Add generation args to ModelConfig

docs/sphinx_doc/source/tutorial/trinity_installation.md

@@ -6,7 +6,7 @@ For installing Trinity-RFT, you have three options: from source (recommended), v
 Before installing, ensure your system meets the following requirements:
 
 - **Python**: Version 3.10 to 3.12 (inclusive)
-- **CUDA**: Version 12.4 to 12.8 (inclusive)
+- **CUDA**: Version >= 12.6
 - **GPUs**: At least 2 GPUs
 
 ---

docs/sphinx_doc/source_zh/tutorial/trinity_installation.md

@@ -6,7 +6,7 @@
 在安装前，请确保您的系统满足以下要求：
 
 - **Python**：3.10 至 3.12（包含）
-- **CUDA**：12.4 至 12.8（包含）
+- **CUDA**：大于等于 12.6
 - **GPU**：至少 2 块 GPU
 
 ---

trinity/common/config.py

@@ -78,10 +78,10 @@ class FormatConfig:
 
 @dataclass
 class GenerationConfig:
-    temperature: float = 1.0
-    top_p: float = 1.0
-    top_k: int = -1
-    logprobs: int = 0  # vLLM return `logprobs + 1` elements
+    temperature: Optional[float] = None  # 1.0
+    top_p: Optional[float] = None  # 1.0
+    top_k: Optional[int] = None  # -1
+    logprobs: Optional[int] = None  # 0  # vLLM return `logprobs + 1` elements
     max_tokens: Optional[int] = None  # if None, use model.max_response_tokens
     # repeat each task for `n` times
     # ! DO NOT SET in `buffer.explorer_input.taskset.rollout_args`
@@ -412,6 +412,12 @@ class ModelConfig:
 
     custom_chat_template: Optional[str] = None
 
+    # rollout args
+    temperature: float = 1.0
+    top_p: float = 1.0
+    top_k: int = -1
+    logprobs: int = 0
+
     # the total number of tokens the model can handle
     max_model_len: Optional[int] = None
 
@@ -447,6 +453,12 @@ class InferenceModelConfig:
     dtype: str = "bfloat16"
     seed: int = 42
 
+    # rollout args, ! DO NOT SET
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    top_k: Optional[int] = None
+    logprobs: Optional[int] = None
+
     # if not set, use `model.max_model_len`
     max_model_len: Optional[int] = None
     # if not set, use `model.max_prompt_tokens`
@@ -853,6 +865,10 @@ def _check_explorer_input(self) -> None:
             set_if_none(taskset, "default_workflow_type", explorer_input.default_workflow_type)
             set_if_none(taskset, "default_reward_fn_type", explorer_input.default_reward_fn_type)
             set_if_none(taskset, "ray_namespace", self.ray_namespace)
+            set_if_none(taskset.rollout_args, "temperature", self.model.temperature)
+            set_if_none(taskset.rollout_args, "top_p", self.model.top_p)
+            set_if_none(taskset.rollout_args, "top_k", self.model.top_k)
+            set_if_none(taskset.rollout_args, "logprobs", self.model.logprobs)
             set_if_none(taskset.rollout_args, "max_tokens", self.model.max_response_tokens)
 
         for idx, dataset in enumerate(explorer_input.eval_tasksets):
@@ -868,6 +884,10 @@ def _check_explorer_input(self) -> None:
             set_if_none(dataset, "default_workflow_type", explorer_input.default_workflow_type)
             set_if_none(dataset, "default_reward_fn_type", explorer_input.default_reward_fn_type)
             set_if_none(dataset, "ray_namespace", self.ray_namespace)
+            set_if_none(dataset.rollout_args, "temperature", self.model.temperature)
+            set_if_none(dataset.rollout_args, "top_p", self.model.top_p)
+            set_if_none(dataset.rollout_args, "top_k", self.model.top_k)
+            set_if_none(dataset.rollout_args, "logprobs", self.model.logprobs)
             set_if_none(dataset.rollout_args, "max_tokens", self.model.max_response_tokens)
 
     def _check_trainer_input(self) -> None:
@@ -1161,18 +1181,20 @@ def check_and_update(self) -> Config:  # noqa: C901
 
         # check explorer
         if self.explorer is not None:
-            self.explorer.rollout_model.model_path = self.model.model_path
-            self.explorer.rollout_model.max_model_len = self.model.max_model_len
-            self.explorer.rollout_model.max_prompt_tokens = self.model.max_prompt_tokens
-            self.explorer.rollout_model.max_response_tokens = self.model.max_response_tokens
-            self.explorer.rollout_model.min_response_tokens = self.model.min_response_tokens
+            rollout_args = ["temperature", "top_p", "top_k", "logprobs"]
+            length_args = [
+                "max_model_len",
+                "max_prompt_tokens",
+                "max_response_tokens",
+                "min_response_tokens",
+            ]
+            for args in ["model_path"] + rollout_args + length_args:
+                setattr(self.explorer.rollout_model, args, getattr(self.model, args))
             for aux_model in self.explorer.auxiliary_models:
                 if not aux_model.model_path:
                     raise ValueError("auxiliary model's model_path is required.")
-                set_if_none(aux_model, "max_model_len", self.model.max_model_len)
-                set_if_none(aux_model, "max_prompt_tokens", self.model.max_prompt_tokens)
-                set_if_none(aux_model, "max_response_tokens", self.model.max_response_tokens)
-                set_if_none(aux_model, "min_response_tokens", self.model.min_response_tokens)
+                for args in rollout_args + length_args:
+                    set_if_none(aux_model, args, getattr(self.model, args))
 
             # for lora configs
             if self.model.lora_configs is not None:

trinity/common/models/model.py

@@ -4,7 +4,7 @@
 import socket
 from abc import ABC, abstractmethod
 from functools import partial
-from typing import Any, List, Optional, Sequence, Tuple, Union
+from typing import List, Optional, Sequence, Tuple, Union
 
 import httpx
 import numpy as np
@@ -83,7 +83,7 @@ class ModelWrapper:
 
     def __init__(
         self,
-        model: Any,
+        model: InferenceModel,
         engine_type: str = "vllm",
         enable_lora: bool = False,
         enable_history: bool = False,

trinity/common/models/vllm_model.py

@@ -83,6 +83,12 @@ def __init__(
             gpu_memory_utilization=config.gpu_memory_utilization,
             enable_chunked_prefill=config.enable_chunked_prefill,
             # max_num_batched_tokens=256, # you can further set this parameter to reduce the vllm peak memory usage
+            override_generation_config={  # TODO: find a way to unittest this
+                "temperature": config.temperature,
+                "top_p": config.top_p,
+                "top_k": config.top_k,
+                "max_new_tokens": config.max_response_tokens,
+            },
             disable_log_stats=True,
             enable_lora=config.enable_lora,
             **config.lora_kwargs,

trinity/common/workflows/agentscope_workflow.py

@@ -47,7 +47,9 @@ def __init__(
             model.get_openai_async_client(),
             generate_kwargs={
                 "temperature": self.task.rollout_args.temperature,
+                "top_p": self.task.rollout_args.top_p,
                 "max_tokens": self.task.rollout_args.max_tokens or 4096,
+                "logprobs": True,
                 "top_logprobs": self.task.rollout_args.logprobs,
             },
         )