adding log_prob option for chat models

src/agentlab/llm/base_api.py

@@ -21,6 +21,7 @@ class BaseModelArgs(ABC):
     max_new_tokens: int = None
     temperature: float = 0.1
     vision_support: bool = False
+    log_probs: bool = False
 
     @abstractmethod
     def make_model(self) -> AbstractChatModel:

src/agentlab/llm/chat_api.py

@@ -87,6 +87,7 @@ def make_model(self):
             model_name=self.model_name,
             temperature=self.temperature,
             max_tokens=self.max_new_tokens,
+            log_probs=self.log_probs,
         )
 
 
@@ -100,6 +101,7 @@ def make_model(self):
             model_name=self.model_name,
             temperature=self.temperature,
             max_tokens=self.max_new_tokens,
+            log_probs=self.log_probs,
         )
 
 
@@ -115,6 +117,7 @@ def make_model(self):
             temperature=self.temperature,
             max_tokens=self.max_new_tokens,
             deployment_name=self.deployment_name,
+            log_probs=self.log_probs,
         )
 
 
@@ -142,6 +145,7 @@ def make_model(self):
                 temperature=self.temperature,
                 max_new_tokens=self.max_new_tokens,
                 n_retry_server=self.n_retry_server,
+                log_probs=self.log_probs,
             )
         elif self.backend == "vllm":
             return VLLMChatModel(
@@ -232,6 +236,7 @@ def __init__(
         client_class=OpenAI,
         client_args=None,
         pricing_func=None,
+        log_probs=False,
     ):
         assert max_retry > 0, "max_retry should be greater than 0"
 
@@ -240,6 +245,7 @@ def __init__(
         self.max_tokens = max_tokens
         self.max_retry = max_retry
         self.min_retry_wait_time = min_retry_wait_time
+        self.log_probs = log_probs
 
         # Get the API key from the environment variable if not provided
         if api_key_env_var:
@@ -286,6 +292,7 @@ def __call__(self, messages: list[dict], n_samples: int = 1, temperature: float
                     n=n_samples,
                     temperature=temperature,
                     max_tokens=self.max_tokens,
+                    log_probs=self.log_probs,
                 )
 
                 if completion.usage is None:
@@ -315,7 +322,10 @@ def __call__(self, messages: list[dict], n_samples: int = 1, temperature: float
             tracking.TRACKER.instance(input_tokens, output_tokens, cost)
 
         if n_samples == 1:
-            return AIMessage(completion.choices[0].message.content)
+            res = AIMessage(completion.choices[0].message.content)
+            if self.log_probs:
+                res["log_probs"] = completion.choices[0].log_probs
+            return res
         else:
             return [AIMessage(c.message.content) for c in completion.choices]
 
@@ -335,6 +345,7 @@ def __init__(
         max_tokens=100,
         max_retry=4,
         min_retry_wait_time=60,
+        log_probs=False,
     ):
         super().__init__(
             model_name=model_name,
@@ -346,6 +357,7 @@ def __init__(
             api_key_env_var="OPENAI_API_KEY",
             client_class=OpenAI,
             pricing_func=tracking.get_pricing_openai,
+            log_probs=log_probs,
         )
 
 
@@ -358,6 +370,7 @@ def __init__(
         max_tokens=100,
         max_retry=4,
         min_retry_wait_time=60,
+        log_probs=False,
     ):
         client_args = {
             "base_url": "https://openrouter.ai/api/v1",
@@ -373,6 +386,7 @@ def __init__(
             client_class=OpenAI,
             client_args=client_args,
             pricing_func=tracking.get_pricing_openrouter,
+            log_probs=log_probs,
         )
 
 
@@ -386,6 +400,7 @@ def __init__(
         max_tokens=100,
         max_retry=4,
         min_retry_wait_time=60,
+        log_probs=False,
     ):
         api_key = api_key or os.getenv("AZURE_OPENAI_API_KEY")
         endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
@@ -406,6 +421,7 @@ def __init__(
             client_class=AzureOpenAI,
             client_args=client_args,
             pricing_func=tracking.get_pricing_openai,
+            log_probs=log_probs,
         )
 
