Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.4 to 3.1.5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pallets/jinja/releases">jinja2's releases</a>.</em></p>
<blockquote>
<h2>3.1.5</h2>
<p>This is the Jinja 3.1.5 security fix release, which fixes security issues and bugs but does not otherwise change behavior and should not result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a href="https://pypi.org/project/Jinja2/3.1.5/">https://pypi.org/project/Jinja2/3.1.5/</a>
Changes: <a href="https://jinja.palletsprojects.com/changes/#version-3-1-5">https://jinja.palletsprojects.com/changes/#version-3-1-5</a>
Milestone: <a href="https://github.com/pallets/jinja/milestone/16?closed=1">https://github.com/pallets/jinja/milestone/16?closed=1</a></p>
<ul>
<li>The sandboxed environment handles indirect calls to <code>str.format</code>, such as by passing a stored reference to a filter that calls its argument. <a href="https://github.com/pallets/jinja/security/advisories/GHSA-q2x7-8rv6-6q7h">GHSA-q2x7-8rv6-6q7h</a></li>
<li>Escape template name before formatting it into error messages, to avoid issues with names that contain f-string syntax. <a href="https://redirect.github.com/pallets/jinja/issues/1792">#1792</a>, <a href="https://github.com/pallets/jinja/security/advisories/GHSA-gmj6-6f8f-6699">GHSA-gmj6-6f8f-6699</a></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on known mutable sequence types. <a href="https://redirect.github.com/pallets/jinja/issues/2032">#2032</a></li>
<li>Calling sync <code>render</code> for an async template uses <code>asyncio.run</code>. <a href="https://redirect.github.com/pallets/jinja/issues/1952">#1952</a></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings. <a href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from <code>Template.generate_async</code>. <a href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in <code>Template.generate_async</code>. <a href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving async generators unclosed in blocks, includes and extends. <a href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>The runtime uses the correct <code>concat</code> function for the current environment when calling block references. <a href="https://redirect.github.com/pallets/jinja/issues/1701">#1701</a></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after another async-aware filter. <a href="https://redirect.github.com/pallets/jinja/issues/1781">#1781</a></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from scientific notation. <a href="https://redirect.github.com/pallets/jinja/issues/1921">#1921</a></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set ... %}</code> call. <a href="https://redirect.github.com/pallets/jinja/issues/2021">#2021</a></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc) interaction with <code>Undefined</code> objects. <a href="https://redirect.github.com/pallets/jinja/issues/2025">#2025</a></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal <code>missing</code> object. <a href="https://redirect.github.com/pallets/jinja/issues/2027">#2027</a></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly. <a href="https://redirect.github.com/pallets/jinja/issues/2061">#2061</a></li>
<li>The error message from <code>FileSystemLoader</code> includes the paths that were searched. <a href="https://redirect.github.com/pallets/jinja/issues/1661">#1661</a></li>
<li><code>PackageLoader</code> shows a clearer error message when the package does not contain the templates directory. <a href="https://redirect.github.com/pallets/jinja/issues/1705">#1705</a></li>
<li>Improve annotations for methods returning copies. <a href="https://redirect.github.com/pallets/jinja/issues/1880">#1880</a></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like <code>@a@b</code>. <a href="https://redirect.github.com/pallets/jinja/issues/1870">#1870</a></li>
<li>Tests decorated with <code>@pass_context</code> can be used with the <code>|select</code> filter. <a href="https://redirect.github.com/pallets/jinja/issues/1624">#1624</a></li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1, 2</code>) does not fail when the target is a namespace attribute. <a href="https://redirect.github.com/pallets/jinja/issues/1413">#1413</a></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{% else %}</code> blocks does not cause the variable to be considered initially undefined. <a href="https://redirect.github.com/pallets/jinja/issues/1253">#1253</a></li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pallets/jinja/blob/main/CHANGES.rst">jinja2's changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.5</h2>
<p>Released 2024-12-21</p>
<ul>
<li>The sandboxed environment handles indirect calls to <code>str.format</code>, such as
by passing a stored reference to a filter that calls its argument.
