mirror of
https://github.com/MLSysBook/TinyTorch.git
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2f23f757e7
This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.
213 lines
7.6 KiB
Python
213 lines
7.6 KiB
Python
"""
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Module 10: Autograd - Integration Tests
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Tests that automatic differentiation works with all previous modules
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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 TestAutogradTensorIntegration:
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"""Test autograd integrates with Tensor system."""
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def test_variable_creation(self):
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"""Test Variable can be created from Tensor-like data."""
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try:
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from tinytorch.core.autograd import Variable
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# Should create Variable from array
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x = Variable(np.array([1.0, 2.0, 3.0]), requires_grad=True)
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assert x.shape == (3,)
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assert x.requires_grad == True
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except ImportError:
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# Skip if autograd not implemented yet
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assert True, "Autograd not implemented yet"
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def test_gradient_computation_basic(self):
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"""Test basic gradient computation."""
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try:
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from tinytorch.core.autograd import Variable
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x = Variable(np.array([2.0]), requires_grad=True)
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y = x * x # y = x²
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if hasattr(y, 'backward'):
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y.backward()
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# dy/dx = 2x = 2*2 = 4
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assert hasattr(x, 'grad'), "Should compute gradients"
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if x.grad is not None:
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assert np.isclose(x.grad, 4.0), f"Expected grad=4, got {x.grad}"
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except (ImportError, AttributeError):
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# Skip if autograd not fully implemented
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assert True, "Autograd backward pass not implemented yet"
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class TestAutogradLayerIntegration:
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"""Test autograd works with layer operations."""
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def test_dense_layer_gradients(self):
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"""Test gradients flow through Dense layer."""
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try:
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from tinytorch.core.autograd import Variable
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from tinytorch.core.layers import Dense
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# Create layer
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layer = Dense(2, 1, use_bias=False)
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# Input with gradients
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x = Variable(np.array([[1.0, 2.0]]), requires_grad=True)
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# Forward pass
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output = layer(x)
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# Should be able to compute gradients
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if hasattr(output, 'backward'):
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loss = output * output # Simple loss
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loss.backward()
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assert hasattr(x, 'grad'), "Input should have gradients"
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except (ImportError, AttributeError):
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assert True, "Dense-autograd integration not ready"
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def test_activation_gradients(self):
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"""Test gradients flow through activations."""
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try:
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from tinytorch.core.autograd import Variable
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from tinytorch.core.activations import ReLU, Sigmoid
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x = Variable(np.array([1.0, -1.0, 2.0]), requires_grad=True)
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relu = ReLU()
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relu_out = relu(x)
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if hasattr(relu_out, 'backward'):
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loss = (relu_out * relu_out).sum()
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loss.backward()
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# ReLU gradient: 1 where x > 0, 0 elsewhere
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expected_grad = np.array([1.0, 0.0, 1.0]) * 2 * relu_out.data
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if x.grad is not None:
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assert np.allclose(x.grad, expected_grad)
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except (ImportError, AttributeError):
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assert True, "Activation-autograd integration not ready"
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class TestAutogradComputationGraph:
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"""Test autograd builds and traverses computation graphs."""
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def test_simple_computation_graph(self):
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"""Test simple multi-operation graph."""
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try:
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from tinytorch.core.autograd import Variable
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x = Variable(np.array([3.0]), requires_grad=True)
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y = Variable(np.array([2.0]), requires_grad=True)
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# z = x * y + x²
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z = x * y + x * x
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if hasattr(z, 'backward'):
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z.backward()
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# dz/dx = y + 2x = 2 + 2*3 = 8
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# dz/dy = x = 3
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if x.grad is not None and y.grad is not None:
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assert np.isclose(x.grad, 8.0)
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assert np.isclose(y.grad, 3.0)
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except (ImportError, AttributeError):
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assert True, "Computation graph not implemented"
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def test_chain_rule(self):
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"""Test chain rule works correctly."""
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try:
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from tinytorch.core.autograd import Variable
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x = Variable(np.array([2.0]), requires_grad=True)
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# Chain: x -> x² -> (x²)²
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y = x * x # y = x²
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z = y * y # z = y² = (x²)²
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if hasattr(z, 'backward'):
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z.backward()
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# dz/dx = dz/dy * dy/dx = 2y * 2x = 2(x²) * 2x = 4x³
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# At x=2: 4 * 2³ = 4 * 8 = 32
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if x.grad is not None:
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assert np.isclose(x.grad, 32.0)
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except (ImportError, AttributeError):
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assert True, "Chain rule not implemented"
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class TestAutogradOptimizationIntegration:
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"""Test autograd enables optimization algorithms."""
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def test_gradient_descent_step(self):
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"""Test manual gradient descent step."""
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try:
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from tinytorch.core.autograd import Variable
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# Parameter to optimize
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x = Variable(np.array([5.0]), requires_grad=True)
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# Loss function: (x - 2)²
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target = 2.0
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loss = (x - target) * (x - target)
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if hasattr(loss, 'backward'):
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loss.backward()
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# Gradient descent step
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learning_rate = 0.1
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if x.grad is not None:
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new_x = x.data - learning_rate * x.grad
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# Should move closer to target
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old_distance = abs(x.data - target)
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new_distance = abs(new_x - target)
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assert new_distance < old_distance
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except (ImportError, AttributeError):
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assert True, "Optimization integration not ready"
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def test_parameter_updates(self):
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"""Test parameter updates work correctly."""
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try:
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from tinytorch.core.autograd import Variable
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from tinytorch.core.layers import Dense
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layer = Dense(1, 1)
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# Convert layer parameters to Variables if needed
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if not isinstance(layer.weights, Variable):
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layer.weights = Variable(layer.weights.data, requires_grad=True)
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# Simple forward pass
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x = Variable(np.array([[1.0]]), requires_grad=True)
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output = layer(x)
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loss = output * output
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if hasattr(loss, 'backward'):
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old_weights = layer.weights.data.copy()
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loss.backward()
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# Update weights
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learning_rate = 0.01
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if layer.weights.grad is not None:
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new_weights = old_weights - learning_rate * layer.weights.grad
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assert not np.array_equal(old_weights, new_weights)
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except (ImportError, AttributeError):
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assert True, "Parameter update integration not ready" |