transformers-4.33.3/src/transformers/utils/model_parallel_utils.py

# coding=utf-8
# Copyright 2020 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 math import ceil


def assert_device_map(device_map, num_blocks):
    blocks = list(range(0, num_blocks))

    device_map_blocks = [item for sublist in list(device_map.values()) for item in sublist]

    # Duplicate check
    duplicate_blocks = []
    for i in device_map_blocks:
        if device_map_blocks.count(i) > 1 and i not in duplicate_blocks:
            duplicate_blocks.append(i)
    # Missing blocks
    missing_blocks = [i for i in blocks if i not in device_map_blocks]
    extra_blocks = [i for i in device_map_blocks if i not in blocks]

    if len(duplicate_blocks) != 0:
        raise ValueError(
            "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
            " These attention blocks were specified more than once: " + str(duplicate_blocks)
        )
    if len(missing_blocks) != 0:
        raise ValueError(
            "There are attention blocks for this model that are not specified in the device_map. Add these attention "
            "blocks to a device on the device_map: " + str(missing_blocks)
        )
    if len(extra_blocks) != 0:
        raise ValueError(
            "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
            + str(extra_blocks)
        )


def get_device_map(n_layers, devices):
    """Returns a dictionary of layers distributed evenly across all devices."""
    layers = list(range(n_layers))
    n_blocks = int(ceil(n_layers / len(devices)))
    layers_list = [layers[i : i + n_blocks] for i in range(0, n_layers, n_blocks)]

    return dict(zip(devices, layers_list))