From 2f00fa46165679e006ea2b4b62ca4e6cedc06af0 Mon Sep 17 00:00:00 2001
From: yyouretoast <yyasso2005@gmail.com>
Date: Wed, 17 Jun 2026 02:41:08 +0300
Subject: [PATCH] Fix torch_logs tutorial to support CPU-only environments

Remove the hard CUDA gate that caused the tutorial to render as 'Skipped'
on the PyTorch docs website. torch.compile works on both CUDA (via Triton)
and CPU (via C++ Inductor), so no guard or early exit is needed.

Changes:
- Use CUDA if available and compute capability >= 7.0, else fall back to CPU (prevents crashes on older CUDA devices while supporting CPU execution).
- Remove try/except compilation probe (unnecessary).
- Remove sys.exit(0) early exit (dangerous in Sphinx-Gallery subprocess mode).
- De-indent all tutorial code from the original if/else block.

Fixes pytorch/pytorch#137285
---
 recipes_source/torch_logs.py | 51 ++++++++++++++++++------------------
 1 file changed, 26 insertions(+), 25 deletions(-)

diff --git a/recipes_source/torch_logs.py b/recipes_source/torch_logs.py
index b5c3f0bd8ac..435d6435a92 100644
--- a/recipes_source/torch_logs.py
+++ b/recipes_source/torch_logs.py
@@ -32,51 +32,52 @@
 
 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
+# Determine device: use CUDA if available and compute capability >= 7.0, otherwise fall back to CPU.
+# torch.compile works on both CUDA (via Triton) and CPU (via C++ Inductor).
+device = "cuda" if torch.cuda.is_available() and torch.cuda.get_device_capability() >= (7, 0) else "cpu"
 
 
-    inputs = (torch.ones(2, 2, device="cuda"), torch.zeros(2, 2, device="cuda"))
+@torch.compile()
+def fn(x, y):
+    z = x + y
+    return z + 2
+
+
+inputs = (torch.ones(2, 2, device=device), torch.zeros(2, 2, device=device))
 
 
 # print separator and reset dynamo
 # between each example
-    def separator(name):
-        print(f"==================={name}=========================")
-        torch._dynamo.reset()
+def separator(name):
+    print(f"==================={name}=========================")
+    torch._dynamo.reset()
 
 
-    separator("Dynamo Tracing")
+separator("Dynamo Tracing")
 # View dynamo tracing
 # TORCH_LOGS="+dynamo"
-    torch._logging.set_logs(dynamo=logging.DEBUG)
-    fn(*inputs)
+torch._logging.set_logs(dynamo=logging.DEBUG)
+fn(*inputs)
 
-    separator("Traced Graph")
+separator("Traced Graph")
 # View traced graph
 # TORCH_LOGS="graph"
-    torch._logging.set_logs(graph=True)
-    fn(*inputs)
+torch._logging.set_logs(graph=True)
+fn(*inputs)
 
-    separator("Fusion Decisions")
+separator("Fusion Decisions")
 # View fusion decisions
 # TORCH_LOGS="fusion"
-    torch._logging.set_logs(fusion=True)
-    fn(*inputs)
+torch._logging.set_logs(fusion=True)
+fn(*inputs)
 
-    separator("Output Code")
+separator("Output Code")
 # View output code generated by inductor
 # TORCH_LOGS="output_code"
-    torch._logging.set_logs(output_code=True)
-    fn(*inputs)
+torch._logging.set_logs(output_code=True)
+fn(*inputs)
 
-    separator("")
+separator("")
 
 ######################################################################
 # Conclusion