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New types of compute hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs.
However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon.
In particular, models that exploit structured input via complex and instance-dependent control flow are difficult to accelerate using existing algorithms and hardware that typically rely on minibatching.
We present an asynchronous model-parallel (AMP) training algorithm that is specifically motivated by training on networks of interconnected devices.
Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently, even for small minibatch sizes, resulting in shorter overall training times.
Our framework opens the door for scaling up a new class of deep learning models that cannot be efficiently trained today.
An emerging category of neural networks show the common trait of reacting in dynamic and unique ways to properties of their input.
Networks like tree-structured recursive neural networks BID35 BID36 and graph neural networks (GNNs) BID31 BID20 BID12 take structured data types as input and and execute a computation that depends on the input structure.
This defies the moden GPU-driven paradigm of minibatch-based processing, and we refer to this new class of models with dynamic control flow as dynamic neural networks.
The development of dynamic neural network frameworks -Chainer BID37 , DyNet BID24 , and PyTorch (PyTorch core team) -speaks to the importance of this class of models and highlights the challenge of how to make it easy for users to describe them.
Yet there is another big challenge: how can we train these models efficiently?Managing minibatches to keep GPUs fully utilized is typically considered a user's responsibility in these dynamic frameworks (with the exception of DyNet's autobatching feature; see Sec. 7).
This means that users have to think about how to change their data feeding pipeline or even the model itself to run efficiently on GPUs, rather spending time innovating to improve the model accuracy.
What if we had a hypothetical device with low memory overhead that allows perfect scaling without batching; i.e., processing 1 item is simply 100x faster than processing 100 items?
Recent work on FPGAs and other specialized hardware BID10 BID4 BID17 for deep learning encourages us to investigate this question.
Our premises are 1.
No batching is required for efficient processing.2.
Each device may not have enough memory to hold the entire model (this is a realistic constraint for current memory systems that approach the perfect scaling we require)Based on these premises, we propose an asynchronous model-parallel (AMP) training algorithm.
Our idea is illustrated in FIG0 .
We need model parallelism because each device may be too small to hold the entire model (premise 2).
However, if we perform synchronous parameter updates following the full forward and backward propagations, the only way to increase device utilization is by pipelining multiple instances into the system (see e.g., .
Pipeline parallelism with synchronous updates is at odds with convergence speed due to a decreased parameter update frequency; compare FIG0 (a) and (b).To overcome this problem, we propose asynchronous parameter updates that occur without global synchronization whenever a pre-specified number of gradients have been accumulated; see Fig. 1 (c) .
With this design we aim for both high device utilization and update frequency.
In this setting, however, model parameters may be updated between the forward and the backward computation of an instance, introducing gradient "staleness".
Despite staleness, we show that AMP training can converge fast with good hardware utilization.
Specifically, our contributions are:• We present the asynchronous model parallel training algorithm for efficient distributed training of dynamic networks.• We present an intermediate representation (IR) with explicit constructs for branching and joining control flow that supports AMP training.
Unlike previous work that considers static computation graphs for static control flow (e.g., Caffe), and dynamic computation graphs for dynamic control flow (e.g., Chainer), our IR encodes a static computation graph to execute dynamic control flow 1 .
This makes training easy to distribute and parallelize.• We show that our IR can readily encode replicas, a form of data parallelism (see Sec. 5).
In addition, our IR includes operators for data aggregation, which recover a form of batching, enabling our methods to be applied even on hardware where batching is beneficial.• We implement AMP training on a multi-core CPU and empirically demonstrate that AMP training converges to similar accuracies as synchronous algorithms on a variety of dynamic neural network models including Tree RNN and gated graph neural networks (GGNN).In summary, our work demonstrates the benefits of AMP training and gives a novel way to design and deploy neural network libraries with dynamic control flow.
In addition, we use our implementation to estimate the performance on a hypothetical device satisfying premises 1 & 2, with 1TFLOPS compute capability (see Appendix C).
Together, these contributions open up new ways to scale up dynamic networks on interconnected compute devices.
