Types: Fix PEP 484 implicit-optional compliance (#1297)

This was done automatically with hauntsaninja/no_implicit_optional.

examples/research_projects/stack_llama_2/scripts/dpo_llama2.py

@@ -78,7 +78,7 @@ class ScriptArguments:
 def get_stack_exchange_paired(
     data_dir: str = "data/rl",
     sanity_check: bool = False,
-    cache_dir: str = None,
+    cache_dir: Optional[str] = None,
     num_proc=24,
 ) -> Dataset:
     """Load the stack-exchange-paired dataset from Hugging Face and convert it to the necessary format.

examples/scripts/dpo.py

@@ -50,7 +50,7 @@
     --lora_alpha=16
 """
 from dataclasses import dataclass, field
-from typing import Dict
+from typing import Dict, Optional
 
 import torch
 from datasets import Dataset, load_dataset
@@ -87,7 +87,7 @@ def extract_anthropic_prompt(prompt_and_response):
     return prompt_and_response[: search_term_idx + len(search_term)]
 
 
-def get_hh(split: str, sanity_check: bool = False, silent: bool = False, cache_dir: str = None) -> Dataset:
+def get_hh(split: str, sanity_check: bool = False, silent: bool = False, cache_dir: Optional[str] = None) -> Dataset:
     """Load the Anthropic Helpful-Harmless dataset from Hugging Face and convert it to the necessary format.
 
     The dataset is converted to a dictionary with the following structure:

trl/core.py

@@ -113,7 +113,7 @@ def whiten(values: torch.Tensor, shift_mean: bool = True) -> torch.Tensor:
     return whitened
 
 
-def masked_mean(values: torch.Tensor, mask: torch.Tensor, axis: bool = None) -> torch.Tensor:
+def masked_mean(values: torch.Tensor, mask: torch.Tensor, axis: Optional[bool] = None) -> torch.Tensor:
     """Compute mean of tensor with a masked values."""
     if axis is not None:
         return (values * mask).sum(axis=axis) / mask.sum(axis=axis)

trl/environment/base_environment.py

@@ -14,6 +14,7 @@
 
 import re
 import warnings
+from typing import Optional
 
 import torch
 from accelerate.utils import extract_model_from_parallel
@@ -416,7 +417,7 @@ def _generate_batched(
         self,
         query_tensors,
         batch_size: int = 16,
-        pad_to_multiple_of: int = None,
+        pad_to_multiple_of: Optional[int] = None,
     ):
         """
         Generate responses for a list of query tensors.

trl/models/modeling_base.py

@@ -15,6 +15,7 @@
 import logging
 import os
 from copy import deepcopy
+from typing import Optional
 
 import torch
 import torch.nn as nn
@@ -600,7 +601,7 @@ def compute_reward_score(self, input_ids, attention_mask=None, **kwargs):
 
 
 def create_reference_model(
-    model: PreTrainedModelWrapper, num_shared_layers: int = None, pattern: str = None
+    model: PreTrainedModelWrapper, num_shared_layers: Optional[int] = None, pattern: Optional[str] = None
 ) -> PreTrainedModelWrapper:
     """
     Creates a static reference copy of a model. Note that model will be in `.eval()` mode.

trl/trainer/dpo_trainer.py

@@ -139,12 +139,12 @@ class DPOTrainer(Trainer):
 
     def __init__(
         self,
-        model: Union[PreTrainedModel, nn.Module, str] = None,
+        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
         ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
         beta: float = 0.1,
         label_smoothing: float = 0,
         loss_type: Literal["sigmoid", "hinge", "ipo", "kto_pair"] = "sigmoid",
-        args: TrainingArguments = None,
+        args: Optional[TrainingArguments] = None,
         data_collator: Optional[DataCollator] = None,
         label_pad_token_id: int = -100,
         padding_value: Optional[int] = None,
@@ -165,11 +165,11 @@ def __init__(
         generate_during_eval: bool = False,
         compute_metrics: Optional[Callable[[EvalLoopOutput], Dict]] = None,
         precompute_ref_log_probs: bool = False,
-        dataset_num_proc: int = None,
+        dataset_num_proc: Optional[int] = None,
         model_init_kwargs: Optional[Dict] = None,
         ref_model_init_kwargs: Optional[Dict] = None,
-        model_adapter_name: str = None,
-        ref_adapter_name: str = None,
+        model_adapter_name: Optional[str] = None,
+        ref_adapter_name: Optional[str] = None,
     ):
         if model_init_kwargs is None:
             model_init_kwargs = {}
@@ -585,7 +585,7 @@ def build_tokenized_answer(self, prompt, answer):
             attention_mask=answer_attention_mask,
         )
 
