mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-03-12 02:53:34 -05:00
6f0c96c130
- Updated Linear layer to use autograd operations (matmul, add) for proper gradient propagation - Fixed Parameter class to wrap Variables with requires_grad=True - Implemented proper MSELoss and CrossEntropyLoss with backward chaining - Added broadcasting support in autograd operations for bias gradients - Fixed memoryview errors in gradient data extraction - All integration tests now pass - neural networks can learn via backpropagation
338 lines
12 KiB
Python
338 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Test gradient flow through the entire system.
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This script tests if gradients properly flow from loss -> linear layers -> parameters.
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"""
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import sys
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sys.path.insert(0, '.')
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sys.path.insert(0, 'modules/05_autograd')
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sys.path.insert(0, 'modules/03_layers')
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sys.path.insert(0, 'modules/04_losses')
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import numpy as np
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import contextlib
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import io
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# Import our autograd system
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from autograd_dev import Variable, multiply, add
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# Import our layers system
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from layers_dev import Linear, Parameter
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# Import our loss functions
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from losses_dev import MSELoss
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def test_simple_gradient_flow():
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"""Test gradient flow through a simple linear layer."""
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print("🔬 Testing Simple Gradient Flow")
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print("=" * 40)
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# Create a simple linear layer: 2 inputs -> 1 output
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layer = Linear(2, 1)
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print("\n📊 Initial State:")
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print(f" Weight shape: {layer.weights.data.data.shape}")
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print(f" Weight values: {layer.weights.data.data}")
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print(f" Bias value: {layer.bias.data.data}")
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print(f" Weight grad: {layer.weights.grad}")
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print(f" Bias grad: {layer.bias.grad}")
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# Create input data (2 features)
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x = Variable([[1.0, 2.0]], requires_grad=False)
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# Forward pass
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print("\n🔄 Forward Pass:")
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output = layer.forward(x)
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print(f" Input: {x.data.data}")
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print(f" Output: {output.data.data}")
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print(f" Output type: {type(output)}")
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print(f" Output requires_grad: {output.requires_grad}")
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# Create target and compute loss
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target = Variable([[0.5]], requires_grad=False)
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loss_fn = MSELoss()
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loss = loss_fn(output, target)
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print(f"\n💔 Loss Computation:")
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print(f" Target: {target.data.data}")
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print(f" Loss: {loss.data.data}")
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print(f" Loss type: {type(loss)}")
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print(f" Loss requires_grad: {loss.requires_grad}")
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# Backward pass
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print(f"\n⬅️ Backward Pass:")
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print(" Calling loss.backward()...")
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try:
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loss.backward(1.0) # Pass scalar gradient for the loss
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print(" ✅ Backward pass completed successfully!")
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# Check gradients
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print(f"\n🎯 Gradient Results:")
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print(f" Weight grad: {layer.weights.grad}")
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print(f" Bias grad: {layer.bias.grad}")
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# Check if gradients exist and are non-zero
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if layer.weights.grad is not None and layer.bias.grad is not None:
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print(" ✅ Gradients successfully computed!")
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# Check if gradients have reasonable values
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# Handle different gradient data structures
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if hasattr(layer.weights.grad, 'data'):
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if hasattr(layer.weights.grad.data, 'data'):
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weight_grad_data = layer.weights.grad.data.data
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else:
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weight_grad_data = layer.weights.grad.data
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else:
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weight_grad_data = layer.weights.grad
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if hasattr(layer.bias.grad, 'data'):
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if hasattr(layer.bias.grad.data, 'data'):
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bias_grad_data = layer.bias.grad.data.data
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else:
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bias_grad_data = layer.bias.grad.data
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else:
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bias_grad_data = layer.bias.grad
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# Convert memoryview to array if needed
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if isinstance(weight_grad_data, memoryview):
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weight_grad_data = np.array(weight_grad_data)
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if isinstance(bias_grad_data, memoryview):
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bias_grad_data = np.array(bias_grad_data)
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weight_grad_norm = np.linalg.norm(weight_grad_data)
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bias_grad_norm = np.linalg.norm(bias_grad_data)
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print(f" Weight gradient norm: {weight_grad_norm:.6f}")
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print(f" Bias gradient norm: {bias_grad_norm:.6f}")
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if weight_grad_norm > 1e-8 and bias_grad_norm > 1e-8:
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print(" ✅ Gradient magnitudes are reasonable!")
