Table Of Contents
Table Of Contents

Source code for mxnet.module.bucketing_module

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# pylint: disable=too-many-instance-attributes, too-many-arguments, protected-access
# pylint: disable=too-many-public-methods
"""A `BucketingModule` implement the `BaseModule` API, and allows multiple
symbols to be used depending on the `bucket_key` provided by each different
mini-batch of data.
"""

import logging
import warnings

from .. import context as ctx

from ..initializer import Uniform

from .base_module import BaseModule, _check_input_names
from .module import Module
from ..name import NameManager

[docs]class BucketingModule(BaseModule): """This module helps to deal efficiently with varying-length inputs. Parameters ---------- sym_gen : function A function when called with a bucket key, returns a triple ``(symbol, data_names, label_names)``. default_bucket_key : str (or any python object) The key for the default bucket. logger : Logger context : Context or list of Context Defaults to ``mx.cpu()`` work_load_list : list of number Defaults to ``None``, indicating uniform workload. fixed_param_names: list of str Defaults to ``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. """
[docs] def __init__(self, sym_gen, default_bucket_key=None, logger=logging, context=ctx.cpu(), work_load_list=None, fixed_param_names=None, state_names=None, group2ctxs=None, compression_params=None): super(BucketingModule, self).__init__(logger=logger) assert default_bucket_key is not None self._default_bucket_key = default_bucket_key self._sym_gen = sym_gen symbol, data_names, label_names = self._call_sym_gen(default_bucket_key) 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) self._compression_params = compression_params self._fixed_param_names = fixed_param_names self._state_names = state_names self._context = context self._work_load_list = work_load_list self._group2ctxs = group2ctxs self._buckets = {} self._curr_module = None self._curr_bucket_key = None self._params_dirty = False self._monitor = None
def _reset_bind(self): """Internal utility function to reset binding.""" self.binded = False self._buckets = {} self._curr_module = None self._curr_bucket_key = None def _call_sym_gen(self, *args, **kwargs): with NameManager(): return self._sym_gen(*args, **kwargs) @property def data_names(self): """A list of names for data required by this module.""" if self.binded: return self._curr_module.data_names else: _, data_names, _ = self._call_sym_gen(self._default_bucket_key) return data_names @property def output_names(self): """A list of names for the outputs of this module.""" if self.binded: return self._curr_module.output_names else: symbol, _, _ = self._call_sym_gen(self._default_bucket_key) return symbol.list_outputs() @property def data_shapes(self): """Get data shapes. Returns ------- A list of `(name, shape)` pairs. """ assert self.binded return self._curr_module.data_shapes @property def label_shapes(self): """Get 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._curr_module.label_shapes @property def output_shapes(self): """Gets output shapes. Returns ------- A list of `(name, shape)` pairs. """ assert self.binded return self._curr_module.output_shapes 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 self._curr_module._params_dirty = self._params_dirty params = self._curr_module.get_params() self._params_dirty = False return params def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False): """Assigns parameters and aux state values. Parameters ---------- arg_params : dict Dictionary of name to value (`NDArray`) mapping. aux_params : dict Dictionary of name to value (`NDArray`) mapping. 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) 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._curr_module.set_params(arg_params, aux_params, allow_missing=allow_missing, force_init=force_init, allow_extra=allow_extra) # because we didn't update self._arg_params, they are dirty now. self._params_dirty = True self.params_initialized = True def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False): """Initializes parameters. Parameters ---------- initializer : Initializer arg_params : dict Defaults to ``None``. Existing parameters. This has higher priority than `initializer`. aux_params : dict Defaults to ``None``. Existing auxiliary states. This has higher priority than `initializer`. allow_missing : bool Allow missing values in `arg_params` and `aux_params` (if not ``None``). In this case, missing values will be filled with `initializer`. force_init : bool Defaults to ``False``. 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: return assert self.binded, 'call bind before initializing the parameters' self._curr_module.init_params(initializer=initializer, arg_params=arg_params, aux_params=aux_params, allow_missing=allow_missing, force_init=force_init, allow_extra=allow_extra) self._params_dirty = False self.params_initialized = True def get_states(self, merge_multi_context=True): """Gets states from all devices. 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 NDArrays or list of list of NDArrays 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`. """ assert self.binded and self.params_initialized return self._curr_module.get_states(merge_multi_context=merge_multi_context) def set_states(self, states=None, value=None): """Sets value for states. Only one of states & values 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._curr_module.set_states(states, value) def bind(self, data_shapes, label_shapes=None, for_training=True, inputs_need_grad=False, force_rebind=False, shared_module=None, grad_req='write'): """Binding for a `BucketingModule` means setting up the buckets and binding the executor for the default bucket key. Executors corresponding to other keys are bound afterwards with `switch_bucket`. Parameters ---------- data_shapes : list of (str, tuple) This should correspond to the symbol for the default bucket. label_shapes : list of (str, tuple) This should correspond to the symbol for the default bucket. for_training : bool Default is ``True``. inputs_need_grad : bool Default is ``False``. force_rebind : bool Default is ``False``. shared_module : BucketingModule Default is ``None``. This