git push --set-upstream origin add-openclaw-training

ajet/backbone/main_trinity.py

@@ -53,7 +53,7 @@ def patched_trainer_get_actor(cls, config: Config):
     Trainer.get_actor = classmethod(patched_trainer_get_actor)
 
     if ajet_config.ajet.enable_interchange_server:
-        from ajet.tuner_lib.experimental.as_oai_model_server import start_interchange_server
+        from ajet.tuner_lib.experimental.oai_model_server import start_interchange_server
         start_interchange_server(ajet_config)
 
 

ajet/backbone/main_verl.py

@@ -251,7 +251,7 @@ def run(self, config):
         from ajet.backbone.trainer_verl import AjetRayPPOTrainer
 
         if config.ajet.enable_interchange_server:
-            from ajet.tuner_lib.experimental.as_oai_model_server import start_interchange_server
+            from ajet.tuner_lib.experimental.oai_model_server import start_interchange_server
             start_interchange_server(config)
 
         # Initialize the PPO trainer.

ajet/backbone/main_vllm.py

@@ -187,7 +187,7 @@ def main(config):
     # atexit.register(lambda: print("Process exiting, performing cleanup..."))
 
     if config.ajet.enable_interchange_server:
-        from ajet.tuner_lib.experimental.as_oai_model_server import start_interchange_server
+        from ajet.tuner_lib.experimental.oai_model_server import start_interchange_server
         start_interchange_server(config)
         if config.ajet.enable_swarm_mode:
             from ajet.tuner_lib.experimental.interchange_utils import http_change_engine_status

ajet/backbone/trainer_verl.py

@@ -859,7 +859,7 @@ def fit(self):  # noqa: C901
 
                 # # when enabled oai request interchange, we need to clear the cache from time to time
                 # if self.config.ajet.enable_interchange_server:
-                #     from ajet.tuner_lib.experimental.as_oai_model_server import ensure_dat_interchange_server_cache_clear
+                #     from ajet.tuner_lib.experimental.oai_model_server import ensure_dat_interchange_server_cache_clear
                 #     ensure_dat_interchange_server_cache_clear()
 
                 if is_last_step:

ajet/context_tracker/single_agent_tracking.py

@@ -185,9 +185,9 @@ def compute_step_level_reward(
     def to_role_content(self, ext_msg_array: List[ExtendedMessage]) -> List:
         result = []
         for ext_msg in ext_msg_array:
-            d = {
+            d: dict = {
                 "role": ext_msg.role,
-                "content": ext_msg.content_for_future,
+                "content": ext_msg.content_for_compare,
             }
             if ext_msg.tool_calls:
                 d.update({"tool_calls": ext_msg.tool_calls})

ajet/context_tracker/timeline_merging/timeline_merging.py

@@ -21,8 +21,8 @@ def is_timeline_mergeable(
         for i in range(len(target_timeline)):
             if timeline_compare_level == "text":
                 same = (
-                    source_timeline[i].content_for_future
-                    == target_timeline[i].content_for_future
+                    source_timeline[i].content_for_compare
+                    == target_timeline[i].content_for_compare
                 )
             elif timeline_compare_level == "token":
                 same = source_timeline[i].token_arr == target_timeline[i].token_arr
@@ -52,12 +52,12 @@ def is_timeline_mergeable(
     #         all_msg_match = False
     #         for i in range(len(target_timeline)):
     #             d = {}
-    #             d["source"] = source_timeline[i].content_for_future
-    #             d["target"] = target_timeline[i].content_for_future
+    #             d["source"] = source_timeline[i].content_for_compare
+    #             d["target"] = target_timeline[i].content_for_compare
     #             if timeline_compare_level == "text":
     #                 same = (
-    #                     source_timeline[i].content_for_future
-    #                     == target_timeline[i].content_for_future
+    #                     source_timeline[i].content_for_compare
+    #                     == target_timeline[i].content_for_compare
     #                 )
     #             elif timeline_compare_level == "token":
     #                 same = source_timeline[i].token_arr == target_timeline[i].token_arr

