mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-03-25 20:49:40 -05:00
ad5404cb2e
New Features: - Add MSEBackward gradient computation for regression tasks - Patch MSELoss in enable_autograd() for gradient tracking - All 3 loss functions now support autograd: MSE, BCE, CrossEntropy Test Suite Organization: - Reorganize tests/ into focused directories - Create tests/integration/ for cross-module tests - Create tests/05_autograd/ for autograd edge cases - Create tests/debugging/ for common student pitfalls - Add comprehensive tests/README.md explaining test philosophy Integration Tests: - Move test_gradient_flow.py to integration/ - 20 comprehensive gradient flow tests - Tests cover: tensors, layers, activations, losses, optimizers - Tests validate: basic ops, chain rule, broadcasting, training loops - 19/20 tests passing (MSE now fixed!) Results: ✅ Perceptron learns: 50% → 93% accuracy ✅ Clean test organization guides future development ✅ Tests catch the exact bugs that broke training Pedagogical Value: - Test organization teaches testing best practices - Gradient flow tests show what integration testing catches - Sets foundation for debugging/diagnostic tests
473 lines
15 KiB
Python
473 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Comprehensive gradient flow testing for TinyTorch.
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This test suite systematically validates that gradients propagate correctly
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through all components of the training stack.
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Run with: pytest tests/test_gradient_flow.py -v
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Or directly: python tests/test_gradient_flow.py
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"""
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import numpy as np
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import sys
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import os
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# Add project root to path
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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from tinytorch import Tensor, Linear, Dropout
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from tinytorch import Sigmoid, ReLU, Tanh, GELU, Softmax
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from tinytorch import MSELoss, CrossEntropyLoss, BinaryCrossEntropyLoss
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from tinytorch import SGD, AdamW
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class TestBasicTensorGradients:
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"""Test gradient computation for basic tensor operations."""
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def test_multiplication_gradient(self):
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"""Test gradient flow through multiplication."""
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x = Tensor([[1.0, 2.0]], requires_grad=True)
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y = x * 3
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loss = y.sum()
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loss.backward()
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# dy/dx = 3
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assert x.grad is not None, "Gradient should be computed"
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assert np.allclose(x.grad, [[3.0, 3.0]]), f"Expected [[3, 3]], got {x.grad}"
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def test_addition_gradient(self):
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"""Test gradient flow through addition."""
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x = Tensor([[1.0, 2.0]], requires_grad=True)
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y = Tensor([[3.0, 4.0]], requires_grad=True)
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z = x + y
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loss = z.sum()
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loss.backward()
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# dz/dx = 1, dz/dy = 1
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assert np.allclose(x.grad, [[1.0, 1.0]]), f"x.grad: {x.grad}"
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assert np.allclose(y.grad, [[1.0, 1.0]]), f"y.grad: {y.grad}"
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def test_chain_rule(self):
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"""Test gradient flow through chain of operations."""
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x = Tensor([[2.0]], requires_grad=True)
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y = x * 3 # y = 3x
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z = y + 1 # z = 3x + 1
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w = z * 2 # w = 2(3x + 1) = 6x + 2
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w.backward()
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# dw/dx = 6
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assert np.allclose(x.grad, [[6.0]]), f"Expected [[6]], got {x.grad}"
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def test_matmul_gradient(self):
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"""Test gradient flow through matrix multiplication."""
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x = Tensor([[1.0, 2.0]], requires_grad=True)
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W = Tensor([[1.0], [2.0]], requires_grad=True)
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y = x.matmul(W) # y = [[5.0]]
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y.backward()
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# dy/dx = W^T = [[1, 2]]
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# dy/dW = x^T = [[1], [2]]
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assert np.allclose(x.grad, [[1.0, 2.0]]), f"x.grad: {x.grad}"
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assert np.allclose(W.grad, [[1.0], [2.0]]), f"W.grad: {W.grad}"
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def test_broadcasting_gradient(self):
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"""Test gradient flow with broadcasting (e.g., bias addition)."""
