feat: use get_rope for gemma2 (#2954)

python/sglang/srt/models/gemma2.py

@@ -20,6 +20,7 @@ from typing import Iterable, Optional, Set, Tuple, Union
 import torch
 from torch import nn
 from transformers import PretrainedConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
 from sglang.srt.layers.activation import GeluAndMul
@@ -48,19 +49,6 @@ def get_attention_sliding_window_size(config):
 from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
 
 
-class GemmaRotaryEmbedding(RotaryEmbedding):
-    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
-        # https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/models/gemma/modeling_gemma.py#L107
-        inv_freq = 1.0 / (
-            base
-            ** (
-                torch.arange(0, self.rotary_dim, 2, dtype=torch.int64).float()
-                / self.rotary_dim
-            )
-        )
-        return inv_freq
-
-
 class Gemma2MLP(nn.Module):
     def __init__(
         self,
@@ -143,14 +131,12 @@ class Gemma2Attention(nn.Module):
             bias=config.attention_bias,
             quant_config=quant_config,
         )
-        # from vLLM: TODO(woosuk): Use the `get_rope` interface.
-        self.rotary_emb = GemmaRotaryEmbedding(
+        self.rotary_emb = get_rope(
             self.head_dim,
-            self.head_dim,
-            max_position_embeddings,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
             base=self.rope_theta,
             is_neox_style=True,
-            dtype=torch.get_default_dtype(),
         )
 
         use_sliding_window = layer_id % 2 == 0 and hasattr(config, "sliding_window")