Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling https://github.com/tensorflow/lingvo
Abstract

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, and experiment configurations are centralized and highly customizable. Distributed training and quantized inference are supported directly within the framework, and it contains existing implementations of a large number of utilities, helper functions, and the newest research ideas. Lingvo has been used in collaboration by dozens of researchers in more than 20 papers over the last two years. This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the framework.

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22footnotetext: Special thanks to Alexander Grushetsky and Adam Sadovsky for the initial design of the Params class.

1 Introduction

This paper presents the open-source Lingvo framework developed by Google for sequence modeling with deep neural networks. To date, this framework has produced a number of state-of-the-art results in machine translation [2, 21, 23], speech recognition [3, 4, 5, 10, 11, 12, 13, 14, 16, 15, 20, 22], speech synthesis [6, 8, 19], and speech translation [7, 9]. It is currently being used by dozens of researchers in their day-to-day work.

We begin by motivating the design of the framework in Section 2, including the development environment it was built for as well as its guiding principles. That is followed by an exposition of its core components and the role possessed by each of them.

Then, in Section 3, we take a deeper dive into how fundamental concepts are implemented and what that means for users of the framework. This covers topics such as how trainable variables are managed and how hyperparameters are configured, as well as the basic APIs involved in composing layers into a model. While there will be some code snippets, those seeking complete examples with code should refer to the codelab [1].

Section 4 provides a consolidated walk-through of the flow of logic during a training run. It outlines the pieces involved from how the model is constructed to how its parameters are updated.

Finally, advanced usage such as distributed training, multi-task models, and inference are described in Section 5.

2 Design

2.1 Motivation

In research, it is critical to be able to quickly prototype and iterate on new ideas. But, when working in a collaborative environment, it is also critical to be able to easily reuse code and document past experiments.

Lingvo evolved out of the need to support a large group of applied researchers working on speech and natural language problems in a single shared codebase.

It follows these guiding principles:

  • Individual pieces should be small and modular, implement the same consistent interface, and be easily extensible;

  • Experiments should be shared, comparable, reproducible, understandable, and correct;

  • Performance should efficiently scale to production-scale datasets and distributed training over hundreds of accelerators;

  • Code should be shared as much as possible when transitioning from research to production.

Modular building blocks.

Lingvo is designed for collaboration, focusing on code with a consistent interface and style that is easy to read and understand, and a flexible modular layering system that promotes code reuse. The same building blocks, such as LSTM or attention layers, can be used as-is across different models with assurance of good quality and performance. Because the blocks are general, an algorithmic improvement in one task (such as the use of multi-head attention in Machine Translation) can be immediately applied to another task (e.g. Speech Recognition). With many people using the same codebase, this makes it extremely easy to employ ideas others are trying in your own models. This also makes it simple to adapt existing models to new datasets.

The building blocks are each individual classes, making it straightforward to extend and override their implementation. Layers are composed in a hierarchical manner, separating low-level implementation details from high-level control flow.

Shared, comparable, reproducible, understandable, and correct experiments.

A big problem in research is the difficulty in reproducing and comparing results, even between people working in the same team. To better document experiments and allow the same experiment to be re-run in the future, Lingvo adopts a system where all the hyperparameters of a model are configured in their own dedicated sub-directory separate from the model logic and are meant to be committed to a shared version control system. As the models are built from the same common layers, this allows our models to be compared with each other without worrying about effects from minute differences in implementation.

All models follow the same overall structure from input processing to loss computation, and all the layers have the same interface. In addition, all the hyperparameters are explicitly declared and their values are logged at runtime. Finally, there are copious amounts of assertions about tensor values and shapes as well as documentation and unit tests. This makes it very easy to read and understand new models when familiar with the framework, and to debug and ensure that the models are correct.

Performance.

Lingvo is used to train on production-scale datasets. As a matter of necessity, its implementation has been optimized, from input processing to the individual layers. Support for synchronous and asynchronous distributed training is provided.

Deployment-readiness.

Ideally, there should be little porting from research to product deployment. In Lingvo, inference-specific graphs are built from the same shared code used for training, and individual classes can be overwritten with device-specific implementations while the high level model architecture remains the same. In addition, quantization support is built directly into the framework.

However, these benefits come at the cost of more discipline and boilerplate, a common trade-off between scalability and fast prototyping.

2.2 Components

The following are the core components of the Lingvo framework.

Models:

A Model is an abstract collection of one or more Tasks. For single-task models the Model is just a transparent wrapper around the Task and the two can be considered the same. For multi-task models, the Model controls how variables are shared between Tasks and how Tasks are sampled for training.

Tasks:

A Task is a specification of a complete optimization problem, such as image classification or speech recognition. It contains an input generator, Layers representing a neural network, a loss value, and an optimizer, and is in charge of updating the model parameters on each training step.

