Source code for mxnet.module.module

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

# pylint: disable=too-many-instance-attributes, too-many-arguments, protected-access, too-many-branches
# pylint: disable=too-many-public-methods
"""A `Module` implement the `BaseModule` API by wrapping a `Symbol` and one or
more `Executor` for data parallelization.
"""

import logging
import warnings

from .. import context as ctx
from .. import optimizer as opt
from .. import ndarray as nd

from .executor_group import DataParallelExecutorGroup
from ..model import _create_kvstore, _initialize_kvstore, _update_params, _update_params_on_kvstore
from ..model import load_checkpoint
from ..initializer import Uniform, InitDesc
from ..io import DataDesc
from ..ndarray import zeros

from .base_module import BaseModule, _check_input_names, _parse_data_desc

[docs]class Module(BaseModule): """Module is a basic module that wrap a `Symbol`. It is functionally the same as the `FeedForward` model, except under the module API. Parameters ---------- symbol : Symbol data_names : list of str Defaults to `('data')` for a typical model used in image classification. label_names : list of str Defaults to `('softmax_label')` for a typical model used in image classification. logger : Logger Defaults to `logging`. context : Context or list of Context Defaults to ``mx.cpu()``. work_load_list : list of number Default ``None``, indicating uniform workload. fixed_param_names: list of str Default ``None``, indicating no network parameters are fixed. state_names : list of str states are similar to data and label, but not provided by data iterator. Instead they are initialized to 0 and can be set by `set_states()`. group2ctxs : dict of str to context or list of context, or list of dict of str to context Default is `None`. Mapping the `ctx_group` attribute to the context assignment. compression_params : dict Specifies type of gradient compression and additional arguments depending on the type of compression being used. For example, 2bit compression requires a threshold. Arguments would then be {'type':'2bit', 'threshold':0.5} See mxnet.KVStore.set_gradient_compression method for more details on gradient compression. """ def __init__(self, symbol, data_names=('data',), label_names=('softmax_label',), logger=logging, context=ctx.cpu(), work_load_list=None, fixed_param_names=None, state_names=None, group2ctxs=None, compression_params=None): super(Module, self).__init__(logger=logger) if isinstance(context, ctx.Context): context = [context] self._context = context if work_load_list is None: work_load_list = [1] * len(self._context) assert len(work_load_list) == len(self._context) self._work_load_list = work_load_list self._group2ctxs = group2ctxs self._symbol = symbol data_names = list(data_names) if data_names is not None else [] label_names = list(label_names) if label_names is not None else [] state_names = list(state_names) if state_names is not None else [] fixed_param_names = list(fixed_param_names) if fixed_param_names is not None else [] _check_input_names(symbol, data_names, "data", True) _check_input_names(symbol, label_names, "label", False) _check_input_names(symbol, state_names, "state", True) _check_input_names(symbol, fixed_param_names, "fixed_param", True) arg_names = symbol.list_arguments() input_names = data_names + label_names + state_names self._param_names = [x for x in arg_names if x not in input_names] self._fixed_param_names = fixed_param_names self._aux_names = symbol.list_auxiliary_states() self._data_names = data_names self._label_names = label_names self._state_names = state_names self._output_names = symbol.list_outputs() self._arg_params = None self._aux_params = None self._params_dirty = False self._compression_params = compression_params self._optimizer = None self._kvstore = None self._update_on_kvstore = None self._updater = None self._preload_opt_states = None self._grad_req = None self._exec_group = None self._data_shapes = None self._label_shapes = None @staticmethod
