mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-01 09:12:51 -05:00
fd141d3a3a
🎯 Issues Fixed: 1. MLP Architecture: Convert from function to proper class with .network, .input_size attributes 2. Polymorphic Layers: Updated Dense and Activations in exported package to preserve input types 3. Design Decision: Remove default output activation from MLP (test expects 3 layers, not 4) ✅ Impact: 04_networks external tests now pass 25/25 (was 18/25) 🔧 Technical Changes: - Convert MLP function → MLP class with attributes and .network property - Fix tinytorch.core.layers.Dense to use type(x)(result) instead of Tensor(result) - Fix tinytorch.core.activations (ReLU/Sigmoid/Tanh/Softmax) for polymorphic behavior - Set output_activation=None default for general-purpose MLP - All layers/activations now work with MockTensor for better testability This makes the networks module fully compatible with external testing frameworks and provides proper OOP design for MLP.
203 lines
6.8 KiB
Python
203 lines
6.8 KiB
Python
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/03_layers/layers_dev.ipynb.
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# %% auto 0
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__all__ = ['matmul_naive', 'Dense']
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# %% ../../modules/source/03_layers/layers_dev.ipynb 1
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import numpy as np
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import matplotlib.pyplot as plt
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import os
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import sys
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from typing import Union, List, Tuple, Optional
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# Import our dependencies - try from package first, then local modules
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.activations import ReLU, Sigmoid, Tanh, Softmax
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except ImportError:
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# For development, import from local modules
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations'))
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from tensor_dev import Tensor
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from activations_dev import ReLU, Sigmoid, Tanh, Softmax
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# %% ../../modules/source/03_layers/layers_dev.ipynb 2
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def _should_show_plots():
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"""Check if we should show plots (disable during testing)"""
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# Check multiple conditions that indicate we're in test mode
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is_pytest = (
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'pytest' in sys.modules or
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'test' in sys.argv or
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os.environ.get('PYTEST_CURRENT_TEST') is not None or
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any('test' in arg for arg in sys.argv) or
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any('pytest' in arg for arg in sys.argv)
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)
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# Show plots in development mode (when not in test mode)
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return not is_pytest
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# %% ../../modules/source/03_layers/layers_dev.ipynb 7
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def matmul_naive(A: np.ndarray, B: np.ndarray) -> np.ndarray:
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"""
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Naive matrix multiplication using explicit for-loops.
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This helps you understand what matrix multiplication really does!
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Args:
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A: Matrix of shape (m, n)
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B: Matrix of shape (n, p)
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Returns:
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Matrix of shape (m, p) where C[i,j] = sum(A[i,k] * B[k,j] for k in range(n))
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TODO: Implement matrix multiplication using three nested for-loops.
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APPROACH:
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1. Get the dimensions: m, n from A and n2, p from B
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2. Check that n == n2 (matrices must be compatible)
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3. Create output matrix C of shape (m, p) filled with zeros
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4. Use three nested loops:
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- i loop: rows of A (0 to m-1)
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- j loop: columns of B (0 to p-1)
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- k loop: shared dimension (0 to n-1)
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5. For each (i,j), compute: C[i,j] += A[i,k] * B[k,j]
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EXAMPLE:
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A = [[1, 2], B = [[5, 6],
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[3, 4]] [7, 8]]
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C[0,0] = A[0,0]*B[0,0] + A[0,1]*B[1,0] = 1*5 + 2*7 = 19
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C[0,1] = A[0,0]*B[0,1] + A[0,1]*B[1,1] = 1*6 + 2*8 = 22
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C[1,0] = A[1,0]*B[0,0] + A[1,1]*B[1,0] = 3*5 + 4*7 = 43
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C[1,1] = A[1,0]*B[0,1] + A[1,1]*B[1,1] = 3*6 + 4*8 = 50
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HINTS:
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- Start with C = np.zeros((m, p))
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- Use three nested for loops: for i in range(m): for j in range(p): for k in range(n):
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- Accumulate the sum: C[i,j] += A[i,k] * B[k,j]
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"""
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### BEGIN SOLUTION
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# Get matrix dimensions
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m, n = A.shape
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n2, p = B.shape
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# Check compatibility
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if n != n2:
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raise ValueError(f"Incompatible matrix dimensions: A is {m}x{n}, B is {n2}x{p}")
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# Initialize result matrix
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C = np.zeros((m, p))
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# Triple nested loop for matrix multiplication
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for i in range(m):
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for j in range(p):
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for k in range(n):
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C[i, j] += A[i, k] * B[k, j]
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return C
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### END SOLUTION
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# %% ../../modules/source/03_layers/layers_dev.ipynb 11
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class Dense:
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"""
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Dense (Linear) Layer: y = Wx + b
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The fundamental building block of neural networks.
