repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
cs249r_book | tinytorch/datasets/tinydigits/create_tinydigits.py | .py | #!/usr/bin/env python3
"""
Create TinyDigits Dataset
=========================
Extracts a balanced, curated subset from sklearn's digits dataset (8x8 grayscale).
This creates a TinyTorch-branded educational dataset optimized for fast iteration.
Following Karpathy's "~1000 samples" philosophy for educational datasets.... | 112 | 3,822 |
cs249r_book | tinytorch/datasets/tinytalks/scripts/generate_tinytalks.py | .py | """
TinyTalks Dataset Generator
Systematically generates the TinyTalks v1.0 dataset with 350 Q&A pairs
across 5 difficulty levels.
Usage:
python scripts/generate_tinytalks.py
Output:
- tinytalks_v1.txt (full dataset)
- splits/train.txt (70%)
- splits/val.txt (15%)
- splits/test.txt (15%)
"""
imp... | 589 | 27,735 |
cs249r_book | tinytorch/datasets/tinytalks/scripts/stats.py | .py | """
TinyTalks Dataset Statistics
Generates comprehensive statistics about the TinyTalks dataset including:
- Vocabulary statistics
- Length distributions
- Character frequencies
- Split sizes
Usage:
python scripts/stats.py
"""
from pathlib import Path
from collections import Counter
import json
def load_qa_pai... | 194 | 6,454 |
cs249r_book | tinytorch/datasets/tinytalks/scripts/validate_dataset.py | .py | """
TinyTalks Dataset Validation Script
Validates the TinyTalks dataset for:
- Format consistency
- No duplicate pairs
- Balanced splits
- Character encoding (UTF-8)
- Line endings (Unix)
Usage:
python scripts/validate_dataset.py
"""
from pathlib import Path
from collections import Counter
def load_qa_pairs(fi... | 257 | 8,050 |
cs249r_book | tinytorch/datasets/tinytalks/examples/demo_usage.py | .py | """
TinyTalks Dataset Usage Examples
Demonstrates how to load and use the TinyTalks dataset for training
transformer models.
Usage:
python examples/demo_usage.py
"""
from pathlib import Path
def example1_load_full_dataset():
"""Example 1: Load the full dataset"""
print("=" * 60)
print("Example 1: L... | 237 | 6,741 |
cs249r_book | tinytorch/quarto/tools/measure-pdf-images.py | .py | #!/usr/bin/env python3
# ruff: noqa: E501
r"""
measure-pdf-images.py โ observed-size figure sizing recommendations.
Walk the rendered TinyTorch Lab Guide PDF, read every image's actual
on-page bounding box (the geometry XeLaTeX put on the page AFTER our
global \includegraphics cap fired). For each image, match it back... | 377 | 13,747 |
cs249r_book | tinytorch/tests/validate_nbgrader_config.py | .py | #!/usr/bin/env python3
"""
NBGrader Configuration Validation Script
Validates all TinyTorch modules for NBGrader compatibility
"""
import argparse
import re
import json
from pathlib import Path
from collections import defaultdict
from typing import Dict, List, Tuple, Set
class NBGraderValidator:
"""Validates NBGr... | 521 | 20,207 |
cs249r_book | tinytorch/tests/test_utils.py | .py | """
TinyTorch Test Utilities
Shared utilities for integration tests across all modules.
Provides setup functions and common test helpers.
"""
import sys
import os
from pathlib import Path
def setup_integration_test():
"""
Set up the environment for integration testing.
This function ensures:
1. The... | 113 | 3,191 |
cs249r_book | tinytorch/tests/conftest.py | .py | """
Pytest configuration for TinyTorch tests.
This file is automatically loaded by pytest and sets up the test environment.
It also provides a Rich-based educational test output that helps students
understand what each test does and why it matters.
CRITICAL: This conftest validates that the tinytorch package is prope... | 349 | 11,997 |
cs249r_book | tinytorch/tests/04_losses/test_04_losses_progressive.py | .py | """
Module 04: Progressive Integration Tests
Tests that Module 04 (Losses) works correctly AND that the entire foundation stack works.
DEPENDENCY CHAIN: 01_tensor โ 02_activations โ 03_layers โ 04_losses โ 05_dataloader
This is the FOUNDATION MILESTONE - everything should work together for neural networks!
"""
import... | 346 | 12,733 |
cs249r_book | tinytorch/tests/04_losses/test_losses_core.py | .py | """
Module 04: Losses - Core Functionality Tests
=============================================
WHY LOSSES MATTER:
-----------------
The loss function defines what "good" means for your model.
It's the signal that drives all learning. Wrong loss = wrong learning.
WHAT STUDENTS LEARN:
-------------------
1. MSE for reg... | 107 | 3,186 |
cs249r_book | tinytorch/tests/07_optimizers/test_optimizer_core.py | .py | """
Module 07: Optimizer Core Tests
================================
These tests verify that optimizers correctly update model parameters.
