Refactored modules/tokenizers to be a subdir of modules/transforms

docs/source/api_ref_modules.rst

@@ -48,10 +48,10 @@ model specific tokenizers.
     :toctree: generated/
     :nosignatures:
 
-    tokenizers.SentencePieceBaseTokenizer
-    tokenizers.TikTokenBaseTokenizer
-    tokenizers.ModelTokenizer
-    tokenizers.BaseTokenizer
+    transforms.tokenizers.SentencePieceBaseTokenizer
+    transforms.tokenizers.TikTokenBaseTokenizer
+    transforms.tokenizers.ModelTokenizer
+    transforms.tokenizers.BaseTokenizer
 
 Tokenizer Utilities
 -------------------
@@ -61,8 +61,8 @@ These are helper methods that can be used by any tokenizer.
     :toctree: generated/
     :nosignatures:
 
-    tokenizers.tokenize_messages_no_special_tokens
-    tokenizers.parse_hf_tokenizer_json
+    transforms.tokenizers.tokenize_messages_no_special_tokens
+    transforms.tokenizers.parse_hf_tokenizer_json
 
 
 PEFT Components

docs/source/basics/tokenizers.rst

@@ -168,7 +168,7 @@ For example, here we change the ``"<|begin_of_text|>"`` and ``"<|end_of_text|>"`
 Base tokenizers
 ---------------
 
-:class:`~torchtune.modules.tokenizers.BaseTokenizer` are the underlying byte-pair encoding modules that perform the actual raw string to token ID conversion and back.
+:class:`~torchtune.modules.transforms.tokenizers.BaseTokenizer` are the underlying byte-pair encoding modules that perform the actual raw string to token ID conversion and back.
 In torchtune, they are required to implement ``encode`` and ``decode`` methods, which are called by the :ref:`model_tokenizers` to convert
 between raw text and token IDs.
 
@@ -202,13 +202,13 @@ between raw text and token IDs.
             """
             pass
 
-If you load any :ref:`model_tokenizers`, you can see that it calls its underlying :class:`~torchtune.modules.tokenizers.BaseTokenizer`
+If you load any :ref:`model_tokenizers`, you can see that it calls its underlying :class:`~torchtune.modules.transforms.tokenizers.BaseTokenizer`
 to do the actual encoding and decoding.
 
 .. code-block:: python
 
     from torchtune.models.mistral import mistral_tokenizer
-    from torchtune.modules.tokenizers import SentencePieceBaseTokenizer
+    from torchtune.modules.transforms.tokenizers import SentencePieceBaseTokenizer
 
     m_tokenizer = mistral_tokenizer("/tmp/Mistral-7B-v0.1/tokenizer.model")
     # Mistral uses SentencePiece for its underlying BPE
@@ -227,7 +227,7 @@ to do the actual encoding and decoding.
 Model tokenizers
 ----------------
 
-:class:`~torchtune.modules.tokenizers.ModelTokenizer` are specific to a particular model. They are required to implement the ``tokenize_messages`` method,
+:class:`~torchtune.modules.transforms.tokenizers.ModelTokenizer` are specific to a particular model. They are required to implement the ``tokenize_messages`` method,
 which converts a list of Messages into a list of token IDs.
 
 .. code-block:: python
@@ -259,7 +259,7 @@ is because they add all the necessary special tokens or prompt templates require
 .. code-block:: python
 
     from torchtune.models.mistral import mistral_tokenizer
-    from torchtune.modules.tokenizers import SentencePieceBaseTokenizer
+    from torchtune.modules.transforms.tokenizers import SentencePieceBaseTokenizer
     from torchtune.data import Message
 
     m_tokenizer = mistral_tokenizer("/tmp/Mistral-7B-v0.1/tokenizer.model")

recipes/eleuther_eval.py

@@ -31,8 +31,8 @@
 from torchtune.modules import TransformerDecoder
 from torchtune.modules.common_utils import local_kv_cache
 from torchtune.modules.model_fusion import DeepFusionModel
-from torchtune.modules.tokenizers import ModelTokenizer
 from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 from torchtune.recipe_interfaces import EvalRecipeInterface
 from torchtune.training import FullModelTorchTuneCheckpointer
 

tests/test_utils.py

@@ -20,8 +20,8 @@
 import torch
 from torch import nn
 from torchtune.data import Message, PromptTemplate, truncate
-from torchtune.modules.tokenizers import ModelTokenizer
 from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 skip_if_cuda_not_available = unittest.skipIf(
     not torch.cuda.is_available(), "CUDA is not available"

tests/torchtune/modules/tokenizers/test_sentencepiece.py → tests/torchtune/modules/transforms/tokenizers/test_sentencepiece.py

