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eafbb4ac8d
🔧 TESTING INFRASTRUCTURE FIXES: - Fixed pytest configuration (removed duplicate timeout) - Exported all modules to tinytorch package using nbdev - Converted .py files to .ipynb for proper NBDev processing - Fixed import issues in test files with fallback strategies 📊 TESTING RESULTS: - 145 tests passing, 15 failing, 16 skipped - Major improvement from previous import errors - All modules now properly exported and testable - Analysis tool working correctly on all modules 🎯 MODULE QUALITY STATUS: - Most modules: Grade C, Scaffolding 3/5 - 01_tensor: Grade C, Scaffolding 2/5 (needs improvement) - 07_autograd: Grade D, Scaffolding 2/5 (needs improvement) - Overall: Functional but needs educational enhancement ✅ RESOLVED ISSUES: - All import errors resolved - NBDev export process working - Test infrastructure functional - Analysis tools operational 🚀 READY FOR NEXT PHASE: Professional report cards and improvements
298 lines
9.4 KiB
Python
298 lines
9.4 KiB
Python
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/01_tensor/tensor_dev.ipynb.
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# %% auto 0
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__all__ = ['Tensor']
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# %% ../../modules/source/01_tensor/tensor_dev.ipynb 1
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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/source/01_tensor/tensor_dev.ipynb 7
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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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TODO: Implement tensor creation with proper type handling.
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STEP-BY-STEP:
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1. Check if data is a scalar (int/float) - convert to numpy array
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2. Check if data is a list - convert to numpy array
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3. Check if data is already a numpy array - use as-is
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4. Apply dtype conversion if specified
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5. Store the result in self._data
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EXAMPLE:
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Tensor(5) → stores np.array(5)
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Tensor([1, 2, 3]) → stores np.array([1, 2, 3])
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Tensor(np.array([1, 2, 3])) → stores the array directly
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HINTS:
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- Use isinstance() to check data types
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- Use np.array() for conversion
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- Handle dtype parameter for type conversion
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- Store the array in self._data
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"""
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### BEGIN SOLUTION
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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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# Use NumPy's auto-detected type, but prefer float32 for floats
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if temp_array.dtype == np.float64:
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dtype = 'float32'
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else:
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dtype = str(temp_array.dtype)
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self._data = np.array(data, dtype=dtype)
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elif isinstance(data, np.ndarray):
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# Already a numpy array
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if dtype is None:
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# Keep existing dtype, but prefer float32 for float64
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if data.dtype == np.float64:
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dtype = 'float32'
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else:
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dtype = str(data.dtype)
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self._data = data.astype(dtype) if dtype != data.dtype else data.copy()
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else:
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# Try to convert unknown types
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self._data = np.array(data, dtype=dtype)
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### END SOLUTION
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@property
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def data(self) -> np.ndarray:
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"""
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Access underlying numpy array.
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TODO: Return the stored numpy array.
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HINT: Return self._data (the array you stored in __init__)
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"""
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### BEGIN SOLUTION
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return self._data
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### END SOLUTION
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@property
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def shape(self) -> Tuple[int, ...]:
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"""
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Get tensor shape.
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TODO: Return the shape of the stored numpy array.
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HINT: Use .shape attribute of the numpy array
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EXAMPLE: Tensor([1, 2, 3]).shape should return (3,)
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"""
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### BEGIN SOLUTION
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return self._data.shape
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### END SOLUTION
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@property
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def size(self) -> int:
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"""
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Get total number of elements.
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TODO: Return the total number of elements in the tensor.
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HINT: Use .size attribute of the numpy array
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EXAMPLE: Tensor([1, 2, 3]).size should return 3
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"""
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### BEGIN SOLUTION
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return self._data.size
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### END SOLUTION
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@property
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def dtype(self) -> np.dtype:
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"""
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Get data type as numpy dtype.
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TODO: Return the data type of the stored numpy array.
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HINT: Use .dtype attribute of the numpy array
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EXAMPLE: Tensor([1, 2, 3]).dtype should return dtype('int32')
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"""
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### BEGIN SOLUTION
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return self._data.dtype
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### END SOLUTION
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def __repr__(self) -> str:
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"""
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String representation.
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TODO: Create a clear string representation of the tensor.