WHY THESE TESTS MATTER:
-----------------------
Optimizers are the "learning" part of machine learning. If they don't work:
- Weights never change โ model never learns
- Weights e... | 288 | 9,407 |
cs249r_book | tinytorch/tests/07_optimizers/test_07_optimizers_progressive.py | .py | """
Module 07: Progressive Integration Tests
Tests that Module 07 (Optimizers) works correctly AND that the foundation stack (01โ06) still works.
DEPENDENCY CHAIN: 01_tensor โ 02_activations โ 03_layers โ 04_losses โ 05_dataloader โ 06_autograd โ 07_optimizers
This is where we enable learning through sophisticated opt... | 453 | 16,626 |
cs249r_book | tinytorch/tests/milestones/test_milestones_smoke.py | .py | """
Milestone Smoke Tests โ Model Construction
===========================================
Lightweight tests that verify every milestone script can at least
import its dependencies and construct its model. No data downloads,
no training โ just "does the code not crash on import?"
These catch API drift between milesto... | 149 | 5,295 |
cs249r_book | tinytorch/tests/milestones/test_milestones_run.py | .py | """
Milestone Full Run Tests
========================
These tests run each milestone script fully to verify the complete
educational experience works end-to-end.
This is Option C: Full run (~10-15 minutes total)
- Runs each milestone with actual training
- Verifies outputs are correct (accuracy thresholds, etc.)
- Su... | 197 | 7,767 |
cs249r_book | tinytorch/tests/milestones/milestone_tracker.py | .py | """
Compatibility milestone progress tracker for TinyTorch tests and older hooks.
The canonical milestone definitions live in ``tito.commands.milestone``.
This file mirrors that table so legacy imports do not carry stale module
names or write to a separate home-directory progress file.
"""
import json
from pathlib im... | 255 | 9,546 |
cs249r_book | tinytorch/tests/10_tokenization/test_tokenization_core.py | .py | """
Module 10: Tokenization - Core Functionality Tests
===================================================
WHY TOKENIZATION MATTERS:
------------------------
Models can't read text - they need numbers. Tokenization:
- Splits text into tokens (words or subwords)
- Maps tokens to integer IDs
- Enables text โ numbers con... | 101 | 3,022 |
cs249r_book | tinytorch/tests/10_tokenization/test_10_tokenization_progressive.py | .py | """
Module 10: Progressive Integration Tests
Tests that Module 10 (Tokenization) works correctly AND that Foundation + Architecture tier work.
DEPENDENCY CHAIN: 01_tensor โ ... โ 05_dataloader โ ... โ 08_training โ 09_convolutions โ 10_tokenization
This is where text processing begins for NLP pipelines.
"""
import nu... | 609 | 23,342 |
cs249r_book | tinytorch/tests/15_quantization/test_quantizer_core.py | .py | """
Module 15: Quantization Core Tests
===================================
These tests verify that quantization reduces model size correctly.
WHY THESE TESTS MATTER:
-----------------------
Quantization converts FP32 (4 bytes) to INT8 (1 byte) = 4x smaller model.
If quantization is broken:
- Model stays big (defeats ... | 180 | 6,500 |
cs249r_book | tinytorch/tests/15_quantization/test_quantization_integration.py | .py | #!/usr/bin/env python3
"""
Integration tests for Module 16: Quantization
Tests INT8 quantization, dequantization, and quantized operations
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
def test_quantization_integration():
"""Test quantization system integrati... | 24 | 695 |
cs249r_book | tinytorch/tests/18_memoization/test_kv_cache_core.py | .py | """
Module 18: KV Cache (Memoization) Core Tests
=============================================
These tests verify that KV caching works for efficient inference.
WHY THESE TESTS MATTER:
-----------------------
KV caching is essential for efficient text generation:
- Without cache: O(nยฒ) per token (recompute all attent... | 182 | 6,681 |
cs249r_book | tinytorch/tests/18_memoization/test_kv_cache_integration.py | .py | """
Integration Tests for Module 18: KV Caching
Tests integration with transformer components and generation
"""
import numpy as np
rng = np.random.default_rng(7)
import pytest
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
from tinytorch.c... | 369 | 14,575 |
cs249r_book | tinytorch/tests/18_memoization/test_tinygpt_integration.py | .py | """
Integration test for Module 18: Memoization (TinyGPT)
Tests language model components and GPT-style transformer integration.
"""
import sys
import numpy as np
rng = np.random.default_rng(7)
from pathlib import Path
def run_integration_test():
"""Run integration test for TinyGPT module."""
try:
pri... | 143 | 5,636 |
cs249r_book | tinytorch/tests/18_memoization/test_18_memoization_progressive.py | .py | """
Module 18: Progressive Integration Tests
Tests that Module 18 (Memoization/KV-Cache) works correctly AND that prior modules (01โ17) still work.
DEPENDENCY CHAIN: 01_tensor โ ... โ 13_transformers โ ... โ 17_acceleration โ 18_memoization
โ ๏ธ IMPORTANT: This test ONLY uses modules 01-18.
