TinyTorch/tinytorch/core/tensor.py

# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/01_tensor/tensor_dev.ipynb.

# %% auto 0
__all__ = ['Tensor']

# %% ../../modules/source/01_tensor/tensor_dev.ipynb 1
import numpy as np

# %% ../../modules/source/01_tensor/tensor_dev.ipynb 6
class Tensor:
    """Educational tensor that grows with student knowledge.

    This class starts simple but includes dormant features for future modules:
    - requires_grad: Will be used for automatic differentiation (Module 05)
    - grad: Will store computed gradients (Module 05)
    - backward(): Will compute gradients (Module 05)

    For now, focus on: data, shape, and basic operations.
    """

    def __init__(self, data, requires_grad=False):
        """
        Create a new tensor from data.

        TODO: Initialize tensor attributes

        APPROACH:
        1. Convert data to NumPy array - handles lists, scalars, etc.
        2. Store shape and size for quick access
        3. Set up gradient tracking (dormant until Module 05)

        EXAMPLE:
        >>> tensor = Tensor([1, 2, 3])
        >>> print(tensor.data)
        [1 2 3]
        >>> print(tensor.shape)
        (3,)

        HINT: np.array() handles type conversion automatically
        """
        ### BEGIN SOLUTION
        # Core tensor data - always present
        self.data = np.array(data, dtype=np.float32)  # Consistent float32 for ML
        self.shape = self.data.shape
        self.size = self.data.size
        self.dtype = self.data.dtype

        # Gradient features (dormant until Module 05)
        self.requires_grad = requires_grad
        self.grad = None
        ### END SOLUTION

    def __repr__(self):
        """String representation of tensor for debugging."""
        grad_info = f", requires_grad={self.requires_grad}" if self.requires_grad else ""
        return f"Tensor(data={self.data}, shape={self.shape}{grad_info})"

    def __str__(self):
        """Human-readable string representation."""
        return f"Tensor({self.data})"

    def numpy(self):
        """Return the underlying NumPy array."""
        return self.data

    # nbgrader={\"grade\": false, \"grade_id\": \"addition-impl\", \"solution\": true}
    def __add__(self, other):
        """
        Add two tensors element-wise with broadcasting support.

        TODO: Implement tensor addition with automatic broadcasting

        APPROACH:
        1. Handle both Tensor and scalar inputs
        2. Use NumPy's broadcasting for automatic shape alignment
        3. Return new Tensor with result (don't modify self)

        EXAMPLE:
        >>> a = Tensor([1, 2, 3])
        >>> b = Tensor([4, 5, 6])
        >>> result = a + b
        >>> print(result.data)
        [5. 7. 9.]

        BROADCASTING EXAMPLE:
        >>> matrix = Tensor([[1, 2], [3, 4]])  # Shape: (2, 2)
        >>> vector = Tensor([10, 20])          # Shape: (2,)
        >>> result = matrix + vector           # Broadcasting: (2,2) + (2,) → (2,2)
        >>> print(result.data)
        [[11. 22.]
         [13. 24.]]

        HINTS:
        - Use isinstance() to check if other is a Tensor
        - NumPy handles broadcasting automatically with +
        - Always return a new Tensor, don't modify self
        - Preserve gradient tracking for future modules
        """
        ### BEGIN SOLUTION
        if isinstance(other, Tensor):
            # Tensor + Tensor: let NumPy handle broadcasting
            result_data = self.data + other.data
        else:
            # Tensor + scalar: NumPy broadcasts automatically
            result_data = self.data + other

        # Create new tensor with result
        result = Tensor(result_data)

        # Preserve gradient tracking if either operand requires gradients
        if hasattr(self, 'requires_grad') and hasattr(other, 'requires_grad'):
            result.requires_grad = self.requires_grad or (isinstance(other, Tensor) and other.requires_grad)
        elif hasattr(self, 'requires_grad'):
            result.requires_grad = self.requires_grad

        return result
        ### END SOLUTION

    # nbgrader={"grade": false, "grade_id": "more-arithmetic", "solution": true}
    def __sub__(self, other):
        """
        Subtract two tensors element-wise.

        Common use: Centering data (x - mean), computing differences for loss functions.
        """
        if isinstance(other, Tensor):
            return Tensor(self.data - other.data)
        else:
            return Tensor(self.data - other)

    def __mul__(self, other):
        """
        Multiply two tensors element-wise (NOT matrix multiplication).

