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Merge pull request #133 from Erotemic/dev/editable-install-fixes
Bump to 1.1.0. Min Python Version is now 3.6. Fixes for editable installs, doctests with decorators.
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| Original file line number | Diff line number | Diff line change |
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| # We should be able to handle these: | ||
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| def issue1(): | ||
| # ISSUE: DOES NOT SKIP CORRECTLY | ||
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| def foo1(): | ||
| """ | ||
| >>> # xdoctest: +SKIP("Undefined variables") | ||
| >>> @custom_sharded_op_impl(torch.nn.functional.linear) | ||
| >>> def my_custom_sharded_linear(types, args, kwargs, process_group): | ||
| >>> ... | ||
| >>> input = torch.rand(10, 32) | ||
| >>> weight = sharded_tensor.rand(32, 16) | ||
| >>> bias = torch.rand(16) | ||
| >>> # This will call 'my_custom_sharded_linear' | ||
| >>> torch.nn.functional.linear(input, weight, bias) | ||
| """ | ||
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| # DOES SKIP CORRECTLY, WHY? | ||
| def foo2(): | ||
| """ | ||
| >>> @custom_sharded_op_impl(torch.nn.functional.linear) | ||
| >>> def my_custom_sharded_linear(types, args, kwargs, process_group): | ||
| >>> ... | ||
| >>> # xdoctest: +SKIP("Undefined variables") | ||
| >>> input = torch.rand(10, 32) | ||
| >>> weight = sharded_tensor.rand(32, 16) | ||
| >>> bias = torch.rand(16) | ||
| >>> # This will call 'my_custom_sharded_linear' | ||
| >>> torch.nn.functional.linear(input, weight, bias) | ||
| """ | ||
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| def issue2(): | ||
| # Should be able to parse that setup | ||
| def CppExtension(name, sources, *args, **kwargs): | ||
| r''' | ||
| Creates a :class:`setuptools.Extension` for C++. | ||
| Convenience method that creates a :class:`setuptools.Extension` with the | ||
| bare minimum (but often sufficient) arguments to build a C++ extension. | ||
| All arguments are forwarded to the :class:`setuptools.Extension` | ||
| constructor. | ||
| Example: | ||
| >>> from setuptools import setup | ||
| >>> from torch.utils.cpp_extension import BuildExtension, CppExtension | ||
| >>> setup( | ||
| name='extension', | ||
| ext_modules=[ | ||
| CppExtension( | ||
| name='extension', | ||
| sources=['extension.cpp'], | ||
| extra_compile_args=['-g']), | ||
| ], | ||
| cmdclass={ | ||
| 'build_ext': BuildExtension | ||
| }) | ||
| ''' | ||
| include_dirs = kwargs.get('include_dirs', []) | ||
| include_dirs += include_paths() | ||
| kwargs['include_dirs'] = include_dirs | ||
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| library_dirs = kwargs.get('library_dirs', []) | ||
| library_dirs += library_paths() | ||
| kwargs['library_dirs'] = library_dirs | ||
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| libraries = kwargs.get('libraries', []) | ||
| libraries.append('c10') | ||
| libraries.append('torch') | ||
| libraries.append('torch_cpu') | ||
| libraries.append('torch_python') | ||
| kwargs['libraries'] = libraries | ||
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| kwargs['language'] = 'c++' | ||
| return setuptools.Extension(name, sources, *args, **kwargs) | ||
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| class LSTMCell(torch.nn.Module): | ||
| r"""A quantizable long short-term memory (LSTM) cell. | ||
| For the description and the argument types, please, refer to :class:`~torch.nn.LSTMCell` | ||
| Examples:: | ||
| >>> import torch.nn.quantizable as nnqa | ||
| >>> rnn = nnqa.LSTMCell(10, 20) | ||
| >>> input = torch.randn(3, 10) | ||
| >>> hx = torch.randn(3, 20) | ||
| >>> cx = torch.randn(3, 20) | ||
| >>> output = [] | ||
| >>> for i in range(6): | ||
| hx, cx = rnn(input[i], (hx, cx)) | ||
| output.append(hx) | ||
| """ | ||
| _FLOAT_MODULE = torch.nn.LSTMCell | ||
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| def non_doctests(): | ||
| """ | ||
| ~/code/pytorch/torch/distributed/launch.py | ||
| 1. Single-Node multi-process distributed training | ||
| :: | ||
| >>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE | ||
| YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other | ||
| arguments of your training script) | ||
| 2. Multi-Node multi-process distributed training: (e.g. two nodes) | ||
| Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* | ||
| :: | ||
| >>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE | ||
| --nnodes=2 --node_rank=0 --master_addr="192.168.1.1" | ||
| --master_port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 | ||
| and all other arguments of your training script) | ||
| Node 2: | ||
| :: | ||
| >>> python -m torch.distributed.launch --nproc_per_node=NUM_GPUS_YOU_HAVE | ||
| --nnodes=2 --node_rank=1 --master_addr="192.168.1.1" | ||
| --master_port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 | ||
| and all other arguments of your training script) | ||
| """ |
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