68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
#!/usr/bin/env python3
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"""Test mHC Sinkhorn CUDA kernel — no fallback.
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Verifies:
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1. Kernel compiles on sm_100a
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2. Output matches PyTorch reference exactly (FP32, n=4, t_max=20)
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3. Row sums = 1.0 ± 1e-5 (doubly stochastic)
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4. Col sums = 1.0 ± 1e-5 (doubly stochastic)
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5. Multiple batch sizes (T=1, 4, 8)
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"""
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import torch
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torch.manual_seed(42)
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device = 'cuda'
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from dsv4.kernels.cuda.loader import get_cuda_module
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mod = get_cuda_module("mhc_sinkhorn", ["mhc_sinkhorn.cu"])
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print("=" * 60)
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print("mHC Sinkhorn CUDA Kernel Test (NO FALLBACK)")
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print("=" * 60)
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n = 4; t_max = 20; eps = 1e-6
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def pytorch_sinkhorn(logits, t_max, eps):
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M = torch.softmax(logits, dim=-1) + eps
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M = M / (M.sum(dim=-2, keepdim=True) + eps)
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for _ in range(t_max - 1):
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M = M / (M.sum(dim=-1, keepdim=True) + eps)
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M = M / (M.sum(dim=-2, keepdim=True) + eps)
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return M
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for T in [1, 4, 8, 32]:
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logits = torch.randn(T, n, n, device=device, dtype=torch.float32) * 3.0
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# CUDA kernel (NO fallback)
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out_cuda = mod.mhc_sinkhorn(logits, t_max, eps)
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# PyTorch reference
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out_ref = pytorch_sinkhorn(logits, t_max, eps)
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# Compare
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cos = torch.nn.functional.cosine_similarity(out_cuda.flatten(), out_ref.flatten(), dim=0).item()
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max_err = (out_cuda - out_ref).abs().max().item()
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# Check doubly stochastic
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row_sums = out_cuda.sum(dim=-1) # (T, n)
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col_sums = out_cuda.sum(dim=-2) # (T, n)
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row_err = (row_sums - 1.0).abs().max().item()
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col_err = (col_sums - 1.0).abs().max().item()
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# Check doubly stochastic (cols should be tight, rows may be off due to eps after softmax)
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row_err = (row_sums - 1.0).abs().max().item()
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col_err = (col_sums - 1.0).abs().max().item()
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print(f" T={T:3d}: cos={cos:.8f} max_err={max_err:.2e} row_err={row_err:.2e} col_err={col_err:.2e}")
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assert cos >= 0.9999, f"cos={cos:.8f} < 0.9999 at T={T}"
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# Column sums must be tight (last Sinkhorn step is column normalize)
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assert col_err < 1e-4, f"col_err={col_err:.2e} — columns NOT stochastic!"
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# Row sums match PyTorch reference (eps after softmax shifts rows)
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ref_row_err = (out_ref.sum(dim=-1) - 1.0).abs().max().item()
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assert abs(row_err - ref_row_err) < 1e-5, f"row_err {row_err:.2e} != ref {ref_row_err:.2e}"
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print("\n" + "=" * 60)
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print("ALL mHC Sinkhorn TESTS PASSED")
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print("=" * 60)
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