Add logging recipe

recipes_source/recipes_index.rst

@@ -144,6 +144,14 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    :link: ../recipes/recipes/module_load_state_dict_tips.html
    :tags: Basics
 
+.. customcarditem::
+   :header: (beta) Using TORCH_LOGS to observe torch.compile
+   :card_description: Learn how to use the torch logging APIs to observe the compilation process.
+   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: ../recipes/torch_logs.html
+   :tags: Basics
+
+
 .. Interpretability
 
 .. customcarditem::
@@ -353,6 +361,7 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
 
    /recipes/recipes/loading_data_recipe
    /recipes/recipes/defining_a_neural_network
+   /recipes/torch_logs
    /recipes/recipes/what_is_state_dict
    /recipes/recipes/saving_and_loading_models_for_inference
    /recipes/recipes/saving_and_loading_a_general_checkpoint

recipes_source/torch_logs.py

@@ -0,0 +1,76 @@
+"""
+(beta) Using TORCH_LOGS python API with torch.compile
+==========================================================================================
+**Author:** `Michael Lazos <https://github.com/mlazos>`_
+"""
+
+import logging
+
+######################################################################
+#
+# This tutorial introduces the TORCH_LOGS environment variable and python API and
+# demonstrates how to apply it to observe the phases torch.compile.
+#
+# .. note::
+#
+#   This tutorial requires PyTorch 2.2.0 or later.
+#
+#
+
+
+######################################################################
+# Setup
+# ~~~~~~~~~~~~~~~~~~~~~
+# In this example we'll setup 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 commandline. The equivalent environment
+#   variable setting is shown for each example.
+
+import torch
+
+
+@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("")