0:
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seg_lse[:, 0] = lse_tensor[:, 0, 0].float()
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# Merge with accumulator using log-sum-exp
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# O_new = (exp(lse_old) * O_old + exp(lse_new) * O_new) / (exp(lse_old) + exp(lse_new))
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# lse_new = ln(exp(lse_old) + exp(lse_new))
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e_old = torch.exp(lse_accum) # (m, 1)
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e_new = torch.exp(seg_lse) # (m, 1)
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e_sum = e_old + e_new
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o_accum = (e_old * o_accum + e_new * seg_o) / e_sum
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lse_accum = torch.log(e_sum)
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cos = torch.nn.functional.cosine_similarity(
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o_accum.flatten().unsqueeze(0), ref_norm.flatten().unsqueeze(0)
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).item()
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print(f' hd={hd}, s_k={s_k} ({n_kv_segments} segments): cos_norm {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
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return cos
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def test():
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print("=== D1: Multi-KV Merge via Log-Sum-Exp (no TMEM round-trip) ===\n")
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test_multi_kv_merge(64, 256)
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test_multi_kv_merge(64, 384)
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test_multi_kv_merge(64, 512)
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test_multi_kv_merge(64, 1024)
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test_multi_kv_merge(128, 256)
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if __name__ == '__main__':
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test()
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