mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-02 16:55:53 -05:00
cf1c9362c3
- Add matmul_naive function with for-loop implementation for learning - Update Dense layer to support both NumPy (@) and naive matrix multiplication - Add comprehensive tests comparing both implementations (correctness & performance) - Include step-by-step computation visualization for 2x2 matrices - Fix missing imports in tensor.py and activations.py - Export both tensor and activations modules to package This provides students with immediate success using NumPy while allowing them to understand the underlying computation through explicit for-loops. The scaffolding includes performance comparisons and educational insights about why NumPy is faster.
217 lines
7.6 KiB
Python
217 lines
7.6 KiB
Python
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/tensor/tensor_dev.ipynb.
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# %% auto 0
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__all__ = ['Tensor']
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# %% ../../modules/tensor/tensor_dev.ipynb 3
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import numpy as np
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import sys
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from typing import Union, List, Tuple, Optional, Any
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# %% ../../modules/tensor/tensor_dev.ipynb 4
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class Tensor:
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"""
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TinyTorch Tensor: N-dimensional array with ML operations.
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The fundamental data structure for all TinyTorch operations.
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Wraps NumPy arrays with ML-specific functionality.
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TODO: Implement the core Tensor class with data handling and properties.
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"""
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def __init__(self, data: Union[int, float, List, np.ndarray], dtype: Optional[str] = None):
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"""
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Create a new tensor from data.
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Args:
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data: Input data (scalar, list, or numpy array)
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dtype: Data type ('float32', 'int32', etc.). Defaults to auto-detect.
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TODO: Implement tensor creation with proper type handling.
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"""
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raise NotImplementedError("Student implementation required")
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@property
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def data(self) -> np.ndarray:
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"""Access underlying numpy array."""
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raise NotImplementedError("Student implementation required")
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@property
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def shape(self) -> Tuple[int, ...]:
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"""Get tensor shape."""
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raise NotImplementedError("Student implementation required")
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@property
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def size(self) -> int:
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"""Get total number of elements."""
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raise NotImplementedError("Student implementation required")
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@property
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def dtype(self) -> np.dtype:
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"""Get data type as numpy dtype."""
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raise NotImplementedError("Student implementation required")
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def __repr__(self) -> str:
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"""String representation."""
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raise NotImplementedError("Student implementation required")
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# %% ../../modules/tensor/tensor_dev.ipynb 5
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class Tensor:
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"""
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TinyTorch Tensor: N-dimensional array with ML operations.
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The fundamental data structure for all TinyTorch operations.
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Wraps NumPy arrays with ML-specific functionality.
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"""
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def __init__(self, data: Union[int, float, List, np.ndarray], dtype: Optional[str] = None):
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"""
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Create a new tensor from data.
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Args:
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data: Input data (scalar, list, or numpy array)
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dtype: Data type ('float32', 'int32', etc.). Defaults to auto-detect.
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"""
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# Convert input to numpy array
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if isinstance(data, (int, float, np.number)):
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# Handle Python and NumPy scalars
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if dtype is None:
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# Auto-detect type: int for integers, float32 for floats
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if isinstance(data, int) or (isinstance(data, np.number) and np.issubdtype(type(data), np.integer)):
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dtype = 'int32'
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else:
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dtype = 'float32'
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self._data = np.array(data, dtype=dtype)
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elif isinstance(data, list):
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# Let NumPy auto-detect type, then convert if needed
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temp_array = np.array(data)
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if dtype is None:
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# Keep NumPy's auto-detected type, but prefer common ML types
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if np.issubdtype(temp_array.dtype, np.integer):
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dtype = 'int32'
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elif np.issubdtype(temp_array.dtype, np.floating):
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dtype = 'float32'
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else:
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dtype = temp_array.dtype
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self._data = temp_array.astype(dtype)
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elif isinstance(data, np.ndarray):
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self._data = data.astype(dtype or data.dtype)
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else:
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raise TypeError(f"Cannot create tensor from {type(data)}")
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@property
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def data(self) -> np.ndarray:
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"""Access underlying numpy array."""
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return self._data
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@property
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def shape(self) -> Tuple[int, ...]:
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"""Get tensor shape."""
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return self._data.shape
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@property
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def size(self) -> int:
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"""Get total number of elements."""
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return self._data.size
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@property
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def dtype(self) -> np.dtype:
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"""Get data type as numpy dtype."""
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return self._data.dtype
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def __repr__(self) -> str:
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"""String representation."""
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return f"Tensor({self._data.tolist()}, shape={self.shape}, dtype={self.dtype})"
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# %% ../../modules/tensor/tensor_dev.ipynb 9
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def _add_arithmetic_methods():
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"""
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Add arithmetic operations to Tensor class.
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TODO: Implement arithmetic methods (__add__, __sub__, __mul__, __truediv__)
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and their reverse operations (__radd__, __rsub__, etc.)
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"""
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def __add__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Addition: tensor + other"""
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raise NotImplementedError("Student implementation required")
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def __sub__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Subtraction: tensor - other"""
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raise NotImplementedError("Student implementation required")
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def __mul__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Multiplication: tensor * other"""
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raise NotImplementedError("Student implementation required")
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def __truediv__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Division: tensor / other"""
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raise NotImplementedError("Student implementation required")
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# Add methods to Tensor class
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Tensor.__add__ = __add__
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Tensor.__sub__ = __sub__
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Tensor.__mul__ = __mul__
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Tensor.__truediv__ = __truediv__
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# %% ../../modules/tensor/tensor_dev.ipynb 10
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def _add_arithmetic_methods():
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"""Add arithmetic operations to Tensor class."""
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def __add__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Addition: tensor + other"""
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if isinstance(other, Tensor):
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return Tensor(self._data + other._data)
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else: # scalar
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return Tensor(self._data + other)
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def __sub__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Subtraction: tensor - other"""
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if isinstance(other, Tensor):
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return Tensor(self._data - other._data)
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else: # scalar
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return Tensor(self._data - other)
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def __mul__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Multiplication: tensor * other"""
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if isinstance(other, Tensor):
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return Tensor(self._data * other._data)
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else: # scalar
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return Tensor(self._data * other)
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def __truediv__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""Division: tensor / other"""
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if isinstance(other, Tensor):
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return Tensor(self._data / other._data)
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else: # scalar
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return Tensor(self._data / other)
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def __radd__(self, other: Union[int, float]) -> 'Tensor':
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"""Reverse addition: scalar + tensor"""
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return Tensor(other + self._data)
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def __rsub__(self, other: Union[int, float]) -> 'Tensor':
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"""Reverse subtraction: scalar - tensor"""
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return Tensor(other - self._data)
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def __rmul__(self, other: Union[int, float]) -> 'Tensor':
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"""Reverse multiplication: scalar * tensor"""
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return Tensor(other * self._data)
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def __rtruediv__(self, other: Union[int, float]) -> 'Tensor':
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"""Reverse division: scalar / tensor"""
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return Tensor(other / self._data)
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# Add methods to Tensor class
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Tensor.__add__ = __add__
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Tensor.__sub__ = __sub__
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Tensor.__mul__ = __mul__
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Tensor.__truediv__ = __truediv__
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Tensor.__radd__ = __radd__
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Tensor.__rsub__ = __rsub__
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Tensor.__rmul__ = __rmul__
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Tensor.__rtruediv__ = __rtruediv__
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# Call the function to add arithmetic methods
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_add_arithmetic_methods()
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