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Mesh TensorFlow - Model Parallelism Made Easier

Introduction

Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations. The purpose of Mesh TensorFlow is to formalize and implement distribution strategies for your computation graph over your hardware/processors. For example: "Split the batch over rows of processors and split the units in the hidden layer across columns of processors." Mesh TensorFlow is implemented as a layer over TensorFlow.

Watch our YouTube video.

Do I need Mesh TensorFlow?

If you just want data-parallel training (batch-splitting), then you do not need Mesh TensorFlow, though Mesh TensorFlow can do this. The most common reasons for more sophisticated parallel computation are:

• The parameters of the model do not fit on one device - e.g. a 5-billion-parameter language model.

• An example is so large that the activations do not fit on one device. - e.g. large 3D image model(experimental/unet.py).

• Lower-latency parallel inference (at batch size 1).

The Mesh TensorFlow Approach to Distributed Computation

• A "Mesh" is an n-dimensional array of processors, connected by a network.

• Each tensor is distributed (split and/or replicated) across all processors in a mesh.

• Tensor dimensions and mesh dimensions are named. The layouts of all tensors follow from a set of user-defined layout rules which specify which tensor-dimensions are split across which mesh-dimensions. This ensures that the corresponding dimensions in different tensors are split in the same manner.

• Layouts do not affect results - only performance.

• The implementation of an operation involves parallel computation on all processors in the mesh, and sometimes also collective communication. A processor usually just manipulates the slices of the input tensors already resident on that processor, and produces the slice of the output that goes on that processor.

Getting Started

Installation

To install the latest stable version, run

pip install mesh-tensorflow

To install the latest development version, run

pip install -e "git+https://github.com/tensorflow/mesh.git#egg=mesh-tensorflow"

Installing mesh-tensorflow does not automatically install or update TensorFlow. We recommend installing it via pip install tensorflow or pip install tensorflow-gpu. See TensorFlow’s installation instructions for details. If you're using a development version of Mesh TensorFlow, you may need to use TensorFlow's nightly package (tf-nightly).

Example Network (MNIST)

To illustrate, let us consider a simple model for the MNIST image-classification task. Our network has one hidden layer with 1024 units, and an output layer with 10 units (corresponding to the 10 digit classes).

The code consists of two parts, the first describing the mathematical operations, and the second describing the devices and tensor/computation layout. For the full example, see examples/mnist.py. TODO(noam): verify that this code works.

# tf_images is a tf.Tensor with shape [100, 28, 28] and dtype tf.float32
# tf_labels is a tf.Tensor with shape [100] and dtype tf.int32
graph = mtf.Graph()
mesh = mtf.Mesh(graph, "my_mesh")
batch_dim = mtf.Dimension("batch", 100)
rows_dim = mtf.Dimension("rows", 28)
cols_dim = mtf.Dimension("cols", 28)
hidden_dim = mtf.Dimension("hidden", 1024)
classes_dim = mtf.Dimension("classes", 10)
images = mtf.import_tf_tensor(
    mesh, tf_images, shape=[batch_dim, rows_dim, cols_dim])
labels = mtf.import_tf_tensor(mesh, tf_labels, [batch_dim])
w1 = mtf.get_variable(mesh, "w1", [rows_dim, cols_dim, hidden_dim])
w2 = mtf.get_variable(mesh, "w2", [hidden_dim, classes_dim])
# einsum is a generalization of matrix multiplication (see numpy.einsum)
hidden = mtf.relu(mtf.einsum(images, w1, output_shape=[batch_dim, hidden_dim]))
logits = mtf.einsum(hidden, w2, output_shape=[batch_dim, classes_dim])
loss = mtf.reduce_mean(mtf.layers.softmax_cross_entropy_with_logits(
    logits, mtf.one_hot(labels, classes_dim), classes_dim))
w1_grad, w2_grad = mtf.gradients([loss], [w1, w2])
update_w1_op = mtf.assign(w1, w1 - w1_grad * 0.001)
update_w2_op = mtf.assign(w2, w2 - w2_grad * 0.001)

