diff --git a/docs/training/logging.md b/docs/training/logging.md index 139f309a4f..d07392c7ab 100644 --- a/docs/training/logging.md +++ b/docs/training/logging.md @@ -197,6 +197,60 @@ When enabled, MLFlow receives: +### Comet ML + +Megatron Bridge can log metrics and experiment metadata to Comet ML, following the same pattern as the W&B and MLFlow integrations. + +#### What Gets Logged + +When enabled, Comet ML receives: + +- Training configuration as experiment parameters +- Scalar metrics (losses, learning rate, batch size, throughput, timers, memory, runtime, norms, energy, etc.) +- Validation loss and perplexity metrics +- Checkpoint save/load metadata + +#### Enable Comet ML Logging + + 1) Install Comet ML: + + ```bash + pip install comet-ml + ``` + + 2) Authenticate: + - Either set `COMET_API_KEY` in the environment, or + - Pass an explicit `comet_api_key` in the logger config. + + 3) Configure logging in your training setup. + + ```python + from megatron.bridge.training.config import LoggerConfig + + cfg.logger = LoggerConfig( + tensorboard_dir="./runs/tensorboard", + comet_project="my_project", + comet_experiment_name="llama32_1b_pretrain_run", + comet_workspace="my_workspace", # optional + comet_tags=["pretrain", "llama32"], # optional + ) + ``` + +```{note} +Comet ML is initialized lazily on the last rank when `comet_project` is set and `comet_experiment_name` is non-empty. +``` + +#### Comet ML Configuration with NeMo Run Launching + +For users launching training scripts with NeMo Run, Comet ML can be optionally configured using the {py:class}`bridge.recipes.run_plugins.CometPlugin`. + +The plugin automatically forwards the `COMET_API_KEY` and by default injects CLI overrides for the following logger parameters: + +- `logger.comet_project` +- `logger.comet_workspace` +- `logger.comet_experiment_name` + + #### Progress Log When `logger.log_progress` is enabled, the framework generates a `progress.txt` file in the checkpoint save directory. diff --git a/pyproject.toml b/pyproject.toml index 9396bc2a70..c55358603f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -89,6 +89,7 @@ dependencies = [ "timm", "open-clip-torch>=3.2.0", "mlflow>=3.5.0", + "comet-ml>=3.50.0", "torch>=2.6.0", ] diff --git a/src/megatron/bridge/recipes/run_plugins.py b/src/megatron/bridge/recipes/run_plugins.py index ac12991169..e935908ad2 100644 --- a/src/megatron/bridge/recipes/run_plugins.py +++ b/src/megatron/bridge/recipes/run_plugins.py @@ -493,6 +493,75 @@ def setup(self, task: Union["run.Partial", "run.Script"], executor: "run.Executo ) +@dataclass +class CometPluginScriptArgs: + """Arguments for CometPlugin to pass to run.Script.""" + + project: str + workspace: Optional[str] + name: Optional[str] + + +def _default_comet_converter(args: CometPluginScriptArgs) -> List[str]: + """Default converter for CometPlugin that generates CLI overrides.""" + cli_overrides = [f"logger.comet_project={args.project}"] + if args.workspace: + cli_overrides.append(f"logger.comet_workspace={args.workspace}") + if args.name: + cli_overrides.append(f"logger.comet_experiment_name={args.name}") + return cli_overrides + + +@dataclass(kw_only=True) +class CometPlugin(Plugin): + """ + A plugin for setting up Comet ML configuration. + + This plugin sets up Comet ML logging configuration. The plugin is only activated + if the ``COMET_API_KEY`` environment variable is set. + The ``COMET_API_KEY`` environment variable will also be set in the executor's environment variables. + Follow https://www.comet.com/docs/v2/guides/getting-started/quickstart/ to retrieve your ``COMET_API_KEY``. + + Args: + project (str): The Comet ML project name. + name (Optional[str]): The name for the Comet ML experiment. + workspace (Optional[str]): The Comet ML workspace. + script_args_converter_fn (Optional[Callable]): A function that takes CometPluginScriptArgs + and returns a list of CLI arguments. + """ + + project: str + name: Optional[str] = None + workspace: Optional[str] = None + script_args_converter_fn: Optional[Callable[[CometPluginScriptArgs], List[str]]] = None + + def setup(self, task: Union["run.Partial", "run.Script"], executor: "run.Executor"): + if not HAVE_NEMO_RUN: + raise ImportError(MISSING_NEMO_RUN_MSG) + + if "COMET_API_KEY" in os.environ: + executor.env_vars["COMET_API_KEY"] = os.environ["COMET_API_KEY"] + + if isinstance(task, Script): + script_args = CometPluginScriptArgs( + project=self.project, + workspace=self.workspace, + name=self.name, + ) + + converter = self.script_args_converter_fn or _default_comet_converter + cli_overrides = converter(script_args) + + task.args.extend(cli_overrides) + logger.info(f"{self.__class__.__name__} added CLI overrides: {', '.join(cli_overrides)}") + else: + raise NotImplementedError("CometPlugin is only supported for run.Script tasks") + else: + logger.warning( + f"Warning: The {self.__class__.__name__} will have no effect as COMET_API_KEY environment variable is not set." + ) + + @dataclass class PerfEnvPluginScriptArgs: """Arguments for PerfEnvPlugin to pass to run.Script.""" diff --git a/src/megatron/bridge/training/checkpointing.py b/src/megatron/bridge/training/checkpointing.py index 34730bcbff..b75642e538 100644 --- a/src/megatron/bridge/training/checkpointing.py +++ b/src/megatron/bridge/training/checkpointing.py @@ -63,7 +63,7 @@ from megatron.bridge.training.state import GlobalState, TrainState from megatron.bridge.training.tokenizers.config import TokenizerConfig from megatron.bridge.training.tokenizers.tokenizer import MegatronTokenizer -from megatron.bridge.training.utils import mlflow_utils, wandb_utils +from megatron.bridge.training.utils import comet_utils, mlflow_utils, wandb_utils from megatron.bridge.training.utils.checkpoint_utils import ( checkpoint_exists, ensure_directory_exists, @@ -829,13 +829,23 @@ def mlflow_finalize_fn() -> None: