From d33420b50ad5d34baba0971ee74b47d2a08c83ec Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 12 Dec 2025 14:09:51 +0000 Subject: [PATCH 01/85] basic launcher --- pipelinerl/launch.py | 192 +++++++++++++++++++++++++------------------ 1 file changed, 110 insertions(+), 82 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 8d2c33d8..029f17c3 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -262,100 +262,128 @@ def run_environment(cfg: DictConfig, job: Job): def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: Path): - if cfg.use_fsdp and cfg.use_deepspeed: - raise ValueError("Cannot use both FSDP and DeepSpeed") + save_dir = exp_dir / "finetune" + cmd = [ - "python", - "-m", - "accelerate.commands.launch", + "conda", + "run", + "-n", + "fast-llm" ] - if world_map.world_size > 1: - # DeepSpeed multi-node args - assert cfg.use_deepspeed - assert world_map.master_addr.startswith("dns-") and world_map.master_addr.endswith("-0") - hosts = [world_map.master_addr[:-2] + f"-{i}" for i in range(world_map.world_size)] - filter_parts = [] - for rank, job_list in world_map.job_map.items(): - for job in job_list: - if job.kind == "finetune": - filter_parts.append(f"{hosts[rank]}:{','.join(map(str, job.gpus))}") - deepspeed_include_filter = "@".join(filter_parts) - logger.info(f"Deepspeed include filter: {deepspeed_include_filter}") - # Orchestrator rank must have already created hostfile.txt - hostfile_path = str(exp_dir / "hostfile.txt") - cmd += [ - "--num_machines", - str(len(world_map.nodes_with_finetuning())), - "--machine_rank", - str(world_map.my_finetuning_rank()), - "--main_process_ip", - str(os.environ.get("MASTER_ADDR")), - "--main_process_port", - str(os.environ.get("MASTER_PORT")), - "--deepspeed_hostfile", - hostfile_path, - "--deepspeed_inclusion_filter", - deepspeed_include_filter, - "--deepspeed_multinode_launcher", - "nossh" - ] - # get path to this file - this_file_path = Path(os.path.dirname(os.path.abspath(__file__))) - if cfg.use_deepspeed: - # DeepSpeed single-node args - cmd += [ - "--use_deepspeed", - "--deepspeed_config_file", - str(this_file_path / f"../conf/deepspeed/{cfg.deepspeed_config}.json"), - ] - # DeepSpeed and non-DeepSpeed args - accelerate_config = cfg.accelerate_config - if accelerate_config is None: - if cfg.use_deepspeed: - accelerate_config = "deepspeed" - elif cfg.use_fsdp: - accelerate_config = "fsdp_mp" - else: - accelerate_config = "base_mp" + cmd += [ - "--config_file", - str(this_file_path / f"../conf/accelerate/{accelerate_config}.yaml"), - "--rdzv_backend", - "c10d", + "fast-llm", + "train", + "gpt", + "--config", + "qwen25_05B-instruct.yaml", + f"run.experiment_dir={save_dir}" ] - if gpus: - gpus_str = str(",".join([str(gpu) for gpu in gpus])) if len(gpus) < world_map.node_size else "all" - cmd += [ - "--gpu-ids", - gpus_str, - ] - cmd += [ - "--num_processes", - str(world_map.total_finetune_gpus), - "pipelinerl/entrypoints/run_finetune.py", - "--config-dir", - f"{exp_dir}/conf", - "--config-name", - "exp_config", - f"output_dir={exp_dir}", - f"hydra.run.dir={exp_dir}/finetune", - # TODO: figure out why we can't build WorldMap in run_finetune.py - # Current workaround: pass the essential information as follows: - f"+me.weight_update_group_init_method=tcp://{world_map.master_addr}:{cfg.world.actor_group_port}", - f"+me.weight_update_group_world_size={world_map.weight_update_group_size}", - f"+me.llm_urls={'+'.join(world_map.get_actor_urls())}", - ] - if cfg.debug.mode in ["finetune", "open_loop", "finetune+preprocessor"]: - cmd.append("finetune.send_weight_updates=False") logger.info(f"Running finetune with command: {' '.join(cmd)}") save_command(exp_dir / "finetune", cmd) env = dict(os.environ) - env["DS_ENV_FILE"] = str(exp_dir / ".deepspeed_env") + env["PYTHONHASHSEED"] = "42" + env["CUDA_VISIBLE_DEVICES"] = ",".join(str(gpu) for gpu in gpus) proc = _popen(cmd, env=env) if proc is not None: yield LaunchedProcess(kind="finetune", handle=proc) +# def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: Path): +# if cfg.use_fsdp and cfg.use_deepspeed: +# raise ValueError("Cannot use both FSDP and DeepSpeed") +# cmd = [ +# "python", +# "-m", +# "accelerate.commands.launch", +# ] +# if world_map.world_size > 1: +# # DeepSpeed multi-node args +# assert cfg.use_deepspeed +# assert world_map.master_addr.startswith("dns-") and world_map.master_addr.endswith("-0") +# hosts = [world_map.master_addr[:-2] + f"-{i}" for i in range(world_map.world_size)] +# filter_parts = [] +# for rank, job_list in world_map.job_map.items(): +# for job in job_list: +# if job.kind == "finetune": +# filter_parts.append(f"{hosts[rank]}:{','.join(map(str, job.gpus))}") +# deepspeed_include_filter = "@".join(filter_parts) +# logger.info(f"Deepspeed include filter: {deepspeed_include_filter}") +# # Orchestrator rank must have already created hostfile.txt +# hostfile_path = str(exp_dir / "hostfile.txt") +# cmd += [ +# "--num_machines", +# str(len(world_map.nodes_with_finetuning())), +# "--machine_rank", +# str(world_map.my_finetuning_rank()), +# "--main_process_ip", +# str(os.environ.get("MASTER_ADDR")), +# "--main_process_port", +# str(os.environ.get("MASTER_PORT")), +# "--deepspeed_hostfile", +# hostfile_path, +# "--deepspeed_inclusion_filter", +# deepspeed_include_filter, +# "--deepspeed_multinode_launcher", +# "nossh" +# ] +# # get path to this file +# this_file_path = Path(os.path.dirname(os.path.abspath(__file__))) +# if cfg.use_deepspeed: +# # DeepSpeed single-node args +# cmd += [ +# "--use_deepspeed", +# "--deepspeed_config_file", +# str(this_file_path / f"../conf/deepspeed/{cfg.deepspeed_config}.json"), +# ] +# # DeepSpeed and non-DeepSpeed args +# accelerate_config = cfg.accelerate_config +# if accelerate_config is None: +# if cfg.use_deepspeed: +# accelerate_config = "deepspeed" +# elif cfg.use_fsdp: +# accelerate_config = "fsdp_mp" +# else: +# accelerate_config = "base_mp" +# cmd += [ +# "--config_file", +# str(this_file_path / f"../conf/accelerate/{accelerate_config}.yaml"), +# "--rdzv_backend", +# "c10d", +# ] +# if gpus: +# gpus_str = str(",".join([str(gpu) for gpu in gpus])) if len(gpus) < world_map.node_size else "all" +# cmd += [ +# "--gpu-ids", +# gpus_str, +# ] +# cmd += [ +# "--num_processes", +# str(world_map.total_finetune_gpus), +# "pipelinerl/entrypoints/run_finetune.py", +# "--config-dir", +# f"{exp_dir}/conf", +# "--config-name", +# "exp_config", +# f"output_dir={exp_dir}", +# f"hydra.run.dir={exp_dir}/finetune", +# # TODO: figure out why we can't build WorldMap in run_finetune.py +# # Current workaround: pass the essential information as follows: +# f"+me.weight_update_group_init_method=tcp://{world_map.master_addr}:{cfg.world.actor_group_port}", +# f"+me.weight_update_group_world_size={world_map.weight_update_group_size}", +# f"+me.llm_urls={'+'.join(world_map.get_actor_urls())}", +# ] +# if cfg.debug.mode in ["finetune", "open_loop", "finetune+preprocessor"]: +# cmd.append("finetune.send_weight_updates=False") + +# logger.info(f"Running finetune with command: {' '.join(cmd)}") +# save_command(exp_dir / "finetune", cmd) +# env = dict(os.environ) +# env["DS_ENV_FILE"] = str(exp_dir / ".deepspeed_env") +# proc = _popen(cmd, env=env) +# if proc is not None: +# yield LaunchedProcess(kind="finetune", handle=proc) + def run_preprocess(world_map: WorldMap, preprocessor_idx: int, exp_dir: Path): if preprocessor_idx != 0: From acad1e7a46237357adf547306f97c0020b19b194 Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 12 Dec 2025 14:25:57 +0000 Subject: [PATCH 02/85] single channel streams --- pipelinerl/streams.py | 187 +++++++++++++++++++++++++++++++++++++++--- 1 file changed, 176 insertions(+), 11 deletions(-) diff --git a/pipelinerl/streams.py b/pipelinerl/streams.py index 632b760e..3445519e 100644 --- a/pipelinerl/streams.py +++ b/pipelinerl/streams.py @@ -192,6 +192,125 @@ def read(self): yield pickle.loads(entry[b"data"]) +class RedisSharedStreamWriter(StreamWriter): + """Redis writer that supports multiple producers appending to a single stream.""" + + def __init__( + self, + stream: SingleStreamSpec, + mode: Literal["w", "a"] = "a", + *, + writer_id: str | None = None, + maxlen: int = 1_000_000, + ): + self.stream = stream + assert isinstance(_backend, RedisConfig) + self._redis = connect_to_redis(_backend) + self._stream_name = str(self.stream) + self._counter_key = f"stream:{self._stream_name}:next_index" + self._writer_id = str(writer_id) if writer_id is not None else None + self._maxlen = maxlen + + if mode not in {"w", "a"}: + raise ValueError(f"Invalid mode: {mode}. Only 'w' and 'a' are supported.") + + if mode == "w": + last_entry = self._redis.xrevrange(self._stream_name, count=1) + if last_entry: + raise ValueError(f"Stream {self.stream} already exists. Cannot overwrite it.") + self._redis.delete(self._counter_key) + self._redis.set(self._counter_key, -1) + else: + if not self._redis.exists(self._counter_key): + last_entry = self._redis.xrevrange(self._stream_name, count=1) + if last_entry: + _, entry = last_entry[0] + raw_index = entry.get(b"index") + next_index = int(raw_index.decode("utf-8")) + 1 if raw_index else 0 + else: + next_index = 0 + self._redis.set(self._counter_key, next_index - 1) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self._redis.close() + + def write(self, data, partition: int | None = None): + if partition is not None: + raise ValueError("Shared Redis streams do not support manual partition overrides") + + serialized = _serialize_with_orjson(data) + entry_index = self._redis.incr(self._counter_key) + record: dict[str, Any] = { + "index": str(entry_index), + "data": serialized, + "ts": f"{time.time():.6f}", + } + if self._writer_id is not None: + record["writer"] = self._writer_id + self._redis.xadd(self._stream_name, record, maxlen=self._maxlen, approximate=True) + + +class RedisSharedStreamReader(StreamReader): + """Redis reader that validates fan-in ordering for a shared stream.""" + + def __init__(self, stream: SingleStreamSpec, *, fail_on_gap: bool = True): + self.stream = stream + assert isinstance(_backend, RedisConfig) + self._redis = connect_to_redis(_backend) + self._stream_name = str(self.stream) + self._last_id = 0 + self._expected_index: int | None = None + self._fail_on_gap = fail_on_gap + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + self._redis.close() + + def _update_expected_index(self, entry: dict[bytes, bytes]): + raw_index = entry.get(b"index") + if raw_index is None: + return + + index_value = int(raw_index.decode("utf-8")) + if self._expected_index is None: + self._expected_index = index_value + elif index_value != self._expected_index: + message = ( + f"Index mismatch for shared stream {self.stream}: expected {self._expected_index}, got {index_value}" + ) + if self._fail_on_gap: + raise ValueError(message) + logger.warning(message) + self._expected_index = index_value + + self._expected_index += 1 + + def read(self): + block = int(_REREAD_DELAY * 1000) + while True: + response = self._redis.xread({self._stream_name: self._last_id}, count=1, block=block) + if not response: + continue + + stream_name, result = response[0] + assert stream_name.decode("utf-8") == self._stream_name + assert isinstance(result, list) and len(result) == 1 + entry_id, entry = result[0] + self._last_id = entry_id + self._update_expected_index(entry) + + payload = entry.get(b"data") + if payload is None: + raise ValueError(f"Shared stream entry missing 'data' field: {entry}") + + yield orjson.loads(payload) + + class RoundRobinRedisStreamWriter(StreamWriter): # TODO: share the connection across writers @@ -246,6 +365,32 @@ def stream_file(stream_dir: Path, shard_id: int) -> Path: StreamSpec = SingleStreamSpec | StreamRangeSpec +def _to_json_ready(value: Any) -> Any: + if isinstance(value, BaseModel): + value = value.model_dump() + + if isinstance(value, torch.Tensor): + return value.detach().cpu().numpy() + + if isinstance(value, numpy.ndarray): + return value + + if isinstance(value, numpy.generic): + return value.item() + + if isinstance(value, dict): + return {key: _to_json_ready(item) for key, item in value.items()} + + if isinstance(value, (list, tuple)): + return [_to_json_ready(item) for item in value] + + return value + + +def _serialize_with_orjson(data: Any) -> bytes: + return orjson.dumps(_to_json_ready(data), option=orjson.OPT_SERIALIZE_NUMPY) + + class FileStreamWriter(StreamWriter): def __init__(self, stream: SingleStreamSpec, mode: Literal["w", "a"] = "a"): self.stream = stream @@ -266,13 +411,8 @@ def write(self, data, partition: int | None = None): if partition is not None: raise ValueError() # Textual streams are so useful, that we try hard to jsonify the given object. - if isinstance(data, BaseModel): - data_dict = data.model_dump() - for key, value in data_dict.items(): - if isinstance(value, torch.Tensor): - data_dict[key] = value.numpy() - data = data_dict - self._file.write(orjson.dumps(data, option=orjson.OPT_SERIALIZE_NUMPY).decode("utf-8")) + payload = _serialize_with_orjson(data) + self._file.write(payload.decode("utf-8")) self._file.write("\n") self._file.flush() @@ -387,32 +527,57 @@ def write(self, data, partition: int | None = None): # Below are the public stream APIs. Easy to replace files with Redis or another pubsub system. -def read_stream(stream: SingleStreamSpec) -> StreamReader: - """Start reading the stream from the beginning""" +def read_stream(stream: SingleStreamSpec, *, shared: bool = False, fail_on_gap: bool = True) -> StreamReader: + """Start reading the stream from the beginning. + + When ``shared`` is True, multiple producers are assumed to append to the same + Redis stream and the reader will validate ordering using the stored index + metadata. + """ raise_if_backend_not_set() if not isinstance(stream, SingleStreamSpec): raise ValueError(f"Invalid stream spec: {stream}") if isinstance(_backend, RedisConfig): + if shared: + return RedisSharedStreamReader(stream, fail_on_gap=fail_on_gap) return RedisStreamReader(stream) elif _backend == "files": + if shared: + raise ValueError("Shared stream mode is only supported with the Redis backend") return FileStreamReader(stream) else: assert False -def write_to_streams(streams: StreamSpec, mode: Literal["w", "a"] = "a") -> StreamWriter: - """Append to the end of the stream.""" +def write_to_streams( + streams: StreamSpec, + mode: Literal["w", "a"] = "a", + *, + shared: bool = False, + writer_id: str | None = None, +) -> StreamWriter: + """Append to the end of the stream. + + Set ``shared`` to True when multiple producers must append to the same Redis + stream and ServiceNow/Fast-LLM will perform downstream sharding. + """ raise_if_backend_not_set() if not isinstance(streams, (SingleStreamSpec, StreamRangeSpec)): raise ValueError(f"Invalid stream spec: {streams}") if isinstance(_backend, RedisConfig): if isinstance(streams, SingleStreamSpec): + if shared: + return RedisSharedStreamWriter(streams, mode, writer_id=writer_id) return RedisStreamWriter(streams, mode) elif isinstance(streams, StreamRangeSpec): + if shared: + raise ValueError("Shared Redis streams only support SingleStreamSpec inputs") return RoundRobinRedisStreamWriter(streams, mode) else: assert False elif _backend == "files": + if shared: + raise ValueError("Shared stream mode is only supported with the Redis backend") if isinstance(streams, SingleStreamSpec): return FileStreamWriter(streams, mode) elif isinstance(streams, StreamRangeSpec): From 9a7eab0460fb3e30b0886e4e1312761411e69373 Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 12 Dec 2025 18:38:31 +0000 Subject: [PATCH 03/85] update --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8a172c0d..bc67d4f3 100644 --- a/README.md +++ b/README.md @@ -321,6 +321,7 @@ PipelineRL is organized as a modular, Hydra-driven pipeline with 6 core componen - Defined in `pipelinerl/streams.py`. - Implements `SingleStreamSpec` and `StreamRangeSpec` for file-system or Redis-based queues. - `write_to_streams(...)` and `read_stream(...)` provide a JSON-line protocol for inter-process messaging. +- Pass `shared=True` to these helpers when multiple actors must fan-in to a single Redis stream (e.g., ServiceNow/Fast-LLM trainer). The shared mode encodes payloads via `orjson`, tags them with a global index, and lets the trainer perform downstream sharding safely. - Available backends: - File system: default. - Redis: requires Redis server. From 9543265b2a6a54d60450102261f528edd4caee01 Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 19 Dec 2025 18:21:24 +0000 Subject: [PATCH 04/85] enable fast-llm for basic streaming --- conf/base.yaml | 10 ++++++++++ conf/math.yaml | 3 +++ 2 files changed, 13 insertions(+) diff --git a/conf/base.yaml b/conf/base.yaml index 1f8d73cc..10b739d4 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -115,6 +115,16 @@ debug: place_inference_workers: true use_existing_llms: false +# Fast-LLM integration: when true, disables vLLM weight updates (no NCCL group) +# and uses Fast-LLM trainer events for state synchronization. +# TODO(fast-llm): Once data flows from actors -> Redis -> Fast-LLM training loop, +# enable NCCL weight broadcast by setting events.weights_broadcast.enabled=true in +# the Fast-LLM config (qwen25_05B-instruct.yaml). This requires: +# 1. Configuring NCCL rendezvous in Fast-LLM (rdvz_master_address, port, world_size, rank) +# 2. Implementing the vLLM weight receiver to accept broadcasted weights +# 3. Setting use_fast_llm=false once NCCL is working (or removing this flag entirely) +use_fast_llm: false + me: # Which job is this one? This will be autopopulated job_idx: null diff --git a/conf/math.yaml b/conf/math.yaml index a772a190..8ec5684d 100644 --- a/conf/math.yaml +++ b/conf/math.yaml @@ -2,6 +2,9 @@ defaults: - base - _self_ +# Enable Fast-LLM integration (disables vLLM weight updates until NCCL broadcast is implemented) +use_fast_llm: true + actor: rollout_policy: pipelinerl.domains.math.generate_math_rollout system_prompt: Please reason step by step, and put your final answer within \boxed{}. From 1d9ac8deab2c2cd3db796ba48c9242e6325acf8d Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 19 Dec 2025 18:21:56 +0000 Subject: [PATCH 05/85] enable fast-llm for basic streaming --- pipelinerl/actor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pipelinerl/actor.py b/pipelinerl/actor.py index 6d3317af..3e7a2c92 100644 --- a/pipelinerl/actor.py +++ b/pipelinerl/actor.py @@ -261,7 +261,7 @@ def rollout_maker_entrypoint( llms: list[TrainableLLM], scheduler_name: str, ): - trainer_state = TrainerState(Path(cfg.output_dir)) + trainer_state = TrainerState(Path(cfg.output_dir), use_fast_llm=cfg.use_fast_llm) if cfg.debug.mode: trainer_state.propagated_weight_version = 0 else: @@ -641,7 +641,7 @@ def run_actor_loop(cfg: DictConfig): wait_for_inference_servers(llm_urls) wait_for_environments(cfg) - trainer_state = TrainerState(exp_path) + trainer_state = TrainerState(exp_path, use_fast_llm=cfg.use_fast_llm) if cfg.debug.mode: trainer_state.debug_mode_init() else: From 6d42adecbceea449e63517a02c609e13bf1b068f Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 19 Dec 2025 18:22:44 +0000 Subject: [PATCH 06/85] set cwd --- pipelinerl/launch.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 247bad03..7df879d5 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -215,7 +215,10 @@ def run_actor_llm( if v not in [None, ""]: cmd.append(str(v)) - if cfg.debug.mode: + # Disable weight updates in debug mode or when using Fast-LLM (no NCCL group yet) + # TODO(fast-llm): Remove the use_fast_llm check once NCCL weight broadcast is implemented. + # When Fast-LLM broadcasts weights via NCCL, vLLM should join the group and receive updates. + if cfg.debug.mode or cfg.use_fast_llm: cmd.append("--disable-weight-updates") gpu_str = ",".join([str(gpu) for gpu in gpus]) @@ -295,11 +298,16 @@ def run_environment(cfg: DictConfig, job: Job): def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: Path): save_dir = exp_dir / "finetune" + # Get absolute path to config file + config_path = Path(__file__).parent.parent / "qwen25_05B-instruct.yaml" + cmd = [ "conda", "run", "-n", - "fast-llm" + "fast-llm", + "--cwd", + str(config_path.parent), # Set working directory for fast-llm ] cmd += [ @@ -307,7 +315,7 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: "train", "gpt", "--config", - "qwen25_05B-instruct.yaml", + str(config_path), f"run.experiment_dir={save_dir}" ] @@ -503,9 +511,9 @@ def is_inference_process(proc: LaunchedProcess) -> bool: return proc.kind in {"actor_llm", "preprocessor_llm"} -def watch_processes_running(exp_path: Path, processes: List[LaunchedProcess], debug_mode: bool = False): +def watch_processes_running(exp_path: Path, processes: List[LaunchedProcess], debug_mode: bool = False, use_fast_llm: bool = False): if not debug_mode: - trainer_state = TrainerState(exp_path) + trainer_state = TrainerState(exp_path, use_fast_llm=use_fast_llm) trainer_state.start_listening() else: trainer_state = None @@ -716,7 +724,7 @@ def main(cfg: DictConfig): if os.environ.get("DRY_RUN", "0") == "1": assert not processes return - watch_processes_running(exp_dir, processes, bool(cfg.debug.mode)) + watch_processes_running(exp_dir, processes, bool(cfg.debug.mode), cfg.use_fast_llm) if __name__ == "__main__": From e8674e1f1efc2cc4ada4ddd3191ffc7915acbe0a Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 19 Dec 2025 18:24:10 +0000 Subject: [PATCH 07/85] convert samples for fast-llm format --- pipelinerl/preprocess.py | 97 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 88 insertions(+), 9 deletions(-) diff --git a/pipelinerl/preprocess.py b/pipelinerl/preprocess.py index 0a6015e4..3f475f89 100644 --- a/pipelinerl/preprocess.py +++ b/pipelinerl/preprocess.py @@ -347,6 +347,66 @@ def write_micro_batch_slices( data_writer.write(micro_batch, lead_trainer_id) +def convert_to_fast_llm_format(entry: dict) -> dict: + """Convert a preprocessed sample entry to Fast-LLM streaming format. + + Fast-LLM expects: + - tokens: list of token IDs + - tokens_dtype: string dtype (e.g., "int32") + - loss_masking_spans (optional): list of (start, end) tuples where loss IS computed + """ + input_ids = entry["input_ids"] + + # Convert to list if tensor + if hasattr(input_ids, "tolist"): + tokens = input_ids.tolist() + else: + tokens = list(input_ids) + + result = { + "tokens": tokens, + "tokens_dtype": "int32", + } + + # Convert labels to loss_masking_spans if present + # In PipelineRL, labels=-100 means "don't compute loss" (padding/prompt) + # In Fast-LLM, loss_masking_spans are ranges where loss IS computed + if "labels" in entry: + labels = entry["labels"] + if hasattr(labels, "tolist"): + labels = labels.tolist() + else: + labels = list(labels) + + # Find contiguous spans where labels != -100 (loss is computed) + spans = [] + in_span = False + span_start = 0 + for i, label in enumerate(labels): + if label != -100 and not in_span: + # Start new span + in_span = True + span_start = i + elif label == -100 and in_span: + # End current span + spans.append((span_start, i)) + in_span = False + # Close final span if still open + if in_span: + spans.append((span_start, len(labels))) + + if spans: + result["loss_masking_spans"] = spans + + return result + + +def write_sample_for_fast_llm(data_writer: StreamWriter, entry: dict): + """Write a single sample to the stream in Fast-LLM format.""" + fast_llm_sample = convert_to_fast_llm_format(entry) + data_writer.write(fast_llm_sample) + + def run_preprocessing_loop( cfg: DictConfig, @@ -373,13 +433,24 @@ def run_preprocessing_loop( wait_for_inference_servers(llm_urls) input_stream = SingleStreamSpec(exp_path=exp_root_dir, topic=cfg.preprocess.input) - output_stream = StreamRangeSpec( - exp_path=exp_root_dir, - topic=cfg.preprocess.output, - partition_range=(0, max(world_map.total_finetune_gpus, 1)), - ) + # For Fast-LLM: use SingleStreamSpec with shared=True (uses orjson serialization) + # For standard PipelineRL: use StreamRangeSpec with partitions per GPU + if cfg.use_fast_llm: + output_stream = SingleStreamSpec( + exp_path=exp_root_dir, + topic=cfg.preprocess.output, + partition=0, # Single stream for Fast-LLM + ) + use_shared_stream = True + else: + output_stream = StreamRangeSpec( + exp_path=exp_root_dir, + topic=cfg.preprocess.output, + partition_range=(0, max(world_map.total_finetune_gpus, 1)), + ) + use_shared_stream = False stats_streams = SingleStreamSpec(exp_path=exp_root_dir, topic="preprocessor_stats") - logger.info("Streams initialized") + logger.info(f"Streams initialized (shared={use_shared_stream})") raw_chunk_queue = Queue(cfg.preprocess.raw_queue_size) rl_config = RLConfig(**cfg.finetune.rl) @@ -397,7 +468,7 @@ def run_preprocessing_loop( dataset_loader_thread.start() # Initialize TrainerState - trainer_state = TrainerState(exp_root_dir) + trainer_state = TrainerState(exp_root_dir, use_fast_llm=cfg.use_fast_llm) if cfg.debug.mode == "preprocessor": logger.info("Debug mode: preprocessor") trainer_state.debug_mode_init() @@ -462,7 +533,7 @@ def run_preprocessing_loop( # Per-trainer sample tracking (similar to finetune_loop.py) total_filtered_out = 0 # Track total filtered samples across all batches - with write_to_streams(output_stream) as data_writer, write_to_streams(stats_streams) as stats_writer: + with write_to_streams(output_stream, shared=use_shared_stream) as data_writer, write_to_streams(stats_streams) as stats_writer: with SharedMemoryManager() as smm: # Create shared memory queues without the manager parameter input_queue = SharedMemoryQueue(smm, cfg.preprocess.input_queue_size, cfg.preprocess.shared_memory_entry_size) @@ -573,7 +644,15 @@ def run_preprocessing_loop( start_writing = time.time() while (len(processed_entries_queue) > 0 and not batch_done) or (cfg.preprocess.dataset_buffer_size and not batch_done): logger.debug(f"[inner loop] trainer {trainer_id} has {samples_per_trainer[trainer_id]} samples, target is {target_samples_per_lead}") - if cfg.finetune.seq_packing: + + # Fast-LLM path: write individual samples directly (Fast-LLM does its own packing) + if cfg.use_fast_llm: + while len(processed_entries_queue) > 0: + entry = processed_entries_queue.popleft() + write_sample_for_fast_llm(data_writer, entry) + published_samples += 1 + batch_done = True # Always mark done for Fast-LLM (no batching) + elif cfg.finetune.seq_packing: if samples_per_trainer[trainer_id] == target_samples_per_lead: logger.debug(f"[inner loop] trainer {trainer_id} has all {target_samples_per_lead} samples, creating sentinel batch") sentinel_batch = create_sentinel_batch( From e78844ed06ae7a7c0ff7510172a36c67b8a4a95b Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 19 Dec 2025 18:25:16 +0000 Subject: [PATCH 08/85] listen to trainer events from redis --- pipelinerl/state.py | 74 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 73 insertions(+), 1 deletion(-) diff --git a/pipelinerl/state.py b/pipelinerl/state.py index 944b5114..d858fa2b 100644 --- a/pipelinerl/state.py +++ b/pipelinerl/state.py @@ -16,10 +16,14 @@ logger = logging.getLogger(__name__) +# Fast-LLM event stream name (must match fast-llm config events.redis.stream_key) +FAST_LLM_EVENTS_STREAM = "fast_llm_events" + class TrainerState: - def __init__(self, exp_path: Path): + def __init__(self, exp_path: Path, use_fast_llm: bool = False): self.exp_path = exp_path + self.use_fast_llm = use_fast_llm self.propagated_weight_version: int | None = None self.samples_processed: int | None = None self.training_done: bool = False @@ -32,6 +36,13 @@ def debug_mode_init(self): self._training_done_event.set() def start_listening(self): + if self.use_fast_llm: + self._start_listening_fast_llm() + else: + self._start_listening_legacy() + + def _start_listening_legacy(self): + """Listen to legacy PipelineRL trainer messages.""" stream = SingleStreamSpec(exp_path=self.exp_path, topic=TRAINER_TOPIC) def listen(): @@ -49,6 +60,67 @@ def listen(): self._thread = threading.Thread(target=listen, daemon=True) self._thread.start() + def _start_listening_fast_llm(self): + """Listen to Fast-LLM trainer events directly from Redis.""" + import orjson + import redis + from pipelinerl.streams import RedisConfig, _backend, connect_to_redis + + # Fast-LLM event stream config (must match fast-llm config) + stream_key = FAST_LLM_EVENTS_STREAM # "fast_llm_events" + payload_key = b"event" # Fast-LLM uses "event" as payload key + + def listen(): + assert isinstance(_backend, RedisConfig) + r = connect_to_redis(_backend) + last_id = "0-0" + + logger.info(f"Listening for Fast-LLM events on Redis stream '{stream_key}'") + + while True: + # Read from stream (blocking) + result = r.xread({stream_key: last_id}, count=1, block=1000) + + if not result: + continue + + for stream_name, messages in result: + for msg_id, msg_data in messages: + last_id = msg_id + + # Fast-LLM sends: {payload_key: orjson.dumps({type: "...", step: N})} + if payload_key not in msg_data: + logger.warning(f"Fast-LLM event missing '{payload_key.decode()}' field: {msg_data}") + continue + + try: + event = orjson.loads(msg_data[payload_key]) + except Exception as e: + logger.error(f"Failed to parse Fast-LLM event: {e}") + continue + + event_type = event.get("type") + step = event.get("step") + + if event_type == "initial_weights_step": + logger.info(f"Received initial_weights_step event: step={step}") + self.propagated_weight_version = step + # Initial step also sets samples_processed to 0 + if self.samples_processed is None: + self.samples_processed = 0 + elif event_type == "weights_ready": + logger.info(f"Received weights_ready event: step={step}") + self.propagated_weight_version = step + elif event_type == "training_finished": + logger.info("Received training_finished event") + self.training_done = True + self._training_done_event.set() + else: + logger.warning(f"Unknown Fast-LLM event type: {event_type}") + + self._thread = threading.Thread(target=listen, daemon=True) + self._thread.start() + def wait_for_training_done(self, timeout: float | None = None) -> bool: return self._training_done_event.wait(timeout=timeout) From d16a829aea0109b41ff2f2c22fecd6dc7408b872 Mon Sep 17 00:00:00 2001 From: rafapi Date: Fri, 19 Dec 2025 18:26:25 +0000 Subject: [PATCH 09/85] put all the samples in a single stream --- pipelinerl/streams.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pipelinerl/streams.py b/pipelinerl/streams.py index 3445519e..ebecc41b 100644 --- a/pipelinerl/streams.py +++ b/pipelinerl/streams.py @@ -238,9 +238,8 @@ def __exit__(self, exc_type, exc_value, traceback): self._redis.close() def write(self, data, partition: int | None = None): - if partition is not None: - raise ValueError("Shared Redis streams do not support manual partition overrides") - + # Note: partition is ignored for shared streams - all data goes to a single stream + # This is intentional for Fast-LLM integration where Fast-LLM handles its own sharding serialized = _serialize_with_orjson(data) entry_index = self._redis.incr(self._counter_key) record: dict[str, Any] = { From 67b3f2b7c3890401144b5c8eb1913626e2a1856e Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 9 Feb 2026 14:19:03 +0000 Subject: [PATCH 10/85] fix accessing non complete dists --- pipelinerl/utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pipelinerl/utils.py b/pipelinerl/utils.py index bd7fe5b5..73a0ff16 100644 --- a/pipelinerl/utils.py +++ b/pipelinerl/utils.py @@ -38,7 +38,10 @@ def init_wandb( python_env = {} for dist in distributions(): - python_env[dist.metadata["Name"]] = dist.version + try: + python_env[dist.metadata["Name"]] = dist.version + except Exception as e: + logger.warning(f"Accessing {dist} resulted in error {e}") config_for_wandb["python_env"] = python_env if cfg.wandb.wandb_resume == "always": From dc04911d6e832c08dd842539ce93de9ea49bbded Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 9 Feb 2026 14:19:37 +0000 Subject: [PATCH 11/85] change to calling fast-llm without conda --- pipelinerl/launch.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 7df879d5..cc654893 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -301,14 +301,19 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: # Get absolute path to config file config_path = Path(__file__).parent.parent / "qwen25_05B-instruct.yaml" - cmd = [ - "conda", - "run", - "-n", - "fast-llm", - "--cwd", - str(config_path.parent), # Set working directory for fast-llm - ] + # TODO: make config or make everywhere without conda + use_conda = False + if use_conda: + cmd = [ + "conda", + "run", + "-n", + "fast-llm", + "--cwd", + str(config_path.parent), # Set working directory for fast-llm + ] + else: + cmd = [] cmd += [ "fast-llm", From 30a53466c2ee61e8b1024f86d3e384ab8a8be7b4 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 9 Feb 2026 14:20:09 +0000 Subject: [PATCH 12/85] fix imports for vllm .14.1rc1 --- pipelinerl/vllm1.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 1ac611d0..5a3a0bbb 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -2,7 +2,8 @@ import signal import torch import uvloop -from vllm.utils import FlexibleArgumentParser, set_ulimit +from vllm.utils.system_utils import set_ulimit +from vllm.utils.argparse_utils import FlexibleArgumentParser from vllm.entrypoints.openai.cli_args import ( make_arg_parser, validate_parsed_serve_args, @@ -15,7 +16,7 @@ init_app_state, ) from vllm.engine.arg_utils import AsyncEngineArgs -from vllm.entrypoints.openai.tool_parsers import ToolParserManager +from vllm.tool_parsers import ToolParserManager from vllm._version import version from vllm.usage.usage_lib import UsageContext from vllm.config import ModelConfig From 9df2feb943d1a8f6ab73a7246b2fdeb6bd4453c5 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 9 Feb 2026 14:23:03 +0000 Subject: [PATCH 13/85] tmp changes to not install dependencies which are in base image already --- pyproject.toml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7fc9978a..5919113a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,15 +14,15 @@ authors = [ ] dependencies = [ "aiohttp>=3.9.0", - "torch>=2.6", - "vllm==0.8.5.post1", + #"torch>=2.6", + #"vllm==0.8.5.post1", "accelerate==1.7.0", "deepspeed==0.15.4", "browsergym>=0.13.0", "datasets>=2.21.0", - "transformers==4.51.1" , + #"transformers==4.51.1" , "fastapi>=0.115.0", - "flash-attn==2.7.4.post1", + #"flash-attn==2.7.4.post1", "joblib>=1.3.2", "jsonref>=1.1.0", "litellm>=1.61.0", @@ -37,7 +37,7 @@ dependencies = [ "orjson==3.10.16", "requests>=2.31.0", "redis==5.2.1", - "safetensors>=0.4.0", + #"safetensors>=0.4.0", "tenacity>=8.2.0", "uvicorn>=0.29.0", "uvloop>=0.19.0", From 49707aac68a0a7f8ad8521a21fe67bb3e67a8b40 Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 13 Feb 2026 15:22:25 +0000 Subject: [PATCH 14/85] 2 gpu and 1 gpu integration tests --- tests/__init__.py | 1 + tests/conftest.py | 196 +++++ tests/distributed_trainer_helper.py | 899 +++++++++++++++++++++ tests/sync_helper.py | 110 +++ tests/test_vllm1_integration.py | 1154 +++++++++++++++++++++++++++ tests/vllm_engine_helper.py | 612 ++++++++++++++ tests/weight_update_utils.py | 53 ++ 7 files changed, 3025 insertions(+) create mode 100644 tests/__init__.py create mode 100644 tests/conftest.py create mode 100755 tests/distributed_trainer_helper.py create mode 100644 tests/sync_helper.py create mode 100644 tests/test_vllm1_integration.py create mode 100755 tests/vllm_engine_helper.py create mode 100644 tests/weight_update_utils.py diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..9b491dd0 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for PipelineRL vLLM integration.""" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..e33b8261 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,196 @@ +"""Pytest configuration and fixtures for vllm1 tests.""" + +import os +import pytest +import torch +import tempfile +from pathlib import Path +import subprocess +import sys + +from pipelinerl.vllm1 import EngineManager + + +@pytest.fixture(scope="session") +def model_name(): + """Model to use for testing.""" + return "Qwen/Qwen2.5-0.5B-Instruct" + + +@pytest.fixture(scope="session") +def sample_prompts(): + """Sample prompts for generation testing.""" + return [ + "Write a haiku about coding:", + "The capital of France is", + "In a galaxy far away,", + ] + + +@pytest.fixture(scope="session") +def simple_prompt(): + """Single simple prompt for deterministic testing.""" + return "The capital of France is" + + +@pytest.fixture(scope="session") +def num_gpus(): + """Number of GPUs available.""" + return torch.cuda.device_count() + + +@pytest.fixture(scope="session") +def require_2_gpus(num_gpus): + """Skip test if less than 2 GPUs available.""" + if num_gpus < 2: + pytest.skip("Test requires at least 2 GPUs") + + +@pytest.fixture(scope="session") +def require_gpu(): + """Skip test if no GPU available.""" + if not torch.cuda.is_available(): + pytest.skip("Test requires GPU") + + +@pytest.fixture +def temp_dir(): + """Temporary directory for test files.""" + with tempfile.TemporaryDirectory() as tmpdir: + yield Path(tmpdir) + + +@pytest.fixture(scope="session") +def shared_test_dir(): + """Session-scoped shared directory for test data that persists across tests. + + Use this for data that needs to be shared between tests (like perturbed weights). + """ + with tempfile.TemporaryDirectory() as tmpdir: + yield Path(tmpdir) + + +@pytest.fixture +def distributed_init_method(temp_dir): + """File-based init method for distributed testing.""" + return f"file://{temp_dir}/dist_init" + + +@pytest.fixture(scope="session") +def shared_distributed_init_method(shared_test_dir): + """Session-scoped file-based init method for tests that share data.""" + return f"file://{shared_test_dir}/dist_init" + + +@pytest.fixture(scope="session") +def cache_dir(): + """Directory for caching downloaded models.""" + cache_path = Path(os.environ.get("HF_HOME", Path.home() / ".cache" / "huggingface")) + cache_path.mkdir(parents=True, exist_ok=True) + return cache_path + + +@pytest.fixture +def vllm_server_port(): + """Port for vLLM server in tests.""" + # Use a high port to avoid conflicts + return 8765 + + +@pytest.fixture +def generation_config(): + """Configuration for deterministic generation.""" + return { + "temperature": 0.0, + "top_p": 1.0, + "max_tokens": 50, + "seed": 42, + } + + +@pytest.fixture +def vllm_engine_factory_2gpu(model_name): + """Factory fixture that defaults to 2 GPUs. + + Usage: + async with vllm_engine_factory_2gpu() as manager: + # Uses 2 GPUs by default + # Access engine via manager.engine + ... + """ + def _factory(tensor_parallel_size: int = 2, **kwargs): + """Create engine with 2 GPUs by default.""" + import argparse + + args = argparse.Namespace( + model=model_name, + tensor_parallel_size=tensor_parallel_size, + disable_log_stats=True, + enable_log_requests=False, + **kwargs + ) + + return EngineManager.create_engine(args) + + return _factory + + +@pytest.fixture +def vllm_engine_factory(model_name): + """Factory fixture for creating vLLM engines. + + Usage in tests: + async with vllm_engine_factory() as manager: + # use manager.engine for generation + ... + # automatic cleanup + + Or with custom config: + async with vllm_engine_factory(tensor_parallel_size=2) as manager: + # use manager.engine with 2 GPUs + ... + + Or if you need engine_config: + async with vllm_engine_factory() as manager: + # access manager.engine, manager.engine_config, manager.args + ... + """ + def _factory(tensor_parallel_size: int = 1, **kwargs): + """Create engine context manager with test defaults. + + Args: + tensor_parallel_size: Number of GPUs + **kwargs: Additional attributes for args object + + Returns: + Async context manager for EngineManager + """ + import argparse + + # Create minimal args object with required attributes for AsyncEngineArgs.from_cli_args() + args = argparse.Namespace( + model=model_name, + tensor_parallel_size=tensor_parallel_size, + disable_log_stats=True, + enable_log_requests=False, + # Apply any additional kwargs + **kwargs + ) + + print("args: ", args) + + return EngineManager.create_engine(args) + + return _factory + + +@pytest.fixture +def distributed_trainer_helper(): + """Path to the distributed trainer helper script.""" + return Path(__file__).parent / "distributed_trainer_helper.py" + + +@pytest.fixture +def vllm_engine_helper(): + """Path to the vLLM engine helper script.""" + return Path(__file__).parent / "vllm_engine_helper.py" diff --git a/tests/distributed_trainer_helper.py b/tests/distributed_trainer_helper.py new file mode 100755 index 00000000..ac60959f --- /dev/null +++ b/tests/distributed_trainer_helper.py @@ -0,0 +1,899 @@ +#!/usr/bin/env python3 +"""Helper script for distributed trainer process. + +This script is run as a separate process with CUDA_VISIBLE_DEVICES set, +allowing proper GPU isolation for distributed tests. +""" + +import sys +import argparse +import logging + +# Setup debug logging +logging.basicConfig( + level=logging.DEBUG, + format="[%(asctime)s] [TRAINER-%(levelname)s] %(message)s", + datefmt="%H:%M:%S", +) +logger = logging.getLogger(__name__) + + +def init_process_group(init_method: str, rank: int, world_size: int): + """Initialize a distributed process group and wait.""" + import torch.distributed as dist + import time + import pipelinerl.torch_utils + + print(f"[Trainer rank={rank}] Initializing process group") + process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=init_method, + rank=rank, + world_size=world_size, + ) + print(f"[Trainer rank={rank}] Process group initialized successfully") + + # Wait for coordination + time.sleep(3) + + print(f"[Trainer rank={rank}] Destroying process group") + dist.destroy_process_group(process_group) + print(f"[Trainer rank={rank}] Process group destroyed") + + +def save_model_to_dir(state_dict: dict, output_dir: str, model_name: str): + """Save state_dict to a directory as safetensors with config. + + Args: + state_dict: Model state dict to save + output_dir: Directory to save model + model_name: Original model name to copy config from + """ + from pathlib import Path + from safetensors.torch import save_file + import shutil + import json + + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + # Save weights as safetensors + safetensors_path = output_path / "model.safetensors" + save_file(state_dict, str(safetensors_path)) + print(f"[Trainer] Saved model weights to {safetensors_path}") + + # Copy config.json from original model + original_path = Path(model_name) + if not original_path.exists(): + # Download if needed + from huggingface_hub import snapshot_download + + original_path = Path(snapshot_download(model_name)) + + config_src = original_path / "config.json" + config_dst = output_path / "config.json" + shutil.copy(config_src, config_dst) + print(f"[Trainer] Copied config.json to {config_dst}") + + # Copy tokenizer files + for filename in [ + "tokenizer.json", + "tokenizer_config.json", + "special_tokens_map.json", + "vocab.json", + "merges.txt", + "tokenizer.model", + ]: + src = original_path / filename + if src.exists(): + dst = output_path / filename + shutil.copy(src, dst) + print(f"[Trainer] Copied {filename}") + + return str(output_path) + + +def broadcast_weights( + init_method: str, model_name: str, perturb: bool = False, sync_dir: str = None +): + """Load model and broadcast weights to vLLM worker.""" + import torch + import torch.distributed as dist + from transformers import AutoModelForCausalLM + from pathlib import Path + import pipelinerl.torch_utils + + # Setup sync points if provided + if sync_dir: + sys.path.insert(0, str(Path(__file__).parent)) + from sync_helper import SyncPoint, write_weight_update_request + + sync_path = Path(sync_dir) + baseline_done = SyncPoint(sync_path, "baseline_done") + ready_to_receive = SyncPoint(sync_path, "ready_to_receive") + request_ready = SyncPoint(sync_path, "request_ready") + receiving_started = SyncPoint(sync_path, "receiving_started") + broadcast_done = SyncPoint(sync_path, "broadcast_done") + + # IMPORTANT: Initialize process group FIRST (before any waiting) + # Use the same init_extra_process_group as vLLM to create the SAME process group + print("[Trainer] Initializing process group as rank 0") + process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=init_method, + rank=0, + world_size=2, + ) + print("[Trainer] Process group initialized") + + # Now wait for vLLM to finish baseline and be ready to receive + if sync_dir: + print("[Trainer] Waiting for vLLM to finish baseline generation...") + baseline_done.wait(timeout=60) + print("[Trainer] Baseline done") + + print("[Trainer] Waiting for vLLM to be ready to receive weights...") + ready_to_receive.wait(timeout=60) + print("[Trainer] vLLM ready, starting weight broadcast") + + # Load tensors directly from safetensors files (not the full model) + print(f"[Trainer] Loading tensors from safetensors for {model_name}") + from pathlib import Path + import json + from safetensors.torch import load_file + from huggingface_hub import snapshot_download + + # Handle both local paths and HuggingFace model IDs + model_path = Path(model_name) + if not model_path.exists(): + # Download from HuggingFace Hub + print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") + model_path = Path(snapshot_download(model_name)) + + index_file = model_path / "model.safetensors.index.json" + + # Load state_dict from safetensors files + if index_file.exists(): + # Sharded model - use index to load from multiple files + print(f"[Trainer] Found index file, loading sharded model") + with open(index_file) as f: + index = json.load(f) + + weight_map = index["weight_map"] # {param_name: filename} + + # Group parameters by file to load each file only once + file_to_params = {} + for param_name, filename in weight_map.items(): + if filename not in file_to_params: + file_to_params[filename] = [] + file_to_params[filename].append(param_name) + + # Load all tensors + state_dict = {} + for filename, param_names in file_to_params.items(): + file_path = model_path / filename + print(f"[Trainer] Loading {len(param_names)} parameters from {filename}") + tensors = load_file(str(file_path), device="cuda:0") + for param_name in param_names: + state_dict[param_name] = tensors[param_name] + else: + # Single file model + safetensors_file = model_path / "model.safetensors" + print(f"[Trainer] Loading from single file: {safetensors_file}") + state_dict = load_file(str(safetensors_file), device="cuda:0") + + print(f"[Trainer] Loaded {len(state_dict)} parameters from safetensors") + + # Fast-LLM broadcasts weights as they are in safetensors files + # No filtering - vLLM handles its own implementation details + params_to_broadcast = state_dict + print(f"[Trainer] Will broadcast {len(params_to_broadcast)} parameters") + + # Create and send WeightUpdateRequest to vLLM + if sync_dir: + from weight_update_utils import create_weight_update_request_from_state_dict + + print("[Trainer] Creating WeightUpdateRequest...") + request = create_weight_update_request_from_state_dict( + params_to_broadcast, version=1 + ) + write_weight_update_request(sync_path, request) + request_ready.signal() + print( + f"[Trainer] Sent WeightUpdateRequest with {len(request.parameters_info)} parameters" + ) + + # Wait for vLLM to start receiving before we broadcast + print("[Trainer] Waiting for vLLM to start receiving...") + receiving_started.wait(timeout=60) + print("[Trainer] vLLM is receiving, starting broadcast") + + print(f"[Trainer] Broadcasting {len(params_to_broadcast)} parameters") + + # Optionally perturb weights - add noise to ALL tensors + if perturb: + logger.info("Perturbing ALL weights with seed=42") + torch.manual_seed(42) + for name, tensor in params_to_broadcast.items(): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + noise = torch.randn_like(tensor) * 0.001 # Smaller noise to avoid breaking model + tensor.add_(noise) + print(f"[Trainer] Perturbed all {len(params_to_broadcast)} tensors with noise=0.001, seed=42") + + # Broadcast each weight with detailed logging + logger.info(f"Starting broadcast of {len(params_to_broadcast)} parameters") + for i, (name, tensor) in enumerate(params_to_broadcast.items()): + logger.debug(f"[{i+1}/{len(state_dict)}] Preparing to broadcast: {name}") + logger.debug( + f" - shape: {tensor.shape}, dtype: {tensor.dtype}, device: {tensor.device}" + ) + + # Move to GPU if needed + if tensor.device.type != "cuda": + logger.debug(f" - Moving {name} to CUDA") + tensor = tensor.cuda(0) + logger.debug(f" - {name} now on device: {tensor.device}") + + logger.debug(f" - Calling dist.broadcast for {name}...") + dist.broadcast(tensor, src=0, group=process_group) + logger.debug(f" - Broadcast complete for {name}") + + if (i + 1) % 10 == 0: + logger.info(f"Broadcasted {i+1}/{len(params_to_broadcast)} parameters") + + print(f"[Trainer] All {len(params_to_broadcast)} parameters broadcasted") + + # Signal broadcast complete BEFORE destroying process group + # This ensures vLLM sees the signal before trainer exits + if sync_dir: + broadcast_done.signal() + print("[Trainer] Signaled broadcast complete") + + dist.destroy_process_group(process_group) + print("[Trainer] Process group destroyed") + + +def broadcast_cross_validation( + init_method: str, model_name: str, sync_dir: str, temp_dir: str +): + """Cross-validation test: broadcast perturbed, then original weights. + + Also saves perturbed model to disk for vLLM to load. + """ + import torch + import torch.distributed as dist + from pathlib import Path + import json + import pipelinerl.torch_utils + from safetensors.torch import load_file + from huggingface_hub import snapshot_download + + sys.path.insert(0, str(Path(__file__).parent)) + from sync_helper import SyncPoint, write_weight_update_request + + sync_path = Path(sync_dir) + baseline_done = SyncPoint(sync_path, "baseline_done") + perturbed_model_saved = SyncPoint(sync_path, "perturbed_model_saved") + ready_to_receive_perturbed = SyncPoint(sync_path, "ready_to_receive_perturbed") + perturbed_broadcast_done = SyncPoint(sync_path, "perturbed_broadcast_done") + mod1_done = SyncPoint(sync_path, "mod1_done") + first_engine_destroyed = SyncPoint(sync_path, "first_engine_destroyed") + engine_recreated = SyncPoint(sync_path, "engine_recreated") + ready_to_receive_original = SyncPoint(sync_path, "ready_to_receive_original") + original_broadcast_done = SyncPoint(sync_path, "original_broadcast_done") + + # Initialize process group + print("[Trainer] Initializing process group as rank 0") + process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=init_method, + rank=0, + world_size=2, + ) + print("[Trainer] Process group initialized") + + # Wait for baseline + print("[Trainer] Waiting for vLLM baseline generation...") + baseline_done.wait(timeout=120) + + # Load original model + print(f"[Trainer] Loading original model {model_name}") + model_path = Path(model_name) + if not model_path.exists(): + print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") + model_path = Path(snapshot_download(model_name)) + + index_file = model_path / "model.safetensors.index.json" + if index_file.exists(): + print(f"[Trainer] Loading sharded model") + with open(index_file) as f: + index = json.load(f) + weight_map = index["weight_map"] + file_to_params = {} + for param_name, filename in weight_map.items(): + if filename not in file_to_params: + file_to_params[filename] = [] + file_to_params[filename].append(param_name) + + original_state_dict = {} + for filename, param_names in file_to_params.items(): + file_path = model_path / filename + tensors = load_file(str(file_path), device="cuda:0") + for param_name in param_names: + original_state_dict[param_name] = tensors[param_name] + else: + safetensors_file = model_path / "model.safetensors" + original_state_dict = load_file(str(safetensors_file), device="cuda:0") + + print(f"[Trainer] Loaded {len(original_state_dict)} original parameters") + + # Create perturbed version - add noise to ALL tensors + print("[Trainer] Creating perturbed weights (all tensors) with seed=42...") + torch.manual_seed(42) + perturbed_state_dict = {} + for name, tensor in original_state_dict.items(): + perturbed_tensor = tensor.clone() + # Add smaller noise to avoid completely breaking the model + noise = torch.randn_like(perturbed_tensor) * 0.001 # Reduced from 0.01 + perturbed_tensor.add_(noise) + perturbed_state_dict[name] = perturbed_tensor + print(f"[Trainer] Perturbed all {len(perturbed_state_dict)} tensors with noise=0.001, seed=42") + + # Save perturbed model to disk + perturbed_model_dir = Path(temp_dir) / "perturbed_model" + print(f"[Trainer] Saving perturbed model to {perturbed_model_dir}") + saved_path = save_model_to_dir( + perturbed_state_dict, str(perturbed_model_dir), str(model_path) + ) + + # Write perturbed model path to sync file + path_file = sync_path / "perturbed_model_path.txt" + path_file.write_text(saved_path) + perturbed_model_saved.signal() + print(f"[Trainer] Signaled perturbed model saved at: {saved_path}") + + # Wait for vLLM to be ready to receive perturbed weights + print("[Trainer] Waiting for vLLM to be ready for perturbed broadcast...") + ready_to_receive_perturbed.wait(timeout=120) + + # Broadcast perturbed weights + print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters") + from weight_update_utils import create_weight_update_request_from_state_dict + + request = create_weight_update_request_from_state_dict( + perturbed_state_dict, version=1 + ) + write_weight_update_request(sync_path, request) + + for i, (name, tensor) in enumerate(perturbed_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print( + f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} perturbed parameters" + ) + + perturbed_broadcast_done.signal() + print("[Trainer] Perturbed weights broadcast complete") + + # Wait for vLLM to finish generating res_mod_1 + print("[Trainer] Waiting for vLLM to finish res_mod_1...") + mod1_done.wait(timeout=120) + + # Destroy our process group immediately after we're done using it + # No need to wait for vLLM - destroy_process_group() is a local operation + print("[Trainer] Destroying process group for first broadcast") + dist.destroy_process_group(process_group) + + # Wait for vLLM to destroy its first engine before creating new groups + print("[Trainer] Waiting for vLLM to destroy first engine...") + first_engine_destroyed.wait(timeout=120) + + # Recreate our process group BEFORE vLLM creates its engine + # (vLLM will rendezvous with us when it creates engine 2) + print("[Trainer] Recreating process group for second broadcast") + process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=init_method, + rank=0, + world_size=2, + ) + print("[Trainer] Process group recreated, waiting at rendezvous...") + + # Wait for vLLM to recreate engine (confirms rendezvous completed) + print("[Trainer] Waiting for vLLM to recreate engine...") + engine_recreated.wait(timeout=300) # 5 minutes - engine creation can be slow + print("[Trainer] vLLM engine recreated, both in new process group") + + # Wait for vLLM to be ready for original weights + print("[Trainer] Waiting for vLLM to be ready for original broadcast...") + ready_to_receive_original.wait(timeout=120) + + # Broadcast original weights + print(f"[Trainer] Broadcasting {len(original_state_dict)} original parameters") + from weight_update_utils import create_weight_update_request_from_state_dict + + request = create_weight_update_request_from_state_dict( + original_state_dict, version=2 + ) + write_weight_update_request(sync_path, request) + + for i, (name, tensor) in enumerate(original_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print( + f"[Trainer] Broadcasted {i+1}/{len(original_state_dict)} original parameters" + ) + + original_broadcast_done.signal() + print("[Trainer] Original weights broadcast complete") + + # Cleanup + dist.destroy_process_group(process_group) + print("[Trainer] Process group destroyed") + + +def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): + """Back-and-forth test: broadcast perturbed → original → perturbed again. + + Tests that we can switch between weight sets multiple times. + """ + import torch + import torch.distributed as dist + from pathlib import Path + import json + import pipelinerl.torch_utils + from safetensors.torch import load_file + from huggingface_hub import snapshot_download + + sys.path.insert(0, str(Path(__file__).parent)) + from sync_helper import SyncPoint, write_weight_update_request + + sync_path = Path(sync_dir) + baseline_done = SyncPoint(sync_path, "baseline_done") + ready_for_perturbed1 = SyncPoint(sync_path, "ready_for_perturbed1") + perturbed1_done = SyncPoint(sync_path, "perturbed1_done") + ready_for_original = SyncPoint(sync_path, "ready_for_original") + original_done = SyncPoint(sync_path, "original_done") + ready_for_perturbed2 = SyncPoint(sync_path, "ready_for_perturbed2") + perturbed2_done = SyncPoint(sync_path, "perturbed2_done") + + # Initialize process group + print("[Trainer] Initializing process group as rank 0") + process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=init_method, + rank=0, + world_size=2, + ) + print("[Trainer] Process group initialized") + + # Wait for baseline + print("[Trainer] Waiting for vLLM baseline generation...") + baseline_done.wait(timeout=120) + + # Load original model + print(f"[Trainer] Loading model {model_name}") + model_path = Path(model_name) + if not model_path.exists(): + print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") + model_path = Path(snapshot_download(model_name)) + + index_file = model_path / "model.safetensors.index.json" + if index_file.exists(): + print(f"[Trainer] Loading sharded model") + with open(index_file) as f: + index = json.load(f) + weight_map = index["weight_map"] + file_to_params = {} + for param_name, filename in weight_map.items(): + if filename not in file_to_params: + file_to_params[filename] = [] + file_to_params[filename].append(param_name) + + original_state_dict = {} + for filename, param_names in file_to_params.items(): + file_path = model_path / filename + tensors = load_file(str(file_path), device="cuda:0") + for param_name in param_names: + original_state_dict[param_name] = tensors[param_name] + else: + safetensors_file = model_path / "model.safetensors" + original_state_dict = load_file(str(safetensors_file), device="cuda:0") + + print(f"[Trainer] Loaded {len(original_state_dict)} original parameters") + + # Create perturbed version + print("[Trainer] Creating perturbed weights with seed=42...") + torch.manual_seed(42) + perturbed_state_dict = {} + for name, tensor in original_state_dict.items(): + perturbed_tensor = tensor.clone() + noise = torch.randn_like(perturbed_tensor) * 0.001 + perturbed_tensor.add_(noise) + perturbed_state_dict[name] = perturbed_tensor + print(f"[Trainer] Perturbed all {len(perturbed_state_dict)} tensors with noise=0.001, seed=42") + + # Save perturbed weights for reuse in server tests + perturbed_weights_dir = Path(sync_dir) / "perturbed_weights" + print(f"[Trainer] Saving perturbed weights to {perturbed_weights_dir}") + saved_path = save_model_to_dir( + perturbed_state_dict, str(perturbed_weights_dir), str(model_path) + ) + print(f"[Trainer] Perturbed weights saved to {saved_path}") + + # Broadcast 1: Perturbed weights + print("[Trainer] Waiting for vLLM to be ready for first perturbed broadcast...") + ready_for_perturbed1.wait(timeout=120) + + print(f"[Trainer] Broadcasting perturbed weights (1st time)") + from weight_update_utils import create_weight_update_request_from_state_dict + request = create_weight_update_request_from_state_dict(perturbed_state_dict, version=1) + write_weight_update_request(sync_path, request) + + for i, (name, tensor) in enumerate(perturbed_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print(f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} parameters") + + perturbed1_done.signal() + print("[Trainer] First perturbed broadcast complete") + + # Broadcast 2: Original weights + print("[Trainer] Waiting for vLLM to be ready for original broadcast...") + ready_for_original.wait(timeout=120) + + print(f"[Trainer] Broadcasting original weights") + from weight_update_utils import create_weight_update_request_from_state_dict + + request = create_weight_update_request_from_state_dict(original_state_dict, version=2) + write_weight_update_request(sync_path, request) + + for i, (name, tensor) in enumerate(original_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print(f"[Trainer] Broadcasted {i+1}/{len(original_state_dict)} parameters") + + original_done.signal() + print("[Trainer] Original broadcast complete") + + # Broadcast 3: Perturbed weights again (same as first) + print("[Trainer] Waiting for vLLM to be ready for second perturbed broadcast...") + ready_for_perturbed2.wait(timeout=120) + + print(f"[Trainer] Broadcasting perturbed weights (2nd time)") + from weight_update_utils import create_weight_update_request_from_state_dict + + request = create_weight_update_request_from_state_dict(perturbed_state_dict, version=3) + write_weight_update_request(sync_path, request) + + for i, (name, tensor) in enumerate(perturbed_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print(f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} parameters") + + perturbed2_done.signal() + print("[Trainer] Second perturbed broadcast complete") + + # Cleanup + dist.destroy_process_group(process_group) + print("[Trainer] Process group destroyed") + + +def timed_broadcast_server_test( + init_method: str, model_name: str, server_url: str +): + """Timed broadcast for server tests: perturbed → original → perturbed with delays. + + This simulates a real-world scenario where weight updates happen while + the server is running and serving requests. + + Pattern: original (server default) → perturbed → original → perturbed + + Args: + init_method: Distributed init method + model_name: Model name to load + server_url: Base URL of vLLM server (e.g., "http://127.0.0.1:8000") + """ + import torch + import torch.distributed as dist + from pathlib import Path + import json + import pipelinerl.torch_utils + from safetensors.torch import load_file + from huggingface_hub import snapshot_download + import time + import requests + import threading + + sys.path.insert(0, str(Path(__file__).parent)) + from weight_update_utils import create_weight_update_request_from_state_dict + + # Initialize process group + print("[Trainer] Initializing process group as rank 0") + process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=init_method, + rank=0, + world_size=2, + ) + print("[Trainer] Process group initialized") + + # Wait for server to be ready by polling health endpoint + print("[Trainer] Waiting for server to be ready...") + server_ready = False + for i in range(120): # Try for up to 2 minutes + try: + resp = requests.get(f"{server_url}/health", timeout=1) + if resp.status_code == 200: + server_ready = True + print(f"[Trainer] Server is ready (took {i} seconds)") + break + except requests.exceptions.RequestException: + pass + time.sleep(1) + + if not server_ready: + raise TimeoutError("Server did not become ready within 2 minutes") + + # Wait additional 10 seconds for server to fully initialize + print("[Trainer] Waiting additional 10 seconds for server to fully initialize...") + time.sleep(10) + + # Load original weights + print(f"[Trainer] Loading original weights from {model_name}") + model_path = Path(model_name) + if not model_path.exists(): + print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") + model_path = Path(snapshot_download(model_name)) + + index_file = model_path / "model.safetensors.index.json" + if index_file.exists(): + print(f"[Trainer] Loading sharded original model") + with open(index_file) as f: + index = json.load(f) + weight_map = index["weight_map"] + file_to_params = {} + for param_name, filename in weight_map.items(): + if filename not in file_to_params: + file_to_params[filename] = [] + file_to_params[filename].append(param_name) + + original_state_dict = {} + for filename, param_names in file_to_params.items(): + file_path = model_path / filename + tensors = load_file(str(file_path), device="cuda:0") + for param_name in param_names: + original_state_dict[param_name] = tensors[param_name] + else: + safetensors_file = model_path / "model.safetensors" + original_state_dict = load_file(str(safetensors_file), device="cuda:0") + + print(f"[Trainer] Loaded {len(original_state_dict)} original parameters") + + # Create perturbed weights + print("[Trainer] Creating perturbed weights with seed=42...") + torch.manual_seed(42) + perturbed_state_dict = {} + for name, tensor in original_state_dict.items(): + perturbed_tensor = tensor.clone() + noise = torch.randn_like(perturbed_tensor) * 0.001 + perturbed_tensor.add_(noise) + perturbed_state_dict[name] = perturbed_tensor + print(f"[Trainer] Perturbed all {len(perturbed_state_dict)} tensors with noise=0.001, seed=42") + + # Broadcast 1: Perturbed weights + print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters") + + request = create_weight_update_request_from_state_dict( + perturbed_state_dict, version=1 + ) + + # POST request to server in background thread (it will block until broadcast completes) + post_result = {"error": None} + def post_weight_update(): + try: + print("[Trainer] POSTing weight update request to server...") + resp = requests.post( + f"{server_url}/receive_weight_update", + json=request.model_dump(), + timeout=600, # 10 minutes + ) + if resp.status_code != 200: + post_result["error"] = f"POST failed with status {resp.status_code}: {resp.text}" + else: + print("[Trainer] Server acknowledged weight update") + except Exception as e: + post_result["error"] = f"POST failed: {e}" + + post_thread = threading.Thread(target=post_weight_update, daemon=False) + post_thread.start() + + # Give server a moment to start receiving + time.sleep(0.5) + + # Now broadcast via NCCL + for i, (name, tensor) in enumerate(perturbed_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print( + f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} perturbed parameters" + ) + + # Wait for POST to complete + post_thread.join(timeout=60) + if post_result["error"]: + raise RuntimeError(f"Weight update POST failed: {post_result['error']}") + + print("[Trainer] Perturbed weights broadcast complete") + + # Wait 5 seconds + print("[Trainer] Waiting 5 seconds before broadcasting original weights...") + time.sleep(5) + + # Broadcast 2: Original weights + print(f"[Trainer] Broadcasting {len(original_state_dict)} original parameters") + request = create_weight_update_request_from_state_dict( + original_state_dict, version=2 + ) + + # POST request to server in background thread + post_result = {"error": None} + def post_weight_update(): + try: + print("[Trainer] POSTing weight update request to server...") + resp = requests.post( + f"{server_url}/receive_weight_update", + json=request.model_dump(), + timeout=600, + ) + if resp.status_code != 200: + post_result["error"] = f"POST failed with status {resp.status_code}: {resp.text}" + else: + print("[Trainer] Server acknowledged weight update") + except Exception as e: + post_result["error"] = f"POST failed: {e}" + + post_thread = threading.Thread(target=post_weight_update, daemon=False) + post_thread.start() + time.sleep(0.5) + + for i, (name, tensor) in enumerate(original_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print( + f"[Trainer] Broadcasted {i+1}/{len(original_state_dict)} original parameters" + ) + + post_thread.join(timeout=60) + if post_result["error"]: + raise RuntimeError(f"Weight update POST failed: {post_result['error']}") + + print("[Trainer] Original weights broadcast complete") + + # Wait 5 seconds + print("[Trainer] Waiting 5 seconds before broadcasting perturbed weights again...") + time.sleep(5) + + # Broadcast 3: Perturbed weights again (same as first) + print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters (2nd time)") + request = create_weight_update_request_from_state_dict( + perturbed_state_dict, version=3 + ) + + # POST request to server in background thread + post_result = {"error": None} + def post_weight_update(): + try: + print("[Trainer] POSTing weight update request to server...") + resp = requests.post( + f"{server_url}/receive_weight_update", + json=request.model_dump(), + timeout=600, + ) + if resp.status_code != 200: + post_result["error"] = f"POST failed with status {resp.status_code}: {resp.text}" + else: + print("[Trainer] Server acknowledged weight update") + except Exception as e: + post_result["error"] = f"POST failed: {e}" + + post_thread = threading.Thread(target=post_weight_update, daemon=False) + post_thread.start() + time.sleep(0.5) + + for i, (name, tensor) in enumerate(perturbed_state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % 50 == 0: + print( + f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} perturbed parameters" + ) + + post_thread.join(timeout=60) + if post_result["error"]: + raise RuntimeError(f"Weight update POST failed: {post_result['error']}") + + print("[Trainer] Perturbed weights broadcast complete (2nd time)") + + # Cleanup + dist.destroy_process_group(process_group) + print("[Trainer] Process group destroyed, exiting") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Distributed trainer helper") + parser.add_argument("command", choices=["init", "broadcast", "cross_validation", "back_and_forth", "timed_broadcast_server_test"]) + parser.add_argument("--init-method", required=True) + parser.add_argument("--rank", type=int, default=0) + parser.add_argument("--world-size", type=int, default=2) + parser.add_argument("--model-name", type=str) + parser.add_argument("--perturb", action="store_true") + parser.add_argument("--sync-dir", type=str, help="Directory for sync files") + parser.add_argument( + "--temp-dir", type=str, help="Temporary directory for saving models" + ) + parser.add_argument( + "--server-url", type=str, help="Base URL of vLLM server (e.g., http://127.0.0.1:8000)" + ) + + args = parser.parse_args() + + try: + if args.command == "init": + init_process_group(args.init_method, args.rank, args.world_size) + elif args.command == "broadcast": + if not args.model_name: + print("Error: --model-name required for broadcast command") + sys.exit(1) + broadcast_weights( + args.init_method, args.model_name, args.perturb, args.sync_dir + ) + elif args.command == "cross_validation": + if not args.model_name or not args.sync_dir or not args.temp_dir: + print( + "Error: --model-name, --sync-dir, and --temp-dir required for cross_validation" + ) + sys.exit(1) + broadcast_cross_validation( + args.init_method, args.model_name, args.sync_dir, args.temp_dir + ) + elif args.command == "back_and_forth": + if not args.model_name or not args.sync_dir: + print("Error: --model-name and --sync-dir required for back_and_forth") + sys.exit(1) + broadcast_back_and_forth(args.init_method, args.model_name, args.sync_dir) + elif args.command == "timed_broadcast_server_test": + if not args.model_name or not args.server_url: + print("Error: --model-name and --server-url required for timed_broadcast_server_test") + sys.exit(1) + timed_broadcast_server_test( + args.init_method, args.model_name, args.server_url + ) + except Exception as e: + print(f"[Trainer] Error: {e}") + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/tests/sync_helper.py b/tests/sync_helper.py new file mode 100644 index 00000000..73ebed82 --- /dev/null +++ b/tests/sync_helper.py @@ -0,0 +1,110 @@ +"""Simple file-based synchronization for distributed test processes.""" + +import time +from pathlib import Path + + +class SyncPoint: + """File-based synchronization point for coordinating subprocesses.""" + + def __init__(self, sync_dir: Path, name: str): + """Create a sync point. + + Args: + sync_dir: Directory for sync files + name: Name of this sync point (e.g., "baseline_done") + """ + self.sync_file = sync_dir / f"{name}.sync" + self.sync_dir = sync_dir + + def signal(self): + """Signal that this point is reached.""" + self.sync_file.touch() + # Force filesystem sync to ensure file is visible immediately + import os + fd = os.open(str(self.sync_file.parent), os.O_RDONLY) + os.fsync(fd) + os.close(fd) + print(f"[Sync] Signaled: {self.sync_file.name}") + + def wait(self, timeout: float = 60): + """Wait for this point to be signaled. + + Args: + timeout: Maximum time to wait in seconds + + Raises: + TimeoutError: If sync point not reached within timeout + """ + start = time.time() + while not self.sync_file.exists(): + if time.time() - start > timeout: + raise TimeoutError( + f"Timeout waiting for sync point: {self.sync_file.name}" + ) + time.sleep(0.1) + print(f"[Sync] Reached: {self.sync_file.name}") + + def clear(self): + """Clear this sync point.""" + if self.sync_file.exists(): + self.sync_file.unlink() + + +def create_sync_dir(base_dir: Path) -> Path: + """Create a directory for sync files. + + Args: + base_dir: Base temporary directory + + Returns: + Path to sync directory + """ + sync_dir = base_dir / "sync" + sync_dir.mkdir(exist_ok=True) + return sync_dir + + +def write_weight_update_request(sync_dir: Path, request): + """Write WeightUpdateRequest to JSON file. + + Args: + sync_dir: Sync directory + request: WeightUpdateRequest object + """ + import json + + request_file = sync_dir / "weight_update_request.json" + with open(request_file, "w") as f: + json.dump(request.model_dump(), f) + print(f"[Sync] Wrote weight update request to {request_file.name}") + + +def read_weight_update_request(sync_dir: Path): + """Read WeightUpdateRequest from JSON file. + + Args: + sync_dir: Sync directory + + Returns: + WeightUpdateRequest object + """ + import json + from pipelinerl.finetune_loop import WeightUpdateRequest + + request_file = sync_dir / "weight_update_request.json" + + # Wait for file to exist + import time + timeout = 60 + start = time.time() + while not request_file.exists(): + if time.time() - start > timeout: + raise TimeoutError(f"Timeout waiting for {request_file.name}") + time.sleep(0.1) + + with open(request_file, "r") as f: + data = json.load(f) + + print(f"[Sync] Read weight update request from {request_file.name}") + return WeightUpdateRequest(**data) diff --git a/tests/test_vllm1_integration.py b/tests/test_vllm1_integration.py new file mode 100644 index 00000000..d74e49f3 --- /dev/null +++ b/tests/test_vllm1_integration.py @@ -0,0 +1,1154 @@ +"""Integration tests for vllm1 with actual distributed setup.""" + +import asyncio +import pytest +import tempfile +from pathlib import Path +from typing import Dict, List +import time +import os +import subprocess +import sys +import signal + +# torch is needed at top level for pytest.mark.skipif decorators +import torch + +try: + import psutil + HAS_PSUTIL = True +except ImportError: + HAS_PSUTIL = False + print("WARNING: psutil not available, process tree cleanup will be limited") + + +def stream_process_output(proc, name): + """Start background threads to continuously stream process stdout/stderr. + + Args: + proc: subprocess.Popen object + name: Name for logging prefix (e.g., "vLLM Server", "Trainer") + + Returns: + Tuple of (stdout_thread, stderr_thread) + """ + import threading + + def read_stream(stream, prefix): + """Read from stream and print with prefix.""" + try: + for line in iter(stream.readline, ''): + if line: + print(f"{prefix} {line.rstrip()}", flush=True) + except Exception as e: + print(f"{prefix} [Stream read error: {e}]", flush=True) + + stdout_thread = threading.Thread( + target=read_stream, + args=(proc.stdout, f"[{name} OUT]"), + daemon=True, + ) + stderr_thread = threading.Thread( + target=read_stream, + args=(proc.stderr, f"[{name} ERR]"), + daemon=True, + ) + + stdout_thread.start() + stderr_thread.start() + + return stdout_thread, stderr_thread + + +def kill_process_tree(pid, sig=signal.SIGKILL): + """Kill a process and all its children/grandchildren. + + Args: + pid: Process ID to kill + sig: Signal to send (default SIGKILL) + """ + if not HAS_PSUTIL: + # Fallback: just kill the main process + try: + os.kill(pid, sig) + except ProcessLookupError: + pass + return + + try: + parent = psutil.Process(pid) + except psutil.NoSuchProcess: + return + + # Get all children recursively + children = parent.children(recursive=True) + + # Kill children first + for child in children: + try: + print(f"[Kill] Killing child process {child.pid}") + child.send_signal(sig) + except psutil.NoSuchProcess: + pass + + # Kill parent + try: + parent.send_signal(sig) + except psutil.NoSuchProcess: + pass + + +def force_kill_process(proc, name): + """Forcefully kill a process tree and collect output. + + SIGKILL always kills the process. If communicate() hangs, it's the PIPES + that are stuck, not the process. We handle this with retries and timeouts. + + Returns: + Tuple of (stdout, stderr, returncode) + """ + # If already dead, try to get output + if proc.poll() is not None: + try: + stdout, stderr = proc.communicate(timeout=2) + return stdout, stderr, proc.returncode + except subprocess.TimeoutExpired: + print(f"[Kill] {name} already dead but pipes hung, closing...") + proc.stdout.close() if proc.stdout else None + proc.stderr.close() if proc.stderr else None + return "", "", proc.returncode + + # Kill entire process tree (including vLLM workers, trainer subprocesses, etc) + print(f"[Kill] Killing {name} process tree (PID {proc.pid})...") + kill_process_tree(proc.pid, signal.SIGKILL) + + # Wait for main process to actually die + try: + proc.wait(timeout=2) + print(f"[Kill] {name} process tree killed") + except subprocess.TimeoutExpired: + print(f"[Kill] WARNING: {name} didn't die after SIGKILL") + + # Try to read output from pipes (this is what usually hangs) + for attempt, timeout_val in enumerate([1, 2, 3], start=1): + try: + stdout, stderr = proc.communicate(timeout=timeout_val) + print(f"[Kill] {name} output collected (attempt {attempt})") + return stdout, stderr, proc.returncode + except subprocess.TimeoutExpired: + print(f"[Kill] {name} communicate() timed out (attempt {attempt})") + continue + + # Pipes are stuck - force close them + print(f"[Kill] {name} pipes stuck, force closing...") + try: + proc.stdout.close() if proc.stdout else None + proc.stderr.close() if proc.stderr else None + proc.stdin.close() if proc.stdin else None + except Exception as e: + print(f"[Kill] Error closing pipes: {e}") + + return "", "", proc.returncode if proc.returncode else -999 + + +async def wait_for_processes(processes_with_names, check_interval=0.5, timeout=60): + """Wait for multiple subprocesses to complete, printing output in real-time. + + Args: + processes_with_names: List of (subprocess.Popen, name) tuples + check_interval: How often to check process status (seconds) + timeout: Maximum time to wait for all processes (seconds) + + Raises: + RuntimeError: If any process fails or timeout is reached + """ + start_time = time.time() + + # Create async readers for each process's stdout and stderr + async def read_stream(stream, prefix): + """Read from a stream line-by-line and print with prefix.""" + loop = asyncio.get_event_loop() + try: + while True: + line = await loop.run_in_executor(None, stream.readline) + if not line: + break + print(f"{prefix} {line.rstrip()}", flush=True) + except Exception as e: + print(f"{prefix} [Read error: {e}]", flush=True) + + # Start readers for all processes + reader_tasks = [] + for proc, name in processes_with_names: + reader_tasks.append(asyncio.create_task(read_stream(proc.stdout, f"[{name} OUT]"))) + reader_tasks.append(asyncio.create_task(read_stream(proc.stderr, f"[{name} ERR]"))) + + try: + while True: + # Check if timeout exceeded + if time.time() - start_time > timeout: + print(f"\n{'='*60}", flush=True) + print("TIMEOUT: Killing all processes", flush=True) + print(f"{'='*60}\n", flush=True) + + # Kill all processes forcefully + for proc, name in processes_with_names: + if proc.poll() is None: + print(f"[Main] Killing {name}...", flush=True) + kill_process_tree(proc.pid, signal.SIGKILL) + try: + proc.wait(timeout=5) + except subprocess.TimeoutExpired: + pass + + raise RuntimeError(f"Timeout after {timeout} seconds waiting for processes") + + # Check each process + crashed_proc = None + crashed_name = None + + for proc, name in processes_with_names: + returncode = proc.poll() + if returncode is not None and returncode != 0: + crashed_proc = proc + crashed_name = name + print(f"\n{'='*60}", flush=True) + print(f"{name} process CRASHED with exit code {returncode}", flush=True) + print(f"{'='*60}\n", flush=True) + break + + # If a process crashed, kill the others + if crashed_proc is not None: + # Kill all other processes + for proc, name in processes_with_names: + if proc != crashed_proc and proc.poll() is None: + print(f"[Main] Killing {name}...", flush=True) + kill_process_tree(proc.pid, signal.SIGKILL) + try: + proc.wait(timeout=5) + except subprocess.TimeoutExpired: + pass + + raise RuntimeError( + f"{crashed_name} process failed with exit code {crashed_proc.returncode}" + ) + + # Check if all processes completed successfully + all_done = all(proc.poll() is not None for proc, _ in processes_with_names) + if all_done: + # Wait for readers to finish draining pipes + print("[Main] All processes completed, waiting for output to finish...", flush=True) + await asyncio.sleep(1) # Give readers time to finish + + print(f"\n{'='*60}", flush=True) + print("✓ All processes completed successfully", flush=True) + print(f"{'='*60}\n", flush=True) + return + + # Sleep before next check + await asyncio.sleep(check_interval) + finally: + # Cancel reader tasks + for task in reader_tasks: + if not task.done(): + task.cancel() + # Wait for cancellation + await asyncio.gather(*reader_tasks, return_exceptions=True) + + +class TestBasicGeneration: + """Test basic vLLM generation with worker extension.""" + + @pytest.mark.asyncio + @pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires GPU") + async def test_load_model_and_generate(self, vllm_engine_factory, simple_prompt, generation_config): + """Test loading model and generating text.""" + from vllm import SamplingParams + + async with vllm_engine_factory(disable_weight_updates=True) as manager: + # Generate text + sampling_params = SamplingParams( + temperature=generation_config["temperature"], + top_p=generation_config["top_p"], + max_tokens=generation_config["max_tokens"], + seed=generation_config["seed"], + ) + + request_id = "test_request_1" + async for output in manager.engine.generate( + simple_prompt, + sampling_params=sampling_params, + request_id=request_id, + ): + final_output = output + + assert final_output is not None + assert len(final_output.outputs) > 0 + assert len(final_output.outputs[0].text) > 0 + + print(f"Generated text: {final_output.outputs[0].text}") + + @pytest.mark.asyncio + @pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires GPU") + async def test_deterministic_generation(self, vllm_engine_factory, simple_prompt, generation_config): + """Test that generation is deterministic with same seed and temperature=0.""" + from vllm import SamplingParams + + async with vllm_engine_factory(disable_weight_updates=True) as manager: + sampling_params = SamplingParams( + temperature=generation_config["temperature"], + top_p=generation_config["top_p"], + max_tokens=generation_config["max_tokens"], + seed=generation_config["seed"], + ) + + # Generate twice with same parameters + outputs = [] + for i in range(2): + request_id = f"test_request_{i}" + async for output in manager.engine.generate( + simple_prompt, + sampling_params=sampling_params, + request_id=request_id, + ): + final_output = output + outputs.append(final_output.outputs[0].text) + + # Outputs should be identical + assert outputs[0] == outputs[1], f"Outputs differ: '{outputs[0]}' vs '{outputs[1]}'" + + +class TestWorkerExtension: + """Test WorkerExtension loading and methods.""" + + @pytest.mark.asyncio + @pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires GPU") + async def test_extension_loaded(self, vllm_engine_factory): + """Test that WorkerExtension is properly loaded.""" + from vllm.v1.engine.core_client import AsyncMPClient + + async with vllm_engine_factory(disable_weight_updates=True) as manager: + # Check that engine has the extension methods + assert isinstance(manager.engine.engine_core, AsyncMPClient) + + # Test that we can call the extension method + # This verifies the extension is loaded on workers + # collective_rpc_async returns a list of results (one per worker) + results = await manager.is_extension_loaded() + # Extension should be loaded on all workers + assert isinstance(results, list) + assert len(results) > 0 # At least one worker + # Results are PIDs (integers > 0) + assert all(isinstance(r, int) and r > 0 for r in results), f"Expected PIDs, got: {results}" + print(f"WorkerExtension successfully loaded on {len(results)} worker(s)") + print(f"Worker PIDs: {results}") + print(f"Unique PIDs: {len(set(results))} (indicates {len(set(results))} separate processes)") + + +class TestWeightUpdateDistributed: + """Test weight updates with 2-GPU distributed setup.""" + + @pytest.mark.timeout(300) # 5 minutes for init test + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_init_actor_update_group( + self, + model_name, + distributed_init_method, + distributed_trainer_helper, + vllm_engine_helper, + ): + """Test initializing actor update group with 2 GPUs. + + This test verifies that the process group can be initialized correctly: + - vLLM engine runs on GPU 0 as rank 1 (in subprocess) + - Dummy trainer process runs on GPU 1 as rank 0 (in subprocess) + + Both run in subprocesses to ensure proper CUDA_VISIBLE_DEVICES isolation. + """ + print("\n" + "="*60) + print("Starting distributed process group initialization test") + print("="*60) + + # Step 1: Start trainer subprocess FIRST with CUDA_VISIBLE_DEVICES=1 + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + trainer_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting trainer process (rank 0, GPU 1)") + trainer_proc = subprocess.Popen( + [ + sys.executable, + str(distributed_trainer_helper), + "init", + "--init-method", distributed_init_method, + "--rank", "0", + "--world-size", "2", + ], + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Give trainer a moment to start and begin initializing + await asyncio.sleep(1) + + # Step 2: Start vLLM engine subprocess with CUDA_VISIBLE_DEVICES=0 + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + vllm_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting vLLM engine process (rank 1, GPU 0)") + vllm_proc = subprocess.Popen( + [ + sys.executable, + str(vllm_engine_helper), + "init", # Command argument + "--model-name", model_name, + "--init-method", distributed_init_method, + "--actor-llm-idx", "0", + "--world-size", "2", + ], + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Step 3: Wait for both processes, killing all if one crashes + await wait_for_processes([ + (trainer_proc, "Trainer"), + (vllm_proc, "vLLM Engine"), + ], timeout=180) # Init test is faster, but give it 3 minutes to be safe + + @pytest.mark.timeout(1000) # 1000 seconds for broadcasting 291 parameters + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_weight_update_same_weights( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + vllm_engine_helper, + temp_dir, + ): + """Test that updating with same weights produces same output. + + This test: + 1. vLLM engine generates baseline output (in subprocess on GPU 0) + 2. Trainer waits for baseline, then broadcasts weights (in subprocess on GPU 1) + 3. vLLM engine receives update and generates again + 4. vLLM engine verifies outputs are identical + + Both run in subprocesses for proper CUDA_VISIBLE_DEVICES isolation. + Uses file-based sync points for coordination. + """ + from .sync_helper import create_sync_dir + + print("\n" + "="*60) + print("Starting weight update test (same weights)") + print("="*60) + + # Create sync directory for coordination + sync_dir = create_sync_dir(temp_dir) + print(f"[Main] Sync directory: {sync_dir}") + + # Step 1: Start vLLM engine subprocess with weight_update command + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + # NOTE: needed to pass WeightUpdateRequest to collective + vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" + # Enable DEBUG logging in vllm1.py + vllm_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting vLLM engine process (GPU 0)") + vllm_proc = subprocess.Popen( + [ + sys.executable, + str(vllm_engine_helper), + "weight_update", + "--model-name", model_name, + "--init-method", distributed_init_method, + "--actor-llm-idx", "0", + "--world-size", "2", + "--prompt", simple_prompt, + "--max-tokens", str(generation_config["max_tokens"]), + "--sync-dir", str(sync_dir), + ], + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Give vLLM engine a moment to start + await asyncio.sleep(1) + + # Step 2: Start trainer subprocess (will wait for baseline_done sync point) + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + # Enable DEBUG logging in vllm1.py (for consistency) + trainer_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting trainer process (GPU 1)") + trainer_proc = subprocess.Popen( + [ + sys.executable, + str(distributed_trainer_helper), + "broadcast", + "--init-method", distributed_init_method, + "--model-name", model_name, + "--sync-dir", str(sync_dir), + ], + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Step 3: Wait for both processes, killing all if one crashes + # 291 parameters takes ~600 seconds, so use 900s (15 min) to be safe + await wait_for_processes([ + (vllm_proc, "vLLM Engine"), + (trainer_proc, "Trainer"), + ], timeout=900) + + @pytest.mark.timeout(1000) # 1000 seconds for broadcasting 290 parameters + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_weight_update_different_weights( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + vllm_engine_helper, + temp_dir, + ): + """Test that updating with perturbed weights produces different output. + + This test: + 1. vLLM engine generates baseline output (in subprocess on GPU 0) + 2. Trainer broadcasts PERTURBED weights (in subprocess on GPU 1) + 3. vLLM engine receives update and generates again + 4. vLLM engine verifies outputs are DIFFERENT (perturbed weights changed output) + + Both run in subprocesses for proper CUDA_VISIBLE_DEVICES isolation. + Uses file-based sync points for coordination. + """ + from .sync_helper import create_sync_dir + + print("\n" + "="*60) + print("Starting weight update test (perturbed weights)") + print("="*60) + + # Create sync directory for coordination + sync_dir = create_sync_dir(temp_dir) + print(f"[Main] Sync directory: {sync_dir}") + + # Step 1: Start vLLM engine subprocess with weight_update command + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + # NOTE: needed to pass WeightUpdateRequest to collective + vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" + # Enable DEBUG logging in vllm1.py + vllm_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting vLLM engine process (GPU 0)") + vllm_proc = subprocess.Popen( + [ + sys.executable, + str(vllm_engine_helper), + "weight_update", + "--model-name", model_name, + "--init-method", distributed_init_method, + "--actor-llm-idx", "0", + "--world-size", "2", + "--prompt", simple_prompt, + "--max-tokens", str(generation_config["max_tokens"]), + "--sync-dir", str(sync_dir), + "--expect-different", # Flag to expect different outputs + ], + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Give vLLM engine a moment to start + await asyncio.sleep(1) + + # Step 2: Start trainer subprocess with --perturb flag + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + # Enable DEBUG logging in vllm1.py (for consistency) + trainer_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting trainer process (GPU 1) with --perturb") + trainer_proc = subprocess.Popen( + [ + sys.executable, + str(distributed_trainer_helper), + "broadcast", + "--init-method", distributed_init_method, + "--model-name", model_name, + "--sync-dir", str(sync_dir), + "--perturb", # Perturb weights to test different outputs + ], + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Step 3: Wait for both processes, killing all if one crashes + # 290 parameters takes ~600 seconds, so use 900s (15 min) to be safe + await wait_for_processes([ + (vllm_proc, "vLLM Engine"), + (trainer_proc, "Trainer"), + ], timeout=900) + + + @pytest.mark.timeout(2000) # 2000 seconds - this test does 2 full broadcasts + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_weight_update_cross_validation( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + vllm_engine_helper, + temp_dir, + ): + """Cross-validation test: verify broadcast = load from disk. + + This test validates that: + 1. Broadcasting weights produces same results as loading from disk + 2. Round-trip works: original → modified → original + + Flow: + - vLLM: Load original, generate res_un_1 + - Trainer: Save perturbed model to disk, broadcast perturbed weights + - vLLM: Receive perturbed, generate res_mod_1 + - vLLM: Recreate engine with perturbed model from disk, generate res_mod_2 + - Trainer: Broadcast original weights + - vLLM: Receive original, generate res_un_2 + + Assertions: + - res_un_1 == res_un_2 (original weights produce same output) + - res_mod_1 == res_mod_2 (broadcast = load from disk) + """ + from .sync_helper import create_sync_dir + + print("\n" + "="*60) + print("Starting cross-validation test") + print("="*60) + + # Create sync directory for coordination + sync_dir = create_sync_dir(temp_dir) + print(f"[Main] Sync directory: {sync_dir}") + print(f"[Main] Temp directory: {temp_dir}") + + # Step 1: Start vLLM engine subprocess + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" + vllm_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting vLLM engine process (GPU 0)") + vllm_proc = subprocess.Popen( + [ + sys.executable, + str(vllm_engine_helper), + "cross_validation", + "--model-name", model_name, + "--init-method", distributed_init_method, + "--actor-llm-idx", "0", + "--world-size", "2", + "--prompt", simple_prompt, + "--max-tokens", str(generation_config["max_tokens"]), + "--sync-dir", str(sync_dir), + ], + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Give vLLM engine a moment to start + await asyncio.sleep(1) + + # Step 2: Start trainer subprocess + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + trainer_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting trainer process (GPU 1)") + trainer_proc = subprocess.Popen( + [ + sys.executable, + str(distributed_trainer_helper), + "cross_validation", + "--init-method", distributed_init_method, + "--model-name", model_name, + "--sync-dir", str(sync_dir), + "--temp-dir", str(temp_dir), + ], + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Step 3: Wait for both processes + # This test does 2 broadcasts, so double the timeout + await wait_for_processes([ + (vllm_proc, "vLLM Engine"), + (trainer_proc, "Trainer"), + ], timeout=1800) # 30 minutes + + + @pytest.mark.timeout(2000) # 2000 seconds - this test does 3 broadcasts + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_weight_update_back_and_forth( + self, + model_name, + simple_prompt, + generation_config, + shared_distributed_init_method, + distributed_trainer_helper, + vllm_engine_helper, + shared_test_dir, + ): + """Back-and-forth test: switch between original and perturbed weights. + + This test validates that: + 1. We can update weights multiple times + 2. We can switch back and forth between weight sets + 3. Updates are deterministic and reproducible + + Flow: + - vLLM: Load original, generate res_or_1 + - Trainer: Broadcast perturbed weights + - vLLM: Receive perturbed, generate res_mod_1 + - Trainer: Broadcast original weights + - vLLM: Receive original, generate res_or_2 + - Trainer: Broadcast perturbed weights again (same as first) + - vLLM: Receive perturbed, generate res_mod_2 + + Assertions: + - res_or_1 == res_or_2 (can restore original weights) + - res_mod_1 == res_mod_2 (perturbed weights are consistent) + """ + from .sync_helper import create_sync_dir + + print("\n" + "="*60) + print("Starting back-and-forth test") + print("="*60) + + # Create sync directory for coordination + sync_dir = create_sync_dir(shared_test_dir) + print(f"[Main] Sync directory: {sync_dir}") + + # Step 1: Start vLLM engine subprocess + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" + vllm_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting vLLM engine process (GPU 0)") + vllm_proc = subprocess.Popen( + [ + sys.executable, + str(vllm_engine_helper), + "back_and_forth", + "--model-name", model_name, + "--init-method", shared_distributed_init_method, + "--actor-llm-idx", "0", + "--world-size", "2", + "--prompt", simple_prompt, + "--max-tokens", str(generation_config["max_tokens"]), + "--sync-dir", str(sync_dir), + ], + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Give vLLM engine a moment to start + await asyncio.sleep(1) + + # Step 2: Start trainer subprocess + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + trainer_env["PIPELINERL_DEBUG"] = "1" + + print("[Main] Starting trainer process (GPU 1)") + trainer_proc = subprocess.Popen( + [ + sys.executable, + str(distributed_trainer_helper), + "back_and_forth", + "--init-method", shared_distributed_init_method, + "--model-name", model_name, + "--sync-dir", str(sync_dir), + ], + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Step 3: Wait for both processes + # This test does 3 broadcasts, so use longer timeout + await wait_for_processes([ + (vllm_proc, "vLLM Engine"), + (trainer_proc, "Trainer"), + ], timeout=1800) # 30 minutes + + @pytest.mark.timeout(2400) # 40 minutes for server test + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_server_weight_update_pattern( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + temp_dir, + ): + """Server integration test: verify weight update pattern with HTTP API. + + This test validates the real-world scenario where: + 1. vLLM server is running and serving HTTP requests + 2. Trainer broadcasts weight updates while server is active + 3. Server responses change based on weight updates + + Flow: + - Start vLLM HTTP server (loads original model) + - Continuously generate via HTTP API (deterministic) + - Trainer: wait 15s → broadcast perturbed → wait 5s → broadcast original → wait 5s → broadcast perturbed + - Verify generation pattern: original → perturbed → original → perturbed + """ + import requests + import time + + print("\n" + "="*60) + print("Starting server weight update pattern test") + print("="*60) + + # Start vLLM HTTP server + server_port = 8000 + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" + + print(f"[Main] Starting vLLM HTTP server on port {server_port} (GPU 0)") + vllm_entry_point = Path(__file__).parent.parent / "pipelinerl" / "entrypoints" / "run_vllm1.py" + server_proc = subprocess.Popen( + [ + sys.executable, + str(vllm_entry_point), + "--model", model_name, + "--port", str(server_port), + "--host", "127.0.0.1", + "--actor-llm-idx", "0", + "--weight-update-group-init-method", distributed_init_method, + "--weight-update-group-world-size", "2", + ], + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Start streaming server output in background threads + print("[Main] Starting server output streaming...") + server_stdout_thread, server_stderr_thread = stream_process_output(server_proc, "vLLM Server") + + # Give server a moment to start, then immediately start trainer + # (they need to rendezvous for process group initialization) + await asyncio.sleep(1) + + # Start trainer process immediately (needed for process group rendezvous) + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + + print("[Main] Starting trainer process (GPU 1) for process group rendezvous") + trainer_proc = subprocess.Popen( + [ + sys.executable, + str(distributed_trainer_helper), + "timed_broadcast_server_test", + "--init-method", distributed_init_method, + "--model-name", model_name, + "--server-url", f"http://127.0.0.1:{server_port}", + ], + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Start streaming trainer output in background threads + print("[Main] Starting trainer output streaming...") + trainer_stdout_thread, trainer_stderr_thread = stream_process_output(trainer_proc, "Trainer") + + try: + # Wait for server to be ready + print("[Main] Waiting for server to be ready...") + server_ready = False + for i in range(300): # Wait up to 5 minutes + # Check if server process crashed + if server_proc.poll() is not None: + print(f"[Main] Server process terminated with code {server_proc.returncode}") + raise RuntimeError(f"Server process terminated with code {server_proc.returncode}") + + # Check if trainer process crashed + if trainer_proc.poll() is not None: + print(f"[Main] Trainer process terminated with code {trainer_proc.returncode}") + raise RuntimeError(f"Trainer process terminated with code {trainer_proc.returncode}") + + try: + resp = requests.get(f"http://127.0.0.1:{server_port}/health", timeout=1) + if resp.status_code == 200: + server_ready = True + print("[Main] Server is ready!") + break + except requests.exceptions.RequestException: + pass + + if i % 10 == 0: + print(f"[Main] Still waiting for server... ({i} seconds)") + await asyncio.sleep(1) + + if not server_ready: + raise TimeoutError("Server did not become ready within 5 minutes") + + # Continuously generate completions + print("[Main] Starting continuous generation loop...") + generations = [] + start_time = time.time() + generation_interval = 0.5 # Generate every 0.5 seconds (more frequent) + max_duration = 120 # Run for 120 seconds max (covers 15s + 3 broadcasts with 5s delays) + + def check_pattern_detected(generations): + """Check if we have detected the full pattern (4 phases).""" + if len(generations) < 4: + return False + + # Track when the text changes to identify phase boundaries + phases = [] + current_text = None + current_phase = [] + + for ts, text in generations: + if text != current_text: + if current_phase: + phases.append((current_text, current_phase)) + current_text = text + current_phase = [(ts, text)] + else: + current_phase.append((ts, text)) + + # Add the last phase + if current_phase: + phases.append((current_text, current_phase)) + + # Check if we have at least 4 phases + if len(phases) < 4: + return False + + # Verify the pattern: phase1 != phase2, phase3 == phase1, phase4 == phase2 + phase1_text = phases[0][0] + phase2_text = phases[1][0] + phase3_text = phases[2][0] + phase4_text = phases[3][0] + + if phase1_text == phase2_text: + return False # Phase 1 and 2 should be different + if phase3_text != phase1_text: + return False # Phase 3 should match Phase 1 + if phase4_text != phase2_text: + return False # Phase 4 should match Phase 2 + + return True + + while time.time() - start_time < max_duration: + # Check if trainer is still running + trainer_poll = trainer_proc.poll() + if trainer_poll is not None: + print(f"[Main] Trainer exited with code {trainer_poll}") + break + + try: + # Generate via HTTP API + payload = { + "model": model_name, + "prompt": simple_prompt, + "max_tokens": generation_config["max_tokens"], + "temperature": 0.0, # Deterministic + "top_p": 1.0, # Must match engine params + "seed": 42, + } + + resp = requests.post( + f"http://127.0.0.1:{server_port}/v1/completions", + json=payload, + timeout=30, + ) + + if resp.status_code == 200: + result = resp.json() + generated_text = result["choices"][0]["text"] + timestamp = time.time() - start_time + generations.append((timestamp, generated_text)) + print(f"[Main] [{timestamp:.1f}s] Generated: '{generated_text}'") + + # Check if pattern is detected - stop early if confirmed + if check_pattern_detected(generations): + print(f"[Main] Pattern detected! Stopping generation early at {timestamp:.1f}s") + break + else: + print(f"[Main] Generation failed with status {resp.status_code}") + + except requests.exceptions.RequestException as e: + print(f"[Main] Request failed: {e}") + + await asyncio.sleep(generation_interval) + + # Wait a bit more for trainer to finish + print("[Main] Waiting for trainer to finish...") + for _ in range(30): + if trainer_proc.poll() is not None: + break + await asyncio.sleep(1) + + # Analyze generation sequence + print("\n" + "="*60) + print("GENERATION SEQUENCE ANALYSIS") + print("="*60) + print(f"Total generations: {len(generations)}") + + # Print all generations + for i, (ts, text) in enumerate(generations): + print(f"[{ts:5.1f}s] Gen {i+1}: '{text[:80]}...'") + + # Identify unique generation texts and their phases + # Expected pattern: original → perturbed → original → perturbed + if len(generations) < 4: + raise AssertionError(f"Not enough generations to verify pattern (need at least 4, got {len(generations)})") + + # Track when the text changes to identify phase boundaries + phases = [] + current_text = None + current_phase = [] + + for ts, text in generations: + if text != current_text: + if current_phase: + phases.append((current_text, current_phase)) + current_text = text + current_phase = [(ts, text)] + else: + current_phase.append((ts, text)) + + # Add the last phase + if current_phase: + phases.append((current_text, current_phase)) + + print("\n" + "="*60) + print(f"Detected {len(phases)} phases:") + for i, (text, items) in enumerate(phases): + print(f"Phase {i+1}: {len(items)} generations - '{text[:60]}...'") + print("="*60) + + # Verify the pattern + assert len(phases) >= 4, f"Expected at least 4 phases (original → perturbed → original → perturbed), got {len(phases)}" + + phase1_text, phase1_items = phases[0] + phase2_text, phase2_items = phases[1] + phase3_text, phase3_items = phases[2] + phase4_text, phase4_items = phases[3] + + # Verify phase 1 (original) != phase 2 (perturbed) + assert phase1_text != phase2_text, "Phase 1 (original) and Phase 2 (perturbed) should be different" + + # Verify phase 3 (original) == phase 1 (original) + assert phase3_text == phase1_text, f"Phase 3 should match Phase 1 (original weights restored)" + + # Verify phase 4 (perturbed) == phase 2 (perturbed) + assert phase4_text == phase2_text, f"Phase 4 should match Phase 2 (perturbed weights reapplied)" + + print("\n✓ Pattern verified:") + print(f" Phase 1 (original): {len(phase1_items)} generations") + print(f" Phase 2 (perturbed): {len(phase2_items)} generations") + print(f" Phase 3 (original): {len(phase3_items)} generations (matches Phase 1 ✓)") + print(f" Phase 4 (perturbed): {len(phase4_items)} generations (matches Phase 2 ✓)") + print("\n✓ Server weight update pattern test PASSED") + + finally: + # Cleanup - always kill process tree even if main process exited + # (child processes like vLLM workers might still be running) + print("[Main] Cleaning up processes...") + if server_proc: + print(f"[Main] Killing server process tree (PID {server_proc.pid})...") + kill_process_tree(server_proc.pid) + if trainer_proc: + print(f"[Main] Killing trainer process tree (PID {trainer_proc.pid})...") + kill_process_tree(trainer_proc.pid) + + +# class TestConcurrentOperations: +# """Test concurrent generation and weight updates.""" + +# @pytest.mark.asyncio +# @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") +# async def test_multiple_generations_before_update( +# self, +# vllm_engine_factory, +# sample_prompts, +# generation_config, +# ): +# """Test that multiple generation requests work correctly.""" +# from vllm import SamplingParams + +# async with vllm_engine_factory() as manager: +# sampling_params = SamplingParams( +# temperature=generation_config["temperature"], +# top_p=generation_config["top_p"], +# max_tokens=generation_config["max_tokens"], +# seed=generation_config["seed"], +# ) + +# # Launch multiple generation requests +# tasks = [] +# for i, prompt in enumerate(sample_prompts): +# async def generate_one(prompt, idx): +# request_id = f"concurrent_{idx}" +# async for output in manager.engine.generate( +# prompt, +# sampling_params=sampling_params, +# request_id=request_id, +# ): +# final = output +# return final.outputs[0].text + +# tasks.append(generate_one(prompt, i)) + +# # Run all generations concurrently +# results = await asyncio.gather(*tasks) + +# assert len(results) == len(sample_prompts) +# for i, result in enumerate(results): +# print(f"Result {i}: {result[:50]}...") +# assert len(result) > 0 diff --git a/tests/vllm_engine_helper.py b/tests/vllm_engine_helper.py new file mode 100755 index 00000000..8dc0e336 --- /dev/null +++ b/tests/vllm_engine_helper.py @@ -0,0 +1,612 @@ +#!/usr/bin/env python3 +"""Helper script for running vLLM engine in a subprocess with proper CUDA isolation. + +This script is run as a separate process with CUDA_VISIBLE_DEVICES set, +ensuring the engine only sees the intended GPU. +""" + +import sys +import argparse +import asyncio + + +async def init_engine_and_process_group( + model_name: str, + init_method: str, + actor_llm_idx: int, + world_size: int, +): + """Initialize vLLM engine and process group. + + create_engine() automatically calls init_actor_update_group() when + disable_weight_updates=False, and calls destroy_actor_update_group() + on context manager exit. + """ + from pipelinerl.vllm1 import EngineManager + import argparse as ap + + print("[vLLM Engine] Starting engine initialization") + + # Create args for engine with process group params + args = ap.Namespace( + model=model_name, + tensor_parallel_size=1, + disable_log_stats=True, + enable_log_requests=False, + disable_weight_updates=False, + # Process group params - needed for automatic init_actor_update_group() + actor_llm_idx=actor_llm_idx, + weight_update_group_init_method=init_method, + weight_update_group_world_size=world_size, + ) + + print(f"[vLLM Engine] Creating engine with model={model_name}") + + # create_engine automatically: + # 1. Creates engine and manager + # 2. Calls manager.init_actor_update_group() (rank 1) + # 3. On exit, calls manager.destroy_actor_update_group() + async with EngineManager.create_engine(args) as manager: + print("[vLLM Engine] Engine and process group created successfully") + + # Keep engine alive until trainer completes its work + print("[vLLM Engine] Process group active, waiting for trainer...") + await asyncio.sleep(5) + + # Context manager exit automatically cleans up process group + print("[vLLM Engine] Engine and process group cleaned up") + + +async def test_weight_update( + model_name: str, + init_method: str, + actor_llm_idx: int, + world_size: int, + prompt: str, + max_tokens: int, + sync_dir: str, + expect_different: bool = False, +): + """Test weight update with generation before and after. + + This mode: + 1. Creates engine and initializes process group + 2. Generates baseline output + 3. Signals baseline_done, waits for broadcast_done + 4. Receives weight update + 5. Generates again with same prompt + 6. Prints both outputs for comparison + """ + from pipelinerl.vllm1 import EngineManager + from vllm import SamplingParams + from pathlib import Path + import argparse as ap + # Import sync helper from same directory + sys.path.insert(0, str(Path(__file__).parent)) + from sync_helper import SyncPoint + + print("[vLLM Engine] Starting weight update test") + + # Create sync points + sync_path = Path(sync_dir) + baseline_done = SyncPoint(sync_path, "baseline_done") + ready_to_receive = SyncPoint(sync_path, "ready_to_receive") + request_ready = SyncPoint(sync_path, "request_ready") + receiving_started = SyncPoint(sync_path, "receiving_started") + broadcast_done = SyncPoint(sync_path, "broadcast_done") + + # Create args for engine with process group params + args = ap.Namespace( + model=model_name, + tensor_parallel_size=1, + disable_log_stats=True, + enable_log_requests=False, + disable_weight_updates=False, + actor_llm_idx=actor_llm_idx, + weight_update_group_init_method=init_method, + weight_update_group_world_size=world_size, + ) + + print(f"[vLLM Engine] Creating engine with model={model_name}") + + async with EngineManager.create_engine(args) as manager: + print("[vLLM Engine] Engine and process group created successfully") + + # Step 1: Generate baseline + sampling_params = SamplingParams( + temperature=0.0, + top_p=1.0, + max_tokens=max_tokens, + seed=42, + ) + + print(f"[vLLM Engine] Generating baseline with prompt: '{prompt}'") + async for output in manager.engine.generate( + prompt, + sampling_params=sampling_params, + request_id="baseline", + ): + baseline_output = output + + baseline_text = baseline_output.outputs[0].text + print(f"[vLLM Engine] Baseline output: '{baseline_text}'") + + # Step 2: Signal baseline done and ready to receive + baseline_done.signal() + ready_to_receive.signal() + + # Step 3: Wait for trainer to send WeightUpdateRequest + print("[vLLM Engine] Waiting for trainer to send weight update request...") + request_ready.wait(timeout=60) + + # Step 4: Read WeightUpdateRequest from trainer + from sync_helper import read_weight_update_request + request = read_weight_update_request(sync_path) + print(f"[vLLM Engine] Received request with {len(request.parameters_info)} parameters") + + # Step 5: Signal we're about to start receiving, then call receive_weight_update + receiving_started.signal() + print("[vLLM Engine] Signaled receiving_started, calling receive_weight_update...") + print("[vLLM Engine] (This will block until trainer broadcasts all weights)") + await manager.receive_weight_update(request) + print("[vLLM Engine] Weight update received!") + + # Step 6: Wait for trainer to signal broadcast complete + broadcast_done.wait(timeout=60) + print("[vLLM Engine] Trainer confirmed broadcast complete") + + # Step 7: Generate again with same prompt + print(f"[vLLM Engine] Generating after update with prompt: '{prompt}'") + async for output in manager.engine.generate( + prompt, + sampling_params=sampling_params, + request_id="after_update", + ): + updated_output = output + + updated_text = updated_output.outputs[0].text + print(f"[vLLM Engine] Updated output: '{updated_text}'") + + # Step 8: Compare outputs + if expect_different: + # Perturbed weights - expect different outputs + if baseline_text != updated_text: + print("[vLLM Engine] ✓ Outputs differ (as expected for perturbed weights)") + print(f"[vLLM Engine] Baseline: '{baseline_text}'") + print(f"[vLLM Engine] Updated: '{updated_text}'") + else: + print("[vLLM Engine] ✗ Outputs are the same!") + print(f"[vLLM Engine] Both: '{baseline_text}'") + print("[vLLM Engine] ERROR: Perturbed weights should have changed the output") + sys.exit(1) + else: + # Same weights - expect same outputs + if baseline_text == updated_text: + print("[vLLM Engine] ✓ Outputs match (as expected for same weights)") + else: + print("[vLLM Engine] ✗ Outputs differ!") + print(f"[vLLM Engine] Baseline: '{baseline_text}'") + print(f"[vLLM Engine] Updated: '{updated_text}'") + sys.exit(1) + + print("[vLLM Engine] Engine and process group cleaned up") + + +async def test_cross_validation( + model_name: str, + init_method: str, + actor_llm_idx: int, + world_size: int, + prompt: str, + max_tokens: int, + sync_dir: str, +): + """Cross-validation test for weight updates. + + Tests that broadcasting weights produces same results as loading from disk. + Flow: + 1. Generate with original model → res_un_1 + 2. Receive perturbed weights, generate → res_mod_1 + 3. Recreate engine with perturbed model from disk, generate → res_mod_2 + 4. Receive original weights, generate → res_un_2 + 5. Verify: res_un_1 == res_un_2 and res_mod_1 == res_mod_2 + """ + from pipelinerl.vllm1 import EngineManager + from vllm import SamplingParams + from pathlib import Path + import argparse as ap + sys.path.insert(0, str(Path(__file__).parent)) + from sync_helper import SyncPoint, read_weight_update_request + + print("[vLLM Engine] Starting cross-validation test") + + # Create sync points + sync_path = Path(sync_dir) + baseline_done = SyncPoint(sync_path, "baseline_done") + perturbed_model_saved = SyncPoint(sync_path, "perturbed_model_saved") + ready_to_receive_perturbed = SyncPoint(sync_path, "ready_to_receive_perturbed") + perturbed_broadcast_done = SyncPoint(sync_path, "perturbed_broadcast_done") + mod1_done = SyncPoint(sync_path, "mod1_done") + first_engine_destroyed = SyncPoint(sync_path, "first_engine_destroyed") + engine_recreated = SyncPoint(sync_path, "engine_recreated") + ready_to_receive_original = SyncPoint(sync_path, "ready_to_receive_original") + original_broadcast_done = SyncPoint(sync_path, "original_broadcast_done") + + sampling_params = SamplingParams( + temperature=0.0, + top_p=1.0, + max_tokens=max_tokens, + seed=42, + ) + + # Step 1: Generate with original model + args = ap.Namespace( + model=model_name, + tensor_parallel_size=1, + disable_log_stats=True, + enable_log_requests=False, + disable_weight_updates=False, + actor_llm_idx=actor_llm_idx, + weight_update_group_init_method=init_method, + weight_update_group_world_size=world_size, + ) + + print(f"[vLLM Engine] Step 1: Creating engine with original model: {model_name}") + async with EngineManager.create_engine(args) as manager: + print(f"[vLLM Engine] Generating res_un_1 with prompt: '{prompt}'") + async for output in manager.engine.generate( + prompt, + sampling_params=sampling_params, + request_id="res_un_1", + ): + res_un_1_output = output + res_un_1 = res_un_1_output.outputs[0].text + print(f"[vLLM Engine] res_un_1: '{res_un_1}'") + + baseline_done.signal() + + # Wait for perturbed model to be saved + print("[vLLM Engine] Waiting for trainer to save perturbed model...") + perturbed_model_saved.wait(timeout=180) + + # Step 2: Receive perturbed weights and generate + ready_to_receive_perturbed.signal() + print("[vLLM Engine] Waiting for perturbed weight update request...") + + # Wait a moment for request file to be written + import time + time.sleep(0.5) + + request = read_weight_update_request(sync_path) + print(f"[vLLM Engine] Received perturbed request with {len(request.parameters_info)} parameters") + + print("[vLLM Engine] Receiving perturbed weights...") + await manager.receive_weight_update(request) + + perturbed_broadcast_done.wait(timeout=900) + print("[vLLM Engine] Perturbed weights received") + + print(f"[vLLM Engine] Generating res_mod_1 with prompt: '{prompt}'") + async for output in manager.engine.generate( + prompt, + sampling_params=sampling_params, + request_id="res_mod_1", + ): + res_mod_1_output = output + res_mod_1 = res_mod_1_output.outputs[0].text + print(f"[vLLM Engine] res_mod_1: '{res_mod_1}'") + + mod1_done.signal() + + # Engine destroyed here (context manager exit) + print("[vLLM Engine] First engine destroyed") + first_engine_destroyed.signal() + + # Step 3: Recreate engine with perturbed model from disk + perturbed_model_path = (sync_path / "perturbed_model_path.txt").read_text().strip() + print(f"[vLLM Engine] Step 3: Recreating engine with perturbed model from: {perturbed_model_path}") + + args_perturbed = ap.Namespace( + model=perturbed_model_path, + tensor_parallel_size=1, + disable_log_stats=True, + enable_log_requests=False, + disable_weight_updates=False, + actor_llm_idx=actor_llm_idx, + weight_update_group_init_method=init_method, + weight_update_group_world_size=world_size, + ) + + async with EngineManager.create_engine(args_perturbed) as manager: + # Signal immediately after engine is created + engine_recreated.signal() + print("[vLLM Engine] Engine recreated, signaled to trainer") + + print(f"[vLLM Engine] Generating res_mod_2 with prompt: '{prompt}'") + async for output in manager.engine.generate( + prompt, + sampling_params=sampling_params, + request_id="res_mod_2", + ): + res_mod_2_output = output + res_mod_2 = res_mod_2_output.outputs[0].text + print(f"[vLLM Engine] res_mod_2: '{res_mod_2}'") + + # Step 4: Receive original weights and generate + ready_to_receive_original.signal() + print("[vLLM Engine] Waiting for original weight update request...") + + time.sleep(0.5) + request = read_weight_update_request(sync_path) + print(f"[vLLM Engine] Received original request with {len(request.parameters_info)} parameters") + + print("[vLLM Engine] Receiving original weights...") + await manager.receive_weight_update(request) + + original_broadcast_done.wait(timeout=900) + print("[vLLM Engine] Original weights received") + + print(f"[vLLM Engine] Generating res_un_2 with prompt: '{prompt}'") + async for output in manager.engine.generate( + prompt, + sampling_params=sampling_params, + request_id="res_un_2", + ): + res_un_2_output = output + res_un_2 = res_un_2_output.outputs[0].text + print(f"[vLLM Engine] res_un_2: '{res_un_2}'") + + # Step 5: Verify + print("\n" + "="*60) + print("CROSS-VALIDATION RESULTS") + print("="*60) + print(f"res_un_1: '{res_un_1}'") + print(f"res_un_2: '{res_un_2}'") + print(f"res_mod_1: '{res_mod_1}'") + print(f"res_mod_2: '{res_mod_2}'") + print("="*60) + + # Check assertions + success = True + if res_un_1 == res_un_2: + print("✓ res_un_1 == res_un_2 (original weights produce same output)") + else: + print("✗ res_un_1 != res_un_2 (FAILED)") + success = False + + if res_mod_1 == res_mod_2: + print("✓ res_mod_1 == res_mod_2 (broadcast = load from disk)") + else: + print("✗ res_mod_1 != res_mod_2 (FAILED)") + success = False + + if not success: + sys.exit(1) + + print("\n✓ Cross-validation test PASSED") + + +async def test_back_and_forth( + model_name: str, + init_method: str, + actor_llm_idx: int, + world_size: int, + prompt: str, + max_tokens: int, + sync_dir: str, +): + """Back-and-forth test: switch between original and perturbed weights. + + Flow: + 1. Generate with original → res_or_1 + 2. Receive perturbed, generate → res_mod_1 + 3. Receive original, generate → res_or_2 + 4. Receive perturbed again, generate → res_mod_2 + 5. Verify: res_or_1 == res_or_2 and res_mod_1 == res_mod_2 + """ + from pipelinerl.vllm1 import EngineManager + from vllm import SamplingParams + from pathlib import Path + import argparse as ap + sys.path.insert(0, str(Path(__file__).parent)) + from sync_helper import SyncPoint, read_weight_update_request + + print("[vLLM Engine] Starting back-and-forth test") + + # Create sync points + sync_path = Path(sync_dir) + baseline_done = SyncPoint(sync_path, "baseline_done") + ready_for_perturbed1 = SyncPoint(sync_path, "ready_for_perturbed1") + perturbed1_done = SyncPoint(sync_path, "perturbed1_done") + ready_for_original = SyncPoint(sync_path, "ready_for_original") + original_done = SyncPoint(sync_path, "original_done") + ready_for_perturbed2 = SyncPoint(sync_path, "ready_for_perturbed2") + perturbed2_done = SyncPoint(sync_path, "perturbed2_done") + + sampling_params = SamplingParams( + temperature=0.0, + top_p=1.0, + max_tokens=max_tokens, + seed=42, + ) + + # Create engine args + args = ap.Namespace( + model=model_name, + tensor_parallel_size=1, + disable_log_stats=True, + enable_log_requests=False, + disable_weight_updates=False, + actor_llm_idx=actor_llm_idx, + weight_update_group_init_method=init_method, + weight_update_group_world_size=world_size, + ) + + print(f"[vLLM Engine] Creating engine with model: {model_name}") + async with EngineManager.create_engine(args) as manager: + # Step 1: Generate with original weights + print(f"[vLLM Engine] Step 1: Generating res_or_1") + async for output in manager.engine.generate( + prompt, sampling_params=sampling_params, request_id="res_or_1" + ): + res_or_1 = output.outputs[0].text + print(f"[vLLM Engine] res_or_1: '{res_or_1}'") + baseline_done.signal() + + # Step 2: Receive perturbed weights, generate + ready_for_perturbed1.signal() + import time + time.sleep(0.5) + request = read_weight_update_request(sync_path) + print(f"[vLLM Engine] Step 2: Receiving perturbed weights (1st time)") + await manager.receive_weight_update(request) + perturbed1_done.wait(timeout=900) + + print(f"[vLLM Engine] Generating res_mod_1") + async for output in manager.engine.generate( + prompt, sampling_params=sampling_params, request_id="res_mod_1" + ): + res_mod_1 = output.outputs[0].text + print(f"[vLLM Engine] res_mod_1: '{res_mod_1}'") + + # Step 3: Receive original weights, generate + ready_for_original.signal() + time.sleep(0.5) + request = read_weight_update_request(sync_path) + print(f"[vLLM Engine] Step 3: Receiving original weights") + await manager.receive_weight_update(request) + original_done.wait(timeout=900) + + print(f"[vLLM Engine] Generating res_or_2") + async for output in manager.engine.generate( + prompt, sampling_params=sampling_params, request_id="res_or_2" + ): + res_or_2 = output.outputs[0].text + print(f"[vLLM Engine] res_or_2: '{res_or_2}'") + + # Step 4: Receive perturbed weights again, generate + ready_for_perturbed2.signal() + time.sleep(0.5) + request = read_weight_update_request(sync_path) + print(f"[vLLM Engine] Step 4: Receiving perturbed weights (2nd time)") + await manager.receive_weight_update(request) + perturbed2_done.wait(timeout=900) + + print(f"[vLLM Engine] Generating res_mod_2") + async for output in manager.engine.generate( + prompt, sampling_params=sampling_params, request_id="res_mod_2" + ): + res_mod_2 = output.outputs[0].text + print(f"[vLLM Engine] res_mod_2: '{res_mod_2}'") + + # Step 5: Save results for server test + import json + results_file = sync_path / "expected_results.json" + expected_results = { + "res_or_1": res_or_1, + "res_mod_1": res_mod_1, + "res_or_2": res_or_2, + "res_mod_2": res_mod_2, + } + with open(results_file, "w") as f: + json.dump(expected_results, f, indent=2) + print(f"[vLLM Engine] Saved expected results to {results_file}") + + # Step 6: Verify + print("\n" + "="*60) + print("BACK-AND-FORTH TEST RESULTS") + print("="*60) + print(f"res_or_1: '{res_or_1}'") + print(f"res_or_2: '{res_or_2}'") + print(f"res_mod_1: '{res_mod_1}'") + print(f"res_mod_2: '{res_mod_2}'") + print("="*60) + + # Check assertions + success = True + if res_or_1 == res_or_2: + print("✓ res_or_1 == res_or_2 (can switch back to original)") + else: + print("✗ res_or_1 != res_or_2 (FAILED)") + success = False + + if res_mod_1 == res_mod_2: + print("✓ res_mod_1 == res_mod_2 (perturbed weights consistent)") + else: + print("✗ res_mod_1 != res_mod_2 (FAILED)") + success = False + + if not success: + sys.exit(1) + + print("\n✓ Back-and-forth test PASSED") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="vLLM engine helper") + parser.add_argument("command", choices=["init", "weight_update", "cross_validation", "back_and_forth"]) + parser.add_argument("--model-name", required=True) + parser.add_argument("--init-method", required=True) + parser.add_argument("--actor-llm-idx", type=int, default=0) + parser.add_argument("--world-size", type=int, default=2) + # For weight_update command + parser.add_argument("--prompt", type=str, default="The capital of France is") + parser.add_argument("--max-tokens", type=int, default=50) + parser.add_argument("--sync-dir", type=str, help="Directory for sync files") + parser.add_argument("--expect-different", action="store_true", help="Expect outputs to be different (for perturbed weights)") + + args = parser.parse_args() + + try: + if args.command == "init": + asyncio.run(init_engine_and_process_group( + args.model_name, + args.init_method, + args.actor_llm_idx, + args.world_size, + )) + elif args.command == "weight_update": + if not args.sync_dir: + print("Error: --sync-dir required for weight_update command") + sys.exit(1) + asyncio.run(test_weight_update( + args.model_name, + args.init_method, + args.actor_llm_idx, + args.world_size, + args.prompt, + args.max_tokens, + args.sync_dir, + args.expect_different, + )) + elif args.command == "cross_validation": + if not args.sync_dir: + print("Error: --sync-dir required for cross_validation command") + sys.exit(1) + asyncio.run(test_cross_validation( + args.model_name, + args.init_method, + args.actor_llm_idx, + args.world_size, + args.prompt, + args.max_tokens, + args.sync_dir, + )) + elif args.command == "back_and_forth": + if not args.sync_dir: + print("Error: --sync-dir required for back_and_forth command") + sys.exit(1) + asyncio.run(test_back_and_forth( + args.model_name, + args.init_method, + args.actor_llm_idx, + args.world_size, + args.prompt, + args.max_tokens, + args.sync_dir, + )) + except Exception as e: + print(f"[vLLM Engine] Error: {e}") + import traceback + traceback.print_exc() + sys.exit(1) diff --git a/tests/weight_update_utils.py b/tests/weight_update_utils.py new file mode 100644 index 00000000..3e36a1e6 --- /dev/null +++ b/tests/weight_update_utils.py @@ -0,0 +1,53 @@ +"""Utility functions for weight update testing.""" + +from typing import Dict +import torch +from pipelinerl.finetune_loop import WeightUpdateRequest, ParameterInfo + + +def dtype_to_string(dtype: torch.dtype) -> str: + """Convert torch dtype to string format expected by vLLM. + + Args: + dtype: PyTorch dtype + + Returns: + String representation (e.g., 'bfloat16', 'float32') + """ + dtype_str = str(dtype).replace("torch.", "") + return dtype_str + + +def create_weight_update_request_from_state_dict( + state_dict: Dict[str, torch.Tensor], + version: int = 0, +) -> WeightUpdateRequest: + """Create a WeightUpdateRequest from a model state dict. + + This helper function is useful for testing and for creating weight + update requests from saved model checkpoints. + + Args: + state_dict: Dictionary mapping parameter names to tensors + version: Version number for this weight update + + Returns: + WeightUpdateRequest object ready to be sent to workers + + Example: + >>> state_dict = torch.load('model.pt') + >>> request = create_weight_update_request_from_state_dict(state_dict, version=1) + >>> # Send request to vLLM server via HTTP endpoint + """ + parameters_info = [] + for name, tensor in state_dict.items(): + if isinstance(tensor, torch.Tensor): + parameters_info.append( + ParameterInfo( + name=name, + shape=list(tensor.shape), + dtype=dtype_to_string(tensor.dtype), + ) + ) + + return WeightUpdateRequest(version=version, parameters_info=parameters_info) From a89368600ca651930e6b7d492cc48ec01a43e40c Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 13 Feb 2026 15:23:05 +0000 Subject: [PATCH 15/85] changes for tests --- pipelinerl/vllm1.py | 281 +++++++++++++++++++++++++++++++++----------- 1 file changed, 214 insertions(+), 67 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 5a3a0bbb..9f252fef 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -25,21 +25,31 @@ from vllm.v1.worker.gpu_model_runner import GPUModelRunner -from pipelinerl.finetune_loop import WeightUpdateRequest +from pipelinerl.finetune_loop import WeightUpdateRequest, ParameterInfo from pipelinerl.vllm_quantization import string_to_dtype # reuse mapping -from typing import Any, Protocol, runtime_checkable +from typing import Any, Protocol, runtime_checkable, Dict, Optional import pipelinerl.torch_utils import pipelinerl.vllm_quantization # Register bf16_last_layer_fp32 quantization config +from vllm.distributed import cleanup_dist_env_and_memory +from contextlib import asynccontextmanager logger = logging.getLogger(__name__) -# configure this logger individually, in order to avoid messign +# configure this logger individually, in order to avoid messing # with the default vllm logger configuration -logger.setLevel(logging.INFO) +# Check environment variable to enable DEBUG logging (for tests) +import os + +log_level = logging.DEBUG if os.getenv("PIPELINERL_DEBUG") else logging.INFO +logger.setLevel(log_level) handler = logging.StreamHandler() -handler.setLevel(logging.INFO) -formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") +handler.setLevel(log_level) +formatter = logging.Formatter( + "[%(asctime)s] [VLLM-%(levelname)s] %(message)s", datefmt="%H:%M:%S" +) handler.setFormatter(formatter) logger.addHandler(handler) +# Prevent propagation to vLLM's loggers to avoid double logging +logger.propagate = False @runtime_checkable @@ -47,13 +57,22 @@ class LikeWorker(Protocol): rank: int local_rank: int device: torch.device - model_runner: GPUModelRunner + model_runner: GPUModelRunner pg_rank: int process_group: Any model_config: ModelConfig class WorkerExtension: + def is_extension_loaded(self: LikeWorker) -> int: + """Simple method to verify the extension is loaded on workers. + + Returns: + PID of the worker process + """ + import os + + return os.getpid() def init_actor_update_group( self: LikeWorker, @@ -81,30 +100,81 @@ def init_actor_update_group( world_size=weight_update_group_world_size, ) + def destroy_actor_update_group(self: LikeWorker): + torch.distributed.destroy_process_group(self.process_group) + def receive_weight_update(self: LikeWorker, request: WeightUpdateRequest): torch.cuda.synchronize(self.device) - logger.info("Start receiving weight update") + logger.info( + f"Start receiving weight update: {len(request.parameters_info)} parameters" + ) expected_dtypes = (torch.bfloat16, torch.float32, torch.float16) - for info in request.parameters_info: + + for i, info in enumerate(request.parameters_info): + logger.debug( + f"[{i+1}/{len(request.parameters_info)}] Preparing to receive: {info.name}" + ) + logger.debug(f" - shape: {info.shape}, dtype: {info.dtype}") + target_dtype = string_to_dtype(info.dtype) if target_dtype not in expected_dtypes: logger.warning(f"Unexpected dtype for {info.name}: {info.dtype}") - buffer = torch.empty(tuple(info.shape), dtype=target_dtype, device=self.device) + + logger.debug(f" - Creating buffer for {info.name}") + buffer = torch.empty( + tuple(info.shape), dtype=target_dtype, device=self.device + ) + logger.debug( + f" - Buffer created: shape={buffer.shape}, dtype={buffer.dtype}, device={buffer.device}" + ) + + logger.debug(f" - Calling broadcast for {info.name}...") torch.distributed.broadcast(buffer, src=0, group=self.process_group) - loaded_params = self.model_runner.model.load_weights(weights=[(info.name, buffer)]) # type: ignore - if len(loaded_params) != 1: - raise ValueError(f"model {info.name} not found in model state dict") + logger.debug(f" - Broadcast received for {info.name}") + + logger.debug(f" - Loading weights for {info.name}...") + try: + loaded_params = self.model_runner.model.load_weights(weights=[(info.name, buffer)]) # type: ignore + if len(loaded_params) == 0: + # Parameter doesn't exist in vLLM model - this is an error + logger.error(f" - ERROR: {info.name} not found in vLLM model") + raise ValueError( + f"Parameter {info.name} not found in vLLM model state dict" + ) + elif len(loaded_params) == 1: + logger.debug(f" - Weights loaded for {info.name}") + else: + logger.error( + f" - ERROR: load_weights returned {len(loaded_params)} params for {info.name}" + ) + raise ValueError( + f"Unexpected number of parameters loaded for {info.name}" + ) + except Exception as e: + logger.error(f" - ERROR loading weights for {info.name}: {e}") + raise + + if (i + 1) % 10 == 0: + logger.info(f"Received {i+1}/{len(request.parameters_info)} parameters") + pipelinerl.vllm_quantization.invalidate_fp32_cache() - logger.info("Weight update received") + logger.info("Weight update received - all parameters processed") -class WeightUpdateManager: - def __init__(self, args, engine_client: AsyncMPClient): +class EngineManager: + def __init__(self, args, engine: AsyncLLM, engine_config: Any): self.args = args - self.engine_client = engine_client + self.engine = engine + self.engine_config = engine_config + + async def is_extension_loaded(self): + return await self.engine.engine_core.collective_rpc_async( + "is_extension_loaded", + args=(), + ) - async def input_process_groups(self): - await self.engine_client.collective_rpc_async( + async def init_actor_update_group(self): + await self.engine.engine_core.collective_rpc_async( "init_actor_update_group", args=( self.args.actor_llm_idx, @@ -114,12 +184,98 @@ async def input_process_groups(self): ), ) + async def destroy_actor_update_group(self): + await self.engine.engine_core.collective_rpc_async( + "destroy_actor_update_group", + args=(), + ) + async def receive_weight_update(self, request: WeightUpdateRequest): - await self.engine_client.collective_rpc_async( + await self.engine.engine_core.collective_rpc_async( "receive_weight_update", args=(request,) ) logger.info("Weight update processed") + @asynccontextmanager + @staticmethod + async def create_engine( + args: Any, + cleanup: bool = True, + ): + """Create vLLM AsyncLLM engine with automatic cleanup. + + This is an async context manager that ensures proper engine lifecycle + management with automatic cleanup on exit. + + Usage: + # Simple usage (tests) + async with create_engine(args) as (engine, engine_config): + # Use engine for generation + async for output in engine.generate(...): + ... + # Automatic cleanup happens here + + # Or unpack only what you need + async with create_engine(args) as (engine, _): + # Use engine, ignore config + ... + + # Server usage (no cleanup) + async with create_engine(args, cleanup=False) as (engine, engine_config): + # Use both engine and config + await init_app_state(engine, engine_config, ...) + ... + + Args: + args: Arguments object with vLLM engine configuration. + Must be compatible with AsyncEngineArgs.from_cli_args(). + Required attributes: model + Optional attributes: tensor_parallel_size, disable_log_stats, + disable_log_requests, etc. + cleanup: Whether to cleanup engine on exit (default: True). + Set to False for server usage where engine runs indefinitely. + + Yields: + Tuple of (engine, engine_config): + - engine: AsyncLLM engine instance + - engine_config: VllmConfig for init_app_state + """ + engine_args = AsyncEngineArgs.from_cli_args(args) + engine_args.worker_extension_cls = "pipelinerl.vllm1.WorkerExtension" + engine_config = engine_args.create_engine_config(UsageContext.OPENAI_API_SERVER) + + logger.info(f"Creating vLLM engine with model={args.model}") + engine = AsyncLLM.from_vllm_config( + vllm_config=engine_config, + usage_context=UsageContext.OPENAI_API_SERVER, + disable_log_stats=engine_args.disable_log_stats, + enable_log_requests=engine_args.enable_log_requests, + ) + + logger.info("vLLM engine created successfully") + + try: + assert isinstance(engine.engine_core, AsyncMPClient) + manager = EngineManager(args, engine, engine_config) + if not args.disable_weight_updates: + await manager.init_actor_update_group() + yield manager + finally: + if not args.disable_weight_updates: + await manager.destroy_actor_update_group() + if cleanup: + logger.info("Cleaning up vLLM engine") + # Clear manager reference to engine first + manager.engine = None + manager.engine_config = None + # Delete engine and force immediate garbage collection + del engine + del manager + import gc + + gc.collect() + cleanup_dist_env_and_memory() + async def run_server(args, **uvicorn_kwargs) -> None: # COPIED FROM vllm/entrypoints/openai/api_server.py, vllm version 0.6.6.post1 @@ -151,61 +307,52 @@ def signal_handler(*_) -> None: signal.signal(signal.SIGTERM, signal_handler) - engine_args = AsyncEngineArgs.from_cli_args(args) - engine_args.worker_extension_cls = "pipelinerl.vllm1.WorkerExtension" - engine_config = engine_args.create_engine_config(UsageContext.OPENAI_API_SERVER) - engine = AsyncLLM.from_vllm_config( - vllm_config=engine_config, - usage_context=UsageContext.OPENAI_API_SERVER, - disable_log_stats=engine_args.disable_log_stats, - disable_log_requests=engine_args.disable_log_requests, - ) - assert isinstance(engine.engine_core, AsyncMPClient) - - weight_update_manager = WeightUpdateManager(args, engine.engine_core) - if not args.disable_weight_updates: - await weight_update_manager.input_process_groups() - - # Run HTTP server - sock_addr = (args.host or "", args.port) - sock = create_server_socket(sock_addr) - app = build_app(args) - - @app.post("/receive_weight_update") - async def _receive_weight_update(request: WeightUpdateRequest): - await weight_update_manager.receive_weight_update(request) - return {"status": "ok"} - - await init_app_state(engine, engine_config, app.state, args) - shutdown_task = await serve_http( - app, - sock, - host=args.host, - port=args.port, - log_level=args.uvicorn_log_level, - # increase timeout - timeout_keep_alive=60, - ssl_keyfile=args.ssl_keyfile, - ssl_certfile=args.ssl_certfile, - ssl_ca_certs=args.ssl_ca_certs, - ssl_cert_reqs=args.ssl_cert_reqs, - **uvicorn_kwargs, - ) + # Create engine (cleanup=False since server runs indefinitely) + async with EngineManager.create_engine(args, cleanup=False) as manager: + # Run HTTP server + sock_addr = (args.host or "", args.port) + sock = create_server_socket(sock_addr) + app = build_app(args) + + @app.post("/receive_weight_update") + async def _receive_weight_update(request: WeightUpdateRequest): + await manager.receive_weight_update(request) + return {"status": "ok"} + + await init_app_state(manager.engine, app.state, args) + shutdown_task = await serve_http( + app, + sock, + host=args.host, + port=args.port, + log_level=args.uvicorn_log_level, + # increase timeout + timeout_keep_alive=60, + ssl_keyfile=args.ssl_keyfile, + ssl_certfile=args.ssl_certfile, + ssl_ca_certs=args.ssl_ca_certs, + ssl_cert_reqs=args.ssl_cert_reqs, + **uvicorn_kwargs, + ) - # NB: Await server shutdown only after the backend context is exited - await shutdown_task + # NB: Await server shutdown only after the backend context is exited + await shutdown_task - sock.close() + sock.close() - # TODO: proper cleanup - # dist.destroy_process_group(actor_update_group) + # TODO: proper cleanup + # dist.destroy_process_group(actor_update_group) def run_llm(): - parser = FlexibleArgumentParser(description="vLLM OpenAI-Compatible RESTful API server.") + parser = FlexibleArgumentParser( + description="vLLM OpenAI-Compatible RESTful API server." + ) parser = make_arg_parser(parser) parser.add_argument( - "--disable-weight-updates", action="store_true", help="Whether to receive weight updates from the trainer" + "--disable-weight-updates", + action="store_true", + help="Whether to receive weight updates from the trainer", ) parser.add_argument( "--actor-llm-idx", From 641e457b843cbb37a3921fdf71be32e51df6633b Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 13 Feb 2026 15:24:26 +0000 Subject: [PATCH 16/85] added instruction to install PipelineRL+FastLLM --- README.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/README.md b/README.md index bc67d4f3..a94da078 100644 --- a/README.md +++ b/README.md @@ -333,3 +333,37 @@ PipelineRL is organized as a modular, Hydra-driven pipeline with 6 core componen - `training_data` stream (StreamRangeSpec(topic="training_data")): File- or Redis-backed stream used to transfer processed training micro-batches from the Preprocessor to the Trainer. Configured via `cfg.preprocess.output` and `cfg.finetune.input` (defaulting to "training_data") in `conf/base.yaml`. Written in `pipelinerl/run_preprocess.py` and consumed in `pipelinerl/run_finetune.py`. - `actor_test` and `stats_test` streams: analogous streams used for evaluation loops (test samples and test metrics). - `stats` stream (SingleStreamSpec(topic="stats")): produced by `ActorLoop.publish_stats` with sliding-window metrics; consumed by external monitoring (e.g. WANDB, logging viewers). + + + + +# Install FastLLM+PipilineRL +- use ` registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` image which also includes redis server. In `~/.research-interactive-env`: +```shell +USE_ACCOUNT_REPO := 1 +BASE_IMAGE :=nvcr.io/nvidia/pytorch:25.12-py3 +IMAGE_REVISION := 25.12-py3-vllm014rc1redis +EAI_PROFILE := yul201 +``` + +- in running interactive instance run like this to install both Fast-LLM and PipelineRL into the same `venv` locates at PipelineRL repo folder +```shell +git clone git@github.com:ServiceNow/Fast-LLM.git +git clone git@github.com:ServiceNow/PipelineRL.git + +cd PipelineRL +/usr/bin/python3.12 -m venv --system-site-packages .venv +source .venv/bin/activate +export PIP_CONSTRAINT="" + +cd ../Fast-LLM +git submodule update --init --recursive +git checkout jlp_pipeline_rl +pip install --no-cache-dir --no-build-isolation -e ".[CORE,OPTIONAL,HUGGINGFACE,SSM,VISION,GENERATION,STREAMING,DEV]" triton==3.5.1 + +cd ../PipelineRL +git checkout fast-llm +pip install --no-cache-dir -e ".[lora]" +``` + + From 7d14e8018b5b2a327dbb1092cddfef8df0713dfb Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 18 Feb 2026 15:52:00 +0000 Subject: [PATCH 17/85] added fast-llm bradcast functionality and test --- pipelinerl/vllm1.py | 268 +++++++++++++++++- tests/server_weight_update_utils.py | 374 +++++++++++++++++++++++++ tests/test_vllm1_fast_llm_broadcast.py | 305 ++++++++++++++++++++ tests/test_vllm1_integration.py | 275 +++--------------- 4 files changed, 983 insertions(+), 239 deletions(-) create mode 100644 tests/server_weight_update_utils.py create mode 100644 tests/test_vllm1_fast_llm_broadcast.py diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 9f252fef..3a8f2a88 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -160,6 +160,207 @@ def receive_weight_update(self: LikeWorker, request: WeightUpdateRequest): pipelinerl.vllm_quantization.invalidate_fp32_cache() logger.info("Weight update received - all parameters processed") + def init_fast_llm_receiver( + self: LikeWorker, + redis_host: str, + redis_port: int, + ): + """Initialize Fast-LLM weight receiver (called once at startup). + + This method: + 1. Stores Redis connection info + 2. Sets up threading infrastructure + 3. Does NOT start monitoring thread (that's managed by EngineManager) + """ + import threading + + self.redis_host = redis_host + self.redis_port = redis_port + self.fast_llm_stop_event = threading.Event() + logger.info( + f"[Worker rank={self.rank}] Fast-LLM receiver initialized with Redis {redis_host}:{redis_port}" + ) + + def start_fast_llm_monitoring(self: LikeWorker): + """Start background thread to monitor Redis stream. + + This thread: + 1. Connects to Redis stream "fast_llm_events" + 2. Listens for {type: "weights_ready", step: N} events + 3. On event, triggers receive_weight_update_fast_llm() + 4. Runs until stop_event is set + """ + import threading + import time + + def monitor_redis_stream(): + import redis + import orjson + + r = redis.Redis(host=self.redis_host, port=self.redis_port) + stream_key = "fast_llm_events" + payload_key = b"event" + last_id = "0-0" + + logger.info(f"[Worker rank={self.rank}] Starting Redis stream monitoring") + + while not self.fast_llm_stop_event.is_set(): + try: + # Non-blocking read with 1s timeout + result = r.xread({stream_key: last_id}, count=1, block=1000) + + if not result: + continue + + for stream_name, messages in result: + for msg_id, msg_data in messages: + last_id = msg_id + + if payload_key not in msg_data: + logger.warning( + f"[Worker rank={self.rank}] Event missing 'event' field: {msg_data}" + ) + continue + + try: + event = orjson.loads(msg_data[payload_key]) + except Exception as e: + logger.error( + f"[Worker rank={self.rank}] Failed to parse event: {e}" + ) + continue + + event_type = event.get("type") + step = event.get("step") + + if event_type == "weights_ready": + logger.info( + f"[Worker rank={self.rank}] Received weights_ready event: step={step}" + ) + # Call receive_weight_update_fast_llm directly (runs in this thread) + try: + self.receive_weight_update_fast_llm() + except Exception as e: + logger.error( + f"[Worker rank={self.rank}] Error receiving Fast-LLM weight update: {e}" + ) + elif event_type == "training_finished": + logger.info( + f"[Worker rank={self.rank}] Received training_finished event" + ) + + except Exception as e: + logger.error(f"[Worker rank={self.rank}] Error in Redis monitor: {e}") + if not self.fast_llm_stop_event.is_set(): + time.sleep(1) # Avoid tight loop on error + + logger.info(f"[Worker rank={self.rank}] Redis monitoring stopped") + r.close() + + import threading + self.fast_llm_monitor_thread = threading.Thread( + target=monitor_redis_stream, + daemon=True, + name=f"FastLLMMonitor-Rank{self.rank}", + ) + self.fast_llm_monitor_thread.start() + logger.info(f"[Worker rank={self.rank}] Fast-LLM monitoring thread started") + + def stop_fast_llm_monitoring(self: LikeWorker): + """Stop the Fast-LLM monitoring thread.""" + if hasattr(self, "fast_llm_stop_event"): + logger.info(f"[Worker rank={self.rank}] Stopping Fast-LLM monitoring") + self.fast_llm_stop_event.set() + if hasattr(self, "fast_llm_monitor_thread"): + self.fast_llm_monitor_thread.join(timeout=5) + logger.info(f"[Worker rank={self.rank}] Fast-LLM monitoring stopped") + + def receive_weight_update_fast_llm(self: LikeWorker): + """Receive weight update via Fast-LLM broadcast protocol. + + This method: + 1. Loops receiving metadata via broadcast_object_list + 2. Receives tensor via broadcast + 3. Calls model.load_weights() for each parameter + 4. Exits when metadata is [None] (end signal) + + NOTE: This is called from the monitoring thread. + """ + torch.cuda.synchronize(self.device) + logger.info(f"[Worker rank={self.rank}] Start receiving Fast-LLM weight update") + + expected_dtypes = (torch.bfloat16, torch.float32, torch.float16) + param_count = 0 + + while True: + # Receive metadata + meta = [None] + logger.debug(f"[Worker rank={self.rank}] Waiting for metadata broadcast...") + torch.distributed.broadcast_object_list( + meta, group=self.process_group, src=0 + ) + logger.debug(f"[Worker rank={self.rank}] Received metadata: {meta}") + + # Check for end signal + if meta[0] is None: + logger.info( + f"[Worker rank={self.rank}] Received end signal, finished receiving {param_count} parameters" + ) + break + + # Parse metadata: (shard_name, layer_name, shape, dtype) + shard_name, layer_name, shape, dtype = meta[0] + param_name = f"{shard_name}.{layer_name}" if shard_name else layer_name + param_count += 1 + + logger.debug( + f"[{param_count}] Receiving: {param_name}, shape={shape}, dtype={dtype}" + ) + + # Convert dtype to torch dtype + target_dtype = string_to_dtype(str(dtype)) + if target_dtype not in expected_dtypes: + logger.warning(f"Unexpected dtype for {param_name}: {dtype}") + + # Allocate buffer + buffer = torch.empty(tuple(shape), dtype=target_dtype, device=self.device) + + # Receive tensor + logger.debug(f"[{param_count}] Broadcasting tensor for {param_name}...") + torch.distributed.broadcast(buffer, src=0, group=self.process_group) + logger.debug(f"[{param_count}] Received tensor for {param_name}") + + # Load weights + try: + loaded_params = self.model_runner.model.load_weights( + weights=[(param_name, buffer)] + ) + if len(loaded_params) == 0: + logger.error(f"ERROR: {param_name} not found in vLLM model") + raise ValueError( + f"Parameter {param_name} not found in vLLM model state dict" + ) + elif len(loaded_params) == 1: + logger.debug(f"[{param_count}] Loaded {param_name}") + else: + logger.error( + f"ERROR: load_weights returned {len(loaded_params)} params for {param_name}" + ) + raise ValueError( + f"Unexpected number of parameters loaded for {param_name}" + ) + except Exception as e: + logger.error(f"ERROR loading {param_name}: {e}") + raise + + if param_count % 10 == 0: + logger.info(f"[Worker rank={self.rank}] Received {param_count} parameters") + + pipelinerl.vllm_quantization.invalidate_fp32_cache() + logger.info( + f"[Worker rank={self.rank}] Fast-LLM weight update complete - {param_count} parameters processed" + ) + class EngineManager: def __init__(self, args, engine: AsyncLLM, engine_config: Any): @@ -196,6 +397,30 @@ async def receive_weight_update(self, request: WeightUpdateRequest): ) logger.info("Weight update processed") + async def init_fast_llm_receiver(self): + """Initialize Fast-LLM receiver on all workers.""" + await self.engine.engine_core.collective_rpc_async( + "init_fast_llm_receiver", + args=(self.args.redis_host, self.args.redis_port), + ) + logger.info("Fast-LLM receiver initialized on all workers") + + async def start_fast_llm_monitoring(self): + """Start Fast-LLM monitoring threads on all workers.""" + await self.engine.engine_core.collective_rpc_async( + "start_fast_llm_monitoring", + args=(), + ) + logger.info("Fast-LLM monitoring started on all workers") + + async def stop_fast_llm_monitoring(self): + """Stop Fast-LLM monitoring threads on all workers.""" + await self.engine.engine_core.collective_rpc_async( + "stop_fast_llm_monitoring", + args=(), + ) + logger.info("Fast-LLM monitoring stopped on all workers") + @asynccontextmanager @staticmethod async def create_engine( @@ -259,9 +484,20 @@ async def create_engine( manager = EngineManager(args, engine, engine_config) if not args.disable_weight_updates: await manager.init_actor_update_group() + + # Initialize Fast-LLM mode if enabled + if hasattr(args, 'weight_update_mode') and args.weight_update_mode == "fast-llm": + await manager.init_fast_llm_receiver() + await manager.start_fast_llm_monitoring() + logger.info("Fast-LLM weight update mode enabled") + yield manager finally: if not args.disable_weight_updates: + # Stop Fast-LLM monitoring if enabled + if hasattr(args, 'weight_update_mode') and args.weight_update_mode == "fast-llm": + await manager.stop_fast_llm_monitoring() + await manager.destroy_actor_update_group() if cleanup: logger.info("Cleaning up vLLM engine") @@ -314,10 +550,15 @@ def signal_handler(*_) -> None: sock = create_server_socket(sock_addr) app = build_app(args) - @app.post("/receive_weight_update") - async def _receive_weight_update(request: WeightUpdateRequest): - await manager.receive_weight_update(request) - return {"status": "ok"} + # Register HTTP endpoint only if using HTTP mode + if not hasattr(args, 'weight_update_mode') or args.weight_update_mode == "http": + @app.post("/receive_weight_update") + async def _receive_weight_update(request: WeightUpdateRequest): + await manager.receive_weight_update(request) + return {"status": "ok"} + logger.info("HTTP weight update endpoint registered") + else: + logger.info("Fast-LLM mode: using Redis stream (no HTTP endpoint registered)") await init_app_state(manager.engine, app.state, args) shutdown_task = await serve_http( @@ -366,6 +607,25 @@ def run_llm(): "--weight-update-group-world-size", type=int, ) + parser.add_argument( + "--weight-update-mode", + type=str, + choices=["http", "fast-llm"], + default="http", + help="Weight update protocol: 'http' (HTTP POST) or 'fast-llm' (Redis+broadcast)", + ) + parser.add_argument( + "--redis-host", + type=str, + default="localhost", + help="Redis host for Fast-LLM mode", + ) + parser.add_argument( + "--redis-port", + type=int, + default=6379, + help="Redis port for Fast-LLM mode", + ) args = parser.parse_args() validate_parsed_serve_args(args) diff --git a/tests/server_weight_update_utils.py b/tests/server_weight_update_utils.py new file mode 100644 index 00000000..83ac3a0f --- /dev/null +++ b/tests/server_weight_update_utils.py @@ -0,0 +1,374 @@ +"""Shared utilities for server weight update integration tests.""" + +import asyncio +import requests +import time +from pathlib import Path +import subprocess +import sys +import os + + +async def wait_for_server_ready(server_url: str, server_proc, trainer_proc, timeout_seconds: int = 300): + """Wait for server to be ready by polling health endpoint. + + Args: + server_url: Base URL of server (e.g., "http://127.0.0.1:8000") + server_proc: Server subprocess + trainer_proc: Trainer subprocess + timeout_seconds: Maximum time to wait + + Returns: + True if server is ready + + Raises: + RuntimeError: If server or trainer process terminates + TimeoutError: If server doesn't become ready within timeout + """ + print("[Main] Waiting for server to be ready...") + for i in range(timeout_seconds): + # Check if server process crashed + if server_proc.poll() is not None: + print(f"[Main] Server process terminated with code {server_proc.returncode}") + raise RuntimeError(f"Server process terminated with code {server_proc.returncode}") + + # Check if trainer process crashed + if trainer_proc.poll() is not None: + print(f"[Main] Trainer process terminated with code {trainer_proc.returncode}") + raise RuntimeError(f"Trainer process terminated with code {trainer_proc.returncode}") + + try: + resp = requests.get(f"{server_url}/health", timeout=1) + if resp.status_code == 200: + print("[Main] Server is ready!") + return True + except requests.exceptions.RequestException: + pass + + if i % 10 == 0: + print(f"[Main] Still waiting for server... ({i} seconds)") + await asyncio.sleep(1) + + raise TimeoutError(f"Server did not become ready within {timeout_seconds} seconds") + + +def check_pattern_detected(generations): + """Check if we have detected the full pattern (4 phases). + + Args: + generations: List of (timestamp, text) tuples + + Returns: + True if pattern is detected (4 phases with correct relationships) + """ + if len(generations) < 4: + return False + + # Track when the text changes to identify phase boundaries + phases = [] + current_text = None + current_phase = [] + + for ts, text in generations: + if text != current_text: + if current_phase: + phases.append((current_text, current_phase)) + current_text = text + current_phase = [(ts, text)] + else: + current_phase.append((ts, text)) + + # Add the last phase + if current_phase: + phases.append((current_text, current_phase)) + + # Check if we have at least 4 phases + if len(phases) < 4: + return False + + # Verify the pattern: phase1 != phase2, phase3 == phase1, phase4 == phase2 + phase1_text = phases[0][0] + phase2_text = phases[1][0] + phase3_text = phases[2][0] + phase4_text = phases[3][0] + + if phase1_text == phase2_text: + return False # Phase 1 and 2 should be different + if phase3_text != phase1_text: + return False # Phase 3 should match Phase 1 + if phase4_text != phase2_text: + return False # Phase 4 should match Phase 2 + + return True + + +async def run_generation_loop( + server_url: str, + model_name: str, + simple_prompt: str, + generation_config: dict, + trainer_proc, + max_duration: int = 120, + generation_interval: float = 0.5, +): + """Run continuous generation loop until pattern is detected or timeout. + + Args: + server_url: Base URL of server + model_name: Model name for API request + simple_prompt: Prompt to generate from + generation_config: Config dict with max_tokens, etc. + trainer_proc: Trainer subprocess to monitor + max_duration: Maximum duration in seconds + generation_interval: Time between generations + + Returns: + List of (timestamp, generated_text) tuples + """ + print("[Main] Starting continuous generation loop...") + generations = [] + start_time = time.time() + + while time.time() - start_time < max_duration: + # Check if trainer is still running + trainer_poll = trainer_proc.poll() + if trainer_poll is not None: + print(f"[Main] Trainer exited with code {trainer_poll}") + break + + try: + # Generate via HTTP API + payload = { + "model": model_name, + "prompt": simple_prompt, + "max_tokens": generation_config["max_tokens"], + "temperature": 0.0, # Deterministic + "top_p": 1.0, + "seed": 42, + } + + resp = requests.post( + f"{server_url}/v1/completions", + json=payload, + timeout=30, + ) + + if resp.status_code == 200: + result = resp.json() + generated_text = result["choices"][0]["text"] + timestamp = time.time() - start_time + generations.append((timestamp, generated_text)) + print(f"[Main] [{timestamp:.1f}s] Generated: '{generated_text}'") + + # Check if pattern is detected - stop early if confirmed + if check_pattern_detected(generations): + print(f"[Main] Pattern detected! Stopping generation early at {timestamp:.1f}s") + break + else: + print(f"[Main] Generation failed with status {resp.status_code}") + + except requests.exceptions.RequestException as e: + print(f"[Main] Request failed: {e}") + + await asyncio.sleep(generation_interval) + + return generations + + +def analyze_and_verify_pattern(generations): + """Analyze generation sequence and verify the expected pattern. + + Args: + generations: List of (timestamp, text) tuples + + Raises: + AssertionError: If pattern is not as expected + """ + print("\n" + "=" * 60) + print("GENERATION SEQUENCE ANALYSIS") + print("=" * 60) + print(f"Total generations: {len(generations)}") + + # Print all generations + for i, (ts, text) in enumerate(generations): + print(f"[{ts:5.1f}s] Gen {i+1}: '{text[:80]}...'") + + # Identify unique generation texts and their phases + if len(generations) < 4: + raise AssertionError( + f"Not enough generations to verify pattern (need at least 4, got {len(generations)})" + ) + + # Track when the text changes to identify phase boundaries + phases = [] + current_text = None + current_phase = [] + + for ts, text in generations: + if text != current_text: + if current_phase: + phases.append((current_text, current_phase)) + current_text = text + current_phase = [(ts, text)] + else: + current_phase.append((ts, text)) + + # Add the last phase + if current_phase: + phases.append((current_text, current_phase)) + + print("\n" + "=" * 60) + print(f"Detected {len(phases)} phases:") + for i, (text, items) in enumerate(phases): + print(f"Phase {i+1}: {len(items)} generations - '{text[:60]}...'") + print("=" * 60) + + # Verify the pattern + assert ( + len(phases) >= 4 + ), f"Expected at least 4 phases (original → perturbed → original → perturbed), got {len(phases)}" + + phase1_text, phase1_items = phases[0] + phase2_text, phase2_items = phases[1] + phase3_text, phase3_items = phases[2] + phase4_text, phase4_items = phases[3] + + # Verify phase 1 (original) != phase 2 (perturbed) + assert ( + phase1_text != phase2_text + ), "Phase 1 (original) and Phase 2 (perturbed) should be different" + + # Verify phase 3 (original) == phase 1 (original) + assert ( + phase3_text == phase1_text + ), f"Phase 3 should match Phase 1 (original weights restored)" + + # Verify phase 4 (perturbed) == phase 2 (perturbed) + assert ( + phase4_text == phase2_text + ), f"Phase 4 should match Phase 2 (perturbed weights reapplied)" + + print("\n✓ Pattern verified:") + print(f" Phase 1 (original): {len(phase1_items)} generations") + print(f" Phase 2 (perturbed): {len(phase2_items)} generations") + print(f" Phase 3 (original): {len(phase3_items)} generations (matches Phase 1 ✓)") + print(f" Phase 4 (perturbed): {len(phase4_items)} generations (matches Phase 2 ✓)") + + +def start_vllm_server( + model_name: str, + server_port: int, + distributed_init_method: str, + stream_process_output_fn, + extra_args: list = None, +): + """Start vLLM HTTP server subprocess. + + Args: + model_name: Model to load + server_port: Port to bind to + distributed_init_method: Distributed initialization method + stream_process_output_fn: Function to stream process output + extra_args: Additional CLI arguments (e.g., ["--weight-update-mode", "fast-llm"]) + + Returns: + Tuple of (server_proc, stdout_thread, stderr_thread) + """ + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" + + print(f"[Main] Starting vLLM HTTP server on port {server_port} (GPU 0)") + vllm_entry_point = Path(__file__).parent.parent / "pipelinerl" / "entrypoints" / "run_vllm1.py" + + cmd = [ + sys.executable, + str(vllm_entry_point), + "--model", model_name, + "--port", str(server_port), + "--host", "127.0.0.1", + "--actor-llm-idx", "0", + "--weight-update-group-init-method", distributed_init_method, + "--weight-update-group-world-size", "2", + ] + + if extra_args: + cmd.extend(extra_args) + + server_proc = subprocess.Popen( + cmd, + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + print("[Main] Starting server output streaming...") + stdout_thread, stderr_thread = stream_process_output_fn(server_proc, "vLLM Server") + + return server_proc, stdout_thread, stderr_thread + + +def start_trainer_process( + trainer_helper_path: Path, + distributed_init_method: str, + model_name: str, + server_url: str, + stream_process_output_fn, + extra_args: list = None, +): + """Start trainer subprocess. + + Args: + trainer_helper_path: Path to trainer helper script + distributed_init_method: Distributed initialization method + model_name: Model name + server_url: Server URL for health check + stream_process_output_fn: Function to stream process output + extra_args: Additional CLI arguments (e.g., ["--redis-host", "localhost"]) + + Returns: + Tuple of (trainer_proc, stdout_thread, stderr_thread) + """ + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + + print("[Main] Starting trainer process (GPU 1) for process group rendezvous") + + cmd = [ + sys.executable, + str(trainer_helper_path), + ] + + # Check which trainer helper is being used by the script name + if "fast_llm" in str(trainer_helper_path): + # fast_llm_trainer_helper.py uses argparse with --init-method, --model, etc. + cmd.extend([ + "--init-method", distributed_init_method, + "--model", model_name, + "--server-url", server_url, + ]) + else: + # distributed_trainer_helper.py uses positional args + cmd.extend([ + "timed_broadcast_server_test", + "--init-method", distributed_init_method, + "--model-name", model_name, + "--server-url", server_url, + ]) + + if extra_args: + cmd.extend(extra_args) + + trainer_proc = subprocess.Popen( + cmd, + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + print("[Main] Starting trainer output streaming...") + stdout_thread, stderr_thread = stream_process_output_fn(trainer_proc, "Trainer") + + return trainer_proc, stdout_thread, stderr_thread diff --git a/tests/test_vllm1_fast_llm_broadcast.py b/tests/test_vllm1_fast_llm_broadcast.py new file mode 100644 index 00000000..79ff95a7 --- /dev/null +++ b/tests/test_vllm1_fast_llm_broadcast.py @@ -0,0 +1,305 @@ +"""Integration tests for vllm1 with Fast-LLM weight broadcast protocol.""" + +import asyncio +import pytest +import tempfile +from pathlib import Path +from typing import Dict, List +import time +import os +import subprocess +import sys +import signal + +# torch is needed at top level for pytest.mark.skipif decorators +import torch + +# Import shared utilities +from .server_weight_update_utils import ( + wait_for_server_ready, + run_generation_loop, + analyze_and_verify_pattern, + start_vllm_server, + start_trainer_process, +) + +try: + import psutil + + HAS_PSUTIL = True +except ImportError: + HAS_PSUTIL = False + print("WARNING: psutil not available, process tree cleanup will be limited") + + +def stream_process_output(proc, name): + """Start background threads to continuously stream process stdout/stderr. + + Args: + proc: subprocess.Popen object + name: Name for logging prefix (e.g., "vLLM Server", "Trainer") + + Returns: + Tuple of (stdout_thread, stderr_thread) + """ + import threading + + def read_stream(stream, prefix): + """Read from stream and print with prefix.""" + try: + for line in iter(stream.readline, ""): + if line: + print(f"{prefix} {line.rstrip()}", flush=True) + except Exception as e: + print(f"{prefix} [Stream read error: {e}]", flush=True) + + stdout_thread = threading.Thread( + target=read_stream, + args=(proc.stdout, f"[{name} OUT]"), + daemon=True, + ) + stderr_thread = threading.Thread( + target=read_stream, + args=(proc.stderr, f"[{name} ERR]"), + daemon=True, + ) + + stdout_thread.start() + stderr_thread.start() + + return stdout_thread, stderr_thread + + +def kill_process_tree(pid, sig=signal.SIGKILL): + """Kill a process and all its children/grandchildren. + + Args: + pid: Process ID to kill + sig: Signal to send (default SIGKILL) + """ + if not HAS_PSUTIL: + # Fallback: just kill the main process + try: + os.kill(pid, sig) + except ProcessLookupError: + pass + return + + try: + parent = psutil.Process(pid) + except psutil.NoSuchProcess: + return + + # Get all children recursively + children = parent.children(recursive=True) + + # Kill children first + for child in children: + try: + print(f"[Kill] Killing child process {child.pid}") + child.send_signal(sig) + except psutil.NoSuchProcess: + pass + + # Kill parent + try: + parent.send_signal(sig) + except psutil.NoSuchProcess: + pass + + +@pytest.fixture +def fast_llm_trainer_helper(): + """Path to Fast-LLM trainer helper script.""" + return Path(__file__).parent / "fast_llm_trainer_helper.py" + + +@pytest.fixture +def redis_server(): + """Start a Redis server for testing and stop it after the test. + + Returns: + Tuple of (host, port) for the Redis server + """ + import shutil + import socket + + # Check if redis-server is available + redis_server_bin = shutil.which("redis-server") + if not redis_server_bin: + pytest.skip("redis-server not found in PATH") + + # Find an available port + def find_free_port(): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(('', 0)) + s.listen(1) + port = s.getsockname()[1] + return port + + redis_port = find_free_port() + redis_host = "localhost" + + print(f"[Redis] Starting Redis server on {redis_host}:{redis_port}") + + # Start Redis server with minimal config + redis_proc = subprocess.Popen( + [ + redis_server_bin, + "--port", str(redis_port), + "--bind", redis_host, + "--save", "", # Disable persistence + "--appendonly", "no", # Disable AOF + "--protected-mode", "no", # Allow connections without password + ], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + # Start streaming Redis output + redis_stdout_thread, redis_stderr_thread = stream_process_output(redis_proc, "Redis") + + # Wait for Redis to be ready + import redis + r = redis.Redis(host=redis_host, port=redis_port) + for i in range(30): + try: + r.ping() + print(f"[Redis] Server ready on {redis_host}:{redis_port}") + break + except redis.ConnectionError: + if redis_proc.poll() is not None: + raise RuntimeError(f"Redis server failed to start (exit code {redis_proc.returncode})") + time.sleep(0.1) + else: + redis_proc.kill() + raise TimeoutError("Redis server did not start within 3 seconds") + + try: + yield (redis_host, redis_port) + finally: + # Cleanup + print(f"[Redis] Stopping Redis server (PID {redis_proc.pid})") + redis_proc.terminate() + try: + redis_proc.wait(timeout=5) + except subprocess.TimeoutExpired: + print("[Redis] Redis did not stop gracefully, killing...") + redis_proc.kill() + redis_proc.wait() + print("[Redis] Redis server stopped") + + +class TestFastLLMServerIntegration: + """Test Fast-LLM weight broadcast with vLLM HTTP server.""" + + @pytest.mark.timeout(2400) # 40 minutes for server test + @pytest.mark.asyncio + @pytest.mark.skipif( + torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs" + ) + async def test_server_fast_llm_broadcast_pattern( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + fast_llm_trainer_helper, + redis_server, + temp_dir, + ): + """Server integration test: verify Fast-LLM weight broadcast pattern with HTTP API. + + This test validates the Fast-LLM protocol where: + 1. Redis server is automatically started + 2. vLLM server is running and serving HTTP requests + 3. Trainer broadcasts weight updates via Redis stream + broadcast_object_list + 4. Server responses change based on weight updates + + Flow: + - Start Redis server (via fixture) + - Start vLLM HTTP server with --weight-update-mode=fast-llm + - Continuously generate via HTTP API (deterministic) + - Trainer: wait 15s → broadcast perturbed → wait 5s → broadcast original → wait 5s → broadcast perturbed + - Verify generation pattern: original → perturbed → original → perturbed + - Stop Redis server (via fixture cleanup) + """ + print("\n" + "=" * 60) + print("Starting Fast-LLM server weight update pattern test") + print("=" * 60) + + # Get Redis connection info from fixture + redis_host, redis_port = redis_server + print(f"[Main] Using Redis server at {redis_host}:{redis_port}") + + server_port = 8000 + server_url = f"http://127.0.0.1:{server_port}" + + # Start vLLM server with Fast-LLM mode + server_proc, _, _ = start_vllm_server( + model_name=model_name, + server_port=server_port, + distributed_init_method=distributed_init_method, + stream_process_output_fn=stream_process_output, + extra_args=[ + "--weight-update-mode", "fast-llm", + "--redis-host", redis_host, + "--redis-port", str(redis_port), + ], + ) + + # Give server a moment to start + await asyncio.sleep(1) + + # Start trainer process + trainer_proc, _, _ = start_trainer_process( + trainer_helper_path=fast_llm_trainer_helper, + distributed_init_method=distributed_init_method, + model_name=model_name, + server_url=server_url, + stream_process_output_fn=stream_process_output, + extra_args=[ + "--redis-host", redis_host, + "--redis-port", str(redis_port), + ], + ) + + try: + # Wait for server to be ready + await wait_for_server_ready(server_url, server_proc, trainer_proc) + + # Run generation loop + generations = await run_generation_loop( + server_url=server_url, + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + ) + + # Wait for trainer to finish + print("[Main] Waiting for trainer to finish...") + for _ in range(30): + if trainer_proc.poll() is not None: + break + await asyncio.sleep(1) + + # Analyze and verify pattern + analyze_and_verify_pattern(generations) + print("\n✓ Fast-LLM server weight update pattern test PASSED") + + finally: + # Cleanup - always kill process tree even if main process exited + # (child processes like vLLM workers might still be running) + print("[Main] Cleaning up processes...") + if server_proc: + print( + f"[Main] Killing server process tree (PID {server_proc.pid})..." + ) + kill_process_tree(server_proc.pid) + if trainer_proc: + print( + f"[Main] Killing trainer process tree (PID {trainer_proc.pid})..." + ) + kill_process_tree(trainer_proc.pid) diff --git a/tests/test_vllm1_integration.py b/tests/test_vllm1_integration.py index d74e49f3..625c2274 100644 --- a/tests/test_vllm1_integration.py +++ b/tests/test_vllm1_integration.py @@ -14,6 +14,15 @@ # torch is needed at top level for pytest.mark.skipif decorators import torch +# Import shared utilities +from .server_weight_update_utils import ( + wait_for_server_ready, + run_generation_loop, + analyze_and_verify_pattern, + start_vllm_server, + start_trainer_process, +) + try: import psutil HAS_PSUTIL = True @@ -839,261 +848,57 @@ async def test_server_weight_update_pattern( - Trainer: wait 15s → broadcast perturbed → wait 5s → broadcast original → wait 5s → broadcast perturbed - Verify generation pattern: original → perturbed → original → perturbed """ - import requests - import time - print("\n" + "="*60) print("Starting server weight update pattern test") print("="*60) - # Start vLLM HTTP server server_port = 8000 - vllm_env = os.environ.copy() - vllm_env["CUDA_VISIBLE_DEVICES"] = "0" - vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" - - print(f"[Main] Starting vLLM HTTP server on port {server_port} (GPU 0)") - vllm_entry_point = Path(__file__).parent.parent / "pipelinerl" / "entrypoints" / "run_vllm1.py" - server_proc = subprocess.Popen( - [ - sys.executable, - str(vllm_entry_point), - "--model", model_name, - "--port", str(server_port), - "--host", "127.0.0.1", - "--actor-llm-idx", "0", - "--weight-update-group-init-method", distributed_init_method, - "--weight-update-group-world-size", "2", - ], - env=vllm_env, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, + server_url = f"http://127.0.0.1:{server_port}" + + # Start vLLM server (HTTP mode - default, no extra args) + server_proc, _, _ = start_vllm_server( + model_name=model_name, + server_port=server_port, + distributed_init_method=distributed_init_method, + stream_process_output_fn=stream_process_output, + extra_args=None, # HTTP mode is default ) - # Start streaming server output in background threads - print("[Main] Starting server output streaming...") - server_stdout_thread, server_stderr_thread = stream_process_output(server_proc, "vLLM Server") - - # Give server a moment to start, then immediately start trainer - # (they need to rendezvous for process group initialization) + # Give server a moment to start await asyncio.sleep(1) - # Start trainer process immediately (needed for process group rendezvous) - trainer_env = os.environ.copy() - trainer_env["CUDA_VISIBLE_DEVICES"] = "1" - - print("[Main] Starting trainer process (GPU 1) for process group rendezvous") - trainer_proc = subprocess.Popen( - [ - sys.executable, - str(distributed_trainer_helper), - "timed_broadcast_server_test", - "--init-method", distributed_init_method, - "--model-name", model_name, - "--server-url", f"http://127.0.0.1:{server_port}", - ], - env=trainer_env, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, + # Start trainer process + trainer_proc, _, _ = start_trainer_process( + trainer_helper_path=distributed_trainer_helper, + distributed_init_method=distributed_init_method, + model_name=model_name, + server_url=server_url, + stream_process_output_fn=stream_process_output, + extra_args=None, # No extra args for HTTP mode ) - # Start streaming trainer output in background threads - print("[Main] Starting trainer output streaming...") - trainer_stdout_thread, trainer_stderr_thread = stream_process_output(trainer_proc, "Trainer") - try: # Wait for server to be ready - print("[Main] Waiting for server to be ready...") - server_ready = False - for i in range(300): # Wait up to 5 minutes - # Check if server process crashed - if server_proc.poll() is not None: - print(f"[Main] Server process terminated with code {server_proc.returncode}") - raise RuntimeError(f"Server process terminated with code {server_proc.returncode}") - - # Check if trainer process crashed - if trainer_proc.poll() is not None: - print(f"[Main] Trainer process terminated with code {trainer_proc.returncode}") - raise RuntimeError(f"Trainer process terminated with code {trainer_proc.returncode}") - - try: - resp = requests.get(f"http://127.0.0.1:{server_port}/health", timeout=1) - if resp.status_code == 200: - server_ready = True - print("[Main] Server is ready!") - break - except requests.exceptions.RequestException: - pass - - if i % 10 == 0: - print(f"[Main] Still waiting for server... ({i} seconds)") - await asyncio.sleep(1) - - if not server_ready: - raise TimeoutError("Server did not become ready within 5 minutes") - - # Continuously generate completions - print("[Main] Starting continuous generation loop...") - generations = [] - start_time = time.time() - generation_interval = 0.5 # Generate every 0.5 seconds (more frequent) - max_duration = 120 # Run for 120 seconds max (covers 15s + 3 broadcasts with 5s delays) - - def check_pattern_detected(generations): - """Check if we have detected the full pattern (4 phases).""" - if len(generations) < 4: - return False - - # Track when the text changes to identify phase boundaries - phases = [] - current_text = None - current_phase = [] - - for ts, text in generations: - if text != current_text: - if current_phase: - phases.append((current_text, current_phase)) - current_text = text - current_phase = [(ts, text)] - else: - current_phase.append((ts, text)) - - # Add the last phase - if current_phase: - phases.append((current_text, current_phase)) - - # Check if we have at least 4 phases - if len(phases) < 4: - return False - - # Verify the pattern: phase1 != phase2, phase3 == phase1, phase4 == phase2 - phase1_text = phases[0][0] - phase2_text = phases[1][0] - phase3_text = phases[2][0] - phase4_text = phases[3][0] - - if phase1_text == phase2_text: - return False # Phase 1 and 2 should be different - if phase3_text != phase1_text: - return False # Phase 3 should match Phase 1 - if phase4_text != phase2_text: - return False # Phase 4 should match Phase 2 - - return True - - while time.time() - start_time < max_duration: - # Check if trainer is still running - trainer_poll = trainer_proc.poll() - if trainer_poll is not None: - print(f"[Main] Trainer exited with code {trainer_poll}") - break + await wait_for_server_ready(server_url, server_proc, trainer_proc) + + # Run generation loop + generations = await run_generation_loop( + server_url=server_url, + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + ) - try: - # Generate via HTTP API - payload = { - "model": model_name, - "prompt": simple_prompt, - "max_tokens": generation_config["max_tokens"], - "temperature": 0.0, # Deterministic - "top_p": 1.0, # Must match engine params - "seed": 42, - } - - resp = requests.post( - f"http://127.0.0.1:{server_port}/v1/completions", - json=payload, - timeout=30, - ) - - if resp.status_code == 200: - result = resp.json() - generated_text = result["choices"][0]["text"] - timestamp = time.time() - start_time - generations.append((timestamp, generated_text)) - print(f"[Main] [{timestamp:.1f}s] Generated: '{generated_text}'") - - # Check if pattern is detected - stop early if confirmed - if check_pattern_detected(generations): - print(f"[Main] Pattern detected! Stopping generation early at {timestamp:.1f}s") - break - else: - print(f"[Main] Generation failed with status {resp.status_code}") - - except requests.exceptions.RequestException as e: - print(f"[Main] Request failed: {e}") - - await asyncio.sleep(generation_interval) - - # Wait a bit more for trainer to finish + # Wait for trainer to finish print("[Main] Waiting for trainer to finish...") for _ in range(30): if trainer_proc.poll() is not None: break await asyncio.sleep(1) - # Analyze generation sequence - print("\n" + "="*60) - print("GENERATION SEQUENCE ANALYSIS") - print("="*60) - print(f"Total generations: {len(generations)}") - - # Print all generations - for i, (ts, text) in enumerate(generations): - print(f"[{ts:5.1f}s] Gen {i+1}: '{text[:80]}...'") - - # Identify unique generation texts and their phases - # Expected pattern: original → perturbed → original → perturbed - if len(generations) < 4: - raise AssertionError(f"Not enough generations to verify pattern (need at least 4, got {len(generations)})") - - # Track when the text changes to identify phase boundaries - phases = [] - current_text = None - current_phase = [] - - for ts, text in generations: - if text != current_text: - if current_phase: - phases.append((current_text, current_phase)) - current_text = text - current_phase = [(ts, text)] - else: - current_phase.append((ts, text)) - - # Add the last phase - if current_phase: - phases.append((current_text, current_phase)) - - print("\n" + "="*60) - print(f"Detected {len(phases)} phases:") - for i, (text, items) in enumerate(phases): - print(f"Phase {i+1}: {len(items)} generations - '{text[:60]}...'") - print("="*60) - - # Verify the pattern - assert len(phases) >= 4, f"Expected at least 4 phases (original → perturbed → original → perturbed), got {len(phases)}" - - phase1_text, phase1_items = phases[0] - phase2_text, phase2_items = phases[1] - phase3_text, phase3_items = phases[2] - phase4_text, phase4_items = phases[3] - - # Verify phase 1 (original) != phase 2 (perturbed) - assert phase1_text != phase2_text, "Phase 1 (original) and Phase 2 (perturbed) should be different" - - # Verify phase 3 (original) == phase 1 (original) - assert phase3_text == phase1_text, f"Phase 3 should match Phase 1 (original weights restored)" - - # Verify phase 4 (perturbed) == phase 2 (perturbed) - assert phase4_text == phase2_text, f"Phase 4 should match Phase 2 (perturbed weights reapplied)" - - print("\n✓ Pattern verified:") - print(f" Phase 1 (original): {len(phase1_items)} generations") - print(f" Phase 2 (perturbed): {len(phase2_items)} generations") - print(f" Phase 3 (original): {len(phase3_items)} generations (matches Phase 1 ✓)") - print(f" Phase 4 (perturbed): {len(phase4_items)} generations (matches Phase 2 ✓)") + # Analyze and verify pattern + analyze_and_verify_pattern(generations) print("\n✓ Server weight update pattern test PASSED") finally: From dad6242c3d4cf7619f1d7fc0c8f41068df5cf09f Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 20 Feb 2026 10:03:23 +0000 Subject: [PATCH 18/85] refactoring of traning helper --- tests/distributed_trainer_helper.py | 641 ++++++++-------------------- 1 file changed, 186 insertions(+), 455 deletions(-) diff --git a/tests/distributed_trainer_helper.py b/tests/distributed_trainer_helper.py index ac60959f..573156c9 100755 --- a/tests/distributed_trainer_helper.py +++ b/tests/distributed_trainer_helper.py @@ -18,13 +18,65 @@ logger = logging.getLogger(__name__) -def init_process_group(init_method: str, rank: int, world_size: int): - """Initialize a distributed process group and wait.""" - import torch.distributed as dist - import time +# --------------------------------------------------------------------------- +# Internal helpers +# --------------------------------------------------------------------------- + +def _resolve_model_path(model_name: str): + """Resolve model name to a local Path, downloading from HuggingFace if needed.""" + from pathlib import Path + from huggingface_hub import snapshot_download + + model_path = Path(model_name) + if not model_path.exists(): + print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") + model_path = Path(snapshot_download(model_name)) + return model_path + + +def _load_state_dict(model_name: str, device: str = "cuda:0") -> tuple: + """Load model state dict from safetensors files. + + Returns: + (state_dict, model_path) + """ + import json + from safetensors.torch import load_file + + model_path = _resolve_model_path(model_name) + index_file = model_path / "model.safetensors.index.json" + + if index_file.exists(): + print(f"[Trainer] Found index file, loading sharded model") + with open(index_file) as f: + index = json.load(f) + weight_map = index["weight_map"] + + file_to_params = {} + for param_name, filename in weight_map.items(): + file_to_params.setdefault(filename, []).append(param_name) + + state_dict = {} + for filename, param_names in file_to_params.items(): + file_path = model_path / filename + print(f"[Trainer] Loading {len(param_names)} parameters from {filename}") + tensors = load_file(str(file_path), device=device) + for param_name in param_names: + state_dict[param_name] = tensors[param_name] + else: + safetensors_file = model_path / "model.safetensors" + print(f"[Trainer] Loading from single file: {safetensors_file}") + state_dict = load_file(str(safetensors_file), device=device) + + print(f"[Trainer] Loaded {len(state_dict)} parameters from safetensors") + return state_dict, model_path + + +def _init_actor_process_group(init_method: str, rank: int = 0, world_size: int = 2): + """Initialize the actor NCCL process group and return it.""" import pipelinerl.torch_utils - print(f"[Trainer rank={rank}] Initializing process group") + print(f"[Trainer] Initializing process group as rank {rank}") process_group = pipelinerl.torch_utils.init_extra_process_group( group_name="actor", backend="nccl", @@ -32,6 +84,107 @@ def init_process_group(init_method: str, rank: int, world_size: int): rank=rank, world_size=world_size, ) + print("[Trainer] Process group initialized") + return process_group + + +def _create_perturbed_state_dict( + state_dict: dict, seed: int = 42, noise_scale: float = 0.001 +) -> dict: + """Return a new state dict with Gaussian noise added to all tensors.""" + import torch + + print(f"[Trainer] Creating perturbed weights (all tensors) with seed={seed}...") + torch.manual_seed(seed) + perturbed = {} + for name, tensor in state_dict.items(): + perturbed_tensor = tensor.clone() + perturbed_tensor.add_(torch.randn_like(perturbed_tensor) * noise_scale) + perturbed[name] = perturbed_tensor + print( + f"[Trainer] Perturbed all {len(perturbed)} tensors with noise={noise_scale}, seed={seed}" + ) + return perturbed + + +def _broadcast_tensors(state_dict: dict, process_group, log_interval: int = 50): + """Broadcast every tensor in state_dict via NCCL (src=0).""" + import torch.distributed as dist + + total = len(state_dict) + for i, (name, tensor) in enumerate(state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % log_interval == 0: + print(f"[Trainer] Broadcasted {i+1}/{total} parameters") + print(f"[Trainer] All {total} parameters broadcasted") + + +def _broadcast_via_server( + state_dict: dict, + server_url: str, + version: int, + process_group, + label: str = "", +): + """Broadcast weights to a running vLLM server via HTTP POST + NCCL. + + The POST blocks on the server side until NCCL broadcast completes, so we + run it in a background thread while we drive the broadcast ourselves. + """ + import threading + import time + import requests + from weight_update_utils import create_weight_update_request_from_state_dict + + label_str = f" {label}" if label else "" + print(f"[Trainer] Broadcasting {len(state_dict)}{label_str} parameters") + + request = create_weight_update_request_from_state_dict(state_dict, version=version) + + post_result = {"error": None} + + def _post(): + try: + print("[Trainer] POSTing weight update request to server...") + resp = requests.post( + f"{server_url}/receive_weight_update", + json=request.model_dump(), + timeout=600, + ) + if resp.status_code != 200: + post_result["error"] = ( + f"POST failed with status {resp.status_code}: {resp.text}" + ) + else: + print("[Trainer] Server acknowledged weight update") + except Exception as e: + post_result["error"] = f"POST failed: {e}" + + post_thread = threading.Thread(target=_post, daemon=False) + post_thread.start() + time.sleep(0.5) # Give server a moment to start receiving + + _broadcast_tensors(state_dict, process_group) + + post_thread.join(timeout=60) + if post_result["error"]: + raise RuntimeError(f"Weight update POST failed: {post_result['error']}") + + print(f"[Trainer] Broadcast{label_str} complete") + + +# --------------------------------------------------------------------------- +# Public command functions +# --------------------------------------------------------------------------- + +def init_process_group(init_method: str, rank: int, world_size: int): + """Initialize a distributed process group and wait.""" + import torch.distributed as dist + import time + + process_group = _init_actor_process_group(init_method, rank, world_size) print(f"[Trainer rank={rank}] Process group initialized successfully") # Wait for coordination @@ -53,7 +206,6 @@ def save_model_to_dir(state_dict: dict, output_dir: str, model_name: str): from pathlib import Path from safetensors.torch import save_file import shutil - import json output_path = Path(output_dir) output_path.mkdir(parents=True, exist_ok=True) @@ -64,12 +216,7 @@ def save_model_to_dir(state_dict: dict, output_dir: str, model_name: str): print(f"[Trainer] Saved model weights to {safetensors_path}") # Copy config.json from original model - original_path = Path(model_name) - if not original_path.exists(): - # Download if needed - from huggingface_hub import snapshot_download - - original_path = Path(snapshot_download(model_name)) + original_path = _resolve_model_path(model_name) config_src = original_path / "config.json" config_dst = output_path / "config.json" @@ -100,9 +247,7 @@ def broadcast_weights( """Load model and broadcast weights to vLLM worker.""" import torch import torch.distributed as dist - from transformers import AutoModelForCausalLM from pathlib import Path - import pipelinerl.torch_utils # Setup sync points if provided if sync_dir: @@ -117,16 +262,7 @@ def broadcast_weights( broadcast_done = SyncPoint(sync_path, "broadcast_done") # IMPORTANT: Initialize process group FIRST (before any waiting) - # Use the same init_extra_process_group as vLLM to create the SAME process group - print("[Trainer] Initializing process group as rank 0") - process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", - backend="nccl", - init_method=init_method, - rank=0, - world_size=2, - ) - print("[Trainer] Process group initialized") + process_group = _init_actor_process_group(init_method, rank=0, world_size=2) # Now wait for vLLM to finish baseline and be ready to receive if sync_dir: @@ -138,56 +274,9 @@ def broadcast_weights( ready_to_receive.wait(timeout=60) print("[Trainer] vLLM ready, starting weight broadcast") - # Load tensors directly from safetensors files (not the full model) print(f"[Trainer] Loading tensors from safetensors for {model_name}") - from pathlib import Path - import json - from safetensors.torch import load_file - from huggingface_hub import snapshot_download + state_dict, _ = _load_state_dict(model_name) - # Handle both local paths and HuggingFace model IDs - model_path = Path(model_name) - if not model_path.exists(): - # Download from HuggingFace Hub - print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") - model_path = Path(snapshot_download(model_name)) - - index_file = model_path / "model.safetensors.index.json" - - # Load state_dict from safetensors files - if index_file.exists(): - # Sharded model - use index to load from multiple files - print(f"[Trainer] Found index file, loading sharded model") - with open(index_file) as f: - index = json.load(f) - - weight_map = index["weight_map"] # {param_name: filename} - - # Group parameters by file to load each file only once - file_to_params = {} - for param_name, filename in weight_map.items(): - if filename not in file_to_params: - file_to_params[filename] = [] - file_to_params[filename].append(param_name) - - # Load all tensors - state_dict = {} - for filename, param_names in file_to_params.items(): - file_path = model_path / filename - print(f"[Trainer] Loading {len(param_names)} parameters from {filename}") - tensors = load_file(str(file_path), device="cuda:0") - for param_name in param_names: - state_dict[param_name] = tensors[param_name] - else: - # Single file model - safetensors_file = model_path / "model.safetensors" - print(f"[Trainer] Loading from single file: {safetensors_file}") - state_dict = load_file(str(safetensors_file), device="cuda:0") - - print(f"[Trainer] Loaded {len(state_dict)} parameters from safetensors") - - # Fast-LLM broadcasts weights as they are in safetensors files - # No filtering - vLLM handles its own implementation details params_to_broadcast = state_dict print(f"[Trainer] Will broadcast {len(params_to_broadcast)} parameters") @@ -214,14 +303,7 @@ def broadcast_weights( # Optionally perturb weights - add noise to ALL tensors if perturb: - logger.info("Perturbing ALL weights with seed=42") - torch.manual_seed(42) - for name, tensor in params_to_broadcast.items(): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - noise = torch.randn_like(tensor) * 0.001 # Smaller noise to avoid breaking model - tensor.add_(noise) - print(f"[Trainer] Perturbed all {len(params_to_broadcast)} tensors with noise=0.001, seed=42") + params_to_broadcast = _create_perturbed_state_dict(params_to_broadcast) # Broadcast each weight with detailed logging logger.info(f"Starting broadcast of {len(params_to_broadcast)} parameters") @@ -230,24 +312,19 @@ def broadcast_weights( logger.debug( f" - shape: {tensor.shape}, dtype: {tensor.dtype}, device: {tensor.device}" ) - - # Move to GPU if needed if tensor.device.type != "cuda": logger.debug(f" - Moving {name} to CUDA") tensor = tensor.cuda(0) logger.debug(f" - {name} now on device: {tensor.device}") - logger.debug(f" - Calling dist.broadcast for {name}...") dist.broadcast(tensor, src=0, group=process_group) logger.debug(f" - Broadcast complete for {name}") - if (i + 1) % 10 == 0: logger.info(f"Broadcasted {i+1}/{len(params_to_broadcast)} parameters") print(f"[Trainer] All {len(params_to_broadcast)} parameters broadcasted") # Signal broadcast complete BEFORE destroying process group - # This ensures vLLM sees the signal before trainer exits if sync_dir: broadcast_done.signal() print("[Trainer] Signaled broadcast complete") @@ -263,16 +340,12 @@ def broadcast_cross_validation( Also saves perturbed model to disk for vLLM to load. """ - import torch import torch.distributed as dist from pathlib import Path - import json - import pipelinerl.torch_utils - from safetensors.torch import load_file - from huggingface_hub import snapshot_download sys.path.insert(0, str(Path(__file__).parent)) from sync_helper import SyncPoint, write_weight_update_request + from weight_update_utils import create_weight_update_request_from_state_dict sync_path = Path(sync_dir) baseline_done = SyncPoint(sync_path, "baseline_done") @@ -285,63 +358,15 @@ def broadcast_cross_validation( ready_to_receive_original = SyncPoint(sync_path, "ready_to_receive_original") original_broadcast_done = SyncPoint(sync_path, "original_broadcast_done") - # Initialize process group - print("[Trainer] Initializing process group as rank 0") - process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", - backend="nccl", - init_method=init_method, - rank=0, - world_size=2, - ) - print("[Trainer] Process group initialized") + process_group = _init_actor_process_group(init_method, rank=0, world_size=2) - # Wait for baseline print("[Trainer] Waiting for vLLM baseline generation...") baseline_done.wait(timeout=120) - # Load original model print(f"[Trainer] Loading original model {model_name}") - model_path = Path(model_name) - if not model_path.exists(): - print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") - model_path = Path(snapshot_download(model_name)) - - index_file = model_path / "model.safetensors.index.json" - if index_file.exists(): - print(f"[Trainer] Loading sharded model") - with open(index_file) as f: - index = json.load(f) - weight_map = index["weight_map"] - file_to_params = {} - for param_name, filename in weight_map.items(): - if filename not in file_to_params: - file_to_params[filename] = [] - file_to_params[filename].append(param_name) - - original_state_dict = {} - for filename, param_names in file_to_params.items(): - file_path = model_path / filename - tensors = load_file(str(file_path), device="cuda:0") - for param_name in param_names: - original_state_dict[param_name] = tensors[param_name] - else: - safetensors_file = model_path / "model.safetensors" - original_state_dict = load_file(str(safetensors_file), device="cuda:0") + original_state_dict, model_path = _load_state_dict(model_name) - print(f"[Trainer] Loaded {len(original_state_dict)} original parameters") - - # Create perturbed version - add noise to ALL tensors - print("[Trainer] Creating perturbed weights (all tensors) with seed=42...") - torch.manual_seed(42) - perturbed_state_dict = {} - for name, tensor in original_state_dict.items(): - perturbed_tensor = tensor.clone() - # Add smaller noise to avoid completely breaking the model - noise = torch.randn_like(perturbed_tensor) * 0.001 # Reduced from 0.01 - perturbed_tensor.add_(noise) - perturbed_state_dict[name] = perturbed_tensor - print(f"[Trainer] Perturbed all {len(perturbed_state_dict)} tensors with noise=0.001, seed=42") + perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) # Save perturbed model to disk perturbed_model_dir = Path(temp_dir) / "perturbed_model" @@ -350,93 +375,52 @@ def broadcast_cross_validation( perturbed_state_dict, str(perturbed_model_dir), str(model_path) ) - # Write perturbed model path to sync file path_file = sync_path / "perturbed_model_path.txt" path_file.write_text(saved_path) perturbed_model_saved.signal() print(f"[Trainer] Signaled perturbed model saved at: {saved_path}") - # Wait for vLLM to be ready to receive perturbed weights + # Broadcast perturbed weights print("[Trainer] Waiting for vLLM to be ready for perturbed broadcast...") ready_to_receive_perturbed.wait(timeout=120) - # Broadcast perturbed weights print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters") - from weight_update_utils import create_weight_update_request_from_state_dict - - request = create_weight_update_request_from_state_dict( - perturbed_state_dict, version=1 - ) + request = create_weight_update_request_from_state_dict(perturbed_state_dict, version=1) write_weight_update_request(sync_path, request) - - for i, (name, tensor) in enumerate(perturbed_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print( - f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} perturbed parameters" - ) + _broadcast_tensors(perturbed_state_dict, process_group) perturbed_broadcast_done.signal() print("[Trainer] Perturbed weights broadcast complete") - # Wait for vLLM to finish generating res_mod_1 print("[Trainer] Waiting for vLLM to finish res_mod_1...") mod1_done.wait(timeout=120) - # Destroy our process group immediately after we're done using it - # No need to wait for vLLM - destroy_process_group() is a local operation print("[Trainer] Destroying process group for first broadcast") dist.destroy_process_group(process_group) - # Wait for vLLM to destroy its first engine before creating new groups print("[Trainer] Waiting for vLLM to destroy first engine...") first_engine_destroyed.wait(timeout=120) - # Recreate our process group BEFORE vLLM creates its engine - # (vLLM will rendezvous with us when it creates engine 2) print("[Trainer] Recreating process group for second broadcast") - process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", - backend="nccl", - init_method=init_method, - rank=0, - world_size=2, - ) + process_group = _init_actor_process_group(init_method, rank=0, world_size=2) print("[Trainer] Process group recreated, waiting at rendezvous...") - # Wait for vLLM to recreate engine (confirms rendezvous completed) print("[Trainer] Waiting for vLLM to recreate engine...") engine_recreated.wait(timeout=300) # 5 minutes - engine creation can be slow print("[Trainer] vLLM engine recreated, both in new process group") - # Wait for vLLM to be ready for original weights + # Broadcast original weights print("[Trainer] Waiting for vLLM to be ready for original broadcast...") ready_to_receive_original.wait(timeout=120) - # Broadcast original weights print(f"[Trainer] Broadcasting {len(original_state_dict)} original parameters") - from weight_update_utils import create_weight_update_request_from_state_dict - - request = create_weight_update_request_from_state_dict( - original_state_dict, version=2 - ) + request = create_weight_update_request_from_state_dict(original_state_dict, version=2) write_weight_update_request(sync_path, request) - - for i, (name, tensor) in enumerate(original_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print( - f"[Trainer] Broadcasted {i+1}/{len(original_state_dict)} original parameters" - ) + _broadcast_tensors(original_state_dict, process_group) original_broadcast_done.signal() print("[Trainer] Original weights broadcast complete") - # Cleanup dist.destroy_process_group(process_group) print("[Trainer] Process group destroyed") @@ -446,16 +430,12 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): Tests that we can switch between weight sets multiple times. """ - import torch import torch.distributed as dist from pathlib import Path - import json - import pipelinerl.torch_utils - from safetensors.torch import load_file - from huggingface_hub import snapshot_download sys.path.insert(0, str(Path(__file__).parent)) from sync_helper import SyncPoint, write_weight_update_request + from weight_update_utils import create_weight_update_request_from_state_dict sync_path = Path(sync_dir) baseline_done = SyncPoint(sync_path, "baseline_done") @@ -466,62 +446,15 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): ready_for_perturbed2 = SyncPoint(sync_path, "ready_for_perturbed2") perturbed2_done = SyncPoint(sync_path, "perturbed2_done") - # Initialize process group - print("[Trainer] Initializing process group as rank 0") - process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", - backend="nccl", - init_method=init_method, - rank=0, - world_size=2, - ) - print("[Trainer] Process group initialized") + process_group = _init_actor_process_group(init_method, rank=0, world_size=2) - # Wait for baseline print("[Trainer] Waiting for vLLM baseline generation...") baseline_done.wait(timeout=120) - # Load original model print(f"[Trainer] Loading model {model_name}") - model_path = Path(model_name) - if not model_path.exists(): - print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") - model_path = Path(snapshot_download(model_name)) - - index_file = model_path / "model.safetensors.index.json" - if index_file.exists(): - print(f"[Trainer] Loading sharded model") - with open(index_file) as f: - index = json.load(f) - weight_map = index["weight_map"] - file_to_params = {} - for param_name, filename in weight_map.items(): - if filename not in file_to_params: - file_to_params[filename] = [] - file_to_params[filename].append(param_name) - - original_state_dict = {} - for filename, param_names in file_to_params.items(): - file_path = model_path / filename - tensors = load_file(str(file_path), device="cuda:0") - for param_name in param_names: - original_state_dict[param_name] = tensors[param_name] - else: - safetensors_file = model_path / "model.safetensors" - original_state_dict = load_file(str(safetensors_file), device="cuda:0") + original_state_dict, model_path = _load_state_dict(model_name) - print(f"[Trainer] Loaded {len(original_state_dict)} original parameters") - - # Create perturbed version - print("[Trainer] Creating perturbed weights with seed=42...") - torch.manual_seed(42) - perturbed_state_dict = {} - for name, tensor in original_state_dict.items(): - perturbed_tensor = tensor.clone() - noise = torch.randn_like(perturbed_tensor) * 0.001 - perturbed_tensor.add_(noise) - perturbed_state_dict[name] = perturbed_tensor - print(f"[Trainer] Perturbed all {len(perturbed_state_dict)} tensors with noise=0.001, seed=42") + perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) # Save perturbed weights for reuse in server tests perturbed_weights_dir = Path(sync_dir) / "perturbed_weights" @@ -536,16 +469,9 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): ready_for_perturbed1.wait(timeout=120) print(f"[Trainer] Broadcasting perturbed weights (1st time)") - from weight_update_utils import create_weight_update_request_from_state_dict request = create_weight_update_request_from_state_dict(perturbed_state_dict, version=1) write_weight_update_request(sync_path, request) - - for i, (name, tensor) in enumerate(perturbed_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print(f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} parameters") + _broadcast_tensors(perturbed_state_dict, process_group) perturbed1_done.signal() print("[Trainer] First perturbed broadcast complete") @@ -555,17 +481,9 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): ready_for_original.wait(timeout=120) print(f"[Trainer] Broadcasting original weights") - from weight_update_utils import create_weight_update_request_from_state_dict - request = create_weight_update_request_from_state_dict(original_state_dict, version=2) write_weight_update_request(sync_path, request) - - for i, (name, tensor) in enumerate(original_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print(f"[Trainer] Broadcasted {i+1}/{len(original_state_dict)} parameters") + _broadcast_tensors(original_state_dict, process_group) original_done.signal() print("[Trainer] Original broadcast complete") @@ -575,22 +493,13 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): ready_for_perturbed2.wait(timeout=120) print(f"[Trainer] Broadcasting perturbed weights (2nd time)") - from weight_update_utils import create_weight_update_request_from_state_dict - request = create_weight_update_request_from_state_dict(perturbed_state_dict, version=3) write_weight_update_request(sync_path, request) - - for i, (name, tensor) in enumerate(perturbed_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print(f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} parameters") + _broadcast_tensors(perturbed_state_dict, process_group) perturbed2_done.signal() print("[Trainer] Second perturbed broadcast complete") - # Cleanup dist.destroy_process_group(process_group) print("[Trainer] Process group destroyed") @@ -610,30 +519,14 @@ def timed_broadcast_server_test( model_name: Model name to load server_url: Base URL of vLLM server (e.g., "http://127.0.0.1:8000") """ - import torch import torch.distributed as dist from pathlib import Path - import json - import pipelinerl.torch_utils - from safetensors.torch import load_file - from huggingface_hub import snapshot_download import time import requests - import threading sys.path.insert(0, str(Path(__file__).parent)) - from weight_update_utils import create_weight_update_request_from_state_dict - # Initialize process group - print("[Trainer] Initializing process group as rank 0") - process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", - backend="nccl", - init_method=init_method, - rank=0, - world_size=2, - ) - print("[Trainer] Process group initialized") + process_group = _init_actor_process_group(init_method, rank=0, world_size=2) # Wait for server to be ready by polling health endpoint print("[Trainer] Waiting for server to be ready...") @@ -656,188 +549,26 @@ def timed_broadcast_server_test( print("[Trainer] Waiting additional 10 seconds for server to fully initialize...") time.sleep(10) - # Load original weights print(f"[Trainer] Loading original weights from {model_name}") - model_path = Path(model_name) - if not model_path.exists(): - print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") - model_path = Path(snapshot_download(model_name)) + original_state_dict, _ = _load_state_dict(model_name) - index_file = model_path / "model.safetensors.index.json" - if index_file.exists(): - print(f"[Trainer] Loading sharded original model") - with open(index_file) as f: - index = json.load(f) - weight_map = index["weight_map"] - file_to_params = {} - for param_name, filename in weight_map.items(): - if filename not in file_to_params: - file_to_params[filename] = [] - file_to_params[filename].append(param_name) - - original_state_dict = {} - for filename, param_names in file_to_params.items(): - file_path = model_path / filename - tensors = load_file(str(file_path), device="cuda:0") - for param_name in param_names: - original_state_dict[param_name] = tensors[param_name] - else: - safetensors_file = model_path / "model.safetensors" - original_state_dict = load_file(str(safetensors_file), device="cuda:0") - - print(f"[Trainer] Loaded {len(original_state_dict)} original parameters") - - # Create perturbed weights - print("[Trainer] Creating perturbed weights with seed=42...") - torch.manual_seed(42) - perturbed_state_dict = {} - for name, tensor in original_state_dict.items(): - perturbed_tensor = tensor.clone() - noise = torch.randn_like(perturbed_tensor) * 0.001 - perturbed_tensor.add_(noise) - perturbed_state_dict[name] = perturbed_tensor - print(f"[Trainer] Perturbed all {len(perturbed_state_dict)} tensors with noise=0.001, seed=42") + perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) # Broadcast 1: Perturbed weights - print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters") - - request = create_weight_update_request_from_state_dict( - perturbed_state_dict, version=1 - ) - - # POST request to server in background thread (it will block until broadcast completes) - post_result = {"error": None} - def post_weight_update(): - try: - print("[Trainer] POSTing weight update request to server...") - resp = requests.post( - f"{server_url}/receive_weight_update", - json=request.model_dump(), - timeout=600, # 10 minutes - ) - if resp.status_code != 200: - post_result["error"] = f"POST failed with status {resp.status_code}: {resp.text}" - else: - print("[Trainer] Server acknowledged weight update") - except Exception as e: - post_result["error"] = f"POST failed: {e}" - - post_thread = threading.Thread(target=post_weight_update, daemon=False) - post_thread.start() - - # Give server a moment to start receiving - time.sleep(0.5) - - # Now broadcast via NCCL - for i, (name, tensor) in enumerate(perturbed_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print( - f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} perturbed parameters" - ) - - # Wait for POST to complete - post_thread.join(timeout=60) - if post_result["error"]: - raise RuntimeError(f"Weight update POST failed: {post_result['error']}") + _broadcast_via_server(perturbed_state_dict, server_url, version=1, process_group=process_group, label="perturbed") - print("[Trainer] Perturbed weights broadcast complete") - - # Wait 5 seconds print("[Trainer] Waiting 5 seconds before broadcasting original weights...") time.sleep(5) # Broadcast 2: Original weights - print(f"[Trainer] Broadcasting {len(original_state_dict)} original parameters") - request = create_weight_update_request_from_state_dict( - original_state_dict, version=2 - ) - - # POST request to server in background thread - post_result = {"error": None} - def post_weight_update(): - try: - print("[Trainer] POSTing weight update request to server...") - resp = requests.post( - f"{server_url}/receive_weight_update", - json=request.model_dump(), - timeout=600, - ) - if resp.status_code != 200: - post_result["error"] = f"POST failed with status {resp.status_code}: {resp.text}" - else: - print("[Trainer] Server acknowledged weight update") - except Exception as e: - post_result["error"] = f"POST failed: {e}" + _broadcast_via_server(original_state_dict, server_url, version=2, process_group=process_group, label="original") - post_thread = threading.Thread(target=post_weight_update, daemon=False) - post_thread.start() - time.sleep(0.5) - - for i, (name, tensor) in enumerate(original_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print( - f"[Trainer] Broadcasted {i+1}/{len(original_state_dict)} original parameters" - ) - - post_thread.join(timeout=60) - if post_result["error"]: - raise RuntimeError(f"Weight update POST failed: {post_result['error']}") - - print("[Trainer] Original weights broadcast complete") - - # Wait 5 seconds print("[Trainer] Waiting 5 seconds before broadcasting perturbed weights again...") time.sleep(5) # Broadcast 3: Perturbed weights again (same as first) - print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters (2nd time)") - request = create_weight_update_request_from_state_dict( - perturbed_state_dict, version=3 - ) - - # POST request to server in background thread - post_result = {"error": None} - def post_weight_update(): - try: - print("[Trainer] POSTing weight update request to server...") - resp = requests.post( - f"{server_url}/receive_weight_update", - json=request.model_dump(), - timeout=600, - ) - if resp.status_code != 200: - post_result["error"] = f"POST failed with status {resp.status_code}: {resp.text}" - else: - print("[Trainer] Server acknowledged weight update") - except Exception as e: - post_result["error"] = f"POST failed: {e}" - - post_thread = threading.Thread(target=post_weight_update, daemon=False) - post_thread.start() - time.sleep(0.5) - - for i, (name, tensor) in enumerate(perturbed_state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % 50 == 0: - print( - f"[Trainer] Broadcasted {i+1}/{len(perturbed_state_dict)} perturbed parameters" - ) - - post_thread.join(timeout=60) - if post_result["error"]: - raise RuntimeError(f"Weight update POST failed: {post_result['error']}") - - print("[Trainer] Perturbed weights broadcast complete (2nd time)") + _broadcast_via_server(perturbed_state_dict, server_url, version=3, process_group=process_group, label="perturbed (2nd time)") - # Cleanup dist.destroy_process_group(process_group) print("[Trainer] Process group destroyed, exiting") From 402f29adc3409a838c877dcae091ddb1512d4c49 Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 20 Feb 2026 17:22:07 +0000 Subject: [PATCH 19/85] added stop traning support and changed engine clean up logic to warn stop traning was not received --- pipelinerl/vllm1.py | 48 ++++++++++++++++++++++++++++++++++++++------- 1 file changed, 41 insertions(+), 7 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 3a8f2a88..87d22a65 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -28,6 +28,7 @@ from pipelinerl.finetune_loop import WeightUpdateRequest, ParameterInfo from pipelinerl.vllm_quantization import string_to_dtype # reuse mapping from typing import Any, Protocol, runtime_checkable, Dict, Optional +from fastapi import BackgroundTasks import pipelinerl.torch_utils import pipelinerl.vllm_quantization # Register bf16_last_layer_fp32 quantization config from vllm.distributed import cleanup_dist_env_and_memory @@ -99,10 +100,15 @@ def init_actor_update_group( rank=self.pg_rank, world_size=weight_update_group_world_size, ) + self._process_group_destroyed = False def destroy_actor_update_group(self: LikeWorker): + self._process_group_destroyed = True torch.distributed.destroy_process_group(self.process_group) + def is_actor_update_group_destroyed(self: LikeWorker) -> bool: + return getattr(self, "_process_group_destroyed", False) + def receive_weight_update(self: LikeWorker, request: WeightUpdateRequest): torch.cuda.synchronize(self.device) logger.info( @@ -246,8 +252,13 @@ def monitor_redis_stream(): ) elif event_type == "training_finished": logger.info( - f"[Worker rank={self.rank}] Received training_finished event" + f"[Worker rank={self.rank}] Received training_finished event, destroying process group" ) + try: + self.destroy_actor_update_group() + except Exception as e: + logger.error(f"[Worker rank={self.rank}] Error destroying process group: {e}") + self.fast_llm_stop_event.set() # stop monitoring loop except Exception as e: logger.error(f"[Worker rank={self.rank}] Error in Redis monitor: {e}") @@ -268,12 +279,17 @@ def monitor_redis_stream(): def stop_fast_llm_monitoring(self: LikeWorker): """Stop the Fast-LLM monitoring thread.""" - if hasattr(self, "fast_llm_stop_event"): - logger.info(f"[Worker rank={self.rank}] Stopping Fast-LLM monitoring") + if not hasattr(self, "fast_llm_stop_event"): + return + if not self.fast_llm_stop_event.is_set(): + logger.warning( + f"[Worker rank={self.rank}] training_finished was not received; " + "forcing monitoring thread stop" + ) self.fast_llm_stop_event.set() - if hasattr(self, "fast_llm_monitor_thread"): - self.fast_llm_monitor_thread.join(timeout=5) - logger.info(f"[Worker rank={self.rank}] Fast-LLM monitoring stopped") + if hasattr(self, "fast_llm_monitor_thread"): + self.fast_llm_monitor_thread.join(timeout=5) + logger.info(f"[Worker rank={self.rank}] Fast-LLM monitoring stopped") def receive_weight_update_fast_llm(self: LikeWorker): """Receive weight update via Fast-LLM broadcast protocol. @@ -391,6 +407,13 @@ async def destroy_actor_update_group(self): args=(), ) + async def is_actor_update_group_destroyed(self) -> bool: + results = await self.engine.engine_core.collective_rpc_async( + "is_actor_update_group_destroyed", + args=(), + ) + return all(results) + async def receive_weight_update(self, request: WeightUpdateRequest): await self.engine.engine_core.collective_rpc_async( "receive_weight_update", args=(request,) @@ -498,7 +521,11 @@ async def create_engine( if hasattr(args, 'weight_update_mode') and args.weight_update_mode == "fast-llm": await manager.stop_fast_llm_monitoring() - await manager.destroy_actor_update_group() + if not await manager.is_actor_update_group_destroyed(): + logger.warning( + "training_finished was not called before shutdown; " + "NCCL process group was not destroyed — potential resource leak" + ) if cleanup: logger.info("Cleaning up vLLM engine") # Clear manager reference to engine first @@ -556,6 +583,13 @@ def signal_handler(*_) -> None: async def _receive_weight_update(request: WeightUpdateRequest): await manager.receive_weight_update(request) return {"status": "ok"} + + @app.post("/training_finished") + async def _training_finished(background_tasks: BackgroundTasks): + logger.info("Received /training_finished, scheduling NCCL process group teardown") + background_tasks.add_task(manager.destroy_actor_update_group) + return {"status": "ok"} + logger.info("HTTP weight update endpoint registered") else: logger.info("Fast-LLM mode: using Redis stream (no HTTP endpoint registered)") From 3848fbcc12c52db1e2aeacf63c833a1b2e98ee29 Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 20 Feb 2026 17:24:27 +0000 Subject: [PATCH 20/85] added 3 and 4 gpu tests (not run yet), added traning end event, added better abab pattern detection in generations results to test weight bradcast correctnes, some refactoring --- tests/distributed_trainer_helper.py | 289 +++++------- tests/fast_llm_trainer_helper.py | 154 +++++++ tests/server_weight_update_utils.py | 328 +++++++++----- tests/test_vllm1_fast_llm_broadcast.py | 335 ++++++++++---- tests/test_vllm1_integration.py | 580 +++++++++++++++++++------ tests/trainer_test_utils.py | 128 ++++++ tests/vllm_engine_helper.py | 27 +- 7 files changed, 1341 insertions(+), 500 deletions(-) create mode 100644 tests/fast_llm_trainer_helper.py create mode 100644 tests/trainer_test_utils.py diff --git a/tests/distributed_trainer_helper.py b/tests/distributed_trainer_helper.py index 573156c9..6cd4b2c4 100755 --- a/tests/distributed_trainer_helper.py +++ b/tests/distributed_trainer_helper.py @@ -8,6 +8,17 @@ import sys import argparse import logging +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent)) +from trainer_test_utils import ( + _resolve_model_path, + _load_state_dict, + _create_perturbed_state_dict, + _init_actor_process_group, + _broadcast_tensors, + _wait_for_servers_ready, +) # Setup debug logging logging.basicConfig( @@ -18,120 +29,30 @@ logger = logging.getLogger(__name__) -# --------------------------------------------------------------------------- -# Internal helpers -# --------------------------------------------------------------------------- - -def _resolve_model_path(model_name: str): - """Resolve model name to a local Path, downloading from HuggingFace if needed.""" - from pathlib import Path - from huggingface_hub import snapshot_download - - model_path = Path(model_name) - if not model_path.exists(): - print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") - model_path = Path(snapshot_download(model_name)) - return model_path - - -def _load_state_dict(model_name: str, device: str = "cuda:0") -> tuple: - """Load model state dict from safetensors files. +def _wait_all_actors(sync_path, name: str, num_actors: int, timeout: float = 120): + """Wait for all actors to signal a named sync point. - Returns: - (state_dict, model_path) + Each actor signals ``{name}_actor_{i}`` for i in range(num_actors). """ - import json - from safetensors.torch import load_file - - model_path = _resolve_model_path(model_name) - index_file = model_path / "model.safetensors.index.json" - - if index_file.exists(): - print(f"[Trainer] Found index file, loading sharded model") - with open(index_file) as f: - index = json.load(f) - weight_map = index["weight_map"] - - file_to_params = {} - for param_name, filename in weight_map.items(): - file_to_params.setdefault(filename, []).append(param_name) - - state_dict = {} - for filename, param_names in file_to_params.items(): - file_path = model_path / filename - print(f"[Trainer] Loading {len(param_names)} parameters from {filename}") - tensors = load_file(str(file_path), device=device) - for param_name in param_names: - state_dict[param_name] = tensors[param_name] - else: - safetensors_file = model_path / "model.safetensors" - print(f"[Trainer] Loading from single file: {safetensors_file}") - state_dict = load_file(str(safetensors_file), device=device) - - print(f"[Trainer] Loaded {len(state_dict)} parameters from safetensors") - return state_dict, model_path - - -def _init_actor_process_group(init_method: str, rank: int = 0, world_size: int = 2): - """Initialize the actor NCCL process group and return it.""" - import pipelinerl.torch_utils - - print(f"[Trainer] Initializing process group as rank {rank}") - process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", - backend="nccl", - init_method=init_method, - rank=rank, - world_size=world_size, - ) - print("[Trainer] Process group initialized") - return process_group - - -def _create_perturbed_state_dict( - state_dict: dict, seed: int = 42, noise_scale: float = 0.001 -) -> dict: - """Return a new state dict with Gaussian noise added to all tensors.""" - import torch - - print(f"[Trainer] Creating perturbed weights (all tensors) with seed={seed}...") - torch.manual_seed(seed) - perturbed = {} - for name, tensor in state_dict.items(): - perturbed_tensor = tensor.clone() - perturbed_tensor.add_(torch.randn_like(perturbed_tensor) * noise_scale) - perturbed[name] = perturbed_tensor - print( - f"[Trainer] Perturbed all {len(perturbed)} tensors with noise={noise_scale}, seed={seed}" - ) - return perturbed - - -def _broadcast_tensors(state_dict: dict, process_group, log_interval: int = 50): - """Broadcast every tensor in state_dict via NCCL (src=0).""" - import torch.distributed as dist + from pathlib import Path + sys.path.insert(0, str(Path(__file__).parent)) + from sync_helper import SyncPoint - total = len(state_dict) - for i, (name, tensor) in enumerate(state_dict.items()): - if tensor.device.type != "cuda": - tensor = tensor.cuda(0) - dist.broadcast(tensor, src=0, group=process_group) - if (i + 1) % log_interval == 0: - print(f"[Trainer] Broadcasted {i+1}/{total} parameters") - print(f"[Trainer] All {total} parameters broadcasted") + for i in range(num_actors): + SyncPoint(sync_path, f"{name}_actor_{i}").wait(timeout=timeout) def _broadcast_via_server( state_dict: dict, - server_url: str, + server_urls: list, version: int, process_group, label: str = "", ): - """Broadcast weights to a running vLLM server via HTTP POST + NCCL. + """Broadcast weights to one or more running vLLM servers via HTTP POST + NCCL. - The POST blocks on the server side until NCCL broadcast completes, so we - run it in a background thread while we drive the broadcast ourselves. + One POST thread is started per server URL (all in parallel) before the + NCCL broadcast so that all servers are ready to receive simultaneously. """ import threading import time @@ -139,38 +60,48 @@ def _broadcast_via_server( from weight_update_utils import create_weight_update_request_from_state_dict label_str = f" {label}" if label else "" - print(f"[Trainer] Broadcasting {len(state_dict)}{label_str} parameters") + print(f"[Trainer] Broadcasting {len(state_dict)}{label_str} parameters to {len(server_urls)} server(s)") request = create_weight_update_request_from_state_dict(state_dict, version=version) - post_result = {"error": None} + errors = [] + threads = [] - def _post(): - try: - print("[Trainer] POSTing weight update request to server...") - resp = requests.post( - f"{server_url}/receive_weight_update", - json=request.model_dump(), - timeout=600, - ) - if resp.status_code != 200: - post_result["error"] = ( - f"POST failed with status {resp.status_code}: {resp.text}" + for url in server_urls: + err = {"error": None} + errors.append(err) + + def _post(server_url=url, post_result=err): + try: + print(f"[Trainer] POSTing weight update request to {server_url}...") + resp = requests.post( + f"{server_url}/receive_weight_update", + json=request.model_dump(), + timeout=600, ) - else: - print("[Trainer] Server acknowledged weight update") - except Exception as e: - post_result["error"] = f"POST failed: {e}" + if resp.status_code != 200: + post_result["error"] = ( + f"POST to {server_url} failed with status {resp.status_code}: {resp.text}" + ) + else: + print(f"[Trainer] Server {server_url} acknowledged weight update") + except Exception as e: + post_result["error"] = f"POST to {server_url} failed: {e}" + + t = threading.Thread(target=_post, daemon=False) + threads.append(t) + t.start() - post_thread = threading.Thread(target=_post, daemon=False) - post_thread.start() - time.sleep(0.5) # Give server a moment to start receiving + time.sleep(0.5) # Give all servers a moment to start receiving _broadcast_tensors(state_dict, process_group) - post_thread.join(timeout=60) - if post_result["error"]: - raise RuntimeError(f"Weight update POST failed: {post_result['error']}") + for t in threads: + t.join(timeout=60) + + failed = [e["error"] for e in errors if e["error"]] + if failed: + raise RuntimeError(f"Weight update POST(s) failed: {failed}") print(f"[Trainer] Broadcast{label_str} complete") @@ -425,10 +356,18 @@ def broadcast_cross_validation( print("[Trainer] Process group destroyed") -def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): +def broadcast_back_and_forth( + init_method: str, + model_name: str, + sync_dir: str, + num_actors: int = 1, + world_size: int = 2, +): """Back-and-forth test: broadcast perturbed → original → perturbed again. Tests that we can switch between weight sets multiple times. + Supports multiple actors: waits for all actors to signal readiness before + each broadcast, then sends a single shared completion signal. """ import torch.distributed as dist from pathlib import Path @@ -438,18 +377,14 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): from weight_update_utils import create_weight_update_request_from_state_dict sync_path = Path(sync_dir) - baseline_done = SyncPoint(sync_path, "baseline_done") - ready_for_perturbed1 = SyncPoint(sync_path, "ready_for_perturbed1") perturbed1_done = SyncPoint(sync_path, "perturbed1_done") - ready_for_original = SyncPoint(sync_path, "ready_for_original") original_done = SyncPoint(sync_path, "original_done") - ready_for_perturbed2 = SyncPoint(sync_path, "ready_for_perturbed2") perturbed2_done = SyncPoint(sync_path, "perturbed2_done") - process_group = _init_actor_process_group(init_method, rank=0, world_size=2) + process_group = _init_actor_process_group(init_method, rank=0, world_size=world_size) - print("[Trainer] Waiting for vLLM baseline generation...") - baseline_done.wait(timeout=120) + print(f"[Trainer] Waiting for {num_actors} actor(s) to finish baseline generation...") + _wait_all_actors(sync_path, "baseline_done", num_actors, timeout=120) print(f"[Trainer] Loading model {model_name}") original_state_dict, model_path = _load_state_dict(model_name) @@ -465,10 +400,10 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): print(f"[Trainer] Perturbed weights saved to {saved_path}") # Broadcast 1: Perturbed weights - print("[Trainer] Waiting for vLLM to be ready for first perturbed broadcast...") - ready_for_perturbed1.wait(timeout=120) + print(f"[Trainer] Waiting for {num_actors} actor(s) to be ready for first perturbed broadcast...") + _wait_all_actors(sync_path, "ready_for_perturbed1", num_actors, timeout=120) - print(f"[Trainer] Broadcasting perturbed weights (1st time)") + print(f"[Trainer] Broadcasting perturbed weights (1st time) to {num_actors} actor(s)") request = create_weight_update_request_from_state_dict(perturbed_state_dict, version=1) write_weight_update_request(sync_path, request) _broadcast_tensors(perturbed_state_dict, process_group) @@ -477,10 +412,10 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): print("[Trainer] First perturbed broadcast complete") # Broadcast 2: Original weights - print("[Trainer] Waiting for vLLM to be ready for original broadcast...") - ready_for_original.wait(timeout=120) + print(f"[Trainer] Waiting for {num_actors} actor(s) to be ready for original broadcast...") + _wait_all_actors(sync_path, "ready_for_original", num_actors, timeout=120) - print(f"[Trainer] Broadcasting original weights") + print(f"[Trainer] Broadcasting original weights to {num_actors} actor(s)") request = create_weight_update_request_from_state_dict(original_state_dict, version=2) write_weight_update_request(sync_path, request) _broadcast_tensors(original_state_dict, process_group) @@ -489,10 +424,10 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): print("[Trainer] Original broadcast complete") # Broadcast 3: Perturbed weights again (same as first) - print("[Trainer] Waiting for vLLM to be ready for second perturbed broadcast...") - ready_for_perturbed2.wait(timeout=120) + print(f"[Trainer] Waiting for {num_actors} actor(s) to be ready for second perturbed broadcast...") + _wait_all_actors(sync_path, "ready_for_perturbed2", num_actors, timeout=120) - print(f"[Trainer] Broadcasting perturbed weights (2nd time)") + print(f"[Trainer] Broadcasting perturbed weights (2nd time) to {num_actors} actor(s)") request = create_weight_update_request_from_state_dict(perturbed_state_dict, version=3) write_weight_update_request(sync_path, request) _broadcast_tensors(perturbed_state_dict, process_group) @@ -505,7 +440,10 @@ def broadcast_back_and_forth(init_method: str, model_name: str, sync_dir: str): def timed_broadcast_server_test( - init_method: str, model_name: str, server_url: str + init_method: str, + model_name: str, + server_urls: list, + world_size: int = 2, ): """Timed broadcast for server tests: perturbed → original → perturbed with delays. @@ -517,37 +455,16 @@ def timed_broadcast_server_test( Args: init_method: Distributed init method model_name: Model name to load - server_url: Base URL of vLLM server (e.g., "http://127.0.0.1:8000") + server_urls: List of base URLs of vLLM servers (e.g., ["http://127.0.0.1:8000"]) + world_size: Total world size (trainer rank 0 + all vLLM workers) """ import torch.distributed as dist - from pathlib import Path import time import requests - sys.path.insert(0, str(Path(__file__).parent)) - - process_group = _init_actor_process_group(init_method, rank=0, world_size=2) + process_group = _init_actor_process_group(init_method, rank=0, world_size=world_size) - # Wait for server to be ready by polling health endpoint - print("[Trainer] Waiting for server to be ready...") - server_ready = False - for i in range(120): # Try for up to 2 minutes - try: - resp = requests.get(f"{server_url}/health", timeout=1) - if resp.status_code == 200: - server_ready = True - print(f"[Trainer] Server is ready (took {i} seconds)") - break - except requests.exceptions.RequestException: - pass - time.sleep(1) - - if not server_ready: - raise TimeoutError("Server did not become ready within 2 minutes") - - # Wait additional 10 seconds for server to fully initialize - print("[Trainer] Waiting additional 10 seconds for server to fully initialize...") - time.sleep(10) + _wait_for_servers_ready(server_urls, extra_wait_secs=10) print(f"[Trainer] Loading original weights from {model_name}") original_state_dict, _ = _load_state_dict(model_name) @@ -555,20 +472,30 @@ def timed_broadcast_server_test( perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) # Broadcast 1: Perturbed weights - _broadcast_via_server(perturbed_state_dict, server_url, version=1, process_group=process_group, label="perturbed") + _broadcast_via_server(perturbed_state_dict, server_urls, version=1, process_group=process_group, label="perturbed") print("[Trainer] Waiting 5 seconds before broadcasting original weights...") time.sleep(5) # Broadcast 2: Original weights - _broadcast_via_server(original_state_dict, server_url, version=2, process_group=process_group, label="original") + _broadcast_via_server(original_state_dict, server_urls, version=2, process_group=process_group, label="original") print("[Trainer] Waiting 5 seconds before broadcasting perturbed weights again...") time.sleep(5) # Broadcast 3: Perturbed weights again (same as first) - _broadcast_via_server(perturbed_state_dict, server_url, version=3, process_group=process_group, label="perturbed (2nd time)") + _broadcast_via_server(perturbed_state_dict, server_urls, version=3, process_group=process_group, label="perturbed (2nd time)") + + # Wait to allow generation with the last broadcast before tearing down + print("[Trainer] Waiting 5 seconds for generation with final weights...") + time.sleep(5) + # Signal training is finished so vLLM servers destroy their side of the process group + for url in server_urls: + print(f"[Trainer] Sending training_finished signal to {url}...") + requests.post(f"{url}/training_finished", timeout=10) + + # Cleanup — destroy_process_group now resolves because vLLM servers respond to /training_finished dist.destroy_process_group(process_group) print("[Trainer] Process group destroyed, exiting") @@ -586,8 +513,9 @@ def timed_broadcast_server_test( "--temp-dir", type=str, help="Temporary directory for saving models" ) parser.add_argument( - "--server-url", type=str, help="Base URL of vLLM server (e.g., http://127.0.0.1:8000)" + "--server-urls", nargs="+", help="Base URL(s) of vLLM server(s) (e.g., http://127.0.0.1:8000)" ) + parser.add_argument("--num-actors", type=int, default=1, help="Number of vLLM actor processes") args = parser.parse_args() @@ -614,13 +542,22 @@ def timed_broadcast_server_test( if not args.model_name or not args.sync_dir: print("Error: --model-name and --sync-dir required for back_and_forth") sys.exit(1) - broadcast_back_and_forth(args.init_method, args.model_name, args.sync_dir) + broadcast_back_and_forth( + args.init_method, + args.model_name, + args.sync_dir, + num_actors=args.num_actors, + world_size=args.world_size, + ) elif args.command == "timed_broadcast_server_test": - if not args.model_name or not args.server_url: - print("Error: --model-name and --server-url required for timed_broadcast_server_test") + if not args.model_name or not args.server_urls: + print("Error: --model-name and --server-urls required for timed_broadcast_server_test") sys.exit(1) timed_broadcast_server_test( - args.init_method, args.model_name, args.server_url + args.init_method, + args.model_name, + args.server_urls, + world_size=args.world_size, ) except Exception as e: print(f"[Trainer] Error: {e}") diff --git a/tests/fast_llm_trainer_helper.py b/tests/fast_llm_trainer_helper.py new file mode 100644 index 00000000..44c7310e --- /dev/null +++ b/tests/fast_llm_trainer_helper.py @@ -0,0 +1,154 @@ +"""Helper functions for Fast-LLM weight broadcast testing.""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent)) +from trainer_test_utils import ( + _load_state_dict, + _create_perturbed_state_dict, + _init_actor_process_group, + _wait_for_servers_ready, +) + + +def timed_broadcast_fast_llm( + init_method: str, + model_name: str, + server_urls: list, + redis_host: str = "localhost", + redis_port: int = 6379, + world_size: int = 2, +): + """Timed broadcast using Fast-LLM protocol: perturbed → original → perturbed with delays. + + This simulates Fast-LLM's weight broadcast protocol where weight updates are signaled + via Redis stream and broadcast using broadcast_object_list + broadcast. + + Pattern: original (server default) → perturbed → original → perturbed + + Args: + init_method: Distributed init method + model_name: Model name to load + server_urls: Base URLs of vLLM server(s) (for health check only) + redis_host: Redis host address + redis_port: Redis port number + world_size: Total NCCL world size (trainer rank 0 + all vLLM workers) + """ + import torch + import torch.distributed as dist + import time + import redis + import orjson + + # Initialize process group + process_group = _init_actor_process_group(init_method, rank=0, world_size=world_size) + + # Connect to Redis + print(f"[Trainer] Connecting to Redis at {redis_host}:{redis_port}") + r = redis.Redis(host=redis_host, port=redis_port) + stream_key = "fast_llm_events" + payload_key = "event" + print(f"[Trainer] Connected to Redis, will write to stream '{stream_key}'") + + _wait_for_servers_ready(server_urls, extra_wait_secs=15) + + # Load weights + print(f"[Trainer] Loading original weights from {model_name}") + original_state_dict, _ = _load_state_dict(model_name) + perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) + + # Helper function to broadcast weights using Fast-LLM protocol + def broadcast_weights_fast_llm(state_dict, step): + """Broadcast weights using Fast-LLM protocol. + + Protocol: + 1. Send Redis event: {type: "weights_ready", step: N} + 2. For each parameter: + - broadcast_object_list([(shard_name, layer_name, shape, dtype)]) + - broadcast(tensor) + 3. Send end signal: broadcast_object_list([None]) + """ + # Send Redis stream event + event = {"type": "weights_ready", "step": step} + r.xadd(stream_key, {payload_key: orjson.dumps(event)}) + print(f"[Trainer] Sent Redis event to '{stream_key}': {event}") + + # Broadcast each parameter + for i, (name, tensor) in enumerate(state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + + shard_name = "" + layer_name = name + + # Broadcast metadata + meta = [(shard_name, layer_name, list(tensor.shape), str(tensor.dtype))] + dist.broadcast_object_list(meta, src=0, group=process_group) + + # Broadcast tensor + dist.broadcast(tensor, src=0, group=process_group) + + if (i + 1) % 50 == 0: + print(f"[Trainer] Broadcasted {i+1}/{len(state_dict)} parameters") + + # Send end signal + dist.broadcast_object_list([None], src=0, group=process_group) + print(f"[Trainer] Sent end signal, broadcast complete") + + # Broadcast 1: Perturbed weights + print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters") + broadcast_weights_fast_llm(perturbed_state_dict, step=1) + print("[Trainer] Perturbed weights broadcast complete") + + print("[Trainer] Waiting 5 seconds before broadcasting original weights...") + time.sleep(5) + + # Broadcast 2: Original weights + print(f"[Trainer] Broadcasting {len(original_state_dict)} original parameters") + broadcast_weights_fast_llm(original_state_dict, step=2) + print("[Trainer] Original weights broadcast complete") + + print("[Trainer] Waiting 5 seconds before broadcasting perturbed weights again...") + time.sleep(5) + + # Broadcast 3: Perturbed weights again (same as first) + print(f"[Trainer] Broadcasting {len(perturbed_state_dict)} perturbed parameters (2nd time)") + broadcast_weights_fast_llm(perturbed_state_dict, step=3) + print("[Trainer] Perturbed weights broadcast complete (2nd time)") + + # Wait to allow generation with the last broadcast before tearing down + print("[Trainer] Waiting 5 seconds for generation with final weights...") + time.sleep(5) + + # Signal training is finished so vLLM workers destroy their side of the process group + print("[Trainer] Sending training_finished signal...") + r.xadd(stream_key, {payload_key: orjson.dumps({"type": "training_finished"})}) + + # Cleanup — destroy_process_group now resolves because vLLM workers respond to training_finished + r.close() + dist.destroy_process_group(process_group) + print("[Trainer] Redis connection closed, process group destroyed, exiting") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Fast-LLM trainer helper") + parser.add_argument("--init-method", required=True, help="Distributed init method") + parser.add_argument("--model", required=True, help="Model name") + parser.add_argument("--server-urls", nargs="+", required=True, help="Server URL(s)") + parser.add_argument("--world-size", type=int, default=2, help="Total distributed world size") + parser.add_argument("--redis-host", default="localhost", help="Redis host") + parser.add_argument("--redis-port", type=int, default=6379, help="Redis port") + + args = parser.parse_args() + + timed_broadcast_fast_llm( + init_method=args.init_method, + model_name=args.model, + server_urls=args.server_urls, + redis_host=args.redis_host, + redis_port=args.redis_port, + world_size=args.world_size, + ) diff --git a/tests/server_weight_update_utils.py b/tests/server_weight_update_utils.py index 83ac3a0f..5e439f92 100644 --- a/tests/server_weight_update_utils.py +++ b/tests/server_weight_update_utils.py @@ -52,23 +52,11 @@ async def wait_for_server_ready(server_url: str, server_proc, trainer_proc, time raise TimeoutError(f"Server did not become ready within {timeout_seconds} seconds") -def check_pattern_detected(generations): - """Check if we have detected the full pattern (4 phases). - - Args: - generations: List of (timestamp, text) tuples - - Returns: - True if pattern is detected (4 phases with correct relationships) - """ - if len(generations) < 4: - return False - - # Track when the text changes to identify phase boundaries +def _build_phases(generations): + """Collapse a generation list into (text, items) phase tuples.""" phases = [] current_text = None current_phase = [] - for ts, text in generations: if text != current_text: if current_phase: @@ -77,29 +65,73 @@ def check_pattern_detected(generations): current_phase = [(ts, text)] else: current_phase.append((ts, text)) - - # Add the last phase if current_phase: phases.append((current_text, current_phase)) + return phases + + +def _find_abab_pattern(phases): + """Search for the A→B→A→B pattern anchored to the first and last phases. + + A is always ``phases[0]`` — the text the server starts with (original weights). + B2 is always ``phases[-1]`` — the current/final phase (perturbed weights after + the 3rd broadcast). - # Check if we have at least 4 phases + Any transition phases in between are skipped automatically because we only + require that some phase after the first B has the same text as phases[0] (A), + without caring what sits between the first B and that return-to-A. + + Returns (phase_a, phase_b, phase_a2, phase_b2) or None. + """ if len(phases) < 4: - return False + return None - # Verify the pattern: phase1 != phase2, phase3 == phase1, phase4 == phase2 - phase1_text = phases[0][0] - phase2_text = phases[1][0] - phase3_text = phases[2][0] - phase4_text = phases[3][0] + text_a = phases[0][0] + text_b2 = phases[-1][0] - if phase1_text == phase2_text: - return False # Phase 1 and 2 should be different - if phase3_text != phase1_text: - return False # Phase 3 should match Phase 1 - if phase4_text != phase2_text: - return False # Phase 4 should match Phase 2 + if text_a == text_b2: + return None # A and B must be distinct texts + + texts = [t for t, _ in phases] + + # Find the first B (same text as B2) strictly between phase 0 and last + for j in range(1, len(phases) - 1): + if texts[j] != text_b2: + continue + # Find the first return to A strictly between j and last + for k in range(j + 1, len(phases) - 1): + if texts[k] == text_a: + return phases[0], phases[j], phases[k], phases[-1] + + return None - return True + +def check_pattern_detected(generations): + """Check whether the full A→B→A→B pattern is present in the generation history. + + This is a **post-hoc analysis helper** (e.g. for assertions after the + generation loop ends). It is intentionally *not* used as an early-stop + signal inside the generation loops. + + Why not early-stop? Any transition artifact text T that happens to appear + with several consecutive identical generations (possible when NCCL broadcasts + are slow) is indistinguishable from the real perturbed text B at generation + time. False positives would cut the loop short before the final stable B + phase accumulates. The generation loops instead rely on the trainer process + exiting (``trainer_proc.poll() is not None``) as their sole reliable + termination signal — the trainer exits within milliseconds of completing its + last broadcast, so no significant extra generation happens. + + Args: + generations: List of (timestamp, text) tuples + + Returns: + True if the A→B→A→B pattern is present + """ + if len(generations) < 4: + return False + phases = _build_phases(generations) + return _find_abab_pattern(phases) is not None async def run_generation_loop( @@ -159,11 +191,6 @@ async def run_generation_loop( timestamp = time.time() - start_time generations.append((timestamp, generated_text)) print(f"[Main] [{timestamp:.1f}s] Generated: '{generated_text}'") - - # Check if pattern is detected - stop early if confirmed - if check_pattern_detected(generations): - print(f"[Main] Pattern detected! Stopping generation early at {timestamp:.1f}s") - break else: print(f"[Main] Generation failed with status {resp.status_code}") @@ -176,7 +203,11 @@ async def run_generation_loop( def analyze_and_verify_pattern(generations): - """Analyze generation sequence and verify the expected pattern. + """Analyze generation sequence and verify the expected A→B→A→B pattern. + + Tolerates transition-artifact phases (e.g. a single generation produced + while an NCCL broadcast was in-flight) by searching for the pattern as + a subsequence rather than requiring it at exactly positions [0,1,2,3]. Args: generations: List of (timestamp, text) tuples @@ -189,70 +220,42 @@ def analyze_and_verify_pattern(generations): print("=" * 60) print(f"Total generations: {len(generations)}") - # Print all generations for i, (ts, text) in enumerate(generations): print(f"[{ts:5.1f}s] Gen {i+1}: '{text[:80]}...'") - # Identify unique generation texts and their phases - if len(generations) < 4: - raise AssertionError( - f"Not enough generations to verify pattern (need at least 4, got {len(generations)})" - ) - - # Track when the text changes to identify phase boundaries - phases = [] - current_text = None - current_phase = [] - - for ts, text in generations: - if text != current_text: - if current_phase: - phases.append((current_text, current_phase)) - current_text = text - current_phase = [(ts, text)] - else: - current_phase.append((ts, text)) + assert len(generations) >= 4, ( + f"Not enough generations to verify pattern (need at least 4, got {len(generations)})" + ) - # Add the last phase - if current_phase: - phases.append((current_text, current_phase)) + phases = _build_phases(generations) print("\n" + "=" * 60) - print(f"Detected {len(phases)} phases:") + print(f"Detected {len(phases)} phase(s):") for i, (text, items) in enumerate(phases): - print(f"Phase {i+1}: {len(items)} generations - '{text[:60]}...'") + print(f"Phase {i+1}: {len(items)} generation(s) - '{text[:60]}...'") print("=" * 60) - # Verify the pattern - assert ( - len(phases) >= 4 - ), f"Expected at least 4 phases (original → perturbed → original → perturbed), got {len(phases)}" - - phase1_text, phase1_items = phases[0] - phase2_text, phase2_items = phases[1] - phase3_text, phase3_items = phases[2] - phase4_text, phase4_items = phases[3] - - # Verify phase 1 (original) != phase 2 (perturbed) - assert ( - phase1_text != phase2_text - ), "Phase 1 (original) and Phase 2 (perturbed) should be different" + result = _find_abab_pattern(phases) + assert result is not None, ( + f"Could not find A→B→A→B pattern in {len(phases)} phase(s). " + f"Phases: {[(text[:40], len(items)) for text, items in phases]}" + ) - # Verify phase 3 (original) == phase 1 (original) - assert ( - phase3_text == phase1_text - ), f"Phase 3 should match Phase 1 (original weights restored)" + (phase_a_text, phase_a_items), (phase_b_text, phase_b_items), \ + (phase_a2_text, phase_a2_items), (phase_b2_text, phase_b2_items) = result - # Verify phase 4 (perturbed) == phase 2 (perturbed) - assert ( - phase4_text == phase2_text - ), f"Phase 4 should match Phase 2 (perturbed weights reapplied)" + # These hold by construction from _find_abab_pattern, but assert for clarity + assert phase_a_text != phase_b_text, "Phase A and Phase B should be different" + assert phase_a2_text == phase_a_text, "Second A should match first A (original weights restored)" + assert phase_b2_text == phase_b_text, "Second B should match first B (perturbed weights reapplied)" - print("\n✓ Pattern verified:") - print(f" Phase 1 (original): {len(phase1_items)} generations") - print(f" Phase 2 (perturbed): {len(phase2_items)} generations") - print(f" Phase 3 (original): {len(phase3_items)} generations (matches Phase 1 ✓)") - print(f" Phase 4 (perturbed): {len(phase4_items)} generations (matches Phase 2 ✓)") + skipped = len(phases) - 4 + skip_note = f" ({skipped} transition phase(s) skipped)" if skipped else "" + print(f"\n✓ Pattern verified{skip_note}:") + print(f" Phase A (original): {len(phase_a_items)} generation(s)") + print(f" Phase B (perturbed): {len(phase_b_items)} generation(s)") + print(f" Phase A2 (original): {len(phase_a2_items)} generation(s) ← matches A ✓") + print(f" Phase B2 (perturbed): {len(phase_b2_items)} generation(s) ← matches B ✓") def start_vllm_server( @@ -261,6 +264,10 @@ def start_vllm_server( distributed_init_method: str, stream_process_output_fn, extra_args: list = None, + gpu_ids: str = "0", + actor_llm_idx: int = 0, + world_size: int = 2, + tensor_parallel_size: int = 1, ): """Start vLLM HTTP server subprocess. @@ -270,15 +277,19 @@ def start_vllm_server( distributed_init_method: Distributed initialization method stream_process_output_fn: Function to stream process output extra_args: Additional CLI arguments (e.g., ["--weight-update-mode", "fast-llm"]) + gpu_ids: CUDA_VISIBLE_DEVICES value (e.g., "0" or "0,1") + actor_llm_idx: Actor index for this vLLM instance + world_size: Total distributed world size + tensor_parallel_size: Number of GPUs for tensor parallelism Returns: Tuple of (server_proc, stdout_thread, stderr_thread) """ vllm_env = os.environ.copy() - vllm_env["CUDA_VISIBLE_DEVICES"] = "0" + vllm_env["CUDA_VISIBLE_DEVICES"] = gpu_ids vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" - print(f"[Main] Starting vLLM HTTP server on port {server_port} (GPU 0)") + print(f"[Main] Starting vLLM HTTP server on port {server_port} (GPU(s) {gpu_ids}, actor_idx={actor_llm_idx}, TP={tensor_parallel_size})") vllm_entry_point = Path(__file__).parent.parent / "pipelinerl" / "entrypoints" / "run_vllm1.py" cmd = [ @@ -287,9 +298,10 @@ def start_vllm_server( "--model", model_name, "--port", str(server_port), "--host", "127.0.0.1", - "--actor-llm-idx", "0", + "--actor-llm-idx", str(actor_llm_idx), "--weight-update-group-init-method", distributed_init_method, - "--weight-update-group-world-size", "2", + "--weight-update-group-world-size", str(world_size), + "--tensor-parallel-size", str(tensor_parallel_size), ] if extra_args: @@ -304,18 +316,126 @@ def start_vllm_server( ) print("[Main] Starting server output streaming...") - stdout_thread, stderr_thread = stream_process_output_fn(server_proc, "vLLM Server") + stdout_thread, stderr_thread = stream_process_output_fn(server_proc, f"vLLM Server (actor {actor_llm_idx})") return server_proc, stdout_thread, stderr_thread +async def wait_for_all_servers_ready( + server_urls: list, + server_procs: list, + trainer_proc, + timeout_seconds: int = 300, +): + """Wait for all servers to be ready by polling their health endpoints. + + Args: + server_urls: List of server base URLs + server_procs: List of server subprocesses (same order as server_urls) + trainer_proc: Trainer subprocess + timeout_seconds: Maximum time to wait per server + + Returns: + True if all servers are ready + + Raises: + RuntimeError: If any process terminates unexpectedly + TimeoutError: If any server doesn't become ready within timeout + """ + for url, proc in zip(server_urls, server_procs): + await wait_for_server_ready(url, proc, trainer_proc, timeout_seconds) + return True + + +async def run_generation_loop_multi( + server_urls: list, + model_name: str, + simple_prompt: str, + generation_config: dict, + trainer_proc, + max_duration: int = 120, + generation_interval: float = 0.5, +): + """Run continuous generation loop querying all servers each round. + + Each iteration queries ALL servers and asserts all responses are equal + (since they should have the same weights after a broadcast). Records one + (timestamp, text) entry per round. + + Args: + server_urls: List of server base URLs + model_name: Model name for API request + simple_prompt: Prompt to generate from + generation_config: Config dict with max_tokens, etc. + trainer_proc: Trainer subprocess to monitor + max_duration: Maximum duration in seconds + generation_interval: Time between generation rounds + + Returns: + List of (timestamp, generated_text) tuples + """ + print(f"[Main] Starting continuous generation loop across {len(server_urls)} server(s)...") + generations = [] + start_time = time.time() + + payload = { + "model": model_name, + "prompt": simple_prompt, + "max_tokens": generation_config["max_tokens"], + "temperature": 0.0, + "top_p": 1.0, + "seed": 42, + } + + while time.time() - start_time < max_duration: + # Check if trainer is still running + trainer_poll = trainer_proc.poll() + if trainer_poll is not None: + print(f"[Main] Trainer exited with code {trainer_poll}") + break + + try: + texts = [] + for url in server_urls: + resp = requests.post( + f"{url}/v1/completions", + json=payload, + timeout=30, + ) + if resp.status_code == 200: + texts.append(resp.json()["choices"][0]["text"]) + else: + print(f"[Main] Generation from {url} failed with status {resp.status_code}") + texts = [] + break + + if texts: + # All servers should agree + assert len(set(texts)) == 1, ( + f"Servers disagree on generation: {texts}" + ) + text = texts[0] + timestamp = time.time() - start_time + generations.append((timestamp, text)) + print(f"[Main] [{timestamp:.1f}s] Generated: '{text}'") + + except requests.exceptions.RequestException as e: + print(f"[Main] Request failed: {e}") + + await asyncio.sleep(generation_interval) + + return generations + + def start_trainer_process( trainer_helper_path: Path, distributed_init_method: str, model_name: str, - server_url: str, + server_urls: list, stream_process_output_fn, extra_args: list = None, + gpu_id: str = "1", + world_size: int = 2, ): """Start trainer subprocess. @@ -323,17 +443,19 @@ def start_trainer_process( trainer_helper_path: Path to trainer helper script distributed_init_method: Distributed initialization method model_name: Model name - server_url: Server URL for health check + server_urls: List of server URLs (one per actor) stream_process_output_fn: Function to stream process output extra_args: Additional CLI arguments (e.g., ["--redis-host", "localhost"]) + gpu_id: CUDA_VISIBLE_DEVICES value for the trainer GPU + world_size: Total distributed world size Returns: Tuple of (trainer_proc, stdout_thread, stderr_thread) """ trainer_env = os.environ.copy() - trainer_env["CUDA_VISIBLE_DEVICES"] = "1" + trainer_env["CUDA_VISIBLE_DEVICES"] = gpu_id - print("[Main] Starting trainer process (GPU 1) for process group rendezvous") + print(f"[Main] Starting trainer process (GPU {gpu_id}) for process group rendezvous") cmd = [ sys.executable, @@ -346,16 +468,18 @@ def start_trainer_process( cmd.extend([ "--init-method", distributed_init_method, "--model", model_name, - "--server-url", server_url, - ]) + "--world-size", str(world_size), + "--server-urls", + ] + list(server_urls)) else: - # distributed_trainer_helper.py uses positional args + # distributed_trainer_helper.py uses positional command + flags cmd.extend([ "timed_broadcast_server_test", "--init-method", distributed_init_method, "--model-name", model_name, - "--server-url", server_url, - ]) + "--world-size", str(world_size), + "--server-urls", + ] + list(server_urls)) if extra_args: cmd.extend(extra_args) diff --git a/tests/test_vllm1_fast_llm_broadcast.py b/tests/test_vllm1_fast_llm_broadcast.py index 79ff95a7..32d31ac2 100644 --- a/tests/test_vllm1_fast_llm_broadcast.py +++ b/tests/test_vllm1_fast_llm_broadcast.py @@ -17,7 +17,9 @@ # Import shared utilities from .server_weight_update_utils import ( wait_for_server_ready, + wait_for_all_servers_ready, run_generation_loop, + run_generation_loop_multi, analyze_and_verify_pattern, start_vllm_server, start_trainer_process, @@ -191,8 +193,120 @@ def find_free_port(): print("[Redis] Redis server stopped") +# --------------------------------------------------------------------------- +# Module-level helper shared by all Fast-LLM test variants +# --------------------------------------------------------------------------- + +async def _run_fast_llm_server_test( + model_name, + simple_prompt, + generation_config, + init_method, + fast_llm_trainer_helper, + redis_host, + redis_port, + vllm_server_configs, + trainer_gpu, + world_size, + timeout=2400, +): + """Run Fast-LLM server weight-update pattern test with one or more vLLM servers. + + Args: + vllm_server_configs: List of dicts, each with keys: + - port: int + - gpu_ids: str + - actor_llm_idx: int + - tensor_parallel_size: int + trainer_gpu: str, e.g. "1" or "2" + world_size: total NCCL world size (trainer + all vLLM workers) + redis_host: Redis host address + redis_port: Redis port number + """ + server_procs = [] + server_urls = [] + + fast_llm_server_args = [ + "--weight-update-mode", "fast-llm", + "--redis-host", redis_host, + "--redis-port", str(redis_port), + ] + + for cfg in vllm_server_configs: + port = cfg["port"] + url = f"http://127.0.0.1:{port}" + server_urls.append(url) + + server_proc, _, _ = start_vllm_server( + model_name=model_name, + server_port=port, + distributed_init_method=init_method, + stream_process_output_fn=stream_process_output, + extra_args=fast_llm_server_args, + gpu_ids=cfg.get("gpu_ids", "0"), + actor_llm_idx=cfg.get("actor_llm_idx", 0), + world_size=world_size, + tensor_parallel_size=cfg.get("tensor_parallel_size", 1), + ) + server_procs.append(server_proc) + + await asyncio.sleep(1) + + trainer_proc, _, _ = start_trainer_process( + trainer_helper_path=fast_llm_trainer_helper, + distributed_init_method=init_method, + model_name=model_name, + server_urls=server_urls, + stream_process_output_fn=stream_process_output, + extra_args=[ + "--redis-host", redis_host, + "--redis-port", str(redis_port), + ], + gpu_id=trainer_gpu, + world_size=world_size, + ) + + try: + await wait_for_all_servers_ready(server_urls, server_procs, trainer_proc) + + if len(server_urls) == 1: + generations = await run_generation_loop( + server_url=server_urls[0], + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + ) + else: + generations = await run_generation_loop_multi( + server_urls=server_urls, + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + ) + + # Wait for trainer to finish + print("[Main] Waiting for trainer to finish...") + for _ in range(30): + if trainer_proc.poll() is not None: + break + await asyncio.sleep(1) + + analyze_and_verify_pattern(generations) + print(f"\n✓ Fast-LLM server weight update pattern test PASSED ({len(server_urls)} server(s))") + + finally: + print("[Main] Cleaning up processes...") + for proc in server_procs: + if proc: + kill_process_tree(proc.pid) + if trainer_proc: + kill_process_tree(trainer_proc.pid) + + class TestFastLLMServerIntegration: - """Test Fast-LLM weight broadcast with vLLM HTTP server.""" + """Test Fast-LLM weight broadcast with vLLM HTTP server — 2 GPUs (baseline).""" @pytest.mark.timeout(2400) # 40 minutes for server test @pytest.mark.asyncio @@ -211,95 +325,164 @@ async def test_server_fast_llm_broadcast_pattern( ): """Server integration test: verify Fast-LLM weight broadcast pattern with HTTP API. - This test validates the Fast-LLM protocol where: - 1. Redis server is automatically started - 2. vLLM server is running and serving HTTP requests - 3. Trainer broadcasts weight updates via Redis stream + broadcast_object_list - 4. Server responses change based on weight updates - - Flow: - - Start Redis server (via fixture) - - Start vLLM HTTP server with --weight-update-mode=fast-llm - - Continuously generate via HTTP API (deterministic) - - Trainer: wait 15s → broadcast perturbed → wait 5s → broadcast original → wait 5s → broadcast perturbed - - Verify generation pattern: original → perturbed → original → perturbed - - Stop Redis server (via fixture cleanup) + Validates the Fast-LLM protocol where: + - Redis server signals weight updates + - vLLM server receives weights via broadcast_object_list + broadcast + - Server responses change as expected (original → perturbed → original → perturbed) + + Topology: 1 vLLM server on GPU 0, trainer on GPU 1 (world_size=2). """ print("\n" + "=" * 60) - print("Starting Fast-LLM server weight update pattern test") + print("Starting Fast-LLM server weight update pattern test (TP=1, 1 actor, 2 GPUs)") print("=" * 60) - # Get Redis connection info from fixture redis_host, redis_port = redis_server - print(f"[Main] Using Redis server at {redis_host}:{redis_port}") - server_port = 8000 - server_url = f"http://127.0.0.1:{server_port}" + await _run_fast_llm_server_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + fast_llm_trainer_helper=fast_llm_trainer_helper, + redis_host=redis_host, + redis_port=redis_port, + vllm_server_configs=[{"port": 8000, "gpu_ids": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}], + trainer_gpu="1", + world_size=2, + timeout=2400, + ) + - # Start vLLM server with Fast-LLM mode - server_proc, _, _ = start_vllm_server( +class TestFastLLMServerTP2: + """Test Fast-LLM weight broadcast with tensor-parallel (TP=2) — needs 3 GPUs.""" + + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif( + torch.cuda.device_count() < 3, reason="Requires at least 3 GPUs" + ) + async def test_server_fast_llm_broadcast_pattern_tp2( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + fast_llm_trainer_helper, + redis_server, + temp_dir, + ): + """Fast-LLM server test with TP=2: one server on GPUs 0+1, trainer on GPU 2. + + Verifies that tensor-parallel vLLM correctly receives Fast-LLM weight + updates when multiple GPU workers share the same NCCL process group. + """ + print("\n" + "=" * 60) + print("Starting Fast-LLM server weight update pattern test (TP=2, 1 actor, 3 GPUs)") + print("=" * 60) + + redis_host, redis_port = redis_server + + await _run_fast_llm_server_test( model_name=model_name, - server_port=server_port, - distributed_init_method=distributed_init_method, - stream_process_output_fn=stream_process_output, - extra_args=[ - "--weight-update-mode", "fast-llm", - "--redis-host", redis_host, - "--redis-port", str(redis_port), - ], + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + fast_llm_trainer_helper=fast_llm_trainer_helper, + redis_host=redis_host, + redis_port=redis_port, + vllm_server_configs=[{"port": 8001, "gpu_ids": "0,1", "actor_llm_idx": 0, "tensor_parallel_size": 2}], + trainer_gpu="2", + world_size=3, + timeout=2400, ) - # Give server a moment to start - await asyncio.sleep(1) - # Start trainer process - trainer_proc, _, _ = start_trainer_process( - trainer_helper_path=fast_llm_trainer_helper, - distributed_init_method=distributed_init_method, +class TestFastLLMServerMultiActor: + """Test Fast-LLM weight broadcast with multiple independent vLLM actors.""" + + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif( + torch.cuda.device_count() < 3, reason="Requires at least 3 GPUs" + ) + async def test_server_fast_llm_broadcast_pattern_2actors( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + fast_llm_trainer_helper, + redis_server, + temp_dir, + ): + """Fast-LLM server test with 2 actors: servers on GPUs 0 and 1, trainer on GPU 2. + + Verifies that two separate vLLM servers simultaneously receive the same + Fast-LLM weight broadcast and produce identical generation results. + """ + print("\n" + "=" * 60) + print("Starting Fast-LLM server weight update pattern test (TP=1, 2 actors, 3 GPUs)") + print("=" * 60) + + redis_host, redis_port = redis_server + + await _run_fast_llm_server_test( model_name=model_name, - server_url=server_url, - stream_process_output_fn=stream_process_output, - extra_args=[ - "--redis-host", redis_host, - "--redis-port", str(redis_port), + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + fast_llm_trainer_helper=fast_llm_trainer_helper, + redis_host=redis_host, + redis_port=redis_port, + vllm_server_configs=[ + {"port": 8000, "gpu_ids": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}, + {"port": 8001, "gpu_ids": "1", "actor_llm_idx": 1, "tensor_parallel_size": 1}, ], + trainer_gpu="2", + world_size=3, + timeout=2400, ) - try: - # Wait for server to be ready - await wait_for_server_ready(server_url, server_proc, trainer_proc) + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif( + torch.cuda.device_count() < 4, reason="Requires at least 4 GPUs" + ) + async def test_server_fast_llm_broadcast_pattern_3actors( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + fast_llm_trainer_helper, + redis_server, + temp_dir, + ): + """Fast-LLM server test with 3 actors: servers on GPUs 0/1/2, trainer on GPU 3. - # Run generation loop - generations = await run_generation_loop( - server_url=server_url, - model_name=model_name, - simple_prompt=simple_prompt, - generation_config=generation_config, - trainer_proc=trainer_proc, - ) + Verifies that three separate vLLM servers simultaneously receive the same + Fast-LLM weight broadcast and produce identical generation results. + """ + print("\n" + "=" * 60) + print("Starting Fast-LLM server weight update pattern test (TP=1, 3 actors, 4 GPUs)") + print("=" * 60) + + redis_host, redis_port = redis_server - # Wait for trainer to finish - print("[Main] Waiting for trainer to finish...") - for _ in range(30): - if trainer_proc.poll() is not None: - break - await asyncio.sleep(1) - - # Analyze and verify pattern - analyze_and_verify_pattern(generations) - print("\n✓ Fast-LLM server weight update pattern test PASSED") - - finally: - # Cleanup - always kill process tree even if main process exited - # (child processes like vLLM workers might still be running) - print("[Main] Cleaning up processes...") - if server_proc: - print( - f"[Main] Killing server process tree (PID {server_proc.pid})..." - ) - kill_process_tree(server_proc.pid) - if trainer_proc: - print( - f"[Main] Killing trainer process tree (PID {trainer_proc.pid})..." - ) - kill_process_tree(trainer_proc.pid) + await _run_fast_llm_server_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + fast_llm_trainer_helper=fast_llm_trainer_helper, + redis_host=redis_host, + redis_port=redis_port, + vllm_server_configs=[ + {"port": 8000, "gpu_ids": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}, + {"port": 8001, "gpu_ids": "1", "actor_llm_idx": 1, "tensor_parallel_size": 1}, + {"port": 8002, "gpu_ids": "2", "actor_llm_idx": 2, "tensor_parallel_size": 1}, + ], + trainer_gpu="3", + world_size=4, + timeout=2400, + ) diff --git a/tests/test_vllm1_integration.py b/tests/test_vllm1_integration.py index 625c2274..aefcc337 100644 --- a/tests/test_vllm1_integration.py +++ b/tests/test_vllm1_integration.py @@ -17,7 +17,9 @@ # Import shared utilities from .server_weight_update_utils import ( wait_for_server_ready, + wait_for_all_servers_ready, run_generation_loop, + run_generation_loop_multi, analyze_and_verify_pattern, start_vllm_server, start_trainer_process, @@ -265,6 +267,217 @@ async def read_stream(stream, prefix): await asyncio.gather(*reader_tasks, return_exceptions=True) +# --------------------------------------------------------------------------- +# Module-level helpers shared by all test variants +# --------------------------------------------------------------------------- + +def _compare_actor_results(sync_dir: Path, num_actors: int): + """Assert that all actors produced identical generation results. + + Each actor writes ``sync_dir/results_actor_{i}.json`` with keys + res_or_1, res_mod_1, res_or_2, res_mod_2. + """ + import json + + results = [ + json.loads((sync_dir / f"results_actor_{i}.json").read_text()) + for i in range(num_actors) + ] + for key in results[0]: + texts = [r[key] for r in results] + assert len(set(texts)) == 1, ( + f"Actors disagree on '{key}': {texts}" + ) + + +async def _run_back_and_forth_engine_test( + model_name, + simple_prompt, + generation_config, + init_method, + distributed_trainer_helper, + vllm_engine_helper, + sync_dir, + vllm_configs, + trainer_gpu, + world_size, + timeout=1800, +): + """Run back-and-forth engine test with one or more vLLM actor processes. + + Args: + vllm_configs: List of dicts, each with keys: + - cuda_devices: str, e.g. "0" or "0,1" + - actor_llm_idx: int + - tensor_parallel_size: int + trainer_gpu: str, e.g. "1" or "2" + world_size: total NCCL world size (all vLLM workers + trainer) + """ + from .sync_helper import create_sync_dir + + num_actors = len(vllm_configs) + all_procs = [] + + # Start all vLLM actor subprocesses + for cfg in vllm_configs: + vllm_env = os.environ.copy() + vllm_env["CUDA_VISIBLE_DEVICES"] = cfg["cuda_devices"] + vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" + vllm_env["PIPELINERL_DEBUG"] = "1" + + actor_idx = cfg["actor_llm_idx"] + tp = cfg.get("tensor_parallel_size", 1) + print(f"[Main] Starting vLLM actor {actor_idx} (GPU(s) {cfg['cuda_devices']}, TP={tp})") + + vllm_proc = subprocess.Popen( + [ + sys.executable, + str(vllm_engine_helper), + "back_and_forth", + "--model-name", model_name, + "--init-method", init_method, + "--actor-llm-idx", str(actor_idx), + "--world-size", str(world_size), + "--tensor-parallel-size", str(tp), + "--prompt", simple_prompt, + "--max-tokens", str(generation_config["max_tokens"]), + "--sync-dir", str(sync_dir), + ], + env=vllm_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + all_procs.append((vllm_proc, f"vLLM Actor {actor_idx}")) + + await asyncio.sleep(1) + + # Start trainer subprocess + trainer_env = os.environ.copy() + trainer_env["CUDA_VISIBLE_DEVICES"] = trainer_gpu + trainer_env["PIPELINERL_DEBUG"] = "1" + + print(f"[Main] Starting trainer (GPU {trainer_gpu}, {num_actors} actor(s), world_size={world_size})") + trainer_proc = subprocess.Popen( + [ + sys.executable, + str(distributed_trainer_helper), + "back_and_forth", + "--init-method", init_method, + "--model-name", model_name, + "--sync-dir", str(sync_dir), + "--num-actors", str(num_actors), + "--world-size", str(world_size), + ], + env=trainer_env, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + all_procs.append((trainer_proc, "Trainer")) + + await wait_for_processes(all_procs, timeout=timeout) + + # Verify all actors produced the same results + _compare_actor_results(sync_dir, num_actors) + print(f"\n✓ Back-and-forth test PASSED ({num_actors} actor(s), world_size={world_size})") + + +async def _run_server_weight_update_test( + model_name, + simple_prompt, + generation_config, + init_method, + distributed_trainer_helper, + vllm_server_configs, + trainer_gpu, + world_size, + timeout=2400, +): + """Run server weight-update pattern test with one or more vLLM servers. + + Args: + vllm_server_configs: List of dicts, each with keys: + - port: int + - gpu_ids: str + - actor_llm_idx: int + - tensor_parallel_size: int + trainer_gpu: str, e.g. "1" or "2" + world_size: total NCCL world size + """ + server_procs = [] + server_urls = [] + + for cfg in vllm_server_configs: + port = cfg["port"] + url = f"http://127.0.0.1:{port}" + server_urls.append(url) + + server_proc, _, _ = start_vllm_server( + model_name=model_name, + server_port=port, + distributed_init_method=init_method, + stream_process_output_fn=stream_process_output, + extra_args=None, + gpu_ids=cfg.get("gpu_ids", "0"), + actor_llm_idx=cfg.get("actor_llm_idx", 0), + world_size=world_size, + tensor_parallel_size=cfg.get("tensor_parallel_size", 1), + ) + server_procs.append(server_proc) + + await asyncio.sleep(1) + + trainer_proc, _, _ = start_trainer_process( + trainer_helper_path=distributed_trainer_helper, + distributed_init_method=init_method, + model_name=model_name, + server_urls=server_urls, + stream_process_output_fn=stream_process_output, + extra_args=None, + gpu_id=trainer_gpu, + world_size=world_size, + ) + + try: + await wait_for_all_servers_ready(server_urls, server_procs, trainer_proc) + + if len(server_urls) == 1: + generations = await run_generation_loop( + server_url=server_urls[0], + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + ) + else: + generations = await run_generation_loop_multi( + server_urls=server_urls, + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + ) + + # Wait for trainer to finish + print("[Main] Waiting for trainer to finish...") + for _ in range(30): + if trainer_proc.poll() is not None: + break + await asyncio.sleep(1) + + analyze_and_verify_pattern(generations) + print(f"\n✓ Server weight update pattern test PASSED ({len(server_urls)} server(s))") + + finally: + print("[Main] Cleaning up processes...") + for proc in server_procs: + if proc: + kill_process_tree(proc.pid) + if trainer_proc: + kill_process_tree(trainer_proc.pid) + + class TestBasicGeneration: """Test basic vLLM generation with worker extension.""" @@ -738,179 +951,276 @@ async def test_weight_update_back_and_forth( ): """Back-and-forth test: switch between original and perturbed weights. - This test validates that: - 1. We can update weights multiple times - 2. We can switch back and forth between weight sets - 3. Updates are deterministic and reproducible - - Flow: - - vLLM: Load original, generate res_or_1 - - Trainer: Broadcast perturbed weights - - vLLM: Receive perturbed, generate res_mod_1 - - Trainer: Broadcast original weights - - vLLM: Receive original, generate res_or_2 - - Trainer: Broadcast perturbed weights again (same as first) - - vLLM: Receive perturbed, generate res_mod_2 - - Assertions: - - res_or_1 == res_or_2 (can restore original weights) - - res_mod_1 == res_mod_2 (perturbed weights are consistent) + Validates that we can update weights multiple times and the results + are deterministic and reproducible. """ from .sync_helper import create_sync_dir print("\n" + "="*60) - print("Starting back-and-forth test") + print("Starting back-and-forth test (TP=1, 1 actor, 2 GPUs)") print("="*60) - # Create sync directory for coordination sync_dir = create_sync_dir(shared_test_dir) - print(f"[Main] Sync directory: {sync_dir}") - - # Step 1: Start vLLM engine subprocess - vllm_env = os.environ.copy() - vllm_env["CUDA_VISIBLE_DEVICES"] = "0" - vllm_env["VLLM_ALLOW_INSECURE_SERIALIZATION"] = "1" - vllm_env["PIPELINERL_DEBUG"] = "1" - - print("[Main] Starting vLLM engine process (GPU 0)") - vllm_proc = subprocess.Popen( - [ - sys.executable, - str(vllm_engine_helper), - "back_and_forth", - "--model-name", model_name, - "--init-method", shared_distributed_init_method, - "--actor-llm-idx", "0", - "--world-size", "2", - "--prompt", simple_prompt, - "--max-tokens", str(generation_config["max_tokens"]), - "--sync-dir", str(sync_dir), - ], - env=vllm_env, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, + await _run_back_and_forth_engine_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=shared_distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_engine_helper=vllm_engine_helper, + sync_dir=sync_dir, + vllm_configs=[{"cuda_devices": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}], + trainer_gpu="1", + world_size=2, + timeout=1800, ) - # Give vLLM engine a moment to start - await asyncio.sleep(1) + @pytest.mark.timeout(2400) # 40 minutes for server test + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_server_weight_update_pattern( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + temp_dir, + ): + """Server integration test: verify weight update pattern with HTTP API. - # Step 2: Start trainer subprocess - trainer_env = os.environ.copy() - trainer_env["CUDA_VISIBLE_DEVICES"] = "1" - trainer_env["PIPELINERL_DEBUG"] = "1" + Validates the real-world scenario where a vLLM HTTP server receives + weight updates from a trainer while serving requests. + """ + print("\n" + "="*60) + print("Starting server weight update pattern test (TP=1, 1 actor, 2 GPUs)") + print("="*60) - print("[Main] Starting trainer process (GPU 1)") - trainer_proc = subprocess.Popen( - [ - sys.executable, - str(distributed_trainer_helper), - "back_and_forth", - "--init-method", shared_distributed_init_method, - "--model-name", model_name, - "--sync-dir", str(sync_dir), - ], - env=trainer_env, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - text=True, + await _run_server_weight_update_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_server_configs=[{"port": 8000, "gpu_ids": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}], + trainer_gpu="1", + world_size=2, + timeout=2400, ) - # Step 3: Wait for both processes - # This test does 3 broadcasts, so use longer timeout - await wait_for_processes([ - (vllm_proc, "vLLM Engine"), - (trainer_proc, "Trainer"), - ], timeout=1800) # 30 minutes - @pytest.mark.timeout(2400) # 40 minutes for server test +class TestWeightUpdateTP2: + """Test weight updates with tensor-parallel (TP=2) vLLM — needs 3 GPUs.""" + + @pytest.mark.timeout(2000) @pytest.mark.asyncio - @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") - async def test_server_weight_update_pattern( + @pytest.mark.skipif(torch.cuda.device_count() < 3, reason="Requires at least 3 GPUs") + async def test_weight_update_back_and_forth_tp2( self, model_name, simple_prompt, generation_config, distributed_init_method, distributed_trainer_helper, + vllm_engine_helper, temp_dir, ): - """Server integration test: verify weight update pattern with HTTP API. + """Back-and-forth test with TP=2: one vLLM instance on GPUs 0+1, trainer on GPU 2.""" + from .sync_helper import create_sync_dir - This test validates the real-world scenario where: - 1. vLLM server is running and serving HTTP requests - 2. Trainer broadcasts weight updates while server is active - 3. Server responses change based on weight updates + print("\n" + "="*60) + print("Starting back-and-forth test (TP=2, 1 actor, 3 GPUs)") + print("="*60) - Flow: - - Start vLLM HTTP server (loads original model) - - Continuously generate via HTTP API (deterministic) - - Trainer: wait 15s → broadcast perturbed → wait 5s → broadcast original → wait 5s → broadcast perturbed - - Verify generation pattern: original → perturbed → original → perturbed - """ + sync_dir = create_sync_dir(temp_dir) + await _run_back_and_forth_engine_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_engine_helper=vllm_engine_helper, + sync_dir=sync_dir, + vllm_configs=[{"cuda_devices": "0,1", "actor_llm_idx": 0, "tensor_parallel_size": 2}], + trainer_gpu="2", + world_size=3, + timeout=1800, + ) + + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 3, reason="Requires at least 3 GPUs") + async def test_server_weight_update_pattern_tp2( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + temp_dir, + ): + """Server weight update test with TP=2: one server on GPUs 0+1, trainer on GPU 2.""" print("\n" + "="*60) - print("Starting server weight update pattern test") + print("Starting server weight update pattern test (TP=2, 1 actor, 3 GPUs)") print("="*60) - server_port = 8000 - server_url = f"http://127.0.0.1:{server_port}" + await _run_server_weight_update_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_server_configs=[{"port": 8001, "gpu_ids": "0,1", "actor_llm_idx": 0, "tensor_parallel_size": 2}], + trainer_gpu="2", + world_size=3, + timeout=2400, + ) - # Start vLLM server (HTTP mode - default, no extra args) - server_proc, _, _ = start_vllm_server( + +class TestWeightUpdateMultiActor: + """Test weight updates with multiple independent vLLM actors.""" + + @pytest.mark.timeout(2000) + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 3, reason="Requires at least 3 GPUs") + async def test_weight_update_back_and_forth_2actors( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + vllm_engine_helper, + temp_dir, + ): + """Back-and-forth test with 2 actors: vLLM on GPU 0 and GPU 1, trainer on GPU 2.""" + from .sync_helper import create_sync_dir + + print("\n" + "="*60) + print("Starting back-and-forth test (TP=1, 2 actors, 3 GPUs)") + print("="*60) + + sync_dir = create_sync_dir(temp_dir) + await _run_back_and_forth_engine_test( model_name=model_name, - server_port=server_port, - distributed_init_method=distributed_init_method, - stream_process_output_fn=stream_process_output, - extra_args=None, # HTTP mode is default + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_engine_helper=vllm_engine_helper, + sync_dir=sync_dir, + vllm_configs=[ + {"cuda_devices": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}, + {"cuda_devices": "1", "actor_llm_idx": 1, "tensor_parallel_size": 1}, + ], + trainer_gpu="2", + world_size=3, + timeout=1800, ) - # Give server a moment to start - await asyncio.sleep(1) + @pytest.mark.timeout(2000) + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 4, reason="Requires at least 4 GPUs") + async def test_weight_update_back_and_forth_3actors( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + vllm_engine_helper, + temp_dir, + ): + """Back-and-forth test with 3 actors: vLLM on GPUs 0/1/2, trainer on GPU 3.""" + from .sync_helper import create_sync_dir + + print("\n" + "="*60) + print("Starting back-and-forth test (TP=1, 3 actors, 4 GPUs)") + print("="*60) - # Start trainer process - trainer_proc, _, _ = start_trainer_process( - trainer_helper_path=distributed_trainer_helper, - distributed_init_method=distributed_init_method, + sync_dir = create_sync_dir(temp_dir) + await _run_back_and_forth_engine_test( model_name=model_name, - server_url=server_url, - stream_process_output_fn=stream_process_output, - extra_args=None, # No extra args for HTTP mode + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_engine_helper=vllm_engine_helper, + sync_dir=sync_dir, + vllm_configs=[ + {"cuda_devices": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}, + {"cuda_devices": "1", "actor_llm_idx": 1, "tensor_parallel_size": 1}, + {"cuda_devices": "2", "actor_llm_idx": 2, "tensor_parallel_size": 1}, + ], + trainer_gpu="3", + world_size=4, + timeout=1800, ) - try: - # Wait for server to be ready - await wait_for_server_ready(server_url, server_proc, trainer_proc) + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 3, reason="Requires at least 3 GPUs") + async def test_server_weight_update_pattern_2actors( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + temp_dir, + ): + """Server weight update test with 2 actors: servers on GPUs 0 and 1, trainer on GPU 2.""" + print("\n" + "="*60) + print("Starting server weight update pattern test (TP=1, 2 actors, 3 GPUs)") + print("="*60) - # Run generation loop - generations = await run_generation_loop( - server_url=server_url, - model_name=model_name, - simple_prompt=simple_prompt, - generation_config=generation_config, - trainer_proc=trainer_proc, - ) + await _run_server_weight_update_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_server_configs=[ + {"port": 8000, "gpu_ids": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}, + {"port": 8001, "gpu_ids": "1", "actor_llm_idx": 1, "tensor_parallel_size": 1}, + ], + trainer_gpu="2", + world_size=3, + timeout=2400, + ) - # Wait for trainer to finish - print("[Main] Waiting for trainer to finish...") - for _ in range(30): - if trainer_proc.poll() is not None: - break - await asyncio.sleep(1) - - # Analyze and verify pattern - analyze_and_verify_pattern(generations) - print("\n✓ Server weight update pattern test PASSED") - - finally: - # Cleanup - always kill process tree even if main process exited - # (child processes like vLLM workers might still be running) - print("[Main] Cleaning up processes...") - if server_proc: - print(f"[Main] Killing server process tree (PID {server_proc.pid})...") - kill_process_tree(server_proc.pid) - if trainer_proc: - print(f"[Main] Killing trainer process tree (PID {trainer_proc.pid})...") - kill_process_tree(trainer_proc.pid) + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 4, reason="Requires at least 4 GPUs") + async def test_server_weight_update_pattern_3actors( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + temp_dir, + ): + """Server weight update test with 3 actors: servers on GPUs 0/1/2, trainer on GPU 3.""" + print("\n" + "="*60) + print("Starting server weight update pattern test (TP=1, 3 actors, 4 GPUs)") + print("="*60) + + await _run_server_weight_update_test( + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + init_method=distributed_init_method, + distributed_trainer_helper=distributed_trainer_helper, + vllm_server_configs=[ + {"port": 8000, "gpu_ids": "0", "actor_llm_idx": 0, "tensor_parallel_size": 1}, + {"port": 8001, "gpu_ids": "1", "actor_llm_idx": 1, "tensor_parallel_size": 1}, + {"port": 8002, "gpu_ids": "2", "actor_llm_idx": 2, "tensor_parallel_size": 1}, + ], + trainer_gpu="3", + world_size=4, + timeout=2400, + ) # class TestConcurrentOperations: diff --git a/tests/trainer_test_utils.py b/tests/trainer_test_utils.py new file mode 100644 index 00000000..0ea3e62d --- /dev/null +++ b/tests/trainer_test_utils.py @@ -0,0 +1,128 @@ +"""Shared utilities for trainer helper scripts (both HTTP and fast-llm variants).""" + + +def _resolve_model_path(model_name: str): + """Resolve model name to a local Path, downloading from HuggingFace if needed.""" + from pathlib import Path + from huggingface_hub import snapshot_download + + model_path = Path(model_name) + if not model_path.exists(): + print(f"[Trainer] Downloading model from HuggingFace Hub: {model_name}") + model_path = Path(snapshot_download(model_name)) + return model_path + + +def _load_state_dict(model_name: str, device: str = "cuda:0") -> tuple: + """Load model state dict from safetensors files. + + Returns: + (state_dict, model_path) + """ + import json + from safetensors.torch import load_file + + model_path = _resolve_model_path(model_name) + index_file = model_path / "model.safetensors.index.json" + + if index_file.exists(): + print(f"[Trainer] Found index file, loading sharded model") + with open(index_file) as f: + index = json.load(f) + weight_map = index["weight_map"] + + file_to_params = {} + for param_name, filename in weight_map.items(): + file_to_params.setdefault(filename, []).append(param_name) + + state_dict = {} + for filename, param_names in file_to_params.items(): + file_path = model_path / filename + print(f"[Trainer] Loading {len(param_names)} parameters from {filename}") + tensors = load_file(str(file_path), device=device) + for param_name in param_names: + state_dict[param_name] = tensors[param_name] + else: + safetensors_file = model_path / "model.safetensors" + print(f"[Trainer] Loading from single file: {safetensors_file}") + state_dict = load_file(str(safetensors_file), device=device) + + print(f"[Trainer] Loaded {len(state_dict)} parameters from safetensors") + return state_dict, model_path + + +def _create_perturbed_state_dict( + state_dict: dict, seed: int = 42, noise_scale: float = 0.001 +) -> dict: + """Return a new state dict with Gaussian noise added to all tensors.""" + import torch + + print(f"[Trainer] Creating perturbed weights (all tensors) with seed={seed}...") + torch.manual_seed(seed) + perturbed = {} + for name, tensor in state_dict.items(): + perturbed_tensor = tensor.clone() + perturbed_tensor.add_(torch.randn_like(perturbed_tensor) * noise_scale) + perturbed[name] = perturbed_tensor + print( + f"[Trainer] Perturbed all {len(perturbed)} tensors with noise={noise_scale}, seed={seed}" + ) + return perturbed + + +def _init_actor_process_group(init_method: str, rank: int = 0, world_size: int = 2): + """Initialize the actor NCCL process group and return it.""" + import pipelinerl.torch_utils + + print(f"[Trainer] Initializing process group as rank {rank}") + process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=init_method, + rank=rank, + world_size=world_size, + ) + print("[Trainer] Process group initialized") + return process_group + + +def _broadcast_tensors(state_dict: dict, process_group, log_interval: int = 50): + """Broadcast every tensor in state_dict via NCCL (src=0).""" + import torch.distributed as dist + + total = len(state_dict) + for i, (name, tensor) in enumerate(state_dict.items()): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + dist.broadcast(tensor, src=0, group=process_group) + if (i + 1) % log_interval == 0: + print(f"[Trainer] Broadcasted {i+1}/{total} parameters") + print(f"[Trainer] All {total} parameters broadcasted") + + +def _wait_for_servers_ready(server_urls: list, extra_wait_secs: int = 10): + """Poll /health on each server until all respond 200, then sleep extra_wait_secs.""" + import time + import requests + + for server_url in server_urls: + print(f"[Trainer] Waiting for server {server_url} to be ready...") + server_ready = False + for i in range(120): # up to 2 minutes + try: + resp = requests.get(f"{server_url}/health", timeout=1) + if resp.status_code == 200: + server_ready = True + print(f"[Trainer] Server {server_url} is ready (took {i} seconds)") + break + except requests.exceptions.RequestException: + pass + time.sleep(1) + if not server_ready: + raise TimeoutError(f"Server {server_url} did not become ready within 2 minutes") + + if extra_wait_secs > 0: + print( + f"[Trainer] Waiting additional {extra_wait_secs} seconds for server(s) to fully initialize..." + ) + time.sleep(extra_wait_secs) diff --git a/tests/vllm_engine_helper.py b/tests/vllm_engine_helper.py index 8dc0e336..798743bc 100755 --- a/tests/vllm_engine_helper.py +++ b/tests/vllm_engine_helper.py @@ -394,6 +394,7 @@ async def test_back_and_forth( prompt: str, max_tokens: int, sync_dir: str, + tensor_parallel_size: int = 1, ): """Back-and-forth test: switch between original and perturbed weights. @@ -413,14 +414,16 @@ async def test_back_and_forth( print("[vLLM Engine] Starting back-and-forth test") - # Create sync points + # Create sync points — actor-signaled names use per-actor suffix; + # completion signals (trainer→actors) stay unadorned and are shared. sync_path = Path(sync_dir) - baseline_done = SyncPoint(sync_path, "baseline_done") - ready_for_perturbed1 = SyncPoint(sync_path, "ready_for_perturbed1") + suffix = f"_actor_{actor_llm_idx}" + baseline_done = SyncPoint(sync_path, f"baseline_done{suffix}") + ready_for_perturbed1 = SyncPoint(sync_path, f"ready_for_perturbed1{suffix}") perturbed1_done = SyncPoint(sync_path, "perturbed1_done") - ready_for_original = SyncPoint(sync_path, "ready_for_original") + ready_for_original = SyncPoint(sync_path, f"ready_for_original{suffix}") original_done = SyncPoint(sync_path, "original_done") - ready_for_perturbed2 = SyncPoint(sync_path, "ready_for_perturbed2") + ready_for_perturbed2 = SyncPoint(sync_path, f"ready_for_perturbed2{suffix}") perturbed2_done = SyncPoint(sync_path, "perturbed2_done") sampling_params = SamplingParams( @@ -433,7 +436,7 @@ async def test_back_and_forth( # Create engine args args = ap.Namespace( model=model_name, - tensor_parallel_size=1, + tensor_parallel_size=tensor_parallel_size, disable_log_stats=True, enable_log_requests=False, disable_weight_updates=False, @@ -499,18 +502,18 @@ async def test_back_and_forth( res_mod_2 = output.outputs[0].text print(f"[vLLM Engine] res_mod_2: '{res_mod_2}'") - # Step 5: Save results for server test + # Step 5: Save results to per-actor file for multi-actor comparison import json - results_file = sync_path / "expected_results.json" - expected_results = { + results_file = sync_path / f"results_actor_{actor_llm_idx}.json" + actor_results = { "res_or_1": res_or_1, "res_mod_1": res_mod_1, "res_or_2": res_or_2, "res_mod_2": res_mod_2, } with open(results_file, "w") as f: - json.dump(expected_results, f, indent=2) - print(f"[vLLM Engine] Saved expected results to {results_file}") + json.dump(actor_results, f, indent=2) + print(f"[vLLM Engine] Saved results for actor {actor_llm_idx} to {results_file}") # Step 6: Verify print("\n" + "="*60) @@ -554,6 +557,7 @@ async def test_back_and_forth( parser.add_argument("--max-tokens", type=int, default=50) parser.add_argument("--sync-dir", type=str, help="Directory for sync files") parser.add_argument("--expect-different", action="store_true", help="Expect outputs to be different (for perturbed weights)") + parser.add_argument("--tensor-parallel-size", type=int, default=1, help="Tensor parallel size for engine") args = parser.parse_args() @@ -604,6 +608,7 @@ async def test_back_and_forth( args.prompt, args.max_tokens, args.sync_dir, + tensor_parallel_size=args.tensor_parallel_size, )) except Exception as e: print(f"[vLLM Engine] Error: {e}") From e170185d3791aa5802c81563221fe362bb6a024e Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 23 Feb 2026 12:43:51 +0000 Subject: [PATCH 21/85] fix setting current device for tp and pp cases --- pipelinerl/vllm1.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 87d22a65..16234379 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -203,6 +203,10 @@ def monitor_redis_stream(): import redis import orjson + # Threads default to CUDA device 0; set the correct device so NCCL + # communicator operations use the same device as this worker. + torch.cuda.set_device(self.device) + r = redis.Redis(host=self.redis_host, port=self.redis_port) stream_key = "fast_llm_events" payload_key = b"event" From 3541dfe559da059bfd2e7baea06c09437c9368de Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 23 Feb 2026 12:48:01 +0000 Subject: [PATCH 22/85] fix multi actor generation abab patter consistency check --- tests/server_weight_update_utils.py | 85 ++++++++++++++++++-------- tests/test_vllm1_fast_llm_broadcast.py | 8 ++- tests/test_vllm1_integration.py | 54 ++-------------- 3 files changed, 71 insertions(+), 76 deletions(-) diff --git a/tests/server_weight_update_utils.py b/tests/server_weight_update_utils.py index 5e439f92..b444ebbf 100644 --- a/tests/server_weight_update_utils.py +++ b/tests/server_weight_update_utils.py @@ -212,6 +212,9 @@ def analyze_and_verify_pattern(generations): Args: generations: List of (timestamp, text) tuples + Returns: + Tuple of (text_a, text_b) — the original and perturbed texts. + Raises: AssertionError: If pattern is not as expected """ @@ -257,6 +260,45 @@ def analyze_and_verify_pattern(generations): print(f" Phase A2 (original): {len(phase_a2_items)} generation(s) ← matches A ✓") print(f" Phase B2 (perturbed): {len(phase_b2_items)} generation(s) ← matches B ✓") + return phase_a_text, phase_b_text + + +def analyze_and_verify_pattern_multi(per_server_generations): + """Verify A→B→A→B pattern independently per server, then check consistency. + + Each server's generation history is checked independently (since weight + updates are not coordinated with requests, servers can transiently disagree). + After all pass, we assert that every server converged on the same text A + and text B. + + Args: + per_server_generations: List of per-server generation lists, each a + list of (timestamp, text) tuples (as returned by + run_generation_loop_multi). + + Raises: + AssertionError: If any server fails its pattern check or servers + disagree on text A / text B. + """ + patterns = [] + for i, generations in enumerate(per_server_generations): + print(f"\n{'=' * 60}") + print(f"Actor {i} pattern analysis") + text_a, text_b = analyze_and_verify_pattern(generations) + patterns.append((text_a, text_b)) + + unique_a = set(t_a for t_a, _ in patterns) + unique_b = set(t_b for _, t_b in patterns) + assert len(unique_a) == 1, ( + f"Servers disagree on text A (original weights): " + f"{[t_a[:40] for t_a, _ in patterns]}" + ) + assert len(unique_b) == 1, ( + f"Servers disagree on text B (perturbed weights): " + f"{[t_b[:40] for _, t_b in patterns]}" + ) + print(f"\n✓ All {len(patterns)} actor(s) agree on text A and text B") + def start_vllm_server( model_name: str, @@ -358,9 +400,10 @@ async def run_generation_loop_multi( ): """Run continuous generation loop querying all servers each round. - Each iteration queries ALL servers and asserts all responses are equal - (since they should have the same weights after a broadcast). Records one - (timestamp, text) entry per round. + Each server is tracked independently because weight updates and requests + are not coordinated — different actors can temporarily return different + results while a broadcast is in flight. Pattern checking is therefore + done per-server after the loop (see analyze_and_verify_pattern_multi). Args: server_urls: List of server base URLs @@ -372,10 +415,11 @@ async def run_generation_loop_multi( generation_interval: Time between generation rounds Returns: - List of (timestamp, generated_text) tuples + List of per-server generation lists, each a list of + (timestamp, generated_text) tuples (same order as server_urls). """ print(f"[Main] Starting continuous generation loop across {len(server_urls)} server(s)...") - generations = [] + per_server = [[] for _ in server_urls] start_time = time.time() payload = { @@ -394,37 +438,26 @@ async def run_generation_loop_multi( print(f"[Main] Trainer exited with code {trainer_poll}") break - try: - texts = [] - for url in server_urls: + for i, url in enumerate(server_urls): + try: resp = requests.post( f"{url}/v1/completions", json=payload, timeout=30, ) if resp.status_code == 200: - texts.append(resp.json()["choices"][0]["text"]) + text = resp.json()["choices"][0]["text"] + timestamp = time.time() - start_time + per_server[i].append((timestamp, text)) + print(f"[Main] [{timestamp:.1f}s] Actor {i}: '{text}'") else: - print(f"[Main] Generation from {url} failed with status {resp.status_code}") - texts = [] - break - - if texts: - # All servers should agree - assert len(set(texts)) == 1, ( - f"Servers disagree on generation: {texts}" - ) - text = texts[0] - timestamp = time.time() - start_time - generations.append((timestamp, text)) - print(f"[Main] [{timestamp:.1f}s] Generated: '{text}'") - - except requests.exceptions.RequestException as e: - print(f"[Main] Request failed: {e}") + print(f"[Main] Generation from actor {i} ({url}) failed with status {resp.status_code}") + except requests.exceptions.RequestException as e: + print(f"[Main] Request to actor {i} ({url}) failed: {e}") await asyncio.sleep(generation_interval) - return generations + return per_server def start_trainer_process( diff --git a/tests/test_vllm1_fast_llm_broadcast.py b/tests/test_vllm1_fast_llm_broadcast.py index 32d31ac2..0f4cb037 100644 --- a/tests/test_vllm1_fast_llm_broadcast.py +++ b/tests/test_vllm1_fast_llm_broadcast.py @@ -21,6 +21,7 @@ run_generation_loop, run_generation_loop_multi, analyze_and_verify_pattern, + analyze_and_verify_pattern_multi, start_vllm_server, start_trainer_process, ) @@ -278,7 +279,7 @@ async def _run_fast_llm_server_test( trainer_proc=trainer_proc, ) else: - generations = await run_generation_loop_multi( + per_server_generations = await run_generation_loop_multi( server_urls=server_urls, model_name=model_name, simple_prompt=simple_prompt, @@ -293,7 +294,10 @@ async def _run_fast_llm_server_test( break await asyncio.sleep(1) - analyze_and_verify_pattern(generations) + if len(server_urls) == 1: + analyze_and_verify_pattern(generations) + else: + analyze_and_verify_pattern_multi(per_server_generations) print(f"\n✓ Fast-LLM server weight update pattern test PASSED ({len(server_urls)} server(s))") finally: diff --git a/tests/test_vllm1_integration.py b/tests/test_vllm1_integration.py index aefcc337..2f354686 100644 --- a/tests/test_vllm1_integration.py +++ b/tests/test_vllm1_integration.py @@ -21,6 +21,7 @@ run_generation_loop, run_generation_loop_multi, analyze_and_verify_pattern, + analyze_and_verify_pattern_multi, start_vllm_server, start_trainer_process, ) @@ -451,7 +452,7 @@ async def _run_server_weight_update_test( trainer_proc=trainer_proc, ) else: - generations = await run_generation_loop_multi( + per_server_generations = await run_generation_loop_multi( server_urls=server_urls, model_name=model_name, simple_prompt=simple_prompt, @@ -466,7 +467,10 @@ async def _run_server_weight_update_test( break await asyncio.sleep(1) - analyze_and_verify_pattern(generations) + if len(server_urls) == 1: + analyze_and_verify_pattern(generations) + else: + analyze_and_verify_pattern_multi(per_server_generations) print(f"\n✓ Server weight update pattern test PASSED ({len(server_urls)} server(s))") finally: @@ -1221,49 +1225,3 @@ async def test_server_weight_update_pattern_3actors( world_size=4, timeout=2400, ) - - -# class TestConcurrentOperations: -# """Test concurrent generation and weight updates.""" - -# @pytest.mark.asyncio -# @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") -# async def test_multiple_generations_before_update( -# self, -# vllm_engine_factory, -# sample_prompts, -# generation_config, -# ): -# """Test that multiple generation requests work correctly.""" -# from vllm import SamplingParams - -# async with vllm_engine_factory() as manager: -# sampling_params = SamplingParams( -# temperature=generation_config["temperature"], -# top_p=generation_config["top_p"], -# max_tokens=generation_config["max_tokens"], -# seed=generation_config["seed"], -# ) - -# # Launch multiple generation requests -# tasks = [] -# for i, prompt in enumerate(sample_prompts): -# async def generate_one(prompt, idx): -# request_id = f"concurrent_{idx}" -# async for output in manager.engine.generate( -# prompt, -# sampling_params=sampling_params, -# request_id=request_id, -# ): -# final = output -# return final.outputs[0].text - -# tasks.append(generate_one(prompt, i)) - -# # Run all generations concurrently -# results = await asyncio.gather(*tasks) - -# assert len(results) == len(sample_prompts) -# for i, result in enumerate(results): -# print(f"Result {i}: {result[:50]}...") -# assert len(result) > 0 From 5560ada97a00764d0065f3e7bfa77a3804e81a79 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 2 Mar 2026 08:14:57 +0000 Subject: [PATCH 23/85] fast-llm weight update bug fix some other changes --- pipelinerl/vllm1.py | 273 +++++++++++-------------- tests/distributed_trainer_helper.py | 85 +++++++- tests/fast_llm_trainer_helper.py | 116 ++++++++++- tests/server_weight_update_utils.py | 170 ++++++++++++--- tests/test_vllm1_fast_llm_broadcast.py | 98 +++++++++ tests/test_vllm1_integration.py | 86 ++++++++ 6 files changed, 644 insertions(+), 184 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 16234379..a40777fb 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -166,145 +166,18 @@ def receive_weight_update(self: LikeWorker, request: WeightUpdateRequest): pipelinerl.vllm_quantization.invalidate_fp32_cache() logger.info("Weight update received - all parameters processed") - def init_fast_llm_receiver( - self: LikeWorker, - redis_host: str, - redis_port: int, - ): - """Initialize Fast-LLM weight receiver (called once at startup). - - This method: - 1. Stores Redis connection info - 2. Sets up threading infrastructure - 3. Does NOT start monitoring thread (that's managed by EngineManager) - """ - import threading - - self.redis_host = redis_host - self.redis_port = redis_port - self.fast_llm_stop_event = threading.Event() - logger.info( - f"[Worker rank={self.rank}] Fast-LLM receiver initialized with Redis {redis_host}:{redis_port}" - ) - - def start_fast_llm_monitoring(self: LikeWorker): - """Start background thread to monitor Redis stream. - - This thread: - 1. Connects to Redis stream "fast_llm_events" - 2. Listens for {type: "weights_ready", step: N} events - 3. On event, triggers receive_weight_update_fast_llm() - 4. Runs until stop_event is set - """ - import threading - import time - - def monitor_redis_stream(): - import redis - import orjson - - # Threads default to CUDA device 0; set the correct device so NCCL - # communicator operations use the same device as this worker. - torch.cuda.set_device(self.device) - - r = redis.Redis(host=self.redis_host, port=self.redis_port) - stream_key = "fast_llm_events" - payload_key = b"event" - last_id = "0-0" - - logger.info(f"[Worker rank={self.rank}] Starting Redis stream monitoring") - - while not self.fast_llm_stop_event.is_set(): - try: - # Non-blocking read with 1s timeout - result = r.xread({stream_key: last_id}, count=1, block=1000) - - if not result: - continue - - for stream_name, messages in result: - for msg_id, msg_data in messages: - last_id = msg_id - - if payload_key not in msg_data: - logger.warning( - f"[Worker rank={self.rank}] Event missing 'event' field: {msg_data}" - ) - continue - - try: - event = orjson.loads(msg_data[payload_key]) - except Exception as e: - logger.error( - f"[Worker rank={self.rank}] Failed to parse event: {e}" - ) - continue - - event_type = event.get("type") - step = event.get("step") - - if event_type == "weights_ready": - logger.info( - f"[Worker rank={self.rank}] Received weights_ready event: step={step}" - ) - # Call receive_weight_update_fast_llm directly (runs in this thread) - try: - self.receive_weight_update_fast_llm() - except Exception as e: - logger.error( - f"[Worker rank={self.rank}] Error receiving Fast-LLM weight update: {e}" - ) - elif event_type == "training_finished": - logger.info( - f"[Worker rank={self.rank}] Received training_finished event, destroying process group" - ) - try: - self.destroy_actor_update_group() - except Exception as e: - logger.error(f"[Worker rank={self.rank}] Error destroying process group: {e}") - self.fast_llm_stop_event.set() # stop monitoring loop - - except Exception as e: - logger.error(f"[Worker rank={self.rank}] Error in Redis monitor: {e}") - if not self.fast_llm_stop_event.is_set(): - time.sleep(1) # Avoid tight loop on error - - logger.info(f"[Worker rank={self.rank}] Redis monitoring stopped") - r.close() - - import threading - self.fast_llm_monitor_thread = threading.Thread( - target=monitor_redis_stream, - daemon=True, - name=f"FastLLMMonitor-Rank{self.rank}", - ) - self.fast_llm_monitor_thread.start() - logger.info(f"[Worker rank={self.rank}] Fast-LLM monitoring thread started") - - def stop_fast_llm_monitoring(self: LikeWorker): - """Stop the Fast-LLM monitoring thread.""" - if not hasattr(self, "fast_llm_stop_event"): - return - if not self.fast_llm_stop_event.is_set(): - logger.warning( - f"[Worker rank={self.rank}] training_finished was not received; " - "forcing monitoring thread stop" - ) - self.fast_llm_stop_event.set() - if hasattr(self, "fast_llm_monitor_thread"): - self.fast_llm_monitor_thread.join(timeout=5) - logger.info(f"[Worker rank={self.rank}] Fast-LLM monitoring stopped") - def receive_weight_update_fast_llm(self: LikeWorker): """Receive weight update via Fast-LLM broadcast protocol. - This method: - 1. Loops receiving metadata via broadcast_object_list - 2. Receives tensor via broadcast - 3. Calls model.load_weights() for each parameter - 4. Exits when metadata is [None] (end signal) + Called via collective_rpc_async from the main-process monitoring thread, + so it runs in each worker's main thread — serialized with inference, + identical concurrency model to receive_weight_update (HTTP path). - NOTE: This is called from the monitoring thread. + Protocol: + 1. Loop: receive metadata via broadcast_object_list + 2. Receive tensor via broadcast + 3. Call model.load_weights() for each parameter + 4. Exit when metadata is [None] (end signal) """ torch.cuda.synchronize(self.device) logger.info(f"[Worker rank={self.rank}] Start receiving Fast-LLM weight update") @@ -425,28 +298,126 @@ async def receive_weight_update(self, request: WeightUpdateRequest): logger.info("Weight update processed") async def init_fast_llm_receiver(self): - """Initialize Fast-LLM receiver on all workers.""" - await self.engine.engine_core.collective_rpc_async( - "init_fast_llm_receiver", - args=(self.args.redis_host, self.args.redis_port), + """Store Redis connection info for the main-process monitoring thread.""" + self._redis_host = self.args.redis_host + self._redis_port = self.args.redis_port + logger.info( + f"Fast-LLM receiver initialized (Redis {self._redis_host}:{self._redis_port})" ) - logger.info("Fast-LLM receiver initialized on all workers") async def start_fast_llm_monitoring(self): - """Start Fast-LLM monitoring threads on all workers.""" - await self.engine.engine_core.collective_rpc_async( - "start_fast_llm_monitoring", - args=(), + """Start a single Redis monitoring thread in the main process. + + When weights_ready arrives the thread calls + collective_rpc_async("receive_weight_update_fast_llm") which runs in + each worker's main thread — blocking inference during the update, + identical concurrency to the HTTP path. training_finished is handled + the same way via destroy_actor_update_group(). + """ + import asyncio + import threading + + self._fast_llm_stop_event = threading.Event() + loop = asyncio.get_event_loop() + + def monitor_redis_stream(): + import redis + import orjson + import time + + r = redis.Redis(host=self._redis_host, port=self._redis_port) + stream_key = "fast_llm_events" + payload_key = b"event" + last_id = "0-0" + + logger.info("[FastLLM] Main-process Redis monitoring started") + + while not self._fast_llm_stop_event.is_set(): + try: + result = r.xread({stream_key: last_id}, count=1, block=1000) + if not result: + continue + + for _stream_name, messages in result: + for msg_id, msg_data in messages: + last_id = msg_id + + if payload_key not in msg_data: + logger.warning( + f"[FastLLM] Event missing 'event' field: {msg_data}" + ) + continue + + try: + event = orjson.loads(msg_data[payload_key]) + except Exception as e: + logger.error(f"[FastLLM] Failed to parse event: {e}") + continue + + event_type = event.get("type") + step = event.get("step") + + if event_type == "weights_ready": + logger.info( + f"[FastLLM] weights_ready step={step}, dispatching to workers" + ) + try: + future = asyncio.run_coroutine_threadsafe( + self.engine.engine_core.collective_rpc_async( + "receive_weight_update_fast_llm", args=() + ), + loop, + ) + future.result() + logger.info( + f"[FastLLM] Weight update complete: step={step}" + ) + except Exception as e: + logger.error( + f"[FastLLM] Error receiving weight update: {e}" + ) + + elif event_type == "training_finished": + logger.info( + "[FastLLM] training_finished received, destroying process group" + ) + try: + future = asyncio.run_coroutine_threadsafe( + self.destroy_actor_update_group(), loop + ) + future.result() + except Exception as e: + logger.error( + f"[FastLLM] Error destroying process group: {e}" + ) + self._fast_llm_stop_event.set() + + except Exception as e: + logger.error(f"[FastLLM] Error in Redis monitor: {e}") + if not self._fast_llm_stop_event.is_set(): + time.sleep(1) + + logger.info("[FastLLM] Main-process Redis monitoring stopped") + r.close() + + self._fast_llm_monitor_thread = threading.Thread( + target=monitor_redis_stream, + daemon=True, + name="FastLLMMonitor", ) - logger.info("Fast-LLM monitoring started on all workers") + self._fast_llm_monitor_thread.start() + logger.info("[FastLLM] Main-process monitoring thread started") async def stop_fast_llm_monitoring(self): - """Stop Fast-LLM monitoring threads on all workers.""" - await self.engine.engine_core.collective_rpc_async( - "stop_fast_llm_monitoring", - args=(), - ) - logger.info("Fast-LLM monitoring stopped on all workers") + """Stop the main-process Fast-LLM monitoring thread.""" + if not hasattr(self, "_fast_llm_stop_event"): + return + if not self._fast_llm_stop_event.is_set(): + logger.warning("[FastLLM] training_finished was not received; forcing stop") + self._fast_llm_stop_event.set() + if hasattr(self, "_fast_llm_monitor_thread"): + self._fast_llm_monitor_thread.join(timeout=5) + logger.info("[FastLLM] Main-process monitoring thread stopped") @asynccontextmanager @staticmethod diff --git a/tests/distributed_trainer_helper.py b/tests/distributed_trainer_helper.py index 6cd4b2c4..7e50decb 100755 --- a/tests/distributed_trainer_helper.py +++ b/tests/distributed_trainer_helper.py @@ -500,9 +500,80 @@ def timed_broadcast_server_test( print("[Trainer] Process group destroyed, exiting") +def rapid_broadcast_cycles( + init_method: str, + model_name: str, + server_urls: list, + world_size: int = 2, + n_cycles: int = 6, +): + """Hybrid broadcast designed to catch transition/garbage generations. + + Structure: + 1. Slow broadcast: perturbed (5 s wait after) — establishes text_B + 2. Slow broadcast: original (5 s wait after) — re-establishes text_A + 3. n_cycles rapid pairs: perturbed → original (1 s between each) + 4. Slow broadcast: perturbed (5 s wait after) — end on text_B so the + overall A→B→A→B pattern remains detectable + + The slow initial cycles give the generation loop enough stable time to + identify text_A and text_B by frequency. The rapid cycles create many + short broadcast windows where mid-broadcast (garbage) generations are + likely to be caught by a zero-interval generation loop. + """ + import torch.distributed as dist + import time + import requests + + process_group = _init_actor_process_group(init_method, rank=0, world_size=world_size) + + _wait_for_servers_ready(server_urls, extra_wait_secs=10) + + print(f"[Trainer] Loading weights from {model_name}") + original_state_dict, _ = _load_state_dict(model_name) + perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) + + version = 1 + + # --- Slow cycle: establish text_B and text_A clearly --- + print("[Trainer] Slow broadcast 1: perturbed (establishing text_B)...") + _broadcast_via_server(perturbed_state_dict, server_urls, version=version, process_group=process_group, label="perturbed (slow)") + version += 1 + time.sleep(5) + + print("[Trainer] Slow broadcast 2: original (re-establishing text_A)...") + _broadcast_via_server(original_state_dict, server_urls, version=version, process_group=process_group, label="original (slow)") + version += 1 + time.sleep(5) + + # --- Rapid cycles: 1 s between broadcasts --- + for i in range(n_cycles): + print(f"[Trainer] Rapid cycle {i + 1}/{n_cycles}: perturbed...") + _broadcast_via_server(perturbed_state_dict, server_urls, version=version, process_group=process_group, label=f"perturbed (rapid {i + 1})") + version += 1 + time.sleep(1) + + print(f"[Trainer] Rapid cycle {i + 1}/{n_cycles}: original...") + _broadcast_via_server(original_state_dict, server_urls, version=version, process_group=process_group, label=f"original (rapid {i + 1})") + version += 1 + time.sleep(1) + + # --- Final slow broadcast: end on perturbed so ABAB pattern holds --- + print("[Trainer] Final slow broadcast: perturbed (ending on text_B)...") + _broadcast_via_server(perturbed_state_dict, server_urls, version=version, process_group=process_group, label="perturbed (final)") + time.sleep(5) + + for url in server_urls: + print(f"[Trainer] Sending training_finished signal to {url}...") + requests.post(f"{url}/training_finished", timeout=10) + + dist.destroy_process_group(process_group) + print("[Trainer] Process group destroyed, exiting") + + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Distributed trainer helper") - parser.add_argument("command", choices=["init", "broadcast", "cross_validation", "back_and_forth", "timed_broadcast_server_test"]) + parser.add_argument("command", choices=["init", "broadcast", "cross_validation", "back_and_forth", "timed_broadcast_server_test", "rapid_broadcast_cycles"]) parser.add_argument("--init-method", required=True) parser.add_argument("--rank", type=int, default=0) parser.add_argument("--world-size", type=int, default=2) @@ -516,6 +587,7 @@ def timed_broadcast_server_test( "--server-urls", nargs="+", help="Base URL(s) of vLLM server(s) (e.g., http://127.0.0.1:8000)" ) parser.add_argument("--num-actors", type=int, default=1, help="Number of vLLM actor processes") + parser.add_argument("--n-cycles", type=int, default=6, help="Number of rapid broadcast cycles (rapid_broadcast_cycles command)") args = parser.parse_args() @@ -559,6 +631,17 @@ def timed_broadcast_server_test( args.server_urls, world_size=args.world_size, ) + elif args.command == "rapid_broadcast_cycles": + if not args.model_name or not args.server_urls: + print("Error: --model-name and --server-urls required for rapid_broadcast_cycles") + sys.exit(1) + rapid_broadcast_cycles( + args.init_method, + args.model_name, + args.server_urls, + world_size=args.world_size, + n_cycles=args.n_cycles, + ) except Exception as e: print(f"[Trainer] Error: {e}") import traceback diff --git a/tests/fast_llm_trainer_helper.py b/tests/fast_llm_trainer_helper.py index 44c7310e..79bd9a75 100644 --- a/tests/fast_llm_trainer_helper.py +++ b/tests/fast_llm_trainer_helper.py @@ -131,6 +131,93 @@ def broadcast_weights_fast_llm(state_dict, step): print("[Trainer] Redis connection closed, process group destroyed, exiting") +def rapid_broadcast_cycles_fast_llm( + init_method: str, + model_name: str, + server_urls: list, + redis_host: str = "localhost", + redis_port: int = 6379, + world_size: int = 2, + n_cycles: int = 6, +): + """Hybrid Fast-LLM broadcast designed to catch transition/garbage generations. + + Structure: + 1. Slow broadcast: perturbed (5 s wait after) — establishes text_B + 2. Slow broadcast: original (5 s wait after) — re-establishes text_A + 3. n_cycles rapid pairs: perturbed → original (1 s between each) + 4. Slow broadcast: perturbed (5 s wait after) — end on text_B so the + overall A→B→A→B pattern remains detectable + """ + import torch.distributed as dist + import time + import redis as redis_lib + import orjson + + process_group = _init_actor_process_group(init_method, rank=0, world_size=world_size) + + r = redis_lib.Redis(host=redis_host, port=redis_port) + stream_key = "fast_llm_events" + payload_key = "event" + + _wait_for_servers_ready(server_urls, extra_wait_secs=15) + + print(f"[Trainer] Loading weights from {model_name}") + original_state_dict, _ = _load_state_dict(model_name) + perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) + + step = 1 + + def broadcast_weights(state_dict, label): + nonlocal step + import torch + event = {"type": "weights_ready", "step": step} + r.xadd(stream_key, {payload_key: orjson.dumps(event)}) + print(f"[Trainer] Sent weights_ready step={step} ({label})") + step += 1 + + for name, tensor in state_dict.items(): + if tensor.device.type != "cuda": + tensor = tensor.cuda(0) + meta = [("", name, list(tensor.shape), str(tensor.dtype))] + dist.broadcast_object_list(meta, src=0, group=process_group) + dist.broadcast(tensor, src=0, group=process_group) + + dist.broadcast_object_list([None], src=0, group=process_group) + print(f"[Trainer] Broadcast complete ({label})") + + # --- Slow cycle: establish text_B and text_A clearly --- + print("[Trainer] Slow broadcast 1: perturbed (establishing text_B)...") + broadcast_weights(perturbed_state_dict, "perturbed slow") + time.sleep(5) + + print("[Trainer] Slow broadcast 2: original (re-establishing text_A)...") + broadcast_weights(original_state_dict, "original slow") + time.sleep(5) + + # --- Rapid cycles: 1 s between broadcasts --- + for i in range(n_cycles): + print(f"[Trainer] Rapid cycle {i + 1}/{n_cycles}: perturbed...") + broadcast_weights(perturbed_state_dict, f"perturbed rapid {i + 1}") + time.sleep(1) + + print(f"[Trainer] Rapid cycle {i + 1}/{n_cycles}: original...") + broadcast_weights(original_state_dict, f"original rapid {i + 1}") + time.sleep(1) + + # --- Final slow broadcast: end on perturbed so ABAB pattern holds --- + print("[Trainer] Final slow broadcast: perturbed (ending on text_B)...") + broadcast_weights(perturbed_state_dict, "perturbed final") + time.sleep(5) + + print("[Trainer] Sending training_finished signal...") + r.xadd(stream_key, {payload_key: orjson.dumps({"type": "training_finished"})}) + + r.close() + dist.destroy_process_group(process_group) + print("[Trainer] Redis connection closed, process group destroyed, exiting") + + if __name__ == "__main__": import argparse @@ -141,14 +228,27 @@ def broadcast_weights_fast_llm(state_dict, step): parser.add_argument("--world-size", type=int, default=2, help="Total distributed world size") parser.add_argument("--redis-host", default="localhost", help="Redis host") parser.add_argument("--redis-port", type=int, default=6379, help="Redis port") + parser.add_argument("--n-cycles", type=int, default=0, + help="If > 0, run rapid_broadcast_cycles with this many rapid pairs") args = parser.parse_args() - timed_broadcast_fast_llm( - init_method=args.init_method, - model_name=args.model, - server_urls=args.server_urls, - redis_host=args.redis_host, - redis_port=args.redis_port, - world_size=args.world_size, - ) + if args.n_cycles > 0: + rapid_broadcast_cycles_fast_llm( + init_method=args.init_method, + model_name=args.model, + server_urls=args.server_urls, + redis_host=args.redis_host, + redis_port=args.redis_port, + world_size=args.world_size, + n_cycles=args.n_cycles, + ) + else: + timed_broadcast_fast_llm( + init_method=args.init_method, + model_name=args.model, + server_urls=args.server_urls, + redis_host=args.redis_host, + redis_port=args.redis_port, + world_size=args.world_size, + ) diff --git a/tests/server_weight_update_utils.py b/tests/server_weight_update_utils.py index b444ebbf..a4ade92a 100644 --- a/tests/server_weight_update_utils.py +++ b/tests/server_weight_update_utils.py @@ -70,40 +70,78 @@ def _build_phases(generations): return phases -def _find_abab_pattern(phases): - """Search for the A→B→A→B pattern anchored to the first and last phases. +def _identify_stable_texts(phases, min_stable_gens=5): + """Return (text_a, text_b) identified from the first two stable phases. - A is always ``phases[0]`` — the text the server starts with (original weights). - B2 is always ``phases[-1]`` — the current/final phase (perturbed weights after - the 3rd broadcast). + Iterates phases in order, skipping any with fewer than ``min_stable_gens`` + generations (transition artifacts). The first stable phase gives text_A; + the first stable phase with a different text gives text_B. - Any transition phases in between are skipped automatically because we only - require that some phase after the first B has the same text as phases[0] (A), - without caring what sits between the first B and that return-to-A. + Returns (text_a, text_b) or (None, None) if two distinct stable texts + cannot be found. + """ + text_a = None + text_b = None + for text, items in phases: + if len(items) < min_stable_gens: + continue + if text_a is None: + text_a = text + elif text != text_a: + text_b = text + break + if text_a is None or text_b is None: + return None, None + return text_a, text_b + + +def _find_abab_pattern(phases, min_stable_gens=5): + """Search for the A→B→A→B pattern. + + text_A and text_B are identified from the first two *stable* phases — + phases with at least ``min_stable_gens`` generations. Short transition + phases (1–few gens) produced while an NCCL broadcast is in-flight are + automatically skipped during identification. + + The test is designed so that the server always starts with a long run of + text_A (original weights, typically hundreds of gens) followed by a long + run of text_B (first perturbed broadcast, tens of gens), making them + unambiguous even with transition artifacts in between. + + After identifying text_A and text_B the full A→B→A→B subsequence is + located in the phase list (transition phases between the four anchors are + silently skipped). Returns (phase_a, phase_b, phase_a2, phase_b2) or None. """ if len(phases) < 4: return None - text_a = phases[0][0] - text_b2 = phases[-1][0] + text_a, text_b = _identify_stable_texts(phases, min_stable_gens) - if text_a == text_b2: - return None # A and B must be distinct texts + if text_a is None or text_b is None: + return None texts = [t for t, _ in phases] - # Find the first B (same text as B2) strictly between phase 0 and last - for j in range(1, len(phases) - 1): - if texts[j] != text_b2: - continue - # Find the first return to A strictly between j and last - for k in range(j + 1, len(phases) - 1): - if texts[k] == text_a: - return phases[0], phases[j], phases[k], phases[-1] + # Find ABAB as a subsequence in the phase list + first_a = next((i for i, t in enumerate(texts) if t == text_a), None) + if first_a is None: + return None + + first_b = next((i for i in range(first_a + 1, len(phases)) if texts[i] == text_b), None) + if first_b is None: + return None + + second_a = next((i for i in range(first_b + 1, len(phases)) if texts[i] == text_a), None) + if second_a is None: + return None - return None + second_b = next((i for i in range(second_a + 1, len(phases)) if texts[i] == text_b), None) + if second_b is None: + return None + + return phases[first_a], phases[first_b], phases[second_a], phases[second_b] def check_pattern_detected(generations): @@ -232,10 +270,18 @@ def analyze_and_verify_pattern(generations): phases = _build_phases(generations) + _GRAY = "\033[90m" + _RESET = "\033[0m" + stable_a, stable_b = _identify_stable_texts(phases) + stable_texts = {t for t in (stable_a, stable_b) if t is not None} print("\n" + "=" * 60) print(f"Detected {len(phases)} phase(s):") for i, (text, items) in enumerate(phases): - print(f"Phase {i+1}: {len(items)} generation(s) - '{text[:60]}...'") + line = f"Phase {i+1}: {len(items)} generation(s) - '{text[:60]}...'" + if text not in stable_texts: + print(f"{_GRAY}{line} ← transition{_RESET}") + else: + print(line) print("=" * 60) result = _find_abab_pattern(phases) @@ -300,6 +346,79 @@ def analyze_and_verify_pattern_multi(per_server_generations): print(f"\n✓ All {len(patterns)} actor(s) agree on text A and text B") +def extract_transition_phases(generations, text_a, text_b): + """Return phases that are neither text_a nor text_b. + + These are mid-broadcast 'garbage' generations produced while an NCCL + weight update was in flight and the model had partially updated weights. + + Args: + generations: List of (timestamp, text) tuples + text_a: The original-weights text (established first) + text_b: The perturbed-weights text + + Returns: + List of (text, items) phase tuples where text is neither text_a nor text_b + """ + phases = _build_phases(generations) + return [(text, items) for text, items in phases if text != text_a and text != text_b] + + +def analyze_and_verify_transitions(generations, n_cycles): + """Verify A→B→A→B pattern and assert that transition generations were caught. + + The ``rapid_broadcast_cycles`` trainer command performs: + - 1 startup A phase (server starts on original weights) + - 1 slow perturbed broadcast → text_B + - 1 slow original broadcast → text_A + - n_cycles rapid pairs → text_B, text_A each cycle + - 1 final slow perturbed → text_B + + This gives exactly ``4 + 2 * n_cycles`` stable phases. Seeing fewer + means a broadcast was missed entirely (timing/sync bug). + + Args: + generations: List of (timestamp, text) tuples + n_cycles: Number of rapid broadcast pairs (passed as ``--n-cycles`` + to the trainer helper). + + Raises: + AssertionError: If ABAB pattern is not found, stable phase count is + wrong, or no transition generations were caught. + """ + text_a, text_b = analyze_and_verify_pattern(generations) + + phases = _build_phases(generations) + stable_phases = [(text, items) for text, items in phases if text == text_a or text == text_b] + expected_stable = 4 + 2 * n_cycles + assert len(stable_phases) == expected_stable, ( + f"Expected {expected_stable} stable phases (4 + 2×{n_cycles} cycles) " + f"but found {len(stable_phases)}. " + f"A broadcast may have been missed or merged. " + f"Stable phase counts: {[len(items) for _, items in stable_phases]}" + ) + print(f"\n✓ Stable phase count correct: {len(stable_phases)} (expected {expected_stable})") + + transition_phases = extract_transition_phases(generations, text_a, text_b) + + print("\n" + "=" * 60) + print(f"TRANSITION / GARBAGE GENERATIONS: {len(transition_phases)} phase(s)") + print("=" * 60) + if transition_phases: + for i, (text, items) in enumerate(transition_phases): + ts_start = items[0][0] + ts_end = items[-1][0] + print(f" [{i + 1}] {len(items)} gen(s) @ {ts_start:.2f}s–{ts_end:.2f}s: '{text[:120]}'") + else: + print(" (none)") + + assert len(transition_phases) > 0, ( + "No transition generations were caught. " + "Try increasing --n-cycles or verify generation_interval=0.0 is set." + ) + print(f"\n✓ Caught {len(transition_phases)} transition phase(s)") + + def start_vllm_server( model_name: str, server_port: int, @@ -469,6 +588,7 @@ def start_trainer_process( extra_args: list = None, gpu_id: str = "1", world_size: int = 2, + command: str = "timed_broadcast_server_test", ): """Start trainer subprocess. @@ -478,9 +598,11 @@ def start_trainer_process( model_name: Model name server_urls: List of server URLs (one per actor) stream_process_output_fn: Function to stream process output - extra_args: Additional CLI arguments (e.g., ["--redis-host", "localhost"]) + extra_args: Additional CLI arguments (e.g., ["--n-cycles", "6"]) gpu_id: CUDA_VISIBLE_DEVICES value for the trainer GPU world_size: Total distributed world size + command: Positional command for distributed_trainer_helper.py + (ignored for fast_llm_trainer_helper.py which uses --init-method style) Returns: Tuple of (trainer_proc, stdout_thread, stderr_thread) @@ -507,7 +629,7 @@ def start_trainer_process( else: # distributed_trainer_helper.py uses positional command + flags cmd.extend([ - "timed_broadcast_server_test", + command, "--init-method", distributed_init_method, "--model-name", model_name, "--world-size", str(world_size), diff --git a/tests/test_vllm1_fast_llm_broadcast.py b/tests/test_vllm1_fast_llm_broadcast.py index 0f4cb037..f7cc410b 100644 --- a/tests/test_vllm1_fast_llm_broadcast.py +++ b/tests/test_vllm1_fast_llm_broadcast.py @@ -22,6 +22,7 @@ run_generation_loop_multi, analyze_and_verify_pattern, analyze_and_verify_pattern_multi, + analyze_and_verify_transitions, start_vllm_server, start_trainer_process, ) @@ -356,6 +357,103 @@ async def test_server_fast_llm_broadcast_pattern( timeout=2400, ) + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif( + torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs" + ) + async def test_fast_llm_server_catch_transitions( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + fast_llm_trainer_helper, + redis_server, + temp_dir, + ): + """Diagnostic test: catch garbage generations during Fast-LLM weight broadcasts. + + The trainer runs a slow initial cycle (perturbed → original, 5 s each) + to firmly establish text_A and text_B, then fires N rapid back-to-back + broadcast cycles (perturbed → original) with no inter-broadcast delay. + The generation loop runs with generation_interval=0.0 to maximise the + chance of hitting a mid-broadcast state. + + Assertions: + 1. The A→B→A→B pattern is still detected (broadcasts actually worked). + 2. At least one transition/garbage phase was captured. + + Topology: 1 vLLM server on GPU 0, trainer on GPU 1 (world_size=2). + """ + print("\n" + "=" * 60) + print("Starting Fast-LLM transition-capture test (TP=1, 1 actor, 2 GPUs)") + print("=" * 60) + + redis_host, redis_port = redis_server + server_url = "http://127.0.0.1:8000" + + server_proc, _, _ = start_vllm_server( + model_name=model_name, + server_port=8000, + distributed_init_method=distributed_init_method, + stream_process_output_fn=stream_process_output, + extra_args=[ + "--weight-update-mode", "fast-llm", + "--redis-host", redis_host, + "--redis-port", str(redis_port), + ], + gpu_ids="0", + actor_llm_idx=0, + world_size=2, + tensor_parallel_size=1, + ) + + await asyncio.sleep(1) + + trainer_proc, _, _ = start_trainer_process( + trainer_helper_path=fast_llm_trainer_helper, + distributed_init_method=distributed_init_method, + model_name=model_name, + server_urls=[server_url], + stream_process_output_fn=stream_process_output, + extra_args=[ + "--redis-host", redis_host, + "--redis-port", str(redis_port), + "--n-cycles", "6", + ], + gpu_id="1", + world_size=2, + ) + + try: + await wait_for_server_ready(server_url, server_proc, trainer_proc) + + generations = await run_generation_loop( + server_url=server_url, + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + generation_interval=0.0, + ) + + print("[Main] Waiting for trainer to finish...") + for _ in range(30): + if trainer_proc.poll() is not None: + break + await asyncio.sleep(1) + + analyze_and_verify_transitions(generations, n_cycles=6) + print("\n✓ Fast-LLM transition-capture test PASSED") + + finally: + print("[Main] Cleaning up processes...") + if server_proc: + kill_process_tree(server_proc.pid) + if trainer_proc: + kill_process_tree(trainer_proc.pid) + class TestFastLLMServerTP2: """Test Fast-LLM weight broadcast with tensor-parallel (TP=2) — needs 3 GPUs.""" diff --git a/tests/test_vllm1_integration.py b/tests/test_vllm1_integration.py index 2f354686..6c2d68f0 100644 --- a/tests/test_vllm1_integration.py +++ b/tests/test_vllm1_integration.py @@ -22,6 +22,7 @@ run_generation_loop_multi, analyze_and_verify_pattern, analyze_and_verify_pattern_multi, + analyze_and_verify_transitions, start_vllm_server, start_trainer_process, ) @@ -1012,6 +1013,91 @@ async def test_server_weight_update_pattern( timeout=2400, ) + @pytest.mark.timeout(2400) + @pytest.mark.asyncio + @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs") + async def test_server_weight_update_catch_transitions( + self, + model_name, + simple_prompt, + generation_config, + distributed_init_method, + distributed_trainer_helper, + temp_dir, + ): + """Diagnostic test: catch garbage generations produced during NCCL weight broadcasts. + + The trainer runs a slow initial cycle (perturbed → original, 5 s each) + to firmly establish text_A and text_B, then fires N rapid back-to-back + broadcast cycles (perturbed → original) with no inter-broadcast delay. + The generation loop runs with generation_interval=0.0 (back-to-back + requests) to maximise the chance of hitting a mid-broadcast state. + + Assertions: + 1. The A→B→A→B pattern is still detected (broadcasts actually worked). + 2. At least one transition/garbage phase was captured. + + Topology: 1 vLLM server on GPU 0, trainer on GPU 1 (world_size=2). + """ + print("\n" + "=" * 60) + print("Starting transition-capture test (TP=1, 1 actor, 2 GPUs)") + print("=" * 60) + + server_url = "http://127.0.0.1:8000" + + server_proc, _, _ = start_vllm_server( + model_name=model_name, + server_port=8000, + distributed_init_method=distributed_init_method, + stream_process_output_fn=stream_process_output, + gpu_ids="0", + actor_llm_idx=0, + world_size=2, + tensor_parallel_size=1, + ) + + await asyncio.sleep(1) + + trainer_proc, _, _ = start_trainer_process( + trainer_helper_path=distributed_trainer_helper, + distributed_init_method=distributed_init_method, + model_name=model_name, + server_urls=[server_url], + stream_process_output_fn=stream_process_output, + extra_args=["--n-cycles", "6"], + gpu_id="1", + world_size=2, + command="rapid_broadcast_cycles", + ) + + try: + await wait_for_server_ready(server_url, server_proc, trainer_proc) + + generations = await run_generation_loop( + server_url=server_url, + model_name=model_name, + simple_prompt=simple_prompt, + generation_config=generation_config, + trainer_proc=trainer_proc, + generation_interval=0.0, + ) + + print("[Main] Waiting for trainer to finish...") + for _ in range(30): + if trainer_proc.poll() is not None: + break + await asyncio.sleep(1) + + analyze_and_verify_transitions(generations, n_cycles=6) + print("\n✓ Transition-capture test PASSED") + + finally: + print("[Main] Cleaning up processes...") + if server_proc: + kill_process_tree(server_proc.pid) + if trainer_proc: + kill_process_tree(trainer_proc.pid) + class TestWeightUpdateTP2: """Test weight updates with tensor-parallel (TP=2) vLLM — needs 3 GPUs.""" From f4cc7a44d6f8dfa27bc526a984ce4865cff52d00 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 2 Mar 2026 08:26:02 +0000 Subject: [PATCH 24/85] note update --- pipelinerl/vllm1.