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x = Tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) # (2, 2)
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bias = Tensor([1.0, 2.0], requires_grad=True) # (2,)
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y = x + bias # Broadcasting happens
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loss = y.sum()
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loss.backward()
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# Gradient should sum over broadcast dimension
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assert x.grad.shape == (2, 2), f"x.grad shape: {x.grad.shape}"
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assert bias.grad.shape == (2,), f"bias.grad shape: {bias.grad.shape}"
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assert np.allclose(bias.grad, [2.0, 2.0]), f"bias.grad: {bias.grad}"
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class TestLayerGradients:
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"""Test gradient computation through neural network layers."""
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def test_linear_layer_gradients(self):
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"""Test gradient flow through Linear layer."""
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layer = Linear(2, 3)
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x = Tensor([[1.0, 2.0]], requires_grad=True)
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w_before = layer.weight.data.copy()
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b_before = layer.bias.data.copy()
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out = layer(x)
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loss = out.sum()
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loss.backward()
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# All gradients should exist
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assert layer.weight.grad is not None, "Weight gradient missing"
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assert layer.bias.grad is not None, "Bias gradient missing"
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assert x.grad is not None, "Input gradient missing"
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# Gradient shapes should match parameter shapes
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assert layer.weight.grad.shape == layer.weight.shape
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assert layer.bias.grad.shape == layer.bias.shape
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def test_multi_layer_gradients(self):
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"""Test gradient flow through multiple layers."""
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layer1 = Linear(2, 3)
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layer2 = Linear(3, 1)
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x = Tensor([[1.0, 2.0]], requires_grad=True)
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h = layer1(x)
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out = layer2(h)
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loss = out.sum()
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loss.backward()
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# All layers should have gradients
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assert layer1.weight.grad is not None
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assert layer1.bias.grad is not None
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assert layer2.weight.grad is not None
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assert layer2.bias.grad is not None
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class TestActivationGradients:
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"""Test gradient computation through activation functions."""
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def test_sigmoid_gradient(self):
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"""Test gradient flow through Sigmoid."""
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x = Tensor([[0.0, 1.0, -1.0]], requires_grad=True)
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sigmoid = Sigmoid()
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y = sigmoid(x)
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loss = y.sum()
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loss.backward()
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assert x.grad is not None, "Sigmoid gradient missing"
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# Sigmoid gradient: σ'(x) = σ(x)(1 - σ(x))
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# At x=0: σ(0) = 0.5, σ'(0) = 0.25
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assert x.grad[0, 0] > 0, "Gradient should be positive"
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def test_relu_gradient(self):
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"""Test gradient flow through ReLU."""
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x = Tensor([[-1.0, 0.0, 1.0]], requires_grad=True)
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relu = ReLU()
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y = relu(x)
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loss = y.sum()
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loss.backward()
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# ReLU gradient: 1 if x > 0, else 0
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# Note: We haven't implemented ReLU backward yet, so this will fail
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# TODO: Implement ReLU backward in autograd
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def test_tanh_gradient(self):
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"""Test gradient flow through Tanh."""
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x = Tensor([[0.0, 1.0]], requires_grad=True)
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tanh = Tanh()
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y = tanh(x)
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loss = y.sum()
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# TODO: Implement Tanh backward
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# loss.backward()
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class TestLossGradients:
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"""Test gradient computation through loss functions."""
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def test_bce_gradient(self):
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"""Test gradient flow through Binary Cross-Entropy."""
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predictions = Tensor([[0.7, 0.3, 0.9]], requires_grad=True)
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targets = Tensor([[1.0, 0.0, 1.0]])
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loss_fn = BinaryCrossEntropyLoss()
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loss = loss_fn(predictions, targets)
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loss.backward()
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assert predictions.grad is not None, "BCE gradient missing"
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assert predictions.grad.shape == predictions.shape
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# Gradient should be negative for correct predictions
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assert predictions.grad[0, 0] < 0, "Gradient sign incorrect"
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def test_mse_gradient(self):
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"""Test gradient flow through MSE loss."""