Below we highlight three models with dynamic control flow that will be studied in depth in this paper:Variable-length RNNs iterate over the tokens of variable-length sequences.
Pseudo-code for a simple vanilla RNN is given in Figure 2 .
The linear (fully connected) layer and rectified linear unit (ReLU) can be substituted with a more sophisticated unit such as a gated recurrent unit BID8 .
Though each instance has a different length, it is possible to add padding to enable batching.
However this may lead to significant redundant compute due to variability in sequence lengths.
Isu inc dec inc(s) = s{t=s.t+1} dec(s) = s{t=s.
Tree-structured neural networks are powerful models used for parsing of natural language and images, semantic representation, and sentiment analysis BID34 BID3 BID35 BID36 .
They require evaluation of (potentially multiple) trees with shared parameters but different topology for each instance.
Each tree structure is instance-specific and batching requires nontrivial planning BID21 .
A simple form of tree neural network performs a bottom up traversal of the instance, starting from an embedding of the leaves.
At each level the values from the child nodes are concatenated and sent through a specialized unit (e.g. LSTM).
The result is then propagated further up the tree.
Backpropagation over the tree structure is known as backpropagation through structure BID13 .
BID31 BID20 BID12 combine both the temporal recurrence and recurrence over the structure.
GNNs can be seen as performing aggregation/distribution operations over a general graph structure with shared parameters.
Apart from the models above, there exist many recently proposed models with flexible control flow (e.g. hierarchical memory networks BID5 , neural programmer interpreters BID29 , adaptive computation networks BID14 BID11 , and sparsely-gated mixture of experts BID33 , to which our framework can be applied.
In AMP training, each node of a computation graph (including control flow nodes -see next section) is associated with a worker, which is an abstraction of a compute device.
Neural network training and inference is carried out by message passing among workers following algorithm 1.
All workers run in parallel without synchronization.
Each message contains a payload (float or int tensor), as well as a state that includes the IDs of the source and sink nodes and a label indicating the type of message (forward, backward, or update).
The state is used to keep track of algorithm and control flow information.
For example, in a variable-length RNN the state also contains the instance identifier, the current position in the sequence, and the total sequence length for the instance.
More generally, if the neural model use (possibly nested) loops, then the state for the messages that arrive to and are produced from nodes that logically belong in loop bodies will contain sets of loop counters that together with the instance id uniquely identify the messages throughout the course of the computation.
When a worker receives a message labeled as forward (or backward) then it performs the operation of the node indicated by the sink node ID on the supplied payload.
This produces one or more outgoing messages that are then enqueued into the queues of the workers hosting the next sink nodes in the computation graph.
The final loss layer initiates backward propagation.
If the message type is update, the worker will carry out weight updates on the sink node using gradients accumulated in the appropriate slot in the worker's local memory.
Since both the operation weights and weight gradients can be stored locally on the worker then workers only need to communicate activations and activation gradients, which are typically an order of magnitude smaller than the weights.
The update message is typically sent from the sink node to itself as part of the backward process but it can also be sent from a controller node to simulate synchronous pipelined training.
There are two important details that are not fully spelled out in Algorithm 1.
First, since the messages arrive asynchronously (and possibly out of order), any operation that has more than one parent nodes need to store the payload into its local cache until all the parents send the corresponding payloads.
Thus output message(s) can only be produced when all the payloads become available.
The cache needs to be able to distinguish payloads received from different parents and payloads with different instance ID, and different counters (all encoded in the message states).
The same is true in the backward pass for a node with multiple child nodes.
Second, op * denotes the adjoint operation of op and takes the backward message msg and potentially the forward message fwd msg stored in the cache.
For a nonlinear activation node (e.g., ReLU), the node will not change the state of the message in the forward pass.
Thus in the backward pass, the adjoint operation will just multiply the partial derivative of the activation function to the payload of the received backward message keeping the state unchanged.
By contrast, an operation that only changes the state of the message in the forward pass (e.g., increment the loop counter) will reverse the change in the backward pass leaving the payload unchanged.
