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https://github.com/MLSysBook/TinyTorch.git
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0357591991
Added full test suite following TinyTorch testing conventions: ✅ UNIT TESTS (test_api_simplification.py): - 23 comprehensive tests covering all API components - Tests Parameter function, Module base class, Linear/Conv2d layers - Tests functional interface (F.relu, F.flatten, F.max_pool2d) - Tests optimizer integration and backward compatibility - Tests complete model workflows (MLP, CNN) ✅ INTEGRATION TESTS (test_api_simplification_integration.py): - Cross-component integration testing - Complete workflow validation (model → optimizer → training setup) - PyTorch compatibility verification - Nested module parameter collection testing ✅ EXAMPLE FIXES: - Fixed optimizer parameter names (lr → learning_rate) - Examples demonstrate real-world usage patterns - Show dramatic code simplification vs old API 🎯 TEST RESULTS: - Unit Tests: 23/23 PASS ✅ - Integration Tests: 8/8 PASS ✅ - API simplification validated with comprehensive coverage The testing validates that the API simplification maintains educational value while providing clean PyTorch-compatible interfaces.
443 lines
16 KiB
Python
443 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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Test Suite for TinyTorch API Simplification
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This test suite validates all the new PyTorch-compatible API features
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introduced in the API simplification project.
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Test Hierarchy:
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1. Unit Tests: Individual components (Parameter, Module, Linear, Conv2d)
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2. Integration Tests: Components working together
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3. End-to-End Tests: Complete workflows (model creation, training setup)
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"""
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import unittest
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import sys
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import os
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import numpy as np
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# Add project root to path
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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class TestParameterFunction(unittest.TestCase):
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"""Unit tests for the Parameter() helper function."""
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def setUp(self):
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from tinytorch.core.tensor import Parameter, Tensor
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self.Parameter = Parameter
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self.Tensor = Tensor
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def test_parameter_creation(self):
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"""Test basic Parameter creation."""
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param = self.Parameter([[1.0, 2.0], [3.0, 4.0]])
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self.assertTrue(param.requires_grad, "Parameter should have requires_grad=True")
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self.assertEqual(param.shape, (2, 2), "Parameter should preserve shape")
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self.assertEqual(param.dtype, np.float32, "Parameter should default to float32")
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def test_parameter_vs_tensor(self):
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"""Test Parameter vs Tensor differences."""
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data = [[1.0, 2.0]]
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tensor = self.Tensor(data)
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param = self.Parameter(data)
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self.assertFalse(tensor.requires_grad, "Tensor should default requires_grad=False")
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self.assertTrue(param.requires_grad, "Parameter should have requires_grad=True")
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def test_parameter_with_dtype(self):
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"""Test Parameter with explicit dtype."""
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param = self.Parameter([1, 2, 3], dtype='int32')
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self.assertEqual(param.dtype, np.int32, "Parameter should respect dtype")
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self.assertTrue(param.requires_grad, "Parameter should still have requires_grad=True")
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class TestModuleBaseClass(unittest.TestCase):
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"""Unit tests for the Module base class."""
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def setUp(self):
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import tinytorch.nn as nn
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from tinytorch.core.tensor import Parameter
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self.nn = nn
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self.Parameter = Parameter
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def test_module_creation(self):
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"""Test basic Module creation."""
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module = self.nn.Module()
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self.assertEqual(len(module._parameters), 0, "New module should have no parameters")
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self.assertEqual(len(module._modules), 0, "New module should have no submodules")
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self.assertEqual(len(list(module.parameters())), 0, "parameters() should return empty list")
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def test_parameter_registration(self):
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"""Test automatic parameter registration."""
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Parameter = self.Parameter
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class TestModule(self.nn.Module):
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def __init__(self):
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super().__init__()
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self.weight = Parameter([[1.0, 2.0]])
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self.bias = Parameter([0.5])
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self.non_param = "not a parameter"
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module = TestModule()
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params = list(module.parameters())
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self.assertEqual(len(params), 2, "Should register 2 parameters")
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self.assertTrue(all(p.requires_grad for p in params), "All parameters should require gradients")
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def test_submodule_registration(self):
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"""Test automatic submodule registration."""
