Llama 3.1: Fix incorrect inv_freq assignment (#32330)

fix 💩

src/transformers/modeling_rope_utils.py

@@ -328,14 +328,14 @@ def _compute_llama3_parameters(
     wavelen = 2 * math.pi / inv_freq
     # wavelen < high_freq_wavelen: do nothing
     # wavelen > low_freq_wavelen: divide by factor
-    inv_freq_new = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
+    inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
     # otherwise: interpolate between the two, using a smooth factor
     smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
-    smoothed_inv_freq = (1 - smooth_factor) * inv_freq_new / factor + smooth_factor * inv_freq_new
+    smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
     is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
-    inv_freq_new = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_new)
+    inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
 
-    return inv_freq, attention_factor
+    return inv_freq_llama, attention_factor
 
 
 # This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters

tests/models/llama/test_modeling_llama.py

@@ -22,7 +22,7 @@
 from packaging import version
 from parameterized import parameterized
 
-from transformers import LlamaConfig, StaticCache, is_torch_available, set_seed
+from transformers import AutoTokenizer, LlamaConfig, StaticCache, is_torch_available, set_seed
 from transformers.testing_utils import (
     require_bitsandbytes,
     require_flash_attn,
@@ -718,6 +718,34 @@ def setUpClass(cls):
             # 8 is for A100 / A10 and 7 for T4
             cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
 
+    @slow
+    @require_read_token
+    def test_llama_3_1_hard(self):
+        """
+        An integration test for llama 3.1. It tests against a long output to ensure the subtle numerical differences
+        from llama 3.1.'s RoPE can be detected
+        """
+        EXPECTED_TEXT = (
+            "Tell me about the french revolution. The french revolution was a period of radical social and political "
+            "upheaval in France that lasted from 1789 until 1799. It was a time of great change and upheaval, marked "
+            "by the overthrow of the monarchy, the rise of the middle class, and the eventual establishment of the "
+            "First French Republic.\nThe revolution began in 1789 with the Estates-General, a representative "
+            "assembly that had not met since 1614. The Third Estate, which represented the common people, "
+            "demanded greater representation and eventually broke away to form the National Assembly. This marked "
+            "the beginning of the end of the absolute monarchy and the rise of the middle class.\n"
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
+        model = LlamaForCausalLM.from_pretrained(
+            "meta-llama/Meta-Llama-3.1-8B-Instruct", device_map="auto", torch_dtype=torch.bfloat16
+        )
+        input_text = ["Tell me about the french revolution."]
+        model_inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+        generated_ids = model.generate(**model_inputs, max_new_tokens=128, do_sample=False)
+        generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(generated_text, EXPECTED_TEXT)
+
     @slow
     @require_read_token
     def test_model_7b_logits_bf16(self):