Add filter_fn to all generic dataset classes and builders API

torchtune/datasets/_alpaca.py

@@ -6,7 +6,7 @@
 
 from functools import partial
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 from torchtune.data._messages import AlpacaToMessages
 
@@ -22,6 +22,7 @@ def alpaca_dataset(
     column_map: Optional[Dict[str, str]] = None,
     train_on_input: bool = True,
     packed: bool = False,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> Union[SFTDataset, PackedDataset]:
@@ -52,6 +53,9 @@ def alpaca_dataset(
             the default column names ``"instruction``, ``"input"``, and ``"output"`` in ``tatsu-lab/alpaca``.
         train_on_input (bool): Whether the model is trained on the prompt or not. Default is False.
         packed (bool): Whether or not to pack the dataset to ``max_seq_len`` prior to training. Default is False.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``. See Hugging
@@ -78,6 +82,7 @@ def alpaca_dataset(
         source=source,
         message_transform=message_transform,
         model_transform=tokenizer,
+        filter_fn=filter_fn,
         split=split,
         **load_dataset_kwargs,
     )

torchtune/datasets/_chat.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 from torchtune.data._messages import OpenAIToMessages, ShareGPTToMessages
 from torchtune.datasets._packed import PackedDataset
@@ -21,6 +21,7 @@ def chat_dataset(
     train_on_input: bool = False,
     new_system_prompt: Optional[str] = None,
     packed: bool = False,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> Union[SFTDataset, PackedDataset]:
@@ -82,6 +83,9 @@ def chat_dataset(
         new_system_prompt (Optional[str]): if specified, prepend a system message. This can
             serve as instructions to guide the model response. Default is None.
         packed (bool): Whether or not to pack the dataset to ``max_seq_len`` prior to training. Default is False.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``,
@@ -171,6 +175,7 @@ def chat_dataset(
         message_transform=message_transform,
         model_transform=tokenizer,
         split=split,
+        filter_fn=filter_fn,
         **load_dataset_kwargs,
     )
     if packed:

torchtune/datasets/_cnn_dailymail.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional
 
 from torchtune.datasets._text_completion import TextCompletionDataset
 
@@ -15,6 +15,7 @@ def cnn_dailymail_articles_dataset(
     tokenizer: ModelTokenizer,
     source: str = "ccdv/cnn_dailymail",
     max_seq_len: Optional[int] = None,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> TextCompletionDataset:
@@ -30,6 +31,9 @@ def cnn_dailymail_articles_dataset(
         max_seq_len (Optional[int]): Maximum number of tokens in the returned input and label token id lists.
             Default is None, disabling truncation. We recommend setting this to the highest you can fit in memory
             and is supported by the model. For example, llama2-7B supports up to 4096 for sequence length.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.
@@ -43,6 +47,7 @@ def cnn_dailymail_articles_dataset(
         source=source,
         column="article",
         max_seq_len=max_seq_len,
+        filter_fn=filter_fn,
         split=split,
         # This is used to specify the version of the dataset, a required argument
         # by the cnn_dailymail dataset builder:

torchtune/datasets/_grammar.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 from torchtune.data import InputOutputToMessages
 from torchtune.datasets._packed import PackedDataset
@@ -21,6 +21,7 @@ def grammar_dataset(
     train_on_input: bool = False,
     new_system_prompt: Optional[str] = None,
     packed: bool = False,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> Union[SFTDataset, PackedDataset]:
@@ -53,6 +54,9 @@ def grammar_dataset(
             serve as instructions to guide the model response. Setting this will OVERRIDE any system
             messages already present in the dataset. Default is None.
         packed (bool): Whether or not to pack the dataset to tokenizer's ``max_seq_len`` prior to training. Default is False.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.
@@ -79,6 +83,7 @@ def grammar_dataset(
         source=source,
         message_transform=message_transform,
         model_transform=tokenizer,
+        filter_fn=filter_fn,
         split=split,
         **load_dataset_kwargs,
     )

torchtune/datasets/_hh_rlhf_helpful.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional
 
 from torchtune.data import ChosenRejectedToMessages
 from torchtune.datasets._preference import PreferenceDataset
@@ -18,6 +18,7 @@ def hh_rlhf_helpful_dataset(
     column_map: Optional[Dict[str, str]] = None,
     train_on_input: bool = False,
     new_system_prompt: Optional[str] = None,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> PreferenceDataset:
@@ -42,6 +43,9 @@ def hh_rlhf_helpful_dataset(
         new_system_prompt (Optional[str]): if specified, prepend a system message to every sample for both chosen
             and rejected. This can serve as instructions to guide the model response. Setting this will OVERRIDE
             any system messages already present in the dataset. Default is None.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.
@@ -60,6 +64,7 @@ def hh_rlhf_helpful_dataset(
         source=source,
         message_transform=message_transform,
         tokenizer=tokenizer,
+        filter_fn=filter_fn,
         split=split,
         **load_dataset_kwargs,
     )

torchtune/datasets/_instruct.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 from torchtune.data import InputOutputToMessages
 from torchtune.datasets._packed import PackedDataset
@@ -20,6 +20,7 @@ def instruct_dataset(
     train_on_input: bool = False,
     new_system_prompt: Optional[str] = None,
     packed: bool = False,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> Union[SFTDataset, PackedDataset]:
@@ -65,6 +66,9 @@ def instruct_dataset(
         new_system_prompt (Optional[str]): if specified, prepend a system message. This can
             serve as instructions to guide the model response. Default is None.
         packed (bool): Whether or not to pack the dataset to tokenizer's ``max_seq_len`` prior to training. Default is False.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``,
@@ -137,9 +141,11 @@ def instruct_dataset(
         source=source,
         message_transform=message_transform,
         model_transform=tokenizer,
+        filter_fn=filter_fn,
         split=split,
         **load_dataset_kwargs,
     )
+
     if packed:
         if tokenizer.max_seq_len is None:
             raise ValueError(

