Removed direct doc formatting

[vitrioil]

Fixes #76034

This does not make python remove all __doc__ because in some places __doc__ is assigned to a string.

Example:

pytorch/torch/nn/modules/conv.py

Lines 174 to 233 in 04b3313

	class Conv1d(_ConvNd):
	__doc__ = r"""Applies a 1D convolution over an input signal composed of several input
	planes.
	In the simplest case, the output value of the layer with input size
	:math:`(N, C_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` can be
	precisely described as:
	.. math::
	\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
	\sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k)
	\star \text{input}(N_i, k)
	where :math:`\star` is the valid `cross-correlation`_ operator,
	:math:`N` is a batch size, :math:`C` denotes a number of channels,
	:math:`L` is a length of signal sequence.
	""" + r"""
	This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
	* :attr:`stride` controls the stride for the cross-correlation, a single
	number or a one-element tuple.
	* :attr:`padding` controls the amount of padding applied to the input. It
	can be either a string {{'valid', 'same'}} or a tuple of ints giving the
	amount of implicit padding applied on both sides.
	* :attr:`dilation` controls the spacing between the kernel points; also
	known as the à trous algorithm. It is harder to describe, but this `link`_
	has a nice visualization of what :attr:`dilation` does.
	{groups_note}
	Note:
	{depthwise_separable_note}
	Note:
	{cudnn_reproducibility_note}
	Note:
	``padding='valid'`` is the same as no padding. ``padding='same'`` pads
	the input so the output has the shape as the input. However, this mode
	doesn't support any stride values other than 1.
	Args:
	in_channels (int): Number of channels in the input image
	out_channels (int): Number of channels produced by the convolution
	kernel_size (int or tuple): Size of the convolving kernel
	stride (int or tuple, optional): Stride of the convolution. Default: 1
	padding (int, tuple or str, optional): Padding added to both sides of
	the input. Default: 0
	padding_mode (string, optional): ``'zeros'``, ``'reflect'``,
	``'replicate'`` or ``'circular'``. Default: ``'zeros'``
	dilation (int or tuple, optional): Spacing between kernel
	elements. Default: 1
	groups (int, optional): Number of blocked connections from input
	channels to output channels. Default: 1
	bias (bool, optional): If ``True``, adds a learnable bias to the
	output. Default: ``True``
	""".format(**reproducibility_notes, **convolution_notes) + r"""

Since there are quite a few of these, I will add all of them together in this PR later. (Basically still a lot of docstring will persist even with -OO enabled.)

[facebook-github-bot]

🔗 Helpful links

• 🧪  See artifacts and rendered test results at hud.pytorch.org/pr/76619
• 📄  Preview Python docs built from this PR
• 📄  Preview C++ docs built from this PR
• ❓Need help or want to give feedback on the CI? Visit our office hours

💊 CI failures summary and remediations

As of commit d205f79 (more details on the Dr. CI page):

Expand to see more

---

• 1/1 failures introduced in this PR

---

🕵️ 1 new failure recognized by patterns

The following CI failures do not appear to be due to upstream breakages

pull / linux-xenial-py3.7-gcc5.4 / test (backwards_compat, 1, 1, linux.2xlarge) (1/1)

Step: "Test" (full log | diagnosis details | 🔁 rerun)

2022-04-30T15:51:04.4105398Z The PR is introduc...m to confirm whether this change is wanted or not.

2022-04-30T15:51:04.4088764Z processing existing schema:  text(__torch__.torch.classes.profiling.SourceRef _0) -> (str _0)
2022-04-30T15:51:04.4090073Z processing existing schema:  count(__torch__.torch.classes.profiling.InstructionStats _0) -> (int _0)
2022-04-30T15:51:04.4092059Z processing existing schema:  duration_ns(__torch__.torch.classes.profiling.InstructionStats _0) -> (int _0)
2022-04-30T15:51:04.4093696Z processing existing schema:  source(__torch__.torch.classes.profiling.SourceStats _0) -> (__torch__.torch.classes.profiling.SourceRef _0)
2022-04-30T15:51:04.4096426Z processing existing schema:  line_map(__torch__.torch.classes.profiling.SourceStats _0) -> (Dict(int, __torch__.torch.classes.profiling.InstructionStats) _0)
2022-04-30T15:51:04.4097279Z processing existing schema:  __init__(__torch__.torch.classes.profiling._ScriptProfile _0) -> (NoneType _0)
2022-04-30T15:51:04.4099175Z processing existing schema:  enable(__torch__.torch.classes.profiling._ScriptProfile _0) -> (NoneType _0)
2022-04-30T15:51:04.4100805Z processing existing schema:  disable(__torch__.torch.classes.profiling._ScriptProfile _0) -> (NoneType _0)
2022-04-30T15:51:04.4103254Z processing existing schema:  _dump_stats(__torch__.torch.classes.profiling._ScriptProfile _0) -> (__torch__.torch.classes.profiling.SourceStats[] _0)
2022-04-30T15:51:04.4104874Z processing existing schema:  __init__(__torch__.torch.classes.dist_rpc.WorkerInfo _0, str _1, int _2) -> (NoneType _0)
2022-04-30T15:51:04.4105398Z The PR is introducing backward incompatible changes to the operator library. Please contact PyTorch team to confirm whether this change is wanted or not. 
2022-04-30T15:51:04.4105409Z 
2022-04-30T15:51:04.4105514Z Broken ops: [
2022-04-30T15:51:04.4106215Z 	aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
2022-04-30T15:51:04.4106553Z 	prim::CudaFusionIvalGuard(...) -> (bool)
2022-04-30T15:51:04.4107022Z 	aten::linalg_ldl_factor_ex(Tensor self, *, bool hermitian=False, bool check_errors=False) -> (Tensor LD, Tensor pivots, Tensor info)
2022-04-30T15:51:04.4107657Z 	aten::linalg_ldl_factor_ex.out(Tensor self, *, bool hermitian=False, bool check_errors=False, Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info)
2022-04-30T15:51:04.4108032Z 	aten::linalg_ldl_solve(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False) -> (Tensor)
2022-04-30T15:51:04.4108471Z 	aten::linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> (Tensor(a!))
2022-04-30T15:51:04.4108833Z 	aten::linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots)
2022-04-30T15:51:04.4109316Z 	aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots)

---

This comment was automatically generated by Dr. CI (expand for details).

Please report bugs/suggestions to the (internal) Dr. CI Users group.

Click here to manually regenerate this comment.

[mrshenli]

Changes in distributed/rpc folder LGTM.

[albanD]

Thanks for fixing this!

[albanD]

Basically still a lot of docstring will persist even with -OO enabled.

I would be curious to know if there is a significant perf hit with leaving these?

[albanD]

@pytorchbot merge this please

[github-actions]

Hey @vitrioil.
You've committed this PR, but it does not have both a 'release notes: ...' and 'topics: ...' label. Please add one of each to the PR. The 'release notes: ...' label should represent the part of PyTorch that this PR changes (fx, autograd, distributed, etc) and the 'topics: ...' label should represent the kind of PR it is (not user facing, new feature, bug fix, perf improvement, etc). The list of valid labels can be found here for the 'release notes: ...' and here for the 'topics: ...'.
For changes that are 'topic: not user facing' there is no need for a release notes label.