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return True
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else:
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print(" ❌ Gradients are too small - might be zero!")
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return False
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else:
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print(" ❌ Gradients are None - backpropagation failed!")
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return False
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except Exception as e:
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print(f" ❌ Backward pass failed with error: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_two_layer_network():
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"""Test gradient flow through a two-layer network."""
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print("\n\n🔬 Testing Two-Layer Network")
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print("=" * 40)
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# Create two-layer network: 3 -> 2 -> 1
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layer1 = Linear(3, 2)
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layer2 = Linear(2, 1)
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print("\n📊 Network Structure:")
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print(f" Layer 1: 3 -> 2 (weights: {layer1.weights.data.data.shape})")
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print(f" Layer 2: 2 -> 1 (weights: {layer2.weights.data.data.shape})")
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# Input data
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x = Variable([[1.0, 2.0, 3.0]], requires_grad=False)
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# Forward pass through network
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print(f"\n🔄 Forward Pass:")
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h1 = layer1.forward(x)
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print(f" Input: {x.data.data}")
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print(f" Hidden: {h1.data.data}")
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output = layer2.forward(h1)
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print(f" Output: {output.data.data}")
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# Loss computation
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target = Variable([[1.0]], requires_grad=False)
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loss_fn = MSELoss()
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loss = loss_fn(output, target)
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print(f"\n💔 Loss: {loss.data.data}")
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# Backward pass
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print(f"\n⬅️ Backward Pass:")
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try:
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loss.backward(1.0) # Pass scalar gradient
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print(" ✅ Backward pass completed!")
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# Check all gradients
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print(f"\n🎯 All Gradients:")
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print(f" Layer 1 weight grad: {layer1.weights.grad is not None}")
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print(f" Layer 1 bias grad: {layer1.bias.grad is not None}")
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print(f" Layer 2 weight grad: {layer2.weights.grad is not None}")
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print(f" Layer 2 bias grad: {layer2.bias.grad is not None}")
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if all([
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layer1.weights.grad is not None,
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layer1.bias.grad is not None,
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layer2.weights.grad is not None,
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layer2.bias.grad is not None
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]):
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# Calculate gradient norms
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# Handle different gradient data structures
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def extract_grad_data(grad):
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if hasattr(grad, 'data'):
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if hasattr(grad.data, 'data'):
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data = grad.data.data
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else:
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data = grad.data
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else:
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data = grad
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# Convert memoryview to array if needed
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if isinstance(data, memoryview):
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data = np.array(data)
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return data
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l1_w_data = extract_grad_data(layer1.weights.grad)
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l1_b_data = extract_grad_data(layer1.bias.grad)
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l2_w_data = extract_grad_data(layer2.weights.grad)
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l2_b_data = extract_grad_data(layer2.bias.grad)
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l1_w_norm = np.linalg.norm(l1_w_data)
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l1_b_norm = np.linalg.norm(l1_b_data)
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l2_w_norm = np.linalg.norm(l2_w_data)
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l2_b_norm = np.linalg.norm(l2_b_data)
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print(f" Layer 1 weight grad norm: {l1_w_norm:.6f}")
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print(f" Layer 1 bias grad norm: {l1_b_norm:.6f}")
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print(f" Layer 2 weight grad norm: {l2_w_norm:.6f}")
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print(f" Layer 2 bias grad norm: {l2_b_norm:.6f}")
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print(" ✅ All gradients computed successfully!")
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return True
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else:
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print(" ❌ Some gradients missing!")
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return False
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except Exception as e:
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print(f" ❌ Error in backward pass: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_optimizer_step():
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"""Test that optimizer can use gradients to update parameters."""