In the experiments we vary two hyper parameters to control the effect of asynchrony: min update interval: determines the minimum number of gradients that a parameterized operation needs to accumulate before it can update its parameters (using the update message).
The staleness of a gradient can be measured by the number of updates between the forward and backward computation that produces the gradient.
Small min update interval may increase gradient staleness.
On the other hand, large min update interval can reduce the variance of the gradient but can result in very infrequent updates and also slow down convergence.
max active keys: controls the maximum number of active instances that are in-flight at any point in time.
By setting max active keys = 1 we restrict to single-instance processing, typically equivalent to synchronous training.
More in-flight messages generally increase hardware utilization, but may also increase gradient staleness.
Section 6 demonstrates the effects of these parameters in a multi-core CPU runtime.
In our asynchronous execution model, the optimal assignment of N neural network computation graph nodes to W workers (referred to as affinitization) is in general a non-trivial scheduling problem.
We investigated several heuristics for assigning affinities (for example k-hop coloring BID15 to ensure subsequent heavy operations were assigned different workers).
However, we find that the following procedure achieves high performance in practice in our multi-core CPU implementation, and is adopted throughout our experiments for simplicity.
We first partition the nodes into H 'heavy' operations (namely matrix multiplies) and (N − H) 'light' operations, and then balance the heavy nodes across the workers by affinitizing the h th with the (h mod W ) th worker.
Finally, the light operations are affinitized randomly among the rest of the workers.
Note that in scenarios where communication is over a physical network affinitization will become more critical for high performance.
Overview Computation graphs are expressed using a static intermediate representation (IR) that can be a compilation target for high-level libraries (e.g. TensorFlow or our own Python and C++ frontends), and can itself admit multiple backends (e.g. the multi-core CPU runtime in this paper, or a network of accelerators).
Static means that the IR graph is instance-independent.
Nevertheless, it can execute dynamic and instance-dependent control flow decisions, in a forward and backward manner, by storing instance-and iteration-dependent information as the computation evolves.
Each IR node comes with a forward and a backward semantics.
A model is specified by (i) an IR graph, and (ii) a specialized controller loop that pumps instances and other data (e.g. initial hidden states or labels), and is responsible for throttling asynchrony.
In the rest of this section we discuss the most important IR nodes along with their operational semantics, and show how they are used in the example models.
Payload transformations Parameterized payload transform (PPT) nodes can be used to encode, for instance, fully connected layers.
They apply a transform in the forward pass, but also record the activation in order to use it to compute gradients in the backward pass.
An activation is recorded by keying on the state of the message, allowing thus to process forward and backwards messages completely asynchronously, and -in the extreme case -out of order.
A PPT node requires specification of the forward and the backward transformation.
It may decide to independently apply accumulated gradients to update its parameters.
For transformations that do not involve parameters (e.g. ReLUs) our IR offers a simpler non-parameterized payload transform.
Loops, state, and control flow A condition node (Cond f ) is parameterized by a function f that queries the state (but not the payload) of the incoming message and, based on the response, routes the input to one of the successor nodes.
A join node (Phi) propagates the messages it receives from each of its ancestor nodes but records the origin (using the state of the message as the key) so that in the backward pass it can backpropagate them to the correct origin.
An invertible state update node (Isu f f −1 ) is parameterized by two functions f and f −1 that operate on the state of a message, and satisfy f −1 (f (x)) = x. Typically these are loop counter update functions.
Figure 2 shows how to encode an RNN.
The loop at the heart of the RNN is implemented with Cond, Phi and Isu nodes.
The controller pumps sequences in a lookup table (just another PPT layer), and our Ungroup node (to be described in the next section) generates a stream of tensors each corresponding to a single token, tagged with the current time-step (loop counter).
For each forward message, the Isu node increments the time-step, and the conditional node tests whether the end of the sequence has been reached.
Depending on the answer it either propagates the hidden state back to Phi, or pushes the hidden state to the final linear and loss layers.