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Parameter = self.Parameter
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class SubModule(self.nn.Module):
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def __init__(self):
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super().__init__()
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self.weight = Parameter([[1.0]])
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class MainModule(self.nn.Module):
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def __init__(self):
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super().__init__()
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self.sub1 = SubModule()
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self.sub2 = SubModule()
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self.weight = Parameter([[2.0]])
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module = MainModule()
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params = list(module.parameters())
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self.assertEqual(len(params), 3, "Should collect parameters from submodules: 2 + 1 = 3")
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self.assertEqual(len(module._modules), 2, "Should register 2 submodules")
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def test_callable_interface(self):
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"""Test that modules are callable via __call__."""
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class TestModule(self.nn.Module):
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def forward(self, x):
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return x + 1
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module = TestModule()
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result = module(5) # Should call forward(5)
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self.assertEqual(result, 6, "Module should be callable and delegate to forward()")
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class TestLinearLayer(unittest.TestCase):
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"""Unit tests for the Linear layer (renamed from Dense)."""
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def setUp(self):
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import tinytorch.nn as nn
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from tinytorch.core.tensor import Tensor
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self.nn = nn
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self.Tensor = Tensor
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def test_linear_creation(self):
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"""Test Linear layer creation."""
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linear = self.nn.Linear(5, 3)
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self.assertEqual(linear.input_size, 5, "Should store input size")
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self.assertEqual(linear.output_size, 3, "Should store output size")
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self.assertEqual(linear.weights.shape, (5, 3), "Weights should have correct shape")
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self.assertEqual(linear.bias.shape, (3,), "Bias should have correct shape")
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self.assertTrue(linear.weights.requires_grad, "Weights should require gradients")
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self.assertTrue(linear.bias.requires_grad, "Bias should require gradients")
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def test_linear_no_bias(self):
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"""Test Linear layer without bias."""
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linear = self.nn.Linear(5, 3, use_bias=False)
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self.assertIsNone(linear.bias, "Should have no bias when use_bias=False")
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self.assertEqual(len(list(linear.parameters())), 1, "Should only have weight parameter")
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def test_linear_forward(self):
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"""Test Linear layer forward pass."""
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linear = self.nn.Linear(3, 2)
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x = self.Tensor([[1.0, 2.0, 3.0]])
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output = linear(x)
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self.assertEqual(output.shape, (1, 2), "Output should have correct shape")
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self.assertIsInstance(output, self.Tensor, "Output should be a Tensor")
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def test_linear_parameter_collection(self):
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"""Test that Linear parameters are properly collected."""
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linear = self.nn.Linear(4, 2)
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params = list(linear.parameters())
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self.assertEqual(len(params), 2, "Should have 2 parameters (weight + bias)")
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shapes = [p.shape for p in params]
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self.assertIn((4, 2), shapes, "Should include weight shape")
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self.assertIn((2,), shapes, "Should include bias shape")
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class TestConv2dLayer(unittest.TestCase):
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"""Unit tests for the Conv2d layer (renamed from MultiChannelConv2D)."""
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def setUp(self):
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import tinytorch.nn as nn
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from tinytorch.core.tensor import Tensor
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self.nn = nn
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self.Tensor = Tensor
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def test_conv2d_creation(self):
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"""Test Conv2d layer creation."""
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conv = self.nn.Conv2d(3, 16, (3, 3))
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self.assertEqual(conv.in_channels, 3, "Should store input channels")
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self.assertEqual(conv.out_channels, 16, "Should store output channels")
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self.assertEqual(conv.kernel_size, (3, 3), "Should store kernel size")
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self.assertEqual(conv.weight.shape, (16, 3, 3, 3), "Weight should have correct shape")
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self.assertEqual(conv.bias.shape, (16,), "Bias should have correct shape")
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def test_conv2d_parameter_collection(self):
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"""Test that Conv2d parameters are properly collected."""
