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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
# The file has been adapted from the following Megatron-LM file:
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/mpu/mappings.py
# Git commit hash: 9dc3c42a84aa656f583703cf8b6b4f79f712b796
# We retain the following copyright from the original files:
# 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.
import torch
import deepspeed
from deepspeed.utils.bwc import (bwc_tensor_model_parallel_world_size, bwc_tensor_model_parallel_rank,
bwc_tensor_model_parallel_group)
def _gather_tokens(input_, dim=0):
"""Gather tensors and concatenate them along a dimension"""
mpu = deepspeed.utils.groups.mpu
input_ = input_.contiguous()
# Size and dimension.
rank = bwc_tensor_model_parallel_rank(mpu)
tensor_list = [torch.empty_like(input_) for _ in range(bwc_tensor_model_parallel_world_size(mpu))]
tensor_list[rank] = input_
deepspeed.comm.all_gather(tensor_list, input_, group=bwc_tensor_model_parallel_group(mpu))
# Note: torch.cat already creates a contiguous tensor.
output = torch.cat(tensor_list, dim=dim).contiguous()
return output
def _drop_tokens(input_, dim=0):
"""Divide a tensor among the tensor parallel ranks"""
mpu = deepspeed.utils.groups.mpu
total_chunks = bwc_tensor_model_parallel_world_size(mpu)
this_chunk = bwc_tensor_model_parallel_rank(mpu)
assert input_.shape[
dim] % total_chunks == 0, f"input dimension {dim} ({input_.shape[dim]}) is not divisible by tensor parallel world size ({total_chunks})"
chunk_size = input_.shape[dim] // total_chunks
return torch.narrow(input_, dim, this_chunk * chunk_size, chunk_size)
class _GatherTokens(torch.autograd.Function):
"""All gather tokens among the tensor parallel ranks"""
@staticmethod
def symbolic(graph, input_, dim):
return _gather_tokens(input_, dim)
@staticmethod
def forward(ctx, input_, dim):
ctx.dim = dim
return _gather_tokens(input_, dim)
@staticmethod
def backward(ctx, grad_output):
return _drop_tokens(grad_output, ctx.dim), None
class _DropTokens(torch.autograd.Function):
"Divide tokens equally among the tensor parallel ranks"
@staticmethod
def symbolic(graph, input_, dim):
return _drop_tokens(input_, dim)
@staticmethod
def forward(ctx, input_, dim):
ctx.dim = dim
return _drop_tokens(input_, dim)
@staticmethod
def backward(ctx, input_):
return _gather_tokens(input_, ctx.dim), None
def gather_tokens(input_, dim=0):
mpu = deepspeed.utils.groups.mpu
if mpu is None or bwc_tensor_model_parallel_world_size(mpu) == 1:
# no tensor parallelism for non-experts
return input_
return _GatherTokens.apply(input_, dim)
def drop_tokens(input_, dim=0):
mpu = deepspeed.utils.groups.mpu
if mpu is None or bwc_tensor_model_parallel_world_size(mpu) == 1:
# no tensor parallelism for non-experts
return input_
return _DropTokens.apply(input_, dim)
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