NAME
AI::MXNet::Module::Base - Base class for AI::MXNet::Module and AI::MXNet::Module::Bucketing
DESCRIPTION
The base class of a modules. A module represents a computation component. The design
purpose of a module is that it abstract a computation "machine", that one can run forward,
backward, update parameters, etc. We aim to make the APIs easy to use, especially in the
case when we need to use imperative API to work with multiple modules (e.g. stochastic
depth network).
A module has several states:
- Initial state. Memory is not allocated yet, not ready for computation yet.
- Binded. Shapes for inputs, outputs, and parameters are all known, memory allocated,
ready for computation.
- Parameter initialized. For modules with parameters, doing computation before initializing
the parameters might result in undefined outputs.
- Optimizer installed. An optimizer can be installed to a module. After this, the parameters
of the module can be updated according to the optimizer after gradients are computed
(forward-backward).
In order for a module to interact with others, a module should be able to report the
following information in its raw stage (before binded)
- data_names: array ref of string indicating the names of required data.
- output_names: array ref of string indicating the names of required outputs.
And also the following richer information after binded:
- state information
- binded: bool, indicating whether the memory buffers needed for computation
has been allocated.
- for_training: whether the module is binded for training (if binded).
- params_initialized: bool, indicating whether the parameters of this modules
has been initialized.
- optimizer_initialized: bool, indicating whether an optimizer is defined
and initialized.
- inputs_need_grad: bool, indicating whether gradients with respect to the
input data is needed. Might be useful when implementing composition of modules.
- input/output information
- data_shapes: am array ref of [name, shape]. In theory, since the memory is allocated,
we could directly provide the data arrays. But in the case of data parallelization,
the data arrays might not be of the same shape as viewed from the external world.
- label_shapes: an array ref of [name, shape]. This might be [] if the module does
not need labels (e.g. it does not contains a loss function at the top), or a module
is not binded for training.
- output_shapes: an array ref of [name, shape] for outputs of the module.
- parameters (for modules with parameters)
- get_params(): return an array ($arg_params, $aux_params). Each of those
is a hash ref of name to NDArray mapping. Those NDArrays always on
CPU. The actual parameters used for computing might be on other devices (GPUs),
this function will retrieve (a copy of) the latest parameters. Therefore, modifying
- get_params($arg_params, $aux_params): assign parameters to the devices
doing the computation.
- init_params(...): a more flexible interface to assign or initialize the parameters.
- setup
- bind(): prepare environment for computation.
- init_optimizer(): install optimizer for parameter updating.
- computation
- forward(data_batch): forward operation.
- backward(out_grads=): backward operation.
- update(): update parameters according to installed optimizer.
- get_outputs(): get outputs of the previous forward operation.
- get_input_grads(): get the gradients with respect to the inputs computed
in the previous backward operation.
- update_metric(metric, labels): update performance metric for the previous forward
computed results.
- other properties (mostly for backward compatability)
- symbol: the underlying symbolic graph for this module (if any)
This property is not necessarily constant. For example, for AI::MXNet::Module::Bucketing,
this property is simply the *current* symbol being used. For other modules,
this value might not be well defined.
When those intermediate-level API are implemented properly, the following
high-level API will be automatically available for a module:
- fit: train the module parameters on a data set
- predict: run prediction on a data set and collect outputs
- score: run prediction on a data set and evaluate performance
forward_backward
A convenient function that calls both forward and backward.
score
Run prediction on eval_data and evaluate the performance according to
eval_metric.
Parameters
----------
$eval_data : AI::MXNet::DataIter
$eval_metric : AI::MXNet::EvalMetric
:$num_batch= : Maybe[Int]
Number of batches to run. Default is undef, indicating run until the AI::MXNet::DataIter
finishes.
:$batch_end_callback= : Maybe[Callback]
Could also be a array ref of functions.
:$reset=1 : Bool
Default 1, indicating whether we should reset $eval_data before starting
evaluating.
$epoch=0 : Int
Default is 0. For compatibility, this will be passed to callbacks (if any). During
training, this will correspond to the training epoch number.
iter_predict
Iterate over predictions.
Parameters
----------
$eval_data : AI::MXNet::DataIter
:$num_batch= : Maybe[Int]
Default is undef, indicating running all the batches in the data iterator.
:$reset=1 : bool
Default is 1, indicating whether we should reset the data iter before start
doing prediction.
predict
Run prediction and collect the outputs.
Parameters
----------
$eval_data : AI::MXNet::DataIter
:$num_batch= : Maybe[Int]
Default is undef, indicating running all the batches in the data iterator.