-    def tokenize_row(self, feature, model: Union[PreTrainedModel, nn.Module] = None) -> Dict:
+    def tokenize_row(self, feature, model: Optional[Union[PreTrainedModel, nn.Module]] = None) -> Dict:
         """Tokenize a single row from a DPO specific dataset.
 
         At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation

trl/trainer/iterative_sft_trainer.py

@@ -60,9 +60,9 @@ class IterativeSFTTrainer(Trainer):
 
     def __init__(
         self,
-        model: PreTrainedModel = None,
-        args: TrainingArguments = None,
-        tokenizer: PreTrainedTokenizerBase = None,
+        model: Optional[PreTrainedModel] = None,
+        args: Optional[TrainingArguments] = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
         optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
             None,
             None,

trl/trainer/ppo_trainer.py

@@ -142,10 +142,10 @@ class PPOTrainer(BaseTrainer):
 
     def __init__(
         self,
-        config: PPOConfig = None,
-        model: PreTrainedModelWrapper = None,
+        config: Optional[PPOConfig] = None,
+        model: Optional[PreTrainedModelWrapper] = None,
         ref_model: Optional[PreTrainedModelWrapper] = None,
-        tokenizer: PreTrainedTokenizerBase = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
         dataset: Optional[Union[torch.utils.data.Dataset, Dataset]] = None,
         optimizer: Optional[torch.optim.Optimizer] = None,
         data_collator: Optional[typing.Callable] = None,
@@ -431,7 +431,7 @@ def _remove_unused_columns(self, dataset: "Dataset"):
     def generate(
         self,
         query_tensor: Union[torch.Tensor, List[torch.Tensor]],
-        length_sampler: Callable = None,
+        length_sampler: Optional[Callable] = None,
         batch_size: int = 4,
         return_prompt: bool = True,
         generate_ref_response: bool = False,
@@ -508,10 +508,10 @@ def _generate_batched(
         self,
         model: PreTrainedModelWrapper,
         query_tensors: List[torch.Tensor],
-        length_sampler: Callable = None,
+        length_sampler: Optional[Callable] = None,
         batch_size: int = 4,
         return_prompt: bool = True,
-        pad_to_multiple_of: int = None,
+        pad_to_multiple_of: Optional[int] = None,
         remove_padding: bool = True,
         **generation_kwargs,
     ):

trl/trainer/reward_trainer.py

@@ -55,7 +55,7 @@ class RewardTrainer(Trainer):
 
     def __init__(
         self,
-        model: Union[PreTrainedModel, nn.Module] = None,
+        model: Optional[Union[PreTrainedModel, nn.Module]] = None,
         args: Optional[RewardConfig] = None,
         data_collator: Optional[DataCollator] = None,
         train_dataset: Optional[Dataset] = None,

trl/trainer/sft_trainer.py

@@ -121,8 +121,8 @@ class SFTTrainer(Trainer):
 
     def __init__(
         self,
-        model: Union[PreTrainedModel, nn.Module, str] = None,
-        args: TrainingArguments = None,
+        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
+        args: Optional[TrainingArguments] = None,
         data_collator: Optional[DataCollator] = None,
         train_dataset: Optional[Dataset] = None,
         eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,

trl/trainer/utils.py

@@ -82,7 +82,7 @@ class DataCollatorForCompletionOnlyLM(DataCollatorForLanguageModeling):
     def __init__(
         self,
         response_template: Union[str, List[int]],
-        instruction_template: Union[str, List[int]] = None,
+        instruction_template: Optional[Union[str, List[int]]] = None,
         *args,
         mlm: bool = False,
         ignore_index: int = -100,