In backward mode, messages pass through the Isu (which decrements the time-step), and reach the Phi node.
The Phi node will (based on information from the forward phase) either back-propagate to the Cond node, or to the controller to terminate.
Hence the loop is executed in both the forward and backward direction.
Aggregation and disaggregation Our IR offers several constructs for aggregation and disagreggation; for example RNN requires us to concatenate (Concat) hidden states and embeddings with matching timesteps and instance ids (states).
We offer a construct for broadcasting (Bcast) a message to multiple successors.
We offer an ungrouping construct (Ungroup) that ungroups a matrix and emits all resulting messages tagged with an extra user-provided increasing loop counter.
This allows us to insert the stream of token embeddings in the middle of the RNN loop in Figure 2 .
In backward mode Ungroup groups back all the incoming gradients.
A simpler variant of Ungroup is Replicate, which replicates a message with an extra loop counter in the state.
In backwards mode Replicate sums up all incoming gradients that correspond to the state without the extra loop counter.
Figure 4(a) describes a GNN that combines aggregation on the structure of a graph instance with an outer loop.
The controller pumps data that contain the feature embeddings of all nodes of an input graph.
In addition it pumps in a map specifiying graph topology.
The Distribute node uses that information along with the graph itself to create sub-matrices (here each corresponding to edge types) and pass them through linear (fully connected) layers.
The Collect node collects and re-groups the results based on graph topology.
These nodes correspond to a form of dynamic partition and merging.
Schematically the Distribute behaviour is given in FIG1 .
In backward mode, based on the control information received during the forward pass (gray line) re-groups the gradients and sums together those that correspond to the same index.
The Collect operator is essentially symmetric.
Pipelined model parallelism can often be augmented with forms of data parallelism.
Consider the RNN in Fig. 2 .
The only heavy operation (Linear-1) in the body of the loop will act as a bottleneck for computation.
One solution is to split the linear layer into smaller tiles and compute them in parallel.
This is expressible in our IR but the linear operation needs to be large enough to benefit from tiling in this way.
Another approach is to replicate the linear layer in full.
This requires only minimal new machinery -we can replicate the linear layer and place the replicas inside Cond and Phi nodes as in FIG2 (b).
Different instances or messages from the same instance but with different position in the sequence can be processed in an (pipeline-)parallel fashion by being sent to one of the replicas chosen by a random or deterministic function of the message state.
To enable parameters to be shared among the replicas, we use infrequent end-of-epoch replica synchronization (averaging) that incurs negligible communication cost.
We also tried more elaborate message-passing protocols for group synchronization, but found that infrequent global synchronization was sufficient for fast convergence.
We evaluate AMPNet using the dynamic models introduced in Section 2.
For completeness, we additionally consider a multi-layer perceptron (MLP) as an example of a static, batched network that AMPNet is not specifically designed to tackle.
Brief details of the models and data sets in the experiments are presented below, and further details are given in Appendix B. Results Our asynchronous runtime is motivated by the promise of emerging hardware (e.g. FPGA accelerators) that fulfill the premises in section 1 and are well suited to dynamic neural network execution.
Here we are primarily interested in how the performance of our runtime improves as we increase the degree of asynchrony (by varying max active keys) while keeping other factors fixed.
The aim is to answer the question of whether AMP training is a promising direction for novel distributed hardware that deviates from the CPU/GPU batching paradigm.
To answer this question using resources available today we run the AMPNet training using a multi-core CPU runtime where each worker is a hardware thread (see Appendix A).
Additionally we forecast the performance on a hypothetical 1TFLOPS device satisfying our premises by replacing all computation nodes by configurable sleep nodes.
This allows us to estimate the performance on a new hardware keeping the control flow decisions dynamic.
See Appendix C.It is also interesting to compare how the raw CPU performance of the AMP runtime compares with existing frameworks (TensorFlow, TensorFlow Fold and DyNet) to see that our implementation is already competitive with state of the art methods even on CPUs that do not conform to our target hardware model.
We provide additional analysis for each experiment below.