Layers:

A Layer represents an arbitrary function possibly with trainable parameters. A Layer can contain other Layers as children. SoftMax, LSTM, Attention, and even a Task are all examples of Layers.

Input Generators:

Lingvo input generators are specialized for sequences, allowing batching input of different lengths in multiple buckets and automatically padding them to the same length. Large datasets that span multiple input files are also supported. The flexibility of the generic_input function enables simple and efficient implementations of custom input processors.

Params:

The Params object contains hyperparameters for the model. They can be viewed as local versions of tf.flags. Layers, Tasks, and Models are all constructed in accordance to the specifications in their Params.

Params are hierarchical, meaning that the Params for an object can contain Params configuring child objects.

Experiment Configurations:

Each experiment is defined in its own class and fully defines all aspects of the experiment from hyperparameters like learning rate and optimizer parameters to options that affect the model graph structure to input datasets and other miscellaneous options.

These standalone configuration classes make it easy to keep track of the params used for each experiment and to reproduce past experiments. It also allows configurations to inherit from other configurations.

All experiment params are registered in a central registry, and can be referenced by its name, e.g. image.mnist.LeNet5.

Job Runners:

Lingvo’s training setup is broken into separate jobs. For example, the Controller job is in charge of writing checkpoints while the Evaler job evaluates the model on the latest checkpoint. For a full description of the different job runners see Section 5.1.

NestedMap:

A NestedMap is a generic dictionary structure for arbitrary structured data similar to tf.contrib.framework.nest. It is used throughout Lingvo to pass data around. Most python objects in the code are instances of either Tensor, a subclass of BaseLayer, or NestedMap.

Custom Ops:

Lingvo supports custom op kernels written in C++ for high-performance code. For example, custom ops are used for the input pipeline, beam search, and tokenization.

3 Implementation

This section provides a more detailed look into the core Lingvo APIs. Section 3.1 introduces the Params class which is used to configure everything. Section 3.2 covers how Layers are constructed, how they work, and how they can be composed. Section 3.3 describes how variables are created and managed by each Layer. Section 3.4 goes over input reading and processing, and Sections 3.5, 3.6, and 3.7 briefly go over model registration, overriding params, and runtime assertions. Finally, Section 3.8 gives a simple overview of the layout of the source code.

3.1 Params

The Params class is a dictionary with explicitly defined keys used for configuration. Keys should be defined when the object is created, and trying to access or modify a nonexistent key will raise an exception. In practice, every Layer has a Params classmethod, which creates a new params object and defines the keys used to configure the layer with a reasonable default value. Then, in a separate experiment configuration class, these default values are overridden with experiment-specific values.

3.2 Layers

In order to construct a Layer, an instance of these layer’s Params is required. The params includes details such as:

  • cls: the layer’s class,

  • name: the layer’s name, and

  • params_init: how the variables created by this layer should be initialized.

Because the class is contained in the params, the following ways of constructing the layer are equivalent:

p = SomeLayerClass.Params()
layer = SomeLayerClass(p)  # Call the constructor.
layer = p.cls(p)           # Same, but call through the params.

All layers have a FProp() function, which is called during the forward step of a computation. Child layers can be created in the constructor using self.CreateChild(’child_name’, child_params), and they can be referenced by self.child_name.

3.3 Variable Management

Each Layer creates and manages its own variables.

Variables are created in the layer’s __init__() method through a call to self.CreateVariable(), which registers the variable in self.vars and the value of the variable (potentially after a transform like adding variational noise) in self.theta. In FProp(), because it may be executed on different devices in distributed training, for performance reasons it is best to access the variables through the theta parameter passed in to the function rather than self.vars or self.theta.

Variable placement is determined by the cluster.GetPlacer() function. The default policy is to place each variable on the parameter server that has the least bytes allocated. For model parallelism, an explicit policy based on e.g. variable scope can be adopted.

There are many benefits to explicitly managing variables instead of using tf.get_variable:

  • It supports research ideas such as weight noise.

  • The variable_scope construct can be error prone and less readable, for example accidental reuse of a variable.

  • For sync replica training, sharing the weights between multiple workers on the same machine is otherwise awkward.

3.4 Input Processing

Lingvo supports inputs in either plain text or TFRecord format. Sequence inputs can be bucketed by length through the bucket_upper_bound and
bucket_batch_limit params.

A tokenizer can be specified for text inputs. Available tokenizers include VocabFileTokenizer which uses a look-up table provided as a file, BpeTokenizer for byte pair encoding [18], and WpmTokenizer for word-piece models [17].

The input file pattern should be specified as “type:glob_pattern” through the file_pattern param. The input processor should implement the
_DataSourceFromFilePattern() method, which returns an op that when executed reads from the file and returns some tensors. Often this op is implemented as a custom C++ op using the RecordProcessor interface. The tensors returned by this op can be retrieved by calling _BuildDataSource(), and can be used to fill in an input batch NestedMap to be returned by the InputBatch() method. Finally, batch-level preprocessing can also be implemented in PreprocessInputBatch().

In addition to using a custom RecordProcessor op, an input processor can also be defined directly in Python through the generic_input op.

3.5 Model Registration

Configuration classes lie inside lingvo/tasks/<task>/params/<param>.py and are annotated with @model_registry.RegisterSingleTaskModel for the typical case of a single-task model. This annotation adds the class to the model registry with a key of <task>.<param>.<classname> (e.g. image.mnist.LeNet5).

The class should be a subclass of SingleTaskModelParams and implement the Task() method, which returns a Params instance configuring a Task. The registration code will automatically wrap the Task into a SingleTaskModel.

The class should also implement the Train(), Test(), and maybe Dev() methods. These methods return a Params instance configuring an input generator, and represent different datasets.

An example is shown in Figure 1.

@model_registry.RegisterSingleTaskModel
class MyTaskParams(base_model_params.SingleTaskModelParams):
  @classmethod
  def Train(cls):
    ...  # Input params.
  @classmethod
  def Task(cls):
    p = my_model.MyNetwork.Params()
    p.name = ’my_task’
    ...
    return p
Figure 1: Registering a single-task model.

3.6 Overriding Params from the Command Line

It is possible to override the values of any hyperparameter for a specific run using the --model_params_override or --model_params_file_override flags. This makes it simple to start similar jobs for hyperparameter tuning.

3.7 Assertions

py_utils.py contains functions for run-time assertions about values and shapes as well as CheckNumerics() for detecting NaNs. Assertions can be disabled with the command-line --enable_asserts=false. Similarly, CheckNumerics can be disabled with --enable_check_numerics=false.

3.8 Code Layout

{forest}

for tree=font=, grow’=0, folder indent=.9em, folder icons, edge=dotted, align=left [lingvo [trainer.py
Entry point., is file] [model_imports.py
Imports and registers all model params in the global registry., is file] [core [base_input_generator.py, is file] [base_layer.py, is file] [base_model.py, is file] [cluster.py
Contains the policy for op placement., is file] [hyperparams.py, is file] [attention.py, layers.py, rnn_cell.py, rnn_layers.py
Contains implementations for many common layers., is file] [optimizer.py, is file] [py_utils.py
Most utility functions are here., is file] [recurrent.py
The functional RNN., is file] [summary_utils.py
Contains utilities for dealing with summaries., is file] [ops
Folder for custom C++ ops. [record_*.*
The input processing pipeline., is file] [py_x_ops.py
Python bindings for the C++ ops., is file] [x_ops.cc
C++ op definitions., is file] ] ] [tasks [<task>
Folder for an individual task/domain/project. [params
Folder for model params.] ] ] [tools] ]

4 Life of a Training Run

Figure 2: An overview of the Lingvo framework, outlining how models are instantiated, trained, and exported for evaluation and serving.

This section gives an overview of what happens behind the scenes from the start of a training run to the end of the first step for a single-task model.

Training is started by launching the trainer with the name of a model and the path to a log directory. The model name is resolved using the model registry to obtain the Params for the model, and the various job runners for training are created.

The Params at this point is just the params for the top level Model with the overrides in the experiment configuration corresponding to the model name specified. No Layers have been instantiated yet.

Each runner then independently instantiates the model and builds the tensorflow graphs and ops that they will execute based on their job. For example, the Trainer will build a train_op spanning both the FProp() and BProp() graphs and involves updating model parameters, while the Evaler and Decoder will build a eval_metrics op involving only the FProp() graph with p.is_eval set. There can be multiple Evalers and Decoders, one for each evaluation dataset.

Instantiating the model calls its __init__() method, which constructs the Params for child layers and instantiates them recursively through calls to self.CreateChild(). These child layer params could be exposed as part of the top level model params, perhaps as a “params template”, allowing them to be configured in the params files, or they could be constructed completely from scratch in the __init__() method based on the information available at that time. The __init__() method is also in charge of creating the variables managed by the Layer through self.CreateVariable().

Once the graphs are built, the Trainer runner will wait for the Controller runner to initialize the variables or restore them from a checkpoint, while the evaluation runners will wait for a new checkpoint to be written to the log directory.

After the variables are initialized, the Trainer will run training, i.e. calling session.run with the model’s train_op, in a loop, and the Controller will produce training summaries for each step. When enough steps have passed, the Controller writes a new checkpoint to the log directory. The Evaler and Decoder detect this new checkpoint and evaluates the model at that checkpoint to generate summaries. This process then continues until the user terminates all jobs or p.train.max_steps is reached.

For more details about the various runners such as the difference between Evalers and Decoders, as well as information about which devices individual ops in the graph will be placed on during distributed training, see Section 5.1.

