From 097de0ded4946a0603cbb75bb1f04ec24ae65743 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Fri, 9 May 2025 15:33:43 -0700 Subject: [PATCH 001/109] Fixed ~100 type errors Signed-off-by: Sahil Jain --- examples/run_grpo_math.py | 38 ++++++++++++++++-------- nemo_rl/algorithms/grpo.py | 21 ++++++++----- nemo_rl/data/interfaces.py | 16 +++++----- nemo_rl/distributed/batched_data_dict.py | 6 ++-- nemo_rl/distributed/model_utils.py | 5 ++-- nemo_rl/models/policy/__init__.py | 1 + nemo_rl/utils/checkpoint.py | 1 + nemo_rl/utils/timer.py | 8 ++--- 8 files changed, 59 insertions(+), 37 deletions(-) diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index df3ef3da7d..6dfdd54963 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -16,8 +16,9 @@ import os import pprint from collections import defaultdict -from typing import Any, Dict +from typing import Any, Dict, Tuple +import torch from omegaconf import OmegaConf from transformers import AutoTokenizer @@ -27,8 +28,14 @@ from nemo_rl.data.datasets import AllTaskProcessedDataset from nemo_rl.data.hf_datasets.deepscaler import DeepScalerDataset from nemo_rl.data.hf_datasets.openmathinstruct2 import OpenMathInstruct2Dataset -from nemo_rl.data.interfaces import DatumSpec, LLMMessageLogType, TaskDataSpec +from nemo_rl.data.interfaces import ( + DatumSpec, + LLMMessageLogType, + TaskDataProcessFnCallable, + TaskDataSpec, +) from nemo_rl.distributed.virtual_cluster import init_ray +from nemo_rl.environments.interfaces import EnvironmentInterface from nemo_rl.environments.math_environment import MathEnvironment from nemo_rl.models.generation.interfaces import configure_generation_config from nemo_rl.utils.config import load_config, parse_hydra_overrides @@ -53,6 +60,7 @@ def parse_args(): # =============================================================================== +# TaskDataProcessFnCallable def hf_data_processor( datum_dict: Dict[str, Any], task_data_spec: TaskDataSpec, @@ -91,7 +99,7 @@ def hf_data_processor( ] loss_multiplier = 0.0 - output = { + output: DatumSpec = { "message_log": message_log, "length": length, "extra_env_info": extra_env_info, @@ -103,6 +111,7 @@ def hf_data_processor( # Example of a generic math data processor +# TaskDataProcessFnCallable def math_data_processor( datum_dict: Dict[str, Any], task_data_spec: TaskDataSpec, @@ -119,17 +128,18 @@ def math_data_processor( # system prompt if task_data_spec.system_prompt: - sys_message = {"role": "system", "content": task_data_spec.system_prompt} - message = tokenizer.apply_chat_template( - [sys_message], + sys_prompt: dict[str, str | torch.Tensor] = { + "role": "system", + "content": task_data_spec.system_prompt, + } + sys = tokenizer.apply_chat_template( + [sys_prompt], tokenize=False, add_generation_prompt=False, add_special_tokens=False, ) - sys_message["token_ids"] = tokenizer(message, return_tensors="pt")["input_ids"][ - 0 - ] - message_log.append(sys_message) + sys_prompt["token_ids"] = tokenizer(sys, return_tensors="pt")["input_ids"][0] + message_log.append(sys_prompt) # user prompt if task_data_spec.prompt: @@ -156,7 +166,7 @@ def math_data_processor( ] loss_multiplier = 0.0 - output = { + output: DatumSpec = { "message_log": message_log, "length": length, "extra_env_info": extra_env_info, @@ -188,7 +198,9 @@ def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): else: raise ValueError(f"No processor for dataset {data_config['dataset_name']}.") - task_data_processors = defaultdict(lambda: (math_task_spec, hf_data_processor)) + task_data_processors: Dict[str, Tuple[TaskDataSpec, TaskDataProcessFnCallable]] = ( + defaultdict(lambda: (math_task_spec, hf_data_processor)) + ) task_data_processors["math"] = (math_task_spec, hf_data_processor) math_env = MathEnvironment.options( @@ -213,7 +225,7 @@ def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): max_seq_length=data_config["max_input_seq_length"], ) - task_to_env = defaultdict(lambda: math_env) + task_to_env: Dict[str, EnvironmentInterface] = defaultdict(lambda: math_env) task_to_env["math"] = math_env return dataset, val_dataset, task_to_env, task_to_env diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index 0b977f92bc..eff4884b19 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -66,11 +66,13 @@ class GRPOConfig(TypedDict): num_prompts_per_step: int num_generations_per_prompt: int max_num_steps: int + max_rollout_turns: int normalize_rewards: bool use_leave_one_out_baseline: bool val_period: int val_batch_size: int val_at_start: bool + max_val_samples: int checkpoint_dir: str @@ -88,13 +90,17 @@ def _default_grpo_save_state() -> GRPOSaveState: } +class GRPOLoggerConfig(LoggerConfig): + num_val_samples_to_print: int # number of val samples to print to stdout + + class MasterConfig(TypedDict): policy: PolicyConfig loss_fn: ClippedPGLossConfig env_configs: Dict[str, Any] data: DataConfig grpo: GRPOConfig - logger: LoggerConfig + logger: GRPOLoggerConfig cluster: ClusterConfig checkpointing: CheckpointingConfig @@ -317,7 +323,7 @@ def grpo_train( val_task_to_env: Optional[Dict[str, EnvironmentInterface]], logger: Logger, checkpointer: CheckpointManager, - grpo_save_state: Optional[GRPOSaveState], + grpo_save_state: GRPOSaveState, master_config: MasterConfig, ): """Run GRPO training algorithm.""" @@ -328,6 +334,7 @@ def grpo_train( policy_generation = policy NEED_REFIT = False POLICY_GENERATION_STALE = True # tracks if generation needs a refit before running + assert policy_generation is not None # for mypy type check # common config/state itmes step = grpo_save_state["step"] @@ -571,7 +578,7 @@ def grpo_train( metrics[k] = np.sum(v).item() metrics.update(rollout_metrics) - timing_metrics = timer.get_timing_metrics(reduction_op="sum") + timing_metrics: dict[str, float] = timer.get_timing_metrics(reduction_op="sum") # type: ignore print(f" • Loss: {metrics['loss']:.4f}") print(f" • Avg Reward: {np.mean(rewards.numpy()):.4f}") @@ -603,16 +610,16 @@ def grpo_train( def validate( policy_generation: GenerationInterface, - val_dataloader: StatefulDataLoader, + val_dataloader: Optional[StatefulDataLoader], tokenizer, - val_task_to_env: Dict[str, EnvironmentInterface], + val_task_to_env: Optional[Dict[str, EnvironmentInterface]], step: int, master_config: MasterConfig, ) -> Tuple[Dict[str, Any], Dict[str, Any]]: """Run validation on the validation dataset.""" if val_dataloader is None: print(" ⚠️ No validation dataloader provided, skipping validation") - return + return {}, {} timer = Timer() with timer.time("total_validation_time"): @@ -649,7 +656,7 @@ def validate( to_env = get_keys_from_message_log( val_batch["message_log"], ["role", "content"] ) - all_message_logs.extend(to_env) + all_message_logs.append(to_env) # Calculate validation metrics accuracy = sum(total_rewards) / len(total_rewards) diff --git a/nemo_rl/data/interfaces.py b/nemo_rl/data/interfaces.py index 54d9a5cf6d..5b426463f8 100644 --- a/nemo_rl/data/interfaces.py +++ b/nemo_rl/data/interfaces.py @@ -13,7 +13,7 @@ # limitations under the License. import os from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Protocol, TypedDict, Union +from typing import Any, Dict, List, NotRequired, Optional, Protocol, TypedDict, Union import torch @@ -31,22 +31,22 @@ class DatumSpec(TypedDict): extra_env_info: Dict[str, Any] loss_multiplier: float # multiplier for the loss for this datum. 0 to mask out (say the sample is invalid) idx: int - task_name: Optional[str] = "default" - stop_strings: Optional[List[str]] = None # Optional stop strings for generation - __extra__: Any # This allows additional fields of any type + task_name: NotRequired[str] = "default" + stop_strings: NotRequired[List[str]] = None # Optional stop strings for generation + __extra__: NotRequired[Any] # This allows additional fields of any type @dataclass class TaskDataSpec: task_name: Optional[str] = None # prompt - prompt_file: Optional[os.PathLike] = None + prompt_file: Optional[str | os.PathLike | str] = None - system_prompt_file: Optional[Union[str, os.PathLike]] = None + system_prompt_file: Optional[str | os.PathLike] = None def __post_init__(self): def load_prompt_file( - prompt_file: Optional[os.PathLike], + prompt_file: Optional[str | os.PathLike], ) -> Optional[str]: """Load prompt from file if it exists, otherwise return as is.""" if prompt_file is None: @@ -59,7 +59,7 @@ def load_prompt_file( # Load prompts from files if they exist self.system_prompt = load_prompt_file(self.system_prompt_file) - self.prompt = load_prompt_file(self.prompt_file) + self.prompt = load_prompt_file(self.prompt_file) or "{}" def copy_defaults(self, from_spec: "TaskDataSpec"): """Apply default values from another Task instance for any None attributes.""" diff --git a/nemo_rl/distributed/batched_data_dict.py b/nemo_rl/distributed/batched_data_dict.py index 957fcdd121..24583f0489 100644 --- a/nemo_rl/distributed/batched_data_dict.py +++ b/nemo_rl/distributed/batched_data_dict.py @@ -13,7 +13,7 @@ # limitations under the License. from collections import UserDict from copy import deepcopy -from typing import Any, Dict, Generic, Iterator, List, Optional, TypeVar, Union +from typing import Any, Dict, Generic, Iterator, List, Mapping, Optional, TypeVar, Union import torch from typing_extensions import Self @@ -23,7 +23,7 @@ rebalance_nd_tensor, ) -DictT = TypeVar("DictT", bound=Dict[str, Any]) +DictT = TypeVar("DictT", bound=Mapping[str, Any]) class BatchedDataDict(UserDict, Generic[DictT]): @@ -325,7 +325,7 @@ def size(self) -> int: return len(self.data[key]) return self.data[key].shape[0] - def to(self, device: torch.device) -> Self: + def to(self, device: str | torch.device) -> Self: """Move tensors in batched dict to device.""" for k, v in self.data.items(): if torch.is_tensor(v): diff --git a/nemo_rl/distributed/model_utils.py b/nemo_rl/distributed/model_utils.py index 40e21c1f0e..e0166629d8 100644 --- a/nemo_rl/distributed/model_utils.py +++ b/nemo_rl/distributed/model_utils.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Tuple +from typing import Sequence, Tuple import torch @@ -99,8 +99,9 @@ def forward( @staticmethod def backward( - ctx, grad_output: torch.Tensor + ctx, *grad_outputs: torch.Tensor ) -> Tuple[torch.Tensor, None, None, None, None, None, None]: + grad_output = grad_outputs[0] softmax, target_mask, masked_target = ctx.saved_tensors partition_vocab_size = softmax.size(-1) diff --git a/nemo_rl/models/policy/__init__.py b/nemo_rl/models/policy/__init__.py index fbe728a840..5eb91ff304 100644 --- a/nemo_rl/models/policy/__init__.py +++ b/nemo_rl/models/policy/__init__.py @@ -41,6 +41,7 @@ class PolicyConfig(TypedDict): precision: str dtensor_cfg: DTensorConfig make_sequence_length_divisible_by: int + max_total_sequence_length: int max_grad_norm: Optional[Union[float, int]] fsdp_offload_enabled: bool activation_checkpointing_enabled: bool diff --git a/nemo_rl/utils/checkpoint.py b/nemo_rl/utils/checkpoint.py index 5f23a0bd68..5e1efe2f11 100644 --- a/nemo_rl/utils/checkpoint.py +++ b/nemo_rl/utils/checkpoint.py @@ -44,6 +44,7 @@ class CheckpointingConfig(TypedDict): checkpoint_dir: os.PathLike metric_name: str higher_is_better: bool + save_period: int keep_top_k: Optional[int] diff --git a/nemo_rl/utils/timer.py b/nemo_rl/utils/timer.py index 188ee14166..1f6c5d782e 100644 --- a/nemo_rl/utils/timer.py +++ b/nemo_rl/utils/timer.py @@ -13,7 +13,7 @@ # limitations under the License. import time from contextlib import contextmanager -from typing import Dict, List, Optional, Union +from typing import Callable, Dict, List, Optional, Sequence, Union import numpy as np @@ -57,7 +57,7 @@ class Timer: """ # Define valid reduction types and their corresponding NumPy functions - _REDUCTION_FUNCTIONS = { + _REDUCTION_FUNCTIONS: Dict[str, Callable[[Sequence[float]], float]] = { "mean": np.mean, "median": np.median, "min": np.min, @@ -194,7 +194,7 @@ def reduce(self, label: str, operation: str = "mean") -> float: def get_timing_metrics( self, reduction_op: Union[str, Dict[str, str]] = "mean" - ) -> Dict[str, List[float]]: + ) -> Dict[str, float | List[float]]: """Get all timing measurements with optional reduction. Args: @@ -214,7 +214,7 @@ def get_timing_metrics( if isinstance(reduction_op, str): reduction_op = {label: reduction_op for label in self._timers} - results = {} + results: dict[str, float | list[float]] = {} for label, op in reduction_op.items(): if label not in self._timers: continue From 21afaf7cde4a8c044678f881f8c2eb5a5dbdc0f9 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Fri, 9 May 2025 16:33:20 -0700 Subject: [PATCH 002/109] fixed 50 more type issues Signed-off-by: Sahil Jain --- examples/run_grpo_math.py | 4 +-- nemo_rl/algorithms/dpo.py | 2 +- nemo_rl/algorithms/grpo.py | 8 +++-- nemo_rl/algorithms/sft.py | 2 +- nemo_rl/distributed/batched_data_dict.py | 6 ++-- nemo_rl/distributed/collectives.py | 7 +++-- nemo_rl/distributed/model_utils.py | 2 +- nemo_rl/models/policy/__init__.py | 8 ++++- .../models/policy/dtensor_policy_worker.py | 8 +++-- nemo_rl/models/policy/hf_policy.py | 31 ++++++++++++------- nemo_rl/models/{ => policy}/interfaces.py | 15 +++++++-- nemo_rl/utils/logger.py | 4 +-- 12 files changed, 65 insertions(+), 32 deletions(-) rename nemo_rl/models/{ => policy}/interfaces.py (84%) diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index 6dfdd54963..ef65c85301 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -189,12 +189,12 @@ def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): # Load OpenMathInstruct2Dataset using nemo rl datasets if data_config["dataset_name"] == "OpenMathInstruct-2": print("Loading nvidia/OpenMathInstruct2Dataset for training and validation") - data = OpenMathInstruct2Dataset() + data: Any = OpenMathInstruct2Dataset() elif data_config["dataset_name"] == "DeepScaler": print( "Loading agentica-org/DeepScaleR-Preview-Dataset for training and validation" ) - data = DeepScalerDataset() + data: Any = DeepScalerDataset() else: raise ValueError(f"No processor for dataset {data_config['dataset_name']}.") diff --git a/nemo_rl/algorithms/dpo.py b/nemo_rl/algorithms/dpo.py index 0077887245..134e86305b 100644 --- a/nemo_rl/algorithms/dpo.py +++ b/nemo_rl/algorithms/dpo.py @@ -31,9 +31,9 @@ from nemo_rl.data.datasets import AllTaskProcessedDataset, dpo_collate_fn from nemo_rl.data.interfaces import TaskDataSpec from nemo_rl.distributed.virtual_cluster import ClusterConfig, RayVirtualCluster -from nemo_rl.models.interfaces import PolicyInterface from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.hf_policy import HfPolicy +from nemo_rl.models.policy.interfaces import PolicyInterface from nemo_rl.utils.checkpoint import CheckpointingConfig, CheckpointManager from nemo_rl.utils.logger import Logger, LoggerConfig from nemo_rl.utils.timer import Timer diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index eff4884b19..7d9e2c34bf 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -141,6 +141,10 @@ def setup( logger_config = master_config["logger"] cluster_config = master_config["cluster"] + assert generation_config is not None, ( + "A generation config in the PolicyConfig is required for GRPO" + ) + # ========================== # Logger # ========================== @@ -334,7 +338,7 @@ def grpo_train( policy_generation = policy NEED_REFIT = False POLICY_GENERATION_STALE = True # tracks if generation needs a refit before running - assert policy_generation is not None # for mypy type check + assert policy_generation is not None # for mypy type check # common config/state itmes step = grpo_save_state["step"] @@ -578,7 +582,7 @@ def grpo_train( metrics[k] = np.sum(v).item() metrics.update(rollout_metrics) - timing_metrics: dict[str, float] = timer.get_timing_metrics(reduction_op="sum") # type: ignore + timing_metrics: dict[str, float] = timer.get_timing_metrics(reduction_op="sum") # type: ignore print(f" • Loss: {metrics['loss']:.4f}") print(f" • Avg Reward: {np.mean(rewards.numpy()):.4f}") diff --git a/nemo_rl/algorithms/sft.py b/nemo_rl/algorithms/sft.py index 7ee857f15a..9fb376b979 100644 --- a/nemo_rl/algorithms/sft.py +++ b/nemo_rl/algorithms/sft.py @@ -34,9 +34,9 @@ ) from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.distributed.virtual_cluster import ClusterConfig, RayVirtualCluster -from nemo_rl.models.interfaces import PolicyInterface from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.hf_policy import HfPolicy +from nemo_rl.models.policy.interfaces import PolicyInterface from nemo_rl.utils.checkpoint import CheckpointingConfig, CheckpointManager from nemo_rl.utils.logger import Logger, LoggerConfig from nemo_rl.utils.timer import Timer diff --git a/nemo_rl/distributed/batched_data_dict.py b/nemo_rl/distributed/batched_data_dict.py index 24583f0489..be6ed66d59 100644 --- a/nemo_rl/distributed/batched_data_dict.py +++ b/nemo_rl/distributed/batched_data_dict.py @@ -13,7 +13,7 @@ # limitations under the License. from collections import UserDict from copy import deepcopy -from typing import Any, Dict, Generic, Iterator, List, Mapping, Optional, TypeVar, Union +from typing import Any, Dict, Generic, Iterator, List, Mapping, Optional, Type, TypeVar, Union import torch from typing_extensions import Self @@ -29,7 +29,7 @@ class BatchedDataDict(UserDict, Generic[DictT]): @classmethod def from_batches( - cls: Self, + cls: Type[Self], batches: List[Dict], pad_value_dict: Optional[Dict[str, int]] = None, ) -> Self: @@ -44,7 +44,7 @@ def from_batches( Returns: BatchedDataDict: A new BatchedDataDict containing the stacked data. """ - stacked_dict = cls() + stacked_dict: Self = cls() pad_value_dict = pad_value_dict or {} for k in sorted(batches[0]): diff --git a/nemo_rl/distributed/collectives.py b/nemo_rl/distributed/collectives.py index fddf3fe881..91a3932215 100644 --- a/nemo_rl/distributed/collectives.py +++ b/nemo_rl/distributed/collectives.py @@ -11,7 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional +from typing import Optional, cast import torch @@ -48,10 +48,11 @@ def rebalance_nd_tensor( B = batch_num_per_rank.sum() other_dims = tensor.shape[1:] + dims = (B, *other_dims) indices = batch_num_per_rank.cumsum(dim=0) output_tensor = torch.zeros( - B, *other_dims, dtype=tensor.dtype, device=torch.cuda.current_device() + *dims, dtype=tensor.dtype, device=torch.cuda.current_device() ) # tensor_split is a view we can copy into @@ -93,4 +94,4 @@ def gather_jagged_object_lists( torch.distributed.all_gather_object(gathered_lists, local_objects, group=group) # Flatten into single list while preserving order - return [obj for sublist in gathered_lists for obj in sublist] + return [obj for sublist in cast(list[list], gathered_lists) for obj in sublist] diff --git a/nemo_rl/distributed/model_utils.py b/nemo_rl/distributed/model_utils.py index e0166629d8..aef8147bd7 100644 --- a/nemo_rl/distributed/model_utils.py +++ b/nemo_rl/distributed/model_utils.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Sequence, Tuple +from typing import Tuple import torch diff --git a/nemo_rl/models/policy/__init__.py b/nemo_rl/models/policy/__init__.py index 5eb91ff304..04dad1b4eb 100644 --- a/nemo_rl/models/policy/__init__.py +++ b/nemo_rl/models/policy/__init__.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional, TypedDict, Union +from typing import Any, NotRequired, Optional, TypedDict, Union from nemo_rl.models.generation.interfaces import GenerationConfig @@ -30,6 +30,11 @@ class TokenizerConfig(TypedDict): chat_template: str +class PytorchOptimizerConfig(TypedDict): + name: str + kwargs: dict[str, Any] + + class PolicyConfig(TypedDict): model_name: str tokenizer: TokenizerConfig @@ -46,3 +51,4 @@ class PolicyConfig(TypedDict): fsdp_offload_enabled: bool activation_checkpointing_enabled: bool refit_buffer_size_gb: int + optimizer: NotRequired[PytorchOptimizerConfig] = None diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index 849c2d75eb..c55b50a4e5 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -42,6 +42,10 @@ to_local_if_dtensor, ) from nemo_rl.models.policy import PolicyConfig +from nemo_rl.models.policy.interfaces import ( + LogprobOutputSpec, + ReferenceLogprobOutputSpec, +) from nemo_rl.models.policy.utils import ( get_gpu_info, import_class_from_path, @@ -452,7 +456,7 @@ def train( def get_logprobs( self, data: BatchedDataDict, micro_batch_size: int = None - ) -> BatchedDataDict: + ) -> BatchedDataDict[LogprobOutputSpec]: """Get the logprobs of the model for a batch of data. Uses the configured logprob_batch_size to do microbatching. @@ -586,7 +590,7 @@ def use_reference_model(self): def get_reference_policy_logprobs( self, data: BatchedDataDict, micro_batch_size: int = None - ) -> BatchedDataDict: + ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get the logprobs from the reference policy for a batch of data. Returns: diff --git a/nemo_rl/models/policy/hf_policy.py b/nemo_rl/models/policy/hf_policy.py index 2d2dbf3d4c..dbba94bc1d 100644 --- a/nemo_rl/models/policy/hf_policy.py +++ b/nemo_rl/models/policy/hf_policy.py @@ -13,7 +13,7 @@ # limitations under the License. import os from collections import defaultdict -from typing import List, Optional, Union +from typing import List, Optional, Type, Union import ray from transformers import AutoTokenizer @@ -27,10 +27,14 @@ GenerationInterface, GenerationOutputSpec, ) -from nemo_rl.models.interfaces import PolicyInterface from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.dtensor_policy_worker import DTensorPolicyWorker from nemo_rl.models.policy.fsdp1_policy_worker import FSDP1PolicyWorker +from nemo_rl.models.policy.interfaces import ( + LogprobOutputSpec, + PolicyInterface, + ReferenceLogprobOutputSpec, +) class HfPolicy(PolicyInterface, GenerationInterface): @@ -55,11 +59,11 @@ def __init__( self.tensor_parallel_size = 1 if config["dtensor_cfg"]["enabled"]: - worker_builder_cls = DTensorPolicyWorker + worker_builder_cls: Type = DTensorPolicyWorker self.tensor_parallel_size = config["dtensor_cfg"]["tensor_parallel_size"] node_bundle_indices = self._get_tied_worker_bundle_indices(cluster) else: - worker_builder_cls = FSDP1PolicyWorker + worker_builder_cls: Type = FSDP1PolicyWorker worker_builder = RayWorkerBuilder( worker_builder_cls, @@ -111,7 +115,7 @@ def _get_tied_worker_bundle_indices(self, cluster): def get_logprobs( self, data: BatchedDataDict[GenerationDatumSpec] - ) -> BatchedDataDict: + ) -> BatchedDataDict[LogprobOutputSpec]: """Get the logprobs of the model for a data dict. Returns: @@ -123,14 +127,16 @@ def get_logprobs( futures = self.worker_group.run_all_workers_multiple_data( "get_logprobs", sharded_data, only_on="all_tied_workers" ) - logprobs = BatchedDataDict.from_batches( + logprobs: BatchedDataDict[LogprobOutputSpec] = BatchedDataDict.from_batches( self.worker_group.get_all_worker_results(futures) ) return logprobs def get_reference_policy_logprobs( - self, data: BatchedDataDict[GenerationDatumSpec], micro_batch_size: int = None - ) -> BatchedDataDict: + self, + data: BatchedDataDict[GenerationDatumSpec], + micro_batch_size: Optional[int] = None, + ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get the logprobs of the reference policy for a data dict. Returns: Identical to get_logprobs. @@ -142,8 +148,10 @@ def get_reference_policy_logprobs( common_kwargs={"micro_batch_size": micro_batch_size}, only_on="all_tied_workers", ) - logprobs = BatchedDataDict.from_batches( - self.worker_group.get_all_worker_results(futures) + logprobs: BatchedDataDict[ReferenceLogprobOutputSpec] = ( + BatchedDataDict.from_batches( + self.worker_group.get_all_worker_results(futures) + ) ) return logprobs @@ -210,7 +218,8 @@ def generate( common_kwargs={"greedy": greedy}, only_on="all_tied_workers", ) - result = BatchedDataDict.from_batches( + assert self.cfg["generation"] is not None, "Generation config is not set" + result: BatchedDataDict[GenerationOutputSpec] = BatchedDataDict.from_batches( self.worker_group.get_all_worker_results(futures), pad_value_dict={"output_ids": self.cfg["generation"]["pad_token_id"]}, ) diff --git a/nemo_rl/models/interfaces.py b/nemo_rl/models/policy/interfaces.py similarity index 84% rename from nemo_rl/models/interfaces.py rename to nemo_rl/models/policy/interfaces.py index f59e3ade0b..2b50c1a785 100644 --- a/nemo_rl/models/interfaces.py +++ b/nemo_rl/models/policy/interfaces.py @@ -12,12 +12,21 @@ # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod -from typing import Any, Dict +from typing import Any, Dict, TypedDict + +import torch from nemo_rl.algorithms.interfaces import LossFunction from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.models.generation.interfaces import GenerationDatumSpec +class LogprobOutputSpec(TypedDict): + """logprobs: Tensor of log probabilities.""" + logprobs: torch.Tensor + +class ReferenceLogprobOutputSpec(TypedDict): + """logprobs: Tensor of log probabilities.""" + reference_logprobs: torch.Tensor class PolicyInterface(ABC): """Abstract base class defining the interface for RL policies.""" @@ -25,7 +34,7 @@ class PolicyInterface(ABC): @abstractmethod def get_logprobs( self, data: BatchedDataDict[GenerationDatumSpec] - ) -> BatchedDataDict: + ) -> BatchedDataDict[LogprobOutputSpec]: """Get logprobs of actions from observations. Args: @@ -40,7 +49,7 @@ def get_logprobs( @abstractmethod def get_reference_policy_logprobs( self, data: BatchedDataDict[GenerationDatumSpec] - ) -> BatchedDataDict: + ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get logprobs of actions from observations. Args: diff --git a/nemo_rl/utils/logger.py b/nemo_rl/utils/logger.py index 0c56296e2b..b38cbe1106 100644 --- a/nemo_rl/utils/logger.py +++ b/nemo_rl/utils/logger.py @@ -21,7 +21,7 @@ import threading import time from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional, TypedDict +from typing import Any, Dict, List, Mapping, Optional, TypedDict import ray import requests @@ -562,7 +562,7 @@ def log_metrics( for logger in self.loggers: logger.log_metrics(metrics, step, prefix, step_metric) - def log_hyperparams(self, params: Dict[str, Any]) -> None: + def log_hyperparams(self, params: Mapping[str, Any]) -> None: """Log hyperparameters to all enabled backends. Args: From f38ab81691687846f6e005c1ef5751ff4c0639c2 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Fri, 9 May 2025 16:34:03 -0700 Subject: [PATCH 003/109] lint Signed-off-by: Sahil Jain --- nemo_rl/distributed/batched_data_dict.py | 13 ++++++++++++- nemo_rl/models/policy/interfaces.py | 5 +++++ 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/nemo_rl/distributed/batched_data_dict.py b/nemo_rl/distributed/batched_data_dict.py index be6ed66d59..f8a7737fe5 100644 --- a/nemo_rl/distributed/batched_data_dict.py +++ b/nemo_rl/distributed/batched_data_dict.py @@ -13,7 +13,18 @@ # limitations under the License. from collections import UserDict from copy import deepcopy -from typing import Any, Dict, Generic, Iterator, List, Mapping, Optional, Type, TypeVar, Union +from typing import ( + Any, + Dict, + Generic, + Iterator, + List, + Mapping, + Optional, + Type, + TypeVar, + Union, +) import torch from typing_extensions import Self diff --git a/nemo_rl/models/policy/interfaces.py b/nemo_rl/models/policy/interfaces.py index 2b50c1a785..0764e0ffa1 100644 --- a/nemo_rl/models/policy/interfaces.py +++ b/nemo_rl/models/policy/interfaces.py @@ -20,14 +20,19 @@ from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.models.generation.interfaces import GenerationDatumSpec + class LogprobOutputSpec(TypedDict): """logprobs: Tensor of log probabilities.""" + logprobs: torch.Tensor + class ReferenceLogprobOutputSpec(TypedDict): """logprobs: Tensor of log probabilities.""" + reference_logprobs: torch.Tensor + class PolicyInterface(ABC): """Abstract base class defining the interface for RL policies.""" From e5296bc8d889a26fa7f0cc9dba668142a286480f Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Sun, 11 May 2025 16:10:18 -0700 Subject: [PATCH 004/109] Added mypy config, fixed 50 more type errors, and updated old typing.{Dict, List, Tuple} to primitive dict, list tuple Signed-off-by: Sahil Jain --- examples/run_dpo.py | 4 +- examples/run_grpo_math.py | 10 +- examples/run_grpo_sliding_puzzle.py | 8 +- examples/run_sft.py | 4 +- mypy.ini | 12 + nemo_rl/algorithms/dpo.py | 4 +- nemo_rl/algorithms/grpo.py | 14 +- nemo_rl/algorithms/interfaces.py | 4 +- nemo_rl/algorithms/loss_functions.py | 24 +- nemo_rl/algorithms/sft.py | 4 +- nemo_rl/data/datasets.py | 12 +- nemo_rl/data/interfaces.py | 12 +- nemo_rl/data/llm_message_utils.py | 53 +- nemo_rl/distributed/batched_data_dict.py | 30 +- nemo_rl/distributed/model_utils.py | 3 +- nemo_rl/distributed/virtual_cluster.py | 4 +- nemo_rl/distributed/worker_groups.py | 18 +- nemo_rl/environments/games/sliding_puzzle.py | 26 +- nemo_rl/environments/interfaces.py | 16 +- nemo_rl/environments/math_environment.py | 30 +- nemo_rl/environments/utils.py | 4 +- nemo_rl/evals/eval.py | 4 +- nemo_rl/experience/rollouts.py | 10 +- nemo_rl/models/dtensor/parallelize.py | 10 +- nemo_rl/models/generation/interfaces.py | 8 +- nemo_rl/models/generation/vllm.py | 19 +- nemo_rl/models/policy/__init__.py | 11 + .../models/policy/dtensor_policy_worker.py | 44 +- nemo_rl/models/policy/fsdp1_policy_worker.py | 59 +- nemo_rl/models/policy/hf_policy.py | 4 +- nemo_rl/models/policy/interfaces.py | 4 +- nemo_rl/utils/checkpoint.py | 26 +- nemo_rl/utils/config.py | 9 +- nemo_rl/utils/logger.py | 36 +- nemo_rl/utils/timer.py | 14 +- pyproject.toml | 4 + tests/check_metrics.py | 3 +- tests/unit/algorithms/test_grpo.py | 13 +- tests/unit/data/test_llm_message_utils.py | 19 +- tests/unit/test_envs.py | 20 +- tests/unit/test_utils.py | 10 +- uv.lock | 4856 +++++++++-------- 42 files changed, 2793 insertions(+), 2686 deletions(-) create mode 100644 mypy.ini diff --git a/examples/run_dpo.py b/examples/run_dpo.py index 2ebfa6bc05..a9702ed93e 100644 --- a/examples/run_dpo.py +++ b/examples/run_dpo.py @@ -16,7 +16,7 @@ import os import pprint import warnings -from typing import Any, Dict +from typing import Any from omegaconf import OmegaConf @@ -49,7 +49,7 @@ def parse_args(): # Data Processing # ======================================================= def dpo_preprocessor( - datum_dict: Dict[str, Any], + datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, tokenizer, max_seq_length: int, diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index ef65c85301..b8b6c4253a 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -16,7 +16,7 @@ import os import pprint from collections import defaultdict -from typing import Any, Dict, Tuple +from typing import Any import torch from omegaconf import OmegaConf @@ -62,7 +62,7 @@ def parse_args(): # TaskDataProcessFnCallable def hf_data_processor( - datum_dict: Dict[str, Any], + datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, tokenizer, max_seq_length: int, @@ -113,7 +113,7 @@ def hf_data_processor( # Example of a generic math data processor # TaskDataProcessFnCallable def math_data_processor( - datum_dict: Dict[str, Any], + datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, tokenizer, max_seq_length: int, @@ -198,7 +198,7 @@ def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): else: raise ValueError(f"No processor for dataset {data_config['dataset_name']}.") - task_data_processors: Dict[str, Tuple[TaskDataSpec, TaskDataProcessFnCallable]] = ( + task_data_processors: dict[str, tuple[TaskDataSpec, TaskDataProcessFnCallable]] = ( defaultdict(lambda: (math_task_spec, hf_data_processor)) ) task_data_processors["math"] = (math_task_spec, hf_data_processor) @@ -225,7 +225,7 @@ def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): max_seq_length=data_config["max_input_seq_length"], ) - task_to_env: Dict[str, EnvironmentInterface] = defaultdict(lambda: math_env) + task_to_env: dict[str, EnvironmentInterface] = defaultdict(lambda: math_env) task_to_env["math"] = math_env return dataset, val_dataset, task_to_env, task_to_env diff --git a/examples/run_grpo_sliding_puzzle.py b/examples/run_grpo_sliding_puzzle.py index fbdd22da75..9694d0a733 100644 --- a/examples/run_grpo_sliding_puzzle.py +++ b/examples/run_grpo_sliding_puzzle.py @@ -17,7 +17,7 @@ import os import pprint import random -from typing import Any, Dict, Iterator, Tuple +from typing import Any, Iterator from omegaconf import OmegaConf from torch.utils.data import IterableDataset @@ -58,7 +58,7 @@ def generate_puzzle_datum( ) -> DatumSpec: """Generates a single sliding puzzle datum (prompt and metadata).""" - def generate_random_config(max_config: Dict[str, Any]) -> Dict[str, Any]: + def generate_random_config(max_config: dict[str, Any]) -> dict[str, Any]: """Generate a random config for the sliding puzzle game.""" shuffle_moves = random.randint(1, max_config.get("shuffle_moves")) if shuffle_moves % 2 == 0: @@ -148,12 +148,12 @@ def __len__(self): def setup_puzzle_data( tokenizer: AutoTokenizer, - env_cfg: Dict[str, Any], + env_cfg: dict[str, Any], task_name: str, length: int, val_length: int, add_system_prompt: bool, -) -> Tuple[IterableDataset, IterableDataset | None, Dict, Dict]: +) -> tuple[IterableDataset, IterableDataset | None, dict, dict]: """Sets up the iterable data generator and env map for the sliding puzzle task.""" print("Setting up Sliding Puzzle iterable data and environment...") env_config = env_cfg[task_name] diff --git a/examples/run_sft.py b/examples/run_sft.py index cc64033703..d78df336cd 100644 --- a/examples/run_sft.py +++ b/examples/run_sft.py @@ -16,7 +16,7 @@ import os import pprint from functools import partial -from typing import Any, Dict +from typing import Any from omegaconf import OmegaConf from transformers import AutoTokenizer @@ -49,7 +49,7 @@ def parse_args(): # Data Processing # ======================================================= def sft_preprocessor( - datum_dict: Dict[str, Any], + datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, tokenizer, max_seq_length: int, diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 0000000000..fa28d77a8e --- /dev/null +++ b/mypy.ini @@ -0,0 +1,12 @@ +[mypy] +disable_error_code = no-redef +check_untyped_defs = True + +[mypy-pynvml.*] +ignore_missing_imports = True + +[mypy-hydra._internal.*] +ignore_missing_imports = True + +[mypy-hydra.core.override_parser.*] +ignore_missing_imports = True \ No newline at end of file diff --git a/nemo_rl/algorithms/dpo.py b/nemo_rl/algorithms/dpo.py index 134e86305b..f41be89cc1 100644 --- a/nemo_rl/algorithms/dpo.py +++ b/nemo_rl/algorithms/dpo.py @@ -16,7 +16,7 @@ from collections import defaultdict from functools import partial from pathlib import Path -from typing import Optional, Tuple, TypedDict +from typing import Optional, TypedDict import numpy as np import torch @@ -94,7 +94,7 @@ def setup( tokenizer: AutoTokenizer, train_dataset: AllTaskProcessedDataset, val_dataset: AllTaskProcessedDataset, -) -> Tuple[ +) -> tuple[ HfPolicy, RayVirtualCluster, StatefulDataLoader, diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index 7d9e2c34bf..ad4b716114 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -13,7 +13,7 @@ # limitations under the License. import os from pathlib import Path -from typing import Any, Dict, Optional, Tuple, TypedDict +from typing import Any, Optional, TypedDict import numpy as np import torch @@ -97,7 +97,7 @@ class GRPOLoggerConfig(LoggerConfig): class MasterConfig(TypedDict): policy: PolicyConfig loss_fn: ClippedPGLossConfig - env_configs: Dict[str, Any] + env_configs: dict[str, Any] data: DataConfig grpo: GRPOConfig logger: GRPOLoggerConfig @@ -115,7 +115,7 @@ def setup( tokenizer: AutoTokenizer, dataset: AllTaskProcessedDataset, val_dataset: Optional[AllTaskProcessedDataset], -) -> Tuple[ +) -> tuple[ PolicyInterface, GenerationInterface, RayVirtualCluster, @@ -323,8 +323,8 @@ def grpo_train( val_dataloader: Optional[StatefulDataLoader], tokenizer, loss_fn: LossFunction, - task_to_env: Dict[str, EnvironmentInterface], - val_task_to_env: Optional[Dict[str, EnvironmentInterface]], + task_to_env: dict[str, EnvironmentInterface], + val_task_to_env: Optional[dict[str, EnvironmentInterface]], logger: Logger, checkpointer: CheckpointManager, grpo_save_state: GRPOSaveState, @@ -616,10 +616,10 @@ def validate( policy_generation: GenerationInterface, val_dataloader: Optional[StatefulDataLoader], tokenizer, - val_task_to_env: Optional[Dict[str, EnvironmentInterface]], + val_task_to_env: Optional[dict[str, EnvironmentInterface]], step: int, master_config: MasterConfig, -) -> Tuple[Dict[str, Any], Dict[str, Any]]: +) -> tuple[dict[str, Any], dict[str, Any]]: """Run validation on the validation dataset.""" if val_dataloader is None: print(" ⚠️ No validation dataloader provided, skipping validation") diff --git a/nemo_rl/algorithms/interfaces.py b/nemo_rl/algorithms/interfaces.py index b36e266104..8cf63f6e3f 100644 --- a/nemo_rl/algorithms/interfaces.py +++ b/nemo_rl/algorithms/interfaces.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Dict, Protocol, Tuple +from typing import Any, Protocol import torch @@ -28,7 +28,7 @@ class LossFunction(Protocol): def __call__( self, next_token_logits: torch.Tensor, data: BatchedDataDict - ) -> Tuple[torch.Tensor, Dict[str, Any]]: + ) -> tuple[torch.Tensor, dict[str, Any]]: """Compute loss and metrics from logprobs and other data. Args: diff --git a/nemo_rl/algorithms/loss_functions.py b/nemo_rl/algorithms/loss_functions.py index 21f4f794ec..f0d77aee3d 100644 --- a/nemo_rl/algorithms/loss_functions.py +++ b/nemo_rl/algorithms/loss_functions.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import enum -from typing import Any, Tuple, TypedDict +from typing import Any, TypedDict import torch @@ -114,7 +114,7 @@ def __call__( next_token_logits: torch.Tensor, data: BatchedDataDict[ClippedPGLossDataDict], total_valid_tokens_or_seqs: torch.Tensor, - ) -> Tuple[torch.Tensor, dict]: + ) -> tuple[torch.Tensor, dict]: """Clipped Policy Gradient RL loss function.""" token_mask = data["token_mask"][:, 1:] sample_mask = data["sample_mask"] @@ -155,7 +155,7 @@ def __call__( next_token_logprobs = torch.nn.functional.log_softmax( next_token_logits, dim=-1 ) - next_tokens = data.get("input_ids")[:, 1:].cuda() # Skip first token + next_tokens = data["input_ids"][:, 1:].cuda() # Skip first token curr_logprobs = next_token_logprobs.gather( dim=-1, index=next_tokens.unsqueeze(-1) ).squeeze(-1) @@ -294,7 +294,7 @@ def __call__( total_valid_tokens_or_seqs: torch.Tensor, dpo_loss: bool = False, dpo_average_log_probs: bool = False, - ) -> Tuple[torch.Tensor, dict]: + ) -> tuple[torch.Tensor, dict]: # logits shape: [batch_size, seq_len, vocab_size] # Get the next token logits for each position token_mask = data["token_mask"][:, 1:] @@ -309,7 +309,7 @@ def __call__( next_token_logits, data["input_ids"] ) else: - next_tokens = data.get("input_ids")[:, 1:].cuda() # Skip first token + next_tokens = data["input_ids"][:, 1:].cuda() # Skip first token next_token_logprobs = torch.nn.functional.log_softmax( next_token_logits, dim=-1 ) @@ -343,10 +343,10 @@ def __call__( class DPOLossConfig(TypedDict): reference_policy_kl_penalty: float - preference_loss_weight: float = 1.0 - sft_loss_weight: float = 0.0 - preference_average_log_probs: bool = False - sft_average_log_probs: bool = False + preference_loss_weight: float + sft_loss_weight: float + preference_average_log_probs: bool + sft_average_log_probs: bool class DPOLossDataDict(TypedDict): @@ -405,7 +405,7 @@ class DPOLossFn(LossFunction): - sft_average_log_probs (bool): Whether to average log probs across tokens in SFT loss Returns: - Tuple[torch.Tensor, dict]: A tuple containing: + tuple[torch.Tensor, dict]: A tuple containing: - The total loss value - A dictionary with metrics including: - loss: Total loss value @@ -443,7 +443,7 @@ def preference_loss( next_token_logits, data["input_ids"] ) else: - next_tokens = data.get("input_ids")[:, 1:].cuda() # Skip first token + next_tokens = data["input_ids"][:, 1:].cuda() # Skip first token next_token_logprobs = torch.nn.functional.log_softmax( next_token_logits, dim=-1 ) @@ -499,7 +499,7 @@ def __call__( next_token_logits: torch.Tensor, data: BatchedDataDict[DPOLossDataDict], total_valid_tokens_or_seqs: torch.Tensor, - ) -> Tuple[torch.Tensor, dict]: + ) -> tuple[torch.Tensor, dict]: sft_loss_chosen = torch.tensor(0.0) if self.sft_loss_weight > 0: sft_loss, _ = self.sft_loss( diff --git a/nemo_rl/algorithms/sft.py b/nemo_rl/algorithms/sft.py index 9fb376b979..2aa0086fb5 100644 --- a/nemo_rl/algorithms/sft.py +++ b/nemo_rl/algorithms/sft.py @@ -14,7 +14,7 @@ import os import warnings from pathlib import Path -from typing import Optional, Tuple, TypedDict +from typing import Optional, TypedDict import numpy as np import torch @@ -87,7 +87,7 @@ def setup( tokenizer: AutoTokenizer, train_dataset: AllTaskProcessedDataset, val_dataset: AllTaskProcessedDataset, -) -> Tuple[ +) -> tuple[ HfPolicy, RayVirtualCluster, StatefulDataLoader, diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index 70b1dd9786..6ee72705bf 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -11,7 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Dict, List, Tuple, Union +from typing import Any, Union import torch from datasets import Dataset @@ -49,7 +49,7 @@ def __init__( tokenizer, default_task_data_spec: TaskDataSpec, task_data_processors: Union[ - Dict[str, Tuple[TaskDataSpec, TaskDataProcessFnCallable]], + dict[str, tuple[TaskDataSpec, TaskDataProcessFnCallable]], TaskDataProcessFnCallable, ], max_seq_length=None, @@ -71,7 +71,7 @@ def __init__( def __len__(self): return len(self.dataset) - def encode_single(self, text: Union[str, List[str]]) -> Tuple[List[int], int]: + def encode_single(self, text: Union[str, list[str]]) -> tuple[list[int], int]: """Takes either a single string or a list of strings that represent multiple turns for the same conversation. Returns a single (concatenated) list of tokenized ids and the length of the tokenized ids. @@ -108,7 +108,7 @@ def __getitem__(self, idx: int) -> DatumSpec: return datum_spec -def rl_collate_fn(data_batch: List[DatumSpec]) -> BatchedDataDict: +def rl_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: """Collate function for RL training.""" message_log = [datum_spec["message_log"] for datum_spec in data_batch] length = torch.tensor([datum_spec["length"] for datum_spec in data_batch]) @@ -140,7 +140,7 @@ def rl_collate_fn(data_batch: List[DatumSpec]) -> BatchedDataDict: return output -def eval_collate_fn(data_batch: List[DatumSpec]) -> BatchedDataDict: +def eval_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: """Collate function for evaluation. Takes a list of data samples and combines them into a single batched dictionary @@ -192,7 +192,7 @@ def eval_collate_fn(data_batch: List[DatumSpec]) -> BatchedDataDict: def dpo_collate_fn( - data_batch: List[DatumSpec], tokenizer, make_sequence_length_divisible_by: int + data_batch: list[DatumSpec], tokenizer, make_sequence_length_divisible_by: int ) -> BatchedDataDict: """Collate function for DPO training. diff --git a/nemo_rl/data/interfaces.py b/nemo_rl/data/interfaces.py index 5b426463f8..1781876ba6 100644 --- a/nemo_rl/data/interfaces.py +++ b/nemo_rl/data/interfaces.py @@ -13,26 +13,26 @@ # limitations under the License. import os from dataclasses import dataclass -from typing import Any, Dict, List, NotRequired, Optional, Protocol, TypedDict, Union +from typing import Any, NotRequired, Optional, Protocol, TypedDict, Union import torch # OpenAI-API-like message log, but every messsage may contain associated tensors (i.e. tokenized strings and logprobs) in addition to the original "content" string -LLMMessageLogType = List[Dict[str, Union[str, torch.Tensor]]] +LLMMessageLogType = list[dict[str, Union[str, torch.Tensor]]] # Flattened message log where all tensors and data are concatenated together for a conversation # Converts a conversation from list-of-turns format to key-value format with concatenated tensors -FlatMessagesType = Dict[str, Union[List[str], torch.Tensor]] +FlatMessagesType = dict[str, Union[list[str], torch.Tensor]] class DatumSpec(TypedDict): message_log: LLMMessageLogType length: int # total (concatenated) length of the message tensors - extra_env_info: Dict[str, Any] + extra_env_info: dict[str, Any] loss_multiplier: float # multiplier for the loss for this datum. 