KVStore API

.. note:: Direct interactions with KVStore are dangerous and not recommended.

Basic Push and Pull

Provides basic operation over multiple devices (GPUs) on a single device.

Initialization

Let’s consider a simple example. It initializes a (int, NDArray) pair into the store, and then pulls the value out.

>>> kv = mx.kv.create('local') # create a local kv store.
>>> shape = (2,3)
>>> kv.init(3, mx.nd.ones(shape)*2)
>>> a = mx.nd.zeros(shape)
>>> kv.pull(3, out = a)
>>> print a.asnumpy()
[[ 2.  2.  2.]
 [ 2.  2.  2.]]

Push, Aggregation, and Updater

For any key that’s been initialized, you can push a new value with the same shape to the key, as follows:

>>> kv.push(3, mx.nd.ones(shape)*8)
>>> kv.pull(3, out = a) # pull out the value
>>> print a.asnumpy()
[[ 8.  8.  8.]
 [ 8.  8.  8.]]

The data that you want to push can be stored on any device. Furthermore, you can push multiple values into the same key, where KVStore first sums all of these values, and then pushes the aggregated value, as follows:

>>> gpus = [mx.gpu(i) for i in range(4)]
>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
>>> kv.push(3, b)
>>> kv.pull(3, out = a)
>>> print a.asnumpy()
[[ 4.  4.  4.]
 [ 4.  4.  4.]]

For each push command, KVStore applies the pushed value to the value stored by an updater. The default updater is ASSIGN. You can replace the default to control how data is merged.

>>> def update(key, input, stored):
>>>     print "update on key: %d" % key
>>>     stored += input * 2
>>> kv._set_updater(update)
>>> kv.pull(3, out=a)
>>> print a.asnumpy()
[[ 4.  4.  4.]
 [ 4.  4.  4.]]
>>> kv.push(3, mx.nd.ones(shape))
update on key: 3
>>> kv.pull(3, out=a)
>>> print a.asnumpy()
[[ 6.  6.  6.]
 [ 6.  6.  6.]]

Pull

You’ve already seen how to pull a single key-value pair. Similar to the way that you use the push command, you can pull the value into several devices with a single call.

>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
>>> kv.pull(3, out = b)
>>> print b[1].asnumpy()
[[ 6.  6.  6.]
 [ 6.  6.  6.]]

List Key-Value Pairs

All of the operations that we’ve discussed so far are performed on a single key. KVStore also provides the interface for generating a list of key-value pairs. For a single device, use the following:

>>> keys = [5, 7, 9]
>>> kv.init(keys, [mx.nd.ones(shape)]*len(keys))
>>> kv.push(keys, [mx.nd.ones(shape)]*len(keys))
update on key: 5
update on key: 7
update on key: 9
>>> b = [mx.nd.zeros(shape)]*len(keys)
>>> kv.pull(keys, out = b)
>>> print b[1].asnumpy()
[[ 3.  3.  3.]
 [ 3.  3.  3.]]

For multiple devices:

>>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
>>> kv.push(keys, b)
update on key: 5
update on key: 7
update on key: 9
>>> kv.pull(keys, out = b)
>>> print b[1][1].asnumpy()
[[ 11.  11.  11.]
 [ 11.  11.  11.]]

API Reference

Key value store interface of MXNet for parameter synchronization.

class mxnet.kvstore.KVStore(handle)[source]

A key-value store for synchronization of values, over multiple devices.

init(key, value)[source]

Initializes a single or a sequence of key-value pairs into the store.

For each key, one must init it before calling push or pull. When multiple workers invoke init for the same key, only the value supplied by worker with rank 0 is used. This function returns after data has been initialized successfully.

Parameters:

Examples

>>> # init a single key-value pair
>>> shape = (2,3)
>>> kv = mx.kv.create('local')
>>> kv.init('3', mx.nd.ones(shape)*2)
>>> a = mx.nd.zeros(shape)
>>> kv.pull('3', out=a)
>>> print a.asnumpy()
[[ 2.  2.  2.]
[ 2.  2.  2.]]

>>> # init a list of key-value pairs
>>> keys = ['5', '7', '9']
>>> kv.init(keys, [mx.nd.ones(shape)]*len(keys))

>>> # init a row_sparse value
>>> kv.init('4', mx.nd.ones(shape).tostype('row_sparse'))
>>> b = mx.nd.sparse.zeros('row_sparse', shape)
>>> kv.row_sparse_pull('4', row_ids=mx.nd.array([0, 1]), out=b)
>>> print b
push(key, value, priority=0)[source]

Pushes a single or a sequence of key-value pairs into the store.

This function returns immediately after adding an operator to the engine. The actual operation is executed asynchronously. If there are consecutive pushes to the same key, there is no guarantee on the serialization of pushes. The execution of a push does not guarantee that all previous pushes are finished. There is no synchronization between workers. One can use _barrier() to sync all workers.

Parameters:
  • key (str, int, or sequence of str or int) – Keys.
  • value (NDArray, RowSparseNDArray, list of NDArray or RowSparseNDArray,) – or list of list of NDArray or RowSparseNDArray Values corresponding to the keys.
  • priority (int, optional) – The priority of the push operation. Higher priority push operations are likely to be executed before other push actions.

