Source code for netket.experimental.nn.rnn.layers_fast

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# Licensed under the Apache License, Version 2.0 (the "License");
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from flax import linen as nn
from flax.linen.dtypes import promote_dtype
from jax import numpy as jnp
from jax.nn.initializers import zeros

from netket.utils.types import Array

from .layers import RNNLayer

[docs] class FastRNNLayer(RNNLayer): """ Recurrent neural network layer with fast sampling. See :class:`netket.models.FastARNNSequential` for a brief explanation of fast autoregressive sampling. """ size: int = None """number of sites."""
[docs] @nn.compact def update_site(self, inputs: Array, index: int) -> Array: """ Applies the RNN cell to a batch of input sites at a given index, and stores the updated memories in the cache. Args: inputs: an input site with dimensions (batch, features). index: the index of the output site. The index of the input site should be `index - self.exclusive`. Returns: The output site with dimensions (batch, features). """ batch_size = inputs.shape[0] inputs = promote_dtype(inputs, dtype=self.cell.param_dtype)[0] if self.reorder_idx is None: prev_neighbors = None else: prev_neighbors = jnp.asarray(self.prev_neighbors) _cell_mem = self.variable( "cache", "cell_mem", zeros, None, (batch_size, self.cell.features), inputs.dtype, ) _outputs = self.variable( "cache", "outputs", zeros, None, (batch_size, self.size, self.cell.features), inputs.dtype, ) cell_mem = _cell_mem.value outputs = _outputs.value hidden = self._extract_hidden(outputs, index, prev_neighbors) cell_mem, hidden = self.cell(inputs, cell_mem, hidden) initializing = self.is_mutable_collection("params") if not initializing: _cell_mem.value = cell_mem _outputs.value =[:, index, :].set(hidden) return hidden
[docs] def __call__(self, inputs: Array) -> Array: return RNNLayer.__call__(self, inputs)