[docs] def load(prefix, epoch, load_optimizer_states=False, **kwargs): """Creates a model from previously saved checkpoint. Parameters ---------- prefix : str path prefix of saved model files. You should have "prefix-symbol.json", "prefix-xxxx.params", and optionally "prefix-xxxx.states", where xxxx is the epoch number. epoch : int epoch to load. load_optimizer_states : bool whether to load optimizer states. Checkpoint needs to have been made with save_optimizer_states=True. data_names : list of str Default is `('data')` for a typical model used in image classification. label_names : list of str Default is `('softmax_label')` for a typical model used in image classification. logger : Logger Default is `logging`. context : Context or list of Context Default is ``cpu()``. work_load_list : list of number Default ``None``, indicating uniform workload. fixed_param_names: list of str Default ``None``, indicating no network parameters are fixed. """ sym, args, auxs = load_checkpoint(prefix, epoch) mod = Module(symbol=sym, **kwargs) mod._arg_params = args mod._aux_params = auxs mod.params_initialized = True if load_optimizer_states: mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch) return mod
[docs] def save_checkpoint(self, prefix, epoch, save_optimizer_states=False): """Saves current progress to checkpoint. Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training. Parameters ---------- prefix : str The file prefix to checkpoint to. epoch : int The current epoch number. save_optimizer_states : bool Whether to save optimizer states to continue training. """ self._symbol.save('%s-symbol.json'%prefix) param_name = '%s-%04d.params' % (prefix, epoch) self.save_params(param_name) logging.info('Saved checkpoint to \"%s\"', param_name) if save_optimizer_states: state_name = '%s-%04d.states' % (prefix, epoch) self.save_optimizer_states(state_name) logging.info('Saved optimizer state to \"%s\"', state_name)
def _reset_bind(self): """Internal function to reset binded state.""" self.binded = False self._exec_group = None self._data_shapes = None self._label_shapes = None @property def data_names(self): """A list of names for data required by this module.""" return self._data_names @property def label_names(self): """A list of names for labels required by this module.""" return self._label_names @property def output_names(self): """A list of names for the outputs of this module.""" return self._output_names @property def data_shapes(self): """Gets data shapes. Returns ------- A list of `(name, shape)` pairs. """ assert self.binded return self._data_shapes @property def label_shapes(self): """Gets label shapes. Returns ------- A list of `(name, shape)` pairs. The return value could be ``None`` if the module does not need labels, or if the module is not bound for training (in this case, label information is not available). """ assert self.binded return self._label_shapes @property def output_shapes(self): """Gets output shapes. Returns ------- A list of `(name, shape)` pairs. """ assert self.binded return self._exec_group.get_output_shapes()
[docs] def get_params(self): """Gets current parameters. Returns ------- `(arg_params, aux_params)` A pair of dictionaries each mapping parameter names to NDArray values. """ assert self.binded and self.params_initialized if self._params_dirty: self._sync_params_from_devices() return (self._arg_params, self._aux_params)
[docs] def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False): """Initializes the parameters and auxiliary states. Parameters ---------- initializer : Initializer Called to initialize parameters if needed. arg_params : dict If not ``None``, should be a dictionary of existing arg_params. Initialization will be copied from that. aux_params : dict If not ``None``, should be a dictionary of existing aux_params. Initialization will be copied from that. allow_missing : bool If ``True``, params could contain missing values, and the initializer will be called to fill those missing params. force_init : bool If ``True``, will force re-initialize even if already initialized. allow_extra : boolean, optional Whether allow extra parameters that are not needed by symbol. If this is True, no error will be thrown when arg_params or aux_params contain extra parameters that is not needed by the executor. """ if self.params_initialized and not force_init: warnings.warn("Parameters already initialized and force_init=False. " "init_params call ignored.", stacklevel=2) return assert self.binded, 'call bind before initializing the parameters' def _impl(name, arr, cache): """Internal helper for parameter initialization""" if cache is not None: if name in cache: cache_arr = cache[name] # just in case the cached array is just the target itself if cache_arr is not arr: cache_arr.copyto(arr) else: if not allow_missing: raise RuntimeError("%s is not presented" % name) if initializer is not None: initializer(name, arr) else: initializer(name, arr) attrs = self._symbol.attr_dict() for name, arr in sorted(self._arg_params.items()): desc = InitDesc(name, attrs.get(name, None)) _impl(desc, arr, arg_params) for name, arr in sorted(self._aux_params.items()): desc = InitDesc(name, attrs.get(name, None)) _impl(desc, arr, aux_params) self.params_initialized = True self._params_dirty = False # copy the initialized parameters to devices self._exec_group.set_params(self._arg_params, self._aux_params, allow_extra=allow_extra)
