Fix: init range

Author: S1ro1

problems/nvidia/nvfp4_dual_gemm/reference.py

@@ -112,13 +112,13 @@ def generate_input(
 
     # Generate uint8 tensor, then convert to float4e2m1fn_x2 data type
     a_ref = torch.randint(
-        0, 2, (l, m, k // 2), dtype=torch.int8, device="cuda"
+        0, 4, (l, m, k // 2), dtype=torch.int8, device="cuda"
     ).permute(1, 2, 0)
     b1_ref = torch.randint(
-        0, 2, (l, n, k // 2), dtype=torch.int8, device="cuda"
+        0, 4, (l, n, k // 2), dtype=torch.int8, device="cuda"
     ).permute(1, 2, 0)
     b2_ref = torch.randint(
-        0, 2, (l, n, k // 2), dtype=torch.int8, device="cuda"
+        0, 4, (l, n, k // 2), dtype=torch.int8, device="cuda"
     ).permute(1, 2, 0)
     a_ref = a_ref.view(torch.float4_e2m1fn_x2)
     b1_ref = b1_ref.view(torch.float4_e2m1fn_x2)
@@ -137,7 +137,7 @@ def create_scale_factor_tensors(l, mn, sf_k):
         ref_shape = (l, mn, sf_k)
         ref_permute_order = (1, 2, 0)
         # Init with uint8 tensor, then convert to float8_e4m3fn
-        ref_f8_random_int = torch.randint(-1, 2, ref_shape, dtype=torch.int8, device='cuda')
+        ref_f8_random_int = torch.randint(0, 3, ref_shape, dtype=torch.int8, device='cuda')
         ref_f8_torch_tensor = ref_f8_random_int.to(dtype=torch.float8_e4m3fn)
         # permute to match ref_permute_order
         ref_f8_torch_tensor_permuted = ref_f8_torch_tensor.permute(*ref_permute_order)