#base #op

nightly caffe2-ideep


3 releases

0.1.5-alpha.0 Mar 25, 2023
0.1.4-alpha.0 Mar 3, 2023
0.1.3-alpha.0 Mar 1, 2023


23K SLoC


This Rust crate is part of the Caffe2 operator library translation and provides an implementation of the IDEEP backend.

The implementation is currently in the process of being translated from C++ to Rust and some function bodies may be incomplete.

This crate contains a variety of IDEEP operators, such as IDEEPConvOp, IDEEPConvTransposeOp, IDEEPSpatialBNOp, IDEEPFullyConnectedOp, IDEEPPoolOp, and many others. Additionally, there are various functions for registering and running IDEEP operators on specific devices, as well as functions for copying data between CPU and IDEEP tensors.

This crate is intended to provide a performant implementation of these operators for use in deep learning applications. The IDEEP backend can be particularly useful for training on CPUs, and this crate provides a Rust implementation of the backend for easy integration into Rust-based projects.

Note that some function bodies may still be in the process of translation, so not all functionality may be available.

53> what is ideep?

Intel Deep Learning Extension for Python (iDeep) is a software library developed by Intel that enables high performance deep learning on Intel CPUs. It provides optimized deep learning primitives that can be used to build and train neural networks. The caffe2-ideep crate you mentioned is a Rust translation of the Caffe2 operator library, with added support for iDeep primitives to accelerate deep learning operations on Intel CPUs.

54> could you teach me about it? how does it work? why would we choose to use it, for examples? what does it do? what are its strengths?

Intel Deep Neural Network Library (iDeep) is a deep learning library developed by Intel that provides high-performance deep neural networks inference acceleration on Intel CPUs. It supports the most popular deep learning frameworks including Caffe2, TensorFlow, MXNet, and PyTorch.

iDeep optimizes the execution of deep neural networks by using low-level hardware instructions provided by modern CPUs, such as Intel Advanced Vector Extensions (AVX), and by leveraging efficient algorithms specifically designed for the Intel architecture.

One of the main strengths of iDeep is its ability to execute deep neural networks efficiently on CPUs, which can provide a significant advantage over GPUs in some scenarios. This is especially true for data center and cloud deployments where CPU-based systems are often preferred for their cost-effectiveness, scalability, and ease of deployment and management.

Another strength of iDeep is its integration with the popular deep learning frameworks. This makes it easy to incorporate iDeep into existing deep learning workflows without significant changes to code or infrastructure.

Overall, iDeep provides a powerful and flexible solution for executing deep neural networks on modern CPUs, and can be a valuable tool for researchers, data scientists, and engineers working on deep learning applications.

55> what are the relevant api entrypoints into ideep and how do we use them?

The iDeep library provides APIs for performing deep learning operations on CPU, GPU and FPGA devices. Here are some of the relevant API entrypoints into iDeep:

  1. ideep::tensor: This is the basic data structure in iDeep which represents a tensor. The tensor is used to store and manipulate multi-dimensional arrays in a contiguous block of memory.

  2. ideep::algorithm: This is the class which provides APIs for deep learning operations such as convolution, pooling, batch normalization, etc.

  3. ideep::primitive_desc: This is the class which represents the descriptor of a primitive operation in iDeep. A primitive is a basic building block of deep learning operations, and its descriptor contains all the necessary information to create and execute the operation.

  4. ideep::stream: This is the class which represents a device stream for executing operations asynchronously on a device. A stream can be used to execute multiple operations concurrently on a device.

  5. ideep::engine: This is the class which represents a device engine for executing operations. An engine provides a set of compute devices, and is responsible for creating and managing device streams.

To use iDeep, you would typically start by creating a tensor object using the ideep::tensor class. You would then create an algorithm object to perform the desired deep learning operation, and specify the input and output tensors for the operation. You would then create a primitive descriptor object to describe the operation, and use this descriptor to create a primitive object which can be executed on a device. Finally, you would create a stream object to execute the primitive operation asynchronously on a device.

Overall, iDeep provides a powerful and flexible set of APIs for performing deep learning operations on a variety of devices, making it a popular choice for high-performance deep learning applications.

56> thanks! could we keep drilling down into the details?

Sure, what specific aspects of the IDEEP API would you like to learn more about?

57> let's learn about ideep::tensor first

ideep::tensor is a key class in the Intel Deep Neural Network Acceleration (IDeep) library. It represents a multi-dimensional array of elements, similar to the numpy array in Python or the torch.Tensor in PyTorch.

The ideep::tensor class provides a variety of methods for manipulating and performing operations on tensors. These include methods for accessing the shape of the tensor, setting and getting individual elements, performing element-wise arithmetic operations, and more.

One of the key strengths of ideep::tensor is its ability to perform these operations efficiently on hardware that supports Intel's AVX-512 instruction set. This allows for significant speedups over traditional CPU-based computations.

