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[legacy] clean up legacy code (#4743)

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* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci
This commit is contained in:
Hongxin Liu
2023-09-18 16:31:06 +08:00
committed by GitHub
parent 32e7f99416
commit b5f9e37c70
342 changed files with 2919 additions and 4182 deletions
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14
colossalai/legacy/communication/collective.py
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@@ -6,8 +6,8 @@ import torch.distributed as dist
from torch import Tensor
from torch.distributed import ReduceOp
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.legacy.context import ParallelMode
from colossalai.legacy.core import global_context as gpc
_all_gather_func = dist._all_gather_base \
if "all_gather_into_tensor" not in dir(dist) else dist.all_gather_into_tensor
@@ -26,7 +26,7 @@ def all_gather(tensor: Tensor, dim: int, parallel_mode: ParallelMode, async_op:
Args:
tensor (:class:`torch.Tensor`): Tensor to be gathered.
dim (int): The dimension concatenating in.
parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
parallel_mode (:class:`colossalai.legacy.context.ParallelMode`): Parallel group mode used in this communication.
async_op (bool, optional): Whether operations are asynchronous.
Returns:
@@ -65,7 +65,7 @@ def reduce_scatter(tensor: Tensor,
Args:
tensor (:class:`torch.Tensor`): Tensor to be reduce_scattered.
dim (int): The dimension concatenating in.
parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
parallel_mode (:class:`colossalai.legacy.context.ParallelMode`): Parallel group mode used in this communication.
op (torch.distributed.ReduceOp, optional): The type of reduce operation,
should be included in [SUM, AVG, PRODUCT, MIN, MAX, BAND, BOR, BXOR].
More details about ReduceOp please refer to
@@ -105,7 +105,7 @@ def all_reduce(tensor: Tensor,
Args:
tensor (:class:`torch.Tensor`): Tensor to be all-reduced.
parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
parallel_mode (:class:`colossalai.legacy.context.ParallelMode`): Parallel group mode used in this communication.
op (torch.distributed.ReduceOp, optional): The type of reduce operation,
should be included in [SUM, AVG, PRODUCT, MIN, MAX, BAND, BOR, BXOR].
More details about ReduceOp please refer to
@@ -141,7 +141,7 @@ def broadcast(tensor: Tensor, src: int, parallel_mode: ParallelMode, async_op: b
Args:
tensor (:class:`torch.Tensor`): Tensor to be broadcast.
src (int): Source rank.
parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
parallel_mode (:class:`colossalai.legacy.context.ParallelMode`): Parallel group mode used in this communication.
async_op (bool, optional): Whether operations are asynchronous.
Returns:
@@ -173,7 +173,7 @@ def reduce(tensor: Tensor, dst: int, parallel_mode: ParallelMode, op: ReduceOp =
Args:
tensor (:class:`torch.Tensor`): Tensor to be reduced.
dst (int): Destination rank.
parallel_mode (:class:`colossalai.context.ParallelMode`): Parallel group mode used in this communication.
parallel_mode (:class:`colossalai.legacy.context.ParallelMode`): Parallel group mode used in this communication.
async_op (bool, optional): Whether operations are asynchronous.
Returns: