chore: remove legacy dead code

packages/prime-rl-configs/src/prime_rl/configs/orchestrator.py

@@ -527,8 +527,8 @@ class NIXLWeightBroadcastConfig(InMemoryWeightBroadcastConfig):
 
 class ConcurrencyConfig(BaseConfig):
     """Adaptive in-flight concurrency control. The orchestrator sizes the
-    in-flight episode cap from engine KV capacity and learned per-env episode
-    costs; these fields only bound and seed it."""
+    in-flight episode cap from engine KV pressure; these fields only bound and
+    seed it."""
 
     initial_inflight: int | None = Field(None, ge=1)
     """Optional initial in-flight episodes to start from. Set it when a good value is known to skip the initial ramp; otherwise auto-derive a pessimistic bound at runtime."""

src/prime_rl/eval/runner.py

@@ -34,10 +34,6 @@
 from prime_rl.orchestrator.eval_source import EvalSource
 from prime_rl.orchestrator.inference_metrics import InferenceMetricsCollector
 from prime_rl.orchestrator.metrics import dispatch_failure_metrics
-from prime_rl.orchestrator.patches import (
-    monkey_patch_chat_completion_logprobs,
-    monkey_patch_oai_iterable_types,
-)
 from prime_rl.orchestrator.periodic_logger import PeriodicLogger
 from prime_rl.orchestrator.types import DispatchFailure, EvalBatch, GroupCancellation, Policy
 from prime_rl.orchestrator.utils import (
@@ -49,9 +45,6 @@
 from prime_rl.utils.logger import format_time, get_logger
 from prime_rl.utils.pathing import get_config_dir
 
-monkey_patch_oai_iterable_types()
-monkey_patch_chat_completion_logprobs()
-
 # How often ``run_epoch`` re-checks for a superseding checkpoint while it waits for episodes.
 POLL_INTERVAL_S = 2.0
 

src/prime_rl/inference/vllm/worker/filesystem.py

@@ -17,10 +17,6 @@
 class FileSystemWeightUpdateWorker(Worker):
     """vLLM worker extension for updating weights in-place using shared filesystem."""
 
-    def init_broadcaster(self) -> None:
-        """Initialize the broadcaster."""
-        ...
-
     def liveness_probe(self) -> None:
         """No-op RPC used by the API server liveness endpoint."""
         return None

src/prime_rl/orchestrator/concurrency.py

@@ -163,7 +163,7 @@ def bind(
 
     # ── inbound hooks ────────────────────────────────────────────────────────
 
-    def record_episode(self, env_name: str, kind: str, tokens: int, duration: float) -> None:
+    def record_episode(self, tokens: int) -> None:
         """One completed unit (from the dispatcher). Advances the turnover
         clock and, while the last poll was green, grows the cap — so growth
         is paced by the pipeline itself at any concurrency scale."""

src/prime_rl/orchestrator/dispatcher.py

@@ -143,7 +143,7 @@ def __init__(
         max_off_policy_steps: int,
         run_id: str,
         run_name: str | None,
-        on_episode_complete: Callable[[str, str, int, float], None] | None = None,
+        on_episode_complete: Callable[[int], None] | None = None,
     ) -> None:
         self.policy = policy
         self.progress = progress
@@ -157,7 +157,7 @@ def __init__(
         self.max_off_policy_steps = max_off_policy_steps
         self.run_id = run_id
         self.run_name = run_name
-        # ``(env_name, kind, total_tokens, duration_s)`` per completed episode
+        # Called with ``total_tokens`` per completed episode
         self.on_episode_complete = on_episode_complete
 
         # Starting value of the dynamic cap (the concurrency controller moves
@@ -694,9 +694,7 @@ async def handle_completed_request(self, task: asyncio.Task) -> None:
         if not episode.ok and not episode.traces:
             self.metrics.record_error(kind=meta.kind, env_name=meta.env_name)
         if self.on_episode_complete is not None and meta.started_at > 0:
-            self.on_episode_complete(
-                meta.env_name, meta.kind, episode.num_total_tokens, time.monotonic() - meta.started_at
-            )
+            self.on_episode_complete(episode.num_total_tokens)
         await self.emit_episode(meta, group, episode)
 
     def complete_group_member(self, meta: InflightEpisode, group: GroupState | None) -> int:

src/prime_rl/orchestrator/orchestrator.py

@@ -48,10 +48,6 @@
 from prime_rl.orchestrator.inference_metrics import InferenceMetricsCollector
 from prime_rl.orchestrator.metrics import TrainEpisodes, dispatch_failure_metrics
 from prime_rl.orchestrator.packing import BatchPacker
-from prime_rl.orchestrator.patches import (
-    monkey_patch_chat_completion_logprobs,
-    monkey_patch_oai_iterable_types,
-)
 from prime_rl.orchestrator.periodic_logger import PeriodicLogger
 from prime_rl.orchestrator.train_sink import TrainSink
 from prime_rl.orchestrator.train_source import TrainSource
@@ -78,12 +74,8 @@
 from prime_rl.utils.async_utils import EventLoopLagMonitor, EventLoopLagStats, safe_cancel
 from prime_rl.utils.heartbeat import Heartbeat
 from prime_rl.utils.logger import format_time, get_logger, setup_logger
-from prime_rl.utils.pathing import get_broadcast_dir, get_config_dir
-from prime_rl.utils.utils import clean_exit, resolve_latest_ckpt_step
-
-monkey_patch_oai_iterable_types()
-monkey_patch_chat_completion_logprobs()
-
+from prime_rl.utils.pathing import get_broadcast_dir, get_config_dir, resolve_latest_ckpt_step
+from prime_rl.utils.utils import clean_exit
 
