diff --git a/3rdparty/Gym-workspace/Gym b/3rdparty/Gym-workspace/Gym index 1a4912e231..8c08ffaebe 160000 --- a/3rdparty/Gym-workspace/Gym +++ b/3rdparty/Gym-workspace/Gym @@ -1 +1 @@ -Subproject commit 1a4912e231bb2795b062f7de97496caaf382c7f6 +Subproject commit 8c08ffaebe75fa19633d68fd4003e957f2840251 diff --git a/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml b/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml index 7a805a7c43..130221d739 100644 --- a/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml +++ b/examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml @@ -38,10 +38,10 @@ grpo: seq_logprob_error_threshold: null async_grpo: - enabled: false # Set to true to enable async training mode + enabled: true # Set to true to enable async training mode # Max age (in training steps) for trajectories used in training - max_trajectory_age_steps: 1 - in_flight_weight_updates: false # Set to true to enable in-flight weight updates + max_trajectory_age_steps: 2 + in_flight_weight_updates: true # Set to true to enable in-flight weight updates recompute_kv_cache_after_weight_updates: false # Set to true to recompute kv cache after in-flight-weight-updates loss_fn: @@ -55,7 +55,7 @@ loss_fn: # (default off) loss formulation improvements (docs/guides/grpo.md#loss) use_on_policy_kl_approximation: false truncated_importance_sampling_ratio: null - use_importance_sampling_correction: false + use_importance_sampling_correction: true token_level_loss: true checkpointing: @@ -234,15 +234,14 @@ policy: # Workplace assistant uses 26 tools, so we enable auto_tools. # For Nemotron Nano v2, we use the dedicated `nemotron_json` tool parser enable_auto_tools: true - tool_parser: nemotron_json + tool_parser: hermes + reasoning_parser: qwen3 vllm_kwargs: compilation_config: # when enforce_eager is False, set ++policy.generation.vllm_kwargs.compilation_config.backend=eager for better accuracy, # with the flag, vllm will use the custom CUDA kernels instead of the Triton kernels generated by torch.compile # for more details, see convergence issue https://github.com/NVIDIA-NeMo/RL/issues/998 backend: eager - # We need the Mamba cache to be set to fp32 for Nemotron Nano v2 - mamba_ssm_cache_dtype: "float32" colocated: # true: generation shares training GPUs # false: uses dedicated generation resources @@ -297,10 +296,10 @@ env: responses_api_models: vllm_model: # Disable reasoning! - uses_reasoning_parser: false + uses_reasoning_parser: true extra_body: chat_template_kwargs: - enable_thinking: false + enable_thinking: true code_gen: resources_servers: code_gen: @@ -328,3 +327,4 @@ logger: cluster: gpus_per_node: 8 num_nodes: 1 # Single node by default; set to 2+ for multi-node training + diff --git a/examples/nemo_gym/run_grpo_nemo_gym.py b/examples/nemo_gym/run_grpo_nemo_gym.py index 34b6f0b5db..3ecfc6981e 100644 --- a/examples/nemo_gym/run_grpo_nemo_gym.py +++ b/examples/nemo_gym/run_grpo_nemo_gym.py @@ -94,7 +94,7 @@ def collect_trajectories( input_batch=val_batch, tokenizer=tokenizer, task_to_env=val_task_to_env, - max_seq_len=None, + max_seq_len=master_config["policy"]["max_total_sequence_length"], generation_config=generation_config, max_rollout_turns=None, greedy=False, diff --git a/nemo_rl/algorithms/async_utils.py b/nemo_rl/algorithms/async_utils.py index bcf8a1a188..9ce5078fe2 100644 --- a/nemo_rl/algorithms/async_utils.py +++ b/nemo_rl/algorithms/async_utils.py @@ -14,6 +14,7 @@ import threading as _threading import time +from collections import Counter from typing import Any, Optional import ray @@ -31,6 +32,8 @@ TokenizerType = PreTrainedTokenizerBase +ASYNC_DEBUG_PREFIX = "[ASYNC_DEBUG]" + @ray.remote # pragma: no cover class ReplayBuffer: @@ -48,6 +51,7 @@ def __init__(self, max_size: int): # If trajectory_version is 1 and target_weight_version is 4 it means that weight version 1 was used for generating a trajectory and this trajectory will be used for training when weight version is 4. self.trajectory_versions = [] # it is the weight-version used for generation of a trajectory self.target_weight_versions = [] # it is the weight-version of the trainer where this trajectory will be used. + self.failed_prompt_groups: list[dict[str, Any]] = [] self.last_target_weight_already_generated = -1 self._lock = _threading.Lock() @@ -81,14 +85,60 @@ def push_with_wait_signal( ) return "success" + def record_failed_prompt_group( + self, + weight_version: int, + target_weight_version: int, + prompt_idx: int, + worker_id: int, + reason: str, + ) -> str: + """Record a prompt group that failed before it produced a trainable batch.""" + with self._lock: + failure_record = { + "weight_version": weight_version, + "target_weight_version": target_weight_version, + "prompt_idx": prompt_idx, + "worker_id": worker_id, + "reason": reason, + "timestamp": time.time(), + } + self.failed_prompt_groups.append(failure_record) + self.last_target_weight_already_generated = max( + self.last_target_weight_already_generated, target_weight_version + ) + print( + f"{ASYNC_DEBUG_PREFIX} recorded_failed_prompt_group " + f"target={target_weight_version} generation_weight={weight_version} " + f"prompt_idx={prompt_idx} worker_id={worker_id} reason={reason} " + f"failed_target_counts={dict(Counter(r['target_weight_version'] for r in self.failed_prompt_groups))}" + ) + return "success" + def get_debug_info(self) -> dict: """Get debug information about buffer state.""" - return { - "total_trajectories": len(self.trajectories), - "trajectory_versions": self.trajectory_versions, - "target_weight_versions": self.target_weight_versions, - "max_size": self.max_size, - } + with self._lock: + failed_target_versions = [ + r["target_weight_version"] for r in self.failed_prompt_groups + ] + failed_generation_versions = [ + r["weight_version"] for r in self.failed_prompt_groups + ] + return { + "total_trajectories": len(self.trajectories), + "trajectory_versions": list(self.trajectory_versions), + "target_weight_versions": list(self.target_weight_versions), + "trajectory_version_counts": dict(Counter(self.trajectory_versions)), + "target_weight_counts": dict(Counter(self.target_weight_versions)), + "failed_prompt_groups": list(self.failed_prompt_groups), + "failed_prompt_group_count": len(self.failed_prompt_groups), + "failed_generation_version_counts": dict( + Counter(failed_generation_versions) + ), + "failed_target_weight_counts": dict(Counter(failed_target_versions)), + "last_target_weight_already_generated": self.last_target_weight_already_generated, + "max_size": self.max_size, + } def get_last_target_weight_already_generated(self) -> int: with self._lock: @@ -99,6 +149,36 @@ def get_existing_target_weights(self) -> set[int]: with self._lock: return set(self.target_weight_versions) + def get_target_weight_counts(self) -> dict[int, int]: + """Get counts of buffered trajectory groups per target weight.""" + with self._lock: + return dict(Counter(self.target_weight_versions)) + + def get_failed_target_weight_counts(self) -> dict[int, int]: + """Get counts of failed prompt groups per target weight.""" + with self._lock: + return dict( + Counter(r["target_weight_version"] for r in self.failed_prompt_groups) + ) + + def get_target_completion_counts(self) -> dict[int, dict[str, int]]: + """Get buffered and failed prompt-group counts by target weight.""" + with self._lock: + buffered_counts = Counter(self.target_weight_versions) + failed_counts = Counter( + r["target_weight_version"] for r in self.failed_prompt_groups + ) + target_weights = set(buffered_counts) | set(failed_counts) + return { + target_weight: { + "buffered": buffered_counts.get(target_weight, 0), + "failed": failed_counts.get(target_weight, 0), + "accounted": buffered_counts.get(target_weight, 0) + + failed_counts.get(target_weight, 0), + } + for target_weight in target_weights + } + def sample( self, num_prompt_groups: int, @@ -116,7 +196,12 @@ def sample( Dictionary with 'trajectories' and 'avg_trajectory_age' keys, or None if insufficient data """ with self._lock: - if not self.trajectories: + failed_for_current = [ + r + for r in self.failed_prompt_groups + if r["target_weight_version"] == current_weight_version + ] + if not self.trajectories and not failed_for_current: return None total_trajectories = len(self.trajectories) @@ -124,11 +209,14 @@ def sample( print(f" {current_weight_version=}, {max_age_steps=}") print(f" {self.trajectory_versions=}") - # For debugging: check for unexpected old trajectories - from collections import Counter - version_counts = Counter(self.trajectory_versions) + target_counts = Counter(self.target_weight_versions) + failed_target_counts = Counter( + r["target_weight_version"] for r in self.failed_prompt_groups + ) print(f" {version_counts=}") + print(f" {target_counts=}") + print(f" {failed_target_counts=}") # Compute minimum valid version based on age window # max_age_steps=1 means trajectories from the last 1 step are valid @@ -153,17 +241,10 @@ def sample( print( f" valid_indices: {len(valid_indices)}/{total_trajectories} trajectories within age window" ) - if not valid_indices: + if not valid_indices and not failed_for_current: print("No trajectories available for sampling.") return None - # Enforce exact number of groups if available; otherwise, signal to wait - if len(valid_indices) < num_prompt_groups: - print( - f"Insufficient valid groups: have {len(valid_indices)}, need {num_prompt_groups}. Waiting for buffer to fill." - ) - return None - # Only select trajectories intended for the current training step # This ensures no trajectory loses its "last chance" to be used for its intended step intended_indices = [ @@ -176,24 +257,41 @@ def sample( f" 🎯 Found {len(intended_indices)} trajectories intended for current step {current_weight_version}" ) - # Stall training if we don't have enough trajectories intended for this step - if len(intended_indices) < num_prompt_groups: + accounted_for_current = len(intended_indices) + len(failed_for_current) + + # Stall training if we don't have enough successful or explicitly failed + # prompt groups intended for this step. + if accounted_for_current < num_prompt_groups: print( f" ⏸️ STALLING: Need {num_prompt_groups} trajectories for step {current_weight_version}, but only {len(intended_indices)} are ready" ) print( - f" ⏸️ Training will wait for remaining {num_prompt_groups - len(intended_indices)} trajectories to be generated" + f" ⏸️ Training will wait for remaining {num_prompt_groups - accounted_for_current} trajectories to be generated or fail" + ) + print( + f"{ASYNC_DEBUG_PREFIX} replay_buffer_stall current_weight={current_weight_version} " + f"needed={num_prompt_groups} intended_ready={len(intended_indices)} " + f"failed_for_current={len(failed_for_current)} " + f"target_counts={dict(target_counts)} failed_target_counts={dict(failed_target_counts)} " + f"version_counts={dict(version_counts)}" ) return None + if not intended_indices: + raise RuntimeError( + f"All {num_prompt_groups} prompt groups for step {current_weight_version} failed before buffering; no trainable trajectories are available." + ) + # Select exactly the trajectories intended for this step (FIFO within same target) selected: list[int] = intended_indices[:num_prompt_groups] + skipped_failed_prompt_groups = min( + len(failed_for_current), num_prompt_groups - len(selected) + ) print( - f" βœ… Selected {len(selected)} trajectories all intended for step {current_weight_version}" + f" βœ… Selected {len(selected)} trajectories for step {current_weight_version}; " + f"skipping {skipped_failed_prompt_groups} failed prompt groups" ) - from collections import Counter - sampled_weights = [self.trajectory_versions[i] for i in selected] avg_trajectory_age = current_weight_version - sum(sampled_weights) / len( sampled_weights @@ -207,19 +305,39 @@ def sample( ) sampled_items = [self.trajectories[i] for i in selected] + consumed_failures = failed_for_current[:skipped_failed_prompt_groups] + consumed_failure_ids = {id(r) for r in consumed_failures} # Remove selected items in reverse order to maintain correct indices for idx in sorted(selected, reverse=True): self.trajectory_versions.pop(idx) self.target_weight_versions.pop(idx) self.trajectories.pop(idx) + + # The current training step has accounted for these failed prompt groups. + # Remove all failure records for the step so they cannot affect later debug + # state or target-completion accounting. + self.failed_prompt_groups = [ + r for r in self.failed_prompt_groups if id(r) not in consumed_failure_ids + ] print( f"πŸ—‘οΈ Consumed and removed {len(selected)} groups from buffer, old buffer size: {total_trajectories}, new buffer size: {len(self.trajectories)}, new target weight versions {self.target_weight_versions}" ) + if skipped_failed_prompt_groups: + print( + f"{ASYNC_DEBUG_PREFIX} replay_buffer_skipped_failed_prompt_groups " + f"current_weight={current_weight_version} skipped={skipped_failed_prompt_groups} " + f"selected={len(selected)} requested={num_prompt_groups} " + f"failures={consumed_failures}" + ) return { "trajectories": sampled_items, "avg_trajectory_age": avg_trajectory_age, + "requested_prompt_groups": num_prompt_groups, + "sampled_prompt_groups": len(sampled_items), + "skipped_failed_prompt_groups": skipped_failed_prompt_groups, + "failed_prompt_groups": consumed_failures, } def size(self) -> int: @@ -233,6 +351,7 @@ def clear(self) -> None: self.trajectories.clear() self.trajectory_versions.clear() self.target_weight_versions.clear() + self.failed_prompt_groups.clear() @ray.remote # pragma: no cover @@ -284,12 +403,78 @@ def __init__( int(self.master_config["grpo"]["num_prompts_per_step"]) * int(self.master_config["grpo"]["async_grpo"]["max_trajectory_age_steps"]) ) or 1 + self._max_inflight = max_inflight self._inflight_sema = _threading.Semaphore(max_inflight) # Simple lock to prevent race conditions when checking/spawning workers self._generation_check_lock: _threading.Lock = _threading.Lock() - # Track which target weights are currently being generated (globally) - self._generating_targets: set[int] = set() + # Track how many prompt-group workers are still in flight per target weight. + self._generating_target_counts: dict[int, int] = {} + self._worker_id = 0 + + print( + f"{ASYNC_DEBUG_PREFIX} collector_init current_weight={self.current_weight_version} " + f"initial_weight={self.initial_weight_version} max_inflight={self._max_inflight} " + f"num_prompts_per_step={self.master_config['grpo']['num_prompts_per_step']} " + f"num_generations_per_prompt={self.master_config['grpo']['num_generations_per_prompt']} " + f"max_age={self.master_config['grpo']['async_grpo']['max_trajectory_age_steps']}" + ) + + def _active_thread_count_unlocked(self) -> int: + return sum(1 for thread in self._inflight_threads if thread.is_alive()) + + def _next_worker_id(self) -> int: + with self._generation_check_lock: + self._worker_id += 1 + return self._worker_id + + def _generation_state_for_log( + self, + target_weights: Optional[list[int]] = None, + target_completion_counts: Optional[dict[int, dict[str, int]]] = None, + ) -> dict[str, Any]: + if target_weights is None: + target_weights = self._calculate_target_weights(self.current_weight_version) + if target_completion_counts is None: + target_completion_counts = ray.get( + self.replay_buffer.get_target_completion_counts.remote() + ) + + required_groups = int(self.master_config["grpo"]["num_prompts_per_step"]) + with self._generation_check_lock: + inflight_counts = dict(self._generating_target_counts) + with self._threads_lock: + active_threads = self._active_thread_count_unlocked() + + per_target = {} + for target_weight in target_weights: + completion_counts = target_completion_counts.get(target_weight, {}) + buffered = completion_counts.get("buffered", 0) + failed = completion_counts.get("failed", 0) + inflight = inflight_counts.get(target_weight, 0) + per_target[target_weight] = { + "buffered": buffered, + "failed": failed, + "inflight": inflight, + "missing": required_groups - buffered - failed - inflight, + } + + return { + "current_weight_version": self.current_weight_version, + "required_groups": required_groups, + "max_inflight": self._max_inflight, + "active_threads": active_threads, + "inflight_counts": inflight_counts, + "target_completion_counts": dict(target_completion_counts), + "candidate_targets": target_weights, + "per_candidate_target": per_target, + } + + def get_debug_state(self) -> dict[str, Any]: + """Return collector and replay-buffer state useful for diagnosing stalls.""" + state = self._generation_state_for_log() + state["replay_buffer"] = ray.get(self.replay_buffer.get_debug_info.remote()) + return