:ghsa:<code>q2x7-8rv6-6q7h</code></li>
<li>Escape template name before formatting it into error messages, to avoid
issues with names that contain f-string syntax.
:issue:<code>1792</code>, :ghsa:<code>gmj6-6f8f-6699</code></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on known mutable sequence
types. :issue:<code>2032</code></li>
<li>Calling sync <code>render</code> for an async template uses <code>asyncio.run</code>.
:pr:<code>1952</code></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings. :pr:<code>1960</code></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving async generators unclosed in blocks, includes and extends.
:pr:<code>1960</code></li>
<li>The runtime uses the correct <code>concat</code> function for the current environment
when calling block references. :issue:<code>1701</code></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after another
async-aware filter. :issue:<code>1781</code></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from scientific notation.
:issue:<code>1921</code></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set ... %}</code>
call. :issue:<code>2021</code></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc) interaction with <code>Undefined</code>
objects. :issue:<code>2025</code></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal <code>missing</code> object.
:issue:<code>2027</code></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly. :pr:<code>2061</code></li>
<li>The error message from <code>FileSystemLoader</code> includes the paths that were
searched. :issue:<code>1661</code></li>
<li><code>PackageLoader</code> shows a clearer error message when the package does not
contain the templates directory. :issue:<code>1705</code></li>
<li>Improve annotations for methods returning copies. :pr:<code>1880</code></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like <code>@a@b</code>. :pr:<code>1870</code></li>
<li>Tests decorated with <code>@pass_context`` can be used with the ``|select`` filter. :issue:</code>1624`</li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1, 2</code>) does not fail when the
target is a namespace attribute. :issue:<code>1413</code></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{% else %}</code> blocks
does not cause the variable to be considered initially undefined.
:issue:<code>1253</code></li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="877f6e51be"><code>877f6e5</code></a> release version 3.1.5</li>
<li><a href="8d58859265"><code>8d58859</code></a> remove test pypi</li>
<li><a href="eda8fe86fd"><code>eda8fe8</code></a> update dev dependencies</li>
<li><a href="c8fdce1e03"><code>c8fdce1</code></a> Fix bug involving calling set on a template parameter within all branches of ...</li>
<li><a href="66587ce989"><code>66587ce</code></a> Fix bug where set would sometimes fail within if</li>
<li><a href="fbc3a696c7"><code>fbc3a69</code></a> Add support for namespaces in tuple parsing (<a href="https://redirect.github.com/pallets/jinja/issues/1664">#1664</a>)</li>
<li><a href="b8f4831d41"><code>b8f4831</code></a> more comments about nsref assignment</li>
<li><a href="ee832194cd"><code>ee83219</code></a> Add support for namespaces in tuple assignment</li>
<li><a href="1d55cddbb2"><code>1d55cdd</code></a> Triple quotes in docs (<a href="https://redirect.github.com/pallets/jinja/issues/2064">#2064</a>)</li>
<li><a href="8a8eafc6b9"><code>8a8eafc</code></a> edit block assignment section</li>
<li>Additional commits viewable in <a href="https://github.com/pallets/jinja/compare/3.1.4...3.1.5">compare view</a></li>
</ul>
</details>
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</details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143844
Approved by: https://github.com/Skylion007
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Before #143552
```py
Traceback (most recent call last):
File "/home/jansel/pytorch/repro.py", line 51, in <module>
fp32_compiled = optimized_model(low_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/eval_frame.py", line 576, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 1381, in __call__
return self._torchdynamo_orig_callable(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 1165, in __call__
result = self._inner_convert(
^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 547, in __call__
return _compile(
^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 987, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 715, in compile_inner
return _compile_inner(code, one_graph, hooks, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_utils_internal.py", line 95, in wrapper_function
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 750, in _compile_inner
out_code = transform_code_object(code, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object
transformations(instructions, code_options)
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 231, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 662, in transform
tracer.run()
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 2870, in run
super().run()
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 1053, in run
while self.step():
^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 963, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 3050, in RETURN_VALUE
self._return(inst)
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 3035, in _return
self.output.compile_subgraph(
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1101, in compile_subgraph
self.compile_and_call_fx_graph(
