.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "recipes/torch_logs.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_recipes_torch_logs.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_recipes_torch_logs.py:


(beta) Using TORCH_LOGS python API with torch.compile
==========================================================================================
**Author:** `Michael Lazos <https://github.com/mlazos>`_

.. GENERATED FROM PYTHON SOURCE LINES 6-9

.. code-block:: Python


    import logging








.. GENERATED FROM PYTHON SOURCE LINES 10-18

This tutorial introduces the ``TORCH_LOGS`` environment variable, as well as the Python API, and
demonstrates how to apply it to observe the phases  of ``torch.compile``.

.. note::

  This tutorial requires PyTorch 2.2.0 or later.



.. GENERATED FROM PYTHON SOURCE LINES 22-32

Setup
~~~~~~~~~~~~~~~~~~~~~
In this example, we'll set up a simple Python function which performs an elementwise
add and observe the compilation process with ``TORCH_LOGS`` Python API.

.. note::

  There is also an environment variable ``TORCH_LOGS``, which can be used to
  change logging settings at the command line. The equivalent environment
  variable setting is shown for each example.

.. GENERATED FROM PYTHON SOURCE LINES 32-81

.. code-block:: Python


    import torch

    # exit cleanly if we are on a device that doesn't support torch.compile
    if torch.cuda.get_device_capability() < (7, 0):
        print("Skipping because torch.compile is not supported on this device.")
    else:
        @torch.compile()
        def fn(x, y):
            z = x + y
            return z + 2


        inputs = (torch.ones(2, 2, device="cuda"), torch.zeros(2, 2, device="cuda"))


    # print separator and reset dynamo
    # between each example
        def separator(name):
            print(f"==================={name}=========================")
            torch._dynamo.reset()


        separator("Dynamo Tracing")
    # View dynamo tracing
    # TORCH_LOGS="+dynamo"
        torch._logging.set_logs(dynamo=logging.DEBUG)
        fn(*inputs)

        separator("Traced Graph")
    # View traced graph
    # TORCH_LOGS="graph"
        torch._logging.set_logs(graph=True)
        fn(*inputs)

        separator("Fusion Decisions")
    # View fusion decisions
    # TORCH_LOGS="fusion"
        torch._logging.set_logs(fusion=True)
        fn(*inputs)

        separator("Output Code")
    # View output code generated by inductor
    # TORCH_LOGS="output_code"
        torch._logging.set_logs(output_code=True)
        fn(*inputs)

        separator("")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    ===================Dynamo Tracing=========================
    I0718 23:09:24.870000 22423 torch/_dynamo/utils.py:1603] [0/0] ChromiumEventLogger initialized with id 8b89c919-423f-4f5f-a8c7-e576d37b8ef6
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0] torchdynamo start compiling fn /var/lib/workspace/recipes_source/torch_logs.py:39, stack (elided 5 frames):
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/bin/sphinx-build", line 8, in <module>
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     sys.exit(main())
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/cmd/build.py", line 313, in main
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     return make_main(argv)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/cmd/build.py", line 195, in make_main
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     return make_mode.run_make_mode(argv[1:])
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/cmd/make_mode.py", line 160, in run_make_mode
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     return make.run_generic_build(args[0])
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/cmd/make_mode.py", line 148, in run_generic_build
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     return build_main(args + opts)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/cmd/build.py", line 276, in build_main
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     app = Sphinx(args.sourcedir, args.confdir, args.outputdir,
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/application.py", line 262, in __init__
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     self._init_builder()
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/application.py", line 335, in _init_builder
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     self.events.emit('builder-inited')
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx/events.py", line 94, in emit
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     results.append(listener.handler(self.app, *args))
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_gallery.py", line 743, in generate_gallery_rst
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     ) = generate_dir_rst(
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 598, in generate_dir_rst
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     results = parallel(
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 599, in <genexpr>
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     p_fun(fname, target_dir, src_dir, gallery_conf) for fname in iterator
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/var/lib/workspace/conf.py", line 79, in wrapper
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     p.start()
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/lib/python3.10/multiprocessing/process.py", line 121, in start
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     self._popen = self._Popen(self)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/lib/python3.10/multiprocessing/context.py", line 224, in _Popen