Examples

>>> # push a single key-value pair
>>> kv.push('3', mx.nd.ones(shape)*8)
>>> kv.pull('3', out=a) # pull out the value
>>> print a.asnumpy()
[[ 8.  8.  8.]
[ 8.  8.  8.]]

>>> # aggregate the value and the push
>>> gpus = [mx.gpu(i) for i in range(4)]
>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
>>> kv.push('3', b)
>>> kv.pull('3', out=a)
>>> print a.asnumpy()
[[ 4.  4.  4.]
[ 4.  4.  4.]]

>>> # push a list of keys.
>>> # single device
>>> keys = ['4', '5', '6']
>>> kv.push(keys, [mx.nd.ones(shape)]*len(keys))
>>> b = [mx.nd.zeros(shape)]*len(keys)
>>> kv.pull(keys, out=b)
>>> print b[1].asnumpy()
[[ 1.  1.  1.]
[ 1.  1.  1.]]

>>> # multiple devices:
>>> keys = ['7', '8', '9']
>>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
>>> kv.push(keys, b)
>>> kv.pull(keys, out=b)
>>> print b[1][1].asnumpy()
[[ 4.  4.  4.]
[ 4.  4.  4.]]

>>> # push a row_sparse value
>>> b = mx.nd.sparse.zeros('row_sparse', shape)
>>> kv.init('10', mx.nd.sparse.zeros('row_sparse', shape))
>>> kv.push('10', mx.nd.ones(shape).tostype('row_sparse'))
>>> # pull out the value
>>> kv.row_sparse_pull('10', row_ids=mx.nd.array([0, 1]), out=b)
>>> print b
pull(key, out=None, priority=0, ignore_sparse=True)[source]

Pulls a single value or a sequence of values from the store.

This function returns immediately after adding an operator to the engine. Subsequent attempts to read from the out variable will be blocked until the pull operation completes.

pull is executed asynchronously after all previous pull calls and only the last push call for the same input key(s) are finished.

The returned values are guaranteed to be the latest values in the store.

pull with RowSparseNDArray is not supported for dist kvstore. Please use row_sparse_pull instead.

Parameters:
  • key (str, int, or sequence of str or int) – Keys.
  • out (NDArray or list of NDArray or list of list of NDArray) – Values corresponding to the keys.
  • priority (int, optional) – The priority of the pull operation. Higher priority pull operations are likely to be executed before other pull actions.
  • ignore_sparse (bool, optional, default True) – Whether to ignore sparse arrays in the request.

Examples

>>> # pull a single key-value pair
>>> a = mx.nd.zeros(shape)
>>> kv.pull('3', out=a)
>>> print a.asnumpy()
[[ 2.  2.  2.]
[ 2.  2.  2.]]

>>> # pull into multiple devices
>>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
>>> kv.pull('3', out=b)
>>> print b[1].asnumpy()
[[ 2.  2.  2.]
[ 2.  2.  2.]]

>>> # pull a list of key-value pairs.
>>> # On single device
>>> keys = ['5', '7', '9']
>>> b = [mx.nd.zeros(shape)]*len(keys)
>>> kv.pull(keys, out=b)
>>> print b[1].asnumpy()
[[ 2.  2.  2.]
[ 2.  2.  2.]]
>>> # On multiple devices
>>> keys = ['6', '8', '10']
>>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
>>> kv.pull(keys, out=b)
>>> print b[1][1].asnumpy()
[[ 2.  2.  2.]
[ 2.  2.  2.]]
row_sparse_pull(key, out=None, priority=0, row_ids=None)[source]

Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull is invoked just once and the result is broadcast to all the rest of outputs.

row_sparse_pull is executed asynchronously after all previous pull/row_sparse_pull calls and the last push call for the same input key(s) are finished.

The returned values are guaranteed to be the latest values in the store.

Parameters:
  • key (str, int, or sequence of str or int) – Keys.
  • out (RowSparseNDArray or list of RowSparseNDArray or list of list of RowSparseNDArray) – Values corresponding to the keys. The stype is expected to be row_sparse
  • priority (int, optional) – The priority of the pull operation. Higher priority pull operations are likely to be executed before other pull actions.
  • row_ids (NDArray or list of NDArray) – The row_ids for which to pull for each value. Each row_id is an 1-D NDArray whose values don’t have to be unique nor sorted.

Examples

>>> shape = (3, 3)
>>> kv.init('3', mx.nd.ones(shape).tostype('row_sparse'))
>>> a = mx.nd.sparse.zeros('row_sparse', shape)
>>> row_ids = mx.nd.array([0, 2], dtype='int64')
>>> kv.row_sparse_pull('3', out=a, row_ids=row_ids)
>>> print a.asnumpy()
[[ 1.  1.  1.]
[ 0.  0.  0.]
[ 1.  1.  1.]]
>>> duplicate_row_ids = mx.nd.array([2, 2], dtype='int64')
>>> kv.row_sparse_pull('3', out=a, row_ids=duplicate_row_ids)
>>> print a.asnumpy()
[[ 0.  0.  0.]
[ 0.  0.  0.]
[ 1.  1.  1.]]
>>> unsorted_row_ids = mx.nd.array([1, 0], dtype='int64')
>>> kv.row_sparse_pull('3', out=a, row_ids=unsorted_row_ids)
>>> print a.asnumpy()
[[ 1.  1.  1.]
[ 1.  1.  1.]
[ 0.  0.  0.]]
set_gradient_compression(compression_params)[source]

Specifies type of low-bit quantization for gradient compression and additional arguments depending on the type of compression being used.