On MNIST, TAB3 shows 3x speedup from synchrony (max active keys = 1) to asynchrony (max active keys = 4) in terms of throughput.
This is almost ideal as the first three linear layers are the heaviest operations.
The number of epochs to reach the target validation accuracy increases from 3 to 4 but the overall speedup in terms of the wall clock time is 2.2x.
We have also compared AMP training against pipeline parallel training ( FIG0 ).
FIG3 (a) shows that while AMP training achieves 3x throughput gain already with max active keys = 4, pipeline parallelism can only achieve 2x (in fact from FIG0 , 3m/(m + 2) is the best case for max active keys = m) and higher throughput at max active keys = 8 is achieved at the cost of the sharp increase in the number of epochs to convergence.
The list reduction dataset demonstrates the power of replicas.
As there is only one heavy operation (Linear-1, Figure 2 ), the speedup from asynchrony is mild (1.3x).
However we get 2.5x and 3.5x speedup for 2 and 4 replicas, respectively, which is nearly ideal.
Again, the # of epochs to convergence is not affected by increasing max active keys.
The slowdown in convergence for 4 replicas is due to the increased effective minibatch size -also commonly observed in data parallel training.
Next the sentiment tree-RNN dataset shows that our runtime is competitive without batching to TensorFlow Fold BID21 using dynamic batching of batch size 100.
It is worth mentioning that our runtime allows us to specify different min update interval parameter for each parameterized operation.
We set this parameter to 1000 for the embedding layer, which is initialized by Glove vectors, and 50 for all other layers.
This reduced gradient staleness in the embedding layer.
The QM9 dataset demonstrates that increasing asynchrony helps on real-world tasks with complex control flow, and our method outperforms an efficient TensorFlow implementation on CPUs.
Finally, we have implemented the BiLSTM w/ char model in BID25 on Wikiner dataset BID26 .
Our preliminary implementation without any batching achieves 130 sentences/s at max active keys = 32 without any noticeable loss in accuracy (around 94 % after one epoch).
This is competitive with DyNet's performance on the same machine (23 sentences/s without and 220 with autobatching, respectively); See Sec. B.5 for details.
Asynchrony Finally we provide additional analysis on the effect of asynchrony.
The degree of asynchrony is controlled by hyperparameters min update interval and max active keys.
In FIG3 we use an 8-replica RNN model on the list reduction dataset to investigate how these parameters affect the data and time required to converge to 96% validation accuracy.
We find, in analogy with minibatch size in traditional systems, that min update interval must neither be too large nor too small.
Increasing max active keys (increasing asynchrony) monotonically increases performance when the number of keys is similar to the number of individually affinitized heavy operations in the model 8 in this case).
Increasing max active keys significantly beyond this point produces diminishing returns.
One approach to the task of training networks with instance dependent control flow is to define the computation graph dynamically per-instance.
This approach is taken in Chainer BID37 , DyNet BID24 , and PyTorch (PyTorch core team).
There are key challenges in accelerating this approach: Model parallelism would require the dynamically generated computation graph to be scheduled on-the-fly, and BLAS level parallelism would require operations to be batched on-the-fly.
Automatic dynamic batching has been implemented in DyNet BID24 , and is an interesting alternative to our asynchronous execution.
Similar methods are used in TensorFlow Fold BID21 .
The basic idea is to inspect and merge together (by depth) the unrolled computation graphs of several instances to create batched BLAS operations.
The effectiveness of automatic batching greatly depends on the model -for example, it would not perform well on random permutations of a sequence of operations.
By contrast, our IR would very succinctly express and achieve pipeline parallelism using a static computation graph that is easy to distribute and optimize.
Theano BID1 and TensorFlow (TF) can syntactically handle instance dependent control flow with abstractions for conditional execution (ifelse in Theano and cond in TF) and loops (scan and while loop, respectively); TF also provides higher-order functions, such as map, foldl, foldr, and scan.
The main difference between AMPNet and the above frameworks is that AMPNet is streaming and asynchronous whereas Theano is non-streaming and synchronous.
