mirror of
https://github.com/pytorch/pytorch.git
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f3683453ae
This PR introduces a way to compile a region of FX graph using `fx.traceback.annotate`.
### UX
1) In the user code, mark the region that you want to be compiled with inductor using `with fx_traceback.annotate({"compile_with_inductor": 0})`. As of now, we just rely on the string `compile_with_inductor` and ignore the integer. As the needs arise, we can update the logic.
Example
```
def fn(x, y):
sin = torch.sin(x)
with fx_traceback.annotate({"compile_with_inductor": 0}):
mul = sin * y
add = mul + 1
return torch.sin(add)
```
2) You have to instruct the compiler to use the annotations with `compile_fx_annotated_nodes_with_inductor` transformation. This is somewhat controversial, and a user might expect that just setting annotation is enough. But for now to control the blast radius, we need to explicitly do this. One such example is
```
# Set the fw and bw compiler of aot_autograd to `compile_fx_annotated_nodes_with_inductor`
def aot_eager_regional_inductor():
return aot_autograd(
fw_compiler=compile_fx_annotated_nodes_with_inductor,
bw_compiler=compile_fx_annotated_nodes_with_inductor,
)
```
3) Fixable in short-term - You have to wrap the user code in `torch.fx.traceback.preserve_node_meta` to ensure that annotations are propagated to the compiler. This is fixable, just need to make CI happy.
### Implementation
1) Relies on `CapabilityBasedPartitioner` to "scoop" out regions based on annotations, and then create subgraphs in the main graph.
2) Call `torch._inductor.standalone_compile` on these subgraphs, and jam the returned callable into the FX graph at the place of call_module
Resulting graph looks something like this - search for `torch__inductor_standalone_compile_inner`
Forward graph
```
class GraphModule(torch.nn.Module):
def forward(self, primals_1: "f32[10]", primals_2: "f32[10]"):
# File: /data/users/anijain/pytorch2/test/dynamo/test_regional_inductor.py:64 in fn, code: sin = torch.sin(x)
sin: "f32[10]" = torch.ops.aten.sin.default(primals_1)
# No stacktrace found for following nodes
inner = torch__inductor_standalone_compile_inner(sin, primals_2)
# File: /data/users/anijain/pytorch2/test/dynamo/test_regional_inductor.py:68 in fn, code: add = mul + 1
getitem: "f32[10]" = inner[0]; inner = None
# File: /data/users/anijain/pytorch2/test/dynamo/test_regional_inductor.py:70 in fn, code: return torch.sin(add)
sin_1: "f32[10]" = torch.ops.aten.sin.default(getitem)
return (sin_1, primals_1, primals_2, sin, getitem)
```
Backward graph
```
class GraphModule(torch.nn.Module):
def forward(self, primals_1: "f32[10]", primals_2: "f32[10]", sin: "f32[10]", add: "f32[10]", tangents_1: "f32[10]"):
# File: /data/users/anijain/pytorch2/test/dynamo/test_regional_inductor.py:64 in fn, code: sin = torch.sin(x)
cos_1: "f32[10]" = torch.ops.aten.cos.default(primals_1); primals_1 = None
# File: /data/users/anijain/pytorch2/test/dynamo/test_regional_inductor.py:70 in fn, code: return torch.sin(add)
cos: "f32[10]" = torch.ops.aten.cos.default(add); add = None
mul_1: "f32[10]" = torch.ops.aten.mul.Tensor(tangents_1, cos); tangents_1 = cos = None
# No stacktrace found for following nodes
inner = torch__inductor_standalone_compile_inner(mul_1, sin, primals_2); mul_1 = sin = primals_2 = None
# File: /data/users/anijain/pytorch2/test/dynamo/test_regional_inductor.py:67 in fn, code: mul = sin * y
getitem: "f32[10]" = inner[0]
getitem_1: "f32[10]" = inner[1]; inner = None
# File: /data/users/anijain/pytorch2/test/dynamo/test_regional_inductor.py:64 in fn, code: sin = torch.sin(x)
mul_4: "f32[10]" = torch.ops.aten.mul.Tensor(getitem_1, cos_1); getitem_1 = cos_1 = None
return (mul_4, getitem)
```
### Some issue raised in the HOP meeting
1) CSE will not differentiate different meta custom nodes and do wrong thing.
2) SAC - The recomputed forward will be smaller than the forward. Will we compile a smaller region than?
3) What happens if you have a op in the middle which does not disturb the topology, is it still 1 subgraph?
4) What happens with the nesting of `fx_traceback.annotate`? Are there any ordering requirements?
5) What are we going to use the annotations for?
a) compile flex
b) streams
c) nn.Module info to organize MoE components for pipelining
d) PP stages
e) Rename graph nodes for more debugging
f) No nested regional compile
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164776
Approved by: https://github.com/SherlockNoMad
ghstack dependencies: #165188
3551 lines
96 KiB
Python
3551 lines
96 KiB
Python
#
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# PyTorch documentation build configuration file, created by
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# sphinx-quickstart on Fri Dec 23 13:31:47 2016.
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#
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# This file is execfile()d with the current directory set to its
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# containing dir.
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#
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# Note that not all possible configuration values are present in this
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# autogenerated file.
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#
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# All configuration values have a default; values that are commented out
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# serve to show the default.
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import inspect
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import os
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# import sys
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import pkgutil
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import re
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from os import path
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# source code directory, relative to this file, for sphinx-autobuild
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# sys.path.insert(0, os.path.abspath('../..'))
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import torch
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# If extensions (or modules to document with autodoc) are in another directory,
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# add these directories to sys.path here. If the directory is relative to the
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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try:
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import torchvision # noqa: F401
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except ImportError:
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import warnings
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warnings.warn('unable to load "torchvision" package')
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RELEASE = os.environ.get("RELEASE", False)
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import pytorch_sphinx_theme2
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html_theme = "pytorch_sphinx_theme2"
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html_theme_path = [pytorch_sphinx_theme2.get_html_theme_path()]
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# -- General configuration ------------------------------------------------
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.
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extensions = [
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"sphinx.ext.autodoc",
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"sphinx.ext.autosummary",
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"sphinx.ext.doctest",
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"sphinx.ext.intersphinx",
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"sphinx.ext.todo",
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"sphinx.ext.coverage",
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"sphinx.ext.napoleon",
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"sphinx.ext.autosectionlabel",
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"sphinxcontrib.katex",
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"sphinx_copybutton",
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"sphinx_design",
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"myst_nb",
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"sphinx.ext.linkcode",
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"sphinxcontrib.mermaid",
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"sphinx_sitemap",
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]
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myst_enable_extensions = [
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"colon_fence",
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"deflist",
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"html_image",
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]
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html_baseurl = "https://docs.pytorch.org/docs/stable/" # needed for sphinx-sitemap
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sitemap_locales = [None]
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sitemap_excludes = [
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"search.html",
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"genindex.html",
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]
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sitemap_url_scheme = "{link}"
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html_additional_pages = {
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"404": "404.html",
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}
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# build the templated autosummary files
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autosummary_generate = True
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numpydoc_show_class_members = False
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# autosectionlabel throws warnings if section names are duplicated.
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# The following tells autosectionlabel to not throw a warning for
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# duplicated section names that are in different documents.
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autosectionlabel_prefix_document = True
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# katex options
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#
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#
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katex_prerender = True
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# General information about the project.
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project = "PyTorch"
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copyright = "PyTorch Contributors"
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author = "PyTorch Contributors"
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torch_version = str(torch.__version__)
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# The version info for the project you're documenting, acts as replacement for
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# |version| and |release|, also used in various other places throughout the
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# built documents.
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#
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# The short X.Y version.
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# TODO: change to [:2] at v1.0
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version = "main (" + torch_version + " )"
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# The full version, including alpha/beta/rc tags.
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release = "main"
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# Customized html_title here.
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# Default is " ".join(project, release, "documentation") if not set
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if RELEASE:
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# Turn 1.11.0aHASH into 1.11
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# Note: the release candidates should no longer have the aHASH suffix, but in any
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# case we wish to leave only major.minor, even for rc builds.
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version = ".".join(torch_version.split(".")[:2])
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html_title = " ".join((project, version, "documentation"))
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release = version
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switcher_version = "main" if not RELEASE else version
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html_static_path = ["_static"]
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html_theme_options = {
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"logo": {"text": "Home"},
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"analytics_id": "GTM-T8XT4PS",
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"canonical_url": "https://docs.pytorch.org/docs/stable/",
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"switcher": {
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"json_url": "https://docs.pytorch.org/docs/pytorch-versions.json",
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"version_match": switcher_version,
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},
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"show_toc_level": 2,
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"navigation_with_keys": False,
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"external_links": [
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{
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"name": "Tutorials",
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"url": "https://docs.pytorch.org/tutorials/",
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},
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],
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"show_version_warning_banner": True,
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"icon_links": [
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{
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"name": "X",
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"url": "https://x.com/PyTorch",
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"icon": "fa-brands fa-x-twitter",
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},
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{
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"name": "GitHub",