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     return _default_context.get_context().Process._Popen(process_obj)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/lib/python3.10/multiprocessing/context.py", line 281, in _Popen
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     return Popen(process_obj)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     self._launch(process_obj)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/lib/python3.10/multiprocessing/popen_fork.py", line 71, in _launch
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     code = process_obj._bootstrap(parent_sentinel=child_r)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     self.run()
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     self._target(*self._args, **self._kwargs)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/var/lib/workspace/conf.py", line 67, in call_fn
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     result = func(*args, **kwargs)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 1346, in generate_file_rst
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     output_blocks, time_elapsed = execute_script(
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 1164, in execute_script
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     execute_code_block(
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 1020, in execute_code_block
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     is_last_expr, mem_max = _exec_and_get_memory(
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 865, in _exec_and_get_memory
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     mem_max, _ = call_memory(
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 1700, in _sg_call_memory_noop
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     return 0.0, func()
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/usr/local/lib/python3.10/dist-packages/sphinx_gallery/gen_rst.py", line 783, in __call__
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     exec(self.code, self.fake_main.__dict__)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]   File "/var/lib/workspace/recipes_source/torch_logs.py", line 59, in <module>
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0]     fn(*inputs)
    V0718 23:09:24.871000 22423 torch/_dynamo/convert_frame.py:1003] [0/0] 
    I0718 23:09:24.875000 22423 torch/_dynamo/symbolic_convert.py:3324] [0/0] Step 1: torchdynamo start tracing fn /var/lib/workspace/recipes_source/torch_logs.py:39
    I0718 23:09:24.876000 22423 torch/fx/experimental/symbolic_shapes.py:3334] [0/0] create_env
    V0718 23:09:24.879000 22423 torch/_dynamo/symbolic_convert.py:1216] [0/0] [__trace_source] TRACE starts_line /var/lib/workspace/recipes_source/torch_logs.py:41 in fn (fn)
    V0718 23:09:24.879000 22423 torch/_dynamo/symbolic_convert.py:1216] [0/0] [__trace_source]             z = x + y
    V0718 23:09:24.880000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE LOAD_FAST x []
    V0718 23:09:24.881000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE LOAD_FAST y [LazyVariableTracker()]
    V0718 23:09:24.881000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE BINARY_ADD None [LazyVariableTracker(), LazyVariableTracker()]
    V0718 23:09:24.882000 22423 torch/_dynamo/variables/builder.py:3025] [0/0] wrap_to_fake L['x'] (2, 2) StatefulSymbolicContext(dynamic_sizes=[<DimDynamic.STATIC: 2>, <DimDynamic.STATIC: 2>], dynamic_strides=[<DimDynamic.INFER_STRIDE: 4>, <DimDynamic.INFER_STRIDE: 4>], constraint_sizes=[None, None], constraint_strides=[None, None], view_base_context=None, tensor_source=LocalSource(local_name='x', is_input=True, dynamism=None, is_derefed_cell_contents=False), shape_env_to_source_to_symbol_cache={}) <class 'torch.Tensor'>
    V0718 23:09:24.883000 22423 torch/_dynamo/output_graph.py:2271] [0/0] create_graph_input L_x_ L['x'] FakeTensor(..., device='cuda:0', size=(2, 2)) at debug_level 0 before=False
    V0718 23:09:24.885000 22423 torch/_dynamo/variables/builder.py:3025] [0/0] wrap_to_fake L['y'] (2, 2) StatefulSymbolicContext(dynamic_sizes=[<DimDynamic.STATIC: 2>, <DimDynamic.STATIC: 2>], dynamic_strides=[<DimDynamic.INFER_STRIDE: 4>, <DimDynamic.INFER_STRIDE: 4>], constraint_sizes=[None, None], constraint_strides=[None, None], view_base_context=None, tensor_source=LocalSource(local_name='y', is_input=True, dynamism=None, is_derefed_cell_contents=False), shape_env_to_source_to_symbol_cache={}) <class 'torch.Tensor'>
    V0718 23:09:24.885000 22423 torch/_dynamo/output_graph.py:2271] [0/0] create_graph_input L_y_ L['y'] FakeTensor(..., device='cuda:0', size=(2, 2)) at debug_level 0 before=False
    V0718 23:09:24.888000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE STORE_FAST z [TensorVariable()]
    V0718 23:09:24.888000 22423 torch/_dynamo/symbolic_convert.py:1216] [0/0] [__trace_source] TRACE starts_line /var/lib/workspace/recipes_source/torch_logs.py:42 in fn (fn)
    V0718 23:09:24.888000 22423 torch/_dynamo/symbolic_convert.py:1216] [0/0] [__trace_source]             return z + 2
    V0718 23:09:24.889000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE LOAD_FAST z []
    V0718 23:09:24.889000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE LOAD_CONST 2 [TensorVariable()]
    V0718 23:09:24.889000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE BINARY_ADD None [TensorVariable(), ConstantVariable(int: 2)]
    V0718 23:09:24.890000 22423 torch/_dynamo/symbolic_convert.py:1239] [0/0] [__trace_bytecode] TRACE RETURN_VALUE None [TensorVariable()]
    I0718 23:09:24.891000 22423 torch/_dynamo/symbolic_convert.py:3681] [0/0] Step 1: torchdynamo done tracing fn (RETURN_VALUE)
