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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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39
colossalai/legacy/nn/layer/parallel_2d/layers.py
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@@ -8,6 +8,7 @@ import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter
from colossalai.accelerator import get_accelerator
from colossalai.legacy.communication import broadcast
from colossalai.legacy.context import ParallelMode, seed
from colossalai.legacy.core import global_context as gpc
@@ -18,7 +19,6 @@ from colossalai.legacy.utils.checkpointing import (
partition_tensor_parallel_state_dict,
)
from colossalai.nn import init as init
from colossalai.utils.device import get_current_device
from ..base_layer import ParallelLayer
from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
@@ -82,7 +82,7 @@ class Linear2D(ParallelLayer):
self.hidden_size_per_partition = divide(self.out_features, self.summa_dim)
# create weight, shape: [k/q, h/q]
factory_kwargs = {"device": get_current_device(), "dtype": dtype}
factory_kwargs = {"device": get_accelerator().get_current_device(), "dtype": dtype}
self.weight = Parameter(
torch.empty(self.input_size_per_partition, self.hidden_size_per_partition, **factory_kwargs)
)
@@ -259,7 +259,7 @@ class LayerNorm2D(ParallelLayer):
self.partitioned_partition = divide(normalized_shape, self.summa_dim**2)
# create parameters
factory_kwargs = {"device": get_current_device(), "dtype": dtype}
factory_kwargs = {"device": get_accelerator().get_current_device(), "dtype": dtype}
self.weight = Parameter(torch.ones(self.partitioned_partition, **factory_kwargs))
if bias:
@@ -438,18 +438,24 @@ class PatchEmbedding2D(ParallelLayer):
self.weight = Parameter(
torch.empty(
(self.embed_size_per_partition, in_chans, *self.patch_size),
device=get_current_device(),
device=get_accelerator().get_current_device(),
dtype=dtype,
)
)
self.bias = Parameter(torch.empty(self.embed_size_per_partition, device=get_current_device(), dtype=dtype))
self.bias = Parameter(
torch.empty(self.embed_size_per_partition, device=get_accelerator().get_current_device(), dtype=dtype)
)
self.cls_token = Parameter(
torch.zeros((1, 1, self.embed_size_per_partition), device=get_current_device(), dtype=dtype)
torch.zeros(
(1, 1, self.embed_size_per_partition), device=get_accelerator().get_current_device(), dtype=dtype
)
)
self.pos_embed = Parameter(
torch.zeros(
(1, self.num_patches + 1, self.embed_size_per_partition), device=get_current_device(), dtype=dtype
(1, self.num_patches + 1, self.embed_size_per_partition),
device=get_accelerator().get_current_device(),
dtype=dtype,
)
)
@@ -619,7 +625,9 @@ class Embedding2D(ParallelLayer):
self.embed_kwargs = kwargs
self.weight = Parameter(
torch.empty((num_embeddings, embed_dim_per_partition), device=get_current_device(), dtype=dtype)
torch.empty(
(num_embeddings, embed_dim_per_partition), device=get_accelerator().get_current_device(), dtype=dtype
)
)
self.reset_parameters(weight_initializer)
@@ -758,7 +766,7 @@ class VocabParallelEmbedding2D(ParallelLayer):
self.weight = Parameter(
torch.empty(
(self.num_embeddings_per_partition, self.embed_dim_per_partition),
device=get_current_device(),
device=get_accelerator().get_current_device(),
dtype=dtype,
)
)
@@ -895,11 +903,18 @@ class Classifier2D(ParallelLayer):
self.has_weight = False
else:
self.weight = Parameter(
torch.empty(self.num_classes, self.input_size_per_partition, device=get_current_device(), dtype=dtype)
torch.empty(
self.num_classes,
self.input_size_per_partition,
device=get_accelerator().get_current_device(),
dtype=dtype,
)
)
self.has_weight = True
if bias:
self.bias = Parameter(torch.zeros(self.num_classes, device=get_current_device(), dtype=dtype))
self.bias = Parameter(
torch.zeros(self.num_classes, device=get_accelerator().get_current_device(), dtype=dtype)
)
else:
self.bias = None
@@ -1052,7 +1067,7 @@ class VocabParallelClassifier2D(ParallelLayer):
self.output_size_per_partition = divide(num_classes, self.summa_dim)
# create weight, shape: [k/q, h/q]
factory_kwargs = {"device": get_current_device(), "dtype": dtype}
factory_kwargs = {"device": get_accelerator().get_current_device(), "dtype": dtype}
if weight is not None:
self.weight = weight
self.has_weight = False