communication.py

# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  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.

"""Communications utilities."""


import torch

from megatron import mpu


def broadcast_from_last_pipeline_stage(size, dtype, tensor=None):
    """Broadcast a tensor from last pipeline stage to all ranks."""

    if mpu.is_pipeline_last_stage():
        assert tensor is not None
        assert tensor.is_cuda
        assert tensor.is_contiguous()
    else:
        tensor = torch.empty(size,
                             dtype=dtype,
                             device=torch.cuda.current_device())
    # Get the group and corresponding source rank.
    src = mpu.get_pipeline_model_parallel_last_rank()
    group = mpu.get_pipeline_model_parallel_group()
    torch.distributed.broadcast(tensor, src, group)

    return tensor


def copy_from_last_to_first_pipeline_stage(size, dtype, tensor=None):
    """Copy tensor values from last stage into the first stage.
    Note that the input tensor is updated in place."""

    # Only first and last stage pipeline stages need to be involved.
    is_last_stage = mpu.is_pipeline_last_stage()
    is_first_stage = mpu.is_pipeline_first_stage()
    if is_last_stage or is_first_stage:
        src = mpu.get_pipeline_model_parallel_last_rank()
        group = mpu.get_embedding_group()
        if is_last_stage:
            assert tensor is not None
            assert tensor.is_cuda
            tensor_ = tensor.contiguous()
        else:
            tensor_ = torch.empty(size,
                                  dtype=dtype,
                                  device=torch.cuda.current_device())
        # Broadcast from last stage into the first stage.
        torch.distributed.broadcast(tensor_, src, group)
        # Update the first stage tensor
        if is_first_stage:
            tensor[...] = tensor_


def broadcast_tensor(size, dtype, tensor=None, rank=0):
    """ Given size and type of a tensor on all ranks and the tensor value
        only on a specific rank, broadcast from that rank to all other ranks.
    """

    if torch.distributed.get_rank() == rank:
        assert tensor is not None
        assert tensor.is_cuda
    else:
        tensor = torch.empty(size,
                             dtype=dtype,
                             device=torch.cuda.current_device())

    torch.distributed.broadcast(tensor, rank)

    return tensor


def broadcast_list(size, dtype, list_values=None, rank=0):
    """Broadcast a list of values with a given type."""

    tensor = None
    if torch.distributed.get_rank() == rank:
        tensor = torch.tensor(list_values, dtype=dtype,
                              device=torch.cuda.current_device())

    return broadcast_tensor(size, dtype, tensor=tensor, rank=rank)


def broadcast_int_list(size, int_list=None, rank=0):
    """Broadcast a list of interger values."""

    return broadcast_list(size, torch.int64, list_values=int_list, rank=rank)


def broadcast_float_list(size, float_list=None, rank=0):
    """Broadcast a list of float values."""

    return broadcast_list(size, torch.float32, list_values=float_list,
                          rank=rank)