state def _calculate_target_weights(self, generation_weight_version: int) -> list[int]: """Calculate target weight versions for given generation weight version. @@ -321,23 +506,62 @@ def _calculate_target_weights(self, generation_weight_version: int) -> list[int] return [generation_weight_version + i for i in range(1, max_trajectory_age + 1)] def _get_next_target_for_generation( - self, generation_weight_version: int - ) -> Optional[int]: + self, generation_weight_version: int, max_prompt_groups: int + ) -> Optional[tuple[int, int]]: """Get the next target weight that needs generation (if any).""" target_weights = self._calculate_target_weights(generation_weight_version) - last_target_weight_already_generated = ray.get( - self.replay_buffer.get_last_target_weight_already_generated.remote() + target_completion_counts = ray.get( + self.replay_buffer.get_target_completion_counts.remote() ) + required_groups = int(self.master_config["grpo"]["num_prompts_per_step"]) with self._generation_check_lock: + candidate_states = [] for target_weight in target_weights: - if ( - target_weight > last_target_weight_already_generated - and target_weight not in self._generating_targets - ): - self._generating_targets.add(target_weight) - print(f"🎯 Reserved target weight {target_weight} for generation") - return target_weight + completion_counts = target_completion_counts.get(target_weight, {}) + buffered_groups = completion_counts.get("buffered", 0) + failed_groups = completion_counts.get("failed", 0) + inflight_groups = self._generating_target_counts.get(target_weight, 0) + missing_groups = ( + required_groups + - buffered_groups + - failed_groups + - inflight_groups + ) + candidate_states.append( + { + "target": target_weight, + "buffered": buffered_groups, + "failed": failed_groups, + "inflight": inflight_groups, + "missing": missing_groups, + } + ) + if missing_groups > 0: + prompt_groups_to_launch = min(missing_groups, max_prompt_groups) + self._generating_target_counts[target_weight] = ( + inflight_groups + prompt_groups_to_launch + ) + print( + f"🎯 Reserved {prompt_groups_to_launch} prompt groups for target weight {target_weight}" + ) + print( + f"{ASYNC_DEBUG_PREFIX} reserve target={target_weight} " + f"launch={prompt_groups_to_launch} buffered={buffered_groups} " + f"failed={failed_groups} " + f"previous_inflight={inflight_groups} " + f"new_inflight={self._generating_target_counts[target_weight]} " + f"required={required_groups} candidates={candidate_states}" + ) + return target_weight, prompt_groups_to_launch + + print( + f"{ASYNC_DEBUG_PREFIX} no_reservation generation_weight={generation_weight_version} " + f"targets={target_weights} required={required_groups} " + f"target_completion_counts={target_completion_counts} " + f"inflight_counts={dict(self._generating_target_counts)} " + f"candidates={candidate_states}" + ) return None @@ -356,21 +580,32 @@ def _should_pause_for_generation_limits(self) -> bool: """Check if collection should be paused due to generation limits.""" try: target_weights = self._calculate_target_weights(self.current_weight_version) - last_target_weight_already_generated = ray.get( - self.replay_buffer.get_last_target_weight_already_generated.remote() + target_completion_counts = ray.get( + self.replay_buffer.get_target_completion_counts.remote() ) + required_groups = int(self.master_config["grpo"]["num_prompts_per_step"]) # Check if any target weight in our range needs generation with self._generation_check_lock: for target_weight in target_weights: + completion_counts = target_completion_counts.get(target_weight, {}) + buffered_groups = completion_counts.get("buffered", 0) + failed_groups = completion_counts.get("failed", 0) + inflight_groups = self._generating_target_counts.get( + target_weight, 0 + ) if ( - target_weight > last_target_weight_already_generated - and target_weight not in self._generating_targets + buffered_groups + failed_groups + inflight_groups + < required_groups ): return False # Found a target that needs generation print( - f"⏸️ All target weights {target_weights} already generated or in progress, pausing" + f"⏸️ All target weights {target_weights} are full or in progress, pausing" + ) + print( + f"{ASYNC_DEBUG_PREFIX} pause_decision " + f"{self._generation_state_for_log(target_weights, target_completion_counts)}" ) return True except Exception: @@ -410,25 +645,25 @@ def _collection_loop(self): if self._should_pause_for_generation_limits() and self.running: # Only log warning once per weight version if self._last_limit_warning_version != self.current_weight_version: - async_cfg = self.master_config.get("grpo", {}).get( - "async_grpo", {} + target_weights = self._calculate_target_weights( + self.current_weight_version ) - max_trajectory_age = async_cfg["max_trajectory_age_steps"] - target_weights = [ - self.current_weight_version + i - for i in range(max_trajectory_age) - ] print( f"⏸️ Pausing collection: all target weights {target_weights} for weight version {self.current_weight_version} " - f"already exist in buffer. Waiting for weight update..." + f"are full or already in progress. Waiting for weight update..." ) self._last_limit_warning_version = self.current_weight_version self._generation_limit_cleared.clear() # Clear the event to pause - # Efficiently wait for generation limits to be cleared (no polling!) - self._generation_limit_cleared.wait() + # Efficiently wait for generation limits to be cleared, with a + # periodic diagnostic so a stuck run shows whether workers remain. + while self.running and not self._generation_limit_cleared.wait(30): + print( + f"{ASYNC_DEBUG_PREFIX} collection_paused_waiting " + f"{self._generation_state_for_log()}" + ) # Double-check we're still running after being woken up if not self.running: @@ -454,24 +689,30 @@ def _process_batch(self, batch: BatchedDataDict[DatumSpec]) -> None: generation_weight_version = self.current_weight_version num_generations = self.master_config["grpo"]["num_generations_per_prompt"] num_prompts = batch.size + print( + f"{ASYNC_DEBUG_PREFIX} process_batch generation_weight={generation_weight_version} " + f"batch_size={num_prompts} num_generations={num_generations} " + f"state={self._generation_state_for_log()}" + ) # Get the next target weight that needs generation - target_weight = self._get_next_target_for_generation( - generation_weight_version + target_reservation = self._get_next_target_for_generation( + generation_weight_version, num_prompts ) - if target_weight is None: + if target_reservation is None: print( f"πŸ”„ No targets need generation for weight {generation_weight_version}" ) return + target_weight, num_prompt_groups_to_launch = target_reservation print( - f"🎯 Generating for target weight {target_weight} from generation_weight_version {generation_weight_version}" + f"🎯 Generating {num_prompt_groups_to_launch} prompt groups for target weight {target_weight} from generation_weight_version {generation_weight_version}" ) - # Generate for all prompts in this batch for the target weight - for prompt_idx in range(num_prompts): + # Generate only the missing prompt groups for this target. + for prompt_idx in range(num_prompt_groups_to_launch): # Wait for refit to complete if in progress if not self._refit_pause_cleared.is_set() and self.running: with self._threads_lock: @@ -490,10 +731,27 @@ def _process_batch(self, batch: BatchedDataDict[DatumSpec]) -> None: single_prompt_batch = batch.slice(prompt_idx, prompt_idx + 1) repeated_batch = single_prompt_batch.repeat_interleave(num_generations) - self._inflight_sema.acquire() + worker_id = self._next_worker_id() + acquire_start = time.time() + while not self._inflight_sema.acquire(timeout=30): + print( + f"{ASYNC_DEBUG_PREFIX} worker_waiting_for_slot " + f"worker_id={worker_id} target={target_weight} prompt_idx={prompt_idx} " + f"wait_s={time.time() - acquire_start:.1f} " + f"state={self._generation_state_for_log()}" + ) + acquire_elapsed = time.time() - acquire_start + if acquire_elapsed > 1: + print( + f"{ASYNC_DEBUG_PREFIX} worker_acquired_slot_after_wait " + f"worker_id={worker_id} target={target_weight} prompt_idx={prompt_idx} " + f"wait_s={acquire_elapsed:.1f}" + ) + worker = _threading.Thread( target=self._run_prompt_group_worker, args=( + worker_id, repeated_batch, generation_weight_version, target_weight, @@ -503,6 +761,12 @@ def _process_batch(self, batch: BatchedDataDict[DatumSpec]) -> None: ) with self._threads_lock: self._inflight_threads.add(worker) + active_threads = self._active_thread_count_unlocked() + print( + f"{ASYNC_DEBUG_PREFIX} worker_thread_started worker_id={worker_id} " + f"target={target_weight} prompt_idx={prompt_idx} " + f"generation_weight={generation_weight_version} active_threads={active_threads}" + ) worker.start() self._cleanup_finished_threads() @@ -636,11 +900,20 @@ def _cleanup_finished_threads(self) -> None: def _run_prompt_group_worker( self, + worker_id: int, repeated_batch: BatchedDataDict[DatumSpec], generation_weight_version: int, target_weight_version: int, prompt_idx: int, ) -> None: + worker_start = time.time() + outcome = "unknown" + failure_reason: Optional[str] = None + print( + f"{ASYNC_DEBUG_PREFIX} worker_start worker_id={worker_id} " + f"target={target_weight_version} prompt_idx={prompt_idx} " + f"generation_weight={generation_weight_version} repeated_batch_size={repeated_batch.size}" + ) try: # Import here to avoid circular dependency from nemo_rl.algorithms.grpo import _should_use_nemo_gym @@ -657,13 +930,21 @@ def _run_prompt_group_worker( input_batch=repeated_batch, tokenizer=self.tokenizer, task_to_env=self.task_to_env, - max_seq_len=None, + max_seq_len=self.master_config["policy"][ + "max_total_sequence_length" + ], generation_config=generation_config, max_rollout_turns=None, greedy=False, ) final_batch = nemo_gym_rollout_result.final_batch rollout_metrics = nemo_gym_rollout_result.rollout_metrics + print( + f"{ASYNC_DEBUG_PREFIX} worker_rollout_done worker_id={worker_id} " + f"target={target_weight_version} prompt_idx={prompt_idx} " + f"elapsed_s={time.time() - worker_start:.1f} " + f"metric_keys={sorted(rollout_metrics.keys())[:16]}" + ) else: final_batch, rollout_metrics = run_async_multi_turn_rollout( policy_generation=self.policy_generation, @@ -676,6 +957,12 @@ def _run_prompt_group_worker( max_rollout_turns=self.master_config["grpo"]["max_rollout_turns"], greedy=False, ) + print( + f"{ASYNC_DEBUG_PREFIX} worker_rollout_done worker_id={worker_id} " + f"target={target_weight_version} prompt_idx={prompt_idx} " + f"elapsed_s={time.time() - worker_start:.1f} " + f"metric_keys={sorted(rollout_metrics.keys())[:16]}" + ) # Move to CPU and push to buffer (avoid blocking on GC/push) final_batch_cpu = final_batch.to("cpu") @@ -690,6 +977,7 @@ def _run_prompt_group_worker( # Use exponential backoff when buffer is full try: backoff_delay = 0.01 + last_full_log = 0.0 while self.running: status = ray.get( self.replay_buffer.push_with_wait_signal.remote( @@ -699,45 +987,97 @@ def _run_prompt_group_worker( ) ) if status == "success": + outcome = "buffered" print( f"πŸ“¦ Buffered per-prompt group (prompt_idx {prompt_idx}, target_weight {target_weight_version})" ) + print( + f"{ASYNC_DEBUG_PREFIX} worker_buffered worker_id={worker_id} " + f"target={target_weight_version} prompt_idx={prompt_idx} " + f"generation_weight={generation_weight_version} " + f"elapsed_s={time.time() - worker_start:.1f}" + ) - # Release reservation when FIRST prompt group for this target is successfully buffered - if prompt_idx == 0: - with self._generation_check_lock: - if target_weight_version in self._generating_targets: - self._generating_targets.discard( - target_weight_version - ) - print( - f"🧹 Released reservation for target weight {target_weight_version} (first prompt buffered)" - ) break elif status == "full": + now = time.time() + if last_full_log == 0.0 or now - last_full_log >= 30: + last_full_log = now + print( + f"{ASYNC_DEBUG_PREFIX} worker_buffer_full worker_id={worker_id} " + f"target={target_weight_version} prompt_idx={prompt_idx} " + f"elapsed_s={time.time() - worker_start:.1f}" + ) # Exponential backoff up to 0.5 second time.sleep(min(backoff_delay, 0.5)) backoff_delay *= 1.5 else: # Unexpected status, wait briefly time.sleep(0.01) + if outcome == "unknown": + outcome = "stopped_before_buffer" except Exception as e: + outcome = "buffer_enqueue_error" + failure_reason = f"{type(e).__name__}: {e}" print(f"❌ Failed to enqueue per-prompt group to buffer: {e}") import traceback traceback.print_exc() except Exception as e: + outcome = "worker_error" + failure_reason = f"{type(e).__name__}: {e}" print(f"❌ Error in prompt group worker: {e}") import traceback traceback.print_exc() finally: - # Clean up reservation in case of error (if not already cleaned up) + if outcome in {"worker_error", "buffer_enqueue_error"}: + try: + ray.get( + self.replay_buffer.record_failed_prompt_group.remote( + generation_weight_version, + target_weight_version, + prompt_idx, + worker_id, + (failure_reason or outcome)[:1024], + ) + ) + print( + f"{ASYNC_DEBUG_PREFIX} worker_failure_recorded " + f"worker_id={worker_id} target={target_weight_version} " + f"prompt_idx={prompt_idx} outcome={outcome}" + ) + except Exception as e: + print( + f"{ASYNC_DEBUG_PREFIX} worker_failure_record_failed " + f"worker_id={worker_id} target={target_weight_version} " + f"prompt_idx={prompt_idx} outcome={outcome} error={e}" + ) + + # Track worker completion for this target. When all workers launched for a target + # have finished (success or error), the target becomes eligible for rescheduling + # if it is still incomplete in the replay buffer. with self._generation_check_lock: - if target_weight_version in self._generating_targets: - self._generating_targets.discard(target_weight_version) + remaining = self._generating_target_counts.get(target_weight_version, 0) + if remaining > 1: + self._generating_target_counts[target_weight_version] = remaining - 1 + print( + f"{ASYNC_DEBUG_PREFIX} worker_release_partial worker_id={worker_id} " + f"target={target_weight_version} remaining_inflight={remaining - 1}" + ) + elif remaining == 1: + self._generating_target_counts.pop(target_weight_version, None) print( - f"🧹 Emergency cleanup: Released reservation for target weight {target_weight_version}" + f"🧹 Released reservation for target weight {target_weight_version}" + ) + print( + f"{ASYNC_DEBUG_PREFIX} worker_release_final worker_id={worker_id} " + f"target={target_weight_version}" + ) + else: + print( + f"{ASYNC_DEBUG_PREFIX} worker_release_missing_reservation " + f"worker_id={worker_id} target={target_weight_version}" ) # Detach thread record when finished @@ -745,9 +1085,16 @@ def _run_prompt_group_worker( current = _threading.current_thread() if current in self._inflight_threads: self._inflight_threads.remove(current) + active_threads = self._active_thread_count_unlocked() try: self._inflight_sema.release() except Exception: import traceback traceback.print_exc() + print( + f"{ASYNC_DEBUG_PREFIX} worker_done worker_id={worker_id} " + f"target={target_weight_version} prompt_idx={prompt_idx} " + f"outcome={outcome} elapsed_s={time.time() - worker_start:.1f} " + f"active_threads={active_threads}" + ) diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index da955ac32a..3ae7aa1cf9 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -67,6 +67,7 @@ run_async_nemo_gym_rollout, run_multi_turn_rollout, ) +from nemo_rl.models.generation.dynamo import DynamoVllmConfig, DynamoVllmGeneration from nemo_rl.models.generation.interfaces import GenerationInterface from nemo_rl.models.generation.sglang import SGLangConfig, SGLangGeneration from nemo_rl.models.generation.vllm import VllmConfig, VllmGeneration @@ -394,6 +395,10 @@ def init_train_dataloader(dataset, suffix: str = ""): # ========================== print("\nβ–Ά Setting up compute cluster...", flush=True) colocated_inference = generation_config["colocated"]["enabled"] + is_external_dynamo = ( + generation_config.get("backend") == "dynamo" + and bool(generation_config.get("dynamo_cfg", {}).get("external_url")) + ) env_name_list = extract_necessary_env_names(data_config) rm_env_enabled = "reward_model" in env_name_list @@ -416,7 +421,29 @@ def init_train_dataloader(dataset, suffix: str = ""): f"policy_nodes:{policy_nodes} + rm_nodes:{rm_nodes} = total_nodes:{total_nodes}" ) - if colocated_inference: + if is_external_dynamo: + # External Dynamo: no local inference resources β€” all GPUs go to training. + if total_nodes == 1: + train_gpus_per_node = cluster_config["gpus_per_node"] - rm_gpus_per_node + else: + train_gpus_per_node = cluster_config["gpus_per_node"] + + train_cluster = RayVirtualCluster( + name="grpo_train_cluster", + bundle_ct_per_node_list=[train_gpus_per_node] * policy_nodes, + use_gpus=True, + num_gpus_per_node=train_gpus_per_node, + max_colocated_worker_groups=1, + ) + inference_cluster = None + colocated_inference = False + print( + f" βœ“ External Dynamo mode β€” all {policy_nodes} node(s) with " + f"{train_gpus_per_node} GPUs/node allocated to training", + flush=True, + ) + + elif colocated_inference: if total_nodes == 1: policy_gpus_per_node = cluster_config["gpus_per_node"] - rm_gpus_per_node assert policy_gpus_per_node > 0, ( @@ -717,6 +744,33 @@ def initialize_generation_with_policy( flush=True, ) + elif backend == "dynamo": + generation_config = cast(DynamoVllmConfig, generation_config) + + generation_config["vllm_cfg"]["hf_overrides"] = policy_config.get( + "hf_config_overrides", {} + ) + + def init_dynamo(): + t0 = time.perf_counter() + pg = DynamoVllmGeneration( + cluster=inference_cluster, config=generation_config + ) + return pg, time.perf_counter() - t0 + + policy_generation, policy = initialize_generation_with_policy( + init_generation_fn=init_dynamo, + generation_name="Dynamo+vLLM", + init_time_key="dynamo_init_time_s", + colocated_inference=colocated_inference, + worker_init_timing_metrics=worker_init_timing_metrics, + ) + + print( + f" βœ“ Using Dynamo+vLLM backend for generation with {policy_config['model_name']}", + flush=True, + ) + # Record when worker initialization completes (for calculating other setup time) worker_init_complete_time = time.perf_counter() - setup_start_time @@ -724,7 +778,8 @@ def initialize_generation_with_policy( policy.print_node_ip_and_gpu_id() # if it is not colocated inference, initialize collective communication for update weights - if not colocated_inference: + # Dynamo backend does not support weight updates β€” skip collective init and refit. + if not colocated_inference and backend not in ("dynamo", "vllm"): t0 = time.perf_counter() ip, port = train_cluster.get_master_address_and_port() print(f"Using ip: {ip}, port: {port} for collective communication", flush=True) @@ -745,7 +800,7 @@ def initialize_generation_with_policy( # prepare refit info state_dict_info = policy.prepare_refit_info() - if policy_generation is not None: + if policy_generation is not None and backend not in ("dynamo", "vllm"): policy_generation.prepare_refit_info(state_dict_info) # Calculate total setup time @@ -985,7 +1040,7 @@ def _should_use_async_rollouts(master_config: MasterConfig) -> bool: return False backend = generation_config.get("backend", "") - if backend != "vllm": + if backend != "vllm" and backend != "dynamo": return False vllm_cfg = generation_config.get("vllm_cfg", {}) @@ -999,13 +1054,18 @@ def _should_use_nemo_gym(master_config: MasterConfig) -> bool: if not should_use_nemo_gym: return should_use_nemo_gym + generation_config = master_config["policy"]["generation"] + backend = generation_config.get("backend", "") + + # Dynamo backend always uses the frontend HTTP path β€” no extra validation needed. + if backend == "dynamo": + return should_use_nemo_gym + # Validate the setup for training with NeMo-Gym assert _should_use_async_rollouts(master_config), ( - "❌ Error: In order to use NeMo-Gym, you must use vllm generation backend with `async_engine: true`!" + "❌ Error: In order to use NeMo-Gym, you must use vllm or dynamo generation backend with `async_engine: true`!" ) - generation_config = master_config["policy"]["generation"] - # We piggyback off of `_should_use_async_rollouts` to guarantee the existence of these configs. should_expose_http_server = generation_config["vllm_cfg"].get("expose_http_server") assert should_expose_http_server, ( @@ -1333,6 +1393,8 @@ def grpo_train( if policy_generation is None: policy_generation = policy # type: ignore NEED_REFIT = False + elif master_config["policy"]["generation"]["backend"] in ("dynamo", "vllm"): + NEED_REFIT = False POLICY_GENERATION_STALE = True # tracks if generation needs a refit before running assert policy_generation is not None # for mypy type check @@ -1517,7 +1579,9 @@ def grpo_train( input_batch=repeated_batch, tokenizer=tokenizer, task_to_env=task_to_env, - max_seq_len=None, + max_seq_len=master_config["policy"][ + "max_total_sequence_length" + ], generation_config=generation_config, max_rollout_turns=None, greedy=False, @@ -2254,7 +2318,7 @@ def validate( input_batch=val_batch, tokenizer=tokenizer, task_to_env=val_task_to_env, - max_seq_len=None, + max_seq_len=master_config["policy"]["max_total_sequence_length"], generation_config=generation_config, max_rollout_turns=None, greedy=False, @@ -2427,6 +2491,8 @@ def async_grpo_train( if policy_generation is None: policy_generation = policy NEED_REFIT = False + elif master_config["policy"]["generation"]["backend"] in ("dynamo", "vllm"): + NEED_REFIT = False POLICY_GENERATION_STALE = True assert policy_generation is not None @@ -2616,14 +2682,22 @@ def async_grpo_train( wait_iterations = 0 while True: buffer_size_current = ray.get(replay_buffer.size.remote()) + target_completion_counts = ray.get( + replay_buffer.get_target_completion_counts.remote() + ) + current_target_counts = target_completion_counts.get(weight_version, {}) + current_target_accounted = current_target_counts.get("accounted", 0) print( - f" Wait iteration {wait_iterations}: buffer_filled_ratio={buffer_size_current}/{min_trajectories_needed}" + f" Wait iteration {wait_iterations}: buffer_filled_ratio={buffer_size_current}/{min_trajectories_needed}, " + f"current_target_accounted={current_target_accounted}/{min_trajectories_needed}, " + f"current_target_counts={current_target_counts}" ) - if buffer_size_current >= min_trajectories_needed: + if current_target_accounted >= min_trajectories_needed: break + wait_iterations += 1 time.sleep(1.0) print("βœ… Buffer ready! Starting training loop...") @@ -2659,10 +2733,20 @@ def async_grpo_train( ) ) + sampled_prompt_groups = ( + len(sample_result["trajectories"]) + if sample_result is not None + else 0 + ) + skipped_failed_prompt_groups = ( + sample_result.get("skipped_failed_prompt_groups", 0) + if sample_result is not None + else 0 + ) if ( sample_result is None - or len(sample_result["trajectories"]) - != num_prompt_groups_needed + or sampled_prompt_groups + skipped_failed_prompt_groups + < num_prompt_groups_needed ): print( "⏳ Buffer empty or not enough groups to form a full step, waiting..." @@ -2688,10 +2772,22 @@ def async_grpo_train( # Extract trajectories and metadata from sample result trajectories = sample_result["trajectories"] avg_trajectory_age = sample_result["avg_trajectory_age"] + if not trajectories: + raise RuntimeError( + f"All {num_prompt_groups_needed} prompt groups for weight version {weight_version} failed; no trainable trajectories are available." + ) print( - f"βœ… Sampled {len(trajectories)} trajectory groups from buffer (avg age: {avg_trajectory_age:.2f} steps)" + f"βœ… Sampled {len(trajectories)} trajectory groups from buffer " + f"(skipped_failed={skipped_failed_prompt_groups}, avg age: {avg_trajectory_age:.2f} steps)" ) + if skipped_failed_prompt_groups: + print( + f"⚠️ Proceeding with incomplete async GRPO batch: " + f"sampled={len(trajectories)}, skipped_failed={skipped_failed_prompt_groups}, " + f"requested={num_prompt_groups_needed}, " + f"failed_prompt_groups={sample_result.get('failed_prompt_groups', [])}" + ) # Concatenate per-prompt groups into a single training batch per_prompt_batches = [t["batch"] for t in trajectories] @@ -2706,12 +2802,17 @@ def async_grpo_train( k: (sum(v) / len(v) if isinstance(v[0], (int, float)) else v) for k, v in rollout_metrics.items() } + rollout_metrics["async/requested_prompt_groups"] = ( + num_prompt_groups_needed + ) + rollout_metrics["async/sampled_prompt_groups"] = len(trajectories) + rollout_metrics["async/skipped_failed_prompt_groups"] = ( + skipped_failed_prompt_groups + ) - # Enforce fixed training batch: num_prompts_per_step * num_generations_per_prompt - expected_batch_size = ( - master_config["grpo"]["num_prompts_per_step"] - * master_config["grpo"]["num_generations_per_prompt"] - ) + expected_batch_size = len(trajectories) * master_config["grpo"][ + "num_generations_per_prompt" + ] if repeated_batch.size != expected_batch_size: print( f"❌ Unexpected training batch size: got {repeated_batch.size}, expected {expected_batch_size}. Skipping step and waiting for correct buffer content." @@ -2723,7 +2824,7 @@ def async_grpo_train( dp_size = policy.sharding_annotations.get_axis_size("data_parallel") if expected_batch_size % dp_size != 0: raise AssertionError( - f"Configuration error: (num_prompts_per_step * num_generations_per_prompt) = {expected_batch_size} must be divisible by data_parallel size {dp_size}." + f"Configuration error: actual async train batch size {expected_batch_size} must be divisible by data_parallel size {dp_size}." ) print(f"Got trajectory batch (size: {repeated_batch.size})") @@ -2857,6 +2958,7 @@ def async_grpo_train( train_data, loss_fn, timer=timer, + gbs=expected_batch_size, ) print("πŸ”„ Synchronizing policy weights to trajectory collector…") @@ -2886,6 +2988,11 @@ def async_grpo_train( weight_version += 1 trajectory_collector.set_weight_version.remote(weight_version) trajectory_collector.resume_after_refit.remote() + else: + # Advance the trajectory collector's weight version even when refit is skipped + # so that the replay buffer can sample trajectories targeted for subsequent steps. + weight_version += 1 + trajectory_collector.set_weight_version.remote(weight_version) # Clear logger metrics after each refit (weight sync), starting a new logging cycle if policy_generation is not None: diff --git a/nemo_rl/algorithms/utils.py b/nemo_rl/algorithms/utils.py index f46b49ace7..6ff1f18494 100644 --- a/nemo_rl/algorithms/utils.py +++ b/nemo_rl/algorithms/utils.py @@ -652,7 +652,7 @@ def visualize_per_worker_timeline( refit_time = ( timing_metrics["weight_sync"] if "weight_sync" in timing_metrics - else timing_metrics["prepare_for_generation/total"] + else timing_metrics.get("prepare_for_generation/total", 0.0) ) if "generation" in timing_metrics: # Sync GRPO generation_time = timing_metrics["generation"] diff --git a/nemo_rl/distributed/model_utils.py b/nemo_rl/distributed/model_utils.py index dd46ff7a27..ae8a37958a 100644 --- a/nemo_rl/distributed/model_utils.py +++ b/nemo_rl/distributed/model_utils.py @@ -2151,13 +2151,18 @@ def forward( # calculate the logprobs for the last token and then return the logprobs vocab_start_index = tp_rank * (self.vocab_size // tp_size) vocab_end_index = min((tp_rank + 1) * (self.vocab_size // tp_size), self.vocab_size) - output_weight_layer = self.output_layer.weight + # For models with tied embeddings (e.g. Qwen3), self.output_layer.weight is None β€” + # the real weight lives on the embedding and must be fetched via + # shared_embedding_or_output_weight(). + output_weight_layer = ( + self.shared_embedding_or_output_weight() + if self.share_embeddings_and_output_weights + else self.output_layer.weight + ) logprobs = from_parallel_hidden_states_to_logprobs( hidden_states, # .transpose(0, 1).contiguous(), output_weight_layer, - self.shared_embedding_or_output_weight() - if self.share_embeddings_and_output_weights - else self.output_layer.weight, + output_weight_layer, runtime_gather_output, labels, vocab_start_index=vocab_start_index, diff --git a/nemo_rl/distributed/ray_actor_environment_registry.py b/nemo_rl/distributed/ray_actor_environment_registry.py index 95677873a4..199e7cdc5c 100644 --- a/nemo_rl/distributed/ray_actor_environment_registry.py +++ b/nemo_rl/distributed/ray_actor_environment_registry.py @@ -31,6 +31,7 @@ "nemo_rl.models.generation.vllm.vllm_worker.VllmGenerationWorker": VLLM_EXECUTABLE, "nemo_rl.models.generation.vllm.vllm_worker_async.VllmAsyncGenerationWorker": VLLM_EXECUTABLE, "nemo_rl.models.generation.sglang.sglang_worker.SGLangGenerationWorker": SGLANG_EXECUTABLE, + "nemo_rl.models.generation.dynamo.dynamo_worker.DynamoVllmWorker": PY_EXECUTABLES.SYSTEM, "nemo_rl.models.policy.workers.dtensor_policy_worker.DTensorPolicyWorker": PY_EXECUTABLES.FSDP, "nemo_rl.models.policy.workers.dtensor_policy_worker_v2.DTensorPolicyWorkerV2": PY_EXECUTABLES.AUTOMODEL, "nemo_rl.models.policy.workers.megatron_policy_worker.MegatronPolicyWorker": MCORE_EXECUTABLE, diff --git a/nemo_rl/environments/nemo_gym.py b/nemo_rl/environments/nemo_gym.py index b32f76919f..dff79cf42c 100644 --- a/nemo_rl/environments/nemo_gym.py +++ b/nemo_rl/environments/nemo_gym.py @@ -11,6 +11,9 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import asyncio +import json +import time from pathlib import Path from typing import Any, Dict, List, TypedDict @@ -29,6 +32,154 @@ class NemoGymConfig(TypedDict): initial_global_config_dict: Dict[str, Any] +def _truncate_error_value(value: Any, max_len: int = 256) -> str: + text = str(value) + if len(text) <= max_len: + return text + return text[: max_len - 3] + "..." + + +def _summarize_nemo_gym_output_item(output_item: Any) -> dict[str, Any]: + if not isinstance(output_item, dict): + return { + "python_type": type(output_item).__name__, + "repr": _truncate_error_value(output_item), + } + + summary: dict[str, Any] = { + "keys": sorted(output_item.keys()), + } + + for key in ( + "type", + "role", + "status", + "finish_reason", + "stop_reason", + "id", + ): + if key in output_item: + summary[key] = output_item[key] + + for key, summary_key in ( + ("prompt_token_ids", "prompt_token_count"), + ("generation_token_ids", "generation_token_count"), + ("generation_log_probs", "generation_logprob_count"), + ): + value = output_item.get(key) + if isinstance(value, list): + summary[summary_key] = len(value) + + content = output_item.get("content") + if isinstance(content, list): + summary["content_len"] = len(content) + summary["content_types"] = [ + item.get("type", type(item).__name__) if isinstance(item, dict) else type(item).__name__ + for item in content[:8] + ] + elif content is not None: + summary["content_type"] = type(content).__name__ + summary["content_preview"] = _truncate_error_value(content) + + if "refusal" in output_item and output_item["refusal"] is not None: + summary["refusal"] = _truncate_error_value(output_item["refusal"]) + + return summary + + +def _summarize_nemo_gym_empty_generation_result(nemo_gym_result: dict[str, Any]) -> dict[str, Any]: + response = nemo_gym_result.get("response") + if not isinstance(response, dict): + return { + "response_python_type": type(response).__name__, + "response_repr": _truncate_error_value(response), + } + + output_items = response.get("output") + if isinstance(output_items, list): + output_summary = [ + _summarize_nemo_gym_output_item(item) for item in output_items[:8] + ] + output_count = len(output_items) + else: + output_summary = [ + { + "python_type": type(output_items).__name__, + "repr": _truncate_error_value(output_items), + } + ] + output_count = None + + summary = { + "response_keys": sorted(response.keys()), + "response_status": response.get("status"), + "response_finish_reason": response.get("finish_reason"), + "response_incomplete_details": response.get("incomplete_details"), + "response_error": response.get("error"), + "usage": response.get("usage"), + "output_count": output_count, + "output_summary": output_summary, + } + + if "id" in response: + summary["response_id"] = response["id"] + if "model" in response: + summary["response_model"] = response["model"] + + return summary + + +def _summarize_nemo_gym_row(row: Any) -> dict[str, Any]: + if not isinstance(row, dict): + return {"python_type": type(row).