import os import sys import unittest import torch import torch.nn.functional 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.ptf_log_softmax import PtfLogSoftmax def compute_log_softmax(x, dim=-1): return torch.nn.functional.log_softmax(x, dim=dim) class TestPtfLogSoftmax(unittest.TestCase): def setUp(self): """Set up test instance.""" self.node = PtfLogSoftmax() # Test cases: (Input Tensor, Expected LogSoftmax Output) self.test_cases = [ # Rank-1 tensor (torch.tensor([0.0, -100.0, 100.0]), compute_log_softmax(torch.tensor([0.0, -100.0, 100.0]))), # # Rank-2 tensor dim=-1 (torch.tensor([[-1.0, 2.0, 3.0], [0.0, 1.0, -1.0]]), compute_log_softmax(torch.tensor([[-1.0, 2.0, 3.0], [0.0, 1.0, -1.0]]))), # # Rank-2 tensor dim=1 (torch.tensor([[-1.0, 2.0, 3.0], [0.0, 1.0, -1.0]]), compute_log_softmax(torch.tensor([[-1.0, 2.0, 3.0], [0.0, 1.0, -1.0]]), dim=1)), # # Rank-3 tensor (torch.tensor([[[1.0, 2.0], [3.0, 4.0]], [[0.0, -1.0], [-2.0, -3.0]]]), compute_log_softmax(torch.tensor([[[1.0, 2.0], [3.0, 4.0]], [[0.0, -1.0], [-2.0, -3.0]]]))), ] def test_log_softmax_function(self): """Test the returned LogSoftmax function.""" log_softmax_function, = self.node.f() # Ensure return value is callable self.assertTrue(callable(log_softmax_function), "Returned value is not callable") for input_tensor, expected in self.test_cases: with self.subTest(input_tensor=input_tensor): result = log_softmax_function(input_tensor) # Ensure output is a tensor self.assertTrue(isinstance(result, torch.Tensor), "Output is not a tensor") # Ensure result matches expected output self.assertTrue(torch.allclose(result, expected, atol=1e-6), f"Expected {expected}, got {result}") if __name__ == "__main__": unittest.main()