47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
import os
|
|
import sys
|
|
import unittest
|
|
import torch
|
|
|
|
|
|
PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
|
|
MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
|
|
sys.path.append(MODULE_ROOT)
|
|
|
|
from pytorch_wrapper.pt_rand import PtRand # Importing the PtRand class
|
|
from pytorch_wrapper.utils import DTYPE_MAPPING # Importing the dtype mapping
|
|
|
|
|
|
class TestPtRand(unittest.TestCase):
|
|
def setUp(self):
|
|
"""Set up test instance."""
|
|
self.node = PtRand()
|
|
|
|
# Tensor size to test
|
|
self.size_str = "[2, 3]" # Using a small 2D tensor size for all tests
|
|
self.expected_shape = (2, 3)
|
|
|
|
# Only floating-point types are valid for torch.rand()
|
|
self.valid_dtypes = {k: v for k, v in DTYPE_MAPPING.items() if v in {torch.float32, torch.float16, torch.bfloat16, torch.float64}}
|
|
|
|
def test_rand_tensor_all_floating_dtypes(self):
|
|
"""Test random tensor creation for all supported floating-point data types."""
|
|
for data_type, torch_dtype in self.valid_dtypes.items():
|
|
with self.subTest(data_type=data_type):
|
|
result, = self.node.f(self.size_str, data_type)
|
|
|
|
# Ensure return value is a tensor
|
|
self.assertTrue(isinstance(result, torch.Tensor), "Output is not a tensor")
|
|
|
|
# Ensure result has correct shape
|
|
self.assertEqual(result.shape, torch.Size(self.expected_shape), f"Expected shape {self.expected_shape}, got {result.shape}")
|
|
|
|
# Ensure result has correct dtype
|
|
self.assertEqual(result.dtype, torch_dtype, f"Expected dtype {torch_dtype}, got {result.dtype}")
|
|
|
|
# Ensure all elements are within range [0,1)
|
|
self.assertTrue(torch.all(result >= 0) and torch.all(result < 1), f"Tensor values are not in the range [0,1) for dtype {data_type}")
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|