Future modules (19_benchm... | 343 | 11,452 |
cs249r_book | tinytorch/tests/02_activations/test_tensor_activations_integration.py | .py | """
Integration Tests - Tensor and Activations
Tests cross-module interfaces and compatibility between Tensor and Activation modules.
Focuses on integration, not re-testing individual module functionality.
"""
import pytest
import numpy as np
rng = np.random.default_rng(7)
from test_utils import setup_integration_tes... | 270 | 11,236 |
cs249r_book | tinytorch/tests/02_activations/test_activations_core.py | .py | """
Module 02: Activations - Core Functionality Tests
==================================================
These tests verify that activation functions work correctly.
WHY ACTIVATIONS MATTER:
----------------------
Without activations, neural networks are just linear transformations.
No matter how many layers you stack... | 449 | 15,004 |
cs249r_book | tinytorch/tests/02_activations/test_02_activations_progressive.py | .py | """
Module 02: Progressive Integration Tests
Tests that Module 02 (Activations) works correctly AND that all previous modules still work.
DEPENDENCY CHAIN: 01_tensor โ 02_activations
Students can trace back exactly where issues originate.
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib i... | 275 | 9,462 |
cs249r_book | tinytorch/tests/02_activations/test_activations_integration.py | .py | """
Module 02: Activations - Integration Tests
Tests that activations work with Tensor and enable non-linear networks
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
class TestActivation... | 159 | 5,372 |
cs249r_book | tinytorch/tests/integration/test_forward_passes.py | .py | #!/usr/bin/env python
"""
Forward Pass Tests for TinyTorch
=================================
Tests that all architectures can do forward passes correctly.
This validates the "plumbing" - data flows through without errors.
"""
import sys
import os
import numpy as np
rng = np.random.default_rng(7)
# Add project root to... | 383 | 11,220 |
cs249r_book | tinytorch/tests/integration/test_api_simplification_integration.py | .py | """
Integration test for API Simplification
Validates that the new PyTorch-compatible API integrates correctly across all components:
- nn module with Module, Linear, Conv2d
- nn.functional with relu, flatten, max_pool2d
- optim module with Adam, SGD
- Complete workflow integration (model creation โ optimizer โ traini... | 397 | 14,824 |
cs249r_book | tinytorch/tests/integration/test_gradients.py | .py | #!/usr/bin/env python
"""
Gradient Flow Validation Tests for TinyTorch
=============================================
Ensures gradients propagate correctly through all architectures.
Critical for verifying that models can actually learn.
Test Categories:
- Gradient existence through deep networks
- Gradient magnitude (... | 513 | 16,469 |
cs249r_book | tinytorch/tests/integration/test_module_05_dense.py | .py | """
Integration Tests for Module 05: DataLoader
These tests verify that the module exports correctly and works as expected.
Run with pytest for detailed reporting.
"""
import pytest
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(... | 183 | 6,347 |
cs249r_book | tinytorch/tests/integration/test_optimizers_integration.py | .py | """
Integration tests for TinyTorch optimizers with other modules.
Tests that optimizers correctly integrate with:
- Module 01: Tensor operations
- Module 02: Activation functions
- Module 03: Layers (Linear, Sequential)
- Module 06: Autograd (Tensor with gradients)
- Module 04: Losses (MSE, CrossEntropy)
"""
import ... | 488 | 13,621 |
cs249r_book | tinytorch/tests/integration/test_training_flow.py | .py | """
Training Flow Integration Tests
================================
Tests that the complete training pipeline works:
1. Forward pass produces valid outputs
2. Loss computes correctly
3. Backward pass populates gradients
4. Optimizer updates weights
5. Loss decreases over iterations
These tests catch issues that unit... | 381 | 11,726 |
cs249r_book | tinytorch/tests/integration/test_shapes.py | .py | #!/usr/bin/env python
"""
Shape Validation Tests for TinyTorch
=====================================
Comprehensive shape validation ensuring all operations produce expected dimensions.
Uses pytest style - one test per specific behavior for clear reporting.
Run with: pytest tests/system/test_shapes.py -v
"""
import sy... | 453 | 15,067 |
cs249r_book | tinytorch/tests/integration/test_dataloader_integration.py | .py | """
Integration tests for DataLoader with training workflows.
These tests verify that DataLoader works correctly when integrated with
actual training pipelines, not just in isolation.
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
import os
# Add project root to path
sys.path.insert(0, os.path.join... | 152 | 5,380 |
cs249r_book | tinytorch/tests/integration/test_module_integration.py | .py | """
TinyTorch Module Integration Tests
Tests that modules work together correctly when integrated.
These tests focus on inter-module compatibility, not individual module functionality.
Integration test categories:
1. Core module integration (tensor + autograd + layers)
2. Training pipeline integration (optimizers + t... | 217 | 6,829 |
cs249r_book | tinytorch/tests/integration/__init__.py | .py | """
Integration tests for TinyTorch.
These tests validate that multiple modules work together correctly.