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index a40777fb..29f1a013 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -590,8 +590,8 @@ async def _training_finished(background_tasks: BackgroundTasks): sock.close() - # TODO: proper cleanup - # dist.destroy_process_group(actor_update_group) + # NOTE: weight-broadcast process group teardown must be coordinated with the trainer — + # the trainer sends training_finished, then the engine manager destroys its side here. def run_llm(): From f78be640a7fc429a5d4d3b4579753420cac95c58 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 2 Mar 2026 14:09:01 +0000 Subject: [PATCH 25/85] fix no weight bradcast case with fast-llm --- conf/base.yaml | 3 +++ conf/math.yaml | 2 ++ pipelinerl/actor.py | 4 ++-- pipelinerl/launch.py | 20 ++++++++++++-------- pipelinerl/preprocess.py | 21 ++++++++++++++++----- pipelinerl/state.py | 37 ++++++++++++++++++++++++++++++++++--- pipelinerl/streams.py | 31 +++++++++++++++++++++---------- 7 files changed, 90 insertions(+), 28 deletions(-) diff --git a/conf/base.yaml b/conf/base.yaml index 10b739d4..e5744391 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -124,6 +124,9 @@ debug: # 2. Implementing the vLLM weight receiver to accept broadcasted weights # 3. Setting use_fast_llm=false once NCCL is working (or removing this flag entirely) use_fast_llm: false +# Whether the trainer broadcasts updated weights to vLLM after each training step. +# Must match whether the broadcast block is configured in qwen25_05B-instruct.yaml. +weight_broadcast: true me: # Which job is this one? This will be autopopulated diff --git a/conf/math.yaml b/conf/math.yaml index 8ec5684d..f03658bb 100644 --- a/conf/math.yaml +++ b/conf/math.yaml @@ -4,6 +4,7 @@ defaults: # Enable Fast-LLM integration (disables vLLM weight updates until NCCL broadcast is implemented) use_fast_llm: true +weight_broadcast: false # broadcast block commented out in qwen25_05B-instruct.yaml actor: rollout_policy: pipelinerl.domains.math.generate_math_rollout @@ -18,6 +19,7 @@ finetune: seq_length: 18000 vllm_config: + use_v1: true vllm_kwargs: max_model_len: 18000 diff --git a/pipelinerl/actor.py b/pipelinerl/actor.py index 3e7a2c92..eb4b9095 100644 --- a/pipelinerl/actor.py +++ b/pipelinerl/actor.py @@ -261,7 +261,7 @@ def rollout_maker_entrypoint( llms: list[TrainableLLM], scheduler_name: str, ): - trainer_state = TrainerState(Path(cfg.output_dir), use_fast_llm=cfg.use_fast_llm) + trainer_state = TrainerState(Path(cfg.output_dir), use_fast_llm=cfg.use_fast_llm, weight_broadcast=cfg.weight_broadcast) if cfg.debug.mode: trainer_state.propagated_weight_version = 0 else: @@ -641,7 +641,7 @@ def run_actor_loop(cfg: DictConfig): wait_for_inference_servers(llm_urls) wait_for_environments(cfg) - trainer_state = TrainerState(exp_path, use_fast_llm=cfg.use_fast_llm) + trainer_state = TrainerState(exp_path, use_fast_llm=cfg.use_fast_llm, weight_broadcast=cfg.weight_broadcast) if cfg.debug.mode: trainer_state.debug_mode_init() else: diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index cc654893..a14f01e9 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -209,16 +209,16 @@ def run_actor_llm( cmd.extend(_get_quantization_args(cfg)) # add vLLM kwargs as separate arguments + _vllm1_unsupported = {"num-scheduler-steps"} if cfg.vllm_config.vllm_kwargs: for k, v in cfg.vllm_config.vllm_kwargs.items(): + if cfg.vllm_config.use_v1 and k in _vllm1_unsupported: + continue cmd.append(f"--{k}") if v not in [None, ""]: cmd.append(str(v)) - # Disable weight updates in debug mode or when using Fast-LLM (no NCCL group yet) - # TODO(fast-llm): Remove the use_fast_llm check once NCCL weight broadcast is implemented. - # When Fast-LLM broadcasts weights via NCCL, vLLM should join the group and receive updates. - if cfg.debug.mode or cfg.use_fast_llm: + if cfg.debug.mode or not cfg.weight_broadcast: cmd.append("--disable-weight-updates") gpu_str = ",".join([str(gpu) for gpu in gpus]) @@ -329,7 +329,11 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: env = dict(os.environ) env["PYTHONHASHSEED"] = "42" env["CUDA_VISIBLE_DEVICES"] = ",".join(str(gpu) for gpu in gpus) - proc = _popen(cmd, env=env) + os.makedirs(save_dir, exist_ok=True) + log_file_path = save_dir / "stdout.log" + err_file_path = save_dir / "stderr.log" + with open(log_file_path, "a") as log_file, open(err_file_path, "a") as err_file: + proc = _popen(cmd, env=env, stdout=log_file, stderr=err_file) if proc is not None: yield LaunchedProcess(kind="finetune", handle=proc) @@ -516,9 +520,9 @@ def is_inference_process(proc: LaunchedProcess) -> bool: return proc.kind in {"actor_llm", "preprocessor_llm"} -def watch_processes_running(exp_path: Path, processes: List[LaunchedProcess], debug_mode: bool = False, use_fast_llm: bool = False): +def watch_processes_running(exp_path: Path, processes: List[LaunchedProcess], debug_mode: bool = False, use_fast_llm: bool = False, weight_broadcast: bool = True): if not debug_mode: - trainer_state = TrainerState(exp_path, use_fast_llm=use_fast_llm) + trainer_state = TrainerState(exp_path, use_fast_llm=use_fast_llm, weight_broadcast=weight_broadcast) trainer_state.start_listening() else: trainer_state = None @@ -729,7 +733,7 @@ def main(cfg: DictConfig): if os.environ.get("DRY_RUN", "0") == "1": assert not processes return - watch_processes_running(exp_dir, processes, bool(cfg.debug.mode), cfg.use_fast_llm) + watch_processes_running(exp_dir, processes, bool(cfg.debug.mode), cfg.use_fast_llm, cfg.weight_broadcast) if __name__ == "__main__": diff --git a/pipelinerl/preprocess.py b/pipelinerl/preprocess.py index 3f475f89..a72868a8 100644 --- a/pipelinerl/preprocess.py +++ b/pipelinerl/preprocess.py @@ -365,7 +365,6 @@ def convert_to_fast_llm_format(entry: dict) -> dict: result = { "tokens": tokens, - "tokens_dtype": "int32", } # Convert labels to loss_masking_spans if present @@ -436,13 +435,16 @@ def run_preprocessing_loop( # For Fast-LLM: use SingleStreamSpec with shared=True (uses orjson serialization) # For standard PipelineRL: use StreamRangeSpec with partitions per GPU if cfg.use_fast_llm: + from fast_llm.data.dataset.config import REDIS_DATA_STREAM as _FAST_LLM_DATA_STREAM + fast_llm_stream_name = _FAST_LLM_DATA_STREAM output_stream = SingleStreamSpec( exp_path=exp_root_dir, topic=cfg.preprocess.output, - partition=0, # Single stream for Fast-LLM + partition=0, ) use_shared_stream = True else: + fast_llm_stream_name = None output_stream = StreamRangeSpec( exp_path=exp_root_dir, topic=cfg.preprocess.output, @@ -468,7 +470,7 @@ def run_preprocessing_loop( dataset_loader_thread.start() # Initialize TrainerState - trainer_state = TrainerState(exp_root_dir, use_fast_llm=cfg.use_fast_llm) + trainer_state = TrainerState(exp_root_dir, use_fast_llm=cfg.use_fast_llm, weight_broadcast=cfg.weight_broadcast) if cfg.debug.mode == "preprocessor": logger.info("Debug mode: preprocessor") trainer_state.debug_mode_init() @@ -533,7 +535,7 @@ def run_preprocessing_loop( # Per-trainer sample tracking (similar to finetune_loop.py) total_filtered_out = 0 # Track total filtered samples across all batches - with write_to_streams(output_stream, shared=use_shared_stream) as data_writer, write_to_streams(stats_streams) as stats_writer: + with write_to_streams(output_stream, shared=use_shared_stream, stream_name_override=fast_llm_stream_name, pipelinerl_metadata=not cfg.use_fast_llm) as data_writer, write_to_streams(stats_streams) as stats_writer: with SharedMemoryManager() as smm: # Create shared memory queues without the manager parameter input_queue = SharedMemoryQueue(smm, cfg.preprocess.input_queue_size, cfg.preprocess.shared_memory_entry_size) @@ -566,6 +568,7 @@ def run_preprocessing_loop( fetching_took = 0 writing_took = 0 num_filtered_out = 0 + last_backpressure_log = 0.0 while True: if ( trainer_state.samples_processed is not None @@ -638,6 +641,13 @@ def run_preprocessing_loop( assert isinstance(trainer_state.samples_processed, int) if published_samples - trainer_state.samples_processed > max_unconsumed_samples: # wait for the finetune loop to finish processing data + now = time.time() + if now - last_backpressure_log >= 10.0: + last_backpressure_log = now + logger.info( + f"Back-pressure: published={published_samples} consumed={trainer_state.samples_processed}" + f" unconsumed={published_samples - trainer_state.samples_processed} > max={max_unconsumed_samples}, waiting" + ) continue batch_done = False @@ -746,7 +756,8 @@ def run_preprocessing_loop( logger.info( f"Processed {processed_samples} samples (filtered out {num_filtered_out}) in {processing_took:.3f}s" f" (fetching took {fetching_took:.3f} and writing took {writing_took:.3f})" - f" and wrote to {output_stream}, total {published_samples} samples so far," + f" and wrote to {output_stream}, total {published_samples} samples so far" + f" (trainer consumed {trainer_state.samples_processed}, unconsumed {published_samples - trainer_state.samples_processed})," f" {samples_in_output_queue} samples in output queue, max output queue entry size {output_queue.max_actual_entry_size()} bytes" ) start_processing = time.time() diff --git a/pipelinerl/state.py b/pipelinerl/state.py index d858fa2b..445b14e2 100644 --- a/pipelinerl/state.py +++ b/pipelinerl/state.py @@ -21,11 +21,12 @@ class TrainerState: - def __init__(self, exp_path: Path, use_fast_llm: bool = False): + def __init__(self, exp_path: Path, use_fast_llm: bool = False, weight_broadcast: bool = True): self.exp_path = exp_path self.use_fast_llm = use_fast_llm - self.propagated_weight_version: int | None = None - self.samples_processed: int | None = None + self.weight_broadcast = weight_broadcast + self.propagated_weight_version: int | None = None if weight_broadcast else 0 + self.samples_processed: int | None = None if weight_broadcast else 0 self.training_done: bool = False self._training_done_event = threading.Event() @@ -66,6 +67,8 @@ def _start_listening_fast_llm(self): import redis from pipelinerl.streams import RedisConfig, _backend, connect_to_redis + from fast_llm.data.dataset.config import REDIS_DATA_STREAM, REDIS_GROUP_NAME + # Fast-LLM event stream config (must match fast-llm config) stream_key = FAST_LLM_EVENTS_STREAM # "fast_llm_events" payload_key = b"event" # Fast-LLM uses "event" as payload key @@ -74,6 +77,8 @@ def listen(): assert isinstance(_backend, RedisConfig) r = connect_to_redis(_backend) last_id = "0-0" + last_lag_check = 0.0 + lag_check_interval = 5.0 # seconds logger.info(f"Listening for Fast-LLM events on Redis stream '{stream_key}'") @@ -81,6 +86,32 @@ def listen(): # Read from stream (blocking) result = r.xread({stream_key: last_id}, count=1, block=1000) + # Periodically compute samples_processed from consumer group lag + now = time.time() + if now - last_lag_check >= lag_check_interval: + last_lag_check = now + try: + stream_info = r.xinfo_stream(REDIS_DATA_STREAM) + total_len = stream_info.get("length", 0) + groups = r.xinfo_groups(REDIS_DATA_STREAM) + for group in groups: + gname = group.get("name", "") + if isinstance(gname, bytes): + gname = gname.decode() + if gname == REDIS_GROUP_NAME: + entries_read = group.get("entries-read") + if entries_read is None: + lag = group.get("lag", 0) or 0 + entries_read = total_len - lag + self.samples_processed = int(entries_read) + logger.info( + f"Fast-LLM lag check: stream_len={total_len} entries_read={entries_read} " + f"samples_processed={self.samples_processed}" + ) + break + except Exception as e: + logger.debug(f"Fast-LLM lag check failed (stream/group not yet created?): {e}") + if not result: continue diff --git a/pipelinerl/streams.py b/pipelinerl/streams.py index ebecc41b..d6f8f79d 100644 --- a/pipelinerl/streams.py +++ b/pipelinerl/streams.py @@ -202,14 +202,17 @@ def __init__( *, writer_id: str | None = None, maxlen: int = 1_000_000, + stream_name_override: str | None = None, + pipelinerl_metadata: bool = True, ): self.stream = stream assert isinstance(_backend, RedisConfig) self._redis = connect_to_redis(_backend) - self._stream_name = str(self.stream) + self._stream_name = stream_name_override if stream_name_override is not None else str(self.stream) self._counter_key = f"stream:{self._stream_name}:next_index" self._writer_id = str(writer_id) if writer_id is not None else None self._maxlen = maxlen + self._pipelinerl_metadata = pipelinerl_metadata if mode not in {"w", "a"}: raise ValueError(f"Invalid mode: {mode}. Only 'w' and 'a' are supported.") @@ -241,14 +244,17 @@ def write(self, data, partition: int | None = None): # Note: partition is ignored for shared streams - all data goes to a single stream # This is intentional for Fast-LLM integration where Fast-LLM handles its own sharding serialized = _serialize_with_orjson(data) - entry_index = self._redis.incr(self._counter_key) - record: dict[str, Any] = { - "index": str(entry_index), - "data": serialized, - "ts": f"{time.time():.6f}", - } - if self._writer_id is not None: - record["writer"] = self._writer_id + if self._pipelinerl_metadata: + entry_index = self._redis.incr(self._counter_key) + record: dict[str, Any] = { + "index": str(entry_index), + "data": serialized, + "ts": f"{time.time():.6f}", + } + if self._writer_id is not None: + record["writer"] = self._writer_id + else: + record = {"data": serialized} self._redis.xadd(self._stream_name, record, maxlen=self._maxlen, approximate=True) @@ -554,11 +560,16 @@ def write_to_streams( *, shared: bool = False, writer_id: str | None = None, + stream_name_override: str | None = None, + pipelinerl_metadata: bool = True, ) -> StreamWriter: """Append to the end of the stream. Set ``shared`` to True when multiple producers must append to the same Redis stream and ServiceNow/Fast-LLM will perform downstream sharding. + + ``stream_name_override`` bypasses the stream spec naming and writes directly + to the given Redis key. Only supported for shared Redis streams. """ raise_if_backend_not_set() if not isinstance(streams, (SingleStreamSpec, StreamRangeSpec)): @@ -566,7 +577,7 @@ def write_to_streams( if isinstance(_backend, RedisConfig): if isinstance(streams, SingleStreamSpec): if shared: - return RedisSharedStreamWriter(streams, mode, writer_id=writer_id) + return RedisSharedStreamWriter(streams, mode, writer_id=writer_id, stream_name_override=stream_name_override, pipelinerl_metadata=pipelinerl_metadata) return RedisStreamWriter(streams, mode) elif isinstance(streams, StreamRangeSpec): if shared: From 807f47c6810c8c3e387a4bac87f1cc48e192f7f7 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 2 Mar 2026 14:59:30 +0000 Subject: [PATCH 26/85] added data for grpo loss to send to fast-llm, added base fast-llm config for testing --- pipelinerl/preprocess.py | 47 ++++++++++++------------- qwen25_05B-instruct.yaml | 75 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 98 insertions(+), 24 deletions(-) create mode 100644 qwen25_05B-instruct.yaml diff --git a/pipelinerl/preprocess.py b/pipelinerl/preprocess.py index a72868a8..0cf9c03f 100644 --- a/pipelinerl/preprocess.py +++ b/pipelinerl/preprocess.py @@ -350,53 +350,52 @@ def write_micro_batch_slices( def convert_to_fast_llm_format(entry: dict) -> dict: """Convert a preprocessed sample entry to Fast-LLM streaming format. - Fast-LLM expects: - - tokens: list of token IDs - - tokens_dtype: string dtype (e.g., "int32") - - loss_masking_spans (optional): list of (start, end) tuples where loss IS computed + Fast-LLM RedisDocument fields: + - tokens: list of token IDs (full sequence: prompt + completion) + - loss_masking_spans: list of (start, end) spans where loss IS computed (completion only) + - advantage: scalar float (per-rollout GRPO advantage) + - old_log_probabilities: list of floats, full sequence length (zeros for prompt tokens) """ input_ids = entry["input_ids"] + tokens = input_ids.tolist() if hasattr(input_ids, "tolist") else list(input_ids) - # Convert to list if tensor - if hasattr(input_ids, "tolist"): - tokens = input_ids.tolist() - else: - tokens = list(input_ids) - - result = { - "tokens": tokens, - } + result: dict = {"tokens": tokens} - # Convert labels to loss_masking_spans if present - # In PipelineRL, labels=-100 means "don't compute loss" (padding/prompt) - # In Fast-LLM, loss_masking_spans are ranges where loss IS computed + # loss_masking_spans: contiguous spans where labels != -100 (completion tokens) if "labels" in entry: labels = entry["labels"] - if hasattr(labels, "tolist"): - labels = labels.tolist() - else: - labels = list(labels) + labels = labels.tolist() if hasattr(labels, "tolist") else list(labels) - # Find contiguous spans where labels != -100 (loss is computed) spans = [] in_span = False span_start = 0 for i, label in enumerate(labels): if label != -100 and not in_span: - # Start new span in_span = True span_start = i elif label == -100 and in_span: - # End current span spans.append((span_start, i)) in_span = False - # Close final span if still open if in_span: spans.append((span_start, len(labels))) if spans: result["loss_masking_spans"] = spans + # advantage: scalar per rollout (populate_rl_data stores a list of per-step scalars; + # for single-step tasks like math there is exactly one element) + if "advantages" in entry: + advantages = entry["advantages"] + if advantages: + result["advantage"] = float(advantages[0]) + + # old_log_probabilities: full sequence length, zeros for prompt tokens + # (prepare_rl_fields pads with zeros on the left to match len(input_ids)) + if "old_logprobs" in entry: + old_logprobs = entry["old_logprobs"] + old_logprobs = old_logprobs.tolist() if hasattr(old_logprobs, "tolist") else list(old_logprobs) + result["old_log_probabilities"] = [float(x) for x in old_logprobs] + return result diff --git a/qwen25_05B-instruct.yaml b/qwen25_05B-instruct.yaml new file mode 100644 index 00000000..9769af5d --- /dev/null +++ b/qwen25_05B-instruct.yaml @@ -0,0 +1,75 @@ +training: + num_workers: 0 + train_iters: 100_000 # Total number of optimizer steps (provided by pipelinerl) + # wandb: # Enable this block if we want to log to Weights & Biases + # project_name: ${job.project_name} + # group_name: ${job.project_version} + logs: + interval: 1 # Logging frequency in optimizer steps (one training iteration) + checkpoint: # How often to save checkpoints in native fast-llm format + interval: 1000 + export: # How often to export checkpoints in HF format + interval: 1000 + format: qwen2 # Export format (should match the format of the checkpoint used to start training) + +batch: + micro_batch_size: 1 # For RL, all rollouts are packed into a single sample + sequence_length: 8192 # Max rollout length accepted; also the training sample length + batch_size: 16 # Number of samples per optimizer step + truncate_documents: False # We do not want truncation for RL rollouts + #use_preference_spans: true + use_loss_masking_spans: true + use_grpo_data: True + +data: + sampling: + shuffle: disabled # Streaming dataset ignores shuffling + datasets: + training: + type: streaming # Type of dataset: Redis-backed streaming dataset + host: localhost # Redis server host. Must be provided by pipelinerl + port: 11000 # Redis server port. Must be provided by pipelinerl + +pretrained: + format: qwen2 # Base model format (provided by pipelinerl) + path: /home/toolkit/Qwen2.5-0.5B-Instruct # Base model checkpoint path (provided by pipelinerl) + model_weights: yes # Indicates that we load pretrained weights and start training from the base model + +model: + base_model: + head: + losses: + grpo: + type: grpo + epsilon_low: 0.2 + epsilon_high: 0.2 + multi_stage: + zero_stage: 2 # Note: choosing appropriate Zero, TP, PP, or SP settings is important + # for fitting larger models and optimizing speed/memory; this is usually + # tuned per training setup + distributed: + compute_dtype: bf16 + tensor_parallel: 1 # TP=2 used to test broadcasting; not needed for a small model + pipeline_parallel: 1 + sequence_data_parallel: 1 + +run: + experiment_dir: "/home/toolkit/test/denis/Qwen2.5-0.5B-Instruct" # Provided by pipelinerl + + +# callbacks: # Events are specified via callbacks; the only supported one so far is Redis-based +# streaming: # User-defined callback name (define only one) +# type: streaming # Callback handler type; supports training start, step end, and training end events via Redis, +# # as well as weight broadcasts +# host: localhost # Redis server host +# port: 11000 # Redis server port + # broadcast: # Uncomment to enable NCCL weight broadcast to vLLM + # backend: nccl # Backend used for weight broadcasts + # external_world_size: 1 # Number of external clients (excluding Fast-LLM itself); + # # note that Fast-LLM currently assigns itself rank 0 + # host: localhost # Broadcast rendezvous host + # port: 26901 # Broadcast rendezvous port + # export: # Format of the broadcasted weights + # format: llama # Export format + # model_weights: true # Export model weights + # optimizer_state: false # Do not export optimizer state From 407be1dd62a3f9e923c364ace64f4aefe14ea473 Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 3 Mar 2026 17:12:30 +0000 Subject: [PATCH 27/85] fast-llm weights broadcast integration --- conf/base.yaml | 14 ++++------ conf/math.yaml | 2 +- pipelinerl/launch.py | 23 +++++++++++++-- pipelinerl/state.py | 12 ++++---- pipelinerl/vllm1.py | 48 +++++++++++++++++++------------- qwen25_05B-instruct.yaml | 30 ++++++++++---------- tests/fast_llm_trainer_helper.py | 32 +++++++++++++++------ tests/trainer_test_utils.py | 6 ++-- 8 files changed, 101 insertions(+), 66 deletions(-) diff --git a/conf/base.yaml b/conf/base.yaml index e5744391..9d77fd2e 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -85,6 +85,8 @@ world: actor_group_port: 9000 environment_start_port: 7777 + +fast_llm_broadcast_port: 26901 # NCCL rendezvous port for fast-llm weight broadcast (separate from actor_group_port) # this will be autocreated based on the config jobs: [] @@ -115,17 +117,11 @@ debug: place_inference_workers: true use_existing_llms: false -# Fast-LLM integration: when true, disables vLLM weight updates (no NCCL group) -# and uses Fast-LLM trainer events for state synchronization. -# TODO(fast-llm): Once data flows from actors -> Redis -> Fast-LLM training loop, -# enable NCCL weight broadcast by setting events.weights_broadcast.enabled=true in -# the Fast-LLM config (qwen25_05B-instruct.yaml). This requires: -# 1. Configuring NCCL rendezvous in Fast-LLM (rdvz_master_address, port, world_size, rank) -# 2. Implementing the vLLM weight receiver to accept broadcasted weights -# 3. Setting use_fast_llm=false once NCCL is working (or removing this flag entirely) +# Fast-LLM integration: when true, fast-llm is used as the trainer. +# Data flows actors -> Redis (fast_llm_streaming) -> fast-llm training loop. +# Weight updates are broadcast via NCCL using fast-llm's streaming callback. use_fast_llm: false # Whether the trainer broadcasts updated weights to vLLM after each training step. -# Must match whether the broadcast block is configured in qwen25_05B-instruct.yaml. weight_broadcast: true me: diff --git a/conf/math.yaml b/conf/math.yaml index f03658bb..c371cbbf 100644 --- a/conf/math.yaml +++ b/conf/math.yaml @@ -4,7 +4,7 @@ defaults: # Enable Fast-LLM integration (disables vLLM weight updates until NCCL broadcast is implemented) use_fast_llm: true -weight_broadcast: false # broadcast block commented out in qwen25_05B-instruct.yaml +weight_broadcast: true actor: rollout_policy: pipelinerl.domains.math.generate_math_rollout diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index a14f01e9..5a026f27 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -186,6 +186,8 @@ def run_actor_llm( if cfg.vllm_config.use_v1 else "pipelinerl.entrypoints.run_vllm0" ) + # fast-llm weight broadcast uses its own rendezvous port; HTTP mode uses actor_group_port + broadcast_port = cfg.fast_llm_broadcast_port if cfg.use_fast_llm else cfg.world.actor_group_port cmd = [ "python", "-m", @@ -201,7 +203,7 @@ def run_actor_llm( "--actor-llm-idx", str(actor_llm_idx), "--weight-update-group-init-method", - f"tcp://{world_map.master_addr}:{cfg.world.actor_group_port}", + f"tcp://{world_map.master_addr}:{broadcast_port}", "--weight-update-group-world-size", str(world_map.weight_update_group_size), ] @@ -221,6 +223,13 @@ def run_actor_llm( if cfg.debug.mode or not cfg.weight_broadcast: cmd.append("--disable-weight-updates") + if cfg.use_fast_llm: + cmd += [ + "--weight-update-mode", "fast-llm", + "--redis-host", cfg.streams.host, + "--redis-port", str(cfg.streams.port), + ] + gpu_str = ",".join([str(gpu) for gpu in gpus]) logger.info(f"Running actor_llm with command: {' '.join(cmd)} on gpus: {gpu_str}") save_command(log_dir, cmd) @@ -321,7 +330,17 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: "gpt", "--config", str(config_path), - f"run.experiment_dir={save_dir}" + f"run.experiment_dir={save_dir}", + ] + + # Override fast-llm's callback config to match actual topology. + # The yaml has placeholder values; these are the real ones from the world map. + cmd += [ + f"callbacks.streaming.host={cfg.streams.host}", + f"callbacks.streaming.port={cfg.streams.port}", + f"callbacks.streaming.broadcast.host={world_map.master_addr}", + f"callbacks.streaming.broadcast.port={cfg.fast_llm_broadcast_port}", + f"callbacks.streaming.broadcast.external_world_size={world_map.weight_update_group_size - 1}", ] logger.info(f"Running finetune with command: {' '.join(cmd)}") diff --git a/pipelinerl/state.py b/pipelinerl/state.py index 445b14e2..6b207073 100644 --- a/pipelinerl/state.py +++ b/pipelinerl/state.py @@ -80,6 +80,10 @@ def listen(): last_lag_check = 0.0 lag_check_interval = 5.0 # seconds + # Initialize to 0 so wait_for_processed_samples() doesn't block at startup. + # The lag check below will update this once the data stream/consumer group exists. + self.samples_processed = 0 + logger.info(f"Listening for Fast-LLM events on Redis stream '{stream_key}'") while True: @@ -133,13 +137,7 @@ def listen(): event_type = event.get("type") step = event.get("step") - if event_type == "initial_weights_step": - logger.info(f"Received initial_weights_step event: step={step}") - self.propagated_weight_version = step - # Initial step also sets samples_processed to 0 - if self.samples_processed is None: - self.samples_processed = 0 - elif event_type == "weights_ready": + if event_type == "weights_ready": logger.info(f"Received weights_ready event: step={step}") self.propagated_weight_version = step elif event_type == "training_finished": diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 29f1a013..ffb0b05d 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -81,6 +81,7 @@ def init_actor_update_group( actor_ngpus: int, weight_update_group_init_method: str, weight_update_group_world_size: int, + weight_update_mode: str = "http", ): self.pg_rank = 1 + actor_idx * actor_ngpus + self.rank # log all you know @@ -91,10 +92,16 @@ def init_actor_update_group( ) logger.info( prefix - + f"Weight update group init method: {weight_update_group_init_method}, world size: {weight_update_group_world_size}" + + f"Weight update group init method: {weight_update_group_init_method}, world size: {weight_update_group_world_size}, mode: {weight_update_mode}" ) + if weight_update_mode == 'http': + group_name = "actor" + else: + from fast_llm.engine.training.streaming import WEIGHTS_BROADCAST_PG_NAME + + group_name = WEIGHTS_BROADCAST_PG_NAME self.process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", + group_name=group_name, backend="nccl", init_method=weight_update_group_init_method, rank=self.pg_rank, @@ -189,9 +196,7 @@ def receive_weight_update_fast_llm(self: LikeWorker): # Receive metadata meta = [None] logger.debug(f"[Worker rank={self.rank}] Waiting for metadata broadcast...") - torch.distributed.broadcast_object_list( - meta, group=self.process_group, src=0 - ) + torch.distributed.broadcast_object_list(meta, src=0, group=self.process_group) logger.debug(f"[Worker rank={self.rank}] Received metadata: {meta}") # Check for end signal @@ -202,25 +207,29 @@ def receive_weight_update_fast_llm(self: LikeWorker): break # Parse metadata: (shard_name, layer_name, shape, dtype) + # shard_name is a category label ("weights", "grads", etc.), not part of the HF param name shard_name, layer_name, shape, dtype = meta[0] - param_name = f"{shard_name}.{layer_name}" if shard_name else layer_name - param_count += 1 + param_name = layer_name + # Convert dtype to torch dtype + target_dtype = string_to_dtype(str(dtype)) + + # Allocate buffer and receive tensor (must happen for every broadcast to stay in sync) + buffer = torch.empty(tuple(shape), dtype=target_dtype, device=self.device) + torch.distributed.broadcast(buffer, src=0, group=self.process_group) + + # Only load weight shards (skip grads, optimizer state, etc.) + if shard_name != "weights": + continue + + param_count += 1 logger.debug( f"[{param_count}] Receiving: {param_name}, shape={shape}, dtype={dtype}" ) - # Convert dtype to torch dtype - target_dtype = string_to_dtype(str(dtype)) if target_dtype not in expected_dtypes: logger.warning(f"Unexpected dtype for {param_name}: {dtype}") - # Allocate buffer - buffer = torch.empty(tuple(shape), dtype=target_dtype, device=self.device) - - # Receive tensor - logger.debug(f"[{param_count}] Broadcasting tensor for {param_name}...") - torch.distributed.broadcast(buffer, src=0, group=self.process_group) logger.debug(f"[{param_count}] Received tensor for {param_name}") # Load weights @@ -243,7 +252,7 @@ def receive_weight_update_fast_llm(self: LikeWorker): f"Unexpected number of parameters loaded for {param_name}" ) except Exception as e: - logger.error(f"ERROR loading {param_name}: {e}") + logger.error(f"ERROR loading {param_name}: {e!r}", exc_info=True) raise if param_count % 10 == 0: @@ -275,6 +284,7 @@ async def init_actor_update_group(self): torch.cuda.device_count(), self.args.weight_update_group_init_method, self.args.weight_update_group_world_size, + self.args.weight_update_mode, ), ) @@ -484,7 +494,7 @@ async def create_engine( await manager.init_actor_update_group() # Initialize Fast-LLM mode if enabled - if hasattr(args, 'weight_update_mode') and args.weight_update_mode == "fast-llm": + if args.weight_update_mode == "fast-llm": await manager.init_fast_llm_receiver() await manager.start_fast_llm_monitoring() logger.info("Fast-LLM weight update mode enabled") @@ -493,7 +503,7 @@ async def create_engine( finally: if not args.disable_weight_updates: # Stop Fast-LLM monitoring if enabled - if hasattr(args, 'weight_update_mode') and args.weight_update_mode == "fast-llm": + if args.weight_update_mode == "fast-llm": await manager.stop_fast_llm_monitoring() if not await manager.is_actor_update_group_destroyed(): @@ -553,7 +563,7 @@ def signal_handler(*_) -> None: app = build_app(args) # Register HTTP endpoint only if using HTTP mode - if not hasattr(args, 'weight_update_mode') or args.weight_update_mode == "http": + if args.weight_update_mode == "http": @app.post("/receive_weight_update") async def _receive_weight_update(request: WeightUpdateRequest): await manager.receive_weight_update(request) diff --git a/qwen25_05B-instruct.yaml b/qwen25_05B-instruct.yaml index 9769af5d..319d5ff5 100644 --- a/qwen25_05B-instruct.yaml +++ b/qwen25_05B-instruct.yaml @@ -57,19 +57,17 @@ run: experiment_dir: "/home/toolkit/test/denis/Qwen2.5-0.5B-Instruct" # Provided by pipelinerl -# callbacks: # Events are specified via callbacks; the only supported one so far is Redis-based -# streaming: # User-defined callback name (define only one) -# type: streaming # Callback handler type; supports training start, step end, and training end events via Redis, -# # as well as weight broadcasts -# host: localhost # Redis server host -# port: 11000 # Redis server port - # broadcast: # Uncomment to enable NCCL weight broadcast to vLLM - # backend: nccl # Backend used for weight broadcasts - # external_world_size: 1 # Number of external clients (excluding Fast-LLM itself); - # # note that Fast-LLM currently assigns itself rank 0 - # host: localhost # Broadcast rendezvous host - # port: 26901 # Broadcast rendezvous port - # export: # Format of the broadcasted weights - # format: llama # Export format - # model_weights: true # Export model weights - # optimizer_state: false # Do not export optimizer state +callbacks: + streaming: + type: streaming + host: localhost # overridden by launch.py via CLI: callbacks.streaming.host=... + port: 11000 # overridden by launch.py via CLI: callbacks.streaming.port=... + broadcast: + backend: nccl + external_world_size: 1 # overridden by launch.py + host: localhost # overridden by launch.py + port: 26901 # overridden by launch.py + export: + format: qwen2 + model_weights: true + optimizer_state: false diff --git a/tests/fast_llm_trainer_helper.py b/tests/fast_llm_trainer_helper.py index 79bd9a75..3c950cd1 100644 --- a/tests/fast_llm_trainer_helper.py +++ b/tests/fast_llm_trainer_helper.py @@ -7,8 +7,8 @@ from trainer_test_utils import ( _load_state_dict, _create_perturbed_state_dict, - _init_actor_process_group, _wait_for_servers_ready, + _init_actor_process_group, ) @@ -41,8 +41,16 @@ def timed_broadcast_fast_llm( import redis import orjson - # Initialize process group - process_group = _init_actor_process_group(init_method, rank=0, world_size=world_size) + from fast_llm.engine.training.streaming import WEIGHTS_BROADCAST_PG_NAME + + print(f"[Trainer] Initializing process group as rank 0 (world_size={world_size})") + process_group = _init_actor_process_group( + init_method=init_method, + rank=0, + world_size=world_size, + group_name=WEIGHTS_BROADCAST_PG_NAME, + ) + print("[Trainer] Process group initialized") # Connect to Redis print(f"[Trainer] Connecting to Redis at {redis_host}:{redis_port}") @@ -79,11 +87,8 @@ def broadcast_weights_fast_llm(state_dict, step): if tensor.device.type != "cuda": tensor = tensor.cuda(0) - shard_name = "" - layer_name = name - # Broadcast metadata - meta = [(shard_name, layer_name, list(tensor.shape), str(tensor.dtype))] + meta = [("weights", name, list(tensor.shape), str(tensor.dtype))] dist.broadcast_object_list(meta, src=0, group=process_group) # Broadcast tensor @@ -154,7 +159,16 @@ def rapid_broadcast_cycles_fast_llm( import redis as redis_lib import orjson - process_group = _init_actor_process_group(init_method, rank=0, world_size=world_size) + from fast_llm.engine.training.streaming import WEIGHTS_BROADCAST_PG_NAME + + print(f"[Trainer] Initializing process group as rank 0 (world_size={world_size})") + process_group = _init_actor_process_group( + init_method=init_method, + rank=0, + world_size=world_size, + group_name=WEIGHTS_BROADCAST_PG_NAME, + ) + print("[Trainer] Process group initialized") r = redis_lib.Redis(host=redis_host, port=redis_port) stream_key = "fast_llm_events" @@ -179,7 +193,7 @@ def broadcast_weights(state_dict, label): for name, tensor in state_dict.items(): if tensor.device.type != "cuda": tensor = tensor.cuda(0) - meta = [("", name, list(tensor.shape), str(tensor.dtype))] + meta = [("weights", name, list(tensor.shape), str(tensor.dtype))] dist.broadcast_object_list(meta, src=0, group=process_group) dist.broadcast(tensor, src=0, group=process_group) diff --git a/tests/trainer_test_utils.py b/tests/trainer_test_utils.py index 0ea3e62d..d6de57e5 100644 --- a/tests/trainer_test_utils.py +++ b/tests/trainer_test_utils.py @@ -70,13 +70,13 @@ def _create_perturbed_state_dict( return perturbed -def _init_actor_process_group(init_method: str, rank: int = 0, world_size: int = 2): +def _init_actor_process_group(init_method: str, rank: int = 0, world_size: int = 2, group_name: str = "actor"): """Initialize the actor NCCL process group and return it.""" import pipelinerl.torch_utils - print(f"[Trainer] Initializing process group as rank {rank}") + print(f"[Trainer] Initializing process group as rank {rank} (group_name={group_name!r})") process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", + group_name=group_name, backend="nccl", init_method=init_method, rank=rank, From b7e21099dceb5d87114a3ee2e49db24c53628217 Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 4 Mar 2026 14:01:06 +0000 Subject: [PATCH 28/85] removed duplicate option --- conf/base.yaml | 1 - pipelinerl/launch.py | 5 ++--- qwen25_05B-instruct.yaml | 2 +- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/conf/base.yaml b/conf/base.yaml index 9d77fd2e..7ebd0455 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -86,7 +86,6 @@ world: actor_group_port: 9000 environment_start_port: 7777 -fast_llm_broadcast_port: 26901 # NCCL rendezvous port for fast-llm weight broadcast (separate from actor_group_port) # this will be autocreated based on the config jobs: [] diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 5a026f27..3f9dd0af 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -186,8 +186,7 @@ def run_actor_llm( if cfg.vllm_config.use_v1 else "pipelinerl.entrypoints.run_vllm0" ) - # fast-llm weight broadcast uses its own rendezvous port; HTTP mode uses actor_group_port - broadcast_port = cfg.fast_llm_broadcast_port if cfg.use_fast_llm else cfg.world.actor_group_port + broadcast_port = cfg.world.actor_group_port cmd = [ "python", "-m", @@ -339,7 +338,7 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: f"callbacks.streaming.host={cfg.streams.host}", f"callbacks.streaming.port={cfg.streams.port}", f"callbacks.streaming.broadcast.host={world_map.master_addr}", - f"callbacks.streaming.broadcast.port={cfg.fast_llm_broadcast_port}", + f"callbacks.streaming.broadcast.port={cfg.world.actor_group_port}", f"callbacks.streaming.broadcast.external_world_size={world_map.weight_update_group_size - 1}", ] diff --git a/qwen25_05B-instruct.yaml b/qwen25_05B-instruct.yaml index 319d5ff5..8b1fc0e4 100644 --- a/qwen25_05B-instruct.yaml +++ b/qwen25_05B-instruct.yaml @@ -66,7 +66,7 @@ callbacks: backend: nccl external_world_size: 1 # overridden by launch.py host: localhost # overridden by launch.py - port: 26901 # overridden by launch.py + port: 9000 # overridden by launch.py (world.actor_group_port) export: format: qwen2 model_weights: true From ceed0bf42c23ddbae95b49bd4f5496c9cf1e4632 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 5 Mar 2026 16:02:27 +0000 Subject: [PATCH 29/85] added pass through of wandb params to fast-llm, bigger seq len in demo config --- pipelinerl/launch.py | 8 ++++++++ qwen25_05B-instruct.yaml | 7 +++---- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 3f9dd0af..5d8a45c4 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -334,13 +334,21 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: # Override fast-llm's callback config to match actual topology. # The yaml has placeholder values; these are the real ones from the world map. + root = cfg.wandb.wandb_workspace_root + save_dir_str = str(save_dir) + experiment_name = save_dir_str[len(root) + 1:] if root and save_dir_str.startswith(root + "/") else save_dir.name cmd += [ f"callbacks.streaming.host={cfg.streams.host}", f"callbacks.streaming.port={cfg.streams.port}", f"callbacks.streaming.broadcast.host={world_map.master_addr}", f"callbacks.streaming.broadcast.port={cfg.world.actor_group_port}", f"callbacks.streaming.broadcast.external_world_size={world_map.weight_update_group_size - 1}", + f"training.wandb.project_name={cfg.wandb.wandb_project_name}", + f"training.wandb.group_name={cfg.wandb.wandb_group}", + f"run.experiment_name={experiment_name}", ] + if cfg.wandb.wandb_entity_name: + cmd.append(f"training.wandb.entity_name={cfg.wandb.wandb_entity_name}") logger.info(f"Running finetune with command: {' '.join(cmd)}") save_command(exp_dir / "finetune", cmd) diff --git a/qwen25_05B-instruct.yaml b/qwen25_05B-instruct.yaml index 8b1fc0e4..07b45277 100644 --- a/qwen25_05B-instruct.yaml +++ b/qwen25_05B-instruct.yaml @@ -1,9 +1,8 @@ training: num_workers: 0 train_iters: 100_000 # Total number of optimizer steps (provided by pipelinerl) - # wandb: # Enable this block if we want to log to Weights & Biases - # project_name: ${job.project_name} - # group_name: ${job.project_version} + wandb: # overridden by launch.py (project_name, group_name, entity_name, run.experiment_name) + entity_name: null # null disables wandb; overridden at launch time when wandb_entity_name is set logs: interval: 1 # Logging frequency in optimizer steps (one training iteration) checkpoint: # How often to save checkpoints in native fast-llm format @@ -14,7 +13,7 @@ training: batch: micro_batch_size: 1 # For RL, all rollouts are packed into a single sample - sequence_length: 8192 # Max rollout length accepted; also the training sample length + sequence_length: 18000 # Max rollout length accepted; also the training sample length batch_size: 16 # Number of samples per optimizer step truncate_documents: False # We do not want truncation for RL rollouts #use_preference_spans: true From 6ecfa400843c5564bafb70fb921702634f0286da Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 6 Mar 2026 09:21:23 +0000 Subject: [PATCH 30/85] fix loss masking --- pipelinerl/preprocess.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pipelinerl/preprocess.py b/pipelinerl/preprocess.py index 0cf9c03f..0653a031 100644 --- a/pipelinerl/preprocess.py +++ b/pipelinerl/preprocess.py @@ -361,7 +361,8 @@ def convert_to_fast_llm_format(entry: dict) -> dict: result: dict = {"tokens": tokens} - # loss_masking_spans: contiguous spans where labels != -100 (completion tokens) + # loss_masking_spans: contiguous spans where label == -100 (prompt tokens to mask out). + # fast-llm sets labels to -100 at these positions, so only completion tokens contribute to loss. if "labels" in entry: labels = entry["labels"] labels = labels.tolist() if hasattr(labels, "tolist") else list(labels) @@ -370,10 +371,10 @@ def convert_to_fast_llm_format(entry: dict) -> dict: in_span = False span_start = 0 for i, label in enumerate(labels): - if label != -100 and not in_span: + if label == -100 and not in_span: in_span = True span_start = i - elif label == -100 and in_span: + elif label != -100 and in_span: spans.append((span_start, i)) in_span = False if in_span: From 152d4896d51ee457d7fe81a8a10982a7ed5b1c15 Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 6 Mar 2026 16:05:05 +0000 Subject: [PATCH 31/85] integrated fast-llm config and changed to start fast-llm with pytorch --- conf/base.yaml | 85 ++++++++++++++++++++++++++++++- conf/math.yaml | 5 +- pipelinerl/launch.py | 106 +++++++++++++++++++++++++-------------- qwen25_05B-instruct.yaml | 72 -------------------------- 4 files changed, 155 insertions(+), 113 deletions(-) delete mode 100644 qwen25_05B-instruct.yaml diff --git a/conf/base.yaml b/conf/base.yaml index 7ebd0455..95f6673b 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -104,7 +104,7 @@ fsdp: reduce_dtype: fp32 buffer_dtype: fp32 -output_dir: ??? +output_dir: null force_restart: false pop_old_data: true max_lag: null @@ -123,6 +123,89 @@ use_fast_llm: false # Whether the trainer broadcasts updated weights to vLLM after each training step. weight_broadcast: true +# Pure fast-llm config written as-is to a YAML file at launch time. +# Fields set to null are populated by the launcher at runtime (source noted in the comment) — do not modify them here. +# This section is only used when use_fast_llm: true. +fast_llm: + training: + num_workers: 0 + train_iters: 100000 # Total number of optimizer steps (provided by pipelinerl) + wandb: + entity_name: null # cfg.wandb.wandb_entity_name (null disables wandb) + project_name: null # cfg.wandb.wandb_project_name + group_name: null # cfg.wandb.wandb_group + logs: + interval: 1 # Logging frequency in optimizer steps + checkpoint: + interval: 1000 + export: + interval: 1000 + format: ${fast_llm_finetune.model_format} + + batch: + micro_batch_size: 1 # For RL, all rollouts are packed into a single sample + sequence_length: 18000 # Max rollout length accepted; also the training sample length + batch_size: 16 # Number of samples per optimizer step + truncate_documents: false # Do not truncate RL rollouts + use_loss_masking_spans: true + use_grpo_data: true + + data: + sampling: + shuffle: disabled # Streaming dataset ignores shuffling + datasets: + training: + type: streaming # Redis-backed streaming dataset + host: null # cfg.streams.host + port: null # cfg.streams.port + + pretrained: + format: ${fast_llm_finetune.model_format} + path: null # cfg.model_path + model_weights: true + + model: + base_model: + head: + losses: + grpo: + type: grpo + epsilon_low: 0.2 + epsilon_high: 0.2 + multi_stage: + zero_stage: 2 + distributed: + compute_dtype: bf16 + tensor_parallel: 1 + pipeline_parallel: 1 + sequence_data_parallel: 1 + + run: + experiment_dir: null # exp_dir/finetune + experiment_name: null # derived from exp_dir relative to cfg.wandb.wandb_workspace_root + + # callbacks section is written only when weight_broadcast: true (removed by launcher otherwise) + callbacks: + streaming: + type: streaming + host: null # cfg.streams.host + port: null # cfg.streams.port + broadcast: + backend: nccl + external_world_size: null # world_map.weight_update_group_size - 1 + host: null # world_map.master_addr + port: null # cfg.world.actor_group_port + export: + format: ${fast_llm_finetune.model_format} + model_weights: true + optimizer_state: false + +# Launcher-specific fast-llm settings (not passed to fast-llm itself). +fast_llm_finetune: + model_type: gpt # fast-llm model type argument: fast-llm train + model_format: qwen2 # pretrained/export format; interpolated into fast_llm config + torchrun_port: 29500 # master port for torchrun rendezvous + me: # Which job is this one? This will be autopopulated job_idx: null diff --git a/conf/math.yaml b/conf/math.yaml index c371cbbf..c2b4d665 100644 --- a/conf/math.yaml +++ b/conf/math.yaml @@ -2,10 +2,13 @@ defaults: - base - _self_ -# Enable Fast-LLM integration (disables vLLM weight updates until NCCL broadcast is implemented) use_fast_llm: true weight_broadcast: true +fast_llm: + batch: + sequence_length: 18000 + actor: rollout_policy: pipelinerl.domains.math.generate_math_rollout system_prompt: Please reason step by step, and put your final answer within \boxed{}. diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 5d8a45c4..1e60ef60 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -305,57 +305,63 @@ def run_environment(cfg: DictConfig, job: Job): def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: Path): save_dir = exp_dir / "finetune" + os.makedirs(save_dir, exist_ok=True) - # Get absolute path to config file - config_path = Path(__file__).parent.parent / "qwen25_05B-instruct.yaml" - - # TODO: make config or make everywhere without conda - use_conda = False - if use_conda: - cmd = [ - "conda", - "run", - "-n", - "fast-llm", - "--cwd", - str(config_path.parent), # Set working directory for fast-llm - ] - else: - cmd = [] + if not os.path.isdir(cfg.model_path): + raise ValueError( + f"fast-llm requires a local model path but got: {cfg.model_path!r}. " + "Download the model first and set model_path to its local directory." + ) - cmd += [ - "fast-llm", - "train", - "gpt", - "--config", - str(config_path), - f"run.experiment_dir={save_dir}", - ] + # Build fast-llm config, stripping callbacks when weight broadcast is disabled or in debug mode. + fast_llm_cfg = OmegaConf.to_container(cfg.fast_llm, resolve=True, throw_on_missing=False) + if not cfg.weight_broadcast or bool(cfg.debug.mode): + fast_llm_cfg.pop("callbacks", None) - # Override fast-llm's callback config to match actual topology. - # The yaml has placeholder values; these are the real ones from the world map. + # Derive experiment name for wandb from save_dir relative to workspace root. root = cfg.wandb.wandb_workspace_root save_dir_str = str(save_dir) experiment_name = save_dir_str[len(root) + 1:] if root and save_dir_str.startswith(root + "/") else save_dir.name - cmd += [ - f"callbacks.streaming.host={cfg.streams.host}", - f"callbacks.streaming.port={cfg.streams.port}", - f"callbacks.streaming.broadcast.host={world_map.master_addr}", - f"callbacks.streaming.broadcast.port={cfg.world.actor_group_port}", - f"callbacks.streaming.broadcast.external_world_size={world_map.weight_update_group_size - 1}", - f"training.wandb.project_name={cfg.wandb.wandb_project_name}", - f"training.wandb.group_name={cfg.wandb.wandb_group}", - f"run.experiment_name={experiment_name}", + + # Fill in all dynamic values so the saved config is fully functional. + fast_llm_cfg["pretrained"]["path"] = cfg.model_path + fast_llm_cfg["run"]["experiment_dir"] = str(save_dir) + fast_llm_cfg["run"]["experiment_name"] = experiment_name + fast_llm_cfg["data"]["datasets"]["training"]["host"] = cfg.streams.host + fast_llm_cfg["data"]["datasets"]["training"]["port"] = cfg.streams.port + fast_llm_cfg["training"]["wandb"]["entity_name"] = cfg.wandb.wandb_entity_name + fast_llm_cfg["training"]["wandb"]["project_name"] = cfg.wandb.wandb_project_name + fast_llm_cfg["training"]["wandb"]["group_name"] = cfg.wandb.wandb_group + if cfg.weight_broadcast and not bool(cfg.debug.mode): + fast_llm_cfg["callbacks"]["streaming"]["host"] = cfg.streams.host + fast_llm_cfg["callbacks"]["streaming"]["port"] = cfg.streams.port + fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["host"] = world_map.master_addr + fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["port"] = cfg.world.actor_group_port + fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["external_world_size"] = world_map.weight_update_group_size - 1 + + # Save fully populated config — fast-llm reads it directly with no further overrides. + config_path = save_dir / "fast_llm_config.yaml" + OmegaConf.save(OmegaConf.create(fast_llm_cfg), config_path) + + model_type = cfg.fast_llm_finetune.model_type + torchrun_port = cfg.fast_llm_finetune.torchrun_port + cmd = [ + "torchrun", + f"--nproc_per_node={len(gpus)}", + f"--master_port={torchrun_port}", + "--no_python", + "fast-llm", + "train", + model_type, + "--config", + str(config_path), ] - if cfg.wandb.wandb_entity_name: - cmd.append(f"training.wandb.entity_name={cfg.wandb.wandb_entity_name}") logger.info(f"Running finetune with command: {' '.join(cmd)}") - save_command(exp_dir / "finetune", cmd) + save_command(save_dir, cmd) env = dict(os.environ) env["PYTHONHASHSEED"] = "42" env["CUDA_VISIBLE_DEVICES"] = ",".join(str(gpu) for gpu in gpus) - os.makedirs(save_dir, exist_ok=True) log_file_path = save_dir / "stdout.log" err_file_path = save_dir / "stderr.log" with open(log_file_path, "a") as log_file, open(err_file_path, "a") as err_file: @@ -741,6 +747,28 @@ def main(cfg: DictConfig): raise ValueError(f"Expected {init_msg}, got {msg}") logger.info(f"Orchestrator {world_map.my_rank} heard that the exp folder is ready.") + # Pre-create the broadcast rendezvous TCPStore on actor_group_port so that + # fast-llm (launched via torchrun) can connect as a client. Torchrun sets + # TORCHELASTIC_USE_AGENT_STORE=True which makes PyTorch treat ALL ranks as + # clients in _create_c10d_store; without a pre-existing server the port is + # never opened and both fast-llm and vLLM hang forever. Only the master + # node (my_rank == 0) hosts the server; vLLM workers connect via master_addr. + broadcast_store = None + if cfg.use_fast_llm and cfg.weight_broadcast and world_map.my_rank == 0: + from torch.distributed import TCPStore + broadcast_store = TCPStore( + host_name=world_map.master_addr, + port=cfg.world.actor_group_port, + world_size=world_map.weight_update_group_size, + is_master=True, + wait_for_workers=False, + ) + logger.info( + f"Broadcast TCPStore server started on " + f"{world_map.master_addr}:{cfg.world.actor_group_port} " + f"(world_size={world_map.weight_update_group_size})" + ) + if cfg.debug.mode == "finetune": processes.extend(launch_jobs(cfg, world_map, ["finetune"])) elif cfg.debug.mode == "actor": diff --git a/qwen25_05B-instruct.yaml b/qwen25_05B-instruct.yaml deleted file mode 100644 index 07b45277..00000000 --- a/qwen25_05B-instruct.yaml +++ /dev/null @@ -1,72 +0,0 @@ -training: - num_workers: 0 - train_iters: 100_000 # Total number of optimizer steps (provided by pipelinerl) - wandb: # overridden by launch.py (project_name, group_name, entity_name, run.experiment_name) - entity_name: null # null disables wandb; overridden at launch time when wandb_entity_name is set - logs: - interval: 1 # Logging frequency in optimizer steps (one training iteration) - checkpoint: # How often to save checkpoints in native fast-llm format - interval: 1000 - export: # How often to export checkpoints in HF format - interval: 1000 - format: qwen2 # Export format (should match the format of the checkpoint used to start training) - -batch: - micro_batch_size: 1 # For RL, all rollouts are packed into a single sample - sequence_length: 18000 # Max rollout length accepted; also the training sample length - batch_size: 16 # Number of samples per optimizer step - truncate_documents: False # We do not want truncation for RL rollouts - #use_preference_spans: true - use_loss_masking_spans: true - use_grpo_data: True - -data: - sampling: - shuffle: disabled # Streaming dataset ignores shuffling - datasets: - training: - type: streaming # Type of dataset: Redis-backed streaming dataset - host: localhost # Redis server host. Must be provided by pipelinerl - port: 11000 # Redis server port. Must be provided by pipelinerl - -pretrained: - format: qwen2 # Base model format (provided by pipelinerl) - path: /home/toolkit/Qwen2.5-0.5B-Instruct # Base model checkpoint path (provided by pipelinerl) - model_weights: yes # Indicates that we load pretrained weights and start training from the base model - -model: - base_model: - head: - losses: - grpo: - type: grpo - epsilon_low: 0.2 - epsilon_high: 0.2 - multi_stage: - zero_stage: 2 # Note: choosing appropriate Zero, TP, PP, or SP settings is important - # for fitting larger models and optimizing speed/memory; this is usually - # tuned per training setup - distributed: - compute_dtype: bf16 - tensor_parallel: 1 # TP=2 used to test broadcasting; not needed for a small model - pipeline_parallel: 1 - sequence_data_parallel: 1 - -run: - experiment_dir: "/home/toolkit/test/denis/Qwen2.5-0.5B-Instruct" # Provided by pipelinerl - - -callbacks: - streaming: - type: streaming - host: localhost # overridden by launch.py via CLI: callbacks.streaming.host=... - port: 11000 # overridden by launch.py via CLI: callbacks.streaming.port=... - broadcast: - backend: nccl - external_world_size: 1 # overridden by launch.py - host: localhost # overridden by launch.py - port: 9000 # overridden by launch.py (world.actor_group_port) - export: - format: qwen2 - model_weights: true - optimizer_state: false From c1aeaacb5d4c2646f5925dc94ca64fe8e2e50830 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 19 Mar 2026 14:40:46 +0000 Subject: [PATCH 32/85] migrate fast-llm weight broadcast to ProcessGroupPool API init_extra_process_group was removed from fast-llm; replace with ProcessGroupPool throughout: - vllm1.py: use ProcessGroupPool in init_actor_update_group (fast-llm path); switch receive_weight_update_fast_llm to use fast_llm.core.distributed.broadcast_object/broadcast which work directly on ProcessGroupNCCL backends (unregistered, so torch.distributed ops reject them); handle shutdown() vs destroy_process_group in destroy_actor_update_group - fast_llm_trainer_helper.py: same ProcessGroupPool + broadcast_object/ broadcast + shutdown() pattern for the test trainer helper --- pipelinerl/vllm1.py | 44 ++++++++++++++++++++------------ tests/fast_llm_trainer_helper.py | 37 +++++++++++++++------------ 2 files changed, 48 insertions(+), 33 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index ffb0b05d..849235a2 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -95,23 +95,32 @@ def init_actor_update_group( + f"Weight update group init method: {weight_update_group_init_method}, world size: {weight_update_group_world_size}, mode: {weight_update_mode}" ) if weight_update_mode == 'http': - group_name = "actor" + self.process_group = pipelinerl.torch_utils.init_extra_process_group( + group_name="actor", + backend="nccl", + init_method=weight_update_group_init_method, + rank=self.pg_rank, + world_size=weight_update_group_world_size, + ) else: - from fast_llm.engine.training.streaming import WEIGHTS_BROADCAST_PG_NAME - - group_name = WEIGHTS_BROADCAST_PG_NAME - self.process_group = pipelinerl.torch_utils.init_extra_process_group( - group_name=group_name, - backend="nccl", - init_method=weight_update_group_init_method, - rank=self.pg_rank, - world_size=weight_update_group_world_size, - ) + from fast_llm.engine.distributed.config import DistributedBackend + from fast_llm.engine.distributed.distributed import ProcessGroupPool + + self.process_group = ProcessGroupPool( + rank=self.pg_rank, + world_size=weight_update_group_world_size, + local_world_size=1, + init_method=weight_update_group_init_method, + backend=DistributedBackend.nccl, + ).get_process_group(range(weight_update_group_world_size), self.pg_rank) self._process_group_destroyed = False def destroy_actor_update_group(self: LikeWorker): self._process_group_destroyed = True - torch.distributed.destroy_process_group(self.process_group) + if isinstance(self.process_group, torch.distributed.ProcessGroup): + torch.distributed.destroy_process_group(self.process_group) + else: + self.process_group.shutdown() def is_actor_update_group_destroyed(self: LikeWorker) -> bool: return getattr(self, "_process_group_destroyed", False) @@ -192,15 +201,16 @@ def receive_weight_update_fast_llm(self: LikeWorker): expected_dtypes = (torch.bfloat16, torch.float32, torch.float16) param_count = 0 + from fast_llm.core.distributed import broadcast as _broadcast, broadcast_object as _broadcast_object + while True: # Receive metadata - meta = [None] logger.debug(f"[Worker rank={self.rank}] Waiting for metadata broadcast...") - torch.distributed.broadcast_object_list(meta, src=0, group=self.process_group) + meta = _broadcast_object(None, self.process_group, src=0) logger.debug(f"[Worker rank={self.rank}] Received metadata: {meta}") # Check for end signal - if meta[0] is None: + if meta is None: logger.info( f"[Worker rank={self.rank}] Received end signal, finished receiving {param_count} parameters" ) @@ -208,7 +218,7 @@ def receive_weight_update_fast_llm(self: LikeWorker): # Parse metadata: (shard_name, layer_name, shape, dtype) # shard_name is a category label ("weights", "grads", etc.), not part of the HF param name - shard_name, layer_name, shape, dtype = meta[0] + shard_name, layer_name, shape, dtype = meta param_name = layer_name # Convert dtype to torch dtype @@ -216,7 +226,7 @@ def receive_weight_update_fast_llm(self: LikeWorker): # Allocate buffer and receive tensor (must happen for every broadcast to stay in sync) buffer = torch.empty(tuple(shape), dtype=target_dtype, device=self.device) - torch.distributed.broadcast(buffer, src=0, group=self.process_group) + _broadcast(buffer, 0, self.process_group) # Only load weight shards (skip grads, optimizer state, etc.) if shard_name != "weights": diff --git a/tests/fast_llm_trainer_helper.py b/tests/fast_llm_trainer_helper.py index 3c950cd1..854aa410 100644 --- a/tests/fast_llm_trainer_helper.py +++ b/tests/fast_llm_trainer_helper.py @@ -41,15 +41,17 @@ def timed_broadcast_fast_llm( import redis import orjson - from fast_llm.engine.training.streaming import WEIGHTS_BROADCAST_PG_NAME + from fast_llm.engine.distributed.config import DistributedBackend + from fast_llm.engine.distributed.distributed import ProcessGroupPool print(f"[Trainer] Initializing process group as rank 0 (world_size={world_size})") - process_group = _init_actor_process_group( - init_method=init_method, + process_group = ProcessGroupPool( rank=0, world_size=world_size, - group_name=WEIGHTS_BROADCAST_PG_NAME, - ) + local_world_size=1, + init_method=init_method, + backend=DistributedBackend.nccl, + ).get_process_group(range(world_size), 0) print("[Trainer] Process group initialized") # Connect to Redis @@ -132,7 +134,7 @@ def broadcast_weights_fast_llm(state_dict, step): # Cleanup — destroy_process_group now resolves because vLLM workers respond to training_finished r.close() - dist.destroy_process_group(process_group) + process_group.shutdown() print("[Trainer] Redis connection closed, process group destroyed, exiting") @@ -159,15 +161,17 @@ def rapid_broadcast_cycles_fast_llm( import redis as redis_lib import orjson - from fast_llm.engine.training.streaming import WEIGHTS_BROADCAST_PG_NAME + from fast_llm.engine.distributed.config import DistributedBackend + from fast_llm.engine.distributed.distributed import ProcessGroupPool print(f"[Trainer] Initializing process group as rank 0 (world_size={world_size})") - process_group = _init_actor_process_group( - init_method=init_method, + process_group = ProcessGroupPool( rank=0, world_size=world_size, - group_name=WEIGHTS_BROADCAST_PG_NAME, - ) + local_world_size=1, + init_method=init_method, + backend=DistributedBackend.nccl, + ).get_process_group(range(world_size), 0) print("[Trainer] Process group initialized") r = redis_lib.Redis(host=redis_host, port=redis_port) @@ -190,14 +194,15 @@ def broadcast_weights(state_dict, label): print(f"[Trainer] Sent weights_ready step={step} ({label})") step += 1 + from fast_llm.core.distributed import broadcast as _broadcast, broadcast_object as _broadcast_object + for name, tensor in state_dict.items(): if tensor.device.type != "cuda": tensor = tensor.cuda(0) - meta = [("weights", name, list(tensor.shape), str(tensor.dtype))] - dist.broadcast_object_list(meta, src=0, group=process_group) - dist.broadcast(tensor, src=0, group=process_group) + _broadcast_object(("weights", name, list(tensor.shape), str(tensor.dtype)), process_group, src=0) + _broadcast(tensor, 0, process_group) - dist.broadcast_object_list([None], src=0, group=process_group) + _broadcast_object(None, process_group, src=0) print(f"[Trainer] Broadcast complete ({label})") # --- Slow cycle: establish text_B and text_A clearly --- @@ -228,7 +233,7 @@ def broadcast_weights(state_dict, label): r.xadd(stream_key, {payload_key: orjson.dumps({"type": "training_finished"})}) r.close() - dist.destroy_process_group(process_group) + process_group.shutdown() print("[Trainer] Redis connection closed, process group destroyed, exiting") From 29964cc8a7a0e07fbdf0dc0530ef97fe399733a0 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 19 Mar 2026 14:40:56 +0000 Subject: [PATCH 33/85] fix fast-llm config schema and subprocess launcher Config schema changes (fast-llm dropped batch:, flattened data.sampling): - conf/base.yaml: remove batch: section; add schedule.depth_first_micro_batches (sequential grad accum, avoids OOM from breadth-first); migrate sequence_length -> data.micro_batch_size; flatten data.sampling.shuffle -> data.shuffle; remove auto-derived use_grpo_data/use_loss_masking_spans - conf/math.yaml: fast_llm.batch.sequence_length -> fast_llm.data.micro_batch_size Subprocess launcher fixes: - Use sys.executable instead of "python" so subprocesses use the venv - Use Path(sys.executable).parent / "fast-llm" for the fast-llm binary --- conf/base.yaml | 14 +++++--------- conf/math.yaml | 4 ++-- pipelinerl/launch.py | 12 ++++++------ 3 files changed, 13 insertions(+), 17 deletions(-) diff --git a/conf/base.yaml b/conf/base.yaml index 95f6673b..99f27535 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -142,17 +142,13 @@ fast_llm: interval: 1000 format: ${fast_llm_finetune.model_format} - batch: - micro_batch_size: 1 # For RL, all rollouts are packed into a single sample - sequence_length: 18000 # Max rollout length accepted; also the training sample length - batch_size: 16 # Number of samples per optimizer step - truncate_documents: false # Do not truncate RL rollouts - use_loss_masking_spans: true - use_grpo_data: true + schedule: + depth_first_micro_batches: 16 # Gradient accumulation steps (sequential, one sample at a time) data: - sampling: - shuffle: disabled # Streaming dataset ignores shuffling + micro_batch_size: 18000 # Tokens per sample; also the max rollout length accepted + truncate_documents: false # Do not truncate RL rollouts + shuffle: disabled # Streaming dataset ignores shuffling datasets: training: type: streaming # Redis-backed streaming dataset diff --git a/conf/math.yaml b/conf/math.yaml index c2b4d665..9339628b 100644 --- a/conf/math.yaml +++ b/conf/math.yaml @@ -6,8 +6,8 @@ use_fast_llm: true weight_broadcast: true fast_llm: - batch: - sequence_length: 18000 + data: + micro_batch_size: 18000 actor: rollout_policy: pipelinerl.domains.math.generate_math_rollout diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 1e60ef60..7c41630f 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -132,7 +132,7 @@ def run_ref_llm(cfg: DictConfig, preprocessor_llm_idx: int, local_idx: int, gpus os.makedirs(log_dir, exist_ok=True) cmd = [ - "python", + sys.executable, "-m", "vllm.entrypoints.openai.api_server", "--model", @@ -188,7 +188,7 @@ def run_actor_llm( ) broadcast_port = cfg.world.actor_group_port cmd = [ - "python", + sys.executable, "-m", entrypoint, "--model", @@ -251,7 +251,7 @@ def run_actor(world_map: WorldMap, actor_idx: int, exp_dir: Path): raise NotImplementedError("Can only do 1 actor yet") llm_urls = "+".join(world_map.get_actor_urls()) cmd = [ - "python", + sys.executable, "-m", "pipelinerl.entrypoints.run_actor", "--config-dir", @@ -276,7 +276,7 @@ def run_environment(cfg: DictConfig, job: Job): # run in a subprocess like in the rest of the code run_dir = Path(cfg.output_dir) / f"environment_{job.replica_idx}" cmd = [ - "python", + sys.executable, "-m", "pipelinerl.entrypoints.run_environment", "--config-dir", @@ -350,7 +350,7 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: f"--nproc_per_node={len(gpus)}", f"--master_port={torchrun_port}", "--no_python", - "fast-llm", + str(Path(sys.executable).parent / "fast-llm"), "train", model_type, "--config", @@ -470,7 +470,7 @@ def run_preprocess(world_map: WorldMap, preprocessor_idx: int, exp_dir: Path): raise NotImplementedError("Can only do 1 preprocessor yet") llm_urls = "+".join(world_map.get_preprocessor_urls()) cmd = [ - "python", + sys.executable, "-m", "pipelinerl.entrypoints.run_preprocess", "--config-dir", From f48386b5a2ae08893e347bb3cc85b97c4b87507c Mon Sep 17 00:00:00 2001 From: bigximik Date: Fri, 20 Mar 2026 07:48:47 +0000 Subject: [PATCH 34/85] fix missed broadcast API in timed_broadcast_fast_llm inner function The inner broadcast_weights_fast_llm local function inside timed_broadcast_fast_llm was still using dist.broadcast_object_list / dist.broadcast, which reject ProcessGroupPool's unregistered ProcessGroupNCCL backends. Switch to fast_llm.core.distributed broadcast_object / broadcast to match the outer function fixed earlier. This was the cause of test_server_fast_llm_broadcast_pattern silently failing to apply weight updates. --- tests/fast_llm_trainer_helper.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/tests/fast_llm_trainer_helper.py b/tests/fast_llm_trainer_helper.py index 854aa410..6ff173e2 100644 --- a/tests/fast_llm_trainer_helper.py +++ b/tests/fast_llm_trainer_helper.py @@ -68,6 +68,8 @@ def timed_broadcast_fast_llm( original_state_dict, _ = _load_state_dict(model_name) perturbed_state_dict = _create_perturbed_state_dict(original_state_dict) + from fast_llm.core.distributed import broadcast as _broadcast, broadcast_object as _broadcast_object + # Helper function to broadcast weights using Fast-LLM protocol def broadcast_weights_fast_llm(state_dict, step): """Broadcast weights using Fast-LLM protocol. @@ -75,9 +77,9 @@ def broadcast_weights_fast_llm(state_dict, step): Protocol: 1. Send Redis event: {type: "weights_ready", step: N} 2. For each parameter: - - broadcast_object_list([(shard_name, layer_name, shape, dtype)]) + - broadcast_object((shard_name, layer_name, shape, dtype)) - broadcast(tensor) - 3. Send end signal: broadcast_object_list([None]) + 3. Send end signal: broadcast_object(None) """ # Send Redis stream event event = {"type": "weights_ready", "step": step} @@ -89,18 +91,14 @@ def broadcast_weights_fast_llm(state_dict, step): if tensor.device.type != "cuda": tensor = tensor.cuda(0) - # Broadcast metadata - meta = [("weights", name, list(tensor.shape), str(tensor.dtype))] - dist.broadcast_object_list(meta, src=0, group=process_group) - - # Broadcast tensor - dist.broadcast(tensor, src=0, group=process_group) + _broadcast_object(("weights", name, list(tensor.shape), str(tensor.dtype)), process_group, src=0) + _broadcast(tensor, 0, process_group) if (i + 1) % 50 == 0: print(f"[Trainer] Broadcasted {i+1}/{len(state_dict)} parameters") # Send end signal - dist.broadcast_object_list([None], src=0, group=process_group) + _broadcast_object(None, process_group, src=0) print(f"[Trainer] Sent end signal, broadcast complete") # Broadcast 1: Perturbed weights From b3a0f6461f277e832230e8128014a5a90978a296 Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 24 Mar 2026 14:21:16 +0000 Subject: [PATCH 35/85] fix fast-llm lag check interval: reduce from 5s to 0s --- pipelinerl/state.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pipelinerl/state.py b/pipelinerl/state.py index 6b207073..4e25a40d 100644 --- a/pipelinerl/state.py +++ b/pipelinerl/state.py @@ -78,7 +78,7 @@ def listen(): r = connect_to_redis(_backend) last_id = "0-0" last_lag_check = 0.0 - lag_check_interval = 5.0 # seconds + lag_check_interval = 0.0 # seconds # Initialize to 0 so wait_for_processed_samples() doesn't block at startup. # The lag check below will update this once the data stream/consumer group exists. From 58a8798ef1f3fd81b78a577cacb0b6bcf51dbe88 Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 24 Mar 2026 14:21:19 +0000 Subject: [PATCH 36/85] update base and counting configs for fast-llm: max_ready_samples, vllm v1, counting fast-llm overrides --- conf/base.yaml | 4 ++-- conf/counting.yaml | 15 +++++++++++++++ 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/conf/base.yaml b/conf/base.yaml index 99f27535..50488a58 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -39,7 +39,7 @@ preprocess: # ring buffer to replace old samples with new ones when training is slow ring_buffer_size: 128 # "virtual" sample queue per lead trainer - max_ready_samples_per_lead: 64 + max_ready_samples_per_lead: 512 pop_old_data: ${..pop_old_data} shared_memory_entry_size: 100000000 log_every_n_samples: 128 @@ -57,7 +57,7 @@ test_llm: top_k: 50 vllm_config: - use_v1: false + use_v1: true quantization: null # or bf16_last_layer_fp32 vllm_kwargs: dtype: bfloat16 diff --git a/conf/counting.yaml b/conf/counting.yaml index 97f61a88..fb857cfd 100644 --- a/conf/counting.yaml +++ b/conf/counting.yaml @@ -3,6 +3,21 @@ defaults: finetune: seq_length: 4000 gradient_accumulation_passes: 1024 +fast_llm: + training: + num_workers: 1 + schedule: + depth_first_micro_batches: 256 + model: + base_model: + head: + losses: + grpo: + epsilon_low: 0.1 + epsilon_high: 0.1 + optimizer: + learning_rate: + base: 1e-5 llm: parameters: max_tokens: 1000 From 979bdf14f3525715cbd9fafa125ce6090ac4065e Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 24 Mar 2026 14:23:19 +0000 Subject: [PATCH 37/85] split fast-llm lag polling into dedicated thread separate from event listener --- pipelinerl/state.py | 76 ++++++++++++++++++++++----------------------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/pipelinerl/state.py b/pipelinerl/state.py index 4e25a40d..a9539812 100644 --- a/pipelinerl/state.py +++ b/pipelinerl/state.py @@ -64,7 +64,6 @@ def listen(): def _start_listening_fast_llm(self): """Listen to Fast-LLM trainer events directly from Redis.""" import orjson - import redis from pipelinerl.streams import RedisConfig, _backend, connect_to_redis from fast_llm.data.dataset.config import REDIS_DATA_STREAM, REDIS_GROUP_NAME @@ -73,49 +72,20 @@ def _start_listening_fast_llm(self): stream_key = FAST_LLM_EVENTS_STREAM # "fast_llm_events" payload_key = b"event" # Fast-LLM uses "event" as payload key - def listen(): + # Initialize to 0 so wait_for_processed_samples() doesn't block at startup. + # The lag thread below will update this once the data stream/consumer group exists. + self.samples_processed = 0 + + def listen_events(): assert isinstance(_backend, RedisConfig) r = connect_to_redis(_backend) last_id = "0-0" - last_lag_check = 0.0 - lag_check_interval = 0.0 # seconds - - # Initialize to 0 so wait_for_processed_samples() doesn't block at startup. - # The lag check below will update this once the data stream/consumer group exists. - self.samples_processed = 0 logger.info(f"Listening for Fast-LLM events on Redis stream '{stream_key}'") while True: - # Read from stream (blocking) result = r.xread({stream_key: last_id}, count=1, block=1000) - # Periodically compute samples_processed from consumer group lag - now = time.time() - if now - last_lag_check >= lag_check_interval: - last_lag_check = now - try: - stream_info = r.xinfo_stream(REDIS_DATA_STREAM) - total_len = stream_info.get("length", 0) - groups = r.xinfo_groups(REDIS_DATA_STREAM) - for group in groups: - gname = group.get("name", "") - if isinstance(gname, bytes): - gname = gname.decode() - if gname == REDIS_GROUP_NAME: - entries_read = group.get("entries-read") - if entries_read is None: - lag = group.get("lag", 0) or 0 - entries_read = total_len - lag - self.samples_processed = int(entries_read) - logger.info( - f"Fast-LLM lag check: stream_len={total_len} entries_read={entries_read} " - f"samples_processed={self.samples_processed}" - ) - break - except Exception as e: - logger.debug(f"Fast-LLM lag check failed (stream/group not yet created?): {e}") - if not result: continue @@ -123,7 +93,6 @@ def listen(): for msg_id, msg_data in messages: last_id = msg_id - # Fast-LLM sends: {payload_key: orjson.dumps({type: "...", step: N})} if payload_key not in msg_data: logger.warning(f"Fast-LLM event missing '{payload_key.decode()}' field: {msg_data}") continue @@ -147,8 +116,39 @@ def listen(): else: logger.warning(f"Unknown Fast-LLM event type: {event_type}") - self._thread = threading.Thread(target=listen, daemon=True) - self._thread.start() + def poll_lag(): + assert isinstance(_backend, RedisConfig) + r = connect_to_redis(_backend) + lag_check_interval = 0.5 # seconds + + while True: + try: + stream_info = r.xinfo_stream(REDIS_DATA_STREAM) + total_len = stream_info.get("length", 0) + groups = r.xinfo_groups(REDIS_DATA_STREAM) + for group in groups: + gname = group.get("name", "") + if isinstance(gname, bytes): + gname = gname.decode() + if gname == REDIS_GROUP_NAME: + entries_read = group.get("entries-read") + if entries_read is None: + lag = group.get("lag", 0) or 0 + entries_read = total_len - lag + self.samples_processed = int(entries_read) + logger.info( + f"Fast-LLM lag check: stream_len={total_len} entries_read={entries_read} " + f"samples_processed={self.samples_processed}" + ) + break + except Exception as e: + logger.debug(f"Fast-LLM lag check failed (stream/group not yet created?): {e}") + time.sleep(lag_check_interval) + + self._event_thread = threading.Thread(target=listen_events, daemon=True) + self._lag_thread = threading.Thread(target=poll_lag, daemon=True) + self._event_thread.start() + self._lag_thread.start() def wait_for_training_done(self, timeout: float | None = None) -> bool: return self._training_done_event.wait(timeout=timeout) From 38783a502137ba5a8bfbcb6fd0cf3892904ff552 Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 24 Mar 2026 14:54:48 +0000 Subject: [PATCH 38/85] fix seq_length validation to use micro_batch_size for fast-llm path --- pipelinerl/launch.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 7c41630f..d8355c2d 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -79,9 +79,15 @@ def validate_config(cfg: DictConfig): raise ValueError("value_loss_coef must be greater than 0 when using causal-language-modeling-with-value-head") # Check that model being tuned to the max length accepted by inference - if cfg.finetune.seq_length < cfg.vllm_config.vllm_kwargs.max_model_len: + if cfg.use_fast_llm: + max_seq_length = cfg.fast_llm.data.micro_batch_size + seq_length_label = "fast_llm.data.micro_batch_size" + else: + max_seq_length = cfg.finetune.seq_length + seq_length_label = "finetune.seq_length" + if max_seq_length < cfg.vllm_config.vllm_kwargs.max_model_len: raise ValueError( - f"seq_length {cfg.finetune.seq_length} must be greater than or equal to " + f"{seq_length_label} {max_seq_length} must be greater than or equal to " f"vllm_kwargs.max_model_len {cfg.vllm_config.vllm_kwargs.max_model_len}" ) From b8b9885acbecd7b48bc35fb23dc8ff6a0739ef49 Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 24 Mar 2026 14:54:53 +0000 Subject: [PATCH 39/85] set use_fast_llm=true as default, add max_model_len and vllm v1 to counting config --- conf/base.yaml | 2 +- conf/counting.yaml | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/conf/base.yaml b/conf/base.yaml index 50488a58..e892e542 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -119,7 +119,7 @@ debug: # Fast-LLM integration: when true, fast-llm is used as the trainer. # Data flows actors -> Redis (fast_llm_streaming) -> fast-llm training loop. # Weight updates are broadcast via NCCL using fast-llm's streaming callback. -use_fast_llm: false +use_fast_llm: true # Whether the trainer broadcasts updated weights to vLLM after each training step. weight_broadcast: true diff --git a/conf/counting.yaml b/conf/counting.yaml index fb857cfd..0b3f96e4 100644 --- a/conf/counting.yaml +++ b/conf/counting.yaml @@ -3,6 +3,9 @@ defaults: finetune: seq_length: 4000 gradient_accumulation_passes: 1024 +vllm_config: + vllm_kwargs: + max_model_len: 4000 fast_llm: training: num_workers: 1 From 1f3ca5eeb80cf9ee0d5984b3c7d8cb967d8f36f2 Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 24 Mar 2026 15:05:09 +0000 Subject: [PATCH 40/85] increase grpo epsilon from 0.1 to 0.2 in counting config --- conf/counting.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/conf/counting.yaml b/conf/counting.yaml index 0b3f96e4..9eaff581 100644 --- a/conf/counting.yaml +++ b/conf/counting.yaml @@ -16,8 +16,8 @@ fast_llm: head: losses: grpo: - epsilon_low: 0.1 - epsilon_high: 0.1 + epsilon_low: 0.2 + epsilon_high: 0.2 optimizer: learning_rate: base: 1e-5 From bd18f461343e60683a03a596e43492e41aee356f Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 25 Mar 2026 15:04:04 +0000 Subject: [PATCH 41/85] add data pipeline diagnostic logging (log_data_pipeline flag) Adds debug.log_data_pipeline config flag that enables per-sample WRITE event logging from the preprocessor and propagates log_data_pipeline=True to fast-llm's StreamingDatasetConfig and ScheduleConfig for per-read and per-micro-batch timing logs. --- conf/base.yaml | 1 + pipelinerl/launch.py | 3 +++ pipelinerl/preprocess.py | 24 ++++++++++++++++++++++++ 3 files changed, 28 insertions(+) diff --git a/conf/base.yaml b/conf/base.yaml index e892e542..4aa50d1d 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -115,6 +115,7 @@ debug: streams_from: null place_inference_workers: true use_existing_llms: false + log_data_pipeline: false # Fast-LLM integration: when true, fast-llm is used as the trainer. # Data flows actors -> Redis (fast_llm_streaming) -> fast-llm training loop. diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index d8355c2d..ee2a41e6 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -335,6 +335,9 @@ def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: fast_llm_cfg["run"]["experiment_name"] = experiment_name fast_llm_cfg["data"]["datasets"]["training"]["host"] = cfg.streams.host fast_llm_cfg["data"]["datasets"]["training"]["port"] = cfg.streams.port + if cfg.debug.log_data_pipeline: + fast_llm_cfg["data"]["datasets"]["training"]["log_data_pipeline"] = True + fast_llm_cfg.setdefault("schedule", {})["log_data_pipeline"] = True fast_llm_cfg["training"]["wandb"]["entity_name"] = cfg.wandb.wandb_entity_name fast_llm_cfg["training"]["wandb"]["project_name"] = cfg.wandb.wandb_project_name fast_llm_cfg["training"]["wandb"]["group_name"] = cfg.wandb.wandb_group diff --git a/pipelinerl/preprocess.py b/pipelinerl/preprocess.py index 0653a031..f847a41f 100644 --- a/pipelinerl/preprocess.py +++ b/pipelinerl/preprocess.py @@ -535,6 +535,15 @@ def run_preprocessing_loop( # Per-trainer sample tracking (similar to finetune_loop.py) total_filtered_out = 0 # Track total filtered samples across all batches + pipeline_log_file = None + if cfg.use_fast_llm and cfg.debug.get("log_data_pipeline", False): + import json as _json + import pathlib as _pathlib + # Write alongside fast-llm rank files: {exp_dir}/finetune/data_pipeline_log/ + _log_dir = _pathlib.Path(cfg.output_dir) / "finetune" / "data_pipeline_log" + _log_dir.mkdir(parents=True, exist_ok=True) + pipeline_log_file = open(_log_dir / "preprocessor.jsonl", "a") + with write_to_streams(output_stream, shared=use_shared_stream, stream_name_override=fast_llm_stream_name, pipelinerl_metadata=not cfg.use_fast_llm) as data_writer, write_to_streams(stats_streams) as stats_writer: with SharedMemoryManager() as smm: # Create shared memory queues without the manager parameter @@ -657,10 +666,25 @@ def run_preprocessing_loop( # Fast-LLM path: write individual samples directly (Fast-LLM does its own packing) if cfg.use_fast_llm: + write_start = time.time() if pipeline_log_file else None + write_samples = 0 + write_tokens = 0 while len(processed_entries_queue) > 0: entry = processed_entries_queue.popleft() + if pipeline_log_file is not None: + write_samples += 1 + write_tokens += len(entry.get("input_ids", [])) write_sample_for_fast_llm(data_writer, entry) published_samples += 1 + if pipeline_log_file is not None and write_samples > 0: + pipeline_log_file.write(_json.dumps({ + "event": "WRITE", + "t_start": round(write_start, 3), + "t_end": round(time.time(), 3), + "samples": write_samples, + "tokens": write_tokens, + }) + "\n") + pipeline_log_file.flush() batch_done = True # Always mark done for Fast-LLM (no batching) elif cfg.finetune.seq_packing: if samples_per_trainer[trainer_id] == target_samples_per_lead: From 31820d2d798e85c5d7fa5d67fe271ae815cb93f6 Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 25 Mar 2026 15:19:51 +0000 Subject: [PATCH 42/85] add Redis verbose logging to file for crash diagnosis --- pipelinerl/launch.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index ee2a41e6..17ea75a6 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -502,6 +502,8 @@ def run_preprocess(world_map: WorldMap, preprocessor_idx: int, exp_dir: Path): def run_redis(cfg: DictConfig): # Launch redis-server + redis_dir = Path(cfg.output_dir) / "redis" + os.makedirs(redis_dir, exist_ok=True) cmd = [ "redis-server", "--bind", @@ -514,6 +516,10 @@ def run_redis(cfg: DictConfig): "no", "--save", cfg.streams.save, + "--logfile", + str(redis_dir / "redis.log"), + "--loglevel", + "verbose", ] logger.info(f"Running redis with command: {' '.join(cmd)}") save_command(Path(cfg.output_dir) / "redis", cmd) From c51d6bd7c9592ef0bdf3bb52a528671951a47cd8 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 16 Apr 2026 09:50:54 +0000 Subject: [PATCH 43/85] handle vLLM 4xx errors gracefully instead of crashing the actor A single bad prompt (e.g., max_tokens + input_tokens > max_model_len) caused an HTTP 400 from vLLM that killed the entire actor process, stopping all rollout generation. Now HTTP 4xx errors create an empty RolloutResult instead of crashing. Groups where all rollouts fail are dropped entirely. Partial groups (some succeed, some fail) submit only the valid results. --- pipelinerl/actor.py | 111 ++++++++++- tests/test_actor_error_handling.py | 290 +++++++++++++++++++++++++++++ 2 files changed, 393 insertions(+), 8 deletions(-) create mode 100644 tests/test_actor_error_handling.py diff --git a/pipelinerl/actor.py b/pipelinerl/actor.py index eb4b9095..542a06c8 100644 --- a/pipelinerl/actor.py +++ b/pipelinerl/actor.py @@ -129,6 +129,19 @@ async def schedule_rollouts( """ loop = asyncio.get_running_loop() + # Diagnostic logging (Process B side) – enabled by debug.log_data_pipeline + _pb_log_file = None + if cfg.debug.get("log_data_pipeline", False): + import json as _json_b + import pathlib as _pathlib_b + _log_dir_b = _pathlib_b.Path(cfg.output_dir) / "actor" / "data_pipeline_log" + _log_dir_b.mkdir(parents=True, exist_ok=True) + # Use scheduler_name to distinguish multiple workers + _safe_name = scheduler_name.replace(" ", "_").replace("/", "_").replace(",", "") + _pb_log_file = open(_log_dir_b / f"process_b_{_safe_name}.jsonl", "a") + _pb_problem_queue_empty_count = 0 + _pb_llm_busy_count = 0 + # Track active tasks per LLM active_rollouts = [0] * len(llms) started_rollouts = 0 @@ -168,12 +181,26 @@ async def rollout_and_maybe_produce_result( llm_index: int, session: aiohttp.ClientSession, ): - nonlocal started_rollouts, finished_rollouts + nonlocal started_rollouts, finished_rollouts, _pb_problem_queue_empty_count, _pb_llm_busy_count try: llm = llms[llm_index] model_version = trainer_state.propagated_weight_version assert model_version is not None - rollout_result = await rollout_policy(cfg, llm, problem, session) + try: + rollout_result = await rollout_policy(cfg, llm, problem, session) + except aiohttp.ClientResponseError as e: + if 400 <= e.status < 500: + logger.warning( + f"Rollout failed with HTTP {e.status} for group {group_id}, " + f"skipping this rollout: {e.message}" + ) + rollout_result = RolloutResult( + training_texts=[], + metrics=BaseMetrics(reward=0.0, success=False, no_error=False, no_answer=True), + latency=0.0, + ) + else: + raise rollout_result.model_version = model_version # Make a group id that will be different from groups made by another rollout maker full_group_id = f"{scheduler_name}_{group_id}" @@ -186,10 +213,36 @@ async def rollout_and_maybe_produce_result( sample.group_id = full_group_id group_rollouts[group_id].append(rollout_result) if len(group_rollouts[group_id]) == attempts: + # Filter out empty results (failed rollouts with no training data) + valid_results = [r for r in group_rollouts[group_id] if r.training_texts] + if not valid_results: + logger.warning( + f"Dropping group {group_id}: all {attempts} rollouts failed " + f"(no training samples produced)" + ) + del group_rollouts[group_id] + finished_rollouts += 1 + return # This is blocking call, but there's just one other thread reading from this queue. - random.shuffle(group_rollouts[group_id]) - result_queue.put(group_rollouts[group_id]) + random.shuffle(valid_results) + _t_put_start = time.monotonic() + result_queue.put(valid_results) + _put_duration = time.monotonic() - _t_put_start del group_rollouts[group_id] + if _pb_log_file is not None: + _pb_log_file.write(_json_b.dumps({ + "wall": time.time(), + "event": "put", + "put_blocked_s": _put_duration, + "result_queue_depth_after": result_queue.qsize(), + "active_rollouts": sum(active_rollouts), + "groups_in_progress": len(group_rollouts), + "problem_queue_empty_since_last": _pb_problem_queue_empty_count, + "llm_busy_since_last": _pb_llm_busy_count, + }) + "\n") + _pb_log_file.flush() + _pb_problem_queue_empty_count = 0 + _pb_llm_busy_count = 0 finished_rollouts += 1 except Exception as e: handle_rollout_exception(e) @@ -226,6 +279,7 @@ async def rollout_and_maybe_produce_result( problem = problem_queue.get(block=False) except Empty: # give some quality time for other couroutines to work + _pb_problem_queue_empty_count += 1 await asyncio.sleep(0.01) continue group_id += 1 @@ -235,6 +289,7 @@ async def rollout_and_maybe_produce_result( next_llm = active_rollouts.index(min(active_rollouts)) if active_rollouts[next_llm] == cfg.actor.llm_max_rollouts: # all llms are busy, wait for one to finish + _pb_llm_busy_count += 1 await asyncio.sleep(0.01) continue active_rollouts[next_llm] += 1 @@ -251,6 +306,8 @@ async def rollout_and_maybe_produce_result( ) group_rollout_index += 1 logger.info("Rollout scheduler finished") + if _pb_log_file is not None: + _pb_log_file.close() def rollout_maker_entrypoint( @@ -396,6 +453,16 @@ def run(self, dataset: list[tuple[str, dict]]): published_samples = 0 submitted_groups = 0 finished_groups = 0 + + # Diagnostic logging setup (enabled by debug.log_data_pipeline) + _pipeline_log_file = None + if self.is_training and self.cfg.debug.get("log_data_pipeline", False): + import json as _json + import pathlib as _pathlib + _log_dir = _pathlib.Path(self.cfg.output_dir) / "actor" / "data_pipeline_log" + _log_dir.mkdir(parents=True, exist_ok=True) + _pipeline_log_file = open(_log_dir / "process_a.jsonl", "a") + _last_publish_wall = None # wall clock of last successful publish expected_rollouts = -1 if self.is_training else len(dataset) if expected_rollouts > 0: logger.info(f"Will stop after {expected_rollouts} rollouts") @@ -484,14 +551,16 @@ def run(self, dataset: list[tuple[str, dict]]): except queue.Empty: continue + _t_got = time.monotonic() + if isinstance(rollout_results, Exception): logger.error("Stop actor loop due to error") raise rollout_results assert isinstance(rollout_results, list) assert isinstance(rollout_results[0], RolloutResult) - assert len(rollout_results) == attempts, ( - f"Expected {attempts} rollouts, got {len(rollout_results)}" + assert 0 < len(rollout_results) <= attempts, ( + f"Expected 1-{attempts} rollouts, got {len(rollout_results)}" ) group_samples = sum(len(r.training_texts) for r in rollout_results) @@ -501,7 +570,9 @@ def run(self, dataset: list[tuple[str, dict]]): for r in rollout_results: for text in r.training_texts: all_text_dumps.append(text.model_dump()) + _t_before_redis = time.monotonic() data_stream_writer.write(all_text_dumps) + _t_after_redis = time.monotonic() in_progress = submitted_groups - finished_groups logger.info( f"Published {group_samples} {'train' if self.is_training else 'test'} samples" @@ -515,10 +586,12 @@ def run(self, dataset: list[tuple[str, dict]]): time_to_publish_train_stats = ( self.is_training and trainer_version_to_publish is not None - ) or self.debug_mode + ) or self.debug_mode time_to_publish_test_stats = finished_groups == expected_rollouts # Publish stats at every new model version or if all tapes are finished + _t_before_stats = None + _t_after_stats = None if time_to_publish_train_stats or time_to_publish_test_stats: if self.is_training: loop_stats = { @@ -526,7 +599,7 @@ def run(self, dataset: list[tuple[str, dict]]): "problem_queue_size": self.problem_queue.qsize(), "result_queue_size": self.result_queue.qsize(), "finished_groups": finished_groups, - "trainer_model_version": trainer_version_to_publish, + "trainer_model_version": trainer_version_to_publish, "time_since_start": time.time() - loop_start_time, } trainer_version_to_publish = None @@ -535,16 +608,38 @@ def run(self, dataset: list[tuple[str, dict]]): "trainer_model_version": last_trainer_version } + _t_before_stats = time.monotonic() self.publish_stats( stats_writer=stats_writer, loop_stats=loop_stats, ) + _t_after_stats = time.monotonic() + + if _pipeline_log_file is not None: + _now = time.monotonic() + _entry = { + "wall": time.time(), + "finished_groups": finished_groups, + "result_queue_depth": self.result_queue.qsize(), + "inter_publish_gap_s": _t_got - _last_publish_wall if _last_publish_wall is not None else None, + "process_s": _t_before_redis - _t_got, + "redis_write_s": _t_after_redis - _t_before_redis, + "stats_write_s": (_t_after_stats - _t_before_stats) if _t_before_stats is not None else None, + "total_cycle_s": _now - _t_got, + "group_samples": group_samples, + } + _pipeline_log_file.write(_json.dumps(_entry) + "\n") + _pipeline_log_file.flush() + _last_publish_wall = _t_got if finished_groups == expected_rollouts: logger.info(f"Finished {expected_rollouts} rollouts, stopping actor loop") break + if _pipeline_log_file is not None: + _pipeline_log_file.close() + def publish_stats(self, stats_writer: StreamWriter, loop_stats: Dict): split_name = "test_" if not self.is_training else "" diff --git a/tests/test_actor_error_handling.py b/tests/test_actor_error_handling.py new file mode 100644 index 00000000..61adc5eb --- /dev/null +++ b/tests/test_actor_error_handling.py @@ -0,0 +1,290 @@ +"""Test that actor rollout error handling doesn't crash the entire actor. + +Specifically tests that: +1. HTTP 4xx errors from vLLM (e.g., max_tokens too large) are handled gracefully +2. Groups where ALL rollouts fail are dropped (not submitted) +3. Groups where SOME rollouts fail submit only valid results +4. HTTP 5xx errors still propagate as fatal +""" + +import asyncio +import queue +from unittest.mock import MagicMock, AsyncMock, patch + +import aiohttp +import pytest +from omegaconf import OmegaConf + +from pipelinerl.rollouts import BaseMetrics, RolloutResult, TrainingText + + +# --------------------------------------------------------------------------- +# Helpers – lightweight stand-ins for heavy classes used by schedule_rollouts +# --------------------------------------------------------------------------- + +class FakeQueue: + """Minimal stand-in for SharedMemoryQueue (no shared memory needed).""" + + def __init__(self): + self._q = queue.Queue() + + def put(self, item, block=True, timeout=None): + self._q.put(item) + + def get(self, block=True, timeout=None): + return self._q.get(block=block, timeout=timeout) + + def qsize(self): + return self._q.qsize() + + def max_actual_entry_size(self): + return 0 + + def get_memory_size(self): + return 0 + + +class FakeTrainerState: + def __init__(self): + self.propagated_weight_version = 1 + self.samples_processed = 0 + + +def make_good_result() -> RolloutResult: + """A valid rollout result with one training sample.""" + return RolloutResult( + training_texts=[ + TrainingText( + text="prompt output", + n_predicted=6, + reward=1.0, + input_ids=[1, 2, 3], + labels=[-100, 2, 3], + finished=True, + prompt_tokens=5, + output_tokens=6, + ) + ], + metrics=BaseMetrics(reward=1.0, success=True, no_error=True, no_answer=False), + latency=0.5, + ) + + +def make_client_response_error(status: int, message: str = "Bad Request"): + """Create an aiohttp.ClientResponseError.""" + mock_req = MagicMock() + mock_req.url = "http://localhost:8080/v1/chat/completions" + return aiohttp.ClientResponseError( + request_info=mock_req, + history=(), + status=status, + message=message, + ) + + +# --------------------------------------------------------------------------- +# Core test: exercise rollout_and_maybe_produce_result + group completion +# --------------------------------------------------------------------------- + +@pytest.mark.asyncio +async def test_all_rollouts_fail_group_dropped(): + """When all rollouts in a group fail with 4xx, the group should be dropped.""" + attempts = 4 + problem_q = FakeQueue() + result_q = FakeQueue() + trainer_state = FakeTrainerState() + + # Put one problem in the queue + problem_q.put({"task": "What is 2+2?", "answer": "4"}) + + call_count = 0 + + async def failing_rollout_policy(cfg, llm, problem, session): + nonlocal call_count + call_count += 1 + raise make_client_response_error(400, "max_tokens too large") + + cfg = OmegaConf.create({ + "actor": { + "rollout_policy": "not_used", # we patch it + "llm_max_rollouts": 64, + }, + "finetune": { + "train_batch_size": 1000, + "gradient_accumulation_passes": 1, + "train_iters": 100, + "interrupt_train_steps": None, + }, + "debug": {}, + }) + + llms = [MagicMock()] # 1 LLM + + # We can't easily run schedule_rollouts (too many dependencies), + # so we directly test the inner logic by reimplementing the key parts. + # This mirrors rollout_and_maybe_produce_result + group completion. + + group_rollouts = {} + group_id = 0 + group_rollouts[group_id] = [] + finished_rollouts = 0 + warnings_logged = [] + + for rollout_index in range(attempts): + try: + rollout_result = await failing_rollout_policy(cfg, llms[0], {"task": "x"}, None) + except aiohttp.ClientResponseError as e: + if 400 <= e.status < 500: + warnings_logged.append(str(e.status)) + rollout_result = RolloutResult( + training_texts=[], + metrics=BaseMetrics(reward=0.0, success=False, no_error=False, no_answer=True), + latency=0.0, + ) + else: + raise + + rollout_result.model_version = 1 + rollout_result.group_id = f"test_{group_id}" + group_rollouts[group_id].append(rollout_result) + + # Now check group completion logic + assert len(group_rollouts[group_id]) == attempts + valid_results = [r for r in group_rollouts[group_id] if r.training_texts] + + # All failed → group should be dropped + assert len(valid_results) == 0, "Expected all results to be empty" + assert call_count == attempts + assert len(warnings_logged) == attempts + + # In real code: del group_rollouts[group_id], don't put in result_q + del group_rollouts[group_id] + assert result_q.qsize() == 0, "No group should be in the result queue" + + +@pytest.mark.asyncio +async def test_partial_failure_submits_valid_only(): + """When some rollouts fail but others succeed, submit only valid ones.""" + attempts = 4 + result_q = FakeQueue() + + call_count = 0 + + async def mixed_rollout_policy(cfg, llm, problem, session): + nonlocal call_count + call_count += 1 + # First 2 calls fail, last 2 succeed + if call_count <= 2: + raise make_client_response_error(400, "max_tokens too large") + return make_good_result() + + group_rollouts = {} + group_id = 0 + group_rollouts[group_id] = [] + + for rollout_index in range(attempts): + try: + rollout_result = await mixed_rollout_policy(None, None, {"task": "x"}, None) + except aiohttp.ClientResponseError as e: + if 400 <= e.status < 500: + rollout_result = RolloutResult( + training_texts=[], + metrics=BaseMetrics(reward=0.0, success=False, no_error=False, no_answer=True), + latency=0.0, + ) + else: + raise + + rollout_result.model_version = 1 + rollout_result.group_id = f"test_{group_id}" + group_rollouts[group_id].append(rollout_result) + + assert len(group_rollouts[group_id]) == attempts + + valid_results = [r for r in group_rollouts[group_id] if r.training_texts] + + # 2 failed, 2 succeeded + assert len(valid_results) == 2, f"Expected 2 valid results, got {len(valid_results)}" + + # In real code: result_queue.put(valid_results) + result_q.put(valid_results) + got = result_q.get(block=False) + assert len(got) == 2 + assert all(len(r.training_texts) > 0 for r in got) + + +@pytest.mark.asyncio +async def test_5xx_errors_still_propagate(): + """HTTP 5xx errors should NOT be caught — they indicate server failure.""" + + async def server_error_policy(cfg, llm, problem, session): + raise make_client_response_error(500, "Internal Server Error") + + with pytest.raises(aiohttp.ClientResponseError) as exc_info: + try: + await server_error_policy(None, None, {"task": "x"}, None) + except aiohttp.ClientResponseError as e: + if 400 <= e.status < 500: + pass # Would be caught in real code + else: + raise # 5xx re-raised + + assert exc_info.value.status == 500 + + +@pytest.mark.asyncio +async def test_all_succeed_normal_path(): + """When all rollouts succeed, the full group is submitted.""" + attempts = 4 + result_q = FakeQueue() + + async def good_policy(cfg, llm, problem, session): + return make_good_result() + + group_rollouts = {} + group_id = 0 + group_rollouts[group_id] = [] + + for rollout_index in range(attempts): + try: + rollout_result = await good_policy(None, None, {"task": "x"}, None) + except aiohttp.ClientResponseError as e: + if 400 <= e.status < 500: + rollout_result = RolloutResult( + training_texts=[], + metrics=BaseMetrics(reward=0.0, success=False, no_error=False, no_answer=True), + latency=0.0, + ) + else: + raise + + rollout_result.model_version = 1 + rollout_result.group_id = f"test_{group_id}" + group_rollouts[group_id].append(rollout_result) + + valid_results = [r for r in group_rollouts[group_id] if r.training_texts] + assert len(valid_results) == attempts, "All rollouts should be valid" + + result_q.put(valid_results) + got = result_q.get(block=False) + assert len(got) == attempts + + +@pytest.mark.asyncio +async def test_consumer_assertion_accepts_partial_group(): + """The consumer-side assertion should accept groups with fewer than `attempts` results.""" + attempts = 8 + # Simulate a partial group with 5 valid results + partial_count = 5 + + results = [make_good_result() for _ in range(partial_count)] + + # This mirrors the relaxed assertion in actor.py + assert isinstance(results, list) + assert isinstance(results[0], RolloutResult) + assert 0 < len(results) <= attempts, ( + f"Expected 1-{attempts} rollouts, got {len(results)}" + ) + + group_samples = sum(len(r.training_texts) for r in results) + assert group_samples == partial_count From 00ddfc33e62a6587c39232414d5593c81f0b4897 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 16 Apr 2026 09:52:08 +0000 Subject: [PATCH 44/85] fix vLLM port race condition when starting multiple actor servers Give each actor a distinct VLLM_PORT base (30000 + idx * 20) so concurrent get_open_port() calls don't collide with EADDRINUSE. --- pipelinerl/launch.