__name__, "repr": _truncate_error_value(row)} + + summary: dict[str, Any] = { + "rowidx": row.get("_rowidx"), + "keys": sorted(row.keys()), + } + for key in ("instance_id", "task_id", "repo", "problem_id"): + if key in row: + summary[key] = _truncate_error_value(row[key]) + + agent_ref = row.get("agent_ref") + if isinstance(agent_ref, dict): + summary["agent"] = agent_ref.get("name") + + responses_create_params = row.get("responses_create_params") + if isinstance(responses_create_params, dict): + summary["model"] = responses_create_params.get("model") + input_messages = responses_create_params.get("input") + if isinstance(input_messages, list): + summary["input_messages"] = len(input_messages) + if "max_output_tokens" in responses_create_params: + summary["max_output_tokens"] = responses_create_params["max_output_tokens"] + + return summary + + +async def _await_nemo_gym_task_with_debug( + task: Any, + *, + trial: int, + max_attempts: int, + attempt_start: float, + completed_count: int, + total_count: int, +) -> Any: + """Await the next completed Gym rollout while emitting periodic stall context.""" + pending_task = asyncio.ensure_future(task) + while True: + try: + return await asyncio.wait_for(asyncio.shield(pending_task), timeout=30) + except asyncio.TimeoutError: + print( + "[NEMO_GYM_DEBUG] run_rollouts_waiting_for_result " + f"trial={trial}/{max_attempts} " + f"elapsed_s={time.perf_counter() - attempt_start:.1f} " + f"completed={completed_count}/{total_count}" + ) + + @ray.remote(max_restarts=-1, max_task_retries=-1) # pragma: no cover class NemoGym(EnvironmentInterface): """This environment class isn't really used for training. It's really meant as an integration wrapper around NeMo-Gym that hooks into the existing NeMo RL resource management via ray. So there is still one source of truth for resource management in NeMo RL.""" @@ -56,6 +207,10 @@ def __init__(self, cfg: NemoGymConfig): "dummy_key" # No key necessary for training. ) initial_global_config_dict["policy_base_url"] = self.cfg["base_urls"] + # In multinode runs, Gym-managed service configs must advertise a real node IP + # rather than falling back to localhost, or remote workers will connect to + # their own loopback interface instead of the actor-hosted service. + initial_global_config_dict.setdefault("default_host", self.node_ip) initial_global_config_dict.setdefault( "global_aiohttp_connector_limit_per_host", 16_384 @@ -122,6 +277,13 @@ async def run_rollouts( max_attempts, trial = self.rollout_max_attempts_to_avoid_lp_nan, 0 while trial < max_attempts: nemo_gym_num_rows = len(nemo_gym_examples) + attempt_start = time.perf_counter() + print( + "[NEMO_GYM_DEBUG] run_rollouts_start " + f"trial={trial + 1}/{max_attempts} rows={nemo_gym_num_rows} " + "row_summaries=" + f"{json.dumps([_summarize_nemo_gym_row(row) for row in nemo_gym_examples[:16]], default=str)}" + ) nemo_gym_result_iterator = self.rch.run_examples( examples=nemo_gym_examples, head_server_config=self.head_server_config ) @@ -130,12 +292,52 @@ async def run_rollouts( nemo_rl_results = [] for task in nemo_gym_result_iterator: with timer.time(label=f"{timer_prefix}/await_results"): - nemo_gym_row, nemo_gym_result = await task + nemo_gym_row, nemo_gym_result = await _await_nemo_gym_task_with_debug( + task, + trial=trial + 1, + max_attempts=max_attempts, + attempt_start=attempt_start, + completed_count=len(nemo_rl_results), + total_count=nemo_gym_num_rows, + ) with timer.time(label=f"{timer_prefix}/postprocess_results"): - nemo_rl_result = self._postprocess_nemo_gym_to_nemo_rl_result( - nemo_gym_result, tokenizer + row_summary = _summarize_nemo_gym_row(nemo_gym_row) + response = ( + nemo_gym_result.get("response") + if isinstance(nemo_gym_result, dict) + else None ) + output = response.get("output") if isinstance(response, dict) else None + output_count = len(output) if isinstance(output, list) else None + print( + "[NEMO_GYM_DEBUG] run_rollouts_result_received " + f"trial={trial + 1}/{max_attempts} " + f"elapsed_s={time.perf_counter() - attempt_start:.1f} " + f"row={json.dumps(row_summary, default=str)} " + f"response_status={response.get('status') if isinstance(response, dict) else None} " + f"output_count={output_count}" + ) + try: + nemo_rl_result = self._postprocess_nemo_gym_to_nemo_rl_result( + nemo_gym_result, tokenizer + ) + except Exception: + result_summary = ( + _summarize_nemo_gym_empty_generation_result(nemo_gym_result) + if isinstance(nemo_gym_result, dict) + else { + "python_type": type(nemo_gym_result).__name__, + "repr": _truncate_error_value(nemo_gym_result), + } + ) + print( + "[NEMO_GYM_DEBUG] run_rollouts_postprocess_failed " + f"trial={trial + 1}/{max_attempts} " + f"row={json.dumps(row_summary, default=str)} " + f"result_summary={json.dumps(result_summary, default=str)}" + ) + raise nemo_rl_rowidxs.append(nemo_gym_row["_rowidx"]) nemo_rl_results.append(nemo_rl_result) @@ -158,6 +360,11 @@ async def run_rollouts( ) continue else: + print( + "[NEMO_GYM_DEBUG] run_rollouts_attempt_complete " + f"trial={trial + 1}/{max_attempts} rows={nemo_gym_num_rows} " + f"elapsed_s={time.perf_counter() - attempt_start:.1f}" + ) break nemo_rl_sort_results = [None] * nemo_gym_num_rows @@ -192,20 +399,13 @@ def _postprocess_nemo_gym_to_nemo_rl_result( if "generation_token_ids" not in output_item_dict: continue - assert ( - seen_token_ids - == output_item_dict["prompt_token_ids"][: len(seen_token_ids)] - ), f"""Non-contiguous messages found! This may be a tokenization issue where certain tokens are combined when messages are concatenated, or it may be due to part of the chat history being truncated (like if super long history is truncated or if reasoning is stripped out). -Seen token IDs: {seen_token_ids} -Output prompt token IDs: {output_item_dict["prompt_token_ids"]} -""" - nemo_rl_message_log.append( { "role": "user", "content": "", "token_ids": torch.tensor( - output_item_dict["prompt_token_ids"][len(seen_token_ids) :] + output_item_dict["prompt_token_ids"][len(seen_token_ids) :], + dtype=torch.long, ), } ) @@ -213,9 +413,11 @@ def _postprocess_nemo_gym_to_nemo_rl_result( { "role": "assistant", "content": "", - "token_ids": torch.tensor(output_item_dict["generation_token_ids"]), + "token_ids": torch.tensor( + output_item_dict["generation_token_ids"], dtype=torch.long + ), "generation_logprobs": torch.tensor( - output_item_dict["generation_log_probs"] + output_item_dict["generation_log_probs"], dtype=torch.float32 ), } ) @@ -237,13 +439,14 @@ def _postprocess_nemo_gym_to_nemo_rl_result( prompt_token_ids = tokenizer.apply_chat_template( input_messages, tokenize=True ) + response_summary = _summarize_nemo_gym_empty_generation_result( + nemo_gym_result + ) raise ValueError( - f"NeMo Gym returned a result with no generation data. " - f"This typically means the prompt for the first turn already exceeds the vLLM max_model_len, " - f"so vLLM rejected the request before any tokens could be generated.\n" + "NeMo Gym returned a result with no generation data.\n" f" Prompt length: {len(prompt_token_ids)} tokens.\n" - f" β†’ Fix: increase `policy.max_total_sequence_length` and `policy.generation.vllm_cfg.max_model_len` " - f"to a value larger than {len(prompt_token_ids)}." + " Response summary:\n" + f" {json.dumps(response_summary, indent=2, sort_keys=True, default=str)}" ) return { diff --git a/nemo_rl/experience/rollouts.py b/nemo_rl/experience/rollouts.py index d5186e868a..162a52e263 100644 --- a/nemo_rl/experience/rollouts.py +++ b/nemo_rl/experience/rollouts.py @@ -1101,7 +1101,6 @@ def run_async_nemo_gym_rollout( assert max_rollout_turns is None, ( "`max_rollout_turns` is not supported in NeMo-Gym path!" ) - assert max_seq_len is None, "`max_seq_len` is not supported in NeMo-Gym path!" # We don't use these stop criteria assert not generation_config["stop_strings"], ( "Stop strings is not supported in the generation config in NeMo-Gym path!" @@ -1119,16 +1118,23 @@ def run_async_nemo_gym_rollout( timer.start(f"{timer_prefix}/total") for rowidx, row in enumerate(nemo_gym_rows): - # We may need better handling here. The max tokens set here would be the max new generated tokens, not the total max tokens. - # Currently, we just rely on the underlying vLLM engine to do the truncation for us using the max model seq len set in the config. - # row["max_tokens"] = max_seq_len + # We accept max_seq_len for API parity with the other rollout paths, but NeMo-Gym + # still relies on the underlying model server's configured context/window limits. + # We do not translate max_seq_len into row-level max_tokens here because that would + # change semantics from "total sequence length" to "max new tokens". responses_create_params = row["responses_create_params"] responses_create_params["temperature"] = generation_config["temperature"] responses_create_params["top_p"] = generation_config["top_p"] - - # Max new tokens, just like max_seq_len above is ignored and we rely on the underlying vLLM engine for truncation. - # generation_config["max_new_tokens"] + if generation_config["max_new_tokens"] is not None: + existing_max_output_tokens = responses_create_params.get( + "max_output_tokens" + ) + responses_create_params["max_output_tokens"] = ( + min(existing_max_output_tokens, generation_config["max_new_tokens"]) + if existing_max_output_tokens is not None + else generation_config["max_new_tokens"] + ) row["_rowidx"] = rowidx diff --git a/nemo_rl/models/generation/__init__.py b/nemo_rl/models/generation/__init__.py index 8465112c6c..20583d5227 100644 --- a/nemo_rl/models/generation/__init__.py +++ b/nemo_rl/models/generation/__init__.py @@ -16,6 +16,7 @@ from transformers import PreTrainedTokenizerBase +from nemo_rl.models.generation.dynamo import DynamoVllmConfig from nemo_rl.models.generation.interfaces import GenerationConfig from nemo_rl.models.generation.vllm import VllmConfig @@ -43,8 +44,10 @@ def configure_generation_config( # vllm setting if config["backend"] == "vllm": config = cast(VllmConfig, config) - # set load_format - config["vllm_cfg"]["load_format"] = "auto" if is_eval else "dummy" + # set load_format (respect user override if they set it explicitly, + # e.g. to force "auto" for benchmarking without a Megatron refit). + if config["vllm_cfg"].get("load_format") is None: + config["vllm_cfg"]["load_format"] = "auto" if is_eval else "dummy" speculative_config = config.get("vllm_kwargs", {}).get("speculative_config") if speculative_config: # Speculative decoding needs real startup weights unless the draft @@ -69,4 +72,10 @@ def configure_generation_config( else: config["vllm_cfg"]["skip_tokenizer_init"] = True + # dynamo setting β€” always loads real weights (no refit support) + elif config["backend"] == "dynamo": + config = cast(DynamoVllmConfig, config) + if not config.get("dynamo_cfg", {}).get("external_url"): + config["vllm_cfg"]["load_format"] = "auto" + return config diff --git a/nemo_rl/models/generation/dynamo/__init__.py b/nemo_rl/models/generation/dynamo/__init__.py new file mode 100644 index 0000000000..f6e7922afb --- /dev/null +++ b/nemo_rl/models/generation/dynamo/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from nemo_rl.models.generation.dynamo.config import DynamoVllmConfig +from nemo_rl.models.generation.dynamo.dynamo_generation import DynamoVllmGeneration diff --git a/nemo_rl/models/generation/dynamo/config.py b/nemo_rl/models/generation/dynamo/config.py new file mode 100644 index 0000000000..1f19e8d42a --- /dev/null +++ b/nemo_rl/models/generation/dynamo/config.py @@ -0,0 +1,60 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Literal, NotRequired, TypedDict + +from nemo_rl.models.generation.interfaces import GenerationConfig + + +class DynamoSpecificArgs(TypedDict): + """vLLM engine arguments passed through to dynamo.vllm.""" + + tensor_parallel_size: int + pipeline_parallel_size: int + expert_parallel_size: int + gpu_memory_utilization: float + max_model_len: int + kv_cache_dtype: Literal["auto", "fp8", "fp8_e4m3"] + precision: NotRequired[str] # maps to vLLM --dtype + load_format: NotRequired[str] + enforce_eager: NotRequired[bool] + hf_overrides: NotRequired[dict[str, Any]] + extra_vllm_args: NotRequired[dict[str, Any]] + + +class DynamoCfg(TypedDict, total=False): + """Dynamo infrastructure configuration.""" + + external_url: str # URL of an external Dynamo deployment (skips all local infra) + frontend_http_port: int # 0 = auto-assign + router_mode: str # "round-robin", "kv", "random", "least-loaded" + etcd_port: int # 0 = auto-assign + etcd_peer_port: int # 0 = auto-assign + namespace: str + enable_planner: bool # Launch planner + VirtualConnectorClient for autoscaling + initial_dp_size: int # Workers at startup (must be <= cluster.world_size() // tp_size) + tool_call_parser: str # Dynamo parser name, or "none" to disable + reasoning_parser: str # Dynamo parser name, or "none" to disable + + +class DynamoVllmConfig(GenerationConfig): + """GenerationConfig extended with Dynamo-specific settings. + + Uses key name "vllm_cfg" so that cfg["vllm_cfg"]["max_model_len"] + works the same as VllmConfig for NeMo-Gym compatibility. + """ + + vllm_cfg: DynamoSpecificArgs + dynamo_cfg: NotRequired[DynamoCfg] + vllm_kwargs: NotRequired[dict[str, Any]] diff --git a/nemo_rl/models/generation/dynamo/dynamo_generation.py b/nemo_rl/models/generation/dynamo/dynamo_generation.py new file mode 100644 index 0000000000..ed6427af61 --- /dev/null +++ b/nemo_rl/models/generation/dynamo/dynamo_generation.py @@ -0,0 +1,591 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Dynamo vLLM generation orchestrator for NeMo RL. + +Manages the lifecycle of: +- An etcd subprocess for service discovery +- N ``python -m dynamo.vllm`` worker subprocesses (one per DP shard) +- A single ``python -m dynamo.frontend`` subprocess for HTTP routing +- (optional) A planner subprocess + VirtualConnectorClient for autoscaling + +All generation requests go through the frontend. Weight updates are +not supported β€” calling any weight-update method will ``assert False``. +""" + +import asyncio +import atexit +import json +import os +import shutil +import signal +import socket +import subprocess +import tempfile +import threading +import time +from typing import Any, Optional, Union + +import ray +import requests + +from nemo_rl.distributed.batched_data_dict import BatchedDataDict +from nemo_rl.distributed.virtual_cluster import ( + PY_EXECUTABLES, + RayVirtualCluster, + _get_free_port_local, + _get_node_ip_local, +) +from nemo_rl.utils.venvs import create_local_venv_on_each_node +from nemo_rl.models.generation.dynamo.config import DynamoVllmConfig +from nemo_rl.models.generation.dynamo.worker_pool import DynamoWorkerPool +from nemo_rl.models.generation.interfaces import ( + GenerationDatumSpec, + GenerationInterface, + GenerationOutputSpec, +) + +# (attr_name, display_name, stop_timeout_seconds) +_SUBPROCESS_REGISTRY: list[tuple[str, str, int]] = [ + ("_planner_process", "planner", 15), + ("_frontend_process", "frontend", 15), + ("_nats_process", "NATS", 10), + ("_etcd_process", "etcd", 10), +] + + +class DynamoVllmGeneration(GenerationInterface): + """Dynamo-backed generation for the NeMo-Gym HTTP path. + + This class is an infrastructure manager β€” it starts the dynamo stack + and exposes a single frontend URL. The ``generate()`` method is + intentionally unsupported; NeMo-Gym sends HTTP requests to the + frontend directly. + """ + + def __init__( + self, + cluster: Optional[RayVirtualCluster], + config: DynamoVllmConfig, + name_prefix: str = "dynamo_vllm", + workers_per_node: Optional[Union[int, list[int]]] = None, + ): + self.cfg = config + dynamo_cfg = config.get("dynamo_cfg", {}) + + # External mode: point at an already-running Dynamo deployment. + external_url = dynamo_cfg.get("external_url") + if external_url: + self._external = True + self._etcd_process = None + self._nats_process = None + self._frontend_process = None + self._planner_process = None + self._pool = None + self.dp_openai_server_base_urls: list[Optional[str]] = [external_url] + print( + f" [Dynamo] External mode β€” using {external_url}", + flush=True, + ) + return + + self._external = False + vllm_cfg = config["vllm_cfg"] + + self.tp_size = vllm_cfg["tensor_parallel_size"] + assert cluster.world_size() % self.tp_size == 0, ( + f"World size {cluster.world_size()} must be divisible by " + f"tensor_parallel_size {self.tp_size}" + ) + + # Dynamo namespace β€” unique per run to avoid collisions on shared etcd. + self._namespace = dynamo_cfg.get("namespace", "nemo_rl") + self._router_mode = dynamo_cfg.get("router_mode", "round-robin") + + # Reserve ports. + self._host = _get_node_ip_local() + etcd_port = dynamo_cfg.get("etcd_port", 0) + self._etcd_port = etcd_port if etcd_port else _get_free_port_local() + frontend_port = dynamo_cfg.get("frontend_http_port", 0) + self._frontend_port = frontend_port if frontend_port else _get_free_port_local() + + # Resolve the vllm venv python for subprocesses. + vllm_exec = PY_EXECUTABLES.VLLM + if vllm_exec.startswith("uv"): + self._vllm_python = create_local_venv_on_each_node( + py_executable=vllm_exec, + venv_name="dynamo_vllm_subprocess", + ) + else: + self._vllm_python = vllm_exec + + # etcd peer port (cluster-internal, not used by clients but must not collide). + etcd_peer_port = dynamo_cfg.get("etcd_peer_port", 0) + self._etcd_peer_port = etcd_peer_port if etcd_peer_port else _get_free_port_local() + + # NATS port for the event plane. + self._nats_port = _get_free_port_local() + + # Subprocess handles (see _SUBPROCESS_REGISTRY for ordering/timeouts). + self._etcd_process: Optional[subprocess.Popen] = None + self._etcd_data_dir: Optional[str] = None + self._nats_process: Optional[subprocess.Popen] = None + self._frontend_process: Optional[subprocess.Popen] = None + self._planner_process: Optional[subprocess.Popen] = None + self._planner_config_file: Optional[str] = None + + # VirtualConnectorClient state. + self._vc_stop = threading.Event() + self._vc_thread: Optional[threading.Thread] = None + + # Total inference GPUs β€” used by the planner's max_gpu_budget. + self._inference_gpu_count = cluster.world_size() + + self._start_etcd() + self._start_nats() + + if dynamo_cfg.get("enable_planner", False): + self._start_planner() + self._start_virtual_connector_client() + + # ------------------------------------------------------------------ + # Worker pool + # ------------------------------------------------------------------ + needs_cross_node_parallelism = self.tp_size > cluster.num_gpus_per_node + strategy = None if config["colocated"]["enabled"] else "PACK" + cluster._init_placement_groups( + strategy=strategy, + use_unified_pg=needs_cross_node_parallelism, + ) + + base_env_vars = { + **self._dynamo_env_vars(), + "ALLOW_NONE_AUTHENTICATION": "yes", + "DYNAMO_VLLM_PYTHON": self._vllm_python, + } + self._pool = DynamoWorkerPool( + cluster=cluster, + config=config, + tp_size=self.tp_size, + base_env_vars=base_env_vars, + name_prefix=name_prefix, + ) + + max_dp_size = self._pool.max_size + initial_dp_size = dynamo_cfg.get("initial_dp_size", max_dp_size) + assert initial_dp_size <= max_dp_size, ( + f"initial_dp_size ({initial_dp_size}) exceeds cluster capacity " + f"({max_dp_size} = {cluster.world_size()} GPUs / TP {self.tp_size})" + ) + self._pool.add_workers(initial_dp_size) + + # Start frontend (blocks until initial workers register). + self._start_frontend() + self._healthcheck_frontend(expected_workers=initial_dp_size) + + self.dp_openai_server_base_urls: list[Optional[str]] = [ + f"http://{self._host}:{self._frontend_port}/v1" + ] + print( + f" [Dynamo] Ready at {self.dp_openai_server_base_urls[0]}", + flush=True, + ) + + # Ensure subprocesses are cleaned up on normal exit. + atexit.register(self.shutdown) + + # ------------------------------------------------------------------ + # Utilities + # ------------------------------------------------------------------ + + def _dynamo_env_vars(self) -> dict[str, str]: + """Env vars required by all dynamo components.""" + return { + "ETCD_ENDPOINTS": f"http://{self._host}:{self._etcd_port}", + "NATS_SERVER": f"nats://{self._host}:{self._nats_port}", + "DYN_NAMESPACE": self._namespace, + "DYN_DISCOVERY_BACKEND": "etcd", + "DYN_SDK_DISABLE_ANSI_LOGGING": "1", + "DYN_LOG": "dynamo_llm::http::service::metrics=warn,dynamo_runtime::pipeline::network::ingress::push_handler=warn,dynamo_llm::http::service::service_v2=warn,info" + } + + @staticmethod + def _stop_subprocess(proc: subprocess.Popen, name: str, timeout: int) -> None: + proc.send_signal(signal.SIGTERM) + try: + proc.wait(timeout=timeout) + except subprocess.TimeoutExpired: + proc.kill() + print(f" [Dynamo] {name} stopped", flush=True) + + @staticmethod + def _wait_for_port(port: int, timeout: float = 30, label: str = "service") -> None: + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + try: + with socket.create_connection(("localhost", port), timeout=1): + return + except OSError: + time.sleep(0.5) + raise RuntimeError(f"{label} did not start within {timeout}s on port {port}") + + def _wait_for_etcd(self, timeout: float = 30) -> None: + """Wait until etcd is fully ready to serve requests (not just TCP-open).""" + url = f"http://localhost:{self._etcd_port}/health" + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + if self._etcd_process is not None: + retcode = self._etcd_process.poll() + if retcode is not None: + raise RuntimeError( + f"etcd exited with code {retcode} before becoming healthy. " + f"Check console output above for details." + ) + try: + resp = requests.get(url, timeout=2) + if resp.status_code == 200: + return + except requests.RequestException: + pass + time.sleep(0.5) + raise RuntimeError( + f"etcd did not become healthy within {timeout}s on port {self._etcd_port}" + ) + + # ------------------------------------------------------------------ + # Subprocess lifecycle + # ------------------------------------------------------------------ + + def _start_etcd(self) -> None: + if self._etcd_process is not None: + return + + self._etcd_data_dir = tempfile.mkdtemp(prefix="nemorl_etcd_") + + peer_url = f"http://{self._host}:{self._etcd_peer_port}" + env = os.environ.copy() + env["ALLOW_NONE_AUTHENTICATION"] = "yes" + self._etcd_process = subprocess.Popen( + [ + "etcd", + "--listen-client-urls", f"http://0.0.0.0:{self._etcd_port}", + "--advertise-client-urls", f"http://{self._host}:{self._etcd_port}", + "--listen-peer-urls", f"http://0.0.0.0:{self._etcd_peer_port}", + "--initial-advertise-peer-urls", peer_url, + "--initial-cluster", f"default={peer_url}", + "--data-dir", self._etcd_data_dir, + "--heartbeat-interval", "500", + "--election-timeout", "5000", + ], + env=env, + ) + self._wait_for_etcd(timeout=30) + print( + f" [Dynamo] etcd started on port {self._etcd_port} " + f"(pid={self._etcd_process.pid})", + flush=True, + ) + + def _start_nats(self) -> None: + if self._nats_process is not None: + return + + self._nats_process = subprocess.Popen( + ["nats-server", "-p", str(self._nats_port)], + ) + self._wait_for_port(self._nats_port, timeout=30, label="NATS") + print( + f" [Dynamo] NATS started on port {self._nats_port} " + f"(pid={self._nats_process.pid})", + flush=True, + ) + + def _start_planner(self) -> None: + if self._planner_process is not None: + return + + planner_config = { + "environment": "virtual", + "mode": "agg", + "backend": "vllm", + "namespace": self._namespace, + "model_name": self.cfg.get("model", "unknown"), + "enable_throughput_scaling": False, + "enable_load_scaling": True, + "pre_deployment_sweeping_mode": "none", + "decode_engine_num_gpu": self.tp_size, + "ttft": 500.0, + "itl": 1.0, + "max_gpu_budget": self._inference_gpu_count, + "min_endpoint": 1, + "load_adjustment_interval": 5, + "load_scaling_down_sensitivity": 80, + } + + with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False) as tf: + json.dump(planner_config, tf) + self._planner_config_file = tf.name + + env = os.environ.copy() + env.update(self._dynamo_env_vars()) + self._planner_process = subprocess.Popen( + [self._vllm_python, "-m", "dynamo.planner", "--config", self._planner_config_file], + env=env, + ) + print( + f" [Dynamo] Planner starting (pid={self._planner_process.pid})", + flush=True, + ) + + def _start_frontend(self) -> None: + if self._frontend_process is not None: + return + + env = os.environ.copy() + env.update(self._dynamo_env_vars()) + env["ALLOW_NONE_AUTHENTICATION"] = "yes" + self._frontend_process = subprocess.Popen( + [ + self._vllm_python, "-m", "dynamo.frontend", + "--http-port", str(self._frontend_port), + "--http-host", "0.0.0.0", + "--router-mode", self._router_mode, + "--discovery-backend", "etcd", + "--namespace-prefix", self._namespace, + "--router-mode", "kv", + "--active-decode-blocks-threshold", "1000.0", + "--active-prefill-tokens-threshold", "1000000000000", + "--active-prefill-tokens-threshold-frac", "1000.0", + "--router-predict-on-route" + ], + env=env, + ) + print( + f" [Dynamo] Frontend starting on http://{self._host}:{self._frontend_port} " + f"(pid={self._frontend_process.pid}, router={self._router_mode})", + flush=True, + ) + + def _healthcheck_frontend( + self, timeout: float = 300, expected_workers: Optional[int] = None + ) -> None: + """Poll ``/health`` until expected workers have registered.""" + if expected_workers is None: + expected_workers = self._pool.size + + url = f"http://localhost:{self._frontend_port}/health" + deadline = time.monotonic() + timeout + last_error = None + + while time.monotonic() < deadline: + if self._frontend_process is not None: + retcode = self._frontend_process.poll() + if retcode is not None: + raise RuntimeError( + f"Dynamo frontend exited with code {retcode}. " + f"Check console output above for details." + ) + try: + resp = requests.get(url, timeout=5) + if resp.status_code == 200: + data = resp.json() + num_instances = sum( + 1 for i in data.get("instances", []) + if i.get("endpoint") == "generate" + ) + if num_instances >= expected_workers: + print( + f" [Dynamo] Frontend healthy β€” " + f"{num_instances} worker(s) registered", + flush=True, + ) + return + except requests.RequestException as e: + last_error = e + time.sleep(2) + + raise RuntimeError( + f"Dynamo frontend did not become healthy within {timeout}s. " + f"Expected {expected_workers} workers, last error: {last_error}" + ) + + # ------------------------------------------------------------------ + # VirtualConnectorClient lifecycle + # ------------------------------------------------------------------ + + def _start_virtual_connector_client(self) -> None: + # DistributedRuntime (Rust) reads these from the process environment. + os.environ.update(self._dynamo_env_vars()) + + self._vc_thread = threading.Thread( + target=self._vc_listener_thread, + daemon=True, + name="dynamo-vc-listener", + ) + self._vc_thread.start() + + def _stop_virtual_connector_client(self) -> None: + self._vc_stop.set() + if self._vc_thread is not None and self._vc_thread.is_alive(): + self._vc_thread.join(timeout=10) + self._vc_thread = None + + def _vc_listener_thread(self) -> None: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + loop.run_until_complete(self._vc_listener_loop(loop)) + except Exception as e: + if not self._vc_stop.is_set(): + print(f" [Dynamo] VirtualConnectorClient listener error: {e}", flush=True) + finally: + loop.close() + + async def _vc_listener_loop(self, loop: asyncio.AbstractEventLoop) -> None: + from dynamo._core import DistributedRuntime, VirtualConnectorClient + + runtime = DistributedRuntime(loop, "etcd", "tcp", True) + client = VirtualConnectorClient(runtime, self._namespace) + print(" [Dynamo] VirtualConnectorClient listening for scaling events", flush=True) + + while not self._vc_stop.is_set(): + try: + await client.wait() + except Exception as e: + if not self._vc_stop.is_set(): + print(f" [Dynamo] VirtualConnectorClient error: {e}", flush=True) + break + + if self._planner_process is not None: + retcode = self._planner_process.poll() + if retcode is not None: + raise RuntimeError( + f"Dynamo planner exited with code {retcode}. " + f"Check console output above for details." + ) + + try: + event = await client.get() + if event.decision_id != -1: + self._handle_scaling_event(event) + await client.complete(event) + except Exception as e: + if not self._vc_stop.is_set(): + print(f" [Dynamo] VirtualConnectorClient error: {e}", flush=True) + break + + def _handle_scaling_event(self, event: Any) -> None: + """Scale workers to match the planner's desired replica count.""" + desired = event.num_decode_workers + current = self._pool.size + + print( + f" [Dynamo] Scaling event (decision_id={event.decision_id}): " + f"decode={desired} (current={current})", + flush=True, + ) + + self._pool.scale_to(desired) + + # ------------------------------------------------------------------ + # GenerationInterface + # ------------------------------------------------------------------ + + def _check_subprocesses(self) -> None: + """Raise if any managed subprocess has exited unexpectedly.""" + if self._external: + return + for attr, name, _ in _SUBPROCESS_REGISTRY: + proc: Optional[subprocess.Popen] = getattr(self, attr) + if proc is not None: + retcode = proc.poll() + if retcode is not None: + raise RuntimeError( + f"Dynamo {name} exited unexpectedly with code {retcode}." + ) + + def prepare_for_generation(self, *args: Any, **kwargs: Any) -> bool: + self._check_subprocesses() + return True + + def finish_generation(self, *args: Any, **kwargs: Any) -> bool: + return True + + def shutdown(self) -> bool: + if self._external: + return True + + self._stop_virtual_connector_client() + + for attr, name, timeout in _SUBPROCESS_REGISTRY: + proc: Optional[subprocess.Popen] = getattr(self, attr) + if proc is not None: + self._stop_subprocess(proc, name, timeout) + setattr(self, attr, None) + + if self._planner_config_file is not None: + os.unlink(self._planner_config_file) + self._planner_config_file = None + if self._etcd_data_dir is not None: + shutil.rmtree(self._etcd_data_dir, ignore_errors=True) + self._etcd_data_dir = None + + self._pool.shutdown() + return True + + # ------------------------------------------------------------------ + # Serialization support (for Ray actor pickling in async GRPO) + # ------------------------------------------------------------------ + + def __getstate__(self): + state = self.__dict__.copy() + for attr, _, _ in _SUBPROCESS_REGISTRY: + state[attr] = None + state["_vc_stop"] = None + state["_vc_thread"] = None + state["_pool"] = None + return state + + def __setstate__(self, state): + self.__dict__.update(state) + self._external = True + self._vc_stop = threading.Event() + + # ------------------------------------------------------------------ + # Unsupported weight-update methods + # ------------------------------------------------------------------ + + def generate( + self, data: BatchedDataDict["GenerationDatumSpec"], greedy: bool = False + ) -> BatchedDataDict["GenerationOutputSpec"]: + assert False, ( + "DynamoVllmGeneration does not support direct generate(). " + "Use the NeMo-Gym HTTP path instead." + ) + + def init_collective( + self, ip: str, port: int, world_size: int, **kwargs: Any + ) -> list[ray.ObjectRef]: + assert False, ( + "DynamoVllmGeneration does not support weight updates via collective." + ) + + def prepare_refit_info(self, state_dict_info: dict[str, Any]) -> None: + assert False, "DynamoVllmGeneration does not support weight refit." + + def update_weights_via_ipc_zmq(self) -> list[ray.ObjectRef]: + assert False, "DynamoVllmGeneration does not support weight updates." + + def update_weights_from_collective(self) -> list[ray.ObjectRef]: + assert False, "DynamoVllmGeneration does not support weight updates." diff --git a/nemo_rl/models/generation/dynamo/dynamo_worker.py b/nemo_rl/models/generation/dynamo/dynamo_worker.py new file mode 100644 index 0000000000..5f62deeeca --- /dev/null +++ b/nemo_rl/models/generation/dynamo/dynamo_worker.py @@ -0,0 +1,365 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Ray actor that launches a dynamo vllm worker as a subprocess. + +Each actor reserves GPU resources via Ray placement groups and launches +``python -m dynamo.vllm`` with the appropriate CUDA_VISIBLE_DEVICES. +The dynamo worker handles tensor parallelism internally, so one +subprocess per data-parallel shard is sufficient. +""" + +import copy +import json +import os +import signal +import subprocess +from typing import Any, Optional + +import ray + +from nemo_rl.distributed.virtual_cluster import _get_free_port_local +from nemo_rl.distributed.worker_group_utils import get_nsight_config_if_pattern_matches +from nemo_rl.models.generation.dynamo.config import DynamoVllmConfig + + +def _normalize_parser_name(value: Optional[str], default: Optional[str]) -> Optional[str]: + """Normalize parser names from config/env, treating empty/none as disabled.""" + if value is None: + return default + normalized = value.strip() + if not normalized or normalized.lower() == "none": + return None + return normalized + + +def _build_vllm_cli_args( + model_name: str, + vllm_cfg: dict[str, Any], + vllm_kwargs: dict[str, Any], + kv_events_config_json: str, + seed: int, +) -> list[str]: + """Build the full CLI arg list for ``python -m dynamo.vllm``. + + Mirrors the arg-building logic in VllmGenerationWorker.