        Common use: Scaling features, applying masks, gating mechanisms in neural networks.
        Note: This is * operator, not @ (which will be matrix multiplication).
        """
        if isinstance(other, Tensor):
            return Tensor(self.data * other.data)
        else:
            return Tensor(self.data * other)

    def __truediv__(self, other):
        """
        Divide two tensors element-wise.

        Common use: Normalization (x / std), converting counts to probabilities.
        """
        if isinstance(other, Tensor):
            return Tensor(self.data / other.data)
        else:
            return Tensor(self.data / other)

    # nbgrader={"grade": false, "grade_id": "matmul-impl", "solution": true}
    def matmul(self, other):
        """
        Matrix multiplication of two tensors.

        TODO: Implement matrix multiplication using np.dot with proper validation

        APPROACH:
        1. Validate inputs are Tensors
        2. Check dimension compatibility (inner dimensions must match)
        3. Use np.dot for optimized computation
        4. Return new Tensor with result

        EXAMPLE:
        >>> a = Tensor([[1, 2], [3, 4]])  # 2×2
        >>> b = Tensor([[5, 6], [7, 8]])  # 2×2
        >>> result = a.matmul(b)          # 2×2 result
        >>> # Result: [[1×5+2×7, 1×6+2×8], [3×5+4×7, 3×6+4×8]] = [[19, 22], [43, 50]]

        SHAPE RULES:
        - (M, K) @ (K, N) → (M, N)  ✓ Valid
        - (M, K) @ (J, N) → Error   ✗ K ≠ J

        COMPLEXITY: O(M×N×K) for (M×K) @ (K×N) matrices

        HINTS:
        - np.dot handles the optimization for us
        - Check self.shape[-1] == other.shape[-2] for compatibility
        - Provide clear error messages for debugging
        """
        ### BEGIN SOLUTION
        if not isinstance(other, Tensor):
            raise TypeError(f"Expected Tensor for matrix multiplication, got {type(other)}")

        # Handle edge cases
        if self.shape == () or other.shape == ():
            # Scalar multiplication
            return Tensor(self.data * other.data)

        # For matrix multiplication, we need at least 1D tensors
        if len(self.shape) == 0 or len(other.shape) == 0:
            return Tensor(self.data * other.data)

        # Check dimension compatibility for matrix multiplication
        if len(self.shape) >= 2 and len(other.shape) >= 2:
            if self.shape[-1] != other.shape[-2]:
                raise ValueError(
                    f"Cannot perform matrix multiplication: {self.shape} @ {other.shape}. "
                    f"Inner dimensions must match: {self.shape[-1]} ≠ {other.shape[-2]}. "
                    f"💡 HINT: For (M,K) @ (K,N) → (M,N), the K dimensions must be equal."
                )
        elif len(self.shape) == 1 and len(other.shape) == 2:
            # Vector @ Matrix
            if self.shape[0] != other.shape[0]:
                raise ValueError(
                    f"Cannot multiply vector {self.shape} with matrix {other.shape}. "
                    f"Vector length {self.shape[0]} must match matrix rows {other.shape[0]}."
                )
        elif len(self.shape) == 2 and len(other.shape) == 1:
            # Matrix @ Vector
            if self.shape[1] != other.shape[0]:
                raise ValueError(
                    f"Cannot multiply matrix {self.shape} with vector {other.shape}. "
                    f"Matrix columns {self.shape[1]} must match vector length {other.shape[0]}."
                )

        # Perform optimized matrix multiplication
        result_data = np.dot(self.data, other.data)
        return Tensor(result_data)
        ### END SOLUTION

    # nbgrader={"grade": false, "grade_id": "shape-ops", "solution": true}
    def reshape(self, *shape):
        """
        Reshape tensor to new dimensions.

        TODO: Implement tensor reshaping with validation

        APPROACH:
        1. Handle different calling conventions: reshape(2, 3) vs reshape((2, 3))
        2. Validate total elements remain the same
        3. Use NumPy's reshape for the actual operation
        4. Return new Tensor (keep immutability)

        EXAMPLE:
        >>> tensor = Tensor([1, 2, 3, 4, 5, 6])  # Shape: (6,)
        >>> reshaped = tensor.reshape(2, 3)      # Shape: (2, 3)
        >>> print(reshaped.data)
        [[1. 2. 3.]
         [4. 5. 6.]]

