Fix learn_to_ask and tinker example.

docs/sphinx_doc/source/tutorial/example_tinker_backend.md

@@ -23,7 +23,6 @@ Configure the Tinker backend in your YAML configuration file by setting the `mod
 model:
   tinker:
     enable: true
-    base_model: null
     rank: 32
     seed: null
     train_mlp: true
@@ -35,7 +34,6 @@ model:
 
 - **`tinker`**: Tinker-specific configuration section. **Important**: When Tinker is enabled, any LoRA configuration settings (`model.lora_configs`) will be ignored.
   - **`enable`**: Whether to activate the Tinker backend. Default: `false`
-  - **`base_model`**: Path to the base model for Tinker. If not specified (`null`), it defaults to the `model_path` defined elsewhere in your config
   - **`rank`**: The LoRA rank that controls the size of the adaptation matrices. Default: `32`
   - **`seed`**: Random seed for reproducible Tinker operations. If not specified (`null`), no specific seed is set
   - **`train_mlp`**: Whether to train the MLP (feed-forward) layers. Default: `true`
@@ -94,7 +92,6 @@ model:
   custom_chat_template: "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
   tinker:
     enable: true
-    base_model: meta-llama/Llama-3.2-3B
 cluster:
   node_num: 1
   gpu_per_node: 8

docs/sphinx_doc/source_zh/tutorial/example_tinker_backend.md

@@ -23,7 +23,6 @@ ray start --head
 model:
   tinker:
     enable: true
-    base_model: null
     rank: 32
     seed: null
     train_mlp: true
@@ -35,7 +34,6 @@ model:
 
 - **`tinker`**：Tinker 专用配置部分。**注意**：启用 Tinker 后，所有 LoRA 配置（`model.lora_configs`）将被忽略。
   - **`enable`**：是否启用 Tinker 后端。默认值：`false`
-  - **`base_model`**：Tinker 的基础模型路径。如果未指定（`null`），则默认为配置中其他位置的 `model_path`
   - **`rank`**：LoRA 的秩，控制适应矩阵的大小。默认值：`32`
   - **`seed`**：Tinker 操作的随机种子。未指定（`null`）时不设定特定种子
   - **`train_mlp`**：是否训练 MLP（前馈）层。默认值：`true`
@@ -93,7 +91,6 @@ model:
   custom_chat_template: "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
   tinker:
     enable: true
-    base_model: meta-llama/Llama-3.2-3B
 cluster:
   node_num: 1
   gpu_per_node: 8

examples/learn_to_ask/README.md

@@ -1,6 +1,7 @@
 # Learn2Ask: Getting Started
 
 This guide demonstrates how to train a proactive LLM using the **Learn2Ask** framework from [Grounded in Reality: Learning and Deploying Proactive LLM from Offline Logs](https://arxiv.org/abs/2510.25441).
+
 **Hardware requirement**: ≥32 H20 (or equivalent) GPUs for full-scale reproduction.
 
 All relevant files are located under `examples/learn_to_ask/`:

examples/learn_to_ask/data_prepare/3_rollout_then_evaluate.py

@@ -6,6 +6,7 @@
 import copy
 import gc
 import json
+import math
 import os
 import re
 import time
@@ -14,9 +15,6 @@
 from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
 
-from trinity.common.constants import PLUGIN_DIRS_ENV_VAR
-from trinity.utils.plugin_loader import load_plugins
-
 
 def init_llm(model_path):
     tokenizer = AutoTokenizer.from_pretrained(model_path)
@@ -37,9 +35,15 @@ def init_llm(model_path):
 
 
 def rollout(llm, tokenizer, sampling_params, input_file_path, output_file_path, rollout_repeat=3):
-    from examples.learn_to_ask.workflow.prompt_learn2ask import (
-        rollout_prompt_med as rollout_prompt,
+    import importlib
+
+    spec = importlib.util.spec_from_file_location(
+        "prompt_learn2ask",
+        os.path.join(os.path.dirname(__file__), "..", "workflow", "prompt_learn2ask.py"),
     )
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    rollout_prompt = module.rollout_prompt_med
 