Although not designed for streaming, TF can support streaming programmatically as it exposes first-class queues, as well as data prefetching with so called input pipelines.
In our IR, all the queuing is implicit and stream-based execution is the default.
TF additionally does support static description of dynamic control flow and state update, but we depart from the classic dataflow architecture that TF follows BID2 : First, instead of having nodes that represent mutable reference cells, we encapsulate the state with which a message should be processed through the graph in the message itself.
Second, because we encapsulate algorithmic state in the messages, we do not introduce the notion of control dependencies (which can be used to impose a specific execution order on TF operations).
Our choices complicate algorithmic state management from a programming point of view and make the task of designing a high-level compiler non-trivial, but allow every node to run asynchronously and independently without a scheduler and without the need for control messages: For example, nodes that dynamically take a control flow path or split the data simply consult the state of the incoming message, instead of having to accept additional control inputs.
For "small" states (e.g. nested loop counters or edge and node ids) this might be preferable than out-of-band signaling.
Our IR can implement loops by simply using state-update, conditional, and phi nodes, because the state accompanies the payload throughout its lifetime, whereas TF introduces specialized operators from timely dataflow BID23 ) to achieve the same effect.
Asynchronous data parallel training BID28 BID9 BID7 ) is another popular approach to scale out optimization by removing synchronization (orthogonal to and combinable with model-parallel training).
For example, convolutional layers are more amenable to data-parallel training than fully connected layers, because the weights are smaller than the activations.
Moreover, when control flow differs per data instance, data parallelism is one way to get an effective minibatch size > 1, which may improve convergence by reducing variance.
The impact of staleness on convergence BID28 and optimization dynamics BID22 have been studied for data parallelism.
It would be interesting to extend those results to our setting.
BID16 , like us, aim to to train different parts of a model in a decoupled or asynchronous manner.
More precisely, their goal is to approximate a gradient with a synthetic gradient computed by a small neural network that is locally attached to each layer.
Hence, the local gradient calculation becomes independent of other layers (except for the training of the gradient predictor network) and allows asynchronous parameter updates.
This would be especially useful if the evaluation of the local network is cheaper than the computation of the real gradient; for example, if the computation of the real gradient required significant communication of forward/backward messages between devices.
We have presented an asynchronous model-parallel SGD algorithm for distributed neural network training.
We have described an IR and multi-core CPU runtime for models with irregular and/or instance-dependent control flow.
Looking forward, we aim to deploy our system on specialized hardware.
Equally importantly, we plan to build a compiler that automatically deduces the information to be placed in the states and generates state keying functions from a higher-level description of the models.
By unlocking scalable distributed training of dynamic models, we hope to enable exploration of this class of models that are currently only on the horizon but may become more mainstream in the future.
A AMPNET RUNTIME IMPLEMENTATIONWe have implemented an AMPNet runtime for multi-core CPUs.
Our runtime spawns multiple workers each associated with a hardware thread and hosting one or more IR nodes -in a more general setting each worker corresponds to a compute device.
To remain faithful to a distributed environment communication is only through message passing.
Each worker is equipped with a multiple-producer single-consumer queue that can accept messages for any IR node hosted on that worker.
The main worker loop periodically offloads messages from the concurrent queue to a worker-local priority queue that assigns higher priority to backward messages.
Backward prioritization is designed for situations when multiple IR nodes with a dependency on the IR graph end up hosted on the same worker.
As a consequence, backpropagation can complete faster and new instances can be pumped in by the controller.
We dequeue the top message and invoke the forward or backward method of the target IR node.
These methods may update internal IR node state (such as cache the state of the incoming message and wait for more messages) or post new forward or backward messages.
How to update the parameters using the gradients is a configuration option that selects amongst a range of optimization algorithms.
We have implemented runtime configuration options for selecting several well-known schemes such as (momentum-)SGD and Adam BID18 , and for controlling the training hyper-parameters.
We provide more details of the experiment and analysis in this section.