 # Wall-clock budget for post-training cleanup; force-exit if graceful
 # shutdown wedges (env-server ZMQ recv, vLLM admin aclose, etc)

src/prime_rl/trainer/ckpt.py

@@ -27,7 +27,7 @@
 from prime_rl.trainer.optim import OffloadOptimizer, OptimizerLike
 from prime_rl.trainer.world import get_world
 from prime_rl.utils.logger import format_time, get_logger
-from prime_rl.utils.utils import get_all_ckpt_steps, get_ckpt_dir, get_step_path
+from prime_rl.utils.pathing import get_all_ckpt_steps, get_ckpt_dir, get_step_path
 
 
 @dataclass

src/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py

@@ -23,7 +23,6 @@
 from transformers.modeling_outputs import BaseModelOutputWithPast
 from transformers.processing_utils import Unpack
 from transformers.utils import TransformersKwargs, auto_docstring
-from transformers.utils.deprecation import deprecate_kwarg
 
 from prime_rl.trainer.models.base import PreTrainedModelPrimeRL
 from prime_rl.trainer.models.glm4_moe.configuration_glm4_moe import Glm4MoeConfig
@@ -89,7 +88,6 @@ def __init__(self, config: Glm4MoeConfig, layer_idx: int):
         self.input_layernorm = RMSNorm(RMSNormConfig(hidden_size=config.hidden_size, eps=config.rms_norm_eps))
         self.post_attention_layernorm = RMSNorm(RMSNormConfig(hidden_size=config.hidden_size, eps=config.rms_norm_eps))
 
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/prime_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py

@@ -10,7 +10,6 @@
 from transformers.modeling_outputs import BaseModelOutputWithPast
 from transformers.processing_utils import Unpack
 from transformers.utils import TransformersKwargs, auto_docstring
-from transformers.utils.deprecation import deprecate_kwarg
 
 from prime_rl.trainer.models.base import PreTrainedModelPrimeRL
 from prime_rl.trainer.models.glm_moe_dsa.configuration_glm_moe_dsa import GlmMoeDsaConfig, _index_cache_skip_topk
@@ -87,7 +86,6 @@ def __init__(self, config: GlmMoeDsaConfig, layer_idx: int):
         self.input_layernorm = RMSNorm(RMSNormConfig(hidden_size=config.hidden_size, eps=config.rms_norm_eps))
         self.post_attention_layernorm = RMSNorm(RMSNormConfig(hidden_size=config.hidden_size, eps=config.rms_norm_eps))
 
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/prime_rl/trainer/models/llama/modeling_llama.py

@@ -32,7 +32,6 @@
 from transformers.models.llama.configuration_llama import LlamaConfig
 from transformers.processing_utils import Unpack
 from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
-from transformers.utils.deprecation import deprecate_kwarg
 
 from prime_rl.trainer.models.base import PreTrainedModelPrimeRL
 from prime_rl.trainer.models.layers.attn import ATTN_IMPL2CLASS, AttentionConfig
@@ -71,7 +70,6 @@ def __init__(self, config: LlamaConfig, layer_idx: int):
         self.input_layernorm = RMSNorm(RMSNormConfig(hidden_size=config.hidden_size, eps=config.rms_norm_eps))
         self.post_attention_layernorm = RMSNorm(RMSNormConfig(hidden_size=config.hidden_size, eps=config.rms_norm_eps))
 
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/prime_rl/trainer/rl/train.py

@@ -71,7 +71,8 @@
 from prime_rl import monitors
 from prime_rl.utils.config import cli
 from prime_rl.utils.process import set_proc_title
-from prime_rl.utils.utils import clean_exit, resolve_latest_ckpt_step
+from prime_rl.utils.pathing import resolve_latest_ckpt_step
+from prime_rl.utils.utils import clean_exit
 
 
 @clean_exit

src/prime_rl/utils/utils.py

@@ -2,33 +2,14 @@
 import functools
 import importlib
 import math
-import os
 import sys
-from collections import defaultdict
 from contextlib import contextmanager
-from pathlib import Path
 from typing import Any, Callable
 
 import wandb
 
 from prime_rl.utils.logger import get_logger
 
-# TODO: Change all imports to use utils.pathing
-# ruff: noqa: F401
-from prime_rl.utils.pathing import (
-    get_all_ckpt_steps,
-    get_batch_dir,
-    get_broadcast_dir,
-    get_ckpt_dir,
-    get_eval_dir,
-    get_file_monitor_dir,
-    get_log_dir,
-    get_step_path,
-    resolve_latest_ckpt_step,
-    sync_wait_for_path,
-    wait_for_path,
-)
-
 
 def import_object(dotted_path: str) -> Any:
     """Import an object from a dotted path like 'my_module.submodule.MyClass'."""