0 to mask out (say the sample is invalid) idx: int task_name: NotRequired[str] = "default" - stop_strings: NotRequired[List[str]] = None # Optional stop strings for generation + stop_strings: NotRequired[list[str]] = None # Optional stop strings for generation __extra__: NotRequired[Any] # This allows additional fields of any type @@ -78,7 +78,7 @@ class TaskDataProcessFnCallable(Protocol): def __call__( self, - datum_dict: Dict[str, Any], + datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, tokenizer, max_seq_length: int, diff --git a/nemo_rl/data/llm_message_utils.py b/nemo_rl/data/llm_message_utils.py index 51cd5a279d..b452627afd 100644 --- a/nemo_rl/data/llm_message_utils.py +++ b/nemo_rl/data/llm_message_utils.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import warnings -from typing import Dict, List +from typing import cast import torch from datasets import Dataset @@ -58,13 +58,13 @@ def message_log_to_flat_messages( tensor([1, 2, 3, 4, 5, 6, 7]) ``` """ - result = {} + result: dict[str, list] = {} if len(message_log) == 0: - return result + return cast(FlatMessagesType, result) # Get all unique keys across all messages - all_keys = set() + all_keys: set[str] = set() for msg in message_log: all_keys.update(msg.keys()) @@ -79,22 +79,23 @@ def message_log_to_flat_messages( result[key].append(msg[key]) # Concatenate tensors for each key + concat: FlatMessagesType = {} for key in result: if result[key] and isinstance(result[key][0], torch.Tensor): try: - result[key] = torch.cat(result[key]) + concat[key] = torch.cat(result[key]) except RuntimeError as e: if "same number of dimensions" in str(e): raise RuntimeError( f"tensors for {key=} must have same number of dimensions: {[t.shape for t in result[key]]}" ) from e raise - - return result + output: FlatMessagesType = {**result, **concat} + return output def get_keys_from_message_log( - message_log: LLMMessageLogType, keys: List[str] + message_log: LLMMessageLogType, keys: list[str] ) -> LLMMessageLogType: """Return a new LLMMessageLogType containing only the specified keys from each message. @@ -108,36 +109,32 @@ def get_keys_from_message_log( def add_loss_mask_to_message_log( - message_log: LLMMessageLogType, - roles_to_train_on: List[str] = ["assistant"], + batch_message_log: list[LLMMessageLogType], + roles_to_train_on: list[str] = ["assistant"], only_unmask_final: bool = False, ) -> None: """Add token-level loss masks to each message in a message log. Args: message_log (LLMMessageLogType): List of message dictionaries containing token IDs and metadata - roles_to_train_on (List[str]): List of strings indicating which speakers to unmask. Default: ["assistant"] + roles_to_train_on (list[str]): List of strings indicating which speakers to unmask. Default: ["assistant"] only_unmask_final (bool): If True, only unmask the final message in the log. Default: False """ for i, role in enumerate(roles_to_train_on): roles_to_train_on[i] = role.lower() - for message in message_log: - for i, sentence in enumerate(message): + for message_log in batch_message_log: + for i, message in enumerate(message_log): if only_unmask_final: - if i == len(message) - 1: - sentence["token_loss_mask"] = torch.ones_like(sentence["token_ids"]) + if i == len(message_log) - 1: + message["token_loss_mask"] = torch.ones_like(message["token_ids"]) else: - sentence["token_loss_mask"] = torch.zeros_like( - sentence["token_ids"] - ) + message["token_loss_mask"] = torch.zeros_like(message["token_ids"]) else: - if sentence["role"] in roles_to_train_on: - sentence["token_loss_mask"] = torch.ones_like(sentence["token_ids"]) + if message["role"] in roles_to_train_on: + message["token_loss_mask"] = torch.ones_like(message["token_ids"]) else: - sentence["token_loss_mask"] = torch.zeros_like( - sentence["token_ids"] - ) + message["token_loss_mask"] = torch.zeros_like(message["token_ids"]) def _pad_tensor( @@ -172,7 +169,7 @@ def _pad_tensor( ) -def _validate_tensor_consistency(tensors: List[torch.Tensor]) -> None: +def _validate_tensor_consistency(tensors: list[torch.Tensor]) -> None: """Validate that all tensors have consistent dtypes and devices. Args: @@ -196,8 +193,8 @@ def _validate_tensor_consistency(tensors: List[torch.Tensor]) -> None: def batched_message_log_to_flat_message( - message_log_batch: List[LLMMessageLogType], - pad_value_dict: Dict[str, int] = None, + message_log_batch: list[LLMMessageLogType], + pad_value_dict: dict[str, int] = None, make_sequence_length_divisible_by: int = 1, ) -> tuple[BatchedDataDict[FlatMessagesType], torch.Tensor]: """Process and pad a batch of message logs for model input. @@ -329,7 +326,7 @@ def batched_message_log_to_flat_message( return result, input_lengths_tensor -def message_log_shape(message_log: LLMMessageLogType) -> List[Dict[str, List[int]]]: +def message_log_shape(message_log: LLMMessageLogType) -> list[dict[str, list[int]]]: """Get the shape of the tensors in the message log. This utility function examines each message in the message log and reports @@ -437,7 +434,7 @@ def get_formatted_message_log( def remap_dataset_keys( dataset: Dataset, - mapping_dict: Dict[str, str], + mapping_dict: dict[str, str], ) -> Dataset: """Remap dataset keys as per mapping. diff --git a/nemo_rl/distributed/batched_data_dict.py b/nemo_rl/distributed/batched_data_dict.py index f8a7737fe5..6183a2a951 100644 --- a/nemo_rl/distributed/batched_data_dict.py +++ b/nemo_rl/distributed/batched_data_dict.py @@ -15,10 +15,8 @@ from copy import deepcopy from typing import ( Any, - Dict, Generic, Iterator, - List, Mapping, Optional, Type, @@ -41,16 +39,16 @@ class BatchedDataDict(UserDict, Generic[DictT]): @classmethod def from_batches( cls: Type[Self], - batches: List[Dict], - pad_value_dict: Optional[Dict[str, int]] = None, + batches: list[dict], + pad_value_dict: Optional[dict[str, int]] = None, ) -> Self: """Given a list of batches, stack the tensors/lists within and put them in a single dictionary. Pad sequences to the max length in the batch using either 0(default) or a non-default value for a given key provided in pad_value_dict. Args: - batches (List[Dict]): A list of dictionaries, each containing a batch of data. - pad_value_dict (Optional[Dict[str, int]]): An optional dict mapping keys to non-default(0) padding values. + batches (list[Dict]): A list of dictionaries, each containing a batch of data. + pad_value_dict (Optional[dict[str, int]]): An optional dict mapping keys to non-default(0) padding values. Returns: BatchedDataDict: A new BatchedDataDict containing the stacked data. @@ -62,9 +60,11 @@ def from_batches( list_of_tensors = [item[k] for item in batches] if isinstance(list_of_tensors[0], list): - tensor = [item for sublist in list_of_tensors for item in sublist] + tensor_or_list: list | torch.Tensor = [ + item for sublist in list_of_tensors for item in sublist + ] elif all(x.ndim == 1 for x in list_of_tensors): - tensor = torch.cat(list_of_tensors) + tensor_or_list: torch.Tensor = torch.cat(list_of_tensors) elif isinstance(list_of_tensors[0], torch.Tensor): pad_value = pad_value_dict.get(k, 0) @@ -72,7 +72,7 @@ def from_batches( row.flatten() for tensor in list_of_tensors for row in tensor ] # TODO: can we avoid padding locally then padding globally? - tensor = torch.nn.utils.rnn.pad_sequence( + tensor_or_list: torch.Tensor = torch.nn.utils.rnn.pad_sequence( list_of_tensors, batch_first=True, padding_value=pad_value ) else: @@ -82,7 +82,7 @@ def from_batches( "Please provide either a tensor or a list of picklable objects." ) ) - stacked_dict[k] = tensor + stacked_dict[k] = tensor_or_list return stacked_dict @@ -92,7 +92,7 @@ def all_gather(self, group: torch.distributed.ProcessGroup) -> "BatchedDataDict" If using reshard, it will treat PP as DP ranks. Works with data that is either tensors or string lists. """ - global_rollout_batch = type(self)() + global_rollout_batch: Self = type(self)() for k, value in self.data.items(): if isinstance(value, torch.Tensor): @@ -152,7 +152,7 @@ def shard_by_batch_size( shards: int, batch_size: Optional[int] = None, allow_uneven_shards: bool = False, - ) -> List["SlicedDataDict"]: + ) -> list["SlicedDataDict"]: """Shards a batch by first dividing it into chunks of size batch_size, then further dividing each chunk into shards equal parts. Finally aggregates the sub-shards by their position. If batch_size is None, there will be no chunking beforehand (will default to the total batch size). @@ -168,7 +168,7 @@ def shard_by_batch_size( If True, the last shard may be smaller than the others. Returns: - List[BatchedDataDict]: A list of BatchedDataDicts, length equal to shards. + list[BatchedDataDict]: A list of BatchedDataDicts, length equal to shards. Examples: ```{doctest} @@ -301,7 +301,7 @@ def repeat_interleave(self, num_repeats: int) -> "BatchedDataDict": i.e: {"key": torch.tensor([1, 2, 3]), "other_key": [1, 2, 3]} -> {"key": torch.tensor([1, 1, 2, 2, 3, 3]), "other_key": [1, 1, 2, 2, 3, 3]} """ - repeated_batch = BatchedDataDict() + repeated_batch: Self = BatchedDataDict() for k, v in self.data.items(): if torch.is_tensor(v): # For tensors, use repeat_interleave to repeat each element @@ -344,7 +344,7 @@ def to(self, device: str | torch.device) -> Self: return self def select_indices( - self, indices: Union[List[int], torch.Tensor] + self, indices: Union[list[int], torch.Tensor] ) -> "BatchedDataDict": """Selects specific rows from the batch based on indices. diff --git a/nemo_rl/distributed/model_utils.py b/nemo_rl/distributed/model_utils.py index aef8147bd7..659cffa0b1 100644 --- a/nemo_rl/distributed/model_utils.py +++ b/nemo_rl/distributed/model_utils.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Tuple import torch @@ -100,7 +99,7 @@ def forward( @staticmethod def backward( ctx, *grad_outputs: torch.Tensor - ) -> Tuple[torch.Tensor, None, None, None, None, None, None]: + ) -> tuple[torch.Tensor, None, None, None, None, None, None]: grad_output = grad_outputs[0] softmax, target_mask, masked_target = ctx.saved_tensors partition_vocab_size = softmax.size(-1) diff --git a/nemo_rl/distributed/virtual_cluster.py b/nemo_rl/distributed/virtual_cluster.py index f9f5be46b8..45f9a334bd 100644 --- a/nemo_rl/distributed/virtual_cluster.py +++ b/nemo_rl/distributed/virtual_cluster.py @@ -15,7 +15,7 @@ import os import sys import time -from typing import List, Optional, TypedDict +from typing import Optional, TypedDict import ray from ray.util.placement_group import placement_group, remove_placement_group @@ -168,7 +168,7 @@ class RayVirtualCluster: def __init__( self, - bundle_ct_per_node_list: List[int], + bundle_ct_per_node_list: list[int], use_gpus: bool = True, max_colocated_worker_groups: int = 1, num_gpus_per_node: int = 8, diff --git a/nemo_rl/distributed/worker_groups.py b/nemo_rl/distributed/worker_groups.py index 76c1be6279..3251758472 100644 --- a/nemo_rl/distributed/worker_groups.py +++ b/nemo_rl/distributed/worker_groups.py @@ -15,7 +15,7 @@ import warnings from copy import deepcopy from dataclasses import dataclass -from typing import Any, Dict, List, Literal, Optional, Union +from typing import Any, Literal, Optional, Union import ray from ray.util.placement_group import PlacementGroup @@ -30,8 +30,8 @@ class MultiWorkerFuture: """Container for Ray futures with associated worker information.""" - futures: List[ray.ObjectRef] - used_workers: List[int] + futures: list[ray.ObjectRef] + used_workers: list[int] respect_tied_workers: bool = True def get_results(self, worker_group): @@ -92,7 +92,7 @@ def __call__( placement_group_bundle_index: int, num_gpus: int, bundle_indices: Optional[tuple] = None, - **extra_options: Dict[str, Any], + **extra_options: dict[str, Any], ): """Create a Ray worker with the specified configuration. @@ -187,9 +187,9 @@ def __init__( self, cluster: RayVirtualCluster, remote_worker_builder: RayWorkerBuilder, - workers_per_node: Optional[Union[int, List[int]]] = None, + workers_per_node: Optional[Union[int, list[int]]] = None, name_prefix: str = "", - bundle_indices_list: Optional[List[tuple]] = None, + bundle_indices_list: Optional[list[tuple]] = None, ): """Initialize a group of distributed Ray workers. @@ -367,8 +367,8 @@ def group_count(self): def run_all_workers_multiple_data( self, method_name: str, - data: List[SlicedDataDict], - common_kwargs: Optional[Dict[str, Any]] = None, + data: list[SlicedDataDict], + common_kwargs: Optional[dict[str, Any]] = None, only_on: Literal["all", "tied_leader", "all_tied_workers"] = "all", ): """Run a method on all workers in parallel with different data. @@ -470,7 +470,7 @@ def run_all_workers_single_data( *args, **kwargs: Arguments to pass to the method Returns: - List[ray.ObjectRef]: A list of ray futures + list[ray.ObjectRef]: A list of ray futures """ futures = [] diff --git a/nemo_rl/environments/games/sliding_puzzle.py b/nemo_rl/environments/games/sliding_puzzle.py index b8bc536f41..27a338fece 100644 --- a/nemo_rl/environments/games/sliding_puzzle.py +++ b/nemo_rl/environments/games/sliding_puzzle.py @@ -14,7 +14,7 @@ import copy import random -from typing import Any, Dict, List, Optional, Tuple, TypedDict +from typing import Any, Optional, TypedDict import ray import torch @@ -34,14 +34,14 @@ class SlidingPuzzleConfig(TypedDict): class SlidingPuzzleMetadata(TypedDict): - game_state: Dict[str, Any] # Stores the dict returned by SlidingPuzzleGame methods + game_state: dict[str, Any] # Stores the dict returned by SlidingPuzzleGame methods num_moves: int max_moves: int class SlidingPuzzleGameLogic: @staticmethod - def generate(config: Dict[str, Any]) -> Dict[str, Any]: + def generate(config: dict[str, Any]) -> dict[str, Any]: """Generate a new Sliding Puzzle.""" size = config.get("size", 4) # Default to 4x4 (15-puzzle) shuffle_moves = config.get( @@ -92,7 +92,7 @@ def generate(config: Dict[str, Any]) -> Dict[str, Any]: } @staticmethod - def init(game_state: Dict[str, Any]) -> str: + def init(game_state: dict[str, Any]) -> str: """Initialize Sliding Puzzle game and return welcome message.""" size = game_state["size"] @@ -106,8 +106,8 @@ def init(game_state: Dict[str, Any]) -> str: @staticmethod def step( - action: str, game_state: Dict[str, Any] - ) -> Tuple[str, float, bool, Dict[str, Any]]: + action: str, game_state: dict[str, Any] + ) -> tuple[str, float, bool, dict[str, Any]]: """Process an action in the Sliding Puzzle game.""" size = game_state["size"] grid = game_state["grid"] @@ -201,7 +201,7 @@ def step( return response, reward, is_terminated, new_state @staticmethod - def render(game_state: Dict[str, Any]) -> str: + def render(game_state: dict[str, Any]) -> str: """Render the current Sliding Puzzle game state.""" grid = game_state["grid"] size = game_state["size"] @@ -267,11 +267,11 @@ def process_turn( self, message_log: LLMMessageLogType, metadata: SlidingPuzzleMetadata, - ) -> Tuple[ - Dict[str, str], + ) -> tuple[ + dict[str, str], float, bool, - Optional[List[str]], + Optional[list[str]], Optional[SlidingPuzzleMetadata], ]: """Processes a single turn for the sliding puzzle task.""" @@ -351,8 +351,8 @@ def __init__(self, cfg: Optional[SlidingPuzzleConfig] = None): def step( self, - message_log_batch: List[LLMMessageLogType], - metadata_batch: List[SlidingPuzzleMetadata], + message_log_batch: list[LLMMessageLogType], + metadata_batch: list[SlidingPuzzleMetadata], ) -> EnvironmentReturn: """Processes a batch of sliding puzzle interactions.""" # Since logic is synchronous, process sequentially (can parallelize if logic becomes heavy) @@ -391,7 +391,7 @@ def shutdown(self): def global_post_process_and_metrics( self, batch: BatchedDataDict - ) -> Tuple[BatchedDataDict, dict]: + ) -> tuple[BatchedDataDict, dict]: # Calculate success rate based on final reward == 1.0 final_rewards = batch.get( "total_reward", torch.tensor([0.0] * len(batch["idx"])) diff --git a/nemo_rl/environments/interfaces.py b/nemo_rl/environments/interfaces.py index e432b829f8..1cd7612745 100644 --- a/nemo_rl/environments/interfaces.py +++ b/nemo_rl/environments/interfaces.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import abc -from typing import Dict, List, NamedTuple, Optional, Tuple +from typing import NamedTuple, Optional from torch import Tensor @@ -37,9 +37,9 @@ class EnvironmentReturn(NamedTuple): terminateds: whether the episode ended this turn. """ - observations: List[Dict[str, str]] - metadata: List[Optional[dict]] - next_stop_strings: List[Optional[List[str]]] + observations: list[dict[str, str]] + metadata: list[Optional[dict]] + next_stop_strings: list[Optional[list[str]]] rewards: Tensor terminateds: Tensor @@ -48,15 +48,15 @@ class EnvironmentInterface(abc.ABC): @abc.abstractmethod def step( self, - message_log_batch: List[LLMMessageLogType], - metadata: List[Optional[dict]], + message_log_batch: list[LLMMessageLogType], + metadata: list[Optional[dict]], *args, **kwargs, ) -> EnvironmentReturn: """Runs a step in the environment. Allows for asynchrony with remote servers, but it's not required (this function is a ray remote). message_log_batch: batch of OpenAI-API-like message logs that represent interactions with the LLM. - Each element is a List[Dict[str, Union[str, torch.Tensor]]]. + Each element is a list[dict[str, Union[str, torch.Tensor]]]. For example, if this were a Math Environment, then the message log would be [ @@ -81,5 +81,5 @@ def step( @abc.abstractmethod def global_post_process_and_metrics( self, batch: BatchedDataDict - ) -> Tuple[BatchedDataDict, dict]: + ) -> tuple[BatchedDataDict, dict]: """Post processing function after all rollouts are done for the batch and returns metrics.""" diff --git a/nemo_rl/environments/math_environment.py b/nemo_rl/environments/math_environment.py index daf80b9064..2993ea9c6b 100644 --- a/nemo_rl/environments/math_environment.py +++ b/nemo_rl/environments/math_environment.py @@ -14,7 +14,7 @@ import contextlib import io import logging -from typing import Dict, List, Optional, Tuple, TypedDict +from typing import Optional, TypedDict import ray import torch @@ -35,7 +35,7 @@ class MathEnvConfig(TypedDict): num_workers: int - stop_strings: Optional[List[str]] = None # Default stop strings for this env + stop_strings: Optional[list[str]] = None # Default stop strings for this env @contextlib.contextmanager @@ -66,16 +66,16 @@ def __init__(self): ) def verify( - self, pred_responses: List[str], ground_truths: List[str] - ) -> List[float]: + self, pred_responses: list[str], ground_truths: list[str] + ) -> list[float]: """Verify the correctness of the predicted responses against the ground truth. Args: - pred_responses: List[str]. The predicted responses from the LLM. - ground_truths: List[str]. The ground truth responses. + pred_responses: list[str]. The predicted responses from the LLM. + ground_truths: list[str]. The ground truth responses. Returns: - List[float]. The rewards for each predicted response. + list[float]. The rewards for each predicted response. """ results = [] for response, ground_truth in zip(pred_responses, ground_truths): @@ -120,20 +120,20 @@ def shutdown(self): def step( self, - message_log_batch: List[List[Dict[str, str]]], - metadata: List[MathEnvironmentMetadata], + message_log_batch: list[list[dict[str, str]]], + metadata: list[MathEnvironmentMetadata], ) -> EnvironmentReturn: """Runs a step in the math environment. Args: - message_log: List[List[Dict[str, str]]]. A batch of OpenAI-API-like message logs that represent interactions with the LLM. - metadata: List[MathEnvironmentMetadata]. The grader will use the 'ground_truth' key to evaluate correctness. + message_log: list[list[dict[str, str]]]. A batch of OpenAI-API-like message logs that represent interactions with the LLM. + metadata: list[MathEnvironmentMetadata]. The grader will use the 'ground_truth' key to evaluate correctness. Returns: EnvironmentReturn: A tuple containing: - - List[Dict[str, str]]: Observations/responses batch - - List[Dict]: Updated metadata - - List[str]: Next stop strings for the next turn + - list[dict[str, str]]: Observations/responses batch + - list[dict]: Updated metadata + - list[str]: Next stop strings for the next turn - Tensor: Rewards tensor - Tensor: Done flags tensor """ @@ -193,7 +193,7 @@ def step( def global_post_process_and_metrics( self, batch: BatchedDataDict - ) -> Tuple[BatchedDataDict, dict]: + ) -> tuple[BatchedDataDict, dict]: """Computes metrics for this environment given a global rollout batch. Every rank will run this function, so you're free to use distributed diff --git a/nemo_rl/environments/utils.py b/nemo_rl/environments/utils.py index f6442151d9..0ea2bd637e 100644 --- a/nemo_rl/environments/utils.py +++ b/nemo_rl/environments/utils.py @@ -11,10 +11,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, List +from typing import Any -def chunk_list_to_workers(to_chunk: List[Any], num_workers: int) -> List[List[Any]]: +def chunk_list_to_workers(to_chunk: list[Any], num_workers: int) -> list[list[Any]]: """Chunk a list into a list of lists, where each sublist is assigned to a worker. Keeps ordering of elements. If the list is not divisible by the number of workers, the last worker may have fewer elements. diff --git a/nemo_rl/evals/eval.py b/nemo_rl/evals/eval.py index 46c87067a1..9ca1e90762 100644 --- a/nemo_rl/evals/eval.py +++ b/nemo_rl/evals/eval.py @@ -13,7 +13,7 @@ # limitations under the License. import os -from typing import Tuple, TypedDict +from typing import TypedDict import ray from torch.utils.data import DataLoader @@ -57,7 +57,7 @@ def setup( master_config: MasterConfig, tokenizer: AutoTokenizer, dataset: AllTaskProcessedDataset, -) -> Tuple[ +) -> tuple[ VllmGeneration, DataLoader, MasterConfig, diff --git a/nemo_rl/experience/rollouts.py b/nemo_rl/experience/rollouts.py index 567add0dfc..b860135843 100644 --- a/nemo_rl/experience/rollouts.py +++ b/nemo_rl/experience/rollouts.py @@ -15,7 +15,7 @@ # Generate rollouts for arbitrary environments # Supports multi-turn rollouts and many simultaneous environments (E.g. you can train on math, code, multi-turn games and more at once) -from typing import Any, Dict, List, Tuple +from typing import Any import ray import torch @@ -48,7 +48,7 @@ def generate_responses( input_lengths: torch.Tensor, include_logprobs: bool = True, greedy: bool = False, -) -> Tuple[BatchedDataDict[DatumSpec], List[torch.Tensor], dict]: +) -> tuple[BatchedDataDict[DatumSpec], list[torch.Tensor], dict]: """Generate responses from policy.""" # Add stop_strings to generation_input_data if present in the batch if "stop_strings" in batch: @@ -102,7 +102,7 @@ def generate_responses( def calculate_rewards( batch: BatchedDataDict[DatumSpec], - task_to_env: Dict[str, EnvironmentInterface], + task_to_env: dict[str, EnvironmentInterface], ) -> EnvironmentReturn: """Calculate rewards for generated responses and get environment feedback. @@ -200,11 +200,11 @@ def run_multi_turn_rollout( policy_generation: GenerationInterface, input_batch: BatchedDataDict[DatumSpec], tokenizer: AutoTokenizer, - task_to_env: Dict[str, EnvironmentInterface], + task_to_env: dict[str, EnvironmentInterface], max_seq_len: int, max_rollout_turns: int = 999999, greedy: bool = False, -) -> Tuple[BatchedDataDict[DatumSpec], Dict[str, Any]]: +) -> tuple[BatchedDataDict[DatumSpec], dict[str, Any]]: """Runs a multi-turn rollout loop, interacting with the environment. Args: diff --git a/nemo_rl/models/dtensor/parallelize.py b/nemo_rl/models/dtensor/parallelize.py index 940f0930ac..d30d101063 100644 --- a/nemo_rl/models/dtensor/parallelize.py +++ b/nemo_rl/models/dtensor/parallelize.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Union +from typing import Union import torch from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( @@ -410,7 +410,7 @@ def to_local_if_dtensor(tensor: Union[torch.Tensor, DTensor]) -> torch.Tensor: def clip_grad_by_total_norm_( - parameters: Union[List[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]], + parameters: Union[list[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]], max_grad_norm: Union[int, float], total_norm: float, dtype: torch.dtype = torch.float32, @@ -422,7 +422,7 @@ def clip_grad_by_total_norm_( Note that the gradients are modified in place. Args: - parameters (Union[List[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]]): + parameters (Union[list[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]]): An iterable of Tensors or DTensors, or a single Tensor or DTensor that will have gradients normalized. max_grad_norm (Union[float, int]): Maximum norm of the gradients. @@ -447,7 +447,7 @@ def clip_grad_by_total_norm_( def get_grad_norm( - parameters: Union[List[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]], + parameters: Union[list[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]], dp_group: torch.distributed.ProcessGroup, tp_group: torch.distributed.ProcessGroup, norm_type: Union[int, float] = 2, @@ -458,7 +458,7 @@ def get_grad_norm( Taken and modified from: https://github.com/NVIDIA/Megatron-LM/blob/a695b2bd2a0ca9ca63385a48c41a1c5a033cdd1e/megatron/core/optimizer/clip_grads.py#L51 Args: - parameters (Union[List[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]]): + parameters (Union[list[Union[torch.Tensor, DTensor]], Union[torch.Tensor, DTensor]]): An iterable of Tensors or DTensors, or a single Tensor or DTensor that will have gradient norm calculated. dp_group (torch.distributed.ProcessGroup): Process group for data parallel communication. diff --git a/nemo_rl/models/generation/interfaces.py b/nemo_rl/models/generation/interfaces.py index 17082143ea..98806d0c48 100644 --- a/nemo_rl/models/generation/interfaces.py +++ b/nemo_rl/models/generation/interfaces.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod -from typing import Any, List, Optional, Tuple, TypedDict, Union +from typing import Any, Optional, TypedDict, Union import torch from transformers import AutoTokenizer @@ -26,7 +26,7 @@ def verify_right_padding( ], pad_value: int = 0, raise_error: bool = True, -) -> Tuple[bool, Union[str, None]]: +) -> tuple[bool, Union[str, None]]: """Verify that a tensor is right-padded according to the provided lengths. Arguments: @@ -108,7 +108,7 @@ class GenerationConfig(TypedDict): top_p: float top_k: int model_name: str - stop_token_ids: List[int] + stop_token_ids: list[int] pad_token_id: int @@ -166,7 +166,7 @@ class GenerationDatumSpec(TypedDict): input_ids: torch.Tensor input_lengths: torch.Tensor - stop_strings: Optional[List[str]] = None + stop_strings: Optional[list[str]] = None __extra__: Any diff --git a/nemo_rl/models/generation/vllm.py b/nemo_rl/models/generation/vllm.py index 6e38f80008..5aabe82725 100644 --- a/nemo_rl/models/generation/vllm.py +++ b/nemo_rl/models/generation/vllm.py @@ -12,9 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +import copy import gc import os -from typing import List, Optional, TypedDict, Union +from typing import NotRequired, Optional, TypedDict, Union import ray import torch @@ -45,6 +46,7 @@ class VllmSpecificArgs(TypedDict): class VllmConfig(GenerationConfig): vllm_cfg: VllmSpecificArgs + vllm_kwargs: NotRequired[dict] @ray.remote @@ -158,7 +160,7 @@ def __init__( "vLLM is not installed. Please check that VllmGenerationWorker.DEFAULT_PY_EXECUTABLE covers the vllm dependency. " "If you are working interactively, you can install by running `uv sync --extra vllm` anywhere in the repo." ) - vllm_kwargs = self.cfg.get("vllm_kwargs", {}).copy() + vllm_kwargs: dict = copy.deepcopy(self.cfg.get("vllm_kwargs", {})) # Special handling for tensor parallel case if self.tensor_parallel_size > 1: @@ -172,6 +174,7 @@ def __init__( ) # Set bundle indices for tensor parallelism workers + assert bundle_indices is not None os.environ["VLLM_RAY_BUNDLE_INDICES"] = ",".join(map(str, bundle_indices)) # Use Ray for distributed execution in TP mode @@ -238,7 +241,7 @@ def generate( input_ids = data["input_ids"] input_lengths = data["input_lengths"] # this function requires all generations have the same stop strings, so we collect all here - batch_stop_strings = data.get("stop_strings", []) + batch_stop_strings: list = data.get("stop_strings", []) stop_strings = set() for sample_stop_strings in batch_stop_strings: if sample_stop_strings: @@ -405,7 +408,9 @@ def generate_text( texts = [output.outputs[0].text for output in outputs] # Convert to BatchedDataDict - return_data = BatchedDataDict({"texts": texts}) + return_data: BatchedDataDict[GenerationOutputSpec] = BatchedDataDict( + {"texts": texts} + ) return return_data def shutdown(self): @@ -469,7 +474,7 @@ def __init__( cluster: RayVirtualCluster, config: VllmConfig, name_prefix: str = "vllm_policy", - workers_per_node: Optional[Union[int, List[int]]] = None, + workers_per_node: Optional[Union[int, list[int]]] = None, ): """Initialize a vLLM policy with distributed workers.""" # Store config @@ -565,7 +570,7 @@ def generate( results = self.worker_group.get_all_worker_results(future_bundle) # Combine results from all tied worker groups - combined = BatchedDataDict.from_batches( + combined: BatchedDataDict[GenerationOutputSpec] = BatchedDataDict.from_batches( results, pad_value_dict={"output_ids": self.cfg["pad_token_id"]} ) @@ -608,7 +613,7 @@ def generate_text( results = self.worker_group.get_all_worker_results(future_bundle) # Combine results from all tied worker groups - combined = BatchedDataDict.from_batches( + combined: BatchedDataDict[GenerationOutputSpec] = BatchedDataDict.from_batches( results, pad_value_dict={"output_ids": self.cfg["pad_token_id"]} ) diff --git a/nemo_rl/models/policy/__init__.py b/nemo_rl/models/policy/__init__.py index 04dad1b4eb..7a6869f273 100644 --- a/nemo_rl/models/policy/__init__.py +++ b/nemo_rl/models/policy/__init__.py @@ -35,6 +35,14 @@ class PytorchOptimizerConfig(TypedDict): kwargs: dict[str, Any] +class SinglePytorchSchedulerConfig(TypedDict): + name: str + kwargs: dict[str, Any] + + +SchedulerMilestones = dict[str, list[int]] + + class PolicyConfig(TypedDict): model_name: str tokenizer: TokenizerConfig @@ -52,3 +60,6 @@ class PolicyConfig(TypedDict): activation_checkpointing_enabled: bool refit_buffer_size_gb: int optimizer: NotRequired[PytorchOptimizerConfig] = None + scheduler: NotRequired[list[SinglePytorchSchedulerConfig] | SchedulerMilestones] = ( + None + ) diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index c55b50a4e5..0f02374470 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -15,8 +15,8 @@ import gc import os from collections import defaultdict -from contextlib import contextmanager, nullcontext -from typing import Any, Dict, Iterable, Optional, Tuple, Union +from contextlib import AbstractContextManager, contextmanager, nullcontext +from typing import Any, Iterable, Optional, Union import ray import torch @@ -73,20 +73,20 @@ def unshard_fsdp2_model(model: nn.Module): @torch.no_grad() def get_cpu_state_dict( - state_generator: Iterable[Tuple[str, Union[torch.Tensor, DTensor]]], + state_generator: Iterable[tuple[str, Union[torch.Tensor, DTensor]]], pin_memory: bool = False, -) -> Dict[str, torch.Tensor]: +) -> dict[str, torch.Tensor]: """Copy the state dict generator to CPU memory. Args: - state_generator (Iterable[Tuple[str, Union[torch.Tensor, DTensor]]]): + state_generator (Iterable[tuple[str, Union[torch.Tensor, DTensor]]]): An iterable that yields (key, tensor) pairs from a model state. pin_memory (bool, optional): Whether to allocate the CPU tensors in pinned memory for faster GPU transfer. Defaults to False. Returns: - Dict[str, torch.Tensor]: A dictionary mapping parameter names to CPU tensors. + dict[str, torch.Tensor]: A dictionary mapping parameter names to CPU tensors. """ new_state_dict = {} for k, v in state_generator: @@ -231,7 +231,7 @@ def __init__( "unknown scheduler config: ", scheduler_cfg, ) - milestones = scheduler_cfg["milestones"] + milestones: list[int] = scheduler_cfg["milestones"] self.scheduler = torch.optim.lr_scheduler.SequentialLR( self.optimizer, schedulers, milestones @@ -268,7 +268,7 @@ def train( eval_mode: bool = False, gbs: Optional[int] = None, mbs: Optional[int] = None, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: """Train the policy on a batch of data with a given loss function.""" skip_tie_check = os.environ.get("NRL_SKIP_TIED_WEIGHT_CHECK") if ( @@ -285,11 +285,11 @@ def train( if mbs is None: mbs = self.cfg["train_micro_batch_size"] local_gbs = gbs // self.dp_size - dataset_size = data.get("input_ids").shape[0] + dataset_size = data["input_ids"].shape[0] num_global_batches = dataset_size // local_gbs if eval_mode: - ctx = torch.no_grad() + ctx: AbstractContextManager[Any] = torch.no_grad() self.model.eval() else: ctx = nullcontext() @@ -341,9 +341,9 @@ def train( num_microbatches = min(local_gbs, dataset_size - gb_start) // mbs for mb in global_batch.make_microbatch_iterator(mbs): - input_ids = mb.get("input_ids").cuda() + input_ids = mb["input_ids"].cuda() - input_lengths = mb.get("input_lengths") + input_lengths = mb["input_lengths"] batch_size, seq_len = input_ids.shape attention_mask = torch.zeros( @@ -407,7 +407,7 @@ def train( mb_losses.append(loss.item()) all_mb_metrics.append(loss_metrics) - grad_norm = None + grad_norm: Optional[float | torch.Tensor] = None if not eval_mode: with torch.no_grad(): grad_norm = get_grad_norm( @@ -455,7 +455,7 @@ def train( return metrics def get_logprobs( - self, data: BatchedDataDict, micro_batch_size: int = None + self, data: BatchedDataDict, micro_batch_size: Optional[int] = None ) -> BatchedDataDict[LogprobOutputSpec]: """Get the logprobs of the model for a batch of data. @@ -480,12 +480,12 @@ def get_logprobs( with unshard_fsdp2_model(self.model), torch.no_grad(): data.to("cuda") for lp_batch in data.make_microbatch_iterator(logprob_batch_size): - input_ids = lp_batch.get("input_ids") + input_ids = lp_batch["input_ids"] batch_size, seq_len = input_ids.shape # Create attention mask - input_lengths = lp_batch.get("input_lengths") + input_lengths = lp_batch["input_lengths"] # Create attention mask for right-padded data attention_mask = torch.zeros( @@ -548,7 +548,7 @@ def get_logprobs( all_log_probs.append(token_logprobs) # Concatenate all batches - return_data = BatchedDataDict() + return_data = BatchedDataDict[LogprobOutputSpec]() return_data["logprobs"] = torch.cat(all_log_probs, dim=0).cpu() return return_data @@ -589,7 +589,7 @@ def use_reference_model(self): val.copy_(curr_buffers[k]) def get_reference_policy_logprobs( - self, data: BatchedDataDict, micro_batch_size: int = None + self, data: BatchedDataDict, micro_batch_size: Optional[int] = None ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get the logprobs from the reference policy for a batch of data. @@ -601,7 +601,7 @@ def get_reference_policy_logprobs( with self.use_reference_model(): reference_logprobs = self.get_logprobs(data, micro_batch_size) - return_data = BatchedDataDict() + return_data = BatchedDataDict[ReferenceLogprobOutputSpec]() return_data["reference_logprobs"] = reference_logprobs["logprobs"].cpu() return return_data @@ -640,9 +640,13 @@ def prepare_weights_for_ipc(self): return state_dict_info @torch.no_grad() - def get_weights_ipc_handles(self, keys): + def get_weights_ipc_handles(self, keys: Iterable[str]): from torch.multiprocessing.reductions import reduce_tensor + assert self._held_sharded_state_dict_reference is not None, ( + "prepare_weights_for_ipc must be called before get_weights_ipc_handles" + ) + converted_params = {} for key in keys: # Get full_tensor for dtensor (GPU > 1) diff --git a/nemo_rl/models/policy/fsdp1_policy_worker.py b/nemo_rl/models/policy/fsdp1_policy_worker.py index c09eddef81..87a5209527 100644 --- a/nemo_rl/models/policy/fsdp1_policy_worker.py +++ b/nemo_rl/models/policy/fsdp1_policy_worker.py @@ -17,7 +17,7 @@ import warnings from collections import defaultdict from contextlib import contextmanager, nullcontext -from typing import Any, Dict, Optional +from typing import Any, Optional import ray import torch @@ -43,6 +43,10 @@ verify_right_padding, ) from nemo_rl.models.policy import PolicyConfig +from nemo_rl.models.policy.interfaces import ( + LogprobOutputSpec, + ReferenceLogprobOutputSpec, +) from nemo_rl.models.policy.utils import ( get_gpu_info, import_class_from_path, @@ -196,7 +200,7 @@ def do_fsdp(model): "unknown scheduler config: ", scheduler_cfg, ) - milestones = scheduler_cfg["milestones"] + milestones: list[int] = scheduler_cfg["milestones"] self.scheduler = torch.optim.lr_scheduler.SequentialLR( self.optimizer, schedulers, milestones @@ -236,7 +240,7 @@ def train( eval_mode: bool = False, gbs: Optional[int] = None, mbs: Optional[int] = None, - ) -> Dict[str, Any]: + ) -> dict[str, Any]: """Train the policy on a batch of data with a given loss function.""" # Check if the model has tied weights skip_tie_check = os.environ.get("NRL_SKIP_TIED_WEIGHT_CHECK") @@ -250,7 +254,7 @@ def train( if mbs is None: mbs = self.cfg["train_micro_batch_size"] local_gbs = gbs // torch.distributed.get_world_size() - dataset_size = data.get("input_ids").shape[0] + dataset_size = data["input_ids"].shape[0] num_global_batches = dataset_size // local_gbs if eval_mode: @@ -302,9 +306,9 @@ def train( num_microbatches = min(local_gbs, dataset_size - gb_start) // mbs for mb in global_batch.make_microbatch_iterator(mbs): - input_ids = mb.get("input_ids") + input_ids = mb["input_ids"] - input_lengths = mb.get("input_lengths") + input_lengths = mb["input_lengths"] batch_size, seq_len = input_ids.shape attention_mask = torch.ones( (batch_size, seq_len), dtype=torch.long, device=input_ids.device @@ -355,7 +359,7 @@ def train( all_mb_metrics.append(loss_metrics) # Clip gradients - grad_norm = None + grad_norm: Optional[torch.Tensor] = None if not eval_mode: if isinstance(self.model, FullyShardedDataParallel): # when using FSDP1, use FSDP's clip_grad_norm_ @@ -398,8 +402,8 @@ def train( return metrics def get_logprobs( - self, data: BatchedDataDict, micro_batch_size: int = None - ) -> BatchedDataDict: + self, data: BatchedDataDict, micro_batch_size: Optional[int] = None + ) -> BatchedDataDict[LogprobOutputSpec]: """Get the logprobs of the model for a batch of data. If no micro-batch size is provided, uses the configured logprob_batch_size to do microbatching. @@ -424,11 +428,11 @@ def get_logprobs( with torch.no_grad(): data.to("cuda") for lp_batch in data.make_microbatch_iterator(logprob_batch_size): - input_ids = lp_batch.get("input_ids") + input_ids = lp_batch["input_ids"] batch_size, seq_len = input_ids.shape # Create attention mask - input_lengths = lp_batch.get("input_lengths") + input_lengths = lp_batch["input_lengths"] # Create attention mask for right-padded data attention_mask = torch.zeros( @@ -473,7 +477,7 @@ def get_logprobs( all_log_probs.append(token_logprobs) # Concatenate all batches - return_data = BatchedDataDict() + return_data = BatchedDataDict[LogprobOutputSpec]() return_data["logprobs"] = torch.cat(all_log_probs, dim=0).cpu() return return_data @@ -511,8 +515,8 @@ def use_reference_model(self): torch.cuda.empty_cache() def get_reference_policy_logprobs( - self, data: BatchedDataDict, micro_batch_size: int = None - ) -> BatchedDataDict: + self, data: BatchedDataDict, micro_batch_size: Optional[int] = None + ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get the logprobs from the reference policy for a batch of data. Returns: @@ -523,7 +527,7 @@ def get_reference_policy_logprobs( with self.use_reference_model(): reference_logprobs = self.get_logprobs(data, micro_batch_size) - return_data = BatchedDataDict() + return_data = BatchedDataDict[ReferenceLogprobOutputSpec]() return_data["reference_logprobs"] = reference_logprobs["logprobs"].cpu() return return_data @@ -564,6 +568,9 @@ def generate( ): # Get generation config from self.cfg generation_batch_size = self.cfg["generation_batch_size"] + assert self.cfg["generation"] is not None, ( + "Generation config is not set while trying to generate" + ) gen_cfg = self.cfg["generation"] micro_batches = [] @@ -572,8 +579,8 @@ def generate( max_length = 0 for gen_batch in data.make_microbatch_iterator(generation_batch_size): # Create attention mask from input_lengths if needed for the model - input_ids = gen_batch.get("input_ids").cuda() - input_lengths = gen_batch.get("input_lengths").cuda() + input_ids = gen_batch["input_ids"].cuda() + input_lengths = gen_batch["input_lengths"].cuda() batch_size, seq_len = input_ids.shape # Convert right padding to left padding @@ -650,17 +657,19 @@ def generate( micro_batches.append(mb) # Get lengths, pad, and concatenate all batches - return_data = BatchedDataDict.from_batches( - micro_batches, - pad_value_dict={ - "left_padded_output_ids": self.cfg["generation"]["pad_token_id"] - }, + return_data: BatchedDataDict[GenerationOutputSpec] = ( + BatchedDataDict.from_batches( + micro_batches, + pad_value_dict={ + "left_padded_output_ids": self.cfg["generation"]["pad_token_id"] + }, + ) ) # Calculate the lengths of generations for each sequence by finding stop tokens generation_lengths = [] unpadded_sequence_lengths = [] - input_length = data.get("input_ids").size(1) + input_length = data["input_ids"].size(1) # Convert left-padded outputs back to right-padded format batch_size = len(return_data["left_padded_output_ids"]) @@ -824,6 +833,10 @@ def get_weights_ipc_handles(self, keys): from torch.distributed.tensor import DTensor from torch.multiprocessing.reductions import reduce_tensor + assert self._held_sharded_state_dict_reference is not None, ( + "prepare_weights_for_ipc must be called before get_weights_ipc_handles" + ) + converted_params = {} for key in keys: # Get full_tensor for dtensor (GPU > 1) diff --git a/nemo_rl/models/policy/hf_policy.py b/nemo_rl/models/policy/hf_policy.py index dbba94bc1d..a45f3cb335 100644 --- a/nemo_rl/models/policy/hf_policy.py +++ b/nemo_rl/models/policy/hf_policy.py @@ -13,7 +13,7 @@ # limitations under the License. import os from collections import defaultdict -from typing import List, Optional, Type, Union +from typing import Optional, Type, Union import ray from transformers import AutoTokenizer @@ -44,7 +44,7 @@ def __init__( config: PolicyConfig, tokenizer: AutoTokenizer, name_prefix: str = "hf_policy", - workers_per_node: Optional[Union[int, List[int]]] = None, + workers_per_node: Optional[Union[int, list[int]]] = None, init_optimizer: bool = True, weights_path: Optional[str] = None, optimizer_path: Optional[str] = None, diff --git a/nemo_rl/models/policy/interfaces.py b/nemo_rl/models/policy/interfaces.py index 0764e0ffa1..162950749b 100644 --- a/nemo_rl/models/policy/interfaces.py +++ b/nemo_rl/models/policy/interfaces.