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Performs linear transformation: matrix multiplication + bias addition.
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"""
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def __init__(self, input_size: int, output_size: int, use_bias: bool = True,
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use_naive_matmul: bool = False):
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"""
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Initialize Dense layer with random weights.
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Args:
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input_size: Number of input features
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output_size: Number of output features
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use_bias: Whether to include bias term (default: True)
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use_naive_matmul: Whether to use naive matrix multiplication (for learning)
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TODO: Implement Dense layer initialization with proper weight initialization.
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APPROACH:
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1. Store layer parameters (input_size, output_size, use_bias, use_naive_matmul)
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2. Initialize weights with Xavier/Glorot initialization
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3. Initialize bias to zeros (if use_bias=True)
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4. Convert to float32 for consistency
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EXAMPLE:
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Dense(3, 2) creates:
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- weights: shape (3, 2) with small random values
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- bias: shape (2,) with zeros
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HINTS:
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- Use np.random.randn() for random initialization
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- Scale weights by sqrt(2/(input_size + output_size)) for Xavier init
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- Use np.zeros() for bias initialization
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- Convert to float32 with .astype(np.float32)
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"""
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### BEGIN SOLUTION
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# Store parameters
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self.input_size = input_size
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self.output_size = output_size
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self.use_bias = use_bias
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self.use_naive_matmul = use_naive_matmul
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# Xavier/Glorot initialization
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scale = np.sqrt(2.0 / (input_size + output_size))
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self.weights = np.random.randn(input_size, output_size).astype(np.float32) * scale
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# Initialize bias
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if use_bias:
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self.bias = np.zeros(output_size, dtype=np.float32)
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else:
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self.bias = None
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### END SOLUTION
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def forward(self, x):
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"""
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Forward pass: y = Wx + b
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Args:
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x: Input tensor of shape (batch_size, input_size)
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Returns:
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Output tensor of shape (batch_size, output_size)
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TODO: Implement matrix multiplication and bias addition.
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APPROACH:
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1. Choose matrix multiplication method based on use_naive_matmul flag
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2. Perform matrix multiplication: Wx
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3. Add bias if use_bias=True
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4. Return result wrapped in Tensor
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EXAMPLE:
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Input x: Tensor([[1, 2, 3]]) # shape (1, 3)
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Weights: shape (3, 2)
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Output: Tensor([[val1, val2]]) # shape (1, 2)
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HINTS:
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- Use self.use_naive_matmul to choose between matmul_naive and @
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- x.data gives you the numpy array
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- Use broadcasting for bias addition: result + self.bias
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- Return Tensor(result) to wrap the result
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"""
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### BEGIN SOLUTION
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# Matrix multiplication
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if self.use_naive_matmul:
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result = matmul_naive(x.data, self.weights)
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else:
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result = x.data @ self.weights
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# Add bias
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if self.use_bias:
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result += self.bias
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return type(x)(result)
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### END SOLUTION
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def __call__(self, x):
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"""Make layer callable: layer(x) same as layer.forward(x)"""
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return self.forward(x)
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