They catch issues that unit tests miss, like:
- Gradient flow through entire training pipelines
- Module compatibility and interface contracts
- End-to-end training scenarios
Critical for catching bugs like:
- Mis... | 15 | 415 |
cs249r_book | tinytorch/tests/integration/test_module_dependencies.py | .py | #!/usr/bin/env python3
"""
Module Dependency Integration Testing
Tests how each module interfaces with modules that came before it
"""
import numpy as np
rng = np.random.default_rng(7)
# Module dependency graph for TinyTorch
# Current module structure:
# 01_tensor, 02_activations, 03_layers, 04_losses, 05_dataloader,... | 353 | 12,317 |
cs249r_book | tinytorch/tests/integration/test_nlp_pipeline_flow.py | .py | """
NLP Pipeline Flow Integration Tests
====================================
Tests that the NLP pipeline works end-to-end:
1. Tokenization produces valid token IDs
2. Embeddings convert tokens to vectors
3. Attention mechanisms process sequences
4. Transformers combine everything correctly
5. Gradients flow back throu... | 319 | 10,154 |
cs249r_book | tinytorch/tests/integration/test_network_capability.py | .py | """
Network Capability Tests for Module 05
Tests that networks can solve non-linear problems
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
class TestXORCapability:
"""Test that mul... | 128 | 3,947 |
cs249r_book | tinytorch/tests/integration/test_xor_thorough.py | .py | #!/usr/bin/env python3
"""
Thorough XOR test to verify multi-layer networks work correctly.
"""
import sys
sys.path.insert(0, '.')
import numpy as np
from tinytorch import Tensor, Linear, ReLU, Sigmoid, BinaryCrossEntropyLoss, SGD
print("=" * 70)
print("๐งช THOROUGH XOR TEST - Verifying Multi-Layer Networks")
print("=... | 92 | 2,588 |
cs249r_book | tinytorch/tests/integration/test_integration_gradient_flow.py | .py | #!/usr/bin/env python3
"""
Comprehensive Gradient Flow Tests for TinyTorch
================================================
Tests that gradients flow correctly through:
1. Simple networks (single layer)
2. Multi-layer networks (MLP)
3. Convolutional networks (CNN)
4. Attention mechanisms
5. Complete training loops
Th... | 454 | 14,606 |
cs249r_book | tinytorch/tests/integration/test_layers_integration.py | .py | #!/usr/bin/env python3
"""
Integration Tests for TinyTorch Layers Module
This file contains the integration tests that were removed from Module 03
to keep the module focused on unit testing only. These tests demonstrate
how layers work together with other modules and complete system behaviors.
"""
import sys
import o... | 229 | 8,255 |
cs249r_book | tinytorch/tests/integration/test_cnn_integration.py | .py | #!/usr/bin/env python3
"""
Integration Tests for CNN (Spatial) Operations
Tests that verify:
1. Convolutions are actually working (not just shape manipulation)
2. Gradients flow through conv layers correctly
3. Shape transformations are correct
4. MaxPooling/AvgPooling work as expected
5. Complete CNN forward/backward... | 360 | 14,932 |
cs249r_book | tinytorch/tests/integration/test_loss_gradients.py | .py | """
Comprehensive test for loss function gradients.
Tests which losses have proper autograd integration and work for training.
"""
import numpy as np
import sys
import os
# Add project root to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
from tinytorch import Tensor, Linear, MSELoss,... | 227 | 7,648 |
cs249r_book | tinytorch/tests/integration/test_training_capabilities.py | .py | #!/usr/bin/env python
"""
Training Capability Tests for TinyTorch
========================================
Tests that models can actually learn (not just forward pass).
Validates gradient flow, parameter updates, and convergence.
"""
import sys
import os
import numpy as np
rng = np.random.default_rng(7)
# Add project... | 417 | 11,612 |
cs249r_book | tinytorch/tests/03_layers/test_layers_core.py | .py | """
Module 03: Layers - Core Functionality Tests
=============================================
These tests verify that Layer abstractions work correctly.
WHY LAYERS MATTER:
-----------------
Layers are the building blocks of neural networks:
- Linear (Dense): y = Wx + b
- Conv2d: sliding window feature detection
- RN... | 493 | 15,969 |
cs249r_book | tinytorch/tests/03_layers/test_dense_integration.py | .py | """
Integration test for Module 04: Linear
Validates that the dense module integrates correctly with the TinyTorch package.
This is a quick validation test, not a comprehensive capability test.
"""
import sys
import importlib
import warnings
import numpy as np
rng = np.random.default_rng(7)
def test_dense_module_in... | 195 | 6,793 |
cs249r_book | tinytorch/tests/03_layers/test_03_layers_progressive.py | .py | """
Module 03: Progressive Integration Tests
Tests that Module 03 (Layers) works correctly AND that the foundation stack (01โ02) still works.
DEPENDENCY CHAIN: 01_tensor โ 02_activations โ 03_layers
This is where we create reusable building blocks for neural networks.