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod -from typing import Any, Dict, TypedDict +from typing import Any, TypedDict import torch @@ -67,7 +67,7 @@ def get_reference_policy_logprobs( pass @abstractmethod - def train(self, data: BatchedDataDict, loss_fn: LossFunction) -> Dict[str, Any]: + def train(self, data: BatchedDataDict, loss_fn: LossFunction) -> dict[str, Any]: """Train the policy on a global batch of data. Args: diff --git a/nemo_rl/utils/checkpoint.py b/nemo_rl/utils/checkpoint.py index 5e1efe2f11..1ab2bf956f 100644 --- a/nemo_rl/utils/checkpoint.py +++ b/nemo_rl/utils/checkpoint.py @@ -22,7 +22,7 @@ import os import shutil from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple, TypedDict +from typing import Any, Optional, TypedDict import numpy as np import torch @@ -83,8 +83,8 @@ def __init__(self, config: CheckpointingConfig): def init_tmp_checkpoint( self, step: int, - training_info: Dict[str, Any], - run_config: Optional[Dict[str, Any]] = None, + training_info: dict[str, Any], + run_config: Optional[dict[str, Any]] = None, ) -> os.PathLike: """Initialize a temporary checkpoint directory. @@ -96,8 +96,8 @@ def init_tmp_checkpoint( Args: step (int): The training step number. - training_info (Dict[str, Any]): Dictionary containing training metrics and info. - run_config (Optional[Dict[str, Any]]): Optional configuration for the training run. + training_info (dict[str, Any]): Dictionary containing training metrics and info. + run_config (Optional[dict[str, Any]]): Optional configuration for the training run. Returns: os.PathLike: Path to the temporary checkpoint directory. @@ -214,13 +214,13 @@ def get_best_checkpoint_path(self) -> Optional[str]: ) return str(checkpoint_history[0][1]) - def get_latest_checkpoint_path(self) -> str: + def get_latest_checkpoint_path(self) -> Optional[str]: """Get the path to the latest checkpoint. Returns the path to the checkpoint with the highest step number. Returns: - str: Path to the latest checkpoint, or None if no checkpoints exist. + Optional[str]: Path to the latest checkpoint, or None if no checkpoints exist. """ # find checkpoint directory with highest step number step_dirs = glob.glob(str(self.checkpoint_dir / "step_*")) @@ -231,7 +231,7 @@ def get_latest_checkpoint_path(self) -> str: def load_training_info( self, checkpoint_path: Optional[os.PathLike] = None - ) -> Dict[str, Any]: + ) -> Optional[dict[str, Any]]: """Load the training info from a checkpoint. Args: @@ -239,7 +239,7 @@ def load_training_info( returns None. Returns: - Dict[str, Any]: Dictionary containing the training info, or None if + Optional[dict[str, Any]]: Dictionary containing the training info, or None if checkpoint_path is None. """ if checkpoint_path is None: @@ -250,17 +250,17 @@ def load_training_info( def _load_checkpoint_history( checkpoint_dir: Path, -) -> List[Tuple[int, os.PathLike, Dict[str, Any]]]: +) -> list[tuple[int, str | os.PathLike, dict[str, Any]]]: """Load the history of checkpoints and their metrics. Args: checkpoint_dir (Path): Directory containing the checkpoints. Returns: - List[Tuple[int, os.PathLike, Dict[str, Any]]]: List of tuples containing + list[tuple[int, os.PathLike, dict[str, Any]]]: List of tuples containing (step_number, checkpoint_path, info) for each checkpoint. """ - checkpoint_history: List[Tuple[int, os.PathLike, Dict[str, Any]]] = [] + checkpoint_history: list[tuple[int, str | os.PathLike, dict[str, Any]]] = [] # Find all step directories step_dirs = glob.glob(str(checkpoint_dir / "step_*")) @@ -269,7 +269,7 @@ def _load_checkpoint_history( info_file = Path(step_dir) / "training_info.json" if info_file.exists(): with open(info_file) as f: - info = json.load(f) + info: dict[str, Any] = json.load(f) step = int(Path(step_dir).name.split("_")[1]) checkpoint_history.append((step, step_dir, info)) diff --git a/nemo_rl/utils/config.py b/nemo_rl/utils/config.py index 82c4be462b..48e2c711c4 100644 --- a/nemo_rl/utils/config.py +++ b/nemo_rl/utils/config.py @@ -13,7 +13,7 @@ # limitations under the License. from pathlib import Path -from typing import Optional, Union +from typing import Optional, Union, cast from hydra._internal.config_loader_impl import ConfigLoaderImpl from hydra.core.override_parser.overrides_parser import OverridesParser @@ -46,6 +46,9 @@ def load_config_with_inheritance( base_dir = Path(base_dir) config = OmegaConf.load(config_path) + assert isinstance(config, DictConfig), ( + "Config must be a Dictionary Config (List Config not supported)" + ) # Handle inheritance if "defaults" in config: @@ -60,10 +63,10 @@ def load_config_with_inheritance( for default in defaults: parent_path = resolve_path(base_dir, default) parent_config = load_config_with_inheritance(parent_path, base_dir) - base_config = OmegaConf.merge(base_config, parent_config) + base_config = cast(DictConfig, OmegaConf.merge(base_config, parent_config)) # Merge with current config - config = OmegaConf.merge(base_config, config) + config = cast(DictConfig, OmegaConf.merge(base_config, config)) return config diff --git a/nemo_rl/utils/logger.py b/nemo_rl/utils/logger.py index b38cbe1106..f9a445d8da 100644 --- a/nemo_rl/utils/logger.py +++ b/nemo_rl/utils/logger.py @@ -21,7 +21,7 @@ import threading import time from abc import ABC, abstractmethod -from typing import Any, Dict, List, Mapping, Optional, TypedDict +from typing import Any, Mapping, Optional, TypedDict import ray import requests @@ -72,7 +72,7 @@ class LoggerInterface(ABC): @abstractmethod def log_metrics( self, - metrics: Dict[str, Any], + metrics: dict[str, Any], step: int, prefix: Optional[str] = "", step_metric: Optional[str] = None, @@ -81,7 +81,7 @@ def log_metrics( pass @abstractmethod - def log_hyperparams(self, params: Dict[str, Any]) -> None: + def log_hyperparams(self, params: dict[str, Any]) -> None: """Log dictionary of hyperparameters.""" pass @@ -95,7 +95,7 @@ def __init__(self, cfg: TensorboardConfig, log_dir: Optional[str] = None): def log_metrics( self, - metrics: Dict[str, Any], + metrics: dict[str, Any], step: int, prefix: Optional[str] = "", step_metric: Optional[str] = None, # ignored in TensorBoard @@ -113,7 +113,7 @@ def log_metrics( name = f"{prefix}/{name}" self.writer.add_scalar(name, value, step) - def log_hyperparams(self, params: Dict[str, Any]) -> None: + def log_hyperparams(self, params: dict[str, Any]) -> None: """Log hyperparameters to Tensorboard. Args: @@ -147,7 +147,7 @@ def define_metric( def log_metrics( self, - metrics: Dict[str, Any], + metrics: dict[str, Any], step: int, prefix: Optional[str] = "", step_metric: Optional[str] = None, @@ -174,7 +174,7 @@ def log_metrics( else: self.run.log(metrics, step=step) - def log_hyperparams(self, params: Dict[str, Any]) -> None: + def log_hyperparams(self, params: dict[str, Any]) -> None: """Log hyperparameters to wandb. Args: @@ -185,7 +185,7 @@ def log_hyperparams(self, params: Dict[str, Any]) -> None: class GpuMetricSnapshot(TypedDict): step: int - metrics: Dict[str, Any] + metrics: dict[str, Any] class RayGpuMonitorLogger: @@ -284,7 +284,7 @@ def _collection_loop(self): ) time.sleep(self.collection_interval) # Continue despite errors - def _parse_gpu_metric(self, sample: Sample, node_idx: int) -> Dict[str, Any]: + def _parse_gpu_metric(self, sample: Sample, node_idx: int) -> dict[str, Any]: """Parse a GPU metric sample into a standardized format. Args: @@ -318,7 +318,7 @@ def _parse_gpu_metric(self, sample: Sample, node_idx: int) -> Dict[str, Any]: metric_name = f"node.{node_idx}.gpu.{index}.{metric_name}" return {metric_name: value} - def _parse_gpu_sku(self, sample: Sample, node_idx: int) -> Dict[str, str]: + def _parse_gpu_sku(self, sample: Sample, node_idx: int) -> dict[str, str]: """Parse a GPU metric sample into a standardized format. Args: @@ -352,7 +352,7 @@ def _parse_gpu_sku(self, sample: Sample, node_idx: int) -> Dict[str, str]: metric_name = f"node.{node_idx}.gpu.{index}.type" return {metric_name: value} - def _collect_gpu_sku(self) -> Dict[str, str]: + def _collect_gpu_sku(self) -> dict[str, str]: """Collect GPU SKU from all Ray nodes. Note: This is an internal API and users are not expected to call this. @@ -365,7 +365,7 @@ def _collect_gpu_sku(self) -> Dict[str, str]: # be the same return self._collect(sku=True) - def _collect_metrics(self) -> Dict[str, Any]: + def _collect_metrics(self) -> dict[str, Any]: """Collect GPU metrics from all Ray nodes. Returns: @@ -373,7 +373,7 @@ def _collect_metrics(self) -> Dict[str, Any]: """ return self._collect(metrics=True) - def _collect(self, metrics: bool = False, sku: bool = False) -> Dict[str, Any]: + def _collect(self, metrics: bool = False, sku: bool = False) -> dict[str, Any]: """Collect GPU metrics from all Ray nodes. Returns: @@ -545,7 +545,7 @@ def __init__(self, cfg: LoggerConfig): def log_metrics( self, - metrics: Dict[str, Any], + metrics: dict[str, Any], step: int, prefix: Optional[str] = "", step_metric: Optional[str] = None, @@ -572,7 +572,7 @@ def log_hyperparams(self, params: Mapping[str, Any]) -> None: logger.log_hyperparams(params) def log_batched_dict_as_jsonl( - self, to_log: BatchedDataDict | Dict[str, Any], filename: str + self, to_log: BatchedDataDict | dict[str, Any], filename: str ) -> None: """Log a list of dictionaries to a JSONL file. @@ -603,7 +603,7 @@ def __del__(self): self.gpu_monitor.stop() -def flatten_dict(d: Dict[str, Any], sep: str = ".") -> Dict[str, Any]: +def flatten_dict(d: dict[str, Any], sep: str = ".") -> dict[str, Any]: """Flatten a nested dictionary. Handles nested dictionaries and lists by creating keys with separators. @@ -690,8 +690,8 @@ def configure_rich_logging( def print_message_log_samples( - message_logs: List[LLMMessageLogType], - rewards: List[float], + message_logs: list[LLMMessageLogType], + rewards: list[float], num_samples: int = 5, step: int = 0, ) -> None: diff --git a/nemo_rl/utils/timer.py b/nemo_rl/utils/timer.py index 1f6c5d782e..1a028e1d44 100644 --- a/nemo_rl/utils/timer.py +++ b/nemo_rl/utils/timer.py @@ -13,7 +13,7 @@ # limitations under the License. import time from contextlib import contextmanager -from typing import Callable, Dict, List, Optional, Sequence, Union +from typing import Callable, Optional, Sequence, Union import numpy as np @@ -57,7 +57,7 @@ class Timer: """ # Define valid reduction types and their corresponding NumPy functions - _REDUCTION_FUNCTIONS: Dict[str, Callable[[Sequence[float]], float]] = { + _REDUCTION_FUNCTIONS: dict[str, Callable[[Sequence[float]], float]] = { "mean": np.mean, "median": np.median, "min": np.min, @@ -72,8 +72,8 @@ def __init__(self): # We store a list of times for each label rather than a single value # to support multiple timing runs with the same label (e.g., in loops) # This allows calculating reductions like mean, min, max, and std dev - self._timers: Dict[str, List[float]] = {} - self._start_times: Dict[str, float] = {} + self._timers: dict[str, list[float]] = {} + self._start_times: dict[str, float] = {} def start(self, label: str) -> None: """Start timing for the given label.""" @@ -121,7 +121,7 @@ def time(self, label: str): finally: self.stop(label) - def get_elapsed(self, label: str) -> List[float]: + def get_elapsed(self, label: str) -> list[float]: """Get all elapsed time measurements for a specific label. Args: @@ -193,8 +193,8 @@ def reduce(self, label: str, operation: str = "mean") -> float: return reduction_func(self._timers[label]) def get_timing_metrics( - self, reduction_op: Union[str, Dict[str, str]] = "mean" - ) -> Dict[str, float | List[float]]: + self, reduction_op: Union[str, dict[str, str]] = "mean" + ) -> dict[str, float | list[float]]: """Get all timing measurements with optional reduction. Args: diff --git a/pyproject.toml b/pyproject.toml index cfb2f3f507..8af0a2a070 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,10 @@ test = [ "pytest>=7.0.0", "pytest-timeout", "pytest-cov", + "mypy", +] +types = [ + "types-PyYAML", ] diff --git a/tests/check_metrics.py b/tests/check_metrics.py index 18f86c9936..a48c2f4875 100644 --- a/tests/check_metrics.py +++ b/tests/check_metrics.py @@ -15,7 +15,6 @@ import json import statistics import sys -from typing import Dict, Tuple from rich.console import Console from rich.table import Table @@ -37,7 +36,7 @@ def mean(value): return statistics.mean(float(v) for v in value.values()) -def evaluate_check(data: Dict, check: str) -> Tuple[bool, str, object]: +def evaluate_check(data: dict, check: str) -> tuple[bool, str, object]: """Evaluate a check against the data. Returns: diff --git a/tests/unit/algorithms/test_grpo.py b/tests/unit/algorithms/test_grpo.py index c0a24fee26..c81cf8a686 100644 --- a/tests/unit/algorithms/test_grpo.py +++ b/tests/unit/algorithms/test_grpo.py @@ -11,7 +11,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Tuple import pytest import ray @@ -28,12 +27,12 @@ @ray.remote(num_cpus=0) class MockEnvironment(EnvironmentInterface): - def __init__(self, rewards: List[float]): + def __init__(self, rewards: list[float]): self.rewards = rewards self._calls = 0 def step( - self, messages: List[LLMMessageLogType], env_info: List[dict] + self, messages: list[LLMMessageLogType], env_info: list[dict] ) -> EnvironmentReturn: self._calls += 1 return ( @@ -53,15 +52,15 @@ def reset_calls(self): def global_post_process_and_metrics( self, batch: BatchedDataDict - ) -> Tuple[BatchedDataDict, dict]: + ) -> tuple[BatchedDataDict, dict]: return batch, {} def create_mock_batch( num_samples: int, - task_names: List[str], - message_logs: List[LLMMessageLogType], - extra_env_info: List[dict] = None, + task_names: list[str], + message_logs: list[LLMMessageLogType], + extra_env_info: list[dict] = None, ) -> BatchedDataDict[DatumSpec]: """Helper function to create a mock batch for testing.""" if extra_env_info is None: diff --git a/tests/unit/data/test_llm_message_utils.py b/tests/unit/data/test_llm_message_utils.py index fc4c6c6b8d..3b0ddfd303 100644 --- a/tests/unit/data/test_llm_message_utils.py +++ b/tests/unit/data/test_llm_message_utils.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List import pytest import torch @@ -61,7 +60,7 @@ def multiple_messages_log() -> LLMMessageLogType: @pytest.fixture -def uneven_message_logs() -> List[LLMMessageLogType]: +def uneven_message_logs() -> list[LLMMessageLogType]: """Fixture for message logs of different lengths.""" return [ [ # First sequence (shorter) @@ -80,7 +79,7 @@ def uneven_message_logs() -> List[LLMMessageLogType]: @pytest.fixture -def raw_chat_message_log() -> List[LLMMessageLogType]: +def raw_chat_message_log() -> list[LLMMessageLogType]: """Fixture for chat message logs.""" return [ {"role": "system", "content": "You are a helpful assistant."}, @@ -90,7 +89,7 @@ def raw_chat_message_log() -> List[LLMMessageLogType]: @pytest.fixture -def tokenized_non_chat_message_log() -> List[LLMMessageLogType]: +def tokenized_non_chat_message_log() -> list[LLMMessageLogType]: return [ [ { @@ -104,7 +103,7 @@ def tokenized_non_chat_message_log() -> List[LLMMessageLogType]: @pytest.fixture -def tokenized_chat_message_log() -> List[LLMMessageLogType]: +def tokenized_chat_message_log() -> list[LLMMessageLogType]: return [ [ { @@ -278,7 +277,7 @@ def test_get_keys_from_messages() -> None: @pytest.mark.parametrize("make_sequence_length_divisible_by", [1, 8]) def test_batch_pad_message_log_divisible_by( - uneven_message_logs: List[LLMMessageLogType], make_sequence_length_divisible_by: int + uneven_message_logs: list[LLMMessageLogType], make_sequence_length_divisible_by: int ) -> None: """Test batch_pad_message_log padding to a multiple.""" result, input_lengths = batched_message_log_to_flat_message( @@ -293,7 +292,7 @@ def test_batch_pad_message_log_divisible_by( def test_batch_pad_message_log_basic( - uneven_message_logs: List[LLMMessageLogType], + uneven_message_logs: list[LLMMessageLogType], ) -> None: """Test batch_pad_message_log with right padding.""" result, input_lengths = batched_message_log_to_flat_message(uneven_message_logs) @@ -311,10 +310,10 @@ def test_batch_pad_message_log_basic( def test_batch_pad_message_log_custom_pad_value( - uneven_message_logs: List[LLMMessageLogType], + uneven_message_logs: list[LLMMessageLogType], ) -> None: """Test batch_pad_message_log with custom padding values.""" - pad_value_dict: Dict[str, int] = {"input_ids": -100} + pad_value_dict: dict[str, int] = {"input_ids": -100} result, input_lengths = batched_message_log_to_flat_message( uneven_message_logs, pad_value_dict=pad_value_dict ) @@ -442,7 +441,7 @@ def test_formatted_message_log_empty_message(): def test_add_loss_mask_to_chat_message_log( - tokenized_chat_message_log: LLMMessageLogType, + tokenized_chat_message_log: list[LLMMessageLogType], ): add_loss_mask_to_message_log( tokenized_chat_message_log, roles_to_train_on=["assistant"] diff --git a/tests/unit/test_envs.py b/tests/unit/test_envs.py index a46466979f..101b7a80f6 100644 --- a/tests/unit/test_envs.py +++ b/tests/unit/test_envs.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Dict, List, Optional, Tuple, TypedDict +from typing import Optional, TypedDict import ray import torch @@ -37,7 +37,7 @@ class _MultiStepCalculatorLogic: def __init__(self): pass - def _parse_tool_call(self, text: str) -> Optional[Tuple[float, float, str]]: + def _parse_tool_call(self, text: str) -> Optional[tuple[float, float, str]]: """Parses '[opA, opB, operation]'.""" # Use a more distinct tool call suffix tool_call_suffix = "" @@ -95,11 +95,11 @@ def process_turn( self, message_log: LLMMessageLogType, metadata: MultiStepCalcMetadata, - ) -> Tuple[ - Dict[str, str], + ) -> tuple[ + dict[str, str], float, bool, - Optional[List[str]], + Optional[list[str]], Optional[MultiStepCalcMetadata], ]: """Processes a single turn for the multi-step calculator task.""" @@ -182,13 +182,13 @@ class MultiStepCalculatorEnv(EnvironmentInterface): DEFAULT_PY_EXECUTABLE = PY_EXECUTABLES.SYSTEM """Multi-step calculator environment (Ray Actor).""" - def __init__(self, cfg: Optional[Dict] = None): + def __init__(self, cfg: Optional[dict] = None): self.logic = _MultiStepCalculatorLogic() def step( self, - message_log_batch: List[LLMMessageLogType], - metadata_batch: List[MultiStepCalcMetadata], + message_log_batch: list[LLMMessageLogType], + metadata_batch: list[MultiStepCalcMetadata], ) -> EnvironmentReturn: """Processes a batch of interactions using the calculator logic.""" futures = [ @@ -205,7 +205,7 @@ def step( all_next_metadata = [] for obs, rew, term, stops, meta in results: - observations.append(obs) # obs is already Dict[str, str] + observations.append(obs) # obs is already dict[str, str] rewards.append(rew) terminateds.append(term) all_stop_strings.append(stops) @@ -230,7 +230,7 @@ def shutdown(self): def global_post_process_and_metrics( self, batch: BatchedDataDict - ) -> Tuple[BatchedDataDict, dict]: + ) -> tuple[BatchedDataDict, dict]: # Example: could calculate success rate based on final reward final_rewards = batch.get( "total_reward", torch.tensor([0.0] * len(batch["idx"])) diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py index 83c5abff49..b45f25c0bd 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= "types-pyyaml" }, ] docs = [ { name = "myst-parser" }, @@ -1900,14 +1902,10 @@ docs = [ { name = "sphinx-copybutton" }, ] test = [ - { name = "mypy" }, { name = "pytest" }, { name = "pytest-cov" }, { name = "pytest-timeout" }, ] -types = [ - { name = "types-pyyaml" }, -] [package.metadata] requires-dist = [ @@ -1938,8 +1936,10 @@ build = [ { name = "torch", specifier = "==2.6.0" }, ] dev = [ + { name = "mypy" }, { name = "pre-commit", specifier = "==3.6.0" }, { name = "ruff", specifier = "==0.9.9" }, + { name = "types-pyyaml" }, ] docs = [ { name = "myst-parser" }, @@ -1950,12 +1950,10 @@ docs = [ { name = "sphinx-copybutton" }, ] test = [ - { name = "mypy" }, { name = "pytest", specifier = ">=7.0.0" }, { name = "pytest-cov" }, { name = "pytest-timeout" }, ] -types = [{ name = "types-pyyaml" }] [[package]] name = "nest-asyncio" From 89cf9cf48f2b968c2a4d10f8a6ada17b1e4db53a Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Sun, 11 May 2025 16:54:46 -0700 Subject: [PATCH 006/109] Down to 100 errors Signed-off-by: Sahil Jain --- mypy.ini | 5 ++- nemo_rl/algorithms/loss_functions.py | 50 +++++++++++++------------- nemo_rl/distributed/virtual_cluster.py | 19 ++++++---- nemo_rl/models/dtensor/parallelize.py | 6 ++-- nemo_rl/models/generation/vllm.py | 31 ++++++++++++---- pyproject.toml | 1 + uv.lock | 14 ++++++++ 7 files changed, 85 insertions(+), 41 deletions(-) diff --git a/mypy.ini b/mypy.ini index fa28d77a8e..3d391ea96c 100644 --- a/mypy.ini +++ b/mypy.ini @@ -9,4 +9,7 @@ ignore_missing_imports = True ignore_missing_imports = True [mypy-hydra.core.override_parser.*] -ignore_missing_imports = True \ No newline at end of file +ignore_missing_imports = True + +[mypy-datasets.*] +ignore_missing_imports = True \ No newline at end of file diff --git a/nemo_rl/algorithms/loss_functions.py b/nemo_rl/algorithms/loss_functions.py index f0d77aee3d..5637d8fa38 100644 --- a/nemo_rl/algorithms/loss_functions.py +++ b/nemo_rl/algorithms/loss_functions.py @@ -26,6 +26,8 @@ get_logprobs_from_vocab_parallel_logits, ) +Tensor = torch.Tensor + class LossType(enum.Enum): TOKEN_LEVEL = "token_level" @@ -45,13 +47,13 @@ class ClippedPGLossConfig(TypedDict): class ClippedPGLossDataDict(TypedDict): """Required keys for the Clipped Policy Gradient loss function.""" - input_ids: torch.Tensor - advantages: torch.Tensor - prev_logprobs: torch.Tensor - generation_logprobs: torch.Tensor - reference_policy_logprobs: torch.Tensor - token_mask: torch.Tensor - sample_mask: torch.Tensor + input_ids: Tensor + advantages: Tensor + prev_logprobs: Tensor + generation_logprobs: Tensor + reference_policy_logprobs: Tensor + token_mask: Tensor + sample_mask: Tensor __extra__: Any @@ -111,10 +113,10 @@ def __init__(self, cfg: ClippedPGLossConfig): def __call__( self, - next_token_logits: torch.Tensor, + next_token_logits: Tensor, data: BatchedDataDict[ClippedPGLossDataDict], total_valid_tokens_or_seqs: torch.Tensor, - ) -> tuple[torch.Tensor, dict]: + ) -> tuple[Tensor, dict]: """Clipped Policy Gradient RL loss function.""" token_mask = data["token_mask"][:, 1:] sample_mask = data["sample_mask"] @@ -289,12 +291,12 @@ class NLLLoss(LossFunction): def __call__( self, - next_token_logits: torch.Tensor, + next_token_logits: Tensor, data: BatchedDataDict, total_valid_tokens_or_seqs: torch.Tensor, dpo_loss: bool = False, dpo_average_log_probs: bool = False, - ) -> tuple[torch.Tensor, dict]: + ) -> tuple[Tensor, dict]: # logits shape: [batch_size, seq_len, vocab_size] # Get the next token logits for each position token_mask = data["token_mask"][:, 1:] @@ -352,10 +354,10 @@ class DPOLossConfig(TypedDict): class DPOLossDataDict(TypedDict): """Required keys for the DPO loss function.""" - input_ids: torch.Tensor - reference_policy_logprobs: torch.Tensor - token_mask: torch.Tensor - sample_mask: torch.Tensor + input_ids: Tensor + reference_policy_logprobs: Tensor + token_mask: Tensor + sample_mask: Tensor class DPOLossFn(LossFunction): @@ -424,15 +426,15 @@ def __init__(self, cfg: DPOLossConfig): self.loss_type = LossType.SEQUENCE_LEVEL - def split_output_tensor(self, tensor: torch.Tensor): + def split_output_tensor(self, tensor: Tensor): return tensor[::2], tensor[1::2] - def preference_loss( + def _preference_loss( self, - next_token_logits: torch.Tensor, + next_token_logits: Tensor, data: BatchedDataDict[DPOLossDataDict], - total_valid_tokens_or_seqs: torch.Tensor, - ) -> torch.Tensor: + total_valid_tokens_or_seqs: Tensor, + ) -> tuple[Tensor, Tensor, Tensor, Tensor]: ## TODO(@ashors): there's some duplicate code here with the NLLLoss function. We should refactor token_mask = data["token_mask"][:, 1:] sample_mask = data["sample_mask"] @@ -496,10 +498,10 @@ def preference_loss( def __call__( self, - next_token_logits: torch.Tensor, + next_token_logits: Tensor, data: BatchedDataDict[DPOLossDataDict], - total_valid_tokens_or_seqs: torch.Tensor, - ) -> tuple[torch.Tensor, dict]: + total_valid_tokens_or_seqs: Tensor, + ) -> tuple[Tensor, dict]: sft_loss_chosen = torch.tensor(0.0) if self.sft_loss_weight > 0: sft_loss, _ = self.sft_loss( @@ -521,7 +523,7 @@ def __call__( accuracy, rewards_chosen_mean, rewards_rejected_mean, - ) = self.preference_loss(next_token_logits, data, total_valid_tokens_or_seqs) + ) = self._preference_loss(next_token_logits, data, total_valid_tokens_or_seqs) dpo_loss = ( self.sft_loss_weight * sft_loss_chosen diff --git a/nemo_rl/distributed/virtual_cluster.py b/nemo_rl/distributed/virtual_cluster.py index 45f9a334bd..289f83bb35 100644 --- a/nemo_rl/distributed/virtual_cluster.py +++ b/nemo_rl/distributed/virtual_cluster.py @@ -18,7 +18,11 @@ from typing import Optional, TypedDict import ray -from ray.util.placement_group import placement_group, remove_placement_group +from ray.util.placement_group import ( + PlacementGroup, + placement_group, + remove_placement_group, +) from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy logging.basicConfig(level=logging.INFO) @@ -188,7 +192,7 @@ def __init__( """ self._bundle_ct_per_node_list = bundle_ct_per_node_list self._world_size = sum(self._bundle_ct_per_node_list) - self._node_placement_groups = None + self._node_placement_groups: Optional[list[PlacementGroup]] = None self.num_gpus_per_node = num_gpus_per_node self.use_gpus = use_gpus @@ -302,6 +306,9 @@ def get_placement_groups(self): Returns: List of placement groups that have at least one bundle """ + assert self._node_placement_groups is not None, ( + "Placement groups must be initialized before calling get_placement_groups" + ) return [pg for pg in self._node_placement_groups if pg.bundle_specs] def world_size(self): @@ -417,7 +424,7 @@ def _create_visualization_grid(self, worker_groups=None, is_global_view=False): # Initialize worker cells arrays (one per worker group) for i in range(len(worker_groups)): - node_row["worker_cells"].append([]) + node_row["worker_cells"].append([]) # type: ignore # Process each GPU position in the row for gpu_idx in range(max_gpus_per_node): @@ -435,10 +442,10 @@ def _create_visualization_grid(self, worker_groups=None, is_global_view=False): worker_cells = [" " * cell_width] * len(worker_groups) # Add cells to the row - node_row["gpu_cells"].append(gpu_cell) + node_row["gpu_cells"].append(gpu_cell) # type: ignore for i, cell in enumerate(worker_cells): - if i < len(node_row["worker_cells"]): - node_row["worker_cells"][i].append(cell) + if i < len(node_row["worker_cells"]): # type: ignore + node_row["worker_cells"][i].append(cell) # type: ignore # Add the completed row to the grid grid_data["rows"].append(node_row) diff --git a/nemo_rl/models/dtensor/parallelize.py b/nemo_rl/models/dtensor/parallelize.py index d30d101063..2084eb54f1 100644 --- a/nemo_rl/models/dtensor/parallelize.py +++ b/nemo_rl/models/dtensor/parallelize.py @@ -485,7 +485,7 @@ def get_grad_norm( # Calculate norm. if norm_type == torch.inf: - total_norm = max(grad.abs().max() for grad in grads_for_norm) + total_norm = max(grad.abs().max().item() for grad in grads_for_norm) total_norm_cuda = torch.tensor( [float(total_norm)], dtype=torch.float, device="cuda" ) @@ -503,7 +503,7 @@ def get_grad_norm( grad_norm = torch.norm(grad, norm_type) total_norm += grad_norm**norm_type - total_norm = total_norm.cuda() + total_norm = total_norm.cuda() # type: ignore # Sum across all data-parallel GPUs if using FSDP and then all model-parallel GPUs. torch.distributed.all_reduce( total_norm, op=torch.distributed.ReduceOp.SUM, group=dp_group @@ -511,7 +511,7 @@ def get_grad_norm( torch.distributed.all_reduce( total_norm, op=torch.distributed.ReduceOp.SUM, group=tp_group ) - total_norm = total_norm.item() ** (1.0 / norm_type) + total_norm = total_norm.item() ** (1.0 / norm_type) # type: ignore return total_norm diff --git a/nemo_rl/models/generation/vllm.py b/nemo_rl/models/generation/vllm.py index 5aabe82725..c9377e2a3b 100644 --- a/nemo_rl/models/generation/vllm.py +++ b/nemo_rl/models/generation/vllm.py @@ -203,9 +203,6 @@ def __init__( **vllm_kwargs, ) - def llm(self): - return self.llm - def is_alive(self): """Check if the worker is alive.""" return True @@ -242,7 +239,7 @@ def generate( input_lengths = data["input_lengths"] # this function requires all generations have the same stop strings, so we collect all here batch_stop_strings: list = data.get("stop_strings", []) - stop_strings = set() + stop_strings: set[str] = set() for sample_stop_strings in batch_stop_strings: if sample_stop_strings: stop_strings.update(sample_stop_strings) @@ -251,7 +248,7 @@ def generate( if self.cfg.get("stop_strings", None): stop_strings.update(self.cfg["stop_strings"]) - stop_strings = list(stop_strings) + stop_strings: list[str] = list(stop_strings) # verify inputs have correct padding verify_right_padding(data, pad_value=self.cfg["pad_token_id"]) @@ -289,6 +286,9 @@ def generate( ) # Generate outputs + assert self.llm is not None, ( + "Attempting to generate with either an uninitialized vLLM or non-model-owner" + ) outputs = self.llm.generate(prompts, sampling_params) # Process the outputs - but preserve the original input padding structure @@ -380,7 +380,7 @@ def generate_text( ) # This function requires all generations have the same stop strings, so we collect all here - stop_strings = set() + stop_strings: set[str] = set() for sample_stop_strings in batch_stop_strings: if sample_stop_strings: stop_strings.update(sample_stop_strings) @@ -389,7 +389,9 @@ def generate_text( if self.cfg.get("stop_strings", None): stop_strings.update(self.cfg["stop_strings"]) - stop_strings = list(stop_strings) if len(stop_strings) > 0 else None + stop_strings: Optional[list[str]] = ( + list(stop_strings) if len(stop_strings) > 0 else None + ) # Read generation parameters from config top_k = self.cfg["top_k"] if self.cfg["top_k"] is not None else -1 @@ -404,6 +406,9 @@ def generate_text( ) # Generate outputs + assert self.llm is not None, ( + "Attempting to generate with either an uninitialized vLLM or non-model-owner" + ) outputs = self.llm.generate(data["prompts"], sampling_params) texts = [output.outputs[0].text for output in outputs] @@ -430,6 +435,9 @@ def shutdown(self): return False def report_device_id(self) -> str: + assert self.llm is not None, ( + "Attempting to report device id with either an uninitialized vLLM or non-model-owner" + ) return self.llm.collective_rpc("report_device_id", args=tuple())[0] def update_weights_from_ipc_handles(self, ipc_handles): @@ -442,6 +450,9 @@ def update_weights_from_ipc_handles(self, ipc_handles): bool: True if weights were successfully updated, False otherwise. """ try: + assert self.llm is not None, ( + "Attempting to update weights with either an uninitialized vLLM or non-model-owner" + ) # Use collective_rpc to delegate to the UpdatableVllmInternalWorker implementation self.llm.collective_rpc( "update_weights_from_ipc_handles", args=(ipc_handles,) @@ -452,6 +463,9 @@ def update_weights_from_ipc_handles(self, ipc_handles): return False def sleep(self): + assert self.llm is not None, ( + "Attempting to sleep with either an uninitialized vLLM or non-model-owner" + ) # Reset the prefix cache to ensure that prefix cache is not reused after weights are updated self.llm.llm_engine.reset_prefix_cache() self.llm.sleep(level=1) @@ -459,6 +473,9 @@ def sleep(self): torch.cuda.empty_cache() def wake_up(self, **kwargs): + assert self.llm is not None, ( + "Attempting to wake up with either an uninitialized vLLM or non-model-owner" + ) # tags like ["weights", "kv_cache"] # We can call this function with just tags=["weights"] while doing refit to # avoid spiking memory with the kv_cache while the training fwk is awake. diff --git a/pyproject.toml b/pyproject.toml index 80bcb33ff4..6c78853d92 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,7 @@ dev = [ "ruff==0.9.9", "mypy", "types-PyYAML", + "types-requests", ] test = [ "pytest>=7.0.0", diff --git a/uv.lock b/uv.lock index eb5607b2ce..5e76ffdafe 100644 --- a/uv.lock +++ b/uv.lock @@ -1891,6 +1891,7 @@ dev = [ { name = "pre-commit" }, { name = "ruff" }, { name = "types-pyyaml" }, + { name = "types-requests" }, ] docs = [ { name = "myst-parser" }, @@ -1940,6 +1941,7 @@ dev = [ { name = "pre-commit", specifier = "==3.6.0" }, { name = "ruff", specifier = "==0.9.9" }, { name = "types-pyyaml" }, + { name = "types-requests" }, ] docs = [ { name = "myst-parser" }, @@ -4291,6 +4293,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ed/56/1fe61db05685fbb512c07ea9323f06ea727125951f1eb4dff110b3311da3/types_pyyaml-6.0.12.20250402-py3-none-any.whl", hash = "sha256:652348fa9e7a203d4b0d21066dfb00760d3cbd5a15ebb7cf8d33c88a49546681", size = 20329, upload-time = "2025-04-02T02:55:59.382Z" }, ] +[[package]] +name = "types-requests" +version = "2.32.0.20250328" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/00/7d/eb174f74e3f5634eaacb38031bbe467dfe2e545bc255e5c90096ec46bc46/types_requests-2.32.0.20250328.tar.gz", hash = "sha256:c9e67228ea103bd811c96984fac36ed2ae8da87a36a633964a21f199d60baf32", size = 22995, upload-time = "2025-03-28T02:55:13.271Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cc/15/3700282a9d4ea3b37044264d3e4d1b1f0095a4ebf860a99914fd544e3be3/types_requests-2.32.0.20250328-py3-none-any.whl", hash = "sha256:72ff80f84b15eb3aa7a8e2625fffb6a93f2ad5a0c20215fc1dcfa61117bcb2a2", size = 20663, upload-time = "2025-03-28T02:55:11.946Z" }, +] + [[package]] name = "typing-extensions" version = "4.12.2" From e8160c3048b7d775f4b0b8cf1185c45fdafb39b7 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Sun, 11 May 2025 16:56:20 -0700 Subject: [PATCH 007/109] Update pyproject Signed-off-by: Sahil Jain --- pyproject.toml | 6 +++--- uv.lock | 12 ++++++------ 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6c78853d92..b5d8b38a3c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,14 +59,14 @@ docs = [ dev = [ "pre-commit==3.6.0", "ruff==0.9.9", - "mypy", - "types-PyYAML", - "types-requests", ] test = [ "pytest>=7.0.0", "pytest-timeout", "pytest-cov", + "mypy", + "types-PyYAML", + "types-requests", ] diff --git a/uv.lock b/uv.lock index 5e76ffdafe..53dc2a6310 100644 --- a/uv.lock +++ b/uv.lock @@ -1887,11 +1887,8 @@ build = [ { name = "torch" }, ] dev = [ - { name = "mypy" }, { name = "pre-commit" }, { name = "ruff" }, - { name = "types-pyyaml" }, - { name = "types-requests" }, ] docs = [ { name = "myst-parser" }, @@ -1903,9 +1900,12 @@ docs = [ { name = "sphinx-copybutton" }, ] test = [ + { name = "mypy" }, { name = "pytest" }, { name = "pytest-cov" }, { name = "pytest-timeout" }, + { name = "types-pyyaml" }, + { name = "types-requests" }, ] [package.metadata] @@ -1937,11 +1937,8 @@ build = [ { name = "torch", specifier = "==2.6.0" }, ] dev = [ - { name = "mypy" }, { name = "pre-commit", specifier = "==3.6.0" }, { name = "ruff", specifier = "==0.9.9" }, - { name = "types-pyyaml" }, - { name = "types-requests" }, ] docs = [ { name = "myst-parser" }, @@ -1952,9 +1949,12 @@ docs = [ { name = "sphinx-copybutton" }, ] test = [ + { name = "mypy" }, { name = "pytest", specifier = ">=7.0.0" }, { name = "pytest-cov" }, { name = "pytest-timeout" }, + { name = "types-pyyaml" }, + { name = "types-requests" }, ] [[package]] From 6a494b9cbc1f6ede6bf4110f2b4ed69b696dde0d Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Sun, 11 May 2025 18:37:21 -0700 Subject: [PATCH 008/109] Down to 50 errors Signed-off-by: Sahil Jain --- examples/run_eval.py | 2 +- examples/run_grpo_math.py | 2 +- examples/run_grpo_sliding_puzzle.py | 2 +- nemo_rl/data/datasets.py | 21 ++++---- nemo_rl/data/interfaces.py | 9 ++++ nemo_rl/data/llm_message_utils.py | 52 ++++++++++++------- nemo_rl/distributed/batched_data_dict.py | 12 ++--- nemo_rl/distributed/worker_groups.py | 19 ++++--- nemo_rl/environments/interfaces.py | 3 +- nemo_rl/environments/math_environment.py | 4 +- nemo_rl/models/generation/__init__.py | 44 ++++++++++++++++ nemo_rl/models/generation/interfaces.py | 25 --------- nemo_rl/models/policy/__init__.py | 3 ++ nemo_rl/models/policy/fsdp1_policy_worker.py | 26 ++++++---- nemo_rl/utils/logger.py | 16 +++--- tests/unit/experience/test_rollouts.py | 2 +- .../models/generation/test_vllm_generation.py | 2 +- .../unit/models/policy/test_dtensor_worker.py | 2 +- tests/unit/models/policy/test_fsdp1_worker.py | 2 +- 19 files changed, 149 insertions(+), 99 deletions(-) diff --git a/examples/run_eval.py b/examples/run_eval.py index abe85bcca8..871910d2db 100644 --- a/examples/run_eval.py +++ b/examples/run_eval.py @@ -32,7 +32,7 @@ from nemo_rl.distributed.virtual_cluster import init_ray from nemo_rl.environments.math_environment import MathEnvironment from nemo_rl.evals.eval import MasterConfig, run_env_eval, setup -from nemo_rl.models.generation.interfaces import configure_generation_config +from nemo_rl.models.generation import configure_generation_config def parse_args(): diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index b8b6c4253a..2a2bdaeca2 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -37,7 +37,7 @@ from nemo_rl.distributed.virtual_cluster import init_ray from nemo_rl.environments.interfaces import EnvironmentInterface from nemo_rl.environments.math_environment import MathEnvironment -from nemo_rl.models.generation.interfaces import configure_generation_config +from nemo_rl.models.generation import configure_generation_config from nemo_rl.utils.config import load_config, parse_hydra_overrides from nemo_rl.utils.logger import get_next_experiment_dir diff --git a/examples/run_grpo_sliding_puzzle.py b/examples/run_grpo_sliding_puzzle.py index 9694d0a733..24f0df2cee 100644 --- a/examples/run_grpo_sliding_puzzle.py +++ b/examples/run_grpo_sliding_puzzle.py @@ -33,7 +33,7 @@ SlidingPuzzleGameLogic, SlidingPuzzleMetadata, ) -from nemo_rl.models.generation.interfaces import configure_generation_config +from nemo_rl.models.generation import configure_generation_config from nemo_rl.utils.config import load_config, parse_hydra_overrides from nemo_rl.utils.logger import get_next_experiment_dir diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index 6ee72705bf..62e679942b 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -18,6 +18,7 @@ from nemo_rl.data.interfaces import ( DatumSpec, + DPODatumSpec, TaskDataProcessFnCallable, TaskDataSpec, ) @@ -127,7 +128,7 @@ def rl_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: # Extract stop_strings if present stop_strings = [datum.get("stop_strings", None) for datum in data_batch] - output = BatchedDataDict( + output: BatchedDataDict = BatchedDataDict( message_log=message_log, length=length, loss_multiplier=loss_multiplier, @@ -183,7 +184,7 @@ def eval_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: extra_env_info = [datum_spec["extra_env_info"] for datum_spec in data_batch] idx = [datum_spec["idx"] for datum_spec in data_batch] - output = BatchedDataDict( + output: BatchedDataDict = BatchedDataDict( message_log=message_log, extra_env_info=extra_env_info, idx=idx, @@ -192,7 +193,7 @@ def eval_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: def dpo_collate_fn( - data_batch: list[DatumSpec], tokenizer, make_sequence_length_divisible_by: int + data_batch: list[DPODatumSpec], tokenizer, make_sequence_length_divisible_by: int ) -> BatchedDataDict: """Collate function for DPO training. @@ -214,15 +215,15 @@ def dpo_collate_fn( loss_multiplier.extend([datum_spec["loss_multiplier"]] * 2) idx.extend([datum_spec["idx"]] * 2) task_names.extend([datum_spec.get("task_name", None)] * 2) - length = torch.tensor(length) - loss_multiplier = torch.tensor(loss_multiplier) + length_batch: torch.Tensor = torch.tensor(length) + loss_multiplier_batch: torch.Tensor = torch.tensor(loss_multiplier) - batch_max_length = torch.ones_like(length) * length.max() + batch_max_length = torch.ones_like(length_batch) * length_batch.max() - batch = BatchedDataDict( + batch: BatchedDataDict = BatchedDataDict( message_log=message_log, - length=length, - loss_multiplier=loss_multiplier, + length=length_batch, + loss_multiplier=loss_multiplier_batch, task_name=task_names, idx=idx, batch_max_length=batch_max_length, @@ -245,7 +246,7 @@ def dpo_collate_fn( "input_ids": cat_and_padded["token_ids"], "input_lengths": input_lengths, "token_mask": cat_and_padded["token_loss_mask"], - "sample_mask": loss_multiplier, + "sample_mask": loss_multiplier_batch, } ) diff --git a/nemo_rl/data/interfaces.py b/nemo_rl/data/interfaces.py index 1781876ba6..779e8a35a8 100644 --- a/nemo_rl/data/interfaces.py +++ b/nemo_rl/data/interfaces.py @@ -36,6 +36,15 @@ class DatumSpec(TypedDict): __extra__: NotRequired[Any] # This allows additional fields of any type +class DPODatumSpec(TypedDict): + message_log_chosen: LLMMessageLogType + message_log_rejected: LLMMessageLogType + length_chosen: int + length_rejected: int + loss_multiplier: float + idx: int + + @dataclass class TaskDataSpec: task_name: Optional[str] = None diff --git a/nemo_rl/data/llm_message_utils.py b/nemo_rl/data/llm_message_utils.py index b452627afd..7947091b30 100644 --- a/nemo_rl/data/llm_message_utils.py +++ b/nemo_rl/data/llm_message_utils.