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 17ea75a6..55477919 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -240,7 +240,9 @@ def run_actor_llm( save_command(log_dir, cmd) log_file_path = os.path.join(log_dir, "stdout.log") err_file_path = os.path.join(log_dir, "stderr.log") - env = {**os.environ, "CUDA_VISIBLE_DEVICES": gpu_str, **_get_quantization_env(cfg)} + # Give each actor a distinct base port so vLLM's get_open_port() race condition + # (TOCTOU: find-free-port then bind) doesn't cause EADDRINUSE when multiple servers start simultaneously. + env = {**os.environ, "CUDA_VISIBLE_DEVICES": gpu_str, "VLLM_PORT": str(30000 + actor_llm_idx * 20), **_get_quantization_env(cfg)} with open(log_file_path, "a") as log_file, open(err_file_path, "a") as err_file: proc = _popen( cmd, From e10e8ce7006f1f5f2369a6fbed05d0c572c76194 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 16 Apr 2026 15:04:57 +0000 Subject: [PATCH 45/85] add 8-GPU math 7B submit script and actor error handling tests - submit_eai_math_7b_8gpu.sh: 2 vLLM actors + 6 trainer GPUs, SDP=2, prefetch_factor matching depth_first_micro_batches for data/training overlap - tests/test_actor_error_handling.py: tests for graceful 4xx handling, partial group submission, and 5xx propagation --- submit_eai_math_7b_8gpu.sh | 69 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100755 submit_eai_math_7b_8gpu.sh diff --git a/submit_eai_math_7b_8gpu.sh b/submit_eai_math_7b_8gpu.sh new file mode 100755 index 00000000..90ac9e44 --- /dev/null +++ b/submit_eai_math_7b_8gpu.sh @@ -0,0 +1,69 @@ +#!/bin/bash +# Submit an 8-GPU eai job for math task with Qwen2.5-7B-Instruct: +# 2 vLLM actors (1 GPU each, TP=1) + 6-GPU fast-llm trainer (DP=3, ZeRO-2, SDP=2) +# 16K/14K sequences, depth_first_micro_batches=1024, full recompute, prefetch=1024 +# Run `eai login` before executing this script. + +IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" +RESULTS_DIR="/mnt/shared/denis/math_7b_results" +MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B-Instruct}" +MICROBATCHES="${1:-32}" + +TIMESTAMP=$(date +%Y%m%d_%H%M%S) +EXP_DIR="${RESULTS_DIR}/math_7b_8gpu_mb${MICROBATCHES}_${TIMESTAMP}" +JOB_NAME="math_7b_8gpu_mb${MICROBATCHES}_${TIMESTAMP}" + +CMD=" +set -e +mkdir -p ${EXP_DIR} +source /home/toolkit/code/PipelineRL/.venv/bin/activate +PYTHONHASHSEED=42 python -m pipelinerl.launch \ + --config-path /home/toolkit/code/PipelineRL/conf \ + --config-name math \ + 'streams=redis' \ + world.replicas=1 \ + world.actor_fraction=2 \ + world.preprocessor_fraction=0 \ + world.finetune_fraction=6 \ + model_path=${MODEL_PATH} \ + output_dir=${EXP_DIR} \ + wandb.wandb_workspace_root=${RESULTS_DIR} \ + wandb.wandb_entity_name=denisko-se \ + wandb.wandb_project_name=watermelon \ + wandb.wandb_group=eai_math7b_16k_sdp2_fastllm_integration \ + 'vllm_config.vllm_kwargs.gpu-memory-utilization=0.85' \ + 'vllm_config.vllm_kwargs.max-num-batched-tokens=8192' \ + 'vllm_config.vllm_kwargs.max_model_len=16000' \ + 'fast_llm.data.micro_batch_size=16000' \ + 'llm.parameters.max_tokens=14000' \ + 'test_llm.parameters.max_tokens=14000' \ + 'eval_every_n_versions=0' \ + 'fast_llm.training.num_workers=1' \ + '+fast_llm.training.prefetch_factor=${MICROBATCHES}' \ + 'fast_llm.schedule.depth_first_micro_batches=${MICROBATCHES}' \ + 'fast_llm.model.distributed.sequence_data_parallel=2' \ + '+fast_llm.model.distributed.timeout=3600' \ + '+fast_llm.model.base_model.decoder.block.mlp.recompute_level=full' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=0.02' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=0.02' \ + '+fast_llm.optimizer.learning_rate.base=1e-5' \ + '+fast_llm.optimizer.learning_rate.warmup_iterations=10' \ + '+fast_llm.optimizer.learning_rate.decay_style=cosine' \ + '+fast_llm.optimizer.learning_rate.decay_iterations=100000' \ + '+fast_llm.optimizer.beta_2=0.95' \ + '+fast_llm.optimizer.gradient_norm_clipping=0.3' \ + '+wandb.wandb_run_name=math7b_16k_sdp2_mb${MICROBATCHES}_lr1e5' +" + +eai job new \ + --preemptable \ + --gpu 8 \ + --cpu 128 \ + --mem 800 \ + --name "$JOB_NAME" \ + -i "$IMAGE" \ + --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ + --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ + --env "HOME=/home/toolkit" \ + --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ + -- /bin/bash -c "$CMD" From 562214aecb5e32b6780615c5d01f8123bd6f06bd Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 08:44:21 +0000 Subject: [PATCH 46/85] vllm1: default weight_update_mode to 'http' when args lacks the attr Tests build a minimal argparse Namespace for EngineManager.create_engine and do not set --weight-update-mode. Use getattr(args, 'weight_update_mode', 'http') at the three call sites so test helpers stay minimal and the full launcher (which always sets it) is unaffected. --- pipelinerl/vllm1.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 7357e116..c9c82ae8 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -329,7 +329,7 @@ async def init_actor_update_group(self): torch.cuda.device_count(), self.args.weight_update_group_init_method, self.args.weight_update_group_world_size, - self.args.weight_update_mode, + getattr(self.args, "weight_update_mode", "http"), ), ) @@ -564,11 +564,12 @@ async def create_engine( try: assert isinstance(engine.engine_core, AsyncMPClient) manager = EngineManager(args, engine, engine_config) + weight_update_mode = getattr(args, "weight_update_mode", "http") if not args.disable_weight_updates: await manager.init_actor_update_group() # Initialize Fast-LLM mode if enabled - if args.weight_update_mode == "fast-llm": + if weight_update_mode == "fast-llm": await manager.init_fast_llm_receiver() await manager.start_fast_llm_monitoring() logger.info("Fast-LLM weight update mode enabled") @@ -577,7 +578,7 @@ async def create_engine( finally: if not args.disable_weight_updates: # Stop Fast-LLM monitoring if enabled - if args.weight_update_mode == "fast-llm": + if weight_update_mode == "fast-llm": await manager.stop_fast_llm_monitoring() if not await manager.is_actor_update_group_destroyed(): @@ -643,7 +644,7 @@ def signal_handler(*_) -> None: app = build_app(args, supported_tasks) # Register HTTP endpoint only if using HTTP mode - if args.weight_update_mode == "http": + if getattr(args, "weight_update_mode", "http") == "http": @app.post("/receive_weight_update") async def _receive_weight_update(request: WeightUpdateRequest): await manager.receive_weight_update(request) From 0cde57705d87d46d9bb642539bd2af33f7456a89 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 08:47:03 +0000 Subject: [PATCH 47/85] launch: dispatch run_finetune to DeepSpeed when use_fast_llm=false fast-llm branch replaced the DeepSpeed run_finetune with a fast-llm-only implementation that unconditionally reads cfg.fast_llm + cfg.streams.host. Running with use_fast_llm=false hit ConfigAttributeError on cfg.streams.host. Split run_finetune into a dispatcher plus two helpers: - _run_finetune_fast_llm: existing fast-llm torchrun launch - _run_finetune_deepspeed: restored from vllm_v1 (accelerate+deepspeed launch of pipelinerl/entrypoints/run_finetune.py) Restores the HTTP/DeepSpeed training path without affecting fast-llm. --- pipelinerl/launch.py | 82 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index dd84a38a..713365e0 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -338,6 +338,88 @@ def run_environment(cfg: DictConfig, job: Job): def run_finetune(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: Path): + if cfg.use_fast_llm: + yield from _run_finetune_fast_llm(cfg, world_map, gpus, exp_dir) + else: + yield from _run_finetune_deepspeed(cfg, world_map, gpus, exp_dir) + + +def _run_finetune_deepspeed(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: Path): + if cfg.use_fsdp and cfg.use_deepspeed: + raise ValueError("Cannot use both FSDP and DeepSpeed") + cmd = [ + "python", + "-m", + "accelerate.commands.launch", + ] + if world_map.world_size > 1: + assert cfg.use_deepspeed + assert world_map.master_addr.startswith("dns-") and world_map.master_addr.endswith("-0") + hosts = [world_map.master_addr[:-2] + f"-{i}" for i in range(world_map.world_size)] + filter_parts = [] + for rank, job_list in world_map.job_map.items(): + for job in job_list: + if job.kind == "finetune": + filter_parts.append(f"{hosts[rank]}:{','.join(map(str, job.gpus))}") + deepspeed_include_filter = "@".join(filter_parts) + logger.info(f"Deepspeed include filter: {deepspeed_include_filter}") + hostfile_path = str(exp_dir / "hostfile.txt") + cmd += [ + "--num_machines", str(len(world_map.nodes_with_finetuning())), + "--machine_rank", str(world_map.my_finetuning_rank()), + "--main_process_ip", str(os.environ.get("MASTER_ADDR")), + "--main_process_port", str(os.environ.get("MASTER_PORT")), + "--deepspeed_hostfile", hostfile_path, + "--deepspeed_inclusion_filter", deepspeed_include_filter, + "--deepspeed_multinode_launcher", "nossh", + ] + this_file_path = Path(os.path.dirname(os.path.abspath(__file__))) + if cfg.use_deepspeed: + cmd += [ + "--use_deepspeed", + "--deepspeed_config_file", + str(this_file_path / f"../conf/deepspeed/{cfg.deepspeed_config}.json"), + ] + accelerate_config = cfg.accelerate_config + if accelerate_config is None: + if cfg.use_deepspeed: + accelerate_config = "deepspeed" + elif cfg.use_fsdp: + accelerate_config = "fsdp_mp" + else: + accelerate_config = "base_mp" + cmd += [ + "--config_file", + str(this_file_path / f"../conf/accelerate/{accelerate_config}.yaml"), + "--rdzv_backend", "c10d", + ] + if gpus: + gpus_str = str(",".join([str(gpu) for gpu in gpus])) if len(gpus) < world_map.node_size else "all" + cmd += ["--gpu-ids", gpus_str] + cmd += [ + "--num_processes", str(world_map.total_finetune_gpus), + "pipelinerl/entrypoints/run_finetune.py", + "--config-dir", f"{exp_dir}/conf", + "--config-name", "exp_config", + f"output_dir={exp_dir}", + f"hydra.run.dir={exp_dir}/finetune", + f"+me.weight_update_group_init_method=tcp://{world_map.master_addr}:{cfg.world.actor_group_port}", + f"+me.weight_update_group_world_size={world_map.weight_update_group_size}", + f"+me.llm_urls={'+'.join(world_map.get_actor_urls())}", + ] + if cfg.debug.mode in ["finetune", "open_loop", "finetune+preprocessor"]: + cmd.append("finetune.send_weight_updates=False") + + logger.info(f"Running DeepSpeed finetune with command: {' '.join(cmd)}") + save_command(exp_dir / "finetune", cmd) + env = dict(os.environ) + env["DS_ENV_FILE"] = str(exp_dir / ".deepspeed_env") + proc = _popen(cmd, env=env) + if proc is not None: + yield LaunchedProcess(kind="finetune", handle=proc) + + +def _run_finetune_fast_llm(cfg: DictConfig, world_map: WorldMap, gpus: list[int], exp_dir: Path): save_dir = exp_dir / "finetune" os.makedirs(save_dir, exist_ok=True) From e0f01a4759b5d09470ed3aea68d233a2da7df6e5 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 09:31:19 +0000 Subject: [PATCH 48/85] vllm1 HTTP path: use StatelessProcessGroup to match the trainer MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The merge kept fast-llm's HEAD choice of pipelinerl.torch_utils.init_extra_process_group for the HTTP worker path. But the trainer (pipelinerl/finetune_loop.py:421) creates a StatelessProcessGroup via torch_utils.stateless_init_process_group — the two rendezvous mechanisms are incompatible, causing an indefinite hang at init_actor_update_group when use_fast_llm=false (DeepSpeed). Switch the HTTP worker init to stateless_init_process_group to match the trainer, and dispatch the broadcast call in receive_weight_update on model_update_group type: torch.distributed.broadcast for the fast-llm ProcessGroupPool path, and StatelessProcessGroup.broadcast() for the HTTP path. Also: skip rollouts with empty logprobs in the guessing domain rather than letting make_training_text raise. vLLM V1 occasionally returns finish_reason='abort' with empty logprobs when a request races the mid-step weight-update pause; dropping the partial rollout keeps the actor alive. Not a real fix — see writeup. --- pipelinerl/domains/guessing/guessing.py | 5 ++++- pipelinerl/vllm1.py | 17 ++++++++++++----- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/pipelinerl/domains/guessing/guessing.py b/pipelinerl/domains/guessing/guessing.py index c14a6c09..d6b34686 100644 --- a/pipelinerl/domains/guessing/guessing.py +++ b/pipelinerl/domains/guessing/guessing.py @@ -68,7 +68,10 @@ async def generate_guessing_rollout( break latency = time.time() - time_start - training_texts = [make_training_text(llm, llm_call) for llm_call in llm_calls] + # vLLM can occasionally return finish_reason='abort' (race with mid-rollout + # weight updates) with an empty logprobs array. Skip those rather than letting + # make_training_text raise; the rest of the rollout is still useful. + training_texts = [make_training_text(llm, llm_call) for llm_call in llm_calls if llm_call.logprobs] for text in training_texts: text.reward = reward diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index c9c82ae8..20a3da07 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -120,12 +120,13 @@ def init_actor_update_group( os.environ.pop(_k, None) if weight_update_mode == 'http': - self.model_update_group = pipelinerl.torch_utils.init_extra_process_group( - group_name="actor", - backend="nccl", + # HTTP mode uses vLLM's StatelessProcessGroup to match the trainer, + # which in pipelinerl/finetune_loop.py uses torch_utils.stateless_init_process_group. + self.model_update_group = stateless_init_process_group( init_method=weight_update_group_init_method, rank=self.pg_rank, world_size=weight_update_group_world_size, + device=self.device, ) else: from fast_llm.engine.distributed.config import DistributedBackend @@ -145,8 +146,9 @@ def destroy_actor_update_group(self: LikeWorker): self._process_group_destroyed = True if isinstance(self.model_update_group, torch.distributed.ProcessGroup): torch.distributed.destroy_process_group(self.model_update_group) - else: + elif hasattr(self.model_update_group, "shutdown"): self.model_update_group.shutdown() + # StatelessProcessGroup has no shutdown method; rely on GC. def is_actor_update_group_destroyed(self: LikeWorker) -> bool: return getattr(self, "_process_group_destroyed", False) @@ -178,7 +180,12 @@ def receive_weight_update(self: LikeWorker, request_json: str): ) logger.debug(f" - Calling broadcast for {info.name}...") - torch.distributed.broadcast(buffer, src=0, group=self.model_update_group) + # StatelessProcessGroup exposes .broadcast(); torch.distributed.ProcessGroup + # (fast-llm path) uses the functional torch.distributed.broadcast. + if isinstance(self.model_update_group, torch.distributed.ProcessGroup): + torch.distributed.broadcast(buffer, src=0, group=self.model_update_group) + else: + self.model_update_group.broadcast(buffer, src=0, stream=torch.cuda.current_stream()) logger.debug(f" - Broadcast received for {info.name}") logger.debug(f" - Loading weights for {info.name}...") From 8893d2ce5a8645743d1d473d1f60b2f1ea73624c Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 09:34:31 +0000 Subject: [PATCH 49/85] preprocess: guard unpacked-mode popleft against partial queue When seq_packing=false, run_preprocessing_loop drains processed_entries_queue with a fixed-count popleft for train_batch_size entries without first checking the queue has enough. If the queue holds fewer than train_batch_size, the loop raises IndexError: pop from an empty deque and kills the preprocessor. The bug was masked on fast-llm because fast-llm always takes the use_fast_llm branch and never enters the unpacked code path. It surfaced as soon as we ran DeepSpeed/HTTP mode with seq_packing=false to work around the missing flash-attn wheel for torch 2.10. Guard with a length check and break out of the inner writing loop; the outer loop then refills the queue and retries. --- pipelinerl/preprocess.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pipelinerl/preprocess.py b/pipelinerl/preprocess.py index f847a41f..38d5c7cc 100644 --- a/pipelinerl/preprocess.py +++ b/pipelinerl/preprocess.py @@ -728,8 +728,11 @@ def run_preprocessing_loop( current_length = 0 logger.debug(f"[inner loop] Packed microbatch with {len(current_batch)} samples for trainer {trainer_id}") else: + # Unpacked path: need a full micro-batch before collating. + if len(processed_entries_queue) < cfg.finetune.train_batch_size: + break # wait for more data; outer loop will refill the queue batch_entries = [] - for _ in range(cfg.finetune.train_batch_size ): + for _ in range(cfg.finetune.train_batch_size): batch_entries.append(processed_entries_queue.popleft()) batch_encoding = collate(batch_entries, tokenizer=tokenizer) write_micro_batch_slices(trainer_id, data_writer, batch_encoding, cfg.finetune.seq_parallel) From 842736c44ad1bcd7e7a346a18340cd92e8fe14fc Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 10:09:01 +0000 Subject: [PATCH 50/85] launch: use absolute paths for redis --dir and --logfile redis-server chdir's to --dir before opening --logfile, so a relative --logfile fails with 'No such file or directory' when the pipeline launches redis from the repo root. Resolve output_dir to absolute early so both --dir and --logfile point at stable paths. --- pipelinerl/launch.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 713365e0..3a3cc143 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -611,8 +611,10 @@ def run_preprocess(world_map: WorldMap, preprocessor_idx: int, exp_dir: Path): def run_redis(cfg: DictConfig): - # Launch redis-server - redis_dir = Path(cfg.output_dir) / "redis" + # Launch redis-server. Resolve paths to absolutes because redis-server + # chdir's to --dir before opening --logfile, which breaks relative paths. + output_dir = Path(cfg.output_dir).resolve() + redis_dir = output_dir / "redis" os.makedirs(redis_dir, exist_ok=True) cmd = [ "redis-server", @@ -621,7 +623,7 @@ def run_redis(cfg: DictConfig): "--port", str(cfg.streams.port), "--dir", - str(cfg.output_dir), + str(output_dir), "--protected-mode", "no", "--save", From b753eceac3df4743b22a0458c93aad52647e7e8b Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 11:06:07 +0000 Subject: [PATCH 51/85] vllm1: drop pause/resume wrap on fast-llm path The merge added a pause_generation/resume_generation wrap to both HTTP and fast-llm weight-update paths symmetrically. On the fast-llm path this deadlocks the initial (step=0) weight broadcast: engine.pause_generation blocks waiting for in-flight requests to drain from a generator that hasn't started yet, so the NCCL broadcast send from fast-llm never gets a receiver. Origin/fast-llm calls the worker RPC directly with no wrap, and the baseline run on counting.yaml completed 10/10 iterations cleanly. This commit restores that behavior: EngineManager no longer has a receive_weight_update_fast_llm method, and start_fast_llm_monitoring calls collective_rpc_async directly again. HTTP path keeps the pause/resume wrap (PR #137's intended fix). --- pipelinerl/vllm1.py | 43 ++++++++++++++++++------------------------- 1 file changed, 18 insertions(+), 25 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 20a3da07..80ffef18 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -379,25 +379,6 @@ async def init_fast_llm_receiver(self): f"Fast-LLM receiver initialized (Redis {self._redis_host}:{self._redis_port})" ) - async def receive_weight_update_fast_llm(self): - """Run a fast-llm broadcast weight update, paused-for-the-duration. - - Pause/resume wraps the collective RPC symmetrically with the HTTP path - (see receive_weight_update) so that in-flight generation doesn't race - against mid-broadcast parameter swaps. - """ - async with self.update_lock: - logger.info("Pausing generation for fast-llm weight update") - await self.engine.pause_generation(mode="keep", clear_cache=False) - try: - await self.engine.engine_core.collective_rpc_async( - "receive_weight_update_fast_llm", args=() - ) - logger.info("Fast-llm weight update processed") - finally: - logger.info("Resuming generation after fast-llm weight update") - await self.engine.resume_generation() - async def start_fast_llm_monitoring(self): """Start a single Redis monitoring thread in the main process. @@ -456,7 +437,9 @@ def monitor_redis_stream(): ) try: future = asyncio.run_coroutine_threadsafe( - self.receive_weight_update_fast_llm(), + self.engine.engine_core.collective_rpc_async( + "receive_weight_update_fast_llm", args=() + ), loop, ) future.result() @@ -645,10 +628,17 @@ def signal_handler(*_) -> None: # Run HTTP server sock_addr = (args.host or "", args.port) sock = create_server_socket(sock_addr) - # vLLM 0.18.1 requires supported_tasks to build the app and app state. - supported_tasks = await manager.engine.get_supported_tasks() - logger.info(f"Supported tasks: {supported_tasks}") - app = build_app(args, supported_tasks) + # vLLM 0.18.1+ requires supported_tasks to build the app and app state; + # older vllm (e.g. 0.14.x) has 1-arg build_app / 3-arg init_app_state. + import inspect as _inspect + _build_app_params = _inspect.signature(build_app).parameters + if "supported_tasks" in _build_app_params and hasattr(manager.engine, "get_supported_tasks"): + supported_tasks = await manager.engine.get_supported_tasks() + logger.info(f"Supported tasks: {supported_tasks}") + app = build_app(args, supported_tasks) + else: + supported_tasks = None + app = build_app(args) # Register HTTP endpoint only if using HTTP mode if getattr(args, "weight_update_mode", "http") == "http": @@ -667,7 +657,10 @@ async def _training_finished(background_tasks: BackgroundTasks): else: logger.info("Fast-LLM mode: using Redis stream (no HTTP endpoint registered)") - await init_app_state(manager.engine, app.state, args, supported_tasks) + if "supported_tasks" in _inspect.signature(init_app_state).parameters: + await init_app_state(manager.engine, app.state, args, supported_tasks) + else: + await init_app_state(manager.engine, app.state, args) shutdown_task = await serve_http( app, sock, From 54d4eeb0ea58dfc3a0745e35cbf1a8439b6d1b43 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 11:33:48 +0000 Subject: [PATCH 52/85] vllm1: re-add pause/resume wrap on fast-llm path with startup gate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Re-adds EngineManager.receive_weight_update_fast_llm() with the same pause/resume wrap as the HTTP path, fixing the logprob-drift regression that was introduced when the wrap was dropped in b753ece. Root cause of the previous deadlock: the first weights_ready event arrives before the actor has started generating (it is blocked in wait_for_model_version), so vLLM has zero in-flight requests at that point. pause_generation(wait_for_inflight_requests=True) then blocks forever waiting for requests that never come, hanging the NCCL collective. Fix: first_weights_ready_seen boolean in monitor_redis_stream. The first weights_ready event (step can be 0 on a fresh start or k on resume) takes the raw collective_rpc_async path with no pause wrap — matching the prior behaviour that worked. Every subsequent event calls receive_weight_update_fast_llm() which does pause → RPC → resume. Also fixes both pause_generation call sites: the old mode="keep" kwarg was removed in vllm 0.14.0rc1+; replaced with wait_for_inflight_requests=True, clear_cache=False (equivalent semantics: drain existing requests without aborting them, preserve KV cache). Verified: 10/10 iterations on counting task, ~2s per weight update (pause → NCCL → resume), no deadlocks or NCCL timeouts. --- pipelinerl/vllm1.py | 57 +++++++++++++++++++++++++++++++++++++-------- 1 file changed, 47 insertions(+), 10 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index 80ffef18..aee8e616 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -356,7 +356,7 @@ async def is_actor_update_group_destroyed(self) -> bool: async def receive_weight_update(self, request: WeightUpdateRequest): async with self.update_lock: logger.info("Pausing generation for weight update") - await self.engine.pause_generation(mode="keep", clear_cache=False) + await self.engine.pause_generation(wait_for_inflight_requests=True, clear_cache=False) try: logger.info("Starting weight update...") await self.engine.engine_core.collective_rpc_async( @@ -379,6 +379,31 @@ async def init_fast_llm_receiver(self): f"Fast-LLM receiver initialized (Redis {self._redis_host}:{self._redis_port})" ) + async def receive_weight_update_fast_llm(self): + """Run a fast-llm broadcast weight update paused-for-the-duration. + + Pause/resume wraps the collective RPC symmetrically with the HTTP path + so that in-flight generation cannot interleave with a mid-broadcast + parameter swap (the source of logprob drift PR #137 closed). + + NOTE: this must NOT be used for the very first weights_ready event + after process startup, because at that point the actor has not yet + begun issuing rollouts (it's blocked in wait_for_model_version) and + pause_generation will deadlock waiting for an in-flight-decode state + that never arrives. The monitor thread gates this accordingly. + """ + async with self.update_lock: + logger.info("Pausing generation for fast-llm weight update") + await self.engine.pause_generation(wait_for_inflight_requests=True, clear_cache=False) + try: + await self.engine.engine_core.collective_rpc_async( + "receive_weight_update_fast_llm", args=() + ) + logger.info("Fast-llm weight update processed") + finally: + logger.info("Resuming generation after fast-llm weight update") + await self.engine.resume_generation() + async def start_fast_llm_monitoring(self): """Start a single Redis monitoring thread in the main process. @@ -403,6 +428,13 @@ def monitor_redis_stream(): stream_key = "fast_llm_events" payload_key = b"event" last_id = "0-0" + # First weights_ready event since this vLLM process started is the + # initial broadcast (step can be 0 on fresh start or k>0 on resume). + # Actor is still blocked in wait_for_model_version at this point, so + # vLLM has zero in-flight requests — pause_generation would deadlock. + # Take the raw RPC path for the first event; wrap with pause/resume + # thereafter, matching PR #137's guard against mid-rollout weight swaps. + first_weights_ready_seen = False logger.info("[FastLLM] Main-process Redis monitoring started") @@ -432,16 +464,21 @@ def monitor_redis_stream(): step = event.get("step") if event_type == "weights_ready": - logger.info( - f"[FastLLM] weights_ready step={step}, dispatching to workers" - ) - try: - future = asyncio.run_coroutine_threadsafe( - self.engine.engine_core.collective_rpc_async( - "receive_weight_update_fast_llm", args=() - ), - loop, + if not first_weights_ready_seen: + logger.info( + f"[FastLLM] weights_ready step={step} (initial broadcast — no pause wrap)" ) + coro = self.engine.engine_core.collective_rpc_async( + "receive_weight_update_fast_llm", args=() + ) + first_weights_ready_seen = True + else: + logger.info( + f"[FastLLM] weights_ready step={step}, dispatching to workers" + ) + coro = self.receive_weight_update_fast_llm() + try: + future = asyncio.run_coroutine_threadsafe(coro, loop) future.result() logger.info( f"[FastLLM] Weight update complete: step={step}" From 612b9bfee5dc33ef26f74a98aa7400a0fe9fbd7e Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 23 Apr 2026 13:45:10 +0000 Subject: [PATCH 53/85] vllm1: use _pause_generation helper that drains in-flight requests MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit pause_generation(mode="keep") freezes requests mid-generation and they get aborted during the NCCL flush, producing empty logprobs. The fix is mode="wait" which drains all in-flight decodes before the weight update and resumes cleanly after. Add _pause_generation() that detects the installed vLLM API at runtime via inspect.signature and calls mode="wait" on newer builds or the equivalent wait_for_inflight_requests=True on older ones. Also remove the guessing.py band-aid that silently dropped aborted rollouts — with proper draining the band-aid is unnecessary and hides real failures. --- pipelinerl/domains/guessing/guessing.py | 5 +---- pipelinerl/vllm1.py | 17 +++++++++++++++-- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/pipelinerl/domains/guessing/guessing.py b/pipelinerl/domains/guessing/guessing.py index d6b34686..c14a6c09 100644 --- a/pipelinerl/domains/guessing/guessing.py +++ b/pipelinerl/domains/guessing/guessing.py @@ -68,10 +68,7 @@ async def generate_guessing_rollout( break latency = time.time() - time_start - # vLLM can occasionally return finish_reason='abort' (race with mid-rollout - # weight updates) with an empty logprobs array. Skip those rather than letting - # make_training_text raise; the rest of the rollout is still useful. - training_texts = [make_training_text(llm, llm_call) for llm_call in llm_calls if llm_call.logprobs] + training_texts = [make_training_text(llm, llm_call) for llm_call in llm_calls] for text in training_texts: text.reward = reward diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index aee8e616..ff0a7562 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -1,4 +1,5 @@ import asyncio +import inspect import logging import os import signal @@ -315,6 +316,18 @@ def close_communicator(self): logger.info("Weight update communicator closed") +async def _pause_generation(engine: AsyncLLM) -> None: + """Pause generation, draining in-flight requests before returning. + + Adapts to the installed vLLM version at runtime: newer builds expose + pause_generation(mode=) while older ones use wait_for_inflight_requests=. + """ + if 'mode' in inspect.signature(engine.pause_generation).parameters: + await engine.pause_generation(mode="wait", clear_cache=False) + else: + await engine.pause_generation(wait_for_inflight_requests=True, clear_cache=False) + + class EngineManager: def __init__(self, args, engine: AsyncLLM, engine_config: Any): self.args = args @@ -356,7 +369,7 @@ async def is_actor_update_group_destroyed(self) -> bool: async def receive_weight_update(self, request: WeightUpdateRequest): async with self.update_lock: logger.info("Pausing generation for weight update") - await self.engine.pause_generation(wait_for_inflight_requests=True, clear_cache=False) + await _pause_generation(self.engine) try: logger.info("Starting weight update...") await self.engine.engine_core.collective_rpc_async( @@ -394,7 +407,7 @@ async def receive_weight_update_fast_llm(self): """ async with self.update_lock: logger.info("Pausing generation for fast-llm weight update") - await self.engine.pause_generation(wait_for_inflight_requests=True, clear_cache=False) + await _pause_generation(self.engine) try: await self.engine.engine_core.collective_rpc_async( "receive_weight_update_fast_llm", args=() From 6829195389fc8e5618145afece2ca823a1db2ae0 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 10:14:57 +0000 Subject: [PATCH 54/85] multinode: fix DeepSpeed path, log capture, Redis host, and add tests - launch.py: fix _run_finetune_deepspeed to use absolute path for run_finetune.py (relative path resolved against /home/toolkit in EAI pods) - launch.py: add stdout/stderr log capture to _run_finetune_deepspeed - launch.py: fix dns_address_map saved after address_map overwrite (captured pod IPs instead of DNS names); save before the pod IP exchange loop - launch.py: fix assertion crashes in main() and _run_finetune_deepspeed after pod IP exchange by using dns_address_map fallback for hostfile/filter - launch.py: set streams.host to rank-0 pod IP in multi-node so DeepSpeed workers on other nodes can reach Redis - world.py: expose dns_address_map attribute for post-exchange DNS lookups - tests/test_world_multinode.py: 33 tests covering pod IP exchange, DeepSpeed DNS name invariants, hostfile, Redis host, and absolute entrypoint path - README.md: add Multi-Node Requirements section (ports, config params, per-role connection topology, assumptions) - remove submit_eai_math_7b_8gpu.sh (local-only convenience script) Both fast-llm and DeepSpeed 2-node jobs validated to complete training step 1. --- README.md | 33 ++ pipelinerl/launch.py | 147 ++++++-- pipelinerl/world.py | 20 ++ submit_eai_math_7b_8gpu.sh | 69 ---- tests/test_world_multinode.py | 649 ++++++++++++++++++++++++++++++++++ 5 files changed, 829 insertions(+), 89 deletions(-) delete mode 100755 submit_eai_math_7b_8gpu.sh create mode 100644 tests/test_world_multinode.py diff --git a/README.md b/README.md index 4d870499..751f49eb 100644 --- a/README.md +++ b/README.md @@ -376,6 +376,39 @@ PipelineRL is organized as a modular, Hydra-driven pipeline with 6 core componen +## Multi-Node Requirements + +PipelineRL can span multiple nodes, with actor (vLLM) and trainer roles on separate machines. Each role opens outbound TCP connections to other roles; every target port must be reachable from the source node. + +### Ports and config params + +| Port (default) | Config param | Direction | Purpose | +|---|---|---|---| +| `streams.port` (11000) | `conf/streams/redis.yaml` | all nodes → rank-0 node | Redis data streams (actor → preprocessor → trainer) | +| `world.actor_group_port` (9000) | `conf/base.yaml` | actor node → trainer node | Weight-broadcast process group (NCCL TCPStore rendezvous) | +| `world.environment_start_port` (7777) | `conf/base.yaml` | actor node → environment node | Remote environment HTTP server | +| `8080 + gpu_local_idx` | derived from GPU placement | trainer node → actor node | vLLM HTTP endpoints for weight updates, one per GPU | +| `MASTER_PORT` env var | set by your cluster launcher | trainer nodes ↔ each other | torchrun / accelerate rendezvous between finetune ranks | + +### What each node connects to + +**Trainer node** opens connections to: +- `{actor_node_ip}:{8080 + i}` for each vLLM GPU `i` — to POST updated weights after each optimizer step. +- `{rank_0_ip}:{streams.port}` — to read training batches from Redis (when `streams=redis`). + +**Actor node** opens connections to: +- `{rank_0_ip}:{streams.port}` — to publish rollout data to Redis. +- `{rank_0_ip}:{world.actor_group_port}` — to join the NCCL weight-broadcast process group (vLLM workers connect as clients; the trainer creates the TCPStore server on this port). +- `{env_node_ip}:{world.environment_start_port + i}` — to call remote environment servers (if `environments[*].mode=remote`). + +**All finetune nodes** connect to each other on `MASTER_PORT` for the distributed training rendezvous (rank-0 finetune node is the server). + +### Topology assumptions + +- With fast-llm (`use_fast_llm=true`), each component must occupy whole nodes — torchrun requires every finetune rank to see a complete, identical GPU set. +- With `world.preprocessor_fraction=0`, every node is either a pure actor node or a pure trainer node (no mixing). +- The DeepSpeed hostfile and `--deepspeed_inclusion_filter` use DNS/hostname names (not IPs), so the cluster rendezvous port (`MASTER_PORT`) must be reachable via those names. All other cross-node connections use IP addresses and are independent of DNS. + # Install FastLLM+PipilineRL - use ` registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` image which also includes redis server. In `~/.research-interactive-env`: ```shell diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 3a3cc143..abb3e9c9 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -2,6 +2,7 @@ import math import os import shutil +import socket import subprocess import sys import time @@ -354,8 +355,9 @@ def _run_finetune_deepspeed(cfg: DictConfig, world_map: WorldMap, gpus: list[int ] if world_map.world_size > 1: assert cfg.use_deepspeed - assert world_map.master_addr.startswith("dns-") and world_map.master_addr.endswith("-0") - hosts = [world_map.master_addr[:-2] + f"-{i}" for i in range(world_map.world_size)] + # Use original DNS names (pod IP exchange may have replaced address_map with IPs). + dns_map = getattr(world_map, "dns_address_map", world_map.address_map) + hosts = [dns_map[i] for i in range(world_map.world_size)] filter_parts = [] for rank, job_list in world_map.job_map.items(): for job in job_list: @@ -398,7 +400,7 @@ def _run_finetune_deepspeed(cfg: DictConfig, world_map: WorldMap, gpus: list[int cmd += ["--gpu-ids", gpus_str] cmd += [ "--num_processes", str(world_map.total_finetune_gpus), - "pipelinerl/entrypoints/run_finetune.py", + str(this_file_path / "entrypoints/run_finetune.py"), "--config-dir", f"{exp_dir}/conf", "--config-name", "exp_config", f"output_dir={exp_dir}", @@ -414,7 +416,12 @@ def _run_finetune_deepspeed(cfg: DictConfig, world_map: WorldMap, gpus: list[int save_command(exp_dir / "finetune", cmd) env = dict(os.environ) env["DS_ENV_FILE"] = str(exp_dir / ".deepspeed_env") - proc = _popen(cmd, env=env) + save_dir = exp_dir / "finetune" + os.makedirs(save_dir, exist_ok=True) + log_file_path = save_dir / "stdout.log" + err_file_path = save_dir / "stderr.log" + with open(log_file_path, "a") as log_file, open(err_file_path, "a") as err_file: + proc = _popen(cmd, env=env, stdout=log_file, stderr=err_file) if proc is not None: yield LaunchedProcess(kind="finetune", handle=proc) @@ -454,7 +461,9 @@ def _run_finetune_fast_llm(cfg: DictConfig, world_map: WorldMap, gpus: list[int] if cfg.weight_broadcast and not bool(cfg.debug.mode): fast_llm_cfg["callbacks"]["streaming"]["host"] = cfg.streams.host fast_llm_cfg["callbacks"]["streaming"]["port"] = cfg.streams.port - fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["host"] = world_map.master_addr + # fast-llm runs on node 0 (same node as the TCPStore server); use localhost + # to avoid DNS self-resolution issues. vLLM (on node 1) uses master_addr. + fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["host"] = "localhost" fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["port"] = cfg.world.actor_group_port fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["external_world_size"] = world_map.weight_update_group_size - 1 @@ -464,17 +473,38 @@ def _run_finetune_fast_llm(cfg: DictConfig, world_map: WorldMap, gpus: list[int] model_type = cfg.fast_llm_finetune.model_type torchrun_port = cfg.fast_llm_finetune.torchrun_port - cmd = [ - "torchrun", - f"--nproc_per_node={len(gpus)}", - f"--master_port={torchrun_port}", - "--no_python", - str(Path(sys.executable).parent / "fast-llm"), - "train", - model_type, - "--config", - str(config_path), - ] + finetune_nodes = world_map.nodes_with_finetuning() + if len(finetune_nodes) > 1: + finetune_master = world_map.address_map[finetune_nodes[0]] + cmd = [ + "torchrun", + f"--nproc_per_node={len(gpus)}", + f"--nnodes={len(finetune_nodes)}", + f"--node_rank={world_map.my_finetuning_rank()}", + "--rdzv_backend=static", + "--rdzv_id=0", + f"--rdzv_endpoint={finetune_master}:{torchrun_port}", + "--rdzv_conf=timeout=3600", + "--max_restarts=0", + "--no_python", + str(Path(sys.executable).parent / "fast-llm"), + "train", + model_type, + "--config", + str(config_path), + ] + else: + cmd = [ + "torchrun", + f"--nproc_per_node={len(gpus)}", + f"--master_port={torchrun_port}", + "--no_python", + str(Path(sys.executable).parent / "fast-llm"), + "train", + model_type, + "--config", + str(config_path), + ] logger.info(f"Running finetune with command: {' '.join(cmd)}") save_command(save_dir, cmd) @@ -798,6 +828,65 @@ def setup_logging(log_file: Path): logger.info("Logging setup complete") +def _get_pod_ip() -> str: + """Return this pod's primary IP (bypasses Kubernetes Service kube-proxy).""" + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + try: + s.connect(("8.8.8.8", 80)) + return s.getsockname()[0] + finally: + s.close() + + +def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path) -> None: + """Exchange pod IPs across replicas via the shared NFS mount. + + Kubernetes Services only expose the declared master port; all other ports + (Redis, vLLM HTTP, TCPStore) are silently dropped for Service ClusterIPs. + Using pod IPs bypasses kube-proxy and gives full port access. + + After the exchange, all Job.url and Job.hostname fields are updated to use + pod IPs so every cross-node HTTP/TCP connection bypasses the Service. + """ + # Save DNS names before overwriting so DeepSpeed hostfile can use them. + world_map.dns_address_map = dict(world_map.address_map) + + ip_dir = exp_dir / ".pod_ips" + ip_dir.mkdir(parents=True, exist_ok=True) + my_ip = _get_pod_ip() + ip_file = ip_dir / f"rank_{world_map.my_rank}.txt" + ip_file.write_text(my_ip) + logger.info(f"Pod IP exchange: rank {world_map.my_rank} pod IP = {my_ip}") + + pod_ips = {} + for rank in range(world_map.world_size): + peer_file = ip_dir / f"rank_{rank}.txt" + waited = 0 + while not peer_file.exists(): + time.sleep(0.5) + waited += 0.5 + if waited % 10 == 0: + logger.info(f"Waiting for pod IP from rank {rank} ({waited:.0f}s)...") + pod_ip = peer_file.read_text().strip() + pod_ips[rank] = pod_ip + world_map.address_map[rank] = pod_ip + logger.info(f"Pod IP exchange: rank {rank} → {pod_ip}") + + world_map.master_addr = pod_ips[0] + logger.info(f"Updated master_addr to pod IP: {world_map.master_addr}") + + # Update all Job URLs and hostnames to pod IPs so cross-node connections + # bypass the Kubernetes Service (which only exposes declared ports). + for node, jobs in world_map.job_map.items(): + pod_ip = pod_ips[node] + dns_name = world_map.dns_address_map[node] + for job in jobs: + job.hostname = pod_ip + if job.url: + job.url = job.url.replace(dns_name, pod_ip) + logger.info("Updated all job URLs to pod IPs for direct pod-to-pod connectivity.") + + @hydra.main( config_path="../conf/", config_name="base", @@ -813,6 +902,15 @@ def main(cfg: DictConfig): log_file = exp_dir / "launcher" / f"launcher_{os.environ.get('RANK', 0)}.log" setup_logging(log_file) world_map = WorldMap(cfg, verbose=True) + + # In multi-node EAI jobs the `dns--` names are Kubernetes Services + # that expose only the declared master port. Connecting to those Service IPs + # on any other port (Redis, vLLM HTTP, TCPStore) gets SYN-dropped by kube-proxy. + # Pod IPs bypass kube-proxy and have all ports open, so we exchange pod IPs via + # a shared NFS file and update address_map before any TCP connections are made. + if world_map.world_size > 1: + _exchange_pod_ips(world_map, exp_dir) + cfg.jobs = [job.model_dump() for job in world_map.get_all_jobs()] group = str(exp_dir) @@ -832,7 +930,15 @@ def main(cfg: DictConfig): ) cfg.finetune.gradient_accumulation_passes = new_accum_passes if cfg.streams.backend == "redis": - cfg.streams.host = world_map.master_addr + if world_map.world_size > 1: + # Multi-node: use the pod IP of rank 0 (world_map.master_addr after pod IP + # exchange). Pod-to-pod connections are unrestricted on all ports, so rank 0 + # can reach its own Redis via its pod IP, and rank 1 via the cross-node pod IP. + # Using the pod IP (not localhost or a DNS name) also ensures the saved + # exp_config.yaml has a reachable address for DeepSpeed workers on node 1. + cfg.streams.host = world_map.master_addr + else: + cfg.streams.host = "localhost" set_streams_backend(**cfg.streams) processes = [] @@ -850,8 +956,9 @@ def main(cfg: DictConfig): redis.flushall() if world_map.world_size > 1: - assert world_map.master_addr.startswith("dns-") and world_map.master_addr.endswith("-0") - hosts = [world_map.master_addr[:-2] + f"-{i}" for i in range(world_map.world_size)] + # Use original DNS names (pod IP exchange may have replaced address_map with IPs). + dns_map = getattr(world_map, "dns_address_map", world_map.address_map) + hosts = [dns_map[i] for i in range(world_map.world_size)] hostfile_lines = [f"{host} slots=8" for host in hosts] deepspeed_hostfile_content = "\n".join(hostfile_lines) hostfile_path = str(exp_dir / "hostfile.txt") @@ -884,7 +991,7 @@ def main(cfg: DictConfig): if cfg.use_fast_llm and cfg.weight_broadcast and world_map.my_rank == 0: from torch.distributed import TCPStore broadcast_store = TCPStore( - host_name=world_map.master_addr, + host_name="0.0.0.0", port=cfg.world.actor_group_port, world_size=world_map.weight_update_group_size, is_master=True, diff --git a/pipelinerl/world.py b/pipelinerl/world.py index 517634c7..4db4de53 100644 --- a/pipelinerl/world.py +++ b/pipelinerl/world.py @@ -152,6 +152,26 @@ def _split_gpus_by_purpose(self, cfg): max(int(total_gpus * preprocessor_fraction), self.gpus_per_llm) if cfg.world.preprocessor_fraction else 0 ) desired_finetune_gpu_share = total_gpus - desired_actor_gpu_share - desired_preprocessor_gpu_share + + # For multi-node fast-llm spanning more than one node, every component + # must occupy whole nodes so torchrun's rdzv gets a clean full-node GPU + # set. Snap all three components; actor takes whatever remains. + # When fast-llm lands on a single node proportional allocation is fine. + if self.world_size > 1 and cfg.get("use_fast_llm", False): + finetune_frac = cfg.world.finetune_fraction / fraction_sum + finetune_nodes = max(1, round(self.world_size * finetune_frac)) + preprocessor_nodes = ( + max(1, round(self.world_size * preprocessor_fraction)) + if cfg.world.preprocessor_fraction else 0 + ) + actor_nodes = self.world_size - finetune_nodes - preprocessor_nodes + if cfg.world.actor_fraction > 0 and actor_nodes < 1: + finetune_nodes -= 1 + actor_nodes += 1 + if finetune_nodes > 1: + desired_finetune_gpu_share = finetune_nodes * self.node_size + desired_preprocessor_gpu_share = preprocessor_nodes * self.node_size + desired_actor_gpu_share = actor_nodes * self.node_size self._log_info( f"Desired GPU share: {desired_actor_gpu_share} for actors," f"{desired_preprocessor_gpu_share} for preprocessors, {desired_finetune_gpu_share} for finetune" diff --git a/submit_eai_math_7b_8gpu.sh b/submit_eai_math_7b_8gpu.sh deleted file mode 100755 index 90ac9e44..00000000 --- a/submit_eai_math_7b_8gpu.sh +++ /dev/null @@ -1,69 +0,0 @@ -#!/bin/bash -# Submit an 8-GPU eai job for math task with Qwen2.5-7B-Instruct: -# 2 vLLM actors (1 GPU each, TP=1) + 6-GPU fast-llm trainer (DP=3, ZeRO-2, SDP=2) -# 16K/14K sequences, depth_first_micro_batches=1024, full recompute, prefetch=1024 -# Run `eai login` before executing this script. - -IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" -RESULTS_DIR="/mnt/shared/denis/math_7b_results" -MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B-Instruct}" -MICROBATCHES="${1:-32}" - -TIMESTAMP=$(date +%Y%m%d_%H%M%S) -EXP_DIR="${RESULTS_DIR}/math_7b_8gpu_mb${MICROBATCHES}_${TIMESTAMP}" -JOB_NAME="math_7b_8gpu_mb${MICROBATCHES}_${TIMESTAMP}" - -CMD=" -set -e -mkdir -p ${EXP_DIR} -source /home/toolkit/code/PipelineRL/.venv/bin/activate -PYTHONHASHSEED=42 python -m pipelinerl.launch \ - --config-path /home/toolkit/code/PipelineRL/conf \ - --config-name math \ - 'streams=redis' \ - world.replicas=1 \ - world.actor_fraction=2 \ - world.preprocessor_fraction=0 \ - world.finetune_fraction=6 \ - model_path=${MODEL_PATH} \ - output_dir=${EXP_DIR} \ - wandb.wandb_workspace_root=${RESULTS_DIR} \ - wandb.wandb_entity_name=denisko-se \ - wandb.wandb_project_name=watermelon \ - wandb.wandb_group=eai_math7b_16k_sdp2_fastllm_integration \ - 'vllm_config.vllm_kwargs.gpu-memory-utilization=0.85' \ - 'vllm_config.vllm_kwargs.max-num-batched-tokens=8192' \ - 'vllm_config.vllm_kwargs.max_model_len=16000' \ - 'fast_llm.data.micro_batch_size=16000' \ - 'llm.parameters.max_tokens=14000' \ - 'test_llm.parameters.max_tokens=14000' \ - 'eval_every_n_versions=0' \ - 'fast_llm.training.num_workers=1' \ - '+fast_llm.training.prefetch_factor=${MICROBATCHES}' \ - 'fast_llm.schedule.depth_first_micro_batches=${MICROBATCHES}' \ - 'fast_llm.model.distributed.sequence_data_parallel=2' \ - '+fast_llm.model.distributed.timeout=3600' \ - '+fast_llm.model.base_model.decoder.block.mlp.recompute_level=full' \ - 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=0.02' \ - 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=0.02' \ - '+fast_llm.optimizer.learning_rate.base=1e-5' \ - '+fast_llm.optimizer.learning_rate.warmup_iterations=10' \ - '+fast_llm.optimizer.learning_rate.decay_style=cosine' \ - '+fast_llm.optimizer.learning_rate.decay_iterations=100000' \ - '+fast_llm.optimizer.beta_2=0.95' \ - '+fast_llm.optimizer.gradient_norm_clipping=0.3' \ - '+wandb.wandb_run_name=math7b_16k_sdp2_mb${MICROBATCHES}_lr1e5' -" - -eai job new \ - --preemptable \ - --gpu 8 \ - --cpu 128 \ - --mem 800 \ - --name "$JOB_NAME" \ - -i "$IMAGE" \ - --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ - --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ - --env "HOME=/home/toolkit" \ - --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ - -- /bin/bash -c "$CMD" diff --git a/tests/test_world_multinode.py b/tests/test_world_multinode.py new file mode 100644 index 00000000..351615c1 --- /dev/null +++ b/tests/test_world_multinode.py @@ -0,0 +1,649 @@ +"""Tests for multi-node WorldMap topology and fast-llm torchrun command assembly.""" + +import os +import sys +import tempfile +from pathlib import Path +from unittest.mock import patch, MagicMock + +import pytest +from omegaconf import OmegaConf + + +def _make_cfg( + actor_fraction=1, + finetune_fraction=1, + preprocessor_fraction=0, + replicas=1, + use_fast_llm=True, + tp=1, + pp=1, + seq_parallel=1, +): + """Minimal config for WorldMap construction.""" + return OmegaConf.create({ + "world": { + "actor_fraction": actor_fraction, + "finetune_fraction": finetune_fraction, + "preprocessor_fraction": preprocessor_fraction, + "replicas": replicas, + "actor_group_port": 9000, + "environment_start_port": 7777, + }, + "vllm_config": { + "vllm_kwargs": { + "tensor-parallel-size": tp, + "pipeline-parallel-size": pp, + } + }, + "finetune": {"seq_parallel": seq_parallel}, + "use_fast_llm": use_fast_llm, + "debug": {"mode": "", "place_inference_workers": True}, + }) + + +def _make_world_map(cfg, world_size, rank=0, master_addr="dns-test-0"): + from pipelinerl.world import WorldMap + env = { + "WORLD_SIZE": str(world_size), + "RANK": str(rank), + "MASTER_ADDR": master_addr, + } + with patch.dict(os.environ, env, clear=False): + # collect_environment_specs needs cfg fields that don't exist in minimal cfg; + # patch it out to avoid AttributeError. + with patch("pipelinerl.world.WorldMap._place_environments"): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + return WorldMap(cfg, verbose=False) + + +# --------------------------------------------------------------------------- +# WorldMap topology tests +# --------------------------------------------------------------------------- + +class TestWorldMapMultiNode: + + def test_2node_1actor_1finetune_whole_nodes(self): + """2 nodes: 1 actor node + 1 finetune node — each gets all 8 GPUs.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=2) + + assert wm.total_finetune_gpus == 8, "finetune should get exactly 1 full node" + assert wm.total_finetune_gpus % wm.node_size == 0 + assert len(wm.nodes_with_finetuning()) == 1 + + def test_4node_1actor_3finetune_whole_nodes(self): + """4 nodes: 1 actor node + 3 finetune nodes.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3) + wm = _make_world_map(cfg, world_size=4) + + assert wm.total_finetune_gpus == 24, "finetune should get exactly 3 full nodes" + assert wm.total_finetune_gpus % wm.node_size == 0 + assert len(wm.nodes_with_finetuning()) == 3 + + def test_4node_2actor_2finetune_whole_nodes(self): + """4 nodes: 2 actor nodes + 2 finetune nodes.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=2) + wm = _make_world_map(cfg, world_size=4) + + assert wm.total_finetune_gpus == 16 + assert wm.total_finetune_gpus % wm.node_size == 0 + assert len(wm.nodes_with_finetuning()) == 2 + + def test_finetune_always_at_least_one_node(self): + """Even with a large actor fraction, finetune gets at least 1 full node.""" + cfg = _make_cfg(actor_fraction=3, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=4) + + assert len(wm.nodes_with_finetuning()) >= 1 + assert wm.total_finetune_gpus >= wm.node_size + assert wm.total_finetune_gpus % wm.node_size == 0 + + def test_actors_never_exceed_world_size_minus_one(self): + """Actor nodes never consume all nodes — at least 1 reserved for finetune.""" + cfg = _make_cfg(actor_fraction=10, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=4) + + finetune_nodes = len(wm.nodes_with_finetuning()) + assert finetune_nodes >= 1 + assert finetune_nodes < 4 + + def test_single_node_unchanged(self): + """Single-node path is not affected by the multi-node rounding.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6) + # Single-node: world_size=1, node_size = actual device count (mocked) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + assert wm.total_finetune_gpus == 6 + assert wm.world_size == 1 + + def test_nodes_with_finetuning_returns_sorted_ranks(self): + """nodes_with_finetuning() returns a sorted list of node ranks.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3) + wm = _make_world_map(cfg, world_size=4) + + fn = wm.nodes_with_finetuning() + assert fn == sorted(fn) + + def test_my_finetuning_rank_on_finetune_node(self): + """my_finetuning_rank() returns 0 for the first finetune node.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + # With 2 nodes, finetune is on node 0 (actor on node 1 due to reversed placement) + wm = _make_world_map(cfg, world_size=2, rank=0) + + finetune_nodes = wm.nodes_with_finetuning() + # my_rank=0 should be a finetune node + assert 0 in finetune_nodes + assert wm.my_finetuning_rank() == finetune_nodes.index(0) + + def test_4node_with_preprocessor_all_whole_nodes(self): + """4 nodes, actor=1, preprocessor=1, finetune=6: all three get whole nodes.""" + cfg = _make_cfg(actor_fraction=1, preprocessor_fraction=1, finetune_fraction=6) + wm = _make_world_map(cfg, world_size=4) + + assert wm.total_finetune_gpus % wm.node_size == 0, "finetune must be whole nodes" + # preprocessor and actor GPU shares should also be multiples of node_size + total = wm.world_size * wm.node_size + actor_gpus = total - wm.total_finetune_gpus - wm.gpus_per_preprocessor * cfg.world.replicas + assert actor_gpus % wm.node_size == 0, "actor must be whole nodes" + assert (wm.gpus_per_preprocessor * cfg.world.replicas) % wm.node_size == 0, "preprocessor must be whole nodes" + assert wm.total_finetune_gpus + actor_gpus + wm.gpus_per_preprocessor * cfg.world.replicas == total + + def test_3node_with_preprocessor_all_whole_nodes(self): + """3 nodes, actor=1, preprocessor=1, finetune=1: each component gets 1 node.""" + cfg = _make_cfg(actor_fraction=1, preprocessor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=3) + + assert wm.total_finetune_gpus % wm.node_size == 0 + total = wm.world_size * wm.node_size + actor_gpus = total - wm.total_finetune_gpus - wm.gpus_per_preprocessor * cfg.world.replicas + assert actor_gpus % wm.node_size == 0 + assert (wm.gpus_per_preprocessor * cfg.world.replicas) % wm.node_size == 0 + + def test_address_map_derived_from_master_addr(self): + """address_map entries follow the dns-- pattern.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + assert wm.address_map[0] == "dns-abc123-0" + assert wm.address_map[1] == "dns-abc123-1" + + +# --------------------------------------------------------------------------- +# torchrun command assembly test +# --------------------------------------------------------------------------- + +class TestTorchrunCommand: + + def _capture_cmd(self, world_map, cfg_extra=None): + """Run _run_finetune_fast_llm with mocked I/O and capture the torchrun command.""" + from pipelinerl.launch import _run_finetune_fast_llm + + cfg = OmegaConf.create({ + "model_path": "/tmp/fake_model", + "weight_broadcast": False, + "debug": {"mode": "", "log_data_pipeline": False}, + "streams": {"host": "localhost", "port": 11000}, + "wandb": { + "wandb_workspace_root": "/tmp", + "wandb_entity_name": "test", + "wandb_project_name": "test", + "wandb_group": "test", + }, + "fast_llm": { + "training": { + "train_iters": 10, + "wandb": {"entity_name": None, "project_name": None, "group_name": None}, + }, + "data": {"datasets": {"training": {"type": "streaming", "host": None, "port": None}}}, + "pretrained": {"format": "llama", "path": None, "model_weights": True}, + "run": {"experiment_dir": None, "experiment_name": None}, + "callbacks": {}, + }, + "fast_llm_finetune": { + "model_type": "llama", + "torchrun_port": 29500, + "model_format": "llama", + }, + }) + if cfg_extra: + cfg = OmegaConf.merge(cfg, OmegaConf.create(cfg_extra)) + + captured_cmd = [] + + def mock_popen(cmd, **kwargs): + captured_cmd.extend(cmd) + return None # no process spawned + + with tempfile.TemporaryDirectory() as tmp: + exp_dir = Path(tmp) + # Patch os.path.isdir to pass the model_path check + with patch("pipelinerl.launch._popen", side_effect=mock_popen): + with patch("pipelinerl.launch.save_command"): + with patch("os.path.isdir", return_value=True): + list(_run_finetune_fast_llm(cfg, world_map, gpus=[0, 1, 2, 3], exp_dir=exp_dir)) + + return captured_cmd + + def test_single_node_uses_master_port(self): + """Single-node torchrun uses --master_port, no rdzv flags.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + cmd = self._capture_cmd(wm) + assert "--master_port=29500" in cmd + assert "--rdzv_backend=static" not in cmd + assert "--nnodes=6" not in cmd + + def test_2node_1finetune_uses_single_node_torchrun(self): + """2-node job with 1 actor + 1 finetune node: fast-llm spans 1 node → single-node torchrun.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=2, rank=0, master_addr="dns-abc-0") + + assert len(wm.nodes_with_finetuning()) == 1, "only 1 finetune node in 2-node job" + cmd = self._capture_cmd(wm) + # Should use simple --master_port, not rdzv + assert "--master_port=29500" in cmd + assert "--rdzv_backend=static" not in cmd + + def test_multi_node_uses_static_rdzv(self): + """Fast-llm spanning multiple nodes uses static rdzv with correct nnodes and node_rank.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3) + wm = _make_world_map(cfg, world_size=4, rank=0, master_addr="dns-abc-0") + + assert len(wm.nodes_with_finetuning()) == 3 + cmd = self._capture_cmd(wm) + assert "--rdzv_backend=static" in cmd + assert "--rdzv_id=0" in cmd + assert "--max_restarts=0" in cmd + finetune_count = len(wm.nodes_with_finetuning()) + assert f"--nnodes={finetune_count}" in cmd + assert f"--node_rank={wm.my_finetuning_rank()}" in cmd + finetune_master = wm.address_map[wm.nodes_with_finetuning()[0]] + assert any(f"--rdzv_endpoint={finetune_master}:29500" in arg for arg in cmd) + assert not any("--master_port" in arg for arg in cmd) + + def test_multi_node_4nodes_correct_nnodes(self): + """4-node job: torchrun nnodes = 3 (finetune nodes only).""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3) + wm = _make_world_map(cfg, world_size=4, rank=0) + + cmd = self._capture_cmd(wm) + finetune_count = len(wm.nodes_with_finetuning()) + assert finetune_count == 3 + assert f"--nnodes={finetune_count}" in cmd + + +# --------------------------------------------------------------------------- +# DeepSpeed regression: snapping must NOT apply when use_fast_llm=False +# --------------------------------------------------------------------------- + +class TestWorldMapDeepSpeed: + + def test_deepspeed_single_node_fractional_split(self): + """Single-node DeepSpeed split is unchanged — 2 actor GPUs + 6 finetune GPUs.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6, use_fast_llm=False) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + assert wm.total_finetune_gpus == 6 + assert wm.world_size == 1 + + def test_deepspeed_multinode_no_rounding(self): + """Multi-node DeepSpeed: no whole-node snapping (handled by DeepSpeed itself).""" + # 2 nodes, actor_fraction=1, finetune_fraction=1 → 8 finetune GPUs (happens to be whole node) + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2) + # Should still compute correctly without triggering fast-llm rounding path + assert wm.total_finetune_gpus > 0 + assert wm.world_size == 2 + + def test_fast_llm_single_node_unchanged(self): + """Single-node fast-llm: fractional split within one node is preserved.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6, use_fast_llm=True) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + assert wm.total_finetune_gpus == 6 + assert wm.world_size == 1 + + +# --------------------------------------------------------------------------- +# Pod IP exchange: dns_address_map, job URL rewriting, DeepSpeed/fast-llm compat +# --------------------------------------------------------------------------- + +def _simulate_pod_ip_exchange(wm, pod_ips: dict): + """Simulate _exchange_pod_ips without NFS I/O. + + Sets dns_address_map to original DNS names, updates address_map and job + URLs/hostnames to pod IPs — mirrors the real function's side-effects. + """ + from pipelinerl.launch import _exchange_pod_ips as real_fn # noqa: F401 (not called) + # Save DNS names first (matches the real implementation order) + wm.dns_address_map = dict(wm.address_map) + # Overwrite address_map with pod IPs + for rank, ip in pod_ips.items(): + wm.address_map[rank] = ip + wm.master_addr = pod_ips[0] + # Rewrite job URLs/hostnames + for node, jobs in wm.job_map.items(): + dns_name = wm.dns_address_map[node] + pod_ip = pod_ips[node] + for job in jobs: + job.hostname = pod_ip + if job.url: + job.url = job.url.replace(dns_name, pod_ip) + + +class TestPodIPExchange: + + def test_dns_address_map_holds_original_dns_names(self): + """After pod IP exchange, dns_address_map contains original DNS names, not pod IPs.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + pod_ips = {0: "10.0.0.1", 1: "10.0.0.2"} + _simulate_pod_ip_exchange(wm, pod_ips) + + assert wm.dns_address_map[0] == "dns-abc123-0" + assert wm.dns_address_map[1] == "dns-abc123-1" + + def test_address_map_updated_to_pod_ips(self): + """After pod IP exchange, address_map and master_addr hold pod IPs.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + pod_ips = {0: "10.0.0.1", 1: "10.0.0.2"} + _simulate_pod_ip_exchange(wm, pod_ips) + + assert wm.address_map[0] == "10.0.0.1" + assert wm.address_map[1] == "10.0.0.2" + assert wm.master_addr == "10.0.0.1" + + def test_job_urls_rewritten_to_pod_ips(self): + """After pod IP exchange, actor_llm job URLs use pod IPs, not DNS names.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + # Verify that actor_llm jobs have DNS-based URLs before exchange + actor_urls_before = [job.url for job in wm.get_all_jobs() if job.kind == "actor_llm"] + assert all("dns-abc123-1" in u for u in actor_urls_before) + + pod_ips = {0: "10.0.0.1", 1: "10.0.0.2"} + _simulate_pod_ip_exchange(wm, pod_ips) + + actor_urls_after = [job.url for job in wm.get_all_jobs() if job.kind == "actor_llm"] + assert all("10.0.0.2" in u for u in actor_urls_after), f"Expected pod IP in URLs: {actor_urls_after}" + assert all("dns-abc123" not in u for u in actor_urls_after) + + def test_no_dns_address_map_without_exchange(self): + """Without pod IP exchange, dns_address_map is not set (no AttributeError).""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + assert not hasattr(wm, "dns_address_map") + + +# --------------------------------------------------------------------------- +# DeepSpeed command assembly: hostfile and inclusion filter use DNS names +# --------------------------------------------------------------------------- + +class TestDeepSpeedCommand: + + def _make_ds_cfg(self): + return OmegaConf.create({ + "use_deepspeed": True, + "use_fsdp": False, + "deepspeed_config": "zero2", + "accelerate_config": None, + "world": {"actor_group_port": 9000}, + "debug": {"mode": ""}, + }) + + def _capture_ds_cmd(self, world_map, cfg_extra=None): + """Run _run_finetune_deepspeed with mocked I/O and capture the command.""" + from pipelinerl.launch import _run_finetune_deepspeed + + cfg = self._make_ds_cfg() + if cfg_extra: + cfg = OmegaConf.merge(cfg, OmegaConf.create(cfg_extra)) + + captured_cmd = [] + + def mock_popen(cmd, **kwargs): + captured_cmd.extend(cmd) + return None + + with tempfile.TemporaryDirectory() as tmp: + exp_dir = Path(tmp) + (exp_dir / "hostfile.txt").write_text("") # pre-create + with patch("pipelinerl.launch._popen", side_effect=mock_popen): + with patch("pipelinerl.launch.save_command"): + with patch.dict(os.environ, {"MASTER_ADDR": "dns-test-0", "MASTER_PORT": "29501"}): + list(_run_finetune_deepspeed(cfg, world_map, gpus=[0, 1, 2, 3], exp_dir=exp_dir)) + + return captured_cmd + + def test_deepspeed_multinode_uses_dns_names_without_exchange(self): + """DeepSpeed 2-node without pod IP exchange: inclusion filter uses DNS names.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + cmd = self._capture_ds_cmd(wm) + # The deepspeed_inclusion_filter should contain the DNS hostname for the finetune node + filter_arg = next((c for c in cmd if "dns-abc123" in c), None) + assert filter_arg is not None, f"Expected DNS name in cmd, got: {cmd}" + + def test_deepspeed_multinode_after_pod_ip_exchange_uses_dns_names(self): + """After pod IP exchange, DeepSpeed inclusion filter still uses DNS names (not pod IPs).""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + # Simulate pod IP exchange + _simulate_pod_ip_exchange(wm, {0: "10.0.0.1", 1: "10.0.0.2"}) + + cmd = self._capture_ds_cmd(wm) + # Inclusion filter must still use DNS names, not pod IPs + filter_arg = next((c for c in cmd if "dns-abc123" in c), None) + assert filter_arg is not None, f"Expected DNS name in DS filter after pod IP exchange, got: {cmd}" + # Pod IPs must NOT appear in the inclusion filter + assert not any("10.0.0" in c for c in cmd if "--deepspeed_inclusion_filter" not in c and "@" in c), \ + f"Pod IP leaked into DS filter: {cmd}" + + def test_deepspeed_single_node_no_pod_ip_exchange(self): + """Single-node DeepSpeed: no world_size>1 branch, pod IP exchange never runs.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6, use_fast_llm=False) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + assert wm.world_size == 1 + assert not hasattr(wm, "dns_address_map") + # Should not crash even without dns_address_map + cmd = self._capture_ds_cmd(wm) + assert "--num_machines" not in cmd # single-node, no multi-machine flags + + +# --------------------------------------------------------------------------- +# Hostfile creation in main(): uses dns_address_map after pod IP exchange +# --------------------------------------------------------------------------- + +class TestHostfileCreation: + + def test_hostfile_uses_dns_names_after_pod_ip_exchange(self): + """The DeepSpeed hostfile written by main() uses DNS names even after pod IP exchange.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + # Simulate pod IP exchange + _simulate_pod_ip_exchange(wm, {0: "10.0.0.1", 1: "10.0.0.2"}) + + dns_map = getattr(wm, "dns_address_map", wm.address_map) + hosts = [dns_map[i] for i in range(wm.world_size)] + + assert hosts[0] == "dns-abc123-0" + assert hosts[1] == "dns-abc123-1" + assert "10.0.0" not in hosts[0] + assert "10.0.0" not in hosts[1] + + def test_hostfile_uses_address_map_without_exchange(self): + """Without pod IP exchange, dns_address_map is absent — falls back to address_map.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + dns_map = getattr(wm, "dns_address_map", wm.address_map) + hosts = [dns_map[i] for i in range(wm.world_size)] + + assert hosts[0] == "dns-abc123-0" + assert hosts[1] == "dns-abc123-1" + + +# --------------------------------------------------------------------------- +# Redis host in saved exp_config.yaml for multi-node (DeepSpeed + Redis) +# --------------------------------------------------------------------------- + +class TestRedisHostMultiNode: + + def _compute_streams_host(self, world_map, my_rank: int) -> str: + """Mirror the launch.py logic for cfg.streams.host selection.""" + if world_map.world_size > 1: + return world_map.master_addr + return "localhost" + + def test_single_node_redis_host_is_localhost(self): + """Single-node: Redis host is localhost regardless of pod IP exchange.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6, use_fast_llm=False) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + host = self._compute_streams_host(wm, my_rank=0) + assert host == "localhost" + + def test_multinode_rank0_redis_host_is_pod_ip(self): + """Multi-node rank 0: Redis host is pod IP (not localhost) after exchange. + + This ensures the saved exp_config.yaml has a reachable address for + DeepSpeed workers on other nodes. + """ + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + _simulate_pod_ip_exchange(wm, {0: "10.0.0.1", 1: "10.0.0.2"}) + + host = self._compute_streams_host(wm, my_rank=0) + assert host == "10.0.0.1", "rank 0 should use pod IP so saved config is reachable cross-node" + assert host != "localhost" + + def test_multinode_rank1_redis_host_is_pod_ip(self): + """Multi-node rank 1: Redis host is pod IP of rank 0.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0", rank=1) + _simulate_pod_ip_exchange(wm, {0: "10.0.0.1", 1: "10.0.0.2"}) + + host = self._compute_streams_host(wm, my_rank=1) + assert host == "10.0.0.1", "rank 1 should use rank 0's pod IP to reach Redis" + + def test_multinode_both_ranks_same_redis_host(self): + """Both ranks in a 2-node job resolve to the same Redis host (pod IP of rank 0).""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm0 = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0", rank=0) + wm1 = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0", rank=1) + + _simulate_pod_ip_exchange(wm0, {0: "10.0.0.1", 1: "10.0.0.2"}) + _simulate_pod_ip_exchange(wm1, {0: "10.0.0.1", 1: "10.0.0.2"}) + + host0 = self._compute_streams_host(wm0, my_rank=0) + host1 = self._compute_streams_host(wm1, my_rank=1) + + assert host0 == host1 == "10.0.0.1" + + def test_multinode_without_pod_ip_exchange_uses_master_addr(self): + """Without pod IP exchange, multi-node uses master_addr (DNS name) for Redis. + + This is a fallback; the pod IP exchange should always run in practice + but the code must not crash without it. + """ + cfg = _make_cfg(actor_fraction=1, finetune_fraction=1, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=2, master_addr="dns-abc123-0") + + # No pod IP exchange — master_addr is still a DNS name + assert wm.master_addr == "dns-abc123-0" + host = self._compute_streams_host(wm, my_rank=0) + assert host == "dns-abc123-0" # DNS name (port filtering may apply, but code doesn't crash) + + +# --------------------------------------------------------------------------- +# DeepSpeed run_finetune.py path: must be absolute (not relative to CWD) +# --------------------------------------------------------------------------- + +class TestDeepSpeedEntrypointPath: + + def _capture_ds_cmd(self, world_map): + from pipelinerl.launch import _run_finetune_deepspeed + from omegaconf import OmegaConf + + cfg = OmegaConf.create({ + "use_deepspeed": True, + "use_fsdp": False, + "deepspeed_config": "zero2", + "accelerate_config": None, + "world": {"actor_group_port": 9000}, + "debug": {"mode": ""}, + }) + captured_cmd = [] + + def mock_popen(cmd, **kwargs): + captured_cmd.extend(cmd) + return None + + with tempfile.TemporaryDirectory() as tmp: + exp_dir = Path(tmp) + with patch("pipelinerl.launch._popen", side_effect=mock_popen): + with patch("pipelinerl.launch.save_command"): + with patch.dict(os.environ, {"MASTER_ADDR": "dns-test-0", "MASTER_PORT": "29501"}): + list(_run_finetune_deepspeed(cfg, world_map, gpus=[0, 1, 2, 3], exp_dir=exp_dir)) + + return captured_cmd + + def test_run_finetune_path_is_absolute(self): + """run_finetune.py must be an absolute path so it works regardless of CWD. + + When EAI starts the pod, CWD is /home/toolkit (not the repo root). A relative + path like 'pipelinerl/entrypoints/run_finetune.py' resolves to + '/home/toolkit/pipelinerl/...' which doesn't exist. + """ + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6, use_fast_llm=False) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + cmd = self._capture_ds_cmd(wm) + + # Find the run_finetune.py argument + finetune_script = next((c for c in cmd if "run_finetune.py" in c), None) + assert finetune_script is not None, f"run_finetune.py not found in cmd: {cmd}" + assert Path(finetune_script).is_absolute(), ( + f"run_finetune.py path must be absolute but got: {finetune_script!r}. " + "A relative path resolves against CWD which is /home/toolkit in EAI pods." + ) + assert Path(finetune_script).exists(), ( + f"run_finetune.py absolute path must exist: {finetune_script!r}" + ) From 56f4a8fff09c73cd7528b92b9a111cdb96fea77b Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 11:45:14 +0000 Subject: [PATCH 55/85] launch: write per-node output files in multinode finetune to avoid NFS races Both fast-llm and DeepSpeed finetune paths now append _node{rank} to all per-node output files (config, start.sh, stdout.log, stderr.log) when running across multiple finetune nodes. Single-node runs keep the original names. --- pipelinerl/launch.py | 34 +++--- tests/test_world_multinode.py | 193 ++++++++++++++++++++++++++++++++++ 2 files changed, 214 insertions(+), 13 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index abb3e9c9..7637221d 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -412,14 +412,18 @@ def _run_finetune_deepspeed(cfg: DictConfig, world_map: WorldMap, gpus: list[int if cfg.debug.mode in ["finetune", "open_loop", "finetune+preprocessor"]: cmd.append("finetune.send_weight_updates=False") + finetune_nodes = world_map.nodes_with_finetuning() + finetune_rank = world_map.my_finetuning_rank() + node_suffix = f"_node{finetune_rank}" if len(finetune_nodes) > 1 else "" + logger.info(f"Running DeepSpeed finetune with command: {' '.join(cmd)}") - save_command(exp_dir / "finetune", cmd) + save_command(exp_dir / "finetune", cmd, suffix=node_suffix) env = dict(os.environ) env["DS_ENV_FILE"] = str(exp_dir / ".deepspeed_env") save_dir = exp_dir / "finetune" os.makedirs(save_dir, exist_ok=True) - log_file_path = save_dir / "stdout.log" - err_file_path = save_dir / "stderr.log" + log_file_path = save_dir / f"stdout{node_suffix}.log" + err_file_path = save_dir / f"stderr{node_suffix}.log" with open(log_file_path, "a") as log_file, open(err_file_path, "a") as err_file: proc = _popen(cmd, env=env, stdout=log_file, stderr=err_file) if proc is not None: @@ -467,20 +471,24 @@ def _run_finetune_fast_llm(cfg: DictConfig, world_map: WorldMap, gpus: list[int] fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["port"] = cfg.world.actor_group_port fast_llm_cfg["callbacks"]["streaming"]["broadcast"]["external_world_size"] = world_map.weight_update_group_size - 1 - # Save fully populated config — fast-llm reads it directly with no further overrides. - config_path = save_dir / "fast_llm_config.yaml" - OmegaConf.save(OmegaConf.create(fast_llm_cfg), config_path) - + # Use per-node suffixes for all output files to avoid NFS write races when multiple + # finetune nodes share the same experiment directory. model_type = cfg.fast_llm_finetune.model_type torchrun_port = cfg.fast_llm_finetune.torchrun_port finetune_nodes = world_map.nodes_with_finetuning() + finetune_rank = world_map.my_finetuning_rank() + node_suffix = f"_node{finetune_rank}" if len(finetune_nodes) > 1 else "" + + config_path = save_dir / f"fast_llm_config{node_suffix}.yaml" + OmegaConf.save(OmegaConf.create(fast_llm_cfg), config_path) + if len(finetune_nodes) > 1: finetune_master = world_map.address_map[finetune_nodes[0]] cmd = [ "torchrun", f"--nproc_per_node={len(gpus)}", f"--nnodes={len(finetune_nodes)}", - f"--node_rank={world_map.my_finetuning_rank()}", + f"--node_rank={finetune_rank}", "--rdzv_backend=static", "--rdzv_id=0", f"--rdzv_endpoint={finetune_master}:{torchrun_port}", @@ -507,12 +515,12 @@ def _run_finetune_fast_llm(cfg: DictConfig, world_map: WorldMap, gpus: list[int] ] logger.info(f"Running finetune with command: {' '.join(cmd)}") - save_command(save_dir, cmd) + save_command(save_dir, cmd, suffix=node_suffix) env = dict(os.environ) env["PYTHONHASHSEED"] = "42" env["CUDA_VISIBLE_DEVICES"] = ",".join(str(gpu) for gpu in gpus) - log_file_path = save_dir / "stdout.log" - err_file_path = save_dir / "stderr.log" + log_file_path = save_dir / f"stdout{node_suffix}.log" + err_file_path = save_dir / f"stderr{node_suffix}.log" with open(log_file_path, "a") as log_file, open(err_file_path, "a") as err_file: proc = _popen(cmd, env=env, stdout=log_file, stderr=err_file) if proc is not None: @@ -670,9 +678,9 @@ def run_redis(cfg: DictConfig): yield LaunchedProcess(kind="redis", handle=proc) -def save_command(script_dir: Path, cmd): +def save_command(script_dir: Path, cmd, suffix: str = ""): os.makedirs(script_dir, exist_ok=True) - script_path = script_dir / "start.sh" + script_path = script_dir / f"start{suffix}.sh" with open(script_path, "w") as f: f.write("#!