TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. TensorFlow, which TensorFlow Eager extends, requires users to represent computations as dataflow graphs; this permits compiler optimizations and simplifies deployment but hinders rapid prototyping and run-time dynamism. TensorFlow Eager eliminates these usability costs without sacrificing the benefits furnished by graphs: It provides an imperative front-end to TensorFlow that executes operations immediately and a JIT tracer that translates Python functions composed of TensorFlow operations into executable dataflow graphs. TensorFlow Eager thus offers a multi-stage programming model that makes it easy to interpolate between imperative and staged execution in a single package.
is not the first Python library to offer a multi-stage programming model. JAX @cite_26 , a tracing-JIT compiler that generates code for heterogeneous devices via XLA @cite_4 , provides a similar programming paradigm; MXNet and Gluon also let users interpolate between imperative and staged computations, but at a level of abstraction that is higher than ours @cite_25 @cite_19 ; and PyTorch is implementing a staging tracer that is similar to ours @cite_9 . Outside of differentiable programming, Terra is a Lua-embedded DSL that supports code generation, and the paper in which it was introduced presents a thorough treatment of multi-stage programming that is more formal than ours @cite_31 ; as another example, OptiML is a Scala-embedded DSL for machine learning with support for staging and code generation but without support for automatic differentiation @cite_10 . Outside of DSLs, there are several projects that provide just-in-time (JIT) compilation for Python, of which Numba @cite_20 and PyPy @cite_7 are two examples.
@cite_26 @cite_4 @cite_7 @cite_9 @cite_19 @cite_31 @cite_10 @cite_25 @cite_20
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We attempt to apply the technique of Tracing JIT Compilers in the context of the PyPy project, i.e., to programs that are interpreters for some dynamic languages, including Python. Tracing JIT compilers can greatly speed up programs that spend most of their time in loops in which they take similar code paths. However, applying an unmodified tracing JIT to a program that is itself a bytecode interpreter results in very limited or no speedup. In this paper we show how to guide tracing JIT compilers to greatly improve the speed of bytecode interpreters. One crucial point is to unroll the bytecode dispatch loop, based on two kinds of hints provided by the implementer of the bytecode interpreter. We evaluate our technique by applying it to two PyPy interpreters: one is a small example, and the other one is the full Python interpreter. High-performance computing applications, such as auto-tuners and domain-specific languages, rely on generative programming techniques to achieve high performance and portability. However, these systems are often implemented in multiple disparate languages and perform code generation in a separate process from program execution, making certain optimizations difficult to engineer. We leverage a popular scripting language, Lua, to stage the execution of a novel low-level language, Terra. Users can implement optimizations in the high-level language, and use built-in constructs to generate and execute high-performance Terra code. To simplify meta-programming, Lua and Terra share the same lexical environment, but, to ensure performance, Terra code can execute independently of Lua's runtime. We evaluate our design by reimplementing existing multi-language systems entirely in Terra. Our Terra-based auto-tuner for BLAS routines performs within 20 of ATLAS, and our DSL for stencil computations runs 2.3x faster than hand-written C. As the size of datasets continues to grow, machine learning applications are becoming increasingly limited by the amount of available computational power. Taking advantage of modern hardware requires using multiple parallel programming models targeted at different devices (e.g. CPUs and GPUs). However, programming these devices to run efficiently and correctly is difficult, error-prone, and results in software that is harder to read and maintain. We present OptiML, a domain-specific language (DSL) for machine learning. OptiML is an implicitly parallel, expressive and high performance alternative to MATLAB and C++. OptiML performs domain-specific analyses and optimizations and automatically generates CUDA code for GPUs. We show that OptiML outperforms explicitly parallelized MATLAB code in nearly all cases. MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines. Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized native machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learn- ing algorithms implemented with Theano are from 1:6 to 7:5 faster than competitive alternatives (including those implemented with C C++, NumPy SciPy and MATLAB) when compiled for the CPU and between 6:5 and 44 faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.