@@ -7,7 +7,7 @@
 import pytest
 
 from tests.common import ASSETS
-from torchtune.modules.tokenizers import SentencePieceBaseTokenizer
+from torchtune.modules.transforms.tokenizers import SentencePieceBaseTokenizer
 
 
 class TestSentencePieceBaseTokenizer:

tests/torchtune/modules/tokenizers/test_tiktoken.py → tests/torchtune/modules/transforms/tokenizers/test_tiktoken.py

@@ -8,7 +8,7 @@
 
 from tests.common import ASSETS
 from torchtune.models.llama3._tokenizer import CL100K_PATTERN
-from torchtune.modules.tokenizers import TikTokenBaseTokenizer
+from torchtune.modules.transforms.tokenizers import TikTokenBaseTokenizer
 
 
 class TestTikTokenBaseTokenizer:

tests/torchtune/modules/tokenizers/test_utils.py → tests/torchtune/modules/transforms/tokenizers/test_utils.py

@@ -9,7 +9,7 @@
 from tests.test_utils import DummyTokenizer
 from torchtune.data import Message
 
-from torchtune.modules.tokenizers import tokenize_messages_no_special_tokens
+from torchtune.modules.transforms.tokenizers import tokenize_messages_no_special_tokens
 
 
 class TestTokenizerUtils:

torchtune/data/_messages.py

@@ -22,9 +22,10 @@
 class Message:
     """
     This class represents individual messages in a fine-tuning dataset. It supports
-    text-only content, text with interleaved images, and tool calls. The :class:`~torchtune.modules.tokenizers.ModelTokenizer`
-    will tokenize the content of the message using ``tokenize_messages`` and attach
-    the appropriate special tokens based on the flags set in this class.
+    text-only content, text with interleaved images, and tool calls. The
+    :class:`~torchtune.modules.transforms.tokenizers.ModelTokenizer` will tokenize
+    the content of the message using ``tokenize_messages`` and attach the appropriate
+    special tokens based on the flags set in this class.
 
     Args:
         role (Role): role of the message writer. Can be "system" for system prompts,

torchtune/datasets/_alpaca.py

@@ -12,7 +12,7 @@
 
 from torchtune.datasets._packed import PackedDataset
 from torchtune.datasets._sft import SFTDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def alpaca_dataset(

torchtune/datasets/_chat.py

@@ -9,7 +9,7 @@
 from torchtune.data._messages import OpenAIToMessages, ShareGPTToMessages
 from torchtune.datasets._packed import PackedDataset
 from torchtune.datasets._sft import SFTDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def chat_dataset(

torchtune/datasets/_cnn_dailymail.py

@@ -8,7 +8,7 @@
 
 from torchtune.datasets._text_completion import TextCompletionDataset
 
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def cnn_dailymail_articles_dataset(

torchtune/datasets/_grammar.py

@@ -10,7 +10,7 @@
 from torchtune.data import InputOutputToMessages
 from torchtune.datasets._packed import PackedDataset
 from torchtune.datasets._sft import SFTDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def grammar_dataset(

torchtune/datasets/_hh_rlhf_helpful.py

@@ -8,7 +8,7 @@
 
 from torchtune.data import ChosenRejectedToMessages
 from torchtune.datasets._preference import PreferenceDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def hh_rlhf_helpful_dataset(

torchtune/datasets/_instruct.py

@@ -9,7 +9,7 @@
 from torchtune.data import InputOutputToMessages
 from torchtune.datasets._packed import PackedDataset
 from torchtune.datasets._sft import SFTDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def instruct_dataset(

torchtune/datasets/_preference.py

@@ -11,10 +11,10 @@
 from torch.utils.data import Dataset
 
 from torchtune.data import ChosenRejectedToMessages, CROSS_ENTROPY_IGNORE_IDX
-
-from torchtune.modules.tokenizers import ModelTokenizer
 from torchtune.modules.transforms import Transform
 