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import warnings -from typing import cast +from typing import Optional, cast import torch from datasets import Dataset @@ -24,6 +24,8 @@ ) from nemo_rl.distributed.batched_data_dict import BatchedDataDict +Tensor = torch.Tensor + def message_log_to_flat_messages( message_log: LLMMessageLogType, @@ -81,7 +83,7 @@ def message_log_to_flat_messages( # Concatenate tensors for each key concat: FlatMessagesType = {} for key in result: - if result[key] and isinstance(result[key][0], torch.Tensor): + if result[key] and isinstance(result[key][0], Tensor): try: concat[key] = torch.cat(result[key]) except RuntimeError as e: @@ -127,22 +129,30 @@ def add_loss_mask_to_message_log( for i, message in enumerate(message_log): if only_unmask_final: if i == len(message_log) - 1: - message["token_loss_mask"] = torch.ones_like(message["token_ids"]) + message["token_loss_mask"] = torch.ones_like( + cast(Tensor, message["token_ids"]) + ) else: - message["token_loss_mask"] = torch.zeros_like(message["token_ids"]) + message["token_loss_mask"] = torch.zeros_like( + cast(Tensor, message["token_ids"]) + ) else: if message["role"] in roles_to_train_on: - message["token_loss_mask"] = torch.ones_like(message["token_ids"]) + message["token_loss_mask"] = torch.ones_like( + cast(Tensor, message["token_ids"]) + ) else: - message["token_loss_mask"] = torch.zeros_like(message["token_ids"]) + message["token_loss_mask"] = torch.zeros_like( + cast(Tensor, message["token_ids"]) + ) def _pad_tensor( - tensor: torch.Tensor, + tensor: Tensor, max_len: int, pad_side: str, pad_value: int = 0, -) -> torch.Tensor: +) -> Tensor: """Pad a tensor to the specified length. Args: @@ -169,7 +179,7 @@ def _pad_tensor( ) -def _validate_tensor_consistency(tensors: list[torch.Tensor]) -> None: +def _validate_tensor_consistency(tensors: list[Tensor]) -> None: """Validate that all tensors have consistent dtypes and devices. Args: @@ -194,9 +204,9 @@ def _validate_tensor_consistency(tensors: list[torch.Tensor]) -> None: def batched_message_log_to_flat_message( message_log_batch: list[LLMMessageLogType], - pad_value_dict: dict[str, int] = None, + pad_value_dict: Optional[dict[str, int]] = None, make_sequence_length_divisible_by: int = 1, -) -> tuple[BatchedDataDict[FlatMessagesType], torch.Tensor]: +) -> tuple[BatchedDataDict[FlatMessagesType], Tensor]: """Process and pad a batch of message logs for model input. For each message log in the batch: @@ -265,7 +275,7 @@ def batched_message_log_to_flat_message( tensor_keys = [] for seq in sequenced_lists: for key, value in seq.items(): - if isinstance(value, torch.Tensor): + if isinstance(value, Tensor): tensor_keys.append(key) max_len = max(max_len, value.size(0)) @@ -276,7 +286,7 @@ def batched_message_log_to_flat_message( # Handle non-tensor case if not tensor_keys: - result = BatchedDataDict( + result: BatchedDataDict[FlatMessagesType] = BatchedDataDict( { k: [seq[k][0] if k in seq else None for seq in sequenced_lists] for k in all_keys @@ -291,7 +301,7 @@ def batched_message_log_to_flat_message( # Use maximum here since there may be keys that aren't populated for all messages yet. # For example, logprobs don't get populated for non-generated tokens until post-processing. seq_len = max( - (v.size(0) for v in seq.values() if isinstance(v, torch.Tensor)), default=0 + (v.size(0) for v in seq.values() if isinstance(v, Tensor)), default=0 ) input_lengths.append(seq_len) input_lengths_tensor = torch.tensor(input_lengths, dtype=torch.int32) @@ -300,16 +310,18 @@ def batched_message_log_to_flat_message( result = BatchedDataDict() for key in all_keys: values = [seq.get(key) for seq in sequenced_lists] - if not values or not isinstance(values[0], torch.Tensor): + if not values or not isinstance(values[0], Tensor): result[key] = values continue # Filter out None values and validate consistency - tensors = [t for t in values if t is not None] + # casting bc we break on str cases above + values: list[Tensor | None] = cast(list[Tensor | None], values) + tensors = cast(list[Tensor], [t for t in values if t is not None]) _validate_tensor_consistency(tensors) # Create zero tensors for None values - values = [ + filled_values: list[Tensor] = [ ( torch.zeros(0, dtype=tensors[0].dtype, device=tensors[0].device) if v is None @@ -320,7 +332,7 @@ def batched_message_log_to_flat_message( # Pad and stack tensors (always right padding) pad_value = pad_value_dict.get(key, 0) if pad_value_dict else 0 - padded = [_pad_tensor(t, max_len, "right", pad_value) for t in values] + padded = [_pad_tensor(t, max_len, "right", pad_value) for t in filled_values] result[key] = torch.stack(padded) return result, input_lengths_tensor @@ -342,7 +354,7 @@ def message_log_shape(message_log: LLMMessageLogType) -> list[dict[str, list[int for message in message_log: shape = {} for k in message.keys(): - if isinstance(message[k], torch.Tensor): + if isinstance(message[k], Tensor): shape[k] = message[k].shape elif isinstance(message[k], list): shape[k] = [message_log_shape(v) for v in message[k]] @@ -422,7 +434,7 @@ def get_formatted_message_log( # if there is an empty message, the empty `token_ids` tensor ends up being in fp32, # which causes `_validate_tensor_consistency` to fail. To fix this, we convert the # empty tensor to int64. - new_message["token_ids"] = new_message["token_ids"].to(torch.int64) + new_message["token_ids"] = new_message["token_ids"].to(torch.int64) # type: ignore new_message["content"] = message_chunk new_message_log.append(new_message) diff --git a/nemo_rl/distributed/batched_data_dict.py b/nemo_rl/distributed/batched_data_dict.py index 6183a2a951..20ffce3fa0 100644 --- a/nemo_rl/distributed/batched_data_dict.py +++ b/nemo_rl/distributed/batched_data_dict.py @@ -86,7 +86,7 @@ def from_batches( return stacked_dict - def all_gather(self, group: torch.distributed.ProcessGroup) -> "BatchedDataDict": + def all_gather(self, group: torch.distributed.ProcessGroup) -> Self: """Gathers batches with possibly jagged leading dimensions across the DP ranks. If using reshard, it will treat PP as DP ranks. @@ -294,14 +294,14 @@ def slice(self, start: int, end: int) -> "SlicedDataDict": sliced_batch[k] = self.data[k][start:end] return sliced_batch - def repeat_interleave(self, num_repeats: int) -> "BatchedDataDict": + def repeat_interleave(self, num_repeats: int) -> Self: """Repeats the batch num_repeats times. For each element in the batch, repeat each value num_repeats times. i.e: {"key": torch.tensor([1, 2, 3]), "other_key": [1, 2, 3]} -> {"key": torch.tensor([1, 1, 2, 2, 3, 3]), "other_key": [1, 1, 2, 2, 3, 3]} """ - repeated_batch: Self = BatchedDataDict() + repeated_batch: Self = type(self)() for k, v in self.data.items(): if torch.is_tensor(v): # For tensors, use repeat_interleave to repeat each element @@ -343,9 +343,7 @@ def to(self, device: str | torch.device) -> Self: self.data[k] = v.to(device) return self - def select_indices( - self, indices: Union[list[int], torch.Tensor] - ) -> "BatchedDataDict": + def select_indices(self, indices: Union[list[int], torch.Tensor]) -> Self: """Selects specific rows from the batch based on indices. Args: @@ -354,7 +352,7 @@ def select_indices( Returns: BatchedDataDict: A new BatchedDataDict containing only the selected rows. """ - selected_batch = BatchedDataDict() + selected_batch: Self = type(self)() for k, v in self.data.items(): if torch.is_tensor(v): selected_batch[k] = v[indices] diff --git a/nemo_rl/distributed/worker_groups.py b/nemo_rl/distributed/worker_groups.py index 3251758472..df8de41865 100644 --- a/nemo_rl/distributed/worker_groups.py +++ b/nemo_rl/distributed/worker_groups.py @@ -34,7 +34,7 @@ class MultiWorkerFuture: used_workers: list[int] respect_tied_workers: bool = True - def get_results(self, worker_group): + def get_results(self, worker_group) -> list[Any]: """Get results from the futures, optionally respecting tied workers. When respect_tied_workers is True, this method deduplicates results by returning @@ -60,7 +60,7 @@ def get_results(self, worker_group): return all_results # Create tied worker sets based on used workers - active_tied_workers = {} + active_tied_workers: dict[int, list[int]] = {} for i, worker_idx in enumerate(self.used_workers): tied_worker_idx = worker_group.worker_to_tied_group_index.get(worker_idx) if tied_worker_idx is None: @@ -117,7 +117,7 @@ def __call__( # Set up worker arguments and resources worker_class = self.ray_actor_class worker_kwargs = dict(self.kwargs) - options = deepcopy(extra_options) + options: dict[str, Any] = deepcopy(extra_options) # Use the worker's configuration interface if available if hasattr(worker_class, "configure_worker"): @@ -162,13 +162,16 @@ def __call__( # local venv first and then replace the py_executable with the local venv's python. # The directory the venv will be created in is controlled by the env var # NEMO_RL_VENV_DIR and defaults to $GIT_ROOT/venvs/. + assert hasattr(worker_class, "__ray_actor_class__"), ( + "Worker class must be decorated with @ray.remote" + ) unwrapped_cls = worker_class.__ray_actor_class__ venv_python = create_local_venv( py_executable=options["runtime_env"]["py_executable"], venv_name=f"{unwrapped_cls.__module__}.{unwrapped_cls.__name__}", ) options["runtime_env"]["py_executable"] = venv_python - return worker_class.options(**options).remote(*self.args, **worker_kwargs) + return worker_class.options(**options).remote(*self.args, **worker_kwargs) # type: ignore class RayWorkerGroup: @@ -203,15 +206,15 @@ def __init__( Each tuple defines a tied group of workers placed on the same node. If provided, workers_per_node is ignored. """ - self._workers = [] - self._worker_metadata = [] + self._workers: list[ray.ObjectRef] = [] + self._worker_metadata: list[dict[str, Any]] = [] self.cluster = cluster self.name_prefix = name_prefix - self.tied_workers_groups = [] + self.tied_workers_groups: list[list[int]] = [] # Maps worker indices to their corresponding tied group index # For example, if worker with index 3 belongs to tied worker group 1, # then worker_to_tied_group_index[3] = 1 - self.worker_to_tied_group_index = {} + self.worker_to_tied_group_index: dict[int, int] = {} # If explicit bundle indices are provided, use those if bundle_indices_list is None: diff --git a/nemo_rl/environments/interfaces.py b/nemo_rl/environments/interfaces.py index 1cd7612745..16624ec456 100644 --- a/nemo_rl/environments/interfaces.py +++ b/nemo_rl/environments/interfaces.py @@ -16,7 +16,6 @@ from torch import Tensor -from nemo_rl.data.interfaces import LLMMessageLogType from nemo_rl.distributed.batched_data_dict import BatchedDataDict @@ -48,7 +47,7 @@ class EnvironmentInterface(abc.ABC): @abc.abstractmethod def step( self, - message_log_batch: list[LLMMessageLogType], + message_log_batch: list[list[dict[str, str]]], metadata: list[Optional[dict]], *args, **kwargs, diff --git a/nemo_rl/environments/math_environment.py b/nemo_rl/environments/math_environment.py index 2993ea9c6b..96e013b312 100644 --- a/nemo_rl/environments/math_environment.py +++ b/nemo_rl/environments/math_environment.py @@ -107,7 +107,7 @@ def __init__(self, cfg: MathEnvConfig): self.cfg = cfg self.num_workers = cfg["num_workers"] self.workers = [ - HFVerifyWorker.options( + HFVerifyWorker.options( # type: ignore # (decorated with @ray.remote) runtime_env={"py_executable": HFVerifyWorker.DEFAULT_PY_EXECUTABLE} ).remote() for _ in range(self.num_workers) @@ -118,7 +118,7 @@ def shutdown(self): for worker in self.workers: ray.kill(worker) - def step( + def step( # type: ignore[override] self, message_log_batch: list[list[dict[str, str]]], metadata: list[MathEnvironmentMetadata], diff --git a/nemo_rl/models/generation/__init__.py b/nemo_rl/models/generation/__init__.py index e69de29bb2..469d556f52 100644 --- a/nemo_rl/models/generation/__init__.py +++ b/nemo_rl/models/generation/__init__.py @@ -0,0 +1,44 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import cast + +from transformers import AutoTokenizer + +from nemo_rl.models.generation.interfaces import GenerationConfig +from nemo_rl.models.generation.vllm import VllmConfig + + +def configure_generation_config( + config: GenerationConfig, tokenizer: AutoTokenizer, is_eval=False +): + """Apply specific configurations to generation config.""" + # tokenizer setting + config["pad_token_id"] = tokenizer.pad_token_id + if config["stop_token_ids"] is None: + config["stop_token_ids"] = [tokenizer.eos_token_id] + + # vllm setting + if config["backend"] == "vllm": + config = cast(VllmConfig, config) + # set load_format + if is_eval: + config["vllm_cfg"]["load_format"] = "auto" + + # set skip_tokenizer_init + if is_eval or config["stop_strings"] is not None: + config["vllm_cfg"]["skip_tokenizer_init"] = False + else: + config["vllm_cfg"]["skip_tokenizer_init"] = True + + return config diff --git a/nemo_rl/models/generation/interfaces.py b/nemo_rl/models/generation/interfaces.py index 98806d0c48..1fcec2cf7e 100644 --- a/nemo_rl/models/generation/interfaces.py +++ b/nemo_rl/models/generation/interfaces.py @@ -15,7 +15,6 @@ from typing import Any, Optional, TypedDict, Union import torch -from transformers import AutoTokenizer from nemo_rl.distributed.batched_data_dict import BatchedDataDict @@ -112,30 +111,6 @@ class GenerationConfig(TypedDict): pad_token_id: int -def configure_generation_config( - config: GenerationConfig, tokenizer: AutoTokenizer, is_eval=False -): - """Apply specific configurations to generation config.""" - # tokenizer setting - config["pad_token_id"] = tokenizer.pad_token_id - if config["stop_token_ids"] is None: - config["stop_token_ids"] = [tokenizer.eos_token_id] - - # vllm setting - if config["backend"] == "vllm": - # set load_format - if is_eval: - config["vllm_cfg"]["load_format"] = "auto" - - # set skip_tokenizer_init - if is_eval or config["stop_strings"] is not None: - config["vllm_cfg"]["skip_tokenizer_init"] = False - else: - config["vllm_cfg"]["skip_tokenizer_init"] = True - - return config - - class GenerationDatumSpec(TypedDict): """Specification for input data required by generation models. diff --git a/nemo_rl/models/policy/__init__.py b/nemo_rl/models/policy/__init__.py index 7a6869f273..7ebb88a1db 100644 --- a/nemo_rl/models/policy/__init__.py +++ b/nemo_rl/models/policy/__init__.py @@ -51,6 +51,9 @@ class PolicyConfig(TypedDict): learning_rate: float logprob_batch_size: int generation: Optional[GenerationConfig] + generation_batch_size: NotRequired[ + int + ] # used in static batched (framework) generation precision: str dtensor_cfg: DTensorConfig make_sequence_length_divisible_by: int diff --git a/nemo_rl/models/policy/fsdp1_policy_worker.py b/nemo_rl/models/policy/fsdp1_policy_worker.py index 87a5209527..1a3e54aab4 100644 --- a/nemo_rl/models/policy/fsdp1_policy_worker.py +++ b/nemo_rl/models/policy/fsdp1_policy_worker.py @@ -16,7 +16,7 @@ import os import warnings from collections import defaultdict -from contextlib import contextmanager, nullcontext +from contextlib import AbstractContextManager, contextmanager, nullcontext from typing import Any, Optional import ray @@ -168,7 +168,7 @@ def do_fsdp(model): self.model = self.manual_load_to_gpu(self.model) # used for streaming update inference engine weights - self._held_sharded_state_dict_reference = None + self._held_sharded_state_dict_reference: Optional[dict[str, Any]] = None self._held_streamed_param_reference = None # register_fsdp_forward_method(self.model, "generate") @@ -258,7 +258,7 @@ def train( num_global_batches = dataset_size // local_gbs if eval_mode: - ctx = torch.no_grad() + ctx: AbstractContextManager = torch.no_grad() self.model.eval() else: ctx = nullcontext() @@ -359,23 +359,27 @@ def train( all_mb_metrics.append(loss_metrics) # Clip gradients - grad_norm: Optional[torch.Tensor] = None if not eval_mode: + if self.cfg["max_grad_norm"] is None: + max_grad_norm = 9999999999.0 + else: + max_grad_norm = self.cfg["max_grad_norm"] + if isinstance(self.model, FullyShardedDataParallel): # when using FSDP1, use FSDP's clip_grad_norm_ # to ensure grad norm is being computed over all parameters # see https://pytorch.org/docs/stable/fsdp.html#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_ - grad_norm = self.model.clip_grad_norm_( - max_norm=self.cfg["max_grad_norm"] - ) + grad_norm = self.model.clip_grad_norm_(max_norm=max_grad_norm) else: grad_norm = torch.nn.utils.clip_grad_norm_( - self.model.parameters(), max_norm=self.cfg["max_grad_norm"] + self.model.parameters(), max_norm=max_grad_norm ) grad_norm = grad_norm.cpu() # Update parameters self.optimizer.step() + else: + grad_norm = None losses.append(torch.tensor(mb_losses).sum().item()) # increment scheduler after all batches in rollout are processed @@ -598,7 +602,7 @@ def generate( left_padded_attention_mask[i, seq_len - length :] = 1 # this function requires all generations have the same stop strings, so we collect all here - batch_stop_strings = gen_batch.get("stop_strings", []) + batch_stop_strings: list[list[str]] = gen_batch.get("stop_strings", []) stop_strings = set() for sample_stop_strings in batch_stop_strings: if sample_stop_strings: @@ -608,7 +612,9 @@ def generate( if gen_cfg.get("stop_strings", None): stop_strings.update(gen_cfg["stop_strings"]) - stop_strings = list(stop_strings) if len(stop_strings) > 0 else None + stop_strings: list[str] | None = ( + list(stop_strings) if len(stop_strings) > 0 else None + ) if isinstance( self.model, torch.distributed.fsdp.FullyShardedDataParallel diff --git a/nemo_rl/utils/logger.py b/nemo_rl/utils/logger.py index f9a445d8da..188c1b690e 100644 --- a/nemo_rl/utils/logger.py +++ b/nemo_rl/utils/logger.py @@ -21,7 +21,7 @@ import threading import time from abc import ABC, abstractmethod -from typing import Any, Mapping, Optional, TypedDict +from typing import Any, Mapping, Optional, TypedDict, cast import ray import requests @@ -81,7 +81,7 @@ def log_metrics( pass @abstractmethod - def log_hyperparams(self, params: dict[str, Any]) -> None: + def log_hyperparams(self, params: Mapping[str, Any]) -> None: """Log dictionary of hyperparameters.""" pass @@ -113,7 +113,7 @@ def log_metrics( name = f"{prefix}/{name}" self.writer.add_scalar(name, value, step) - def log_hyperparams(self, params: dict[str, Any]) -> None: + def log_hyperparams(self, params: Mapping[str, Any]) -> None: """Log hyperparameters to Tensorboard. Args: @@ -174,7 +174,7 @@ def log_metrics( else: self.run.log(metrics, step=step) - def log_hyperparams(self, params: dict[str, Any]) -> None: + def log_hyperparams(self, params: Mapping[str, Any]) -> None: """Log hyperparameters to wandb. Args: @@ -217,7 +217,7 @@ def __init__( ] = [] # Store metrics with timestamps self.last_flush_time = time.time() self.is_running = False - self.collection_thread = None + self.collection_thread: Optional[threading.Thread] = None self.lock = threading.Lock() def start(self): @@ -501,7 +501,7 @@ def __init__(self, cfg: LoggerConfig): - gpu_collection_interval - gpu_flush_interval """ - self.loggers = [] + self.loggers: list[LoggerInterface] = [] self.wandb_logger = None self.base_log_dir = cfg["log_dir"] @@ -603,7 +603,7 @@ def __del__(self): self.gpu_monitor.stop() -def flatten_dict(d: dict[str, Any], sep: str = ".") -> dict[str, Any]: +def flatten_dict(d: Mapping[str, Any], sep: str = ".") -> dict[str, Any]: """Flatten a nested dictionary. Handles nested dictionaries and lists by creating keys with separators. @@ -827,7 +827,7 @@ def safe_render(content, role_color): # Format each message in the conversation message_parts = [] for msg in message_log: - role = msg.get("role", "unknown").upper() + role = cast(str, msg.get("role", "unknown")).upper() content = msg.get("content", "") # Choose color based on role - using muted, elegant colors diff --git a/tests/unit/experience/test_rollouts.py b/tests/unit/experience/test_rollouts.py index bcfa1b84d2..edf4651406 100644 --- a/tests/unit/experience/test_rollouts.py +++ b/tests/unit/experience/test_rollouts.py @@ -30,7 +30,7 @@ SlidingPuzzleMetadata, ) from nemo_rl.experience.rollouts import run_multi_turn_rollout -from nemo_rl.models.generation.interfaces import configure_generation_config +from nemo_rl.models.generation import configure_generation_config from nemo_rl.models.generation.vllm import VllmConfig, VllmGeneration from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.hf_policy import HfPolicy diff --git a/tests/unit/models/generation/test_vllm_generation.py b/tests/unit/models/generation/test_vllm_generation.py index c64ca1d096..de6e338910 100644 --- a/tests/unit/models/generation/test_vllm_generation.py +++ b/tests/unit/models/generation/test_vllm_generation.py @@ -23,7 +23,7 @@ from nemo_rl.algorithms.utils import get_tokenizer from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.distributed.virtual_cluster import RayVirtualCluster -from nemo_rl.models.generation.interfaces import configure_generation_config +from nemo_rl.models.generation import configure_generation_config from nemo_rl.models.generation.vllm import VllmConfig, VllmGeneration from nemo_rl.models.policy import PolicyConfig diff --git a/tests/unit/models/policy/test_dtensor_worker.py b/tests/unit/models/policy/test_dtensor_worker.py index c7bcee7f46..ec8d708300 100644 --- a/tests/unit/models/policy/test_dtensor_worker.py +++ b/tests/unit/models/policy/test_dtensor_worker.py @@ -28,7 +28,7 @@ from nemo_rl.algorithms.utils import get_tokenizer from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.distributed.virtual_cluster import RayVirtualCluster -from nemo_rl.models.generation.interfaces import configure_generation_config +from nemo_rl.models.generation import configure_generation_config from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.dtensor_policy_worker import DTensorPolicyWorker from nemo_rl.models.policy.hf_policy import HfPolicy diff --git a/tests/unit/models/policy/test_fsdp1_worker.py b/tests/unit/models/policy/test_fsdp1_worker.py index e3b90b979f..e31bd90a4e 100644 --- a/tests/unit/models/policy/test_fsdp1_worker.py +++ b/tests/unit/models/policy/test_fsdp1_worker.py @@ -25,7 +25,7 @@ from nemo_rl.algorithms.utils import get_tokenizer from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.distributed.virtual_cluster import RayVirtualCluster -from nemo_rl.models.generation.interfaces import configure_generation_config +from nemo_rl.models.generation import configure_generation_config from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.hf_policy import HfPolicy from tests.unit.test_utils import SimpleLoss, SimpleNLLLoss From 8a95964261cc091e72e660c9f0210f91847470fe Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Sun, 11 May 2025 22:34:19 -0700 Subject: [PATCH 009/109] Added testing doc Signed-off-by: Sahil Jain --- docs/testing.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/docs/testing.md b/docs/testing.md index 35825ab50d..583123f6a0 100644 --- a/docs/testing.md +++ b/docs/testing.md @@ -133,3 +133,19 @@ in Docker with this script: ```sh CONTAINER=... bash run_functional_in_docker.sh functional/sft.sh ``` + + +## Static Type Checking with [MyPy](https://mypy-lang.org/) +Static type checking can be run with no GPU resources: + +```sh +uv run --group test mypy {program}.py +``` + +For example, +```sh +uv run --group test mypy examples/run_grpo_math.py +uv run --group test mypy examples/run_sft.py +``` + +mypy.ini controls the configuration of mypy. \ No newline at end of file From 3b0bfbc87e84cf5d2c88b77eecc50bbf25e44802 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 00:14:00 -0700 Subject: [PATCH 010/109] Fixed missing import Signed-off-by: Sahil Jain --- examples/run_grpo_math.py | 18 +++++++++++------- mypy.ini | 5 ++++- nemo_rl/algorithms/grpo.py | 2 +- nemo_rl/algorithms/interfaces.py | 8 +++++++- nemo_rl/algorithms/loss_functions.py | 8 +------- nemo_rl/distributed/model_utils.py | 10 ++++++---- nemo_rl/models/policy/dtensor_policy_worker.py | 3 +-- nemo_rl/models/policy/fsdp1_policy_worker.py | 3 +-- nemo_rl/models/policy/utils.py | 6 ++++-- nemo_rl/utils/logger.py | 2 +- nemo_rl/utils/nvml.py | 14 ++++++++++++-- nemo_rl/utils/timer.py | 6 +++--- tests/unit/test_utils.py | 2 +- 13 files changed, 53 insertions(+), 34 deletions(-) diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index 2a2bdaeca2..fc86030328 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -42,7 +42,7 @@ from nemo_rl.utils.logger import get_next_experiment_dir -def parse_args(): +def parse_args() -> tuple[argparse.Namespace, list[str]]: """Parse command line arguments.""" parser = argparse.ArgumentParser(description="Run GRPO training with configuration") parser.add_argument( @@ -58,13 +58,13 @@ def parse_args(): # =============================================================================== # Math Data Processor # =============================================================================== - +TokenizerType = AutoTokenizer # TaskDataProcessFnCallable def hf_data_processor( datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, - tokenizer, + tokenizer: TokenizerType, max_seq_length: int, idx: int, ) -> DatumSpec: @@ -115,7 +115,7 @@ def hf_data_processor( def math_data_processor( datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, - tokenizer, + tokenizer: TokenizerType, max_seq_length: int, idx: int, ) -> DatumSpec: @@ -178,7 +178,11 @@ def math_data_processor( return output -def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): +def setup_data( + tokenizer: TokenizerType, + data_config: DataConfig, + env_configs: dict[str, Any], +) -> tuple: print("\n▶ Setting up data...") math_task_spec = TaskDataSpec( task_name="math", @@ -203,7 +207,7 @@ def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): ) task_data_processors["math"] = (math_task_spec, hf_data_processor) - math_env = MathEnvironment.options( + math_env = MathEnvironment.options( # type: ignore # it's wrapped with ray.remote runtime_env={ "py_executable": MathEnvironment.DEFAULT_PY_EXECUTABLE, "env_vars": dict(os.environ), # Pass thru all user environment variables @@ -230,7 +234,7 @@ def setup_data(tokenizer: AutoTokenizer, data_config: DataConfig, env_configs): return dataset, val_dataset, task_to_env, task_to_env -def main(): +def main() -> None: """Main entry point.""" # Parse arguments args, overrides = parse_args() diff --git a/mypy.ini b/mypy.ini index 3d391ea96c..568455b826 100644 --- a/mypy.ini +++ b/mypy.ini @@ -12,4 +12,7 @@ ignore_missing_imports = True ignore_missing_imports = True [mypy-datasets.*] -ignore_missing_imports = True \ No newline at end of file +ignore_missing_imports = True + +[mypy-transformers.*] +ignore_missing_imports = True diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index ad4b716114..4785e19400 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -46,7 +46,7 @@ GenerationInterface, ) from nemo_rl.models.generation.vllm import VllmGeneration -from nemo_rl.models.interfaces import PolicyInterface +from nemo_rl.models.policy.interfaces import PolicyInterface from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.hf_policy import HfPolicy from nemo_rl.utils.checkpoint import CheckpointingConfig, CheckpointManager diff --git a/nemo_rl/algorithms/interfaces.py b/nemo_rl/algorithms/interfaces.py index 8cf63f6e3f..889dc2f1e4 100644 --- a/nemo_rl/algorithms/interfaces.py +++ b/nemo_rl/algorithms/interfaces.py @@ -12,12 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. +import enum from typing import Any, Protocol import torch from nemo_rl.distributed.batched_data_dict import BatchedDataDict +class LossType(enum.Enum): + TOKEN_LEVEL = "token_level" + SEQUENCE_LEVEL = "sequence_level" + class LossFunction(Protocol): """Signature for loss functions used in reinforcement learning algorithms. @@ -25,9 +30,10 @@ class LossFunction(Protocol): Loss functions compute a scalar loss value and associated metrics from model logprobs and other data contained in a BatchedDataDict. """ + loss_type: LossType def __call__( - self, next_token_logits: torch.Tensor, data: BatchedDataDict + self, next_token_logits: torch.Tensor, data: BatchedDataDict, *args, **kwargs ) -> tuple[torch.Tensor, dict[str, Any]]: """Compute loss and metrics from logprobs and other data. diff --git a/nemo_rl/algorithms/loss_functions.py b/nemo_rl/algorithms/loss_functions.py index 5637d8fa38..9f5d1d4124 100644 --- a/nemo_rl/algorithms/loss_functions.py +++ b/nemo_rl/algorithms/loss_functions.py @@ -11,12 +11,11 @@ # 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 enum from typing import Any, TypedDict import torch -from nemo_rl.algorithms.interfaces import LossFunction +from nemo_rl.algorithms.interfaces import LossFunction, LossType from nemo_rl.algorithms.utils import ( calculate_kl_penalty_joschu2020, masked_mean, @@ -29,11 +28,6 @@ Tensor = torch.Tensor -class LossType(enum.Enum): - TOKEN_LEVEL = "token_level" - SEQUENCE_LEVEL = "sequence_level" - - class ClippedPGLossConfig(TypedDict): reference_policy_kl_penalty: float ratio_clip_min: float diff --git a/nemo_rl/distributed/model_utils.py b/nemo_rl/distributed/model_utils.py index 659cffa0b1..f1e2e6ac81 100644 --- a/nemo_rl/distributed/model_utils.py +++ b/nemo_rl/distributed/model_utils.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Any import torch @@ -62,14 +63,14 @@ class DistributedLogprob(torch.autograd.Function): @staticmethod def forward( - ctx, + ctx: Any, vocab_parallel_logits: torch.Tensor, target: torch.Tensor, vocab_start_index: int, vocab_end_index: int, group: torch.distributed.ProcessGroup, inference_only: bool = False, - ): + ) -> torch.Tensor: # Create a mask of valid vocab ids (1 means it needs to be masked). target_mask = (target < vocab_start_index) | (target >= vocab_end_index) masked_target = target - vocab_start_index @@ -98,7 +99,8 @@ def forward( @staticmethod def backward( - ctx, *grad_outputs: torch.Tensor + ctx: Any, + *grad_outputs: torch.Tensor, ) -> tuple[torch.Tensor, None, None, None, None, None, None]: grad_output = grad_outputs[0] softmax, target_mask, masked_target = ctx.saved_tensors @@ -144,7 +146,7 @@ def from_parallel_logits_to_logprobs( Taken from: https://github.com/NVIDIA/NeMo-Aligner/blob/9faab404f21994a7eb1d6ed5890b76152b941636/nemo_aligner/utils/distributed.py#L354 """ target = target.roll(shifts=-1, dims=-1) - probs = DistributedLogprob.apply( + probs: torch.Tensor = DistributedLogprob.apply( # type: ignore vocab_parallel_logits, target, vocab_start_index, diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index 0f02374470..01c8b27a25 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -28,8 +28,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.integrations.accelerate import find_tied_parameters -from nemo_rl.algorithms.interfaces import LossFunction -from nemo_rl.algorithms.loss_functions import LossType +from nemo_rl.algorithms.interfaces import LossFunction, LossType from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.distributed.virtual_cluster import ( PY_EXECUTABLES, diff --git a/nemo_rl/models/policy/fsdp1_policy_worker.py b/nemo_rl/models/policy/fsdp1_policy_worker.py index 1a3e54aab4..8471ff7d03 100644 --- a/nemo_rl/models/policy/fsdp1_policy_worker.py +++ b/nemo_rl/models/policy/fsdp1_policy_worker.py @@ -31,8 +31,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.integrations.accelerate import find_tied_parameters -from nemo_rl.algorithms.interfaces import LossFunction -from nemo_rl.algorithms.loss_functions import LossType +from nemo_rl.algorithms.interfaces import LossFunction, LossType from nemo_rl.distributed.batched_data_dict import BatchedDataDict from nemo_rl.distributed.virtual_cluster import ( PY_EXECUTABLES, diff --git a/nemo_rl/models/policy/utils.py b/nemo_rl/models/policy/utils.py index 2fbbff9d4e..9973bc8879 100644 --- a/nemo_rl/models/policy/utils.py +++ b/nemo_rl/models/policy/utils.py @@ -14,11 +14,13 @@ import importlib import os +from typing import Any +import torch from transformers import AutoConfig -def import_class_from_path(name): +def import_class_from_path(name: str) -> Any: """Import a class from a string path (e.g. 