Rebuild Docs V0.8.0 (#8392)

* rebuild for 5 module * fix bug * fix for doctree and content in nn and * fix * fix * fix * add some * fix for oneflow.rst * update oneflow oneflow.nn * update tensor * update tensor module * update * test * update * update * fix for undone desc * docs: oneflow.utils.data (#8485) * feat(utils.data): add oneflow.utils.data * docs(dataloader): change the docstring of DataLoader * docs(tensor): add methods to oneflow.Tensor document * docs(optim): change docstring of optimizer and add a note to the doucument * nn.graph * fix for graph * fix bug * review nn and linalg document (#8515) * docs(nn): add contents to oneflow.nn document * docs(linalg): refactor oneflow.linalg document * change attributes.rst and review nn.functional.rst (#8514) * change attributes.rst and review nn.functional.rst * reconstruction oneflow.cuda * fix cuda and rebuild comm demo (#8582) * update image * add distributed * oneembedding & refine graph * update for sdisributed one_embedding * fix rnn.py (#8616) * 重构 oneflow.nn.init 文档 (#8622) docs(nn.init): refactore nn.init document * docs(nn.init): remove the comments * docs(utils.data): remove the comments * update and fix bug * docs(review): refine the documents (#8646) * docs(review): refine oneflow, nn, Tensor, nn.init, linalg, utils.data, optim modules * docs(optim): modify the code examples * docs(tensor): edit note * 重构 oneflow.autograd 文档 (#8594) * docs(autograd): refactor oneflow.autograd * docs(autograd): edit "Default gradient layouts". * docs(autograd): reedit "Default gradient layouts" * docs(autograd): add comment * docs(autograd): add reference * update * docs(tensor): change autoclass to autosummary * update * update * add oneflow.linalg.diagonal (#8653) * docs(linalg): add oneflow.linalg.diagonal * update enviorment variable * Update docs/source/distributed.rst Co-authored-by: Houjiang Chen <chenhoujiangcug@gmail.com> * Update docs/source/distributed.rst Co-authored-by: Houjiang Chen <chenhoujiangcug@gmail.com> * update enviorment variable * update for ev & distributed * update distribued * update ev * update distribute desc * Update docs/source/distributed.rst Co-authored-by: Houjiang Chen <chenhoujiangcug@gmail.com> * update * 修改 docstring 描述 (#8656) * docs: move pytorch refernce to end * docs: add some docstring * docs(refs): add refs * Update docs/source/distributed.rst * updte for distributed details and environment_variable * docs(docstring): Modify all reference links to version 1.10 (#8663) * fix bug * fix bug * fix all warning Co-authored-by: Guoliang Cheng <1876953310@qq.com> Co-authored-by: liu xuan <85344642+laoliu97@users.noreply.github.com> Co-authored-by: Guoliang Cheng <lmyybh_lazy@163.com> Co-authored-by: laoliu97 <841637247@qq.com> Co-authored-by: Yao Chi <later@usopp.net> Co-authored-by: Houjiang Chen <chenhoujiangcug@gmail.com>
Oneflow-Inc · Jul 19, 2022 · e15a8bc · e15a8bc
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diff --git a/.gitignore b/.gitignore
@@ -2,6 +2,7 @@
 /build-*
 /docs/build/
 /docs/build-cn/
+/docs/source/generated
 /cmake-build-*
 /dist
 /third_party/

diff --git a/docs/source/autograd.rst b/docs/source/autograd.rst
@@ -1,12 +1,87 @@
 oneflow.autograd
-================================================
-Functions and classes for autograd.
----------------------------------------------------
+====================================================
+
+.. The documentation is referenced from:
+   https://pytorch.org/docs/1.10/autograd.html
+
+``oneflow.autograd`` provides classes and functions implementing automatic differentiation of arbitrary scalar 
+valued functions. It requires minimal changes to the existing code - you only need to declare ``Tensor`` s 
+for which gradients should be computed with the ``requires_grad=True`` keyword. As of now, we only support 
+autograd for floating point ``Tensor`` types ( half, float, double and bfloat16).
+
+
 .. currentmodule:: oneflow.autograd
-.. autoclass:: oneflow.autograd.Function
-    :members: apply,
-    :special-members: __call__,
 
