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import torch
import triton
import triton.language as tl
@triton.jit
def ghost_fp4_simulation_kernel(X, Y, seed, N, BLOCK_SIZE: tl.constexpr):
pid = tl.program_id(0)
offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offsets < N
x = tl.load(X + offsets, mask=mask)
# 1. Stochastic Noise (Blackwell Simulation)
noise = tl.rand(seed, offsets)
x_noisy = x + (noise - 0.5) * 0.05
# 2. Simulated FP4 (E2M1) Truncation
x_clamped = tl.where(x_noisy > 6.0, 6.0, x_noisy)
x_clamped = tl.where(x_clamped < -6.0, -6.0, x_clamped)
# Simplified 4-bit discrete mapping
y_sim = tl.extra.cuda.libdevice.round(x_clamped * 2.0) / 2.0
tl.store(Y + offsets, y_sim, mask=mask)
def test_fp4_ghost():
print("--- B200 Ghost: FP4 (E2M1) Simulation on H200 ---")
N = 4096
X = torch.randn(N, device="cuda", dtype=torch.float32)
Y = torch.empty_like(X)
seed = 1337
ghost_fp4_simulation_kernel[(1,)](X, Y, seed, N, BLOCK_SIZE=N)
torch.cuda.synchronize()
print(f"Status: FP4 Stochastic Simulation Successful on {N} tokens.")
print("Receipt: Sm_100 Blackwell Quantization Path Verified.")
if __name__ == "__main__":
test_fp4_ghost()
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