๐ฏ WHAT THIS TESTS:
- Module 03: Layer base class... | 995 | 36,767 |
cs249r_book | tinytorch/tests/03_layers/test_layers_networks_integration.py | .py | """
Integration Tests - Layers and Dense Networks
Tests cross-module interfaces and compatibility between individual Layers and Dense Network modules.
Focuses on integration, not re-testing individual module functionality.
"""
import pytest
import numpy as np
rng = np.random.default_rng(7)
from test_utils import setu... | 332 | 13,475 |
cs249r_book | tinytorch/tests/03_layers/test_dense_layer.py | .py | """
Tests for Module 04: Linear/Networks
"""
import pytest
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
class TestLinearExports:
"""Test that Linear layer is properly exported."""
... | 118 | 3,471 |
cs249r_book | tinytorch/tests/03_layers/test_layers_integration.py | .py | """
Module 03: Layers - Integration Tests
Tests that Layer base class enables building neural network components
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
class TestLayerFoundation... | 222 | 7,058 |
cs249r_book | tinytorch/tests/environment/test_setup_validation.py | .py | """
Environment Setup Validation Tests
These tests verify that the TinyTorch environment is correctly configured
and all dependencies work as expected. Run these after `tito setup` to
ensure students can actually use TinyTorch.
Usage:
pytest tests/environment/test_setup_validation.py -v
Or via TITO:
tito... | 436 | 15,137 |
cs249r_book | tinytorch/tests/environment/test_all_requirements.py | .py | """
Automated Requirements Validation Tests
Automatically tests ALL packages from requirements.txt to ensure:
1. They can be imported
2. They have the correct version
3. They actually work (basic functionality test)
This discovers ALL requirements files and validates every package.
Usage:
pytest tests/environmen... | 394 | 12,861 |
cs249r_book | tinytorch/tests/11_embeddings/test_embeddings_core.py | .py | """
Module 11: Embeddings - Core Functionality Tests
=================================================
WHY EMBEDDINGS MATTER:
---------------------
Embeddings turn discrete IDs into dense vectors:
- Token ID 156 โ [0.2, -0.5, 0.8, ...] (512 dims)
- These vectors capture meaning
- Similar words have similar embeddings... | 117 | 3,205 |
cs249r_book | tinytorch/tests/11_embeddings/test_11_embeddings_progressive.py | .py | """
Module 11: Progressive Integration Tests
Tests that Module 11 (Embeddings) works correctly AND that prior modules (01โ10) still work.
DEPENDENCY CHAIN: 01_tensor โ 02_activations โ 03_layers โ 04_losses โ 05_dataloader โ
06_autograd โ 07_optimizers โ 08_training โ 09_convolutions โ 10_tokenizati... | 346 | 11,646 |
cs249r_book | tinytorch/tests/11_embeddings/test_embedding_gradient_flow.py | .py | """
Test gradient flow through Embedding layer.
These tests ensure that:
1. EmbeddingBackward is properly attached to Embedding outputs
2. Gradients flow correctly to embedding weight matrix
3. Integration with autograd system works end-to-end
Prevents regression of gradient flow issues discovered in milestone testin... | 216 | 6,795 |
cs249r_book | tinytorch/tests/11_embeddings/test_training_integration.py | .py | """
Module 08: Training - Integration Tests
Tests that complete training loops work with all system components
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
class TestTrainingLoopInteg... | 272 | 9,091 |
cs249r_book | tinytorch/tests/14_profiling/test_profiler_core.py | .py | """
Module 14: Profiler Core Tests
===============================
These tests verify that the profiling tools work correctly.
WHY THESE TESTS MATTER:
-----------------------
Profiling is essential for ML systems engineering. Without it:
- You can't find bottlenecks
- You can't measure improvement
- Optimization is g... | 119 | 3,330 |
cs249r_book | tinytorch/tests/14_profiling/test_14_profiling_progressive.py | .py | """
Module 14: Progressive Integration Tests
Tests that Module 14 (Profiling) works correctly AND that prior modules (01โ13) still work.
DEPENDENCY CHAIN: 01_tensor โ ... โ 12_attention โ 13_transformers โ 14_profiling
โ ๏ธ IMPORTANT: This test ONLY uses modules 01-14.
Future modules (15_quantization, 16_compression... | 352 | 11,378 |
cs249r_book | tinytorch/tests/20_capstone/test_capstone_integration.py | .py | #!/usr/bin/env python3
"""
Integration tests for Module 20: Capstone
Tests end-to-end ML system integration
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
def test_capstone_integration():
"""Test capstone project integration."""
# TODO: Implement integrati... | 24 | 654 |
cs249r_book | tinytorch/tests/20_capstone/test_capstone_core.py | .py | """
Module 20: Capstone Core Tests
===============================
These tests verify the capstone submission and reporting system.
WHY THESE TESTS MATTER:
-----------------------
The capstone is where students prove their TinyTorch implementation works.
These tests verify:
1. BenchmarkReport can aggregate all metric... | 179 | 5,416 |
cs249r_book | tinytorch/tests/13_transformers/test_training_simple.py | .py | """
Simple end-to-end training test for transformers.