[docs] def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False): """Assigns parameter and aux state values. Parameters ---------- arg_params : dict Dictionary of name to `NDArray`. aux_params : dict Dictionary of name to `NDArray`. allow_missing : bool If ``True``, params could contain missing values, and the initializer will be called to fill those missing params. force_init : bool If ``True``, will force re-initialize even if already initialized. allow_extra : boolean, optional Whether allow extra parameters that are not needed by symbol. If this is True, no error will be thrown when arg_params or aux_params contain extra parameters that is not needed by the executor. Examples -------- >>> # An example of setting module parameters. >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, n_epoch_load) >>> mod.set_params(arg_params=arg_params, aux_params=aux_params) """ if not allow_missing: self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params, allow_missing=allow_missing, force_init=force_init, allow_extra=allow_extra) return if self.params_initialized and not force_init: warnings.warn("Parameters already initialized and force_init=False. " "set_params call ignored.", stacklevel=2) return self._exec_group.set_params(arg_params, aux_params, allow_extra=allow_extra) # because we didn't update self._arg_params, they are dirty now. self._params_dirty = True self.params_initialized = True
[docs] def bind(self, data_shapes, label_shapes=None, for_training=True, inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'): """Binds the symbols to construct executors. This is necessary before one can perform computation with the module. Parameters ---------- data_shapes : list of (str, tuple) Typically is ``data_iter.provide_data``. label_shapes : list of (str, tuple) Typically is ``data_iter.provide_label``. for_training : bool Default is ``True``. Whether the executors should be bound for training. inputs_need_grad : bool Default is ``False``. Whether the gradients to the input data need to be computed. Typically this is not needed. But this might be needed when implementing composition of modules. force_rebind : bool Default is ``False``. This function does nothing if the executors are already bound. But with this ``True``, the executors will be forced to rebind. shared_module : Module Default is ``None``. This is used in bucketing. When not ``None``, the shared module essentially corresponds to a different bucket -- a module with different symbol but with the same sets of parameters (e.g. unrolled RNNs with different lengths). """ # force rebinding is typically used when one want to switch from # training to prediction phase. if force_rebind: self._reset_bind() if self.binded: self.logger.warning('Already bound, ignoring bind()') return self.for_training = for_training self.inputs_need_grad = inputs_need_grad self.binded = True self._grad_req = grad_req if not for_training: assert not inputs_need_grad else: pass # this is not True, as some module might not contains a loss function # that consumes the labels # assert label_shapes is not None self._data_shapes, self._label_shapes = _parse_data_desc( self.data_names, self.label_names, data_shapes, label_shapes) if shared_module is not None: assert isinstance(shared_module, Module) and \ shared_module.binded and shared_module.params_initialized shared_group = shared_module._exec_group assert len(shared_group.execs) >= len(self._context) else: shared_group = None self._exec_group = DataParallelExecutorGroup(self._symbol, self._context, self._work_load_list, self._data_shapes, self._label_shapes, self._param_names, for_training, inputs_need_grad, shared_group, logger=self.logger, fixed_param_names=self._fixed_param_names, grad_req=grad_req, group2ctxs=self._group2ctxs, state_names=self._state_names) self._total_exec_bytes = self._exec_group._total_exec_bytes if shared_module is not None: self.params_initialized = True self._arg_params = shared_module._arg_params self._aux_params = shared_module._aux_params elif self.params_initialized: # if the parameters are already initialized, we are re-binding # so automatically copy the already initialized params self._exec_group.set_params(self._arg_params, self._aux_params) else: assert self._arg_params is None and self._aux_params is None param_arrays = [ zeros(shape=x[0].shape, dtype=x[0].dtype, stype=x[0].stype) for x in self._exec_group.param_arrays ] self._arg_params = {name:arr for name, arr in zip(self._param_names, param_arrays)} aux_arrays = [ zeros(x[0].shape, dtype=x[0].dtype) for x in self._exec_group.aux_arrays ] self._aux_params = {name:arr for name, arr in zip(self._aux_names, aux_arrays)} if shared_module is not None and shared_module.optimizer_initialized: self.borrow_optimizer(shared_module)