'torch.optim.AdamW'). Args: @@ -32,7 +34,7 @@ def import_class_from_path(name): return cls_instance -def get_gpu_info(model): +def get_gpu_info(model: torch.nn.Module) -> dict: """Return information about the GPU being used by this worker.""" import torch diff --git a/nemo_rl/utils/logger.py b/nemo_rl/utils/logger.py index 188c1b690e..a7bdbf07a0 100644 --- a/nemo_rl/utils/logger.py +++ b/nemo_rl/utils/logger.py @@ -876,7 +876,7 @@ def safe_render(content, role_color): console.rule("[bold bright_white on purple4]End of Samples") -def get_next_experiment_dir(base_log_dir): +def get_next_experiment_dir(base_log_dir: str) -> str: """Create a new experiment directory with an incremented ID. Args: diff --git a/nemo_rl/utils/nvml.py b/nemo_rl/utils/nvml.py index 137374e00b..9b954c1a86 100644 --- a/nemo_rl/utils/nvml.py +++ b/nemo_rl/utils/nvml.py @@ -13,12 +13,13 @@ # limitations under the License. import contextlib import os +from typing import Generator import pynvml @contextlib.contextmanager -def nvml_context(): +def nvml_context() -> Generator[None, None, None]: """Context manager for NVML initialization and shutdown. Raises: @@ -60,7 +61,16 @@ def get_device_uuid(device_idx: int) -> str: with nvml_context(): try: handle = pynvml.nvmlDeviceGetHandleByIndex(global_device_idx) - return pynvml.nvmlDeviceGetUUID(handle) + uuid = pynvml.nvmlDeviceGetUUID(handle) + # Ensure the UUID is returned as a string, not bytes + if isinstance(uuid, bytes): + return uuid.decode("utf-8") + elif isinstance(uuid, str): + return uuid + else: + raise RuntimeError( + f"Unexpected UUID type: {type(uuid)} for device {device_idx} (global index: {global_device_idx})" + ) except pynvml.NVMLError as e: raise RuntimeError( f"Failed to get device UUID for device {device_idx} (global index: {global_device_idx}): {e}" diff --git a/nemo_rl/utils/timer.py b/nemo_rl/utils/timer.py index 1a028e1d44..4fdaffee98 100644 --- a/nemo_rl/utils/timer.py +++ b/nemo_rl/utils/timer.py @@ -13,7 +13,7 @@ # limitations under the License. import time from contextlib import contextmanager -from typing import Callable, Optional, Sequence, Union +from typing import Callable, Generator, Optional, Sequence, Union import numpy as np @@ -67,7 +67,7 @@ class Timer: "count": len, } - def __init__(self): + def __init__(self) -> None: # Dictionary mapping labels to lists of elapsed times # We store a list of times for each label rather than a single value # to support multiple timing runs with the same label (e.g., in loops) @@ -106,7 +106,7 @@ def stop(self, label: str) -> float: return elapsed @contextmanager - def time(self, label: str): + def time(self, label: str) -> Generator[None, None, None]: """Context manager for timing a block of code. Args: diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py index b45f25c0bd..bec93c5049 100644 --- a/tests/unit/test_utils.py +++ b/tests/unit/test_utils.py @@ -15,7 +15,7 @@ import torch -from nemo_rl.algorithms.loss_functions import LossType +from nemo_rl.algorithms.interfaces import LossType from nemo_rl.distributed.batched_data_dict import BatchedDataDict From 588cfdaab553d37650dc7ce07ba78b816aba6b2c Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 10:33:52 -0700 Subject: [PATCH 011/109] Fixed tokenizer type Signed-off-by: Sahil Jain --- examples/run_grpo_math.py | 10 +++++----- nemo_rl/algorithms/grpo.py | 6 +++--- nemo_rl/algorithms/utils.py | 6 +++--- nemo_rl/distributed/collectives.py | 13 +++++++------ 4 files changed, 18 insertions(+), 17 deletions(-) diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index fc86030328..98d5ab1173 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -20,7 +20,7 @@ import torch from omegaconf import OmegaConf -from transformers import AutoTokenizer +from transformers import PreTrainedTokenizerBase from nemo_rl.algorithms.grpo import MasterConfig, grpo_train, setup from nemo_rl.algorithms.utils import get_tokenizer @@ -58,7 +58,7 @@ def parse_args() -> tuple[argparse.Namespace, list[str]]: # =============================================================================== # Math Data Processor # =============================================================================== -TokenizerType = AutoTokenizer +TokenizerType = PreTrainedTokenizerBase # TaskDataProcessFnCallable def hf_data_processor( @@ -78,7 +78,7 @@ def hf_data_processor( "role": "user", "content": task_data_spec.prompt.format(problem), } - message = tokenizer.apply_chat_template( + message: list[str] = tokenizer.apply_chat_template( # type: ignore [user_message], tokenize=False, add_generation_prompt=True, @@ -93,8 +93,8 @@ def hf_data_processor( loss_multiplier = 1.0 if length > max_seq_length: # make smaller and mask out - for message in message_log: - message["token_ids"] = message["token_ids"][ + for chat_message in message_log: + chat_message["token_ids"] = chat_message["token_ids"][ : min(4, max_seq_length // len(message_log)) ] loss_multiplier = 0.0 diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index 4785e19400..4bb7dd6759 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -18,7 +18,7 @@ import numpy as np import torch from torchdata.stateful_dataloader import StatefulDataLoader -from transformers import AutoTokenizer +from transformers import PreTrainedTokenizerBase from nemo_rl.algorithms.interfaces import LossFunction from nemo_rl.algorithms.loss_functions import ( @@ -60,7 +60,7 @@ # =============================================================================== # Configuration # =============================================================================== - +TokenizerType = PreTrainedTokenizerBase class GRPOConfig(TypedDict): num_prompts_per_step: int @@ -112,7 +112,7 @@ class MasterConfig(TypedDict): def setup( master_config: MasterConfig, - tokenizer: AutoTokenizer, + tokenizer: TokenizerType, dataset: AllTaskProcessedDataset, val_dataset: Optional[AllTaskProcessedDataset], ) -> tuple[ diff --git a/nemo_rl/algorithms/utils.py b/nemo_rl/algorithms/utils.py index 5357bdd8cd..e8c888848d 100644 --- a/nemo_rl/algorithms/utils.py +++ b/nemo_rl/algorithms/utils.py @@ -18,7 +18,7 @@ import numpy as np import torch -from transformers import AutoTokenizer +from transformers import AutoTokenizer, PreTrainedTokenizerBase from nemo_rl.data import hf_datasets from nemo_rl.models.policy import TokenizerConfig @@ -142,7 +142,7 @@ def set_seed(seed: int): torch.cuda.manual_seed_all(seed) -def get_tokenizer(tokenizer_config: TokenizerConfig) -> AutoTokenizer: +def get_tokenizer(tokenizer_config: TokenizerConfig) -> PreTrainedTokenizerBase: """Get the tokenizer and set pad token to eos token if it is not already set. This function initializes a tokenizer from the Hugging Face transformers library @@ -160,7 +160,7 @@ def get_tokenizer(tokenizer_config: TokenizerConfig) -> AutoTokenizer: If not specified, the tokenizer's default template will be used. Returns: - AutoTokenizer: The configured tokenizer instance + PreTrainedTokenizerBase: The configured tokenizer instance Examples: ```{doctest} diff --git a/nemo_rl/distributed/collectives.py b/nemo_rl/distributed/collectives.py index 91a3932215..4402816ee0 100644 --- a/nemo_rl/distributed/collectives.py +++ b/nemo_rl/distributed/collectives.py @@ -11,14 +11,15 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Optional, cast +from typing import Any, Optional, TypeVar, cast import torch +T = TypeVar('T') def rebalance_nd_tensor( tensor: torch.Tensor, group: Optional[torch.distributed.ProcessGroup] = None -): +) -> torch.Tensor: """Takes tensors with variable leading sizes (at dim=0) and stacks them into a single tensor. This function handles the case where different GPUs have tensors with different batch sizes @@ -64,8 +65,8 @@ def rebalance_nd_tensor( def gather_jagged_object_lists( - local_objects: list, group: Optional[torch.distributed.ProcessGroup] = None -): + local_objects: list[T], group: Optional[torch.distributed.ProcessGroup] = None +) -> list[T]: """Gathers jagged lists of picklable objects from all ranks and flattens them into a single list. This function handles the case where different GPUs have lists of different lengths @@ -90,8 +91,8 @@ def gather_jagged_object_lists( """ # Gather all lists across ranks world_size = torch.distributed.get_world_size(group=group) - gathered_lists = [None] * world_size + gathered_lists: list[list[T]] = [None] * world_size # type: ignore torch.distributed.all_gather_object(gathered_lists, local_objects, group=group) # Flatten into single list while preserving order - return [obj for sublist in cast(list[list], gathered_lists) for obj in sublist] + return [obj for sublist in gathered_lists for obj in sublist] From bc07e7611b160467c53e4ca7d458a7d13a250bcd Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 11:29:54 -0700 Subject: [PATCH 012/109] Down to 50 errors Signed-off-by: Sahil Jain --- examples/run_grpo_math.py | 8 +++--- nemo_rl/algorithms/grpo.py | 16 ++++++----- nemo_rl/data/datasets.py | 2 +- nemo_rl/experience/rollouts.py | 7 +++-- nemo_rl/models/generation/__init__.py | 5 ++-- nemo_rl/models/policy/fsdp1_policy_worker.py | 4 +-- nemo_rl/models/policy/hf_policy.py | 3 +- nemo_rl/models/policy/interfaces.py | 30 ++++++++++++++++++++ nemo_rl/utils/checkpoint.py | 2 +- 9 files changed, 56 insertions(+), 21 deletions(-) diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index 98d5ab1173..b7c1809d74 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -16,7 +16,7 @@ import os import pprint from collections import defaultdict -from typing import Any +from typing import Any, cast import torch from omegaconf import OmegaConf @@ -133,7 +133,7 @@ def math_data_processor( "content": task_data_spec.system_prompt, } sys = tokenizer.apply_chat_template( - [sys_prompt], + [cast(dict[str, str], sys_prompt)], tokenize=False, add_generation_prompt=False, add_special_tokens=False, @@ -160,8 +160,8 @@ def math_data_processor( loss_multiplier = 1.0 if length > max_seq_length: # make smaller and mask out - for message in message_log: - message["token_ids"] = message["token_ids"][ + for indiv_message in message_log: + indiv_message["token_ids"] = indiv_message["token_ids"][ : min(4, max_seq_length // len(message_log)) ] loss_multiplier = 0.0 diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index 4bb7dd6759..0c99f1cda7 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -13,7 +13,7 @@ # limitations under the License. import os from pathlib import Path -from typing import Any, Optional, TypedDict +from typing import Any, Optional, TypedDict, cast import numpy as np import torch @@ -45,8 +45,8 @@ from nemo_rl.models.generation.interfaces import ( GenerationInterface, ) -from nemo_rl.models.generation.vllm import VllmGeneration -from nemo_rl.models.policy.interfaces import PolicyInterface +from nemo_rl.models.generation.vllm import VllmGeneration, VllmConfig +from nemo_rl.models.policy.interfaces import ColocatablePolicyInterface from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.hf_policy import HfPolicy from nemo_rl.utils.checkpoint import CheckpointingConfig, CheckpointManager @@ -116,7 +116,7 @@ def setup( dataset: AllTaskProcessedDataset, val_dataset: Optional[AllTaskProcessedDataset], ) -> tuple[ - PolicyInterface, + ColocatablePolicyInterface, GenerationInterface, RayVirtualCluster, StatefulDataLoader, @@ -193,9 +193,10 @@ def setup( print(f" ✓ Training dataloader loaded with {len(dataset)} samples") # Load validation dataset if provided - val_dataloader = None + val_dataloader: Optional[StatefulDataLoader] = None # If validation is enabled, load the validation dataloader if grpo_config["val_period"] > 0 or grpo_config["val_at_start"]: + assert val_dataset is not None, "Validation dataset is required if validation is enabled" val_dataloader = StatefulDataLoader( val_dataset, batch_size=grpo_config["val_batch_size"], @@ -232,6 +233,7 @@ def setup( policy_generation = None print(f" ✓ Using HF backend for generation with {policy_config['model_name']}") elif backend == "vllm": + generation_config = cast(VllmConfig, generation_config) policy_generation = VllmGeneration(cluster=cluster, config=generation_config) # Worker groups are not initialized until the first call to run something on workergroups. # vllm 0.8 fails in initialization if its called in the first training step since it has no clean view of the GPU memory (HF is sharing the same memory). @@ -279,7 +281,7 @@ def setup( def refit_policy_generation( - policy: PolicyInterface, + policy: ColocatablePolicyInterface, policy_generation: GenerationInterface, refit_buffer_size_gb: int, # GB ): @@ -317,7 +319,7 @@ def refit_policy_generation( def grpo_train( - policy: PolicyInterface, + policy: ColocatablePolicyInterface, policy_generation: Optional[GenerationInterface], dataloader: StatefulDataLoader, val_dataloader: Optional[StatefulDataLoader], diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index 62e679942b..1380249319 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -72,7 +72,7 @@ def __init__( def __len__(self): return len(self.dataset) - def encode_single(self, text: Union[str, list[str]]) -> tuple[list[int], int]: + def encode_single(self, text: Union[str, list[str]]) -> tuple[list[int] | torch.Tensor, int]: """Takes either a single string or a list of strings that represent multiple turns for the same conversation. Returns a single (concatenated) list of tokenized ids and the length of the tokenized ids. diff --git a/nemo_rl/experience/rollouts.py b/nemo_rl/experience/rollouts.py index b860135843..0b3357e914 100644 --- a/nemo_rl/experience/rollouts.py +++ b/nemo_rl/experience/rollouts.py @@ -19,7 +19,7 @@ import ray import torch -from transformers import AutoTokenizer +from transformers import PreTrainedTokenizerBase from nemo_rl.data.interfaces import ( DatumSpec, @@ -39,12 +39,13 @@ GenerationInterface, ) +TokenizerType = PreTrainedTokenizerBase def generate_responses( policy_generation: GenerationInterface, generation_input_data: BatchedDataDict[GenerationDatumSpec], batch: BatchedDataDict[DatumSpec], - tokenizer: AutoTokenizer, + tokenizer: TokenizerType, input_lengths: torch.Tensor, include_logprobs: bool = True, greedy: bool = False, @@ -199,7 +200,7 @@ def calculate_rewards( def run_multi_turn_rollout( policy_generation: GenerationInterface, input_batch: BatchedDataDict[DatumSpec], - tokenizer: AutoTokenizer, + tokenizer: TokenizerType, task_to_env: dict[str, EnvironmentInterface], max_seq_len: int, max_rollout_turns: int = 999999, diff --git a/nemo_rl/models/generation/__init__.py b/nemo_rl/models/generation/__init__.py index 469d556f52..e3fba19ac8 100644 --- a/nemo_rl/models/generation/__init__.py +++ b/nemo_rl/models/generation/__init__.py @@ -13,14 +13,15 @@ # limitations under the License. from typing import cast -from transformers import AutoTokenizer +from transformers import PreTrainedTokenizerBase from nemo_rl.models.generation.interfaces import GenerationConfig from nemo_rl.models.generation.vllm import VllmConfig +TokenizerType = PreTrainedTokenizerBase def configure_generation_config( - config: GenerationConfig, tokenizer: AutoTokenizer, is_eval=False + config: GenerationConfig, tokenizer: TokenizerType, is_eval=False ): """Apply specific configurations to generation config.""" # tokenizer setting diff --git a/nemo_rl/models/policy/fsdp1_policy_worker.py b/nemo_rl/models/policy/fsdp1_policy_worker.py index 8471ff7d03..18f4def6ec 100644 --- a/nemo_rl/models/policy/fsdp1_policy_worker.py +++ b/nemo_rl/models/policy/fsdp1_policy_worker.py @@ -28,7 +28,7 @@ MixedPrecision, ) from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy -from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers import AutoModelForCausalLM, PreTrainedTokenizerBase from transformers.integrations.accelerate import find_tied_parameters from nemo_rl.algorithms.interfaces import LossFunction, LossType @@ -74,7 +74,7 @@ def __repr__(self): def __init__( self, config: PolicyConfig, - tokenizer: AutoTokenizer, + tokenizer: PreTrainedTokenizerBase, weights_path: Optional[str] = None, optimizer_path: Optional[str] = None, init_optimizer: bool = True, diff --git a/nemo_rl/models/policy/hf_policy.py b/nemo_rl/models/policy/hf_policy.py index a45f3cb335..aad5f53116 100644 --- a/nemo_rl/models/policy/hf_policy.py +++ b/nemo_rl/models/policy/hf_policy.py @@ -33,11 +33,12 @@ from nemo_rl.models.policy.interfaces import ( LogprobOutputSpec, PolicyInterface, + ColocatablePolicyInterface, ReferenceLogprobOutputSpec, ) -class HfPolicy(PolicyInterface, GenerationInterface): +class HfPolicy(ColocatablePolicyInterface, GenerationInterface): def __init__( self, cluster: RayVirtualCluster, diff --git a/nemo_rl/models/policy/interfaces.py b/nemo_rl/models/policy/interfaces.py index 162950749b..c771446385 100644 --- a/nemo_rl/models/policy/interfaces.py +++ b/nemo_rl/models/policy/interfaces.py @@ -82,3 +82,33 @@ def prepare_for_training(self, *args, **kwargs): @abstractmethod def finish_training(self, *args, **kwargs): pass + + @abstractmethod + def save_checkpoint(self, *args, **kwargs): + pass + + @abstractmethod + def load_checkpoint(self, *args, **kwargs): + pass + + @abstractmethod + def shutdown(self): + pass + + +class ColocatablePolicyInterface(PolicyInterface): + @abstractmethod + def offload_before_refit(self): + pass + + @abstractmethod + def offload_after_refit(self): + pass + + @abstractmethod + def prepare_weights_for_ipc(self, *args, **kwargs): + pass + + @abstractmethod + def get_weights_ipc_handles(self, keys: list[str]) -> dict[str, Any]: + pass diff --git a/nemo_rl/utils/checkpoint.py b/nemo_rl/utils/checkpoint.py index 1ab2bf956f..3d9b1300a0 100644 --- a/nemo_rl/utils/checkpoint.py +++ b/nemo_rl/utils/checkpoint.py @@ -230,7 +230,7 @@ def get_latest_checkpoint_path(self) -> Optional[str]: return str(step_dirs[-1]) def load_training_info( - self, checkpoint_path: Optional[os.PathLike] = None + self, checkpoint_path: Optional[str | os.PathLike] = None ) -> Optional[dict[str, Any]]: """Load the training info from a checkpoint. From 72e48191413ffb93c9f2ce6c6a5b6055d7f1a0b5 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 13:59:00 -0700 Subject: [PATCH 013/109] fixed 150 strict mypy errors Signed-off-by: Sahil Jain --- examples/run_grpo_math.py | 1 + nemo_rl/algorithms/grpo.py | 26 +++++---- nemo_rl/algorithms/interfaces.py | 2 + nemo_rl/data/datasets.py | 4 +- nemo_rl/data/interfaces.py | 15 +++-- nemo_rl/distributed/collectives.py | 5 +- nemo_rl/distributed/virtual_cluster.py | 43 ++++++++------ nemo_rl/distributed/worker_groups.py | 4 +- nemo_rl/environments/metrics.py | 4 +- nemo_rl/experience/rollouts.py | 1 + nemo_rl/models/generation/__init__.py | 1 + nemo_rl/models/generation/interfaces.py | 8 +-- nemo_rl/models/generation/vllm.py | 12 ++-- .../models/policy/dtensor_policy_worker.py | 28 ++++----- nemo_rl/models/policy/hf_policy.py | 57 ++++++++++--------- nemo_rl/models/policy/interfaces.py | 18 +++--- nemo_rl/models/policy/utils.py | 4 +- nemo_rl/utils/checkpoint.py | 26 +++++---- 18 files changed, 142 insertions(+), 117 deletions(-) diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index b7c1809d74..3b8fb30bf4 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -60,6 +60,7 @@ def parse_args() -> tuple[argparse.Namespace, list[str]]: # =============================================================================== TokenizerType = PreTrainedTokenizerBase + # TaskDataProcessFnCallable def hf_data_processor( datum_dict: dict[str, Any], diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index 0c99f1cda7..e39ffea7c9 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -45,10 +45,10 @@ from nemo_rl.models.generation.interfaces import ( GenerationInterface, ) -from nemo_rl.models.generation.vllm import VllmGeneration, VllmConfig -from nemo_rl.models.policy.interfaces import ColocatablePolicyInterface +from nemo_rl.models.generation.vllm import VllmConfig, VllmGeneration from nemo_rl.models.policy import PolicyConfig from nemo_rl.models.policy.hf_policy import HfPolicy +from nemo_rl.models.policy.interfaces import ColocatablePolicyInterface from nemo_rl.utils.checkpoint import CheckpointingConfig, CheckpointManager from nemo_rl.utils.logger import ( Logger, @@ -62,6 +62,7 @@ # =============================================================================== TokenizerType = PreTrainedTokenizerBase + class GRPOConfig(TypedDict): num_prompts_per_step: int num_generations_per_prompt: int @@ -117,7 +118,7 @@ def setup( val_dataset: Optional[AllTaskProcessedDataset], ) -> tuple[ ColocatablePolicyInterface, - GenerationInterface, + Optional[GenerationInterface], RayVirtualCluster, StatefulDataLoader, Optional[StatefulDataLoader], @@ -196,7 +197,9 @@ def setup( val_dataloader: Optional[StatefulDataLoader] = None # If validation is enabled, load the validation dataloader if grpo_config["val_period"] > 0 or grpo_config["val_at_start"]: - assert val_dataset is not None, "Validation dataset is required if validation is enabled" + assert val_dataset is not None, ( + "Validation dataset is required if validation is enabled" + ) val_dataloader = StatefulDataLoader( val_dataset, batch_size=grpo_config["val_batch_size"], @@ -284,16 +287,17 @@ def refit_policy_generation( policy: ColocatablePolicyInterface, policy_generation: GenerationInterface, refit_buffer_size_gb: int, # GB -): +) -> None: """Refit the policy generation interface with the latest policy weights.""" policy.offload_before_refit() policy_generation.prepare_for_generation(tags=["weights"]) # Streaming update weights to save memory - state_dict_info = policy.prepare_weights_for_ipc() + state_dict_info: dict[str, int] = policy.prepare_weights_for_ipc() # group keys to save time available_bytes = refit_buffer_size_gb * (1024**3) - split_keys, keys = [], [] - for key, size_in_bytes in state_dict_info: + split_keys: list[list[str]] = [] + keys: list[str] = [] + for key, size_in_bytes in state_dict_info.items(): if size_in_bytes > available_bytes: if keys: split_keys.append(keys) @@ -323,7 +327,7 @@ def grpo_train( policy_generation: Optional[GenerationInterface], dataloader: StatefulDataLoader, val_dataloader: Optional[StatefulDataLoader], - tokenizer, + tokenizer: TokenizerType, loss_fn: LossFunction, task_to_env: dict[str, EnvironmentInterface], val_task_to_env: Optional[dict[str, EnvironmentInterface]], @@ -331,13 +335,13 @@ def grpo_train( checkpointer: CheckpointManager, grpo_save_state: GRPOSaveState, master_config: MasterConfig, -): +) -> None: """Run GRPO training algorithm.""" timer = Timer() NEED_REFIT = True # If policy_generation is None, use the policy as the generation interface (hf framework backend) if policy_generation is None: - policy_generation = policy + policy_generation = policy # type: ignore NEED_REFIT = False POLICY_GENERATION_STALE = True # tracks if generation needs a refit before running assert policy_generation is not None # for mypy type check diff --git a/nemo_rl/algorithms/interfaces.py b/nemo_rl/algorithms/interfaces.py index 889dc2f1e4..b41f5fab92 100644 --- a/nemo_rl/algorithms/interfaces.py +++ b/nemo_rl/algorithms/interfaces.py @@ -19,6 +19,7 @@ from nemo_rl.distributed.batched_data_dict import BatchedDataDict + class LossType(enum.Enum): TOKEN_LEVEL = "token_level" SEQUENCE_LEVEL = "sequence_level" @@ -30,6 +31,7 @@ class LossFunction(Protocol): Loss functions compute a scalar loss value and associated metrics from model logprobs and other data contained in a BatchedDataDict. """ + loss_type: LossType def __call__( diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index 1380249319..34f9503553 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -72,7 +72,9 @@ def __init__( def __len__(self): return len(self.dataset) - def encode_single(self, text: Union[str, list[str]]) -> tuple[list[int] | torch.Tensor, int]: + def encode_single( + self, text: Union[str, list[str]] + ) -> tuple[list[int] | torch.Tensor, int]: """Takes either a single string or a list of strings that represent multiple turns for the same conversation. Returns a single (concatenated) list of tokenized ids and the length of the tokenized ids. diff --git a/nemo_rl/data/interfaces.py b/nemo_rl/data/interfaces.py index 779e8a35a8..6f5bf41834 100644 --- a/nemo_rl/data/interfaces.py +++ b/nemo_rl/data/interfaces.py @@ -16,6 +16,7 @@ from typing import Any, NotRequired, Optional, Protocol, TypedDict, Union import torch +from transformers import PreTrainedTokenizerBase # OpenAI-API-like message log, but every messsage may contain associated tensors (i.e. tokenized strings and logprobs) in addition to the original "content" string LLMMessageLogType = list[dict[str, Union[str, torch.Tensor]]] @@ -24,6 +25,8 @@ # Converts a conversation from list-of-turns format to key-value format with concatenated tensors FlatMessagesType = dict[str, Union[list[str], torch.Tensor]] +PathLike = Union[str, "os.PathLike[Any]"] +TokenizerType = PreTrainedTokenizerBase class DatumSpec(TypedDict): message_log: LLMMessageLogType @@ -49,13 +52,13 @@ class DPODatumSpec(TypedDict): class TaskDataSpec: task_name: Optional[str] = None # prompt - prompt_file: Optional[str | os.PathLike | str] = None + prompt_file: Optional[PathLike] = None - system_prompt_file: Optional[str | os.PathLike] = None + system_prompt_file: Optional[PathLike] = None - def __post_init__(self): + def __post_init__(self) -> None: def load_prompt_file( - prompt_file: Optional[str | os.PathLike], + prompt_file: Optional[PathLike], ) -> Optional[str]: """Load prompt from file if it exists, otherwise return as is.""" if prompt_file is None: @@ -70,7 +73,7 @@ def load_prompt_file( self.system_prompt = load_prompt_file(self.system_prompt_file) self.prompt = load_prompt_file(self.prompt_file) or "{}" - def copy_defaults(self, from_spec: "TaskDataSpec"): + def copy_defaults(self, from_spec: "TaskDataSpec") -> None: """Apply default values from another Task instance for any None attributes.""" default_attrs = { "system_prompt": from_spec.system_prompt, @@ -89,7 +92,7 @@ def __call__( self, datum_dict: dict[str, Any], task_data_spec: TaskDataSpec, - tokenizer, + tokenizer: TokenizerType, max_seq_length: int, idx: int, ) -> DatumSpec: diff --git a/nemo_rl/distributed/collectives.py b/nemo_rl/distributed/collectives.py index 4402816ee0..ddce1919d7 100644 --- a/nemo_rl/distributed/collectives.py +++ b/nemo_rl/distributed/collectives.py @@ -11,11 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Optional, TypeVar, cast +from typing import Optional, TypeVar import torch -T = TypeVar('T') +T = TypeVar("T") + def rebalance_nd_tensor( tensor: torch.Tensor, group: Optional[torch.distributed.ProcessGroup] = None diff --git a/nemo_rl/distributed/virtual_cluster.py b/nemo_rl/distributed/virtual_cluster.py index 289f83bb35..cbe133d5a2 100644 --- a/nemo_rl/distributed/virtual_cluster.py +++ b/nemo_rl/distributed/virtual_cluster.py @@ -15,7 +15,7 @@ import os import sys import time -from typing import Optional, TypedDict +from typing import Any, Optional, TypedDict import ray from ray.util.placement_group import ( @@ -51,7 +51,7 @@ class PY_EXECUTABLES: @ray.remote -def _get_node_ip_and_free_port(): +def _get_node_ip_and_free_port() -> tuple[str, int]: import socket # Get the IP address of the current node @@ -64,7 +64,7 @@ def _get_node_ip_and_free_port(): return node_ip, port -def init_ray(log_dir: Optional[str] = None): +def init_ray(log_dir: Optional[str] = None) -> None: """Initialise Ray. Try to attach to an existing local cluster. @@ -223,7 +223,7 @@ def __init__( f"Maximum number of retries reached ({max_retries}). Cluster resources may be insufficient or cluster itself is highly unstable. Please check your cluster configuration and your cluster logs." ) - def _init_placement_groups(self, strategy: str): + def _init_placement_groups(self, strategy: str) -> list[PlacementGroup]: """Creates placement groups for each node in the cluster. Has empty groups for nodes that don't have any bundles. Args: @@ -298,7 +298,7 @@ def _init_placement_groups(self, strategy: str): return self._node_placement_groups - def get_placement_groups(self): + def get_placement_groups(self) -> list[PlacementGroup]: """Returns a list of placement groups that have at least one bundle, filtering out empty nodes. This represents the "virtual cluster" - only nodes that are actually being used. @@ -311,13 +311,13 @@ def get_placement_groups(self): ) return [pg for pg in self._node_placement_groups if pg.bundle_specs] - def world_size(self): + def world_size(self) -> int: return self._world_size - def node_count(self): + def node_count(self) -> int: return len(self.get_placement_groups()) - def get_master_address_and_port(self): + def get_master_address_and_port(self) -> tuple[str, int]: """Gets the master address and port for the distributed training setup. Returns: @@ -344,7 +344,7 @@ def get_master_address_and_port(self): raise RuntimeError("No valid placement groups found to get master address") - def shutdown(self): + def shutdown(self) -> bool: """Cleans up and releases all resources associated with this virtual cluster. This includes removing all placement groups and resetting the internal state. @@ -365,7 +365,9 @@ def shutdown(self): return True - def _create_visualization_grid(self, worker_groups=None, is_global_view=False): + def _create_visualization_grid( + self, worker_groups: Optional[Any] = None, is_global_view: bool = False + ) -> dict[str, Any]: """Create a visualization grid for the cluster with optional worker groups. Args: @@ -453,8 +455,13 @@ def _create_visualization_grid(self, worker_groups=None, is_global_view=False): return grid_data def _get_worker_cells( - self, node_idx, gpu_idx, worker_groups, cell_width, is_global_view - ): + self, + node_idx: int, + gpu_idx: int, + worker_groups: list[Any], + cell_width: int, + is_global_view: bool, + ) -> list[str]: """Get the worker cell content for each worker group at a specific GPU location. Args: @@ -491,7 +498,7 @@ def _get_worker_cells( return worker_cells - def _print_visualization(self, grid_data): + def _print_visualization(self, grid_data: dict[str, Any]) -> None: """Print the visualization based on the grid data. Args: @@ -551,7 +558,7 @@ def _print_visualization(self, grid_data): # Print legend self._print_legend(grid_data) - def _print_legend(self, grid_data): + def _print_legend(self, grid_data: dict[str, Any]) -> None: """Print the legend for the visualization.""" if grid_data["is_global_view"]: # Legend for global view @@ -572,7 +579,7 @@ def _print_legend(self, grid_data): print("#.#: Node.GPU identifier") - def print_cluster_grid(self, worker_group=None): + def print_cluster_grid(self, worker_group: Optional[Any] = None) -> None: """Prints a compact grid visualization of the virtual cluster, similar to JAX's visualize_array_sharding. If a worker_group is provided, it will also show worker assignments on each device. @@ -583,7 +590,9 @@ def print_cluster_grid(self, worker_group=None): grid_data = self._create_visualization_grid(worker_group, is_global_view=False) self._print_visualization(grid_data) - def print_all_worker_groups(self, worker_groups=None): + def print_all_worker_groups( + self, worker_groups: Optional[list[Any]] = None + ) -> None: """Prints a visualization showing all worker groups in the cluster. This provides a global view of all workers across all worker groups. @@ -595,7 +604,7 @@ def print_all_worker_groups(self, worker_groups=None): grid_data = self._create_visualization_grid(worker_groups, is_global_view=True) self._print_visualization(grid_data) - def __del__(self): + def __del__(self) -> None: """Shutsdown the virtual cluster when the object is deleted or is garbage collected. This is an extra safety net in case the user forgets to call shutdown and the pointer to diff --git a/nemo_rl/distributed/worker_groups.py b/nemo_rl/distributed/worker_groups.py index df8de41865..f3f71921c1 100644 --- a/nemo_rl/distributed/worker_groups.py +++ b/nemo_rl/distributed/worker_groups.py @@ -504,7 +504,7 @@ def run_all_workers_single_data( return futures - def get_all_worker_results(self, future_bundle): + def get_all_worker_results(self, future_bundle: MultiWorkerFuture) -> list[Any]: """Get results from all workers, optionally filtering to get just one result per tied worker group. Args: @@ -522,7 +522,7 @@ def shutdown( cleanup_method: Optional[str] = None, timeout: Optional[float] = 30.0, force: bool = False, - ): + ) -> bool: """Shutdown all workers in the worker group. Args: diff --git a/nemo_rl/environments/metrics.py b/nemo_rl/environments/metrics.py index eee81d7993..8c8eba53c7 100644 --- a/nemo_rl/environments/metrics.py +++ b/nemo_rl/environments/metrics.py @@ -14,7 +14,9 @@ import torch -def calculate_pass_rate_per_prompt(prompts, is_correct): +def calculate_pass_rate_per_prompt( + prompts: torch.Tensor, is_correct: torch.Tensor +) -> float: """Function to compute fraction of prompts that have at least one correct answer (reward > 0). prompts: tensor (b, s) Tensor of prompts the model used. May be on any device diff --git a/nemo_rl/experience/rollouts.py b/nemo_rl/experience/rollouts.py index 0b3357e914..e0c74fe086 100644 --- a/nemo_rl/experience/rollouts.py +++ b/nemo_rl/experience/rollouts.py @@ -41,6 +41,7 @@ TokenizerType = PreTrainedTokenizerBase + def generate_responses( policy_generation: GenerationInterface, generation_input_data: BatchedDataDict[GenerationDatumSpec], diff --git a/nemo_rl/models/generation/__init__.py b/nemo_rl/models/generation/__init__.py index e3fba19ac8..5c44705a5d 100644 --- a/nemo_rl/models/generation/__init__.py +++ b/nemo_rl/models/generation/__init__.py @@ -20,6 +20,7 @@ TokenizerType = PreTrainedTokenizerBase + def configure_generation_config( config: GenerationConfig, tokenizer: TokenizerType, is_eval=False ): diff --git a/nemo_rl/models/generation/interfaces.py b/nemo_rl/models/generation/interfaces.py index 1fcec2cf7e..8f57de9185 100644 --- a/nemo_rl/models/generation/interfaces.py +++ b/nemo_rl/models/generation/interfaces.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod -from typing import Any, Optional, TypedDict, Union +from typing import Any, NotRequired, Optional, TypedDict, Union import torch @@ -108,7 +108,7 @@ class GenerationConfig(TypedDict): top_k: int model_name: str stop_token_ids: list[int] - pad_token_id: int + pad_token_id: NotRequired[int] class GenerationDatumSpec(TypedDict): @@ -202,9 +202,9 @@ def generate( pass @abstractmethod - def prepare_for_generation(self, *args, **kwargs): + def prepare_for_generation(self, *args: Any, **kwargs: Any) -> bool: pass @abstractmethod - def finish_generation(self, *args, **kwargs): + def finish_generation(self, *args: Any, **kwargs: Any) -> bool: pass diff --git a/nemo_rl/models/generation/vllm.py b/nemo_rl/models/generation/vllm.py index c9377e2a3b..0875e53749 100644 --- a/nemo_rl/models/generation/vllm.py +++ b/nemo_rl/models/generation/vllm.py @@ -15,7 +15,7 @@ import copy import gc import os -from typing import NotRequired, Optional, TypedDict, Union +from typing import Any, NotRequired, Optional, TypedDict, Union import ray import torch @@ -498,7 +498,7 @@ def __init__( self.cfg = config # Ensure all required VllmConfig fields are present missing_keys = [ - key for key in VllmConfig.__annotations__ if key not in self.cfg + key for key in VllmConfig.__required_keys__ if key not in self.cfg ] assert not missing_keys, ( f"VLLM Configuration Error: Missing required keys in VllmConfig.\n" @@ -644,8 +644,8 @@ def generate_text( return combined - def prepare_for_generation(self, *args, **kwargs): - """Abstract method that must be implemented by subclasses.""" + def prepare_for_generation(self, *args: Any, **kwargs: Any) -> bool: + """Wake workers up""" try: # Use run_all_workers_single_data for methods that don't need data futures = self.worker_group.run_all_workers_single_data( @@ -658,8 +658,8 @@ def prepare_for_generation(self, *args, **kwargs): print(f"Error during policy preparation: {e}") return False - def finish_generation(self, *args, **kwargs): - """Abstract method that must be implemented by subclasses.""" + def finish_generation(self, *args: Any, **kwargs: Any) -> bool: + """Sleep workers""" try: # Use run_all_workers_single_data for methods that don't need data futures = self.worker_group.run_all_workers_single_data( diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index 01c8b27a25..69ee27d46a 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -16,7 +16,7 @@ import os from collections import defaultdict from contextlib import AbstractContextManager, contextmanager, nullcontext -from typing import Any, Iterable, Optional, Union +from typing import Any, Iterable, Optional, Union, Generator import ray import torch @@ -57,7 +57,7 @@ @contextmanager -def unshard_fsdp2_model(model: nn.Module): +def unshard_fsdp2_model(model: nn.Module) -> Generator[None, None, None]: """Explicitly unshard and then reshard the FSDP2 modules. Useful for logprob inference.""" try: for module in model.modules(): @@ -108,7 +108,7 @@ def get_cpu_state_dict( class DTensorPolicyWorker: DEFAULT_PY_EXECUTABLE = PY_EXECUTABLES.BASE - def __repr__(self): + def __repr__(self) -> str: """Customizes the actor's prefix in the Ray logs. This makes it easier to identify which worker is producing specific log messages. @@ -250,19 +250,19 @@ def __init__( "No weights path provided. Starting from scratch (default policy init)" ) - def is_alive(self): + def is_alive(self) -> bool: return True - def reset_peak_memory_stats(self): + def reset_peak_memory_stats(self) -> None: torch.cuda.reset_peak_memory_stats() - def get_gpu_info(self): + def get_gpu_info(self) -> dict[str, Any]: """Return information about the GPU being used by this worker.""" return get_gpu_info(self.model) def train( self, - data: BatchedDataDict, + data: BatchedDataDict[Any], loss_fn: LossFunction, eval_mode: bool = False, gbs: Optional[int] = None, @@ -302,7 +302,7 @@ def train( losses = [] all_mb_metrics = [] for gb_start in range(0, dataset_size, local_gbs): - global_batch: BatchedDataDict = data.slice( + global_batch: BatchedDataDict[Any] = data.slice( gb_start, gb_start + local_gbs ) @@ -454,7 +454,7 @@ def train( return metrics def get_logprobs( - self, data: BatchedDataDict, micro_batch_size: Optional[int] = None + self, data: BatchedDataDict[Any], micro_batch_size: Optional[int] = None ) -> BatchedDataDict[LogprobOutputSpec]: """Get the logprobs of the model for a batch of data. @@ -553,7 +553,7 @@ def get_logprobs( return return_data @contextmanager - def use_reference_model(self): + def use_reference_model(self) -> Generator[None, None, None]: """Context manager that temporarily swaps the reference model and active model. On entry: Moves model to CPU, moves reference_model to CUDA. Swaps the references @@ -588,7 +588,7 @@ def use_reference_model(self): val.copy_(curr_buffers[k]) def get_reference_policy_logprobs( - self, data: BatchedDataDict, micro_batch_size: Optional[int] = None + self, data: BatchedDataDict[Any], micro_batch_size: Optional[int] = None ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get the logprobs from the reference policy for a batch of data. @@ -604,7 +604,7 @@ def get_reference_policy_logprobs( return_data["reference_logprobs"] = reference_logprobs["logprobs"].cpu() return return_data - def _add_noise_to_weights(self): + def _add_noise_to_weights(self) -> None: """Add small Gaussian noise to the weights of the model. Note that this is used for testing purposes only.""" noise_std = 0.01 # Standard deviation for the noise for p in self.model.parameters(): @@ -627,7 +627,7 @@ def report_device_id(self) -> str: return get_device_uuid(device_idx) @torch.no_grad() - def prepare_weights_for_ipc(self): + def prepare_weights_for_ipc(self) -> dict[str, int]: self.model = self.move_to_cuda(self.model) self._held_sharded_state_dict_reference = self.model.state_dict() # Collect info for streaming multiple tensors @@ -749,7 +749,7 @@ def move_to_device(self, model, device): model = self.move_buffer_to_device(model, device) return model.to(device) - def move_buffer_to_device(self, model, device): + def move_buffer_to_device(self, model: nn.Module, device: str) -> nn.Module: # FSDP modules do not move buffers to the device automatically for v in model.buffers(): v.data = v.data.to(device) diff --git a/nemo_rl/models/policy/hf_policy.py b/nemo_rl/models/policy/hf_policy.py index aad5f53116..779dd75d5a 100644 --- a/nemo_rl/models/policy/hf_policy.py +++ b/nemo_rl/models/policy/hf_policy.py @@ -13,10 +13,10 @@ # limitations under the License. import os from collections import defaultdict -from typing import Optional, Type, Union +from typing import Any, Optional, Union, cast, Type import ray -from transformers import AutoTokenizer +from transformers import PreTrainedTokenizerBase from nemo_rl.algorithms.interfaces import LossFunction from nemo_rl.distributed.batched_data_dict import BatchedDataDict @@ -31,24 +31,24 @@ from nemo_rl.models.policy.dtensor_policy_worker import DTensorPolicyWorker from nemo_rl.models.policy.fsdp1_policy_worker import FSDP1PolicyWorker from nemo_rl.models.policy.interfaces import ( - LogprobOutputSpec, - PolicyInterface, ColocatablePolicyInterface, + LogprobOutputSpec, ReferenceLogprobOutputSpec, ) +PathLike = Union[str, "os.PathLike[Any]"] class HfPolicy(ColocatablePolicyInterface, GenerationInterface): def __init__( self, cluster: RayVirtualCluster, config: PolicyConfig, - tokenizer: AutoTokenizer, + tokenizer: PreTrainedTokenizerBase, name_prefix: str = "hf_policy", workers_per_node: Optional[Union[int, list[int]]] = None, init_optimizer: bool = True, - weights_path: Optional[str] = None, - optimizer_path: Optional[str] = None, + weights_path: Optional[PathLike] = None, + optimizer_path: Optional[PathLike] = None, init_reference_model: bool = True, ): if weights_path: @@ -59,12 +59,13 @@ def __init__( node_bundle_indices = None self.tensor_parallel_size = 1 + worker_builder_cls: Union[Type[DTensorPolicyWorker], Type[FSDP1PolicyWorker]] if config["dtensor_cfg"]["enabled"]: - worker_builder_cls: Type = DTensorPolicyWorker + worker_builder_cls = DTensorPolicyWorker self.tensor_parallel_size = config["dtensor_cfg"]["tensor_parallel_size"] node_bundle_indices = self._get_tied_worker_bundle_indices(cluster) else: - worker_builder_cls: Type = FSDP1PolicyWorker + worker_builder_cls = FSDP1PolicyWorker worker_builder = RayWorkerBuilder( worker_builder_cls, @@ -85,7 +86,7 @@ def __init__( self.dp_size = self.worker_group.world_size // self.tensor_parallel_size self.cfg = config - def _get_tied_worker_bundle_indices(self, cluster): + def _get_tied_worker_bundle_indices(self, cluster: RayVirtualCluster) -> list[tuple[int, list[int]]]: """Calculate bundle indices for tensor parallel workers.""" # Get the placement groups (nodes) from the cluster placement_groups = cluster.get_placement_groups() @@ -158,12 +159,12 @@ def get_reference_policy_logprobs( def train( self, - data: BatchedDataDict, + data: BatchedDataDict[Any], loss_fn: LossFunction, eval_mode: bool = False, gbs: Optional[int] = None, mbs: Optional[int] = None, - ): + ) -> dict[str, Any]: """Train the policy on a batch of data with a given loss function.""" batch_size = gbs or self.cfg["train_global_batch_size"] micro_batch_size = mbs or self.cfg["train_micro_batch_size"] @@ -240,32 +241,32 @@ def generate( return result - def prepare_for_generation(self, *args, **kwargs): + def prepare_for_generation(self, *args: Any, **kwargs: Any) -> bool: # We don't need to do anything here - pass + return True - def prepare_for_training(self, *args, **kwargs): + def prepare_for_training(self, *args: Any, **kwargs: Any) -> None: # onload everything to the GPU futures = self.worker_group.run_all_workers_single_data( "prepare_for_training", only_on="all_tied_workers" ) ray.get(futures) - def prepare_for_lp_inference(self, *args, **kwargs): + def prepare_for_lp_inference(self, *args: Any, **kwargs: Any) -> None: futures = self.worker_group.run_all_workers_single_data( "prepare_for_lp_inference", only_on="all_tied_workers" ) ray.get(futures) - def finish_generation(self, *args, **kwargs): + def finish_generation(self, *args: Any, **kwargs: Any) -> bool: # We don't need to do anything here - pass + return True - def finish_training(self, *args, **kwargs): + def finish_training(self, *args: Any, **kwargs: Any) -> None: # Placeholder implementation pass - def prepare_weights_for_ipc(self): + def prepare_weights_for_ipc(self) -> dict[str, int]: """Prepare the weights for IPC. Returns: @@ -275,18 +276,18 @@ def prepare_weights_for_ipc(self): "prepare_weights_for_ipc", only_on="all_tied_workers" ) # only get the first worker's result is enough