     with open(input_file_path, "r") as lines:
         sample_list = [json.loads(line.strip()) for line in lines]
@@ -70,9 +74,15 @@ def rollout(llm, tokenizer, sampling_params, input_file_path, output_file_path,
 
 
 def eval_sample(llm, tokenizer, sampling_params, input_file_path, output_file_path):
-    from examples.learn_to_ask.workflow.prompt_learn2ask import (
-        reward_prompt_med as grader_prompt,
+    import importlib
+
+    spec = importlib.util.spec_from_file_location(
+        "prompt_learn2ask",
+        os.path.join(os.path.dirname(__file__), "..", "workflow", "prompt_learn2ask.py"),
     )
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    grader_prompt = module.reward_prompt_med
 
     print(f"input_file_path: {input_file_path}")
     print(f"output_file_path: {output_file_path}")
@@ -156,6 +166,53 @@ def msg2str(msg_list):
         print("\n======================\n")
 
 
+def compute_score(input_file_path):
+    with open(input_file_path, "r") as lines:
+        sample_list = [json.loads(line.strip()) for line in lines]
+    continue_count, continue_content_score, continue_content_full = 0, 0, 0
+    continue_decision_score = 0
+    stop_count, stop_decision_score = 0, 0
+    total_reward, total_format = 0, 0
+    continue_count_correct, continue_content_score_correct, continue_content_full_correct = 0, 0, 0
+    for sample in sample_list:
+        for rollout, grade in zip(sample["rollouts"], sample["grades"]):
+            if math.isnan(grade["content_score"]) or math.isnan(grade["format_score"]):
+                continue
+            if sample["decision_truth"] == "continue":
+                continue_count += 1
+                continue_content_score += grade["content_score"]
+                continue_content_full += 1 if grade["content_score"] == 1 else 0
+                continue_decision_score += grade["action_score"]
+                if "<stop />" not in rollout:
+                    continue_count_correct += 1
+                    continue_content_score_correct += grade["content_score"]
+                    continue_content_full_correct += 1 if grade["content_score"] == 1 else 0
+
+            else:
+                stop_count += 1
+                stop_decision_score += grade["action_score"]
+            total_reward += (
+                grade["action_score"] * (1 + 2 * grade["content_score"]) + grade["format_score"]
+            )
+            total_format += grade["format_score"]
+
+    result = {
+        "ave_continue_content": continue_content_score / continue_count,
+        "win_continue_content": continue_content_full / continue_count,
+        "ave_continue_content if correct": continue_content_score_correct / continue_count_correct,
+        "win_continue_content if correct": continue_content_full_correct / continue_count_correct,
+        "ave_continue_decision": continue_decision_score / continue_count,
+        "ave_stop_decision": stop_decision_score / stop_count,
+        "ave_total_decision": (continue_decision_score + stop_decision_score)
+        / (continue_count + stop_count),
+        "ave_total_format": total_format / (continue_count + stop_count),
+        "ave_total_reward": total_reward / (continue_count + stop_count),
+    }
+
+    print(f"total count: {continue_count + stop_count}")
+    print(json.dumps(result, ensure_ascii=False, indent=4))
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--rollout_repeat", type=int, default=3)
@@ -177,9 +234,6 @@ def msg2str(msg_list):
 
     args = parser.parse_args()
 
-    os.environ[PLUGIN_DIRS_ENV_VAR] = os.path.join(os.path.dirname(__file__), "..", "workflow")
-    load_plugins()
-
     # rollout stage
     llm, tokenizer, sampling_params = init_llm(args.eval_model_path)
     rollout(
@@ -197,3 +251,4 @@ def msg2str(msg_list):
     # eval stage
     llm2, tokenizer2, sampling_params2 = init_llm(args.grader_model_path)
     eval_sample(llm2, tokenizer2, sampling_params2, args.rollout_file_path, args.eval_file_path)
+    compute_score(args.eval_file_path)