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print("\n\n🔬 Testing Optimizer Integration")
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print("=" * 40)
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# Simple optimization test
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layer = Linear(1, 1)
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# Get initial weight
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initial_weight = layer.weights.data.data.copy()
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initial_bias = layer.bias.data.data.copy()
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print(f" Initial weight: {initial_weight}")
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print(f" Initial bias: {initial_bias}")
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# Forward pass with known input/output
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x = Variable([[2.0]], requires_grad=False)
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output = layer.forward(x)
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# Target for specific gradient direction
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target = Variable([[0.0]], requires_grad=False) # Want output to be smaller
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loss_fn = MSELoss()
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loss = loss_fn(output, target)
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print(f" Loss before update: {loss.data.data}")
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# Backward pass
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loss.backward(1.0) # Pass scalar gradient
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# Simple gradient descent update
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learning_rate = 0.1
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if layer.weights.grad is not None:
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# Extract gradient data properly
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if hasattr(layer.weights.grad, 'data'):
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if hasattr(layer.weights.grad.data, 'data'):
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weight_grad_data = layer.weights.grad.data.data
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else:
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weight_grad_data = layer.weights.grad.data
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else:
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weight_grad_data = layer.weights.grad
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if isinstance(weight_grad_data, memoryview):
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weight_grad_data = np.array(weight_grad_data)
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# Subtract gradient (gradient descent)
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new_weight = layer.weights.data.data - learning_rate * weight_grad_data
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layer.weights.data.data[:] = new_weight # Update in place
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if layer.bias.grad is not None:
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# Extract gradient data properly
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if hasattr(layer.bias.grad, 'data'):
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if hasattr(layer.bias.grad.data, 'data'):
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bias_grad_data = layer.bias.grad.data.data
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else:
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bias_grad_data = layer.bias.grad.data
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else:
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bias_grad_data = layer.bias.grad
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if isinstance(bias_grad_data, memoryview):
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bias_grad_data = np.array(bias_grad_data)
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new_bias = layer.bias.data.data - learning_rate * bias_grad_data
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layer.bias.data.data[:] = new_bias
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print(f" Updated weight: {layer.weights.data.data}")
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print(f" Updated bias: {layer.bias.data.data}")
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# Verify parameters actually changed
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weight_changed = not np.allclose(initial_weight, layer.weights.data.data)
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bias_changed = not np.allclose(initial_bias, layer.bias.data.data)
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if weight_changed and bias_changed:
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print(" ✅ Parameters updated successfully!")
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# Test forward pass with updated parameters
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# Reset gradients first
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layer.weights.grad = None
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layer.bias.grad = None
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new_output = layer.forward(x)
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new_loss = loss_fn(new_output, target)
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print(f" Loss after update: {new_loss.data.data}")
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# Loss should be smaller (we did gradient descent)
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if new_loss.data.data < loss.data.data:
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print(" ✅ Loss decreased - optimization working!")
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return True
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else:
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print(" ⚠️ Loss didn't decrease - might be learning rate or other issue")
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return True # Still counts as parameter update working
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else:
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print(" ❌ Parameters didn't change!")
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return False
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if __name__ == "__main__":
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print("🚀 Testing Gradient Flow in TinyTorch")
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print("=" * 50)
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results = []
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# Run all tests
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results.append(("Simple gradient flow", test_simple_gradient_flow()))
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results.append(("Two-layer network", test_two_layer_network()))
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results.append(("Optimizer integration", test_optimizer_step()))
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# Summary
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print("\n\n📊 FINAL RESULTS")
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print("=" * 30)
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all_passed = True
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for test_name, passed in results:
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status = "✅ PASS" if passed else "❌ FAIL"
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print(f" {test_name:20}: {status}")
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all_passed = all_passed and passed
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if all_passed:
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print(f"\n🎉 ALL TESTS PASSED! Gradient flow is working correctly.")
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print(f" Your fixes have successfully enabled PyTorch-style gradient flow!")
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print(f" Neural networks can now learn via backpropagation! 🧠✨")
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else:
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print(f"\n❌ Some tests failed. Gradient flow needs more work.")
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print(f" Check the error messages above for debugging guidance.") |