Move Inspector Tutorial Code into individual code cells (#814)

Summary:
Pull Request resolved: #814

The generated Notebook Cell types were being set to markdown.

This diff just makes them into code cells

Differential Revision: D50147708

fbshipit-source-id: c73cfda9e46cbe9f8a7101669636e8b097447b73

Author: Jack-Khuu

docs/source/tutorials_source/sdk-integration-tutorial.py

@@ -57,39 +57,43 @@
 # edge dialect model (``EdgeProgramManager``), the ExecuTorch dialect model
 # (``ExecutorchProgramManager``), and an optional dictionary of additional models
 #
-# In this tutorial, the mobilenet v2 example model is used to demonstrate::
-#
-#   # Imports
-#   import copy
-#   import torch
-#
-#   from executorch.examples.models.mobilenet_v2 import MV2Model
-#   from executorch.exir import (
-#       EdgeCompileConfig,
-#       EdgeProgramManager,
-#       ExecutorchProgramManager,
-#       to_edge,
-#   )
-#   from executorch.sdk import generate_etrecord
-#   from torch.export import export, ExportedProgram
-#
-#   # Generate MV2 Model
-#   model: torch.nn.Module = MV2Model()
-#
-#   aten_model: ExportedProgram = export(
-#       model.get_eager_model().eval(),
-#       model.get_example_inputs(),
-#   )
-#
-#   edge_program_manager: EdgeProgramManager = to_edge(aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True))
-#   edge_program_manager_copy = copy.deepcopy(edge_program_manager)
-#   et_program_manager: ExecutorchProgramManager = edge_program_manager_copy.to_executorch()
-#
-#
-#   # Generate ETRecord
-#   etrecord_path = "etrecord.bin"
-#   generate_etrecord(etrecord_path, edge_program_manager, et_program_manager)
-#
+# In this tutorial, the mobilenet v2 example model is used to demonstrate.
+
+# Imports
+import copy
+
+import torch
+
+from executorch.examples.models.mobilenet_v2 import MV2Model
+from executorch.exir import (
+    EdgeCompileConfig,
+    EdgeProgramManager,
+    ExecutorchProgramManager,
+    to_edge,
+)
+from executorch.sdk import generate_etrecord
+from torch.export import export, ExportedProgram
+
+# Generate MV2 Model
+model: torch.nn.Module = MV2Model()
+
+aten_model: ExportedProgram = export(
+    model.get_eager_model().eval(),
+    model.get_example_inputs(),
+)
+
+edge_program_manager: EdgeProgramManager = to_edge(
+    aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True)
+)
+edge_program_manager_copy = copy.deepcopy(edge_program_manager)
+et_program_manager: ExecutorchProgramManager = edge_program_manager_copy.to_executorch()
+
+
+# Generate ETRecord
+etrecord_path = "etrecord.bin"
+generate_etrecord(etrecord_path, edge_program_manager, et_program_manager)
+
+######################################################################
 # .. warning::
 #    Users should do a deepcopy of the output of to_edge() and pass in the
 #    deepcopy to the generate_etrecord API. This is needed because the
@@ -131,15 +135,14 @@
 # Note: An ``ETRecord`` is not required. If an ``ETRecord`` is not provided,
 # the Inspector will show runtime results without operator correlation.
 #
-# To visualize all runtime events, call ``print_data_tabular``::
-#
-#   from executorch.sdk import Inspector
-#
-#   etdump_path = "etdump.etdp"
-#   inspector = Inspector(etdump_path=etdump_path, etrecord_path=etrecord_path)
-#   inspector.print_data_tabular()
-#
+# To visualize all runtime events, call Inspector's ``print_data_tabular``.
 
