Fix strides of randomly generated data

hatlib/arg_value.py

@@ -145,7 +145,7 @@ def get_dimension_arg_indices(array_arg: ArgInfo, all_arguments: List[ArgInfo])
     return indices
 
 
-def _gen_random_data(dtype, shape):
+def _gen_random_data(dtype, shape, strides):
     dtype = np.uint16 if dtype == "bfloat16" else dtype
     if isinstance(dtype, np.dtype):
         dtype = dtype.type
@@ -157,7 +157,7 @@ def _gen_random_data(dtype, shape):
     else:
         data = np.random.random(tuple(shape)).astype(dtype)
 
-    return data
+    return np.lib.stride_tricks.as_strided(data, strides=strides)
 
 
 def generate_arg_values(arguments: List[ArgInfo], dim_names_to_values={}) -> List[ArgValue]:
@@ -198,7 +198,7 @@ def generate_arg_values(arguments: List[ArgInfo], dim_names_to_values={}) -> Lis
                             shape.append(v if isinstance(v, np.integer) or type(v) == int else v[0])
 
             # materialize an array input using the generated shape
-            runtime_array_inputs = _gen_random_data(arg.numpy_dtype, shape)
+            runtime_array_inputs = _gen_random_data(arg.numpy_dtype, shape, arg.numpy_strides)
             values.append(ArgValue(arg, runtime_array_inputs))
 
         elif arg.name in dim_names_to_values and arg.usage == hat_file.UsageType.Input:
@@ -214,7 +214,7 @@ def generate_arg_values(arguments: List[ArgInfo], dim_names_to_values={}) -> Lis
                     arg.numpy_strides = list(map(lambda x: x * arg.element_num_bytes, arg.shape[1:] + [1]))
 
             if arg.usage != hat_file.UsageType.Output:
-                arg_data = _gen_random_data(arg.numpy_dtype, arg.shape)
+                arg_data = _gen_random_data(arg.numpy_dtype, arg.shape, arg.numpy_strides)
                 values.append(ArgValue(arg, arg_data))
             else:
                 values.append(ArgValue(arg))