    V0718 23:09:24.891000 22423 torch/_dynamo/symbolic_convert.py:3685] [0/0] RETURN_VALUE triggered compile
    V0718 23:09:24.891000 22423 torch/_dynamo/output_graph.py:1008] [0/0] COMPILING GRAPH due to GraphCompileReason(reason='return_value', user_stack=[<FrameSummary file /var/lib/workspace/recipes_source/torch_logs.py, line 42 in fn>], graph_break=False)
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code] TRACED GRAPH
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]  ===== __compiled_fn_1 =====
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]  /usr/local/lib/python3.10/dist-packages/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module):
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]     def forward(self, L_x_: "f32[2, 2][2, 1]cuda:0", L_y_: "f32[2, 2][2, 1]cuda:0"):
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         l_x_ = L_x_
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         l_y_ = L_y_
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]          # File: /var/lib/workspace/recipes_source/torch_logs.py:41 in fn, code: z = x + y
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         z: "f32[2, 2][2, 1]cuda:0" = l_x_ + l_y_;  l_x_ = l_y_ = None
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]          # File: /var/lib/workspace/recipes_source/torch_logs.py:42 in fn, code: return z + 2
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         add_1: "f32[2, 2][2, 1]cuda:0" = z + 2;  z = None
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         return (add_1,)
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code]         
    V0718 23:09:24.893000 22423 torch/_dynamo/output_graph.py:1408] [0/0] [__graph_code] 
    I0718 23:09:24.895000 22423 torch/_dynamo/output_graph.py:1515] [0/0] Step 2: calling compiler function inductor
    I0718 23:09:26.259000 22423 torch/fx/experimental/symbolic_shapes.py:4734] [0/0] produce_guards
    I0718 23:09:26.264000 22423 torch/_dynamo/output_graph.py:1520] [0/0] Step 2: done compiler function inductor
    I0718 23:09:26.266000 22423 torch/fx/experimental/symbolic_shapes.py:4734] [0/0] produce_guards
    V0718 23:09:26.267000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['x'].size()[0] 2 None
    V0718 23:09:26.267000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['x'].size()[1] 2 None
    V0718 23:09:26.267000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['x'].stride()[0] 2 None
    V0718 23:09:26.267000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['x'].stride()[1] 1 None
    V0718 23:09:26.268000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['x'].storage_offset() 0 None
    V0718 23:09:26.268000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['y'].size()[0] 2 None
    V0718 23:09:26.268000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['y'].size()[1] 2 None
    V0718 23:09:26.269000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['y'].stride()[0] 2 None
    V0718 23:09:26.269000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['y'].stride()[1] 1 None
    V0718 23:09:26.269000 22423 torch/fx/experimental/symbolic_shapes.py:4954] [0/0] track_symint L['y'].storage_offset() 0 None
    V0718 23:09:26.270000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['x'].size()[0] == 2
    V0718 23:09:26.270000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['x'].size()[1] == 2
    V0718 23:09:26.270000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['x'].stride()[0] == 2
    V0718 23:09:26.271000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['x'].stride()[1] == 1
    V0718 23:09:26.271000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['x'].storage_offset() == 0
    V0718 23:09:26.271000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['y'].size()[0] == 2
    V0718 23:09:26.272000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['y'].size()[1] == 2
    V0718 23:09:26.272000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['y'].stride()[0] == 2
    V0718 23:09:26.272000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['y'].stride()[1] == 1
    V0718 23:09:26.273000 22423 torch/fx/experimental/symbolic_shapes.py:5156] [0/0] Skipping guard L['y'].storage_offset() == 0
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2557] [0/0] [__guards] GUARDS:
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] 
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] TREE_GUARD_MANAGER:
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] +- RootGuardManager
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | +- DEFAULT_DEVICE: utils_device.CURRENT_DEVICE == None                           # _dynamo/output_graph.py:520 in init_ambient_guards
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | +- GLOBAL_STATE: ___check_global_state()
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | +- TORCH_FUNCTION_MODE_STACK: ___check_torch_function_mode_stack()
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | +- GuardManager: source=L['x'], accessed_by=FrameLocalsGuardAccessor(key='x', framelocals_idx=0)
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | | +- TENSOR_MATCH: check_tensor(L['x'], Tensor, DispatchKeySet(CUDA, BackendSelect, ADInplaceOrView, AutogradCUDA), torch.float32, device=0, requires_grad=False, size=[2, 2], stride=[2, 1])  # z = x + y  # ar/lib/workspace/recipes_source/torch_logs.py:41 in fn
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | | +- NO_HASATTR: hasattr(L['x'], '_dynamo_dynamic_indices') == False           # z = x + y  # ar/lib/workspace/recipes_source/torch_logs.py:41 in fn
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | | +- NO_TENSOR_ALIASING: check_no_aliasing(L['x'], L['y'])