:$merge_batches=1 : Bool
Default is 1.
:$reset=1 : Bool
Default is 1, indicating whether we should reset the data iter before start
doing prediction.
:$always_output_list=0 : Bool
Default is 0, see the doc for return values.
Returns
-------
When $merge_batches is 1 (by default), the return value will be an array ref
[$out1, $out2, $out3] where each element is concatenation of the outputs for
all the mini-batches. If $always_output_list` also is 0 (by default),
then in the case of a single output, $out1 is returned in stead of [$out1].
When $merge_batches is 0, the return value will be a nested array ref like
[[$out1_batch1, $out2_batch1], [$out1_batch2], ...]. This mode is useful because
in some cases (e.g. bucketing), the module does not necessarily produce the same
number of outputs.
The objects in the results are AI::MXNet::NDArray`s. If you need to work with pdl array,
just call ->aspdl() on each AI::MXNet::NDArray.
fit
Train the module parameters.
Parameters
----------
$train_data : AI::MXNet::DataIter
:$eval_data= : Maybe[AI::MXNet::DataIter]
If not undef, it will be used as a validation set to evaluate the performance
after each epoch.
:$eval_metric='acc' : str or AI::MXNet::EvalMetric subclass object.
Default is 'accuracy'. The performance measure used to display during training.
Other possible predefined metrics are:
'ce' (CrossEntropy), 'f1', 'mae', 'mse', 'rmse', 'top_k_accuracy'
:$epoch_end_callback= : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions.
Each callback will be called with the current $epoch, $symbol, $arg_params
and $aux_params.
:$batch_end_callback= : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions.
Each callback will be called with a AI::MXNet::BatchEndParam.
:$kvstore='local' : str or AI::MXNet::KVStore
Default is 'local'.
:$optimizer : str or AI::MXNet::Optimizer
Default is 'sgd'
:$optimizer_params : hash ref
Default { learning_rate => 0.01 }.
The parameters for the optimizer constructor.
:$eval_end_callback= : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions
These will be called at the end of each full evaluation, with the metrics over
the entire evaluation set.
:$eval_batch_end_callback : Maybe[Callback]|ArrayRef[Callback] function or array ref of functions
These will be called at the end of each minibatch during evaluation
:$initializer= : Initializer
Will be called to initialize the module parameters if not already initialized.
:$arg_params= : hash ref
Default undef, if not undef, must be an existing parameters from a trained
model or loaded from a checkpoint (previously saved model). In this case,
the value here will be used to initialize the module parameters, unless they
are already initialized by the user via a call to init_params or fit.
$arg_params have higher priority than the $initializer.
:$aux_params= : hash ref
Default is undef. This is similar to the $arg_params, except for auxiliary states.
:$allow_missing=0 : Bool
Default is 0. Indicates whether we allow missing parameters when $arg_params
and $aux_params are not undefined. If this is 1, then the missing parameters
will be initialized via the $initializer.
:$force_rebind=0 : Bool
Default is 0. Whether to force rebinding the executors if already binded.
:$force_init=0 : Bool
Default is 0. Indicates whether we should force initialization even if the
parameters are already initialized.
:$begin_epoch=0 : Int
Default is 0. Indicates the starting epoch. Usually, if we are resuming from a
checkpoint saved at a previous training phase at epoch N, then we should specify
this value as N+1.
:$num_epoch : Int
Number of epochs for the training.
get_symbol
The symbol used by this module.
data_names
An array ref of names for data required by this module.
output_names
An array ref of names for the outputs of this module.
data_shapes
An array ref of AI::MXNet::DataDesc objects specifying the data inputs to this module.
label_shapes
A array ref of AI::MXNet::DataDesc objects specifying the label inputs to this module.
If this module does not accept labels -- either it is a module without a loss
function, or it is not binded for training, then this should return an empty
array ref.
output_shapes
An array ref of (name, shape) array refs specifying the outputs of this module.
get_params
The parameters, these are potentially a copies of the the actual parameters used
to do computation on the device.
Returns
-------
($arg_params, $aux_params), a pair of hash refs of name to value mapping.
init_params
Initialize the parameters and auxiliary states.
Parameters
----------
:$initializer : Maybe[AI::MXNet::Initializer]
Called to initialize parameters if needed.
:$arg_params= : Maybe[HashRef[AI::MXNet::NDArray]]
If not undef, should be a hash ref of existing arg_params.
:$aux_params : Maybe[HashRef[AI::MXNet::NDArray]]
If not undef, should be a hash ref of existing aux_params.