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"url": "https://github.com/pytorch/pytorch",
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"icon": "fa-brands fa-github",
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},
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{
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"name": "PyTorch Forum",
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"url": "https://discuss.pytorch.org/",
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"icon": "fa-brands fa-discourse",
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},
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{
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"name": "PyPi",
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"url": "https://pypi.org/project/torch/",
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"icon": "fa-brands fa-python",
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},
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],
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"navbar_align": "left",
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"navbar_start": ["version-switcher", "navbar-logo"],
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"navbar_center": ["navbar-nav"],
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"navbar_end": ["search-field-custom", "theme-switcher", "navbar-icon-links"],
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"header_links_before_dropdown": 6,
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"navbar_persistent": [],
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"use_edit_page_button": True,
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"pytorch_project": "docs",
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}
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theme_variables = pytorch_sphinx_theme2.get_theme_variables()
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html_context = {
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"github_url": "https://github.com",
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"github_user": "pytorch",
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"github_repo": "pytorch",
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"feedback_url": "https://github.com/pytorch/pytorch",
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"github_version": "main",
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"pytorch_project": "docs",
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"doc_path": "docs/source",
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"theme_variables": theme_variables,
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# library links are defined in
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# pytorch_sphinx_theme2/pytorch_sphinx_theme2/links.json
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"library_links": theme_variables.get("library_links", []),
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"version": version,
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"date_info": {
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"paths_to_skip": ["generated/", "index"],
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},
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}
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napoleon_use_ivar = True
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# Add any paths that contain templates here, relative to this directory.
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templates_path = [
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"_templates",
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os.path.join(os.path.dirname(pytorch_sphinx_theme2.__file__), "templates"),
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]
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# TODO: document these and remove them from here.
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coverage_ignore_functions = [
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# torch
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"typename",
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# torch.cuda._sanitizer
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"zip_arguments",
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"zip_by_key",
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# torch.distributed.autograd
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"is_available",
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# torch.distributed.checkpoint.state_dict
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"gc_context",
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# torch.distributed.elastic.events
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"record_rdzv_event",
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# torch.distributed.elastic.metrics
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"initialize_metrics",
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# torch.distributed.elastic.rendezvous.registry
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"get_rendezvous_handler",
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# torch.distributed.launch
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"launch",
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"main",
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"parse_args",
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# torch.distributed.rpc
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"is_available",
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# torch.distributed.run
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"config_from_args",
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"determine_local_world_size",
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"get_args_parser",
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"get_rdzv_endpoint",
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"get_use_env",
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"main",
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"parse_args",
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"parse_min_max_nnodes",
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"run",
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"run_script_path",
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# torch.distributions.constraints
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"is_dependent",
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# torch.hub
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"import_module",
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# torch.jit
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"export_opnames",
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# torch.jit.unsupported_tensor_ops
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"execWrapper",
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# torch.onnx
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"unregister_custom_op_symbolic",
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# torch.ao.quantization
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"default_eval_fn",
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# torch.backends
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"disable_global_flags",
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"flags_frozen",
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# torch.distributed.algorithms.ddp_comm_hooks
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"register_ddp_comm_hook",
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# torch.nn.parallel
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"DistributedDataParallelCPU",
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# torch.utils
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"set_module",
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"burn_in_info",
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"get_info_and_burn_skeleton",
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"get_inline_skeleton",
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"get_model_info",
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"get_storage_info",
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"hierarchical_pickle",
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# torch.amp.autocast_mode
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"autocast_decorator",
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# torch.ao.nn.quantized.dynamic.modules.rnn
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"apply_permutation",
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"pack_weight_bias",
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# torch.ao.nn.quantized.reference.modules.rnn
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"get_quantized_weight",
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# torch.ao.ns.fx.graph_matcher
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"get_matching_subgraph_pairs",
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# torch.ao.ns.fx.graph_passes
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"add_loggers_to_model",
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"create_a_shadows_b",
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# torch.ao.ns.fx.mappings
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"add_op_to_sets_of_related_ops",
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"get_base_name_for_op",
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"get_base_name_to_sets_of_related_ops",
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"get_node_type_to_io_type_map",
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"get_unmatchable_types_map",
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# torch.ao.ns.fx.n_shadows_utils
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"create_add_loggers_graph",
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"create_n_transformed_and_logged_copies_of_subgraph",
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"create_one_transformed_and_logged_copy_of_subgraph",
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"create_results_comparison",
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"create_submodule_from_subgraph",
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"extract_weight_comparison",
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"group_results_by_subgraph",
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"print_n_shadows_summary",
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# torch.ao.ns.fx.pattern_utils
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"end_node_matches_reversed_fusion",
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"get_reversed_fusions",
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"get_type_a_related_to_b",
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# torch.ao.ns.fx.utils
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"get_arg_indices_of_inputs_to_log",
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"get_node_first_input_and_output_type",
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"get_node_input_qparams",
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"get_normalized_nth_input",
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"get_number_of_non_param_args",
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"get_target_type_str",
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"maybe_add_missing_fqns",
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"maybe_dequantize_first_two_tensor_args_and_handle_tuples",
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"op_type_supports_shadowing",
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"rekey_logger_info_on_node_name_of_model",
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"return_first_non_observer_node",
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# torch.ao.ns.fx.weight_utils
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"extract_weight_from_node",
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"get_conv_fun_weight",
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"get_conv_mod_weight",
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"get_linear_fun_weight",
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"get_linear_mod_weight",
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"get_lstm_mod_weights",
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"get_lstm_weight",
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"get_op_to_type_to_weight_extraction_fn",
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"get_qconv_fun_weight",
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"get_qlinear_fun_weight",
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"get_qlstm_weight",
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"mod_0_weight_detach",
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"mod_weight_bias_0",
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"mod_weight_detach",
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# torch.ao.pruning.sparsifier.utils
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"fqn_to_module",
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"get_arg_info_from_tensor_fqn",
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"module_contains_param",
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"module_to_fqn",
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"swap_module",
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# torch.ao.quantization.backend_config.executorch
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"get_executorch_backend_config",
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# torch.ao.quantization.backend_config.fbgemm
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"get_fbgemm_backend_config",
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# torch.ao.quantization.backend_config.native
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"get_native_backend_config",
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"get_native_backend_config_dict",
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"get_test_only_legacy_native_backend_config",
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"get_test_only_legacy_native_backend_config_dict",
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# torch.ao.quantization.backend_config.onednn
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"get_onednn_backend_config",
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# torch.ao.quantization.backend_config.qnnpack
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"get_qnnpack_backend_config",
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# torch.ao.quantization.backend_config.tensorrt