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
+
 
 class PreferenceDataset(Dataset):
     """
@@ -84,7 +84,7 @@ class requires the dataset to have "chosen" and "rejected" model responses. Thes
             of messages are stored in the ``"chosen"`` and ``"rejected"`` keys.
         tokenizer (ModelTokenizer): Tokenizer used by the model that implements the ``tokenize_messages`` method.
             Since PreferenceDataset only supports text data, it requires a
-            :class:`~torchtune.modules.tokenizers.ModelTokenizer` instead of the ``model_transform`` in
+            :class:`~torchtune.modules.transforms.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

torchtune/datasets/_samsum.py

@@ -10,7 +10,7 @@
 from torchtune.data import InputOutputToMessages
 from torchtune.datasets._packed import PackedDataset
 from torchtune.datasets._sft import SFTDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def samsum_dataset(

torchtune/datasets/_sft.py

@@ -69,11 +69,13 @@ class SFTDataset(Dataset):
     multimodal datasets requires processing the images in a way specific to the vision
     encoder being used by the model and is agnostic to the specific dataset.
 
-    Tokenization is handled by the ``model_transform``. All :class:`~torchtune.modules.tokenizers.ModelTokenizer`
-    can be treated as a ``model_transform`` since it uses the model-specific tokenizer to
-    transform the list of messages outputted from the ``message_transform`` into tokens
-    used by the model for training. Text-only datasets will simply pass the :class:`~torchtune.modules.tokenizers.ModelTokenizer`
-    into ``model_transform``. Tokenizers handle prompt templating, if configured.
+    Tokenization is handled by the ``model_transform``. All
+    :class:`~torchtune.modules.transforms.tokenizers.ModelTokenizer` can be treated as
+    a ``model_transform`` since it uses the model-specific tokenizer to transform the
+    list of messages outputted from the ``message_transform`` into tokens used by the
+    model for training. Text-only datasets will simply pass the
+    :class:`~torchtune.modules.transforms.tokenizers.ModelTokenizer` into ``model_transform``.
+    Tokenizers handle prompt templating, if configured.
 
     Args:
         source (str): path to dataset repository on Hugging Face. For local datasets,

torchtune/datasets/_slimorca.py

@@ -10,7 +10,7 @@
 from torchtune.datasets._packed import PackedDataset
 
 from torchtune.datasets._sft import SFTDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def slimorca_dataset(

torchtune/datasets/_stack_exchange_paired.py

@@ -8,8 +8,8 @@
 
 from torchtune.data import Message
 from torchtune.datasets._preference import PreferenceDataset
-from torchtune.modules.tokenizers import ModelTokenizer
 from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 class StackExchangePairedToMessages(Transform):

torchtune/datasets/_text_completion.py

@@ -10,7 +10,7 @@
 from torch.utils.data import Dataset
 from torchtune.data._utils import truncate
 from torchtune.datasets._packed import PackedDataset
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 class TextCompletionDataset(Dataset):

torchtune/datasets/_wikitext.py

@@ -13,7 +13,7 @@
     TextCompletionDataset,
 )
 
-from torchtune.modules.tokenizers import ModelTokenizer
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 def wikitext_dataset(

torchtune/models/clip/_tokenizer.py

@@ -7,7 +7,7 @@
 
 import regex as re
 
-from torchtune.modules.tokenizers._utils import BaseTokenizer
+from torchtune.modules.transforms.tokenizers._utils import BaseTokenizer
 
 WORD_BOUNDARY = "</w>"
 

torchtune/models/gemma/_tokenizer.py

@@ -7,12 +7,12 @@
 from typing import Any, List, Mapping, Optional, Tuple
 
 from torchtune.data import Message, PromptTemplate
-from torchtune.modules.tokenizers import (
+from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import (
     ModelTokenizer,
     SentencePieceBaseTokenizer,
     tokenize_messages_no_special_tokens,
 )
-from torchtune.modules.transforms import Transform
 
 WHITESPACE_CHARS = [" ", "\n", "\t", "\r", "\v"]
 

torchtune/models/llama2/_tokenizer.py

@@ -8,12 +8,12 @@
 
 from torchtune.data import Message, PromptTemplate
 from torchtune.models.llama2._prompt_template import Llama2ChatTemplate
-from torchtune.modules.tokenizers import (
+from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import (
     ModelTokenizer,
     SentencePieceBaseTokenizer,
     tokenize_messages_no_special_tokens,
 )
-from torchtune.modules.transforms import Transform
 
