this should work?

src/axolotl/datasets.py

@@ -52,6 +52,8 @@ def process(self, dataset):
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
             map_kwargs["batch_size"] = 100
+        import pdb
+        pdb.set_trace()
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
             num_proc=num_proc,

src/axolotl/prompt_strategies/stepwise.py → src/axolotl/prompt_strategies/stepwise_supervised.py

@@ -5,15 +5,16 @@
 
 from itertools import chain
 
-from typing import Dict, List, Optional, Union
+from typing import Dict, Generator, List, Optional, Union
 
-from transformers import BatchEncoding
+from transformers import BatchEncoding, PreTrainedTokenizer
 
 from axolotl.prompt_tokenizers import IGNORE_INDEX, PromptTokenizingStrategy
 from axolotl.prompters import Prompter
+from axolotl.utils.dict import DictDefault
 
 
-class StepwiseSupervisedPromptTokenizingStrategy(PromptTokenizingStrategy):
+class StepwiseSupervisedPromptTokenizingStrategy:
     """
     Tokenizing strategy for supervised stepwise datasets, typically used for COT-reasoning.
     These datasets should include the following columns:
@@ -24,7 +25,6 @@ class StepwiseSupervisedPromptTokenizingStrategy(PromptTokenizingStrategy):
 
     def __init__(
         self,
-        prompter: Prompter,
         tokenizer,
         train_on_inputs: bool = False,
         sequence_len: int = 2048,
@@ -33,7 +33,9 @@ def __init__(
         train_on_last_step_only: bool = False,
         is_eval: bool = False,
     ):
-        super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
+        self.tokenizer = tokenizer
+        self.train_on_inputs = train_on_inputs
+        self.sequence_len = sequence_len
         self.step_separator = step_separator
         self.max_completion_length = max_completion_length
         self.train_on_last_step_only = train_on_last_step_only
@@ -42,6 +44,8 @@ def __init__(
     def tokenize_prompt(
         self, prompt: Dict[str, Union[str, List[str]]]
     ) -> BatchEncoding:
+        # Inspired by TRL's PRMTRainer
+        # https://github.com/huggingface/trl/blob/ed7de87dc766478c024b68f12530d1b0e7c3ff23/trl/trainer/prm_trainer.py#L206
         prompt_ids = self.tokenizer(prompt["prompt"], add_special_tokens=False)[
             "input_ids"
         ]
@@ -98,3 +102,17 @@ def tokenize_prompt(
                 "attention_mask": [1] * len(input_ids),
             }
         )
+
+    @property
+    def supports_batched(self):
+        return False
+
+
+def load(
+    tokenizer: PreTrainedTokenizer, cfg: DictDefault
+) -> StepwiseSupervisedPromptTokenizingStrategy:
+    return StepwiseSupervisedPromptTokenizingStrategy(
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )

src/axolotl/utils/data/sft.py

@@ -6,9 +6,9 @@
 from typing import List, Tuple, Union
 
 from datasets import (
+    concatenate_datasets,
     Dataset,
     DatasetDict,
-    concatenate_datasets,
     load_dataset,
     load_from_disk,
 )
@@ -456,13 +456,6 @@ def get_dataset_wrapper(
             dataset,
             **ds_kwargs,
         )
-    elif ds_strategy := config_dataset.type == "stepwise_supervised":
-        dataset_prompter = UnsupportedPrompter()
-        dataset_wrapper = TokenizedPromptDataset(
-            ds_strategy,
-            dataset,
-            **ds_kwargs,
-        )
     elif ds_strategy := load(
         config_dataset.type, tokenizer, cfg, config_dataset, processor=processor
     ):