123 lines
4.7 KiB
Python
123 lines
4.7 KiB
Python
"""
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FMHA D1: Test O rescale with multiple KV tiles (s_k > 128).
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DSV4 Pro uses top_k=1024 → s_k=1024 → n_kv_tiles=8.
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The O rescale code (kt>0) is guarded with const_expr(n_kv_tiles > 1)
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and uses hand-constructed TMEM atoms. Untested and likely broken.
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This test verifies O rescale correctness at s_k=256 (2 KV tiles).
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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(hd=64, 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
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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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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=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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# Compile with first PV tile
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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'hd={hd}, s_k={s_k} (n_kv_tiles={n_kv_tiles}, pv_n_tile={pv_n_tile}): 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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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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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}, s_k={s_k}: cos_unnorm {cos_unnorm:.6f} cos_norm {cos_norm:.6f} lse_err {lse_err:.6f} {status}')
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return cos_unnorm, cos_norm, lse_err
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def test():
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print("=== D1: Multi-KV-Tile O Rescale Test ===\n")
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# First: s_k=128 baseline (1 KV tile, no rescale needed)
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print("--- Baseline: s_k=128 (1 KV tile) ---")
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test_multi_kv(64, 128)
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# Critical test: s_k=256 (2 KV tiles, O rescale exercised)
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print("\n--- s_k=256 (2 KV tiles, O rescale needed) ---")
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test_multi_kv(64, 256)
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# s_k=384 (3 KV tiles)
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print("\n--- s_k=384 (3 KV tiles) ---")
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test_multi_kv(64, 384)
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# s_k=512 (4 KV tiles — Flash decode config)
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print("\n--- s_k=512 (4 KV tiles, Flash decode) ---")
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test_multi_kv(64, 512)
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# hd=128 with multi-KV
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print("\n--- hd=128, s_k=256 ---")
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test_multi_kv(128, 256)
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if __name__ == '__main__':
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test()
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