/bin/bash\n") # Properly quote arguments for the shell script diff --git a/tests/test_world_multinode.py b/tests/test_world_multinode.py index 351615c1..14d1474a 100644 --- a/tests/test_world_multinode.py +++ b/tests/test_world_multinode.py @@ -647,3 +647,196 @@ def test_run_finetune_path_is_absolute(self): assert Path(finetune_script).exists(), ( f"run_finetune.py absolute path must exist: {finetune_script!r}" ) + + +# --------------------------------------------------------------------------- +# Per-node file naming: fast-llm and DeepSpeed avoid NFS write races +# --------------------------------------------------------------------------- + +class TestPerNodeFileNaming: + """Verify that multinode fast-llm and DeepSpeed finetune runs write separate + output files per node (config, start.sh, stdout, stderr) to avoid NFS races.""" + + def _capture_fast_llm_files(self, world_map, gpus=None): + """Run _run_finetune_fast_llm and return captured file suffix info.""" + from pipelinerl.launch import _run_finetune_fast_llm + + cfg = OmegaConf.create({ + "model_path": "/tmp/fake_model", + "weight_broadcast": False, + "debug": {"mode": "", "log_data_pipeline": False}, + "streams": {"host": "localhost", "port": 11000}, + "wandb": { + "wandb_workspace_root": "/tmp", + "wandb_entity_name": "test", + "wandb_project_name": "test", + "wandb_group": "test", + }, + "fast_llm": { + "training": { + "train_iters": 10, + "wandb": {"entity_name": None, "project_name": None, "group_name": None}, + }, + "data": {"datasets": {"training": {"type": "streaming", "host": None, "port": None}}}, + "pretrained": {"format": "llama", "path": None, "model_weights": True}, + "run": {"experiment_dir": None, "experiment_name": None}, + "callbacks": {}, + }, + "fast_llm_finetune": { + "model_type": "llama", + "torchrun_port": 29500, + "model_format": "llama", + }, + }) + + written_files = {} + + real_open = open + + def mock_popen(cmd, **kwargs): + written_files["stdout"] = str(kwargs.get("stdout", {}).name if hasattr(kwargs.get("stdout"), "name") else "") + written_files["stderr"] = str(kwargs.get("stderr", {}).name if hasattr(kwargs.get("stderr"), "name") else "") + return None + + captured_save = {} + + def mock_save_command(script_dir, cmd, suffix=""): + captured_save["suffix"] = suffix + captured_save["dir"] = str(script_dir) + + captured_config = {} + + real_omegaconf_save = None + + with tempfile.TemporaryDirectory() as tmp: + exp_dir = Path(tmp) + with patch("pipelinerl.launch._popen", side_effect=mock_popen): + with patch("pipelinerl.launch.save_command", side_effect=mock_save_command): + with patch("os.path.isdir", return_value=True): + with patch("omegaconf.OmegaConf.save") as mock_cfg_save: + list(_run_finetune_fast_llm(cfg, world_map, gpus=gpus or [0, 1, 2, 3], exp_dir=exp_dir)) + if mock_cfg_save.call_args: + # OmegaConf.save(cfg, path) — second positional arg is path + args = mock_cfg_save.call_args[0] + captured_config["path"] = str(args[1]) if len(args) > 1 else "" + + return { + "config_path": captured_config.get("path", ""), + "save_suffix": captured_save.get("suffix", ""), + "stdout": written_files.get("stdout", ""), + "stderr": written_files.get("stderr", ""), + } + + def _capture_deepspeed_files(self, world_map, gpus=None): + """Run _run_finetune_deepspeed and return captured file suffix.""" + from pipelinerl.launch import _run_finetune_deepspeed + + cfg = OmegaConf.create({ + "use_deepspeed": True, + "use_fsdp": False, + "deepspeed_config": "zero2", + "accelerate_config": None, + "world": {"actor_group_port": 9000}, + "debug": {"mode": ""}, + }) + + captured_save = {} + written_files = {} + + def mock_popen(cmd, **kwargs): + written_files["stdout"] = str(kwargs.get("stdout", {}).name if hasattr(kwargs.get("stdout"), "name") else "") + written_files["stderr"] = str(kwargs.get("stderr", {}).name if hasattr(kwargs.get("stderr"), "name") else "") + return None + + def mock_save_command(script_dir, cmd, suffix=""): + captured_save["suffix"] = suffix + + with tempfile.TemporaryDirectory() as tmp: + exp_dir = Path(tmp) + with patch("pipelinerl.launch._popen", side_effect=mock_popen): + with patch("pipelinerl.launch.save_command", side_effect=mock_save_command): + with patch.dict(os.environ, {"MASTER_ADDR": "dns-test-0", "MASTER_PORT": "29501"}): + list(_run_finetune_deepspeed(cfg, world_map, gpus=gpus or [0, 1, 2, 3], exp_dir=exp_dir)) + + return { + "save_suffix": captured_save.get("suffix", ""), + "stdout": written_files.get("stdout", ""), + "stderr": written_files.get("stderr", ""), + } + + # --- fast-llm single-node: no suffix --- + + def test_fast_llm_single_node_no_suffix(self): + """Single-node fast-llm: no _node0 suffix — backward compat.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + result = self._capture_fast_llm_files(wm) + assert result["save_suffix"] == "", f"Single-node must have no suffix, got: {result['save_suffix']!r}" + assert "_node" not in result["config_path"], f"Single-node config must have no _node suffix: {result['config_path']}" + + # --- fast-llm multinode: each node gets its own suffix --- + + def test_fast_llm_multinode_node0_suffix(self): + """4-node fast-llm, finetune node 0: files get _node0 suffix. + Actor takes the last node (rank 3), so ranks 0/1/2 are finetune.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3) + wm = _make_world_map(cfg, world_size=4, rank=0) # rank 0 = first finetune node + + result = self._capture_fast_llm_files(wm) + assert result["save_suffix"] == "_node0", f"Expected _node0, got: {result['save_suffix']!r}" + assert "_node0" in result["config_path"], f"Config path must contain _node0: {result['config_path']}" + + def test_fast_llm_multinode_node1_suffix(self): + """4-node fast-llm, finetune node 1: files get _node1 suffix.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3) + wm = _make_world_map(cfg, world_size=4, rank=1) # rank 1 = second finetune node + + result = self._capture_fast_llm_files(wm) + assert result["save_suffix"] == "_node1", f"Expected _node1, got: {result['save_suffix']!r}" + assert "_node1" in result["config_path"] + + def test_fast_llm_multinode_node2_suffix(self): + """4-node fast-llm, finetune node 2: files get _node2 suffix.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3) + wm = _make_world_map(cfg, world_size=4, rank=2) # rank 2 = third finetune node + + result = self._capture_fast_llm_files(wm) + assert result["save_suffix"] == "_node2", f"Expected _node2, got: {result['save_suffix']!r}" + + # --- DeepSpeed single-node: no suffix --- + + def test_deepspeed_single_node_no_suffix(self): + """Single-node DeepSpeed: no _node suffix.""" + cfg = _make_cfg(actor_fraction=2, finetune_fraction=6, use_fast_llm=False) + with patch("torch.cuda.device_count", return_value=8): + with patch("pipelinerl.utils.collect_environment_specs", return_value=[]): + with patch("pipelinerl.world.WorldMap._place_environments"): + from pipelinerl.world import WorldMap + wm = WorldMap(cfg, verbose=False) + + result = self._capture_deepspeed_files(wm) + assert result["save_suffix"] == "", f"Single-node must have no suffix, got: {result['save_suffix']!r}" + + # --- DeepSpeed multinode: each node gets its own suffix --- + + def test_deepspeed_multinode_node0_suffix(self): + """4-node DeepSpeed, finetune node 0: save_command gets _node0 suffix. + Actor takes the last node (rank 3), so ranks 0/1/2 are finetune.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=4, rank=0) # rank 0 = first finetune node + + result = self._capture_deepspeed_files(wm) + assert result["save_suffix"] == "_node0", f"Expected _node0, got: {result['save_suffix']!r}" + + def test_deepspeed_multinode_node2_suffix(self): + """4-node DeepSpeed, finetune node 2: save_command gets _node2 suffix.""" + cfg = _make_cfg(actor_fraction=1, finetune_fraction=3, use_fast_llm=False) + wm = _make_world_map(cfg, world_size=4, rank=2) # rank 2 = third finetune node + + result = self._capture_deepspeed_files(wm) + assert result["save_suffix"] == "_node2", f"Expected _node2, got: {result['save_suffix']!r}" From 4dce398acebd8431ec64838c4a7d9207bdc87f44 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 14:06:06 +0000 Subject: [PATCH 56/85] vllm v1: enable processed_logprobs mode by default Add logprobs-mode=processed_logprobs to the V1 defaults in _get_vllm_kwargs alongside the existing prefix-caching and async-scheduling fixes. processed_logprobs returns log-probs from the forward pass rather than recomputing them, preventing stale values when weights change between generation and scoring. --- conf/base.yaml | 3 +-- pipelinerl/launch.py | 3 +++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/conf/base.yaml b/conf/base.yaml index 6093c45e..ef057e86 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -71,8 +71,7 @@ vllm_config: pipeline-parallel-size: 1 generation-config: vllm max_model_len: 10000 - # V1 specific settings - # logprobs-mode: processed_logprobs + # V1 specific settings (enable-prefix-caching, async-scheduling, logprobs-mode set programmatically) # V0 specific settings disable-log-requests: "" disable-frontend-multiprocessing: "" diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 7637221d..70e234df 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -142,6 +142,9 @@ def _get_vllm_kwargs(cfg: DictConfig, *, use_v1: bool) -> dict: # Keep V1 actor/reference serving closer to the legacy V0 path by default. kwargs.setdefault("enable-prefix-caching", False) kwargs.setdefault("async-scheduling", False) + # processed_logprobs returns log-probs computed during the forward pass, + # avoiding stale values if weights change between generation and scoring. + kwargs.setdefault("logprobs-mode", "processed_logprobs") for legacy_flag in ("disable-log-requests", "disable-frontend-multiprocessing"): if legacy_flag in kwargs: kwargs.pop(legacy_flag) From eaa2a9ab197477d574b861b60b090a47a78614f0 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 14:15:54 +0000 Subject: [PATCH 57/85] vllm1: use mode=keep in pause_generation to match PR #137 Align with the approach in PR #137: pause accepts new requests but lets in-flight ones finish naturally, rather than draining the engine fully before the weight update begins. --- pipelinerl/vllm1.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pipelinerl/vllm1.py b/pipelinerl/vllm1.py index ff0a7562..b75240c3 100644 --- a/pipelinerl/vllm1.py +++ b/pipelinerl/vllm1.py @@ -317,15 +317,15 @@ def close_communicator(self): async def _pause_generation(engine: AsyncLLM) -> None: - """Pause generation, draining in-flight requests before returning. + """Pause generation without draining in-flight requests. Adapts to the installed vLLM version at runtime: newer builds expose pause_generation(mode=) while older ones use wait_for_inflight_requests=. """ if 'mode' in inspect.signature(engine.pause_generation).parameters: - await engine.pause_generation(mode="wait", clear_cache=False) + await engine.pause_generation(mode="keep", clear_cache=False) else: - await engine.pause_generation(wait_for_inflight_requests=True, clear_cache=False) + await engine.pause_generation(wait_for_inflight_requests=False, clear_cache=False) class EngineManager: From eeac65e59bb1cba6129e409955aa12f4b36bf05b Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 15:18:40 +0000 Subject: [PATCH 58/85] actor: retry rollout on vLLM abort instead of crashing When vLLM aborts an in-flight request during a weight update pause it returns finish_reason='abort' with empty logprobs. Previously this propagated to make_training_text which raised ValueError and crashed the entire actor. Raise asyncio.TimeoutError instead so the actor's existing retry logic replays the rollout cleanly. --- pipelinerl/async_llm.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/pipelinerl/async_llm.py b/pipelinerl/async_llm.py index dc6b6bcc..0b7916fd 100644 --- a/pipelinerl/async_llm.py +++ b/pipelinerl/async_llm.py @@ -1,3 +1,4 @@ +import asyncio import base64 import io import logging @@ -131,6 +132,11 @@ async def llm_async_generate( logger.exception(f"Failed to parse llm response: {data}") raise + if finish_reason == "abort": + raise asyncio.TimeoutError( + f"vLLM aborted request (weight update in progress); will retry" + ) + output = LLMOutput(content=content) llm_call = llm.log_output(prompt, output, count_tokens=False) llm_call.prompt_length_tokens = data["usage"]["prompt_tokens"] From 7f1b87a514077d1425f3475f73e82b79a101bc30 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 15:27:19 +0000 Subject: [PATCH 59/85] world: read GPUS_PER_NODE from env instead of hardcoding 8; add resume support to submit script --- pipelinerl/world.py | 2 +- submit_eai_math_7b_multinode.sh | 131 ++++++++++++++++++++++++++++++++ 2 files changed, 132 insertions(+), 1 deletion(-) create mode 100755 submit_eai_math_7b_multinode.sh diff --git a/pipelinerl/world.py b/pipelinerl/world.py index 4db4de53..8173e987 100644 --- a/pipelinerl/world.py +++ b/pipelinerl/world.py @@ -57,7 +57,7 @@ def __init__(self, cfg: DictConfig, verbose: bool = False): tp = llm_kwargs.get("tensor-parallel-size", 1) pp = llm_kwargs.get("pipeline-parallel-size", 1) self.gpus_per_llm = tp * pp - self.node_size = 8 if self.world_size > 1 else torch.cuda.device_count() + self.node_size = int(os.environ.get("GPUS_PER_NODE", torch.cuda.device_count())) place_inference_jobs = not cfg.debug.mode or cfg.debug.place_inference_workers if place_inference_jobs: diff --git a/submit_eai_math_7b_multinode.sh b/submit_eai_math_7b_multinode.sh new file mode 100755 index 00000000..34138003 --- /dev/null +++ b/submit_eai_math_7b_multinode.sh @@ -0,0 +1,131 @@ +#!/bin/bash +# Submit a multi-node EAI job for math task with Qwen2.5-7B-Instruct. +# Topology: 1 actor node (vLLM) + (NODES-1) fast-llm trainer nodes. +# Usage: bash submit_eai_math_7b_multinode.sh [NODES] [TARGET_TOTAL_MB] [TIMESTAMP] +# Example (fresh): bash submit_eai_math_7b_multinode.sh 4 60 +# -> 1 actor node, 3 fast-llm nodes (BDP=12, depth_first=5, total_MBs=60) +# Example (resume): bash submit_eai_math_7b_multinode.sh 4 60 20260427_144646 +# -> resumes experiment math_7b_4node_mb5x12_20260427_144646 (checkpoint + wandb run preserved) +# Run `eai login` before executing this script. + +IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" +RESULTS_DIR="/mnt/shared/denis/math_7b_results" +MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B-Instruct}" +NODES="${1:-2}" +TARGET_TOTAL_MB="${2:-1024}" # target total microbatches/step across all DP ranks +TIMESTAMP="${3:-$(date +%Y%m%d_%H%M%S)}" # experiment ID; omit to start fresh + +FINETUNE_NODES=$((NODES - 1)) +if [ "$FINETUNE_NODES" -lt 1 ]; then + echo "ERROR: NODES must be >= 2 (got $NODES)" >&2 + exit 1 +fi + +# SDP=2 throughout; batch_data_parallel = finetune_gpus / SDP +FINETUNE_GPUS=$((FINETUNE_NODES * 8)) +SDP=2 +BDP=$((FINETUNE_GPUS / SDP)) + +# depth_first_micro_batches and prefetch_factor are per-rank (per DP group) +# Round up so total >= TARGET_TOTAL_MB +DEPTH_FIRST=$(( (TARGET_TOTAL_MB + BDP - 1) / BDP )) +PREFETCH=$DEPTH_FIRST + +EXP_NAME="math_7b_${NODES}node_mb${DEPTH_FIRST}x${BDP}_${TIMESTAMP}" +EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" + +if [ -n "${3:-}" ]; then + JOB_NAME="${EXP_NAME}_resume" + echo "RESUMING: ${EXP_DIR}" +else + JOB_NAME="${EXP_NAME}" +fi + +echo "Config: ${NODES} nodes, ${FINETUNE_NODES} fast-llm nodes, BDP=${BDP}, depth_first=${DEPTH_FIRST}, total_MBs=$((DEPTH_FIRST * BDP))" + +CMD=" +set -e +mkdir -p ${EXP_DIR} +source /home/toolkit/code/PipelineRL/.venv/bin/activate +PYTHONHASHSEED=42 python -m pipelinerl.launch \ + --config-path /home/toolkit/code/PipelineRL/conf \ + --config-name math \ + 'streams=redis' \ + world.replicas=1 \ + world.actor_fraction=1 \ + world.preprocessor_fraction=0 \ + world.finetune_fraction=${FINETUNE_NODES} \ + model_path=${MODEL_PATH} \ + output_dir=${EXP_DIR} \ + wandb.wandb_workspace_root=${RESULTS_DIR} \ + wandb.wandb_entity_name=denisko-se \ + wandb.wandb_project_name=watermelon \ + wandb.wandb_group=eai_math7b_multinode \ + 'vllm_config.vllm_kwargs.gpu-memory-utilization=0.85' \ + 'vllm_config.vllm_kwargs.max-num-batched-tokens=8192' \ + 'vllm_config.vllm_kwargs.max_model_len=16000' \ + 'fast_llm.data.micro_batch_size=16000' \ + 'llm.parameters.max_tokens=14000' \ + 'test_llm.parameters.max_tokens=14000' \ + 'eval_every_n_versions=0' \ + 'fast_llm.training.num_workers=1' \ + '+fast_llm.training.prefetch_factor=${PREFETCH}' \ + 'fast_llm.schedule.depth_first_micro_batches=${DEPTH_FIRST}' \ + 'fast_llm.model.distributed.sequence_data_parallel=${SDP}' \ + '+fast_llm.model.distributed.timeout=3600' \ + '+fast_llm.model.base_model.decoder.block.mlp.recompute_level=full' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=0.02' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=0.02' \ + '+fast_llm.model.base_model.head.losses.grpo.compute_extra_metrics=true' \ + '+fast_llm.model.base_model.head.losses.grpo.compute_entropy_metric=true' \ + 'fast_llm.training.checkpoint.interval=20' \ + '+fast_llm.optimizer.learning_rate.base=1e-5' \ + '+fast_llm.optimizer.learning_rate.warmup_iterations=10' \ + '+fast_llm.optimizer.learning_rate.decay_style=cosine' \ + '+fast_llm.optimizer.learning_rate.decay_iterations=100000' \ + '+fast_llm.optimizer.beta_2=0.95' \ + '+fast_llm.optimizer.gradient_norm_clipping=0.3' +" + +# Generate a job spec YAML with all ports exposed in the Kubernetes Service. +# Ports: 29501 (EAI replica master), 11000 (Redis), 9000 (TCPStore weight-broadcast), +# 8080-8087 (vLLM HTTP servers, one per GPU on the actor node), +# 7777 (environment server, for actor→environment HTTP). +SPEC_YAML=$(mktemp /tmp/eai_job_spec_XXXXXX.yaml) +cat > "$SPEC_YAML" << 'YAML_EOF' +options: + internal-dns: + name: "" + ports: + - port: 29501 + - port: 11000 + - port: 9000 + - port: 7777 + - port: 8080 + - port: 8081 + - port: 8082 + - port: 8083 + - port: 8084 + - port: 8085 + - port: 8086 + - port: 8087 +YAML_EOF + +eai job new \ + --file "$SPEC_YAML" \ + --preemptable \ + --replicas "$NODES" \ + --gpu 8 \ + --cpu 128 \ + --mem 800 \ + --name "$JOB_NAME" \ + -i "$IMAGE" \ + --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ + --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ + --env "HOME=/home/toolkit" \ + --env "GPUS_PER_NODE=8" \ + --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ + --env "TRITON_CACHE_DIR=/tmp/triton_cache" \ + -- /bin/bash -c "$CMD" + +rm -f "$SPEC_YAML" From 8b19f1fd9d352794f8c10d83f3f2909e659c558b Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 15:43:07 +0000 Subject: [PATCH 60/85] launch: clear stale pod IPs on resume; unique resume job names in submit script MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Stale .pod_ips files from a previous run caused the pod IP exchange to return immediately with old IPs — slow-starting ranks were never waited for, breaking torchrun rendezvous on resume. clean_up() now removes the directory so every run waits for all live ranks to write fresh IPs. Submit script appends a timestamp to resume job names so EAI does not reject them as duplicates. --- pipelinerl/launch.py | 5 +++++ submit_eai_math_7b_multinode.sh | 5 +++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 70e234df..cd4f4b60 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -702,6 +702,11 @@ def clean_up(exp_dir, force_restart): os.remove(f"{exp_dir}/streams") if os.path.exists(f"{exp_dir}/dump.rdb"): os.remove(f"{exp_dir}/dump.rdb") + # Remove stale pod IP files so the exchange waits for all live ranks. + pod_ips_dir = Path(exp_dir) / ".pod_ips" + if pod_ips_dir.exists(): + shutil.rmtree(pod_ips_dir) + logger.info("Removed stale .pod_ips directory") if force_restart: if os.path.exists(f"{exp_dir}/finetune"): diff --git a/submit_eai_math_7b_multinode.sh b/submit_eai_math_7b_multinode.sh index 34138003..ce1493dc 100755 --- a/submit_eai_math_7b_multinode.sh +++ b/submit_eai_math_7b_multinode.sh @@ -35,8 +35,9 @@ EXP_NAME="math_7b_${NODES}node_mb${DEPTH_FIRST}x${BDP}_${TIMESTAMP}" EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" if [ -n "${3:-}" ]; then - JOB_NAME="${EXP_NAME}_resume" - echo "RESUMING: ${EXP_DIR}" + RESUME_TS=$(date +%Y%m%d_%H%M%S) + JOB_NAME="${EXP_NAME}_resume_${RESUME_TS}" + echo "RESUMING: ${EXP_DIR} (job: ${JOB_NAME})" else JOB_NAME="${EXP_NAME}" fi From 223687f66891205dd07a077ab892973e273c351f Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 16:08:34 +0000 Subject: [PATCH 61/85] launch: fix pod IP exchange on resume with session-token barrier Stale .pod_ips files from a previous job caused rank 0 to complete the exchange with wrong IPs before other ranks had even started. Then clean_up() deleted rank_0.txt, leaving ranks 1-N waiting forever. Rank 0 now atomically wipes the old directory and writes a UUID session token before any rank writes its IP. Non-zero ranks block on the session token, so they only write after rank 0 has cleared stale data. Remove the incorrect pod_ips deletion from clean_up() (it was too late: exchange already complete, and it wiped rank_0.txt other ranks needed). --- pipelinerl/launch.py | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index cd4f4b60..8e294b47 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -6,6 +6,7 @@ import subprocess import sys import time +import uuid from dataclasses import dataclass from pathlib import Path from typing import List, TextIO @@ -702,12 +703,6 @@ def clean_up(exp_dir, force_restart): os.remove(f"{exp_dir}/streams") if os.path.exists(f"{exp_dir}/dump.rdb"): os.remove(f"{exp_dir}/dump.rdb") - # Remove stale pod IP files so the exchange waits for all live ranks. - pod_ips_dir = Path(exp_dir) / ".pod_ips" - if pod_ips_dir.exists(): - shutil.rmtree(pod_ips_dir) - logger.info("Removed stale .pod_ips directory") - if force_restart: if os.path.exists(f"{exp_dir}/finetune"): logger.info("Cleaning up finetune directory") @@ -868,8 +863,27 @@ def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path) -> None: world_map.dns_address_map = dict(world_map.address_map) ip_dir = exp_dir / ".pod_ips" - ip_dir.mkdir(parents=True, exist_ok=True) my_ip = _get_pod_ip() + session_file = ip_dir / "session" + + if world_map.my_rank == 0: + # Wipe any stale files from a previous job, then create a fresh session token. + # Non-zero ranks wait for this token before writing their own IPs, preventing + # them from seeing stale IP files from the previous job. + if ip_dir.exists(): + shutil.rmtree(ip_dir) + ip_dir.mkdir(parents=True) + session_file.write_text(uuid.uuid4().hex) + logger.info("Pod IP exchange: rank 0 created fresh session") + else: + # Wait until rank 0 has wiped stale data and written the session token. + waited = 0 + while not session_file.exists(): + time.sleep(0.5) + waited += 0.5 + if waited % 10 == 0: + logger.info(f"Waiting for pod IP session token from rank 0 ({waited:.0f}s)...") + ip_file = ip_dir / f"rank_{world_map.my_rank}.txt" ip_file.write_text(my_ip) logger.info(f"Pod IP exchange: rank {world_map.my_rank} pod IP = {my_ip}") From b6ea563faf1fe6c85f36777ca3710dcf396edee3 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 16:17:50 +0000 Subject: [PATCH 62/85] launch: use job-specific MASTER_ADDR as pod IP session token MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The UUID approach was broken: a non-zero rank arriving before rank 0 would see the stale session UUID from the previous job, skip waiting, write its IP — then rank 0 would wipe the dir (deleting the fresh IP) and write a new UUID. Rank 0 then waits forever for that rank's file. Use the rank-0 DNS name from MASTER_ADDR as the token instead. It is unique per EAI job (contains a job UUID), so non-zero ranks reject a stale session by comparing token content to their own MASTER_ADDR. --- pipelinerl/launch.py | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 8e294b47..c69a8f02 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -866,19 +866,23 @@ def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path) -> None: my_ip = _get_pod_ip() session_file = ip_dir / "session" + # Use the job-unique DNS name as the session token. MASTER_ADDR contains a + # job-specific string (e.g. "dns--0") that differs between EAI runs, + # so non-zero ranks can reject a stale session left by a previous job. + job_token = world_map.dns_address_map[0] + if world_map.my_rank == 0: - # Wipe any stale files from a previous job, then create a fresh session token. - # Non-zero ranks wait for this token before writing their own IPs, preventing - # them from seeing stale IP files from the previous job. + # Wipe any stale files from a previous job, then write the job token. if ip_dir.exists(): shutil.rmtree(ip_dir) ip_dir.mkdir(parents=True) - session_file.write_text(uuid.uuid4().hex) - logger.info("Pod IP exchange: rank 0 created fresh session") + session_file.write_text(job_token) + logger.info(f"Pod IP exchange: rank 0 created fresh session (token={job_token})") else: - # Wait until rank 0 has wiped stale data and written the session token. + # Wait until session exists AND contains this job's token. + # A stale session from a previous job has a different token and is ignored. waited = 0 - while not session_file.exists(): + while not (session_file.exists() and session_file.read_text().strip() == job_token): time.sleep(0.5) waited += 0.5 if waited % 10 == 0: From 9fcfb206808d52e9a3bed8ac7df3d53e2acaa457 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 16:22:16 +0000 Subject: [PATCH 63/85] launch: simplify pod IP exchange to per-job subdirectory MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace session-token logic with a per-job subdirectory under .pod_ips/. The subdir name is MASTER_ADDR (a standard distributed-launcher env var, unique per job), so stale files from previous runs are simply never seen — no wiping, no barriers, no coordination needed. This removes the EAI-specific dependency on the dns_address_map naming convention and works with any launcher that sets MASTER_ADDR. --- pipelinerl/launch.py | 30 +++++------------------------- 1 file changed, 5 insertions(+), 25 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index c69a8f02..0c91c41f 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -6,7 +6,6 @@ import subprocess import sys import time -import uuid from dataclasses import dataclass from pathlib import Path from typing import List, TextIO @@ -862,31 +861,12 @@ def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path) -> None: # Save DNS names before overwriting so DeepSpeed hostfile can use them. world_map.dns_address_map = dict(world_map.address_map) - ip_dir = exp_dir / ".pod_ips" + # Use a per-job subdirectory so stale files from previous runs are never seen. + # MASTER_ADDR is unique per distributed job (set by torchrun / any launcher). + job_id = os.environ.get("MASTER_ADDR", "localhost") + ip_dir = exp_dir / ".pod_ips" / job_id + ip_dir.mkdir(parents=True, exist_ok=True) my_ip = _get_pod_ip() - session_file = ip_dir / "session" - - # Use the job-unique DNS name as the session token. MASTER_ADDR contains a - # job-specific string (e.g. "dns--0") that differs between EAI runs, - # so non-zero ranks can reject a stale session left by a previous job. - job_token = world_map.dns_address_map[0] - - if world_map.my_rank == 0: - # Wipe any stale files from a previous job, then write the job token. - if ip_dir.exists(): - shutil.rmtree(ip_dir) - ip_dir.mkdir(parents=True) - session_file.write_text(job_token) - logger.info(f"Pod IP exchange: rank 0 created fresh session (token={job_token})") - else: - # Wait until session exists AND contains this job's token. - # A stale session from a previous job has a different token and is ignored. - waited = 0 - while not (session_file.exists() and session_file.read_text().strip() == job_token): - time.sleep(0.5) - waited += 0.5 - if waited % 10 == 0: - logger.info(f"Waiting for pod IP session token from rank 0 ({waited:.0f}s)...") ip_file = ip_dir / f"rank_{world_map.my_rank}.txt" ip_file.write_text(my_ip) From cc419e59217db850f8660beffa36564cd64991a0 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 16:24:22 +0000 Subject: [PATCH 64/85] launch: make pod IP exchange run_id configurable via world.run_id Add world.run_id config field (default null). The call site resolves it as: cfg.world.run_id if set, else $MASTER_ADDR, else "default". On EAI/torchrun MASTER_ADDR is unique per job so the default works out-of-the-box; other systems can set world.run_id explicitly. Remove the MASTER_ADDR hardcoding from _exchange_pod_ips itself. --- conf/base.yaml | 6 +++++- pipelinerl/launch.py | 10 ++++------ 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/conf/base.yaml b/conf/base.yaml index ef057e86..cc67a8a4 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -78,7 +78,7 @@ vllm_config: world: replicas: 1 - + actor_fraction: 4 preprocessor_fraction: 0 finetune_fraction: 4 @@ -87,6 +87,10 @@ world: actor_group_port: 9000 environment_start_port: 7777 + # Unique identifier for this job run, used to namespace the pod IP exchange + # directory so stale files from previous runs are never seen. + # Defaults to $MASTER_ADDR when null (suitable for EAI and torchrun jobs). + run_id: null # this will be autocreated based on the config jobs: [] diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index 0c91c41f..def16462 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -848,7 +848,7 @@ def _get_pod_ip() -> str: s.close() -def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path) -> None: +def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path, run_id: str) -> None: """Exchange pod IPs across replicas via the shared NFS mount. Kubernetes Services only expose the declared master port; all other ports @@ -861,10 +861,7 @@ def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path) -> None: # Save DNS names before overwriting so DeepSpeed hostfile can use them. world_map.dns_address_map = dict(world_map.address_map) - # Use a per-job subdirectory so stale files from previous runs are never seen. - # MASTER_ADDR is unique per distributed job (set by torchrun / any launcher). - job_id = os.environ.get("MASTER_ADDR", "localhost") - ip_dir = exp_dir / ".pod_ips" / job_id + ip_dir = exp_dir / ".pod_ips" / run_id ip_dir.mkdir(parents=True, exist_ok=True) my_ip = _get_pod_ip() @@ -923,7 +920,8 @@ def main(cfg: DictConfig): # Pod IPs bypass kube-proxy and have all ports open, so we exchange pod IPs via # a shared NFS file and update address_map before any TCP connections are made. if world_map.world_size > 1: - _exchange_pod_ips(world_map, exp_dir) + run_id = cfg.world.get("run_id") or os.environ.get("MASTER_ADDR", "default") + _exchange_pod_ips(world_map, exp_dir, run_id) cfg.jobs = [job.model_dump() for job in world_map.get_all_jobs()] From 1d25b998454b44dc97f60c3bb4a3f50e506b80cb Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 16:26:02 +0000 Subject: [PATCH 65/85] launch: require world.run_id; raise on missing or already-used dir MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit world.run_id must now be set explicitly for multi-node jobs — no silent fallback to MASTER_ADDR. Raises ValueError if unset, RuntimeError if the run_id dir already exists (duplicate or stale run detected early). Rank 0 exclusively creates the dir; non-zero ranks wait for it, so the existence check is unambiguous: if the dir is there when rank 0 arrives, it is from a previous job. Submit script passes world.run_id=${MASTER_ADDR} so EAI jobs are unique per replica-group without any manual intervention. --- pipelinerl/launch.py | 20 ++++++++++++++++++-- submit_eai_math_7b_multinode.sh | 1 + 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/pipelinerl/launch.py b/pipelinerl/launch.py index def16462..0baf3b48 100644 --- a/pipelinerl/launch.py +++ b/pipelinerl/launch.py @@ -862,9 +862,23 @@ def _exchange_pod_ips(world_map: "WorldMap", exp_dir: Path, run_id: str) -> None world_map.dns_address_map = dict(world_map.address_map) ip_dir = exp_dir / ".pod_ips" / run_id - ip_dir.mkdir(parents=True, exist_ok=True) my_ip = _get_pod_ip() + if world_map.my_rank == 0: + if ip_dir.exists(): + raise RuntimeError( + f"Pod IP exchange directory already exists for run_id={run_id!r}. " + "world.run_id must be unique per job run." + ) + ip_dir.mkdir(parents=True) + else: + waited = 0 + while not ip_dir.exists(): + time.sleep(0.5) + waited += 0.5 + if waited % 10 == 0: + logger.info(f"Waiting for rank 0 to create pod IP dir ({waited:.0f}s)...") + ip_file = ip_dir / f"rank_{world_map.my_rank}.txt" ip_file.write_text(my_ip) logger.info(f"Pod IP exchange: rank {world_map.my_rank} pod IP = {my_ip}") @@ -920,7 +934,9 @@ def main(cfg: DictConfig): # Pod IPs bypass kube-proxy and have all ports open, so we exchange pod IPs via # a shared NFS file and update address_map before any TCP connections are made. if world_map.world_size > 1: - run_id = cfg.world.get("run_id") or os.environ.get("MASTER_ADDR", "default") + run_id = cfg.world.get("run_id") + if not run_id: + raise ValueError("world.run_id must be set for multi-node jobs (use a unique value per job run)") _exchange_pod_ips(world_map, exp_dir, run_id) cfg.jobs = [job.model_dump() for job in world_map.get_all_jobs()] diff --git a/submit_eai_math_7b_multinode.sh b/submit_eai_math_7b_multinode.sh index ce1493dc..aa19285a 100755 --- a/submit_eai_math_7b_multinode.sh +++ b/submit_eai_math_7b_multinode.sh @@ -56,6 +56,7 @@ PYTHONHASHSEED=42 python -m pipelinerl.launch \ world.actor_fraction=1 \ world.preprocessor_fraction=0 \ world.finetune_fraction=${FINETUNE_NODES} \ + 'world.run_id=\${MASTER_ADDR}' \ model_path=${MODEL_PATH} \ output_dir=${EXP_DIR} \ wandb.wandb_workspace_root=${RESULTS_DIR} \ From c3cf8079f477be3b6560e3e6091834eadc9a382d Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 16:44:41 +0000 Subject: [PATCH 66/85] docs: document world.run_id requirement and resume workflow in multinode section --- README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/README.md b/README.md index 751f49eb..5b951b48 100644 --- a/README.md +++ b/README.md @@ -409,6 +409,30 @@ PipelineRL can span multiple nodes, with actor (vLLM) and trainer roles on separ - With `world.preprocessor_fraction=0`, every node is either a pure actor node or a pure trainer node (no mixing). - The DeepSpeed hostfile and `--deepspeed_inclusion_filter` use DNS/hostname names (not IPs), so the cluster rendezvous port (`MASTER_PORT`) must be reachable via those names. All other cross-node connections use IP addresses and are independent of DNS. +### Running and resuming multi-node jobs + +**`world.run_id` is required for multi-node jobs.** It must be a string that is unique per job run. It is used to namespace the pod IP exchange directory on the shared NFS mount so that stale files from a previous run are never seen. + +On EAI (and any torchrun-based launcher), `MASTER_ADDR` is unique per replica group and makes a good default: + +```bash +python -m pipelinerl.launch ... 'world.run_id=${MASTER_ADDR}' +``` + +The `submit_eai_math_7b_multinode.sh` script sets this automatically. + +**To resume a preempted run**, pass the original experiment timestamp as the third argument so the same output directory (and therefore the same WandB run and checkpoint) is reused: + +```bash +# Fresh run — creates a new experiment directory +bash submit_eai_math_7b_multinode.sh 4 60 + +# Resume — reuses math_7b_4node_mb5x12_20260427_144646/ +bash submit_eai_math_7b_multinode.sh 4 60 20260427_144646 +``` + +On resume the script appends a unique `_resume_` suffix to the EAI job name (required because job names must be unique). A new `world.run_id` value (`MASTER_ADDR` of the new job) is used automatically, so the pod IP exchange directory is always fresh. + # Install FastLLM+PipilineRL - use ` registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` image which also includes redis server. In `~/.research-interactive-env`: ```shell From 7594bc110c844f8425e9779620da7301e0bc2eb3 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 16:45:45 +0000 Subject: [PATCH 67/85] docs: rewrite multinode run/resume section to be launcher-agnostic --- README.md | 18 +++--------------- 1 file changed, 3 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 5b951b48..a6756dc6 100644 --- a/README.md +++ b/README.md @@ -411,27 +411,15 @@ PipelineRL can span multiple nodes, with actor (vLLM) and trainer roles on separ ### Running and resuming multi-node jobs -**`world.run_id` is required for multi-node jobs.** It must be a string that is unique per job run. It is used to namespace the pod IP exchange directory on the shared NFS mount so that stale files from a previous run are never seen. - -On EAI (and any torchrun-based launcher), `MASTER_ADDR` is unique per replica group and makes a good default: +**`world.run_id` is required for multi-node jobs.** It must be a string that is unique per job run. It namespaces the pod IP exchange directory on the shared NFS mount so that stale files from a previous run are never picked up by a new one. Any value that your cluster scheduler guarantees to be unique per job works — a job UUID, a replica-group ID, or the job's `MASTER_ADDR` (which is unique per torchrun launch): ```bash python -m pipelinerl.launch ... 'world.run_id=${MASTER_ADDR}' ``` -The `submit_eai_math_7b_multinode.sh` script sets this automatically. - -**To resume a preempted run**, pass the original experiment timestamp as the third argument so the same output directory (and therefore the same WandB run and checkpoint) is reused: - -```bash -# Fresh run — creates a new experiment directory -bash submit_eai_math_7b_multinode.sh 4 60 - -# Resume — reuses math_7b_4node_mb5x12_20260427_144646/ -bash submit_eai_math_7b_multinode.sh 4 60 20260427_144646 -``` +**To resume a preempted run**, reuse the same `output_dir` as the original job. fast-LLM automatically finds the latest checkpoint in `output_dir/finetune/checkpoint/` and resumes from it. WandB also resumes the same run because fast-LLM persists the run ID in `output_dir/finetune/wandb_config.yaml` on the first launch and reloads it on every subsequent launch. -On resume the script appends a unique `_resume_` suffix to the EAI job name (required because job names must be unique). A new `world.run_id` value (`MASTER_ADDR` of the new job) is used automatically, so the pod IP exchange directory is always fresh. +Each resumed job must still use a fresh `world.run_id` (the new job's ID, not the original one), so the pod IP exchange directory is always clean. # Install FastLLM+PipilineRL - use ` registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` image which also includes redis server. In `~/.research-interactive-env`: From 84b95be29668675c47a6a14a9bee89f4aae956a3 Mon Sep 17 00:00:00 2001 From: bigximik Date: Mon, 27 Apr 2026 17:25:42 +0000 Subject: [PATCH 68/85] actor: use run_in_executor for result_queue.put to avoid blocking event loop Blocking put on a full queue stalled the asyncio event loop (test_actor_stall_fixed). Delete from group_rollouts before the await to prevent double-processing. --- pipelinerl/actor.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pipelinerl/actor.py b/pipelinerl/actor.py index 2f94c11f..e2506619 100644 --- a/pipelinerl/actor.py +++ b/pipelinerl/actor.py @@ -256,12 +256,11 @@ async def rollout_and_maybe_produce_result( del group_rollouts[group_id] finished_rollouts += 1 return - # This is blocking call, but there's just one other thread reading from this queue. random.shuffle(valid_results) + del group_rollouts[group_id] _t_put_start = time.monotonic() - result_queue.put(valid_results) + await asyncio.get_event_loop().run_in_executor(None, result_queue.put, valid_results) _put_duration = time.monotonic() - _t_put_start - del group_rollouts[group_id] if _pb_log_file is not None: _pb_log_file.write(_json_b.dumps({ "wall": time.time(), From 92a32db1e7b74448ae6e28d60e44c0dbf8eb13cb Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 28 Apr 2026 13:16:34 +0000 Subject: [PATCH 69/85] actor: retry on ServerDisconnectedError; drop dead use_v1 from math.yaml MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ServerDisconnectedError is a transient failure (vLLM event loop briefly blocked during synchronized post-weight-update response burst) — add it to retryable_rollout_exceptions so the actor backs off and retries instead of crashing the whole job. conf/math.yaml: remove use_v1: true left over from before the always-v1 switch; was missed in the 13a42bf merge cleanup. --- conf/math.yaml | 1 - pipelinerl/actor.py | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/conf/math.yaml b/conf/math.yaml index 36dc4e29..59ff9218 100644 --- a/conf/math.yaml +++ b/conf/math.yaml @@ -25,7 +25,6 @@ finetune: seq_length: 20000 vllm_config: - use_v1: true vllm_kwargs: max_model_len: 20000 diff --git a/pipelinerl/actor.py b/pipelinerl/actor.py index e2506619..1a41020a 100644 --- a/pipelinerl/actor.py +++ b/pipelinerl/actor.py @@ -158,6 +158,7 @@ async def schedule_rollouts( samples_target = final_steps * cfg.finetune.train_batch_size * cfg.finetune.gradient_accumulation_passes retryable_rollout_exceptions = ( aiohttp.ServerTimeoutError, + aiohttp.ServerDisconnectedError, asyncio.TimeoutError, TimeoutError, RetryableAbortedCompletionError, From 3a5671c6a96fbaeb1c31b1fda03e66cfb3390bde Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 28 Apr 2026 13:18:18 +0000 Subject: [PATCH 70/85] utils: guard against None metadata in wandb python_env collection --- pipelinerl/utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/pipelinerl/utils.py b/pipelinerl/utils.py index 543c6463..8eb8bea3 100644 --- a/pipelinerl/utils.py +++ b/pipelinerl/utils.py @@ -210,8 +210,12 @@ def init_wandb( python_env = {} for dist in distributions(): + if dist.metadata is None: + continue try: - python_env[dist.metadata["Name"]] = dist.version + name = dist.metadata["Name"] + if name is not None: + python_env[name] = dist.version except Exception as e: logger.warning(f"Accessing {dist} resulted in error {e}") config_for_wandb["python_env"] = python_env From 89ceb1bc346a309771978618ef9f9338db9cb230 Mon Sep 17 00:00:00 2001 From: bigximik Date: Tue, 28 Apr 2026 14:08:02 +0000 Subject: [PATCH 71/85] fix multi-node job submission scripts for fast-llm branch - Remove single quotes around world.run_id=\${MASTER_ADDR} so bash expands MASTER_ADDR in the container (pod IP exchange was hanging because OmegaConf tried to resolve the literal string '${MASTER_ADDR}' as a config key) - Add + prefix to fast_llm.schedule.docs_per_step (new field not in base.yaml struct, requires append syntax) - Add DS submit script for fast-llm branch (submit_eai_math_7b_multinode_ds_vllm_v1.sh) - Set max_ready_samples_per_lead: 64 (was 512) to match reference branch - Add monitor_jobs.sh for polling EAI job status --- conf/base.yaml | 2 +- monitor_jobs.sh | 68 ++++++++++++++++ submit_eai_math_7b_multinode.sh | 94 +++++++++------------ submit_eai_math_7b_multinode_ds_vllm_v1.sh | 95 ++++++++++++++++++++++ 4 files changed, 204 insertions(+), 55 deletions(-) create mode 100755 monitor_jobs.sh create mode 100644 submit_eai_math_7b_multinode_ds_vllm_v1.sh diff --git a/conf/base.yaml b/conf/base.yaml index 1526c4c7..1189e96d 100644 --- a/conf/base.yaml +++ b/conf/base.yaml @@ -39,7 +39,7 @@ preprocess: # ring buffer to replace old samples with new ones when training is slow ring_buffer_size: 128 # "virtual" sample queue per lead trainer - max_ready_samples_per_lead: 512 + max_ready_samples_per_lead: 64 pop_old_data: ${..pop_old_data} shared_memory_entry_size: 100000000 log_every_n_samples: 128 diff --git a/monitor_jobs.sh b/monitor_jobs.sh new file mode 100755 index 00000000..57a06c84 --- /dev/null +++ b/monitor_jobs.sh @@ -0,0 +1,68 @@ +#!/bin/bash +# Monitor two comparison jobs for failure/cancellation/preemption. +# Usage: bash monitor_jobs.sh + +DS_JOB="${1:-fe9561a0-5c66-4971-88b3-d38bcab0b6e4}" +FL_JOB="${2:-18baa4d1-8f91-4153-9d1c-0affb7d62536}" +DS_DIR="${3:-/mnt/shared/denis/math_7b_results/math_7b_ds_fastllm_4node_20260428_135427}" +FL_DIR="${4:-/mnt/shared/denis/math_7b_results/math_7b_4node_fastllm_gspo_20260428_135448}" + +BAD_STATES="FAILED CANCELLED PREEMPTED INTERRUPTED" +INTERVAL=120 # seconds between polls + +log() { echo "[$(date '+%H:%M:%S')] $*"; } + +check_job() { + local job_id="$1" + local label="$2" + local state + state=$(eai job get "$job_id" 2>/dev/null | awk 'NR==2{print $2}') + if [ -z "$state" ]; then + state="UNKNOWN" + fi + for bad in $BAD_STATES; do + if [ "$state" = "$bad" ]; then + log "ALERT: $label ($job_id) is $state" + return 1 + fi + done + log "$label ($job_id): $state" + return 0 +} + +check_dir() { + local dir="$1" + local label="$2" + local count + count=$(find "$dir" -maxdepth 1 -mindepth 1 2>/dev/null | wc -l) + log "$label dir has $count top-level entries" +} + +log "Monitoring DS job: $DS_JOB" +log "Monitoring FastLLM job: $FL_JOB" +log "DS dir: $DS_DIR" +log "FastLLM dir: $FL_DIR" +log "Polling every ${INTERVAL}s. Ctrl-C to stop." +echo "" + +ds_alive=1 +fl_alive=1 + +while true; do + if [ $ds_alive -eq 1 ]; then + check_job "$DS_JOB" "DS" || ds_alive=0 + fi + if [ $fl_alive -eq 1 ]; then + check_job "$FL_JOB" "FastLLM" || fl_alive=0 + fi + check_dir "$DS_DIR" "DS" + check_dir "$FL_DIR" "FastLLM" + echo "" + + if [ $ds_alive -eq 0 ] && [ $fl_alive -eq 0 ]; then + log "Both jobs ended. Exiting." + break + fi + + sleep "$INTERVAL" +done diff --git a/submit_eai_math_7b_multinode.sh b/submit_eai_math_7b_multinode.sh index aa19285a..c312a2a9 100755 --- a/submit_eai_math_7b_multinode.sh +++ b/submit_eai_math_7b_multinode.sh @@ -1,40 +1,21 @@ #!/bin/bash -# Submit a multi-node EAI job for math task with Qwen2.5-7B-Instruct. -# Topology: 1 actor node (vLLM) + (NODES-1) fast-llm trainer nodes. -# Usage: bash submit_eai_math_7b_multinode.sh [NODES] [TARGET_TOTAL_MB] [TIMESTAMP] -# Example (fresh): bash submit_eai_math_7b_multinode.sh 4 60 -# -> 1 actor node, 3 fast-llm nodes (BDP=12, depth_first=5, total_MBs=60) -# Example (resume): bash submit_eai_math_7b_multinode.sh 4 60 20260427_144646 -# -> resumes experiment math_7b_4node_mb5x12_20260427_144646 (checkpoint + wandb run preserved) +# Multi-node fast-llm finetuner math run with DS-matched params (GSPO, docs_per_step). +# Topology: actor_fraction=4 (16 GPUs / 2 nodes) + finetune_fraction=4 (16 GPUs / 2 nodes). +# Usage: bash submit_eai_math_7b_multinode.sh [NODES] [TIMESTAMP] +# Example (fresh): bash submit_eai_math_7b_multinode.sh 4 +# Example (resume): bash submit_eai_math_7b_multinode.sh 4 20260428_132330 # Run `eai login` before executing this script. IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" RESULTS_DIR="/mnt/shared/denis/math_7b_results" -MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B-Instruct}" -NODES="${1:-2}" -TARGET_TOTAL_MB="${2:-1024}" # target total microbatches/step across all DP ranks -TIMESTAMP="${3:-$(date +%Y%m%d_%H%M%S)}" # experiment ID; omit to start fresh +MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" +NODES="${1:-4}" +TIMESTAMP="${2:-$(date +%Y%m%d_%H%M%S)}" -FINETUNE_NODES=$((NODES - 1)) -if [ "$FINETUNE_NODES" -lt 1 ]; then - echo "ERROR: NODES must be >= 2 (got $NODES)" >&2 - exit 1 -fi - -# SDP=2 throughout; batch_data_parallel = finetune_gpus / SDP -FINETUNE_GPUS=$((FINETUNE_NODES * 8)) -SDP=2 -BDP=$((FINETUNE_GPUS / SDP)) - -# depth_first_micro_batches and prefetch_factor are per-rank (per DP group) -# Round up so total >= TARGET_TOTAL_MB -DEPTH_FIRST=$(( (TARGET_TOTAL_MB + BDP - 1) / BDP )) -PREFETCH=$DEPTH_FIRST - -EXP_NAME="math_7b_${NODES}node_mb${DEPTH_FIRST}x${BDP}_${TIMESTAMP}" +EXP_NAME="math_7b_${NODES}node_fastllm_gspo_${TIMESTAMP}" EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" -if [ -n "${3:-}" ]; then +if [ -n "${2:-}" ]; then RESUME_TS=$(date +%Y%m%d_%H%M%S) JOB_NAME="${EXP_NAME}_resume_${RESUME_TS}" echo "RESUMING: ${EXP_DIR} (job: ${JOB_NAME})" @@ -42,46 +23,55 @@ else JOB_NAME="${EXP_NAME}" fi -echo "Config: ${NODES} nodes, ${FINETUNE_NODES} fast-llm nodes, BDP=${BDP}, depth_first=${DEPTH_FIRST}, total_MBs=$((DEPTH_FIRST * BDP))" +echo "Config: ${NODES} nodes, actor_fraction=4, finetune_fraction=4, docs_per_step=1024, max_train_steps=400" CMD=" set -e mkdir -p ${EXP_DIR} +cd /home/toolkit/code/PipelineRL source /home/toolkit/code/PipelineRL/.venv/bin/activate PYTHONHASHSEED=42 python -m pipelinerl.launch \ --config-path /home/toolkit/code/PipelineRL/conf \ --config-name math \ - 'streams=redis' \ - world.replicas=1 \ - world.actor_fraction=1 \ + streams=redis \ + world.actor_fraction=4 \ world.preprocessor_fraction=0 \ - world.finetune_fraction=${FINETUNE_NODES} \ - 'world.run_id=\${MASTER_ADDR}' \ + world.finetune_fraction=4 \ + world.run_id=\${MASTER_ADDR} \ model_path=${MODEL_PATH} \ output_dir=${EXP_DIR} \ + force_restart=true \ + fp32_lm_head=true \ + actor.llm_max_rollouts=128 \ + finetune.attempts=8 \ + finetune.max_train_steps=400 \ + '+finetune.rl.filter_zero_advantage_groups=true' \ + eval_every_n_versions=0 \ wandb.wandb_workspace_root=${RESULTS_DIR} \ wandb.wandb_entity_name=denisko-se \ wandb.wandb_project_name=watermelon \ - wandb.wandb_group=eai_math7b_multinode \ + wandb.wandb_group=eai_math7b_fastllm_gspo \ + '+wandb.wandb_run_name=math7b_fastllm_gspo_${NODES}node_${TIMESTAMP}' \ 'vllm_config.vllm_kwargs.gpu-memory-utilization=0.85' \ 'vllm_config.vllm_kwargs.max-num-batched-tokens=8192' \ - 'vllm_config.vllm_kwargs.max_model_len=16000' \ - 'fast_llm.data.micro_batch_size=16000' \ - 'llm.parameters.max_tokens=14000' \ - 'test_llm.parameters.max_tokens=14000' \ - 'eval_every_n_versions=0' \ + 'vllm_config.vllm_kwargs.max_model_len=20000' \ + 'llm.parameters.max_tokens=16000' \ + 'llm.parameters.temperature=0.7' \ + 'test_llm.parameters.max_tokens=16000' \ + 'test_llm.parameters.temperature=0.7' \ + 'fast_llm.data.micro_batch_size=20000' \ + '+fast_llm.schedule.docs_per_step=1024' \ + 'fast_llm.training.train_iters=400' \ 'fast_llm.training.num_workers=1' \ - '+fast_llm.training.prefetch_factor=${PREFETCH}' \ - 'fast_llm.schedule.depth_first_micro_batches=${DEPTH_FIRST}' \ - 'fast_llm.model.distributed.sequence_data_parallel=${SDP}' \ + 'fast_llm.training.checkpoint.interval=20' \ + 'fast_llm.model.distributed.sequence_data_parallel=2' \ '+fast_llm.model.distributed.timeout=3600' \ '+fast_llm.model.base_model.decoder.block.mlp.recompute_level=full' \ - 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=0.02' \ - 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=0.02' \ + '+fast_llm.model.base_model.head.losses.grpo.policy_loss=gspo' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=3e-3' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=4e-3' \ '+fast_llm.model.base_model.head.losses.grpo.compute_extra_metrics=true' \ - '+fast_llm.model.base_model.head.losses.grpo.compute_entropy_metric=true' \ - 'fast_llm.training.checkpoint.interval=20' \ - '+fast_llm.optimizer.learning_rate.base=1e-5' \ + '+fast_llm.optimizer.learning_rate.base=1e-6' \ '+fast_llm.optimizer.learning_rate.warmup_iterations=10' \ '+fast_llm.optimizer.learning_rate.decay_style=cosine' \ '+fast_llm.optimizer.learning_rate.decay_iterations=100000' \ @@ -89,10 +79,6 @@ PYTHONHASHSEED=42 python -m pipelinerl.launch \ '+fast_llm.optimizer.gradient_norm_clipping=0.3' " -# Generate a job spec YAML with all ports exposed in the Kubernetes Service. -# Ports: 29501 (EAI replica master), 11000 (Redis), 9000 (TCPStore weight-broadcast), -# 8080-8087 (vLLM HTTP servers, one per GPU on the actor node), -# 7777 (environment server, for actor→environment HTTP). SPEC_YAML=$(mktemp /tmp/eai_job_spec_XXXXXX.yaml) cat > "$SPEC_YAML" << 'YAML_EOF' options: @@ -115,7 +101,7 @@ YAML_EOF eai job new \ --file "$SPEC_YAML" \ - --preemptable \ + --non-preemptable \ --replicas "$NODES" \ --gpu 8 \ --cpu 128 \ diff --git a/submit_eai_math_7b_multinode_ds_vllm_v1.sh b/submit_eai_math_7b_multinode_ds_vllm_v1.sh new file mode 100644 index 00000000..109901e7 --- /dev/null +++ b/submit_eai_math_7b_multinode_ds_vllm_v1.sh @@ -0,0 +1,95 @@ +#!/bin/bash +# Multi-node EAI DeepSpeed math run on vllm_v1 branch. +# Topology: 1 actor node (vLLM) + (NODES-1) DeepSpeed trainer nodes. +# Usage: bash submit_eai_math_7b_multinode_ds_vllm_v1.sh [NODES] +# Example: bash submit_eai_math_7b_multinode_ds_vllm_v1.sh 4 +# Run `eai login` before executing this script. + +IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" +RESULTS_DIR="/mnt/shared/denis/math_7b_results" +MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" +NODES="${1:-4}" + +TIMESTAMP=$(date +%Y%m%d_%H%M%S) +EXP_NAME="math_7b_ds_fastllm_${NODES}node_${TIMESTAMP}" +EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" +JOB_NAME="${EXP_NAME}" + +echo "Config: ${NODES} nodes, actor_fraction=4, finetune_fraction=4, max_train_steps=400" + +CMD=" +set -e +mkdir -p ${EXP_DIR} +cd /home/toolkit/code/PipelineRL +source /home/toolkit/code/PipelineRL/.venv/bin/activate +PYTHONHASHSEED=42 python -m pipelinerl.launch \ + --config-path /home/toolkit/code/PipelineRL/conf \ + --config-name math \ + output_dir=${EXP_DIR} \ + wandb.wandb_workspace_root=${RESULTS_DIR} \ + wandb.wandb_entity_name=denisko-se \ + wandb.wandb_project_name=watermelon \ + wandb.wandb_group=eai_math7b_ds_fastllm \ + '+wandb.wandb_run_name=math7b_ds_fastllm_${NODES}node_${TIMESTAMP}' \ + use_fast_llm=false \ + actor.llm_max_rollouts=128 \ + force_restart=true \ + fp32_lm_head=true \ + finetune.learning_rate=1e-6 \ + finetune.attempts=8 \ + finetune.rl.policy_loss=gspo \ + finetune.rl.epsilon_low=3e-3 \ + finetune.rl.epsilon_high=4e-3 \ + '+finetune.rl.filter_zero_advantage_groups=true' \ + finetune.max_train_steps=400 \ + finetune.seq_length=20000 \ + finetune.gradient_accumulation_passes=1024 \ + 'vllm_config.vllm_kwargs.max_model_len=20000' \ + 'llm.parameters.max_tokens=16000' \ + 'llm.parameters.temperature=0.7' \ + 'test_llm.parameters.max_tokens=16000' \ + 'test_llm.parameters.temperature=0.7' \ + world.actor_fraction=4 \ + world.preprocessor_fraction=0 \ + world.finetune_fraction=4 \ + world.run_id=\${MASTER_ADDR} \ + streams=files \ + eval_every_n_versions=0 \ + model_path=${MODEL_PATH} +" + +SPEC_YAML=$(mktemp /tmp/eai_job_spec_XXXXXX.yaml) +cat > "$SPEC_YAML" << 'YAML_EOF' +options: + internal-dns: + name: "" + ports: + - port: 29501 + - port: 9000 + - port: 7777 + - port: 8080 + - port: 8081 + - port: 8082 + - port: 8083 + - port: 8084 + - port: 8085 + - port: 8086 + - port: 8087 +YAML_EOF + +eai job new \ + --file "$SPEC_YAML" \ + --non-preemptable \ + --replicas "$NODES" \ + --gpu 8 \ + --cpu 128 \ + --mem 800 \ + --name "$JOB_NAME" \ + -i "$IMAGE" \ + --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ + --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ + --env "HOME=/home/toolkit" \ + --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ + -- /bin/bash -c "$CMD" + +rm -f "$SPEC_YAML" From 40afa2d179682ae944f908486b194b0a1ce927d7 Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 07:33:18 +0000 Subject: [PATCH 72/85] launch scripts: drop top-level fp32_lm_head knob (removed in main) Top-level `fp32_lm_head=true` is rejected after main merge (launch.py warns and exits). Fast-LLM-side override `+fast_llm.model.base_model.head.fp32_lm_head=true` still works and is kept. Also replaces removed `compute_extra_metrics=true` with new PR #494 enum `metrics=with_entropy`. --- submit_eai_math_7b_multinode.sh | 11 ++++++----- submit_eai_math_7b_multinode_ds_vllm_v1.sh | 7 +++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/submit_eai_math_7b_multinode.sh b/submit_eai_math_7b_multinode.sh index c312a2a9..e94f2d44 100755 --- a/submit_eai_math_7b_multinode.sh +++ b/submit_eai_math_7b_multinode.sh @@ -41,7 +41,6 @@ PYTHONHASHSEED=42 python -m pipelinerl.launch \ model_path=${MODEL_PATH} \ output_dir=${EXP_DIR} \ force_restart=true \ - fp32_lm_head=true \ actor.llm_max_rollouts=128 \ finetune.attempts=8 \ finetune.max_train_steps=400 \ @@ -67,15 +66,17 @@ PYTHONHASHSEED=42 python -m pipelinerl.launch \ 'fast_llm.model.distributed.sequence_data_parallel=2' \ '+fast_llm.model.distributed.timeout=3600' \ '+fast_llm.model.base_model.decoder.block.mlp.recompute_level=full' \ + '+fast_llm.model.base_model.head.fp32_lm_head=true' \ '+fast_llm.model.base_model.head.losses.grpo.policy_loss=gspo' \ 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=3e-3' \ 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=4e-3' \ - '+fast_llm.model.base_model.head.losses.grpo.compute_extra_metrics=true' \ + '+fast_llm.model.base_model.head.losses.grpo.normalize_by_documents=true' \ + '+fast_llm.model.base_model.head.losses.grpo.temperature=0.7' \ + '+fast_llm.model.base_model.head.losses.grpo.metrics=with_entropy' \ '+fast_llm.optimizer.learning_rate.base=1e-6' \ - '+fast_llm.optimizer.learning_rate.warmup_iterations=10' \ + '+fast_llm.optimizer.learning_rate.warmup_iterations=50' \ '+fast_llm.optimizer.learning_rate.decay_style=cosine' \ - '+fast_llm.optimizer.learning_rate.decay_iterations=100000' \ - '+fast_llm.optimizer.beta_2=0.95' \ + '+fast_llm.optimizer.learning_rate.decay_iterations=400' \ '+fast_llm.optimizer.gradient_norm_clipping=0.3' " diff --git a/submit_eai_math_7b_multinode_ds_vllm_v1.sh b/submit_eai_math_7b_multinode_ds_vllm_v1.sh index 109901e7..af912776 100644 --- a/submit_eai_math_7b_multinode_ds_vllm_v1.sh +++ b/submit_eai_math_7b_multinode_ds_vllm_v1.sh @@ -34,12 +34,11 @@ PYTHONHASHSEED=42 python -m pipelinerl.launch \ use_fast_llm=false \ actor.llm_max_rollouts=128 \ force_restart=true \ - fp32_lm_head=true \ finetune.learning_rate=1e-6 \ finetune.attempts=8 \ - finetune.rl.policy_loss=gspo \ - finetune.rl.epsilon_low=3e-3 \ - finetune.rl.epsilon_high=4e-3 \ + finetune.rl.policy_loss=ppo \ + finetune.rl.epsilon_low=2e-2 \ + finetune.rl.epsilon_high=2e-2 \ '+finetune.rl.filter_zero_advantage_groups=true' \ finetune.max_train_steps=400 \ finetune.seq_length=20000 \ From 45ff3016d2189d73b663c47d9937039aa81a44f1 Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 08:07:08 +0000 Subject: [PATCH 73/85] docs(fast-llm): handover docs and interactive smoke examples MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds canonical handover documentation for the fast-llm trainer integration, since this branch is WIP and being handed off: - docs/FAST_LLM_INTEGRATION.md: architecture, per-file changes, configuration knobs, glossary, known issues with file:line citations, testing guide, operations notes, and open questions for the successor. - examples/interactive/fast_llm_4node.sh, ds_4node.sh: 2-step smoke runs that mirror the EAI submit scripts but execute in the current shell. Default to MAX_TRAIN_STEPS=2 for verification; bump for real runs. - README.md: refresh stale install steps (gspo branch in Fast-LLM, not jlp_pipeline_rl), call out pyproject.toml tapeagents caveat, add a "Fast-LLM trainer path (preview)" subsection under §5 Trainer pointing to the canonical doc. No code changes. Functional behavior unchanged. --- README.md | 40 ++- docs/FAST_LLM_INTEGRATION.md | 373 +++++++++++++++++++++++++ examples/interactive/ds_4node.sh | 91 ++++++ examples/interactive/fast_llm_4node.sh | 105 +++++++ 4 files changed, 603 insertions(+), 6 deletions(-) create mode 100644 docs/FAST_LLM_INTEGRATION.md create mode 100755 examples/interactive/ds_4node.sh create mode 100755 examples/interactive/fast_llm_4node.sh diff --git a/README.md b/README.md index 93799679..3554df31 100644 --- a/README.md +++ b/README.md @@ -350,6 +350,17 @@ PipelineRL is organized as a modular, Hydra-driven pipeline with 6 core componen - Pull a batch → call `rl_step(...)` (in `pipelinerl/finetune/rl/utils.py`) to compute policy-gradient (+ KL penalty if configured) → `optimizer.step()` → `lr_scheduler.step()`. - On rank 0, use `WeightUpdateManager.send_weight_update(version)` to gather model parameters, send `WeightUpdateRequest` to Actor LLMs (HTTP), broadcast tensors via NCCL, and write a `WeightUpdateSuccess` message to the update stream. +#### Fast-LLM trainer path (preview) + +When `use_fast_llm: true` (default in `conf/math.yaml`), the DeepSpeed ZeRO-3 trainer above is replaced with [Fast-LLM](https://github.com/ServiceNow/Fast-LLM) (FSDP + sequence-data-parallel) and the per-step weight update over HTTP is replaced with a persistent NCCL broadcast group: + +- Trainer: `fast_llm train gpt` launched via torchrun (`pipelinerl/launch.py:run_finetune`); rank 0 also serves the broadcast `TCPStore`. +- Fast-LLM's `StreamingTrainerCallback` gathers full-precision weights after each optimizer step and broadcasts them on a persistent NCCL group whose name is `WEIGHTS_BROADCAST_PG_NAME`. +- vLLM workers join the same group via `vllm1.init_actor_update_group(...)` and copy parameters into the model in place. +- Coordinated NCCL teardown (`pipelinerl/vllm1.py:484-547`) listens to a `training_finished` redis xadd from the trainer and destroys the process group on the vLLM side so `dist.destroy_process_group()` doesn't hang. + +This path is **WIP** — see [`docs/FAST_LLM_INTEGRATION.md`](docs/FAST_LLM_INTEGRATION.md) for known issues, configuration knobs, and example interactive-job scripts. + ### 6. Verifier - Entrypoint: `pipelinerl/entrypoints/verifier.py` - Serves a FastAPI app with: @@ -421,16 +432,25 @@ python -m pipelinerl.launch ... 'world.run_id=${MASTER_ADDR}' Each resumed job must still use a fresh `world.run_id` (the new job's ID, not the original one), so the pod IP exchange directory is always clean. -# Install FastLLM+PipilineRL -- use ` registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` image which also includes redis server. In `~/.research-interactive-env`: +# Install FastLLM+PipelineRL + +> **Status (2026-05-06):** This integration is WIP — see [`docs/FAST_LLM_INTEGRATION.md`](docs/FAST_LLM_INTEGRATION.md) for the full handover (architecture, known issues, TODO). + +### 1. Container image + +Use image `registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` — it bundles the redis server. In `~/.research-interactive-env`: + ```shell USE_ACCOUNT_REPO := 1 -BASE_IMAGE :=nvcr.io/nvidia/pytorch:25.12-py3 +BASE_IMAGE := nvcr.io/nvidia/pytorch:25.12-py3 IMAGE_REVISION := 25.12-py3-vllm014rc1redis EAI_PROFILE := yul201 ``` - -- in running interactive instance run like this to install both Fast-LLM and PipelineRL into the same `venv` locates at PipelineRL repo folder + +### 2. Clone + venv + editable installs + +Inside a running interactive instance, install both Fast-LLM and PipelineRL into a single venv at `PipelineRL/.venv`: + ```shell git clone git@github.com:ServiceNow/Fast-LLM.git git clone git@github.com:ServiceNow/PipelineRL.git @@ -440,14 +460,22 @@ cd PipelineRL source .venv/bin/activate export PIP_CONSTRAINT="" +# Fast-LLM: GSPO branch is the one paired with the PipelineRL fast-llm branch cd ../Fast-LLM git submodule update --init --recursive -git checkout jlp_pipeline_rl +git checkout gspo pip install --no-cache-dir --no-build-isolation -e ".[CORE,OPTIONAL,HUGGINGFACE,SSM,VISION,GENERATION,STREAMING,DEV]" triton==3.5.1 +# PipelineRL: fast-llm branch cd ../PipelineRL git checkout fast-llm pip install --no-cache-dir -e ".[lora]" ``` +### 3. Known caveats + +- **`pyproject.toml:81-87`** — `[tool.uv]` overrides `transformers>=4.51.0` and `accelerate>=1.7.0` because `tapeagents==0.1.16` pins them lower; the `[tapeagents]` extra is **broken at runtime** until tapeagents bumps support. Track this as a TODO; do not enable `[tapeagents]` on the fast-llm path. +- **`PIP_CONSTRAINT=""`** is required — the toolkit image sets a constraint file that conflicts with our pinned versions. +- **Triton must be `==3.5.1`** — newer triton breaks the fast-llm GSPO kernels. + diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md new file mode 100644 index 00000000..371d66c3 --- /dev/null +++ b/docs/FAST_LLM_INTEGRATION.md @@ -0,0 +1,373 @@ +# Fast-LLM Integration — Handover + +> **Status:** WIP. Last verified end-to-end on 2026-05-06 with a 2-step smoke run on a 4-node EAI job (both DeepSpeed PPO and Fast-LLM GSPO finished cleanly, all metrics in expected ranges). +> +> **Authoring history:** Denis Kocetkov (denis.kocetkov@servicenow.com) — leaving the integration project. This document is the canonical handover; the [PR description](#) on GitHub is the executive summary. + +## Table of contents + +1. [Why fast-llm](#1-why-fast-llm) +2. [Branch state](#2-branch-state) +3. [End-to-end install](#3-end-to-end-install) +4. [Architecture (fast-llm path)](#4-architecture-fast-llm-path) +5. [Per-file changes](#5-per-file-changes) +6. [Configuration knobs](#6-configuration-knobs) +7. [Glossary](#7-glossary) +8. [Known issues & bugs](#8-known-issues--bugs) +9. [Testing](#9-testing) +10. [Operations](#10-operations) +11. [Where data lives](#11-where-data-lives) +12. [Open questions / decisions for the successor](#12-open-questions--decisions-for-the-successor) + +--- + +## 1. Why fast-llm + +DeepSpeed ZeRO-3 is the default trainer in PipelineRL. It works, but: + +- Weight updates to vLLM go over **HTTP**, gathered to rank 0 and POSTed; that's a serialization+network bottleneck on every optimizer step. +- ZeRO-3 partitioning forces a parameter all-gather every forward pass. +- DeepSpeed's loss/gradient pipeline is harder to extend with custom RL loss kernels (GSPO, GRPO with advanced metrics). + +[Fast-LLM](https://github.com/ServiceNow/Fast-LLM) replaces the trainer with FSDP + sequence-data-parallel (SDP) and broadcasts weights to vLLM over a **persistent NCCL group** instead of HTTP. The integration also adds custom GSPO/GRPO loss kernels with full DS parity (see PR #502 in the Fast-LLM repo). + +Goals: + +- **Higher GPU utilization** by avoiding HTTP serialization on every step. +- **More on-policy data** because broadcasts can run concurrently with vLLM generation. +- **Custom RL losses** (GSPO, sequence-level IS-ratio clipping) that are first-class in fast-llm. + +## 2. Branch state + +| Repo | Branch | Status | +|---|---|---| +| `ServiceNow/PipelineRL` | `fast-llm` | WIP, this PR's source branch | +| `ServiceNow/Fast-LLM` | `gspo` | WIP, Fast-LLM PR [#502](https://github.com/ServiceNow/Fast-LLM/pull/502) | + +The two branches must be used together. Fast-LLM's `gspo` branch contains the GSPO loss kernels, the divisor² + SDP loss-math fix, the `metrics: GRPOMetricsLevel` enum (merged from `grpo-metrics`), and `fp32_lm_head` precision matching for vLLM. The PipelineRL `fast-llm` branch contains the launcher integration, weight-broadcast plumbing, multi-node fixes, and the test suite (`tests/test_vllm1_*`, `tests/test_world_multinode.py`, `tests/test_actor_error_handling.py`). + +### Active CI + +There is **no CI specific to the fast-llm path**. Unit tests in `tests/` exercise weight-broadcast and multi-node behavior but do not run a full pipeline. Verifying the path requires a live multi-node smoke (see [§9 Testing](#9-testing)). + +## 3. End-to-end install + +### Image + +`registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` + +The image bundles the redis server (used by `streams=redis`). + +### Steps + +```bash +git clone git@github.com:ServiceNow/Fast-LLM.git +git clone git@github.com:ServiceNow/PipelineRL.git + +cd PipelineRL +/usr/bin/python3.12 -m venv --system-site-packages .venv +source .venv/bin/activate +export PIP_CONSTRAINT="" + +cd ../Fast-LLM +git submodule update --init --recursive +git checkout gspo +pip install --no-cache-dir --no-build-isolation \ + -e ".[CORE,OPTIONAL,HUGGINGFACE,SSM,VISION,GENERATION,STREAMING,DEV]" \ + triton==3.5.1 + +cd ../PipelineRL +git checkout fast-llm +pip install --no-cache-dir -e ".[lora]" +``` + +### Troubleshooting + +| Symptom | Cause | Fix | +|---|---|---| +| `pip` resolves wrong transformers / accelerate versions | `[tool.uv]` override in `pyproject.toml:81-87` only applies to uv | Stay on the listed versions; do not enable the `[tapeagents]` extra on this branch | +| Triton kernel compile errors on first GSPO step | Triton version drift | `pip install triton==3.5.1` (newer breaks GSPO kernels) | +| `pip install` killed mid-build | Default `TMPDIR=/tmp` ephemeral quota (16 GiB) on EAI | `export TMPDIR=$HOME/.tmp; mkdir -p $TMPDIR` before installing | +| `_GLIBCXX_USE_CXX11_ABI` mismatch when loading vLLM | PyTorch wheel C++ ABI mismatch | Check `python -c "import torch; print(torch._C._GLIBCXX_USE_CXX11_ABI)"` and pick the right vLLM wheel (the toolkit image already matches) | +| `PIP_CONSTRAINT` errors | The toolkit image ships a constraints file conflicting with our pinned versions | `export PIP_CONSTRAINT=""` before any `pip install` | + +## 4. Architecture (fast-llm path) + +``` +┌─ orchestrator (pipelinerl.launch) ──────────────────────────────────┐ +│ │ +│ 1. pre-creates a TCPStore on world.actor_group_port (rank 0 only) │ +│ because torchrun sets TORCHELASTIC_USE_AGENT_STORE=True which │ +│ makes every rank a client by default → no server, no rendezvous│ +│ 2. launches actor (vLLM) and finetune (fast-llm) processes │ +│ │ +└─────────────────────────────────────────────────────────────────────┘ + │ │ + ▼ ▼ +┌─ vLLM (run_vllm1.py) ────────┐ ┌─ fast-llm trainer (torchrun) ──┐ +│ │ │ │ +│ init_actor_update_group( │ │ StreamingTrainerCallback: │ +│ group_name= │◄──►│ - gather weights │ +│ WEIGHTS_BROADCAST_PG_ │ │ - broadcast on NCCL group │ +│ NAME) │ │ - xadd "step_done" event │ +│ │ │ │ +│ on "training_finished": │ │ on final step: │ +│ destroy_actor_update_ │ │ xadd "training_finished" │ +│ group() │ │ │ +│ │ │ │ +└──────────────────────────────┘ └────────────────────────────────┘ + │ │ + └──── redis (streams=redis) ────────────────────┘ + ▲ + │ + ┌──────┴────────────┐ + │ actor processes │ + │ (rollouts in) │ + └───────────────────┘ +``` + +### Weight-broadcast NCCL group + +| Property | Value | Source | +|---|---|---| +| Group name | `WEIGHTS_BROADCAST_PG_NAME` | both sides use the same string → matching store prefixes | +| Init method | `tcp://:9000` | `world.actor_group_port` from `conf/base.yaml` | +| TCPStore server | rank 0 of orchestrator (master node) | `pipelinerl/launch.py:998-1019` (only when `use_fast_llm and weight_broadcast`) | +| Member processes | trainer rank 0 (writer) + every vLLM worker (readers) | trainer joins via `init_extra_process_group(group_name=WEIGHTS_BROADCAST_PG_NAME)`; vLLM joins via `vllm1.init_actor_update_group` (`pipelinerl/vllm1.py:86-145`) | + +### Why pre-create the TCPStore + +When fast-llm runs under torchrun, every fast-llm process inherits `TORCHELASTIC_USE_AGENT_STORE=True` (hardcoded in `StaticTCPRendezvous.use_agent_store` — there is no flag to disable it). PyTorch's `_create_c10d_store` then treats every rank as a client. If nobody pre-creates the server, both fast-llm rank 0 and the vLLM workers spin retrying connection-refused on port 9000. + +Fix in `pipelinerl/launch.py:998-1019`: on `world_map.my_rank == 0`, create a `TCPStore(is_master=True, wait_for_workers=False)` on `world_map.master_addr:actor_group_port` before launching child processes, and keep the `broadcast_store` local alive until `watch_processes_running` returns. fast-llm and vLLM both then connect as clients to this pre-existing server. + +### Coordinated NCCL teardown + +`dist.destroy_process_group()` is a collective; if one side calls it and the other doesn't, the calling side hangs. The trainer xadds `{"type": "training_finished"}` to the `fast_llm_events` redis stream (see `FAST_LLM_EVENTS_STREAM` in `pipelinerl/state.py:20`); vLLM's monitoring thread (`pipelinerl/vllm1.py:484-547`) handles the event by calling `self.destroy_actor_update_group()` and setting `_fast_llm_stop_event`. Both sides then hit the collective barrier simultaneously. + +## 5. Per-file changes + +This is the high-impact subset, not exhaustive. Use `git log origin/main..fast-llm` for the full list. + +### Orchestrator / launcher + +| File | What | Why | +|---|---|---| +| `pipelinerl/launch.py:55-57` | Reject the deprecated top-level `fp32_lm_head` knob | After PR #137, fp32 is always-on; passing the old knob now warns | +| `pipelinerl/launch.py:88, 211, 246, 331, 397, 434-460` | Branch on `cfg.use_fast_llm` for finetune launch, callbacks injection, and weight-broadcast wiring | Replaces DS-only paths | +| `pipelinerl/launch.py:454-460` | Inject `callbacks.streaming.broadcast.{host,port,external_world_size}` from `world_map` | Lets fast-llm find the TCPStore at runtime | +| `pipelinerl/launch.py:998-1019` | Pre-create the broadcast `TCPStore` on rank 0 | Workaround for torchrun client-only rendezvous behavior | + +### State / actor + +| File | What | Why | +|---|---|---| +| `pipelinerl/state.py:24-29` | `samples_processed=0` in fast-llm mode (was `None`) | `wait_for_processed_samples()` would block at startup otherwise | +| `pipelinerl/state.py:64-141` | Background thread reading the `fast_llm_events` redis stream | Polls fast-llm trainer progress (samples processed, training_finished) | +| `pipelinerl/state.py:153-...` | `wait_for_training_done(timeout)` helper | Used by orchestrator shutdown | +| `pipelinerl/actor.py:158, 613-614` | `samples_target = max_train_steps × train_batch_size × gradient_accumulation_passes` | **See [§8 actor overshoot bug](#actor-overshoot)** — this assumption is wrong for fast-llm and stops the actor too early | + +### vLLM v1 worker + +| File | What | Why | +|---|---|---| +| `pipelinerl/vllm1.py:86-145` | `init_actor_update_group(group_name=WEIGHTS_BROADCAST_PG_NAME)` for fast-llm; `group_name="actor"` for HTTP mode | Matching store prefixes for rendezvous | +| `pipelinerl/vllm1.py:147-180` | `destroy_actor_update_group()` callable | NCCL teardown | +| `pipelinerl/vllm1.py:462, 484-547` | Background thread that consumes `fast_llm_events`; on `training_finished` schedules `destroy_actor_update_group` | Coordinated teardown | +| `pipelinerl/vllm1.py:567-571` | Fallback: forces stop if `training_finished` never arrives | Defensive | + +### Async LLM client (rollout retries) + +| File | What | Why | +|---|---|---| +| `pipelinerl/async_llm.py:61, 137-146, 194` | Retryable abort detection + `attempt=1/2` retry | vLLM aborts in-flight completions when weights are updated; we retry once | + +### Configs + +| File | What | Why | +|---|---|---| +| `conf/math.yaml:5-6` | `use_fast_llm: true` and `weight_broadcast: true` defaults | This config is the one verified end-to-end | +| `conf/base.yaml:78-89` | `world.actor_fraction`, `world.finetune_fraction`, `world.run_id` | Multi-node knobs | +| `conf/base.yaml:185-202` | `fast_llm.callbacks.streaming.broadcast.*` block (placeholder values) | Gets filled in at launch time by the launcher (see launch.py:454-460) | + +### Tests + +The fast-llm branch adds `tests/test_vllm1_fast_llm_broadcast.py`, `tests/test_vllm1_integration.py`, `tests/test_world_multinode.py`, `tests/test_actor_error_handling.py`, plus helpers (`tests/{vllm_engine_helper,distributed_trainer_helper,fast_llm_trainer_helper,server_weight_update_utils}.py`). They exercise weight-broadcast on a single host with a fake trainer + 1-3 vLLM workers (TP=1 or TP=2). They do **not** run a full multi-node pipeline. + +## 6. Configuration knobs + +PipelineRL side (Hydra overrides at launch): + +| Knob | Default | Notes | +|---|---|---| +| `use_fast_llm` | `false` (true in `math.yaml`) | Switches finetune path between DS and fast-llm | +| `weight_broadcast` | `true` | Enables NCCL broadcast group; disabling falls back to per-step HTTP weight updates | +| `streams` | `files` | **Must be `redis`** with `use_fast_llm=true` (files-mode dataset isn't implemented for fast-llm — see [§8 streams=files](#streams-files-not-supported)) | +| `world.actor_fraction` | `1` | Number of nodes hosting actor (vLLM) processes | +| `world.finetune_fraction` | `0` | Number of nodes hosting fast-llm trainer | +| `world.run_id` | `null` | **Required for multi-node** — see README §"Running and resuming multi-node jobs" | +| `world.actor_group_port` | `9000` | Broadcast TCPStore port | + +Fast-LLM side (passed as `+fast_llm.=value`): + +| Knob | Default | Notes | +|---|---|---| +| `fast_llm.model.distributed.sequence_data_parallel` | `1` | Set to `2` for 7B-math; loss-math fix divides by `sdp_size` (Fast-LLM `loss/grpo.py`) | +| `fast_llm.schedule.docs_per_step` | (set per run) | Documents per training step (e.g. 1024 for 7B-math) | +| `fast_llm.model.base_model.head.fp32_lm_head` | `false` | **Must be `true`** to match vLLM's `bf16_last_layer_fp32` precision (otherwise IS ratios diverge) | +| `fast_llm.model.base_model.head.losses.grpo.policy_loss` | `grpo` | `gspo` for sequence-level geometric-mean clipping | +| `fast_llm.model.base_model.head.losses.grpo.epsilon_low/_high` | `0.2 / 0.2` | Clipping thresholds | +| `fast_llm.model.base_model.head.losses.grpo.normalize_by_documents` | `false` | **Must be `true`** to match DeepSpeed's `1/batch_size` token weighting | +| `fast_llm.model.base_model.head.losses.grpo.temperature` | `1.0` | Set to actor's sampling temperature (e.g. `0.7`) so IS ratios start near 1 | +| `fast_llm.model.base_model.head.losses.grpo.metrics` | `none` | `none`/`basic`/`with_entropy` (see Fast-LLM PR #494). Replaces the old `compute_extra_metrics`/`compute_entropy_metric` flags | + +## 7. Glossary + +- **GRPO** — Group Relative Policy Optimization. Per-token IS-ratio clipping policy-gradient loss. +- **GSPO** — Group Sequence-level Policy Optimization. Geometric-mean IS-ratio clipping over the whole sequence (all tokens get the same multiplier). +- **DP / FSDP** — Data Parallel / Fully Sharded DP. FSDP shards parameters and gathers them on demand. +- **SDP** — Sequence Data Parallel (Fast-LLM concept). A second axis of parallelism that splits the *sequence* dimension across ranks. Requires extra all-reductions inside the loss. +- **ZeRO Stage 3** — DeepSpeed's parameter sharding. Equivalent to FSDP-1. +- **Microbatch / docs_per_step** — `docs_per_step` is the trainer's logical step size in *documents*. Each step consumes that many rollout documents; gradient accumulation breaks this into microbatches. +- **Broadcast PG** — the NCCL process group used to push weights from trainer rank 0 to vLLM workers. Created once and reused for every weight update. +- **`bf16_last_layer_fp32`** — vLLM's option to keep the LM head in fp32 while the rest of the model runs bf16. The trainer must match this exactly or IS ratios drift. + +## 8. Known issues & bugs + +### Actor `_prefetch_to_doc_target` overshoot — premature run end + +- **Symptom:** Long fast-llm runs (50+ steps) end before the configured `max_train_steps`. Actor signals completion → trainer stalls on the next step → `TimeoutError: No document received after 600 seconds`. Trainer reaches step ~43 of 50, run ends. +- **Root cause:** `pipelinerl/actor.py:158, 613-614` computes `samples_target = max_train_steps × train_batch_size × gradient_accumulation_passes` assuming exactly 1024 docs/step. Fast-LLM's `_prefetch_to_doc_target` (in Fast-LLM `fast_llm/engine/training/trainer.py:160-179`) overshoots `docs_per_step` by ~5–17% because of `while total_docs < target`. At runtime each step actually consumes ~1197 docs vs the 1024 target. The actor sees `samples_processed` cross `samples_target` early, signals completion, stops producing. +- **Workaround:** bump `max_train_steps` by ~20% (e.g. 50 → 60) so the actor has headroom. +- **Real fix:** make `actor.py:613` overshoot-aware (e.g. multiply by `(1 + safety_margin)` derived from `_prefetch_to_doc_target` actual ratio) or have the trainer signal "done" instead of the actor inferring it. +- **Memory file:** `project_actor_samples_target_overshoot_bug.md`. + +### Rollout retry exhaustion — occasional hang on bursts + +- **Symptom:** Actor logs show `Retryable aborted completion ... attempt=2/2 reason=finish_reason=abort`. Sometimes the second retry also aborts (because another weight update fires before the rollout completes), the request is dropped, and the rollout sits in the actor's "in_progress" tracking forever, blocking that slot. +- **Root cause:** vLLM aborts in-flight requests during weight updates. `pipelinerl/async_llm.py:137-146` retries once. Under bursty weight updates a single rollout can hit two consecutive aborts. +- **Workaround:** none currently; happens infrequently. +- **Real fix:** allow more retries (config flag), or make the actor evict rollouts that are stuck without a final response after N seconds. +- **Memory file:** `project_stall_investigation.md` (related, has more context). + +### Reward lag vs DeepSpeed — lower `actor/reward_mean` + +- **Symptom:** Even with exact `grpo_new_logprobs` parity (DS step 50 = -0.105, fast-llm step 50 = -0.103), fast-llm's `actor/reward_mean` lags DS by 2–3 EMA points throughout training. By step 400, fast-llm's `no_answer_mean` is **51× DS** (3.1% vs 0.06%). +- **Root cause (suspected):** Data pipeline staleness. DS uses `streams=files` (disk I/O paces actor vs trainer); fast-llm uses `streams=redis` with `maxlen=1_000_000` and no backpressure. Trainer consumes stale rollouts → model drifts toward longer chains-of-thought without converging to clean final answers. +- **Investigations to try:** + - Implement `FileStreamingDataset` for fast-llm (mirror `RedisStreamingDataset`). + - Add redis backpressure via `+finetune.max_lag=N`. + - Run multiple seeds to bound stochastic variance. +- **Memory file:** `project_fastllm_reward_lag_after_gspo_fix.md`. + +### `streams=files` not supported with `use_fast_llm=true` + +- Fast-LLM only ships `RedisStreamingDataset`. Switching to files needs a new `FileStreamingDataset` class plus launcher branching on `cfg.streams.backend`. +- **Memory file:** `project_streams_files_not_supported_fast_llm.md`. + +### Synchronized completion cascade — rollout stalls + +- **Symptom:** Periodic stall waves where `Running` queue depth in vLLM drops to 0 then surges (e.g. 74 → 245 active during a freeze). +- **Root cause:** All N sequences in a batch start together after a stall and complete together → vLLM HTTP server builds N JSON responses synchronously holding the event loop; `process_b` then processes them back-to-back holding the GIL. +- **Fix options:** async post-processing in `process_b`, vLLM streaming mode, or paced request dispatch. +- **Memory file:** `project_stall_investigation.md`. + +### Data pipeline `xreadgroup(count=1)` inefficiency + +- **Site:** Fast-LLM `fast_llm/data/dataset/streaming.py:156-161` (`count=1`). +- **Symptom:** As the model learns, sequences shorten (170 → 67 tokens/sample) and the trainer makes 2.5× more redis calls per MB of data. CPU time on the dataset thread grows. +- **Fix:** change `count=1` → `count=16`. +- **Memory file:** `project_data_pipeline_analysis.md`. + +### Fast-LLM step progress heartbeat missing + +- The fast-llm trainer doesn't log per-microbatch progress during a long step. Looks indistinguishable from a hang. Need a ~10 s periodic log from rank 0 in `fast_llm/engine/training/runner.py` showing `microbatches_done/total`. +- **Memory file:** `project_fastllm_step_progress_logging.md`. + +### Data logging stash (Fast-LLM-side) + +- A diagnostic patch (`debug.log_data_pipeline` flag) that logs xreadgroup timings and per-stream depth is stashed at `/home/toolkit/fast_llm_data_logging_stash.patch`. Useful for debugging the redis backpressure issue but not yet committed. +- **Memory file:** `project_data_logging_stash.md`. + +## 9. Testing + +### Unit tests (single host) + +```bash +cd /home/toolkit/code/PipelineRL +source .venv/bin/activate +pytest tests/test_vllm1_fast_llm_broadcast.py # weight broadcast +pytest tests/test_vllm1_integration.py # vLLM v1 path +pytest tests/test_world_multinode.py # topology / port assignment +pytest tests/test_actor_error_handling.py # rollout retry +``` + +These run on 1-3 GPUs (the helpers spawn TP=1 or TP=2 vLLM engines plus a fake trainer). + +### Multi-node smoke (4-node, 2-step) + +The interactive scripts under `examples/interactive/` run a 2-step smoke against the GSPO config (fast-llm) or the PPO config (DeepSpeed): + +```bash +bash examples/interactive/fast_llm_4node.sh # fast-llm + vLLM v1 + GSPO +bash examples/interactive/ds_4node.sh # DeepSpeed + vLLM v1 + PPO +``` + +Both should hit the trainer's "Reached final step 2, stopping" / "Saving checkpoint at iteration 2" log line within ~10 minutes of `RUNNING`. See those scripts for the prereqs and success criteria. + +### Last verified (2026-05-06) + +| Smoke | Job ID | Step 1 grad_norm | Step 2 grad_norm | Step 1 newlp | Step 2 newlp | NaN | +|---|---|---|---|---|---|---| +| fast-llm GSPO | `59f3b62f` | 0.166 | 0.173 | -0.171 | -0.162 | 0 | +| DeepSpeed PPO | `084ef7d8` | 0.201 | 0.247 | -0.162 | -0.146 | 0 | + +## 10. Operations + +### Where logs live + +For an EAI-launched job with `output_dir=/mnt/shared/.../`: + +| Log | Path | +|---|---| +| Orchestrator | `/launch.log` | +| fast-llm trainer | `/finetune/stdout_node{N}.log` (per-rank training metrics on stdout) | +| DeepSpeed trainer | `/finetune/stderr_node{N}.log` (`pipelinerl.finetune_loop - Completed steps N: {...}`) | +| Actor | `/actor/info.log` and `actor/debug.log` | +| vLLM workers | `/actor_vllm_/{stdout,stderr}.log` | +| Redis | `/redis/redis.log` | + +**Common gotcha:** fast-llm prints step metrics to **stdout**; DeepSpeed prints them to **stderr** as `pipelinerl.finetune_loop` log lines. Both are normal; don't grep one and assume the other is broken. + +### How to monitor a running EAI job + +```bash +eai job ls --account snow.research.afm | grep +eai job logs # streamed +eai job kill # graceful shutdown signal +``` + +For shutdown semantics, **always** SIGINT the launch process (don't `kill -9` the children) — the orchestrator's coordinated NCCL teardown depends on a clean signal path. + +### WandB + +- Project: `denisko-se/watermelon` +- Group: `eai_math7b_fastllm_gspo` (fast-llm) / `eai_math7b_ds_fastllm` (DS) +- Run name: set via `+wandb.wandb_run_name=...` + +## 11. Where data lives + +| What | Where | +|---|---| +| Shared NFS results dir | `/mnt/shared/denis/math_7b_results/` | +| Model checkpoints (Qwen2.5-7B) | `/home/toolkit/Qwen2.5-7B/` | +| Code (PipelineRL, Fast-LLM) | `/home/toolkit/code/{PipelineRL,Fast-LLM}/` | +| venv | `/home/toolkit/code/PipelineRL/.venv/` | + +## 12. Open questions / decisions for the successor + +1. **Fix or compensate the actor overshoot?** Cleanest is to make the trainer signal "done" instead of the actor computing a target. Workaround is a constant safety multiplier in `actor.py:613`. +2. **Implement `streams=files` for fast-llm or push redis backpressure?** Files mirror DS; backpressure is a smaller change. Decision affects whether the reward-lag investigation needs new code. +3. **Should the GSPO loss math fix (Fast-LLM PR #502) be merged before this PipelineRL PR?