 WHITESPACE_CHARS = [" ", "\n", "\t", "\r", "\v"]
 

torchtune/models/llama3/_model_builders.py

@@ -13,7 +13,7 @@
 
 from torchtune.modules import TransformerDecoder
 from torchtune.modules.peft import LORA_ATTN_MODULES
-from torchtune.modules.tokenizers import parse_hf_tokenizer_json
+from torchtune.modules.transforms.tokenizers import parse_hf_tokenizer_json
 
 
 """

torchtune/models/llama3/_tokenizer.py

@@ -8,8 +8,11 @@
 from typing import Any, Dict, List, Mapping, Optional, Tuple
 
 from torchtune.data import Message, PromptTemplate, truncate
-from torchtune.modules.tokenizers import ModelTokenizer, TikTokenBaseTokenizer
 from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import (
+    ModelTokenizer,
+    TikTokenBaseTokenizer,
+)
 
 
 CL100K_PATTERN = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""  # noqa

torchtune/models/llama3_2_vision/_model_builders.py

@@ -20,6 +20,7 @@
 from torchtune.models.llama3_2_vision._transform import Llama3VisionTransform
 from torchtune.modules.model_fusion import DeepFusionModel
 from torchtune.modules.peft import LORA_ATTN_MODULES
+from torchtune.modules.transforms.tokenizers import parse_hf_tokenizer_json
 
 
 def llama3_2_vision_transform(

torchtune/models/llama3_2_vision/_transform.py

@@ -10,8 +10,8 @@
 
 from torchtune.models.clip import CLIPImageTransform
 from torchtune.models.llama3 import llama3_tokenizer
-from torchtune.modules.tokenizers import ModelTokenizer
 from torchtune.modules.transforms import Transform, VisionCrossAttentionMask
+from torchtune.modules.transforms.tokenizers import ModelTokenizer
 
 
 class Llama3VisionTransform(ModelTokenizer, Transform):

torchtune/models/mistral/_tokenizer.py

@@ -8,12 +8,12 @@
 
 from torchtune.data import Message, PromptTemplate
 from torchtune.models.mistral._prompt_template import MistralChatTemplate
-from torchtune.modules.tokenizers import (
+from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import (
     ModelTokenizer,
     SentencePieceBaseTokenizer,
     tokenize_messages_no_special_tokens,
 )
-from torchtune.modules.transforms import Transform
 
 WHITESPACE_CHARS = [" ", "\n", "\t", "\r", "\v"]
 

torchtune/models/phi3/_model_builders.py

@@ -6,7 +6,7 @@
 from torchtune.modules import TransformerDecoder
 from torchtune.modules.peft import LORA_ATTN_MODULES
 from functools import partial
-from torchtune.modules.tokenizers import parse_hf_tokenizer_json
+from torchtune.modules.transforms.tokenizers import parse_hf_tokenizer_json
 from torchtune.data._prompt_templates import _TemplateType
 from torchtune.data._prompt_templates import _get_prompt_template
 

torchtune/models/phi3/_tokenizer.py

@@ -9,8 +9,11 @@
 from torchtune.data._messages import Message
 from torchtune.data._prompt_templates import PromptTemplate
 from torchtune.data._utils import truncate
-from torchtune.modules.tokenizers import ModelTokenizer, SentencePieceBaseTokenizer
 from torchtune.modules.transforms import Transform
+from torchtune.modules.transforms.tokenizers import (
+    ModelTokenizer,
+    SentencePieceBaseTokenizer,
+)
 
 PHI3_SPECIAL_TOKENS = {
     "<|endoftext|>": 32000,

torchtune/models/qwen2/_model_builders.py

@@ -11,7 +11,7 @@
 from torchtune.models.qwen2._tokenizer import QWEN2_SPECIAL_TOKENS, Qwen2Tokenizer
 from torchtune.modules import TransformerDecoder
 from torchtune.modules.peft import LORA_ATTN_MODULES
-from torchtune.modules.tokenizers import parse_hf_tokenizer_json
+from torchtune.modules.transforms.tokenizers import parse_hf_tokenizer_json
 
 """
 Model builders build specific instantiations using component builders. For example