5 Advanced Usage

This section provides some examples of advanced features. This is by no means an exhaustive list of all the existing features, and many new features are continually being added.

Section 5.1 describes the distributed training setup. Section 5.2 details how multi-task models are configured and registered, and Section 5.3 gives a brief look into inference and productionization support.

5.1 Distributed Training

Both synchronous as well as asynchronous distributed training are supported. In asynchronous mode, each individual worker job executes its own training loop and is completely independent from the other workers. In synchronous mode, there is a single training loop driven by a trainer client that distributes work onto the various worker jobs.

Here we summarize the different types of job runners under each configuration.

A shared directory on a distributed file system where checkpoints can be written and loaded is assumed to exist.

5.1.1 Common Runners

Controller:

This job handles initializing variables and saving/loading checkpoints as well as writing training summaries.

Evaler:

This job loads the latest checkpoint and runs and exports evaluation summaries. Multiple evalers can be started for different datasets.

Decoder:

This job loads the latest checkpoint and runs and exports decoding summaries. Multiple decoders can be started for different datasets. Decoders are different from evalers in that the ground-truth is used during evaluation but not during decoding. A concrete example is that Evalers can use teacher-forcing while Decoders may need to rely on beam search.

5.1.2 Asynchronous Training

Trainer:

This is the worker job which runs the training op and sends variable updates.

Parameter Server:

Variable values are stored here. Trainer jobs send updates and receive global values periodically.

Data Processor:

This is an optional job for loading data before dispatching them to trainers, to offload the cost associated with loading and preprocessing data from the trainer to a separate machine.

5.1.3 Synchronous Training

Worker:

The worker job in sync training runs the training op like the trainer job in async training but they do not perform variable updates.

Trainer client:

The trainer client drives the training loop and aggregates their results before updating the variables. There are no parameter servers in sync training. Instead, the worker jobs act as parameter servers, and the trainer client sends the relevant variable updates to each worker.

5.2 Multi-task Models

A multi-task model is composed of individual Tasks sharing variables. Existing options for variable sharing range from sharing just the encoder
(multitask_model.SharedEncoderModel) to fine-grained control with
multitask_model.RegExSharedVariableModel.

Multi-task model params should be a subclass of MultiTaskModelParams and implement the Model() method, which returns a Params instance configuring a MultiTaskModel. The task_params and task_probs attributes define respectively the params and relative weight of each Task.

An example of registering a multi-task model is shown in Figure 3.

@model_registry.RegisterMultiTaskModel
class MyMultiTaskParams(base_model_params.MultiTaskModelParams):
  @classmethod
  def Train(cls):
    p = super(MyMultiTaskParams, cls).Train()
    task1_input_params = ...
    p.Define(’task1’, task1_input_params, ’’)
    # Or, refer to existing single task model params.
    p.Define(’task2’, MyTaskParams.Train(), ’’)
    return p
  @classmethod
  def Model(cls):
    p1 = my_model.MyNetwork.Params()
    p1.name = ’task1’
    ...
    # Or, refer to existing single task model.
    p2 = MyTaskParams.Task()
    p2.name = ’task2’
    p = base_model.MultiTaskModel.Params()
    p.name = ’my_multitask_model’
    p.task_params = hyperparams.Params()
    p.task_params.Define(’task1’, p1, ’’)
    p.task_params.Define(’task2’, p2, ’’)
    p.task_probs = hyperparams.Params()
    p.task_probs.Define(’task1’, 0.5, ’’)
    p.task_probs.Define(’task2’, 0.5, ’’)
    return p
Figure 3: Registering a multi-task model.

Knowledge distillation is also supported via base_model.DistillationTask. For knowledge distillation, the teacher parameters must be loaded from a checkpoint file by specifying params.train.init_from_checkpoint_rules in the Task() definition.

5.3 Inference and Quantization

Once models have been trained, they must be deployed on a server or on an embedded device. Typically, during inference, models will be executed on a device with fixed-point arithmetic. To achieve the best quality, the dynamic range must be kept in check during training. We offer quantized layers that wrap the training and inference computation functions for convenience.

Inference has different computational characteristics than training. For latency reasons, the batch size is smaller, sometimes even equal to just 1. For sequence models, often a beam search is performed. It may even be preferable to drive inference one timestep at a time. Several constraints dominate the choice of how to run the computation: 1) available operations, 2) desired latency, 3) parallelizability, and 4) memory and power consumption. To enable the greatest amount of flexibility given these constraints, we leave it to the designer of the model to express inference in the optimal way by explicitly exporting inference graphs rather than leaving it to a graph converter. A basic inference graph can be written in a few lines of code, reusing the same functions used for building the training graph, while for more complicated inference graphs it is possible to even completely swap out the implementation of a low level layer.

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