since all workers will have the same result - return ray.get(futures)[0] + return cast(dict[str, Any], ray.get(futures)[0]) - def get_weights_ipc_handles(self, key): + def get_weights_ipc_handles(self, keys: list[str]) -> dict[str, Any]: """Fetch weight IPC handles from all workers. Returns: dict: A dictionary mapping device UUIDs to parameter IPC handles. """ # Collect IPC handles from all workers - worker_handles = ray.get( + worker_handles: list[dict[str, Any]] = ray.get( [ - worker.get_weights_ipc_handles.remote(key) + worker.get_weights_ipc_handles.remote(keys) for worker in self.worker_group.workers ] ) @@ -298,14 +299,14 @@ def get_weights_ipc_handles(self, key): return all_handles - def offload_before_refit(self): + def offload_before_refit(self) -> None: """Offload the optimizer and buffers to the CPU.""" futures = self.worker_group.run_all_workers_single_data( "offload_before_refit", only_on="all_tied_workers" ) ray.get(futures) - def offload_after_refit(self): + def offload_after_refit(self) -> None: """Offload the optimizer and buffers to the CPU.""" futures = self.worker_group.run_all_workers_single_data( "offload_after_refit", only_on="all_tied_workers" @@ -317,7 +318,7 @@ def save_checkpoint( weights_path: str, optimizer_path: Optional[str] = None, tokenizer_path: Optional[str] = None, - ): + ) -> None: """Save a checkpoint of the model.""" futures = self.worker_group.run_all_workers_single_data( "save_checkpoint", @@ -337,7 +338,7 @@ def shutdown(self) -> bool: print(f"Error during policy shutdown: {e}") return False - def __del__(self): + def __del__(self) -> None: """Shuts down the worker groups when the object is deleted or is garbage collected. This is an extra safety net in case the user forgets to call worker_group.shutdown() and the pointer to diff --git a/nemo_rl/models/policy/interfaces.py b/nemo_rl/models/policy/interfaces.py index c771446385..9e3c2cd281 100644 --- a/nemo_rl/models/policy/interfaces.py +++ b/nemo_rl/models/policy/interfaces.py @@ -76,37 +76,33 @@ def train(self, data: BatchedDataDict, loss_fn: LossFunction) -> dict[str, Any]: pass @abstractmethod - def prepare_for_training(self, *args, **kwargs): + def prepare_for_training(self, *args: Any, **kwargs: Any) -> None: pass @abstractmethod - def finish_training(self, *args, **kwargs): + def finish_training(self, *args: Any, **kwargs: Any) -> None: pass @abstractmethod - def save_checkpoint(self, *args, **kwargs): + def save_checkpoint(self, *args: Any, **kwargs: Any) -> None: pass @abstractmethod - def load_checkpoint(self, *args, **kwargs): - pass - - @abstractmethod - def shutdown(self): + def shutdown(self) -> bool: pass class ColocatablePolicyInterface(PolicyInterface): @abstractmethod - def offload_before_refit(self): + def offload_before_refit(self) -> None: pass @abstractmethod - def offload_after_refit(self): + def offload_after_refit(self) -> None: pass @abstractmethod - def prepare_weights_for_ipc(self, *args, **kwargs): + def prepare_weights_for_ipc(self, *args: Any, **kwargs: Any) -> dict[str, int]: pass @abstractmethod diff --git a/nemo_rl/models/policy/utils.py b/nemo_rl/models/policy/utils.py index 9973bc8879..38e3c6d4d4 100644 --- a/nemo_rl/models/policy/utils.py +++ b/nemo_rl/models/policy/utils.py @@ -34,7 +34,7 @@ def import_class_from_path(name: str) -> Any: return cls_instance -def get_gpu_info(model: torch.nn.Module) -> dict: +def get_gpu_info(model: torch.nn.Module) -> dict[str, Any]: """Return information about the GPU being used by this worker.""" import torch @@ -100,7 +100,7 @@ def get_gpu_info(model: torch.nn.Module) -> dict: } -def sliding_window_overwrite(model_name: str) -> dict: +def sliding_window_overwrite(model_name: str) -> dict[str, Any]: """Returns configuration overrides to handle sliding window settings based on model rules. Args: diff --git a/nemo_rl/utils/checkpoint.py b/nemo_rl/utils/checkpoint.py index 3d9b1300a0..6255dcc79f 100644 --- a/nemo_rl/utils/checkpoint.py +++ b/nemo_rl/utils/checkpoint.py @@ -22,26 +22,28 @@ import os import shutil from pathlib import Path -from typing import Any, Optional, TypedDict +from typing import Any, Optional, TypedDict, Union import numpy as np import torch import yaml +PathLike = Union[str, "os.PathLike[Any]"] + class CheckpointingConfig(TypedDict): """Configuration for checkpoint management. Attributes: enabled (bool): Whether checkpointing is enabled. - checkpoint_dir (os.PathLike): Directory where checkpoints will be saved. + checkpoint_dir (PathLike): Directory where checkpoints will be saved. metric_name (str): Name of the metric to use for determining best checkpoints. higher_is_better (bool): Whether higher values of the metric indicate better performance. keep_top_k (Optional[int]): Number of best checkpoints to keep. If None, all checkpoints are kept. """ enabled: bool - checkpoint_dir: os.PathLike + checkpoint_dir: PathLike metric_name: str higher_is_better: bool save_period: int @@ -85,7 +87,7 @@ def init_tmp_checkpoint( step: int, training_info: dict[str, Any], run_config: Optional[dict[str, Any]] = None, - ) -> os.PathLike: + ) -> PathLike: """Initialize a temporary checkpoint directory. Creates a temporary directory for a new checkpoint and saves training info @@ -100,7 +102,7 @@ def init_tmp_checkpoint( run_config (Optional[dict[str, Any]]): Optional configuration for the training run. Returns: - os.PathLike: Path to the temporary checkpoint directory. + PathLike: Path to the temporary checkpoint directory. """ # create new step_{step} directory save_dir = self.checkpoint_dir / f"tmp_step_{step}" @@ -121,7 +123,7 @@ def init_tmp_checkpoint( return Path(os.path.abspath(save_dir)) - def finalize_checkpoint(self, checkpoint_path: os.PathLike) -> None: + def finalize_checkpoint(self, checkpoint_path: PathLike) -> None: """Complete a checkpoint by moving it from temporary to permanent location. If a checkpoint at the target location already exists (i.e when resuming training), @@ -129,7 +131,7 @@ def finalize_checkpoint(self, checkpoint_path: os.PathLike) -> None: Also triggers cleanup of old checkpoints based on the keep_top_k setting. Args: - checkpoint_path (os.PathLike): Path to the temporary checkpoint directory. + checkpoint_path (PathLike): Path to the temporary checkpoint directory. """ # rename tmp_step_{step} to step_{step} checkpoint_path = Path(checkpoint_path) @@ -230,12 +232,12 @@ def get_latest_checkpoint_path(self) -> Optional[str]: return str(step_dirs[-1]) def load_training_info( - self, checkpoint_path: Optional[str | os.PathLike] = None + self, checkpoint_path: Optional[PathLike] = None ) -> Optional[dict[str, Any]]: """Load the training info from a checkpoint. Args: - checkpoint_path (Optional[os.PathLike]): Path to the checkpoint. If None, + checkpoint_path (Optional[PathLike]): Path to the checkpoint. If None, returns None. Returns: @@ -250,17 +252,17 @@ def load_training_info( def _load_checkpoint_history( checkpoint_dir: Path, -) -> list[tuple[int, str | os.PathLike, dict[str, Any]]]: +) -> list[tuple[int, PathLike, dict[str, Any]]]: """Load the history of checkpoints and their metrics. Args: checkpoint_dir (Path): Directory containing the checkpoints. Returns: - list[tuple[int, os.PathLike, dict[str, Any]]]: List of tuples containing + list[tuple[int, PathLike, dict[str, Any]]]: List of tuples containing (step_number, checkpoint_path, info) for each checkpoint. """ - checkpoint_history: list[tuple[int, str | os.PathLike, dict[str, Any]]] = [] + checkpoint_history: list[tuple[int, PathLike, dict[str, Any]]] = [] # Find all step directories step_dirs = glob.glob(str(checkpoint_dir / "step_*")) From 9bb8d834c4e4a72f4295eb8dc23f34617539575f Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 13:59:43 -0700 Subject: [PATCH 014/109] lint Signed-off-by: Sahil Jain --- nemo_rl/data/interfaces.py | 1 + nemo_rl/models/generation/vllm.py | 4 ++-- nemo_rl/models/policy/dtensor_policy_worker.py | 2 +- nemo_rl/models/policy/hf_policy.py | 7 +++++-- 4 files changed, 9 insertions(+), 5 deletions(-) diff --git a/nemo_rl/data/interfaces.py b/nemo_rl/data/interfaces.py index 6f5bf41834..a03e9b0c16 100644 --- a/nemo_rl/data/interfaces.py +++ b/nemo_rl/data/interfaces.py @@ -28,6 +28,7 @@ PathLike = Union[str, "os.PathLike[Any]"] TokenizerType = PreTrainedTokenizerBase + class DatumSpec(TypedDict): message_log: LLMMessageLogType length: int # total (concatenated) length of the message tensors diff --git a/nemo_rl/models/generation/vllm.py b/nemo_rl/models/generation/vllm.py index 0875e53749..8317ba6c6e 100644 --- a/nemo_rl/models/generation/vllm.py +++ b/nemo_rl/models/generation/vllm.py @@ -645,7 +645,7 @@ def generate_text( return combined def prepare_for_generation(self, *args: Any, **kwargs: Any) -> bool: - """Wake workers up""" + """Wake workers up.""" try: # Use run_all_workers_single_data for methods that don't need data futures = self.worker_group.run_all_workers_single_data( @@ -659,7 +659,7 @@ def prepare_for_generation(self, *args: Any, **kwargs: Any) -> bool: return False def finish_generation(self, *args: Any, **kwargs: Any) -> bool: - """Sleep workers""" + """Sleep workers.""" try: # Use run_all_workers_single_data for methods that don't need data futures = self.worker_group.run_all_workers_single_data( diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index 69ee27d46a..13c2853f67 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -16,7 +16,7 @@ import os from collections import defaultdict from contextlib import AbstractContextManager, contextmanager, nullcontext -from typing import Any, Iterable, Optional, Union, Generator +from typing import Any, Generator, Iterable, Optional, Union import ray import torch diff --git a/nemo_rl/models/policy/hf_policy.py b/nemo_rl/models/policy/hf_policy.py index 779dd75d5a..078b361b35 100644 --- a/nemo_rl/models/policy/hf_policy.py +++ b/nemo_rl/models/policy/hf_policy.py @@ -13,7 +13,7 @@ # limitations under the License. import os from collections import defaultdict -from typing import Any, Optional, Union, cast, Type +from typing import Any, Optional, Type, Union, cast import ray from transformers import PreTrainedTokenizerBase @@ -38,6 +38,7 @@ PathLike = Union[str, "os.PathLike[Any]"] + class HfPolicy(ColocatablePolicyInterface, GenerationInterface): def __init__( self, @@ -86,7 +87,9 @@ def __init__( self.dp_size = self.worker_group.world_size // self.tensor_parallel_size self.cfg = config - def _get_tied_worker_bundle_indices(self, cluster: RayVirtualCluster) -> list[tuple[int, list[int]]]: + def _get_tied_worker_bundle_indices( + self, cluster: RayVirtualCluster + ) -> list[tuple[int, list[int]]]: """Calculate bundle indices for tensor parallel workers.""" # Get the placement groups (nodes) from the cluster placement_groups = cluster.get_placement_groups() From 73763845a36125a6725088651a5ae338b8aea554 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 14:49:50 -0700 Subject: [PATCH 015/109] Fixed another 100 strict typing mypy errors Signed-off-by: Sahil Jain --- nemo_rl/algorithms/loss_functions.py | 13 +++--- nemo_rl/algorithms/utils.py | 20 +++++----- nemo_rl/data/datasets.py | 25 +++++++----- nemo_rl/distributed/batched_data_dict.py | 8 ++-- nemo_rl/distributed/worker_groups.py | 40 ++++++++++--------- .../models/policy/dtensor_policy_worker.py | 10 +++-- nemo_rl/models/policy/fsdp1_policy_worker.py | 38 +++++++++--------- nemo_rl/utils/logger.py | 18 +++++---- nemo_rl/utils/native_checkpoint.py | 25 +++++++----- 9 files changed, 107 insertions(+), 90 deletions(-) diff --git a/nemo_rl/algorithms/loss_functions.py b/nemo_rl/algorithms/loss_functions.py index 9f5d1d4124..15207e558e 100644 --- a/nemo_rl/algorithms/loss_functions.py +++ b/nemo_rl/algorithms/loss_functions.py @@ -110,7 +110,7 @@ def __call__( next_token_logits: Tensor, data: BatchedDataDict[ClippedPGLossDataDict], total_valid_tokens_or_seqs: torch.Tensor, - ) -> tuple[Tensor, dict]: + ) -> tuple[Tensor, dict[str, Any]]: """Clipped Policy Gradient RL loss function.""" token_mask = data["token_mask"][:, 1:] sample_mask = data["sample_mask"] @@ -157,6 +157,7 @@ def __call__( ).squeeze(-1) # Calculate KL regularization. + kl: float | Tensor if self.reference_policy_kl_penalty != 0: if self.use_on_policy_kl_approximation: # See: docs/guides/grpo.md#on-policy-kl-approximation @@ -187,7 +188,7 @@ def __call__( global_normalization_factor=total_valid_tokens_or_seqs, ) else: - kl = 0 + kl = 0.0 # Calculate clipped loss function if ppo ratio is enabled. if not self.disable_ppo_ratio: @@ -286,11 +287,11 @@ class NLLLoss(LossFunction): def __call__( self, next_token_logits: Tensor, - data: BatchedDataDict, + data: BatchedDataDict[Any], total_valid_tokens_or_seqs: torch.Tensor, dpo_loss: bool = False, dpo_average_log_probs: bool = False, - ) -> tuple[Tensor, dict]: + ) -> tuple[Tensor, dict[str, Any]]: # logits shape: [batch_size, seq_len, vocab_size] # Get the next token logits for each position token_mask = data["token_mask"][:, 1:] @@ -420,7 +421,7 @@ def __init__(self, cfg: DPOLossConfig): self.loss_type = LossType.SEQUENCE_LEVEL - def split_output_tensor(self, tensor: Tensor): + def split_output_tensor(self, tensor: Tensor) -> tuple[Tensor, Tensor]: return tensor[::2], tensor[1::2] def _preference_loss( @@ -495,7 +496,7 @@ def __call__( next_token_logits: Tensor, data: BatchedDataDict[DPOLossDataDict], total_valid_tokens_or_seqs: Tensor, - ) -> tuple[Tensor, dict]: + ) -> tuple[Tensor, dict[str, Any]]: sft_loss_chosen = torch.tensor(0.0) if self.sft_loss_weight > 0: sft_loss, _ = self.sft_loss( diff --git a/nemo_rl/algorithms/utils.py b/nemo_rl/algorithms/utils.py index e8c888848d..0351ec32b2 100644 --- a/nemo_rl/algorithms/utils.py +++ b/nemo_rl/algorithms/utils.py @@ -26,7 +26,7 @@ def calculate_kl_penalty_joschu2020( logprobs_policy: torch.Tensor, logprobs_reference: torch.Tensor -): +) -> torch.Tensor: """Calculates a per-token estimate of the KL Divergence between two log_probs. From Schulman 2020, always positive. @@ -43,7 +43,7 @@ def calculate_baseline_and_std_per_prompt( rewards: torch.Tensor, valid_mask: torch.Tensor, leave_one_out_baseline: bool = True, -): +) -> tuple[torch.Tensor, torch.Tensor]: """Function to compute a baseline for each (prompt, response) pair in the batch. The same baseline is calculated for each prompt. Samples set to 0 in 'valid_mask' @@ -56,13 +56,13 @@ def calculate_baseline_and_std_per_prompt( the baseline is for (from RLOO https://arxiv.org/abs/2402.14740) Returns: - tensor (b,) of baselines on the same device as 'rewards' + tensor (b,), tensor (b,) of baselines and std on the same device as 'rewards' """ unique_prompts = torch.unique(prompts, dim=0) baseline = torch.zeros_like(rewards) sq_baseline = torch.zeros_like(rewards) - reward_device = rewards.get_device() + reward_device: torch.device = rewards.get_device() if reward_device == -1: reward_device = torch.device("cpu") @@ -108,9 +108,9 @@ def calculate_baseline_and_std_per_prompt( return baseline, std -def surpress_user_warnings(f): +def surpress_user_warnings(f): # type: ignore @wraps(f) - def wrapper(*args, **kwargs): + def wrapper(*args, **kwargs): # type: ignore with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=UserWarning) output = f(*args, **kwargs) @@ -120,11 +120,11 @@ def wrapper(*args, **kwargs): def masked_mean( - values, - mask, + values: torch.Tensor, + mask: torch.Tensor, dim: Optional[int] = None, global_normalization_factor: Optional[torch.Tensor] = None, -): +) -> torch.Tensor: """Computes the mean of a microbatch, using a global statistic as the normalization factor.""" normalization_factor = ( torch.sum(mask, dim=dim) @@ -134,7 +134,7 @@ def masked_mean( return torch.sum(values * mask, dim=dim) / (normalization_factor + 1e-8) -def set_seed(seed: int): +def set_seed(seed: int) -> None: """Sets the seed for python, numpy, and pytorch.""" random.seed(seed) np.random.seed(seed) diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index 34f9503553..a5c153c86e 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -15,6 +15,7 @@ import torch from datasets import Dataset +from transformers import PreTrainedTokenizerBase from nemo_rl.data.interfaces import ( DatumSpec, @@ -28,6 +29,8 @@ ) from nemo_rl.distributed.batched_data_dict import BatchedDataDict +TokenizerType = PreTrainedTokenizerBase + # TODO @sahilj handle too-long prompts and masking them out throughout the whole process and renormalizing on loss class AllTaskProcessedDataset: @@ -47,7 +50,7 @@ class AllTaskProcessedDataset: def __init__( self, dataset: Union[Dataset, Any], - tokenizer, + tokenizer: TokenizerType, default_task_data_spec: TaskDataSpec, task_data_processors: Union[ dict[str, tuple[TaskDataSpec, TaskDataProcessFnCallable]], @@ -69,7 +72,7 @@ def __init__( ) in task_data_processors.items(): task_data_spec.copy_defaults(self.default_task_data_spec) - def __len__(self): + def __len__(self) -> int: return len(self.dataset) def encode_single( @@ -111,7 +114,7 @@ def __getitem__(self, idx: int) -> DatumSpec: return datum_spec -def rl_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: +def rl_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict[Any]: """Collate function for RL training.""" message_log = [datum_spec["message_log"] for datum_spec in data_batch] length = torch.tensor([datum_spec["length"] for datum_spec in data_batch]) @@ -130,7 +133,7 @@ def rl_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: # Extract stop_strings if present stop_strings = [datum.get("stop_strings", None) for datum in data_batch] - output: BatchedDataDict = BatchedDataDict( + output: BatchedDataDict[Any] = BatchedDataDict( message_log=message_log, length=length, loss_multiplier=loss_multiplier, @@ -143,7 +146,7 @@ def rl_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: return output -def eval_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: +def eval_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict[Any]: """Collate function for evaluation. Takes a list of data samples and combines them into a single batched dictionary @@ -186,7 +189,7 @@ def eval_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: extra_env_info = [datum_spec["extra_env_info"] for datum_spec in data_batch] idx = [datum_spec["idx"] for datum_spec in data_batch] - output: BatchedDataDict = BatchedDataDict( + output: BatchedDataDict[Any] = BatchedDataDict( message_log=message_log, extra_env_info=extra_env_info, idx=idx, @@ -195,8 +198,10 @@ def eval_collate_fn(data_batch: list[DatumSpec]) -> BatchedDataDict: def dpo_collate_fn( - data_batch: list[DPODatumSpec], tokenizer, make_sequence_length_divisible_by: int -) -> BatchedDataDict: + data_batch: list[DPODatumSpec], + tokenizer: TokenizerType, + make_sequence_length_divisible_by: int, +) -> BatchedDataDict[Any]: """Collate function for DPO training. This function separates the chosen and rejected responses to create @@ -222,7 +227,7 @@ def dpo_collate_fn( batch_max_length = torch.ones_like(length_batch) * length_batch.max() - batch: BatchedDataDict = BatchedDataDict( + batch: BatchedDataDict[Any] = BatchedDataDict( message_log=message_log, length=length_batch, loss_multiplier=loss_multiplier_batch, @@ -243,7 +248,7 @@ def dpo_collate_fn( make_sequence_length_divisible_by=make_sequence_length_divisible_by, ) - train_data: BatchedDataDict = BatchedDataDict( + train_data: BatchedDataDict[Any] = BatchedDataDict( { "input_ids": cat_and_padded["token_ids"], "input_lengths": input_lengths, diff --git a/nemo_rl/distributed/batched_data_dict.py b/nemo_rl/distributed/batched_data_dict.py index 20ffce3fa0..7b7bfcd398 100644 --- a/nemo_rl/distributed/batched_data_dict.py +++ b/nemo_rl/distributed/batched_data_dict.py @@ -39,7 +39,7 @@ class BatchedDataDict(UserDict, Generic[DictT]): @classmethod def from_batches( cls: Type[Self], - batches: list[dict], + batches: list[dict[Any, Any]], pad_value_dict: Optional[dict[str, int]] = None, ) -> Self: """Given a list of batches, stack the tensors/lists within and put them in a single dictionary. @@ -60,7 +60,7 @@ def from_batches( list_of_tensors = [item[k] for item in batches] if isinstance(list_of_tensors[0], list): - tensor_or_list: list | torch.Tensor = [ + tensor_or_list: list[Any] | torch.Tensor = [ item for sublist in list_of_tensors for item in sublist ] elif all(x.ndim == 1 for x in list_of_tensors): @@ -334,7 +334,7 @@ def size(self) -> int: return 0 if not torch.is_tensor(self.data[key]): return len(self.data[key]) - return self.data[key].shape[0] + return self.data[key].shape[0] # type: ignore # it's a tensor here def to(self, device: str | torch.device) -> Self: """Move tensors in batched dict to device.""" @@ -365,7 +365,7 @@ def select_indices(self, indices: Union[list[int], torch.Tensor]) -> Self: ) return selected_batch - def get_dict(self) -> dict: + def get_dict(self) -> dict[Any, Any]: """Get the underlying data dictionary.""" return self.data diff --git a/nemo_rl/distributed/worker_groups.py b/nemo_rl/distributed/worker_groups.py index f3f71921c1..56972b8f11 100644 --- a/nemo_rl/distributed/worker_groups.py +++ b/nemo_rl/distributed/worker_groups.py @@ -34,7 +34,7 @@ class MultiWorkerFuture: used_workers: list[int] respect_tied_workers: bool = True - def get_results(self, worker_group) -> list[Any]: + def get_results(self, worker_group: "RayWorkerGroup") -> list[Any]: """Get results from the futures, optionally respecting tied workers. When respect_tied_workers is True, this method deduplicates results by returning @@ -81,7 +81,7 @@ def get_results(self, worker_group) -> list[Any]: class RayWorkerBuilder: - def __init__(self, ray_actor_class: type, *args, **kwargs): + def __init__(self, ray_actor_class: type, *args: Any, **kwargs: Any): self.ray_actor_class = ray_actor_class self.args = args self.kwargs = kwargs @@ -90,10 +90,10 @@ def __call__( self, placement_group: PlacementGroup, placement_group_bundle_index: int, - num_gpus: int, - bundle_indices: Optional[tuple] = None, - **extra_options: dict[str, Any], - ): + num_gpus: float | int, + bundle_indices: Optional[tuple[int, list[int]]] = None, + **extra_options: Any, + ) -> ray.actor.ActorHandle: """Create a Ray worker with the specified configuration. Order of precedence for worker options configuration (from lowest to highest): @@ -107,7 +107,7 @@ def __call__( Args: placement_group: Ray placement group for resource allocation placement_group_bundle_index: Index of the bundle in the placement group - num_gpus: Number of GPUs to allocate to this worker + num_gpus: Number of GPUs to allocate to this worker (can be fractional) bundle_indices: Tuple of (node_idx, local_bundle_indices) for tensor parallelism (if applicable) extra_options: Additional options to pass to the Ray actor (may be overridden by actor's configure_worker(...) method) @@ -192,7 +192,7 @@ def __init__( remote_worker_builder: RayWorkerBuilder, workers_per_node: Optional[Union[int, list[int]]] = None, name_prefix: str = "", - bundle_indices_list: Optional[list[tuple]] = None, + bundle_indices_list: Optional[list[tuple[int, list[int]]]] = None, ): """Initialize a group of distributed Ray workers. @@ -206,7 +206,7 @@ def __init__( Each tuple defines a tied group of workers placed on the same node. If provided, workers_per_node is ignored. """ - self._workers: list[ray.ObjectRef] = [] + self._workers: list[ray.actor.ActorHandle] = [] self._worker_metadata: list[dict[str, Any]] = [] self.cluster = cluster self.name_prefix = name_prefix @@ -259,8 +259,10 @@ def __init__( ) def _create_workers_from_bundle_indices( - self, remote_worker_builder, bundle_indices_list - ): + self, + remote_worker_builder: RayWorkerBuilder, + bundle_indices_list: list[tuple[int, list[int]]], + ) -> None: """Create workers based on explicit bundle indices for tied worker groups. Args: @@ -355,15 +357,15 @@ def _create_workers_from_bundle_indices( self.tied_workers_groups.append(current_group) @property - def workers(self): + def workers(self) -> list[ray.actor.ActorHandle]: return self._workers @property - def worker_metadata(self): + def worker_metadata(self) -> list[dict[str, Any]]: return self._worker_metadata @property - def group_count(self): + def group_count(self) -> int: """Number of tied worker groups.""" return len(self.tied_workers_groups) @@ -373,7 +375,7 @@ def run_all_workers_multiple_data( data: list[SlicedDataDict], common_kwargs: Optional[dict[str, Any]] = None, only_on: Literal["all", "tied_leader", "all_tied_workers"] = "all", - ): + ) -> MultiWorkerFuture: """Run a method on all workers in parallel with different data. Args: @@ -458,10 +460,10 @@ def run_all_workers_multiple_data( def run_all_workers_single_data( self, method_name: str, - *args, + *args: Any, only_on: Literal["all", "tied_leader", "all_tied_workers"] = "all", - **kwargs, - ): + **kwargs: Any, + ) -> list[ray.ObjectRef]: """Run a method on all workers in parallel with the same data. Args: @@ -578,7 +580,7 @@ def shutdown( return success - def print_worker_layout(self): + def print_worker_layout(self) -> None: """Prints a visual representation of the worker layout across the virtual cluster. This shows which workers are assigned to which nodes and GPUs. diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index 13c2853f67..40a4a9c164 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -756,13 +756,13 @@ def move_buffer_to_device(self, model: nn.Module, device: str) -> nn.Module: return model - def move_to_cuda(self, model): + def move_to_cuda(self, model: torch.nn.Module) -> torch.nn.Module: model = self.move_to_device(model, "cuda") gc.collect() torch.cuda.empty_cache() return model - def move_to_cpu(self, model): + def move_to_cpu(self, model: torch.nn.Module) -> torch.nn.Module: model = self.move_to_device(model, "cpu") gc.collect() torch.cuda.empty_cache() @@ -773,7 +773,7 @@ def save_checkpoint( weights_path: str, optimizer_path: Optional[str] = None, tokenizer_path: Optional[str] = None, - ): + ) -> None: """Save a checkpoint of the model. the optimizer states are saved only if `optimizer` and `optimizer_path` are provided. @@ -788,7 +788,9 @@ def save_checkpoint( tokenizer_path=tokenizer_path, ) - def load_checkpoint(self, weights_path: str, optimizer_path: Optional[str] = None): + def load_checkpoint( + self, weights_path: str, optimizer_path: Optional[str] = None + ) -> None: """Load a checkpoint into the model.""" load_checkpoint( model=self.model, diff --git a/nemo_rl/models/policy/fsdp1_policy_worker.py b/nemo_rl/models/policy/fsdp1_policy_worker.py index 18f4def6ec..39520a7021 100644 --- a/nemo_rl/models/policy/fsdp1_policy_worker.py +++ b/nemo_rl/models/policy/fsdp1_policy_worker.py @@ -17,7 +17,7 @@ import warnings from collections import defaultdict from contextlib import AbstractContextManager, contextmanager, nullcontext -from typing import Any, Optional +from typing import Any, Generator, Optional import ray import torch @@ -61,7 +61,7 @@ class FSDP1PolicyWorker: DEFAULT_PY_EXECUTABLE = PY_EXECUTABLES.BASE - def __repr__(self): + def __repr__(self) -> str: """Customizes the actor's prefix in the Ray logs. This makes it easier to identify which worker is producing specific log messages. @@ -124,7 +124,7 @@ def __init__( # (Initialize device mesh, shard submodules, then shard entire model) # ------------------------------------------------ - def do_fsdp(model): + def do_fsdp(model: torch.nn.Module) -> torch.nn.Module: if world_size == 1: print( "[INFO] Using a single GPU - skipping FSDP wrapper to avoid GPU memory offloading issues" @@ -222,19 +222,19 @@ def do_fsdp(model): "No weights path provided. Starting from scratch (default policy init)" ) - def is_alive(self): + def is_alive(self) -> bool: return True - def reset_peak_memory_stats(self): + def reset_peak_memory_stats(self) -> None: torch.cuda.reset_peak_memory_stats() - def get_gpu_info(self): + def get_gpu_info(self) -> dict[str, Any]: """Return information about the GPU being used by this worker.""" return get_gpu_info(self.model) def train( self, - data: BatchedDataDict, + data: BatchedDataDict[Any], loss_fn: LossFunction, eval_mode: bool = False, gbs: Optional[int] = None, @@ -271,7 +271,7 @@ def train( losses = [] all_mb_metrics = [] for gb_start in range(0, dataset_size, local_gbs): - global_batch: BatchedDataDict = data.slice( + global_batch: BatchedDataDict[Any] = data.slice( gb_start, gb_start + local_gbs ) @@ -405,7 +405,7 @@ def train( return metrics def get_logprobs( - self, data: BatchedDataDict, micro_batch_size: Optional[int] = None + self, data: BatchedDataDict[Any], micro_batch_size: Optional[int] = None ) -> BatchedDataDict[LogprobOutputSpec]: """Get the logprobs of the model for a batch of data. @@ -486,7 +486,7 @@ def get_logprobs( return return_data @contextmanager - def use_reference_model(self): + def use_reference_model(self) -> Generator[None, None, None]: """Context manager that temporarily swaps the reference model and active model. On entry: Moves model to CPU, moves reference_model to CUDA. Swaps the references @@ -518,7 +518,7 @@ def use_reference_model(self): torch.cuda.empty_cache() def get_reference_policy_logprobs( - self, data: BatchedDataDict, micro_batch_size: Optional[int] = None + self, data: BatchedDataDict[Any], micro_batch_size: Optional[int] = None ) -> BatchedDataDict[ReferenceLogprobOutputSpec]: """Get the logprobs from the reference policy for a batch of data. @@ -780,7 +780,7 @@ def generate( return return_data - def _add_noise_to_weights(self): + def _add_noise_to_weights(self) -> None: """Add small Gaussian noise to the weights of the model. Note that this is used for testing purposes only.""" # TODO @sahilj: do this without a summon (maybe FSDP2) noise_std = 0.01 # Standard deviation for the noise @@ -807,7 +807,7 @@ def report_device_id(self) -> str: return get_device_uuid(device_idx) @torch.no_grad() - def prepare_weights_for_ipc(self): + def prepare_weights_for_ipc(self) -> dict[str, int]: from torch.distributed.fsdp.api import ShardedStateDictConfig, StateDictType # If the model is not FSDP, then we need to manually move it to the GPU @@ -834,7 +834,7 @@ def prepare_weights_for_ipc(self): return state_dict_info @torch.no_grad() - def get_weights_ipc_handles(self, keys): + def get_weights_ipc_handles(self, keys: list[str]) -> dict[str, Any]: from torch.distributed.tensor import DTensor from torch.multiprocessing.reductions import reduce_tensor @@ -867,12 +867,12 @@ def get_weights_ipc_handles(self, keys): return {device_uuid: all_handles} - def prepare_for_lp_inference(self): + def prepare_for_lp_inference(self) -> None: self.model = self.manual_load_to_gpu(self.model) self.model.eval() self.offload_before_refit() - def prepare_for_training(self, *args, **kwargs): + def prepare_for_training(self, *args: Any, **kwargs: Any) -> None: # onload models and optimizer state to cuda self.model = self.manual_load_to_gpu(self.model) self.model.train() @@ -933,7 +933,7 @@ def offload_after_refit(self): f"GPU Memory after refit complete: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved" ) - def manual_offload_to_cpu(self, model): + def manual_offload_to_cpu(self, model: torch.nn.Module) -> torch.nn.Module: if self.cfg["fsdp_offload_enabled"]: return model @@ -951,7 +951,7 @@ def manual_offload_to_cpu(self, model): return model - def manual_load_to_gpu(self, model): + def manual_load_to_gpu(self, model: torch.nn.Module) -> torch.nn.Module: if self.cfg["fsdp_offload_enabled"]: return model @@ -974,7 +974,7 @@ def save_checkpoint( weights_path: str, optimizer_path: Optional[str] = None, tokenizer_path: Optional[str] = None, - ): + ) -> None: """Save a checkpoint of the model. The checkpoint is saved in the following format: diff --git a/nemo_rl/utils/logger.py b/nemo_rl/utils/logger.py index a7bdbf07a0..978bef3280 100644 --- a/nemo_rl/utils/logger.py +++ b/nemo_rl/utils/logger.py @@ -21,7 +21,7 @@ import threading import time from abc import ABC, abstractmethod -from typing import Any, Mapping, Optional, TypedDict, cast +from typing import Any, Callable, Mapping, Optional, TypedDict, cast import ray import requests @@ -220,7 +220,7 @@ def __init__( self.collection_thread: Optional[threading.Thread] = None self.lock = threading.Lock() - def start(self): + def start(self) -> None: """Start the GPU monitoring thread.""" if not ray.is_initialized(): raise ValueError( @@ -241,7 +241,7 @@ def start(self): f"GPU monitoring started with collection interval={self.collection_interval}s, flush interval={self.flush_interval}s" ) - def stop(self): + def stop(self) -> None: """Stop the GPU monitoring thread.""" self.is_running = False if self.collection_thread: @@ -251,7 +251,7 @@ def stop(self): self.flush() print("GPU monitoring stopped") - def _collection_loop(self): + def _collection_loop(self) -> None: """Main collection loop that runs in a separate thread.""" while self.is_running: try: @@ -418,7 +418,9 @@ def _collect(self, metrics: bool = False, sku: bool = False) -> dict[str, Any]: print(f"Error collecting GPU metrics: {e}") return {} - def _fetch_and_parse_metrics(self, node_idx, metric_address, parser_fn): + def _fetch_and_parse_metrics( + self, node_idx: int, metric_address: str, parser_fn: Callable[Any, int] + ): """Fetch metrics from a node and parse GPU metrics. Args: @@ -458,7 +460,7 @@ def _fetch_and_parse_metrics(self, node_idx, metric_address, parser_fn): print(f"Error fetching metrics from {metric_address}: {e}") return {} - def flush(self): + def flush(self) -> None: """Flush collected metrics to the parent logger.""" with self.lock: if not self.metrics_buffer: @@ -747,7 +749,7 @@ def print_message_log_samples( discrete_lines.append("[bold bright_white]Discrete Reward Levels:[/]") # Get emoji for each reward level - def get_reward_emoji(reward): + def get_reward_emoji(reward: float) -> str: if reward >= 0.7: return "🔥" # Excellent elif reward >= 0.3: @@ -810,7 +812,7 @@ def get_reward_emoji(reward): console.print("\n[bold bright_white]Sample Conversations[/]") # Helper function to safely render content that might have problematic markups - def safe_render(content, role_color): + def safe_render(content: str, role_color: str) -> str: # Fix common problematic patterns that might break Rich markup # Replace any standalone [/ without matching closing bracket content = content.replace("[/", "\\[/") diff --git a/nemo_rl/utils/native_checkpoint.py b/nemo_rl/utils/native_checkpoint.py index b9c685a628..b857264d31 100644 --- a/nemo_rl/utils/native_checkpoint.py +++ b/nemo_rl/utils/native_checkpoint.py @@ -41,10 +41,10 @@ class ModelState(Stateful): model: The PyTorch model to track. """ - def __init__(self, model): + def __init__(self, model: torch.nn.Module): self.model = model - def state_dict(self): + def state_dict(self) -> dict[str, Any]: """Get the model's state dictionary. Returns: @@ -59,7 +59,7 @@ def state_dict(self): ) return model_state_dict - def load_state_dict(self, state_dict): + def load_state_dict(self, state_dict: dict[str, Any]) -> None: """Load the state dictionary into the model. Args: @@ -84,12 +84,17 @@ class OptimizerState(Stateful): scheduler: Optional learning rate scheduler. """ - def __init__(self, model, optimizer, scheduler=None): + def __init__( + self, + model: torch.nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Optional[Any] = None, + ): self.model = model self.optimizer = optimizer self.scheduler = scheduler - def state_dict(self): + def state_dict(self) -> dict[str, Any]: """Get the optimizer and scheduler state dictionaries. Returns: @@ -112,7 +117,7 @@ def state_dict(self): return state_dict - def load_state_dict(self, state_dict): + def load_state_dict(self, state_dict: dict[str, Any]) -> None: """Load the state dictionaries into the optimizer and scheduler. Args: @@ -126,12 +131,12 @@ def load_state_dict(self, state_dict): ) ## load the scheduler state if it exists - if "sched" in state_dict: + if "sched" in state_dict and self.scheduler is not None: self.scheduler.load_state_dict(state_dict["sched"]) def save_checkpoint( - model, + model: torch.nn.Module, weights_path: str, optimizer: Optional[torch.optim.Optimizer] = None, scheduler: Optional[Any] = None, @@ -168,7 +173,7 @@ def save_checkpoint( def load_checkpoint( - model, + model: torch.nn.Module, weights_path: str, optimizer: Optional[torch.optim.Optimizer] = None, scheduler: Optional[Any] = None, @@ -203,7 +208,7 @@ def convert_dcp_to_hf( model_name_or_path: str, tokenizer_name_or_path: str, overwrite: bool = False, -): +) -> str: """Convert a Torch DCP checkpoint to a Hugging Face checkpoint. This is not an optimized utility. If checkpoint is too large, consider saving DCP during training From b3f509869574b9c742aeb778146700788d6eda32 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 16:33:32 -0700 Subject: [PATCH 016/109] Fixed another 100 strict typing mypy errors (down to 130) Signed-off-by: Sahil Jain --- mypy.ini | 9 ++++ nemo_rl/algorithms/grpo.py | 4 +- nemo_rl/algorithms/loss_functions.py | 3 +- nemo_rl/algorithms/utils.py | 6 ++- nemo_rl/data/hf_datasets/oasst.py | 10 ++-- nemo_rl/data/hf_datasets/squad.py | 6 ++- nemo_rl/models/generation/__init__.py | 2 +- nemo_rl/models/generation/interfaces.py | 4 ++ nemo_rl/models/generation/vllm.py | 50 ++++++++++--------- .../models/policy/dtensor_policy_worker.py | 36 +++++++------ nemo_rl/models/policy/fsdp1_policy_worker.py | 10 ++-- nemo_rl/models/policy/hf_policy.py | 4 +- nemo_rl/models/policy/interfaces.py | 4 +- nemo_rl/utils/logger.py | 12 ++--- 14 files changed, 97 insertions(+), 63 deletions(-) diff --git a/mypy.ini b/mypy.ini index 568455b826..dece5e7119 100644 --- a/mypy.ini +++ b/mypy.ini @@ -16,3 +16,12 @@ ignore_missing_imports = True [mypy-transformers.*] ignore_missing_imports = True + +[mypy-vllm.*] +ignore_missing_imports = True + +[mypy-math_verify.*] +ignore_missing_imports = True + +[mypy-torchdata.