-.. automodule:: oneflow.autograd
-    :members: grad,
-      backward,
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    backward
+    grad
+
+Locally disabling gradient computation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    no_grad
+    enable_grad
+    set_grad_enabled
+    inference_mode
+
+.. TODO(wyg): uncomment this after aligning accumulate grad
+.. Default gradient layouts
+.. ^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. A ``param.grad`` is accumulated by replacing ``.grad`` with a 
+.. new tensor ``.grad + new grad`` during :func:`oneflow.autograd.backward()` or 
+.. :func:`oneflow.Tensor.backward()`.
+
+In-place operations on Tensors
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Supporting in-place operations in autograd is a hard matter, and we discourage
+their use in most cases. Autograd's aggressive buffer freeing and reuse makes
+it very efficient and there are very few occasions when in-place operations
+actually lower memory usage by any significant amount. Unless you're operating
+under heavy memory pressure, you might never need to use them.
+
+Tensor autograd functions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autosummary::
+    :nosignatures:
+
+   oneflow.Tensor.grad
+   oneflow.Tensor.requires_grad
+   oneflow.Tensor.is_leaf
+   oneflow.Tensor.backward
+   oneflow.Tensor.detach
+   oneflow.Tensor.register_hook
+   oneflow.Tensor.retain_grad
+
+Function
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: Function
+.. currentmodule:: oneflow.autograd
+.. autosummary::
+    :toctree generated
+    :nosignatures:
+
+    Function.forward
+    Function.backward
+    Function.apply
+
+Context method mixins
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+When creating a new :class:`Function`, the following methods are available to `ctx`.
+
+.. currentmodule:: oneflow.autograd.autograd_function
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    FunctionAutoGradCaptureState.mark_non_differentiable
+    FunctionAutoGradCaptureState.save_for_backward
+    FunctionAutoGradCaptureState.saved_tensors
diff --git a/docs/source/comm.rst b/docs/source/comm.rst
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -44,10 +44,16 @@
 extensions = [
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",
+    "sphinx.ext.intersphinx",
     "recommonmark",
+    "sphinx.ext.autosummary",
     "sphinx_copybutton",
 ]
 
+# build the templated autosummary files
+autosummary_generate = True
+numpydoc_show_class_members = False
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -107,6 +113,10 @@
 #
 # html_sidebars = {}
 
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+}
 
 # -- Options for HTMLHelp output ---------------------------------------------
 

diff --git a/docs/source/cuda.rst b/docs/source/cuda.rst
@@ -1,22 +1,45 @@
 oneflow.cuda
 ===================================
-ONEFLOW.CUDA 
-----------------------------------
+
+.. The documentation is referenced from: https://pytorch.org/docs/1.10/cuda.html.
+
 .. currentmodule:: oneflow.cuda
-.. automodule:: oneflow.cuda
-    :members: is_available,
-        device_count,
-        current_device,
-        set_device,
-        synchronize,
-        manual_seed_all,
-        manual_seed,
-        empty_cache,
-        HalfTensor,
-        FloatTensor,
-        DoubleTensor,
-        BoolTensor,
-        ByteTensor,
-        CharTensor,
-        IntTensor,
-        LongTensor,
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    is_available
+    device_count
+    current_device
+    set_device
+    synchronize
+
+.. note::
+   The :attr:`current_device` returns local rank as device index. It is different from the 'torch.current_device()' in PyTorch.
+
+
+Random Number Generator
+-------------------------
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    manual_seed_all
+    manual_seed
+
+
+GPU tensor
+-----------------------------
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    HalfTensor
+    FloatTensor
+    DoubleTensor
+    BoolTensor
+    ByteTensor
+    CharTensor
+    IntTensor
+    LongTensor