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1382, in compile_and_call_fx_graph
compiled_fn = self.call_user_compiler(gm)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1432, in call_user_compiler
return self._call_user_compiler(gm)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1483, in _call_user_compiler
raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1462, in _call_user_compiler
compiled_fn = compiler_fn(gm, self.example_inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/repro/after_dynamo.py", line 130, in __call__
compiled_gm = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/__init__.py", line 2314, in __call__
return compile_fx(model_, inputs_, config_patches=self.config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1880, in compile_fx
return aot_autograd(
^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/backends/common.py", line 83, in __call__
cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 1145, in aot_module_simplified
compiled_fn = AOTAutogradCache.load(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/_aot_autograd/autograd_cache.py", line 754, in load
compiled_fn = dispatch_and_compile()
^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 1131, in dispatch_and_compile
compiled_fn, _ = create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 580, in create_aot_dispatcher_function
return _create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 830, in _create_aot_dispatcher_function
compiled_fn, fw_metadata = compiler_fn(
^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 676, in aot_dispatch_autograd
compiled_fw_func = aot_config.fw_compiler(fw_module, adjusted_flat_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 489, in __call__
return self.compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1758, in fw_compiler_base
return inner_compile(
^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 572, in compile_fx_inner
return wrap_compiler_debug(_compile_fx_inner, compiler_name="inductor")(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/repro/after_aot.py", line 102, in debug_wrapper
inner_compiled_fn = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 686, in _compile_fx_inner
mb_compiled_graph = fx_codegen_and_compile(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1129, in fx_codegen_and_compile
return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1044, in codegen_and_compile
compiled_fn = graph.compile_to_module().call
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1975, in compile_to_module
return self._compile_to_module()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1981, in _compile_to_module
self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen()
^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1912, in codegen
self.scheduler = Scheduler(self.operations)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 1880, in __init__
self._init(nodes)
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 1955, in _init
self.nodes = self.fuse_nodes(self.nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 2461, in fuse_nodes
nodes = self.fuse_nodes_once(nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 2773, in fuse_nodes_once
assert False, "a fake error during fusion"
^^^^^
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
AssertionError: a fake error during fusion
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
```
Before this PR
```py
Traceback (most recent call last):
File "/home/jansel/pytorch/repro.py", line 51, in <module>
fp32_compiled = optimized_model(low_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/eval_frame.py", line 580, in _fn
raise e.remove_dynamo_frames() from None # see TORCHDYNAMO_VERBOSE=1
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1484, in _call_user_compiler
raise BackendCompilerFailed(
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1463, in _call_user_compiler
compiled_fn = compiler_fn(gm, self.example_inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/repro/after_dynamo.py", line 130, in __call__
compiled_gm = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/__init__.py", line 2314, in __call__
return compile_fx(model_, inputs_, config_patches=self.config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1880, in compile_fx
return aot_autograd(
^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/backends/common.py", line 83, in __call__
cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 1145, in aot_module_simplified
compiled_fn = AOTAutogradCache.load(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/_aot_autograd/autograd_cache.py", line 754, in load
compiled_fn = dispatch_and_compile()
^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 1131, in dispatch_and_compile
compiled_fn, _ = create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 580, in create_aot_dispatcher_function
return _create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 830, in _create_aot_dispatcher_function
compiled_fn, fw_metadata = compiler_fn(
^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 676, in aot_dispatch_autograd
compiled_fw_func = aot_config.fw_compiler(fw_module, adjusted_flat_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 489, in __call__
return self.compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1758, in fw_compiler_base
return inner_compile(
^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 572, in compile_fx_inner
return wrap_compiler_debug(_compile_fx_inner, compiler_name="inductor")(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/repro/after_aot.py", line 102, in debug_wrapper