ajet/copilot/train-complex-blackbox/SKILL.md

@@ -55,7 +55,7 @@ from ajet.task_reader import RouterTaskReader
 from ajet.utils.thread_executors import PeriodicDrainThreadPoolExecutor
 from ajet.tuner_lib.as_oai_baseurl_apikey import OpenaiBaseUrlAndApiKey
 from ajet.default_config.ajet_default import AjetTaskReader, HuggingfaceDatRepo
-from ajet.tuner_lib.experimental.as_swarm_client import SwarmClient
+from ajet.tuner_lib.experimental.swarm_client import SwarmClient
 
 # python -m tutorial.example_math_swarm.math
 

ajet/copilot/write-swarm-client/SKILL.md

@@ -136,7 +136,7 @@ Below are some reference materials.
     Now, create a python script and start coding:
 
     ```python
-    from ajet.tuner_lib.experimental.as_swarm_client import SwarmClient
+    from ajet.tuner_lib.experimental.swarm_client import SwarmClient
     REMOTE_SWARM_URL = "http://localhost:10086" # Change to your swarm remote url
     swarm_worker = SwarmClient(REMOTE_SWARM_URL)
     ```
@@ -364,7 +364,7 @@ Below are some reference materials.
 
     ```python
     from ajet.copilot.job import AgentJetJob
-    from ajet.tuner_lib.experimental.as_swarm_client import SwarmClient, run_episodes_until_all_complete
+    from ajet.tuner_lib.experimental.swarm_client import SwarmClient, run_episodes_until_all_complete
     from ajet.default_config.ajet_default import AjetTaskReader, HuggingfaceDatRepo
     from ajet.task_reader import RouterTaskReader
     from tutorial.example_academic_trans_swarm.trans import execute_agent

ajet/launcher.py

@@ -153,7 +153,7 @@ def start_swarm_server(env, config):
     assert config.ajet.enable_interchange_server, (
         "Please enable_interchange_server in config to start swarm server."
     )
-    from ajet.tuner_lib.experimental.as_oai_model_server import (
+    from ajet.tuner_lib.experimental.oai_model_server import (
         start_interchange_server,
     )
 

ajet/schema/extended_msg.py

@@ -65,8 +65,6 @@ def __init__(
         token_arr=[],
         token_begin_index=-1,
         token_end_index=-1,
-        clip=False,
-        clip_token_limit=8192,
         tokenizer: PreTrainedTokenizer = None,  # type: ignore
         token_generator="manual",
         build_from_uuid="",
@@ -85,9 +83,8 @@ def __init__(
         self.token_begin_index = token_begin_index
         self.token_end_index = token_end_index
         self.invalid_log_prob_value = INVALID_LOG_PROB_VALUE
-        self._content_for_future = ""
+        self._content_for_compare = ""
         self._info = ""
-        self.clip = clip
         self.tools = tools
         self.tool_calls = tool_calls
         self.tool_call_id = tool_call_id
@@ -101,14 +98,8 @@ def __init__(
         self.manual_loss_mask_override = []
         self.lack_normal_eos = False
 
-        if not clip:
-            self.generate_content_for_future(tokenizer=None, clip=False)
-        else:
-            self.generate_content_for_future(
-                tokenizer=tokenizer,
-                clip=True,
-                clip_token_limit=clip_token_limit,
-            )
+        self.generate_content_for_compare(tokenizer=None)
+
         self.eos_token_id = tokenizer.eos_token_id
 