:$allow_missing=0 : Bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
:$force_init=0 : Bool
If true, will force re-initialize even if already initialized.
set_params
Assign parameter and aux state values.
Parameters
----------
$arg_params= : Maybe[HashRef[AI::MXNet::NDArray]]
Hash ref of name to value (NDArray) mapping.
$aux_params= : Maybe[HashRef[AI::MXNet::NDArray]]
Hash Ref of name to value (`NDArray`) mapping.
:$allow_missing=0 : Bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
:$force_init=0 : Bool
If true, will force re-initialize even if already initialized.
save_params
Save model parameters to file.
Parameters
----------
$fname : str
Path to output param file.
$arg_params= : Maybe[HashRef[AI::MXNet::NDArray]]
$aux_params= : Maybe[HashRef[AI::MXNet::NDArray]]
load_params
Load model parameters from file.
Parameters
----------
$fname : str
Path to input param file.
get_states
The states from all devices
Parameters
----------
$merge_multi_context=1 : Bool
Default is true (1). 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
-------
If $merge_multi_context is 1, it is like [$out1, $out2]. Otherwise, it
is like [[$out1_dev1, $out1_dev2], [$out2_dev1, $out2_dev2]]. All the output
elements are AI::MXNet::NDArray.
set_states
Set value for states. You can specify either $states or $value, not both.
Parameters
----------
$states= : Maybe[ArrayRef[ArrayRef[AI::MXNet::NDArray]]]
source states arrays formatted like [[$state1_dev1, $state1_dev2],
[$state2_dev1, $state2_dev2]].
$value= : Maybe[Num]
a single scalar value for all state arrays.
install_monitor
Install monitor on all executors
Parameters
----------
$mon : AI::MXNet::Monitor
prepare
Prepare the module for processing a data batch.
Usually involves switching a bucket and reshaping.
Parameters
----------
$data_batch : AI::MXNet::DataBatch
forward
Forward computation.
Parameters
----------
$data_batch : DataBatch
Could be anything with similar API implemented.
:$is_train= : Bool
Default is undef, which means is_train takes the value of $self->for_training.
backward
Backward computation.
Parameters
----------
$out_grads : Maybe[AI::MXNet::NDArray|ArrayRef[AI::MXNet::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.
get_outputs
The outputs of the previous forward computation.
Parameters
----------
$merge_multi_context=1 : Bool
The gradients to the inputs, computed in the previous backward computation.
Parameters
----------
$merge_multi_context=1 : Bool
update
Update parameters according to the installed optimizer and the gradients computed
in the previous forward-backward batch.
update_metric
Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
$eval_metric : EvalMetric
$labels : ArrayRef[AI::MXNet::NDArray]
Typically $data_batch->label.
bind
Binds the symbols in order to construct the executors. This is necessary
before the computations can be performed.
Parameters
----------
$data_shapes : ArrayRef[AI::MXNet::DataDesc]
Typically is $data_iter->provide_data.
:$label_shapes= : Maybe[ArrayRef[AI::MXNet::DataDesc]]
Typically is $data_iter->provide_label.
:$for_training=1 : Bool
Default is 1. Whether the executors should be bind for training.
:$inputs_need_grad=0 : Bool
Default is 0. Whether the gradients to the input data need to be computed.
Typically this is not needed. But this might be needed when implementing composition
of modules.
:$force_rebind=0 : Bool
Default is 0. This function does nothing if the executors are already
binded. But with this as 1, the executors will be forced to rebind.
:$shared_module= : A subclass of AI::MXNet::Module::Base
Default is undef. This is used in bucketing. When not undef, the shared module
essentially corresponds to a different bucket -- a module with different symbol
but with the same sets of parameters (e.g. unrolled RNNs with different lengths).
:$grad_req='write' : Str|ArrayRef[Str]|HashRef[Str]
Requirement for gradient accumulation. Can be 'write', 'add', or 'null'
(defaults to 'write').
Can be specified globally (str) or for each argument (array ref, hash ref).
init_optimizer
Install and initialize optimizers.
Parameters
----------
:$kvstore='local' : str or KVStore
:$optimizer='sgd' : str or Optimizer
:$optimizer_params={ learning_rate => 0.01 } : hash ref
:$force_init=0 : Bool
symbol
The symbol associated with this module.
Except for AI::MXNet::Module, for other types of modules (e.g. AI::MXNet::Module::Bucketing), this
property might not be a constant throughout its life time. Some modules might
not even be associated with any symbols.