`BaseModule`#

class BaseModule[source]#

Bases: ABC

Base class for all Modules. Custom modules should inherit from this class.

Methods that must be overridden by subclasses#

`__init__`	BaseModule constructor, must be overridden by subclasses.
`is_ready`	Return True if the module is initialized and ready for training or inference.
`_get_callable`	Returns a `jax.jit`-able and `jax.grad`-able callable that represents the module's forward pass.
`compile`	Compile the module to be used with the given input shape.
`get_output_shape`	Get the output shape of the module given the input shape.
`get_hyperparameters`	Get the hyperparameters of the module.
`get_params`	Get the current trainable parameters of the module.
`set_params`	Set the trainable parameters of the module.

abstractmethod BaseModule.__init__()[source]#

BaseModule constructor, must be overridden by subclasses.

All modules must be able to be initialized without any arguments in order for Model saving and loading to work correctly.

__init__ can take optional parameters, but all aspects of the module must be able to be set by set_hyperparameters, set_params, and set_state.

Always raises NotImplementedError when called on BaseModule s.

BaseModule is not meant to be instantiated directly.

Return type:: None

abstractmethod BaseModule.is_ready()[source]#

Return True if the module is initialized and ready for training or inference.

Returns:: True if the module is ready, False otherwise.
Raises:: NotImplementedError – If the method is not implemented in the subclass.
Return type:: bool

abstractmethod BaseModule._get_callable()[source]#

Returns a jax.jit-able and jax.grad-able callable that represents the module’s forward pass.

This method must be implemented by all subclasses and must return a jax-jit-able and jax-grad-able callable in the form of

module_callable(
    params: parametricmatrixmodels.typing.Params,
    data: parametricmatrixmodels.typing.Data,
    training: bool,
    state: parametricmatrixmodels.typing.State,
    rng: Any,
) -> (
    output: parametricmatrixmodels.typing.Data,
    new_state: parametricmatrixmodels.typing.State,
    )

That is, all hyperparameters are traced out and the callable depends explicitly only on

the module’s parameters, as a PyTree with leaf nodes as JAX arrays,
the input data, as a PyTree with leaf nodes as JAX arrays, each of
which has shape (num_samples, …),
the training flag, as a boolean,
the module’s state, as a PyTree with leaf nodes as JAX arrays

and returns

the output data, as a PyTree with leaf nodes as JAX arrays, each of
which has shape (num_samples, …),
the new module state, as a PyTree with leaf nodes as JAX arrays. The
PyTree structure must match that of the input state and additionally all leaf nodes must have the same shape as the input state leaf nodes.

The training flag will be traced out, so it doesn’t need to be jittable

Returns:

A callable that takes the module’s parameters, input data,
training flag, state, and rng key and returns the output data and
new state.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Return type:

pmm.typing.ModuleCallable

See also

__call__: Calls the module with the current parameters and given input, state, and rng.
ModuleCallable: Typing for the callable returned by this method.
Params: Typing for the module parameters.
Data: Typing for the input and output data.
State: Typing for the module state.

abstractmethod BaseModule.compile(rng, input_shape, /)[source]#

Compile the module to be used with the given input shape.

This method initializes the module’s parameters and state based on the input shape and random key.

This is needed since Model s are built before the input data is given, so before training or inference can be done, the module needs to be compiled and each module passes its output shape to the next module’s compile method.

The RNG key is used to initialize random parameters, if needed.

This is not used to trace or jit the module’s callable, that is done automatically later.

Parameters:

rng (Any) – JAX random key.
input_shape (pmm.typing.DataShape) – PyTree of input shape tuples, e.g. ((num_features,),), to compile the module for. All data passed to the module later must have the same PyTree structure and shape in all leaf array dimensions except the leading batch dimension.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Return type:

None

See also

DataShape: Typing for the input shape.
get_output_shape: Get the output shape of the module

abstractmethod BaseModule.get_output_shape(input_shape, /)[source]#

Get the output shape of the module given the input shape.