[docs] def reshape(self, data_shapes, label_shapes=None): """Reshapes the module for new input shapes. Parameters ---------- data_shapes : list of (str, tuple) Typically is ``data_iter.provide_data``. label_shapes : list of (str, tuple) Typically is ``data_iter.provide_label``. """ assert self.binded self._data_shapes, self._label_shapes = _parse_data_desc( self.data_names, self.label_names, data_shapes, label_shapes) self._exec_group.reshape(self._data_shapes, self._label_shapes)
[docs] def init_optimizer(self, kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), force_init=False): """Installs and initializes optimizers. Parameters ---------- kvstore : str or KVStore Default `'local'`. optimizer : str or Optimizer Default `'sgd'` optimizer_params : dict Default `(('learning_rate', 0.01),)`. The default value is not a dictionary, just to avoid pylint warning of dangerous default values. force_init : bool Default ``False``, indicating whether we should force re-initializing the optimizer in the case an optimizer is already installed. """ assert self.binded and self.params_initialized if self.optimizer_initialized and not force_init: self.logger.warning('optimizer already initialized, ignoring...') return if self._params_dirty: self._sync_params_from_devices() (kvstore, update_on_kvstore) = \ _create_kvstore(kvstore, len(self._context), self._arg_params) batch_size = self._exec_group.batch_size if kvstore and 'dist' in kvstore.type and '_sync' in kvstore.type: batch_size *= kvstore.num_workers rescale_grad = 1.0/batch_size if isinstance(optimizer, str): idx2name = {} if update_on_kvstore: idx2name.update(enumerate(self._exec_group.param_names)) else: for k in range(len(self._context)): idx2name.update({i*len(self._context)+k: n for i, n in enumerate(self._exec_group.param_names)}) optimizer_params = dict(optimizer_params) if 'rescale_grad' not in optimizer_params: optimizer_params['rescale_grad'] = rescale_grad optimizer = opt.create(optimizer, sym=self.symbol, param_idx2name=idx2name, **optimizer_params) else: assert isinstance(optimizer, opt.Optimizer) if optimizer.rescale_grad != rescale_grad: #pylint: disable=no-member warnings.warn( "Optimizer created manually outside Module but rescale_grad " + "is not normalized to 1.0/batch_size/num_workers (%s vs. %s). "%( optimizer.rescale_grad, rescale_grad) + "Is this intended?", stacklevel=2) self._optimizer = optimizer self._kvstore = kvstore self._update_on_kvstore = update_on_kvstore self._updater = None if kvstore: if self._compression_params: kvstore.set_gradient_compression(self._compression_params) if update_on_kvstore: kvstore.set_optimizer(self._optimizer) # copy initialized local parameters to kvstore _initialize_kvstore(kvstore=kvstore, param_arrays=self._exec_group.param_arrays, arg_params=self._arg_params, param_names=self._param_names, update_on_kvstore=update_on_kvstore) if not update_on_kvstore: self._updater = opt.get_updater(optimizer) self.optimizer_initialized = True if self._preload_opt_states is not None: self.load_optimizer_states(self._preload_opt_states) self._preload_opt_states = None
[docs] def borrow_optimizer(self, shared_module): """Borrows optimizer from a shared module. Used in bucketing, where exactly the same optimizer (esp. kvstore) is used. Parameters ---------- shared_module : Module """ assert shared_module.optimizer_initialized self._optimizer = shared_module._optimizer self._kvstore = shared_module._kvstore self._update_on_kvstore = shared_module._update_on_kvstore self._updater = shared_module._updater self.optimizer_initialized = True
