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[npu] change device to accelerator api (#5239)

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* update accelerator

* fix timer

* fix amp

* update

* fix

* update bug

* add error raise

* fix autocast

* fix set device

* remove doc accelerator

* update doc

* update doc

* update doc

* use nullcontext

* update cpu

* update null context

* change time limit for example

* udpate

* update

* update

* update

* [npu] polish accelerator code

---------

Co-authored-by: Xuanlei Zhao <xuanlei.zhao@gmail.com>
Co-authored-by: zxl <43881818+oahzxl@users.noreply.github.com>
This commit is contained in:
Hongxin Liu
2024-01-09 10:20:05 +08:00
committed by GitHub
parent dd2c28a323
commit d202cc28c0
128 changed files with 1773 additions and 868 deletions
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22
colossalai/legacy/trainer/hooks/_metric_hook.py
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@@ -7,12 +7,12 @@ from typing import Callable
import torch
import torch.distributed as dist
from colossalai.accelerator import get_accelerator
from colossalai.legacy.communication import all_reduce
from colossalai.legacy.context import ParallelMode
from colossalai.legacy.core import global_context as gpc
from colossalai.legacy.registry import HOOKS
from colossalai.legacy.utils import is_no_pp_or_last_stage
from colossalai.utils import get_current_device
from ._base_hook import BaseHook
from ._commons_ import _format_number
@@ -82,8 +82,8 @@ class LossMetric(Metric):
def __init__(self, epoch_only):
super().__init__(epoch_only=epoch_only)
self.last_step_loss = torch.zeros(1, device=get_current_device())
self.accum_loss = torch.zeros(1, device=get_current_device())
self.last_step_loss = torch.zeros(1, device=get_accelerator().get_current_device())
self.accum_loss = torch.zeros(1, device=get_accelerator().get_current_device())
self.count = 0
def reset(self) -> None:
@@ -164,10 +164,10 @@ class AccuracyMetric(Metric):
def __init__(self, epoch_only: bool, accuracy_func: Callable):
super().__init__(epoch_only=epoch_only)
self.acc = accuracy_func
self.last_step_sum = torch.zeros(1, device=get_current_device())
self.last_step_correct = torch.zeros(1, device=get_current_device())
self.accumulated_sum = torch.zeros(1, device=get_current_device())
self.accumulated_correct = torch.zeros(1, device=get_current_device())
self.last_step_sum = torch.zeros(1, device=get_accelerator().get_current_device())
self.last_step_correct = torch.zeros(1, device=get_accelerator().get_current_device())
self.accumulated_sum = torch.zeros(1, device=get_accelerator().get_current_device())
self.accumulated_correct = torch.zeros(1, device=get_accelerator().get_current_device())
def reset(self) -> None:
self.last_step_sum.zero_()
@@ -320,10 +320,10 @@ class ThroughputMetric(Metric):
super().__init__(epoch_only=epoch_only)
self.ignored_steps = ignored_steps
self.cur_steps = 0
self.accumulated_num_samples = torch.zeros(1, device=get_current_device())
self.accumulated_used_time = torch.zeros(1, device=get_current_device())
self.last_step_num_samples = torch.zeros(1, device=get_current_device())
self.last_step_used_time = torch.zeros(1, device=get_current_device())
self.accumulated_num_samples = torch.zeros(1, device=get_accelerator().get_current_device())
self.accumulated_used_time = torch.zeros(1, device=get_accelerator().get_current_device())
self.last_step_num_samples = torch.zeros(1, device=get_accelerator().get_current_device())
self.last_step_used_time = torch.zeros(1, device=get_accelerator().get_current_device())
self._tflop_per_step = tflop_per_step
self._use_local = use_local