In the code above, we have built a Mesh TensorFlow graph, which is simply a Python structure. We have completely defined the mathematical operations. In the code below, we specify the mesh of processors and the layout of the computation.

devices = ["gpu:0", "gpu:1", "gpu:2", "gpu:3"]
mesh_shape = [("all_processors", 4)]
layout_rules = [("batch", "all_processors")]
mesh_impl = mtf.placement_mesh_impl.PlacementMeshImpl(
    mesh_shape, layout_rules, devices)
lowering = mtf.Lowering(graph, {mesh:mesh_impl})
tf_update_ops = [lowering.lowered_operation(update_w1_op),
                 lowering.lowered_operation(update_w2_op)]

The particular layout above implements data-parallelism, splitting the batch of examples evenly across all four processors. Any Tensor with a "batch" dimension (e.g. images, h, logits, and their gradients) is split in that dimension across all processors, while any tensor without a "batch" dimension (e.g. the model parameters) is replicated identically on every processor.

Alternatively, for model-parallelism, we can set layout_rules=[("hidden", "all_processors")]. In this case, any tensor with a "hidden" dimension (e.g. hidden, w1, w2) is split, while any other tensor (e.g. image, logits) is fully replicated.

We can even combine data-parallelism and model-parallelism on a 2-dimensional mesh of processors. We split the batch along one dimension of the mesh, and the units in the hidden layer along the other dimension of the mesh, as below. In this case, the hidden layer is actually tiled between the four processors, being split in both the "batch" and "hidden_units" dimensions.

mesh_shape = [("processor_rows", 2), ("processor_cols", 2)]
layout_rules = [("batch", "processor_rows"), ("hidden", "processor_cols")]

Where does the network communication happen?

Some Mesh TensorFlow operations cause network communication. For example, an einsum (generalized matrix multiplication) is computed as follows:

• On each processor, compute the einsum of the slices of the two operands that are local to that processor.
• If no reduced-out dimensions are split, then we are done.
• If reduced-out dimensions are split, then perform an "allreduce" operation on the resulting slices - summing across any mesh dimensions over which the reduced-out dimensions are split.

Where the allreduces happen depends will depend on the computation layout. For example, in a data-parallel layout where the "batch" dimension is split, allreduces will happen when computing the parameter gradients, since this involves matrix multiplications which reduce out the "batch" dimension.

How do I pick a layout?

While results do not depend on layout (except in the realm of roundoff errors and random seeds), performance and memory consumption depend heavily on layout. Fortunately, the auto_mtf subpackage provides a method for automatically choosing a layout. For more information about what auto_mtf is doing to choose a layout, see its README file.

import mesh_tensorflow.auto_mtf

graph = mtf.Graph()
mesh = mtf.Mesh(graph, "my_mesh")
# Insert model code here.
outputs = [logits, loss]  # iterable of mtf.Tensor, the outputs you're computing
mesh_shape = [("processor_rows", 2), ("processor_cols", 2)]
layout_rules = mtf.auto_mtf.layout(graph, mesh_shape, outputs)

It is possible for advanced users to eke out additional performance by tuning the layout (and model) further. Mesh TensorFlow helps by accumulating and printing counters of computation/communication. To start, here are some tricks/guidelines.

• It is illegal for two dimensions of the same tensor to be split across the same mesh dimension.
• For any compute-intense operation (e.g. einsum), make sure that all mesh-dimensions are used to split dimensions of the inputs or outputs. Otherwise, computation is duplicated.
• To keep the ratio of compute/communication high (i.e. not be bandwidth-bound), split dimensions into large chunks. This should be familiar in the data-parallelism case, where we want a large batch size per processor to avoid spending most of our time communicating.