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predictions = Tensor([[1.0, 2.0, 3.0]], requires_grad=True)
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targets = Tensor([[2.0, 2.0, 2.0]])
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loss_fn = MSELoss()
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loss = loss_fn(predictions, targets)
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# TODO: Implement MSE backward
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# loss.backward()
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class TestOptimizerIntegration:
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"""Test optimizer integration with gradient flow."""
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def test_sgd_updates_parameters(self):
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"""Test that SGD actually updates parameters."""
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layer = Linear(2, 1)
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optimizer = SGD(layer.parameters(), lr=0.1)
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w_before = layer.weight.data.copy()
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b_before = layer.bias.data.copy()
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# Forward pass
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x = Tensor([[1.0, 2.0]], requires_grad=True)
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out = layer(x)
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loss = out.sum()
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# Backward pass
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loss.backward()
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# Optimizer step
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optimizer.step()
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# Parameters should change
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assert not np.allclose(layer.weight.data, w_before), "Weights didn't update"
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assert not np.allclose(layer.bias.data, b_before), "Bias didn't update"
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def test_zero_grad_clears_gradients(self):
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"""Test that zero_grad() clears gradients."""
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layer = Linear(2, 1)
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optimizer = SGD(layer.parameters(), lr=0.1)
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# First backward pass
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x = Tensor([[1.0, 2.0]])
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out = layer(x)
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loss = out.sum()
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loss.backward()
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assert layer.weight.grad is not None, "Gradient should exist"
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# Clear gradients
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optimizer.zero_grad()
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assert layer.weight.grad is None, "Gradient should be cleared"
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assert layer.bias.grad is None, "Bias gradient should be cleared"
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def test_adamw_updates_parameters(self):
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"""Test that AdamW optimizer works."""
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layer = Linear(2, 1)
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optimizer = AdamW(layer.parameters(), lr=0.01)
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w_before = layer.weight.data.copy()
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x = Tensor([[1.0, 2.0]])
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out = layer(x)
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loss = out.sum()
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loss.backward()
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optimizer.step()
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assert not np.allclose(layer.weight.data, w_before), "AdamW didn't update weights"
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class TestFullTrainingLoop:
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"""Test complete training scenarios."""
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def test_simple_convergence(self):
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"""Test that a simple model can learn."""
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# Simple task: learn to output 5 from input [1, 2]
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layer = Linear(2, 1)
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optimizer = SGD(layer.parameters(), lr=0.1)
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loss_fn = MSELoss()
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x = Tensor([[1.0, 2.0]])
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target = Tensor([[5.0]])
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initial_loss = None
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final_loss = None
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# Train for a few iterations
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for i in range(50):
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# Forward
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pred = layer(x)
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loss = loss_fn(pred, target)
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if i == 0:
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initial_loss = loss.data
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if i == 49:
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final_loss = loss.data
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# Backward
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loss.backward()
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# Update
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optimizer.step()
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optimizer.zero_grad()
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# Loss should decrease
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assert final_loss < initial_loss, f"Loss didn't decrease: {initial_loss} → {final_loss}"
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def test_binary_classification(self):
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"""Test binary classification training."""
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layer = Linear(2, 1)
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sigmoid = Sigmoid()
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loss_fn = BinaryCrossEntropyLoss()
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optimizer = SGD(layer.parameters(), lr=0.1)
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# Simple dataset: [1, 1] → 1, [0, 0] → 0
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X = Tensor([[1.0, 1.0], [0.0, 0.0]])
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y = Tensor([[1.0], [0.0]])
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initial_loss = None
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final_loss = None
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for i in range(50):
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# Forward
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logits = layer(X)
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probs = sigmoid(logits)
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loss = loss_fn(probs, y)
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if i == 0:
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initial_loss = loss.data
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if i == 49:
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final_loss = loss.data
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# Backward
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loss.backward()
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# Update
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optimizer.step()
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optimizer.zero_grad()
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assert final_loss < initial_loss, "Binary classification didn't learn"
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class TestEdgeCases:
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"""Test edge cases and potential failure modes."""