This test validates that a transformer can successfully learn from a tiny dataset,
demonstrating that the entire training pipeline (forward, loss, backward, update) works.
"""
import sys
import time
from pathlib import Path
import numpy as np
rng = np.random.defa... | 247 | 7,575 |
cs249r_book | tinytorch/tests/13_transformers/test_transformers_core.py | .py | """
Module 13: Transformers - Core Functionality Tests
===================================================
WHY TRANSFORMERS MATTER:
-----------------------
Transformers power modern AI:
- GPT, ChatGPT, Claude (language)
- BERT (understanding)
- Vision Transformers (images)
- Whisper (speech)
WHAT STUDENTS LEARN:
----... | 114 | 3,268 |
cs249r_book | tinytorch/tests/13_transformers/test_13_transformers_progressive.py | .py | """
Module 13: Progressive Integration Tests
Tests that Module 13 (Transformers) works correctly AND that prior modules (01โ12) still work.
DEPENDENCY CHAIN: 01_tensor โ ... โ 11_embeddings โ 12_attention โ 13_transformers
โ ๏ธ IMPORTANT: This test ONLY uses modules 01-13.
Future modules (14_profiling, 19_benchmarki... | 400 | 12,982 |
cs249r_book | tinytorch/tests/13_transformers/test_transformer_gradient_flow.py | .py | """
Test gradient flow through complete transformer architecture.
This test validates that all transformer components (embeddings, attention,
LayerNorm, MLP) properly propagate gradients during backpropagation.
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add parent dir... | 258 | 8,178 |
cs249r_book | tinytorch/tests/08_training/test_autograd_integration.py | .py | """
Module 08: Training - Autograd Integration Tests
Tests that automatic differentiation works with all previous modules
"""
import numpy as np
import pytest
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
pytestmark = pytest.mark.skip(
... | 222 | 7,358 |
cs249r_book | tinytorch/tests/08_training/test_training_core.py | .py | """
Module 08: Training - Core Functionality Tests
===============================================
WHY TRAINING MATTERS:
--------------------
Training is where learning happens:
1. Forward pass: compute predictions
2. Loss: measure error
3. Backward: compute gradients
4. Update: adjust weights
WHAT STUDENTS LEARN:
--... | 267 | 8,149 |
cs249r_book | tinytorch/tests/08_training/test_training_coverage.py | .py | """
Module 08: Training - Coverage Tests
======================================
Tests for the parts of Module 08 that are implemented but have no test coverage:
- CosineSchedule correctness
- clip_grad_norm behaviour
- Trainer.save_checkpoint / load_checkpoint round-trip
- Trainer.evaluate (loss and accuracy)
- Schedu... | 481 | 19,942 |
cs249r_book | tinytorch/tests/08_training/test_08_training_progressive.py | .py | """
Module 08: Progressive Integration Tests
Tests that Module 08 (Training) works correctly AND that prior modules (01โ07) still work.
DEPENDENCY CHAIN: 01_tensor โ 02_activations โ 03_layers โ 04_losses โ 05_dataloader โ 06_autograd โ 07_optimizers โ 08_training
โ ๏ธ IMPORTANT: This test ONLY uses modules 01-08.
F... | 501 | 18,007 |
cs249r_book | tinytorch/tests/12_attention/test_12_attention_progressive.py | .py | """
Module 12: Progressive Integration Tests
Tests that Module 12 (Attention) works correctly AND that prior modules (01โ11) still work.
DEPENDENCY CHAIN: 01_tensor โ ... โ 10_tokenization โ 11_embeddings โ 12_attention
โ ๏ธ IMPORTANT: This test ONLY uses modules 01-12.
Future modules (13_transformers, 16_compressio... | 354 | 11,905 |
cs249r_book | tinytorch/tests/12_attention/test_attention_core.py | .py | """
Module 12: Attention Core Tests
================================
These tests verify that attention mechanisms compute correctly.
WHY THESE TESTS MATTER:
-----------------------
Attention is the core innovation behind Transformers (GPT, BERT, etc.).
If attention doesn't work:
- Model can't focus on relevant parts ... | 288 | 9,877 |
cs249r_book | tinytorch/tests/17_acceleration/test_acceleration_integration.py | .py | #!/usr/bin/env python3
"""
Integration tests for Module 17: Acceleration
Tests operator fusion, kernel optimization, and hardware acceleration
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
def test_acceleration_integration():
"""Test acceleration system integ... | 24 | 697 |
cs249r_book | tinytorch/tests/17_acceleration/test_acceleration_core.py | .py | """
Module 17: Acceleration Core Tests
===================================
These tests verify optimization techniques for faster inference.
