Fix llava & Fix multiprocessing

examples/quick_start/srt_example_llava.py

@@ -7,10 +7,8 @@ def image_qa(s, image_path, question):
     s += sgl.assistant(sgl.gen("answer"))
 
 
-# runtime = sgl.Runtime(model_path="liuhaotian/llava-v1.5-7b",
-#                       tokenizer_path="llava-hf/llava-1.5-7b-hf")
-runtime = sgl.Runtime(model_path="llava-internal/llava-v1.6-7b-hd-224px_3x2-preview-20230103",
-                      tokenizer_path="llava-internal/llava-v1.6-7b-hd-224px_3x2-preview-20230103-tokenizer")
+runtime = sgl.Runtime(model_path="liuhaotian/llava-v1.5-7b",
+                      tokenizer_path="llava-hf/llava-1.5-7b-hf")
 sgl.set_default_backend(runtime)
 
 

python/sglang/srt/managers/tokenizer_manager.py

@@ -1,6 +1,7 @@
 import asyncio
 import concurrent.futures
 import dataclasses
+import multiprocessing as mp
 import os
 from typing import List
 
@@ -101,7 +102,9 @@ class TokenizerManager:
             self.tokenizer = self.processor.tokenizer
             os.environ["TOKENIZERS_PARALLELISM"] = "false"
             self.executor = concurrent.futures.ProcessPoolExecutor(
-                initializer=init_global_processor, initargs=(server_args,)
+                initializer=init_global_processor,
+                mp_context=mp.get_context("fork"),
+                initargs=(server_args,),
             )
         else:
             self.tokenizer = get_tokenizer(

python/sglang/srt/models/llava.py

@@ -34,9 +34,10 @@ class LlavaLlamaForCausalLM(nn.Module):
         self.config.text_config.hidden_size = config.hidden_size
         self.multi_modal_projector = LlavaMultiModalProjector(config)
         self.language_model = LlamaForCausalLM(config, linear_method)
-        if "unpad" in getattr(config, "mm_patch_merge_type"):
+        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
             self.language_model.model.image_newline = nn.Parameter(
-                    torch.empty(config.text_config.hidden_size, dtype=torch.float16))
+                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
+            )
 
     def pad_input_ids(self, input_ids, pad_value, pt_shape=None, image_size=None):
         new_image_feature_len = self.image_feature_len

python/sglang/srt/server.py

@@ -425,8 +425,6 @@ class Runtime:
             random_seed=random_seed,
             log_level=log_level,
         )
-        import torch
-        torch.multiprocessing.set_start_method("spawn", force=True)
 
         self.url = self.server_args.url()
         self.generate_url = (