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"get_tensorrt_backend_config",
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"get_tensorrt_backend_config_dict",
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# torch.ao.quantization.backend_config.utils
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"entry_to_pretty_str",
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"get_fused_module_classes",
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"get_fuser_method_mapping",
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"get_fusion_pattern_to_extra_inputs_getter",
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"get_fusion_pattern_to_root_node_getter",
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"get_module_to_qat_module",
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"get_pattern_to_dtype_configs",
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"get_pattern_to_input_type_to_index",
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"get_qat_module_classes",
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"get_root_module_to_quantized_reference_module",
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"pattern_to_human_readable",
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"remove_boolean_dispatch_from_name",
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# torch.ao.quantization.backend_config.x86
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"get_x86_backend_config",
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# torch.ao.quantization.fuse_modules
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"fuse_known_modules",
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"fuse_modules_qat",
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# torch.ao.quantization.fuser_method_mappings
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"fuse_conv_bn",
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"fuse_conv_bn_relu",
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"fuse_convtranspose_bn",
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"fuse_linear_bn",
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"get_fuser_method",
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"get_fuser_method_new",
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# torch.ao.quantization.fx.convert
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"convert",
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"convert_custom_module",
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"convert_standalone_module",
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"convert_weighted_module",
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# torch.ao.quantization.fx.fuse
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"fuse",
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# torch.ao.quantization.fx.lower_to_fbgemm
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"lower_to_fbgemm",
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# torch.ao.quantization.fx.lower_to_qnnpack
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"lower_to_qnnpack",
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# torch.ao.quantization.fx.pattern_utils
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"get_default_fusion_patterns",
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"get_default_output_activation_post_process_map",
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"get_default_quant_patterns",
|
|
# torch.ao.quantization.fx.prepare
|
|
"insert_observers_for_model",
|
|
"prepare",
|
|
"propagate_dtypes_for_known_nodes",
|
|
# torch.ao.quantization.fx.utils
|
|
"all_node_args_except_first",
|
|
"all_node_args_have_no_tensors",
|
|
"assert_and_get_unique_device",
|
|
"collect_producer_nodes",
|
|
"create_getattr_from_value",
|
|
"create_node_from_old_node_preserve_meta",
|
|
"get_custom_module_class_keys",
|
|
"get_linear_prepack_op_for_dtype",
|
|
"get_new_attr_name_with_prefix",
|
|
"get_non_observable_arg_indexes_and_types",
|
|
"get_qconv_prepack_op",
|
|
"get_skipped_module_name_and_classes",
|
|
"graph_module_from_producer_nodes",
|
|
"maybe_get_next_module",
|
|
"node_arg_is_bias",
|
|
"node_arg_is_weight",
|
|
"return_arg_list",
|
|
# torch.ao.quantization.pt2e.graph_utils
|
|
"bfs_trace_with_node_process",
|
|
"find_sequential_partitions",
|
|
"get_equivalent_types",
|
|
"update_equivalent_types_dict",
|
|
# torch.ao.quantization.pt2e.prepare
|
|
"prepare",
|
|
# torch.ao.quantization.pt2e.representation.rewrite
|
|
"reference_representation_rewrite",
|
|
# torch.ao.quantization.pt2e.utils
|
|
"fold_bn_weights_into_conv_node",
|
|
"remove_tensor_overload_for_qdq_ops",
|
|
# torch.ao.quantization.qconfig
|
|
"get_default_qat_qconfig",
|
|
"get_default_qat_qconfig_dict",
|
|
"get_default_qconfig",
|
|
"get_default_qconfig_dict",
|
|
"qconfig_equals",
|
|
# torch.ao.quantization.quantization_mappings
|
|
"get_default_dynamic_quant_module_mappings",
|
|
"get_default_dynamic_sparse_quant_module_mappings",
|
|
"get_default_float_to_quantized_operator_mappings",
|
|
"get_default_qat_module_mappings",
|
|
"get_default_qconfig_propagation_list",
|
|
"get_default_static_quant_module_mappings",
|
|
"get_default_static_quant_reference_module_mappings",
|
|
"get_default_static_sparse_quant_module_mappings",
|
|
"get_dynamic_quant_module_class",
|
|
"get_embedding_qat_module_mappings",
|
|
"get_embedding_static_quant_module_mappings",
|
|
"get_quantized_operator",
|
|
"get_static_quant_module_class",
|
|
"no_observer_set",
|
|
# torch.ao.quantization.quantize
|
|
"get_default_custom_config_dict",
|
|
# torch.ao.quantization.quantize_fx
|
|
"attach_preserved_attrs_to_model",
|
|
"convert_to_reference_fx",
|
|
# torch.ao.quantization.quantize_jit
|
|
"convert_dynamic_jit",
|
|
"convert_jit",
|
|
"fuse_conv_bn_jit",
|
|
"prepare_dynamic_jit",
|
|
"prepare_jit",
|
|
"quantize_dynamic_jit",
|
|
"quantize_jit",
|
|
"script_qconfig",
|
|
"script_qconfig_dict",
|
|
# torch.ao.quantization.quantize_pt2e
|
|
"convert_pt2e",
|
|
"prepare_pt2e",
|
|
"prepare_qat_pt2e",
|
|
# torch.ao.quantization.quantizer.embedding_quantizer
|
|
"get_embedding_operators_config",
|
|
# torch.ao.quantization.quantizer.xnnpack_quantizer_utils
|
|
"get_bias_qspec",
|
|
"get_input_act_qspec",
|
|
"get_output_act_qspec",
|
|
"get_weight_qspec",
|
|
"propagate_annotation",
|
|
"register_annotator",
|
|
"activation_dtype",
|
|
"check_node",
|
|
"has_no_children_ignoring_parametrizations",
|
|
"is_per_channel",
|
|
"is_per_tensor",
|
|
"op_is_int8_dynamically_quantized",
|
|
"to_underlying_dtype",
|
|
"weight_dtype",
|
|
"weight_is_quantized",
|
|
"weight_is_statically_quantized",
|
|
# torch.backends.cudnn.rnn
|
|
"get_cudnn_mode",
|
|
"init_dropout_state",
|
|
# torch.backends.xeon.run_cpu
|
|
"create_args",
|
|
# torch.cuda.amp.autocast_mode
|
|
"custom_bwd",
|
|
"custom_fwd",
|
|
# torch.cuda.amp.common
|
|
"amp_definitely_not_available",
|
|
# torch.mtia.memory
|
|
"reset_peak_memory_stats",
|
|
# torch.cuda.nccl
|
|
"all_gather",
|
|
"all_reduce",
|
|
"broadcast",
|
|
"init_rank",
|
|
"reduce",
|
|
"reduce_scatter",
|
|
"unique_id",
|
|
"version",
|
|
# torch.cuda.profiler
|
|
"init",
|
|
"profile",
|
|
"start",
|
|
"stop",
|
|
# torch.distributed.algorithms.ddp_comm_hooks.ddp_zero_hook
|
|
"hook_with_zero_step",
|
|
"hook_with_zero_step_interleaved",
|
|
# torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook
|
|
"post_localSGD_hook",
|
|
# torch.distributed.algorithms.ddp_comm_hooks.quantization_hooks
|
|
"quantization_perchannel_hook",
|
|
"quantization_pertensor_hook",
|
|
# torch.distributed.algorithms.model_averaging.utils
|
|
"average_parameters",
|
|
"average_parameters_or_parameter_groups",
|
|
"get_params_to_average",
|
|
# torch.distributed.checkpoint.default_planner
|
|
"create_default_global_load_plan",
|
|
"create_default_global_save_plan",
|
|
"create_default_local_load_plan",
|
|
"create_default_local_save_plan",
|
|
# torch.distributed.checkpoint.optimizer
|
|
"load_sharded_optimizer_state_dict",
|
|
# torch.distributed.checkpoint.planner_helpers
|
|
"create_read_items_for_chunk_list",
|
|
# torch.distributed.checkpoint.state_dict_loader
|
|
"load_state_dict",
|
|
# torch.distributed.checkpoint.state_dict_saver
|
|
"save_state_dict",
|
|
# torch.distributed.checkpoint.utils
|
|
"find_state_dict_object",
|
|
"find_tensor_shard",
|
|
"isend",
|
|
"monitored_barrier",
|
|
"new_group",
|
|
"new_subgroups",
|
|
"new_subgroups_by_enumeration",
|
|
"recv",
|
|
"reduce",
|
|
"reduce_scatter",
|
|
"reduce_scatter_tensor",
|
|
"scatter",
|
|
"scatter_object_list",
|
|
"send",
|
|
"supports_complex",
|
|
# torch.distributed.elastic.events.handlers
|
|
"get_logging_handler",
|
|
# torch.distributed.elastic.metrics.api
|
|
"configure",
|
|
"getStream",
|
|
"get_elapsed_time_ms",
|
|
"prof",
|
|
"profile",
|
|
"publish_metric",
|
|
"put_metric",
|
|
# torch.distributed.elastic.multiprocessing.api
|
|
"get_std_cm",
|
|
"to_map",
|
|
# torch.distributed.elastic.multiprocessing.errors.handlers
|
|
"get_error_handler",
|
|
# torch.distributed.elastic.multiprocessing.redirects
|
|
"get_libc",
|
|
"redirect",
|
|
# torch.distributed.elastic.multiprocessing.tail_log
|
|
"tail_logfile",
|
|
# torch.distributed.elastic.rendezvous.dynamic_rendezvous
|
|
"get_method_name",
|
|
# torch.distributed.elastic.rendezvous.etcd_rendezvous
|
|
"create_rdzv_handler",
|
|
# torch.distributed.elastic.rendezvous.etcd_server
|
|
"find_free_port",
|
|
"stop_etcd",
|
|
# torch.distributed.elastic.rendezvous.etcd_store
|
|
"cas_delay",
|
|
# torch.distributed.elastic.rendezvous.static_tcp_rendezvous
|
|
"create_rdzv_handler",
|
|
# torch.distributed.elastic.rendezvous.utils
|
|
"parse_rendezvous_endpoint",
|
|
# torch.distributed.elastic.timer.api
|
|
"configure",
|
|
"expires",
|
|
# torch.distributed.elastic.utils.api
|
|
"get_env_variable_or_raise",
|
|
"get_socket_with_port",
|
|
# torch.distributed.elastic.utils.distributed
|
|
"create_c10d_store",
|
|
"get_free_port",
|
|
"get_socket_with_port",
|
|
# torch.distributed.elastic.utils.log_level
|
|
"get_log_level",
|
|
# torch.distributed.elastic.utils.logging
|
|
"get_logger",
|
|
# torch.distributed.elastic.utils.store
|
|
"barrier",
|
|
"get_all",
|
|
"synchronize",
|
|
"store_timeout",
|
|
# torch.distributed.fsdp.wrap
|
|
"always_wrap_policy",
|
|
"enable_wrap",
|
|
"lambda_auto_wrap_policy",
|
|
"size_based_auto_wrap_policy",
|
|
"transformer_auto_wrap_policy",
|
|
"wrap",
|
|
# torch.distributed.nn.functional
|
|
"all_to_all",
|
|
"all_to_all_single",
|
|
# torch.distributed.nn.jit.instantiator
|
|
"get_arg_return_types_from_interface",
|
|
"instantiate_non_scriptable_remote_module_template",
|
|
"instantiate_scriptable_remote_module_template",
|
|
# torch.distributed.nn.jit.templates.remote_module_template
|
|
"get_remote_module_template",
|
|
# torch.distributed.optim.utils
|
|
"as_functional_optim",
|
|
"register_functional_optim",
|
|
# torch.distributed.rendezvous
|
|
"register_rendezvous_handler",
|
|
"rendezvous",
|
|
# torch.distributed.rpc.api
|
|
"get_worker_info",
|
|
"method_factory",
|
|
"new_method",
|
|
"remote",
|
|
"rpc_async",
|
|
"rpc_sync",
|
|
"shutdown",
|
|
# torch.distributed.rpc.backend_registry
|
|
"backend_registered",
|
|
"construct_rpc_backend_options",
|
|
"init_backend",
|
|
"register_backend",
|
|
# torch.distributed.rpc.internal
|
|
"deserialize",
|
|
"serialize",
|
|
# torch.distributed.tensor.parallel.api
|
|
"parallelize_module",
|
|
# torch.distributed.tensor.parallel.input_reshard
|
|
"input_reshard",
|
|
# torch.distributed.tensor.parallel.loss
|
|
"loss_parallel",
|
|
# torch.distributed.tensor.parallel.style
|
|
"make_sharded_output_tensor",
|
|
# torch.distributions.utils
|
|
"broadcast_all",
|
|
"clamp_probs",
|
|
"logits_to_probs",
|
|
"probs_to_logits",
|
|
"tril_matrix_to_vec",
|
|
"vec_to_tril_matrix",
|
|
# torch.fx.annotate
|
|
"annotate",
|
|
# torch.fx.experimental.accelerator_partitioner
|
|
"check_dependency",
|
|
"combine_two_partitions",
|
|
"get_bfs_level_partition",
|
|
"get_device_partition_stats",
|
|
"get_device_to_partitions_mapping",
|
|
"get_logical_id_to_device",
|
|
"get_node_to_partition_mapping",
|
|
"reorganize_partitions",
|
|
"reset_partition_device",
|
|
"set_parents_and_children",
|
|
# torch.fx.experimental.const_fold
|
|
"get_unique_attr_name_in_module",
|
|
"split_const_subgraphs",
|
|
# torch.fx.experimental.debug
|
|
"set_trace",
|
|
# torch.fx.experimental.graph_gradual_typechecker
|
|
"adaptiveavgpool2d_check",
|
|
"adaptiveavgpool2d_inference_rule",
|
|
"add_inference_rule",
|
|
"all_eq",
|
|
"bn2d_inference_rule",
|
|
"broadcast_types",
|
|
"calculate_out_dimension",
|
|
"conv2d_inference_rule",
|
|
"conv_refinement_rule",
|
|
"conv_rule",
|
|
"element_wise_eq",
|
|
"expand_to_tensor_dim",
|
|
"first_two_eq",
|
|
"flatten_check",
|
|
"flatten_inference_rule",
|
|
"flatten_refinement_rule",
|
|
"get_attr_inference_rule",
|
|
"get_greatest_upper_bound",
|
|
"get_parameter",
|
|
"linear_check",
|
|
"linear_inference_rule",
|
|
"linear_refinement_rule",
|
|
"maxpool2d_check",
|
|
"maxpool2d_inference_rule",
|
|
"register_algebraic_expressions_inference_rule",
|
|
"register_inference_rule",
|
|
"register_refinement_rule",
|
|
"relu_inference_rule",
|
|
"reshape_inference_rule",
|
|
"transpose_inference_rule",
|
|
# torch.fx.experimental.merge_matmul
|
|
"are_nodes_independent",
|
|
"may_depend_on",
|
|
"merge_matmul",
|
|
"split_result_tensors",
|
|
# torch.fx.experimental.meta_tracer
|
|
"embedding_override",
|
|
"functional_relu_override",
|
|
"gen_constructor_wrapper",
|
|
"nn_layernorm_override",
|
|
"proxys_to_metas",
|
|
"symbolic_trace",
|
|
"torch_abs_override",
|
|
"torch_nn_relu_override",
|
|
"torch_relu_override",
|
|
"torch_where_override",
|
|
# torch.fx.experimental.migrate_gradual_types.constraint
|
|
"is_algebraic_expression",
|
|
"is_bool_expr",
|
|
"is_dim",
|
|
# torch.fx.experimental.migrate_gradual_types.constraint_generator
|
|
"adaptive_inference_rule",
|
|
"add_layer_norm_constraints",
|
|
"add_linear_constraints",
|
|
"arange_inference_rule",
|
|
"assert_inference_rule",
|
|
"batchnorm_inference_rule",
|
|
"bmm_inference_rule",
|
|
"broadcasting_inference_rule",
|
|
"conv2d_inference_rule",
|
|
"cumsum_inference_rule",
|
|
"embedding_inference_rule",
|
|
"embedding_inference_rule_functional",
|
|
"eq_inference_rule",
|
|
"equality_inference_rule",
|
|
"expand_inference_rule",
|
|
"flatten_inference_rule",
|
|
"full_inference_rule",
|
|
"gen_broadcasting_constraints",
|
|
"gen_embedding_rules",
|
|
"gen_layer_norm_constraints",
|
|
"generate_flatten_constraints",
|
|
"get_attr_inference_rule",
|
|
"getitem_inference_rule",
|
|
"gt_inference_rule",
|
|
"index_select_inference_rule",
|
|
"layer_norm_functional",
|
|
"layer_norm_inference_rule",
|
|
"linear_constraints",
|
|
"linear_inference_rule",
|
|
"lt_inference_rule",
|
|
"masked_fill_inference_rule",
|
|
"maxpool_inference_rule",
|
|
"neq_inference_rule",