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def test_zero_gradient(self):
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"""Test that zero gradients don't break training."""
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x = Tensor([[0.0, 0.0]], requires_grad=True)
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y = x * 0
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loss = y.sum()
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loss.backward()
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assert x.grad is not None
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assert np.allclose(x.grad, [[0.0, 0.0]])
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def test_very_small_values(self):
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"""Test gradient flow with very small values."""
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x = Tensor([[1e-8, 1e-8]], requires_grad=True)
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y = x * 2
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loss = y.sum()
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loss.backward()
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assert x.grad is not None
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assert np.allclose(x.grad, [[2.0, 2.0]])
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def test_gradient_accumulation(self):
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"""Test that gradients accumulate correctly across multiple backward passes."""
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x = Tensor([[1.0]], requires_grad=True)
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# First backward
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y1 = x * 2
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y1.backward()
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grad_after_first = x.grad.copy()
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# Second backward (without zero_grad)
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y2 = x * 3
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y2.backward()
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# Gradient should accumulate: 2 + 3 = 5
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expected = grad_after_first + np.array([[3.0]])
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assert np.allclose(x.grad, expected), f"Expected {expected}, got {x.grad}"
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def run_all_tests():
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"""Run all tests and print results."""
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import inspect
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test_classes = [
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TestBasicTensorGradients,
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TestLayerGradients,
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TestActivationGradients,
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TestLossGradients,
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TestOptimizerIntegration,
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TestFullTrainingLoop,
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TestEdgeCases,
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]
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total_tests = 0
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passed_tests = 0
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failed_tests = []
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skipped_tests = []
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print("=" * 80)
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print("🧪 TINYTORCH GRADIENT FLOW TEST SUITE")
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print("=" * 80)
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for test_class in test_classes:
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print(f"\n{'=' * 80}")
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print(f"📦 {test_class.__name__}")
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print(f"{'=' * 80}")
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instance = test_class()
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methods = [m for m in dir(instance) if m.startswith('test_')]
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for method_name in methods:
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total_tests += 1
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method = getattr(instance, method_name)
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# Get docstring
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doc = method.__doc__ or method_name
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doc = doc.strip().split('\n')[0]
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print(f"\n {method_name}")
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print(f" {doc}")
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try:
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method()
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print(f" ✅ PASSED")
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passed_tests += 1
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except NotImplementedError as e:
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print(f" ⏭️ SKIPPED: {e}")
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skipped_tests.append((test_class.__name__, method_name, str(e)))
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except AssertionError as e:
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print(f" ❌ FAILED: {e}")
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failed_tests.append((test_class.__name__, method_name, str(e)))
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except Exception as e:
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print(f" ❌ ERROR: {e}")
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failed_tests.append((test_class.__name__, method_name, str(e)))
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# Summary
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print("\n" + "=" * 80)
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print("📊 TEST SUMMARY")
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print("=" * 80)
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print(f"Total tests: {total_tests}")
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print(f"✅ Passed: {passed_tests}")
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print(f"❌ Failed: {len(failed_tests)}")
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print(f"⏭️ Skipped: {len(skipped_tests)}")
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if failed_tests:
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print("\n" + "=" * 80)
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print("❌ FAILED TESTS:")
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print("=" * 80)
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for class_name, method_name, error in failed_tests:
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print(f"\n {class_name}.{method_name}")
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print(f" {error}")
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if skipped_tests:
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print("\n" + "=" * 80)
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print("⏭️ SKIPPED TESTS (Not Yet Implemented):")
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print("=" * 80)
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for class_name, method_name, reason in skipped_tests:
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print(f" {class_name}.{method_name}")
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print("\n" + "=" * 80)
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return len(failed_tests) == 0
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if __name__ == "__main__":
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success = run_all_tests()
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sys.exit(0 if success else 1)
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