Source code for flax.training.common_utils

# Copyright 2022 The Flax 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
#
#     http://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.

"""Common utilty functions used in data-parallel Flax examples.

This module is a historical grab-bag of utility functions primarily concerned
with helping write pmap-based data-parallel training loops.
"""

import jax
from jax import lax
import jax.numpy as jnp
import numpy as np


[docs]def shard(xs): """Helper for pmap to shard a pytree of arrays by local_device_count. Args: xs: a pytree of arrays. Returns: A matching pytree with arrays' leading dimensions sharded by the local device count. """ local_device_count = jax.local_device_count() return jax.tree_util.tree_map( lambda x: x.reshape((local_device_count, -1) + x.shape[1:]), xs)
[docs]def shard_prng_key(prng_key): """Helper to shard (aka split) a PRNGKey for use with pmap'd functions. PRNG keys can used at train time to drive stochastic modules e.g. Dropout. We would like a different PRNG key for each local device so that we end up with different random numbers on each one, hence we split our PRNG key. Args: prng_key: JAX PRNGKey Returns: A new array of PRNGKeys with leading dimension equal to local device count. """ return jax.random.split(prng_key, num=jax.local_device_count())
[docs]def stack_forest(forest): """Helper function to stack the leaves of a sequence of pytrees. Args: forest: a sequence of pytrees (e.g tuple or list) of matching structure whose leaves are arrays with individually matching shapes. Returns: A single pytree of the same structure whose leaves are individually stacked arrays. """ stack_args = lambda *args: np.stack(args) return jax.tree_util.tree_map(stack_args, *forest)
[docs]def get_metrics(device_metrics): """Helper utility for pmap, gathering replicated timeseries metric data. Args: device_metrics: replicated, device-resident pytree of metric data, whose leaves are presumed to be a sequence of arrays recorded over time. Returns: A pytree of unreplicated, host-resident, stacked-over-time arrays useful for computing host-local statistics and logging. """ # We select the first element of x in order to get a single copy of a # device-replicated metric. device_metrics = jax.tree_util.tree_map(lambda x: x[0], device_metrics) metrics_np = jax.device_get(device_metrics) return stack_forest(metrics_np)
[docs]def onehot(labels, num_classes, on_value=1.0, off_value=0.0): """Create a dense one-hot version of an indexed array. NB: consider using the more standard `jax.nn.one_hot` instead. Args: labels: an n-dim JAX array whose last dimension contains integer indices. num_classes: the maximum possible index. on_value: the "on" value for the one-hot array, defaults to 1.0. off_value: the "off" value for the one-hot array, defaults to 0.0. Returns: A (n+1)-dim array whose last dimension contains one-hot vectors of length num_classes. """ x = (labels[..., None] == jnp.arange(num_classes).reshape((1,) * labels.ndim + (-1,))) x = lax.select(x, jnp.full(x.shape, on_value), jnp.full(x.shape, off_value)) return x.astype(jnp.float32)