Improve Dataset Processing Multiprocessing, Sharding, and Qwen Tokenizer Bug Fix. (#2918)

* Added a feature to save prepared dataset in specified shards, removed limiter on multiprocessing during tokenization, and a bug fix of qwen tokenizer

* removed limiters and fixed config variable name

* black lint

* chore: lint

* feat: update handling of dataset_processes

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

src/axolotl/core/datasets/chat.py

@@ -2,7 +2,6 @@
 chat dataset module
 """
 
-import os
 from typing import Callable, Optional, Union
 
 from datasets import Dataset
@@ -41,14 +40,10 @@ class TokenizedChatDataset(Dataset):
                 )
             return ex.tokenized(model_transform)
 
-        process_or_cpu_count: int = (
-            process_count or os.cpu_count()  # type: ignore[assignment]
-        )
-        num_proc = min(32, process_or_cpu_count)
         features = data.features.keys()
         tokenized_data = data.map(
             map_fn,
-            num_proc=num_proc,
+            num_proc=process_count,
             keep_in_memory=keep_in_memory,
             remove_columns=features,
             desc="Tokenizing Chats",

src/axolotl/datasets.py

@@ -1,7 +1,5 @@
 """Module containing Dataset functionality"""
 
-import os
-
 import torch
 from datasets import Dataset, IterableDataset
 
@@ -46,7 +44,6 @@ class TokenizedPromptDataset(Dataset):
 
     def process(self, dataset):
         features = dataset.features.keys()
-        num_proc = min(64, self.process_count if self.process_count else os.cpu_count())
 
         map_kwargs = {}
         if self.prompt_tokenizer.supports_batched:
@@ -59,13 +56,13 @@ class TokenizedPromptDataset(Dataset):
         ):
             dataset = dataset.filter(
                 self.prompt_tokenizer.filter_rows,
-                num_proc=num_proc,
+                num_proc=self.process_count,
                 desc="Strategy Filtering Rows",
             )
 
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
-            num_proc=num_proc,
+            num_proc=self.process_count,
             remove_columns=features,
             keep_in_memory=self.keep_in_memory,
             desc="Tokenizing Prompts",

src/axolotl/loaders/tokenizer.py

@@ -188,7 +188,8 @@ def load_tokenizer(cfg: DictDefault) -> PreTrainedTokenizer:
         tokenizer.padding_side = "left"
 
     # Qwen base only has single token, so we need to set the special tokens
-    if cfg.is_qwen_derived_model:
+    # the following check is for Qwen1 base models
+    if cfg.is_qwen_derived_model and hasattr(tokenizer, "eod_id"):
         token_ids = ["bos_token_id", "eos_token_id", "pad_token_id", "unk_token_id"]
         for attr_name in token_ids:
             if getattr(tokenizer, attr_name) is None:

src/axolotl/utils/config/__init__.py

@@ -148,8 +148,6 @@ def normalize_config(cfg):
                 f"Invalid value for eval_steps ({eval_steps}) from evals_per_epoch and/or num_epochs. Skipping evaluations."
             )
 
-    cfg.dataset_processes = cfg.dataset_processes or os.cpu_count()
-
     if not cfg.base_model_config:
         cfg.base_model_config = cfg.base_model
 

src/axolotl/utils/data/shared.py

@@ -410,9 +410,8 @@ def save_preprocessed_dataset(
 ) -> None:
     """Save preprocessed dataset to disk and optionally push to the HF Hub."""
     prepared_ds_path = get_prepared_dataset_path(cfg, dataset_hash)
+    num_workers = cfg.dataset_processes
     if isinstance(dataset, IterableDataset):
-        num_workers = cfg.dataset_processes
-
         ds_from_iter = Dataset.from_generator(
             functools.partial(_generate_from_iterable_dataset, dataset),
             features=dataset.features,
@@ -423,10 +422,20 @@ def save_preprocessed_dataset(
                 "num_workers": [num_workers] * num_workers,
             },
         )
-        ds_from_iter.save_to_disk(str(prepared_ds_path))
+        ds_from_iter.save_to_disk(
+            str(prepared_ds_path),
+            num_proc=num_workers,
+            max_shard_size=None,
+            num_shards=cfg.num_dataset_shards_to_save,
+        )
     else:
         os.makedirs(prepared_ds_path, exist_ok=True)
-        dataset.save_to_disk(str(prepared_ds_path))
+        dataset.save_to_disk(
+            str(prepared_ds_path),
+            num_proc=num_workers,
+            max_shard_size=None,
+            num_shards=cfg.num_dataset_shards_to_save,
+        )
     if cfg.push_dataset_to_hub:
         LOG.info(
             "Pushing merged prepared dataset to Huggingface hub at "

src/axolotl/utils/schemas/config.py

@@ -193,6 +193,12 @@ class AxolotlInputConfig(
         json_schema_extra={"description": "Index of shard to use for whole dataset"},
     )
     skip_prepare_dataset: bool | None = False
+    num_dataset_shards_to_save: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Number of shards to save the prepared dataset"
+        },
+    )
 
     pretraining_dataset: (
         Annotated[list[PretrainingDataset | SFTDataset], MinLen(1)] | None
@@ -203,11 +209,12 @@ class AxolotlInputConfig(
         },
     )
     dataset_processes: int | None = Field(
-        default=min(
+        default=None,
-            int(os.environ.get("AXOLOTL_DATASET_PROCESSES", 32)), os.cpu_count()
-        ),  # type: ignore[type-var]
         json_schema_extra={
-            "description": "The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()` if not set."
+            "description": (
+                "The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()` if not set.\n"
+                "For Runpod VMs, it will default to number of vCPUs via RUNPOD_CPU_COUNT."
+            )
         },
     )
     dataset_exact_deduplication: bool | None = Field(
@@ -1199,3 +1206,16 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
             data["dataloader_prefetch_factor"] = 256
 
         return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def default_dataset_processes(cls, data):
+        if data.get("dataset_processes") is None:
+            if axolotl_dataset_processes := os.environ.get("AXOLOTL_DATASET_PROCESSES"):
+                data["dataset_processes"] = int(axolotl_dataset_processes)
+            elif runpod_cpu_count := os.environ.get("RUNPOD_CPU_COUNT"):
+                data["dataset_processes"] = int(runpod_cpu_count)
+            else:
+                data["dataset_processes"] = os.cpu_count()
+
+        return data