[docs] def forward(self, data_batch, is_train=None): """Forward computation. It supports data batches with different shapes, such as different batch sizes or different image sizes. If reshaping of data batch relates to modification of symbol or module, such as changing image layout ordering or switching from training to predicting, module rebinding is required. See Also ---------- :meth:`BaseModule.forward`. Parameters ---------- data_batch : DataBatch Could be anything with similar API implemented. is_train : bool Default is ``None``, which means ``is_train`` takes the value of ``self.for_training``. """ assert self.binded and self.params_initialized curr_data_shapes = tuple(i.shape for i in self._data_shapes) if isinstance(data_batch, list): assert data_batch is not None, "Encountered empty data batch" new_data_shapes = [] for i in range(len(data_batch[0].data)): shape = data_batch[0].data[i].shape for db in data_batch: assert shape == db.data[i].shape, \ "All data batches in a list need to have the same shape" new_batch_size = len(data_batch) * shape[0] new_data_shapes.append((new_batch_size,) + shape[1:]) new_data_shapes = tuple(new_data_shapes) else: new_data_shapes = tuple(i.shape for i in data_batch.data) if curr_data_shapes != new_data_shapes: if hasattr(data_batch, "provide_data") and data_batch.provide_data: new_dshape = data_batch.provide_data else: new_dshape = [DataDesc(i.name, shape, i.dtype, i.layout) \ for i, shape in zip(self._data_shapes, new_data_shapes)] if hasattr(data_batch, "provide_label") and data_batch.provide_label: new_lshape = data_batch.provide_label elif hasattr(data_batch, "label") and data_batch.label: new_lshape = [DataDesc(i.name, j.shape, i.dtype, i.layout) \ for i, j in zip(self._label_shapes, data_batch.label)] else: new_lshape = None self.reshape(new_dshape, new_lshape) self._exec_group.forward(data_batch, is_train)
[docs] def backward(self, out_grads=None): """Backward computation. See Also ---------- :meth:`BaseModule.backward`. Parameters ---------- out_grads : NDArray or list of NDArray, optional Gradient on the outputs to be propagated back. This parameter is only needed when bind is called on outputs that are not a loss function. """ assert self.binded and self.params_initialized self._exec_group.backward(out_grads=out_grads)
[docs] def update(self): """Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch. When KVStore is used to update parameters for multi-device or multi-machine training, a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters, this function does update the copy of parameters in KVStore, but doesn't broadcast the updated parameters to all devices / machines. Please call `prepare` to broadcast `row_sparse` parameters with the next batch of data. See Also ---------- :meth:`BaseModule.update`. """ assert self.binded and self.params_initialized and self.optimizer_initialized self._params_dirty = True if self._update_on_kvstore: _update_params_on_kvstore(self._exec_group.param_arrays, self._exec_group.grad_arrays, self._kvstore, self._exec_group.param_names) else: _update_params(self._exec_group.param_arrays, self._exec_group.grad_arrays, updater=self._updater, num_device=len(self._context), kvstore=self._kvstore, param_names=self._exec_group.param_names)
[docs] def get_outputs(self, merge_multi_context=True): """Gets outputs of the previous forward computation. If ``merge_multi_context`` is ``True``, it is like ``[out1, out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output elements are `NDArray`. When `merge_multi_context` is `False`, those `NDArray` might live on different devices. Parameters ---------- merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the outputs will be collected from multiple devices. A ``True`` value indicate that we should merge the collected results so that they look like from a single executor. Returns ------- list of NDArray or list of list of NDArray Output. """ assert self.binded and self.params_initialized return self._exec_group.get_outputs(merge_multi_context=merge_multi_context)
[docs] def get_input_grads(self, merge_multi_context=True): """Gets the gradients with respect to the inputs of the module. If ``merge_multi_context`` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output elements are `NDArray`. Parameters ---------- merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the outputs will be collected from multiple devices. A ``True`` value indicate that we should merge the collected results so that they look like from a single executor. Returns ------- list of NDArray or list of list of NDArray Input gradients """ assert self.binded and self.params_initialized and self.inputs_need_grad return self._exec_group.get_input_grads(merge_multi_context=merge_multi_context)
[docs] def get_states(self, merge_multi_context=True): """Gets states from all devices. If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output elements are `NDArray`. Parameters ---------- merge_multi_context : bool Default is ``True``. In the case when data-parallelism is used, the states will be collected from multiple devices. A ``True`` value indicate that we should merge the collected results so that they look like from a single executor. Returns ------- list of NDArray or list of list of NDArray States """ assert self.binded and self.params_initialized return self._exec_group.get_states(merge_multi_context=merge_multi_context)
