CI: AMD MI300 tests fix (#30797)

* add fix

* update import

* updated dicts and comments

* remove prints

* Update testing_utils.py

tests/models/llama/test_modeling_llama.py

@@ -715,6 +715,11 @@ class LlamaIntegrationTest(unittest.TestCase):
         NUM_TOKENS_TO_GENERATE = 40
         # Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test
         # was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs.
+        #
+        # Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
+        #
+        # Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
+        # considering differences in hardware processing and potential deviations in generated text.
         EXPECTED_TEXT_COMPLETION = {
             8: [
                 "Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial "
@@ -730,7 +735,15 @@ class LlamaIntegrationTest(unittest.TestCase):
                 "My favorite all time favorite condiment is ketchup. I love it on hamburgers, hot dogs, fries, eggs, "
                 "and even on a good old fashioned cheeseburger. I love it on everything. I love it so",
             ],
+            9: [
+                "Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial"
+                " reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe "
+                "theory of relativ",
+                "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs,"
+                " my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
+            ],
         }
+        expected_text_completion_idx = 8
 
         prompts = [
             "Simply put, the theory of relativity states that ",
@@ -745,7 +758,9 @@ class LlamaIntegrationTest(unittest.TestCase):
         # Dynamic Cache
         generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False)
         dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
-        self.assertEqual(EXPECTED_TEXT_COMPLETION[8], dynamic_text)  # Both GPU architectures have the same output
+        self.assertEqual(
+            EXPECTED_TEXT_COMPLETION[expected_text_completion_idx], dynamic_text
+        )  # Both GPU architectures have the same output
 
         # Static Cache
         generated_ids = model.generate(