More elegant weight initialization for FP6 benchmark

benchmarks/benchmark_fp6.py

@@ -1,18 +1,17 @@
 import torch
 import pandas as pd
 import torchao
-from torchao.dtypes.floatx import from_scaled_tc_floatx
+from torchao.dtypes.floatx import from_scaled_tc_floatx, to_scaled_tc_floatx
 from torchao.utils import benchmark_torch_function_in_microseconds
 from tqdm import tqdm
 
 
 def benchmark(m: int, k: int, n: int):
     ebits = 3
     mbits = 2
-    nbits = 1 + ebits + mbits
 
-    fp6_weight = torch.randint(256, (n, k // 8 * nbits), dtype=torch.uint8, device="cuda")
-    scale = torch.rand(n, device="cuda").half() + 0.5
+    fp32_weight = torch.randn(n, k, device="cuda")
+    fp6_weight, scale = to_scaled_tc_floatx(fp32_weight, ebits, mbits)
     fp16_act = torch.randn(m, k, dtype=torch.half, device="cuda") + 0.5
 
     fp6_output = torchao.ops.quant_llm_linear(ebits, mbits, fp16_act, fp6_weight, scale, splitK=1)