All experiments were carried out on machines with 16 cores and 112 GB of RAM.
The validation curves were averaged over at least 20 independent runs.
The time/epoch to reach a target accuracy was calculated as median of the time an algorithm takes to reach the target accuracy over the repetitions.
We found this approach to be more reliable than reporting the time/epoch when the averaged accuracy reaches the target.
TAB4 shows both the training and validation throughputs we obtained with AMPNet and our TensorFlow baselines.
We train a 4-layer perceptron with ReLUs and 784-dimensional hidden units on MNIST BID19 .
Both the AMP runtime and a TensorFlow baseline use SGD with learning rate 0.1 and batch size of 100.Figure 6(a) shows the validation accuracy vs. time, validation accuracy vs. epochs, and throughputs of synchronous and asynchronous versions of AMPNet as well as TensorFlow.
The throughput greatly increases from synchronous (max active keys = 1) to asynchronous (max active keys = 4) while the speed of convergence (middle panel) is hardly affected for mild amount of asynchrony.
Taking higher max active keys = 8 increase throughput only very little (because there is no more work) and seems to rather make the convergence more unstable.
This is due to the fact that our current scheduler is greedy and pumps in a forward message whenever the first layer is unoccupied, which leads to large gradient staleness.
Clearly a better scheduling will remove this sensitivity.
We train a vanilla RNN to perform reduction operations on variable length lists of digits.
Each training instance is a sequence of at most 10 tokens: The first token indicates which of 4 reduction operations 2 is to be performed, and the remaining tokens represent the list of digits.
The output is the result of the calculation rounded modulo 10.
The dataset consists of 10 5 training and 10 4 validation instances.
We present this task as a classification problem to a vanilla RNN with ReLU activation and a hidden dimension of 128.
All parameterized operations are affinitized on individual workers.
We bucket training instances into batches of 100 sequences (in the baseline and in AMPNet).
Figure 6(b) shows the validation accuracy vs. time and the number of epochs, and throughputs of the methods we discussed in the main text on the list reduction dataset.
We first notice that increasing the asynchrony from synchronous (max active keys=1) to max active keys = 4 and max active keys = 16 affects the convergence very little at least in average.
However, there (d) Sentiment Tree Bank (max active keys = 16) (e) QM9 dataset is also very little speedup without introducing replicas as we discussed in the main text.
Increasing the number of replicas increases the throughput almost linearly from 13k sequences/s (synchronous) to 35k sequences/s (2 replicas) and over 70k sequences/s (4 replicas).
Convergence is almost unaffected for 2 replicas.
This was rather surprising because the parameters of the replicas are only synchronized after each epoch as we described in Sec. 5.
A slight slow-down in convergence can be noticed for 4 replicas.
Since even max active keys = 16 has almost no effect on the convergence without replicas, this is not due to asynchrony.
We also tried to synchronize more frequently but this did not help.
Thus we believe that the slow-down is due to the increase in the effective minibatch size resulting in reduced number of updates per epoch, which is commonly observed in data parallel training.
We consider the sentiment classification dataset from BID35 consisting of binarized constituency parse trees of English sentences with sentiment labels at each node.
Following Tai et al. BID36 , we use 8,544 trees for training, 1,101 trees for validation, and 2,210 trees for testing.
We use a Tree LSTM for this classification task based on the TensorFlow Fold BID21 benchmark model.
Both the AMP and Fold models are trained following BID36 with the additional architectural modifications proposed by BID21 ; BID32 .
Furthermore, we split our Tree-LSTM cell into Leaf LSTM and Branch LSTM cells.
This does not affect the expressiveness of the model because the LSTM cell receives either zero input (on branch) or zero hidden states (on leaves); i.e., the two cells do not share weights except for the bias parameters, which are learned independently in our implementation.
We compare the time to reach 82 % fine grained (5 classes) accuracy (averaged over all the nodes) on the validation set.
Figure 6(c) shows the averaged fine grained validation accuracy for the tree RNN model with different max active keyson the Stanford Sentiment Tree Bank dataset.
