[Misc] Remove Gemma RoPE (vllm-project#7638)

vllm/model_executor/layers/rotary_embedding.py

@@ -93,11 +93,6 @@ def __init__(
 
     def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
         """Compute the inverse frequency."""
-        # NOTE(woosuk): The HF implementation uses `torch.arange(...).float()`.
-        # However, we use `torch.arange(..., dtype=torch.float)` instead to
-        # avoid numerical issues with large base values (e.g., 10000000).
-        # This may cause a slight numerical difference between the HF
-        # implementation and ours.
         # NOTE(woosuk): To exactly match the HF implementation, we need to
         # use CPU to compute the cache and then move it to GPU. However, we
         # create the cache on GPU for faster initialization. This may cause
@@ -724,16 +719,6 @@ def forward(
         return query, key
 
 
-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 Llama3RotaryEmbedding(RotaryEmbedding):
 
     def __init__(

vllm/model_executor/models/gemma.py

@@ -33,7 +33,7 @@
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.model_executor.layers.rotary_embedding import GemmaRotaryEmbedding
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -148,14 +148,12 @@ def __init__(self,
             quant_config=quant_config,
         )
 
-        # TODO(woosuk): Use the `get_rope` interface.
-        self.rotary_emb = GemmaRotaryEmbedding(
+        self.rotary_emb = get_rope(
             self.head_dim,
             rotary_dim=self.head_dim,
-            max_position_embeddings=max_position_embeddings,
+            max_position=max_position_embeddings,
             base=self.rope_theta,
             is_neox_style=True,
-            dtype=torch.get_default_dtype(),
         )
         self.attn = Attention(self.num_heads,
                               self.head_dim,

vllm/model_executor/models/gemma2.py

@@ -32,7 +32,7 @@
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.model_executor.layers.rotary_embedding import GemmaRotaryEmbedding
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -130,14 +130,12 @@ def __init__(self,
             bias=config.attention_bias,
             quant_config=quant_config,
         )
-        # 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(),
         )
 
         # FIXME(woosuk): While Gemma 2 uses sliding window attention for every