mlflow_logger=state.mlflow_logger, ) + def comet_finalize_fn() -> None: + comet_utils.on_save_checkpoint_success( + checkpoint_name, + save_dir, + train_state.step, + comet_logger=state.comet_logger, + ) + if ckpt_cfg.async_save: assert async_save_request is not None async_save_request.add_finalize_fn(wandb_finalize_fn) async_save_request.add_finalize_fn(mlflow_finalize_fn) + async_save_request.add_finalize_fn(comet_finalize_fn) else: wandb_finalize_fn() mlflow_finalize_fn() + comet_finalize_fn() if ckpt_cfg.async_save: schedule_async_save(state, async_save_request) @@ -1795,6 +1805,7 @@ def _load_checkpoint_from_path( if not torch.distributed.is_initialized() or is_last_rank(): wandb_utils.on_load_checkpoint_success(checkpoint_name, load_dir, state.wandb_logger) mlflow_utils.on_load_checkpoint_success(checkpoint_name, load_dir, state.mlflow_logger) + comet_utils.on_load_checkpoint_success(checkpoint_name, load_dir, state.comet_logger) torch.cuda.empty_cache() diff --git a/src/megatron/bridge/training/config.py b/src/megatron/bridge/training/config.py index c6c57ca733..2330b2694b 100644 --- a/src/megatron/bridge/training/config.py +++ b/src/megatron/bridge/training/config.py @@ -1082,6 +1082,21 @@ class LoggerConfig: mlflow_tags: Optional[dict[str, str]] = None """Optional tags to apply to the MLFlow run.""" + comet_project: Optional[str] = None + """The Comet ML project name. Comet logging is disabled when this is None.""" + + comet_experiment_name: Optional[str] = None + """The Comet ML experiment name.""" + + comet_workspace: Optional[str] = None + """The Comet ML workspace. If not set, uses the default workspace for the API key.""" + + comet_api_key: Optional[str] = None + """The Comet ML API key. Can also be set via COMET_API_KEY environment variable.""" + + comet_tags: Optional[list[str]] = None + """Optional list of tags to apply to the Comet ML experiment.""" + logging_level: int = logging.INFO """Set default logging level""" @@ -1125,6 +1140,30 @@ def finalize(self) -> None: "Install it via pip install mlflow or uv add mlflow" ) from exc + if self.comet_project and (self.comet_experiment_name is None or self.comet_experiment_name == ""): + raise ValueError("Set logger.comet_experiment_name when enabling Comet ML logging.") + + using_comet = any( + [ + self.comet_project, + self.comet_experiment_name, + self.comet_workspace, + self.comet_api_key, + self.comet_tags, + ] + ) + + if using_comet: + try: + import importlib + + importlib.import_module("comet_ml") + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "Comet ML logging is configured, but the 'comet_ml' package is not installed. " + "Install it via pip install comet-ml or uv add comet-ml" + ) from exc + @dataclass(kw_only=True) class ProfilingConfig: diff --git a/src/megatron/bridge/training/eval.py b/src/megatron/bridge/training/eval.py index 99b6d6a0af..3c451bb5db 100644 --- a/src/megatron/bridge/training/eval.py +++ b/src/megatron/bridge/training/eval.py @@ -336,6 +336,7 @@ def evaluate_and_print_results( writer = None wandb_writer = state.wandb_logger + comet_logger = state.comet_logger if should_fire(callback_manager, start_event): callback_manager.fire( @@ -386,6 +387,13 @@ def evaluate_and_print_results( if state.cfg.logger.log_validation_ppl_to_tensorboard: wandb_writer.log({"{} validation ppl".format(key): ppl}, state.train_state.step) + if comet_logger and is_last_rank(): + comet_logger.log_metrics( + {"{} validation".format(key): total_loss_dict[key].item()}, step=state.train_state.step + ) + if state.cfg.logger.log_validation_ppl_to_tensorboard: + comet_logger.log_metrics({"{} validation ppl".format(key): ppl}, step=state.train_state.step) + if process_non_loss_data_func is not None and writer and is_last_rank(): process_non_loss_data_func(collected_non_loss_data, state.train_state.step, writer) diff --git a/src/megatron/bridge/training/setup.py b/src/megatron/bridge/training/setup.py index cbed6603ff..55eb0943fd 100644 --- a/src/megatron/bridge/training/setup.py +++ b/src/megatron/bridge/training/setup.py @@ -51,7 +51,6 @@ ) - class SetupOutput(NamedTuple): """Represents the output of the main setup function. @@ -80,6 +79,7 @@ class SetupOutput(NamedTuple): checkpointing_context: dict[str, Any] pg_collection: ProcessGroupCollection + def setup( state: GlobalState, train_valid_test_datasets_provider: Callable[..., tuple[Optional[Any], Optional[Any], Optional[Any]]], @@ -244,13 +244,16 @@ def modelopt_pre_wrap_hook(model): # For PEFT, the pretrained checkpoint is loaded in the pre-wrap hook if cfg.peft is not None: - should_load_checkpoint = (cfg.checkpoint.load is not None and checkpoint_exists(cfg.checkpoint.load)) + should_load_checkpoint = cfg.checkpoint.load is not None and checkpoint_exists(cfg.checkpoint.load) if should_load_checkpoint: # The finetune toggle is explicitly set to True in order to avoid loading optimizer and RNG states # This is switched off here in order to load these states from the checkpoint cfg.checkpoint.finetune = False else: - should_load_checkpoint = (cfg.checkpoint.load is not None and checkpoint_exists(cfg.checkpoint.load)) or (cfg.checkpoint.pretrained_checkpoint is not None and checkpoint_exists(cfg.checkpoint.pretrained_checkpoint)) + should_load_checkpoint = (cfg.checkpoint.load is not None and checkpoint_exists(cfg.checkpoint.load)) or ( + cfg.checkpoint.pretrained_checkpoint is not None + and checkpoint_exists(cfg.checkpoint.pretrained_checkpoint) + ) if should_load_checkpoint: timers("load-checkpoint", log_level=0).start(barrier=True) @@ -271,6 +274,7 @@ def modelopt_pre_wrap_hook(model): model, state.tensorboard_logger, state.wandb_logger, + comet_logger=state.comet_logger, current_training_step=state.train_state.step, ) @@ -288,9 +292,7 @@ def modelopt_pre_wrap_hook(model): if "tokenizer" in inspect.signature(train_valid_test_datasets_provider).parameters: train_valid_test_datasets_provider = partial(train_valid_test_datasets_provider, tokenizer=tokenizer) if "pg_collection" in inspect.signature(train_valid_test_datasets_provider).parameters: - train_valid_test_datasets_provider = partial( - train_valid_test_datasets_provider, pg_collection=pg_collection - ) + train_valid_test_datasets_provider = partial(train_valid_test_datasets_provider, pg_collection=pg_collection) train_data_iterator, valid_data_iterator, test_data_iterator = setup_data_iterators( cfg=cfg, @@ -351,13 +353,13 @@ def _update_model_config_funcs( if len(model) == 1: model_config.param_sync_func = model_config.param_sync_func[0] if optimizer is not None: - model_config.finalize_model_grads_func = partial( - finalize_model_grads, pg_collection=pg_collection - ) + model_config.finalize_model_grads_func = partial(finalize_model_grads, pg_collection=pg_collection) model_config.grad_scale_func = optimizer.scale_loss -def _create_peft_pre_wrap_hook(cfg: ConfigContainer, state: GlobalState) -> Callable[[list[MegatronModule]], list[MegatronModule]]: +def _create_peft_pre_wrap_hook( + cfg: ConfigContainer, state: GlobalState +) -> Callable[[list[MegatronModule]], list[MegatronModule]]: """Create a pre-wrap hook that handles PEFT logic. This hook is executed before the model is wrapped with DDP/FSDP and handles: @@ -371,6 +373,7 @@ def _create_peft_pre_wrap_hook(cfg: ConfigContainer, state: GlobalState) -> Call Returns: A callable hook that can be registered with the model provider """ + def peft_pre_wrap_hook(model: list[MegatronModule]) -> list[MegatronModule]: """Pre-wrap hook that handles PEFT transformation. diff --git a/src/megatron/bridge/training/state.py b/src/megatron/bridge/training/state.py index 44178d3a3d..ce5f49e314 100644 --- a/src/megatron/bridge/training/state.py +++ b/src/megatron/bridge/training/state.py @@ -126,6 +126,7 @@ def __init__(self) -> None: self._tensorboard_logger: Optional[SummaryWriter] = None self._wandb_logger: Optional[Any] = None self._mlflow_logger: Optional[Any] = None + self._comet_logger: Optional[Any] = None self._timers: Optional[Timers] = None self._train_state: Optional[TrainState] = None self.rank_monitor_client: Optional[Any] = None @@ -282,6 +283,54 @@ def _flatten_dict(d: dict[str, Any], parent_key: str = "", sep: str = ".") -> di self._mlflow_logger = None return self._mlflow_logger + @property + def comet_logger(self) -> Optional[Any]: + """The Comet ML Experiment instance, lazily initialized for rank N-1.""" + if self._comet_logger is None: + cfg = self.cfg + if cfg is None: + self._comet_logger = None + return self._comet_logger + + logger_cfg = cfg.logger + if logger_cfg.comet_project and get_rank_safe() == (get_world_size_safe() - 1): + if not logger_cfg.comet_experiment_name: + raise ValueError("Please specify the comet_experiment_name for Comet ML logging!") + + import comet_ml + + init_kwargs: dict[str, Any] = { + "project_name": logger_cfg.comet_project, + "auto_metric_logging": False, + } + + api_key = logger_cfg.comet_api_key + if api_key is None: + api_key = os.environ.get("COMET_API_KEY") + if api_key: + api_key = api_key.strip() + if "COMET_API_KEY" in os.environ: + os.environ["COMET_API_KEY"] = api_key + init_kwargs["api_key"] = api_key + + if logger_cfg.comet_workspace: + init_kwargs["workspace"] = logger_cfg.comet_workspace + + experiment = comet_ml.Experiment(**init_kwargs) + experiment.set_name(logger_cfg.comet_experiment_name) + + if logger_cfg.comet_tags: + experiment.add_tags(logger_cfg.comet_tags) + + config_dict = cfg.to_dict() + sanitized_config = json.loads(json.dumps(config_dict, default=safe_serialize)) + experiment.log_parameters(sanitized_config) + + self._comet_logger = experiment + else: + self._comet_logger = None + return self._comet_logger + @property def timers(self) -> Timers: """The Megatron Timers instance used for tracking execution times.""" @@ -289,6 +338,7 @@ def timers(self) -> Timers: self._timers = Timers(self.cfg.logger.timing_log_level, self.cfg.logger.timing_log_option) self._timers.write_to_wandb = types.MethodType(_timers_write_to_wandb, self._timers) self._timers.write_to_mlflow = types.MethodType(_timers_write_to_mlflow, self._timers) + self._timers.write_to_comet = types.MethodType(_timers_write_to_comet, self._timers) return self._timers @property @@ -394,6 +444,7 @@ def reset_for_restart(self) -> None: self._tensorboard_logger = None self._wandb_logger = None self._mlflow_logger = None + self._comet_logger = None self._energy_monitor = None self._energy_monitor_created = False self._signal_handler = None @@ -447,3 +498,28 @@ def _timers_write_to_mlflow( import warnings warnings.warn("Failed to log timer metrics to MLFlow; continuing without timer metrics.") + + +def _timers_write_to_comet( + self: Timers, + names: list[str], + logger: Any, + iteration: int, + normalizer: float = 1.0, + reset: bool = True, + barrier: bool = False, +) -> None: + """Patch to write timers to Comet ML for Megatron Core Timers.""" + assert normalizer > 0.0 + name_to_min_max_time = self._get_global_min_max_time(names, reset, barrier, normalizer) + if logger is not None: + metrics: dict[str, float] = {} + for name in name_to_min_max_time: + _, max_time = name_to_min_max_time[name] + metrics[name + "-time"] = max_time + try: + logger.log_metrics(metrics, step=iteration) + except Exception: + import warnings + + warnings.warn("Failed to log timer metrics to Comet ML; continuing without timer metrics.", stacklevel=2) diff --git a/src/megatron/bridge/training/tensor_inspect.py b/src/megatron/bridge/training/tensor_inspect.py index 52b74caafd..39a4b5fb96 100644 --- a/src/megatron/bridge/training/tensor_inspect.py +++ b/src/megatron/bridge/training/tensor_inspect.py @@ -72,7 +72,9 @@ def initialize_tensor_inspect_pre_model_initialization(tensor_inspect_config: Te raise -def _maybe_attach_metric_loggers(tensorboard_logger: Any | None, wandb_logger: Any | None) -> None: +def _maybe_attach_metric_loggers( + tensorboard_logger: Any | None, wandb_logger: Any | None, comet_logger: Any | None +) -> None: """Attach supported metric loggers (TensorBoard, W&B raw module).""" try: @@ -95,6 +97,21 @@ def log_scalar(self, name: str, value: float | int, iteration: int, **kwargs): self._wandb.log({name: value}, step=iteration) MetricLogger.add_logger(_WandbModuleLogger(wandb_logger)) + + # Comet ML experiment (with .log_metrics) + if comet_logger is not None and hasattr(comet_logger, "log_metrics"): + if BaseLogger is None: + return + + class _CometExperimentLogger(BaseLogger): # type: ignore + def __init__(self, experiment): + super().