Small fixes to torch.compile tutorial

intermediate_source/torch_compile_tutorial.py

@@ -138,21 +138,18 @@ def init_model():
 # Note that in the call to ``torch.compile``, we have have the additional
 # ``mode`` argument, which we will discuss below.
 
-def evaluate(mod, inp):
-    return mod(inp)
-
 model = init_model()
 
 # Reset since we are using a different mode.
 import torch._dynamo
 torch._dynamo.reset()
 
-evaluate_opt = torch.compile(evaluate, mode="reduce-overhead")
+model_opt = torch.compile(model, mode="reduce-overhead")
 
 inp = generate_data(16)[0]
 with torch.no_grad():
-    print("eager:", timed(lambda: evaluate(model, inp))[1])
-    print("compile:", timed(lambda: evaluate_opt(model, inp))[1])
+    print("eager:", timed(lambda: model(inp))[1])
+    print("compile:", timed(lambda: model_opt(inp))[1])
 
 ######################################################################
 # Notice that ``torch.compile`` takes a lot longer to complete
@@ -166,7 +163,7 @@ def evaluate(mod, inp):
 for i in range(N_ITERS):
     inp = generate_data(16)[0]
     with torch.no_grad():
-        _, eager_time = timed(lambda: evaluate(model, inp))
+        _, eager_time = timed(lambda: model(inp))
     eager_times.append(eager_time)
     print(f"eager eval time {i}: {eager_time}")
 
@@ -176,7 +173,7 @@ def evaluate(mod, inp):
 for i in range(N_ITERS):
     inp = generate_data(16)[0]
     with torch.no_grad():
-        _, compile_time = timed(lambda: evaluate_opt(model, inp))
+        _, compile_time = timed(lambda: model_opt(inp))
     compile_times.append(compile_time)
     print(f"compile eval time {i}: {compile_time}")
 print("~" * 10)
@@ -250,6 +247,10 @@ def train(mod, data):
 # Again, we can see that ``torch.compile`` takes longer in the first
 # iteration, as it must compile the model, but in subsequent iterations, we see
 # significant speedups compared to eager.
+#
+# We remark that the speedup numbers presented in this tutorial are for
+# demonstration purposes only. Official speedup values can be seen at the
+# `TorchInductor performance dashboard <https://hud.pytorch.org/benchmark/compilers>`__.
 
 ######################################################################
 # Comparison to TorchScript and FX Tracing