remove cache configs in model definitions (#4031)

python/sglang/srt/models/grok.py

@@ -359,7 +359,6 @@ class Grok1ForCausalLM(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config=None,
     ) -> None:
         super().__init__()
         self.config = config

python/sglang/srt/models/llama_eagle.py

@@ -106,7 +106,6 @@ class LlamaForCausalLMEagle(LlamaForCausalLM):
         self,
         config: LlamaConfig,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config=None,
     ) -> None:
         nn.Module.__init__(self)
         self.config = config

python/sglang/srt/models/qwen2_eagle.py

@@ -107,7 +107,6 @@ class Qwen2ForCausalLMEagle(Qwen2ForCausalLM):
         self,
         config: Qwen2Config,
         quant_config: Optional[QuantizationConfig] = None,
-        cache_config=None,
     ) -> None:
         nn.Module.__init__(self)
         self.config = config

python/sglang/test/test_utils.py

@@ -159,45 +159,6 @@ def call_generate_guidance(
     return rets if n > 1 else rets[0]
 
 
-async def call_generate_lmql(
-    prompt, temperature, max_tokens, stop=None, n=1, max_len=4096, model=None, **kwargs
-):
-    assert model is not None
-    import lmql
-
-    if stop != None:
-
-        @lmql.query(model=model)
-        async def program(question, max_tokens, stop):
-            '''lmql
-            """{question}[ANSWER]""" where len(TOKENS(ANSWER)) < max_tokens and STOPS_AT(ANSWER, stop)
-            return ANSWER
-            '''
-
-    else:
-
-        @lmql.query(model=model)
-        async def program(question, max_tokens):
-            '''lmql
-            """{question}[ANSWER]""" where len(TOKENS(ANSWER)) < max_tokens
-            return ANSWER
-            '''
-
-    tasks = [
-        program(
-            question=prompt,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            stop=stop,
-            max_len=max_len,
-            **kwargs,
-        )
-        for _ in range(n)
-    ]
-    rets = await asyncio.gather(*tasks)
-    return rets if n > 1 else rets[0]
-
-
 def call_select_lightllm(context, choices, url=None):
     assert url is not None
 
@@ -247,23 +208,6 @@ def call_select_guidance(context, choices, model=None):
     return choices.index(out["answer"])
 
 
-async def call_select_lmql(context, choices, temperature=0, max_len=4096, model=None):
-    assert model is not None
-    import lmql
-
-    @lmql.query(model=model)
-    async def program(ctx, choices):
-        '''lmql
-        """{ctx}[ANSWER]""" where ANSWER in set(choices)
-        return ANSWER
-        '''
-
-    answer = await program(
-        ctx=context, choices=choices, temperature=temperature, max_len=max_len
-    )
-    return choices.index(answer)
-
-
 def add_common_other_args_and_parse(parser: argparse.ArgumentParser):
     parser.add_argument("--parallel", type=int, default=64)
     parser.add_argument("--host", type=str, default="http://127.0.0.1")
@@ -278,7 +222,6 @@ def add_common_other_args_and_parse(parser: argparse.ArgumentParser):
             "lightllm",
             "gserver",
             "guidance",
-            "lmql",
             "srt-raw",
             "llama.cpp",
         ],
@@ -295,7 +238,6 @@ def add_common_other_args_and_parse(parser: argparse.ArgumentParser):
             "vllm": 21000,
             "outlines": 21000,
             "lightllm": 22000,
-            "lmql": 23000,
             "srt-raw": 30000,
             "gserver": 9988,
         }
@@ -343,11 +285,6 @@ def _get_call_generate(args: argparse.Namespace):
         call_generate = partial(call_generate_guidance, model=model)
         call_generate("Hello,", 1.0, 8, ".")
         return call_generate
-    elif args.backend == "lmql":
-        import lmql
-
-        model = lmql.model(args.model_path, endpoint=f"{args.host}:{args.port}")
-        return partial(call_generate_lmql, model=model)
     else:
         raise ValueError(f"Invalid backend: {args.backend}")
 
@@ -365,12 +302,6 @@ def _get_call_select(args: argparse.Namespace):
 
         call_select("Hello,", ["world", "earth"])
         return call_select
-
-    elif args.backend == "lmql":
-        import lmql
-
-        model = lmql.model(args.model_path, endpoint=f"{args.host}:{args.port}")
-        return partial(call_select_lmql, model=model)
     else:
         raise ValueError(f"Invalid backend: {args.backend}")