The Mesh TensorFlow Language

Mesh TensorFlow (v0.0) is implemented as a Python library which can generate part of a TensorFlow graph. The user first builds a mtf.Graph (the analog of a TensorFlow graph) made up of mtf.Tensors and mtf.Operations. As in TensorFlow, this graph consists of simple Python objects. The user then creates a mtf.Lowering object, which lowers the mtf.Graph into TensorFlow, adding to the default TensorFlow graph.

The Mesh TensorFlow language is nearly identical to TensorFlow, with the familiar notion of a Graph, Tensors, Operations, and automatic gradient computation. The principal differences are as follows:

Meshes replace devices

A Mesh is a n-dimensional array of processors with named dimensions. Each Tensor is assigned to a Mesh, instead of a device.

Tensor dimensions are named

Each Tensor has a static Shape, which is a tuple of different "Dimensions". A Dimension is a (name, size) pair. For example, the shape of a Tensor representing a batch of images might be:

[("batch", 100), ("rows", 28"), ("cols", 28), ("channels", 3)].

Layouts

A Tensor is laid out on its mesh with one slice on each processor. A Tensor "layout", is an injective partial map specifying which dimensions of the tensor are (evenly) split across which dimensions of the mesh. No dimension of a tensor may be split across two dimensions of its mesh and no two dimensions of a tensor may be split across the same dimension of its mesh. The user defines a global set of layout rules in the form of (tensor-dimension-name, mesh-dimension-name) pairs. A dimension of a tensor is split across a dimension of its mesh if there is a matching rule.

Example Layouts

Take our example Tensor image_batch with shape: [("batch", 100), ("rows", 28"), ("cols", 28), ("channels", 3)]

Assume that this Tensor is assigned to a mesh of 8 processors with shape: [("processor_rows", 2), ("processor_cols", 4)]

• If we use an empty set of layout rules [], we get no splitting. Each processor contains the whole Tensor.

• If we use the layout rules "batch:processor_cols", then the "batch" dimension of the Tensor is split across the "processor_cols" dimension of the batch. This means that each processor contains a Tensor slice with shape [25, 28, 28, 3]. For example, processors (0, 3) and (1, 3) contain identical slices - image_batch[75:100, :, :, :].

• If we use the layout rules "rows:processor_rows;cols:processor_cols", then the image is split in two dimensions, with each processor containing one spatial tile with shape [100, 14, 7, 3]. For example, processor (0, 1) contains the slice image_batch[:, 0:14, 7:14, :].

Some layout rules would lead to illegal layouts:

• "batch:processor_rows;rows:processor_rows" is illegal because two tensor dimensions could not be split across the same mesh dimension.

• "channels:processor_rows" is illegal because the size of the tensor dimension is not evenly divisible by the size of the mesh dimension.

Einsum

Mesh TensorFlow uses Einstein-summation notation, mtf.einsum(inputs, output_shape), using the (named) Dimensions as the symbols. Matrix multiplication, broadcast, sum-reduction, and transposition can all be expressed as special cases of mtf.einsum, though the familiar interfaces are also supported. The operation is lowered to slice-wise tf.einsums, followed by allreduce across any mesh-dimensions corresponding to the summed-out Tensor dimensions.

Reshape can be expensive

mtf.reshape(x, new_shape) is used to change a Tensor's shape, potentially leading to a new tensor layout and hence network communication.

CPU/GPU/TPU implementations

Mesh TensorFlow works on CPU, GPU and TPU. The TPU implementation is very different from the CPU/GPU implementation.

Multi-CPU/GPU meshes are implemented with PlacementMeshImpl. In this case Mesh TensorFlow emits separate TensorFlow operations placed on the different devices, all in one big TensorFlow graph.

