caffe2op-copy

Description

This Rust crate provides operators and functions for efficiently copying and manipulating data in DSP and machine learning computations. One of the core operators in this crate is CopyRowsToTensorOp, which copies a set of rows from an input tensor to an output tensor, creating a new tensor with the specified rows. The CopyRowsToTensorGradientOp performs the gradient computation for this operation.

Note: This crate is currently being translated from C++ to Rust, and some function bodies may still be in the process of translation.

Other operators in this crate include CopyCPUToGPUOp, which copies data from a CPU tensor to a GPU tensor, and CopyGPUToCPUOp, which performs the reverse operation. The CopyOnDeviceLikeOp copies data from one tensor to another tensor on the same device, and the Identity operator simply returns its input as its output.

This crate also provides functions for working with blobs, including FeedBlob and FetchBlob, which respectively load and retrieve data from a blob, and ResetWorkspace, which clears the workspace of any previously loaded blobs.

Symbols

• CopyRowsToTensorOp: The main operator of this crate, which copies a set of rows from an input tensor to an output tensor.

• CopyRowsToTensorGradientOp: The gradient computation for CopyRowsToTensorOp.

• CopyCPUToGPUOp: Copies data from a CPU tensor to a GPU tensor.

• CopyGPUToCPUOp: Copies data from a GPU tensor to a CPU tensor.

• CopyOnDeviceLikeOp: Copies data from one tensor to another tensor on the same device.

• Identity: Returns its input as its output.

• FeedBlob: Loads data into a blob.

• FetchBlob: Retrieves data from a blob.

• ResetWorkspace: Clears the workspace of any previously loaded blobs.

Mathematical Analysis

The CopyRowsToTensorOp operator can be expressed mathematically as follows, where input and output are the input and output tensors, respectively, and rows is a vector specifying which rows to copy:

output[i, :] = input[rows[i], :]

The gradient computation for this operation, CopyRowsToTensorGradientOp, can be expressed mathematically as follows:

grad_input[rows[i], :] += grad_output[i, :]

where grad_input and grad_output are the gradient tensors for the input and output, respectively.