Parameters:

input_shape (pmm.typing.DataShape) – PyTree of input shape tuples, e.g. ((num_features,),), to get the output shape for.

Returns:

PyTree of output shape tuples, e.g. ((num_output_features,),),
corresponding to the output shape of the module for the given
input shape.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Return type:

pmm.typing.DataShape

See also

DataShape: Typing for the input and output shape.

abstractmethod BaseModule.get_hyperparameters()[source]#

Get the hyperparameters of the module.

Hyperparameters are used to configure the module and are not trainable. They can be set via set_hyperparameters.

Returns:: Dictionary containing the hyperparameters of the module.
Return type:: pmm.typing.HyperParams

See also

set_hyperparameters: Set the hyperparameters of the module.
HyperParams: Typing for the hyperparameters. Simply an alias for Dict[str, Any].

abstractmethod BaseModule.get_params()[source]#

Get the current trainable parameters of the module. If the module has no trainable parameters, this method should return an empty tuple.

Returns:

PyTree with leaf nodes as JAX arrays representing the module’s
trainable parameters.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Return type:

pmm.typing.Params

See also

set_params: Set the trainable parameters of the module.
Params: Typing for the module parameters.

abstractmethod BaseModule.set_params(params, /)[source]#

Set the trainable parameters of the module.

Parameters:: params (pmm.typing.Params) – PyTree with leaf nodes as JAX arrays representing the new trainable parameters of the module.
Raises:: NotImplementedError – If the method is not implemented in the subclass.
Return type:: None

See also

get_params: Get the trainable parameters of the module.
Params: Typing for the module parameters.

Methods with default implementations#

`name`	Returns the name of the module, unless overridden, this is the class name.
`__repr__`	Returns a string representation of the module.
`__call__`	Call the module with the current parameters and given input, state, and rng.
`set_hyperparameters`	Set the hyperparameters of the module.
`get_num_trainable_floats`	Returns the number of trainable floats in the module.
`get_state`	Get the current state of the module.
`set_state`	Set the state of the module.
`set_precision`	Set the precision of the module parameters and state.
`astype`	Convenience wrapper to set_precision using the dtype argument, returns self.
`serialize`	Serialize the module to a dictionary.
`deserialize`	Deserialize the module from a dictionary.

property BaseModule.name: str#

Returns the name of the module, unless overridden, this is the class name.

Returns:: Name of the module.

BaseModule.__repr__()[source]#

Returns a string representation of the module. Unless overridden, this includes the module name, number of trainable floats (if any), and whether the module is initialized (ready) or not.

Returns:: String representation of the module.
Return type:: str

BaseModule.__call__(data, /, *, training=False, state=(), rng=None)[source]#

Call the module with the current parameters and given input, state, and rng.

Parameters:

data (pmm.typing.Data) – PyTree of input arrays of shape (num_samples, …). Only the first dimension (num_samples) is guaranteed to be the same for all input arrays.
training (bool) – Whether the module is in training mode, by default False.
state (pmm.typing.State) – State of the module, by default ().
rng (Any) – JAX random key, by default None.

Returns:

Output array of shape (num_samples, num_output_features) and new
state.

Raises:

ValueError – If the module is not ready (i.e., compile() has not been called).

Return type:

tuple[TypeAliasForwardRef(’pmm.typing.Data’), TypeAliasForwardRef(’pmm.typing.State’)]

See also

_get_callable: Returns a callable that can be used to compute the output and new state given the parameters, input, training flag, state, and rng.
Params: Typing for the module parameters.
Data: Typing for the input and output data.
State: Typing for the module state.

BaseModule.set_hyperparameters(hyperparameters, /)[source]#

Set the hyperparameters of the module.

Hyperparameters are used to configure the module and are not trainable. They can be set via this method.

The default implementation uses setattr to set the hyperparameters as attributes of the class instance.