TPU meshes are implemented in with SimdMeshImpl. In this case, Mesh TensorFlow emits TensorFlow operations (and communication collectives) from the perspective of one core, and this same program runs on every core, relying on the fact that each core actually performs the same operations. This piggy-backs on the TPU data-parallelism infrastructure, which operates the same way. This "SIMD" approach keeps the TensorFlow and XLA graphs from growing with the number of cores. The differences between cores are as follows:

• different slices of the variables (this works now)
• different positions in the collective communication (this works now)
• different slices of the infed and outfed tensors. We currently work around this by requiring that all imported/exported tensors be fully-replicated. In the future, we should handle this correctly.

Experimental features

The input pipeline of Mesh Tensorflow models might become a bottleneck, when training with large input (e.g., high resolution images). We provide new APIs and a new input pipeline for you to run Mesh Tensorflow models. You can find them under the experimental/ folder. We suggest that you give them a try when your input is so large that running Mesh Tensorflow models with the default APIs is almost infeasible. To be more specific:

• The BROADCAST mode in TPUEstimator does not scale up to large inputs (images of tens of millions of pixels). We provide a new input pipeline: experimental/input_reader.py. See experimental/model_executor.py on how to use it.
• If your model takes images as input and has convolution layers. You cannot directly map image height and width dimensions to mesh dimensions, due to the sliding-window nature of convolution. Instead, you should use spatial partitioning. We provide examples in experimental/unet.py.
• If you want more control on the training and evaluation loop, instead of using the default API (TPUEstimator) to run your model, you can use low level APIs in experimental/model_executor.py.

Note that we did not test the experimental code on GPUs. We ran them on TPUs. We believe that some debugging would be required for it to work on GPUs.

Instructions for running on cloud-tpu

Note: It requires tensorflow>=1.11.0.

Prerequisite

Please go through the Transformer tutorial.

Create VM and TPU instance in Cloud console

TODO(trandustin,ylc): update given mtf pypi package

ctpu up -name=ylc-mtf-donut -tf-version=nightly -tpu-size=v2-8 -zone=us-central1-b

SSH into VM

git clone https://github.com/tensorflow/mesh.git
cd mesh/
pip install --user .

Run the Transfomer model (no Tensor2Tensor dependencies)

pip install tensorflow_datasets

cd mesh/
DATA_DIR=gs://noam-mtf/data
MODEL_DIR=gs://noam-mtf/transformer_standalone
TPU=noam-mtf-donut

# MODEL HPARAMS AND DIRECTORY  (uncomment one)
# base model
MODEL=./transformer/gin/model_base.gin
# 5B parameters (too big for this dataset, only trains with model-parallelism)
# MODEL=./transformer/gin/model_5b.gin

# UNCOMMENT ONE OF THESE
# Data-parallelism
LAYOUT=./transformer/gin/layout_data_parallel.gin
# Model-parallelism
# LAYOUT=./transformer/gin/layout_model_parallel.gin
# Data-parallelism and Model-Parallelism
# LAYOUT=./transformer/gin/layout_data_and_model_parallel.gin

# TRAIN
python examples/transformer_standalone.py \
  --tpu=$TPU --data_dir=$DATA_DIR --model_dir=$MODEL_DIR --gin_file=$MODEL \
  --gin_file=$LAYOUT --gin_param="run.mode='train'"

# EVAL
python examples/transformer_standalone.py \
  --tpu=$TPU --data_dir=$DATA_DIR --model_dir=$MODEL_DIR --gin_file=$MODEL \
  --gin_file=$LAYOUT --gin_param="run.mode='evaluate'"

The above code will train on the LM1B language modeling benchmark, as specified in examples/transformer_standalone_defaults.gin. To train a sequence-to-sequence model on WMT14 en-de, change utils.run.dataset to wmt_translate_ende/ende_subwords8k_t2t and set utils.run.mode to True. Note that the wmt_translate_ende/ende_subwords8k_t2t dataset was removed from TensorFlow Datasets in commit 211cb6f, so in order to train a model using this dataset you need to install a version of TFDS before this commit. Then, you can decode the WMT en-de development set and evaluate it using SacreBLEU like so:

