Fix Llava model (#594)

python/sglang/backend/runtime_endpoint.py

@@ -1,15 +1,14 @@
 import json
-from typing import Callable, List, Optional, Union
+from typing import List, Optional
 
 import numpy as np
-import requests
 
 from sglang.backend.base_backend import BaseBackend
 from sglang.global_config import global_config
 from sglang.lang.chat_template import get_chat_template_by_model_path
 from sglang.lang.interpreter import StreamExecutor
-from sglang.lang.ir import SglArgument, SglSamplingParams
+from sglang.lang.ir import SglSamplingParams
-from sglang.utils import encode_image_base64, find_printable_text, http_request
+from sglang.utils import find_printable_text, http_request
 
 
 class RuntimeEndpoint(BaseBackend):

python/sglang/lang/interpreter.py

@@ -523,9 +523,9 @@ class StreamExecutor:
                 self, sampling_params=sampling_params
             )
 
+            self.variables[name] = ""
             self.stream_var_event[name].set()
 
-            self.variables[name] = ""
             for comp, meta_info in generator:
                 self.text_ += comp
                 self.variables[name] += comp

python/sglang/srt/managers/controller/infer_batch.py

@@ -3,7 +3,7 @@
 import warnings
 from dataclasses import dataclass
 from enum import IntEnum, auto
-from typing import List
+from typing import List, Union
 
 import numpy as np
 import torch
@@ -31,7 +31,7 @@ class BaseFinishReason:
 
 
 class FINISH_MATCHED_TOKEN(BaseFinishReason):
-    def __init__(self, matched: int | List[int]):
+    def __init__(self, matched: Union[int, List[int]]):
         super().__init__()
         self.matched = matched
 

python/sglang/srt/model_config.py

@@ -115,6 +115,12 @@ def get_hf_text_config(config: PretrainedConfig):
     """Get the "sub" config relevant to llm for multi modal models.
     No op for pure text models.
     """
+    class_name = config.architectures[0]
+    if class_name.startswith("Llava") and class_name.endswith("ForCausalLM"):
+        # We support non-hf version of llava models, so we do not want to
+        # read the wrong values from the unused default text_config.
+        return config
+
     if hasattr(config, "text_config"):
         # The code operates under the assumption that text_config should have
         # `num_attention_heads` (among others). Assert here to fail early

python/sglang/srt/models/gemma2.py

@@ -1,10 +1,10 @@
 # Adapted from:
 # https://github.com/vllm-project/vllm/blob/56b325e977435af744f8b3dca7af0ca209663558/vllm/model_executor/models/gemma2.py
-from typing import Iterable, List, Optional, Set, Tuple, Union
+from typing import Iterable, Optional, Set, Tuple, Union
 
 import torch
 from torch import nn
-from transformers import Gemma2Config
+from transformers import PretrainedConfig
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 
@@ -131,7 +131,7 @@ class Gemma2Attention(nn.Module):
     def __init__(
         self,
         layer_idx: int,
-        config: Gemma2Config,
+        config: PretrainedConfig,
         hidden_size: int,
         num_heads: int,
         num_kv_heads: int,
@@ -222,7 +222,7 @@ class Gemma2DecoderLayer(nn.Module):
     def __init__(
         self,
         layer_idx: int,
-        config: Gemma2Config,
+        config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
@@ -290,7 +290,7 @@ class Gemma2Model(nn.Module):
 
     def __init__(
         self,
-        config: Gemma2Config,
+        config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
@@ -369,7 +369,7 @@ class Gemma2ForCausalLM(nn.Module):
 
     def __init__(
         self,
-        config: Gemma2Config,
+        config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,