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Clarify CUDA 7.0+ device compatibility requirement in torch_logs.py tutorial #3634

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Clarify CUDA 7.0+ device compatibility requirement in torch_logs.py tutorial #3634

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31 changes: 14 additions & 17 deletions recipes_source/torch_logs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,7 @@
==========================================================================================
**Author:** `Michael Lazos <https://github.com/mlazos>`_
"""

import logging

######################################################################
#
# This tutorial introduces the ``TORCH_LOGS`` environment variable, as well as the Python API, and
Expand All @@ -16,8 +14,6 @@
# This tutorial requires PyTorch 2.2.0 or later.
#
#


######################################################################
# Setup
# ~~~~~~~~~~~~~~~~~~~~~
Expand All @@ -29,55 +25,56 @@
# 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.

import torch

# exit cleanly if we are on a device that doesn't support torch.compile
######################################################################
# Device Compatibility Check
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The following code checks if the device supports torch.compile, which requires
# CUDA compute capability 7.0 or higher.
#
# .. important::
#
# If your device does not meet this requirement, this tutorial will only print
# a skip message and exit. You will NOT see any of the logging output examples
# (Dynamo Tracing, Traced Graph, Fusion Decisions, Output Code) demonstrated
# below. To see the actual logging output, you need a CUDA-capable GPU with
# compute capability 7.0 or higher (e.g., V100, T4, or newer).
#
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("")

######################################################################
# Conclusion
# ~~~~~~~~~~
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