inner_compiled_fn = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 686, in _compile_fx_inner
mb_compiled_graph = fx_codegen_and_compile(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1129, in fx_codegen_and_compile
return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1044, in codegen_and_compile
compiled_fn = graph.compile_to_module().call
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1975, in compile_to_module
return self._compile_to_module()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1981, in _compile_to_module
self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen()
^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1912, in codegen
self.scheduler = Scheduler(self.operations)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 1880, in __init__
self._init(nodes)
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 1955, in _init
self.nodes = self.fuse_nodes(self.nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 2461, in fuse_nodes
nodes = self.fuse_nodes_once(nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 2773, in fuse_nodes_once
assert False, "a fake error during fusion"
^^^^^
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
AssertionError: a fake error during fusion
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
```
After this PR
```py
Traceback (most recent call last):
File "/home/jansel/pytorch/repro.py", line 51, in <module>
fp32_compiled = optimized_model(low_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/eval_frame.py", line 580, in _fn
raise e.remove_dynamo_frames() from None # see TORCHDYNAMO_VERBOSE=1
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 704, in _compile_fx_inner
raise InductorError(e, currentframe()).with_traceback(
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 689, in _compile_fx_inner
mb_compiled_graph = fx_codegen_and_compile(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1138, in fx_codegen_and_compile
return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1053, in codegen_and_compile
compiled_fn = graph.compile_to_module().call
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1975, in compile_to_module
return self._compile_to_module()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1981, in _compile_to_module
self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen()
^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1912, in codegen
self.scheduler = Scheduler(self.operations)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 1880, in __init__
self._init(nodes)
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 1955, in _init
self.nodes = self.fuse_nodes(self.nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 2461, in fuse_nodes
nodes = self.fuse_nodes_once(nodes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 2773, in fuse_nodes_once
assert False, "a fake error during fusion"
^^^^^
torch._inductor.exc.InductorError: AssertionError: a fake error during fusion
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
```
A large numer of frames are removed between:
```py
File "/home/jansel/pytorch/torch/_dynamo/eval_frame.py", line 580, in _fn
raise e.remove_dynamo_frames() from None # see TORCHDYNAMO_VERBOSE=1
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 704, in _compile_fx_inner
raise InductorError(e, currentframe()).with_traceback(
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143610
Approved by: https://github.com/eellison
ghstack dependencies: #143552
Fixes#143406
After this PR the error for missing Triton is:
```py
Traceback (most recent call last):
File "/home/jansel/pytorch/repro.py", line 51, in <module>
fp32_compiled = optimized_model(low_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/eval_frame.py", line 580, in _fn
raise e.remove_dynamo_frames() from None # see TORCHDYNAMO_VERBOSE=1
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 3624, in create_backend
raise TritonMissing(inspect.currentframe())
torch._dynamo.exc.TritonMissing: Cannot find a working triton installation. Either the package is not installed or it is too old. More information on installing Triton can be found at: https://github.com/triton-lang/triton
Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
```
Setting `TORCHDYNAMO_VERBOSE=1` yields something like the old error:
```py
Traceback (most recent call last):
File "/home/jansel/pytorch/repro.py", line 51, in <module>
fp32_compiled = optimized_model(low_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/eval_frame.py", line 580, in _fn
raise e.remove_dynamo_frames() from None # see TORCHDYNAMO_VERBOSE=1
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/eval_frame.py", line 576, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/nn/modules/module.py", line 1750, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 1383, in __call__
return self._torchdynamo_orig_callable(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 1167, in __call__
result = self._inner_convert(
^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 548, in __call__
return _compile(
^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 988, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 716, in compile_inner
return _compile_inner(code, one_graph, hooks, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_utils_internal.py", line 95, in wrapper_function
return function(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 751, in _compile_inner