|
|
"range_check",
|
|
"register_inference_rule",
|
|
"relu_inference_rule",
|
|
"reshape_inference_rule",
|
|
"size_inference_rule",
|
|
"tensor_inference_rule",
|
|
"torch_dim_inference_rule",
|
|
"torch_linear_inference_rule",
|
|
"transpose_inference_rule",
|
|
"type_inference_rule",
|
|
"view_inference_rule",
|
|
# torch.fx.experimental.migrate_gradual_types.constraint_transformation
|
|
"apply_padding",
|
|
"broadcast_dim",
|
|
"calc_last_two_dims",
|
|
"create_equality_constraints_for_broadcasting",
|
|
"gen_all_reshape_possibilities",
|
|
"gen_broadcasting_constraints",
|
|
"gen_consistency_constraints",
|
|
"gen_greatest_upper_bound",
|
|
"gen_lists_of_dims",
|
|
"generate_all_broadcasting_possibilities_no_padding",
|
|
"generate_all_int_dyn_dim_possibilities",
|
|
"generate_binconstraint_d",
|
|
"generate_binconstraint_t",
|
|
"generate_broadcasting",
|
|
"generate_calc_conv",
|
|
"generate_calc_maxpool",
|
|
"generate_calc_product",
|
|
"generate_conj",
|
|
"generate_d_gub",
|
|
"generate_disj",
|
|
"generate_gub",
|
|
"generate_reshape",
|
|
"is_dim_div_by_target",
|
|
"is_target_div_by_dim",
|
|
"no_broadcast_dim_with_index",
|
|
"register_transformation_rule",
|
|
"transform_constraint",
|
|
"transform_get_item",
|
|
"transform_get_item_tensor",
|
|
"transform_index_select",
|
|
"transform_transpose",
|
|
"valid_index",
|
|
"valid_index_tensor",
|
|
# torch.fx.experimental.migrate_gradual_types.transform_to_z3
|
|
"evaluate_conditional_with_constraints",
|
|
# torch.fx.experimental.migrate_gradual_types.util
|
|
"gen_bvar",
|
|
"gen_dvar",
|
|
"gen_nat_constraints",
|
|
"gen_tensor_dims",
|
|
"gen_tvar",
|
|
# torch.fx.experimental.optimization
|
|
"extract_subgraph",
|
|
"fuse",
|
|
"gen_mkl_autotuner",
|
|
"matches_module_pattern",
|
|
"modules_to_mkldnn",
|
|
"optimize_for_inference",
|
|
"remove_dropout",
|
|
"replace_node_module",
|
|
"reset_modules",
|
|
"use_mkl_length",
|
|
# torch.fx.experimental.partitioner_utils
|
|
"get_comm_latency_between",
|
|
"get_extra_size_of",
|
|
"get_latency_of_one_partition",
|
|
"get_latency_of_partitioned_graph",
|
|
"get_partition_to_latency_mapping",
|
|
# torch.fx.experimental.proxy_tensor
|
|
"decompose",
|
|
"disable_autocast_cache",
|
|
"disable_proxy_modes_tracing",
|
|
"dispatch_trace",
|
|
"extract_val",
|
|
"fake_signature",
|
|
"fetch_sym_proxy",
|
|
"fetch_object_proxy",
|
|
"get_innermost_proxy_mode",
|
|
"get_isolated_graphmodule",
|
|
"get_proxy_slot",
|
|
"get_torch_dispatch_modes",
|
|
"has_proxy_slot",
|
|
"is_sym_node",
|
|
"maybe_handle_decomp",
|
|
"proxy_call",
|
|
"set_meta",
|
|
"set_original_aten_op",
|
|
"set_proxy_slot",
|
|
"snapshot_fake",
|
|
"thunkify",
|
|
"track_tensor",
|
|
"track_tensor_tree",
|
|
"wrap_key",
|
|
"wrapper_and_args_for_make_fx",
|
|
# torch.fx.experimental.recording
|
|
"record_shapeenv_event",
|
|
"replay_shape_env_events",
|
|
"shape_env_check_state_equal",
|
|
# torch.fx.experimental.sym_node
|
|
"ceil_impl",
|
|
"floor_ceil_helper",
|
|
"floor_impl",
|
|
"method_to_operator",
|
|
"sympy_is_channels_last_contiguous_2d",
|
|
"sympy_is_channels_last_contiguous_3d",
|
|
"sympy_is_channels_last_strides_2d",
|
|
"sympy_is_channels_last_strides_3d",
|
|
"sympy_is_channels_last_strides_generic",
|
|
"sympy_is_contiguous",
|
|
"sympy_is_contiguous_generic",
|
|
"to_node",
|
|
"wrap_node",
|
|
"sym_sqrt",
|
|
# torch.fx.experimental.symbolic_shapes
|
|
"bind_symbols",
|
|
"cast_symbool_to_symint_guardless",
|
|
"create_contiguous",
|
|
"error",
|
|
"eval_guards",
|
|
"eval_is_non_overlapping_and_dense",
|
|
"expect_true",
|
|
"find_symbol_binding_fx_nodes",
|
|
"free_symbols",
|
|
"free_unbacked_symbols",
|
|
"fx_placeholder_targets",
|
|
"fx_placeholder_vals",
|
|
"guard_bool",
|
|
"guard_float",
|
|
"guard_int",
|
|
"guard_scalar",
|
|
"has_hint",
|
|
"has_symbolic_sizes_strides",
|
|
"is_channels_last_contiguous_2d",
|
|
"is_channels_last_contiguous_3d",
|
|
"is_channels_last_strides_2d",
|
|
"is_channels_last_strides_3d",
|
|
"is_contiguous",
|
|
"is_non_overlapping_and_dense_indicator",
|
|
"is_nested_int",
|
|
"is_symbol_binding_fx_node",
|
|
"is_symbolic",
|
|
# torch.fx.experimental.unification.core
|
|
"reify",
|
|
# torch.fx.experimental.unification.match
|
|
"edge",
|
|
"match",
|
|
"ordering",
|
|
"supercedes",
|
|
# torch.fx.experimental.unification.more
|
|
"reify_object",
|
|
"unifiable",
|
|
"unify_object",
|
|
# torch.fx.experimental.unification.multipledispatch.conflict
|
|
"ambiguities",
|
|
"ambiguous",
|
|
"consistent",
|
|
"edge",
|
|
"ordering",
|
|
"super_signature",
|
|
"supercedes",
|
|
# torch.fx.experimental.unification.multipledispatch.core
|
|
"dispatch",
|
|
"ismethod",
|
|
# torch.fx.experimental.unification.multipledispatch.dispatcher
|
|
"ambiguity_warn",
|
|
"halt_ordering",
|
|
"restart_ordering",
|
|
"source",
|
|
"str_signature",
|
|
"variadic_signature_matches",
|
|
"variadic_signature_matches_iter",
|
|
"warning_text",
|
|
# torch.fx.experimental.unification.multipledispatch.utils
|
|
"expand_tuples",
|
|
"groupby",
|
|
"raises",
|
|
"reverse_dict",
|
|
# torch.fx.experimental.unification.multipledispatch.variadic
|
|
"isvariadic",
|
|
# torch.fx.experimental.unification.unification_tools
|
|
"assoc",
|
|
"assoc_in",
|
|
"dissoc",
|
|
"first",
|
|
"get_in",
|
|
"getter",
|
|
"groupby",
|
|
"itemfilter",
|
|
"itemmap",
|
|
"keyfilter",
|
|
"keymap",
|
|
"merge",
|
|
"merge_with",
|
|
"update_in",
|
|
"valfilter",
|
|
"valmap",
|
|
# torch.fx.experimental.unification.utils
|
|
"freeze",
|
|
"hashable",
|
|
"raises",
|
|
"reverse_dict",
|
|
"transitive_get",
|
|
"xfail",
|
|
# torch.fx.experimental.unification.variable
|
|
"var",
|
|
"vars",
|
|
# torch.fx.experimental.unify_refinements
|
|
"check_for_type_equality",
|
|
"convert_eq",
|
|
"infer_symbolic_types",
|
|
"infer_symbolic_types_single_pass",
|
|
"substitute_all_types",
|
|
"substitute_solution_one_type",
|
|
"unify_eq",
|
|
# torch.fx.experimental.validator
|
|
"bisect",
|
|
"translation_validation_enabled",
|
|
"translation_validation_timeout",
|
|
"z3op",
|
|
"z3str",
|
|
# torch.fx.graph_module
|
|
"reduce_graph_module",
|
|
"reduce_package_graph_module",
|
|
# torch.fx.node
|
|
"has_side_effect",
|
|
"map_aggregate",
|
|
"map_arg",
|
|
# torch.fx.operator_schemas
|
|
"check_for_mutable_operation",
|
|
"create_type_hint",
|
|
"get_signature_for_torch_op",
|
|
"normalize_function",
|
|
"normalize_module",
|
|
"type_matches",
|
|
# torch.fx.passes.annotate_getitem_nodes
|
|
"annotate_getitem_nodes",
|
|
# torch.fx.passes.backends.cudagraphs
|
|
"partition_cudagraphs",
|
|
# torch.fx.passes.dialect.common.cse_pass
|
|
"get_CSE_banned_ops",
|
|
# torch.fx.passes.graph_manipulation
|
|
"get_size_of_all_nodes",
|
|
"get_size_of_node",
|
|
"get_tensor_meta",
|
|
"replace_target_nodes_with",
|
|
# torch.fx.passes.infra.pass_manager
|
|
"pass_result_wrapper",
|
|
"this_before_that_pass_constraint",
|
|
# torch.fx.passes.operator_support
|
|
"any_chain",
|
|
"chain",
|
|
"create_op_support",
|
|
# torch.fx.passes.param_fetch
|
|
"default_matching",
|
|
"extract_attrs_for_lowering",
|
|
"lift_lowering_attrs_to_nodes",
|
|
# torch.fx.passes.pass_manager
|
|
"inplace_wrapper",
|
|
"log_hook",
|
|
"loop_pass",
|
|
"these_before_those_pass_constraint",
|
|
"this_before_that_pass_constraint",
|
|
# torch.fx.passes.regional_inductor
|
|
"regional_inductor",
|
|
# torch.fx.passes.reinplace
|
|
"reinplace",
|
|
# torch.fx.passes.split_module
|
|
"split_module",
|
|
# torch.fx.passes.split_utils
|
|
"getattr_recursive",
|
|
"setattr_recursive",
|
|
"split_by_tags",
|
|
# torch.fx.passes.splitter_base
|
|
"generate_inputs_for_submodules",
|
|
# torch.fx.passes.tools_common
|
|
"get_acc_ops_name",
|
|
"get_node_target",
|
|
"is_node_output_tensor",
|
|
"legalize_graph",
|
|
# torch.fx.passes.utils.common
|
|
"compare_graphs",
|
|
"lift_subgraph_as_module",
|
|
# torch.fx.passes.utils.fuser_utils
|
|
"erase_nodes",
|
|
"fuse_as_graphmodule",
|
|
"fuse_by_partitions",
|
|
"insert_subgm",
|
|
"topo_sort",
|
|
"validate_partition",
|
|
# torch.fx.passes.utils.source_matcher_utils
|
|
"check_subgraphs_connected",
|
|
"get_source_partitions",
|
|
# torch.fx.proxy
|
|
"assert_fn",
|
|
# torch.fx.subgraph_rewriter
|
|
"replace_pattern",
|
|
"replace_pattern_with_filters",
|
|
# torch.fx.tensor_type
|
|
"is_consistent",
|
|
"is_more_precise",
|
|
# torch.fx.traceback
|
|
"format_stack",
|
|
"get_current_meta",
|
|
"has_preserved_node_meta",
|
|
"preserve_node_meta",
|
|
"reset_grad_fn_seq_nr",
|
|
"set_current_meta",
|
|
"set_grad_fn_seq_nr",
|
|
"set_stack_trace",
|
|
# torch.jit.annotations
|
|
"ann_to_type",
|
|
"check_fn",
|
|
"get_enum_value_type",
|
|
"get_param_names",
|
|
"get_signature",
|
|
"get_type_line",
|
|
"is_function_or_method",
|
|
"is_tensor",
|
|
"is_vararg",
|
|
"parse_type_line",
|
|
"split_type_line",
|
|
"try_ann_to_type",
|
|
"try_real_annotations",
|
|
# torch.jit.frontend
|
|
"build_class_def",
|
|
"build_def",
|
|
"build_ignore_context_manager",
|
|
"build_param",
|
|
"build_param_list",
|
|
"build_stmts",
|
|
"build_withitems",
|
|
"find_before",
|
|
"get_class_assigns",
|
|
"get_class_properties",
|
|
"get_default_args",
|
|
"get_default_args_for_class",
|
|
"get_jit_class_def",
|
|
"get_jit_def",
|
|
"is_reserved_name",
|
|
"is_torch_jit_ignore_context_manager",
|
|
# torch.jit.generate_bytecode
|
|
"format_bytecode",
|
|
"generate_upgraders_bytecode",
|
|
# torch.jit.quantized
|
|
"apply_permutation",
|
|
"quantize_linear_modules",
|
|
"quantize_rnn_cell_modules",
|
|
"quantize_rnn_modules",
|
|
# torch.library
|
|
"define",
|
|
"get_ctx",
|
|
"impl",
|
|
"impl_abstract",
|
|
# torch.masked.maskedtensor.core
|
|
"is_masked_tensor",
|
|
# torch.masked.maskedtensor.creation
|
|
"as_masked_tensor",
|
|
"masked_tensor",
|
|
# torch.multiprocessing.pool
|
|
"clean_worker",
|
|
# torch.multiprocessing.reductions
|
|
"fd_id",
|
|
"init_reductions",
|
|
"rebuild_cuda_tensor",
|
|
"rebuild_meta_tensor",
|
|
"rebuild_event",
|
|
"rebuild_nested_tensor",
|
|
"rebuild_sparse_coo_tensor",
|
|
"rebuild_sparse_compressed_tensor",
|
|
"rebuild_storage_empty",
|
|
"rebuild_storage_fd",
|
|
"rebuild_storage_filename",
|
|
"rebuild_tensor",
|
|
"rebuild_typed_storage",
|
|
"rebuild_typed_storage_child",
|
|
"reduce_event",
|
|
"reduce_storage",
|
|
"reduce_tensor",
|
|
"reduce_typed_storage",
|
|
"reduce_typed_storage_child",
|
|
"storage_from_cache",
|
|
# torch.multiprocessing.spawn
|
|
"start_processes",
|
|
# torch.nn.functional
|
|
"adaptive_max_pool1d_with_indices", # documented as adaptive_max_pool1d
|
|
"adaptive_max_pool2d_with_indices", # documented as adaptive_max_pool2d
|
|
"adaptive_max_pool3d_with_indices", # documented as adaptive_max_pool3d
|
|
"assert_int_or_pair", # looks unintentionally public
|
|
"fractional_max_pool2d_with_indices", # documented as fractional_max_pool2d
|
|
"fractional_max_pool3d_with_indices", # documented as fractional_max_pool3d
|
|
"max_pool1d_with_indices", # documented as max_pool1d
|
|
"max_pool2d_with_indices", # documented as max_pool2d
|
|
"max_pool3d_with_indices", # documented as max_pool3d
|
|
"multi_head_attention_forward",
|
|
# torch.nn.grad
|
|
"conv1d_input", # legacy helper for gradient computation
|
|
"conv1d_weight", # legacy helper for gradient computation
|
|
"conv2d_input", # legacy helper for gradient computation
|
|
"conv2d_weight", # legacy helper for gradient computation
|
|
"conv3d_input", # legacy helper for gradient computation
|
|
"conv3d_weight", # legacy helper for gradient computation
|
|
# torch.nn.init
|
|
"constant", # deprecated
|
|
"dirac", # deprecated
|
|
"eye", # deprecated
|
|
"kaiming_normal", # deprecated
|
|
"kaiming_uniform", # deprecated
|
|
"normal", # deprecated
|
|
"orthogonal", # deprecated
|
|
"sparse", # deprecated
|
|
"uniform", # deprecated
|
|
"xavier_normal", # deprecated
|
|
"xavier_uniform", # deprecated
|
|
# torch.nn.modules.rnn
|
|
"apply_permutation", # deprecated
|
|
# torch.nn.modules.utils
|
|
"consume_prefix_in_state_dict_if_present",
|
|
# torch.nn.parallel.comm
|
|
"broadcast",
|
|
"broadcast_coalesced",
|
|
"gather",
|
|
"reduce_add",
|
|
"reduce_add_coalesced",
|
|
"scatter",
|
|
# torch.nn.parallel.data_parallel
|
|
"data_parallel",
|
|
# torch.nn.parallel.parallel_apply
|
|
"get_a_var",
|
|
"parallel_apply",
|
|
# torch.nn.parallel.replicate
|
|
"replicate",
|
|
# torch.nn.parallel.scatter_gather
|
|
"gather",
|
|
"is_namedtuple",
|
|
"scatter",
|
|
"scatter_kwargs",
|
|
# torch.nn.utils.rnn
|
|
"bind", # looks unintentionally public
|
|
# torch.onnx.operators
|
|
"reshape_from_tensor_shape",
|
|
"shape_as_tensor",
|
|
# torch.onnx.symbolic_caffe2
|
|
"add",
|
|
"avg_pool2d",
|
|
"cat",
|
|
"conv2d",
|
|
"conv2d_relu",
|
|
"conv_prepack",
|
|
"dequantize",
|
|
"linear",
|
|
"linear_prepack",
|
|
"max_pool2d",
|
|
"nchw2nhwc",
|
|
"nhwc2nchw",
|
|
"quantize_per_tensor",
|
|
"register_quantized_ops",
|
|
"relu",
|
|
"reshape",
|
|
"sigmoid",
|
|
"slice",
|
|
"upsample_nearest2d",
|
|
# torch.onnx.symbolic_helper
|
|
"args_have_same_dtype",
|
|
"check_training_mode",
|
|
"dequantize_helper",
|
|
"is_complex_value",
|
|
"quantize_helper",
|
|
"quantized_args",
|
|
"requantize_bias_helper",
|
|
# torch.onnx.symbolic_opset10
|
|
"dequantize",
|
|
"div",
|
|
"embedding_bag",
|
|
"fake_quantize_per_tensor_affine",
|
|
"flip",
|
|
"fmod",
|
|
"isfinite",
|
|
"isinf",
|
|
"nan_to_num",
|
|
"quantize_per_tensor",
|
|
"quantized_add",
|
|
"quantized_add_relu",
|
|
"quantized_cat",
|
|
"quantized_conv1d",
|
|
"quantized_conv1d_relu",
|
|
"quantized_conv2d",
|
|
"quantized_conv2d_relu",
|
|
"quantized_conv3d",
|
|
"quantized_conv3d_relu",
|
|
"quantized_conv_transpose1d",
|
|
"quantized_conv_transpose2d",
|
|
"quantized_conv_transpose3d",
|
|
"quantized_group_norm",
|
|
"quantized_hardswish",
|
|
"quantized_instance_norm",
|
|
"quantized_layer_norm",
|
|
"quantized_leaky_relu",
|
|
"quantized_linear",
|
|
"quantized_linear_relu",
|
|
"quantized_mul",
|
|
"quantized_sigmoid",
|
|
"slice",
|
|
"sort",
|
|
"topk",
|
|
# torch.onnx.symbolic_opset11
|
|
"Delete",
|
|
"add",
|
|
"append",
|
|
"arange",
|
|
"argsort",
|
|
"atleast_1d",
|
|
"atleast_2d",
|
|
"atleast_3d",
|
|
"cat",
|
|
"chunk",
|
|
"clamp",
|
|
"clamp_max",
|
|
"clamp_min",
|
|
"constant_pad_nd",
|
|
"cumsum",
|
|
"embedding_bag",
|
|
"embedding_renorm",
|
|
"flatten",
|
|
"gather",
|
|
"hardtanh",
|
|
"hstack",
|
|
"im2col",
|
|
"index",
|
|
"index_copy",
|
|
"index_fill",
|
|
"index_put",
|
|
"insert",
|
|
"linalg_det",
|
|
"linalg_vector_norm",
|
|
"logdet",
|
|
"masked_scatter",
|
|
"masked_select",
|
|
"mm",
|
|
"narrow",
|
|
"normal",
|
|
"pad",
|
|
"pixel_shuffle",
|
|
"pop",
|
|
"prim_constant_chunk",
|
|
"reflection_pad",
|
|
"relu6",
|
|
"remainder",
|
|
"replication_pad",
|
|
"round",
|
|
"scatter",
|
|
"select",
|
|
"size",
|
|
"sort",
|
|
"split",
|
|
"split_with_sizes",
|
|
"squeeze",
|
|
"stack",
|
|
"topk",
|
|
"unbind",
|
|
"unique_dim",
|
|
"unsqueeze",
|
|
"vstack",
|
|
