Add max_prompt_tokens

tests/common/vllm_test.py

@@ -101,13 +101,23 @@ def print_debug(*args):
 
 
 @parameterized_class(
-    ("tensor_parallel_size", "engine_num", "use_v1", "repeat_times", "enable_history", "use_async"),
+    (
+        "tensor_parallel_size",
+        "engine_num",
+        "use_v1",
+        "repeat_times",
+        "enable_history",
+        "use_async",
+        "max_model_len",
+    ),
     [
-        (1, 2, False, 2, True, False),
-        (2, 2, False, 1, False, True),
-        (2, 2, True, 2, True, False),
-        (1, 2, True, 1, False, True),
-        (2, 1, True, 3, True, True),
+        (1, 2, False, 2, True, False, None),
+        (1, 2, False, 2, True, True, 20),
+        (1, 2, False, 2, True, False, 20),
+        (2, 2, False, 1, False, True, None),
+        (2, 2, True, 2, True, False, None),
+        (1, 2, True, 1, False, True, None),
+        (2, 1, True, 3, True, True, None),
     ],
 )
 class ModelWrapperTest(RayUnittestBaseAysnc):
@@ -116,13 +126,17 @@ def setUp(self):
         self.config = get_template_config()
         self.config.mode = "explore"
         self.config.model.model_path = get_model_path()
+        self.config.model.max_model_len = self.max_model_len
         self.config.explorer.rollout_model.engine_num = self.engine_num
         self.config.explorer.rollout_model.tensor_parallel_size = self.tensor_parallel_size
         self.config.explorer.rollout_model.use_v1 = self.use_v1
         self.config.explorer.rollout_model.chat_template = CHAT_TEMPLATE
         self.config.algorithm.repeat_times = self.repeat_times
         self.config.explorer.rollout_model.enable_history = self.enable_history
         self.config.check_and_update()
+        from pprint import pprint
+
+        pprint(self.config)
         self.engines, self.auxiliary_engines = create_inference_models(self.config)
         self.model_wrapper = ModelWrapper(
             self.engines[0], model_type="vllm_async", enable_history=self.enable_history
@@ -191,7 +205,12 @@ async def test_generate(
                 "content": results[0].response_text,
             }
         )
-        exp = self.model_wrapper.convert_messages_to_experience(messages)
+        if self.max_model_len is not None:
+            with self.assertRaises(ValueError):
+                exp = self.model_wrapper.convert_messages_to_experience(messages)
+            return
+        else:
+            exp = self.model_wrapper.convert_messages_to_experience(messages)
         tokenizer = AutoTokenizer.from_pretrained(self.config.model.model_path)
         result_dict = tokenizer.apply_chat_template(
             messages,

trinity/common/config.py

@@ -10,6 +10,7 @@
 
 from trinity.common.constants import (
     EXPLORER_NAME,
+    MAX_MODEL_LEN,
     TRAINER_NAME,
     OpType,
     PromptType,
@@ -178,7 +179,7 @@ class ModelConfig:
     model_path: str = ""
     critic_model_path: str = ""
     max_model_len: Optional[int] = None
-    max_prompt_tokens: Optional[int] = None  # deprecated
+    max_prompt_tokens: Optional[int] = None
     max_response_tokens: Optional[int] = None
     custom_chat_template: Optional[str] = None
 
@@ -203,7 +204,7 @@ class InferenceModelConfig:
     # if not set, use `model.max_model_len`
     max_model_len: Optional[int] = None
     # if not set, use `model.max_prompt_tokens`
-    max_prompt_tokens: Optional[int] = None  # deprecated
+    max_prompt_tokens: Optional[int] = None
     # if not set, use `model.max_response_tokens`
     max_response_tokens: Optional[int] = None
 
@@ -769,24 +770,40 @@ def check_and_update(self) -> None:  # noqa: C901
             self.model.critic_model_path = self.model.model_path
 