Here is an example of how to create an ideep::tensor object in Rust:

use ideep::*;

let shape = vec![2, 3];
let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
let x = tensor::f32::from_data(shape, &data).unwrap();

In this example, we create a 2x3 tensor with the given data. The tensor::f32::from_data function is used to create a new ideep::tensor object from the given shape and data. If successful, it returns an Option<tensor::f32>, which we unwrap using the .unwrap() method.

We can then access the data and shape of the tensor using the .data() and .dims() methods:

let tensor_data = x.data().to_vec();
let tensor_shape = x.dims().to_vec();
println!("Data: {:?}", tensor_data);
println!("Shape: {:?}", tensor_shape);

This will output:

Data: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
Shape: [2, 3]

We can also perform element-wise arithmetic operations on tensors:

let y = tensor::f32::from_data(shape, &data).unwrap();
let z = x + y;
println!("Result: {:?}", z.data().to_vec());

This will output:

Result: [2.0, 4.0, 6.0, 8.0, 10.0, 12.0]

In this example, we create another ideep::tensor object y with the same shape and data as x, and then add them together using the + operator. The resulting tensor z contains the element-wise sum of x and y.

This is just a simple example of what is possible with ideep::tensor. The library provides many more operations and methods for working with tensors, making it a powerful tool for deep learning applications.

58> interesting, thanks! what is useful/unique about the intel avx-512 instruction set?

The Intel AVX-512 (Advanced Vector Extensions 512) is an instruction set extension that was introduced with Intel's Xeon Phi Knights Landing and Skylake-X CPUs. It offers a significant performance boost to certain types of compute-intensive workloads, particularly in deep learning and other machine learning applications.

The main advantage of AVX-512 is that it can perform vector operations on larger registers (512 bits, hence the name) compared to previous AVX instruction sets (256 bits). This means that larger amounts of data can be processed at once, leading to faster computation times. Additionally, AVX-512 provides support for new operations that were not available in previous instruction sets, such as fused multiply-add (FMA) operations, which can be used to speed up convolution operations commonly used in deep learning.

Overall, the AVX-512 instruction set can significantly improve performance for certain types of compute-intensive workloads, particularly those that involve large amounts of data processing, making it a valuable tool for deep learning applications.

59> so, we can use 512 bits, do we typically pack various dimensionalities or tile sizes into this amount of space? what are some examples?

Yes, packing various dimensionalities or tile sizes into the 512 bits of the AVX-512 registers can be useful for performance optimization in deep learning applications. One common technique is to use the registers to hold multiple elements of the same dimension of a tensor, so that multiple operations can be applied to those elements simultaneously. For example, if we have a convolution operation that operates on a 3x3 tile of an image, we could load 8 rows of the image into a single 512-bit register, so that the convolution operation could be applied to those 8 rows in parallel.

Another technique is to use the registers to hold multiple elements of different dimensions of a tensor, so that operations that involve those elements can be performed efficiently. For example, if we have a matrix multiplication operation that involves multiplying a large matrix by a small matrix, we could load the small matrix into a 512-bit register and use it to perform a series of vector-matrix multiplications with the large matrix.

There are many other ways to use the AVX-512 instruction set to optimize performance in deep learning applications, and the specific techniques used will depend on the particular operations being performed and the structure of the tensors involved.

60> this is a cpu architecture correct? next, let's talk about ideep::algorithm

Yes, AVX-512 is a CPU architecture.

Moving on to ideep::algorithm, this is a module in the Intel Deep Learning Library (iDeep) that provides various algorithms for deep learning operations. It provides optimized implementations of the most commonly used neural network algorithms, such as convolution, pooling, and fully connected layers.

Some of the algorithms provided by ideep::algorithm include:

  • Convolution: This algorithm performs convolution of an input tensor with a set of filters, where the filters are learned during training. It provides optimized implementations for 1D, 2D, and 3D convolution.

  • Pooling: This algorithm performs max or average pooling of an input tensor. It provides optimized implementations for 1D, 2D, and 3D pooling.

  • Batch Normalization: This algorithm performs batch normalization of an input tensor to help improve the performance of deep neural networks.

  • Fully Connected: This algorithm performs a fully connected layer, also known as a dense layer, where each neuron is connected to every neuron in the previous layer.

The ideep::algorithm module provides optimized implementations of these algorithms for various hardware architectures, including Intel CPUs and GPUs. These implementations are designed to make efficient use of the hardware and provide high performance for deep learning workloads.

61> thanks, it seems like a standard set of algorithms. is there anything unique about it or something we should watch out for?

The ideep::algorithm module provides a set of highly optimized and scalable deep learning algorithms, so it's very useful for building high-performance deep learning applications. One thing to keep in mind is that these algorithms are highly optimized for Intel architectures and may not perform as well on other architectures. Additionally, the API may not be as intuitive as some other deep learning frameworks, so there may be a bit of a learning curve for those new to the library. Overall, though, the ideep::algorithm module is a powerful tool for building high-performance deep learning models.

62> thanks, next ideep::primitive_desc

ideep::primitive_desc is a type in the Intel oneAPI Deep Neural Network Library (oneDNN) that represents the descriptor of a primitive operation. A primitive operation is a basic computation unit in oneDNN that implements a specific algorithm on input data to produce output data.

primitive_desc provides a way to create a specific instance of a primitive operation, by specifying its input and output tensors, as well as any other required parameters. It also allows for querying information about the primitive, such as its expected memory format, and provides a mechanism for error handling.

For example, to create an instance of the convolution operation, one would first create a convolution_desc object specifying the convolution parameters such as the strides and padding, then create a convolution_primitive_desc object by passing in the convolution_desc object and the memory_desc objects representing the input and output tensors. Finally, one can create an instance of the convolution operation by passing in the convolution_primitive_desc object and the actual tensor data.

The primitive_desc is an important part of the oneDNN API because it allows for efficient implementation of neural network models by optimizing the computations based on the specific hardware platform and input data. It also provides a way to ensure compatibility and reproducibility across different systems and platforms.

63> cool! could we go into more depth?

Sure! ideep::primitive_desc is a class in the Intel Deep Learning Library (iDeep) that provides a description of a specific computation that needs to be performed on a set of data. The description contains information about the computation itself, as well as the memory layout of the input and output data.

One of the main benefits of ideep::primitive_desc is that it allows for efficient memory allocation and management. The class provides information about the optimal memory layout for the input and output data, as well as the amount of memory required for the computation. This information can be used to allocate memory in a way that is optimal for the computation, reducing memory overhead and improving performance.

Another benefit of ideep::primitive_desc is that it allows for dynamic optimization of the computation. The class provides information about the data types and sizes of the input and output data, as well as the computational requirements of the operation. This information can be used to select the optimal implementation of the operation based on the available hardware and other factors.

In summary, ideep::primitive_desc is a key component of the iDeep library that provides a detailed description of a computation, including the memory layout of the input and output data. This information can be used to optimize memory allocation and select the optimal implementation of the computation based on hardware and other factors.

64> ok next, ideep::stream

ideep::stream is a class that encapsulates a stream of computation operations on the CPU. Streams are used to execute independent operations asynchronously, in parallel. This can result in better CPU utilization and increased performance.

In general, streams allow for overlapping of compute and data transfer operations. For example, while the CPU is performing a computation on one set of data, it can concurrently transfer data from another part of memory to a different location. This can lead to more efficient use of system resources and faster overall computation.

Streams can be created using the ideep::stream::create method. Once created, operations can be added to the stream using the ideep::stream::enqueue method. Operations added to a stream can be executed in any order, as long as they don't have data dependencies.

The ideep::stream class also provides a method to synchronize the stream, ideep::stream::wait. This method blocks the calling thread until all operations on the stream have completed.

Overall, ideep::stream is an important class for achieving high-performance computations in a parallelized, asynchronous manner.

65> thanks! ideep::engine

ideep::engine is an abstraction of a computational device or system that can execute computational primitives provided by Intel Math Kernel Library for Deep Neural Networks (MKL-DNN). The engine provides a way to configure and manage the execution of the primitives, such as specifying the device to use, the number of threads, and the memory allocation strategy.

An engine is created with a backend_kind that specifies the device type to use, such as CPU or GPU. The engine also provides a default stream and a default memory allocation mode that can be changed by setting the configuration options.

The engine class has a kind method that returns the type of the backend device that the engine represents. This can be useful when creating primitive_desc objects or when determining if an engine supports a certain type of computation.

The engine class is used extensively throughout the Intel MKL-DNN library, and provides a powerful and flexible way to configure and manage the execution of computational primitives on different types of devices.

66> great! what are some real world use cases for ideep? to my understanding, it is a set of functional building blocks when optimizing deep nets for intel cpu architecture. is there anything else to it?

Yes, you are correct that iDeep is a set of functional building blocks for optimizing deep neural networks on Intel CPUs. Some real-world use cases of iDeep include image and speech recognition, natural language processing, recommendation systems, and many other applications that require deep learning techniques.

One of the key strengths of iDeep is its ability to take advantage of the advanced features of Intel CPUs, such as the AVX-512 instruction set and other hardware optimizations, to achieve faster training and inference times. Additionally, iDeep provides a flexible API that allows developers to customize their neural network architectures and optimize them for specific hardware configurations.

Another benefit of iDeep is its compatibility with other deep learning frameworks such as TensorFlow, PyTorch, and Caffe2, allowing developers to easily integrate it into their existing deep learning workflows.


~362K SLoC