# INFER
pip3 install sacrebleu
mkdir ~/input ~/output
DECODE_INPUT=/home/$USER/input/ende.dev
DECODE_OUTPUT=/home/$USER/output/ende.dev.out
~/.local/bin/sacrebleu -t wmt13 -l en-de --echo src > $DECODE_INPUT
python examples/transformer_standalone.py \
  --tpu=$TPU --data_dir=$DATA_DIR --model_dir=$MODEL_DIR --gin_file=$MODEL \
  --gin_file=$LAYOUT \
  --gin_param="decode_from_file.input_filename='$DECODE_INPUT'" \
  --gin_param="decode_from_file.output_filename='$DECODE_OUTPUT'" \
  --gin_param="run.mode='infer'"

# Compute BLEU score for dev set
cat $DECODE_OUTPUT | ~/.local/bin/sacrebleu -t wmt13 -l en-de -tok intl

Run the Transfomer model with Tensor2Tensor config

git clone https://github.com/tensorflow/tensor2tensor.git
cd tensor2tensor/
pip install --user  .

Before running the model, you need to prepare the training data and bucket for storing checkpoints. Refer to the Transformer tutorial to learn how to generate the training data and create buckets.

CONF=mtf_transformer_paper_tr_0_mesh_8
NAME=ende_$CONF\_0828
MODEL=mtf_transformer
PROBLEM=translate_ende_wmt32k_packed

DATA_DIR=gs://xxxx
OUT_DIR=gs://xxxx
TPU_NAME=ylc-mtf-donut

tensor2tensor/bin/t2t-trainer \
  --model=$MODEL \
  --hparams_set=$CONF \
  --problem=$PROBLEM \
  --train_steps=10000 \
  --eval_steps=200 \
  --data_dir=$DATA_DIR \
  --output_dir=$OUT_DIR \
  --use_tpu=True \
  --cloud_tpu_name=$TPU_NAME

Run the toy model without Tensor2Tensor dependencies

This toy model contains two fully-connected layers which aim to train a identity function: f(x) = x. Since there are 8 TPU cores, we can arbitrary change the FLAGS.mesh_shape and FLAGS.layout to achieve different data-parallelism and model-parallelism strategies.

MODEL_DIR=gs://xxxx
TPU_NAME=ylc-mtf-donut

# 2 ways data-parallelism and 4 ways model-parallelism.
# In this configuration, we split the batch dimension into 2 cores and the
# hidden dimension into 4 cores.
python examples/toy_model_tpu.py \
  --tpu=$TPU \
  --model_dir=$MODEL_DIR \
  --io_size=8 \
  --hidden_size=8 \
  --mesh_shape='x:2;y:4' \
  --layout='batch:x;hidden:y'

# 8 ways model-parallelism.
# In this configuration, We split the hidden dimension into 8 cores.
python examples/toy_model_tpu.py \
  --tpu=$TPU \
  --model_dir=$MODEL_DIR \
  --io_size=8 \
  --hidden_size=8 \
  --mesh_shape='all:8' \
  --layout='hidden:all'

References

N. Shazeer, Y. Cheng, N. Parmar, D. Tran, A. Vaswani, P. Koanantakool, P. Hawkins, H. Lee, M. Hong, C. Young, R. Sepassi, and B. Hechtman. Mesh-TensorFlow: Deep learning for supercomputers. In Neural Information Processing Systems, 2018.

@inproceedings{shazeer2018mesh,
  author = {Noam Shazeer and Youlong Cheng and Niki Parmar and Dustin Tran and Ashish Vaswani and Penporn Koanantakool and Peter Hawkins and HyoukJoong Lee and Mingsheng Hong and Cliff Young and Ryan Sepassi and Blake Hechtman},
  title = {{Mesh-TensorFlow}: Deep Learning for Supercomputers},
  booktitle = {Neural Information Processing Systems},
  year = {2018},
}