 
@@ -419,8 +435,9 @@ def __init__(
         temperature: Optional[int] = 1e-1,
         max_new_tokens: Optional[int] = 512,
         n_retry_server: Optional[int] = 4,
+        log_probs: Optional[bool] = False,
     ):
-        super().__init__(model_name, base_model_name, n_retry_server)
+        super().__init__(model_name, base_model_name, n_retry_server, log_probs)
         if temperature < 1e-3:
             logging.warning("Models might behave weirdly when temperature is too low.")
         self.temperature = temperature
@@ -429,7 +446,7 @@ def __init__(
             token = os.environ["TGI_TOKEN"]
 
         client = InferenceClient(model=model_url, token=token)
-        self.llm = partial(client.text_generation, max_new_tokens=max_new_tokens)
+        self.llm = partial(client.text_generation, max_new_tokens=max_new_tokens, details=log_probs)
 
 
 class VLLMChatModel(ChatModel):

src/agentlab/llm/huggingface_utils.py

@@ -2,12 +2,11 @@
 import time
 from typing import Any, List, Optional, Union
 
-from pydantic import Field
-from transformers import AutoTokenizer, GPT2TokenizerFast
-
 from agentlab.llm.base_api import AbstractChatModel
 from agentlab.llm.llm_utils import AIMessage, Discussion
 from agentlab.llm.prompt_templates import PromptTemplate, get_prompt_template
+from pydantic import Field
+from transformers import AutoTokenizer, GPT2TokenizerFast
 
 
 class HFBaseChatModel(AbstractChatModel):
@@ -40,9 +39,10 @@ class HFBaseChatModel(AbstractChatModel):
         description="The number of times to retry the server if it fails to respond",
     )
 
-    def __init__(self, model_name, base_model_name, n_retry_server):
+    def __init__(self, model_name, base_model_name, n_retry_server, log_probs):
         super().__init__()
         self.n_retry_server = n_retry_server
+        self.log_probs = log_probs
 
         if base_model_name is None:
             self.tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -100,7 +100,11 @@ def __call__(
             while True:
                 try:
                     temperature = temperature if temperature is not None else self.temperature
-                    response = AIMessage(self.llm(prompt, temperature=temperature))
+                    answer = self.llm(prompt, temperature=temperature)
+                    response = AIMessage(answer)
+                    if self.log_probs:
+                        response["content"] = answer.generated_text
+                        response["log_prob"] = answer.details
                     responses.append(response)
                     break
                 except Exception as e:

src/agentlab/llm/llm_utils.py

@@ -382,9 +382,14 @@ def image_to_jpg_base64_url(image: np.ndarray | Image.Image):
 
 
 class BaseMessage(dict):
-    def __init__(self, role: str, content: Union[str, list[dict]]):
+    def __init__(self, role: str, content: Union[str, list[dict]], **kwargs):
+        allowed_attrs = {"log_probs"}
+        invalid_attrs = set(kwargs.keys()) - allowed_attrs
+        if invalid_attrs:
+            raise ValueError(f"Invalid attributes: {invalid_attrs}")
         self["role"] = role
         self["content"] = deepcopy(content)
+        self.update(kwargs)
 
     def __str__(self, warn_if_image=False) -> str:
         if isinstance(self["content"], str):
@@ -464,8 +469,8 @@ def __init__(self, content: Union[str, list[dict]]):
 
 
 class AIMessage(BaseMessage):
-    def __init__(self, content: Union[str, list[dict]]):
-        super().__init__("assistant", content)
+    def __init__(self, content: Union[str, list[dict]], log_probs=None):
+        super().__init__("assistant", content, log_probs=log_probs)
 
 
 class Discussion: