mirror of
https://github.com/MLSysBook/TinyTorch.git
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96880b3133
Re-exported all modules after restructuring: - Updated _modidx.py with new module locations - Removed outdated autogeneration headers - Updated all core modules (tensor, autograd, layers, etc.) - Updated optimization modules (quantization, compression, etc.) - Updated TITO commands for new structure Changes include: - 24 tinytorch/ module files - 24 tito/ command and core files - Updated references from modules/source/ to modules/ All modules re-exported via nbdev from their new locations.
86 lines
2.7 KiB
Python
Generated
86 lines
2.7 KiB
Python
Generated
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/17_compression/compression_dev.ipynb.
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# %% auto 0
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__all__ = ['Tensor', 'Linear', 'Sequential']
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# %% ../../modules/source/17_compression/compression_dev.ipynb 1
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import numpy as np
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import copy
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from typing import List, Dict, Any, Tuple, Optional
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import time
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# Import from previous modules
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# Note: In the full package, these would be imports like:
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# from tinytorch.core.tensor import Tensor
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# from tinytorch.core.layers import Linear
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# For development, we'll create minimal implementations
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class Tensor:
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"""Minimal Tensor class for compression development - imports from Module 01 in practice."""
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def __init__(self, data, requires_grad=False):
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self.data = np.array(data)
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self.shape = self.data.shape
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self.size = self.data.size
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self.requires_grad = requires_grad
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self.grad = None
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def __add__(self, other):
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if isinstance(other, Tensor):
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return Tensor(self.data + other.data)
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return Tensor(self.data + other)
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def __mul__(self, other):
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if isinstance(other, Tensor):
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return Tensor(self.data * other.data)
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return Tensor(self.data * other)
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def matmul(self, other):
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return Tensor(np.dot(self.data, other.data))
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def abs(self):
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return Tensor(np.abs(self.data))
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def sum(self, axis=None):
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return Tensor(self.data.sum(axis=axis))
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def __repr__(self):
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return f"Tensor(shape={self.shape})"
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class Linear:
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"""Minimal Linear layer for compression development - imports from Module 03 in practice."""
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def __init__(self, in_features, out_features, bias=True):
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self.in_features = in_features
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self.out_features = out_features
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# Initialize with He initialization
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self.weight = Tensor(np.random.randn(in_features, out_features) * np.sqrt(2.0 / in_features))
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self.bias = Tensor(np.zeros(out_features)) if bias else None
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def forward(self, x):
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output = x.matmul(self.weight)
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if self.bias is not None:
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output = output + self.bias
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return output
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def parameters(self):
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params = [self.weight]
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if self.bias is not None:
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params.append(self.bias)
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return params
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class Sequential:
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"""Minimal Sequential container for model compression."""
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def __init__(self, *layers):
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self.layers = list(layers)
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def forward(self, x):
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for layer in self.layers:
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x = layer.forward(x)
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return x
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def parameters(self):
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params = []
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for layer in self.layers:
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if hasattr(layer, 'parameters'):
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params.extend(layer.parameters())
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return params
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