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APPROACH:
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1. Convert the numpy array to a list for readable output
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2. Include the shape and dtype information
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3. Format: "Tensor([data], shape=shape, dtype=dtype)"
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EXAMPLE:
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Tensor([1, 2, 3]) → "Tensor([1, 2, 3], shape=(3,), dtype=int32)"
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HINTS:
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- Use .tolist() to convert numpy array to list
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- Include shape and dtype information
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- Keep format consistent and readable
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"""
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### BEGIN SOLUTION
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return f"Tensor({self._data.tolist()}, shape={self.shape}, dtype={self.dtype})"
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### END SOLUTION
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def add(self, other: 'Tensor') -> 'Tensor':
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"""
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Add two tensors element-wise.
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TODO: Implement tensor addition.
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APPROACH:
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1. Add the numpy arrays using +
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2. Return a new Tensor with the result
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3. Handle broadcasting automatically
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EXAMPLE:
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Tensor([1, 2]) + Tensor([3, 4]) → Tensor([4, 6])
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HINTS:
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- Use self._data + other._data
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- Return Tensor(result)
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- NumPy handles broadcasting automatically
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"""
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### BEGIN SOLUTION
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result = self._data + other._data
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return Tensor(result)
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### END SOLUTION
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def multiply(self, other: 'Tensor') -> 'Tensor':
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"""
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Multiply two tensors element-wise.
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TODO: Implement tensor multiplication.
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APPROACH:
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1. Multiply the numpy arrays using *
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2. Return a new Tensor with the result
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3. Handle broadcasting automatically
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EXAMPLE:
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Tensor([1, 2]) * Tensor([3, 4]) → Tensor([3, 8])
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HINTS:
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- Use self._data * other._data
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- Return Tensor(result)
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- This is element-wise, not matrix multiplication
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"""
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### BEGIN SOLUTION
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result = self._data * other._data
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return Tensor(result)
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### END SOLUTION
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def __add__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""
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Addition operator: tensor + other
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TODO: Implement + operator for tensors.
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APPROACH:
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1. If other is a Tensor, use tensor addition
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2. If other is a scalar, convert to Tensor first
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3. Return the result
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EXAMPLE:
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Tensor([1, 2]) + Tensor([3, 4]) → Tensor([4, 6])
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Tensor([1, 2]) + 5 → Tensor([6, 7])
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"""
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### BEGIN SOLUTION
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if isinstance(other, Tensor):
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return self.add(other)
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else:
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return self.add(Tensor(other))
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### END SOLUTION
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def __mul__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""
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Multiplication operator: tensor * other
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TODO: Implement * operator for tensors.
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APPROACH:
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1. If other is a Tensor, use tensor multiplication
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2. If other is a scalar, convert to Tensor first
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3. Return the result
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EXAMPLE:
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Tensor([1, 2]) * Tensor([3, 4]) → Tensor([3, 8])
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Tensor([1, 2]) * 3 → Tensor([3, 6])
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"""
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### BEGIN SOLUTION
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if isinstance(other, Tensor):
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return self.multiply(other)
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else:
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return self.multiply(Tensor(other))
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### END SOLUTION
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def __sub__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""
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Subtraction operator: tensor - other
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TODO: Implement - operator for tensors.
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APPROACH:
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1. Convert other to Tensor if needed
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2. Subtract using numpy arrays
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3. Return new Tensor with result
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EXAMPLE:
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Tensor([5, 6]) - Tensor([1, 2]) → Tensor([4, 4])
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Tensor([5, 6]) - 1 → Tensor([4, 5])
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"""
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### BEGIN SOLUTION
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if isinstance(other, Tensor):
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result = self._data - other._data
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else:
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result = self._data - other
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return Tensor(result)
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### END SOLUTION
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def __truediv__(self, other: Union['Tensor', int, float]) -> 'Tensor':
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"""
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Division operator: tensor / other
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TODO: Implement / operator for tensors.
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APPROACH:
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1. Convert other to Tensor if needed
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2. Divide using numpy arrays
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3. Return new Tensor with result
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EXAMPLE:
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Tensor([6, 8]) / Tensor([2, 4]) → Tensor([3, 2])
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Tensor([6, 8]) / 2 → Tensor([3, 4])
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"""
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### BEGIN SOLUTION
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if isinstance(other, Tensor):
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result = self._data / other._data
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else:
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result = self._data / other
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return Tensor(result)
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### END SOLUTION
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