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7bc4f6f835
- Delete outdated site/ directory - Rename docs/ → site/ to match original architecture intent - Update all GitHub workflows to reference site/: - publish-live.yml: Update paths and build directory - publish-dev.yml: Update paths and build directory - build-pdf.yml: Update paths and artifact locations - Update README.md: - Consolidate site/ documentation (website + PDF) - Update all docs/ links to site/ - Test successful: Local build works with all 40 pages The site/ directory now clearly represents the course website and documentation, making the repository structure more intuitive. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
"""
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Progressive Testing Framework for TinyTorch
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This module provides educational, progressive testing that:
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1. Verifies module capabilities (what students implement)
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2. Checks for regressions (earlier modules still work)
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3. Tests integration (modules work together)
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Tests are designed to be educational - failure messages teach students
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what went wrong and how to fix it.
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"""
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from pathlib import Path
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# Module dependencies - when testing Module N, also test these earlier modules
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MODULE_DEPENDENCIES = {
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"01": [], # Tensor has no dependencies
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"02": ["01"], # Activations need Tensor
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"03": ["01", "02"], # Layers need Tensor, Activations
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"04": ["01", "02", "03"], # Losses need Tensor, Activations, Layers
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"05": ["01", "02", "03", "04"], # Autograd needs all foundation
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"06": ["01", "02", "03", "04", "05"], # Optimizers need Autograd
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"07": ["01", "02", "03", "04", "05", "06"], # Training needs Optimizers
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"08": ["01"], # DataLoader mainly needs Tensor
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"09": ["01", "02", "03", "05"], # Spatial needs Tensor, Layers, Autograd
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"10": ["01"], # Tokenization mainly needs Tensor
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"11": ["01", "05", "10"], # Embeddings need Tensor, Autograd, Tokenization
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"12": ["01", "03", "05", "11"], # Attention needs Layers, Autograd, Embeddings
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"13": ["01", "03", "05", "11", "12"], # Transformers need Attention
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"14": ["01"], # Profiling is mostly standalone
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"15": ["01", "03"], # Quantization needs Tensor, Layers
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"16": ["01", "03"], # Compression needs Tensor, Layers
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"17": ["01", "12", "13"], # Memoization (KV-cache) needs Attention, Transformers
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"18": ["01"], # Acceleration is mostly standalone
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"19": ["01"], # Benchmarking is mostly standalone
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"20": ["01", "02", "03", "04", "05", "06", "07"], # Capstone needs core modules
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}
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# What each module should provide (for capability testing)
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MODULE_CAPABILITIES = {
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"01": {
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"name": "Tensor",
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"exports": ["Tensor"],
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"capabilities": [
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"Create tensors from lists and numpy arrays",
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"Perform element-wise operations (+, -, *, /)",
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"Perform matrix multiplication (matmul)",
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"Reshape and transpose tensors",
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"Support broadcasting",
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],
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},
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"02": {
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"name": "Activations",
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"exports": ["Sigmoid", "ReLU", "Tanh", "GELU", "Softmax"],
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"capabilities": [
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"Apply non-linear transformations",
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"Preserve tensor shapes",
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"Handle batch dimensions",
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],
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},
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"03": {
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"name": "Layers",
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"exports": ["Layer", "Linear", "Dropout"],
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"capabilities": [
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"Linear transformation: y = xW + b",
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"Xavier weight initialization",
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"Parameter collection for optimization",
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],
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},
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"04": {
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"name": "Losses",
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"exports": ["MSELoss", "CrossEntropyLoss", "BinaryCrossEntropyLoss"],
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"capabilities": [
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"Compute scalar loss from predictions and targets",
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"Handle batch inputs",
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"Numerical stability (log-sum-exp trick)",
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],
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},
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"05": {
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"name": "Autograd",
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"exports": ["enable_autograd"],
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"capabilities": [
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"Track computation graph",
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"Compute gradients via backpropagation",
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"Support requires_grad flag",
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],
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},
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# ... continue for other modules
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}
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def get_dependencies(module_num: str) -> list:
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"""Get list of modules that must work for module_num to work."""
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return MODULE_DEPENDENCIES.get(module_num, [])
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def get_capabilities(module_num: str) -> dict:
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"""Get capability information for a module."""
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return MODULE_CAPABILITIES.get(module_num, {})
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