Parameters:: hyperparameters (pmm.typing.HyperParams) – Dictionary containing the hyperparameters to set.
Raises:: TypeError – If hyperparameters is not a dictionary.
Return type:: None

See also

get_hyperparameters: Get the hyperparameters of the module.
HyperParams: Typing for the hyperparameters. Simply an alias for Dict[str, Any].

BaseModule.get_num_trainable_floats()[source]#

Returns the number of trainable floats in the module. If the module does not have trainable parameters, returns 0. If the module is not ready, returns None.

Returns:

Number of trainable floats in the module, or None if the module
is not ready.

Return type:

int | None

BaseModule.get_state()[source]#

Get the current state of the module.

States are used to store “memory” or other information that is not passed between modules, is not trainable, but may be updated during either training or inference. e.g. batch normalization state.

The state is optional, in which case this method should return the empty tuple.

Returns:

PyTree with leaf nodes as JAX arrays representing the module’s
state.

Return type:

pmm.typing.State

See also

set_state: Set the state of the module.
State: Typing for the module state.

BaseModule.set_state(state, /)[source]#

Set the state of the module.

This method is optional.

Parameters:: state (pmm.typing.State) – PyTree with leaf nodes as JAX arrays representing the new state of the module.
Return type:: None

See also

get_state: Get the state of the module.
State: Typing for the module state.

BaseModule.set_precision(prec, /)[source]#

Set the precision of the module parameters and state.

Parameters:

prec (Any | str | int) – Precision to set for the module parameters. Valid options are: For 32-bit precision (all options are equivalent) np.float32, np.complex64, "float32", "complex64", "single", "f32", "c64", 32. For 64-bit precision (all options are equivalent) np.float64, np.complex128, "float64", "complex128", "double", "f64", "c128", 64.

Raises:

ValueError – If the precision is invalid or if 64-bit precision is requested but JAX_ENABLE_X64 is not set.
RuntimeError – If the module is not ready (i.e., compile() has not been called).

Return type:

None

See also

astype: Convenience wrapper to set_precision using the dtype argument, returns self.

BaseModule.astype(dtype, /)[source]#

Convenience wrapper to set_precision using the dtype argument, returns self.

Parameters:

dtype (str | type[Any] | dtype | SupportsDType) – Precision to set for the module parameters. Valid options are: For 32-bit precision (all options are equivalent) np.float32, np.complex64, "float32", "complex64", "single", "f32", "c64", 32 For 64-bit precision (all options are equivalent) np.float64, np.complex128, "float64", "complex128", "double", "f64", "c128", 64

Returns:

BaseModule – The module itself, with updated precision.

Raises:

ValueError – If the precision is invalid or if 64-bit precision is requested but JAX_ENABLE_X64 is not set.
RuntimeError – If the module is not ready (i.e., compile() has not been called).

Return type:

BaseModule

See also

set_precision: Sets the precision of the module parameters and state.

BaseModule.serialize()[source]#

Serialize the module to a dictionary.

This method returns a dictionary representation of the module, including its parameters and state.

The default implementation serializes the module’s name, hyperparameters, trainable parameters, and state via a simple dictionary.

This only works if the module’s hyperparameters are auto-serializable. This includes basic types as well as numpy arrays.

Returns:: Dictionary containing the serialized module data.
Return type:: dict[str, Any]

BaseModule.deserialize(data, /)[source]#

Deserialize the module from a dictionary.

This method sets the module’s parameters and state based on the provided dictionary.

The default implementation expects the dictionary to contain the module’s name, trainable parameters, and state.

Parameters:: data (dict[str, Any]) – Dictionary containing the serialized module data.
Raises:: ValueError – If the serialized data does not contain the expected keys or if the version of the serialized data is not compatible with with the current package version.
Return type:: None

BaseModule#

Methods that must be overridden by subclasses#

Methods with default implementations#

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`BaseModule`#