+from executorch.sdk import Inspector
+
+etrecord_path = "etdump.etdp"
+etdump_path = "etdump.etdp"
+inspector = Inspector(etdump_path=etdump_path, etrecord_path=etrecord_path)
+inspector.print_data_tabular()
 
 ######################################################################
 # Analyzing with an Inspector
@@ -149,68 +152,71 @@
 # and ``DataFrames``. These mediums give users the ability to perform custom
 # analysis about their model performance.
 #
-# Below are examples usages, with both ``EventBlock`` and ``DataFrame`` approaches::
-#
-#   # Set Up
-#
-#   import pprint as pp
-#   import pandas as pd
-#
-#   pd.set_option('display.max_colwidth', None)
-#   pd.set_option('display.max_columns', None)
-#
+# Below are examples usages, with both ``EventBlock`` and ``DataFrame`` approaches.
+
+# Set Up
+import pprint as pp
+import pandas as pd
+
+pd.set_option("display.max_colwidth", None)
+pd.set_option("display.max_columns", None)
+
+######################################################################
 # If a user wants the raw profiling results, they would do something similar to
-# finding the raw runtime data of an ``addmm.out`` event::
-#
-#   for event_block in inspector.event_blocks:
-#       # Via EventBlocks
-#       for event in event_block.events:
-#           if event.name == 'native_call_addmm.out':
-#               print(event.name, event.perf_data.raw)
-#
-#       # Via Dataframe
-#       df = event_block.to_dataframe()
-#       df = df[df.event_name == 'native_call_addmm.out']
-#       print(df[['event_name', 'raw']])
-#       print()
-#
+# finding the raw runtime data of an ``addmm.out`` event.
+
+for event_block in inspector.event_blocks:
+    # Via EventBlocks
+    for event in event_block.events:
+        if event.name == "native_call_addmm.out":
+            print(event.name, event.perf_data.raw)
+
+    # Via Dataframe
+    df = event_block.to_dataframe()
+    df = df[df.event_name == "native_call_addmm.out"]
+    print(df[["event_name', 'raw"]])
+    print()
+
+######################################################################
 # If a user wants to trace an operator back to their model code, they would do
 # something similar to finding the module hierarchy and stack trace of the
-# slowest ``convolution.out`` call::
-#
-#   for event_block in inspector.event_blocks:
-#       # Via EventBlocks
-#       slowest = None
-#       for event in event_block.events:
-#           if event.name == 'native_call_convolution.out':
-#               if slowest is None or event.perf_data.p50 > slowest.perf_data.p50:
-#                   slowest = event
-#       if slowest is not None:
-#           print(slowest.name)
-#           print()
-#           pp.pprint(slowest.stack_traces)
-#           print()
-#           pp.pprint(slowest.module_hierarchy
-#
-#       # Via Dataframe
-#       df = event_block.to_dataframe()
-#       df = df[df.event_name == 'native_call_convolution.out']
-#       if len(df) > 0:
-#           slowest = df.loc[df['p50'].idxmax()]
-#           print(slowest.event_name)
-#           print()
-#           pp.pprint(slowest.stack_traces)
-#           print()
-#           pp.pprint(slowest.module_hierarchy)
-#
-# If a user wants the total runtime of a module::
-#
-#   print(inspector.find_total_for_module("L__self___features"))
-#   print(inspector.find_total_for_module("L__self___features_14"))
-#
+# slowest ``convolution.out`` call.
+
+for event_block in inspector.event_blocks:
+    # Via EventBlocks
+    slowest = None
+    for event in event_block.events:
+        if event.name == "native_call_convolution.out":
+            if slowest is None or event.perf_data.p50 > slowest.perf_data.p50:
+                slowest = event
+    if slowest is not None:
+        print(slowest.name)
+        print()
+        pp.pprint(slowest.stack_traces)
+        print()
+        pp.pprint(slowest.module_hierarchy)
+
+    # Via Dataframe
+    df = event_block.to_dataframe()
+    df = df[df.event_name == "native_call_convolution.out"]
+    if len(df) > 0:
+        slowest = df.loc[df["p50"].idxmax()]
+        print(slowest.event_name)
+        print()
+        pp.pprint(slowest.stack_traces)
+        print()
+        pp.pprint(slowest.module_hierarchy)
+
+######################################################################
+# If a user wants the total runtime of a module, they can use
+# ``find_total_for_module``.
+
+print(inspector.find_total_for_module("L__self___features"))
+print(inspector.find_total_for_module("L__self___features_14"))
+
+######################################################################
 # Note: ``find_total_for_module`` is a special first class method of
 # `Inspector <../sdk-inspector.html>`__
-#
 
 ######################################################################
 # Conclusion