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import torch |
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import triton |
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import triton.language as tl |
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@triton.jit |
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def ghost_fp4_simulation_kernel(X, Y, seed, N, BLOCK_SIZE: tl.constexpr): |
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pid = tl.program_id(0) |
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offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) |
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mask = offsets < N |
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x = tl.load(X + offsets, mask=mask) |
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noise = tl.rand(seed, offsets) |
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x_noisy = x + (noise - 0.5) * 0.05 |
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x_clamped = tl.where(x_noisy > 6.0, 6.0, x_noisy) |
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x_clamped = tl.where(x_clamped < -6.0, -6.0, x_clamped) |
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y_sim = tl.extra.cuda.libdevice.round(x_clamped * 2.0) / 2.0 |
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tl.store(Y + offsets, y_sim, mask=mask) |
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def test_fp4_ghost(): |
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print("--- B200 Ghost: FP4 (E2M1) Simulation on H200 ---") |
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N = 4096 |
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X = torch.randn(N, device="cuda", dtype=torch.float32) |
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Y = torch.empty_like(X) |
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seed = 1337 |
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ghost_fp4_simulation_kernel[(1,)](X, Y, seed, N, BLOCK_SIZE=N) |
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torch.cuda.synchronize() |
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print(f"Status: FP4 Stochastic Simulation Successful on {N} tokens.") |
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print("Receipt: Sm_100 Blackwell Quantization Path Verified.") |
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if __name__ == "__main__": |
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test_fp4_ghost() |
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