# torch.onnx.symbolic_opset12
|
|
"argmax",
|
|
"argmin",
|
|
"binary_cross_entropy_with_logits",
|
|
"celu",
|
|
"cross_entropy_loss",
|
|
"dropout",
|
|
"einsum",
|
|
"ge",
|
|
"le",
|
|
"native_dropout",
|
|
"nll_loss",
|
|
"nll_loss2d",
|
|
"nll_loss_nd",
|
|
"outer",
|
|
"pow",
|
|
"tensordot",
|
|
"unfold",
|
|
# torch.onnx.symbolic_opset13
|
|
"diagonal",
|
|
"fake_quantize_per_channel_affine",
|
|
"fake_quantize_per_tensor_affine",
|
|
"frobenius_norm",
|
|
"log_softmax",
|
|
"nonzero_numpy",
|
|
"quantized_conv1d",
|
|
"quantized_conv1d_relu",
|
|
"quantized_conv2d",
|
|
"quantized_conv2d_relu",
|
|
"quantized_conv3d",
|
|
"quantized_conv3d_relu",
|
|
"quantized_conv_transpose1d",
|
|
"quantized_conv_transpose2d",
|
|
"quantized_conv_transpose3d",
|
|
"quantized_linear",
|
|
"quantized_linear_relu",
|
|
"repeat_interleave",
|
|
"softmax",
|
|
"split",
|
|
"split_with_sizes",
|
|
"tensor_split",
|
|
"tile",
|
|
"unbind",
|
|
"unflatten",
|
|
"unsafe_chunk",
|
|
"unsafe_split",
|
|
"unsafe_split_with_sizes",
|
|
"where",
|
|
# torch.onnx.symbolic_opset14
|
|
"batch_norm",
|
|
"hardswish",
|
|
"quantized_hardswish",
|
|
"reshape",
|
|
"scaled_dot_product_attention",
|
|
"tril",
|
|
"triu",
|
|
# torch.onnx.symbolic_opset15
|
|
"aten__is_",
|
|
"aten__isnot_",
|
|
"bernoulli",
|
|
"prim_unchecked_cast",
|
|
# torch.onnx.symbolic_opset16
|
|
"grid_sampler",
|
|
"scatter_add",
|
|
"scatter_reduce",
|
|
# torch.onnx.symbolic_opset17
|
|
"layer_norm",
|
|
"stft",
|
|
# torch.onnx.symbolic_opset18
|
|
"col2im",
|
|
# torch.onnx.symbolic_opset7
|
|
"max",
|
|
"min",
|
|
# torch.onnx.symbolic_opset8
|
|
"addmm",
|
|
"bmm",
|
|
"empty",
|
|
"empty_like",
|
|
"flatten",
|
|
"full",
|
|
"full_like",
|
|
"gt",
|
|
"lt",
|
|
"matmul",
|
|
"mm",
|
|
"ones",
|
|
"ones_like",
|
|
"prelu",
|
|
"repeat",
|
|
"zeros",
|
|
"zeros_like",
|
|
# torch.onnx.symbolic_opset9
|
|
"abs",
|
|
"acos",
|
|
"adaptive_avg_pool1d",
|
|
"adaptive_avg_pool2d",
|
|
"adaptive_avg_pool3d",
|
|
"adaptive_max_pool1d",
|
|
"adaptive_max_pool2d",
|
|
"adaptive_max_pool3d",
|
|
"add",
|
|
"addcmul",
|
|
"addmm",
|
|
"alias",
|
|
"amax",
|
|
"amin",
|
|
"aminmax",
|
|
"arange",
|
|
"argmax",
|
|
"argmin",
|
|
"as_strided",
|
|
"as_tensor",
|
|
"asin",
|
|
"atan",
|
|
"atan2",
|
|
"avg_pool1d",
|
|
"avg_pool2d",
|
|
"avg_pool3d",
|
|
"baddbmm",
|
|
"batch_norm",
|
|
"bernoulli",
|
|
"bitwise_not",
|
|
"bitwise_or",
|
|
"bmm",
|
|
"broadcast_tensors",
|
|
"broadcast_to",
|
|
"bucketize",
|
|
"cat",
|
|
"cdist",
|
|
"ceil",
|
|
"clamp",
|
|
"clamp_max",
|
|
"clamp_min",
|
|
"clone",
|
|
"constant_pad_nd",
|
|
"contiguous",
|
|
"conv1d",
|
|
"conv2d",
|
|
"conv3d",
|
|
"conv_tbc",
|
|
"conv_transpose1d",
|
|
"conv_transpose2d",
|
|
"conv_transpose3d",
|
|
"convert_element_type",
|
|
"convolution",
|
|
"cos",
|
|
"cosine_similarity",
|
|
"cross",
|
|
"cumsum",
|
|
"detach",
|
|
"dim",
|
|
"div",
|
|
"dot",
|
|
"dropout",
|
|
"elu",
|
|
"embedding",
|
|
"embedding_bag",
|
|
"empty",
|
|
"empty_like",
|
|
"eq",
|
|
"erf",
|
|
"exp",
|
|
"expand",
|
|
"expand_as",
|
|
"eye",
|
|
"fill",
|
|
"flatten",
|
|
"floor",
|
|
"floor_divide",
|
|
"floordiv",
|
|
"frobenius_norm",
|
|
"full",
|
|
"full_like",
|
|
"gather",
|
|
"ge",
|
|
"gelu",
|
|
"get_pool_ceil_padding",
|
|
"glu",
|
|
"group_norm",
|
|
"gru",
|
|
"gt",
|
|
"hann_window",
|
|
"hardshrink",
|
|
"hardsigmoid",
|
|
"hardswish",
|
|
"hardtanh",
|
|
"index",
|
|
"index_add",
|
|
"index_copy",
|
|
"index_fill",
|
|
"index_put",
|
|
"index_select",
|
|
"instance_norm",
|
|
"is_floating_point",
|
|
"is_pinned",
|
|
"isnan",
|
|
"item",
|
|
"kl_div",
|
|
"layer_norm",
|
|
"le",
|
|
"leaky_relu",
|
|
"lerp",
|
|
"lift",
|
|
"linalg_cross",
|
|
"linalg_matrix_norm",
|
|
"linalg_norm",
|
|
"linalg_vector_norm",
|
|
"linear",
|
|
"linspace",
|
|
"log",
|
|
"log10",
|
|
"log1p",
|
|
"log2",
|
|
"log_sigmoid",
|
|
"log_softmax",
|
|
"logical_and",
|
|
"logical_not",
|
|
"logical_or",
|
|
"logical_xor",
|
|
"logit",
|
|
"logsumexp",
|
|
"lstm",
|
|
"lstm_cell",
|
|
"lt",
|
|
"masked_fill",
|
|
"masked_fill_",
|
|
"matmul",
|
|
"max",
|
|
"max_pool1d",
|
|
"max_pool1d_with_indices",
|
|
"max_pool2d",
|
|
"max_pool2d_with_indices",
|
|
"max_pool3d",
|
|
"max_pool3d_with_indices",
|
|
"maximum",
|
|
"meshgrid",
|
|
"min",
|
|
"minimum",
|
|
"mish",
|
|
"mm",
|
|
"movedim",
|
|
"mse_loss",
|
|
"mul",
|
|
"multinomial",
|
|
"mv",
|
|
"narrow",
|
|
"native_layer_norm",
|
|
"ne",
|
|
"neg",
|
|
"new_empty",
|
|
"new_full",
|
|
"new_ones",
|
|
"new_zeros",
|
|
"nonzero",
|
|
"nonzero_numpy",
|
|
"noop_complex_operators",
|
|
"norm",
|
|
"numel",
|
|
"numpy_T",
|
|
"one_hot",
|
|
"ones",
|
|
"ones_like",
|
|
"onnx_placeholder",
|
|
"overload_by_arg_count",
|
|
"pad",
|
|
"pairwise_distance",
|
|
"permute",
|
|
"pixel_shuffle",
|
|
"pixel_unshuffle",
|
|
"pow",
|
|
"prelu",
|
|
"prim_constant",
|
|
"prim_constant_chunk",
|
|
"prim_constant_split",
|
|
"prim_data",
|
|
"prim_device",
|
|
"prim_dtype",
|
|
"prim_if",
|
|
"prim_layout",
|
|
"prim_list_construct",
|
|
"prim_list_unpack",
|
|
"prim_loop",
|
|
"prim_max",
|
|
"prim_min",
|
|
"prim_shape",
|
|
"prim_tolist",
|
|
"prim_tuple_construct",
|
|
"prim_type",
|
|
"prim_unchecked_cast",
|
|
"prim_uninitialized",
|
|
"rand",
|
|
"rand_like",
|
|
"randint",
|
|
"randint_like",
|
|
"randn",
|
|
"randn_like",
|
|
"reciprocal",
|
|
"reflection_pad",
|
|
"relu",
|
|
"relu6",
|
|
"remainder",
|
|
"repeat",
|
|
"repeat_interleave",
|
|
"replication_pad",
|
|
"reshape",
|
|
"reshape_as",
|
|
"rnn_relu",
|
|
"rnn_tanh",
|
|
"roll",
|
|
"rrelu",
|
|
"rsqrt",
|
|
"rsub",
|
|
"scalar_tensor",
|
|
"scatter",
|
|
"scatter_add",
|
|
"select",
|
|
"selu",
|
|
"sigmoid",
|
|
"sign",
|
|
"silu",
|
|
"sin",
|
|
"size",
|
|
"slice",
|
|
"softmax",
|
|
"softplus",
|
|
"softshrink",
|
|
"sort",
|
|
"split",
|
|
"split_with_sizes",
|
|
"sqrt",
|
|
"square",
|
|
"squeeze",
|
|
"stack",
|
|
"std",
|
|
"std_mean",
|
|
"sub",
|
|
"t",
|
|
"take",
|
|
"tan",
|
|
"tanh",
|
|
"tanhshrink",
|
|
"tensor",
|
|
"threshold",
|
|
"to",
|
|
"topk",
|
|
"transpose",
|
|
"true_divide",
|
|
"type_as",
|
|
"unbind",
|
|
"unfold",
|
|
"unsafe_chunk",
|
|
"unsafe_split",
|
|
"unsafe_split_with_sizes",
|
|
"unsqueeze",
|
|
"unsupported_complex_operators",
|
|
"unused",
|
|
"upsample_bilinear2d",
|
|
"upsample_linear1d",
|
|
"upsample_nearest1d",
|
|
"upsample_nearest2d",
|
|
"upsample_nearest3d",
|
|
"upsample_trilinear3d",
|
|
"var",
|
|
"var_mean",
|
|
"view",
|
|
"view_as",
|
|
"where",
|
|
"wrap_logical_op_with_cast_to",
|
|
"wrap_logical_op_with_negation",
|
|
"zero",
|
|
"zeros",
|
|
"zeros_like",
|
|
# torch.onnx.utils
|
|
"disable_apex_o2_state_dict_hook",
|
|
"export",
|
|
"export_to_pretty_string",
|
|
"exporter_context",
|
|
"is_in_onnx_export",
|
|
"model_signature",
|
|
"register_custom_op_symbolic",
|
|
"select_model_mode_for_export",
|
|
"setup_onnx_logging",
|
|
"unconvertible_ops",
|
|
"unpack_quantized_tensor",
|
|
"warn_on_static_input_change",
|
|
# torch.onnx.verification
|
|
"check_export_model_diff",
|
|
"verify",
|
|
"verify_aten_graph",
|
|
# torch.optim.optimizer
|
|
"register_optimizer_step_post_hook",
|
|
"register_optimizer_step_pre_hook",
|
|
# torch.overrides
|
|
"enable_reentrant_dispatch",
|
|
# torch.package.analyze.find_first_use_of_broken_modules
|
|
"find_first_use_of_broken_modules",
|
|
# torch.package.analyze.is_from_package
|
|
"is_from_package",
|
|
# torch.package.analyze.trace_dependencies
|
|
"trace_dependencies",
|
|
# torch.profiler.itt
|
|
"range",
|
|
# torch.profiler.profiler
|
|
"schedule",
|
|
"supported_activities",
|
|
"tensorboard_trace_handler",
|
|
# torch.return_types
|
|
"pytree_register_structseq",
|
|
# torch.serialization
|
|
"check_module_version_greater_or_equal",
|
|
"default_restore_location",
|
|
"load",
|
|
"location_tag",
|
|
"mkdtemp",
|
|
"normalize_storage_type",
|
|
"save",
|
|
"storage_to_tensor_type",
|
|
"validate_cuda_device",
|
|
"validate_hpu_device",
|
|
# torch.signal.windows.windows
|
|
"bartlett",
|
|
"blackman",
|
|
"cosine",
|
|
"exponential",
|
|
"gaussian",
|
|
"general_cosine",
|
|
"general_hamming",
|
|
"hamming",
|
|
"hann",
|
|
"kaiser",
|
|
"nuttall",
|
|
# torch.sparse.semi_structured
|
|
"to_sparse_semi_structured",
|
|
# torch.utils.backend_registration
|
|
"generate_methods_for_privateuse1_backend",
|
|
"rename_privateuse1_backend",
|
|
# torch.utils.benchmark.examples.op_benchmark
|
|
"assert_dicts_equal",
|
|
# torch.utils.benchmark.op_fuzzers.spectral
|
|
"power_range",
|
|
# torch.utils.benchmark.utils.common
|
|
"ordered_unique",
|
|
"select_unit",
|
|
"set_torch_threads",
|
|
"trim_sigfig",
|
|
"unit_to_english",
|
|
# torch.utils.benchmark.utils.compare
|
|
"optional_min",
|
|
# torch.utils.benchmark.utils.compile
|
|
"bench_all",
|
|
"bench_loop",
|
|
"benchmark_compile",
|
|
# torch.utils.benchmark.utils.cpp_jit
|
|
"compile_callgrind_template",
|
|
"compile_timeit_template",
|
|
"get_compat_bindings",
|
|
# torch.utils.benchmark.utils.fuzzer
|
|
"dtype_size",
|
|
"prod",
|
|
# torch.utils.benchmark.utils.timer
|
|
"timer",
|
|
# torch.utils.benchmark.utils.valgrind_wrapper.timer_interface
|
|
"wrapper_singleton",
|
|
# torch.utils.bundled_inputs
|
|
"augment_many_model_functions_with_bundled_inputs",
|
|
"augment_model_with_bundled_inputs",
|
|
"bundle_inputs",
|
|
"bundle_large_tensor",
|
|
"bundle_randn",
|
|
# torch.utils.checkpoint
|
|
"check_backward_validity",
|
|
"detach_variable",
|
|
"get_device_states",
|
|
"noop_context_fn",
|
|
"set_checkpoint_early_stop",
|
|
"set_device_states",
|
|
# torch.utils.collect_env
|
|
"check_release_file",
|
|
"get_cachingallocator_config",
|
|
"get_clang_version",
|
|
"get_cmake_version",
|
|
"get_conda_packages",
|
|
"get_cpu_info",
|
|
"get_cuda_module_loading_config",
|
|
"get_cudnn_version",
|
|
"get_env_info",
|
|
"get_gcc_version",
|
|
"get_gpu_info",
|
|
"get_libc_version",
|
|
"get_lsb_version",
|
|
"get_mac_version",
|
|
"get_nvidia_driver_version",
|
|
"get_nvidia_smi",
|
|
"get_os",
|
|
"get_pip_packages",
|
|
"get_platform",
|
|
"get_pretty_env_info",
|
|
"get_python_platform",
|
|
"get_running_cuda_version",
|
|
"get_windows_version",
|
|
"is_xnnpack_available",
|
|
"pretty_str",
|
|
# torch.utils.cpp_backtrace
|
|
"get_cpp_backtrace",
|
|
# torch.utils.cpp_extension
|
|
"check_compiler_is_gcc",
|
|
"check_compiler_ok_for_platform",
|
|
"get_cxx_compiler",
|
|
"get_default_build_root",
|
|
"library_paths",
|
|
"remove_extension_h_precompiler_headers",
|
|
# torch.utils.data.backward_compatibility
|
|
"worker_init_fn",
|
|
# torch.utils.data.datapipes.dataframe.dataframe_wrapper
|
|
"concat",
|
|
"create_dataframe",
|
|
"get_columns",
|
|
"get_df_wrapper",
|
|
"get_item",
|
|
"get_len",
|
|
"is_column",
|
|
"is_dataframe",
|
|
"iterate",
|
|
"set_df_wrapper",
|
|
# torch.utils.data.datapipes.dataframe.dataframes
|
|
"disable_capture",
|
|
"get_val",
|
|
# torch.utils.data.datapipes.gen_pyi
|
|
"extract_class_name",
|
|
"extract_method_name",
|
|
"find_file_paths",
|
|
"gen_from_template",
|
|
"get_method_definitions",
|
|
"materialize_lines",
|
|
"parse_datapipe_file",
|
|
"parse_datapipe_files",
|
|
"process_signature",
|
|
"split_outside_bracket",
|
|
# torch.utils.data.datapipes.map.callable
|
|
"default_fn",
|
|
# torch.utils.data.datapipes.utils.common
|
|
"get_file_binaries_from_pathnames",
|
|
"get_file_pathnames_from_root",
|
|
"match_masks",
|
|
"validate_input_col",
|
|
"validate_pathname_binary_tuple",
|
|
# torch.utils.data.datapipes.utils.decoder
|
|
"audiohandler",
|
|
"basichandlers",
|
|
"extension_extract_fn",
|
|
"handle_extension",
|
|
"imagehandler",
|
|
"mathandler",
|
|
"videohandler",
|
|
# torch.utils.data.dataset
|
|
"random_split",
|
|
# torch.utils.data.graph
|
|
"traverse",
|
|
"traverse_dps",
|
|
# torch.utils.data.graph_settings
|
|
"apply_random_seed",
|
|
"apply_sharding",
|
|
"apply_shuffle_seed",
|
|
"apply_shuffle_settings",
|
|
"get_all_graph_pipes",
|
|
# torch.utils.flop_counter
|
|
"addmm_flop",
|
|
"baddbmm_flop",
|
|
"bmm_flop",
|
|
"conv_backward_flop",
|
|
"conv_flop",
|
|
"conv_flop_count",
|
|
"convert_num_with_suffix",
|
|
"get_shape",
|
|
"get_suffix_str",
|
|
"mm_flop",
|
|
"normalize_tuple",
|
|
"register_flop_formula",
|
|
"sdpa_backward_flop",
|
|
"sdpa_backward_flop_count",
|
|
"sdpa_flop",
|
|
"sdpa_flop_count",
|
|
"shape_wrapper",
|
|
"transpose_shape",
|
|
# torch.utils.hipify.hipify_python
|
|
"add_dim3",
|
|
"compute_stats",
|
|
"extract_arguments",
|
|
"file_add_header",
|
|
"file_specific_replacement",
|
|
"find_bracket_group",
|
|
"find_closure_group",
|
|
"find_parentheses_group",
|
|
"fix_static_global_kernels",
|
|
"get_hip_file_path",
|
|
"hip_header_magic",
|
|
"hipify",
|
|
"is_caffe2_gpu_file",
|
|
"is_cusparse_file",
|
|
"is_out_of_place",
|
|
"is_pytorch_file",
|
|
"is_special_file",
|
|
"match_extensions",
|
|
"matched_files_iter",
|
|
"openf",
|
|
"preprocess_file_and_save_result",
|
|
"preprocessor",
|
|
"processKernelLaunches",
|
|
"replace_extern_shared",
|
|
"replace_math_functions",
|
|
"str2bool",
|
|
# torch.utils.hooks
|
|
"unserializable_hook",
|
|
"warn_if_has_hooks",
|
|
# torch.utils.jit.log_extract
|
|
"extract_ir",
|
|
"load_graph_and_inputs",
|
|
"make_tensor_from_type",
|
|
"no_fuser",
|
|
"time_cpu",
|
|
"time_cuda",
|
|
# torch.utils.mkldnn
|
|
"to_mkldnn",
|
|
# torch.utils.mobile_optimizer
|
|
"generate_mobile_module_lints",
|
|
# torch.utils.tensorboard.summary
|
|
"audio",
|
|
"compute_curve",
|
|
"custom_scalars",
|
|
"draw_boxes",
|
|
"half_to_int",
|
|
"histogram",
|
|
"histogram_raw",
|
|
"hparams",
|
|
"image",
|
|
"image_boxes",
|
|
"int_to_half",
|
|
"make_histogram",
|
|
"make_image",
|
|
"make_video",
|
|
"mesh",
|
|
"pr_curve",
|
|
"pr_curve_raw",
|
|
"scalar",
|
|
"tensor_proto",
|
|
"text",
|
|
"video",
|
|
# torch.utils.throughput_benchmark
|
|
"format_time",
|
|
]
|
|
|
|
coverage_ignore_classes = [
|
|
# torch
|
|
"FatalError",
|
|
"QUInt2x4Storage",
|
|
"Size",
|
|
"Storage",
|
|
"Stream",
|
|
"Tensor",
|
|
"finfo",
|
|
"iinfo",
|
|
"qscheme",
|
|
"AggregationType",
|
|
"AliasDb",
|
|
"AnyType",
|
|
"Argument",
|
|
"ArgumentSpec",
|
|
"AwaitType",
|
|
"BenchmarkConfig",
|
|
"BenchmarkExecutionStats",
|
|
"Block",
|
|
"BoolType",
|
|
"BufferDict",
|
|
"CallStack",
|
|
"Capsule",
|
|
"ClassType",
|
|
"Code",
|
|
"CompleteArgumentSpec",
|
|
"ComplexType",
|
|
"ConcreteModuleType",
|
|
"ConcreteModuleTypeBuilder",
|
|
"DeepCopyMemoTable",
|
|
"DeserializationStorageContext",
|
|
"DeviceObjType",
|
|
"DictType",
|
|
"DispatchKey",
|
|
"DispatchKeySet",
|
|
"EnumType",
|
|
"ExcludeDispatchKeyGuard",
|
|
"ExecutionPlan",
|
|
"FileCheck",
|
|
"FloatType",
|
|
"FunctionSchema",
|
|
"Gradient",
|
|
"Graph",
|
|
"GraphExecutorState",
|
|
"IODescriptor",
|
|
"InferredType",
|
|
"IntType",
|
|
"InterfaceType",
|
|
"ListType",
|
|
"LockingLogger",
|
|
"MobileOptimizerType",
|
|
"ModuleDict",
|
|
"Node",
|
|
"NoneType",
|
|
"NoopLogger",
|
|
"NumberType",
|
|
"OperatorInfo",
|
|
"OptionalType",
|
|