         # check explorer
+        if self.model.max_model_len is None:
+            from transformers import AutoConfig, AutoTokenizer
+            from transformers.tokenization_utils_base import LARGE_INTEGER
+
+            tokenizer = AutoTokenizer.from_pretrained(self.model.model_path)
+            config = AutoConfig.from_pretrained(self.model.model_path)
+            max_model_len = min(
+                getattr(tokenizer, "model_max_length", LARGE_INTEGER),
+                getattr(config, "max_position_embeddings", LARGE_INTEGER),
+            )
+            if max_model_len >= LARGE_INTEGER:
+                max_model_len = MAX_MODEL_LEN
+                logger.warning(
+                    f"Failed to get `max_model_len` from model {self.model.model_path}, use {MAX_MODEL_LEN} instead."
+                )
+            self.model.max_model_len = max_model_len
+        if (
+            self.model.max_prompt_tokens is None
+            or self.model.max_prompt_tokens >= self.model.max_model_len
+        ):
+            self.model.max_prompt_tokens = self.model.max_model_len - 1
+            logger.warning(f"`max_prompt_tokens` is set to {self.model.max_prompt_tokens}.")
+        if (
+            self.model.max_response_tokens is None
+            or self.model.max_response_tokens > self.model.max_model_len
+        ):
+            self.model.max_response_tokens = self.model.max_model_len
+            logger.warning(f"`max_response_tokens` is set to {self.model.max_response_tokens}.")
+        if self.explorer.rollout_model.max_model_len is None:
+            self.explorer.rollout_model.max_model_len = self.model.max_model_len
         if self.explorer.rollout_model.max_prompt_tokens is None:
             self.explorer.rollout_model.max_prompt_tokens = self.model.max_prompt_tokens
         if self.explorer.rollout_model.max_response_tokens is None:
             self.explorer.rollout_model.max_response_tokens = self.model.max_response_tokens
-        if self.explorer.rollout_model.max_model_len is None:
-            self.explorer.rollout_model.max_model_len = self.model.max_model_len
-        if (
-            self.explorer.rollout_model.max_model_len is None
-            and self.explorer.rollout_model.max_prompt_tokens is not None
-            and self.explorer.rollout_model.max_response_tokens is not None
-        ):
-            logger.warning(
-                "`max_prompt_tokens` is deprecated, please set `max_model_len` directly."
-            )
-            self.explorer.rollout_model.max_model_len = (
-                self.explorer.rollout_model.max_prompt_tokens
-                + self.explorer.rollout_model.max_response_tokens
-            )
 
         # check synchronizer
         self.synchronizer.ray_namespace = self.ray_namespace

trinity/common/constants.py

@@ -16,6 +16,11 @@
 PLUGIN_DIRS_ENV_VAR = "TRINITY_PLUGIN_DIRS"
 
 
+# constants
+
+MAX_MODEL_LEN = 4096
+
+
 # enumerate types
 
 

trinity/common/models/vllm_model.py

@@ -53,7 +53,7 @@ def __init__(
             temperature=0.0,
             max_tokens=config.max_response_tokens,
             min_tokens=1,
-            truncate_prompt_tokens=config.max_model_len - 1,  # type: ignore [operator]
+            truncate_prompt_tokens=config.max_prompt_tokens,
             skip_special_tokens=True,
             include_stop_str_in_output=False,
             output_kind=RequestOutputKind.FINAL_ONLY,
@@ -100,6 +100,10 @@ def __init__(
         self.api_server_host = None
         self.api_server_port = None
 
+    async def _initialize_tokenizer(self):
+        self.tokenizer = await self.async_llm.get_tokenizer()
+        self.tokenizer.truncation_side = "left"
+
     async def chat(self, messages: List[Dict], **kwargs) -> Sequence[Experience]:
         """Chat with the model with a list of messages in async.
 
@@ -111,7 +115,7 @@ async def chat(self, messages: List[Dict], **kwargs) -> Sequence[Experience]:
             A list of experiences.
         """
         if self.tokenizer is None:
-            self.tokenizer = await self.async_llm.get_tokenizer()
+            await self._initialize_tokenizer()
         if self.chat_template is None:
             self.chat_template = self.tokenizer.get_chat_template()
         if messages[-1]["role"] == "assistant":
@@ -141,7 +145,12 @@ async def generate(self, prompt: str, **kwargs) -> Sequence[Experience]:
         Returns:
             A list of experiences.
         """
-        output = await self._generate_internal(prompt=prompt, **kwargs)
+        if self.tokenizer is None:
+            await self._initialize_tokenizer()
+        token_ids = self.tokenizer(  # type: ignore
+            prompt, truncation=True, max_length=self.config.max_prompt_tokens, return_tensors="pt"
+        )["input_ids"][0].tolist()
+        output = await self._generate_internal(prompt={"prompt_token_ids": token_ids}, **kwargs)
         experiences = [
             Experience(
                 tokens=torch.cat(