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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/kernel/jit/option.py
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@@ -1,7 +1,7 @@
import torch
from colossalai.accelerator import get_accelerator
from colossalai.legacy.nn.layer.colossalai_layer import Embedding, Linear
from colossalai.utils import get_current_device
from .bias_dropout_add import bias_dropout_add_fused_train
from .bias_gelu import bias_gelu_impl
@@ -46,11 +46,13 @@ def warmup_jit_fusion(
):
"""Compile JIT functions before the main training steps"""
embed = Embedding(vocab_size, hidden_size).to(get_current_device())
linear_1 = Linear(hidden_size, hidden_size * 4, skip_bias_add=True).to(get_current_device())
linear_2 = Linear(hidden_size * 4, hidden_size, skip_bias_add=True).to(get_current_device())
embed = Embedding(vocab_size, hidden_size).to(get_accelerator().get_current_device())
linear_1 = Linear(hidden_size, hidden_size * 4, skip_bias_add=True).to(get_accelerator().get_current_device())
linear_2 = Linear(hidden_size * 4, hidden_size, skip_bias_add=True).to(get_accelerator().get_current_device())
x = torch.randint(vocab_size, (batch_size, seq_length), dtype=torch.long, device=get_current_device())
x = torch.randint(
vocab_size, (batch_size, seq_length), dtype=torch.long, device=get_accelerator().get_current_device()
)
x = embed(x)
y, y_bias = linear_1(x)
z, z_bias = linear_2(y)
@@ -58,8 +60,8 @@ def warmup_jit_fusion(
# prop and recomputation
for bias_grad, input_grad in zip([True, True], [False, True]):
for _ in range(10):
bias = torch.rand_like(y_bias, dtype=dtype, device=get_current_device())
input_ = torch.rand_like(y, dtype=dtype, device=get_current_device())
bias = torch.rand_like(y_bias, dtype=dtype, device=get_accelerator().get_current_device())
input_ = torch.rand_like(y, dtype=dtype, device=get_accelerator().get_current_device())
bias.requires_grad, input_.requires_grad = bias_grad, input_grad
bias_gelu_impl(input_, bias)
@@ -69,9 +71,9 @@ def warmup_jit_fusion(
# prop and recomputation
for input_grad, bias_grad, residual_grad in zip([False, True], [True, True], [True, True]):
for _ in range(10):
input_ = torch.rand_like(z, dtype=dtype, device=get_current_device())
residual = torch.rand_like(x, dtype=dtype, device=get_current_device())
bias = torch.rand_like(z_bias, dtype=dtype, device=get_current_device())
input_ = torch.rand_like(z, dtype=dtype, device=get_accelerator().get_current_device())
residual = torch.rand_like(x, dtype=dtype, device=get_accelerator().get_current_device())
bias = torch.rand_like(z_bias, dtype=dtype, device=get_accelerator().get_current_device())
input_.requires_grad = input_grad
bias.requires_grad = bias_grad
residual.requires_grad = residual_grad