value is currently not used. grad_req : str, list of str, dict of str to str Requirement for gradient accumulation. Can be 'write', 'add', or 'null' (default to 'write'). Can be specified globally (str) or for each argument (list, dict). bucket_key : str (or any python object) bucket key for binding. by default use the default_bucket_key """ # in case we already initialized params, keep it if self.params_initialized: arg_params, aux_params = self.get_params() # 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 assert shared_module is None, 'shared_module for BucketingModule is not supported' self.for_training = for_training self.inputs_need_grad = inputs_need_grad self.binded = True symbol, data_names, label_names = self._call_sym_gen(self._default_bucket_key) module = Module(symbol, data_names, label_names, logger=self.logger, context=self._context, work_load_list=self._work_load_list, fixed_param_names=self._fixed_param_names, state_names=self._state_names, group2ctxs=self._group2ctxs, compression_params=self._compression_params) module.bind(data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind=False, shared_module=None, grad_req=grad_req) self._curr_module = module self._curr_bucket_key = self._default_bucket_key self._buckets[self._default_bucket_key] = module # copy back saved params, if already initialized if self.params_initialized: self.set_params(arg_params, aux_params) def switch_bucket(self, bucket_key, data_shapes, label_shapes=None): """Switches to a different bucket. This will change ``self.curr_module``. Parameters ---------- bucket_key : str (or any python object) The key of the target bucket. data_shapes : list of (str, tuple) Typically ``data_batch.provide_data``. label_shapes : list of (str, tuple) Typically ``data_batch.provide_label``. """ assert self.binded, 'call bind before switching bucket' if not bucket_key in self._buckets: symbol, data_names, label_names = self._call_sym_gen(bucket_key) module = Module(symbol, data_names, label_names, logger=self.logger, context=self._context, work_load_list=self._work_load_list, fixed_param_names=self._fixed_param_names, state_names=self._state_names, group2ctxs=self._group2ctxs, compression_params=self._compression_params) module.bind(data_shapes, label_shapes, self._curr_module.for_training, self._curr_module.inputs_need_grad, force_rebind=False, shared_module=self._buckets[self._default_bucket_key]) if self._monitor is not None: module.install_monitor(self._monitor) self._buckets[bucket_key] = module self._curr_module = self._buckets[bucket_key] self._curr_bucket_key = bucket_key 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 Defaults to `'local'`. optimizer : str or Optimizer Defaults to `'sgd'` optimizer_params : dict Defaults to `(('learning_rate', 0.01),)`. The default value is not a dictionary, just to avoid pylint warning of dangerous default values. force_init : bool Defaults to ``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 self._curr_module.init_optimizer(kvstore, optimizer, optimizer_params, force_init=force_init) for mod in self._buckets.values(): if mod is not self._curr_module: mod.borrow_optimizer(self._curr_module) self.optimizer_initialized = True 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. 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. ''' # perform bind if haven't done so assert self.binded and self.params_initialized bucket_key = data_batch.bucket_key original_bucket_key = self._curr_bucket_key data_shapes = data_batch.provide_data label_shapes = data_batch.provide_label self.switch_bucket(bucket_key, data_shapes, label_shapes) self._curr_module.prepare(data_batch, sparse_row_id_fn=sparse_row_id_fn) # switch back self.switch_bucket(original_bucket_key, None, None) def forward(self, data_batch, is_train=None): """Forward computation. Parameters ---------- data_batch : DataBatch is_train : bool Defaults to ``None``, in which case `is_train` is take as ``self.for_training``. """ assert self.binded and self.params_initialized self.switch_bucket(data_batch.bucket_key, data_batch.provide_data, data_batch.provide_label) self._curr_module.forward(data_batch, is_train=is_train) def backward(self, out_grads=None): """Backward computation.""" assert self.binded and self.params_initialized self._curr_module.backward(out_grads=out_grads) def update(self): """Updates parameters according to installed optimizer and the gradient computed in the previous forward-backward cycle. 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. """ assert self.binded and self.params_initialized and self.optimizer_initialized self._params_dirty = True self._curr_module.update() def get_outputs(self, merge_multi_context=True): """Gets outputs from a previous forward computation. Parameters ---------- merge_multi_context : bool Defaults to ``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 numpy arrays or list of list of numpy arrays 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 numpy arrays. """ assert self.binded and self.params_initialized return self._curr_module.get_outputs(merge_multi_context=merge_multi_context) def get_input_grads(self, merge_multi_context=True): """Gets the gradients with respect to the inputs of the module. Parameters ---------- merge_multi_context : bool Defaults to ``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 NDArrays or list of list of NDArrays 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`. """ assert self.binded and self.params_initialized and self.inputs_need_grad return self._curr_module.get_input_grads(merge_multi_context=merge_multi_context) def update_metric(self, eval_metric, labels, pre_sliced=False): """Evaluates and accumulates evaluation metric on outputs of the last forward computation. Parameters ---------- eval_metric : EvalMetric labels : list of NDArray Typically ``data_batch.label``. """ assert self.binded and self.params_initialized self._curr_module.update_metric(eval_metric, labels, pre_sliced) @property def symbol(self): """The symbol of the current bucket being used.""" assert self.binded return self._curr_module.symbol def install_monitor(self, mon): """Installs monitor on all executors """ assert self.binded self._monitor = mon for mod in self._buckets.values(): mod.install_monitor(mon)