out_code = transform_code_object(code, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object
transformations(instructions, code_options)
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 232, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/convert_frame.py", line 663, in transform
tracer.run()
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 2870, in run
super().run()
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 1053, in run
while self.step():
^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 963, in step
self.dispatch_table[inst.opcode](self, inst)
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 3050, in RETURN_VALUE
self._return(inst)
File "/home/jansel/pytorch/torch/_dynamo/symbolic_convert.py", line 3035, in _return
self.output.compile_subgraph(
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1102, in compile_subgraph
self.compile_and_call_fx_graph(
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1383, in compile_and_call_fx_graph
compiled_fn = self.call_user_compiler(gm)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1433, in call_user_compiler
return self._call_user_compiler(gm)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/output_graph.py", line 1463, in _call_user_compiler
compiled_fn = compiler_fn(gm, self.example_inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/repro/after_dynamo.py", line 130, in __call__
compiled_gm = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/__init__.py", line 2314, in __call__
return compile_fx(model_, inputs_, config_patches=self.config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1880, in compile_fx
return aot_autograd(
^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/backends/common.py", line 83, in __call__
cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 1145, in aot_module_simplified
compiled_fn = AOTAutogradCache.load(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/_aot_autograd/autograd_cache.py", line 754, in load
compiled_fn = dispatch_and_compile()
^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 1131, in dispatch_and_compile
compiled_fn, _ = create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 580, in create_aot_dispatcher_function
return _create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 830, in _create_aot_dispatcher_function
compiled_fn, fw_metadata = compiler_fn(
^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 676, in aot_dispatch_autograd
compiled_fw_func = aot_config.fw_compiler(fw_module, adjusted_flat_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_functorch/aot_autograd.py", line 489, in __call__
return self.compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1758, in fw_compiler_base
return inner_compile(
^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 572, in compile_fx_inner
return wrap_compiler_debug(_compile_fx_inner, compiler_name="inductor")(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_dynamo/repro/after_aot.py", line 102, in debug_wrapper
inner_compiled_fn = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 686, in _compile_fx_inner
mb_compiled_graph = fx_codegen_and_compile(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1129, in fx_codegen_and_compile
return scheme.codegen_and_compile(gm, example_inputs, inputs_to_check, graph_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/compile_fx.py", line 1044, in codegen_and_compile
compiled_fn = graph.compile_to_module().call
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1975, in compile_to_module
return self._compile_to_module()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1981, in _compile_to_module
self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen()
^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/graph.py", line 1916, in codegen
self.scheduler.codegen()
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 3667, in codegen
return self._codegen()
^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 3761, in _codegen
if device is not None and self.get_backend(device).ready_to_flush():
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 3631, in get_backend
self.backends[device] = self.create_backend(device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jansel/pytorch/torch/_inductor/scheduler.py", line 3624, in create_backend
raise TritonMissing(inspect.currentframe())
torch._dynamo.exc.TritonMissing: Cannot find a working triton installation. Either the package is not installed or it is too old. More information on installing Triton can be found at: https://github.com/triton-lang/triton
You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True
```
This PR also strips dynamo stack frames from other types of backend compile errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143552
Approved by: https://github.com/yanboliang
Changes:
1. Bump `ruff` from 0.7.4 to 0.8.4
2. Change `%`-formatted strings to f-string
3. Change arguments with the `__`-prefix to positional-only arguments with the `/` separator in function signature.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143753
Approved by: https://github.com/Skylion007
Some tests fail for ROCm build on navi arch because of this check: f83361b274/torch/_inductor/fx_passes/pad_mm.py (L211)
There is no need to determine if mm is compute bound for most of the padding tests since they don't specifically test compute bound behavior. We don't have enough empirical data to fine tune this check for AMD gpus yet. I propose to force the shape padding for the tests that we had trouble with to avoid this unnecessary logic path.
Please correct me if I didn't add other tests that can potentially fail with this issue or if I added a test that is dependent on logic below the `force_shape_pad` check here: f83361b274/torch/_inductor/fx_passes/pad_mm.py (L444)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141768
Approved by: https://github.com/jeffdaily
* Will enable us to target `periodic`/distributed CI jobs to 4-GPU runners using a different label `linux.rocm.gpu.4`
* Use 2-GPU runners for `trunk`, `pull` and `slow` (in addition to `inductor-rocm`) as well (although this currently will not change anything, since all our MI2xx runners have both `linux.rocm.gpu` and `linux.rocm.gpu.2` labels... but this will change in the future: see next point)
* Continue to use `linux.rocm.gpu` label for any job that doesn't need more than 1-GPU eg. binary test jobs in `workflows/generated-linux-binary-manywheel-nightly.yml`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143769
Approved by: https://github.com/jeffdaily
This PR aims to add the functionality support of max-autotune for XPU. The current triton templates and configurations are not well optimized for XPU, so the performance is not ready yet. Also the `mm_plus_mm` template have accuracy issues in some cases. We will address these issues in the next PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143266
Approved by: https://github.com/EikanWang, https://github.com/jansel
For #143486
Interestingly enough changing the indexing type seems to degrade performance when a larger width is not needed, even on small sizes, so making this a template param rather than forcing all cases to 64-bit
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143696
Approved by: https://github.com/malfet
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
In hinsight, we never needed a DICT_SUBCLASS_GUARD_MANAGER, because Dynamo would inline through the overridden keys method. In this PR, we ensure that while creating guards and constructing variable trackers, we get the `d.keys()` value by using `dict.keys(d)`. This ensures that we do not call overridden keys method. Therefore, the C++ guard can use `PyDict_Next` directly to check the guards.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143722
Approved by: https://github.com/jansel
Fixes#143071
Operations performed on tensors with `requires_grad=True` such as
```python
import torch
x = torch.tensor(2.0, requires_grad=True)
y = x ** 3
```
and
```python
x = torch.tensor(2.0, requires_grad=True)
y = torch.pow(x,3)
```
are valid operations.
While an operation using `numpy` like
```python
import numpy as np
x = torch.tensor(2.0, requires_grad=True)
y = np.pow(x,3)
# > RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.
```
leads to an error.
However, an operation that uses `math` like
```python
import math
x = torch.tensor(2.0, requires_grad=True)
y = math.pow(x,3)
```
does not cause an error, and `y` is no longer a tensor with a gradient!
This represents a [footgun](https://en.wiktionary.org/wiki/footgun#Noun) for some users, like myself when training small, custom, non-neural network models.
To prevent future undesired behavior, I added a warning when converting tensors with `requires_grad=True` to scalars. Now, when using `math.pow` on a `tensor`, we get a single warning with:
```python
x = torch.tensor(2.0, requires_grad=True)
y = math.pow(x,3)
# > UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
# Consider using tensor.detach() first.
```
Please let me know if you have any questions 👍
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143261
Approved by: https://github.com/albanD
Fixes#104899
Refactors AdamW into Adam by making AdamW a subclass of Adam. Additionally adds a test to assert that the added parameter `decoupled_weight_decay` is True in AdamW and also updates test_defaults_changed_to_foreach to account for the differences in module location for AdamW.
Heavily heavily inspired by #118857 by @tfsingh
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143710
Approved by: https://github.com/janeyx99
Summary: Mostly cosmetic, but one bug fix:
* Bug fix: Make sure compile_id is converted to a string in the compilation metrics so it's printed as, e.g., "0/1" instead of "[0, 1]"
* Sort collections in `collection_to_str`
* Print non-string elements as `"<unknown>"` instead of None (since we don't expect non-strings)
* Move the population of the legacy metrics and any pre-processing to a new factory method in CompilationMetrics
Test Plan:
```
python test/dynamo/test_structured_trace.py
python test/dynamo/test_utils.py
```
Internal testing: https://fburl.com/scuba/dynamo_compile/sandbox/l0me8auf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143332
Approved by: https://github.com/ppanchalia
In some situations we want to profile calls coming from all threads (similar to on-demand), not just the thread that started profiling and the spawned threads that would inherit KinetoThreadLocal state.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143659
Approved by: https://github.com/sraikund16