__init__() + self._experiment = experiment + + def log_scalar(self, name: str, value: float | int, iteration: int, **kwargs): # type: ignore[override] + self._experiment.log_metrics({name: value}, step=iteration) + + MetricLogger.add_logger(_CometExperimentLogger(comet_logger)) except Exception as e: print_rank_0(f"Skipping NVIDIA DLFw Inspect metric logger attach due to error: {e}") @@ -104,6 +121,7 @@ def finalize_tensor_inspect_post_model_initialization( model: list[MegatronModule], tensorboard_logger: Any | None, wandb_logger: Any | None, + comet_logger: Any | None = None, current_training_step: int | None = None, ) -> None: """Finalize setup after model creation: attach loggers, set names and groups.""" @@ -118,7 +136,7 @@ def finalize_tensor_inspect_post_model_initialization( try: from megatron.core.parallel_state import get_tensor_and_data_parallel_group - _maybe_attach_metric_loggers(tensorboard_logger, wandb_logger) + _maybe_attach_metric_loggers(tensorboard_logger, wandb_logger, comet_logger) # Align nvinspect_api's step count with the resumed training step after checkpoint load if current_training_step is not None: diff --git a/src/megatron/bridge/training/train.py b/src/megatron/bridge/training/train.py index 0734b5e577..d1a0c1a962 100644 --- a/src/megatron/bridge/training/train.py +++ b/src/megatron/bridge/training/train.py @@ -643,6 +643,8 @@ def train( wandb_writer = global_state.wandb_logger if wandb_writer: wandb_writer.finish() + if global_state._comet_logger: + global_state._comet_logger.end() fault_tolerance.shutdown(global_state) sys.exit(exit_code) @@ -1308,6 +1310,9 @@ def _finish_train(global_state: GlobalState): if global_state.wandb_logger: global_state.wandb_logger.finish() + if global_state._comet_logger: + global_state._comet_logger.end() + destroy_global_state() diff --git a/src/megatron/bridge/training/utils/comet_utils.py b/src/megatron/bridge/training/utils/comet_utils.py new file mode 100644 index 0000000000..95943b6366 --- /dev/null +++ b/src/megatron/bridge/training/utils/comet_utils.py @@ -0,0 +1,75 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from pathlib import Path +from typing import Any, Optional + +from megatron.bridge.utils.common_utils import print_rank_last + + +def on_save_checkpoint_success( + checkpoint_path: str, + save_dir: str, + iteration: int, + comet_logger: Optional[Any], +) -> None: + """Callback executed after a checkpoint is successfully saved. + + If a Comet ML experiment is provided, records the checkpoint path and iteration + as experiment metadata. + + Args: + checkpoint_path: The path to the specific checkpoint file/directory saved. + save_dir: The base directory where checkpoints are being saved. + iteration: The training iteration at which the checkpoint was saved. + comet_logger: The Comet ML Experiment instance. + If None, this function is a no-op. + """ + if comet_logger is None: + return + + try: + resolved_ckpt = str(Path(checkpoint_path).resolve()) + comet_logger.log_other("last_saved_checkpoint", resolved_ckpt) + comet_logger.log_other("last_saved_iteration", iteration) + except Exception as exc: + print_rank_last(f"Failed to log checkpoint information to Comet ML: {exc}") + + +def on_load_checkpoint_success( + checkpoint_path: str, + load_dir: str, + comet_logger: Optional[Any], +) -> None: + """Callback executed after a checkpoint is successfully loaded. + + For Comet ML, records the loaded checkpoint path and base directory + as experiment metadata. + + Args: + checkpoint_path: The path to the specific checkpoint file/directory loaded. + load_dir: The base directory from which the checkpoint was loaded. + comet_logger: The Comet ML Experiment instance. + If None, this function is a no-op. + """ + if comet_logger is None: + return + + try: + resolved_ckpt = str(Path(checkpoint_path).resolve()) + resolved_load_dir = str(Path(load_dir).resolve()) + comet_logger.log_other("last_loaded_checkpoint", resolved_ckpt) + comet_logger.log_other("checkpoint_base_dir", resolved_load_dir) + except Exception as exc: + print_rank_last(f"Failed to record loaded checkpoint information to Comet ML: {exc}") diff --git a/src/megatron/bridge/training/utils/train_utils.py b/src/megatron/bridge/training/utils/train_utils.py index b2f57afa9d..8e7cb96789 100644 --- a/src/megatron/bridge/training/utils/train_utils.py +++ b/src/megatron/bridge/training/utils/train_utils.py @@ -380,6 +380,7 @@ def training_log( writer = global_state.tensorboard_logger wandb_writer = global_state.wandb_logger mlflow_logger = global_state.mlflow_logger + comet_logger = global_state.comet_logger energy_monitor = global_state.energy_monitor logger_config = config.logger train_config = config.train @@ -462,6 +463,8 @@ def training_log( timers.write_to_wandb(timers_to_log, wandb_writer, iteration, normalizer=total_iterations, reset=True) if hasattr(timers, "write_to_mlflow"): timers.write_to_mlflow(timers_to_log, mlflow_logger, iteration, normalizer=total_iterations, reset=True) + if hasattr(timers, "write_to_comet"): + timers.write_to_comet(timers_to_log, comet_logger, iteration, normalizer=total_iterations, reset=True) if