wehub-resource-sync
119 lines
4.0 KiB
Python
119 lines
4.0 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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# The file has been adapted from the following Megatron-LM file:
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# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/mpu/mappings.py
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# Git commit hash: 9dc3c42a84aa656f583703cf8b6b4f79f712b796
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# We retain the following copyright from the original files:
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import deepspeed
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from deepspeed.utils.bwc import (bwc_tensor_model_parallel_world_size, bwc_tensor_model_parallel_rank,
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bwc_tensor_model_parallel_group)
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def _gather_tokens(input_, dim=0):
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"""Gather tensors and concatenate them along a dimension"""
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mpu = deepspeed.utils.groups.mpu
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input_ = input_.contiguous()
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world_size = bwc_tensor_model_parallel_world_size(mpu)
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if world_size == 1:
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return input_
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gather_buffer = torch.empty(world_size * input_.numel(), dtype=input_.dtype, device=input_.device)
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deepspeed.comm.all_gather_into_tensor(gather_buffer, input_, group=bwc_tensor_model_parallel_group(mpu))
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if dim == 0:
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shape = list(input_.size())
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shape[0] = shape[0] * world_size
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output = gather_buffer.view(shape)
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else:
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tensor_list = [
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gather_buffer.narrow(0,
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input_.numel() * i, input_.numel()).view_as(input_) for i in range(world_size)
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]
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# Note: torch.cat already creates a contiguous tensor.
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output = torch.cat(tensor_list, dim=dim).contiguous()
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return output
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def _drop_tokens(input_, dim=0):
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"""Divide a tensor among the tensor parallel ranks"""
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mpu = deepspeed.utils.groups.mpu
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total_chunks = bwc_tensor_model_parallel_world_size(mpu)
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if total_chunks == 1:
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return input_
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this_chunk = bwc_tensor_model_parallel_rank(mpu)
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assert input_.shape[
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dim] % total_chunks == 0, f"input dimension {dim} ({input_.shape[dim]}) is not divisible by tensor parallel world size ({total_chunks})"
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chunk_size = input_.shape[dim] // total_chunks
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return torch.narrow(input_, dim, this_chunk * chunk_size, chunk_size)
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class _GatherTokens(torch.autograd.Function):
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"""All gather tokens among the tensor parallel ranks"""
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@staticmethod
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def symbolic(graph, input_, dim):
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return _gather_tokens(input_, dim)
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@staticmethod
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def forward(ctx, input_, dim):
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ctx.dim = dim
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return _gather_tokens(input_, dim)
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@staticmethod
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def backward(ctx, grad_output):
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return _drop_tokens(grad_output, ctx.dim), None
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class _DropTokens(torch.autograd.Function):
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"Divide tokens equally among the tensor parallel ranks"
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@staticmethod
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def symbolic(graph, input_, dim):
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return _drop_tokens(input_, dim)
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@staticmethod
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def forward(ctx, input_, dim):
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ctx.dim = dim
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return _drop_tokens(input_, dim)
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@staticmethod
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def backward(ctx, input_):
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return _gather_tokens(input_, ctx.dim), None
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def gather_tokens(input_, dim=0):
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mpu = deepspeed.utils.groups.mpu
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if mpu is None or bwc_tensor_model_parallel_world_size(mpu) == 1:
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# no tensor parallelism for non-experts
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return input_
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return _GatherTokens.apply(input_, dim)
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def drop_tokens(input_, dim=0):
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mpu = deepspeed.utils.groups.mpu
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if mpu is None or bwc_tensor_model_parallel_world_size(mpu) == 1:
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# no tensor parallelism for non-experts
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return input_
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return _DropTokens.apply(input_, dim)
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