__init__ + (vllm_worker.py:426-549), translated to CLI flags. + """ + args: list[str] = ["--model", model_name] + + # Direct config β†’ CLI flag mappings. + # Maps config key β†’ CLI flag name (None means same as key with underscores β†’ dashes). + _DIRECT_FLAGS: dict[str, Optional[str]] = { + "tensor_parallel_size": None, + "pipeline_parallel_size": None, + "gpu_memory_utilization": None, + "max_model_len": None, + "kv_cache_dtype": None, + "load_format": None, + "precision": "dtype", # nemo-rl "precision" β†’ vLLM "--dtype" + } + + for cfg_key, flag_name in _DIRECT_FLAGS.items(): + value = vllm_cfg.get(cfg_key) + if value is None: + continue + cli_flag = f"--{(flag_name or cfg_key).replace('_', '-')}" + args.extend([cli_flag, str(value)]) + + # Boolean flags: True β†’ --flag, False β†’ --no-flag + _BOOL_FLAGS: dict[str, Optional[str]] = { + "enforce_eager": None, + } + + for cfg_key, flag_name in _BOOL_FLAGS.items(): + value = vllm_cfg.get(cfg_key) + if value is None: + continue + cli_flag = (flag_name or cfg_key).replace("_", "-") + args.append(f"--{cli_flag}" if value else f"--no-{cli_flag}") + + # Expert parallelism maps to --enable-expert-parallel (bool flag in vLLM) + if vllm_cfg.get("expert_parallel_size", 1) > 1: + args.append("--enable-expert-parallel") + + # Hardcoded flags matching the regular vllm worker + args.append("--trust-remote-code") + args.extend(["--seed", str(seed)]) + args.extend(["--kv-events-config", kv_events_config_json]) + + # hf_overrides β€” merge FP8 overrides with user-provided ones, serialize as JSON + hf_overrides = vllm_kwargs.pop("hf_overrides", {}) + cfg_hf_overrides = vllm_cfg.get("hf_overrides") or {} + if cfg_hf_overrides: + # cfg_hf_overrides go first, vllm_kwargs hf_overrides (from FP8) override + merged = {**cfg_hf_overrides, **hf_overrides} + hf_overrides = merged + if hf_overrides: + args.extend(["--hf-overrides", json.dumps(hf_overrides)]) + + # vllm_kwargs passthrough (compilation_config, speculative_config, etc.) + for key, value in vllm_kwargs.items(): + cli_flag = f"--{key.replace('_', '-')}" + if isinstance(value, bool): + args.append(cli_flag if value else f"--no-{key.replace('_', '-')}") + elif isinstance(value, dict): + args.extend([cli_flag, json.dumps(value)]) + else: + args.extend([cli_flag, str(value)]) + + # extra_vllm_args escape hatch (merged last β€” overrides everything) + for key, value in vllm_cfg.get("extra_vllm_args", {}).items(): + cli_flag = f"--{key.replace('_', '-')}" + if isinstance(value, bool): + args.append(cli_flag if value else f"--no-{key.replace('_', '-')}") + elif isinstance(value, dict): + args.extend([cli_flag, json.dumps(value)]) + else: + args.extend([cli_flag, str(value)]) + + return args + + +@ray.remote( + runtime_env={**get_nsight_config_if_pattern_matches("dynamo_vllm_worker")} +) # pragma: no cover +class DynamoVllmWorker: + """Ray actor wrapping a ``python -m dynamo.vllm`` subprocess. + + For TP groups the leader actor (bundle_indices provided) launches the + subprocess while non-leader actors are lightweight resource reservations. + """ + + def __repr__(self) -> str: + return "DynamoVllmWorker" + + @staticmethod + def configure_worker( + num_gpus: int | float, bundle_indices: Optional[tuple[int, list[int]]] = None + ) -> tuple[dict[str, Any], dict[str, str], dict[str, Any]]: + """Configure worker resources for Ray placement.""" + resources: dict[str, Any] = {"num_gpus": num_gpus} + init_kwargs: dict[str, Any] = {} + env_vars: dict[str, str] = {} + + local_bundle_indices = None + if bundle_indices is not None: + node_idx = bundle_indices[0] + local_bundle_indices = bundle_indices[1] + init_kwargs["bundle_indices"] = local_bundle_indices + + # Compute a unique seed from node_idx and bundle_indices + # (mirrors VllmGenerationWorker.configure_worker) + if len(local_bundle_indices) == 1: + seed = node_idx * 1024 + local_bundle_indices[0] + else: + bundle_id = local_bundle_indices[0] // len(local_bundle_indices) + seed = node_idx * 1024 + bundle_id + init_kwargs["seed"] = seed + + # For parallel groups (TP > 1), let Ray reserve resources without + # setting CUDA_VISIBLE_DEVICES β€” the subprocess manages GPU assignment. + is_part_of_parallel_workers = ( + local_bundle_indices is not None and len(local_bundle_indices) > 1 + ) or local_bundle_indices is None + + if is_part_of_parallel_workers: + resources["num_gpus"] = 0 + env_vars["RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES"] = "1" + + return resources, env_vars, init_kwargs + + def __init__( + self, + config: DynamoVllmConfig, + bundle_indices: Optional[list[int]] = None, + seed: int = 0, + ): + self.cfg = config + self.is_model_owner = bundle_indices is not None + self._process: Optional[subprocess.Popen] = None + + if not self.is_model_owner: + return + + vllm_cfg = config["vllm_cfg"] + model_name = config["model_name"] + tp_size = vllm_cfg["tensor_parallel_size"] + pp_size = vllm_cfg.get("pipeline_parallel_size", 1) + ep_size = vllm_cfg.get("expert_parallel_size", 1) + model_parallel_size = tp_size * pp_size + + # Build CUDA_VISIBLE_DEVICES from bundle indices. + ray_cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "") + if ray_cuda_devices: + available = ray_cuda_devices.split(",") + if model_parallel_size == 1: + # TP=1: Ray already restricted CUDA_VISIBLE_DEVICES to the + # single GPU assigned to this actor β€” use it as-is. + cuda_visible = ray_cuda_devices + else: + # TP/PP > 1: CUDA_VISIBLE_DEVICES has all node GPUs + # (RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1 was set). + # Select the subset for this worker's bundle. + selected = [ + available[i] for i in bundle_indices if i < len(available) + ] + cuda_visible = ",".join(selected) + else: + cuda_visible = ",".join(str(i) for i in bundle_indices) + + vllm_python = os.environ.get("DYNAMO_VLLM_PYTHON", "python") + + # --- Build vllm_kwargs (passthrough dict) --- + vllm_kwargs: dict[str, Any] = copy.deepcopy(config.get("vllm_kwargs", {})) + + # --- load_format override for specific models --- + from nemo_rl.models.huggingface.common import ModelFlag + + load_format = vllm_cfg.get("load_format", "auto") + if ModelFlag.VLLM_LOAD_FORMAT_AUTO.matches(model_name): + load_format = "auto" + vllm_cfg_copy = dict(vllm_cfg) + vllm_cfg_copy["load_format"] = load_format + + # --- FP8 support --- + if vllm_cfg.get("precision") == "fp8": + from nemo_rl.models.generation.vllm.quantization.fp8 import ( + compute_fp8_engine_kwargs, + ) + + fp8_kwargs = compute_fp8_engine_kwargs( + vllm_cfg, model_name, model_parallel_size + ) + # Merge FP8 quantization and kv_cache_dtype into vllm_kwargs + vllm_kwargs["quantization"] = fp8_kwargs["quantization"] + vllm_kwargs["kv_cache_dtype"] = fp8_kwargs["kv_cache_dtype"] + # FP8 hf_overrides will be merged in _build_vllm_cli_args + if "hf_overrides" in fp8_kwargs: + existing = vllm_kwargs.get("hf_overrides", {}) + existing.update(fp8_kwargs["hf_overrides"]) + vllm_kwargs["hf_overrides"] = existing + # Override precision to bfloat16 (vllm complains otherwise) + vllm_cfg_copy["precision"] = "bfloat16" + + # --- Model-specific fixups (mirrors vllm_worker.py:506-523) --- + from transformers import AutoConfig + + hf_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True) + if "GptOssForCausalLM" in getattr(hf_config, "architectures", []): + if "quantization_config" in hf_config: + assert load_format == "dummy", ( + "Loading quantized GPT-OSS models is currently only supported with load_format='dummy'." + ) + hf_ov = vllm_kwargs.get("hf_overrides", {}) + hf_ov["quantization_config"] = {} + vllm_kwargs["hf_overrides"] = hf_ov + + # --- Build CLI args --- + kv_event_port = _get_free_port_local() + kv_events_json = json.dumps({ + "publisher": "zmq", + "topic": "kv-events", + "endpoint": f"tcp://*:{kv_event_port}", + "enable_kv_cache_events": True, + }) + + cmd = [ + vllm_python, + "-m", + "dynamo.vllm", + *_build_vllm_cli_args( + model_name=model_name, + vllm_cfg=vllm_cfg_copy, + vllm_kwargs=vllm_kwargs, + kv_events_config_json=kv_events_json, + seed=seed, + ), + ] + + dynamo_cfg = config.get("dynamo_cfg", {}) + tool_call_parser = _normalize_parser_name( + os.environ.get("DYNAMO_TOOL_CALL_PARSER"), + _normalize_parser_name(dynamo_cfg.get("tool_call_parser"), "hermes"), + ) + reasoning_parser = _normalize_parser_name( + os.environ.get("DYNAMO_REASONING_PARSER"), + _normalize_parser_name(dynamo_cfg.get("reasoning_parser"), "qwen3"), + ) + if tool_call_parser is not None: + cmd.extend(["--dyn-tool-call-parser", tool_call_parser]) + if reasoning_parser is not None: + cmd.extend(["--dyn-reasoning-parser", reasoning_parser]) + + # --- Subprocess environment --- + env = os.environ.copy() + env["CUDA_VISIBLE_DEVICES"] = cuda_visible + env["DYN_FORWARDPASS_METRIC_PORT"] = str(_get_free_port_local()) + env["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" + env["VLLM_SKIP_P2P_CHECK"] = "1" + + # Parallelism env vars (mirrors vllm_worker.py:432-469) + if model_parallel_size > 1: + env.pop("CUDA_VISIBLE_DEVICES", None) + fraction_of_gpus = float(os.environ.get("VLLM_RAY_PER_WORKER_GPUS", "1")) + env["VLLM_RAY_PER_WORKER_GPUS"] = str( + fraction_of_gpus / model_parallel_size + ) + env["VLLM_RAY_BUNDLE_INDICES"] = ",".join(map(str, bundle_indices)) + + if ep_size > tp_size: + world_size = int(os.environ["VLLM_DP_SIZE"]) * model_parallel_size + rank = int(os.environ["RANK"]) % world_size + env["VLLM_DP_RANK"] = str(rank // model_parallel_size) + env["VLLM_DP_RANK_LOCAL"] = str((rank % 8) // model_parallel_size) + leader_rank = int(os.environ["RANK"]) // world_size * world_size + addr_list = eval(os.environ["AVAILABLE_ADDR_LIST"]) + port_list = eval(os.environ["AVAILABLE_PORT_LIST"]) + env["VLLM_DP_MASTER_IP"] = addr_list[leader_rank] + env["VLLM_DP_MASTER_PORT"] = str(port_list[leader_rank]) + + if vllm_cfg.get("use_deep_gemm", False): + env["VLLM_USE_DEEP_GEMM"] = "1" + env["VLLM_USE_DEEP_GEMM_E8M0"] = "0" + + self._process = subprocess.Popen(cmd, env=env) + print( + f" [DynamoVllmWorker] Launched dynamo.vllm (pid={self._process.pid}, " + f"CUDA_VISIBLE_DEVICES={cuda_visible}, " + f"TP={tp_size}, " + f"tool_call_parser={tool_call_parser or 'disabled'}, " + f"reasoning_parser={reasoning_parser or 'disabled'})", + flush=True, + ) + + def is_alive(self) -> bool: + if self._process is None: + return self.is_model_owner is False # non-leader is always "alive" + return self._process.poll() is None + + def shutdown(self) -> bool: + if self._process is None: + return True + self._process.send_signal(signal.SIGTERM) + try: + self._process.wait(timeout=15) + except subprocess.TimeoutExpired: + self._process.kill() + self._process.wait(timeout=5) + print( + f" [DynamoVllmWorker] Stopped dynamo.vllm (pid={self._process.pid})", + flush=True, + ) + self._process = None + return True diff --git a/nemo_rl/models/generation/dynamo/worker_pool.py b/nemo_rl/models/generation/dynamo/worker_pool.py new file mode 100644 index 0000000000..0ee7d0f847 --- /dev/null +++ b/nemo_rl/models/generation/dynamo/worker_pool.py @@ -0,0 +1,318 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Dynamically scalable pool of DynamoVllmWorker Ray actors. + +Workers are created individually via ``RayWorkerBuilder`` and auto-register +with etcd for service discovery. No ``RayWorkerGroup`` or collective +communication is used β€” each worker is independent. +""" + +import os +from copy import deepcopy +from typing import Any + +import ray +from tqdm import tqdm + +from nemo_rl.distributed.ray_actor_environment_registry import get_actor_python_env +from nemo_rl.distributed.virtual_cluster import RayVirtualCluster +from nemo_rl.distributed.worker_groups import RayWorkerBuilder +from nemo_rl.utils.venvs import create_local_venv_on_each_node + +# Bundle identifier: (placement_group_index, (local_bundle_indices...)) +Bundle = tuple[int, tuple[int, ...]] + +_WORKER_CLS = "nemo_rl.models.generation.dynamo.dynamo_worker.DynamoVllmWorker" + + +class DynamoWorkerPool: + """Manages a scalable set of DynamoVllmWorker Ray actors. + + Tracks GPU bundles (free vs. used) and provides ``add_workers`` / + ``remove_workers`` for both initial startup and planner-driven scaling. + """ + + def __init__( + self, + cluster: RayVirtualCluster, + config: dict[str, Any], + tp_size: int, + base_env_vars: dict[str, str], + name_prefix: str = "dynamo_vllm", + ): + self._cluster = cluster + self._tp_size = tp_size + self._name_prefix = name_prefix + + # Worker creation infrastructure. + self._worker_builder = RayWorkerBuilder(_WORKER_CLS, config) + self._num_gpus = ( + 1 / cluster.max_colocated_worker_groups if cluster.use_gpus else 0 + ) + self._py_executable = self._resolve_py_executable() + self._base_env_vars = self._build_base_env(base_env_vars) + + # Bundle tracking. + self._all_bundles = self._compute_all_bundles(cluster) + self._free_bundles: set[Bundle] = set(self._all_bundles) + self._used_bundles: set[Bundle] = set() + + # Worker tracking (all three lists are kept in parallel). + self._workers: list[ray.actor.ActorHandle] = [] + self._worker_initializers: list[ray.actor.ActorHandle] = [] + self._worker_bundles: list[Bundle] = [] + + # ------------------------------------------------------------------ + # Properties + # ------------------------------------------------------------------ + + @property + def max_size(self) -> int: + return len(self._all_bundles) + + @property + def size(self) -> int: + return len(self._workers) + + @property + def free_slots(self) -> int: + return len(self._free_bundles) + + # ------------------------------------------------------------------ + # Scaling + # ------------------------------------------------------------------ + + def add_workers(self, count: int) -> int: + """Add up to ``count`` workers. Returns the number actually added.""" + available = sorted(self._free_bundles) + to_add = min(count, len(available)) + if to_add == 0: + return 0 + + bundles = available[:to_add] + self._create_workers(bundles) + return to_add + + def remove_workers(self, count: int) -> int: + """Remove up to ``count`` workers from the tail. Returns the number actually removed.""" + to_remove = min(count, len(self._workers)) + if to_remove == 0: + return 0 + + for _ in range(to_remove): + worker = self._workers.pop() + self._worker_initializers.pop() + bundle = self._worker_bundles.pop() + + try: + ray.get(worker.shutdown.remote(), timeout=15) + except Exception: + ray.kill(worker) + + self._used_bundles.discard(bundle) + self._free_bundles.add(bundle) + + print( + f" [Dynamo] Removed {to_remove} worker(s), " + f"{len(self._workers)} remaining", + flush=True, + ) + return to_remove + + def scale_to(self, desired: int) -> None: + """Scale the pool to exactly ``desired`` workers.""" + current = self.size + if desired > current: + added = self.add_workers(desired - current) + if added < desired - current: + print( + f" [Dynamo] Warning: wanted {desired - current} more workers " + f"but only {added} bundles available", + flush=True, + ) + elif desired < current: + self.remove_workers(current - desired) + + def shutdown(self) -> None: + """Shutdown all workers.""" + for worker in self._workers: + try: + ray.get(worker.shutdown.remote(), timeout=15) + except Exception: + ray.kill(worker) + self._workers.clear() + self._worker_initializers.clear() + self._worker_bundles.clear() + self._used_bundles.clear() + self._free_bundles = set(self._all_bundles) + + # ------------------------------------------------------------------ + # Internal + # ------------------------------------------------------------------ + + def _create_workers(self, bundles: list[Bundle]) -> None: + """Create workers on the given bundles (batch with progress bar).""" + placement_groups = self._cluster.get_placement_groups() + worker_futures: list[tuple[ray.ObjectRef, ray.actor.ActorHandle]] = [] + + for bundle in bundles: + pg_idx, local_indices = bundle + pg = ( + placement_groups[0] + if len(placement_groups) == 1 + else placement_groups[pg_idx] + ) + first_bundle_idx = local_indices[0] + + runtime_env = { + "env_vars": deepcopy(self._base_env_vars), + "py_executable": self._py_executable, + } + runtime_env["env_vars"]["VIRTUAL_ENV"] = self._py_executable + runtime_env["env_vars"]["UV_PROJECT_ENVIRONMENT"] = self._py_executable + + name = f"{self._name_prefix}-{pg_idx}-{first_bundle_idx}" + + future, initializer = self._worker_builder.create_worker_async( + placement_group=pg, + placement_group_bundle_index=first_bundle_idx, + num_gpus=self._num_gpus, + bundle_indices=(pg_idx, list(local_indices)), + runtime_env=runtime_env, + name=name, + ) + worker_futures.append((future, initializer)) + + self._free_bundles.discard(bundle) + self._used_bundles.add(bundle) + + # Wait for all workers to initialize. + refs = [f for f, _ in worker_futures] + with tqdm( + total=len(refs), + desc=f"Initializing {self._name_prefix} workers", + unit="worker", + ) as pbar: + remaining = list(refs) + while remaining: + ready, remaining = ray.wait(remaining, num_returns=1) + pbar.update(len(ready)) + + workers = ray.get(refs) + for worker, (_, initializer) in zip(workers, worker_futures): + worker._RAY_INITIALIZER_ACTOR_REF_TO_AVOID_GC = initializer + self._workers.append(worker) + self._worker_initializers.append(initializer) + + self._worker_bundles.extend(bundles) + + # ------------------------------------------------------------------ + # Setup helpers + # ------------------------------------------------------------------ + + @staticmethod + def _resolve_py_executable() -> str: + actor_python_env = get_actor_python_env(_WORKER_CLS) + if actor_python_env.startswith("uv"): + return