torchtune/datasets/_preference.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Callable, Dict, List, Mapping, Optional
 
 import numpy as np
 from datasets import load_dataset
@@ -86,6 +86,9 @@ class requires the dataset to have "chosen" and "rejected" model responses. Thes
             Since PreferenceDataset only supports text data, it requires a
             :class:`~torchtune.modules.tokenizers.ModelTokenizer` instead of the ``model_transform`` in
             :class:`~torchtune.datasets.SFTDataset`.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``. See Hugging
             Face's `API ref <https://huggingface.co/docs/datasets/en/package_reference/loading_methods#datasets.load_dataset>`_
             for more details.
@@ -97,12 +100,16 @@ def __init__(
         source: str,
         message_transform: Transform,
         tokenizer: ModelTokenizer,
+        filter_fn: Optional[Callable] = None,
         **load_dataset_kwargs: Dict[str, Any],
     ) -> None:
         self._tokenizer = tokenizer
         self._message_transform = message_transform
         self._data = load_dataset(source, **load_dataset_kwargs)
 
+        if filter_fn is not None:
+            self._data = self._data.filter(filter_fn)
+
     def __len__(self):
         return len(self._data)
 
@@ -149,6 +156,7 @@ def preference_dataset(
     column_map: Optional[Dict[str, str]] = None,
     train_on_input: bool = False,
     new_system_prompt: Optional[str] = None,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> PreferenceDataset:
@@ -214,6 +222,9 @@ def preference_dataset(
         new_system_prompt (Optional[str]): if specified, prepend a system message to every sample for both chosen
             and rejected. This can serve as instructions to guide the model response. Setting this will OVERRIDE
             any system messages already present in the dataset. Default is None.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.
@@ -291,6 +302,7 @@ def preference_dataset(
         source=source,
         message_transform=message_transform,
         tokenizer=tokenizer,
+        filter_fn=filter_fn,
         split=split,
         **load_dataset_kwargs,
     )

torchtune/datasets/_samsum.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 from torchtune.data import InputOutputToMessages
 from torchtune.datasets._packed import PackedDataset
@@ -21,6 +21,7 @@ def samsum_dataset(
     train_on_input: bool = False,
     new_system_prompt: Optional[str] = None,
     packed: bool = False,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> Union[SFTDataset, PackedDataset]:
@@ -53,6 +54,9 @@ def samsum_dataset(
             serve as instructions to guide the model response. Setting this will OVERRIDE any system
             messages already present in the dataset. Default is None.
         packed (bool): Whether or not to pack the dataset to tokenizer's ``max_seq_len`` prior to training. Default is False.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.
@@ -81,6 +85,7 @@ def samsum_dataset(
         message_transform=message_transform,
         model_transform=tokenizer,
         split=split,
+        filter_fn=filter_fn,
         **load_dataset_kwargs,
     )
     if packed:

torchtune/datasets/_slimorca.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 from torchtune.data import ShareGPTToMessages
 from torchtune.datasets._packed import PackedDataset
@@ -21,6 +21,7 @@ def slimorca_dataset(
     train_on_input: bool = False,
     new_system_prompt: Optional[str] = None,
     packed: bool = False,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> Union[SFTDataset, PackedDataset]:
@@ -50,6 +51,9 @@ def slimorca_dataset(
             serve as instructions to guide the model response. Setting this will OVERRIDE any system
             messages already present in the dataset. Default is None.
         packed (bool): Whether or not to pack the dataset to tokenizer's ``max_seq_len`` prior to training. Default is False.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.
@@ -79,6 +83,7 @@ def slimorca_dataset(
         source=source,
         message_transform=message_transform,
         model_transform=tokenizer,
+        filter_fn=filter_fn,
         split=split,
         **load_dataset_kwargs,
     )

torchtune/datasets/_stack_exchange_paired.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from typing import Any, Dict, Mapping, Optional
+from typing import Any, Callable, Dict, Mapping, Optional
 
 from torchtune.data import Message
 from torchtune.datasets._preference import PreferenceDataset
@@ -78,6 +78,7 @@ def stack_exchange_paired_dataset(
     source: str = "lvwerra/stack-exchange-paired",
     column_map: Optional[Dict[str, str]] = None,
     train_on_input: bool = False,
+    filter_fn: Optional[Callable] = None,
     split: str = "train",
     **load_dataset_kwargs: Dict[str, Any],
 ) -> PreferenceDataset:
@@ -100,6 +101,9 @@ def stack_exchange_paired_dataset(
             Keys should be "prompt", "chosen", and "rejected" and values should be the actual column names.
             Default is None, keeping the default column names.
         train_on_input (bool): Whether the model is trained on the prompt or not. Default is False.
+        filter_fn (Optional[Callable]): callable used to filter the dataset prior to any pre-processing. See
+            the Hugging Face `docs <https://huggingface.co/docs/datasets/v2.20.0/process#select-and-filter>`_ for more
+            details.
         split (str): ``split`` argument for ``datasets.load_dataset``. You can use this argument to load a subset
             of a given split, e.g. ``split="train[:10%]"``. Default is "train".
         **load_dataset_kwargs (Dict[str, Any]): additional keyword arguments to pass to ``load_dataset``.
@@ -122,6 +126,7 @@ def stack_exchange_paired_dataset(
         source=source,
         message_transform=message_transform,
         tokenizer=tokenizer,
+        filter_fn=filter_fn,
         split=split,
         data_dir="data/rl",
         **load_dataset_kwargs,