mirror of
https://github.com/MLSysBook/TinyTorch.git
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9361cbf987
- Create professional examples directory showcasing TinyTorch as real ML framework - Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison - Fix import paths in exported modules (training.py, dense.py) - Update training module with autograd integration for loss functions - Add progressive integration tests for all 16 modules - Document framework capabilities and usage patterns This commit establishes the examples gallery that demonstrates TinyTorch works like PyTorch/TensorFlow, validating the complete framework.
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
"""
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Integration Tests for Module XX: [Module Name]
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Template for testing integration with dependencies
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"""
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import numpy as np
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import sys
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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class TestDependencyIntegration:
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"""Test integration with prerequisite modules."""
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def test_works_with_tensor(self):
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"""Test integration with Tensor (if applicable)."""
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# Example:
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# from tinytorch.core.tensor import Tensor
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# from tinytorch.core.module import MainClass
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#
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# t = Tensor(np.array([1, 2, 3]))
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# obj = MainClass()
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# result = obj.process(t)
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# assert isinstance(result, Tensor)
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pass
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def test_output_consumed_by_next_module(self):
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"""Test output can be consumed by dependent modules."""
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# Example:
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# from tinytorch.core.module import MainClass
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# from tinytorch.next.module import NextClass
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#
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# obj1 = MainClass()
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# output = obj1.process(input_data)
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#
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# obj2 = NextClass()
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# final = obj2.process(output)
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# assert final is not None
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pass
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def test_chaining_operations(self):
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"""Test chaining multiple operations."""
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# Example:
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# obj = MainClass()
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# result = obj.operation1().operation2().operation3()
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# assert result is not None
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pass |