         if token_generator == "auto":
@@ -127,9 +118,9 @@ def auto_tokenize(self, tokenizer, tools):
         if not self.first_message:
             self.token_arr = self.auto_tokenize_non_first_message(tokenizer=tokenizer, tools=tools)
         else:
-            auto_tokenize_target = {
+            auto_tokenize_target:dict = {
                 "role": self.role,
-                "content": self.content_for_future,
+                "content": self.content_for_compare,
             }
             if self.tool_calls:
                 auto_tokenize_target.update({"tool_calls": self.tool_calls})
@@ -144,9 +135,9 @@ def auto_tokenize(self, tokenizer, tools):
     def auto_tokenize_non_first_message(self, tokenizer, tools):
         try:
             # completion_token_arr will contain generation_prompt header
-            auto_tokenize_target = {
+            auto_tokenize_target:dict = {
                 "role": self.role,
-                "content": self.content_for_future,
+                "content": self.content_for_compare,
             }
             if self.tool_calls:
                 auto_tokenize_target.update({"tool_calls": self.tool_calls})
@@ -160,7 +151,7 @@ def auto_tokenize_non_first_message(self, tokenizer, tools):
             )
         except Exception as e:
             raise ValueError(
-                f"Cannot tokenize {self.role} --- {self.content_for_future}, \n\n Error: {e}"
+                f"Cannot tokenize {self.role} --- {self.content_for_compare}, \n\n Error: {e}"
             )
         self.token_arr, _ = self.get_inc_simple(
             text_frag_from=ajet_apply_chat_template(
@@ -175,12 +166,12 @@ def auto_tokenize_non_first_message(self, tokenizer, tools):
         return self.token_arr
 
     @property
-    def content_for_future(self):
-        if self._content_for_future == "":
+    def content_for_compare(self):
+        if self._content_for_compare == "":
             if not self.tool_calls:
-                logger.exception("content_for_future is not set, or previous llm output is empty!")
-                self._content_for_future
-        return self._content_for_future
+                logger.exception("content_for_compare is not set, or previous llm output is empty!")
+                self._content_for_compare
+        return self._content_for_compare
 
     @property
     def need_training(self):
@@ -191,19 +182,9 @@ def need_training(self):
         ), f"author {self.author} is not identified"
         return self.author in NEED_TRAIN_AUTHORS
 
-    def generate_content_for_future(self, tokenizer, clip, clip_token_limit=-1):
+    def generate_content_for_compare(self, tokenizer):
         _content: str = self.content
-        if clip:
-            assert clip_token_limit > 0, "clip_token_limit must be set when clip is True"
-            n_token = len(tokenizer(_content, return_tensors="pt", padding=False)["input_ids"][0])
-            if n_token > clip_token_limit:
-                # 8000 > 4000
-                n_char = len(_content)  # 10,000
-                eps = 100  # token
-                preserve_percent = (clip_token_limit - eps) / n_token  # 3900 / 8000
-                n_char_to_preserve = int(n_char * preserve_percent)
-                _content = _content[:n_char_to_preserve] + "... truncate ..."
-        self._content_for_future = _content
+        self._content_for_compare = _content
 
     def get_loss_mask(self, blackout_token_combo):
         if self.need_training:
@@ -315,7 +296,7 @@ def merge_tool_group(group, tokenizer):
             )
             # re-compute token_arr
             auto_tokenize_targets = [
-                {"role": msg.role, "content": msg.content_for_future} for msg in group
+                {"role": msg.role, "content": msg.content_for_compare} for msg in group
             ]
             merged.token_arr, _ = merged.get_inc_simple(
                 text_frag_from=ajet_apply_chat_template(

ajet/swarm_cli.py

@@ -31,7 +31,7 @@ def start_swarm_server(env, config, port):
     # Set the port in the config
     config.ajet.interchange_server.interchange_server_port = port
 
-    from ajet.tuner_lib.experimental.as_oai_model_server import (
+    from ajet.tuner_lib.experimental.oai_model_server import (
         start_interchange_server,
     )
 
@@ -139,6 +139,24 @@ def main():
     )
     parser_overwatch.set_defaults(func=cmd_overwatch)
 