[docs] def set_states(self, states=None, value=None): """Sets value for states. Only one of the states & value can be specified. Parameters ---------- states : list of list of NDArrays source states arrays formatted like ``[[state1_dev1, state1_dev2], [state2_dev1, state2_dev2]]``. value : number a single scalar value for all state arrays. """ assert self.binded and self.params_initialized self._exec_group.set_states(states, value)
[docs] def update_metric(self, eval_metric, labels, pre_sliced=False): """Evaluates and accumulates evaluation metric on outputs of the last forward computation. See Also ---------- :meth:`BaseModule.update_metric`. Parameters ---------- eval_metric : EvalMetric Evaluation metric to use. labels : list of NDArray if `pre_sliced` parameter is set to `False`, list of lists of NDArray otherwise. Typically `data_batch.label`. pre_sliced: bool Whether the labels are already sliced per device (default: False). """ self._exec_group.update_metric(eval_metric, labels, pre_sliced)
def _sync_params_from_devices(self): """Synchronizes parameters from devices to CPU. This function should be called after calling `update` that updates the parameters on the devices, before one can read the latest parameters from ``self._arg_params`` and ``self._aux_params``. For row_sparse parameters on devices, ther are pulled from KVStore with all row ids. """ self._exec_group.get_params(self._arg_params, self._aux_params) if self._kvstore and self._update_on_kvstore: for param_name, param_val in sorted(self._arg_params.items()): if param_val.stype == 'row_sparse': row_ids = nd.arange(0, param_val.shape[0], dtype='int64') self._kvstore.row_sparse_pull(param_name, param_val, row_ids=row_ids) self._params_dirty = False
[docs] def save_optimizer_states(self, fname): """Saves optimizer (updater) state to a file. Parameters ---------- fname : str Path to output states file. """ assert self.optimizer_initialized if self._update_on_kvstore: self._kvstore.save_optimizer_states(fname) else: with open(fname, 'wb') as fout: fout.write(self._updater.get_states())
[docs] def load_optimizer_states(self, fname): """Loads optimizer (updater) state from a file. Parameters ---------- fname : str Path to input states file. """ assert self.optimizer_initialized if self._update_on_kvstore: self._kvstore.load_optimizer_states(fname) else: self._updater.set_states(open(fname, 'rb').read())
[docs] def install_monitor(self, mon): """Installs monitor on all executors. """ assert self.binded self._exec_group.install_monitor(mon)
[docs] def prepare(self, data_batch, sparse_row_id_fn=None): '''Prepares the module for processing a data batch. Usually involves switching bucket and reshaping. For modules that contain `row_sparse` parameters in KVStore, it prepares the `row_sparse` parameters based on the sparse_row_id_fn. When KVStore is used to update parameters for multi-device or multi-machine training, a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters, the `update()` updates the copy of parameters in KVStore, but doesn't broadcast the updated parameters to all devices / machines. The `prepare` function is used to broadcast `row_sparse` parameters with the next batch of data. Parameters ---------- data_batch : DataBatch The current batch of data for forward computation. sparse_row_id_fn : A callback function The function takes `data_batch` as an input and returns a dict of str -> NDArray. The resulting dict is used for pulling row_sparse parameters from the kvstore, where the str key is the name of the param, and the value is the row id of the param to pull. ''' assert self.binded if sparse_row_id_fn is not None: if not self._kvstore or not self._update_on_kvstore: warnings.warn(UserWarning("Parameters are not updated in the KVStore. " "No need to call sparse_row_id_fn.")) else: row_ids = sparse_row_id_fn(data_batch) assert(isinstance(row_ids, dict)), "Expected dict output from sparse_row_id_fn" for param_name, row_id in row_ids.items(): param_idx = self._exec_group.param_names.index(param_name) param_val = self._exec_group.param_arrays[param_idx] assert(isinstance(param_val, (tuple, list))) if param_val[0].stype != 'row_sparse': warnings.warn(UserWarning("%s.stype is not 'row_sparse'. No need to " "perform row_sparse_pull." % param_name)) else: self._kvstore.row_sparse_pull(param_name, param_val, row_ids=row_id, priority=-param_idx)