From 9773b39a49954c3742d7e96f96f94b2e3a237711 Mon Sep 17 00:00:00 2001
From: JackCaoG <59073027+JackCaoG@users.noreply.github.com>
Date: Thu, 18 Jul 2024 09:36:16 -0700
Subject: [PATCH 1/2] Update eager.md

---
 docs/eager.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/eager.md b/docs/eager.md
index a1da0b5c634..e1c8e85910c 100644
--- a/docs/eager.md
+++ b/docs/eager.md
@@ -59,7 +59,7 @@ The implementation of the `torch_xla.experimental.compile` is actually pretty st
 ```python
 torch_xla.experimental.eager_mode(True)
 
-compiled_model = torch_xla.compile(model, backend="openxla")
+compiled_model = torch.compile(model, backend="openxla")
 ```
 It is recommened to use the `torch.compile` instead of `torch_xla.experimental.compile` for inference to reduce the tracing overhad. 
 

From 026591574280b7caab78b97a373ca8e3138b3cb5 Mon Sep 17 00:00:00 2001
From: JackCaoG <59073027+JackCaoG@users.noreply.github.com>
Date: Thu, 18 Jul 2024 16:15:16 -0700
Subject: [PATCH 2/2] Update eager.md

---
 docs/eager.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/docs/eager.md b/docs/eager.md
index e1c8e85910c..6e5413d63d4 100644
--- a/docs/eager.md
+++ b/docs/eager.md
@@ -79,9 +79,7 @@ step_fn = torch_xla.experimental.compile(step_fn)
 ```
 In training we asked user to refactor the `step_fn` out because it is usually better to compile the model's forward, backward and optimizer together. The long term goal is to also use `torch.compile` for training but right now we recommend user to use `torch_xla.experimental.compile`(for perfomrance reason).
 
-## Performance
-
-# Benchmark
+## Benchmark
 
 I run a 2 layer decoder only model training(it is pretty much just a llama2) with fake data on a single chip of v4-8 for 300 steps. Below is the number I observed.