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | +- GuardManager: source=L['y'], accessed_by=FrameLocalsGuardAccessor(key='y', framelocals_idx=1)
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | | +- TENSOR_MATCH: check_tensor(L['y'], Tensor, DispatchKeySet(CUDA, BackendSelect, ADInplaceOrView, AutogradCUDA), torch.float32, device=0, requires_grad=False, size=[2, 2], stride=[2, 1])  # z = x + y  # ar/lib/workspace/recipes_source/torch_logs.py:41 in fn
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | | +- NO_HASATTR: hasattr(L['y'], '_dynamo_dynamic_indices') == False           # z = x + y  # ar/lib/workspace/recipes_source/torch_logs.py:41 in fn
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] | | +- NO_TENSOR_ALIASING
    V0718 23:09:26.273000 22423 torch/_dynamo/guards.py:2495] [0/0] [__guards] 
    V0718 23:09:26.275000 22423 torch/_dynamo/guards.py:2524] [0/0] [__guards] Guard eval latency = 0.89 us
    I0718 23:09:26.275000 22423 torch/_dynamo/pgo.py:660] [0/0] put_code_state: no cache key, skipping
    I0718 23:09:26.276000 22423 torch/_dynamo/convert_frame.py:1121] [0/0] run_gc_after_compile: running gc
    V0718 23:09:26.279000 22423 torch/_dynamo/convert_frame.py:1395] skipping: _fn (reason: in skipfiles, file: /usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py)
    ===================Traced Graph=========================
    I0718 23:09:26.279000 22423 torch/_dynamo/__init__.py:112] torch._dynamo.reset
    I0718 23:09:26.280000 22423 torch/_dynamo/__init__.py:145] torch._dynamo.reset_code_caches
    ===================Fusion Decisions=========================
    ===================Output Code=========================
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] Output code: 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] # AOT ID: ['0_inference']
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from ctypes import c_void_p, c_long, c_int
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import torch
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import math
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import random
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import os
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import tempfile
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from math import inf, nan
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from cmath import nanj
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.hooks import run_intermediate_hooks
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.utils import maybe_profile
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.codegen.memory_planning import _align as align
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch import device, empty_strided
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.async_compile import AsyncCompile
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.select_algorithm import extern_kernels
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.codegen.multi_kernel import MultiKernelCall
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import triton
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import triton.language as tl
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.runtime.triton_heuristics import start_graph, end_graph
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._C import _cuda_getCurrentRawStream as get_raw_stream
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._C import _cuda_getCurrentRawStream as get_raw_stream
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] aten = torch.ops.aten
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] inductor_ops = torch.ops.inductor
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] _quantized = torch.ops._quantized
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] assert_size_stride = torch._C._dynamo.guards.assert_size_stride
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] alloc_from_pool = torch.ops.inductor._alloc_from_pool
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] async_compile = AsyncCompile()
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] empty_strided_p2p = torch._C._distributed_c10d._SymmetricMemory.empty_strided_p2p
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] # kernel path: /tmp/torchinductor_ci-user/ld/cld7tar7n7kytdxqq7n73fjc5nptwpbw7wqmdbp24zf62axk3q3a.py
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] # Topologically Sorted Source Nodes: [z, add_1], Original ATen: [aten.add]
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] # Source node to ATen node mapping:
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] #   add_1 => add_1
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] #   z => add
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] # Graph fragment:
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] #   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] #   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, 2), kwargs = {})
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', '''
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import triton
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] import triton.language as tl
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.runtime import triton_helpers, triton_heuristics
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, DeviceProperties
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] triton_helpers.set_driver_to_gpu()
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] @triton_heuristics.pointwise(
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     size_hints={'x': 4}, 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     filename=__file__,