        COMMON USAGE:
        >>> # Flatten for MLP input
        >>> image = Tensor(np.random.rand(3, 32, 32))  # (channels, height, width)
        >>> flattened = image.reshape(-1)              # (3072,) - all pixels in vector
        >>>
        >>> # Prepare batch for convolution
        >>> batch = Tensor(np.random.rand(32, 784))    # (batch, features)
        >>> images = batch.reshape(32, 1, 28, 28)      # (batch, channels, height, width)

        HINTS:
        - Handle both reshape(2, 3) and reshape((2, 3)) calling styles
        - Check np.prod(new_shape) == self.size for validation
        - Use descriptive error messages for debugging
        """
        ### BEGIN SOLUTION
        # Handle both reshape(2, 3) and reshape((2, 3)) calling conventions
        if len(shape) == 1 and isinstance(shape[0], (tuple, list)):
            new_shape = tuple(shape[0])
        else:
            new_shape = shape

        # Handle -1 for automatic dimension inference (like NumPy)
        if -1 in new_shape:
            if new_shape.count(-1) > 1:
                raise ValueError("Can only specify one unknown dimension with -1")

            # Calculate the unknown dimension
            known_size = 1
            unknown_idx = new_shape.index(-1)
            for i, dim in enumerate(new_shape):
                if i != unknown_idx:
                    known_size *= dim

            unknown_dim = self.size // known_size
            new_shape = list(new_shape)
            new_shape[unknown_idx] = unknown_dim
            new_shape = tuple(new_shape)

        # Validate total elements remain the same
        if np.prod(new_shape) != self.size:
            raise ValueError(
                f"Cannot reshape tensor of size {self.size} to shape {new_shape}. "
                f"Total elements must match: {self.size} ≠ {np.prod(new_shape)}. "
                f"💡 HINT: Make sure new_shape dimensions multiply to {self.size}"
            )

        # Reshape the data (NumPy handles the memory layout efficiently)
        reshaped_data = np.reshape(self.data, new_shape)
        return Tensor(reshaped_data)
        ### END SOLUTION

    def transpose(self, dim0=None, dim1=None):
        """
        Transpose tensor dimensions.

        TODO: Implement tensor transposition

        APPROACH:
        1. Handle default case (transpose last two dimensions)
        2. Handle specific dimension swapping
        3. Use NumPy's transpose with proper axis specification
        4. Return new Tensor

        EXAMPLE:
        >>> matrix = Tensor([[1, 2, 3], [4, 5, 6]])  # (2, 3)
        >>> transposed = matrix.transpose()          # (3, 2)
        >>> print(transposed.data)
        [[1. 4.]
         [2. 5.]
         [3. 6.]]

        NEURAL NETWORK USAGE:
        >>> # Weight matrix transpose for backward pass
        >>> W = Tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])  # (3, 2)
        >>> W_T = W.transpose()  # (2, 3) - for gradient computation
        >>>
        >>> # Attention mechanism
        >>> Q = Tensor([[1, 2], [3, 4]])  # queries (2, 2)
        >>> K = Tensor([[5, 6], [7, 8]])  # keys (2, 2)
        >>> attention_scores = Q.matmul(K.transpose())  # Q @ K^T

        HINTS:
        - Default: transpose last two dimensions (most common case)
        - Use np.transpose() with axes parameter
        - Handle 1D tensors gracefully (transpose is identity)
        """
        ### BEGIN SOLUTION
        if dim0 is None and dim1 is None:
            # Default: transpose last two dimensions
            if len(self.shape) < 2:
                # For 1D tensors, transpose is identity operation
                return Tensor(self.data.copy())
            else:
                # Transpose last two dimensions (most common in ML)
                axes = list(range(len(self.shape)))
                axes[-2], axes[-1] = axes[-1], axes[-2]
                transposed_data = np.transpose(self.data, axes)
        else:
            # Specific dimensions to transpose
            if dim0 is None or dim1 is None:
                raise ValueError("Both dim0 and dim1 must be specified for specific dimension transpose")

