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Code Issues 7 Packages Projects Releases Wiki Activity

✅ Fix 13_kernels: Move integration test BEFORE testing, clean structure

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CORRECTED PATTERN NOW:
1. ✅ Integration test (test_module_kernel_sequential_model) - BEFORE ## 🧪 Module Testing
2. ✅ ## 🧪 Module Testing (markdown section)
3. ✅ STANDARDIZED MODULE TESTING (nbgrader cell)
4. ✅ if __name__ == '__main__' block with run_module_tests_auto
5. ✅ ## 🎯 Module Summary (immediately after, no code between)

FIXES APPLIED:
✅ Moved integration test function from AFTER testing section to BEFORE it
✅ Removed duplicate integration test function and markdown section
✅ Added integration test to the if __name__ == '__main__' block
✅ Clean STANDARDIZED MODULE TESTING structure

Module 13_kernels now follows the exact pattern
This commit is contained in:
Vijay Janapa Reddi
2025-07-20 09:45:38 -04:00
parent 80fb5cb111
commit 4e28bbc6ff
1 changed files with 26 additions and 29 deletions
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55
modules/source/13_kernels/kernels_dev.py
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@@ -1318,36 +1318,7 @@ def final_performance_test():
# Run the final test
final_performance_test()
# %% [markdown]
"""
## 🧪 Module Testing
Time to test your implementation! This section uses TinyTorch's standardized testing framework to ensure your implementation works correctly.
**This testing section is locked** - it provides consistent feedback across all modules and cannot be modified.
"""
# %% nbgrader={"grade": false, "grade_id": "standardized-testing", "locked": true, "schema_version": 3, "solution": false, "task": false}
# =============================================================================
# STANDARDIZED MODULE TESTING - DO NOT MODIFY
# This cell is locked to ensure consistent testing across all TinyTorch modules
# =============================================================================
"""
## 🔬 Integration Test: Using Kernels in a Sequential Model
"""
# %%
# Mock classes for Dense and ReLU to be used in the test
class Dense:
def __init__(self, in_features, out_features):
self.weights = Tensor(np.random.randn(in_features, out_features))
self.bias = Tensor(np.random.randn(out_features))
class ReLU:
def __call__(self, x: Tensor) -> Tensor:
return vectorized_relu(x)
def test_module_kernel_sequential_model():
"""
Integration test for using optimized kernels in a Sequential model.
@@ -1381,6 +1352,16 @@ def test_module_kernel_sequential_model():
x = vectorized_relu(x)
return x
# Mock classes for Dense and ReLU to be used in the test
class Dense:
def __init__(self, in_features, out_features):
self.weights = Tensor(np.random.randn(in_features, out_features))
self.bias = Tensor(np.random.randn(out_features))
class ReLU:
def __call__(self, x: Tensor) -> Tensor:
return vectorized_relu(x)
# 1. Create baseline and optimized models
baseline_model = BaselineModel()
optimized_model = OptimizedModel(baseline_model)
@@ -1397,6 +1378,20 @@ def test_module_kernel_sequential_model():
print("✅ Integration Test Passed: Kernels correctly integrated into a model.")
# %% [markdown]
"""
## 🧪 Module Testing
Time to test your implementation! This section uses TinyTorch's standardized testing framework to ensure your implementation works correctly.
**This testing section is locked** - it provides consistent feedback across all modules and cannot be modified.
"""
# %% nbgrader={"grade": false, "grade_id": "standardized-testing", "locked": true, "schema_version": 3, "solution": false, "task": false}
# =============================================================================
# STANDARDIZED MODULE TESTING - DO NOT MODIFY
# This cell is locked to ensure consistent testing across all TinyTorch modules
# =============================================================================
if __name__ == "__main__":
# Unit tests
@@ -1407,6 +1402,8 @@ if __name__ == "__main__":
test_simple_kernel_timing()
test_compressed_kernels()
final_performance_test()
# Integration test
test_module_kernel_sequential_model()
from tito.tools.testing import run_module_tests_auto
# Automatically discover and run all tests in this module