+    # Subcommand: top (alias for overwatch)
+    parser_top = subparsers.add_parser("top", help="Monitor the swarm server (alias for overwatch)")
+    parser_top.add_argument(
+        "--swarm-url",
+        type=str,
+        default="http://localhost:10086",
+        required=False,
+        help="Swarm server URL (default: http://localhost:10086)",
+    )
+    parser_top.add_argument(
+        "--refresh-interval",
+        type=float,
+        default=2.0,
+        required=False,
+        help="Refresh interval in seconds (default: 2.0)",
+    )
+    parser_top.set_defaults(func=cmd_overwatch)
+
     args = parser.parse_args()
 
     if not hasattr(args, 'func'):

ajet/task_runner/swarm_runner.py

@@ -66,11 +66,11 @@ def register_episode_and_wait_output(
 
             while True:
                 # <wait for 1/2>:
-                #   <from_sourcefile>: ajet/tuner_lib/experimental/as_swarm_server.py
+                #   <from_sourcefile>: ajet/tuner_lib/experimental/swarm_server.py
                 #   <from_code>: socket.send_string(workflow_output.model_dump_json())
                 #   <expect>: workflow_output: WorkflowOutput
                 # <wait for 2/2>:
-                #   <from_sourcefile>: ajet/tuner_lib/experimental/as_swarm_server.py
+                #   <from_sourcefile>: ajet/tuner_lib/experimental/swarm_server.py
                 #   <from_code>: socket.send_string("RUNNER.SPECIAL.RESET_CONTEXT_TRACKER")
                 #   <expect>: "RUNNER.SPECIAL.RESET_CONTEXT_TRACKER"
                 try:

ajet/tuner.py

@@ -171,7 +171,7 @@ def get_context_tracker(self) -> MultiAgentContextTracker:
     def _enable_interchange_server(self, llm_inference_fn):
         # experimental reverse proxy start
         if self.enable_interchange_server:
-            from ajet.tuner_lib.experimental.as_oai_model_client import InterchangeClient
+            from ajet.tuner_lib.experimental.oai_model_client import InterchangeClient
             self.interchange_client = InterchangeClient(
                 episode_uuid=self.context_tracker.episode_uuid,
                 context_tracker=self.context_tracker,

ajet/tuner_lib/experimental/as_oai_model_client.py → ajet/tuner_lib/experimental/oai_model_client.py

@@ -9,7 +9,7 @@
 
 from loguru import logger
 from typing import TYPE_CHECKING
-from ajet.tuner_lib.experimental.as_oai_model_server import InterchangeCompletionRequest
+from ajet.tuner_lib.experimental.oai_model_server import InterchangeCompletionRequest
 from ajet.utils.thread_executors import SharedInferenceTrackerThreadExecutor, SharedInterchangeThreadExecutor
 from ajet.tuner_lib.experimental.interchange_utils import get_zmq_socket
 from ajet.tuner_lib.experimental.interchange_utils import DEBUG
@@ -107,7 +107,7 @@ def _begin_service_threading(self):
                 try:
 
                     # <wait for>:
-                    #   <from_sourcefile>: ajet/tuner_lib/experimental/as_oai_model_server.py
+                    #   <from_sourcefile>: ajet/tuner_lib/experimental/oai_model_server.py
                     #   <from_code>: socket.send_string(int_req.model_dump_json())
                     #   <expect>: InterchangeCompletionRequest object in JSON string format
                     message = self.socket.recv_string()
@@ -165,7 +165,7 @@ def _begin_service_threading(self):
                 if DEBUG: logger.info(f"[client] {self.episode_uuid} | before send_string (send llm call result)")
 
                 # <send to>
-                #   <to_sourcefile>: ajet/tuner_lib/experimental/as_oai_model_server.py
+                #   <to_sourcefile>: ajet/tuner_lib/experimental/oai_model_server.py
                 #   <to_code>: result_str = socket.recv_string()
                 self.socket.send_string(result)