*] +ignore_missing_imports = True diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index e39ffea7c9..508b72b1bf 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -292,12 +292,12 @@ def refit_policy_generation( policy.offload_before_refit() policy_generation.prepare_for_generation(tags=["weights"]) # Streaming update weights to save memory - state_dict_info: dict[str, int] = policy.prepare_weights_for_ipc() + state_dict_info: list[tuple[str, int]] = policy.prepare_weights_for_ipc() # group keys to save time available_bytes = refit_buffer_size_gb * (1024**3) split_keys: list[list[str]] = [] keys: list[str] = [] - for key, size_in_bytes in state_dict_info.items(): + for key, size_in_bytes in state_dict_info: if size_in_bytes > available_bytes: if keys: split_keys.append(keys) diff --git a/nemo_rl/algorithms/loss_functions.py b/nemo_rl/algorithms/loss_functions.py index 15207e558e..cd3e6148f4 100644 --- a/nemo_rl/algorithms/loss_functions.py +++ b/nemo_rl/algorithms/loss_functions.py @@ -157,7 +157,6 @@ def __call__( ).squeeze(-1) # Calculate KL regularization. - kl: float | Tensor if self.reference_policy_kl_penalty != 0: if self.use_on_policy_kl_approximation: # See: docs/guides/grpo.md#on-policy-kl-approximation @@ -188,7 +187,7 @@ def __call__( global_normalization_factor=total_valid_tokens_or_seqs, ) else: - kl = 0.0 + kl = torch.tensor(0.0) # Calculate clipped loss function if ppo ratio is enabled. if not self.disable_ppo_ratio: diff --git a/nemo_rl/algorithms/utils.py b/nemo_rl/algorithms/utils.py index 0351ec32b2..79d82d6ce1 100644 --- a/nemo_rl/algorithms/utils.py +++ b/nemo_rl/algorithms/utils.py @@ -62,9 +62,11 @@ def calculate_baseline_and_std_per_prompt( baseline = torch.zeros_like(rewards) sq_baseline = torch.zeros_like(rewards) - reward_device: torch.device = rewards.get_device() - if reward_device == -1: + device_ordinal = rewards.get_device() + if device_ordinal == -1: reward_device = torch.device("cpu") + else: + reward_device = torch.device(reward_device) for i in range(len(unique_prompts)): is_matching_prompt = (prompts == unique_prompts[i]).all(1) diff --git a/nemo_rl/data/hf_datasets/oasst.py b/nemo_rl/data/hf_datasets/oasst.py index decf9769dc..a0c19b6909 100644 --- a/nemo_rl/data/hf_datasets/oasst.py +++ b/nemo_rl/data/hf_datasets/oasst.py @@ -25,7 +25,7 @@ SYSTEM_PROMPT = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n" -def parse_conversations(tree_obj, first=False): +def parse_conversations(tree_obj, first: bool = False): """Recusive function that returns all the sub converstaions in a list starting from node tree_obj. Args: @@ -86,7 +86,11 @@ def get_data_records(objs): return output -def download_and_process_oasst(output_directory=".", seed=42, split_ratio=0.95): +def download_and_process_oasst( + output_directory: str = ".", + seed: int = 42, + split_ratio: float = 0.95, +) -> dict[str, list]: os.makedirs(output_directory, exist_ok=True) filename = f"{output_directory}/2023-04-12_oasst_all.trees.jsonl.gz" @@ -119,7 +123,7 @@ def download_and_process_oasst(output_directory=".", seed=42, split_ratio=0.95): class OasstDataset: - def __init__(self, output_dir: str = "."): + def __init__(self, output_dir: str = ".") -> None: self.formatted_ds = download_and_process_oasst(output_dir) self.task_spec = TaskDataSpec( task_name="OASST", diff --git a/nemo_rl/data/hf_datasets/squad.py b/nemo_rl/data/hf_datasets/squad.py index 1400dad5ba..069847b5a1 100644 --- a/nemo_rl/data/hf_datasets/squad.py +++ b/nemo_rl/data/hf_datasets/squad.py @@ -13,12 +13,14 @@ # limitations under the License. +from typing import Any + from datasets import load_dataset from nemo_rl.data.interfaces import TaskDataSpec -def format_squad(data): +def format_squad(data: dict[str, Any]) -> dict[str, list[dict[str, str]]]: return { "messages": [ { @@ -38,7 +40,7 @@ def format_squad(data): class SquadDataset: - def __init__(self): + def __init__(self) -> None: original_ds = load_dataset("rajpurkar/squad") self.formatted_ds = original_ds.map(format_squad) self.task_spec = TaskDataSpec( diff --git a/nemo_rl/models/generation/__init__.py b/nemo_rl/models/generation/__init__.py index 5c44705a5d..2c0de51b48 100644 --- a/nemo_rl/models/generation/__init__.py +++ b/nemo_rl/models/generation/__init__.py @@ -23,7 +23,7 @@ def configure_generation_config( config: GenerationConfig, tokenizer: TokenizerType, is_eval=False -): +) -> GenerationConfig: """Apply specific configurations to generation config.""" # tokenizer setting config["pad_token_id"] = tokenizer.pad_token_id diff --git a/nemo_rl/models/generation/interfaces.py b/nemo_rl/models/generation/interfaces.py index 8f57de9185..a9b6383984 100644 --- a/nemo_rl/models/generation/interfaces.py +++ b/nemo_rl/models/generation/interfaces.py @@ -208,3 +208,7 @@ def prepare_for_generation(self, *args: Any, **kwargs: Any) -> bool: @abstractmethod def finish_generation(self, *args: Any, **kwargs: Any) -> bool: pass + + def update_weights(self, ipc_handles: dict[str, Any]) -> bool: + """Update the model weights from the given IPC handles.""" + raise NotImplementedError diff --git a/nemo_rl/models/generation/vllm.py b/nemo_rl/models/generation/vllm.py index 8317ba6c6e..a58dc47591 100644 --- a/nemo_rl/models/generation/vllm.py +++ b/nemo_rl/models/generation/vllm.py @@ -15,7 +15,7 @@ import copy import gc import os -from typing import Any, NotRequired, Optional, TypedDict, Union +from typing import Any, NotRequired, Optional, TypedDict, Union, cast import ray import torch @@ -46,14 +46,14 @@ class VllmSpecificArgs(TypedDict): class VllmConfig(GenerationConfig): vllm_cfg: VllmSpecificArgs - vllm_kwargs: NotRequired[dict] + vllm_kwargs: NotRequired[dict[str, Any]] @ray.remote class VllmGenerationWorker: DEFAULT_PY_EXECUTABLE = PY_EXECUTABLES.VLLM - def __repr__(self): + def __repr__(self) -> str: """Customizes the actor's prefix in the Ray logs. This makes it easier to identify which worker is producing specific log messages. @@ -62,8 +62,8 @@ def __repr__(self): @staticmethod def configure_worker( - num_gpus: int | float, bundle_indices: Optional[tuple] = None - ) -> tuple[dict, dict, dict]: + num_gpus: int | float, bundle_indices: Optional[tuple[int, list[int]]] = None + ) -> tuple[dict[str, Any], dict[str, str], dict[str, Any]]: """Provides complete worker configuration for vLLM tensor parallelism. This method configures the worker based on its role in tensor parallelism, @@ -80,9 +80,9 @@ def configure_worker( - 'init_kwargs': Parameters to pass to __init__ of the worker """ # Initialize configuration - resources = {"num_gpus": num_gpus} - init_kwargs = {} - env_vars = {} + resources: dict[str, Any] = {"num_gpus": num_gpus} + init_kwargs: dict[str, Any] = {} + env_vars: dict[str, str] = {} local_bundle_indices = None if bundle_indices is not None: @@ -119,7 +119,7 @@ def configure_worker( def __init__( self, config: VllmConfig, - bundle_indices: Optional[list] = None, + bundle_indices: Optional[list[int]] = None, fraction_of_gpus: float = 1.0, seed: Optional[int] = None, ): @@ -160,7 +160,7 @@ def __init__( "vLLM is not installed. Please check that VllmGenerationWorker.DEFAULT_PY_EXECUTABLE covers the vllm dependency. " "If you are working interactively, you can install by running `uv sync --extra vllm` anywhere in the repo." ) - vllm_kwargs: dict = copy.deepcopy(self.cfg.get("vllm_kwargs", {})) + vllm_kwargs: dict[str, Any] = copy.deepcopy(self.cfg.get("vllm_kwargs", {})) # Special handling for tensor parallel case if self.tensor_parallel_size > 1: @@ -203,7 +203,7 @@ def __init__( **vllm_kwargs, ) - def is_alive(self): + def is_alive(self) -> bool: """Check if the worker is alive.""" return True @@ -238,7 +238,7 @@ def generate( input_ids = data["input_ids"] input_lengths = data["input_lengths"] # this function requires all generations have the same stop strings, so we collect all here - batch_stop_strings: list = data.get("stop_strings", []) + batch_stop_strings: list[list[str]] = data.get("stop_strings", []) stop_strings: set[str] = set() for sample_stop_strings in batch_stop_strings: if sample_stop_strings: @@ -375,7 +375,7 @@ def generate_text( - texts: List of generated text responses """ # Extract stop_strings if provided, else use default from config - batch_stop_strings = data.get( + batch_stop_strings: list[list[str]] = data.get( "stop_strings", [self.cfg.get("stop_strings")] * len(data["prompts"]) ) @@ -389,7 +389,7 @@ def generate_text( if self.cfg.get("stop_strings", None): stop_strings.update(self.cfg["stop_strings"]) - stop_strings: Optional[list[str]] = ( + stop_strings: list[str] | None = ( list(stop_strings) if len(stop_strings) > 0 else None ) @@ -418,7 +418,7 @@ def generate_text( ) return return_data - def shutdown(self): + def shutdown(self) -> bool: """Clean up vLLM resources.""" try: # Clear caches and free memory @@ -438,13 +438,13 @@ def report_device_id(self) -> str: assert self.llm is not None, ( "Attempting to report device id with either an uninitialized vLLM or non-model-owner" ) - return self.llm.collective_rpc("report_device_id", args=tuple())[0] + return cast(str, self.llm.collective_rpc("report_device_id", args=tuple())[0]) - def update_weights_from_ipc_handles(self, ipc_handles): + def update_weights_from_ipc_handles(self, ipc_handles: dict[str, Any]) -> bool: """Update weights from IPC handles by delegating to the vLLM Worker implementation. Args: - ipc_handles (dict): Dictionary mapping device UUIDs to parameter IPC handles. + ipc_handles (dict): Dictionary mapping device UUIDs (str) to parameter IPC handles. Returns: bool: True if weights were successfully updated, False otherwise. @@ -462,7 +462,7 @@ def update_weights_from_ipc_handles(self, ipc_handles): print(f"Error updating weights: {e}") return False - def sleep(self): + def sleep(self) -> None: assert self.llm is not None, ( "Attempting to sleep with either an uninitialized vLLM or non-model-owner" ) @@ -472,7 +472,7 @@ def sleep(self): gc.collect() torch.cuda.empty_cache() - def wake_up(self, **kwargs): + def wake_up(self, **kwargs: Any) -> None: assert self.llm is not None, ( "Attempting to wake up with either an uninitialized vLLM or non-model-owner" ) @@ -534,7 +534,9 @@ def __init__( # Number of data parallel groups is the number of tied worker groups self.dp_size = self.worker_group.group_count - def _get_tied_worker_bundle_indices(self, cluster): + def _get_tied_worker_bundle_indices( + self, cluster: RayVirtualCluster + ) -> list[tuple[int, list[int]]]: """Calculate bundle indices for tensor parallel workers.""" # Get the placement groups (nodes) from the cluster placement_groups = cluster.get_placement_groups() @@ -681,13 +683,13 @@ def shutdown(self) -> bool: print(f"Error during policy shutdown: {e}") return False - def update_weights(self, ipc_handles): + def update_weights(self, ipc_handles: dict[str, Any]) -> bool: """Update weights of the policy using IPC handles, considering tensor parallelism. For tp > 1, only the leader in each tensor parallel tied worker group will update weights. Args: - ipc_handles (dict): Dictionary mapping device UUIDs to parameter IPC handles. + ipc_handles (dict): Dictionary mapping device UUIDs (str) to parameter IPC handles. Returns: bool: True if weights were successfully updated, False otherwise. @@ -709,7 +711,7 @@ def update_weights(self, ipc_handles): print(f"Error updating weights: {e}") return False - def __del__(self): + def __del__(self) -> None: """Shuts down the worker groups when the object is deleted or is garbage collected. This is an extra safety net in case the user forgets to call shutdown() and the pointer to diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index 40a4a9c164..0ea74cbabb 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -16,7 +16,7 @@ import os from collections import defaultdict from contextlib import AbstractContextManager, contextmanager, nullcontext -from typing import Any, Generator, Iterable, Optional, Union +from typing import Any, Generator, Iterable, Optional, Union, cast import ray import torch @@ -191,7 +191,9 @@ def __init__( self.model = self.move_buffer_to_device(self.model, "cpu") # used for streaming update inference engine weights - self._held_sharded_state_dict_reference = None + self._held_sharded_state_dict_reference: Optional[dict[str, torch.Tensor]] = ( + None + ) self._held_streamed_param_reference = None if init_reference_model: @@ -212,7 +214,9 @@ def __init__( if "scheduler" in self.cfg and self.optimizer is not None: if isinstance(self.cfg["scheduler"], dict): - scheduler_cls = import_class_from_path(self.cfg["scheduler"]["name"]) + scheduler_cls = import_class_from_path( + cast(str, self.cfg["scheduler"]["name"]) + ) self.scheduler = scheduler_cls( self.optimizer, **self.cfg["scheduler"]["kwargs"] ) @@ -221,7 +225,7 @@ def __init__( for scheduler_cfg in self.cfg["scheduler"]: if "name" in scheduler_cfg: schedulers.append( - import_class_from_path(scheduler_cfg["name"])( + import_class_from_path(cast(str, scheduler_cfg["name"]))( self.optimizer, **scheduler_cfg["kwargs"] ) ) @@ -627,9 +631,11 @@ def report_device_id(self) -> str: return get_device_uuid(device_idx) @torch.no_grad() - def prepare_weights_for_ipc(self) -> dict[str, int]: + def prepare_weights_for_ipc(self) -> list[tuple[str, int]]: self.model = self.move_to_cuda(self.model) - self._held_sharded_state_dict_reference = self.model.state_dict() + self._held_sharded_state_dict_reference: dict[str, torch.Tensor] = ( + self.model.state_dict() + ) # Collect info for streaming multiple tensors state_dict_info = [] for name, tensor in self._held_sharded_state_dict_reference.items(): @@ -639,7 +645,7 @@ def prepare_weights_for_ipc(self) -> dict[str, int]: return state_dict_info @torch.no_grad() - def get_weights_ipc_handles(self, keys: Iterable[str]): + def get_weights_ipc_handles(self, keys: Iterable[str]) -> dict[str, Any]: from torch.multiprocessing.reductions import reduce_tensor assert self._held_sharded_state_dict_reference is not None, ( @@ -671,7 +677,7 @@ def get_weights_ipc_handles(self, keys: Iterable[str]): return {device_uuid: all_handles} - def prepare_for_lp_inference(self): + def prepare_for_lp_inference(self) -> None: if not self.cpu_offload: self.move_to_cuda(self.model) else: @@ -680,7 +686,7 @@ def prepare_for_lp_inference(self): self.model.eval() self.offload_before_refit() - def prepare_for_training(self, *args, **kwargs): + def prepare_for_training(self, *args, **kwargs) -> None: # onload models and optimizer state to cuda if not self.cpu_offload: self.move_to_cuda(self.model) @@ -707,7 +713,7 @@ def prepare_for_training(self, *args, **kwargs): torch.cuda.empty_cache() @torch.no_grad() - def offload_before_refit(self): + def offload_before_refit(self) -> None: """Offload the optimizer to the CPU.""" torch.randn(1).cuda() # wake up torch allocator if hasattr(self, "optimizer") and self.optimizer is not None: @@ -720,7 +726,7 @@ def offload_before_refit(self): torch.cuda.empty_cache() @torch.no_grad() - def offload_after_refit(self): + def offload_after_refit(self) -> None: # Offload as much as possible on the CPU self.model = self.move_to_cpu(self.model) self.model.eval() @@ -745,11 +751,13 @@ def offload_after_refit(self): f"GPU Memory after optimizer offload: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved" ) - def move_to_device(self, model, device): + def move_to_device(self, model: nn.Module, device: str | torch.device) -> nn.Module: model = self.move_buffer_to_device(model, device) return model.to(device) - def move_buffer_to_device(self, model: nn.Module, device: str) -> nn.Module: + def move_buffer_to_device( + self, model: nn.Module, device: str | torch.device + ) -> nn.Module: # FSDP modules do not move buffers to the device automatically for v in model.buffers(): v.data = v.data.to(device) @@ -800,5 +808,5 @@ def load_checkpoint( optimizer_path=optimizer_path, ) - def shutdown(self): + def shutdown(self) -> None: """Shutdown the policy.""" diff --git a/nemo_rl/models/policy/fsdp1_policy_worker.py b/nemo_rl/models/policy/fsdp1_policy_worker.py index 39520a7021..6b6c4fbdfb 100644 --- a/nemo_rl/models/policy/fsdp1_policy_worker.py +++ b/nemo_rl/models/policy/fsdp1_policy_worker.py @@ -17,7 +17,7 @@ import warnings from collections import defaultdict from contextlib import AbstractContextManager, contextmanager, nullcontext -from typing import Any, Generator, Optional +from typing import Any, Generator, Optional, cast import ray import torch @@ -181,7 +181,9 @@ def do_fsdp(model: torch.nn.Module) -> torch.nn.Module: if "scheduler" in self.cfg and self.optimizer is not None: if isinstance(self.cfg["scheduler"], dict): - scheduler_cls = import_class_from_path(self.cfg["scheduler"]["name"]) + scheduler_cls = import_class_from_path( + cast(str, self.cfg["scheduler"]["name"]) + ) self.scheduler = scheduler_cls( self.optimizer, **self.cfg["scheduler"]["kwargs"] ) @@ -190,7 +192,7 @@ def do_fsdp(model: torch.nn.Module) -> torch.nn.Module: for scheduler_cfg in self.cfg["scheduler"]: if "name" in scheduler_cfg: schedulers.append( - import_class_from_path(scheduler_cfg["name"])( + import_class_from_path(cast(str, scheduler_cfg["name"]))( self.optimizer, **scheduler_cfg["kwargs"] ) ) @@ -807,7 +809,7 @@ def report_device_id(self) -> str: return get_device_uuid(device_idx) @torch.no_grad() - def prepare_weights_for_ipc(self) -> dict[str, int]: + def prepare_weights_for_ipc(self) -> list[tuple[str, int]]: from torch.distributed.fsdp.api import ShardedStateDictConfig, StateDictType # If the model is not FSDP, then we need to manually move it to the GPU diff --git a/nemo_rl/models/policy/hf_policy.py b/nemo_rl/models/policy/hf_policy.py index 078b361b35..2171086229 100644 --- a/nemo_rl/models/policy/hf_policy.py +++ b/nemo_rl/models/policy/hf_policy.py @@ -269,7 +269,7 @@ def finish_training(self, *args: Any, **kwargs: Any) -> None: # Placeholder implementation pass - def prepare_weights_for_ipc(self) -> dict[str, int]: + def prepare_weights_for_ipc(self) -> list[tuple[str, int]]: """Prepare the weights for IPC. Returns: @@ -279,7 +279,7 @@ def prepare_weights_for_ipc(self) -> dict[str, int]: "prepare_weights_for_ipc", only_on="all_tied_workers" ) # only get the first worker's result is enough since all workers will have the same result - return cast(dict[str, Any], ray.get(futures)[0]) + return cast(list[tuple[str, int]], ray.get(futures)[0]) def get_weights_ipc_handles(self, keys: list[str]) -> dict[str, Any]: """Fetch weight IPC handles from all workers. diff --git a/nemo_rl/models/policy/interfaces.py b/nemo_rl/models/policy/interfaces.py index 9e3c2cd281..22125d3925 100644 --- a/nemo_rl/models/policy/interfaces.py +++ b/nemo_rl/models/policy/interfaces.py @@ -102,7 +102,9 @@ def offload_after_refit(self) -> None: pass @abstractmethod - def prepare_weights_for_ipc(self, *args: Any, **kwargs: Any) -> dict[str, int]: + def prepare_weights_for_ipc( + self, *args: Any, **kwargs: Any + ) -> list[tuple[str, int]]: pass @abstractmethod diff --git a/nemo_rl/utils/logger.py b/nemo_rl/utils/logger.py index 978bef3280..6135d7feef 100644 --- a/nemo_rl/utils/logger.py +++ b/nemo_rl/utils/logger.py @@ -419,7 +419,7 @@ def _collect(self, metrics: bool = False, sku: bool = False) -> dict[str, Any]: return {} def _fetch_and_parse_metrics( - self, node_idx: int, metric_address: str, parser_fn: Callable[Any, int] + self, node_idx: int, metric_address: str, parser_fn: Callable ): """Fetch metrics from a node and parse GPU metrics. @@ -574,7 +574,7 @@ def log_hyperparams(self, params: Mapping[str, Any]) -> None: logger.log_hyperparams(params) def log_batched_dict_as_jsonl( - self, to_log: BatchedDataDict | dict[str, Any], filename: str + self, to_log: BatchedDataDict[Any] | dict[str, Any], filename: str ) -> None: """Log a list of dictionaries to a JSONL file. @@ -599,7 +599,7 @@ def log_batched_dict_as_jsonl( print(f"Logged data to {filepath}") - def __del__(self): + def __del__(self) -> None: """Clean up resources when the logger is destroyed.""" if self.gpu_monitor: self.gpu_monitor.stop() @@ -631,9 +631,9 @@ def flatten_dict(d: Mapping[str, Any], sep: str = ".") -> dict[str, Any]: {'a.0.b': 1, 'a.1.c': 2} ``` """ - result = {} + result: dict[str, Any] = {} - def _flatten(d, parent_key=""): + def _flatten(d: Mapping[str, Any], parent_key: str = "") -> None: for key, value in d.items(): new_key = f"{parent_key}{sep}{key}" if parent_key else key @@ -830,7 +830,7 @@ def safe_render(content: str, role_color: str) -> str: message_parts = [] for msg in message_log: role = cast(str, msg.get("role", "unknown")).upper() - content = msg.get("content", "") + content = cast(str, msg.get("content", "")) # Choose color based on role - using muted, elegant colors if role == "SYSTEM": From e8b35522400e36ffe4519c90ef90cdd754bb81b3 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 18:06:31 -0700 Subject: [PATCH 017/109] Brought non-strict errors down to 18 Signed-off-by: Sahil Jain --- mypy.ini | 1 + nemo_rl/data/datasets.py | 4 +-- nemo_rl/data/hf_datasets/deepscaler.py | 10 ++++--- nemo_rl/data/hf_datasets/helpsteer3.py | 6 ++-- nemo_rl/data/hf_datasets/openmathinstruct2.py | 10 +++++-- nemo_rl/data/llm_message_utils.py | 9 +++--- nemo_rl/environments/interfaces.py | 2 +- nemo_rl/experience/rollouts.py | 9 +++--- nemo_rl/models/dtensor/parallelize.py | 4 +-- nemo_rl/models/generation/interfaces.py | 1 + .../models/policy/dtensor_policy_worker.py | 4 +-- nemo_rl/models/policy/fsdp1_policy_worker.py | 30 ++++++++++++------- 12 files changed, 55 insertions(+), 35 deletions(-) diff --git a/mypy.ini b/mypy.ini index dece5e7119..df8814a5ee 100644 --- a/mypy.ini +++ b/mypy.ini @@ -1,6 +1,7 @@ [mypy] disable_error_code = no-redef check_untyped_defs = True +python_version = 3.12 [mypy-pynvml.*] ignore_missing_imports = True diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index a5c153c86e..d9427d29a1 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -11,7 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Union +from typing import Any, Optional, Union import torch from datasets import Dataset @@ -56,7 +56,7 @@ def __init__( dict[str, tuple[TaskDataSpec, TaskDataProcessFnCallable]], TaskDataProcessFnCallable, ], - max_seq_length=None, + max_seq_length: Optional[int] = None, ): self.dataset = dataset self.tokenizer = tokenizer diff --git a/nemo_rl/data/hf_datasets/deepscaler.py b/nemo_rl/data/hf_datasets/deepscaler.py index 9734b3e5b8..0c909322df 100644 --- a/nemo_rl/data/hf_datasets/deepscaler.py +++ b/nemo_rl/data/hf_datasets/deepscaler.py @@ -13,12 +13,14 @@ # limitations under the License. -from datasets import load_dataset +from typing import Any + +from datasets import Dataset, load_dataset from nemo_rl.data.interfaces import TaskDataSpec -def format_math(data): +def format_math(data: dict[str, str | float | int]) -> dict[str, list[Any] | str]: return { "messages": [ { @@ -35,7 +37,7 @@ def format_math(data): } -def prepare_deepscaler_dataset(seed=42): +def prepare_deepscaler_dataset(seed: int = 42) -> dict[str, Dataset | None]: """Load and split the DeepScaler dataset into train and test sets.""" # Load the original dataset original_ds = load_dataset("agentica-org/DeepScaleR-Preview-Dataset", split="train") @@ -60,7 +62,7 @@ def prepare_deepscaler_dataset(seed=42): class DeepScalerDataset: - def __init__(self, seed: int = 42): + def __init__(self, seed: int = 42) -> None: """Initialize the DeepScaler dataset with train/test split. Args: diff --git a/nemo_rl/data/hf_datasets/helpsteer3.py b/nemo_rl/data/hf_datasets/helpsteer3.py index a6f67e6032..7d694c4c06 100644 --- a/nemo_rl/data/hf_datasets/helpsteer3.py +++ b/nemo_rl/data/hf_datasets/helpsteer3.py @@ -11,13 +11,15 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import Any + from absl import logging from datasets import load_dataset from nemo_rl.data.interfaces import TaskDataSpec -def format_helpsteer3(data): +def format_helpsteer3(data: dict[str, Any]) -> dict[str, str | dict[str, str]]: response_1 = data["response1"] response_2 = data["response2"] overall_preference = data["overall_preference"] @@ -47,7 +49,7 @@ def format_helpsteer3(data): class HelpSteer3Dataset: """HelpSteer3 preference dataset for DPO training.""" - def __init__(self): + def __init__(self) -> None: ds = load_dataset("nvidia/HelpSteer3", "preference") self.formatted_ds = ds.map(format_helpsteer3) diff --git a/nemo_rl/data/hf_datasets/openmathinstruct2.py b/nemo_rl/data/hf_datasets/openmathinstruct2.py index 457db35c7e..1c35c39489 100644 --- a/nemo_rl/data/hf_datasets/openmathinstruct2.py +++ b/nemo_rl/data/hf_datasets/openmathinstruct2.py @@ -13,12 +13,14 @@ # limitations under the License. -from datasets import load_dataset +from typing import Any + +from datasets import Dataset, load_dataset from nemo_rl.data.interfaces import TaskDataSpec -def format_math(data): +def format_math(data: dict[str, str | float | int]) -> dict[str, list[Any] | str]: return { "messages": [ { @@ -35,7 +37,9 @@ def format_math(data): } -def prepare_openinstructmath2_dataset(split: str = "train_1M", seed=42, test_size=0.05): +def prepare_openinstructmath2_dataset( + split: str = "train_1M", seed: int = 42, test_size: float = 0.05 +) -> dict[str, Dataset | None]: """Load and split the OpenMathInstruct-2 dataset into train and validation sets using HF's train_test_split.""" print( "WARNING: For reproducible experiments, preprocess the dataset once and define your own HfDataset subclass that directly uses the preprocessed datasets." diff --git a/nemo_rl/data/llm_message_utils.py b/nemo_rl/data/llm_message_utils.py index 7947091b30..fe480b6a5f 100644 --- a/nemo_rl/data/llm_message_utils.py +++ b/nemo_rl/data/llm_message_utils.py @@ -315,7 +315,6 @@ def batched_message_log_to_flat_message( continue # Filter out None values and validate consistency - # casting bc we break on str cases above values: list[Tensor | None] = cast(list[Tensor | None], values) tensors = cast(list[Tensor], [t for t in values if t is not None]) _validate_tensor_consistency(tensors) @@ -323,7 +322,7 @@ def batched_message_log_to_flat_message( # Create zero tensors for None values filled_values: list[Tensor] = [ ( - torch.zeros(0, dtype=tensors[0].dtype, device=tensors[0].device) + torch.zeros(0, dtype=tensors[0].dtype, device=tensors[0].device) # type: ignore if v is None else v ) @@ -338,7 +337,7 @@ def batched_message_log_to_flat_message( return result, input_lengths_tensor -def message_log_shape(message_log: LLMMessageLogType) -> list[dict[str, list[int]]]: +def message_log_shape(message_log: LLMMessageLogType) -> list[dict[str, torch.Size]]: """Get the shape of the tensors in the message log. This utility function examines each message in the message log and reports @@ -355,9 +354,9 @@ def message_log_shape(message_log: LLMMessageLogType) -> list[dict[str, list[int shape = {} for k in message.keys(): if isinstance(message[k], Tensor): - shape[k] = message[k].shape + shape[k] = message[k].shape # type: ignore # we know it's a tensor elif isinstance(message[k], list): - shape[k] = [message_log_shape(v) for v in message[k]] + shape[k] = [message_log_shape(v) for v in message[k]] # type: ignore shapes.append(shape) return shapes diff --git a/nemo_rl/environments/interfaces.py b/nemo_rl/environments/interfaces.py index 16624ec456..46bdceb757 100644 --- a/nemo_rl/environments/interfaces.py +++ b/nemo_rl/environments/interfaces.py @@ -38,7 +38,7 @@ class EnvironmentReturn(NamedTuple): observations: list[dict[str, str]] metadata: list[Optional[dict]] - next_stop_strings: list[Optional[list[str]]] + next_stop_strings: list[list[str] | None] | list[None] rewards: Tensor terminateds: Tensor diff --git a/nemo_rl/experience/rollouts.py b/nemo_rl/experience/rollouts.py index e0c74fe086..a86ac8082b 100644 --- a/nemo_rl/experience/rollouts.py +++ b/nemo_rl/experience/rollouts.py @@ -24,6 +24,7 @@ from nemo_rl.data.interfaces import ( DatumSpec, FlatMessagesType, + LLMMessageLogType, ) from nemo_rl.data.llm_message_utils import ( batched_message_log_to_flat_message, @@ -50,7 +51,7 @@ def generate_responses( input_lengths: torch.Tensor, include_logprobs: bool = True, greedy: bool = False, -) -> tuple[BatchedDataDict[DatumSpec], list[torch.Tensor], dict]: +) -> tuple[BatchedDataDict[DatumSpec], list[torch.Tensor], dict[str, float | int]]: """Generate responses from policy.""" # Add stop_strings to generation_input_data if present in the batch if "stop_strings" in batch: @@ -128,7 +129,7 @@ def calculate_rewards( task_names = batch["task_name"] # Group messages by task type - task_groups = {} + task_groups: dict[str, list[tuple[int, LLMMessageLogType]]] = {} for i, task_name in enumerate(task_names): if task_name not in task_groups: task_groups[task_name] = [] @@ -149,7 +150,7 @@ def calculate_rewards( env_info = [batch["extra_env_info"][i] for i in indices] # Submit task to environment and store future - future = task_to_env[task_name].step.remote(messages, env_info) + future = task_to_env[task_name].step.remote(messages, env_info) # type: ignore # ray actor call futures.append(future) future_to_indices[future] = indices @@ -254,7 +255,7 @@ def run_multi_turn_rollout( active_batch = current_batch.select_indices(active_indices) active_stop_strings = [current_stop_strings[i] for i in active_indices.tolist()] - active_flat_messages: FlatMessagesType + active_flat_messages: BatchedDataDict[FlatMessagesType] active_flat_messages, active_input_lengths = ( batched_message_log_to_flat_message( active_batch["message_log"], diff --git a/nemo_rl/models/dtensor/parallelize.py b/nemo_rl/models/dtensor/parallelize.py index 2084eb54f1..cb5cff5e66 100644 --- a/nemo_rl/models/dtensor/parallelize.py +++ b/nemo_rl/models/dtensor/parallelize.py @@ -222,7 +222,7 @@ def _parallelize_llama( if activation_checkpointing: for i in range(len(model.model.layers)): - model.model.layers[i].mlp = checkpoint_wrapper(model.model.layers[i].mlp) + model.model.layers[i].mlp = checkpoint_wrapper(model.model.layers[i].mlp) # type: ignore for layer in model.model.layers: fully_shard( @@ -323,7 +323,7 @@ def _prepare_input_fn(sequence_sharding, mod, inputs, device_mesh): if activation_checkpointing: for i in range(len(model.model.layers)): - model.model.layers[i].mlp = checkpoint_wrapper(model.model.layers[i].mlp) + model.model.layers[i].mlp = checkpoint_wrapper(model.model.layers[i].mlp) # type: ignore for layer in model.model.layers: fully_shard( diff --git a/nemo_rl/models/generation/interfaces.py b/nemo_rl/models/generation/interfaces.py index a9b6383984..8e7a0c1a7e 100644 --- a/nemo_rl/models/generation/interfaces.py +++ b/nemo_rl/models/generation/interfaces.py @@ -108,6 +108,7 @@ class GenerationConfig(TypedDict): top_k: int model_name: str stop_token_ids: list[int] + stop_strings: NotRequired[list[str]] pad_token_id: NotRequired[int] diff --git a/nemo_rl/models/policy/dtensor_policy_worker.py b/nemo_rl/models/policy/dtensor_policy_worker.py index 0ea74cbabb..bcb2c1711d 100644 --- a/nemo_rl/models/policy/dtensor_policy_worker.py +++ b/nemo_rl/models/policy/dtensor_policy_worker.py @@ -194,7 +194,7 @@ def __init__( self._held_sharded_state_dict_reference: Optional[dict[str, torch.Tensor]] = ( None ) - self._held_streamed_param_reference = None + self._held_streamed_param_reference: Optional[dict[str, torch.Tensor]] = None if init_reference_model: self.reference_model_state_dict = get_cpu_state_dict( @@ -225,7 +225,7 @@ def __init__( for scheduler_cfg in self.cfg["scheduler"]: if "name" in scheduler_cfg: schedulers.append( - import_class_from_path(cast(str, scheduler_cfg["name"]))( + import_class_from_path(scheduler_cfg["name"])( self.optimizer, **scheduler_cfg["kwargs"] ) ) diff --git a/nemo_rl/models/policy/fsdp1_policy_worker.py b/nemo_rl/models/policy/fsdp1_policy_worker.py index 6b6c4fbdfb..0418b32d97 100644 --- a/nemo_rl/models/policy/fsdp1_policy_worker.py +++ b/nemo_rl/models/policy/fsdp1_policy_worker.py @@ -168,7 +168,7 @@ def do_fsdp(model: torch.nn.Module) -> torch.nn.Module: # used for streaming update inference engine weights self._held_sharded_state_dict_reference: Optional[dict[str, Any]] = None - self._held_streamed_param_reference = None + self._held_streamed_param_reference: Optional[dict[str, Any]] = None # register_fsdp_forward_method(self.model, "generate") if init_optimizer: @@ -192,7 +192,7 @@ def do_fsdp(model: torch.nn.Module) -> torch.nn.Module: for scheduler_cfg in self.cfg["scheduler"]: if "name" in scheduler_cfg: schedulers.append( - import_class_from_path(cast(str, scheduler_cfg["name"]))( + import_class_from_path(scheduler_cfg["name"])( self.optimizer, **scheduler_cfg["kwargs"] ) ) @@ -623,7 +623,7 @@ def generate( generation_module = self.model.module else: generation_module = self.model - outputs = generation_module.generate( + outputs = generation_module.generate( # type: ignore # we know it's a nn.Module input_ids=left_padded_input_ids, attention_mask=left_padded_attention_mask, max_new_tokens=gen_cfg["max_new_tokens"], @@ -828,7 +828,7 @@ def prepare_weights_for_ipc(self) -> list[tuple[str, int]]: # Collect info for streaming multiple tensors state_dict_info = [] - for name, tensor in self._held_sharded_state_dict_reference.items(): + for name, tensor in self._held_sharded_state_dict_reference.items(): # type: ignore # dtensor's numel will return complete tensor instead of only local tensor size_in_bytes = tensor.element_size() * tensor.numel() state_dict_info.append((name, size_in_bytes)) @@ -890,7 +890,7 @@ def prepare_for_training(self, *args: Any, **kwargs: Any) -> None: torch.cuda.empty_cache() @torch.no_grad() - def offload_before_refit(self): + def offload_before_refit(self) -> None: """Offload the optimizer and buffers to the CPU.""" torch.randn(1).cuda() # wake up torch allocator if not self.cfg["fsdp_offload_enabled"]: @@ -911,7 +911,7 @@ def offload_before_refit(self): ) @torch.no_grad() - def offload_after_refit(self): + def offload_after_refit(self) -> None: # Offload as much as possible on the CPU self.model = self.manual_offload_to_cpu(self.model) self.model.eval() @@ -949,7 +949,11 @@ def manual_offload_to_cpu(self, model: torch.nn.Module) -> torch.nn.Module: buffer.data = buffer.data.to("cpu", non_blocking=True) if hasattr(model, "_fsdp_wrapped_module"): - self.manual_offload_to_cpu(model._fsdp_wrapped_module) + wrapped_module = model._fsdp_wrapped_module + assert isinstance(wrapped_module, torch.nn.Module), ( + f"wrapped_module is not a torch.nn.Module: instead, {type(wrapped_module)}" + ) + self.manual_offload_to_cpu(wrapped_module) return model @@ -967,7 +971,11 @@ def manual_load_to_gpu(self, model: torch.nn.Module) -> torch.nn.Module: buffer.data = buffer.data.to("cuda", non_blocking=True) if hasattr(model, "_fsdp_wrapped_module"): - self.manual_load_to_gpu(model._fsdp_wrapped_module) + wrapped_module = model._fsdp_wrapped_module + assert isinstance(wrapped_module, torch.nn.Module), ( + f"wrapped_module is not a torch.nn.Module: instead, {type(wrapped_module)}" + ) + self.manual_load_to_gpu(wrapped_module) return model @@ -1002,7 +1010,9 @@ def save_checkpoint( tokenizer_path=tokenizer_path, ) - def load_checkpoint(self, weights_path: str, optimizer_path: Optional[str] = None): + def load_checkpoint( + self, weights_path: str, optimizer_path: Optional[str] = None + ) -> None: """Load a checkpoint into the model.""" load_checkpoint( model=self.model, @@ -1012,6 +1022,6 @@ def load_checkpoint(self, weights_path: str, optimizer_path: Optional[str] = Non optimizer_path=optimizer_path, ) - def shutdown(self): + def shutdown(self) -> None: """Shutdown the policy.""" # From 7aa1f4450a47de1d855a6d7867fa2d9cce6144a0 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Mon, 12 May 2025 22:21:45 -0700 Subject: [PATCH 018/109] Brought non-strict errors down further Signed-off-by: Sahil Jain --- examples/run_grpo_math.py | 32 +++++++++++++------ mypy.ini | 1 + nemo_rl/algorithms/grpo.py | 2 +- .../hf_datasets/prompt_response_dataset.py | 6 +++- nemo_rl/data/llm_message_utils.py | 19 +++++++---- nemo_rl/environments/math_environment.py | 10 +++--- nemo_rl/models/dtensor/parallelize.py | 4 +-- 7 files changed, 48 insertions(+), 26 deletions(-) diff --git a/examples/run_grpo_math.py b/examples/run_grpo_math.py index 3b8fb30bf4..f9e3cedc88 100644 --- a/examples/run_grpo_math.py +++ b/examples/run_grpo_math.py @@ -16,7 +16,7 @@ import os import pprint from collections import defaultdict -from typing import Any, cast +from typing import Any, Optional, cast import torch from omegaconf import OmegaConf @@ -86,7 +86,7 @@ def hf_data_processor( add_special_tokens=False, ) user_message["token_ids"] = tokenizer(message, return_tensors="pt")["input_ids"][0] - user_message["content"] = message + user_message["content"] = message[0] message_log.append(user_message) length = sum(len(m["token_ids"]) for m in message_log) @@ -183,7 +183,12 @@ def setup_data( tokenizer: TokenizerType, data_config: DataConfig, env_configs: dict[str, Any], -) -> tuple: +) -> tuple[ + AllTaskProcessedDataset, + Optional[AllTaskProcessedDataset], + dict[str, EnvironmentInterface], + dict[str, EnvironmentInterface], +]: print("\n▶ Setting up data...") math_task_spec = TaskDataSpec( task_name="math", @@ -222,13 +227,17 @@ def setup_data( max_seq_length=data_config["max_input_seq_length"], ) - val_dataset = AllTaskProcessedDataset( - data.formatted_ds["validation"], - tokenizer, - math_task_spec, - task_data_processors, - max_seq_length=data_config["max_input_seq_length"], - ) + val_dataset: Optional[AllTaskProcessedDataset] = None + if data.formatted_ds["validation"]: + val_dataset = AllTaskProcessedDataset( + data.formatted_ds["validation"], + tokenizer, + math_task_spec, + task_data_processors, + max_seq_length=data_config["max_input_seq_length"], + ) + else: + val_dataset = None task_to_env: dict[str, EnvironmentInterface] = defaultdict(lambda: math_env) task_to_env["math"] = math_env @@ -271,6 +280,9 @@ def main() -> None: # setup tokenizer tokenizer = get_tokenizer(config["policy"]["tokenizer"]) + assert config["policy"]["generation"] is not None, ( + "A generation config is required for GRPO" + ) config["policy"]["generation"] = configure_generation_config( config["policy"]["generation"], tokenizer ) diff --git a/mypy.ini b/mypy.ini index df8814a5ee..7871a419dc 100644 --- a/mypy.ini +++ b/mypy.ini @@ -1,5 +1,6 @@ [mypy] disable_error_code = no-redef +allow_redefinition = True check_untyped_defs = True python_version = 3.12 diff --git a/nemo_rl/algorithms/grpo.py b/nemo_rl/algorithms/grpo.py index 508b72b1bf..6b81a74db3 100644 --- a/nemo_rl/algorithms/grpo.py +++ b/nemo_rl/algorithms/grpo.py @@ -98,7 +98,7 @@ class GRPOLoggerConfig(LoggerConfig): class MasterConfig(TypedDict): policy: PolicyConfig loss_fn: ClippedPGLossConfig - env_configs: dict[str, Any] + env: dict[str, Any] data: DataConfig grpo: GRPOConfig logger: GRPOLoggerConfig diff --git a/nemo_rl/data/hf_datasets/prompt_response_dataset.py b/nemo_rl/data/hf_datasets/prompt_response_dataset.py index 1f35d97f01..398459d2b2 100644 --- a/nemo_rl/data/hf_datasets/prompt_response_dataset.py +++ b/nemo_rl/data/hf_datasets/prompt_response_dataset.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Any + from datasets import load_dataset from nemo_rl.data.interfaces import TaskDataSpec @@ -43,7 +45,9 @@ def __init__( "json_dataset", ) - def add_messages_key(self, example): + def add_messages_key( + self, example: dict[str, Any] + ) -> dict[str, list[dict[str, Any]]]: return { "messages": [ {"role": "user", "content": example[self.input_key]}, diff --git a/nemo_rl/data/llm_message_utils.py b/nemo_rl/data/llm_message_utils.py index fe480b6a5f..b960871c0f 100644 --- a/nemo_rl/data/llm_message_utils.py +++ b/nemo_rl/data/llm_message_utils.py @@ -12,10 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. import warnings -from typing import Optional, cast +from typing import Any, Optional, cast import torch from datasets import Dataset +from transformers.tokenization_utils_base import PreTrainedTokenizerBase from nemo_rl.data.interfaces import ( FlatMessagesType, @@ -25,6 +26,7 @@ from nemo_rl.distributed.batched_data_dict import BatchedDataDict Tensor = torch.Tensor +TokenizerType = PreTrainedTokenizerBase def message_log_to_flat_messages( @@ -60,7 +62,7 @@ def message_log_to_flat_messages( tensor([1, 2, 3, 4, 5, 6, 7]) ``` """ - result: dict[str, list] = {} + result: dict[str, list[Any]] = {} if len(message_log) == 0: return cast(FlatMessagesType, result) @@ -361,7 +363,7 @@ def message_log_shape(message_log: LLMMessageLogType) -> list[dict[str, torch.Si return shapes -def get_first_index_that_differs(str1, str2): +def get_first_index_that_differs(str1: str, str2: str) -> int: """Get the first index that differs between two strings.""" for i, (c1, c2) in enumerate(zip(str1, str2)): if c1 != c2: @@ -371,7 +373,7 @@ def get_first_index_that_differs(str1, str2): def get_formatted_message_log( message_log: LLMMessageLogType, - tokenizer, + tokenizer: TokenizerType, task_data_spec: TaskDataSpec, add_bos_token: bool = True, add_eos_token: bool = True, @@ -388,17 +390,20 @@ def get_formatted_message_log( """ new_message_log = [] prev_formatted_message = "" + message_log_strs: list[dict[str, str]] = cast( + list[dict[str, str]], message_log + ) # we just use the str:str parts here for i, message in enumerate(message_log): - formatted_message = tokenizer.apply_chat_template( - message_log[: i + 1], + formatted_message: str = tokenizer.apply_chat_template( # type: ignore + message_log_strs[: i + 1], add_generation_prompt=False, tokenize=False, add_special_tokens=False, ) ## get the length of the previous message, excluding the eos token (if present) - prev_message_len_no_eos = get_first_index_that_differs( + prev_message_len_no_eos: int = get_first_index_that_differs( prev_formatted_message, formatted_message, ) diff --git a/nemo_rl/environments/math_environment.py b/nemo_rl/environments/math_environment.py index 96e013b312..26c041e365 100644 --- a/nemo_rl/environments/math_environment.py +++ b/nemo_rl/environments/math_environment.py @@ -14,7 +14,7 @@ import contextlib import io import logging -from typing import Optional, TypedDict +from typing import Any, Optional, TypedDict import ray import torch @@ -52,7 +52,7 @@ def _mute_output(): class HFVerifyWorker: DEFAULT_PY_EXECUTABLE = PY_EXECUTABLES.SYSTEM - def __init__(self): + def __init__(self) -> None: logging.getLogger("math_verify").setLevel(logging.CRITICAL) # Use Latex and plain math extraction from predictions @@ -113,7 +113,7 @@ def __init__(self, cfg: MathEnvConfig): for _ in range(self.num_workers) ] - def shutdown(self): + def shutdown(self) -> None: # shutdown all workers for worker in self.workers: ray.kill(worker) @@ -192,8 +192,8 @@ def step( # type: ignore[override] ) def global_post_process_and_metrics( - self, batch: BatchedDataDict - ) -> tuple[BatchedDataDict, dict]: + self, batch: BatchedDataDict[Any] + ) -> tuple[BatchedDataDict[Any], dict[str, float | int]]: """Computes metrics for this environment given a global rollout batch. Every rank will run this function, so you're free to use distributed diff --git a/nemo_rl/models/dtensor/parallelize.py b/nemo_rl/models/dtensor/parallelize.py index cb5cff5e66..7e40e54e82 100644 --- a/nemo_rl/models/dtensor/parallelize.py +++ b/nemo_rl/models/dtensor/parallelize.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Union +from typing import Callable, Union import torch from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( @@ -335,7 +335,7 @@ def _prepare_input_fn(sequence_sharding, mod, inputs, device_mesh): ) -PARALLIZE_FUNCTIONS = { +PARALLIZE_FUNCTIONS: dict[type[torch.nn.Module], Callable[..., torch.nn.Module]] = { Qwen2ForCausalLM: _parallelize_qwen, Qwen3ForCausalLM: _parallelize_qwen, LlamaForCausalLM: _parallelize_llama, From 958f5381ce442cb08ab97b5ac3f4beb713d30365 Mon Sep 17 00:00:00 2001 From: Sahil Jain Date: Tue, 13 May 2025 10:39:11 -0700 Subject: [PATCH 019/109] Fixed pynvml test type Signed-off-by: Sahil Jain --- tests/unit/utils/test_pynvml.