Interestingly although TensorFlow Fold achieves higher throughput, AMPNet converges faster (in terms of the number of epochs).
This speedup is mainly due to the fact that we are not batching and updating whenever we have accumulated 50 gradients (except for the lookup table node that updates every 1000 gradients); 50 gradients correspond to roughly 2 trees.
The reason for the lower throughput compared to TensorFlow Fold is that we are only grouping the leaf operations and not the branch operations.
Grouping the branch operations is possible by extending our IR nodes and we are actively working on it.
Figure 6(d) shows the same information for fixed max active keys = 16 and different min update interval.
We can see that as we increase min update interval from the originally used 50 to larger values, the peak of the validation accuracy shifts later and lower becoming closer to the curve obtained by TensorFlow Fold.
This is consistent with the parallels between min update interval and minibatch size we drew in Section 6.
The min update interval parameter has marginal influence on the training throughput.
We study a real-world application for GNNs: prediction of organic molecule properties from structural formulae in the QM9 dataset BID30 ; BID27 .
GNNs have previously been applied to this task in BID12 .We concentrate on prediction of the norm of a molecule's dipole moment using a regression layer build on the propagation model from BID20 (corresponding to the simplest setting in BID12 ).
We use a hidden dimension of 100 and 4 propagation steps, initializing the graph nodes (atoms) following BID12 .
The molecules contain up to 29 atoms and in a TensorFlow baseline we bucket molecules into batches of 20 with atom counts differing by at most 1 within a batch.
Following BID12 , we report regression accuracies in multiples of a target accuracy from the chemistry community.
Figure 6(e) shows that GGNN can tolerate relatively large max active keys = 16, and increased the throughput significantly from 300 graphs/s (synchronous) to 1797 graphs/s (see TAB4 ).
We compare the performance of AMP training with DyNet with and without autobatching using the BiLSTM tagger w/ char model from the DyNet benchmark suite 3 .
This model consists of both character-level and word-level bidirectional LSTMs.
The model uses a learnable word embedding for frequent words (more than 5 times in the corpus) and character-level bidirectional LSTMs for infrequent words.
We use our Distribute and Collect nodes to dynamically route the messages depending on the word frequencies.
Wikiner dataset 4 is a named entity recognition dataset extracted from Wikipedia by BID26 .
We use the training/validation split provided by the DyNet benchmark suite with 142,153 and 1,696 sentences, respectively.
The result is shown in FIG5 .
We achieve more than 3x in throughput from max active keys = 1 to max active keys = 32 without any noticeable loss in the validation accuracy after 2 or 3 epochs.
The slight decrease in the validation accuracy after the third epoch is due to overfitting and it is not related to asynchrony.
In order to estimate the performance of AMPNet on a hypothetical device with 1 TFLOPS compute capability, we replaced all fully connected layers in the network with a dummy operation that simply waits for a specified time.
The dummy operation waits for 2 · k · d in · d out · 10 −12 seconds when the input is k × d in and the weight matrix is d in × d out for forward, backward, and gradient accumulation operation.
It waits for d in · d out · 10 −12 seconds for weight update.
In this way we can maintain all the data-dependent control decisions (e.g., sequence length) identical to the original network and also measure the real time spent for all the other operations.
In order to calculate the time to reach target accuracy, we take the median number of epochs the original network required to reach the target accuracy and calculate the time as time = epochs · n train throughput train + n val throughput val , where n train and n val are the number of training and validation instances, respectively, and throughput train and throughput val are the throughput for training and validation, respectively.
The results are shown in TAB5 .
For the 4-way replicated RNN, we estimate roughly 260k instances/s, which is a 3.7x speedup compared to our CPU runtime.
For tree RNN and GGSNN, we estimate milder 30 -70 % speedups mainly due to the fact that they have more complicated operations like Distribute and Collect, which we did not attempt to extrapolate the computation time because the implementation on a new hardware may be drastically different from the current CPU runtime.

assistant: text: Using asynchronous gradient updates to accelerate dynamic neural network training