config.profiling: if config.profiling.record_memory_history and get_rank_safe() in config.profiling.profile_ranks: @@ -492,6 +495,8 @@ def training_log( wandb_writer.log(throughput_report, iteration) if mlflow_logger: mlflow_logger.log_metrics(_sanitize_mlflow_metrics(throughput_report), step=iteration) + if comet_logger: + comet_logger.log_metrics(throughput_report, step=iteration) if logger_config.log_memory_to_tensorboard: memory_report = report_memory(memory_keys=logger_config.memory_keys) memory_report = {f"memory/{mem_stat}": val for (mem_stat, val) in memory_report.items()} @@ -501,6 +506,8 @@ def training_log( wandb_writer.log(memory_report, iteration) if mlflow_logger: mlflow_logger.log_metrics(_sanitize_mlflow_metrics(memory_report), step=iteration) + if comet_logger: + comet_logger.log_metrics(memory_report, step=iteration) if logger_config.log_runtime_to_tensorboard: runtime_report = report_runtime( train_state=train_state, @@ -515,6 +522,8 @@ def training_log( wandb_writer.log(runtime_report, iteration) if mlflow_logger: mlflow_logger.log_metrics(_sanitize_mlflow_metrics(runtime_report), step=iteration) + if comet_logger: + comet_logger.log_metrics(runtime_report, step=iteration) if logger_config.log_l2_norm_grad_to_tensorboard: l2_report = report_l2_norm_grad(model) for metric, value in l2_report.items(): @@ -523,16 +532,22 @@ def training_log( wandb_writer.log(l2_report, iteration) if mlflow_logger: mlflow_logger.log_metrics(_sanitize_mlflow_metrics(l2_report), step=iteration) + if comet_logger: + comet_logger.log_metrics(l2_report, step=iteration) if wandb_writer: wandb_writer.log({"samples vs steps": train_state.consumed_train_samples}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"samples vs steps": train_state.consumed_train_samples}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"samples vs steps": train_state.consumed_train_samples}, step=iteration) writer.add_scalar("learning-rate", learning_rate, iteration) writer.add_scalar("learning-rate vs samples", learning_rate, train_state.consumed_train_samples) if wandb_writer and learning_rate is not None: wandb_writer.log({"learning-rate": learning_rate}, iteration) if mlflow_logger and learning_rate is not None: mlflow_logger.log_metrics({"learning-rate": learning_rate}, step=iteration) + if comet_logger and learning_rate is not None: + comet_logger.log_metrics({"learning-rate": learning_rate}, step=iteration) if config.optimizer.decoupled_lr is not None: writer.add_scalar("decoupled-learning-rate", decoupled_learning_rate, iteration) if global_state.train_state.skipped_train_samples > 0: @@ -544,12 +559,19 @@ def training_log( {"skipped-train-samples": global_state.train_state.skipped_train_samples}, step=iteration, ) + if comet_logger: + comet_logger.log_metrics( + {"skipped-train-samples": global_state.train_state.skipped_train_samples}, + step=iteration, + ) writer.add_scalar("batch-size", batch_size, iteration) writer.add_scalar("batch-size vs samples", batch_size, global_state.train_state.consumed_train_samples) if wandb_writer: wandb_writer.log({"batch-size": batch_size}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"batch-size": batch_size}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"batch-size": batch_size}, step=iteration) for key in loss_dict: writer.add_scalar(key, loss_dict[key], iteration) writer.add_scalar(key + " vs samples", loss_dict[key], global_state.train_state.consumed_train_samples) @@ -558,6 +580,8 @@ def training_log( if mlflow_logger: loss_metrics = {key: float(val) for key, val in loss_dict.items()} mlflow_logger.log_metrics(loss_metrics, step=iteration) + if comet_logger: + comet_logger.log_metrics({key: float(val) for key, val in loss_dict.items()}, step=iteration) if logger_config.log_loss_scale_to_tensorboard: writer.add_scalar("loss-scale", loss_scale, iteration) writer.add_scalar("loss-scale vs samples", loss_scale, global_state.train_state.consumed_train_samples) @@ -565,6 +589,8 @@ def training_log( wandb_writer.log({"loss-scale": loss_scale}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"loss-scale": loss_scale}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"loss-scale": loss_scale}, step=iteration) if logger_config.log_world_size_to_tensorboard: writer.add_scalar("world-size", get_world_size_safe(), iteration) writer.add_scalar( @@ -574,6 +600,8 @@ def training_log( wandb_writer.log({"world-size": get_world_size_safe()}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"world-size": get_world_size_safe()}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"world-size": get_world_size_safe()}, step=iteration) if grad_norm is not None: writer.add_scalar("grad-norm", grad_norm, iteration) writer.add_scalar("grad-norm vs samples", grad_norm, global_state.train_state.consumed_train_samples) @@ -581,6 +609,8 @@ def training_log( wandb_writer.log({"grad-norm": grad_norm}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"grad-norm": grad_norm}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"grad-norm": grad_norm}, step=iteration) if num_zeros_in_grad is not None: writer.add_scalar("num-zeros", num_zeros_in_grad, iteration) writer.add_scalar( @@ -590,6 +620,8 @@ def training_log( wandb_writer.log({"num-zeros": num_zeros_in_grad}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"num-zeros": num_zeros_in_grad}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"num-zeros": num_zeros_in_grad}, step=iteration) if params_norm is not None: writer.add_scalar("params-norm", params_norm, iteration) writer.add_scalar("params-norm vs samples", params_norm, global_state.train_state.consumed_train_samples) @@ -597,6 +629,8 @@ def training_log( wandb_writer.log({"params-norm": params_norm}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"params-norm": params_norm}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"params-norm": params_norm}, step=iteration) if