Reset package and export modules 01-07 only (skip broken spatial module)

tinytorch/optimization/acceleration.py

@@ -1,8 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/16_acceleration/acceleration_dev.ipynb.
-
-# %% auto 0
-__all__ = []
-
-# %% ../../modules/source/16_acceleration/acceleration_dev.ipynb 0
-#| default_exp optimization.acceleration
-#| export

tinytorch/optimization/compression.py

@@ -1,85 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/18_compression/compression_dev.ipynb.
-
-# %% auto 0
-__all__ = ['Tensor', 'Linear', 'Sequential']
-
-# %% ../../modules/source/18_compression/compression_dev.ipynb 1
-import numpy as np
-import copy
-from typing import List, Dict, Any, Tuple, Optional
-import time
-
-# Import from previous modules
-# Note: In the full package, these would be imports like:
-# from tinytorch.core.tensor import Tensor
-# from tinytorch.core.layers import Linear
-# For development, we'll create minimal implementations
-
-class Tensor:
-    """Minimal Tensor class for compression development - imports from Module 01 in practice."""
-    def __init__(self, data, requires_grad=False):
-        self.data = np.array(data)
-        self.shape = self.data.shape
-        self.size = self.data.size
-        self.requires_grad = requires_grad
-        self.grad = None
-
-    def __add__(self, other):
-        if isinstance(other, Tensor):
-            return Tensor(self.data + other.data)
-        return Tensor(self.data + other)
-
-    def __mul__(self, other):
-        if isinstance(other, Tensor):
-            return Tensor(self.data * other.data)
-        return Tensor(self.data * other)
-
-    def matmul(self, other):
-        return Tensor(np.dot(self.data, other.data))
-
-    def abs(self):
-        return Tensor(np.abs(self.data))
-
-    def sum(self, axis=None):
-        return Tensor(self.data.sum(axis=axis))
-
-    def __repr__(self):
-        return f"Tensor(shape={self.shape})"
-
-class Linear:
-    """Minimal Linear layer for compression development - imports from Module 03 in practice."""
-    def __init__(self, in_features, out_features, bias=True):
-        self.in_features = in_features
-        self.out_features = out_features
-        # Initialize with He initialization
-        self.weight = Tensor(np.random.randn(in_features, out_features) * np.sqrt(2.0 / in_features))
-        self.bias = Tensor(np.zeros(out_features)) if bias else None
-
-    def forward(self, x):
-        output = x.matmul(self.weight)
-        if self.bias is not None:
-            output = output + self.bias
-        return output
-
-    def parameters(self):
-        params = [self.weight]
-        if self.bias is not None:
-            params.append(self.bias)
-        return params
-
-class Sequential:
-    """Minimal Sequential container for model compression."""
-    def __init__(self, *layers):
-        self.layers = list(layers)
-
-    def forward(self, x):
-        for layer in self.layers:
-            x = layer.forward(x)
-        return x
-
-    def parameters(self):
-        params = []
-        for layer in self.layers:
-            if hasattr(layer, 'parameters'):
-                params.extend(layer.parameters())
-        return params

tinytorch/optimization/quantization.py

@@ -1,8 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/17_quantization/quantization_dev.ipynb.
-
-# %% auto 0
-__all__ = []
-
-# %% ../../modules/source/17_quantization/quantization_dev.ipynb 0
-#| default_exp optimization.quantization
-#| export