"ParameterDict",
|
|
"PyObjectType",
|
|
"PyTorchFileReader",
|
|
"PyTorchFileWriter",
|
|
"RRefType",
|
|
"ScriptClass",
|
|
"ScriptClassFunction",
|
|
"ScriptDict",
|
|
"ScriptDictIterator",
|
|
"ScriptDictKeyIterator",
|
|
"ScriptList",
|
|
"ScriptListIterator",
|
|
"ScriptMethod",
|
|
"ScriptModule",
|
|
"ScriptModuleSerializer",
|
|
"ScriptObject",
|
|
"ScriptObjectProperty",
|
|
"SerializationStorageContext",
|
|
"StaticModule",
|
|
"StringType",
|
|
"SymIntType",
|
|
"SymBoolType",
|
|
"ThroughputBenchmark",
|
|
"TracingState",
|
|
"TupleType",
|
|
"Type",
|
|
"UnionType",
|
|
"Use",
|
|
"Value",
|
|
# torch.cuda
|
|
"BFloat16Storage",
|
|
"BFloat16Tensor",
|
|
"BoolStorage",
|
|
"BoolTensor",
|
|
"ByteStorage",
|
|
"ByteTensor",
|
|
"CharStorage",
|
|
"CharTensor",
|
|
"ComplexDoubleStorage",
|
|
"ComplexFloatStorage",
|
|
"CudaError",
|
|
"DeferredCudaCallError",
|
|
"DoubleStorage",
|
|
"DoubleTensor",
|
|
"FloatStorage",
|
|
"FloatTensor",
|
|
"HalfStorage",
|
|
"HalfTensor",
|
|
"IntStorage",
|
|
"IntTensor",
|
|
"LongStorage",
|
|
"LongTensor",
|
|
"ShortStorage",
|
|
"ShortTensor",
|
|
"cudaStatus",
|
|
# torch.cuda._sanitizer
|
|
"Access",
|
|
"AccessType",
|
|
"Await",
|
|
"CUDASanitizer",
|
|
"CUDASanitizerDispatchMode",
|
|
"CUDASanitizerErrors",
|
|
"EventHandler",
|
|
"SynchronizationError",
|
|
"UnsynchronizedAccessError",
|
|
# torch.distributed.elastic.multiprocessing.errors
|
|
"ChildFailedError",
|
|
"ProcessFailure",
|
|
# torch.distributions.constraints
|
|
"cat",
|
|
"greater_than",
|
|
"greater_than_eq",
|
|
"half_open_interval",
|
|
"independent",
|
|
"integer_interval",
|
|
"interval",
|
|
"less_than",
|
|
"multinomial",
|
|
"stack",
|
|
# torch.distributions.transforms
|
|
"AffineTransform",
|
|
"CatTransform",
|
|
"ComposeTransform",
|
|
"CorrCholeskyTransform",
|
|
"CumulativeDistributionTransform",
|
|
"ExpTransform",
|
|
"IndependentTransform",
|
|
"PowerTransform",
|
|
"ReshapeTransform",
|
|
"SigmoidTransform",
|
|
"SoftmaxTransform",
|
|
"SoftplusTransform",
|
|
"StackTransform",
|
|
"StickBreakingTransform",
|
|
"TanhTransform",
|
|
"Transform",
|
|
# torch.jit
|
|
"CompilationUnit",
|
|
"Error",
|
|
"Future",
|
|
"ScriptFunction",
|
|
# torch.onnx
|
|
"CheckerError",
|
|
"ExportTypes",
|
|
# torch.backends
|
|
"ContextProp",
|
|
"PropModule",
|
|
# torch.backends.cuda
|
|
"cuBLASModule",
|
|
"cuFFTPlanCache",
|
|
"cuFFTPlanCacheAttrContextProp",
|
|
"cuFFTPlanCacheManager",
|
|
# torch.distributed.algorithms.ddp_comm_hooks
|
|
"DDPCommHookType",
|
|
# torch.jit.mobile
|
|
"LiteScriptModule",
|
|
# torch.ao.nn.quantized.modules
|
|
"DeQuantize",
|
|
"Quantize",
|
|
# torch.utils.backcompat
|
|
"Warning",
|
|
# torch.ao.nn.intrinsic.modules.fused
|
|
"ConvAdd2d",
|
|
"ConvAddReLU2d",
|
|
"LinearBn1d",
|
|
"LinearLeakyReLU",
|
|
"LinearTanh",
|
|
# torch.ao.nn.intrinsic.qat.modules.conv_fused
|
|
"ConvBnReLU1d",
|
|
"ConvBnReLU2d",
|
|
"ConvBnReLU3d",
|
|
"ConvReLU1d",
|
|
"ConvReLU2d",
|
|
"ConvReLU3d",
|
|
# torch.ao.nn.intrinsic.qat.modules.linear_fused
|
|
"LinearBn1d",
|
|
# torch.ao.nn.intrinsic.qat.modules.linear_relu
|
|
"LinearReLU",
|
|
# torch.ao.nn.intrinsic.quantized.dynamic.modules.linear_relu
|
|
"LinearReLU",
|
|
# torch.ao.nn.intrinsic.quantized.modules.bn_relu
|
|
"BNReLU2d",
|
|
"BNReLU3d",
|
|
# torch.ao.nn.intrinsic.quantized.modules.conv_add
|
|
"ConvAdd2d",
|
|
"ConvAddReLU2d",
|
|
# torch.ao.nn.intrinsic.quantized.modules.conv_relu
|
|
"ConvReLU1d",
|
|
"ConvReLU2d",
|
|
"ConvReLU3d",
|
|
# torch.ao.nn.intrinsic.quantized.modules.linear_relu
|
|
"LinearLeakyReLU",
|
|
"LinearReLU",
|
|
"LinearTanh",
|
|
# torch.ao.nn.qat.modules.conv
|
|
"Conv1d",
|
|
"Conv2d",
|
|
"Conv3d",
|
|
# torch.ao.nn.qat.modules.embedding_ops
|
|
"Embedding",
|
|
"EmbeddingBag",
|
|
# torch.ao.nn.qat.modules.linear
|
|
"Linear",
|
|
# torch.ao.nn.quantizable.modules.activation
|
|
"MultiheadAttention",
|
|
# torch.ao.nn.quantizable.modules.rnn
|
|
"LSTM",
|
|
"LSTMCell",
|
|
# torch.ao.nn.quantized.dynamic.modules.conv
|
|
"Conv1d",
|
|
"Conv2d",
|
|
"Conv3d",
|
|
"ConvTranspose1d",
|
|
"ConvTranspose2d",
|
|
"ConvTranspose3d",
|
|
# torch.ao.nn.quantized.dynamic.modules.linear
|
|
"Linear",
|
|
# torch.ao.nn.quantized.dynamic.modules.rnn
|
|
"GRU",
|
|
"GRUCell",
|
|
"LSTM",
|
|
"LSTMCell",
|
|
"PackedParameter",
|
|
"RNNBase",
|
|
"RNNCell",
|
|
"RNNCellBase",
|
|
# torch.ao.nn.quantized.modules.activation
|
|
"ELU",
|
|
"Hardswish",
|
|
"LeakyReLU",
|
|
"MultiheadAttention",
|
|
"PReLU",
|
|
"ReLU6",
|
|
"Sigmoid",
|
|
"Softmax",
|
|
# torch.ao.nn.quantized.modules.batchnorm
|
|
"BatchNorm2d",
|
|
"BatchNorm3d",
|
|
# torch.ao.nn.quantized.modules.conv
|
|
"Conv1d",
|
|
"Conv2d",
|
|
"Conv3d",
|
|
"ConvTranspose1d",
|
|
"ConvTranspose2d",
|
|
"ConvTranspose3d",
|
|
# torch.ao.nn.quantized.modules.dropout
|
|
"Dropout",
|
|
# torch.ao.nn.quantized.modules.embedding_ops
|
|
"Embedding",
|
|
"EmbeddingBag",
|
|
"EmbeddingPackedParams",
|
|
# torch.ao.nn.quantized.modules.functional_modules
|
|
"FXFloatFunctional",
|
|
"FloatFunctional",
|
|
"QFunctional",
|
|
# torch.ao.nn.quantized.modules.linear
|
|
"Linear",
|
|
"LinearPackedParams",
|
|
# torch.ao.nn.quantized.modules.normalization
|
|
"GroupNorm",
|
|
"InstanceNorm1d",
|
|
"InstanceNorm2d",
|
|
"InstanceNorm3d",
|
|
"LayerNorm",
|
|
# torch.ao.nn.quantized.modules.rnn
|
|
"LSTM",
|
|
# torch.ao.nn.quantized.modules.utils
|
|
"WeightedQuantizedModule",
|
|
# torch.ao.nn.quantized.reference.modules.conv
|
|
"Conv1d",
|
|
"Conv2d",
|
|
"Conv3d",
|
|
"ConvTranspose1d",
|
|
"ConvTranspose2d",
|
|
"ConvTranspose3d",
|
|
# torch.ao.nn.quantized.reference.modules.linear
|
|
"Linear",
|
|
# torch.ao.nn.quantized.reference.modules.rnn
|
|
"GRU",
|
|
"GRUCell",
|
|
"LSTM",
|
|
"LSTMCell",
|
|
"RNNBase",
|
|
"RNNCell",
|
|
"RNNCellBase",
|
|
# torch.ao.nn.quantized.reference.modules.sparse
|
|
"Embedding",
|
|
"EmbeddingBag",
|
|
# torch.ao.nn.quantized.reference.modules.utils
|
|
"ReferenceQuantizedModule",
|
|
# torch.ao.nn.sparse.quantized.dynamic.linear
|
|
"Linear",
|
|
# torch.ao.nn.sparse.quantized.linear
|
|
"Linear",
|
|
"LinearPackedParams",
|
|
# torch.ao.nn.sparse.quantized.utils
|
|
"LinearBlockSparsePattern",
|
|
# torch.ao.ns.fx.graph_matcher
|
|
"SubgraphTypeRelationship",
|
|
# torch.ao.ns.fx.n_shadows_utils
|
|
"OutputProp",
|
|
# torch.ao.ns.fx.ns_types
|
|
"NSSingleResultValuesType",
|
|
"NSSubgraph",
|
|
# torch.ao.ns.fx.qconfig_multi_mapping
|
|
"QConfigMultiMapping",
|
|
# torch.ao.pruning.scheduler.base_scheduler
|
|
"BaseScheduler",
|
|
# torch.ao.pruning.scheduler.cubic_scheduler
|
|
"CubicSL",
|
|
# torch.ao.pruning.scheduler.lambda_scheduler
|
|
"LambdaSL",
|
|
# torch.ao.pruning.sparsifier.base_sparsifier
|
|
"BaseSparsifier",
|
|
# torch.ao.pruning.sparsifier.nearly_diagonal_sparsifier
|
|
"NearlyDiagonalSparsifier",
|
|
# torch.ao.pruning.sparsifier.utils
|
|
"FakeSparsity",
|
|
# torch.ao.pruning.sparsifier.weight_norm_sparsifier
|
|
"WeightNormSparsifier",
|
|
# torch.ao.quantization.backend_config.backend_config
|
|
"BackendConfig",
|
|
"BackendPatternConfig",
|
|
"DTypeConfig",
|
|
# torch.ao.quantization.fake_quantize
|
|
"FakeQuantize",
|
|
"FakeQuantizeBase",
|
|
"FixedQParamsFakeQuantize",
|
|
"FusedMovingAvgObsFakeQuantize",
|
|
# torch.ao.quantization.fx.fuse_handler
|
|
"DefaultFuseHandler",
|
|
"FuseHandler",
|
|
# torch.ao.quantization.fx.graph_module
|
|
"FusedGraphModule",
|
|
"ObservedGraphModule",
|
|
"ObservedStandaloneGraphModule",
|
|
# torch.ao.quantization.fx.quantize_handler
|
|
"BatchNormQuantizeHandler",
|
|
"BinaryOpQuantizeHandler",
|
|
"CatQuantizeHandler",
|
|
"ConvReluQuantizeHandler",
|
|
"CopyNodeQuantizeHandler",
|
|
"CustomModuleQuantizeHandler",
|
|
"DefaultNodeQuantizeHandler",
|
|
"EmbeddingQuantizeHandler",
|
|
"FixedQParamsOpQuantizeHandler",
|
|
"GeneralTensorShapeOpQuantizeHandler",
|
|
"LinearReLUQuantizeHandler",
|
|
"RNNDynamicQuantizeHandler",
|
|
"StandaloneModuleQuantizeHandler",
|
|
# torch.ao.quantization.fx.tracer
|
|
"QuantizationTracer",
|
|
"ScopeContextManager",
|
|
# torch.ao.quantization.fx.utils
|
|
"ObservedGraphModuleAttrs",
|
|
# torch.ao.quantization.observer
|
|
"FixedQParamsObserver",
|
|
"HistogramObserver",
|
|
"MinMaxObserver",
|
|
"MovingAverageMinMaxObserver",
|
|
"MovingAveragePerChannelMinMaxObserver",
|
|
"NoopObserver",
|
|
"ObserverBase",
|
|
"PerChannelMinMaxObserver",
|
|
"PlaceholderObserver",
|
|
"RecordingObserver",
|
|
"ReuseInputObserver",
|
|
"UniformQuantizationObserverBase",
|
|
"default_debug_observer",
|
|
"default_placeholder_observer",
|
|
"default_reuse_input_observer",
|
|
# torch.ao.quantization.pt2e.duplicate_dq_pass
|
|
"DuplicateDQPass",
|
|
# torch.ao.quantization.pt2e.port_metadata_pass
|
|
"PortNodeMetaForQDQ",
|
|
# torch.ao.quantization.qconfig
|
|
"QConfigDynamic",
|
|
# torch.ao.quantization.quant_type
|
|
"QuantType",
|
|
# torch.ao.quantization.quantizer.composable_quantizer
|
|
"ComposableQuantizer",
|
|
# torch.ao.quantization.quantizer.embedding_quantizer
|
|
"EmbeddingQuantizer",
|
|
# torch.ao.quantization.quantizer.quantizer
|
|
"DerivedQuantizationSpec",
|
|
"FixedQParamsQuantizationSpec",
|
|
"QuantizationAnnotation",
|
|
"QuantizationSpec",
|
|
"QuantizationSpecBase",
|
|
"SharedQuantizationSpec",
|
|
# torch.ao.quantization.quantizer.x86_inductor_quantizer
|
|
"X86InductorQuantizer",
|
|
# torch.ao.quantization.quantizer.xpu_inductor_quantizer
|
|
"XPUInductorQuantizer",
|
|
# torch.ao.quantization.quantizer.xnnpack_quantizer
|
|
"XNNPACKQuantizer",
|
|
# torch.ao.quantization.quantizer.xnnpack_quantizer_utils
|
|
"OperatorConfig",
|
|
"QuantizationConfig",
|
|
# torch.ao.quantization.stubs
|
|
"DeQuantStub",
|
|
"QuantStub",
|
|
"QuantWrapper",
|
|
# torch.ao.quantization.utils
|
|
"MatchAllNode",
|
|
# torch.backends.cudnn.rnn
|
|
"Unserializable",
|
|
# torch.amp.grad_scaler
|
|
"GradScaler",
|
|
"OptState",
|
|
# torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook
|
|
"PostLocalSGDState",
|
|
# torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook
|
|
"PowerSGDState",
|
|
# torch.distributed.algorithms.join
|
|
"Join",
|
|
"JoinHook",
|
|
"Joinable",
|
|
# torch.distributed.algorithms.model_averaging.averagers
|
|
"ModelAverager",
|
|
"PeriodicModelAverager",
|
|
# torch.distributed.algorithms.model_averaging.hierarchical_model_averager
|
|
"HierarchicalModelAverager",
|
|
# torch.distributed.argparse_util
|
|
"check_env",
|
|
"env",
|
|
# torch.distributed.checkpoint.api
|
|
"CheckpointException",
|
|
# torch.distributed.checkpoint.default_planner
|
|
"DefaultLoadPlanner",
|
|
"DefaultSavePlanner",
|
|
# torch.distributed.checkpoint.filesystem
|
|
"FileSystemReader",
|
|
"FileSystemWriter",
|
|
# torch.distributed.checkpoint.hf_storage
|
|
"HuggingFaceStorageReader",
|
|
"HuggingFaceStorageWriter",
|
|
# torch.distributed.checkpoint.quantized_hf_storage
|
|
"QuantizedHuggingFaceStorageReader",
|
|
# torch.distributed.checkpoint.metadata
|
|
"BytesStorageMetadata",
|
|
"ChunkStorageMetadata",
|
|
"Metadata",
|
|
"MetadataIndex",
|
|
# torch.distributed.checkpoint.planner
|
|
"LoadItemType",
|
|
"LoadPlanner",
|
|
"SavePlanner",
|
|
"WriteItemType",
|
|
# torch.distributed.checkpoint.state_dict
|
|
"DistributedStateDictOptions",
|
|
# torch.distributed.checkpoint.storage
|
|
"WriteResult",
|
|
# torch.distributed.collective_utils
|
|
"SyncPayload",
|
|
# torch.distributed.distributed_c10d
|
|
"AllToAllOptions",
|
|
"AllreduceCoalescedOptions",
|
|
"AllreduceOptions",
|
|
"Backend",
|
|
"BackendConfig",
|
|
"BarrierOptions",
|
|
"BroadcastOptions",
|
|
"DebugLevel",
|
|
"GatherOptions",
|
|
"GroupMember",
|
|
"ProcessGroup",
|
|
"ProcessGroupGloo",
|
|
"ProcessGroupNCCL",
|
|
"ReduceOptions",
|
|
"ReduceScatterOptions",
|
|
"ScatterOptions",
|
|
"Work",
|
|
"group",
|
|
# torch.distributed.elastic.agent.server.api
|
|
"ElasticAgent",
|
|
"RunResult",
|
|
"SimpleElasticAgent",
|
|
"WorkerSpec",
|
|
# torch.distributed.elastic.events.api
|
|
"Event",
|
|
"RdzvEvent",
|
|
# torch.distributed.elastic.metrics.api
|
|
"ConsoleMetricHandler",
|
|
"MetricData",
|
|
"MetricHandler",
|
|
"MetricStream",
|
|
"MetricsConfig",
|
|
"NullMetricHandler",
|
|
# torch.distributed.elastic.multiprocessing.api
|
|
"MultiprocessContext",
|
|
"PContext",
|
|
"RunProcsResult",
|
|
"SignalException",
|
|
"Std",
|
|
"SubprocessContext",
|
|
"SubprocessHandler",
|
|
# torch.distributed.elastic.multiprocessing.tail_log
|
|
"TailLog",
|
|
# torch.distributed.elastic.rendezvous.api
|
|
"RendezvousHandler",
|
|
"RendezvousHandlerRegistry",
|
|
"RendezvousParameters",
|
|
# torch.distributed.elastic.rendezvous.dynamic_rendezvous
|
|
"DynamicRendezvousHandler",
|
|
"RendezvousSettings",
|
|
# torch.distributed.elastic.rendezvous.etcd_rendezvous
|
|
"EtcdRendezvous",
|
|
"EtcdRendezvousHandler",
|
|
"EtcdRendezvousRetryImmediately",
|
|
"EtcdRendezvousRetryableFailure",
|
|
# torch.distributed.elastic.rendezvous.etcd_server
|
|
"EtcdServer",
|
|
# torch.distributed.elastic.rendezvous.static_tcp_rendezvous
|
|
"StaticTCPRendezvous",
|
|
# torch.distributed.elastic.timer.api
|
|
"RequestQueue",
|
|
"TimerClient",
|
|
"TimerServer",
|
|
# torch.distributed.elastic.timer.file_based_local_timer
|
|
"FileTimerClient",
|
|
"FileTimerRequest",
|
|
"FileTimerServer",
|
|
# torch.distributed.elastic.timer.local_timer
|
|
"LocalTimerClient",
|
|
"LocalTimerServer",
|
|
"MultiprocessingRequestQueue",
|
|
# torch.distributed.elastic.utils.api
|
|
"macros",
|
|
# torch.distributed.elastic.utils.data.cycling_iterator
|
|
"CyclingIterator",
|
|
# torch.distributed.elastic.utils.data.elastic_distributed_sampler
|
|
"ElasticDistributedSampler",
|
|
# torch.distributed.fsdp.api
|
|
"StateDictType",
|
|
# torch.distributed.fsdp.fully_sharded_data_parallel
|
|
"FullyShardedDataParallel",
|
|
"OptimStateKeyType",
|
|
# torch.distributed.fsdp.sharded_grad_scaler
|
|
"ShardedGradScaler",
|
|
# torch.distributed.fsdp.wrap
|
|
"CustomPolicy",
|
|
"ModuleWrapPolicy",
|
|
# torch.distributed.launcher.api
|
|
"LaunchConfig",
|
|
"elastic_launch",
|
|
# torch.distributed.optim.optimizer
|
|
"DistributedOptimizer",
|
|
# torch.distributed.optim.post_localSGD_optimizer
|
|
"PostLocalSGDOptimizer",
|
|
# torch.distributed.optim.zero_redundancy_optimizer
|
|
"ZeroRedundancyOptimizer",
|
|
# torch.distributed.rpc.api
|
|
"AllGatherStates",
|
|
"RRef",
|
|
# torch.distributed.rpc.backend_registry
|
|
"BackendValue",
|
|
# torch.distributed.rpc.internal
|
|
"PythonUDF",
|
|
"RPCExecMode",
|
|
"RemoteException",
|
|
# torch.distributed.rpc.rref_proxy
|
|
"RRefProxy",
|
|
# torch.distributed.tensor.parallel.fsdp
|
|
"DTensorExtensions",
|
|
# torch.distributed.tensor.parallel.style
|
|
"ParallelStyle",
|
|
# torch.distributions.logistic_normal
|
|
"LogisticNormal",
|
|
# torch.distributions.one_hot_categorical
|
|
"OneHotCategoricalStraightThrough",
|
|
# torch.distributions.relaxed_categorical
|
|
"ExpRelaxedCategorical",
|
|
# torch.distributions.utils
|
|
"lazy_property",
|
