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Jack-Khuumergennachin
Jack-Khuu
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Fix ipynb generation (.md -> code block) (#862)
Summary: Pull Request resolved: #862 Currently everything is a .md block, which looks fine in html, but doesn't translate properly to .ipynb This diff fixes the rendering to work in both html and ipynb - Pushed code into code cells - Adding mocking sphinx-gallery/sphinx-gallery#941 to get around runtime code not being run in Notebook - Fixing inline code (.ipynb uses a different whitespace requirement than html sphinx) Reviewed By: tarun292 Differential Revision: D50209589 fbshipit-source-id: deb1c2a8b52edce1783f042e10aa51bad7dd2516
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‎docs/source/tutorials_source/sdk-integration-tutorial.py

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@@ -32,18 +32,18 @@
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#
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# Set up a conda environment. To set up a conda environment in Google Colab::
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#
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# !pip install -q condacolab
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# import condacolab
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# condacolab.install()
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# !pip install -q condacolab
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# import condacolab
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# condacolab.install()
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#
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# !conda create --name executorch python=3.10
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# !conda install -c conda-forge flatbuffers
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# !conda create --name executorch python=3.10
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# !conda install -c conda-forge flatbuffers
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#
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# Install ExecuTorch from source. If cloning is failing on Google Colab, make
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# sure Colab -> Setting -> Github -> Access Private Repo is checked::
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#
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# !git clone https://{github_username}:{token}@github.com/pytorch/executorch.git
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# !cd executorch && bash ./install_requirements.sh
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# !git clone https://{github_username}:{token}@github.com/pytorch/executorch.git
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# !cd executorch && bash ./install_requirements.sh
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######################################################################
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# Generate ETRecord (Optional)
@@ -57,41 +57,48 @@
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# edge dialect model (``EdgeProgramManager``), the ExecuTorch dialect model
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# (``ExecutorchProgramManager``), and an optional dictionary of additional models
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#
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# In this tutorial, the mobilenet v2 example model is used to demonstrate::
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#
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# # Imports
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# import copy
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#
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# import torch
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#
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# from executorch.examples.models.mobilenet_v2 import MV2Model
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# from executorch.exir import (
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# EdgeCompileConfig,
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# EdgeProgramManager,
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# ExecutorchProgramManager,
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# to_edge,
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# )
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# from executorch.sdk import generate_etrecord
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# from torch.export import export, ExportedProgram
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#
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# # Generate MV2 Model
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# model: torch.nn.Module = MV2Model()
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#
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# aten_model: ExportedProgram = export(
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# model.get_eager_model().eval(),
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# model.get_example_inputs(),
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# )
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#
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# edge_program_manager: EdgeProgramManager = to_edge(
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# aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True)
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# )
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# edge_program_manager_copy = copy.deepcopy(edge_program_manager)
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# et_program_manager: ExecutorchProgramManager = edge_program_manager_copy.to_executorch()
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#
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#
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# # Generate ETRecord
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# etrecord_path = "etrecord.bin"
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# generate_etrecord(etrecord_path, edge_program_manager, et_program_manager)
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# In this tutorial, the mobilenet v2 example model is used to demonstrate.
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import copy
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import torch
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from executorch.examples.models.mobilenet_v2 import MV2Model
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from executorch.exir import (
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EdgeCompileConfig,
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EdgeProgramManager,
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ExecutorchProgramManager,
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to_edge,
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)
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from executorch.sdk import generate_etrecord
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from torch.export import export, ExportedProgram
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# Generate MV2 Model
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model: torch.nn.Module = MV2Model()
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aten_model: ExportedProgram = export(
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model.get_eager_model().eval(),
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model.get_example_inputs(),
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)
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edge_program_manager: EdgeProgramManager = to_edge(
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aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True)
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)
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edge_program_manager_copy = copy.deepcopy(edge_program_manager)
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et_program_manager: ExecutorchProgramManager = edge_program_manager_copy.to_executorch()
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# Generate ETRecord
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etrecord_path = "etrecord.bin"
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generate_etrecord(etrecord_path, edge_program_manager, et_program_manager)
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# sphinx_gallery_start_ignore
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from unittest.mock import patch
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# sphinx_gallery_end_ignore
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######################################################################
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#
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# .. warning::
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# Users should do a deepcopy of the output of to_edge() and pass in the
@@ -111,18 +118,18 @@
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#
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# Use Buck::
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#
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# python3 -m examples.sdk.scripts.export_bundled_program -m mv2
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# buck2 run -c executorch.event_tracer_enabled=true examples/sdk/sdk_example_runner:sdk_example_runner -- --bundled_program_path mv2_bundled.bp
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# python3 -m examples.sdk.scripts.export_bundled_program -m mv2
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# buck2 run -c executorch.event_tracer_enabled=true examples/sdk/sdk_example_runner:sdk_example_runner -- --bundled_program_path mv2_bundled.bp
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#
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# **Option 2:**
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#
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# Use CMake::
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#
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# cd executorch
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# rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake -DBUCK2=buck2 -DEXECUTORCH_BUILD_SDK=1 -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=1 ..
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# cd ..
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# cmake --build cmake-out -j8 -t sdk_example_runner
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# ./cmake-out/examples/sdk/sdk_example_runner --bundled_program_path mv2_bundled.bp
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# cd executorch
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# rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake -DBUCK2=buck2 -DEXECUTORCH_BUILD_SDK=1 -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=1 ..
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# cd ..