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conv = self.nn.Conv2d(3, 8, (3, 3))
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params = list(conv.parameters())
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self.assertEqual(len(params), 2, "Should have 2 parameters (weight + bias)")
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weight_params = [p for p in params if len(p.shape) == 4]
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bias_params = [p for p in params if len(p.shape) == 1]
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self.assertEqual(len(weight_params), 1, "Should have 1 weight parameter")
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self.assertEqual(len(bias_params), 1, "Should have 1 bias parameter")
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class TestFunctionalInterface(unittest.TestCase):
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"""Unit tests for the functional interface (F.relu, F.flatten, etc.)."""
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def setUp(self):
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import tinytorch.nn.functional as F
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from tinytorch.core.tensor import Tensor
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self.F = F
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self.Tensor = Tensor
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def test_relu_function(self):
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"""Test F.relu function."""
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x = self.Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
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output = self.F.relu(x)
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expected = np.array([[0.0, 0.0, 0.0, 1.0, 2.0]])
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np.testing.assert_array_equal(output.data, expected, "ReLU should zero negative values")
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def test_flatten_function(self):
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"""Test F.flatten function."""
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x = self.Tensor([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]) # Shape: (1, 2, 2, 2)
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output = self.F.flatten(x)
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self.assertEqual(output.shape, (1, 8), "Flatten should preserve batch dimension")
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expected = np.array([[1, 2, 3, 4, 5, 6, 7, 8]])
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np.testing.assert_array_equal(output.data, expected, "Flatten should preserve order")
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def test_flatten_start_dim(self):
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"""Test F.flatten with custom start_dim."""
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x = self.Tensor([[[1, 2], [3, 4]]]) # Shape: (1, 2, 2) = 4 elements
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output = self.F.flatten(x, start_dim=0) # Flatten everything
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self.assertEqual(output.shape, (4,), "Should flatten from dimension 0")
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expected = np.array([1, 2, 3, 4])
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np.testing.assert_array_equal(output.data, expected, "Should flatten all dimensions")
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class TestOptimizerIntegration(unittest.TestCase):
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"""Integration tests for optimizers with the new API."""
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def setUp(self):
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import tinytorch.nn as nn
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import tinytorch.optim as optim
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self.nn = nn
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self.optim = optim
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def test_adam_with_model_parameters(self):
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"""Test Adam optimizer with model.parameters()."""
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model = self.nn.Linear(5, 3)
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optimizer = self.optim.Adam(model.parameters(), learning_rate=0.001)
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# Check that optimizer received the parameters
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self.assertEqual(len(optimizer.parameters), 2, "Adam should receive 2 parameters")
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# Check parameter types
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for param in optimizer.parameters:
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self.assertTrue(param.requires_grad, "All optimizer parameters should require gradients")
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def test_sgd_with_model_parameters(self):
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"""Test SGD optimizer with model.parameters()."""
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nn = self.nn
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class SimpleNet(nn.Module):
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def __init__(self):
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super().__init__()
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self.fc1 = nn.Linear(4, 8)
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self.fc2 = nn.Linear(8, 2)
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model = SimpleNet()
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optimizer = self.optim.SGD(model.parameters(), learning_rate=0.01)
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# Should collect parameters from both layers
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self.assertEqual(len(optimizer.parameters), 4, "SGD should receive 4 parameters (2 layers × 2 params)")
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class TestBackwardCompatibility(unittest.TestCase):
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"""Integration tests for backward compatibility."""
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def test_dense_alias_works(self):
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"""Test that Dense alias still works."""
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from tinytorch.core.layers import Dense
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dense = Dense(5, 3)
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self.assertEqual(dense.input_size, 5, "Dense alias should work")
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self.assertEqual(dense.output_size, 3, "Dense alias should work")
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def test_multichannel_conv2d_alias_works(self):
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"""Test that MultiChannelConv2D alias still works."""
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from tinytorch.core.spatial import MultiChannelConv2D
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conv = MultiChannelConv2D(3, 16, (3, 3))
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self.assertEqual(conv.in_channels, 3, "MultiChannelConv2D alias should work")
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self.assertEqual(conv.out_channels, 16, "MultiChannelConv2D alias should work")
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class TestCompleteModelWorkflow(unittest.TestCase):
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"""End-to-end integration tests for complete model workflows."""