2bit Gradient Compression takes a positive float threshold. The technique works by thresholding values such that positive values in the gradient above threshold will be set to threshold. Negative values whose absolute values are higher than threshold, will be set to the negative of threshold. Values whose absolute values are less than threshold will be set to 0. By doing so, each value in the gradient is in one of three states. 2bits are used to represent these states, and every 16 float values in the original gradient can be represented using one float. This compressed representation can reduce communication costs. The difference between these thresholded values and original values is stored at the sender’s end as residual and added to the gradient in the next iteration.

When kvstore is ‘local’, gradient compression is used to reduce communication between multiple devices (gpus). Gradient is quantized on each GPU which computed the gradients, then sent to the GPU which merges the gradients. This receiving GPU dequantizes the gradients and merges them. Note that this increases memory usage on each GPU because of the residual array stored.

When kvstore is ‘dist’, gradient compression is used to reduce communication from worker to sender. Gradient is quantized on each worker which computed the gradients, then sent to the server which dequantizes this data and merges the gradients from each worker. Note that this increases CPU memory usage on each worker because of the residual array stored. Only worker to server communication is compressed in this setting. If each machine has multiple GPUs, currently this GPU to GPU or GPU to CPU communication is not compressed. Server to worker communication (in the case of pull) is also not compressed.

To use 2bit compression, we need to specify type as 2bit. Only specifying type would use default value for the threshold. To completely specify the arguments for 2bit compression, we would need to pass a dictionary which includes threshold like: {‘type’: ‘2bit’, ‘threshold’: 0.5}

Parameters:compression_params (dict) – A dictionary specifying the type and parameters for gradient compression. The key type in this dictionary is a required string argument and specifies the type of gradient compression. Currently type can be only 2bit Other keys in this dictionary are optional and specific to the type of gradient compression.
set_optimizer(optimizer)[source]

Registers an optimizer with the kvstore.

When using a single machine, this function updates the local optimizer. If using multiple machines and this operation is invoked from a worker node, it will serialized the optimizer with pickle and send it to all servers. The function returns after all servers have been updated.

Parameters:optimizer (Optimizer) – The new optimizer for the store

Examples

>>> kv = mx.kv.create()
>>> shape = (2, 2)
>>> weight = mx.nd.zeros(shape)
>>> kv.init(3, weight)
>>> # set the optimizer for kvstore as the default SGD optimizer
>>> kv.set_optimizer(mx.optimizer.SGD())
>>> grad = mx.nd.ones(shape)
>>> kv.push(3, grad)
>>> kv.pull(3, out = weight)
>>> # weight is updated via gradient descent
>>> weight.asnumpy()
array([[-0.01, -0.01],
       [-0.01, -0.01]], dtype=float32)
type

Returns the type of this kvstore.

Returns:type – the string type
Return type:str
rank

Returns the rank of this worker node.

Returns:rank – The rank of this node, which is in range [0, num_workers())
Return type:int
num_workers

Returns the number of worker nodes.

Returns:size – The number of worker nodes.
Return type:int
save_optimizer_states(fname, dump_optimizer=False)[source]

Saves the optimizer (updater) state to a file. This is often used when checkpointing the model during training.

Parameters:
  • fname (str) – Path to the output states file.
  • dump_optimizer (bool, default False) – Whether to also save the optimizer itself. This would also save optimizer information such as learning rate and weight decay schedules.
load_optimizer_states(fname)[source]

Loads the optimizer (updater) state from the file.

Parameters:fname (str) – Path to input states file.
mxnet.kvstore.create(name='local')[source]

Creates a new KVStore.

For single machine training, there are two commonly used types:

local: Copies all gradients to CPU memory and updates weights there.

device: Aggregates gradients and updates weights on GPUs. With this setting, the KVStore also attempts to use GPU peer-to-peer communication, potentially accelerating the communication.

For distributed training, KVStore also supports a number of types:

dist_sync: Behaves similarly to local but with one major difference. With dist_sync, batch-size now means the batch size used on each machine. So if there are n machines and we use batch size b, then dist_sync behaves like local with batch size n * b.

dist_device_sync: Identical to dist_sync with the difference similar to device vs local.

dist_async: Performs asynchronous updates. The weights are updated whenever gradients are received from any machine. No two updates happen on the same weight at the same time. However, the order is not guaranteed.

Parameters:name ({'local', 'device', 'nccl', 'dist_sync', 'dist_device_sync', 'dist_async'}) – The type of KVStore.
Returns:kv – The created KVStore.
Return type:KVStore

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