log_max_attention_logit is not None: writer.add_scalar("max-attention-logit", log_max_attention_logit, iteration) writer.add_scalar( @@ -608,6 +642,8 @@ def training_log( wandb_writer.log({"max-attention-logit": log_max_attention_logit}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"max-attention-logit": log_max_attention_logit}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"max-attention-logit": log_max_attention_logit}, step=iteration) if config.model.num_moe_experts is not None: moe_loss_scale = 1 / get_num_microbatches() @@ -674,6 +710,14 @@ def training_log( ), step=iteration, ) + if comet_logger: + comet_logger.log_metrics( + { + "throughput/tflops/device": per_gpu_tf, + "throughput/tflops": per_gpu_tf * get_world_size_safe(), + }, + step=iteration, + ) if logger_config.log_timers_to_tensorboard: if writer: @@ -682,6 +726,8 @@ def training_log( wandb_writer.log({"iteration-time": elapsed_time_per_iteration}, iteration) if mlflow_logger: mlflow_logger.log_metrics({"iteration-time": elapsed_time_per_iteration}, step=iteration) + if comet_logger: + comet_logger.log_metrics({"iteration-time": elapsed_time_per_iteration}, step=iteration) log_string = f" [{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}]" log_string += " iteration {:8d}/{:8d} |".format(iteration, train_config.train_iters) log_string += " consumed samples: {:12d} |".format(global_state.train_state.consumed_train_samples) @@ -708,6 +754,8 @@ def training_log( _sanitize_mlflow_metrics({"iter-energy/gpu": float(energy), "power/gpu": float(power)}), step=iteration, ) + if comet_logger: + comet_logger.log_metrics({"iter-energy/gpu": float(energy), "power/gpu": float(power)}, step=iteration) # Decoupled_learning_rate should be not None only on first and last pipeline stage. log_string += f" learning rate: {learning_rate:.6E} |" diff --git a/tests/unit_tests/training/test_config.py b/tests/unit_tests/training/test_config.py index 2b363d2103..2600a91cf5 100644 --- a/tests/unit_tests/training/test_config.py +++ b/tests/unit_tests/training/test_config.py @@ -2845,3 +2845,54 @@ def test_finalize_with_all_mlflow_settings(self): # Mock mlflow import to avoid slow actual import with patch("importlib.import_module"): config.finalize() # Should not raise + + def test_finalize_no_comet_settings(self): + """Test finalize succeeds when no Comet settings are configured.""" + config = LoggerConfig() + config.finalize() + + def test_finalize_with_comet_project_only_raises_error(self): + """Test finalize raises error when comet_project is set but comet_experiment_name is missing.""" + config = LoggerConfig(comet_project="my_project") + + with pytest.raises(ValueError, match="comet_experiment_name"): + config.finalize() + + def test_finalize_with_comet_project_and_empty_experiment_name_raises_error(self): + """Test finalize raises error when comet_experiment_name is empty string.""" + config = LoggerConfig(comet_project="my_project", comet_experiment_name="") + + with pytest.raises(ValueError, match="comet_experiment_name"): + config.finalize() + + def test_finalize_with_comet_project_and_experiment_name_succeeds(self): + """Test finalize succeeds when both comet_project and comet_experiment_name are set.""" + config = LoggerConfig(comet_project="my_project", comet_experiment_name="my_experiment") + with patch("importlib.import_module"): + config.finalize() + + def test_finalize_comet_not_installed_raises_module_not_found(self): + """Test finalize raises ModuleNotFoundError when comet_ml is configured but not installed.""" + config = LoggerConfig(comet_project="my_project", comet_experiment_name="my_experiment") + + with patch("importlib.import_module", side_effect=ModuleNotFoundError("No module named 'comet_ml'")): + with pytest.raises(ModuleNotFoundError, match="comet_ml"): + config.finalize() + + def test_finalize_with_comet_workspace_only(self): + """Test finalize with only comet_workspace triggers Comet validation.""" + config = LoggerConfig(comet_workspace="my_workspace") + with patch("importlib.import_module"): + config.finalize() + + def test_finalize_with_all_comet_settings(self): + """Test finalize with all Comet settings configured.""" + config = LoggerConfig( + comet_project="my_project", + comet_experiment_name="my_experiment", + comet_workspace="my_workspace", + comet_api_key="my_key", + comet_tags=["sft", "qwen3"], + ) + with patch("importlib.import_module"): + config.finalize() diff --git a/tests/unit_tests/training/test_state.py b/tests/unit_tests/training/test_state.py index e5a30ba6cc..9f218dfd11 100644 --- a/tests/unit_tests/training/test_state.py +++ b/tests/unit_tests/training/test_state.py @@ -821,6 +821,7 @@ def test_reset_for_restart(self): state._tensorboard_logger = MagicMock() state._wandb_logger = MagicMock() state._mlflow_logger = MagicMock() + state._comet_logger = MagicMock() state._energy_monitor = MagicMock() state._energy_monitor_created = True state._signal_handler = MagicMock() @@ -837,6 +838,7 @@ def test_reset_for_restart(self): assert state._tensorboard_logger is None assert state._wandb_logger is None assert state._mlflow_logger is None + assert state._comet_logger is None assert state._energy_monitor is None assert state._energy_monitor_created is False assert state._signal_handler is None @@ -982,3 +984,202 @@ def test_asserts_positive_normalizer(self): with pytest.raises(AssertionError): _timers_write_to_mlflow(mock_timers, names=["forward"], logger=mock_mlflow, iteration=100, normalizer=-1.0) + + +class TestCometLoggerProperty: + """Tests for the comet_logger property on GlobalState.""" + + def test_comet_logger_property_disabled(self): + """Test comet logger when disabled.""" + state = GlobalState() + mock_config = MagicMock() + mock_config.logger.comet_project = None + state._cfg = mock_config + + with ( + patch("megatron.bridge.training.state.get_rank_safe", return_value=3), + patch("megatron.bridge.training.state.get_world_size_safe", return_value=4), + ): + logger = state.comet_logger + + assert logger is None + assert state._comet_logger is None + + def test_comet_logger_property_when_cfg_is_none(self): + """Test comet logger returns None when cfg is None.""" + state = GlobalState() + state._cfg = None + + logger = state.comet_logger + + assert logger is None + assert state._comet_logger is None + + def test_comet_logger_not_on_last_rank(self): + """Test comet logger returns None for non-last rank.""" + state = GlobalState() + mock_config = MagicMock() + mock_config.logger.comet_project = "my_project" + mock_config.logger.comet_experiment_name = "my_experiment" + state._cfg = mock_config + + with ( + patch("megatron.bridge.training.state.get_rank_safe", return_value=0), + patch("megatron.bridge.training.state.get_world_size_safe", return_value=4), + ): + logger = state.comet_logger + + assert logger is None + assert state._comet_logger is None + + def test_comet_logger_property_missing_experiment_name(self): + """Test comet logger raises error when experiment name is empty.""" + state = GlobalState() + mock_config = MagicMock() + mock_config.logger.comet_project = "my_project" + mock_config.logger.comet_experiment_name = "" + state._cfg = mock_config + + with ( + patch("megatron.bridge.training.state.get_rank_safe", return_value=3), + patch("megatron.bridge.training.state.get_world_size_safe", return_value=4), + ): + with pytest.raises(ValueError, match="comet_experiment_name"): + _ = state.comet_logger + + def test_timers_property_has_write_to_comet(self): + """Test that timers property patches write_to_comet method.""" + state = GlobalState() + mock_config = MagicMock() + mock_config.logger.timing_log_level = 1 + mock_config.logger.timing_log_option = "minmax" + state._cfg = mock_config + + mock_timers = MagicMock() + + with patch("megatron.bridge.training.state.Timers", return_value=mock_timers): + _ = state.timers + + assert hasattr(mock_timers, "write_to_comet") + + def test_reset_for_restart_clears_comet_logger(self): + """Test reset_for_restart clears comet logger.""" + state = GlobalState() + state._comet_logger = MagicMock() + + state.reset_for_restart() + + assert state._comet_logger is None + + +class TestTimersWriteToComet: + """Test suite for _timers_write_to_comet function.""" + + def test_writes_metrics_to_comet(self): + """Test that timer metrics are logged to Comet ML.""" + from megatron.bridge.training.state import _timers_write_to_comet + + mock_timers = MagicMock() + mock_timers._get_global_min_max_time.return_value = { + "forward": (0.1, 0.5), + "backward": (0.2, 0.8), + } + + mock_comet = MagicMock() + + _timers_write_to_comet( + mock_timers, names=["forward", "backward"], logger=mock_comet, iteration=100, normalizer=1.0 + ) + + mock_timers._get_global_min_max_time.assert_called_once_with(["forward", "backward"], True, False, 1.0) + mock_comet.log_metrics.assert_called_once() + call_args = mock_comet.log_metrics.call_args + metrics = call_args[0][0] + assert "forward-time" in metrics + assert "backward-time" in metrics + assert metrics["forward-time"] == 0.5 + assert metrics["backward-time"] == 0.8 + assert call_args[1]["step"] == 100 + + def test_preserves_slash_in_metric_names(self): + """Test that Comet preserves slashes in metric names (unlike MLflow).""" + from megatron.bridge.training.state import _timers_write_to_comet + + mock_timers = MagicMock() + mock_timers._get_global_min_max_time.return_value = { + "train/forward": (0.1, 0.5), + "train/backward/compute": (0.2, 0.8), + } + + mock_comet = MagicMock() + + _timers_write_to_comet( + mock_timers, names=["train/forward", "train/backward/compute"], logger=mock_comet, iteration=100 + ) + + call_args = mock_comet.log_metrics.call_args + metrics = call_args[0][0] + assert "train/forward-time" in metrics + assert "train/backward/compute-time" in metrics + + def test_noop_when_logger_is_none(self): + """Test that no error is raised when logger is None.""" + from megatron.bridge.training.state import _timers_write_to_comet + + mock_timers = MagicMock() + mock_timers._get_global_min_max_time.return_value = {"forward": (0.1, 0.5)} + + _timers_write_to_comet(mock_timers, names=["forward"], logger=None, iteration=100) + + def test_handles_exception_gracefully(self): + """Test that exceptions from Comet are caught and logged as warning.""" + import warnings + + from megatron.bridge.training.state import _timers_write_to_comet + + mock_timers = MagicMock() + mock_timers._get_global_min_max_time.return_value = {"forward": (0.1, 0.5)} + + mock_comet = MagicMock() + mock_comet.log_metrics.side_effect = Exception("Comet connection error") + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + _timers_write_to_comet(mock_timers, names=["forward"], logger=mock_comet, iteration=100) + + assert len(w) == 1 + assert "Failed to log timer metrics to Comet ML" in str(w[0].message) + + def test_with_custom_normalizer(self): + """Test timer metrics with custom normalizer.""" + from megatron.bridge.training.state import _timers_write_to_comet + + mock_timers = MagicMock() + mock_timers._get_global_min_max_time.return_value = {"forward": (0.1, 0.5)} + + mock_comet = MagicMock() + + _timers_write_to_comet( + mock_timers, + names=["forward"], + logger=mock_comet, + iteration=100, + normalizer=2.0, + reset=False, + barrier=True, + ) + + mock_timers._get_global_min_max_time.assert_called_once_with(["forward"], False, True, 2.0) + + def test_asserts_positive_normalizer(self): + """Test that normalizer must be positive.""" + from megatron.bridge.training.state import _timers_write_to_comet + + mock_timers = MagicMock() + mock_comet = MagicMock() + + with