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# cmake --build cmake-out -j8 -t sdk_example_runner
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# ./cmake-out/examples/sdk/sdk_example_runner --bundled_program_path mv2_bundled.bp
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######################################################################
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# Creating an Inspector
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# Note: An ``ETRecord`` is not required. If an ``ETRecord`` is not provided,
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# the Inspector will show runtime results without operator correlation.
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#
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# To visualize all runtime events, call Inspector's ``print_data_tabular``::
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#
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# from executorch.sdk import Inspector
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#
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# etdump_path = "etdump.etdp"
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# inspector = Inspector(etdump_path=etdump_path, etrecord_path=etrecord_path)
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# inspector.print_data_tabular()
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#
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# To visualize all runtime events, call Inspector's ``print_data_tabular``.
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from executorch.sdk import Inspector
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# sphinx_gallery_start_ignore
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inspector_patch = patch.object(Inspector, "__init__", return_value=None)
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inspector_patch_print = patch.object(Inspector, "print_data_tabular", return_value=None)
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inspector_patch.start()
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inspector_patch_print.start()
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# sphinx_gallery_end_ignore
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etdump_path = "etdump.etdp"
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inspector = Inspector(etdump_path=etdump_path, etrecord_path=etrecord_path)
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# sphinx_gallery_start_ignore
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inspector.event_blocks = []
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# sphinx_gallery_end_ignore
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inspector.print_data_tabular()
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# sphinx_gallery_start_ignore
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inspector_patch.stop()
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inspector_patch_print.stop()
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# sphinx_gallery_end_ignore
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######################################################################
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# Analyzing with an Inspector
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# and ``DataFrames``. These mediums give users the ability to perform custom
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# analysis about their model performance.
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#
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# Below are examples usages, with both ``EventBlock`` and ``DataFrame`` approaches::
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#
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# # Set Up
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#
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# import pprint as pp
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#
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# import pandas as pd
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#
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# pd.set_option("display.max_colwidth", None)
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# pd.set_option("display.max_columns", None)
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# Below are examples usages, with both ``EventBlock`` and ``DataFrame`` approaches.
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# Set Up
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import pprint as pp
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import pandas as pd
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pd.set_option("display.max_colwidth", None)
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pd.set_option("display.max_columns", None)
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######################################################################
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# If a user wants the raw profiling results, they would do something similar to
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# finding the raw runtime data of an ``addmm.out`` event::
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#
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# for event_block in inspector.event_blocks:
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# # Via EventBlocks
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# for event in event_block.events:
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# if event.name == "native_call_addmm.out":
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# print(event.name, event.perf_data.raw)
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#
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# # Via Dataframe
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# df = event_block.to_dataframe()
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# df = df[df.event_name == "native_call_addmm.out"]
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# print(df[["event_name", "raw"]])
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# print()
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#
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# finding the raw runtime data of an ``addmm.out`` event.
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for event_block in inspector.event_blocks:
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# Via EventBlocks
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for event in event_block.events:
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if event.name == "native_call_addmm.out":
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print(event.name, event.perf_data.raw)
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# Via Dataframe
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df = event_block.to_dataframe()
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df = df[df.event_name == "native_call_addmm.out"]
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print(df[["event_name", "raw"]])
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print()
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######################################################################
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# If a user wants to trace an operator back to their model code, they would do
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# something similar to finding the module hierarchy and stack trace of the
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# slowest ``convolution.out`` call::
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#
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# for event_block in inspector.event_blocks:
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# # Via EventBlocks
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# slowest = None
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# for event in event_block.events:
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# if event.name == "native_call_convolution.out":
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# if slowest is None or event.perf_data.p50 > slowest.perf_data.p50:
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# slowest = event
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# if slowest is not None:
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# print(slowest.name)
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# print()
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# pp.pprint(slowest.stack_traces)
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# print()
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# pp.pprint(slowest.module_hierarchy)
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#
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# # Via Dataframe
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# df = event_block.to_dataframe()
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# df = df[df.event_name == "native_call_convolution.out"]
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# if len(df) > 0:
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# slowest = df.loc[df["p50"].idxmax()]
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# print(slowest.event_name)
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# print()
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# pp.pprint(slowest.stack_traces)
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# print()
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# pp.pprint(slowest.module_hierarchy)
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#
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# slowest ``convolution.out`` call.
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for event_block in inspector.event_blocks:
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# Via EventBlocks
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slowest = None
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for event in event_block.events:
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if event.name == "native_call_convolution.out":
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if slowest is None or event.perf_data.p50 > slowest.perf_data.p50:
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slowest = event
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if slowest is not None:
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print(slowest.name)
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print()
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pp.pprint(slowest.stack_traces)
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print()
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pp.pprint(slowest.module_hierarchy)
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# Via Dataframe
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df = event_block.to_dataframe()
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df = df[df.event_name == "native_call_convolution.out"]
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if len(df) > 0:
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slowest = df.loc[df["p50"].idxmax()]
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print(slowest.event_name)
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print()
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pp.pprint(slowest.stack_traces)
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print()
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pp.pprint(slowest.module_hierarchy)
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######################################################################
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# If a user wants the total runtime of a module, they can use
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# ``find_total_for_module``::
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#
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# print(inspector.find_total_for_module("L__self___features"))
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# print(inspector.find_total_for_module("L__self___features_14"))
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# ``find_total_for_module``.
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235+
print(inspector.find_total_for_module("L__self___features"))
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print(inspector.find_total_for_module("L__self___features_14"))
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######################################################################
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# Note: ``find_total_for_module`` is a special first class method of

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