tf_utils.py

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.

from typing import List, Union, Optional

import numpy as np
import tensorflow as tf

import logging


logger = logging.getLogger(__name__)


def set_tensor_by_indices_to_value(
    tensor: tf.Tensor, indices: tf.Tensor, value: Union[tf.Tensor, int, float]
):
    # create value_tensor since tensor value assignment is not possible in TF
    return tf.where(indices, value, tensor)


def shape_list(tensor: Union[tf.Tensor, np.ndarray]) -> List[int]:
    """
    Deal with dynamic shape in tensorflow cleanly.

    Args:
        tensor (`tf.Tensor` or `np.ndarray`): The tensor we want the shape of.

    Returns:
        `List[int]`: The shape of the tensor as a list.
    """
    if isinstance(tensor, np.ndarray):
        return list(tensor.shape)

    dynamic = tf.shape(tensor)

    if tensor.shape == tf.TensorShape(None):
        return dynamic

    static = tensor.shape.as_list()

    return [dynamic[i] if s is None else s for i, s in enumerate(static)]


def stable_softmax(
    logits: tf.Tensor, axis: Optional[int] = None, name: Optional[str] = None
) -> tf.Tensor:
    """
    Stable wrapper that returns the same output as `tf.nn.softmax`, but that works reliably with XLA on CPU. It is
    meant as a workaround for the [following issue](https://github.com/tensorflow/tensorflow/issues/55682), and will be
    removed after it gets fixed. The arguments and outputs are the same as `tf.nn.softmax`, and relies on the fact that
    `softmax(x) = softmax(x + c)` (see https://ogunlao.github.io/2020/04/26/you_dont_really_know_softmax.html).
    Args:
        logits (`tf.Tensor`):
            Must be one of the following types: half, float32, float64.
        axis (`int`, *optional*):
            The dimension softmax would be performed on. The default is -1 which indicates the last dimension.
        name (`str`, *optional*):
            A name for the operation.
    Returns:
        `tf.Tensor`:
            A Tensor. Has the same type and shape as logits.
    """
    # TODO: When the issue linked above gets sorted, add a check on TF version here and use the original function if
    # it has the fix. After we drop the support for unfixed versions, remove this function.
    return tf.nn.softmax(
        logits=logits + tf.constant(1e-9, dtype=logits.dtype), axis=axis, name=name
    )