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def setUp(self):
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import tinytorch.nn as nn
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import tinytorch.nn.functional as F
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import tinytorch.optim as optim
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from tinytorch.core.tensor import Tensor
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self.nn = nn
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self.F = F
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self.optim = optim
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self.Tensor = Tensor
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def test_complete_mlp_workflow(self):
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"""Test complete MLP creation and setup."""
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nn = self.nn
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F = self.F
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class MLP(nn.Module):
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def __init__(self):
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super().__init__()
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self.fc1 = nn.Linear(4, 8)
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self.fc2 = nn.Linear(8, 2)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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return self.fc2(x)
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# Create model
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model = MLP()
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# Test parameter collection
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params = list(model.parameters())
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self.assertEqual(len(params), 4, "Should collect all parameters")
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# Test optimizer creation
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optimizer = self.optim.Adam(model.parameters(), learning_rate=0.001)
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self.assertIsNotNone(optimizer, "Should create optimizer")
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# Test forward pass
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x = self.Tensor([[1.0, 2.0, 3.0, 4.0]])
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output = model(x)
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self.assertEqual(output.shape, (1, 2), "Should produce correct output shape")
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def test_complete_cnn_workflow(self):
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"""Test complete CNN creation and setup."""
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nn = self.nn
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F = self.F
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class CNN(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 8, (3, 3))
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self.fc1 = nn.Linear(128, 10) # Simplified size
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.flatten(x)
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return self.fc1(x)
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# Create model
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model = CNN()
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# Test parameter collection
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params = list(model.parameters())
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self.assertEqual(len(params), 4, "Should collect conv + fc parameters")
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# Test optimizer creation
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optimizer = self.optim.Adam(model.parameters(), learning_rate=0.001)
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self.assertIsNotNone(optimizer, "Should create optimizer")
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def test_pytorch_like_syntax(self):
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"""Test that the syntax matches PyTorch patterns."""
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# This test verifies the API feels like PyTorch
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import tinytorch.nn as nn
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import tinytorch.nn.functional as F
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import tinytorch.optim as optim
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# Should be able to write PyTorch-like code
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model = nn.Linear(10, 1)
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optimizer = optim.Adam(model.parameters(), learning_rate=0.001)
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# Test that this workflow doesn't crash
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x = self.Tensor([[1.0] * 10])
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output = model(x)
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# Should be able to chain operations
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output = F.relu(output)
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self.assertIsNotNone(output, "PyTorch-like workflow should work")
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if __name__ == '__main__':
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# Run the test suite
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print("🧪 TinyTorch API Simplification Test Suite")
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print("=" * 60)
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# Create test suite
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suite = unittest.TestSuite()
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# Add all test classes
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test_classes = [
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TestParameterFunction,
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TestModuleBaseClass,
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TestLinearLayer,
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TestConv2dLayer,
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TestFunctionalInterface,
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TestOptimizerIntegration,
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TestBackwardCompatibility,
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TestCompleteModelWorkflow
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]
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for test_class in test_classes:
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tests = unittest.TestLoader().loadTestsFromTestCase(test_class)
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suite.addTests(tests)
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# Run tests
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runner = unittest.TextTestRunner(verbosity=2)
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result = runner.run(suite)
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# Print summary
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print("\n" + "=" * 60)
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print(f"🎯 Test Summary:")
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print(f" Tests run: {result.testsRun}")
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print(f" Failures: {len(result.failures)}")
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print(f" Errors: {len(result.errors)}")
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if result.failures:
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print("\n❌ Failures:")
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for test, traceback in result.failures:
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print(f" {test}: {traceback.split('AssertionError:')[-1].strip()}")
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if result.errors:
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print("\n💥 Errors:")
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for test, traceback in result.errors:
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print(f" {test}: {traceback.split('Exception:')[-1].strip()}")
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if result.wasSuccessful():
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print("\n✅ All API simplification tests passed!")
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print("🎉 PyTorch-compatible API is working correctly!")
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else:
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print("\n❌ Some tests failed!")
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print("🔧 API needs fixes before deployment.")
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# Exit with proper code
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sys.exit(0 if result.wasSuccessful() else 1) |