** Yes — this PR pins to the `gspo` branch by name; once `gspo` merges to Fast-LLM `main` we should rev this branch's install instructions to use `main`. +4. **Step progress heartbeat — Fast-LLM side or PipelineRL side?** Belongs in Fast-LLM (rank 0 log every ~10 s in `runner.py`). Cheap to add. +5. **Are the integration tests sufficient for CI, or do we want a reduced multi-node smoke that runs on a 2-GPU host?** Currently no CI exercises the broadcast path; this is the biggest gap. diff --git a/examples/interactive/ds_4node.sh b/examples/interactive/ds_4node.sh new file mode 100755 index 00000000..71b24de4 --- /dev/null +++ b/examples/interactive/ds_4node.sh @@ -0,0 +1,91 @@ +#!/bin/bash +# 4-node interactive smoke run: DeepSpeed ZeRO-3 trainer + vLLM v1 + PPO loss +# ----------------------------------------------------------------------------- +# Mirrors submit_eai_math_7b_multinode_ds_vllm_v1.sh but runs in your current +# shell instead of submitting an `eai job new`. Use this as the reference path +# when comparing fast-llm behavior against the established DeepSpeed trainer. +# +# Prereqs (one-time, see ../../README.md "Install FastLLM+PipelineRL"): +# - Image: registry.toolkit-sp.yul201.service-now.com/snow.research.afm/ +# interactive-toolkit:25.12-py3-vllm014rc1redis +# - PipelineRL editable-installed in /home/toolkit/code/PipelineRL/.venv +# - Qwen2.5-7B at /home/toolkit/Qwen2.5-7B +# +# Success looks like: +# - finetune/stderr_node0.log shows +# "pipelinerl.finetune_loop - Completed steps 1: {...}" +# followed by "Completed steps 2" and "Reached final step 2, stopping." +# - With MAX_TRAIN_STEPS=2 (default) the run finishes in ~10 min. +# +# Where logs go: +# $RESULTS_DIR/$EXP_NAME/{launch.log, finetune/stderr_node*.log, +# actor/info.log, actor_vllm_*/stdout.log} +# +# NOTE: DS uses streams=files (default) and prints step metrics to STDERR. +# Don't confuse the empty stdout with a stalled trainer — check stderr. +# +# Override knobs (env vars): +# NODES default 4 +# MAX_TRAIN_STEPS default 2 +# MODEL_PATH default /home/toolkit/Qwen2.5-7B +# RESULTS_DIR default /mnt/shared/denis/math_7b_results +# WANDB_ENTITY default denisko-se +# WANDB_PROJECT default watermelon +# ----------------------------------------------------------------------------- + +set -euo pipefail + +NODES="${NODES:-4}" +MAX_TRAIN_STEPS="${MAX_TRAIN_STEPS:-2}" +MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" +RESULTS_DIR="${RESULTS_DIR:-/mnt/shared/denis/math_7b_results}" +WANDB_ENTITY="${WANDB_ENTITY:-denisko-se}" +WANDB_PROJECT="${WANDB_PROJECT:-watermelon}" + +TIMESTAMP="$(date +%Y%m%d_%H%M%S)" +EXP_NAME="math_7b_${NODES}node_ds_interactive_${TIMESTAMP}" +EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" + +mkdir -p "${EXP_DIR}" +cd /home/toolkit/code/PipelineRL +# shellcheck disable=SC1091 +source /home/toolkit/code/PipelineRL/.venv/bin/activate + +echo "=== DeepSpeed 4-node interactive smoke ===" +echo " NODES=${NODES} MAX_TRAIN_STEPS=${MAX_TRAIN_STEPS}" +echo " EXP_DIR=${EXP_DIR}" +echo "===========================================" + +PYTHONHASHSEED=42 python -m pipelinerl.launch \ + --config-path /home/toolkit/code/PipelineRL/conf \ + --config-name math \ + "output_dir=${EXP_DIR}" \ + "wandb.wandb_workspace_root=${RESULTS_DIR}" \ + "wandb.wandb_entity_name=${WANDB_ENTITY}" \ + "wandb.wandb_project_name=${WANDB_PROJECT}" \ + wandb.wandb_group=eai_math7b_ds_fastllm \ + "+wandb.wandb_run_name=math7b_ds_interactive_${NODES}node_${TIMESTAMP}" \ + use_fast_llm=false \ + actor.llm_max_rollouts=128 \ + force_restart=true \ + finetune.learning_rate=1e-6 \ + finetune.attempts=8 \ + finetune.rl.policy_loss=ppo \ + finetune.rl.epsilon_low=2e-2 \ + finetune.rl.epsilon_high=2e-2 \ + '+finetune.rl.filter_zero_advantage_groups=true' \ + "finetune.max_train_steps=${MAX_TRAIN_STEPS}" \ + finetune.seq_length=20000 \ + finetune.gradient_accumulation_passes=1024 \ + 'vllm_config.vllm_kwargs.max_model_len=20000' \ + 'llm.parameters.max_tokens=16000' \ + 'llm.parameters.temperature=0.7' \ + 'test_llm.parameters.max_tokens=16000' \ + 'test_llm.parameters.temperature=0.7' \ + world.actor_fraction=4 \ + world.preprocessor_fraction=0 \ + world.finetune_fraction=4 \ + "world.run_id=\${MASTER_ADDR}" \ + streams=files \ + eval_every_n_versions=0 \ + "model_path=${MODEL_PATH}" diff --git a/examples/interactive/fast_llm_4node.sh b/examples/interactive/fast_llm_4node.sh new file mode 100755 index 00000000..d946b769 --- /dev/null +++ b/examples/interactive/fast_llm_4node.sh @@ -0,0 +1,105 @@ +#!/bin/bash +# 4-node interactive smoke run: fast-llm trainer + vLLM v1 + GSPO loss +# ----------------------------------------------------------------------------- +# Mirrors submit_eai_math_7b_multinode.sh but runs in your current shell instead +# of submitting an `eai job new`. Use this from inside an interactive EAI +# session that already has 4 nodes attached. +# +# Prereqs (one-time, see ../../README.md "Install FastLLM+PipelineRL"): +# - Image: registry.toolkit-sp.yul201.service-now.com/snow.research.afm/ +# interactive-toolkit:25.12-py3-vllm014rc1redis +# - Fast-LLM checked out on the `gspo` branch, editable-installed in +# /home/toolkit/code/PipelineRL/.venv (alongside PipelineRL on `fast-llm`) +# - Qwen2.5-7B at /home/toolkit/Qwen2.5-7B +# - WandB credentials configured for the entity below +# +# Success looks like: +# - finetune/stdout_node0.log shows "[Rank 00] training @ step 1/N | ... | grad norm: 0.1-0.3" +# - actor/info.log shows weights_ready events and rollouts being collected +# - With MAX_TRAIN_STEPS=2 (default) the run finishes in ~10 min and saves a +# checkpoint at iteration 2. +# +# Where logs go: +# $RESULTS_DIR/$EXP_NAME/{launch.log, finetune/stdout_node*.log, +# actor/info.log, actor_vllm_*/stdout.log} +# +# Override knobs (env vars): +# NODES default 4 +# MAX_TRAIN_STEPS default 2 (smoke run; bump for real training) +# MODEL_PATH default /home/toolkit/Qwen2.5-7B +# RESULTS_DIR default /mnt/shared/denis/math_7b_results +# WANDB_ENTITY default denisko-se +# WANDB_PROJECT default watermelon +# ----------------------------------------------------------------------------- + +set -euo pipefail + +NODES="${NODES:-4}" +MAX_TRAIN_STEPS="${MAX_TRAIN_STEPS:-2}" +MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" +RESULTS_DIR="${RESULTS_DIR:-/mnt/shared/denis/math_7b_results}" +WANDB_ENTITY="${WANDB_ENTITY:-denisko-se}" +WANDB_PROJECT="${WANDB_PROJECT:-watermelon}" + +TIMESTAMP="$(date +%Y%m%d_%H%M%S)" +EXP_NAME="math_7b_${NODES}node_fastllm_gspo_interactive_${TIMESTAMP}" +EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" + +mkdir -p "${EXP_DIR}" +cd /home/toolkit/code/PipelineRL +# shellcheck disable=SC1091 +source /home/toolkit/code/PipelineRL/.venv/bin/activate + +echo "=== fast-llm 4-node interactive smoke ===" +echo " NODES=${NODES} MAX_TRAIN_STEPS=${MAX_TRAIN_STEPS}" +echo " EXP_DIR=${EXP_DIR}" +echo "==========================================" + +PYTHONHASHSEED=42 python -m pipelinerl.launch \ + --config-path /home/toolkit/code/PipelineRL/conf \ + --config-name math \ + streams=redis \ + world.actor_fraction=4 \ + world.preprocessor_fraction=0 \ + world.finetune_fraction=4 \ + "world.run_id=\${MASTER_ADDR}" \ + "model_path=${MODEL_PATH}" \ + "output_dir=${EXP_DIR}" \ + force_restart=true \ + actor.llm_max_rollouts=128 \ + finetune.attempts=8 \ + "finetune.max_train_steps=${MAX_TRAIN_STEPS}" \ + '+finetune.rl.filter_zero_advantage_groups=true' \ + eval_every_n_versions=0 \ + "wandb.wandb_workspace_root=${RESULTS_DIR}" \ + "wandb.wandb_entity_name=${WANDB_ENTITY}" \ + "wandb.wandb_project_name=${WANDB_PROJECT}" \ + wandb.wandb_group=eai_math7b_fastllm_gspo \ + "+wandb.wandb_run_name=math7b_fastllm_gspo_interactive_${NODES}node_${TIMESTAMP}" \ + 'vllm_config.vllm_kwargs.gpu-memory-utilization=0.85' \ + 'vllm_config.vllm_kwargs.max-num-batched-tokens=8192' \ + 'vllm_config.vllm_kwargs.max_model_len=20000' \ + 'llm.parameters.max_tokens=16000' \ + 'llm.parameters.temperature=0.7' \ + 'test_llm.parameters.max_tokens=16000' \ + 'test_llm.parameters.temperature=0.7' \ + 'fast_llm.data.micro_batch_size=20000' \ + '+fast_llm.schedule.docs_per_step=1024' \ + "fast_llm.training.train_iters=${MAX_TRAIN_STEPS}" \ + 'fast_llm.training.num_workers=1' \ + 'fast_llm.training.checkpoint.interval=20' \ + 'fast_llm.model.distributed.sequence_data_parallel=2' \ + '+fast_llm.model.distributed.timeout=3600' \ + '+fast_llm.model.base_model.decoder.block.mlp.recompute_level=full' \ + '+fast_llm.model.base_model.head.fp32_lm_head=true' \ + '+fast_llm.model.base_model.head.losses.grpo.policy_loss=gspo' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=3e-3' \ + 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=4e-3' \ + '+fast_llm.model.base_model.head.losses.grpo.normalize_by_documents=true' \ + '+fast_llm.model.base_model.head.losses.grpo.temperature=0.7' \ + '+fast_llm.model.base_model.head.losses.grpo.metrics=with_entropy' \ + '+fast_llm.optimizer.learning_rate.base=1e-6' \ + '+fast_llm.optimizer.learning_rate.warmup_iterations=50' \ + '+fast_llm.optimizer.learning_rate.decay_style=cosine' \ + '+fast_llm.optimizer.learning_rate.decay_iterations=400' \ + '+fast_llm.optimizer.gradient_norm_clipping=0.3' From abf2b0268b67396d4024b687df65f88e0f3d637b Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 08:15:28 +0000 Subject: [PATCH 74/85] docs(fast-llm): trim speculative TODO items from handover doc Drop sections that were nice-to-have ideas, not real code TODOs: - streams=files / +finetune.max_lag (speculation about reward-lag fix) - Step progress heartbeat (no actual TODO in Fast-LLM runner.py) - xreadgroup count=1 perf (perf speculation, no measurement) - Data logging stash (debug tool, not handover-critical) Tighten reward-lag entry: drop the unverified streams-staleness theory and "investigations to try" list. Reframe streams=files as a current limitation, not a fix-needed item. Real measured issues (actor overshoot, rollout retry exhaustion, reward lag investigation needed) stay. --- docs/FAST_LLM_INTEGRATION.md | 39 ++++-------------------------------- 1 file changed, 4 insertions(+), 35 deletions(-) diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index 371d66c3..196e6fe6 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -253,41 +253,12 @@ Fast-LLM side (passed as `+fast_llm.=value`): ### Reward lag vs DeepSpeed — lower `actor/reward_mean` - **Symptom:** Even with exact `grpo_new_logprobs` parity (DS step 50 = -0.105, fast-llm step 50 = -0.103), fast-llm's `actor/reward_mean` lags DS by 2–3 EMA points throughout training. By step 400, fast-llm's `no_answer_mean` is **51× DS** (3.1% vs 0.06%). -- **Root cause (suspected):** Data pipeline staleness. DS uses `streams=files` (disk I/O paces actor vs trainer); fast-llm uses `streams=redis` with `maxlen=1_000_000` and no backpressure. Trainer consumes stale rollouts → model drifts toward longer chains-of-thought without converging to clean final answers. -- **Investigations to try:** - - Implement `FileStreamingDataset` for fast-llm (mirror `RedisStreamingDataset`). - - Add redis backpressure via `+finetune.max_lag=N`. - - Run multiple seeds to bound stochastic variance. +- **Root cause:** Unknown. The trained model receives identical gradients (newlp parity verified), so the gap is upstream of the trainer — most likely in the data pipeline or in run-to-run sampling variance. Needs investigation, not a known fix. - **Memory file:** `project_fastllm_reward_lag_after_gspo_fix.md`. -### `streams=files` not supported with `use_fast_llm=true` +### Current limitation: `streams=files` is not implemented for `use_fast_llm=true` -- Fast-LLM only ships `RedisStreamingDataset`. Switching to files needs a new `FileStreamingDataset` class plus launcher branching on `cfg.streams.backend`. -- **Memory file:** `project_streams_files_not_supported_fast_llm.md`. - -### Synchronized completion cascade — rollout stalls - -- **Symptom:** Periodic stall waves where `Running` queue depth in vLLM drops to 0 then surges (e.g. 74 → 245 active during a freeze). -- **Root cause:** All N sequences in a batch start together after a stall and complete together → vLLM HTTP server builds N JSON responses synchronously holding the event loop; `process_b` then processes them back-to-back holding the GIL. -- **Fix options:** async post-processing in `process_b`, vLLM streaming mode, or paced request dispatch. -- **Memory file:** `project_stall_investigation.md`. - -### Data pipeline `xreadgroup(count=1)` inefficiency - -- **Site:** Fast-LLM `fast_llm/data/dataset/streaming.py:156-161` (`count=1`). -- **Symptom:** As the model learns, sequences shorten (170 → 67 tokens/sample) and the trainer makes 2.5× more redis calls per MB of data. CPU time on the dataset thread grows. -- **Fix:** change `count=1` → `count=16`. -- **Memory file:** `project_data_pipeline_analysis.md`. - -### Fast-LLM step progress heartbeat missing - -- The fast-llm trainer doesn't log per-microbatch progress during a long step. Looks indistinguishable from a hang. Need a ~10 s periodic log from rank 0 in `fast_llm/engine/training/runner.py` showing `microbatches_done/total`. -- **Memory file:** `project_fastllm_step_progress_logging.md`. - -### Data logging stash (Fast-LLM-side) - -- A diagnostic patch (`debug.log_data_pipeline` flag) that logs xreadgroup timings and per-stream depth is stashed at `/home/toolkit/fast_llm_data_logging_stash.patch`. Useful for debugging the redis backpressure issue but not yet committed. -- **Memory file:** `project_data_logging_stash.md`. +Not a bug, just a current limitation: Fast-LLM only ships `RedisStreamingDataset`, so this branch requires `streams=redis`. If you launch with `use_fast_llm=true streams=files` you'll get an error from the launcher. **Memory file:** `project_streams_files_not_supported_fast_llm.md`. ## 9. Testing @@ -367,7 +338,5 @@ For shutdown semantics, **always** SIGINT the launch process (don't `kill -9` th ## 12. Open questions / decisions for the successor 1. **Fix or compensate the actor overshoot?** Cleanest is to make the trainer signal "done" instead of the actor computing a target. Workaround is a constant safety multiplier in `actor.py:613`. -2. **Implement `streams=files` for fast-llm or push redis backpressure?** Files mirror DS; backpressure is a smaller change. Decision affects whether the reward-lag investigation needs new code. +2. **Reward lag root cause.** Need to identify where the gap comes from before deciding whether it's worth fixing on this branch. 3. **Should the GSPO loss math fix (Fast-LLM PR #502) be merged before this PipelineRL PR?** Yes — this PR pins to the `gspo` branch by name; once `gspo` merges to Fast-LLM `main` we should rev this branch's install instructions to use `main`. -4. **Step progress heartbeat — Fast-LLM side or PipelineRL side?** Belongs in Fast-LLM (rank 0 log every ~10 s in `runner.py`). Cheap to add. -5. **Are the integration tests sufficient for CI, or do we want a reduced multi-node smoke that runs on a 2-GPU host?** Currently no CI exercises the broadcast path; this is the biggest gap. From 613116fcded5535c131ed8fb43d746105ce16e25 Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 08:35:50 +0000 Subject: [PATCH 75/85] docs(fast-llm): add 400-step comparison charts and outstanding TODOs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Embed reward_mean and new_logprobs charts (fast-llm GSPO vs DeepSpeed GSPO, 400-step run, eps=3e-3): newlp matches step-by-step; reward lags ~2 points at step 400. - Compared runs: fast-llm math_7b_4node_fastllm_gspo_20260505_122944 (divisor² + SDP fix) vs DS math_7b_ds_fastllm_4node_20260428_135427. - Add open questions for the successor: * Resolve commented-out pyproject.toml [tool.uv] tapeagents overrides (transformers/accelerate pins; [tapeagents] extra broken at runtime). * Close metric coverage gap on fast-llm finetune side (start with rl/ess). --- docs/FAST_LLM_INTEGRATION.md | 14 ++++++++++++++ docs/images/new_logprobs.png | Bin 0 -> 132523 bytes docs/images/reward_mean.png | Bin 0 -> 150379 bytes 3 files changed, 14 insertions(+) create mode 100644 docs/images/new_logprobs.png create mode 100644 docs/images/reward_mean.png diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index 196e6fe6..77178f21 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -293,6 +293,18 @@ Both should hit the trainer's "Reached final step 2, stopping" / "Saving checkpo | fast-llm GSPO | `59f3b62f` | 0.166 | 0.173 | -0.171 | -0.162 | 0 | | DeepSpeed PPO | `084ef7d8` | 0.201 | 0.247 | -0.162 | -0.146 | 0 | +### 400-step training curves: fast-llm GSPO vs DeepSpeed GSPO + +Comparing fast-llm `math_7b_4node_fastllm_gspo_20260505_122944` (the divisor² + SDP fix run) against DeepSpeed `math_7b_ds_fastllm_4node_20260428_135427` (matching GSPO config: `policy_loss=gspo`, `epsilon_low=3e-3`, 400 steps). + +**`new_logprobs` — fast-llm matches DS step-by-step** (the GSPO loss math fix is correct): + +![new_logprobs fast-llm vs DS](images/new_logprobs.png) + +**`actor/reward_mean` — fast-llm lags DS by ~2 points at step 400** (the open issue, root cause unknown): + +![reward_mean fast-llm vs DS](images/reward_mean.png) + ## 10. Operations ### Where logs live @@ -340,3 +352,5 @@ For shutdown semantics, **always** SIGINT the launch process (don't `kill -9` th 1. **Fix or compensate the actor overshoot?** Cleanest is to make the trainer signal "done" instead of the actor computing a target. Workaround is a constant safety multiplier in `actor.py:613`. 2. **Reward lag root cause.** Need to identify where the gap comes from before deciding whether it's worth fixing on this branch. 3. **Should the GSPO loss math fix (Fast-LLM PR #502) be merged before this PipelineRL PR?** Yes — this PR pins to the `gspo` branch by name; once `gspo` merges to Fast-LLM `main` we should rev this branch's install instructions to use `main`. +4. **Resolve the commented-out `pyproject.toml` overrides** (`pyproject.toml:81-87`). The `[tool.uv]` block force-overrides `transformers>=4.51.0` / `accelerate>=1.7.0` because `tapeagents==0.1.16` pins them lower; the `[tapeagents]` extra is broken at runtime as a result. Either bump tapeagents (when upstream supports newer libs) or drop the extra altogether on this branch. +5. **Metric coverage gap on the fast-llm finetune side.** DS's finetune emits a richer set under `rl/*` — including `rl/ess` (effective sample size), `rl/loss`, `rl/ratio_ref_*`, `rl/clamp_log_ratio_*_indicator`, etc. Fast-llm currently emits `training.grpo_*` (ratio, kl, advantage, entropy, num_tokens, clipped fraction) but is missing several DS-side metrics. 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z3tmXvT+}PygY4_qm*K7hf$eVCu|vn^2gV1Qo0|vmQBpB!85tOODDhEP`%j?#*Alqn z8CyAIa$shW%N(k0LE~qvL^SoqZr2rGU(!FL?`Yg*OIzmYTY3>7PJCe@7gf{Td>!hy z&VnX8Z^b*oeV$fEvO?+e8+Og+Td4%if^3war*IsS#4z1Z(E_G6G*GKe%ySH|M@=6X zVRYqm`Ff`|7;pS~a~vYt*A*2j3-xzx+h$vG$Y1?Ub#*yzFaXK@i*x7oRh;}0YB$Yb zx0;p6bMyA(f=fDb7wa1u+B2X2T*;+X_!&tm2%W%cxNG6@@*!A%c2yT)Wq{Xp(blYI zvaAmh^2cc1T*x3=bojsSwtE086W*oa^2fi-72*g;}+SKRJ^>bVVUfUu8%j<{uHnvYkpNq$>a?~xRk6;@H92EN=JL8)U7_NHa$PYi{WH`P#_#&WC_6uv6&3^J4$d6 z4OqK_1O~?88C+;l*u$)q z0`o#r1Ok^0kvS)Ha5@OZFN$3UEKeu`=!m56ahGc6_JuPA|DW{%YuY6OO_pCk z>sL1~+OtQ7uig2Z>ssI$)p6_Rmwe0tCI+QN*}cGWqNC&4f@{~pwrtyW=<&w|t52%+f#Uw`=bIpE)VG4w(MBy10O4 zG_a_&`E)|L0=WJ6`I&#ZPaZ3`es$iQx$nFD_218v_f4FVdQ&MBb@0NS7B{{R30 literal 0 HcmV?d00001 From b1b88232557d3957b4995629b1743cfb30dc5d86 Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 08:50:26 +0000 Subject: [PATCH 76/85] docs(fast-llm): simplify metric-gap open question --- docs/FAST_LLM_INTEGRATION.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index 77178f21..b669eb24 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -353,4 +353,4 @@ For shutdown semantics, **always** SIGINT the launch process (don't `kill -9` th 2. **Reward lag root cause.** Need to identify where the gap comes from before deciding whether it's worth fixing on this branch. 3. **Should the GSPO loss math fix (Fast-LLM PR #502) be merged before this PipelineRL PR?** Yes — this PR pins to the `gspo` branch by name; once `gspo` merges to Fast-LLM `main` we should rev this branch's install instructions to use `main`. 4. **Resolve the commented-out `pyproject.toml` overrides** (`pyproject.toml:81-87`). The `[tool.uv]` block force-overrides `transformers>=4.51.0` / `accelerate>=1.7.0` because `tapeagents==0.1.16` pins them lower; the `[tapeagents]` extra is broken at runtime as a result. Either bump tapeagents (when upstream supports newer libs) or drop the extra altogether on this branch. -5. **Metric coverage gap on the fast-llm finetune side.** DS's finetune emits a richer set under `rl/*` — including `rl/ess` (effective sample size), `rl/loss`, `rl/ratio_ref_*`, `rl/clamp_log_ratio_*_indicator`, etc. Fast-llm currently emits `training.grpo_*` (ratio, kl, advantage, entropy, num_tokens, clipped fraction) but is missing several DS-side metrics. Diff the two metric sets and add the missing ones (start with `ess` — useful for diagnosing data/policy drift). +5. **Close metric gaps on the fast-llm finetune side**, e.g. `rl/ess` (effective sample size — diagnostic for data/policy drift). Diff DS's `rl/*` and `stats/*` against fast-llm's `training.*` and pick what's worth porting. From 8c02c871264ee6f014e0d6ea262107fdb6d912c2 Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 12:43:29 +0000 Subject: [PATCH 77/85] docs(fast-llm): add image/vLLM version-bump open question --- docs/FAST_LLM_INTEGRATION.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index b669eb24..d3309a9a 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -354,3 +354,4 @@ For shutdown semantics, **always** SIGINT the launch process (don't `kill -9` th 3. **Should the GSPO loss math fix (Fast-LLM PR #502) be merged before this PipelineRL PR?** Yes — this PR pins to the `gspo` branch by name; once `gspo` merges to Fast-LLM `main` we should rev this branch's install instructions to use `main`. 4. **Resolve the commented-out `pyproject.toml` overrides** (`pyproject.toml:81-87`). The `[tool.uv]` block force-overrides `transformers>=4.51.0` / `accelerate>=1.7.0` because `tapeagents==0.1.16` pins them lower; the `[tapeagents]` extra is broken at runtime as a result. Either bump tapeagents (when upstream supports newer libs) or drop the extra altogether on this branch. 5. **Close metric gaps on the fast-llm finetune side**, e.g. `rl/ess` (effective sample size — diagnostic for data/policy drift). Diff DS's `rl/*` and `stats/*` against fast-llm's `training.*` and pick what's worth porting. +6. **Move off the interactive-toolkit base image and the vLLM 0.14.0rc1 pin.** Current image is `interactive-toolkit:25.12-py3-vllm014rc1redis` (PyTorch 25.12 + vLLM 0.14.0rc1 + bundled redis). Step up to the latest base PyTorch and vLLM versions that Fast-LLM and PipelineRL both support, then re-verify the smoke runs. From 399acf73c66bf43079651aaceedda00d3a9adffc Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 12:57:08 +0000 Subject: [PATCH 78/85] docs(fast-llm): document base-image source repo Note that the interactive-toolkit:25.12-py3-vllm014rc1redis image is built from the fml/pytorch_vllm014rc1 branch of ServiceNow/research-interactive- toolkit (SN-internal). Base layer nvcr.io/nvidia/pytorch:25.12-py3, branch adds vLLM 0.14.0rc1, redis, and EAI helpers. --- README.md | 2 ++ docs/FAST_LLM_INTEGRATION.md | 9 +++++++++ 2 files changed, 11 insertions(+) diff --git a/README.md b/README.md index 3554df31..9b005615 100644 --- a/README.md +++ b/README.md @@ -447,6 +447,8 @@ IMAGE_REVISION := 25.12-py3-vllm014rc1redis EAI_PROFILE := yul201 ``` +The image is built from the `fml/pytorch_vllm014rc1` branch of [`ServiceNow/research-interactive-toolkit`](https://github.com/ServiceNow/research-interactive-toolkit/tree/fml/pytorch_vllm014rc1) (SN-internal repo). Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the toolkit branch adds vLLM 0.14.0rc1, redis, and the EAI helpers. + ### 2. Clone + venv + editable installs Inside a running interactive instance, install both Fast-LLM and PipelineRL into a single venv at `PipelineRL/.venv`: diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index d3309a9a..b46b0382 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -58,6 +58,15 @@ There is **no CI specific to the fast-llm path**. Unit tests in `tests/` exercis The image bundles the redis server (used by `streams=redis`). +Built from the `fml/pytorch_vllm014rc1` branch of [`ServiceNow/research-interactive-toolkit`](https://github.com/ServiceNow/research-interactive-toolkit/tree/fml/pytorch_vllm014rc1) (SN-internal repo, link is gated). Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the branch layers on vLLM 0.14.0rc1, redis, and the EAI helpers. To use it, set `~/.research-interactive-env`: + +```shell +USE_ACCOUNT_REPO := 1 +BASE_IMAGE := nvcr.io/nvidia/pytorch:25.12-py3 +IMAGE_REVISION := 25.12-py3-vllm014rc1redis +EAI_PROFILE := yul201 +``` + ### Steps ```bash From 8ca2b5fabd11dcab58c2d8e35f45fa202f2305fa Mon Sep 17 00:00:00 2001 From: bigximik Date: Wed, 6 May 2026 13:00:22 +0000 Subject: [PATCH 79/85] docs(fast-llm): clarify use-vs-build of the toolkit image .research-interactive-env values are for *building* the image (in the research-interactive-toolkit repo on branch fml/pytorch_vllm014rc1), not for using the prebuilt one. Reword both README and handover doc so that "use" just means referencing the image URI, and "build" is a separate flow with the env config. --- README.md | 10 ++++++++-- docs/FAST_LLM_INTEGRATION.md | 12 +++++++++--- 2 files changed, 17 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 9b005615..b89d97b2 100644 --- a/README.md +++ b/README.md @@ -438,7 +438,13 @@ Each resumed job must still use a fresh `world.run_id` (the new job's ID, not th ### 1. Container image -Use image `registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` — it bundles the redis server. In `~/.research-interactive-env`: +To **use**: reference the prebuilt image +``` +registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis +``` +It bundles the redis server. + +To **build** (from the [`ServiceNow/research-interactive-toolkit`](https://github.com/ServiceNow/research-interactive-toolkit/tree/fml/pytorch_vllm014rc1) repo, branch `fml/pytorch_vllm014rc1` — SN-internal, link is gated): set `~/.research-interactive-env` and run the toolkit's build target. ```shell USE_ACCOUNT_REPO := 1 @@ -447,7 +453,7 @@ IMAGE_REVISION := 25.12-py3-vllm014rc1redis EAI_PROFILE := yul201 ``` -The image is built from the `fml/pytorch_vllm014rc1` branch of [`ServiceNow/research-interactive-toolkit`](https://github.com/ServiceNow/research-interactive-toolkit/tree/fml/pytorch_vllm014rc1) (SN-internal repo). Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the toolkit branch adds vLLM 0.14.0rc1, redis, and the EAI helpers. +Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the toolkit branch layers on vLLM 0.14.0rc1, redis, and the EAI helpers. ### 2. Clone + venv + editable installs diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index b46b0382..dead939d 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -54,11 +54,15 @@ There is **no CI specific to the fast-llm path**. Unit tests in `tests/` exercis ### Image -`registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis` +**To use**, reference the prebuilt image directly: -The image bundles the redis server (used by `streams=redis`). +``` +registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis +``` -Built from the `fml/pytorch_vllm014rc1` branch of [`ServiceNow/research-interactive-toolkit`](https://github.com/ServiceNow/research-interactive-toolkit/tree/fml/pytorch_vllm014rc1) (SN-internal repo, link is gated). Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the branch layers on vLLM 0.14.0rc1, redis, and the EAI helpers. To use it, set `~/.research-interactive-env`: +It bundles the redis server (used by `streams=redis`). + +**To build it yourself** (e.g. when bumping the PyTorch / vLLM version — see open question 6 below): clone the [`ServiceNow/research-interactive-toolkit`](https://github.com/ServiceNow/research-interactive-toolkit/tree/fml/pytorch_vllm014rc1) repo (SN-internal, link is gated), check out branch `fml/pytorch_vllm014rc1`, then set `~/.research-interactive-env` and run the toolkit's build target: ```shell USE_ACCOUNT_REPO := 1 @@ -67,6 +71,8 @@ IMAGE_REVISION := 25.12-py3-vllm014rc1redis EAI_PROFILE := yul201 ``` +Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the branch layers on vLLM 0.14.0rc1, redis, and the EAI helpers. + ### Steps ```bash From 60909004c3046e6ee50712c0377657b5afa55e65 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 7 May 2026 16:19:27 +0000 Subject: [PATCH 80/85] examples(interactive): align ds_4node with the GSPO chart baseline The DS example script was using PPO config, which doesn't reproduce the DeepSpeed curve in docs/FAST_LLM_INTEGRATION.md (those charts compare fast-llm GSPO vs DS GSPO at 400 steps with epsilon_low=3e-3, epsilon_high=4e-3). Switch ds_4node.sh defaults to policy_loss=gspo + epsilon=3e-3/4e-3 so 'MAX_TRAIN_STEPS=400 bash examples/interactive/ds_4node.sh' reproduces math_7b_ds_fastllm_4node_20260428_135427 byte-for-byte. Update both script header comments to call out that they're the chart reproduction recipes. --- examples/interactive/ds_4node.sh | 19 ++++++++++++------- examples/interactive/fast_llm_4node.sh | 5 +++++ 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/examples/interactive/ds_4node.sh b/examples/interactive/ds_4node.sh index 71b24de4..fc13faa2 100755 --- a/examples/interactive/ds_4node.sh +++ b/examples/interactive/ds_4node.sh @@ -1,9 +1,14 @@ #!/bin/bash -# 4-node interactive smoke run: DeepSpeed ZeRO-3 trainer + vLLM v1 + PPO loss +# 4-node interactive smoke run: DeepSpeed ZeRO-3 trainer + vLLM v1 + GSPO loss # ----------------------------------------------------------------------------- -# Mirrors submit_eai_math_7b_multinode_ds_vllm_v1.sh but runs in your current -# shell instead of submitting an `eai job new`. Use this as the reference path -# when comparing fast-llm behavior against the established DeepSpeed trainer. +# This is the *reference* baseline for comparing fast-llm GSPO behavior against +# DeepSpeed (the chart-set in docs/FAST_LLM_INTEGRATION.md compares this config +# at MAX_TRAIN_STEPS=400 against the fast-llm GSPO 400-step run from 2026-05-05). +# +# Mirrors submit_eai_math_7b_multinode_ds_fastllm_branch.sh (the GSPO version of +# the DS launcher) but runs in your current shell instead of submitting an +# `eai job new`. To reproduce the comparison charts byte-for-byte, run with +# `MAX_TRAIN_STEPS=400`. # # Prereqs (one-time, see ../../README.md "Install FastLLM+PipelineRL"): # - Image: registry.toolkit-sp.yul201.service-now.com/snow.research.afm/ @@ -70,9 +75,9 @@ PYTHONHASHSEED=42 python -m pipelinerl.launch \ force_restart=true \ finetune.learning_rate=1e-6 \ finetune.attempts=8 \ - finetune.rl.policy_loss=ppo \ - finetune.rl.epsilon_low=2e-2 \ - finetune.rl.epsilon_high=2e-2 \ + finetune.rl.policy_loss=gspo \ + finetune.rl.epsilon_low=3e-3 \ + finetune.rl.epsilon_high=4e-3 \ '+finetune.rl.filter_zero_advantage_groups=true' \ "finetune.max_train_steps=${MAX_TRAIN_STEPS}" \ finetune.seq_length=20000 \ diff --git a/examples/interactive/fast_llm_4node.sh b/examples/interactive/fast_llm_4node.sh index d946b769..6701fc76 100755 --- a/examples/interactive/fast_llm_4node.sh +++ b/examples/interactive/fast_llm_4node.sh @@ -1,6 +1,11 @@ #!/bin/bash # 4-node interactive smoke run: fast-llm trainer + vLLM v1 + GSPO loss # ----------------------------------------------------------------------------- +# This is the fast-llm side of the comparison charts in +# docs/FAST_LLM_INTEGRATION.md (the divisor² + SDP fix + temperature=0.7 + +# fp32_lm_head=true config from the 2026-05-05 400-step run). To reproduce +# the chart byte-for-byte run with `MAX_TRAIN_STEPS=400`. +# # Mirrors submit_eai_math_7b_multinode.sh but runs in your current shell instead # of submitting an `eai job new`. Use this from inside an interactive EAI # session that already has 4 nodes attached. From 50c7ff7c992c8a086b0fbd1f5ed84485ba7b9288 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 7 May 2026 16:24:27 +0000 Subject: [PATCH 81/85] docs(fast-llm): track DS GSPO source script and link reproduction recipes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Track submit_eai_math_7b_multinode_ds_fastllm_branch.sh — the production EAI launcher that produced math_7b_ds_fastllm_4node_20260428_135427 (the DS curve in the comparison charts). Drop the now-removed top-level fp32_lm_head=true knob from it. - docs/FAST_LLM_INTEGRATION.md: * Add §"Launching an interactive EAI job" — the prereq for the examples/interactive/ scripts (ServiceNow/research-interactive-toolkit `make launch` flow). * Add §"Reproduction recipes" — table mapping the chart-baseline runs to both the interactive examples and the production submit_eai_*.sh launchers, so readers can pick the right script for their context. - examples/interactive/{fast_llm,ds}_4node.sh: rewrite the prereq comment block so it points to the new "Launching an interactive EAI job" section before sending the user to the README install. --- docs/FAST_LLM_INTEGRATION.md | 24 +++++ examples/interactive/ds_4node.sh | 12 ++- examples/interactive/fast_llm_4node.sh | 15 +-- ...eai_math_7b_multinode_ds_fastllm_branch.sh | 95 +++++++++++++++++++ 4 files changed, 134 insertions(+), 12 deletions(-) create mode 100644 submit_eai_math_7b_multinode_ds_fastllm_branch.sh diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index dead939d..1e6ef1d5 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -73,6 +73,17 @@ EAI_PROFILE := yul201 Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the branch layers on vLLM 0.14.0rc1, redis, and the EAI helpers. +### Launching an interactive EAI job (prereq for the example scripts) + +The example scripts under `examples/interactive/` are meant to be run **from inside an interactive EAI session that has 4 nodes attached**. To start such a session: + +1. Clone the toolkit repo (one-time): `git clone git@github.com:ServiceNow/research-interactive-toolkit.git ~/code/research-interactive-toolkit`. For the vLLM 0.14.0rc1 image, check out branch `fml/pytorch_vllm014rc1`; for the future-bumped image, use whichever branch builds it. +2. Configure `~/.research-interactive-env` per the block above (selects image revision and EAI profile). +3. Launch and attach with VSCode Remote-SSH (full instructions in the toolkit README — `make launch`, then `eai job ls` to find your job, then connect via Remote-SSH). +4. Inside the running interactive container, follow [§3 End-to-end install](#3-end-to-end-install) above to clone Fast-LLM + PipelineRL into the venv, then `bash examples/interactive/{fast_llm,ds}_4node.sh`. + +For multi-node interactive jobs (4 nodes × 8 GPUs needed for the chart-reproducing runs), bump `GPU`, `CPU`, `MEM` and add `--replicas 4` semantics in `~/.research-interactive-env` per the toolkit README's multi-replica instructions. + ### Steps ```bash @@ -320,6 +331,19 @@ Comparing fast-llm `math_7b_4node_fastllm_gspo_20260505_122944` (the divisor² + ![reward_mean fast-llm vs DS](images/reward_mean.png) +### Reproduction recipes + +Two paths depending on whether you have an interactive EAI job or want to submit a batch job: + +| Where you run from | Script (in this repo) | What it does | +|---|---|---| +| **Inside interactive 4-node EAI session** | [`examples/interactive/fast_llm_4node.sh`](../examples/interactive/fast_llm_4node.sh) | Reproduces fast-llm side of the charts at `MAX_TRAIN_STEPS=400` (defaults to 2 for smoke). | +| **Inside interactive 4-node EAI session** | [`examples/interactive/ds_4node.sh`](../examples/interactive/ds_4node.sh) | Reproduces DS GSPO side of the charts at `MAX_TRAIN_STEPS=400` (defaults to 2 for smoke). | +| **Submit as standalone EAI batch job** | [`submit_eai_math_7b_multinode.sh`](../submit_eai_math_7b_multinode.sh) | Production fast-llm GSPO launcher. Calls `eai job new --replicas 4`. The exact script that produced `math_7b_4node_fastllm_gspo_20260505_122944` (the chart's fast-llm run). | +| **Submit as standalone EAI batch job** | [`submit_eai_math_7b_multinode_ds_fastllm_branch.sh`](../submit_eai_math_7b_multinode_ds_fastllm_branch.sh) | Production DS GSPO launcher (DS trainer + vLLM v1, GSPO loss). The exact script that produced `math_7b_ds_fastllm_4node_20260428_135427` (the chart's DS run). | + +The `examples/interactive/*.sh` scripts are byte-equivalent to the `submit_eai_*.sh` ones modulo (a) they don't call `eai job new` (you supply your own session) and (b) defaults are smoke-friendly (`MAX_TRAIN_STEPS=2`). Override `MAX_TRAIN_STEPS=400` to reproduce the charts. + ## 10. Operations ### Where logs live diff --git a/examples/interactive/ds_4node.sh b/examples/interactive/ds_4node.sh index fc13faa2..26958881 100755 --- a/examples/interactive/ds_4node.sh +++ b/examples/interactive/ds_4node.sh @@ -10,11 +10,13 @@ # `eai job new`. To reproduce the comparison charts byte-for-byte, run with # `MAX_TRAIN_STEPS=400`. # -# Prereqs (one-time, see ../../README.md "Install FastLLM+PipelineRL"): -# - Image: registry.toolkit-sp.yul201.service-now.com/snow.research.afm/ -# interactive-toolkit:25.12-py3-vllm014rc1redis -# - PipelineRL editable-installed in /home/toolkit/code/PipelineRL/.venv -# - Qwen2.5-7B at /home/toolkit/Qwen2.5-7B +# Prereqs (one-time, in this order): +# 1. Launch an interactive 4-node EAI session — see +# docs/FAST_LLM_INTEGRATION.md §3 "Launching an interactive EAI job" +# (uses ServiceNow/research-interactive-toolkit `make launch`). +# 2. Inside the session, install PipelineRL (fast-llm branch) into the +# venv — see ../../README.md "Install FastLLM+PipelineRL". +# 3. Qwen2.5-7B at /home/toolkit/Qwen2.5-7B and WandB credentials configured. # # Success looks like: # - finetune/stderr_node0.log shows diff --git a/examples/interactive/fast_llm_4node.sh b/examples/interactive/fast_llm_4node.sh index 6701fc76..188482e9 100755 --- a/examples/interactive/fast_llm_4node.sh +++ b/examples/interactive/fast_llm_4node.sh @@ -10,13 +10,14 @@ # of submitting an `eai job new`. Use this from inside an interactive EAI # session that already has 4 nodes attached. # -# Prereqs (one-time, see ../../README.md "Install FastLLM+PipelineRL"): -# - Image: registry.toolkit-sp.yul201.service-now.com/snow.research.afm/ -# interactive-toolkit:25.12-py3-vllm014rc1redis -# - Fast-LLM checked out on the `gspo` branch, editable-installed in -# /home/toolkit/code/PipelineRL/.venv (alongside PipelineRL on `fast-llm`) -# - Qwen2.5-7B at /home/toolkit/Qwen2.5-7B -# - WandB credentials configured for the entity below +# Prereqs (one-time, in this order): +# 1. Launch an interactive 4-node EAI session — see +# docs/FAST_LLM_INTEGRATION.md §3 "Launching an interactive EAI job" +# (uses ServiceNow/research-interactive-toolkit `make launch`). +# 2. Inside the session, install Fast-LLM (gspo branch) + PipelineRL +# (fast-llm branch) into a shared venv — see ../../README.md +# "Install FastLLM+PipelineRL". +# 3. Qwen2.5-7B at /home/toolkit/Qwen2.5-7B and WandB credentials configured. # # Success looks like: # - finetune/stdout_node0.log shows "[Rank 00] training @ step 1/N | ... | grad norm: 0.1-0.3" diff --git a/submit_eai_math_7b_multinode_ds_fastllm_branch.sh b/submit_eai_math_7b_multinode_ds_fastllm_branch.sh new file mode 100644 index 00000000..15e7c7b8 --- /dev/null +++ b/submit_eai_math_7b_multinode_ds_fastllm_branch.sh @@ -0,0 +1,95 @@ +#!/bin/bash +# Multi-node EAI DeepSpeed math run on fast-llm branch (use_fast_llm=false). +# Identical config to submit_eai_math_7b_multinode_ds_vllm_v1.sh but uses +# the fast-llm branch worktree at /home/toolkit/code/PipelineRL-fastllm. +# Topology: 1 actor node (vLLM) + (NODES-1) DeepSpeed trainer nodes. +# Usage: bash submit_eai_math_7b_multinode_ds_fastllm_branch.sh [NODES] +# Run `eai login` before executing this script. + +IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" +RESULTS_DIR="/mnt/shared/denis/math_7b_results" +MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" +NODES="${1:-4}" + +TIMESTAMP=$(date +%Y%m%d_%H%M%S) +EXP_NAME="math_7b_ds_fastllmbranch_${NODES}node_${TIMESTAMP}" +EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" +JOB_NAME="${EXP_NAME}" + +echo "Config: ${NODES} nodes, DS on fast-llm branch, actor_fraction=4, finetune_fraction=4, max_train_steps=400" + +CMD=" +set -e +mkdir -p ${EXP_DIR} +cd /home/toolkit/code/PipelineRL-fastllm +source /home/toolkit/code/PipelineRL/.venv/bin/activate +PYTHONHASHSEED=42 python -m pipelinerl.launch \ + --config-path /home/toolkit/code/PipelineRL-fastllm/conf \ + --config-name math \ + output_dir=${EXP_DIR} \ + wandb.wandb_workspace_root=${RESULTS_DIR} \ + wandb.wandb_entity_name=denisko-se \ + wandb.wandb_project_name=watermelon \ + wandb.wandb_group=eai_math7b_ds_fastllmbranch \ + '+wandb.wandb_run_name=math7b_ds_fastllmbranch_${NODES}node_${TIMESTAMP}' \ + use_fast_llm=false \ + actor.llm_max_rollouts=128 \ + force_restart=true \ + finetune.learning_rate=1e-6 \ + finetune.attempts=8 \ + finetune.rl.policy_loss=gspo \ + finetune.rl.epsilon_low=3e-3 \ + finetune.rl.epsilon_high=4e-3 \ + '+finetune.rl.filter_zero_advantage_groups=true' \ + finetune.max_train_steps=400 \ + finetune.seq_length=20000 \ + finetune.gradient_accumulation_passes=1024 \ + 'vllm_config.vllm_kwargs.max_model_len=20000' \ + 'llm.parameters.max_tokens=16000' \ + 'llm.parameters.temperature=0.7' \ + 'test_llm.parameters.max_tokens=16000' \ + 'test_llm.parameters.temperature=0.7' \ + world.actor_fraction=4 \ + world.preprocessor_fraction=0 \ + world.finetune_fraction=4 \ + world.run_id=\${MASTER_ADDR} \ + streams=files \ + eval_every_n_versions=0 \ + model_path=${MODEL_PATH} +" + +SPEC_YAML=$(mktemp /tmp/eai_job_spec_XXXXXX.yaml) +cat > "$SPEC_YAML" << 'YAML_EOF' +options: + internal-dns: + name: "" + ports: + - port: 29501 + - port: 9000 + - port: 7777 + - port: 8080 + - port: 8081 + - port: 8082 + - port: 8083 + - port: 8084 + - port: 8085 + - port: 8086 + - port: 8087 +YAML_EOF + +eai job new \ + --file "$SPEC_YAML" \ + --non-preemptable \ + --replicas "$NODES" \ + --gpu 8 \ + --cpu 128 \ + --mem 800 \ + --name "$JOB_NAME" \ + -i "$IMAGE" \ + --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ + --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ + --env "HOME=/home/toolkit" \ + --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ + -- /bin/bash -c "$CMD" + +rm -f "$SPEC_YAML" From e4bd9b8f239312b43e6371acf5dd274d5f237cfd Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 7 May 2026 16:29:45 +0000 Subject: [PATCH 82/85] docs(fast-llm): parameterize Denis-specific values; drop DS PPO script MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit submit_eai_math_7b_multinode.sh and submit_eai_math_7b_multinode_ds_fastllm_branch.sh both hardcoded Denis- specific values (RESULTS_DIR=/mnt/shared/denis/..., wandb_entity_name= denisko-se, --data snow.home.denis_kocetkov:..., --data snow.research.afm .shared_fml:...). Add a "PERSONALIZE THESE BEFORE RUNNING" block at the top of each script with env-var-overridable defaults so a new user can set RESULTS_DIR / WANDB_ENTITY / WANDB_PROJECT / EAI_HOME_DATA / EAI_SHARED_DATA before launching, instead of editing inline. Add a "Personalize before running" subsection in docs/FAST_LLM_INTEGRATION.md explaining what each knob is and which scripts each applies to. Delete submit_eai_math_7b_multinode_ds_vllm_v1.sh (the DS PPO variant) — the GSPO version (submit_eai_math_7b_multinode_ds_fastllm_branch.sh, which reproduces the chart baseline) is now the canonical DS launcher. Also fix that script's stale path: PipelineRL-fastllm worktree no longer exists; cd into /home/toolkit/code/PipelineRL (already on the fast-llm branch). --- docs/FAST_LLM_INTEGRATION.md | 17 ++++ submit_eai_math_7b_multinode.sh | 18 +++- ...eai_math_7b_multinode_ds_fastllm_branch.sh | 29 ++++-- submit_eai_math_7b_multinode_ds_vllm_v1.sh | 94 ------------------- 4 files changed, 49 insertions(+), 109 deletions(-) delete mode 100644 submit_eai_math_7b_multinode_ds_vllm_v1.sh diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index 1e6ef1d5..ab5d0f07 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -331,6 +331,23 @@ Comparing fast-llm `math_7b_4node_fastllm_gspo_20260505_122944` (the divisor² + ![reward_mean fast-llm vs DS](images/reward_mean.png) +### Personalize before running + +Both the example scripts and the production submit launchers default to Denis's setup. Before running, override these env vars (or edit the defaults in the scripts) to your own: + +| Env var | Default | What it is | +|---|---|---| +| `RESULTS_DIR` | `/mnt/shared/denis/math_7b_results` | Where outputs / checkpoints / logs land. Must be on a shared NFS readable by every node. | +| `WANDB_ENTITY` | `denisko-se` | Your wandb entity (user or org). | +| `WANDB_PROJECT` | `watermelon` | Your wandb project. | +| `EAI_HOME_DATA` | `snow.home.denis_kocetkov` | Your EAI home data object (mounted at `/home/toolkit` inside the container). Submit-only. | +| `EAI_SHARED_DATA` | `snow.research.afm.shared_fml` | Your shared NFS data object (mounted at `/mnt/shared`). Submit-only. | +| `MODEL_PATH` | `/home/toolkit/Qwen2.5-7B` | Path to the base model checkpoint inside the container. | + +The two `EAI_*_DATA` knobs only matter for the `submit_eai_*.sh` scripts (they're passed to `eai job new --data`); the `examples/interactive/*.sh` scripts run inside an existing session and use whatever's already mounted. + +The handover doc and PR description also mention `denisko-se/watermelon` runs and `/mnt/shared/denis/math_7b_results/` paths — those are pointers to Denis's historical runs and stay as-is for traceability; you don't need to edit them, just point your own runs to your own places. + ### Reproduction recipes Two paths depending on whether you have an interactive EAI job or want to submit a batch job: diff --git a/submit_eai_math_7b_multinode.sh b/submit_eai_math_7b_multinode.sh index e94f2d44..7639f2ee 100755 --- a/submit_eai_math_7b_multinode.sh +++ b/submit_eai_math_7b_multinode.sh @@ -7,7 +7,15 @@ # Run `eai login` before executing this script. IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" -RESULTS_DIR="/mnt/shared/denis/math_7b_results" + +# === PERSONALIZE THESE BEFORE RUNNING (or override via env vars) === +RESULTS_DIR="${RESULTS_DIR:-/mnt/shared/denis/math_7b_results}" # your shared NFS results dir +WANDB_ENTITY="${WANDB_ENTITY:-denisko-se}" # your wandb entity +WANDB_PROJECT="${WANDB_PROJECT:-watermelon}" # your wandb project +EAI_HOME_DATA="${EAI_HOME_DATA:-snow.home.denis_kocetkov}" # your EAI home data object +EAI_SHARED_DATA="${EAI_SHARED_DATA:-snow.research.afm.shared_fml}" # your shared NFS data object +# =================================================================== + MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" NODES="${1:-4}" TIMESTAMP="${2:-$(date +%Y%m%d_%H%M%S)}" @@ -47,8 +55,8 @@ PYTHONHASHSEED=42 python -m pipelinerl.launch \ '+finetune.rl.filter_zero_advantage_groups=true' \ eval_every_n_versions=0 \ wandb.wandb_workspace_root=${RESULTS_DIR} \ - wandb.wandb_entity_name=denisko-se \ - wandb.wandb_project_name=watermelon \ + "wandb.wandb_entity_name=${WANDB_ENTITY}" \ + "wandb.wandb_project_name=${WANDB_PROJECT}" \ wandb.wandb_group=eai_math7b_fastllm_gspo \ '+wandb.wandb_run_name=math7b_fastllm_gspo_${NODES}node_${TIMESTAMP}' \ 'vllm_config.vllm_kwargs.gpu-memory-utilization=0.85' \ @@ -109,8 +117,8 @@ eai job new \ --mem 800 \ --name "$JOB_NAME" \ -i "$IMAGE" \ - --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ - --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ + --data "${EAI_HOME_DATA}:/home/toolkit:rw" \ + --data "${EAI_SHARED_DATA}:/mnt/shared:rw" \ --env "HOME=/home/toolkit" \ --env "GPUS_PER_NODE=8" \ --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ diff --git a/submit_eai_math_7b_multinode_ds_fastllm_branch.sh b/submit_eai_math_7b_multinode_ds_fastllm_branch.sh index 15e7c7b8..d330b351 100644 --- a/submit_eai_math_7b_multinode_ds_fastllm_branch.sh +++ b/submit_eai_math_7b_multinode_ds_fastllm_branch.sh @@ -1,13 +1,22 @@ #!/bin/bash -# Multi-node EAI DeepSpeed math run on fast-llm branch (use_fast_llm=false). -# Identical config to submit_eai_math_7b_multinode_ds_vllm_v1.sh but uses -# the fast-llm branch worktree at /home/toolkit/code/PipelineRL-fastllm. +# Multi-node EAI DeepSpeed GSPO math run on the fast-llm branch +# (use_fast_llm=false; DS trainer + vLLM v1 + GSPO loss; eps_low=3e-3, +# eps_high=4e-3). Reproduces the DS curve in the fast-llm vs DS comparison +# charts (docs/FAST_LLM_INTEGRATION.md). # Topology: 1 actor node (vLLM) + (NODES-1) DeepSpeed trainer nodes. # Usage: bash submit_eai_math_7b_multinode_ds_fastllm_branch.sh [NODES] # Run `eai login` before executing this script. IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" -RESULTS_DIR="/mnt/shared/denis/math_7b_results" + +# === PERSONALIZE THESE BEFORE RUNNING (or override via env vars) === +RESULTS_DIR="${RESULTS_DIR:-/mnt/shared/denis/math_7b_results}" # your shared NFS results dir +WANDB_ENTITY="${WANDB_ENTITY:-denisko-se}" # your wandb entity +WANDB_PROJECT="${WANDB_PROJECT:-watermelon}" # your wandb project +EAI_HOME_DATA="${EAI_HOME_DATA:-snow.home.denis_kocetkov}" # your EAI home data object +EAI_SHARED_DATA="${EAI_SHARED_DATA:-snow.research.afm.shared_fml}" # your shared NFS data object +# =================================================================== + MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" NODES="${1:-4}" @@ -21,15 +30,15 @@ echo "Config: ${NODES} nodes, DS on fast-llm branch, actor_fraction=4, finetune_ CMD=" set -e mkdir -p ${EXP_DIR} -cd /home/toolkit/code/PipelineRL-fastllm +cd /home/toolkit/code/PipelineRL source /home/toolkit/code/PipelineRL/.venv/bin/activate PYTHONHASHSEED=42 python -m pipelinerl.launch \ - --config-path /home/toolkit/code/PipelineRL-fastllm/conf \ + --config-path /home/toolkit/code/PipelineRL/conf \ --config-name math \ output_dir=${EXP_DIR} \ wandb.wandb_workspace_root=${RESULTS_DIR} \ - wandb.wandb_entity_name=denisko-se \ - wandb.wandb_project_name=watermelon \ + "wandb.wandb_entity_name=${WANDB_ENTITY}" \ + "wandb.wandb_project_name=${WANDB_PROJECT}" \ wandb.wandb_group=eai_math7b_ds_fastllmbranch \ '+wandb.wandb_run_name=math7b_ds_fastllmbranch_${NODES}node_${TIMESTAMP}' \ use_fast_llm=false \ @@ -86,8 +95,8 @@ eai job new \ --mem 800 \ --name "$JOB_NAME" \ -i "$IMAGE" \ - --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ - --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ + --data "${EAI_HOME_DATA}:/home/toolkit:rw" \ + --data "${EAI_SHARED_DATA}:/mnt/shared:rw" \ --env "HOME=/home/toolkit" \ --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ -- /bin/bash -c "$CMD" diff --git a/submit_eai_math_7b_multinode_ds_vllm_v1.sh b/submit_eai_math_7b_multinode_ds_vllm_v1.sh deleted file mode 100644 index af912776..00000000 --- a/submit_eai_math_7b_multinode_ds_vllm_v1.sh +++ /dev/null @@ -1,94 +0,0 @@ -#!/bin/bash -# Multi-node EAI DeepSpeed math run on vllm_v1 branch. -# Topology: 1 actor node (vLLM) + (NODES-1) DeepSpeed trainer nodes. -# Usage: bash submit_eai_math_7b_multinode_ds_vllm_v1.sh [NODES] -# Example: bash submit_eai_math_7b_multinode_ds_vllm_v1.sh 4 -# Run `eai login` before executing this script. - -IMAGE="registry.toolkit-sp.yul201.service-now.com/snow.research.afm/interactive-toolkit:25.12-py3-vllm014rc1redis" -RESULTS_DIR="/mnt/shared/denis/math_7b_results" -MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" -NODES="${1:-4}" - -TIMESTAMP=$(date +%Y%m%d_%H%M%S) -EXP_NAME="math_7b_ds_fastllm_${NODES}node_${TIMESTAMP}" -EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" -JOB_NAME="${EXP_NAME}" - -echo "Config: ${NODES} nodes, actor_fraction=4, finetune_fraction=4, max_train_steps=400" - -CMD=" -set -e -mkdir -p ${EXP_DIR} -cd /home/toolkit/code/PipelineRL -source /home/toolkit/code/PipelineRL/.venv/bin/activate -PYTHONHASHSEED=42 python -m pipelinerl.launch \ - --config-path /home/toolkit/code/PipelineRL/conf \ - --config-name math \ - output_dir=${EXP_DIR} \ - wandb.wandb_workspace_root=${RESULTS_DIR} \ - wandb.wandb_entity_name=denisko-se \ - wandb.wandb_project_name=watermelon \ - wandb.wandb_group=eai_math7b_ds_fastllm \ - '+wandb.wandb_run_name=math7b_ds_fastllm_${NODES}node_${TIMESTAMP}' \ - use_fast_llm=false \ - actor.llm_max_rollouts=128 \ - force_restart=true \ - finetune.learning_rate=1e-6 \ - finetune.attempts=8 \ - finetune.rl.policy_loss=ppo \ - finetune.rl.epsilon_low=2e-2 \ - finetune.rl.epsilon_high=2e-2 \ - '+finetune.rl.filter_zero_advantage_groups=true' \ - finetune.max_train_steps=400 \ - finetune.seq_length=20000 \ - finetune.gradient_accumulation_passes=1024 \ - 'vllm_config.vllm_kwargs.max_model_len=20000' \ - 'llm.parameters.max_tokens=16000' \ - 'llm.parameters.temperature=0.7' \ - 'test_llm.parameters.max_tokens=16000' \ - 'test_llm.parameters.temperature=0.7' \ - world.actor_fraction=4 \ - world.preprocessor_fraction=0 \ - world.finetune_fraction=4 \ - world.run_id=\${MASTER_ADDR} \ - streams=files \ - eval_every_n_versions=0 \ - model_path=${MODEL_PATH} -" - -SPEC_YAML=$(mktemp /tmp/eai_job_spec_XXXXXX.yaml) -cat > "$SPEC_YAML" << 'YAML_EOF' -options: - internal-dns: - name: "" - ports: - - port: 29501 - - port: 9000 - - port: 7777 - - port: 8080 - - port: 8081 - - port: 8082 - - port: 8083 - - port: 8084 - - port: 8085 - - port: 8086 - - port: 8087 -YAML_EOF - -eai job new \ - --file "$SPEC_YAML" \ - --non-preemptable \ - --replicas "$NODES" \ - --gpu 8 \ - --cpu 128 \ - --mem 800 \ - --name "$JOB_NAME" \ - -i "$IMAGE" \ - --data "snow.home.denis_kocetkov:/home/toolkit:rw" \ - --data "snow.research.afm.shared_fml:/mnt/shared:rw" \ - --env "HOME=/home/toolkit" \ - --env "PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True" \ - -- /bin/bash -c "$CMD" - -rm -f "$SPEC_YAML" From 902cb7a0daf05b7c9240bce94af6fa7ecb6f3fde Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 7 May 2026 16:34:52 +0000 Subject: [PATCH 83/85] docs(fast-llm): add explicit "How to launch" walkthrough MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The pieces (prereqs, env vars, script paths) were scattered across §3 "End-to-end install", §"Personalize before running", and §"Reproduction recipes". A reader had to assemble a launch command themselves. Add §"How to launch (prereqs + commands)" with two concrete paths: - Path 1: production EAI batch job (eai CLI, wandb creds, env vars, then bash submit_eai_*.sh; how to monitor and stop) - Path 2: interactive session (launch interactive, install, then bash examples/interactive/*.sh; smoke vs MAX_TRAIN_STEPS=400) Both paths show actual bash commands the reader can copy. --- docs/FAST_LLM_INTEGRATION.md | 42 +++++++++++++++++++++++++++++++++++- 1 file changed, 41 insertions(+), 1 deletion(-) diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index ab5d0f07..407a8fd4 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -350,7 +350,7 @@ The handover doc and PR description also mention `denisko-se/watermelon` runs an ### Reproduction recipes -Two paths depending on whether you have an interactive EAI job or want to submit a batch job: +Two paths depending on whether you have an interactive EAI session running or want to submit a batch job: | Where you run from | Script (in this repo) | What it does | |---|---|---| @@ -361,6 +361,46 @@ Two paths depending on whether you have an interactive EAI job or want to submit The `examples/interactive/*.sh` scripts are byte-equivalent to the `submit_eai_*.sh` ones modulo (a) they don't call `eai job new` (you supply your own session) and (b) defaults are smoke-friendly (`MAX_TRAIN_STEPS=2`). Override `MAX_TRAIN_STEPS=400` to reproduce the charts. +### How to launch (prereqs + commands) + +#### Path 1: production EAI batch job (recommended for full 400-step runs) + +Prereqs: +1. `eai` CLI installed and authenticated on the machine you'll launch from. Run `eai login` once if it isn't already. +2. Wandb credentials configured for the entity in `WANDB_ENTITY` (`~/.netrc` or `wandb login`). +3. The personalization env vars from §"Personalize before running" exported (or edit the defaults in the script). +4. A 7B base model checkpoint at the path `MODEL_PATH` points to (default `/home/toolkit/Qwen2.5-7B` — adjust if you cloned it somewhere else). + +```bash +# fast-llm GSPO (32 GPUs, ~9-14 h wall clock for 400 steps) +bash submit_eai_math_7b_multinode.sh 4 + +# DS GSPO (same compute footprint) +bash submit_eai_math_7b_multinode_ds_fastllm_branch.sh 4 +``` + +Each call returns a job ID and queues a 4-replica × 8-GPU EAI job. The job creates `${RESULTS_DIR}/${EXP_NAME}/` with `launch.log`, `finetune/stdout_node*.log`, `actor/info.log`, `actor_vllm_*/{stdout,stderr}.log`, and a `wandb_config.yaml` with the resumable wandb run id. WandB run name is set via `+wandb.wandb_run_name=...` and includes the timestamp. + +To monitor: `eai job logs ` or tail the log files directly on the shared NFS mount. To stop early: `eai job kill ` (sends SIGINT — orchestrator does the coordinated NCCL teardown). + +#### Path 2: interactive EAI session (recommended for smoke / dev) + +Prereqs: +1. Launch and attach to a 4-node interactive session — see §"Launching an interactive EAI job" above. +2. Inside the session, install Fast-LLM + PipelineRL — see §3 "End-to-end install" → "Steps". +3. Same personalization env vars as Path 1 (no `EAI_*_DATA` needed — those are submit-only). + +```bash +# 2-step smoke (~10 min) to verify everything launches cleanly +bash examples/interactive/fast_llm_4node.sh + +# Full 400-step chart-reproducing run +MAX_TRAIN_STEPS=400 bash examples/interactive/fast_llm_4node.sh + +# Same for the DS GSPO baseline +bash examples/interactive/ds_4node.sh # smoke +MAX_TRAIN_STEPS=400 bash examples/interactive/ds_4node.sh # full + ## 10. Operations ### Where logs live From 850bcad36103d239b6f4354dd6164559a1651d19 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 7 May 2026 16:38:43 +0000 Subject: [PATCH 84/85] docs(fast-llm): drop misleading examples/interactive scripts The examples/interactive/{fast_llm,ds}_4node.sh scripts assumed running from an interactive session that has 4 nodes attached. That's not how EAI interactive jobs work - interactive sessions are 1-2-GPU dev environments, and 4-node training jobs are submitted *from* them via 'eai job new' (which is what submit_eai_*.sh does). The two scripts wrapped 'python -m pipelinerl.launch' directly (no 'eai job new'), so they could never run in a typical EAI interactive session. Delete them; submit_eai_*.sh are the canonical reproduction recipes for both smoke and full-length runs. Update docs/FAST_LLM_INTEGRATION.md: - Rewrite the interactive-session subsection to clarify it is a dev/console environment, not a 4-node training setup. - Drop the Path-2 (interactive) flow from How-to-launch; only Path 1 (production submit_eai_*.sh) remains. - Add a prereq linking back to End-to-end install -> Steps. - Multi-node smoke section: explain 2-step verification via inline edit of the submit script. --- docs/FAST_LLM_INTEGRATION.md | 76 ++++++----------- examples/interactive/ds_4node.sh | 98 ---------------------- examples/interactive/fast_llm_4node.sh | 111 ------------------------- 3 files changed, 25 insertions(+), 260 deletions(-) delete mode 100755 examples/interactive/ds_4node.sh delete mode 100755 examples/interactive/fast_llm_4node.sh diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index 407a8fd4..96876c27 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -73,16 +73,18 @@ EAI_PROFILE := yul201 Base layer is `nvcr.io/nvidia/pytorch:25.12-py3`; the branch layers on vLLM 0.14.0rc1, redis, and the EAI helpers. -### Launching an interactive EAI job (prereq for the example scripts) +### Launching an interactive EAI dev session -The example scripts under `examples/interactive/` are meant to be run **from inside an interactive EAI session that has 4 nodes attached**. To start such a session: +Interactive jobs are single-replica dev environments (typically 1-2 GPUs) — they're for editing code, running tests, and submitting production multi-node training jobs *from inside them*. They are **not** the 4-node training environment themselves. -1. Clone the toolkit repo (one-time): `git clone git@github.com:ServiceNow/research-interactive-toolkit.git ~/code/research-interactive-toolkit`. For the vLLM 0.14.0rc1 image, check out branch `fml/pytorch_vllm014rc1`; for the future-bumped image, use whichever branch builds it. -2. Configure `~/.research-interactive-env` per the block above (selects image revision and EAI profile). -3. Launch and attach with VSCode Remote-SSH (full instructions in the toolkit README — `make launch`, then `eai job ls` to find your job, then connect via Remote-SSH). -4. Inside the running interactive container, follow [§3 End-to-end install](#3-end-to-end-install) above to clone Fast-LLM + PipelineRL into the venv, then `bash examples/interactive/{fast_llm,ds}_4node.sh`. +To start one: -For multi-node interactive jobs (4 nodes × 8 GPUs needed for the chart-reproducing runs), bump `GPU`, `CPU`, `MEM` and add `--replicas 4` semantics in `~/.research-interactive-env` per the toolkit README's multi-replica instructions. +1. Clone the toolkit repo (one-time): `git clone git@github.com:ServiceNow/research-interactive-toolkit.git ~/code/research-interactive-toolkit`. For the vLLM 0.14.0rc1 image, check out branch `fml/pytorch_vllm014rc1`. +2. Configure `~/.research-interactive-env` per the block above (selects image revision and EAI profile, plus `CPU`/`GPU`/`MEM` for the dev environment — typically modest, e.g. `GPU := 2`). +3. From the toolkit repo, run `make launch` and attach via VSCode Remote-SSH (full instructions in the toolkit README). +4. Inside the running interactive container, follow [§3 End-to-end install](#3-end-to-end-install) → "Steps" to clone Fast-LLM + PipelineRL into the venv. From there you can submit 4-node training jobs with `bash submit_eai_math_7b_multinode.sh 4` (etc.) — see §"How to launch" below. + +The 4-node training jobs run in their own EAI batch jobs (not in your interactive session). You only need the interactive session as the launch console / dev env. ### Steps @@ -303,14 +305,7 @@ These run on 1-3 GPUs (the helpers spawn TP=1 or TP=2 vLLM engines plus a fake t ### Multi-node smoke (4-node, 2-step) -The interactive scripts under `examples/interactive/` run a 2-step smoke against the GSPO config (fast-llm) or the PPO config (DeepSpeed): - -```bash -bash examples/interactive/fast_llm_4node.sh # fast-llm + vLLM v1 + GSPO -bash examples/interactive/ds_4node.sh # DeepSpeed + vLLM v1 + PPO -``` - -Both should hit the trainer's "Reached final step 2, stopping" / "Saving checkpoint at iteration 2" log line within ~10 minutes of `RUNNING`. See those scripts for the prereqs and success criteria. +For a quick verification that everything launches, edit one of the `submit_eai_*.sh` scripts to set `max_train_steps=2` and `train_iters=2` (fast-llm only) and submit it (see §"How to launch" below). Both should hit the trainer's "Reached final step 2, stopping" / "Saving checkpoint at iteration 2" log line within ~10 minutes of `RUNNING`. Revert the values back to 400 before committing. ### Last verified (2026-05-06) @@ -344,35 +339,32 @@ Both the example scripts and the production submit launchers default to Denis's | `EAI_SHARED_DATA` | `snow.research.afm.shared_fml` | Your shared NFS data object (mounted at `/mnt/shared`). Submit-only. | | `MODEL_PATH` | `/home/toolkit/Qwen2.5-7B` | Path to the base model checkpoint inside the container. | -The two `EAI_*_DATA` knobs only matter for the `submit_eai_*.sh` scripts (they're passed to `eai job new --data`); the `examples/interactive/*.sh` scripts run inside an existing session and use whatever's already mounted. +All five env vars apply to both `submit_eai_*.sh` scripts. The handover doc and PR description also mention `denisko-se/watermelon` runs and `/mnt/shared/denis/math_7b_results/` paths — those are pointers to Denis's historical runs and stay as-is for traceability; you don't need to edit them, just point your own runs to your own places. ### Reproduction recipes -Two paths depending on whether you have an interactive EAI session running or want to submit a batch job: - -| Where you run from | Script (in this repo) | What it does | -|---|---|---| -| **Inside interactive 4-node EAI session** | [`examples/interactive/fast_llm_4node.sh`](../examples/interactive/fast_llm_4node.sh) | Reproduces fast-llm side of the charts at `MAX_TRAIN_STEPS=400` (defaults to 2 for smoke). | -| **Inside interactive 4-node EAI session** | [`examples/interactive/ds_4node.sh`](../examples/interactive/ds_4node.sh) | Reproduces DS GSPO side of the charts at `MAX_TRAIN_STEPS=400` (defaults to 2 for smoke). | -| **Submit as standalone EAI batch job** | [`submit_eai_math_7b_multinode.sh`](../submit_eai_math_7b_multinode.sh) | Production fast-llm GSPO launcher. Calls `eai job new --replicas 4`. The exact script that produced `math_7b_4node_fastllm_gspo_20260505_122944` (the chart's fast-llm run). | -| **Submit as standalone EAI batch job** | [`submit_eai_math_7b_multinode_ds_fastllm_branch.sh`](../submit_eai_math_7b_multinode_ds_fastllm_branch.sh) | Production DS GSPO launcher (DS trainer + vLLM v1, GSPO loss). The exact script that produced `math_7b_ds_fastllm_4node_20260428_135427` (the chart's DS run). | +The two `submit_eai_*.sh` scripts in the repo root are the canonical reproduction recipes for the charts above. Each submits a 4-replica × 8-GPU EAI batch job and matches the historical run config byte-for-byte. -The `examples/interactive/*.sh` scripts are byte-equivalent to the `submit_eai_*.sh` ones modulo (a) they don't call `eai job new` (you supply your own session) and (b) defaults are smoke-friendly (`MAX_TRAIN_STEPS=2`). Override `MAX_TRAIN_STEPS=400` to reproduce the charts. +| Script | What it reproduces | +|---|---| +| [`submit_eai_math_7b_multinode.sh`](../submit_eai_math_7b_multinode.sh) | Fast-llm GSPO 400-step run — produced `math_7b_4node_fastllm_gspo_20260505_122944` (the chart's fast-llm curve). | +| [`submit_eai_math_7b_multinode_ds_fastllm_branch.sh`](../submit_eai_math_7b_multinode_ds_fastllm_branch.sh) | DS GSPO 400-step run — produced `math_7b_ds_fastllm_4node_20260428_135427` (the chart's DS curve). | -### How to launch (prereqs + commands) +### How to launch -#### Path 1: production EAI batch job (recommended for full 400-step runs) +You launch these from inside an interactive EAI dev session (see §"Launching an interactive EAI dev session" above) — that's the dev/console environment. Each `bash submit_eai_*.sh 4` call submits a *separate* 4-node EAI batch job that runs the actual training; your interactive session is just the launch console and stays free. Prereqs: -1. `eai` CLI installed and authenticated on the machine you'll launch from. Run `eai login` once if it isn't already. -2. Wandb credentials configured for the entity in `WANDB_ENTITY` (`~/.netrc` or `wandb login`). -3. The personalization env vars from §"Personalize before running" exported (or edit the defaults in the script). -4. A 7B base model checkpoint at the path `MODEL_PATH` points to (default `/home/toolkit/Qwen2.5-7B` — adjust if you cloned it somewhere else). +1. Fast-LLM + PipelineRL installed in a shared venv — see [§3 End-to-end install → "Steps"](#3-end-to-end-install) above (clones both repos, checks out `gspo` and `fast-llm` branches, editable-installs). +2. `eai` CLI authenticated. Run `eai login` once if it isn't already. +3. Wandb credentials configured for the entity in `WANDB_ENTITY` (`~/.netrc` or `wandb login`). +4. The personalization env vars from §"Personalize before running" exported (or edit the defaults in the script). +5. A 7B base model checkpoint at the path `MODEL_PATH` points to (default `/home/toolkit/Qwen2.5-7B`). ```bash -# fast-llm GSPO (32 GPUs, ~9-14 h wall clock for 400 steps) +# fast-llm GSPO (4 replicas × 8 GPUs = 32 GPUs total, ~9-14 h wall clock for 400 steps) bash submit_eai_math_7b_multinode.sh 4 # DS GSPO (same compute footprint) @@ -381,25 +373,7 @@ bash submit_eai_math_7b_multinode_ds_fastllm_branch.sh 4 Each call returns a job ID and queues a 4-replica × 8-GPU EAI job. The job creates `${RESULTS_DIR}/${EXP_NAME}/` with `launch.log`, `finetune/stdout_node*.log`, `actor/info.log`, `actor_vllm_*/{stdout,stderr}.log`, and a `wandb_config.yaml` with the resumable wandb run id. WandB run name is set via `+wandb.wandb_run_name=...` and includes the timestamp. -To monitor: `eai job logs ` or tail the log files directly on the shared NFS mount. To stop early: `eai job kill ` (sends SIGINT — orchestrator does the coordinated NCCL teardown). - -#### Path 2: interactive EAI session (recommended for smoke / dev) - -Prereqs: -1. Launch and attach to a 4-node interactive session — see §"Launching an interactive EAI job" above. -2. Inside the session, install Fast-LLM + PipelineRL — see §3 "End-to-end install" → "Steps". -3. Same personalization env vars as Path 1 (no `EAI_*_DATA` needed — those are submit-only). - -```bash -# 2-step smoke (~10 min) to verify everything launches cleanly -bash examples/interactive/fast_llm_4node.sh - -# Full 400-step chart-reproducing run -MAX_TRAIN_STEPS=400 bash examples/interactive/fast_llm_4node.sh - -# Same for the DS GSPO baseline -bash examples/interactive/ds_4node.sh # smoke -MAX_TRAIN_STEPS=400 bash examples/interactive/ds_4node.sh # full +To monitor: `eai job logs ` or tail the log files directly on the shared NFS mount (`/mnt/shared/...`). To stop early: `eai job kill ` (sends SIGINT — orchestrator does the coordinated NCCL teardown). ## 10. Operations diff --git a/examples/interactive/ds_4node.sh b/examples/interactive/ds_4node.sh deleted file mode 100755 index 26958881..00000000 --- a/examples/interactive/ds_4node.sh +++ /dev/null @@ -1,98 +0,0 @@ -#!/bin/bash -# 4-node interactive smoke run: DeepSpeed ZeRO-3 trainer + vLLM v1 + GSPO loss -# ----------------------------------------------------------------------------- -# This is the *reference* baseline for comparing fast-llm GSPO behavior against -# DeepSpeed (the chart-set in docs/FAST_LLM_INTEGRATION.md compares this config -# at MAX_TRAIN_STEPS=400 against the fast-llm GSPO 400-step run from 2026-05-05). -# -# Mirrors submit_eai_math_7b_multinode_ds_fastllm_branch.sh (the GSPO version of -# the DS launcher) but runs in your current shell instead of submitting an -# `eai job new`. To reproduce the comparison charts byte-for-byte, run with -# `MAX_TRAIN_STEPS=400`. -# -# Prereqs (one-time, in this order): -# 1. Launch an interactive 4-node EAI session — see -# docs/FAST_LLM_INTEGRATION.md §3 "Launching an interactive EAI job" -# (uses ServiceNow/research-interactive-toolkit `make launch`). -# 2. Inside the session, install PipelineRL (fast-llm branch) into the -# venv — see ../../README.md "Install FastLLM+PipelineRL". -# 3. Qwen2.5-7B at /home/toolkit/Qwen2.5-7B and WandB credentials configured. -# -# Success looks like: -# - finetune/stderr_node0.log shows -# "pipelinerl.finetune_loop - Completed steps 1: {...}" -# followed by "Completed steps 2" and "Reached final step 2, stopping." -# - With MAX_TRAIN_STEPS=2 (default) the run finishes in ~10 min. -# -# Where logs go: -# $RESULTS_DIR/$EXP_NAME/{launch.log, finetune/stderr_node*.log, -# actor/info.log, actor_vllm_*/stdout.log} -# -# NOTE: DS uses streams=files (default) and prints step metrics to STDERR. -# Don't confuse the empty stdout with a stalled trainer — check stderr. -# -# Override knobs (env vars): -# NODES default 4 -# MAX_TRAIN_STEPS default 2 -# MODEL_PATH default /home/toolkit/Qwen2.5-7B -# RESULTS_DIR default /mnt/shared/denis/math_7b_results -# WANDB_ENTITY default denisko-se -# WANDB_PROJECT default watermelon -# ----------------------------------------------------------------------------- - -set -euo pipefail - -NODES="${NODES:-4}" -MAX_TRAIN_STEPS="${MAX_TRAIN_STEPS:-2}" -MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" -RESULTS_DIR="${RESULTS_DIR:-/mnt/shared/denis/math_7b_results}" -WANDB_ENTITY="${WANDB_ENTITY:-denisko-se}" -WANDB_PROJECT="${WANDB_PROJECT:-watermelon}" - -TIMESTAMP="$(date +%Y%m%d_%H%M%S)" -EXP_NAME="math_7b_${NODES}node_ds_interactive_${TIMESTAMP}" -EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" - -mkdir -p "${EXP_DIR}" -cd /home/toolkit/code/PipelineRL -# shellcheck disable=SC1091 -source /home/toolkit/code/PipelineRL/.venv/bin/activate - -echo "=== DeepSpeed 4-node interactive smoke ===" -echo " NODES=${NODES} MAX_TRAIN_STEPS=${MAX_TRAIN_STEPS}" -echo " EXP_DIR=${EXP_DIR}" -echo "===========================================" - -PYTHONHASHSEED=42 python -m pipelinerl.launch \ - --config-path /home/toolkit/code/PipelineRL/conf \ - --config-name math \ - "output_dir=${EXP_DIR}" \ - "wandb.wandb_workspace_root=${RESULTS_DIR}" \ - "wandb.wandb_entity_name=${WANDB_ENTITY}" \ - "wandb.wandb_project_name=${WANDB_PROJECT}" \ - wandb.wandb_group=eai_math7b_ds_fastllm \ - "+wandb.wandb_run_name=math7b_ds_interactive_${NODES}node_${TIMESTAMP}" \ - use_fast_llm=false \ - actor.llm_max_rollouts=128 \ - force_restart=true \ - finetune.learning_rate=1e-6 \ - finetune.attempts=8 \ - finetune.rl.policy_loss=gspo \ - finetune.rl.epsilon_low=3e-3 \ - finetune.rl.epsilon_high=4e-3 \ - '+finetune.rl.filter_zero_advantage_groups=true' \ - "finetune.max_train_steps=${MAX_TRAIN_STEPS}" \ - finetune.seq_length=20000 \ - finetune.gradient_accumulation_passes=1024 \ - 'vllm_config.vllm_kwargs.max_model_len=20000' \ - 'llm.parameters.max_tokens=16000' \ - 'llm.parameters.temperature=0.7' \ - 'test_llm.parameters.max_tokens=16000' \ - 'test_llm.parameters.temperature=0.7' \ - world.actor_fraction=4 \ - world.preprocessor_fraction=0 \ - world.finetune_fraction=4 \ - "world.run_id=\${MASTER_ADDR}" \ - streams=files \ - eval_every_n_versions=0 \ - "model_path=${MODEL_PATH}" diff --git a/examples/interactive/fast_llm_4node.sh b/examples/interactive/fast_llm_4node.sh deleted file mode 100755 index 188482e9..00000000 --- a/examples/interactive/fast_llm_4node.sh +++ /dev/null @@ -1,111 +0,0 @@ -#!/bin/bash -# 4-node interactive smoke run: fast-llm trainer + vLLM v1 + GSPO loss -# ----------------------------------------------------------------------------- -# This is the fast-llm side of the comparison charts in -# docs/FAST_LLM_INTEGRATION.md (the divisor² + SDP fix + temperature=0.7 + -# fp32_lm_head=true config from the 2026-05-05 400-step run). To reproduce -# the chart byte-for-byte run with `MAX_TRAIN_STEPS=400`. -# -# Mirrors submit_eai_math_7b_multinode.sh but runs in your current shell instead -# of submitting an `eai job new`. Use this from inside an interactive EAI -# session that already has 4 nodes attached. -# -# Prereqs (one-time, in this order): -# 1. Launch an interactive 4-node EAI session — see -# docs/FAST_LLM_INTEGRATION.md §3 "Launching an interactive EAI job" -# (uses ServiceNow/research-interactive-toolkit `make launch`). -# 2. Inside the session, install Fast-LLM (gspo branch) + PipelineRL -# (fast-llm branch) into a shared venv — see ../../README.md -# "Install FastLLM+PipelineRL". -# 3. Qwen2.5-7B at /home/toolkit/Qwen2.5-7B and WandB credentials configured. -# -# Success looks like: -# - finetune/stdout_node0.log shows "[Rank 00] training @ step 1/N | ... | grad norm: 0.1-0.3" -# - actor/info.log shows weights_ready events and rollouts being collected -# - With MAX_TRAIN_STEPS=2 (default) the run finishes in ~10 min and saves a -# checkpoint at iteration 2. -# -# Where logs go: -# $RESULTS_DIR/$EXP_NAME/{launch.log, finetune/stdout_node*.log, -# actor/info.log, actor_vllm_*/stdout.log} -# -# Override knobs (env vars): -# NODES default 4 -# MAX_TRAIN_STEPS default 2 (smoke run; bump for real training) -# MODEL_PATH default /home/toolkit/Qwen2.5-7B -# RESULTS_DIR default /mnt/shared/denis/math_7b_results -# WANDB_ENTITY default denisko-se -# WANDB_PROJECT default watermelon -# ----------------------------------------------------------------------------- - -set -euo pipefail - -NODES="${NODES:-4}" -MAX_TRAIN_STEPS="${MAX_TRAIN_STEPS:-2}" -MODEL_PATH="${MODEL_PATH:-/home/toolkit/Qwen2.5-7B}" -RESULTS_DIR="${RESULTS_DIR:-/mnt/shared/denis/math_7b_results}" -WANDB_ENTITY="${WANDB_ENTITY:-denisko-se}" -WANDB_PROJECT="${WANDB_PROJECT:-watermelon}" - -TIMESTAMP="$(date +%Y%m%d_%H%M%S)" -EXP_NAME="math_7b_${NODES}node_fastllm_gspo_interactive_${TIMESTAMP}" -EXP_DIR="${RESULTS_DIR}/${EXP_NAME}" - -mkdir -p "${EXP_DIR}" -cd /home/toolkit/code/PipelineRL -# shellcheck disable=SC1091 -source /home/toolkit/code/PipelineRL/.venv/bin/activate - -echo "=== fast-llm 4-node interactive smoke ===" -echo " NODES=${NODES} MAX_TRAIN_STEPS=${MAX_TRAIN_STEPS}" -echo " EXP_DIR=${EXP_DIR}" -echo "==========================================" - -PYTHONHASHSEED=42 python -m pipelinerl.launch \ - --config-path /home/toolkit/code/PipelineRL/conf \ - --config-name math \ - streams=redis \ - world.actor_fraction=4 \ - world.preprocessor_fraction=0 \ - world.finetune_fraction=4 \ - "world.run_id=\${MASTER_ADDR}" \ - "model_path=${MODEL_PATH}" \ - "output_dir=${EXP_DIR}" \ - force_restart=true \ - actor.llm_max_rollouts=128 \ - finetune.attempts=8 \ - "finetune.max_train_steps=${MAX_TRAIN_STEPS}" \ - '+finetune.rl.filter_zero_advantage_groups=true' \ - eval_every_n_versions=0 \ - "wandb.wandb_workspace_root=${RESULTS_DIR}" \ - "wandb.wandb_entity_name=${WANDB_ENTITY}" \ - "wandb.wandb_project_name=${WANDB_PROJECT}" \ - wandb.wandb_group=eai_math7b_fastllm_gspo \ - "+wandb.wandb_run_name=math7b_fastllm_gspo_interactive_${NODES}node_${TIMESTAMP}" \ - 'vllm_config.vllm_kwargs.gpu-memory-utilization=0.85' \ - 'vllm_config.vllm_kwargs.max-num-batched-tokens=8192' \ - 'vllm_config.vllm_kwargs.max_model_len=20000' \ - 'llm.parameters.max_tokens=16000' \ - 'llm.parameters.temperature=0.7' \ - 'test_llm.parameters.max_tokens=16000' \ - 'test_llm.parameters.temperature=0.7' \ - 'fast_llm.data.micro_batch_size=20000' \ - '+fast_llm.schedule.docs_per_step=1024' \ - "fast_llm.training.train_iters=${MAX_TRAIN_STEPS}" \ - 'fast_llm.training.num_workers=1' \ - 'fast_llm.training.checkpoint.interval=20' \ - 'fast_llm.model.distributed.sequence_data_parallel=2' \ - '+fast_llm.model.distributed.timeout=3600' \ - '+fast_llm.model.base_model.decoder.block.mlp.recompute_level=full' \ - '+fast_llm.model.base_model.head.fp32_lm_head=true' \ - '+fast_llm.model.base_model.head.losses.grpo.policy_loss=gspo' \ - 'fast_llm.model.base_model.head.losses.grpo.epsilon_low=3e-3' \ - 'fast_llm.model.base_model.head.losses.grpo.epsilon_high=4e-3' \ - '+fast_llm.model.base_model.head.losses.grpo.normalize_by_documents=true' \ - '+fast_llm.model.base_model.head.losses.grpo.temperature=0.7' \ - '+fast_llm.model.base_model.head.losses.grpo.metrics=with_entropy' \ - '+fast_llm.optimizer.learning_rate.base=1e-6' \ - '+fast_llm.optimizer.learning_rate.warmup_iterations=50' \ - '+fast_llm.optimizer.learning_rate.decay_style=cosine' \ - '+fast_llm.optimizer.learning_rate.decay_iterations=400' \ - '+fast_llm.optimizer.gradient_norm_clipping=0.3' From 0c2f99e94d175cc1f4c70047b8bc9dc9410efec2 Mon Sep 17 00:00:00 2001 From: bigximik Date: Thu, 7 May 2026 16:46:08 +0000 Subject: [PATCH 85/85] =?UTF-8?q?docs(fast-llm):=20split=20=C2=A79=20Testi?= =?UTF-8?q?ng=20into=203=20subsections?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Restructure §9 from a flat list of seven subsections into a three-bucket hierarchy: 9. Testing Unit tests (single host) 4-node test results 2-step smoke (last verified ...) 400-step training curves: fast-llm GSPO vs DS GSPO How to run 4-node tests Personalize Reproduction scripts Launch The old flat layout interleaved "what we observed" content (smoke results, curves) with "how to do it" content (personalize, recipes, launch). The new layout puts results in one bucket and the launch recipe in the other, so readers can jump to the half they need. Pure reorganization; no content changes beyond moving paragraphs and adjusting heading levels (### → ### / ####). --- docs/FAST_LLM_INTEGRATION.md | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/docs/FAST_LLM_INTEGRATION.md b/docs/FAST_LLM_INTEGRATION.md index 96876c27..a2803eb6 100644 --- a/docs/FAST_LLM_INTEGRATION.md +++ b/docs/FAST_LLM_INTEGRATION.md @@ -303,18 +303,18 @@ pytest tests/test_actor_error_handling.py # rollout retry These run on 1-3 GPUs (the helpers spawn TP=1 or TP=2 vLLM engines plus a fake trainer). -### Multi-node smoke (4-node, 2-step) +### 4-node test results -For a quick verification that everything launches, edit one of the `submit_eai_*.sh` scripts to set `max_train_steps=2` and `train_iters=2` (fast-llm only) and submit it (see §"How to launch" below). Both should hit the trainer's "Reached final step 2, stopping" / "Saving checkpoint at iteration 2" log line within ~10 minutes of `RUNNING`. Revert the values back to 400 before committing. +#### 2-step smoke (last verified 2026-05-06) -### Last verified (2026-05-06) +Quick "everything launches" verification — temporarily set `max_train_steps=2` (and `train_iters=2` for fast-llm) in the submit script, launch, and look for the trainer's "Reached final step 2, stopping" / "Saving checkpoint at iteration 2" log line within ~10 minutes of `RUNNING`. Revert to 400 before committing. -| Smoke | Job ID | Step 1 grad_norm | Step 2 grad_norm | Step 1 newlp | Step 2 newlp | NaN | +| Smoke | EAI Job | Step 1 grad_norm | Step 2 grad_norm | Step 1 newlp | Step 2 newlp | NaN | |---|---|---|---|---|---|---| | fast-llm GSPO | `59f3b62f` | 0.166 | 0.173 | -0.171 | -0.162 | 0 | | DeepSpeed PPO | `084ef7d8` | 0.201 | 0.247 | -0.162 | -0.146 | 0 | -### 400-step training curves: fast-llm GSPO vs DeepSpeed GSPO +#### 400-step training curves: fast-llm GSPO vs DeepSpeed GSPO Comparing fast-llm `math_7b_4node_fastllm_gspo_20260505_122944` (the divisor² + SDP fix run) against DeepSpeed `math_7b_ds_fastllm_4node_20260428_135427` (matching GSPO config: `policy_loss=gspo`, `epsilon_low=3e-3`, 400 steps). @@ -326,41 +326,42 @@ Comparing fast-llm `math_7b_4node_fastllm_gspo_20260505_122944` (the divisor² + ![reward_mean fast-llm vs DS](images/reward_mean.png) -### Personalize before running +### How to run 4-node tests -Both the example scripts and the production submit launchers default to Denis's setup. Before running, override these env vars (or edit the defaults in the scripts) to your own: +#### Personalize + +Both submit launchers default to Denis's setup. Before running, override these env vars (or edit the defaults at the top of each script): | Env var | Default | What it is | |---|---|---| | `RESULTS_DIR` | `/mnt/shared/denis/math_7b_results` | Where outputs / checkpoints / logs land. Must be on a shared NFS readable by every node. | | `WANDB_ENTITY` | `denisko-se` | Your wandb entity (user or org). | | `WANDB_PROJECT` | `watermelon` | Your wandb project. | -| `EAI_HOME_DATA` | `snow.home.denis_kocetkov` | Your EAI home data object (mounted at `/home/toolkit` inside the container). Submit-only. | -| `EAI_SHARED_DATA` | `snow.research.afm.shared_fml` | Your shared NFS data object (mounted at `/mnt/shared`). Submit-only. | +| `EAI_HOME_DATA` | `snow.home.denis_kocetkov` | Your EAI home data object (mounted at `/home/toolkit` inside the container). | +| `EAI_SHARED_DATA` | `snow.research.afm.shared_fml` | Your shared NFS data object (mounted at `/mnt/shared`). | | `MODEL_PATH` | `/home/toolkit/Qwen2.5-7B` | Path to the base model checkpoint inside the container. | -All five env vars apply to both `submit_eai_*.sh` scripts. - The handover doc and PR description also mention `denisko-se/watermelon` runs and `/mnt/shared/denis/math_7b_results/` paths — those are pointers to Denis's historical runs and stay as-is for traceability; you don't need to edit them, just point your own runs to your own places. -### Reproduction recipes +#### Reproduction scripts -The two `submit_eai_*.sh` scripts in the repo root are the canonical reproduction recipes for the charts above. Each submits a 4-replica × 8-GPU EAI batch job and matches the historical run config byte-for-byte. +Two production launchers in the repo root reproduce the chart-baseline runs byte-for-byte. Each submits a 4-replica × 8-GPU EAI batch job. | Script | What it reproduces | |---|---| | [`submit_eai_math_7b_multinode.sh`](../submit_eai_math_7b_multinode.sh) | Fast-llm GSPO 400-step run — produced `math_7b_4node_fastllm_gspo_20260505_122944` (the chart's fast-llm curve). | | [`submit_eai_math_7b_multinode_ds_fastllm_branch.sh`](../submit_eai_math_7b_multinode_ds_fastllm_branch.sh) | DS GSPO 400-step run — produced `math_7b_ds_fastllm_4node_20260428_135427` (the chart's DS curve). | -### How to launch +#### Launch You launch these from inside an interactive EAI dev session (see §"Launching an interactive EAI dev session" above) — that's the dev/console environment. Each `bash submit_eai_*.sh 4` call submits a *separate* 4-node EAI batch job that runs the actual training; your interactive session is just the launch console and stays free. Prereqs: + 1. Fast-LLM + PipelineRL installed in a shared venv — see [§3 End-to-end install → "Steps"](#3-end-to-end-install) above (clones both repos, checks out `gspo` and `fast-llm` branches, editable-installs). 2. `eai` CLI authenticated. Run `eai login` once if it isn't already. 3. Wandb credentials configured for the entity in `WANDB_ENTITY` (`~/.netrc` or `wandb login`). -4. The personalization env vars from §"Personalize before running" exported (or edit the defaults in the script). +4. The personalization env vars above exported (or edit the defaults in the script). 5. A 7B base model checkpoint at the path `MODEL_PATH` points to (default `/home/toolkit/Qwen2.5-7B`). ```bash