Source code for netket.nn.blocks.mlp

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Callable, Union, Optional

import jax
import jax.numpy as jnp

from flax import linen as nn
from jax.nn.initializers import lecun_normal, zeros

from netket.utils.types import NNInitFunc, DType
from netket import nn as nknn

default_kernel_init = lecun_normal()
default_bias_init = zeros




class MLP(nn.Module):
    r"""A Multi-Layer Perceptron with hidden layers.

    This combines multiple dense layers and activations functions into a single object.
    It separates the output layer from the hidden layers,
    since it typically has a different form.
    One can specify the specific activation functions per layer.
    The size of the hidden dimensions can be provided as a number,
    or as a factor relative to the input size (similar as for RBM).
    The default model is a single linear layer without activations.

    Forms a common building block for models such as
    `PauliNet (continuous) <https://www.nature.com/articles/s41557-020-0544-y>`_
    """
    output_dim: int = 1
    """The output dimension"""
    hidden_dims: Optional[Union[int, tuple[int, ...]]] = None
    """The size of the hidden layers, excluding the output layer."""
    hidden_dims_alpha: Optional[Union[int, tuple[int, ...]]] = None
    """The size of the hidden layers provided as number of times the input size.
    One must choose to either specify this or the hidden_dims keyword argument"""
    param_dtype: DType = jnp.float64
    """The dtype of the weights."""
    hidden_activations: Optional[Union[Callable, tuple[Callable, ...]]] = nknn.gelu
    """The nonlinear activation function after each hidden layer.
    Can be provided as a single activation,
    where the same activation will be used for every layer."""
    output_activation: Optional[Callable] = None
    """The nonlinear activation at the output layer.
    If None is provided, the output layer will be essentially linear."""
    use_hidden_bias: bool = True
    """If True uses a bias in the hidden layer."""
    use_output_bias: bool = True
    """If True adds a bias to the output layer."""
    precision: Optional[jax.lax.Precision] = None
    """Numerical precision of the computation see :class:`jax.lax.Precision` for details."""
    kernel_init: NNInitFunc = default_kernel_init
    """Initializer for the Dense layer matrix."""
    bias_init: NNInitFunc = default_bias_init
    """Initializer for the biases."""



    @nn.compact
    def __call__(self, input):
        if self.hidden_dims is None:
            if self.hidden_dims_alpha is not None:
                hidden_dims = [
                    int(nh * input.shape[-1]) for nh in self.hidden_dims_alpha
                ]
            else:
                hidden_dims = []
        else:
            if self.hidden_dims_alpha is not None:
                raise ValueError(
                    "Cannot specify both hidden_dims and alpha_hidden_dims, "
                    "choose one way to provide the hidden dimensions"
                )
            hidden_dims = self.hidden_dims

        if self.hidden_activations is None:
            hidden_activations = [None] * len(hidden_dims)
        elif hasattr(self.hidden_activations, "__len__"):
            hidden_activations = self.hidden_activations
        else:
            hidden_activations = [self.hidden_activations] * len(hidden_dims)

        if len(hidden_activations) != len(hidden_dims):
            raise ValueError(
                "number of hidden activations must be the same "
                "as the length of the hidden dimensions list"
            )

        x = input

        # hidden layers
        for nh, act_h in zip(hidden_dims, hidden_activations):
            x = nn.Dense(
                features=nh,
                param_dtype=self.param_dtype,
                precision=self.precision,
                use_bias=self.use_hidden_bias,
                kernel_init=self.kernel_init,
                bias_init=self.bias_init,
            )(x)
            if act_h:
                x = act_h(x)

        # output layer
        x = nn.Dense(
            features=self.output_dim,
            param_dtype=self.param_dtype,
            precision=self.precision,
            use_bias=self.use_output_bias,
            kernel_init=self.kernel_init,
            bias_init=self.bias_init,
        )(x)
        if self.output_activation:
            x = self.output_activation(x)

        return x