[Bugfix] Fix precisions in Gemma 1

tests/models/test_models.py

@@ -17,6 +17,7 @@
     "stabilityai/stablelm-3b-4e1t",
     # "allenai/OLMo-1B",  # Broken
     "bigcode/starcoder2-3b",
+    "google/gemma-1.1-2b-it",
 ]
 
 

vllm/model_executor/models/gemma.py

@@ -26,14 +26,14 @@
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import GeluAndMul
-from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 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 get_rope
+from vllm.model_executor.layers.rotary_embedding import GemmaRotaryEmbedding
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
@@ -148,12 +148,14 @@ def __init__(self,
             quant_config=quant_config,
         )
 
-        self.rotary_emb = get_rope(
+        # TODO(woosuk): Use the `get_rope` interface.
+        self.rotary_emb = GemmaRotaryEmbedding(
             self.head_dim,
             rotary_dim=self.head_dim,
-            max_position=max_position_embeddings,
+            max_position_embeddings=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,
@@ -204,10 +206,10 @@ def __init__(
             hidden_activation=getattr(config, "hidden_activation", None),
             quant_config=quant_config,
         )
-        self.input_layernorm = RMSNorm(config.hidden_size,
-                                       eps=config.rms_norm_eps)
-        self.post_attention_layernorm = RMSNorm(config.hidden_size,
-                                                eps=config.rms_norm_eps)
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size,
+                                            eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                     eps=config.rms_norm_eps)
 
     def forward(
         self,
@@ -257,7 +259,7 @@ def __init__(
             GemmaDecoderLayer(config, cache_config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
         # Normalize the embedding by sqrt(hidden_size)
         # The normalizer's data type should be downcasted to the model's
@@ -331,7 +333,6 @@ def __init__(
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 
-    @torch.no_grad()
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -388,10 +389,6 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
-                # GemmaRMSNorm is different from Llama's in that it multiplies
-                # (1 + weight) to the output, instead of just weight.
-                if "norm.weight" in name:
-                    loaded_weight += 1.0
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)