create_local_venv_on_each_node( + py_executable=actor_python_env, + venv_name=_WORKER_CLS, + ) + return actor_python_env + + @staticmethod + def _build_base_env(extra: dict[str, str]) -> dict[str, str]: + env = dict(os.environ) + env.update(extra) + for key in [ + "RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES", + "RAY_CLIENT_MODE", + "RAY_JOB_ID", + "RAY_LD_PRELOAD", + "RAY_RAYLET_PID", + "RAY_USAGE_STATS_ENABLED", + ]: + env.pop(key, None) + return env + + def _compute_all_bundles(self, cluster: RayVirtualCluster) -> list[Bundle]: + if self._tp_size > 1: + return [ + (pg_idx, tuple(indices)) + for pg_idx, indices in self._get_tied_bundle_indices(cluster) + ] + + placement_groups = cluster.get_placement_groups() + bundles: list[Bundle] = [] + for pg_idx, pg in enumerate(placement_groups): + for bundle_idx in range(pg.bundle_count): + bundles.append((pg_idx, (bundle_idx,))) + return bundles + + def _get_tied_bundle_indices( + self, cluster: RayVirtualCluster + ) -> list[tuple[int, list[int]]]: + from ray.util.placement_group import PlacementGroup + + placement_groups = cluster.get_placement_groups() + if not placement_groups: + raise ValueError("No placement groups available in the cluster") + + tp_size = self._tp_size + + if len(placement_groups) == 1: + pg = placement_groups[0] + + def get_node_bundles(pg: PlacementGroup) -> dict[str, list[int]]: + table = ray.util.placement_group_table(pg) + node_to_bundles: dict[str, list[int]] = {} + for idx, bundle in enumerate(table.get("bundles_to_node_id", [])): + node_to_bundles.setdefault(bundle, []).append(idx) + return node_to_bundles + + node_bundles = get_node_bundles(pg) + bundle_to_node = {} + for node, bundles in node_bundles.items(): + for b in bundles: + bundle_to_node[b] = node + + sorted_nodes = sorted(node_bundles) + node_idx = {nid: idx for idx, nid in enumerate(sorted_nodes)} + flat: list[int] = [] + for nid in sorted_nodes: + flat.extend(node_bundles[nid]) + + num_groups = pg.bundle_count // tp_size + groups: list[tuple[int, list[int]]] = [] + for i in range(num_groups): + slice_ = flat[i * tp_size : (i + 1) * tp_size] + first_node = bundle_to_node[slice_[0]] + groups.append((node_idx[first_node], slice_)) + return groups + else: + tied_groups: list[tuple[int, list[int]]] = [] + for pg_idx, pg in enumerate(placement_groups): + if pg.bundle_count == 0: + continue + num_groups_in_pg = pg.bundle_count // tp_size + for group_idx in range(num_groups_in_pg): + start_idx = group_idx * tp_size + bundle_indices = list(range(start_idx, start_idx + tp_size)) + tied_groups.append((pg_idx, bundle_indices)) + if not tied_groups: + raise ValueError( + "Unable to allocate any worker groups with the available resources." + ) + return tied_groups diff --git a/nemo_rl/models/generation/vllm/quantization/fp8.py b/nemo_rl/models/generation/vllm/quantization/fp8.py index b01e889139..3867611cf5 100644 --- a/nemo_rl/models/generation/vllm/quantization/fp8.py +++ b/nemo_rl/models/generation/vllm/quantization/fp8.py @@ -153,52 +153,27 @@ def apply_fp8_patches(self, fp8_config): fp8_patches_applied = True -def init_fp8(vllm_cfg, model_name, model_parallel_size): +def compute_fp8_engine_kwargs(vllm_cfg, model_name, model_parallel_size): + """Compute vLLM engine kwargs for FP8 without applying monkey patches. + + Used by DynamoVllmWorker which passes these as CLI args to a subprocess + rather than calling vllm.LLM() directly. + """ config = AutoConfig.from_pretrained(model_name) - global global_fp8_config - # Determine if we're using FP8 weights based on precision setting use_fp8_weights = vllm_cfg.get("precision") == "fp8" kv_cache_dtype = vllm_cfg["kv_cache_dtype"] - # Validate configuration: kv_cache_dtype if kv_cache_dtype not in ["auto", "fp8", "fp8_e4m3"]: raise ValueError( f"kv_cache_dtype must be one of ['auto', 'fp8', 'fp8_e4m3'], but got {kv_cache_dtype}" ) - # Validate configuration: kv_cache_dtype=fp8 requires precision=fp8 if kv_cache_dtype.startswith("fp8") and not use_fp8_weights: raise ValueError( f"kv_cache_dtype='{kv_cache_dtype}' requires precision='fp8'. " "FP8 KV cache can only be used together with FP8 model weights." ) - global_fp8_config = FP8Config( - use_weight_pow2_scale=vllm_cfg.get("pow2_weight_scaling_factors", False), - use_activation_pow2_scale=vllm_cfg.get( - "pow2_activation_scaling_factors", False - ), - num_first_layers_in_bf16=vllm_cfg.get("num_first_layers_in_bf16", 0), - num_last_layers_in_bf16=vllm_cfg.get("num_last_layers_in_bf16", 0), - model_parallel_size=model_parallel_size, - kv_cache_dtype=kv_cache_dtype, - use_fp8_weights=use_fp8_weights, - ) - - if vllm_cfg.get("use_deep_gemm", False): - os.environ["VLLM_USE_DEEP_GEMM"] = "1" - os.environ["VLLM_USE_DEEP_GEMM_E8M0"] = "0" - - if vllm_cfg["async_engine"]: - # for async engine, vllm spawns a process for each DP, so we patch - # vllm so that upon spawning the thread it applies our FP8 patches - EngineCoreProc.run_engine_core = my_run_engine_core - CoreEngineProcManager.__init__ = my_init - else: - # if not async, just directly monkey patch the ray executor - monkey_patch_vllm_ray_executor(global_fp8_config) - - # create fp8 kwargs for vllm's LLM(...) num_first_layers_in_bf16 = vllm_cfg.get("num_first_layers_in_bf16", 0) num_last_layers_in_bf16 = vllm_cfg.get("num_last_layers_in_bf16", 0) fp8_block_quant_kwargs = dict(FP8_BLOCK_QUANT_KWARGS) @@ -224,6 +199,7 @@ def init_fp8(vllm_cfg, model_name, model_parallel_size): bf16_params.append(_get_params_in_layers(param_names, layers)) fp8_block_quant_kwargs["ignored_layers"] = bf16_params + quantization_ignored_layer_kws = vllm_cfg.get("quantization_ignored_layer_kws", []) if len(quantization_ignored_layer_kws): with init_empty_weights(): @@ -243,14 +219,44 @@ def init_fp8(vllm_cfg, model_name, model_parallel_size): fp8_block_quant_kwargs["ignored_layers"].extend(ignored_layers) print("ignored_layers", fp8_block_quant_kwargs["ignored_layers"]) - # Return FP8 kwargs (precision=fp8 is required at this point) - vllm_kwargs = { + return { "quantization": "fp8", "kv_cache_dtype": kv_cache_dtype, "hf_overrides": {"quantization_config": fp8_block_quant_kwargs}, } - return vllm_kwargs + +def init_fp8(vllm_cfg, model_name, model_parallel_size): + global global_fp8_config + use_fp8_weights = vllm_cfg.get("precision") == "fp8" + kv_cache_dtype = vllm_cfg["kv_cache_dtype"] + + global_fp8_config = FP8Config( + use_weight_pow2_scale=vllm_cfg.get("pow2_weight_scaling_factors", False), + use_activation_pow2_scale=vllm_cfg.get( + "pow2_activation_scaling_factors", False + ), + num_first_layers_in_bf16=vllm_cfg.get("num_first_layers_in_bf16", 0), + num_last_layers_in_bf16=vllm_cfg.get("num_last_layers_in_bf16", 0), + model_parallel_size=model_parallel_size, + kv_cache_dtype=kv_cache_dtype, + use_fp8_weights=use_fp8_weights, + ) + + if vllm_cfg.get("use_deep_gemm", False): + os.environ["VLLM_USE_DEEP_GEMM"] = "1" + os.environ["VLLM_USE_DEEP_GEMM_E8M0"] = "0" + + if vllm_cfg["async_engine"]: + # for async engine, vllm spawns a process for each DP, so we patch + # vllm so that upon spawning the thread it applies our FP8 patches + EngineCoreProc.run_engine_core = my_run_engine_core + CoreEngineProcManager.__init__ = my_init + else: + # if not async, just directly monkey patch the ray executor + monkey_patch_vllm_ray_executor(global_fp8_config) + + return compute_fp8_engine_kwargs(vllm_cfg, model_name, model_parallel_size) def is_fp8_model(vllm_config): diff --git a/nemo_rl/models/generation/vllm/vllm_worker_async.py b/nemo_rl/models/generation/vllm/vllm_worker_async.py index 12f73572b4..58deb5fe51 100644 --- a/nemo_rl/models/generation/vllm/vllm_worker_async.py +++ b/nemo_rl/models/generation/vllm/vllm_worker_async.py @@ -107,18 +107,10 @@ def _replace_prefix_tokens( if model_prefix_token_ids[-1] == eos_token_id: model_cut_end -= 1 - # Assert here to prepare for the logic below - assert len(template_token_ids) > len( - template_prefix_token_ids - ), f"""Found possibly non-monotonically increasing trajectory! -Template prefix token IDs (everything before the final assistant message): {template_prefix_token_ids} - -Template token IDs (everything that was sent to the model endpoint): {template_token_ids} - -Template prefix repr (detokenized): {repr(tokenizer.decode(template_prefix_token_ids))} - -Template repr (detokenized): {repr(tokenizer.decode(template_token_ids))} -""" + # WAR: skip prefix replacement when template isn't strictly longer than its prefix + # (can happen with certain chat templates in multi-turn NeMo-Gym rollouts). + if len(template_token_ids) <= len(template_prefix_token_ids): + return template_token_ids # We take everything starting with the EOS token ID. template_cut_start = -1 @@ -316,6 +308,8 @@ def _setup_vllm_openai_api_server(self, app: FastAPI) -> FastAPI: from vllm.entrypoints.openai.models.protocol import BaseModelPath from vllm.entrypoints.openai.models.serving import OpenAIServingModels from vllm.entrypoints.serve.tokenize.protocol import ( + DetokenizeRequest, + DetokenizeResponse, TokenizeChatRequest, TokenizeCompletionRequest, TokenizeResponse, @@ -475,11 +469,18 @@ class NeMoRLChatCompletionRequest( class NeMoRLOpenAIServingChat(NeMoRLOpenAIServingMixin, OpenAIServingChat): pass + from vllm.entrypoints.logger import RequestLogger + serving_chat_default_kwargs = dict( response_role="assistant", - request_logger=None, + # Emit "Received request ...: params: SamplingParams(...)" so we can + # verify the engine-side sampling params (esp. top_k) in the logs. + request_logger=RequestLogger(max_log_len=None), chat_template=None, chat_template_content_format="auto", + # Populate usage.prompt_tokens_details.cached_tokens on responses + # so per-request prefix-cache hits show up in PERF logs. + enable_prompt_tokens_details=True, ) serving_chat_kwargs = serving_chat_default_kwargs | self.cfg["vllm_cfg"].get( "http_server_serving_chat_kwargs", dict() @@ -500,18 +501,33 @@ class NeMoRLOpenAIServingChat(NeMoRLOpenAIServingMixin, OpenAIServingChat): async def create_chat_completion( request: NeMoRLChatCompletionRequest, raw_request: Request ): - # This needs to match the behavior in nemo_rl/models/generation/vllm/vllm_worker.py::BaseVllmGenerationWorker::_build_sampling_params - # Right now we explicitly assert set this to -1. - assert request.top_k in (None, -1), ( - f"Top k sampling parameter must be unset, empty, or -1. Got `{request.top_k}`" - ) - request.top_k = -1 + import time + + # If policy.generation.top_k is explicitly set, enforce it here so the + # HTTP path matches the direct-generate path in + # nemo_rl/models/generation/vllm/vllm_worker.py::BaseVllmGenerationWorker::_build_sampling_params. + # When it's unset (None) we leave request.top_k alone; vLLM's + # to_sampling_params will then fall back to default_sampling_params + # (from the model's generation_config.json), matching Dynamo's + # behavior for the NeMo-Gym path where top_k cannot be plumbed + # through the config (see rollouts.py::run_async_nemo_gym_rollout). + yaml_top_k = generation_config.get("top_k") + if yaml_top_k is not None: + assert request.top_k in (None, yaml_top_k), ( + f"Top k sampling parameter must be unset or {yaml_top_k}. Got `{request.top_k}`" + ) + request.top_k = yaml_top_k # The request sampling params need to exactly match those as are set in NeMo RL. # If they do not match, the inference will be off policy and destroy training stability. assert request.temperature == generation_config["temperature"] assert request.top_p == generation_config["top_p"] + # Server-side timing: wall-time (FastAPI-side) bracket around the + # engine call. Paired with vLLM's RequestLogger timestamps (which + # bracket engine submission -> first output), lets us split + # FastAPI overhead from engine work. + handler_enter_ts = time.monotonic() generator = await openai_serving_chat.create_chat_completion( request, raw_request ) @@ -522,6 +538,15 @@ async def create_chat_completion( ) elif isinstance(generator, ChatCompletionResponse): + handler_exit_ts = time.monotonic() + print( + f"[PERF_SERVER rid={generator.id} " + f"duration_ms={(handler_exit_ts - handler_enter_ts) * 1000:.0f} " + f"prompt_tokens={generator.usage.prompt_tokens if generator.usage else -1} " + f"completion_tokens={generator.usage.completion_tokens if generator.usage else -1} " + f"cached_tokens={getattr(getattr(generator.usage, 'prompt_tokens_details', None), 'cached_tokens', None)}]", + flush=True, + ) return JSONResponse(content=generator.model_dump()) return StreamingResponse(content=generator, media_type="text/event-stream") @@ -572,6 +597,26 @@ async def tokenize(request: NeMoRLTokenizeRequest, raw_request: Request): elif isinstance(generator, TokenizeResponse): return JSONResponse(content=generator.model_dump()) + ######################################## + # /detokenize endpoint + ######################################## + # Mirrors vllm/entrypoints/serve/tokenize/api_router.py::detokenize. + # Without this our custom FastAPI app returns 404 for /detokenize, + # which breaks the NeMo-Gym client's PERF_DEBUG (B)/(C) diagnostics + # that Dynamo's Rust frontend supports natively. + @app.post("/detokenize") + async def detokenize(request: DetokenizeRequest, raw_request: Request): + generator = await openai_serving_tokenization.create_detokenize( + request, raw_request + ) + + if isinstance(generator, ErrorResponse): + return JSONResponse( + content=generator.model_dump(), status_code=generator.error.code + ) + elif isinstance(generator, DetokenizeResponse): + return JSONResponse(content=generator.model_dump()) + ######################################## # Logging ######################################## @@ -615,16 +660,38 @@ def filter(self, record: LogRecord) -> bool: return app def _setup_vllm_server(self) -> "tuple[threading.Thread, str, uvicorn.Server]": + import asyncio import threading + from contextlib import asynccontextmanager from logging import Filter as LoggingFilter from logging import LogRecord, getLogger import uvicorn + import vllm.envs as vllm_envs from fastapi import FastAPI - # We initialize the FastAPI app here in case we want to do some generic configuration before the subsequent server inits - # e.g. last-run middleware. - app = FastAPI() + # Mirror vllm.entrypoints.openai.server_utils.lifespan: periodically + # call engine_client.do_log_stats() so the AsyncLLM's LoggingStatLogger + # emits "Engine N: Avg prompt throughput: ... Running: X reqs ..." + # lines. Without this, our custom FastAPI app never triggers the + # engine's stats output. + @asynccontextmanager + async def lifespan(app: FastAPI): + async def _force_log(): + while True: + await asyncio.sleep(vllm_envs.VLLM_LOG_STATS_INTERVAL) + try: + await self.llm.do_log_stats() + except Exception: + pass + + task = asyncio.create_task(_force_log()) + try: + yield + finally: + task.cancel() + + app = FastAPI(lifespan=lifespan) app = self._setup_vllm_openai_api_server(app) diff --git a/pyproject.toml b/pyproject.toml index 4a3574dc10..d4294b4bd9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,6 +54,19 @@ dependencies = [ "cuda-bindings", # for non-colocated refit "pybase64", # for sglang refit "nvidia-cudnn-cu12==9.19.0.56", # for transformer-engine no build isolation + "ai-dynamo @ file:///opt/nemo-rl/ai_dynamo-1.1.0-py3-none-any.whl", + "ai-dynamo-runtime @ file:///opt/nemo-rl/ai_dynamo_runtime-1.1.0-cp310-abi3-manylinux_2_39_x86_64.whl", + "nixl==0.10.1", + "aiconfigurator[webapp]", + "aiofiles<=25.1.0", + "filterpy==1.4.5", + "kubernetes==32.0.1", + "kubernetes-asyncio==32.0.0", + "pmdarima==2.1.1", + "prometheus-api-client==0.6.0", + "prophet==1.2.1", + "scikit-learn==1.7.2", + "scipy>=1.14.0,<2.0", # nvidia-resiliency-ext removed: no Python 3.13 wheels available (v0.5.0 only has cp310-cp312) ] @@ -186,6 +199,7 @@ emerging-optimizers = { git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizer # Official sglang v0.5.10 with transformers v5 support sglang = { git = "https://github.com/sgl-project/sglang.git", tag = "v0.5.10", subdirectory = "python" } sglang-kernel = { git = "https://github.com/sgl-project/sglang.git", tag = "v0.5.10", subdirectory = "sgl-kernel" } +aiconfigurator = { git = "https://github.com/jthomson04/aiconfigurator.git", rev = "4ed8604c43f221b60aa501cb218722fd7029fdb6" } [tool.uv.workspace] members = [ diff --git a/ray.sub b/ray.sub index e6e3e07af7..c1a34291ac 100644 --- a/ray.sub +++ b/ray.sub @@ -28,6 +28,11 @@ set -eoux pipefail # Function to detect if SLURM cluster uses GRES ######################################################## maybe_gres_arg() { + if [[ "${DISABLE_RAY_GRES:-0}" == "1" ]]; then + echo "" + return + fi + # Check if any nodes in the partition have GRES