Intelegentny_Pszczelarz/.venv/Lib/site-packages/jax/_src/lax/stack.py
2023-06-19 00:49:18 +02:00

79 lines
2.5 KiB
Python

# Copyright 2021 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
"""A bounded functional stack implementation.
Used as a helper for expressing recursive algorithms such as QDWH-eig for
Eigendecomposition on TPU.
"""
from __future__ import annotations
from typing import Any, Tuple
import jax
from jax import lax
import jax.numpy as jnp
class Stack:
"""A bounded functional stack implementation. Elements may be pytrees."""
def __init__(self, size, data):
"""Private constructor."""
self._size = size
self._data = data
def __repr__(self):
return f"Stack({self._size}, {self._data})"
@staticmethod
def create(capacity: int, prototype: Any) -> Stack:
"""Creates a stack with size `capacity` with elements like `prototype`.
`prototype` can be any JAX pytree. This function looks only at its
structure; the specific values are ignored.
"""
return Stack(
jnp.array(0, jnp.int32),
jax.tree_util.tree_map(
lambda x: jnp.zeros((capacity,) + tuple(x.shape), x.dtype), prototype))
def empty(self) -> Any:
"""Returns true if the stack is empty."""
return self._size == 0
def push(self, elem: Any) -> Stack:
"""Pushes `elem` onto the stack, returning the updated stack."""
return Stack(
self._size + 1,
jax.tree_util.tree_map(
lambda x, y: lax.dynamic_update_index_in_dim(x, y, self._size, 0),
self._data, elem))
def pop(self) -> Tuple[Any, Stack]:
"""Pops from the stack, returning an (elem, updated stack) pair."""
elem = jax.tree_util.tree_map(
lambda x: lax.dynamic_index_in_dim(x, self._size - 1, 0, keepdims=False),
self._data)
return elem, Stack(self._size - 1, self._data)
def flatten(self):
leaves, treedef = jax.tree_util.tree_flatten(self._data)
return ([self._size] + leaves), treedef
@staticmethod
def unflatten(treedef, leaves):
return Stack(leaves[0], jax.tree_util.tree_unflatten(treedef, leaves[1:]))
jax.tree_util.register_pytree_node(Stack, Stack.flatten, Stack.unflatten)