Bug Report – 错误报告区块链毕设代写

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Recently HF trainer was extended to support full fp16 eval via --fp16_full_eval. I’d have expected it to be either equal or faster than eval with fp32 model, but surprisingly I have noticed a 25% slowdown when using it.

This may or may not impact deepspeed as well, which also runs eval in fp16, but we can’t compare it to a baseline, since it only runs fp16.

I wonder if someone would like t

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🐛 Bug

Please reproduce using the BoringModel

Currently, the progress bar doesn’t make distinction between running in a real terminal or being piped to logs.

When Lightning run in logs, it produces a prog_bar line for each change.

<img width=”1659″ alt=”Screenshot 2021-03-23 at 09 10 40″ src=”https://user-images.git

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Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

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Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________  self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>      def test_make_tensor(self):  # type: () -> None      
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While setting train_parameters to False very often we also may consider disabling dropout/batchnorm, in other words, to run the pretrained model in eval mode.
We’ve done a little modification to PretrainedTransformerEmbedder that allows providing whether the token embedder should be forced to eval mode during the training phase.

Do you this feature might be handy? Should I open a PR?

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I’m using mxnet to do some work, but there is nothing when I search the mxnet trial and example.

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Please can you train ghostnet.
(i don’t have the imagenet dataset)

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Is it possible to run this on a (recent) Mac, which does not support CUDA? I would have guessed setting –GPU 0 would not attempt to call CUDA, but it fails.

File "/Users/../Desktop/bopbtl/venv/lib/python3.7/site-packages/torch/cuda/__init__.py", line 61, in _check_driver     raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enable 
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Python 9,207


最近,HF训练器通过–fp16 U full U eval扩展到支持完整的fp16评估。我本以为它会等于或快于评估与fp32模型,但令人惊讶的是,我注意到一个25%的减速时,使用它。

这可能也会影响deepspeed,它也会在fp16中运行eval,但我们无法将其与基线进行比较,因为它只运行fp16。

我想知道是否有人愿意t

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我们保持此问题的开放性,以收集用户的功能请求并倾听您的声音。我们的每月发布计划也在这里提供。

您可以:

  1. 投票支持具有👍 或者反对👎. (请记住,开发人员很忙,无法响应所有功能请求,所以请选择您最喜欢的功能!)告诉我们
  2. Vote for a feature request with 👍 or be against with 👎. (Remember that developers are busy and cannot respond to all feature requests, so vote for your most favorable one!)
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🐛 Bug请使用BoringModel重新生成Bug报告,并通过留言建议新功能。
  • 投票支持具有👍 或者反对👎. (请记住,开发人员很忙,无法响应所有功能请求,所以请选择您最喜欢的功能!)告诉我们
  • Please reproduce using the BoringModel

    当前,进度条不区分在实际终端中运行还是通过管道传输到日志。

    当Lightning在日志中运行时,它会为每次更改生成一条程序条线。

    &lt;img width=“1659”alt=“屏幕截图2021-03-23 at 09 10 40”src=”https://user-images.git

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    更改张量数据至张量分离()由于
    pytorch/pytorch#6990(评论)
    张量分离()比张量数据.

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    Bug Report

    这些测试是在s390x上运行的。s390x是big-endian架构。

    帮助程序的故障日志_测试.py

    ________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________  self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>      def test_make_tensor(self):  # type: () -> None      
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    当经常将trainu参数设置为False时,我们还可以考虑禁用dropout/batchnorm,换句话说,在eval模式下运行预训练模型。
    我们对pretrainedtransformerembeder做了一些修改,允许提供是否应强制令牌嵌入器训练阶段的评估模式。

    您认为这个功能可能很方便吗?我应该开公关吗?

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    我正在使用mxnet做一些工作,但是在搜索mxnet试用版和示例时却什么都没有。

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    请您培训ghostnet。
    (我没有imagenet数据集)

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    是否可以在(最近的)不支持CUDA的Mac上运行此功能?我猜设置——GPU0不会尝试调用CUDA,但它失败了。

    File "/Users/../Desktop/bopbtl/venv/lib/python3.7/site-packages/torch/cuda/__init__.py", line 61, in _check_driver     raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enable 
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    Python 9,207

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