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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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16
examples/tutorial/opt/opt/run_clm.py
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@@ -51,13 +51,13 @@ from transformers import (
from transformers.utils.versions import require_version
import colossalai
from colossalai.accelerator import get_accelerator
from colossalai.legacy.context import ParallelMode
from colossalai.legacy.core import global_context as gpc
from colossalai.legacy.tensor import ProcessGroup
from colossalai.legacy.utils import get_dataloader
from colossalai.logging import disable_existing_loggers, get_dist_logger
from colossalai.nn.optimizer import HybridAdam
from colossalai.utils import get_current_device
from colossalai.zero import GeminiOptimizer
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
@@ -249,9 +249,9 @@ def parse_args():
def colo_memory_cap(size_in_GB):
from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device
from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction
cuda_capacity = colo_device_memory_capacity(get_current_device())
cuda_capacity = colo_device_memory_capacity(get_accelerator().get_current_device())
if size_in_GB * (1024**3) < cuda_capacity:
colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity)
print("Using {} GB of GPU memory".format(size_in_GB))
@@ -265,7 +265,9 @@ class DummyDataloader:
self.vocab_size = vocab_size
def generate(self):
input_ids = torch.randint(0, self.vocab_size, (self.batch_size, self.seq_len), device=get_current_device())
input_ids = torch.randint(
0, self.vocab_size, (self.batch_size, self.seq_len), device=get_accelerator().get_current_device()
)
attention_mask = torch.ones_like(input_ids)
return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": input_ids}
@@ -390,7 +392,7 @@ def main():
if args.init_in_cpu:
init_dev = torch.device("cpu")
else:
init_dev = get_current_device()
init_dev = get_accelerator().get_current_device()
cai_version = colossalai.__version__
logger.info(f"using Colossal-AI version {cai_version}")
@@ -439,7 +441,9 @@ def main():
except ImportError:
# this works for unreleased main branch, and this may be released on 0.2.9
from colossalai.zero import GeminiDDP
model = GeminiDDP(model, device=get_current_device(), placement_policy=PLACEMENT_POLICY, pin_memory=True)
model = GeminiDDP(
model, device=get_accelerator().get_current_device(), placement_policy=PLACEMENT_POLICY, pin_memory=True
)
elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
from colossalai.gemini import ChunkManager, GeminiManager