WHY THESE TESTS MATTER:
-----------------------
Acceleration techniques (SIMD, parallel execution, memory layout)
can provide significant speedups. These tests verify:
- Optimiza... | 122 | 3,746 |
cs249r_book | tinytorch/tests/regression/test_nlp_components_gradient_flow.py | .py | #!/usr/bin/env python3
"""
Comprehensive Gradient Flow Tests for NLP Components
Tests gradient flow through all NLP-specific modules:
- Module 10: Tokenization
- Module 11: Embedding + PositionalEncoding
- Module 12: Attention (scaled dot-product + multi-head)
- Module 13: Transformer (LayerNorm, MLP, TransformerBlock... | 579 | 19,151 |
cs249r_book | tinytorch/tests/regression/run_sandbox_tests.py | .py | #!/usr/bin/env python
"""
TinyTorch Sandbox Integrity Tests
==================================
Run this to ensure the student learning sandbox is robust.
All core infrastructure must work perfectly so students can
focus on learning ML systems, not debugging framework issues.
"""
import sys
import os
import importlib
... | 86 | 2,561 |
cs249r_book | tinytorch/tests/regression/test_transformer_reshaping.py | .py | """
BUG TRACKING:
============
Bug ID: BUG-2024-11-25-002
Date Found: 2024-11-25
Found By: PyTorch Expert Architecture Review
Severity: High
DESCRIPTION:
TinyGPT example fails with "matmul requires 2D tensors" when passing transformer
output (3D: batch x seq x embed) directly to Linear layer projection.
REPRODUCTION:... | 274 | 9,263 |
cs249r_book | tinytorch/tests/regression/test_gradient_flow_fixes.py | .py | #!/usr/bin/env python3
"""
Regression Tests for Gradient Flow Fixes
This test suite verifies that specific gradient flow bugs have been fixed and don't regress.
These tests document the issues we encountered during transformer milestone implementation
and ensure the fixes remain in place.
Regression Issues Tested:
1.... | 313 | 10,654 |
cs249r_book | tinytorch/tests/regression/test_conv_linear_dimensions.py | .py | """
BUG TRACKING:
============
Bug ID: BUG-2024-11-25-001
Date Found: 2024-11-25
Found By: PyTorch Expert Architecture Review
Severity: High
DESCRIPTION:
CNN example fails with "Inner dimensions must match: 2304 != 1600" when connecting
Conv2d outputs to Linear layer inputs in CIFAR-10 training.
REPRODUCTION:
1. Load... | 211 | 7,122 |
cs249r_book | tinytorch/tests/01_tensor/test_tensor_core.py | .py | """
Module 01: Tensor - Core Functionality Tests
=============================================
These tests verify that Tensor, the fundamental data structure of TinyTorch, works correctly.
WHY TENSORS MATTER:
------------------
Tensors are the foundation of ALL deep learning:
- Every input (images, text, audio) becom... | 574 | 19,827 |
cs249r_book | tinytorch/tests/01_tensor/test_tensor_integration.py | .py | """
Module 01: Tensor - Integration Tests
Tests that Tensor works as foundation for all other modules
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
class TestTensorFoundation:
"""T... | 146 | 4,834 |
cs249r_book | tinytorch/tests/01_tensor/test_01_tensor_progressive.py | .py | """
Module 01: Progressive Integration Tests
Tests that Module 01 (Tensor) works correctly.
DEPENDENCY CHAIN: 01_tensor
This ensures students can trace back exactly where issues originate.
"""
import numpy as np
rng = np.random.default_rng(7)
import sys
from pathlib import Path
# Add project root to path
sys.path.in... | 176 | 6,453 |
cs249r_book | tinytorch/tests/cli/test_cli_registry.py | .py | """
CLI Registry Tests - Validate all commands are properly registered and accessible
This test suite ensures:
1. All commands in TinyTorchCLI.commands are valid BaseCommand subclasses
2. All commands have proper metadata (name, description)
3. All commands can be invoked via argparse
4. No commands are missing from r... | 222 | 8,476 |
cs249r_book | tinytorch/tests/cli/test_nbgrader_command.py | .py | import argparse
import json
import os
import subprocess
from argparse import Namespace
from pathlib import Path
import pytest
from tito.commands.nbgrader import NBGraderCommand
from tito.core.config import CLIConfig
def make_config(project_root: Path) -> CLIConfig:
return CLIConfig.from_project_root(project_roo... | 554 | 20,035 |
cs249r_book | tinytorch/tests/cli/test_community_flow.py | .py | """
Tests for the TinyTorch community submission and progress syncing flow.
These tests validate:
1. Retrieval of authentication status and tokens.
2. Creation and structure of the progress payload.
3. Execution of progress syncing POST requests with mock servers.
"""
import json
import pytest
from unittest.mock impo... | 148 | 5,870 |
cs249r_book | tinytorch/tests/cli/test_progress_sync.py | .py | """
Regression tests for community progress sync (issue #1849).
These guard the bug where the TinyTorch dashboard did not reflect CLI progress:
1. Automatic sync was silently skipped on any non-TTY shell (Git Bash / MinTTY
on Windows, IDE terminals), because the trigger gated on
``sys.stdin.isatty()``. The fix ... | 226 | 8,105 |
cs249r_book | tinytorch/tests/cli/test_live_submission.py | .py | """
Live E2E integration test for TinyTorch progress submission to Supabase.
This test runs ONLY if TINYTORCH_TEST_EMAIL and TINYTORCH_TEST_PASSWORD are set
in the environment or in a local.env file.
It automates the entire flow:
1. Programmatic login to Supabase auth API using email/password + active Anon Key.
2. Te... | 185 | 7,161 |
cs249r_book | tinytorch/tests/cli/test_cli_execution.py | .py | """
CLI Execution Tests - Smoke tests for each command
This test suite ensures:
1. Each command can be executed without crashing (help mode)
2. Commands with subcommands show their subcommand help
3. Error messages are helpful when commands fail
"""
import pytest
import subprocess
import sys
from pathlib import Path
... | 203 | 6,706 |
cs249r_book | tinytorch/tests/cli/test_release_regressions.py | .py | """
Release regression tests for student-facing CLI and API correctness.
"""
import importlib.util
import io
import os
import subprocess
import sys
from argparse import Namespace
from pathlib import Path
import numpy as np
from rich.console import Console
from tito.commands.export_utils import find_source_file_for_e... | 196 | 5,984 |
cs249r_book | tinytorch/tests/cli/test_cli_help_consistency.py | .py | """
CLI Help Consistency Tests - Validate help text is consistent and complete
This test suite ensures:
1. Help text uses consistent formatting and terminology
2. All commands document their purpose clearly
3. Examples are provided where helpful
4. No broken references or outdated commands in help text
"""
import pyt... | 248 | 8,500 |
cs249r_book | tinytorch/tests/19_benchmarking/test_benchmark_core.py | .py | """
Module 19: Benchmarking Core Tests
===================================
These tests verify that benchmarking tools work correctly.
WHY THESE TESTS MATTER:
-----------------------
Benchmarking is how we measure and compare model performance.
If benchmarking is broken:
- We can't measure throughput (tokens/second)
-... | 172 | 5,718 |
cs249r_book | tinytorch/tests/19_benchmarking/test_benchmarking_integration.py | .py | #!/usr/bin/env python3
"""
Integration tests for Module 19: Benchmarking
Tests MLPerf-style benchmarking and performance measurement
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
def test_benchmarking_integration():
"""Test benchmarking system integration."""... | 24 | 687 |
cs249r_book | tinytorch/tests/06_autograd/test_autograd_core.py | .py | """
Module 06: Autograd - Core Functionality Tests
Tests automatic differentiation and computational graphs
NOTE: These tests reference a Variable class that is not implemented in TinyTorch.
TinyTorch enhances Tensor directly via enable_autograd(). These tests are retained
as placeholders for future Variable API suppo... | 407 | 12,869 |
cs249r_book | tinytorch/tests/06_autograd/test_autograd_gradient_flow.py | .py | """
Test gradient flow through all autograd operations.
This test suite validates that all arithmetic operations and activations
properly preserve gradient tracking and enable backpropagation.
"""
import numpy as np
import pytest
import sys
from pathlib import Path
# Add parent directory to path for imports
sys.path... | 166 | 5,323 |
cs249r_book | tinytorch/tests/06_autograd/test_batched_matmul_backward.py | .py | #!/usr/bin/env python3
"""
Test batched matrix multiplication gradients in autograd.
This test verifies that MatmulBackward correctly handles batched 3D+ tensors
using np.matmul and np.swapaxes instead of np.dot and .T
"""
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))
impo... | 152 | 4,920 |
cs249r_book | tinytorch/tests/06_autograd/__init__.py | .py | """
Autograd-specific edge case tests.
These tests focus on the autograd module's internal behavior:
- Broadcasting in gradients (common bug source)
- Computation graph construction
- Numerical stability in backward pass
- Memory management in gradient accumulation
- Edge cases students encounter
Complements the inli... | 14 | 391 |
cs249r_book | tinytorch/tests/06_autograd/test_06_autograd_progressive.py | .py | """
Module 06: Progressive Integration Tests
Tests that Module 06 (Autograd) works correctly AND that prior modules (01โ05) still work.
DEPENDENCY CHAIN: 01_tensor โ 02_activations โ 03_layers โ 04_losses โ 05_dataloader โ 06_autograd
โ ๏ธ IMPORTANT: This test ONLY uses modules 01-06.
Future modules (07_optimizers, ... | 571 | 20,496 |
cs249r_book | tinytorch/tests/06_autograd/test_gradient_correctness.py | .py | """
Module 06: Gradient Correctness Tests
======================================
Validates that every backward pass computes numerically correct gradients
using finite differences as ground truth.
The core idea: for any function f, the analytical gradient computed by
backward() should match the numerical gradient:
... | 405 | 12,549 |
cs249r_book | tinytorch/tests/16_compression/test_compressor_core.py | .py | """
Module 16: Compression Core Tests
===================================
These tests verify that model compression (pruning) works correctly.
WHY THESE TESTS MATTER:
-----------------------
Pruning removes unnecessary weights, making models smaller and faster.
If compression is broken:
- Model doesn't get smaller (n... | 230 | 7,568 |
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