|
# torch.export.unflatten
|
|
"UnflattenedModule",
|
|
# torch.export.exported_program
|
|
"ConstantArgument",
|
|
"ExportedProgram",
|
|
# torch.fx.experimental.accelerator_partitioner
|
|
"DAG",
|
|
"DAGNode",
|
|
"PartitionResult",
|
|
"Partitioner",
|
|
# torch.fx.experimental.const_fold
|
|
"FoldedGraphModule",
|
|
# torch.fx.experimental.graph_gradual_typechecker
|
|
"Refine",
|
|
# torch.fx.experimental.meta_tracer
|
|
"MetaAttribute",
|
|
"MetaDeviceAttribute",
|
|
"MetaProxy",
|
|
"MetaTracer",
|
|
# torch.fx.experimental.migrate_gradual_types.constraint
|
|
"ApplyBroadcasting",
|
|
"BVar",
|
|
"BinConstraintD",
|
|
"BinConstraintT",
|
|
"BinaryConstraint",
|
|
"CalcConv",
|
|
"CalcMaxPool",
|
|
"CalcProduct",
|
|
"CanReshape",
|
|
"Conj",
|
|
"Constraint",
|
|
"DGreatestUpperBound",
|
|
"DVar",
|
|
"Disj",
|
|
"F",
|
|
"GetItem",
|
|
"GetItemTensor",
|
|
"IndexSelect",
|
|
"Prod",
|
|
"T",
|
|
"TGreatestUpperBound",
|
|
"TVar",
|
|
"Transpose",
|
|
# torch.fx.experimental.migrate_gradual_types.constraint_generator
|
|
"ConstraintGenerator",
|
|
# torch.fx.experimental.normalize
|
|
"NormalizeArgs",
|
|
"NormalizeOperators",
|
|
# torch.fx.experimental.optimization
|
|
"MklSubgraph",
|
|
"UnionFind",
|
|
# torch.fx.experimental.partitioner_utils
|
|
"Device",
|
|
"Partition",
|
|
"PartitionLatency",
|
|
"PartitionMode",
|
|
"PartitionerConfig",
|
|
# torch.fx.experimental.proxy_tensor
|
|
"DecompositionInterpreter",
|
|
"PreDispatchTorchFunctionMode",
|
|
"ProxySymDispatchMode",
|
|
"ProxyTorchDispatchMode",
|
|
"PythonKeyTracer",
|
|
# torch.fx.experimental.recording
|
|
"FakeTensorMeta",
|
|
"NotEqualError",
|
|
"ShapeEnvEvent",
|
|
# torch.fx.experimental.refinement_types
|
|
"Equality",
|
|
# torch.fx.experimental.rewriter
|
|
"AST_Rewriter",
|
|
"RewritingTracer",
|
|
# torch.fx.experimental.schema_type_annotation
|
|
"AnnotateTypesWithSchema",
|
|
# torch.fx.experimental.sym_node
|
|
"SymNode",
|
|
# torch.fx.experimental.symbolic_shapes
|
|
"Constraint",
|
|
"ConstraintViolationError",
|
|
"DynamicDimConstraintPrinter",
|
|
"GuardOnDataDependentSymNode",
|
|
"PendingUnbackedSymbolNotFound",
|
|
"LoggingShapeGuardPrinter",
|
|
"SymExprPrinter",
|
|
"RelaxedUnspecConstraint",
|
|
"RuntimeAssert",
|
|
"ShapeGuardPrinter",
|
|
"ShapeGuardPythonPrinter",
|
|
"SymDispatchMode",
|
|
"SymbolicContext",
|
|
# torch.fx.experimental.unification.match
|
|
"Dispatcher",
|
|
"VarDispatcher",
|
|
# torch.fx.experimental.unification.multipledispatch.conflict
|
|
"AmbiguityWarning",
|
|
# torch.fx.experimental.unification.multipledispatch.dispatcher
|
|
"Dispatcher",
|
|
"MDNotImplementedError",
|
|
"MethodDispatcher",
|
|
# torch.fx.experimental.unification.multipledispatch.variadic
|
|
"Variadic",
|
|
"VariadicSignatureMeta",
|
|
"VariadicSignatureType",
|
|
# torch.fx.experimental.unification.variable
|
|
"Var",
|
|
# torch.fx.experimental.validator
|
|
"BisectValidationException",
|
|
"PopulateValidator",
|
|
"SympyToZ3",
|
|
"ValidationException",
|
|
# torch.fx.graph
|
|
"PythonCode",
|
|
# torch.fx.immutable_collections
|
|
"immutable_dict",
|
|
"immutable_list",
|
|
# torch.fx.interpreter
|
|
"Interpreter",
|
|
# torch.fx.operator_schemas
|
|
"ArgsKwargsPair",
|
|
# torch.fx.passes.backends.cudagraphs
|
|
"CudaGraphsSupport",
|
|
# torch.fx.passes.dialect.common.cse_pass
|
|
"CSEPass",
|
|
# torch.fx.passes.fake_tensor_prop
|
|
"FakeTensorProp",
|
|
# torch.fx.passes.graph_drawer
|
|
"FxGraphDrawer",
|
|
# torch.fx.passes.graph_manipulation
|
|
"size_bytes",
|
|
# torch.fx.passes.infra.partitioner
|
|
"CapabilityBasedPartitioner",
|
|
"Partition",
|
|
# torch.fx.passes.infra.pass_base
|
|
"PassBase",
|
|
"PassResult",
|
|
# torch.fx.passes.infra.pass_manager
|
|
"PassManager",
|
|
# torch.fx.passes.net_min_base
|
|
"FxNetMinimizerBadModuleError",
|
|
"FxNetMinimizerResultMismatchError",
|
|
"FxNetMinimizerRunFuncError",
|
|
# torch.fx.passes.operator_support
|
|
"OpSupports",
|
|
"OperatorSupport",
|
|
"OperatorSupportBase",
|
|
# torch.fx.passes.pass_manager
|
|
"PassManager",
|
|
# torch.fx.passes.shape_prop
|
|
"ShapeProp",
|
|
# torch.fx.passes.split_module
|
|
"Partition",
|
|
# torch.fx.passes.split_utils
|
|
"Component",
|
|
# torch.fx.passes.splitter_base
|
|
"FxNetAccNodesFinder",
|
|
"FxNetSplitterInternalError",
|
|
"SplitResult",
|
|
"Subgraph",
|
|
# torch.fx.passes.tests.test_pass_manager
|
|
"TestPassManager",
|
|
# torch.fx.passes.tools_common
|
|
"FxNetAccFusionsFinder",
|
|
# torch.fx.passes.utils.common
|
|
"HolderModule",
|
|
# torch.fx.passes.utils.matcher_utils
|
|
"InternalMatch",
|
|
"SubgraphMatcher",
|
|
# torch.fx.passes.utils.source_matcher_utils
|
|
"SourcePartition",
|
|
# torch.fx.proxy
|
|
"Attribute",
|
|
"ParameterProxy",
|
|
"Proxy",
|
|
"Scope",
|
|
"ScopeContextManager",
|
|
"TraceError",
|
|
"TracerBase",
|
|
# torch.fx.subgraph_rewriter
|
|
"Match",
|
|
"ReplacedPatterns",
|
|
# torch.jit.annotations
|
|
"EvalEnv",
|
|
"Module",
|
|
# torch.jit.frontend
|
|
"Builder",
|
|
"ExprBuilder",
|
|
"FrontendError",
|
|
"FrontendTypeError",
|
|
"NotSupportedError",
|
|
"StmtBuilder",
|
|
"UnsupportedNodeError",
|
|
"WithItemBuilder",
|
|
# torch.masked.maskedtensor.core
|
|
"MaskedTensor",
|
|
# torch.multiprocessing.pool
|
|
"Pool",
|
|
# torch.multiprocessing.queue
|
|
"ConnectionWrapper",
|
|
"Queue",
|
|
"SimpleQueue",
|
|
# torch.multiprocessing.reductions
|
|
"SharedCache",
|
|
# torch.multiprocessing.spawn
|
|
"ProcessContext",
|
|
"ProcessException",
|
|
"ProcessExitedException",
|
|
"ProcessRaisedException",
|
|
"SpawnContext",
|
|
# torch.nn.cpp
|
|
"ModuleWrapper",
|
|
"OrderedDictWrapper",
|
|
# torch.nn.modules.container
|
|
"Container", # deprecated
|
|
# torch.nn.modules.linear
|
|
"NonDynamicallyQuantizableLinear",
|
|
# torch.nn.modules.module
|
|
# TODO: causes multiple sphinx warnings
|
|
# WARNING: more than one target found for cross-reference 'Module'
|
|
"Module",
|
|
# torch.nn.modules.loss
|
|
"NLLLoss2d", # deprecated
|
|
# torch.nn.modules.normalization
|
|
"CrossMapLRN2d",
|
|
# torch.nn.parallel.data_parallel
|
|
"DataParallel",
|
|
# torch.nn.parallel.distributed
|
|
"DistributedDataParallel",
|
|
# torch.nn.parameter
|
|
"UninitializedTensorMixin",
|
|
# torch.nn.utils.parametrize
|
|
"ParametrizationList",
|
|
# torch.nn.utils.prune
|
|
"CustomFromMask",
|
|
"Identity",
|
|
"L1Unstructured",
|
|
"RandomUnstructured",
|
|
# torch.nn.utils.rnn
|
|
"PackedSequence",
|
|
"PackedSequence_",
|
|
# torch.nn.utils.spectral_norm
|
|
"SpectralNorm",
|
|
"SpectralNormLoadStateDictPreHook",
|
|
"SpectralNormStateDictHook",
|
|
# torch.nn.utils.weight_norm
|
|
"WeightNorm",
|
|
# torch.onnx.errors
|
|
"OnnxExporterError",
|
|
"OnnxExporterWarning",
|
|
"SymbolicValueError",
|
|
"UnsupportedOperatorError",
|
|
# torch.onnx.verification
|
|
"OnnxBackend",
|
|
"OnnxTestCaseRepro",
|
|
# torch.optim.optimizer
|
|
"Optimizer",
|
|
# torch.overrides
|
|
"BaseTorchFunctionMode",
|
|
"TorchFunctionMode",
|
|
# torch.package.file_structure_representation
|
|
"Directory",
|
|
# torch.package.glob_group
|
|
"GlobGroup",
|
|
# torch.package.importer
|
|
"Importer",
|
|
"ObjMismatchError",
|
|
"ObjNotFoundError",
|
|
"OrderedImporter",
|
|
# torch.package.package_exporter
|
|
"PackageExporter",
|
|
"PackagingErrorReason",
|
|
# torch.package.package_importer
|
|
"PackageImporter",
|
|
# torch.profiler.profiler
|
|
"ExecutionTraceObserver",
|
|
"profile",
|
|
# torch.return_types
|
|
"aminmax",
|
|
"aminmax_out",
|
|
"cummax",
|
|
"cummax_out",
|
|
"cummin",
|
|
"cummin_out",
|
|
"frexp",
|
|
"frexp_out",
|
|
"geqrf",
|
|
"geqrf_out",
|
|
"histogram",
|
|
"histogram_out",
|
|
"histogramdd",
|
|
"kthvalue",
|
|
"kthvalue_out",
|
|
"linalg_cholesky_ex",
|
|
"linalg_cholesky_ex_out",
|
|
"linalg_eig",
|
|
"linalg_eig_out",
|
|
"linalg_eigh",
|
|
"linalg_eigh_out",
|
|
"linalg_inv_ex",
|
|
"linalg_inv_ex_out",
|
|
"linalg_ldl_factor",
|
|
"linalg_ldl_factor_ex",
|
|
"linalg_ldl_factor_ex_out",
|
|
"linalg_ldl_factor_out",
|
|
"linalg_lstsq",
|
|
"linalg_lstsq_out",
|
|
"linalg_lu",
|
|
"linalg_lu_factor",
|
|
"linalg_lu_factor_ex",
|
|
"linalg_lu_factor_ex_out",
|
|
"linalg_lu_factor_out",
|
|
"linalg_lu_out",
|
|
"linalg_qr",
|
|
"linalg_qr_out",
|
|
"linalg_slogdet",
|
|
"linalg_slogdet_out",
|
|
"linalg_solve_ex",
|
|
"linalg_solve_ex_out",
|
|
"linalg_svd",
|
|
"linalg_svd_out",
|
|
"lu_unpack",
|
|
"lu_unpack_out",
|
|
"max",
|
|
"max_out",
|
|
"median",
|
|
"median_out",
|
|
"min",
|
|
"min_out",
|
|
"mode",
|
|
"mode_out",
|
|
"nanmedian",
|
|
"nanmedian_out",
|
|
"qr",
|
|
"qr_out",
|
|
"slogdet",
|
|
"slogdet_out",
|
|
"sort",
|
|
"sort_out",
|
|
"svd",
|
|
"svd_out",
|
|
"topk",
|
|
"topk_out",
|
|
"triangular_solve",
|
|
"triangular_solve_out",
|
|
# torch.serialization
|
|
"LoadEndianness",
|
|
"SourceChangeWarning",
|
|
# torch.sparse.semi_structured
|
|
"SparseSemiStructuredTensor",
|
|
# torch.storage
|
|
"UntypedStorage",
|
|
# torch.torch_version
|
|
"TorchVersion",
|
|
# torch.types
|
|
"SymInt",
|
|
# torch.utils.benchmark.examples.compare
|
|
"FauxTorch",
|
|
# torch.utils.benchmark.examples.spectral_ops_fuzz_test
|
|
"Benchmark",
|
|
# torch.utils.benchmark.op_fuzzers.binary
|
|
"BinaryOpFuzzer",
|
|
# torch.utils.benchmark.op_fuzzers.sparse_binary
|
|
"BinaryOpSparseFuzzer",
|
|
# torch.utils.benchmark.op_fuzzers.sparse_unary
|
|
"UnaryOpSparseFuzzer",
|
|
# torch.utils.benchmark.op_fuzzers.spectral
|
|
"SpectralOpFuzzer",
|
|
# torch.utils.benchmark.op_fuzzers.unary
|
|
"UnaryOpFuzzer",
|
|
# torch.utils.benchmark.utils.common
|
|
"Measurement",
|
|
"TaskSpec",
|
|
# torch.utils.benchmark.utils.compare
|
|
"Colorize",
|
|
"Compare",
|
|
"Table",
|
|
# torch.utils.benchmark.utils.fuzzer
|
|
"FuzzedParameter",
|
|
"FuzzedTensor",
|
|
"Fuzzer",
|
|
"ParameterAlias",
|
|
# torch.utils.benchmark.utils.sparse_fuzzer
|
|
"FuzzedSparseTensor",
|
|
# torch.utils.benchmark.utils.timer
|
|
"CPPTimer",
|
|
"Language",
|
|
"Timer",
|
|
# torch.utils.benchmark.utils.valgrind_wrapper.timer_interface
|
|
"CallgrindStats",
|
|
"CopyIfCallgrind",
|
|
"FunctionCount",
|
|
"FunctionCounts",
|
|
"GlobalsBridge",
|
|
"Serialization",
|
|
# torch.utils.bundled_inputs
|
|
"InflatableArg",
|
|
# torch.utils.checkpoint
|
|
"CheckpointError",
|
|
"CheckpointFunction",
|
|
"DefaultDeviceType",
|
|
# torch.utils.collect_env
|
|
"SystemEnv",
|
|
# torch.utils.cpp_extension
|
|
"BuildExtension",
|
|
# torch.utils.data.dataloader
|
|
"DataLoader",
|
|
# torch.utils.data.datapipes.dataframe.dataframe_wrapper
|
|
"PandasWrapper",
|
|
"default_wrapper",
|
|
# torch.utils.data.datapipes.dataframe.dataframes
|
|
"Capture",
|
|
"CaptureA",
|
|
"CaptureAdd",
|
|
"CaptureCall",
|
|
"CaptureControl",
|
|
"CaptureDataFrame",
|
|
"CaptureDataFrameWithDataPipeOps",
|
|
"CaptureF",
|
|
"CaptureGetAttr",
|
|
"CaptureGetItem",
|
|
"CaptureInitial",
|
|
"CaptureLikeMock",
|
|
"CaptureMul",
|
|
"CaptureSetItem",
|
|
"CaptureSub",
|
|
"CaptureVariable",
|
|
"CaptureVariableAssign",
|
|
"DataFrameTracedOps",
|
|
"DataFrameTracer",
|
|
# torch.utils.data.datapipes.dataframe.datapipes
|
|
"ConcatDataFramesPipe",
|
|
"DataFramesAsTuplesPipe",
|
|
"ExampleAggregateAsDataFrames",
|
|
"FilterDataFramesPipe",
|
|
"PerRowDataFramesPipe",
|
|
"ShuffleDataFramesPipe",
|
|
# torch.utils.data.datapipes.dataframe.structures
|
|
"DataChunkDF",
|
|
# torch.utils.data.datapipes.datapipe
|
|
"DFIterDataPipe",
|
|
"DataChunk",
|
|
"IterDataPipe",
|
|
"MapDataPipe",
|
|
# torch.utils.data.datapipes.iter.callable
|
|
"CollatorIterDataPipe",
|
|
"MapperIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.combinatorics
|
|
"SamplerIterDataPipe",
|
|
"ShufflerIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.combining
|
|
"ConcaterIterDataPipe",
|
|
"DemultiplexerIterDataPipe",
|
|
"ForkerIterDataPipe",
|
|
"MultiplexerIterDataPipe",
|
|
"ZipperIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.filelister
|
|
"FileListerIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.fileopener
|
|
"FileOpenerIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.grouping
|
|
"BatcherIterDataPipe",
|
|
"GrouperIterDataPipe",
|
|
"UnBatcherIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.routeddecoder
|
|
"RoutedDecoderIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.selecting
|
|
"FilterIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.sharding
|
|
"SHARDING_PRIORITIES",
|
|
"ShardingFilterIterDataPipe",
|
|
# torch.utils.data.datapipes.iter.utils
|
|
"IterableWrapperIterDataPipe",
|
|
# torch.utils.data.datapipes.map.callable
|
|
"MapperMapDataPipe",
|
|
# torch.utils.data.datapipes.map.combinatorics
|
|
"ShufflerIterDataPipe",
|
|
# torch.utils.data.datapipes.map.combining
|
|
"ConcaterMapDataPipe",
|
|
"ZipperMapDataPipe",
|
|
# torch.utils.data.datapipes.map.grouping
|
|
"BatcherMapDataPipe",
|
|
# torch.utils.data.datapipes.map.utils
|
|
"SequenceWrapperMapDataPipe",
|
|
# torch.utils.data.datapipes.utils.decoder
|
|
"Decoder",
|
|
"ImageHandler",
|
|
"MatHandler",
|
|
# torch.utils.data.dataset
|
|
"ConcatDataset",
|
|
# torch.utils.data.distributed
|
|
"DistributedSampler",
|
|
# torch.utils.dlpack
|
|
"DLDeviceType",
|
|
# torch.utils.file_baton
|
|
"FileBaton",
|
|
# torch.utils.flop_counter
|
|
"FlopCounterMode",
|
|
# torch.utils.hipify.hipify_python
|
|
"CurrentState",
|
|
"GeneratedFileCleaner",
|
|
"HipifyResult",
|
|
"InputError",
|
|
"Trie",
|
|
"bcolors",
|
|
# torch.utils.hooks
|
|
"BackwardHook",
|
|
"RemovableHandle",
|
|
# torch.utils.mkldnn
|
|
"MkldnnBatchNorm",
|
|
"MkldnnConv1d",
|
|
"MkldnnConv2d",
|
|
"MkldnnConv3d",
|
|
"MkldnnLinear",
|
|
"MkldnnPrelu",
|
|
# torch.utils.mobile_optimizer
|
|
"LintCode",
|
|
# torch.utils.show_pickle
|
|
"DumpUnpickler",
|
|
"FakeClass",
|
|
"FakeObject",
|
|
# torch.utils.tensorboard.writer
|
|
"FileWriter",
|
|
"SummaryWriter",
|
|
# torch.utils.throughput_benchmark
|
|
"ExecutionStats",
|
|
# torch.utils.weak
|
|
"WeakIdKeyDictionary",
|
|
"WeakIdRef",
|
|
"WeakTensorKeyDictionary",
|
|
]
|
|
|
|
# The suffix(es) of source filenames.
|
|
# You can specify multiple suffix as a list of string:
|
|
#
|
|
# source_suffix = ['.rst', '.md']
|
|
source_suffix = ".rst"
|
|
|
|
# The master toctree document.
|
|
master_doc = "index"
|
|
|
|
|
|
# Use the linkcode extension to override [SOURCE] links to point
|
|
# to the repo. Use the torch_version variable defined above to
|
|
# determine link
|
|
def linkcode_resolve(domain, info):
|
|
if domain != "py":
|
|
return None
|
|
if not info["module"]:
|
|
return None
|
|
|
|
try:
|
|
module = __import__(info["module"], fromlist=[""])
|
|
obj = module
|
|
for part in info["fullname"].split("."):
|
|
obj = getattr(obj, part)
|
|
# Get the source file and line number
|
|
obj = inspect.unwrap(obj)
|
|
fn = inspect.getsourcefile(obj)
|
|
source, lineno = inspect.getsourcelines(obj)
|
|
except Exception:
|
|
return None
|
|
|
|
# Determine the tag based on the torch_version
|
|
if RELEASE:
|
|
version_parts = torch_version.split(
|
|
"."