            # Validate dimensions exist
            if dim0 >= len(self.shape) or dim1 >= len(self.shape) or dim0 < 0 or dim1 < 0:
                raise ValueError(
                    f"Dimension out of range for tensor with shape {self.shape}. "
                    f"Got dim0={dim0}, dim1={dim1}, but tensor has {len(self.shape)} dimensions."
                )

            # Create axes list and swap the specified dimensions
            axes = list(range(len(self.shape)))
            axes[dim0], axes[dim1] = axes[dim1], axes[dim0]
            transposed_data = np.transpose(self.data, axes)

        return Tensor(transposed_data)
        ### END SOLUTION

    # nbgrader={"grade": false, "grade_id": "reduction-ops", "solution": true}
    def sum(self, axis=None, keepdims=False):
        """
        Sum tensor along specified axis.

        TODO: Implement tensor sum with axis control

        APPROACH:
        1. Use NumPy's sum with axis parameter
        2. Handle axis=None (sum all elements) vs specific axis
        3. Support keepdims to maintain shape for broadcasting
        4. Return new Tensor with result

        EXAMPLE:
        >>> tensor = Tensor([[1, 2], [3, 4]])
        >>> total = tensor.sum()          # Sum all elements: 10
        >>> col_sum = tensor.sum(axis=0)  # Sum columns: [4, 6]
        >>> row_sum = tensor.sum(axis=1)  # Sum rows: [3, 7]

        NEURAL NETWORK USAGE:
        >>> # Batch loss computation
        >>> batch_losses = Tensor([0.1, 0.3, 0.2, 0.4])  # Individual losses
        >>> total_loss = batch_losses.sum()               # Total: 1.0
        >>> avg_loss = batch_losses.mean()                # Average: 0.25
        >>>
        >>> # Global average pooling
        >>> feature_maps = Tensor(np.random.rand(32, 256, 7, 7))  # (batch, channels, h, w)
        >>> global_features = feature_maps.sum(axis=(2, 3))       # (batch, channels)

        HINTS:
        - np.sum handles all the complexity for us
        - axis=None sums all elements (returns scalar)
        - axis=0 sums along first dimension, axis=1 along second, etc.
        - keepdims=True preserves dimensions for broadcasting
        """
        ### BEGIN SOLUTION
        result = np.sum(self.data, axis=axis, keepdims=keepdims)
        return Tensor(result)
        ### END SOLUTION

    def mean(self, axis=None, keepdims=False):
        """
        Compute mean of tensor along specified axis.

        Common usage: Batch normalization, loss averaging, global pooling.
        """
        ### BEGIN SOLUTION
        result = np.mean(self.data, axis=axis, keepdims=keepdims)
        return Tensor(result)
        ### END SOLUTION

    def max(self, axis=None, keepdims=False):
        """
        Find maximum values along specified axis.

        Common usage: Max pooling, finding best predictions, activation clipping.
        """
        ### BEGIN SOLUTION
        result = np.max(self.data, axis=axis, keepdims=keepdims)
        return Tensor(result)
        ### END SOLUTION

    # nbgrader={"grade": false, "grade_id": "gradient-placeholder", "solution": true}
    def backward(self):
        """
        Compute gradients (implemented in Module 05: Autograd).

        TODO: Placeholder implementation for gradient computation

        STUDENT NOTE:
        This method exists but does nothing until Module 05: Autograd.
        Don't worry about it for now - focus on the basic tensor operations.

        In Module 05, we'll implement:
        - Gradient computation via chain rule
        - Automatic differentiation
        - Backpropagation through operations
        - Computation graph construction

        FUTURE IMPLEMENTATION PREVIEW:
        ```python
        def backward(self, gradient=None):
            # Module 05 will implement:
            # 1. Set gradient for this tensor
            # 2. Propagate to parent operations
            # 3. Apply chain rule recursively
            # 4. Accumulate gradients properly
            pass
        ```

        CURRENT BEHAVIOR:
        >>> x = Tensor([1, 2, 3], requires_grad=True)
        >>> y = x * 2
        >>> y.sum().backward()  # Calls this method - does nothing
        >>> print(x.grad)      # Still None
        None
        """
        ### BEGIN SOLUTION
        # Placeholder - will be implemented in Module 05
        # For now, just ensure it doesn't crash when called
        # This allows students to experiment with gradient syntax
        # without getting confusing errors about missing methods
        pass
        ### END SOLUTION