Use model loader from vllm (#459)

python/sglang/srt/models/gemma.py

@@ -1,7 +1,7 @@
 # Adapted from:
-# https://github.com/vllm-project/vllm/blob/d65fac2738f0287a41955b45df76a2d5a919bff6/vllm/model_executor/models/gemma.py
+# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/gemma.py#L1
 """Inference-only Gemma model compatible with HuggingFace weights."""
-from typing import Optional, Tuple
+from typing import Iterable, Optional, Tuple
 
 import torch
 from torch import nn
@@ -18,11 +18,11 @@ from vllm.model_executor.layers.linear import (
 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.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.managers.router.model_runner import InputMetadata
-from sglang.srt.weight_utils import default_weight_loader, hf_model_weights_iterator
 
 
 class GemmaMLP(nn.Module):
@@ -285,13 +285,7 @@ class GemmaForCausalLM(nn.Module):
             input_ids, hidden_states, self.model.embed_tokens.weight, input_metadata
         )
 
-    def load_weights(
-        self,
-        model_name_or_path: str,
-        cache_dir: Optional[str] = None,
-        load_format: str = "auto",
-        revision: Optional[str] = None,
-    ):
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
             ("qkv_proj", "q_proj", "q"),
@@ -302,9 +296,7 @@ class GemmaForCausalLM(nn.Module):
         ]
         params_dict = dict(self.named_parameters())
         loaded_params = set()
-        for name, loaded_weight in hf_model_weights_iterator(
-            model_name_or_path, cache_dir, load_format, revision
-        ):
+        for name, loaded_weight in weights:
             for param_name, shard_name, shard_id in stacked_params_mapping:
                 if shard_name not in name:
                     continue