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/utils/test_pynvml.py b/tests/unit/utils/test_pynvml.py index aa0c9a90fe..e5e60fcf5a 100644 --- a/tests/unit/utils/test_pynvml.py +++ b/tests/unit/utils/test_pynvml.py @@ -56,6 +56,6 @@ def test_get_device_uuid(mock_pynvml, mock_convert_id): uuid = get_device_uuid(0) # Verify - assert uuid == b"GPU-12345" + assert uuid == "GPU-12345" mock_convert_id.assert_called_once_with(0) mock_pynvml.nvmlDeviceGetHandleByIndex.assert_called_once_with(1) From 6a14aec1764c96c5162a6f2e4383b5c40a4bbcca Mon Sep 17 00:00:00 2001 From: Terry Kong Date: Thu, 1 May 2025 23:39:18 -0700 Subject: [PATCH 020/109] feat: support mcore extra (megatron + tron) Signed-off-by: Terry Kong wip Signed-off-by: Terry Kong fix it Signed-off-by: Terry Kong patthing fix Signed-off-by: Terry Kong wip Signed-off-by: Terry Kong doesn't look like i needed that Signed-off-by: Terry Kong fix Signed-off-by: Terry Kong revert stuff Signed-off-by: Terry Kong make it better Signed-off-by: Terry Kong go Signed-off-by: Terry Kong cleanup Signed-off-by: Terry Kong mix it up Signed-off-by: Terry Kong touch up Signed-off-by: Terry Kong clean Signed-off-by: Terry Kong better Signed-off-by: Terry Kong clean up Signed-off-by: Terry Kong add it in Signed-off-by: Terry Kong mcore extra Signed-off-by: Terry Kong instructions Signed-off-by: Terry Kong works Signed-off-by: Terry Kong revert to 3.10, 3.12 didn't seem necessary Signed-off-by: Terry Kong ci has to recursively clone Signed-off-by: Terry Kong bump build workflow Signed-off-by: Terry Kong add megatron.core import Signed-off-by: Terry Kong potential fix for unit test on CI Signed-off-by: Terry Kong fix the test Signed-off-by: Terry Kong this should fix test (it was a collision of namespace) Signed-off-by: Terry Kong remove fp8 from test Signed-off-by: Terry Kong add shallow Signed-off-by: Terry Kong fix base build Signed-off-by: Terry Kong fix instructions Signed-off-by: Terry Kong fix the messed up indenting Signed-off-by: Terry Kong fix Signed-off-by: Terry Kong try nesting Signed-off-by: Terry Kong okay, got it to work Signed-off-by: Terry Kong fix up the readme Signed-off-by: Terry Kong ok Signed-off-by: Terry Kong touchup Signed-off-by: Terry Kong add the lock file back Signed-off-by: Terry Kong got Signed-off-by: Terry Kong --- .github/workflows/_run_test.yml | 2 + .github/workflows/cicd-main.yml | 5 + .gitmodules | 10 + 3rdparty/Megatron-LM-workspace/Megatron-LM | 1 + .../is_megatron_installed.py | 8 + 3rdparty/Megatron-LM-workspace/pyproject.toml | 14 + 3rdparty/Megatron-LM-workspace/setup.py | 85 + 3rdparty/NeMo-workspace/NeMo | 1 + 3rdparty/NeMo-workspace/is_nemo_installed.py | 14 + 3rdparty/NeMo-workspace/pyproject.toml | 10 + 3rdparty/NeMo-workspace/setup.py | 42 + README.md | 11 + docker/Dockerfile | 5 +- nemo_rl/__init__.py | 17 + nemo_rl/distributed/virtual_cluster.py | 7 +- nemo_rl/utils/venvs.py | 2 + pyproject.toml | 70 +- .../test_mcore_extra_installed_correctly.sh | 80 + tests/unit/conftest.py | 10 +- .../unit/distributed/test_virtual_cluster.py | 40 + tests/unit/utils/test_venvs.py | 8 +- uv.lock | 4045 ++++++++++++----- 22 files changed, 3333 insertions(+), 1154 deletions(-) create mode 100644 .gitmodules create mode 160000 3rdparty/Megatron-LM-workspace/Megatron-LM create mode 100644 3rdparty/Megatron-LM-workspace/is_megatron_installed.py create mode 100644 3rdparty/Megatron-LM-workspace/pyproject.toml create mode 100644 3rdparty/Megatron-LM-workspace/setup.py create mode 160000 3rdparty/NeMo-workspace/NeMo create mode 100644 3rdparty/NeMo-workspace/is_nemo_installed.py create mode 100644 3rdparty/NeMo-workspace/pyproject.toml create mode 100644 3rdparty/NeMo-workspace/setup.py create mode 100755 tests/functional/test_mcore_extra_installed_correctly.sh diff --git a/.github/workflows/_run_test.yml b/.github/workflows/_run_test.yml index a3f4fbcd43..fb7dca9a94 100644 --- a/.github/workflows/_run_test.yml +++ b/.github/workflows/_run_test.yml @@ -72,6 +72,8 @@ jobs: - name: Checkout repository uses: actions/checkout@v4 + with: + submodules: 'recursive' - name: Start container run: | diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml index d1baeda007..5ab55b2cfd 100644 --- a/.github/workflows/cicd-main.yml +++ b/.github/workflows/cicd-main.yml @@ -110,6 +110,8 @@ jobs: steps: - name: Checkout repository uses: actions/checkout@v4 + with: + submodules: 'recursive' - name: Check lint run: | pip install pre-commit @@ -124,6 +126,8 @@ jobs: steps: - name: Checkout repository uses: actions/checkout@v4 + with: + submodules: 'recursive' - name: build docs run: | pip install uv @@ -174,6 +178,7 @@ jobs: time uv run --no-sync bash ./tests/functional/grpo_multiturn.sh time uv run --no-sync bash ./tests/functional/dpo.sh time uv run --no-sync bash ./tests/functional/eval.sh + time uv run --no-sync bash ./tests/functional/test_mcore_extra_installed_correctly.sh else echo Skipping functional tests for level ${{ needs.pre-flight.outputs.test_level }} fi diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000000..d94e2e584c --- /dev/null +++ b/.gitmodules @@ -0,0 +1,10 @@ +[submodule "3rdparty/NeMo"] + path = 3rdparty/NeMo-workspace/NeMo + url = https://github.com/NVIDIA/NeMo.git + branch = terryk/hemil/automodel-custom-loop-with-sahil-patch + shallow = true +[submodule "3rdparty/Megatron-LM"] + path = 3rdparty/Megatron-LM-workspace/Megatron-LM + url = https://github.com/terrykong/Megatron-LM.git + branch = terryk/main-2025-05-01-with-sahil-patch + shallow = true diff --git a/3rdparty/Megatron-LM-workspace/Megatron-LM b/3rdparty/Megatron-LM-workspace/Megatron-LM new file mode 160000 index 0000000000..a768e50588 --- /dev/null +++ b/3rdparty/Megatron-LM-workspace/Megatron-LM @@ -0,0 +1 @@ +Subproject commit a768e50588f6e833741dcd25cfda64a8798f4d8e diff --git a/3rdparty/Megatron-LM-workspace/is_megatron_installed.py b/3rdparty/Megatron-LM-workspace/is_megatron_installed.py new file mode 100644 index 0000000000..8d764ab3df --- /dev/null +++ b/3rdparty/Megatron-LM-workspace/is_megatron_installed.py @@ -0,0 +1,8 @@ +try: + from megatron.core import parallel_state # noqa: F401 + + INSTALLED = True +except ImportError: + INSTALLED = False + +print(f"Megatron {INSTALLED=}") diff --git a/3rdparty/Megatron-LM-workspace/pyproject.toml b/3rdparty/Megatron-LM-workspace/pyproject.toml new file mode 100644 index 0000000000..8a87b4389d --- /dev/null +++ b/3rdparty/Megatron-LM-workspace/pyproject.toml @@ -0,0 +1,14 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. + +[build-system] +requires = [ + "setuptools", + "pybind11", +] + +[project] +name = "megatron-core" +dynamic = ["dependencies", "version"] +description = "Megatron Core - a library for efficient and scalable training of transformer based models" +authors = [{ name = "NVIDIA", email = "nemo-toolkit@nvidia.com" }] +maintainers = [{ name = "NVIDIA", email = "nemo-toolkit@nvidia.com" }] \ No newline at end of file diff --git a/3rdparty/Megatron-LM-workspace/setup.py b/3rdparty/Megatron-LM-workspace/setup.py new file mode 100644 index 0000000000..3951c1930e --- /dev/null +++ b/3rdparty/Megatron-LM-workspace/setup.py @@ -0,0 +1,85 @@ +"""Setup for pip package.""" + +import os +import subprocess + +import setuptools +from setuptools import Extension + +############################################################################### +# Extension Making # +# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # + +# --- Configuration Start --- +# These will be populated conditionally or with defaults +final_packages = [] +final_package_dir = {} +final_ext_modules = [] + +# --- megatron.core conditional section --- +# Directory for the megatron.core Python package source +megatron_core_python_package_source_dir = "Megatron-LM/megatron/core" +megatron_core_package_name = "megatron.core" + +# Path for the C++ extension's source file, relative to setup.py +# This path is taken from your original setup.py +megatron_core_cpp_extension_source_file = "megatron/core/datasets/helpers.cpp" + +# Check if the main directory for the megatron.core Python package exists +if os.path.exists(megatron_core_python_package_source_dir): + # Add Python package 'megatron.core' + final_packages.append(megatron_core_package_name) + final_package_dir[megatron_core_package_name] = ( + megatron_core_python_package_source_dir + ) + + # If the Python package is being added, then check if its C++ extension can also be added + # This requires the specific C++ source file to exist + if os.path.exists(megatron_core_cpp_extension_source_file): + megatron_extension = Extension( + "megatron.core.datasets.helpers_cpp", # Name of the extension + sources=[megatron_core_cpp_extension_source_file], # Path to C++ source + language="c++", + extra_compile_args=( + subprocess.check_output(["python3", "-m", "pybind11", "--includes"]) + .decode("utf-8") + .strip() + .split() + ) + + ["-O3", "-Wall", "-std=c++17"], + optional=True, # As in your original setup + ) + final_ext_modules.append(megatron_extension) +# --- End of megatron.core conditional section --- + +setuptools.setup( + name="megatron-core", + version="0.0.0", + packages=final_packages, + package_dir=final_package_dir, + py_modules=["is_megatron_installed"], + ext_modules=final_ext_modules, + # Add in any packaged data. + include_package_data=True, + install_requires=[ + # From requirements/pytorch_25.03/requirements.txt + "einops", + "flask-restful", + "nltk", + "pytest", + "pytest-cov", + "pytest_mock", + "pytest-random-order", + "sentencepiece", + "tiktoken", + "wrapt", + "zarr", + "wandb", + "tensorstore!=0.1.46,!=0.1.72", + "torch", + "nvidia-modelopt[torch]>=0.23.2; sys_platform != 'darwin'", + # From megatron/core/requirements.txt + "torch", # Repeated with ^ just to make it easy to map back to the original requirements.txt + "packaging", + ], +) diff --git a/3rdparty/NeMo-workspace/NeMo b/3rdparty/NeMo-workspace/NeMo new file mode 160000 index 0000000000..ce56431f9b --- /dev/null +++ b/3rdparty/NeMo-workspace/NeMo @@ -0,0 +1 @@ +Subproject commit ce56431f9b051c78367c2a04f2372e94b5eda279 diff --git a/3rdparty/NeMo-workspace/is_nemo_installed.py b/3rdparty/NeMo-workspace/is_nemo_installed.py new file mode 100644 index 0000000000..081f42634e --- /dev/null +++ b/3rdparty/NeMo-workspace/is_nemo_installed.py @@ -0,0 +1,14 @@ +import contextlib +import io + +try: + with ( + contextlib.redirect_stdout(io.StringIO()), + contextlib.redirect_stderr(io.StringIO()), + ): + # Silence the logging because NeMo is very verbose + from nemo.tron.init import initialize_megatron # noqa: F401 + INSTALLED = True +except ImportError: + INSTALLED = False +print(f"NeMo {INSTALLED=}") diff --git a/3rdparty/NeMo-workspace/pyproject.toml b/3rdparty/NeMo-workspace/pyproject.toml new file mode 100644 index 0000000000..3eb6af1c86 --- /dev/null +++ b/3rdparty/NeMo-workspace/pyproject.toml @@ -0,0 +1,10 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] + +[project] +name = "nemo-tron" +dynamic = ["dependencies", "version"] +authors = [{ name = "NVIDIA", email = "nemo-toolkit@nvidia.com" }] +description = "Standalone packaging for the NeMo Tron sub-module." +requires-python = ">=3.10" +# Dependencies will be managed in setup.py diff --git a/3rdparty/NeMo-workspace/setup.py b/3rdparty/NeMo-workspace/setup.py new file mode 100644 index 0000000000..bdd6eef88e --- /dev/null +++ b/3rdparty/NeMo-workspace/setup.py @@ -0,0 +1,42 @@ +import os + +import setuptools + +# --- Configuration Start --- +final_packages = [] +final_package_dir = {} + +# --- nemo package conditional section --- +nemo_package_source_dir = "NeMo/nemo" +nemo_package_name = "nemo" + +if os.path.exists(nemo_package_source_dir): + final_packages.append(nemo_package_name) + final_package_dir[nemo_package_name] = nemo_package_source_dir +# --- End of nemo package conditional section --- + +setuptools.setup( + name="nemo-tron", # Must match [project].name in pyproject.toml + version="0.0.0", # Must match [project].version in pyproject.toml + description="Standalone packaging for the NeMo Tron sub-module.", # Can be sourced from pyproject.toml too + author="NVIDIA", + author_email="nemo-toolkit@nvidia.com", + packages=final_packages, + package_dir=final_package_dir, + py_modules=["is_nemo_installed"], + install_requires=[ + "lightning", + "wget", + "onnx", + "fiddle", + "cloudpickle", + "braceexpand", + "webdataset", + "h5py", + "ijson", + "matplotlib", + "scikit-learn", + "nemo-run", + "hatchling", + ], +) diff --git a/README.md b/README.md index fdec4a228a..ed48936c12 100644 --- a/README.md +++ b/README.md @@ -62,6 +62,17 @@ Clone **NeMo RL**. ```sh git clone git@github.com:NVIDIA/NeMo-RL.git nemo-rl cd nemo-rl + +# If you are using the Megatron backend, download the pinned versions of Megatron-LM and NeMo submodules +# by running: +# git submodule update --init --recursive + +# Different branches of the repo can have different pinned versions of these third-party submodules. Ensure +# submodules are automatically updated after switching branches or pulling updates by configuring git with: +# git config submodule.recurse true + +# **NOTE**: this setting will not download **new** or remove **old** submodules with the branch's changes. +# You will have to run the full `git submodule update --init --recursive` command in these situations. ``` Install `uv`. diff --git a/docker/Dockerfile b/docker/Dockerfile index 449aab32ee..8ea864a8a4 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -28,12 +28,14 @@ WORKDIR /opt/nemo-rl # First copy only the dependency files COPY pyproject.toml uv.lock ./ +COPY --link 3rdparty/ ./3rdparty/ ENV UV_PROJECT_ENVIRONMENT=/opt/nemo_rl_venv ENV VIRTUAL_ENV=/opt/nemo_rl_venv +ENV UV_LINK_MODE=copy # Create and activate virtual environment -RUN <<"EOF" +RUN <<"EOF" bash -exu uv venv /opt/nemo_rl_venv # uv sync has a more reliable resolver than simple uv pip install which can fail @@ -42,6 +44,7 @@ uv venv /opt/nemo_rl_venv # Do everything in one layer to prevent large layers. uv sync --locked --extra vllm --no-install-project +uv sync --locked --extra mcore --no-install-project --no-build-isolation uv sync --locked --all-groups --no-install-project EOF diff --git a/nemo_rl/__init__.py b/nemo_rl/__init__.py index 753a669fc8..5edc568f9c 100644 --- a/nemo_rl/__init__.py +++ b/nemo_rl/__init__.py @@ -12,6 +12,23 @@ # See the License for the specific language governing permissions and # limitations under the License. import os +import sys +from pathlib import Path + +""" +This is a work around to ensure whenever NeMo RL is imported, that we +add Megatron-LM to the python path. This is because the only sub-package +that's officially installed is megatron.core. So we add the whole repo into +the path so we can access megatron.{training,legacy,inference,...} + +Since users may pip install NeMo RL, this is a convenience so they do not +have to manually run with PYTHONPATH=3rdparty/Megatron-LM-workspace/Megatron-LM. +""" +megatron_path = ( + Path(__file__).parent.parent / "3rdparty" / "Megatron-LM-workspace" / "Megatron-LM" +) +if megatron_path.exists() and str(megatron_path) not in sys.path: + sys.path.append(str(megatron_path)) from nemo_rl.package_info import ( __contact_emails__, diff --git a/nemo_rl/distributed/virtual_cluster.py b/nemo_rl/distributed/virtual_cluster.py index f9f5be46b8..fc98148c6c 100644 --- a/nemo_rl/distributed/virtual_cluster.py +++ b/nemo_rl/distributed/virtual_cluster.py @@ -38,13 +38,18 @@ class ClusterConfig(TypedDict): class PY_EXECUTABLES: SYSTEM = sys.executable - # TODO: Debug why run-to-run variance is so high with these options # Use NeMo-RL direct dependencies. BASE = "uv run --locked" # Use NeMo-RL direct dependencies and vllm. VLLM = "uv run --locked --extra vllm" + # Megatron-core (and nemo dependencies) + # We always run with --reinstall to avoid issues where someone runs "uv run ... --extra mcore ..." + # but the submodules are not downloaded yet. This results in errors where it appears Megatron/Nemo + # aren't installed. Simple workaround is to always run the mcore py_executable with --reinstall. + MCORE = "uv run --reinstall --extra mcore --no-build-isolation" + @ray.remote def _get_node_ip_and_free_port(): diff --git a/nemo_rl/utils/venvs.py b/nemo_rl/utils/venvs.py index 184f529c3d..07a8770fa9 100644 --- a/nemo_rl/utils/venvs.py +++ b/nemo_rl/utils/venvs.py @@ -75,6 +75,8 @@ def create_local_venv(py_executable: str, venv_name: str) -> str: # Command doesn't matter, since `uv` syncs the environment no matter the command. exec_cmd.extend(["echo", f"Finished creating venv {venv_path}"]) + # Always run uv sync first to ensure the build requirements are set (for --no-build-isolation packages) + subprocess.run(["uv", "sync"], env=env, check=True) subprocess.run(exec_cmd, env=env, check=True) # Return the path to the python executable in the virtual environment diff --git a/pyproject.toml b/pyproject.toml index cfb2f3f507..f07b1bec00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,13 +2,21 @@ requires = ["setuptools>=42", "wheel"] build-backend = "setuptools.build_meta" + +[tool.setuptools] +packages = ["nemo_rl"] + +[tool.setuptools.dynamic] +version = {attr = "nemo_rl.__version__"} # any module attribute compatible with ast.literal_eval +readme = {file = "README.md", content-type = "text/markdown"} + [project] name = "nemo-rl" dynamic = [ "version", "readme", ] -description = "Nemo-RL: A Scalable and Efficient Post-Training Library for Models Ranging from 1 GPU to 1000s, and from Tiny to >100B Parameters" +description = "Nemo RL: A Scalable and Efficient Post-Training Library for Models Ranging from 1 GPU to 1000s, and from Tiny to >100B Parameters" requires-python = ">=3.10" license = {text = "Apache 2.0"} dependencies = [ @@ -29,24 +37,35 @@ dependencies = [ "hydra-core", ] -[tool.setuptools] -packages = ["nemo_rl"] - -[tool.setuptools.dynamic] -version = {attr = "nemo_rl.__version__"} # any module attribute compatible with ast.literal_eval -readme = {file = "README.md", content-type = "text/markdown"} - [project.optional-dependencies] vllm = [ "vllm==0.8.4", ] +mcore = [ + # also need cudnn (https://developer.nvidia.com/cudnn-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=deb_network) + # wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb + # sudo dpkg -i cuda-keyring_1.1-1_all.deb + # sudo apt-get update + # sudo apt-get install cudnn-cuda-12 + "transformer-engine[pytorch]==1.13.0", + "megatron-core", + "nemo-tron", +] [dependency-groups] + +# This is a default group so that we install these even with bare `uv sync` build = [ + # Build requirement for TE "torch==2.6.0", + # Build requirement for TE "setuptools", "packaging", - "einops" + "einops", + # Build requirement for nemo_run + "hatchling", + # Build requirement for mcore + "pybind11", ] docs = [ "sphinx", @@ -59,6 +78,7 @@ docs = [ dev = [ "pre-commit==3.6.0", "ruff==0.9.9", + "debugpy>=1.8.0", ] test = [ "pytest>=7.0.0", @@ -66,6 +86,30 @@ test = [ "pytest-cov", ] +[tool.uv.sources] +megatron-core = { workspace = true } +nemo-tron = { workspace = true } +# The NeMo Run source to be used by nemo-tron +nemo_run = { git = "https://github.com/NVIDIA/NeMo-Run", rev = "414f0077c648fde2c71bb1186e97ccbf96d6844c" } + +[tool.uv.workspace] +members = [ + "3rdparty/Megatron-LM-workspace", + "3rdparty/NeMo-workspace", +] + +[tool.uv] +# Currently, TE must be built with no build-isolation b/c it requires torch +no-build-isolation-package = ["transformer-engine"] +# Always apply the build group since dependencies like TE/mcore/nemo-run require build dependencies +# and this lets us assume they are implicitly installed with a simply `uv sync`. Ideally, we'd +# avoid including these in the default dependency set, but for now it's required. +default-groups = ["dev", "build"] +# Users may use different link-modes depending on their scenario: +# --link-mode=hardlink (default on linux; may get warnings about switching to --link-mode copy if uv cache and venv on different file-systems) +# --link-mode=copy (slower but more reliable; supresses warning) +# --link-mode=symlink (fastest option when uv cache and venv on different file-system; caveat: venv is brittle since it depends on the environment/container) +link-mode = "copy" [tool.black] line-length = 120 @@ -112,11 +156,3 @@ convention = "google" "*_test.py" = ["D"] # Ignore F401 (import but unused) in __init__.py "__init__.py" = ["F401"] - -[tool.uv] -# Users may use different link-modes depending on their scenario: -# --link-mode=hardlink (default on linux; may get warnings about switching to --link-mode copy if uv cache and venv on different file-systems) -# --link-mode=copy (slower but more reliable; supresses warning) -# --link-mode=symlink (fastest option when uv cache and venv on different file-system; caveat: venv is brittle since it depends on the environment/container) -# -#link-mode = "symlink" diff --git a/tests/functional/test_mcore_extra_installed_correctly.sh b/tests/functional/test_mcore_extra_installed_correctly.sh new file mode 100755 index 0000000000..dfab0828d2 --- /dev/null +++ b/tests/functional/test_mcore_extra_installed_correctly.sh @@ -0,0 +1,80 @@ +#!/bin/bash +set -eoux pipefail + +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd) +cd $SCRIPT_DIR + +uv sync +# Just the first call with --extra mcore is invoked with --reinstall in case submodules were recently updated/downloaded +uv run --reinstall --extra mcore --no-build-isolation python <<"EOF" +import torch +import transformer_engine.pytorch as te +from transformer_engine.common import recipe + +# Set dimensions. +in_features = 768 +out_features = 3072 +hidden_size = 2048 + +# Initialize model and inputs. +model = te.Linear(in_features, out_features, bias=True) +inp = torch.randn(hidden_size, in_features, device="cuda") + +# TODO: Disabling FP8 testing since CI machines may not support FP8 +## Create an FP8 recipe. Note: All input args are optional. +#fp8_recipe = recipe.DelayedScaling(margin=0, fp8_format=recipe.Format.E4M3) +# +## Enable autocasting for the forward pass +#with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe): +# out = model(inp) + +out = model(inp) + +loss = out.sum() +loss.backward() +print("[TE hello world succeessful]") +EOF + +uv run --extra mcore --no-build-isolation python <<"EOF" +import is_megatron_installed +import is_nemo_installed +assert is_megatron_installed.INSTALLED, "Megatron is not installed. Please check if the submodule has been initialized. May need to run `git submodule update --init --recursive`" +assert is_nemo_installed.INSTALLED, "NeMo is not installed. Please check if the submodule has been initialized. May need to run `git submodule update --init --recursive`" + +# This must be the first import to get all of the megatron non-core packages added to the path +import nemo_rl +import megatron.core +from megatron.training.utils import get_ltor_masks_and_position_ids +from nemo.tron.init import initialize_megatron +from nemo.tron.config import ( + ConfigContainer, + TrainingConfig, + LoggerConfig, + OptimizerConfig, + SchedulerConfig, + CheckpointConfig, + DistributedDataParallelConfig, +) +from nemo.tron.utils.common_utils import get_rank_safe +from nemo.tron.config import TokenizerConfig +from nemo.tron.model import get_model_from_config +from nemo.tron.checkpointing import checkpoint_exists, load_checkpoint +from nemo.tron.init import initialize_megatron, set_jit_fusion_options +from nemo.tron.setup import _init_checkpointing_context, _update_model_config_funcs +from nemo.tron.state import GlobalState +from nemo.tron.optim import setup_optimizer +from nemo.tron import fault_tolerance +from nemo.tron.tokenizers.tokenizer import build_tokenizer +from nemo.tron.utils.train_utils import ( + calc_params_l2_norm, + logical_and_across_model_parallel_group, + reduce_max_stat_across_model_parallel_group, +) +from nemo.tron.train import train_step +from nemo.tron.setup import HAVE_FSDP2 +print("[Nemo/Mcore imports successful]") +EOF + +# Sync just to return the environment to the original base state +uv sync +echo Success diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index 823b8cff17..5d3b3650d6 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -30,7 +30,7 @@ dir_path = os.path.dirname(os.path.abspath(__file__)) -_TEST_ASSETS_DIR = os.path.join(dir_path, "test_assets") +TEST_ASSETS_DIR = os.path.join(dir_path, "test_assets") UNIT_RESULTS_FILE = os.path.join(dir_path, "unit_results.json") UNIT_RESULTS_FILE_DATED = os.path.join( dir_path, f"unit_results/{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" @@ -40,16 +40,16 @@ # Mapping between asset and absolute path (each are populated from a session level fixture) class TEST_ASSETS: TINY_LLAMA_MODEL_PATH = os.path.join( - _TEST_ASSETS_DIR, "tiny_llama_with_llama3.2_tokenizer" + TEST_ASSETS_DIR, "tiny_llama_with_llama3.2_tokenizer" ) TINY_LLAMA_TIED_MODEL_PATH = os.path.join( - _TEST_ASSETS_DIR, "tiny_llama_tied_with_llama3.2_tokenizer" + TEST_ASSETS_DIR, "tiny_llama_tied_with_llama3.2_tokenizer" ) TINY_QWEN2_MODEL_PATH = os.path.join( - _TEST_ASSETS_DIR, "tiny_qwen2_with_qwen2_tokenizer" + TEST_ASSETS_DIR, "tiny_qwen2_with_qwen2_tokenizer" ) TINY_QWEN3_MODEL_PATH = os.path.join( - _TEST_ASSETS_DIR, "tiny_qwen3_with_qwen3_tokenizer" + TEST_ASSETS_DIR, "tiny_qwen3_with_qwen3_tokenizer" ) TINY_GEMMA3_MODEL_PATH = os.path.join( _TEST_ASSETS_DIR, "tiny_gemma3_with_gemma3_tokenizer" diff --git a/tests/unit/distributed/test_virtual_cluster.py b/tests/unit/distributed/test_virtual_cluster.py index a06cc92120..077248bc25 100644 --- a/tests/unit/distributed/test_virtual_cluster.py +++ b/tests/unit/distributed/test_virtual_cluster.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. import os +import subprocess +from tempfile import TemporaryDirectory from unittest.mock import MagicMock, patch import pytest @@ -23,6 +25,8 @@ ResourceInsufficientError, _get_node_ip_and_free_port, ) +from nemo_rl.utils.venvs import create_local_venv +from tests.unit.conftest import TEST_ASSETS_DIR def test_get_node_ip_and_free_port_does_not_start_with_zero(): @@ -185,3 +189,39 @@ def test_ray_uses_same_cluster_for_permuted_cuda_devices(): assert mock_ray_init.call_count == 1 assert mock_ray_init.call_args_list[0][1]["address"] == "auto" assert mock_ray_shutdown.call_count == 0 + + +def test_mcore_py_executable(): + # The temporary directory is created within the project. + # For some reason, creating a virtual environment outside of the project + # doesn't work reliably. + with TemporaryDirectory(dir=TEST_ASSETS_DIR) as tempdir: + # Mock os.environ to set NEMO_RL_VENV_DIR for this test + with patch.dict(os.environ, {"NEMO_RL_VENV_DIR": tempdir}): + venv_python = create_local_venv( + py_executable=PY_EXECUTABLES.MCORE, venv_name="test_venv" + ) + assert os.path.exists(venv_python) + assert venv_python == f"{tempdir}/test_venv/bin/python" + + # Run a Python command to see if core dependencies were installed + result = subprocess.run( + [ + venv_python, + "-c", + # Importing nemo_rl must be first to ensure all of megatron is importable + "import nemo_rl; print('nemo_rl is imported'); import transformer_engine.pytorch as te; print('te is imported'); import nemo.tron; print('nemo-tron is imported'); import megatron.core; print('megatron-core is imported'); import megatron.training; print('megatron-training is imported');", + ], + capture_output=True, + text=True, + ) + + # Verify the command executed successfully (return code 0) + assert result.returncode == 0, ( + f"Failed to import mcore libraries: {result.stderr}" + ) + assert "nemo_rl is imported" in result.stdout + assert "te is imported" in result.stdout + assert "nemo-tron is imported" in result.stdout + assert "megatron-core is imported" in result.stdout + assert "megatron-training is imported" in result.stdout diff --git a/tests/unit/utils/test_venvs.py b/tests/unit/utils/test_venvs.py index 59f7c03565..9e54541bb0 100644 --- a/tests/unit/utils/test_venvs.py +++ b/tests/unit/utils/test_venvs.py @@ -17,10 +17,14 @@ from unittest.mock import patch from nemo_rl.utils.venvs import create_local_venv +from tests.unit.conftest import TEST_ASSETS_DIR def test_create_local_venv(): - with TemporaryDirectory() as tempdir: + # The temporary directory is created within the project. + # For some reason, creating a virtual environment outside of the project + # doesn't work reliably. + with TemporaryDirectory(dir=TEST_ASSETS_DIR) as tempdir: # Mock os.environ to set NEMO_RL_VENV_DIR for this test with patch.dict(os.environ, {"NEMO_RL_VENV_DIR": tempdir}): venv_python = create_local_venv( @@ -30,7 +34,7 @@ def test_create_local_venv(): assert venv_python == f"{tempdir}/test_venv/bin/python" # Check if sphinx package is installed in the created venv - # Run a Python command to check if emoji can be imported + # Run a Python command to check if sphinx can be imported result = subprocess.run( [ venv_python, diff --git a/uv.lock b/uv.lock index d331873557..1acb223054 100644 --- a/uv.lock +++ b/uv.lock @@ -3,12 +3,12 @@ revision = 2 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux'", "python_full_version == 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index abe85bcca8..5f2be92aa7 100644 --- a/examples/run_eval.py +++ b/examples/run_eval.py @@ -19,10 +19,10 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from datasets import load_dataset from omegaconf import OmegaConf from transformers import AutoTokenizer +from datasets import load_dataset from examples.run_grpo_math import math_data_processor from nemo_rl.algorithms.utils import get_tokenizer from nemo_rl.data import MathDataConfig diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index 70b1dd9786..b52cbc0a84 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -14,8 +14,8 @@ from typing import Any, Dict, List, Tuple, Union import torch -from datasets import Dataset +from datasets import Dataset from nemo_rl.data.interfaces import ( DatumSpec, TaskDataProcessFnCallable, diff --git a/nemo_rl/data/hf_datasets/deepscaler.py b/nemo_rl/data/hf_datasets/deepscaler.py index 53e330a17a..c4c3d7d992 100644 --- a/nemo_rl/data/hf_datasets/deepscaler.py +++ b/nemo_rl/data/hf_datasets/deepscaler.py @@ -14,7 +14,6 @@ from datasets import load_dataset - from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/dpo.py b/nemo_rl/data/hf_datasets/dpo.py index 03d5c7e872..2513b5b721 100644 --- a/nemo_rl/data/hf_datasets/dpo.py +++ b/nemo_rl/data/hf_datasets/dpo.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. from datasets import load_dataset - from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/helpsteer3.py b/nemo_rl/data/hf_datasets/helpsteer3.py index a6f67e6032..aa967ec595 100644 --- a/nemo_rl/data/hf_datasets/helpsteer3.py +++ b/nemo_rl/data/hf_datasets/helpsteer3.py @@ -12,8 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. from absl import logging -from datasets import load_dataset +from datasets import load_dataset from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/openmathinstruct2.py b/nemo_rl/data/hf_datasets/openmathinstruct2.py index 50c96d74c8..d2edc85ffe 100644 --- a/nemo_rl/data/hf_datasets/openmathinstruct2.py +++ b/nemo_rl/data/hf_datasets/openmathinstruct2.py @@ -14,7 +14,6 @@ from datasets import load_dataset - from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/prompt_response_dataset.py b/nemo_rl/data/hf_datasets/prompt_response_dataset.py index 1f35d97f01..a8740527fb 100644 --- a/nemo_rl/data/hf_datasets/prompt_response_dataset.py +++ b/nemo_rl/data/hf_datasets/prompt_response_dataset.py @@ -13,7 +13,6 @@ # limitations under the License. from datasets import load_dataset - from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/squad.py b/nemo_rl/data/hf_datasets/squad.py index 1400dad5ba..0f16ca8c0e 100644 --- a/nemo_rl/data/hf_datasets/squad.py +++ b/nemo_rl/data/hf_datasets/squad.py @@ -14,7 +14,6 @@ from datasets import load_dataset - from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/llm_message_utils.py b/nemo_rl/data/llm_message_utils.py index acc33437cd..e32a788442 100644 --- a/nemo_rl/data/llm_message_utils.py +++ b/nemo_rl/data/llm_message_utils.py @@ -15,8 +15,8 @@ from typing import Dict, List import torch -from datasets import Dataset +from datasets import Dataset from nemo_rl.data.interfaces import ( FlatMessagesType, LLMMessageLogType, diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index 5d3b3650d6..e11c7ed325 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -52,7 +52,7 @@ class TEST_ASSETS: TEST_ASSETS_DIR, "tiny_qwen3_with_qwen3_tokenizer" ) TINY_GEMMA3_MODEL_PATH = os.path.join( - _TEST_ASSETS_DIR, "tiny_gemma3_with_gemma3_tokenizer" + TEST_ASSETS_DIR, "tiny_gemma3_with_gemma3_tokenizer" ) From b17e1639f7d86ffaa01f2f3ae279c191f7bb9612 Mon Sep 17 00:00:00 2001 From: Terry Kong Date: Sat, 17 May 2025 10:09:23 -0700 Subject: [PATCH 022/109] all good Signed-off-by: Terry Kong --- .../Megatron-LM-workspace/is_megatron_installed.py | 13 +++++++++++++ 3rdparty/Megatron-LM-workspace/setup.py | 13 +++++++++++++ 3rdparty/NeMo-workspace/is_nemo_installed.py | 13 +++++++++++++ 3rdparty/NeMo-workspace/setup.py | 13 +++++++++++++ tools/copyright.sh | 2 +- 5 files changed, 53 insertions(+), 1 deletion(-) diff --git a/3rdparty/Megatron-LM-workspace/is_megatron_installed.py b/3rdparty/Megatron-LM-workspace/is_megatron_installed.py index 8d764ab3df..9a88db404f 100644 --- a/3rdparty/Megatron-LM-workspace/is_megatron_installed.py +++ b/3rdparty/Megatron-LM-workspace/is_megatron_installed.py @@ -1,3 +1,16 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. try: from megatron.core import parallel_state # noqa: F401 diff --git a/3rdparty/Megatron-LM-workspace/setup.py b/3rdparty/Megatron-LM-workspace/setup.py index 3951c1930e..f3492afcd5 100644 --- a/3rdparty/Megatron-LM-workspace/setup.py +++ b/3rdparty/Megatron-LM-workspace/setup.py @@ -1,3 +1,16 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. """Setup for pip package.""" import os diff --git a/3rdparty/NeMo-workspace/is_nemo_installed.py b/3rdparty/NeMo-workspace/is_nemo_installed.py index 081f42634e..4eeadc0006 100644 --- a/3rdparty/NeMo-workspace/is_nemo_installed.py +++ b/3rdparty/NeMo-workspace/is_nemo_installed.py @@ -1,3 +1,16 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import contextlib import io diff --git a/3rdparty/NeMo-workspace/setup.py b/3rdparty/NeMo-workspace/setup.py index bdd6eef88e..6bc940202c 100644 --- a/3rdparty/NeMo-workspace/setup.py +++ b/3rdparty/NeMo-workspace/setup.py @@ -1,3 +1,16 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import os import setuptools diff --git a/tools/copyright.sh b/tools/copyright.sh index 927d148af2..044243adbc 100755 --- a/tools/copyright.sh +++ b/tools/copyright.sh @@ -19,7 +19,7 @@ SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) # Move to the project root cd $SCRIPT_DIR/.. find_files_with_missing_copyright() { -find ./nemo_rl/ ./docs/*.py ./examples/ ./tests/ ./tools/ -type f -name '*.py' | while read path; do +find ./nemo_rl/ ./docs/*.py ./examples/ ./tests/ ./tools/ ./3rdparty/*/*.py -type f -name '*.py' | while read path; do echo -en $path"\t" head -2 $path | grep -iv 'coding=' | head -1 done \ From 5b3b96d4068f2b51734ca61e1c1a0fe56a7f1dbf Mon Sep 17 00:00:00 2001 From: Terry Kong Date: Sat, 17 May 2025 10:12:30 -0700 Subject: [PATCH 023/109] pin pre-commit Signed-off-by: Terry Kong --- .github/workflows/cicd-main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml index 5ab55b2cfd..1dac68f318 100644 --- a/.github/workflows/cicd-main.yml +++ b/.github/workflows/cicd-main.yml @@ -114,7 +114,7 @@ jobs: submodules: 'recursive' - name: Check lint run: | - pip install pre-commit + pip install pre-commit==3.6.0 pre-commit install pre-commit run --all-files --show-diff-on-failure --color=always From 48f8ffcc30a54fd79e1a55f18642ad5f8342d6ec Mon Sep 17 00:00:00 2001 From: Terry Kong Date: Sat, 17 May 2025 16:22:04 -0700 Subject: [PATCH 024/109] undo Signed-off-by: Terry Kong --- examples/run_eval.py | 2 +- nemo_rl/data/datasets.py | 2 +- nemo_rl/data/hf_datasets/deepscaler.py | 1 + nemo_rl/data/hf_datasets/dpo.py | 1 + nemo_rl/data/hf_datasets/helpsteer3.py | 2 +- nemo_rl/data/hf_datasets/openmathinstruct2.py | 1 + nemo_rl/data/hf_datasets/prompt_response_dataset.py | 1 + nemo_rl/data/hf_datasets/squad.py | 1 + nemo_rl/data/llm_message_utils.py | 2 +- 9 files changed, 9 insertions(+), 4 deletions(-) diff --git a/examples/run_eval.py b/examples/run_eval.py index 5f2be92aa7..abe85bcca8 100644 --- a/examples/run_eval.py +++ b/examples/run_eval.py @@ -19,10 +19,10 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from datasets import load_dataset from omegaconf import OmegaConf from transformers import AutoTokenizer -from datasets import load_dataset from examples.run_grpo_math import math_data_processor from nemo_rl.algorithms.utils import get_tokenizer from nemo_rl.data import MathDataConfig diff --git a/nemo_rl/data/datasets.py b/nemo_rl/data/datasets.py index b52cbc0a84..70b1dd9786 100644 --- a/nemo_rl/data/datasets.py +++ b/nemo_rl/data/datasets.py @@ -14,8 +14,8 @@ from typing import Any, Dict, List, Tuple, Union import torch - from datasets import Dataset + from nemo_rl.data.interfaces import ( DatumSpec, TaskDataProcessFnCallable, diff --git a/nemo_rl/data/hf_datasets/deepscaler.py b/nemo_rl/data/hf_datasets/deepscaler.py index c4c3d7d992..53e330a17a 100644 --- a/nemo_rl/data/hf_datasets/deepscaler.py +++ b/nemo_rl/data/hf_datasets/deepscaler.py @@ -14,6 +14,7 @@ from datasets import load_dataset + from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/dpo.py b/nemo_rl/data/hf_datasets/dpo.py index 2513b5b721..03d5c7e872 100644 --- a/nemo_rl/data/hf_datasets/dpo.py +++ b/nemo_rl/data/hf_datasets/dpo.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from datasets import load_dataset + from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/helpsteer3.py b/nemo_rl/data/hf_datasets/helpsteer3.py index aa967ec595..a6f67e6032 100644 --- a/nemo_rl/data/hf_datasets/helpsteer3.py +++ b/nemo_rl/data/hf_datasets/helpsteer3.py @@ -12,8 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. from absl import logging - from datasets import load_dataset + from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/openmathinstruct2.py b/nemo_rl/data/hf_datasets/openmathinstruct2.py index d2edc85ffe..50c96d74c8 100644 --- a/nemo_rl/data/hf_datasets/openmathinstruct2.py +++ b/nemo_rl/data/hf_datasets/openmathinstruct2.py @@ -14,6 +14,7 @@ from datasets import load_dataset + from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/prompt_response_dataset.py b/nemo_rl/data/hf_datasets/prompt_response_dataset.py index a8740527fb..1f35d97f01 100644 --- a/nemo_rl/data/hf_datasets/prompt_response_dataset.py +++ b/nemo_rl/data/hf_datasets/prompt_response_dataset.py @@ -13,6 +13,7 @@ # limitations under the License. from datasets import load_dataset + from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/hf_datasets/squad.py b/nemo_rl/data/hf_datasets/squad.py index 0f16ca8c0e..1400dad5ba 100644 --- a/nemo_rl/data/hf_datasets/squad.py +++ b/nemo_rl/data/hf_datasets/squad.py @@ -14,6 +14,7 @@ from datasets import load_dataset + from nemo_rl.data.interfaces import TaskDataSpec diff --git a/nemo_rl/data/llm_message_utils.py b/nemo_rl/data/llm_message_utils.py index e32a788442..acc33437cd 100644 --- a/nemo_rl/data/llm_message_utils.py +++ b/nemo_rl/data/llm_message_utils.py @@ -15,8 +15,8 @@ from typing import Dict, List import torch - from datasets import Dataset + from nemo_rl.data.interfaces import ( FlatMessagesType, LLMMessageLogType, From 8aa6ff8c23db14ed76918255c42a9b4e569d8348 Mon Sep 17 00:00:00 2001 From: Terry Kong Date: Sat, 17 May 2025 17:45:35 -0700 Subject: [PATCH 025/109] move submodule stuff into comment until Signed-off-by: Terry Kong --- README.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ed48936c12..c0e3a0fdcf 100644 --- a/README.md +++ b/README.md @@ -2,13 +2,14 @@ - [Nemo RL: A Scalable and Efficient Post-Training Library](#nemo-rl-a-scalable-and-efficient-post-training-library) + - [📣 News](#-news) - [Features](#features) - [Prerequisites](#prerequisites) - [GRPO](#grpo) - [GRPO Single Node](#grpo-single-node) - [GRPO Multi-node](#grpo-multi-node) - [GRPO Qwen2.5-32B](#grpo-qwen25-32b) - - [GRPO Multi-Turn/Tool-Use](#grpo-multi-turn) + - [GRPO Multi-Turn](#grpo-multi-turn) - [Supervised Fine-Tuning (SFT)](#supervised-fine-tuning-sft) - [SFT Single Node](#sft-single-node) - [SFT Multi-node](#sft-multi-node) @@ -62,6 +63,12 @@ Clone **NeMo RL**. ```sh git clone git@github.com:NVIDIA/NeMo-RL.git nemo-rl cd nemo-rl +``` + + Install `uv`. ```sh From c5f4305f97f54467cb8fa6844585a0f6f2e4f336 Mon Sep 17 00:00:00 2001 From: Terry Kong Date: Sat, 17 May 2025 17:52:42 -0700 Subject: [PATCH 026/109] ok Signed-off-by: Terry Kong --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c0e3a0fdcf..3f9c57adb5 100644 --- a/README.md +++ b/README.md @@ -66,6 +66,7 @@ cd nemo-rl ```