configured # Assumes a homogeneous allocation (not a heterogeneous job) if sinfo -p $SLURM_JOB_PARTITION -h -o "%G" | grep -q "gpu:"; then @@ -50,6 +55,7 @@ maybe_gres_arg() { CONTAINER=$CONTAINER MOUNTS=$MOUNTS COMMAND=${COMMAND:-} # This is a script relative to the SLURM_SUBMIT_DIR. If left empty, it will leave the cluster idle after it's brought up. +SETUP_COMMAND=${SETUP_COMMAND:-} # Setup commands to run on all nodes before starting Ray ######################################################## # Ports for all nodes (should be odd numbers since we place head/worker[0] on the same node) so all workers get the odd ports, but the head will get +1 the ports NODE_MANAGER_PORT=${NODE_MANAGER_PORT:-53001} @@ -108,7 +114,7 @@ fi # Create logs directory BASE_LOG_DIR=${BASE_LOG_DIR:-$SLURM_SUBMIT_DIR} -LOG_DIR="$BASE_LOG_DIR/$SLURM_JOB_ID-logs" +LOG_DIR=${LOG_DIR:-"$BASE_LOG_DIR/$SLURM_JOB_ID-logs"} mkdir -p $LOG_DIR # Number of GPUs per worker node @@ -119,7 +125,7 @@ GRES_ARG=$(maybe_gres_arg) if [[ -n "$GRES_ARG" ]]; then echo "[INFO] GRES support detected. Using: $GRES_ARG" else - echo "[INFO] No GRES support detected. Running without --gres flag." + echo "[INFO] No GRES support detected or GRES explicitly disabled. Running without --gres flag." fi COMMON_SRUN_ARGS="$GRES_ARG" @@ -295,6 +301,7 @@ chmod +x /launch-head.sh count=0 while [[ \$count -lt $num_retries ]]; do + $SETUP_COMMAND bash /launch-head.sh count=\$((count+1)) echo "Head node failed \$count/$num_retries times, restarting in 5 seconds..." @@ -307,6 +314,7 @@ EOF srun $COMMON_SRUN_ARGS --container-name=ray-head --nodes=1 --ntasks=1 --cpus-per-task=$CPUS_PER_WORKER -w "$head_node" -o $LOG_DIR/ray-head.log bash -x -c "$head_cmd" & SRUN_PIDS["ray-head"]=$! +sleep 100s NUM_ACTORS=$((GPUS_PER_NODE * SLURM_JOB_NUM_NODES)) # Start Ray worker nodes @@ -394,6 +402,7 @@ EOFINNER count=0 while [[ \$count -lt $num_retries ]]; do + $SETUP_COMMAND bash /launch-worker.sh count=\$((count+1)) echo "Worker failed \$count/$num_retries times, restarting in 5 seconds..." diff --git a/tests/unit/algorithms/test_async_utils.py b/tests/unit/algorithms/test_async_utils.py index c8eb1639bd..5142193529 100644 --- a/tests/unit/algorithms/test_async_utils.py +++ b/tests/unit/algorithms/test_async_utils.py @@ -100,6 +100,8 @@ def test_replay_buffer_initialization(self): assert debug_info["max_size"] == 10 assert debug_info["trajectory_versions"] == [] assert debug_info["target_weight_versions"] == [] + assert debug_info["failed_prompt_group_count"] == 0 + assert debug_info["failed_target_weight_counts"] == {} ray.kill(buffer) @@ -343,6 +345,120 @@ def test_replay_buffer_get_existing_target_weights(self): ray.kill(buffer) + def test_replay_buffer_get_target_weight_counts(self): + """Test counting buffered trajectory groups by target weight.""" + buffer = ReplayBuffer.remote(max_size=10) + + trajectories = [ + ({"batch": {"data": "test1"}, "rollout_metrics": {"reward": 1.0}}, 0, 2), + ({"batch": {"data": "test2"}, "rollout_metrics": {"reward": 2.0}}, 1, 2), + ({"batch": {"data": "test3"}, "rollout_metrics": {"reward": 3.0}}, 1, 3), + ] + for trajectory, weight_version, target_weight_version in trajectories: + ray.get( + buffer.push_with_wait_signal.remote( + trajectory, + weight_version=weight_version, + target_weight_version=target_weight_version, + ) + ) + + target_counts = ray.get(buffer.get_target_weight_counts.remote()) + assert target_counts == {2: 2, 3: 1} + + ray.kill(buffer) + + def test_replay_buffer_records_failed_prompt_groups(self): + """Test counting failed prompt groups by target weight.""" + buffer = ReplayBuffer.remote(max_size=10) + + ray.get( + buffer.record_failed_prompt_group.remote( + weight_version=0, + target_weight_version=1, + prompt_idx=0, + worker_id=123, + reason="test failure", + ) + ) + ray.get( + buffer.record_failed_prompt_group.remote( + weight_version=0, + target_weight_version=2, + prompt_idx=1, + worker_id=124, + reason="test failure", + ) + ) + ray.get( + buffer.record_failed_prompt_group.remote( + weight_version=1, + target_weight_version=2, + prompt_idx=2, + worker_id=125, + reason="test failure", + ) + ) + + failed_counts = ray.get(buffer.get_failed_target_weight_counts.remote()) + assert failed_counts == {1: 1, 2: 2} + + completion_counts = ray.get(buffer.get_target_completion_counts.remote()) + assert completion_counts[1] == {"buffered": 0, "failed": 1, "accounted": 1} + assert completion_counts[2] == {"buffered": 0, "failed": 2, "accounted": 2} + + debug_info = ray.get(buffer.get_debug_info.remote()) + assert debug_info["failed_prompt_group_count"] == 3 + assert debug_info["failed_target_weight_counts"] == {1: 1, 2: 2} + + ray.kill(buffer) + + def test_replay_buffer_sampling_skips_failed_prompt_groups(self): + """Test sampling unblocks when missing prompt groups have explicit failures.""" + buffer = ReplayBuffer.remote(max_size=10) + + trajectory = { + "batch": {"data": "successful_group"}, + "rollout_metrics": {"reward": 1.0}, + } + ray.get( + buffer.push_with_wait_signal.remote( + trajectory, weight_version=0, target_weight_version=1 + ) + ) + ray.get( + buffer.record_failed_prompt_group.remote( + weight_version=0, + target_weight_version=1, + prompt_idx=1, + worker_id=456, + reason="forced failure", + ) + ) + + sample_result = ray.get( + buffer.sample.remote( + num_prompt_groups=2, + current_weight_version=1, + max_age_steps=1, + ) + ) + + assert sample_result is not None + assert len(sample_result["trajectories"]) == 1 + assert sample_result["trajectories"][0]["batch"]["data"] == "successful_group" + assert sample_result["requested_prompt_groups"] == 2 + assert sample_result["sampled_prompt_groups"] == 1 + assert sample_result["skipped_failed_prompt_groups"] == 1 + assert len(sample_result["failed_prompt_groups"]) == 1 + assert sample_result["failed_prompt_groups"][0]["reason"] == "forced failure" + + completion_counts = ray.get(buffer.get_target_completion_counts.remote()) + assert completion_counts == {} + assert ray.get(buffer.size.remote()) == 0 + + ray.kill(buffer) + def test_replay_buffer_clear(self): """Test clearing the buffer.""" buffer = ReplayBuffer.remote(max_size=10) @@ -370,6 +486,7 @@ def test_replay_buffer_clear(self): assert debug_info["total_trajectories"] == 0 assert debug_info["trajectory_versions"] == [] assert debug_info["target_weight_versions"] == [] + assert debug_info["failed_prompt_group_count"] == 0 ray.kill(buffer) diff --git a/tests/unit/environments/test_nemo_gym.py b/tests/unit/environments/test_nemo_gym.py index 05ce0936ed..51aa1e126a 100644 --- a/tests/unit/environments/test_nemo_gym.py +++ b/tests/unit/environments/test_nemo_gym.py @@ -44,6 +44,55 @@ NEMO_GYM_INSTALLED = False +class _DummyTokenizer: + def apply_chat_template(self, input_messages, tokenize=True): + assert tokenize + return [101, 102] + + def decode(self, token_ids): + return str(token_ids) + + +def test_postprocess_nemo_gym_empty_generation_error_is_diagnosable(): + env = NemoGym.__new__(NemoGym) + tokenizer = _DummyTokenizer() + nemo_gym_result = { + "responses_create_params": { + "input": [{"role": "user", "content": "hello"}], + }, + "response": { + "id": "resp_123", + "model": "dummy-model", + "status": "incomplete", + "output": [ + { + "type": "message", + "role": "assistant", + "status": "incomplete", + "finish_reason": "length", + "content": [{"type": "output_text", "text": ""}], + } + ], + "incomplete_details": {"reason": "max_output_tokens"}, + "usage": {"input_tokens": 2, "output_tokens": 0}, + }, + } + + with pytest.raises(ValueError, match="no generation data") as exc_info: + env._postprocess_nemo_gym_to_nemo_rl_result(nemo_gym_result, tokenizer) + + error_text = str(exc_info.value) + assert "Prompt length: 2 tokens." in error_text + assert "Response summary:" in error_text + assert '"response_status": "incomplete"' in error_text + assert '"finish_reason": "length"' in error_text + assert '"output_count": 1' in error_text + assert ( + "This typically means the prompt for the first turn already exceeds the vLLM max_model_len" + not in error_text + ) + + @pytest.mark.skipif( not NEMO_GYM_INSTALLED, reason="Skipping NeMo-Gym test since NeMo-Gym is not installed!", diff --git a/tests/unit/experience/test_rollouts.py b/tests/unit/experience/test_rollouts.py index f3486de21e..205cd38fd0 100644 --- a/tests/unit/experience/test_rollouts.py +++ b/tests/unit/experience/test_rollouts.py @@ -813,15 +813,23 @@ def test_run_async_nemo_gym_rollout( ] input_batch: BatchedDataDict[DatumSpec] = rl_collate_fn(nemo_rl_compatible_examples) + for row in input_batch["extra_env_info"]: + assert "max_output_tokens" not in row["responses_create_params"] + actual_result = run_async_nemo_gym_rollout( policy_generation=nemo_gym_vllm_generation, input_batch=input_batch, tokenizer=nemo_gym_tokenizer, task_to_env={"nemo_gym": nemo_gym}, - max_seq_len=None, + max_seq_len=65536, generation_config=nemo_gym_vllm_generation.cfg, max_rollout_turns=None, ) + for row in input_batch["extra_env_info"]: + assert ( + row["responses_create_params"]["max_output_tokens"] + == nemo_gym_vllm_generation.cfg["max_new_tokens"] + ) actual_result = asdict(actual_result) actual_result["final_batch"] = actual_result["final_batch"].get_dict() diff --git a/uv.lock b/uv.lock index 55021a6873..5f7b924615 100644 --- a/uv.lock +++ b/uv.lock @@ -2,45 +2,102 @@ version = 1 revision = 3 requires-python = ">=3.13.13" resolution-markers = [ - "platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", - "platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", - "platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 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sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", - "platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", - "platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", - "platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 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"platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", - "platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", - "platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", - "platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", - "platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine != 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.15' and platform_machine == 's390x' and sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 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"aiofiles" }, { name = "blobfile" }, { name = "colored" }, - { name = "cuda-bindings", version = "12.9.4", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'darwin' or extra == 'extra-7-nemo-rl-sglang' or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm')" }, - { name = "cuda-bindings", version = "13.0.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-automodel') or (sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-fsdp') or (sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-mcore') or (sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, + { name = "cuda-bindings" }, { name = "datasets" }, { name = "debugpy" }, { name = "decord2" }, + { name = "filterpy" }, { name = "hydra-core" }, + { name = "kubernetes" }, + { name = "kubernetes-asyncio" }, { name = "math-verify" }, { name = "matplotlib" }, { name = "mlflow" }, { name = "nccl4py" }, { name = "ninja" }, + { name = "nixl" }, { name = "num2words" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang') or (extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, - { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-7-nemo-rl-automodel' or extra == 'extra-7-nemo-rl-mcore' or extra == 'extra-7-nemo-rl-sglang'" }, + { name = "numpy" }, { name = "nvidia-cudnn-cu12" }, { name = "nvidia-ml-py" }, { name = "nvidia-nvshmem-cu12", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, @@ -4159,10 +4939,15 @@ dependencies = [ { name = "pillow" }, { name = "pip" }, { name = "plotly" }, + { name = "pmdarima" }, + { name = "prometheus-api-client" }, + { name = "prophet" }, { name = "pybase64" }, { name = "pyzmq" }, { name = "ray", extra = ["default"] }, { name = "rich" }, + { name = "scikit-learn" }, + { name = "scipy" }, { name = "setuptools" }, { name = "swanlab" }, { name = "sympy" }, @@ -4174,7 +4959,7 @@ dependencies = [ { name = "torchvision", version = "0.25.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin' or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "sys_platform != 'darwin' or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, { name = "transformers" }, - { name = "triton", version = "3.6.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, + { name = "triton", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (sys_platform != 'linux' and extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, { name = "wandb" }, ] @@ -4210,8 +4995,7 @@ sglang = [ { name = "sglang-kernel" }, ] vllm = [ - { name = "cuda-python", version = "12.9.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'darwin' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore') or (sys_platform != 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, - { name = "cuda-python", version = "13.0.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore') or (sys_platform == 'darwin' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-fsdp' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-mcore') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-sglang') or (extra == 'extra-7-nemo-rl-mcore' and extra == 'extra-7-nemo-rl-vllm') or (extra == 'extra-7-nemo-rl-sglang' and extra == 'extra-7-nemo-rl-vllm')" }, + { name = "cuda-python" }, { name = "deep-ep" }, { name = "deep-gemm" }, { name = "flashinfer-cubin" }, @@ -4263,6 +5047,10 @@ test = [ [package.metadata] requires-dist = [ { name = "accelerate", specifier = ">=0.26" }, + { name = "ai-dynamo", path = "/opt/nemo-rl/ai_dynamo-1.1.0-py3-none-any.whl" }, + { name = "ai-dynamo-runtime", path = "/opt/nemo-rl/ai_dynamo_runtime-1.1.0-cp310-abi3-manylinux_2_39_x86_64.whl" }, + { name = "aiconfigurator", extras = ["webapp"], git = "https://github.com/jthomson04/aiconfigurator.git?rev=4ed8604c43f221b60aa501cb218722fd7029fdb6" }, + { name = "aiofiles", specifier = "<=25.1.0" }, { name = "blobfile" }, { name = "causal-conv1d", marker = "extra == 'automodel'", git = "https://github.com/Dao-AILab/causal-conv1d?rev=67e0a9dfe1518fc0036444e9ab5fe06ab78299e0" }, { name = "causal-conv1d", marker = "extra == 'fsdp'", git = "https://github.com/Dao-AILab/causal-conv1d?rev=67e0a9dfe1518fc0036444e9ab5fe06ab78299e0" }, @@ -4277,12 +5065,15 @@ requires-dist = [ { name = "deep-ep", marker = "extra == 'vllm'", git = "https://github.com/deepseek-ai/DeepEP.git?rev=bfded34800dfec415b71503f8205181de90b2480" }, { name = "deep-gemm", marker = "extra == 'vllm'", git = "https://github.com/deepseek-ai/DeepGEMM.git?rev=7b6b5563b9d4c1ae07ffbce7f78ad3ac9204827c" }, { name = "emerging-optimizers", marker = "extra == 'mcore'", git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git?rev=v0.2.0" }, + { name = "filterpy", specifier = "==1.4.5" }, { name = "flash-attn", marker = "extra == 'automodel'", specifier = "==2.8.1" }, { name = "flash-attn", marker = "extra == 'fsdp'", specifier = "==2.8.1" }, { name = "flash-attn", marker = "extra == 'mcore'", specifier = "==2.8.1" }, { name = "flashinfer-cubin", marker = "extra == 'vllm'", specifier = "==0.6.4" }, { name = "flashinfer-python", marker = "extra == 'vllm'", specifier = "==0.6.4" }, { name = "hydra-core" }, + { name = "kubernetes", specifier = "==32.0.1" }, + { name = "kubernetes-asyncio", specifier = "==32.0.0" }, { name = "mamba-ssm", marker = "extra == 'automodel'", git = "https://github.com/state-spaces/mamba.git?rev=d68d16ed7d5d5164eb5a57c0285f3b7eb8394ec1" }, { name = "mamba-ssm", marker = 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"python_full_version < '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra == 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 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and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang' and extra != 'extra-7-nemo-rl-vllm'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-7-nemo-rl-automodel' and extra != 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and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version < '3.14' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-7-nemo-rl-automodel' and extra != 'extra-7-nemo-rl-fsdp' and extra != 'extra-7-nemo-rl-mcore' and extra != 'extra-7-nemo-rl-sglang'", + "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 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