|
|
) # For release versions, format as "vX.Y.Z" for correct path in repo
|
|
patch_version = (
|
|
version_parts[2].split("+")[0].split("a")[0]
|
|
) # assuming a0 always comes after release version in versions.txt
|
|
version_path = f"v{version_parts[0]}.{version_parts[1]}.{patch_version}"
|
|
else:
|
|
version_path = torch.version.git_version
|
|
fn = os.path.relpath(fn, start=os.path.dirname(torch.__file__))
|
|
return (
|
|
f"https://github.com/pytorch/pytorch/blob/{version_path}/torch/{fn}#L{lineno}"
|
|
)
|
|
|
|
|
|
# The language for content autogenerated by Sphinx. Refer to documentation
|
|
# for a list of supported languages.
|
|
#
|
|
# This is also used if you do content translation via gettext catalogs.
|
|
# Usually you set "language" from the command line for these cases.
|
|
language = "en"
|
|
|
|
# List of patterns, relative to source directory, that match files and
|
|
# directories to ignore when looking for source files.
|
|
# This patterns also effect to html_static_path and html_extra_path
|
|
exclude_patterns = []
|
|
|
|
# The name of the Pygments (syntax highlighting) style to use.
|
|
pygments_style = "sphinx"
|
|
|
|
# If true, `todo` and `todoList` produce output, else they produce nothing.
|
|
# Disable docstring inheritance
|
|
autodoc_inherit_docstrings = False
|
|
|
|
# Show type hints in the description
|
|
autodoc_typehints = "description"
|
|
|
|
# Add parameter types if the parameter is documented in the docstring
|
|
autodoc_typehints_description_target = "documented_params"
|
|
|
|
# Type aliases for common types
|
|
# Sphinx type aliases only works with Postponed Evaluation of Annotations
|
|
# (PEP 563) enabled (via `from __future__ import annotations`), which keeps the
|
|
# type annotations in string form instead of resolving them to actual types.
|
|
# However, PEP 563 does not work well with JIT, which uses the type information
|
|
# to generate the code. Therefore, the following dict does not have any effect
|
|
# until PEP 563 is supported by JIT and enabled in files.
|
|
autodoc_type_aliases = {
|
|
"_size_1_t": "int or tuple[int]",
|
|
"_size_2_t": "int or tuple[int, int]",
|
|
"_size_3_t": "int or tuple[int, int, int]",
|
|
"_size_4_t": "int or tuple[int, int, int, int]",
|
|
"_size_5_t": "int or tuple[int, int, int, int, int]",
|
|
"_size_6_t": "int or tuple[int, int, int, int, int, int]",
|
|
"_size_any_opt_t": "int or None or tuple",
|
|
"_size_2_opt_t": "int or None or 2-tuple",
|
|
"_size_3_opt_t": "int or None or 3-tuple",
|
|
"_ratio_2_t": "float or tuple[float, float]",
|
|
"_ratio_3_t": "float or tuple[float, float, float]",
|
|
"_ratio_any_t": "float or tuple",
|
|
"_tensor_list_t": "Tensor or tuple[Tensor]",
|
|
}
|
|
|
|
# Enable overriding of function signatures in the first line of the docstring.
|
|
autodoc_docstring_signature = True
|
|
|
|
# -- katex javascript in header
|
|
#
|
|
# def setup(app):
|
|
# app.add_javascript("https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.js")
|
|
|
|
|
|
# -- Options for HTML output ----------------------------------------------
|
|
#
|
|
# The theme to use for HTML and HTML Help pages. See the documentation for
|
|
# a list of builtin themes.
|
|
#
|
|
#
|
|
#
|
|
|
|
|
|
# Theme options are theme-specific and customize the look and feel of a theme
|
|
# further. For a list of options available for each theme, see the
|
|
# documentation.
|
|
|
|
# Add any paths that contain custom static files (such as style sheets) here,
|
|
# relative to this directory. They are copied after the builtin static files,
|
|
# so a file named "default.css" will overwrite the builtin "default.css".
|
|
|
|
html_css_files = [
|
|
"css/jit.css",
|
|
"css/custom.css",
|
|
"https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css",
|
|
]
|
|
|
|
html_js_files = ["js/runllm-widget.js"]
|
|
|
|
from sphinx.ext.coverage import CoverageBuilder
|
|
|
|
|
|
# NB: Due to some duplications of the following modules/functions, we keep
|
|
# them as expected failures for the time being instead of return 1
|
|
ignore_duplicated_modules = {
|
|
"torch.nn.utils.weight_norm",
|
|
"torch.nn.utils.spectral_norm",
|
|
"torch.nn.parallel.data_parallel",
|
|
"torch.ao.quantization.quantize",
|
|
}
|
|
|
|
|
|
def coverage_post_process(app, exception):
|
|
if exception is not None:
|
|
return
|
|
|
|
# Only run this test for the coverage build
|
|
if not isinstance(app.builder, CoverageBuilder):
|
|
return
|
|
|
|
if not torch.distributed.is_available():
|
|
raise RuntimeError(
|
|
"The coverage tool cannot run with a version "
|
|
"of PyTorch that was built with USE_DISTRIBUTED=0 "
|
|
"as this module's API changes."
|
|
)
|
|
|
|
# These are all the modules that have "automodule" in an rst file
|
|
# These modules are the ones for which coverage is checked
|
|
# Here, we make sure that no module is missing from that list
|
|
modules = app.env.domaindata["py"]["modules"]
|
|
|
|
# We go through all the torch submodules and make sure they are
|
|
# properly tested
|
|
missing = set()
|
|
|
|
def is_not_internal(modname):
|
|
split_name = modname.split(".")
|
|
for name in split_name:
|
|
if name[0] == "_":
|
|
return False
|
|
return True
|
|
|
|
# The walk function does not return the top module
|
|
if "torch" not in modules:
|
|
missing.add("torch")
|
|
|
|
for _, modname, ispkg in pkgutil.walk_packages(
|
|
path=torch.__path__, prefix=torch.__name__ + "."
|
|
):
|
|
if is_not_internal(modname):
|
|
if modname not in modules and modname not in ignore_duplicated_modules:
|
|
missing.add(modname)
|
|
|
|
output = []
|
|
|
|
if missing:
|
|
mods = ", ".join(missing)
|
|
output.append(
|
|
f"\nYou added the following module(s) to the PyTorch namespace '{mods}' "
|
|
"but they have no corresponding entry in a doc .rst file. You should "
|
|
"either make sure that the .rst file that contains the module's documentation "
|
|
"properly contains either '.. automodule:: mod_name' (if you do not want "
|
|
"the paragraph added by the automodule, you can simply use '.. py:module:: mod_name') "
|
|
" or make the module private (by appending an '_' at the beginning of its name)."
|
|
)
|
|
|
|
# The output file is hard-coded by the coverage tool
|
|
# Our CI is setup to fail if any line is added to this file
|
|
output_file = path.join(app.outdir, "python.txt")
|
|
|
|
if output:
|
|
with open(output_file, "a") as f:
|
|
for o in output:
|
|
f.write(o)
|
|
|
|
|
|
def process_docstring(app, what_, name, obj, options, lines):
|
|
"""
|
|
Custom process to transform docstring lines Remove "Ignore" blocks
|
|
|
|
Args:
|
|
app (sphinx.application.Sphinx): the Sphinx application object
|
|
|
|
what (str):
|
|
the type of the object which the docstring belongs to (one of
|
|
"module", "class", "exception", "function", "method", "attribute")
|
|
|
|
name (str): the fully qualified name of the object
|
|
|
|
obj: the object itself
|
|
|
|
options: the options given to the directive: an object with
|
|
attributes inherited_members, undoc_members, show_inheritance
|
|
and noindex that are true if the flag option of same name was
|
|
given to the auto directive
|
|
|
|
lines (List[str]): the lines of the docstring, see above
|
|
|
|
References:
|
|
https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html
|
|
"""
|
|
import re
|
|
|
|
remove_directives = [
|
|
# Remove all xdoctest directives
|
|
re.compile(r"\s*>>>\s*#\s*x?doctest:\s*.*"),
|
|
re.compile(r"\s*>>>\s*#\s*x?doc:\s*.*"),
|
|
]
|
|
filtered_lines = [
|
|
line for line in lines if not any(pat.match(line) for pat in remove_directives)
|
|
]
|
|
# Modify the lines inplace
|
|
lines[:] = filtered_lines
|
|
|
|
# make sure there is a blank line at the end
|
|
if lines and lines[-1].strip():
|
|
lines.append("")
|
|
|
|
|
|
def setup(app):
|
|
app.connect("build-finished", coverage_post_process)
|
|
app.connect("autodoc-process-docstring", process_docstring)
|
|
app.connect("html-page-context", hide_edit_button_for_pages)
|
|
app.config.add_last_updated = True
|
|
return {"version": "0.1", "parallel_read_safe": True}
|
|
|
|
|
|
def hide_edit_button_for_pages(app, pagename, templatename, context, doctree):
|
|
if pagename.startswith("generated/"):
|
|
context["theme_use_edit_page_button"] = False
|
|
|
|
|
|
# From PyTorch 1.5, we now use autogenerated files to document classes and
|
|
# functions. This breaks older references since
|
|
# https://pytorch.org/docs/stable/torch.html#torch.flip
|
|
# moved to
|
|
# https://pytorch.org/docs/stable/generated/torch.flip.html
|
|
# which breaks older links from blog posts, stack overflow answers and more.
|
|
# To mitigate that, we add an id="torch.flip" in an appropriated place
|
|
# in torch.html by overriding the visit_reference method of html writers.
|
|
# Someday this can be removed, once the old links fade away
|
|
|
|
from sphinx.writers import html, html5
|
|
|
|
|
|
def replace(Klass):
|
|
old_call = Klass.visit_reference
|
|
|
|
def visit_reference(self, node):
|
|
if "refuri" in node and "generated" in node.get("refuri"):
|
|
ref = node.get("refuri")
|
|
ref_anchor = ref.split("#")
|
|
if len(ref_anchor) > 1:
|
|
# Only add the id if the node href and the text match,
|
|
# i.e. the href is "torch.flip#torch.flip" and the content is
|
|
# "torch.flip" or "flip" since that is a signal the node refers
|
|
# to autogenerated content
|
|
anchor = ref_anchor[1]
|
|
txt = node.parent.astext()
|
|
if txt == anchor or txt == anchor.split(".")[-1]:
|
|
self.body.append(f'<p id="{ref_anchor[1]}"/>')
|
|
return old_call(self, node)
|
|
|
|
Klass.visit_reference = visit_reference
|
|
|
|
|
|
replace(html.HTMLTranslator)
|
|
replace(html5.HTML5Translator)
|
|
|
|
# -- Options for HTMLHelp output ------------------------------------------
|
|
|
|
# Output file base name for HTML help builder.
|
|
htmlhelp_basename = "PyTorchdoc"
|
|
|
|
|
|
# -- Options for LaTeX output ---------------------------------------------
|
|
|
|
latex_engine = "lualatex"
|
|
latex_show_urls = "footnote"
|
|
|
|
latex_elements = {
|
|
"papersize": "letterpaper",
|
|
"pointsize": "10pt",
|
|
"tableofcontents": r"\pdfbookmark[0]{Contents}{toc}\tableofcontents",
|
|
"preamble": r"""
|
|
\usepackage{tocloft}
|
|
\setcounter{tocdepth}{3}
|
|
\setcounter{secnumdepth}{3}
|
|
% Fix table column widths
|
|
\renewenvironment{tabulary}{\begin{longtable}{p{0.3\linewidth}p{0.7\linewidth}}}{\end{longtable}}
|
|
|
|
% Ensure tables don't overflow
|
|
\AtBeginEnvironment{tabular}{\sloppy}
|
|
""",
|
|
"fncychap": r"\usepackage[Bjornstrup]{fncychap}",
|
|
"extraclassoptions": "oneside",
|
|
}
|
|
|
|
# Grouping the document tree into LaTeX files. List of tuples
|
|
# (source start file, target name, title,
|
|
# author, documentclass [howto, manual, or own class]).
|
|
|
|
|
|
latex_documents = [
|
|
(
|
|
master_doc,
|
|
"pytorch.tex",
|
|
"PyTorch Documentation",
|
|
"Torch Contributors",
|
|
"manual",
|
|
),
|
|
]
|
|
latex_use_xindy = False
|
|
|
|
|
|
# -- Options for manual page output ---------------------------------------
|
|
|
|
# One entry per manual page. List of tuples
|
|
# (source start file, name, description, authors, manual section).
|
|
man_pages = [(master_doc, "PyTorch", "PyTorch Documentation", [author], 1)]
|
|
|
|
|
|
# -- Options for Texinfo output -------------------------------------------
|
|
|
|
# Grouping the document tree into Texinfo files. List of tuples
|
|
# (source start file, target name, title, author,
|
|
# dir menu entry, description, category)
|
|
texinfo_documents = [
|
|
(
|
|
master_doc,
|
|
"PyTorch",
|
|
"PyTorch Documentation",
|
|
author,
|
|
"PyTorch",
|
|
"One line description of project.",
|
|
"Miscellaneous",
|
|
),
|
|
]
|
|
|
|
|
|
# Example configuration for intersphinx: refer to the Python standard library.
|
|
intersphinx_mapping = {
|
|
"python": ("https://docs.python.org/3", None),
|
|
"numpy": ("https://numpy.org/doc/stable", None),
|
|
}
|
|
|
|
import sphinx.ext.doctest
|
|
|
|
# -- A patch that prevents Sphinx from cross-referencing ivar tags -------
|
|
# See http://stackoverflow.com/a/41184353/3343043
|
|
from docutils import nodes
|
|
from sphinx import addnodes
|
|
from sphinx.util.docfields import TypedField
|
|
|
|
|
|
# Without this, doctest adds any example with a `>>>` as a test
|
|
doctest_test_doctest_blocks = ""
|
|
doctest_default_flags = sphinx.ext.doctest.doctest.ELLIPSIS
|
|
doctest_global_setup = """
|
|
import torch
|
|
try:
|
|
import torchvision
|
|
except ImportError:
|
|
torchvision = None
|
|
"""
|
|
|
|
|
|
def patched_make_field(self, types, domain, items, **kw):
|
|
# `kw` catches `env=None` needed for newer sphinx while maintaining
|
|
# backwards compatibility when passed along further down!
|
|
|
|
# type: (List, unicode, Tuple) -> nodes.field
|
|
def handle_item(fieldarg, content):
|
|
par = nodes.paragraph()
|
|
par += addnodes.literal_strong("", fieldarg) # Patch: this line added
|
|
# par.extend(self.make_xrefs(self.rolename, domain, fieldarg,
|
|
# addnodes.literal_strong))
|
|
if fieldarg in types:
|
|
par += nodes.Text(" (")
|
|
# NOTE: using .pop() here to prevent a single type node to be
|
|
# inserted twice into the doctree, which leads to
|
|
# inconsistencies later when references are resolved
|
|
fieldtype = types.pop(fieldarg)
|
|
if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text):
|
|
typename = fieldtype[0].astext()
|
|
builtin_types = ["int", "long", "float", "bool", "type"]
|
|
for builtin_type in builtin_types:
|
|
pattern = rf"(?<![\w.]){builtin_type}(?![\w.])"
|
|
repl = f"python:{builtin_type}"
|
|
typename = re.sub(pattern, repl, typename)
|
|
par.extend(
|
|
self.make_xrefs(
|
|
self.typerolename,
|
|
domain,
|
|
typename,
|
|
addnodes.literal_emphasis,
|
|
**kw,
|
|
)
|
|
)
|
|
else:
|
|
par += fieldtype
|
|
par += nodes.Text(")")
|
|
par += nodes.Text(" -- ")
|
|
par += content
|
|
return par
|
|
|
|
fieldname = nodes.field_name("", self.label)
|
|
if len(items) == 1 and self.can_collapse:
|
|
fieldarg, content = items[0]
|
|
bodynode = handle_item(fieldarg, content)
|
|
else:
|
|
bodynode = self.list_type()
|
|
for fieldarg, content in items:
|
|
bodynode += nodes.list_item("", handle_item(fieldarg, content))
|
|
fieldbody = nodes.field_body("", bodynode)
|
|
return nodes.field("", fieldname, fieldbody)
|
|
|
|
|
|
TypedField.make_field = patched_make_field
|
|
|
|
copybutton_prompt_text = r">>> |\.\.\. "
|
|
copybutton_prompt_is_regexp = True
|