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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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14
examples/language/gpt/titans/model/embed.py
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@@ -5,6 +5,7 @@ from torch import nn as nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
from colossalai.accelerator import get_accelerator
from colossalai.legacy.context import ParallelMode, seed
from colossalai.legacy.core import global_context as gpc
from colossalai.legacy.nn.layer.base_layer import ParallelLayer
@@ -12,7 +13,6 @@ from colossalai.legacy.nn.layer.parallel_1d._utils import gather_forward_split_b
from colossalai.legacy.nn.layer.parallel_1d.layers import Linear1D_Row
from colossalai.legacy.nn.layer.utils import divide
from colossalai.legacy.registry import LAYERS, LOSSES
from colossalai.utils import get_current_device
class VocabParallelEmbedding(torch.nn.Module):
@@ -96,7 +96,9 @@ class VocabParallelEmbedding(torch.nn.Module):
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if position_ids is None:
position_ids = torch.arange(0, input_shape[-1] + 0, dtype=torch.long, device=get_current_device())
position_ids = torch.arange(
0, input_shape[-1] + 0, dtype=torch.long, device=get_accelerator().get_current_device()
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
position_embeddings = self.position_embeddings(position_ids)
@@ -194,7 +196,7 @@ class VocabParallelEmbedding1D(torch.nn.Module):
self.num_embeddings_per_partition = self.vocab_end_index - self.vocab_start_index
# Allocate weights and initialize.
factory_kwargs = {"device": get_current_device(), "dtype": dtype}
factory_kwargs = {"device": get_accelerator().get_current_device(), "dtype": dtype}
self.weight = Parameter(torch.empty(self.num_embeddings_per_partition, self.embedding_dim, **factory_kwargs))
init.uniform_(self.weight, -1, 1)
@@ -439,7 +441,9 @@ class HiddenParallelEmbedding(torch.nn.Module):
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if position_ids is None:
position_ids = torch.arange(0, input_shape[-1] + 0, dtype=torch.long, device=get_current_device())
position_ids = torch.arange(
0, input_shape[-1] + 0, dtype=torch.long, device=get_accelerator().get_current_device()
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
position_embeddings = self.position_embeddings(position_ids)
@@ -532,7 +536,7 @@ class HiddenParallelEmbedding1D(torch.nn.Module):
self._weight = None
# Allocate weights and initialize.
factory_kwargs = {"device": get_current_device(), "dtype": dtype}
factory_kwargs = {"device": get_accelerator().get_current_device(), "dtype": dtype}
self.weight = Parameter(torch.empty(num_embeddings, embed_dim_per_partition, **factory_kwargs))
init.uniform_(self.weight, -1, 1)