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     triton_meta={'signature': {'in_ptr0': '*fp32', 'in_ptr1': '*fp32', 'out_ptr0': '*fp32', 'xnumel': 'i32', 'XBLOCK': 'constexpr'}, 'device': DeviceProperties(type='cuda', index=0, multi_processor_count=80, cc=86, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, warp_size=32), 'constants': {}, 'configs': [{(0,): [['tt.divisibility', 16]], (1,): [['tt.divisibility', 16]], (2,): [['tt.divisibility', 16]]}]},
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     inductor_meta={'grid_type': 'Grid1D', 'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'optimize_mem': True, 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': '1E2C16421D4C3DBA4AD92BFC4278A3CB24C43DEDA6EE7FF9E3FBB1DBB80802DB', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     min_elem_per_thread=0
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] )
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] @triton.jit
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     xnumel = 4
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     xoffset = tl.program_id(0) * XBLOCK
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     xindex = xoffset + tl.arange(0, XBLOCK)[:]
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     xmask = xindex < xnumel
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     x0 = xindex
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     tmp0 = tl.load(in_ptr0 + (x0), xmask)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     tmp1 = tl.load(in_ptr1 + (x0), xmask)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     tmp2 = tmp0 + tmp1
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     tmp3 = 2.0
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     tmp4 = tmp2 + tmp3
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     tl.store(out_ptr0 + (x0), tmp4, xmask)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] ''', device_str='cuda')
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] async_compile.wait(globals())
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] del async_compile
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] def call(args):
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     arg0_1, arg1_1 = args
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     args.clear()
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     assert_size_stride(arg0_1, (2, 2), (2, 1))
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     assert_size_stride(arg1_1, (2, 2), (2, 1))
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     with torch.cuda._DeviceGuard(0):
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]         torch.cuda.set_device(0)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]         buf0 = empty_strided_cuda((2, 2), (2, 1), torch.float32)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]         # Topologically Sorted Source Nodes: [z, add_1], Original ATen: [aten.add]
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]         stream0 = get_raw_stream(0)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]         triton_poi_fused_add_0.run(arg0_1, arg1_1, buf0, 4, stream=stream0)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]         del arg0_1
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]         del arg1_1
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     return (buf0, )
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] def benchmark_compiled_module(times=10, repeat=10):
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     from torch._dynamo.testing import rand_strided
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     from torch._inductor.utils import print_performance
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     arg0_1 = rand_strided((2, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     arg1_1 = rand_strided((2, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     fn = lambda: call([arg0_1, arg1_1])
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     return print_performance(fn, times=times, repeat=repeat)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] if __name__ == "__main__":
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     from torch._inductor.wrapper_benchmark import compiled_module_main
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code]     compiled_module_main('None', benchmark_compiled_module)
    V0718 23:09:26.369000 22423 torch/_inductor/codecache.py:1093] [0/0] [__output_code] 
    V0718 23:09:26.375000 22423 torch/_inductor/codecache.py:1094] [0/0] [__output_code] Output code written to: /tmp/torchinductor_ci-user/nk/cnk55csixpane7aredk4kvfxz3fx2bb7zgzf4vpzqkzufdznzojb.py
    ============================================




.. GENERATED FROM PYTHON SOURCE LINES 82-97

Conclusion
~~~~~~~~~~

In this tutorial we introduced the TORCH_LOGS environment variable and python API
by experimenting with a small number of the available logging options.
To view descriptions of all available options, run any python script
which imports torch and set TORCH_LOGS to "help".

Alternatively, you can view the `torch._logging documentation`_ to see
descriptions of all available logging options.

For more information on torch.compile, see the `torch.compile tutorial`_.

.. _torch._logging documentation: https://pytorch.org/docs/main/logging.html
.. _torch.compile tutorial: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.823 seconds)


.. _sphx_glr_download_recipes_torch_logs.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: torch_logs.ipynb <torch_logs.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: torch_logs.py <torch_logs.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: torch_logs.zip <torch_logs.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_