pytest.raises(AssertionError): + _timers_write_to_comet(mock_timers, names=["forward"], logger=mock_comet, iteration=100, normalizer=0.0) + + with pytest.raises(AssertionError): + _timers_write_to_comet(mock_timers, names=["forward"], logger=mock_comet, iteration=100, normalizer=-1.0) diff --git a/tests/unit_tests/training/utils/test_comet_utils.py b/tests/unit_tests/training/utils/test_comet_utils.py new file mode 100644 index 0000000000..520f55e841 --- /dev/null +++ b/tests/unit_tests/training/utils/test_comet_utils.py @@ -0,0 +1,143 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tempfile +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest + +from megatron.bridge.training.utils.comet_utils import ( + on_load_checkpoint_success, + on_save_checkpoint_success, +) + + +@pytest.mark.unit +class TestOnSaveCheckpointSuccess: + """Test cases for on_save_checkpoint_success function.""" + + def test_noop_when_comet_logger_is_none(self): + """Test that the function does nothing when comet_logger is None.""" + on_save_checkpoint_success( + checkpoint_path="/path/to/checkpoint", + save_dir="/path/to", + iteration=100, + comet_logger=None, + ) + + def test_logs_checkpoint_metadata(self): + """Test that log_other is called with correct checkpoint information.""" + mock_comet = MagicMock() + + with tempfile.TemporaryDirectory() as tmpdir: + checkpoint_path = Path(tmpdir) / "checkpoint" + checkpoint_path.mkdir() + + on_save_checkpoint_success( + checkpoint_path=str(checkpoint_path), + save_dir=tmpdir, + iteration=1000, + comet_logger=mock_comet, + ) + + assert mock_comet.log_other.call_count == 2 + calls = mock_comet.log_other.call_args_list + assert calls[0][0] == ("last_saved_checkpoint", str(checkpoint_path.resolve())) + assert calls[1][0] == ("last_saved_iteration", 1000) + + def test_handles_exception_gracefully(self): + """Test that exceptions are caught and logged, not raised.""" + mock_comet = MagicMock() + mock_comet.log_other.side_effect = Exception("Comet connection error") + + with patch("megatron.bridge.training.utils.comet_utils.print_rank_last") as mock_print: + on_save_checkpoint_success( + checkpoint_path="/path/to/checkpoint", + save_dir="/path/to", + iteration=100, + comet_logger=mock_comet, + ) + + mock_print.assert_called_once() + error_msg = mock_print.call_args[0][0] + assert "Failed to log checkpoint information to Comet ML" in error_msg + assert "Comet connection error" in error_msg + + +@pytest.mark.unit +class TestOnLoadCheckpointSuccess: + """Test cases for on_load_checkpoint_success function.""" + + def test_noop_when_comet_logger_is_none(self): + """Test that the function does nothing when comet_logger is None.""" + on_load_checkpoint_success( + checkpoint_path="/path/to/checkpoint", + load_dir="/path/to", + comet_logger=None, + ) + + def test_logs_correct_metadata(self): + """Test that log_other is called with correct checkpoint information.""" + mock_comet = MagicMock() + + with tempfile.TemporaryDirectory() as tmpdir: + checkpoint_path = Path(tmpdir) / "checkpoint" + checkpoint_path.mkdir() + + on_load_checkpoint_success( + checkpoint_path=str(checkpoint_path), + load_dir=tmpdir, + comet_logger=mock_comet, + ) + + assert mock_comet.log_other.call_count == 2 + calls = mock_comet.log_other.call_args_list + assert calls[0][0] == ("last_loaded_checkpoint", str(checkpoint_path.resolve())) + assert calls[1][0] == ("checkpoint_base_dir", str(Path(tmpdir).resolve())) + + def test_resolves_relative_paths(self): + """Test that relative paths are resolved to absolute paths.""" + mock_comet = MagicMock() + + with tempfile.TemporaryDirectory() as tmpdir: + checkpoint_path = Path(tmpdir) / "checkpoint" + checkpoint_path.mkdir() + + on_load_checkpoint_success( + checkpoint_path=str(checkpoint_path), + load_dir=tmpdir, + comet_logger=mock_comet, + ) + + calls = mock_comet.log_other.call_args_list + assert Path(calls[0][0][1]).is_absolute() + assert Path(calls[1][0][1]).is_absolute() + + def test_handles_exception_gracefully(self): + """Test that exceptions are caught and logged, not raised.""" + mock_comet = MagicMock() + mock_comet.log_other.side_effect = Exception("Comet API error") + + with patch("megatron.bridge.training.utils.comet_utils.print_rank_last") as mock_print: + on_load_checkpoint_success( + checkpoint_path="/path/to/checkpoint", + load_dir="/path/to", + comet_logger=mock_comet, + ) + + mock_print.assert_called_once() + error_msg = mock_print.call_args[0][0] + assert "Failed to record loaded checkpoint information to Comet ML" in error_msg + assert "Comet API error" in error_msg diff --git a/uv.lock b/uv.lock index 51590c3bf9..d9f049362a 100644 --- a/uv.lock +++ b/uv.lock @@ -5,18 +5,15 @@ resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'linux'", "python_full_version == '3.13.*' and sys_platform == 'linux'", "python_full_version == '3.12.*' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and sys_platform == 'linux'", - "python_full_version < '3.11' and sys_platform == 'linux'", "python_full_version >= '3.14' and sys_platform != 'linux'", "python_full_version == '3.13.*' and sys_platform != 'linux'", "python_full_version == '3.12.*' and sys_platform != 'linux'", + "python_full_version == '3.11.*' and sys_platform == 'linux'", + "python_full_version < '3.11' and sys_platform == 'linux'", "python_full_version == '3.11.*' and sys_platform != 'linux'", "python_full_version < '3.11' and sys_platform != 'linux'", ] -[options] -prerelease-mode = "allow" - [manifest] overrides = [ { name = "cryptography", specifier = ">=43.0.0,<47" }, @@ -40,7 +37,7 @@ wheels = [ [[package]] name = "accelerate" -version = "1.12.0" +version = "1.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -51,9 +48,9 @@ dependencies = [ { name = "safetensors" }, { name = "torch", marker = "sys_platform == 'never'" }, ] 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