Make AlpacaToMessage public.

docs/source/api_ref_data.rst

@@ -64,6 +64,7 @@ Converts data from common schema and conversation JSON formats into a list of to
     ShareGPTToMessages
     OpenAIToMessages
     ChosenRejectedToMessages
+    AlpacaToMessages
 
 Collaters
 ---------

torchtune/data/__init__.py

@@ -17,6 +17,7 @@
 from torchtune.data._converters import get_openai_messages, get_sharegpt_messages
 from torchtune.data._instruct_templates import InstructTemplate
 from torchtune.data._messages import (
+    AlpacaToMessages,
     ChosenRejectedToMessages,
     InputOutputToMessages,
     Message,
@@ -43,6 +44,7 @@
     "SummarizeTemplate",
     "OpenAIToMessages",
     "ShareGPTToMessages",
+    "AlpacaToMessages",
     "truncate",
     "Message",
     "validate_messages",

torchtune/data/_messages.py

@@ -621,3 +621,70 @@ def validate_messages(
                 f"System message at index {i} in messages, but system messages must come first"
             )
         last_turn = message.role
+
+
+class AlpacaToMessages(Transform):
+    """
+    Message transform class for Alpaca-style datasets with "instruction", "input", and "output"
+    (or equivalent fields specified in column_map) columns. User messages are formed from the
+    instruction + input columns and assistant messages are formed from the output column. Prompt
+    templating is conditional on the presence of the "input" column, and thus is handled directly
+    in this transform class instead of a dedicated :class:`~torchtune.data.PromptTemplate` class
+    due to this custom logic.
+
+    Args:
+        train_on_input (bool): Whether the model is trained on the user prompt or not.
+            Default is True.
+        column_map (Optional[Dict[str, str]]): a mapping to change the expected "instruction", "input",
+            and "output" column names to the actual column names in the dataset. Default is None,
+            keeping the default column names.
+    """
+
+    def __init__(
+        self, train_on_input: bool = True, column_map: Optional[Dict[str, str]] = None
+    ):
+        self.train_on_input = train_on_input
+        self.column_map = column_map
+        self.template = {
+            "prompt_input": (
+                "Below is an instruction that describes a task, paired with an input that provides further context. "
+                "Write a response that appropriately completes the request.\n\n"
+                "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
+            ),
+            "prompt_no_input": (
+                "Below is an instruction that describes a task. "
+                "Write a response that appropriately completes the request.\n\n"
+                "### Instruction:\n{instruction}\n\n### Response:\n"
+            ),
+        }
+
+    def __call__(self, sample: Mapping[str, Any]) -> Mapping[str, Any]:
+        column_map = self.column_map or {}
+        key_input = column_map.get("input", "input")
+        key_instruction = column_map.get("instruction", "instruction")
+        key_output = column_map.get("output", "output")
+
+        if key_input in sample and sample[key_input]:
+            prompt = self.template["prompt_input"].format(
+                instruction=sample[key_instruction], input=sample[key_input]
+            )
+        else:
+            prompt = self.template["prompt_no_input"].format(
+                instruction=sample[key_instruction]
+            )
+
+        messages = [
+            Message(
+                role="user",
+                content=prompt,
+                masked=not self.train_on_input,
+                eot=True,
+            ),
+            Message(
+                role="assistant",
+                content=sample[key_output],
+                masked=False,
+                eot=True,
+            ),
+        ]
+        return {"messages": messages}

torchtune/datasets/_alpaca.py

@@ -6,80 +6,13 @@
 
 from functools import partial
 
-from typing import Any, Dict, Mapping, Optional, Union
+from typing import Any, Dict, Optional, Union
+
+from torchtune.data._messages import AlpacaToMessages
 
-from torchtune.data._messages import Message
 from torchtune.datasets._packed import PackedDataset
 from torchtune.datasets._sft import SFTDataset
 from torchtune.modules.tokenizers import ModelTokenizer
-from torchtune.modules.transforms import Transform
-
-
-class AlpacaToMessages(Transform):
-    """
-    Message transform class for Alpaca-style datasets with "instruction", "input", and "output"
-    (or equivalent fields specified in column_map) columns. User messages are formed from the
-    instruction + input columns and assistant messages are formed from the output column. Prompt
-    templating is conditional on the presence of the "input" column, and thus is handled directly
-    in this transform class instead of a dedicated :class:`~torchtune.data.PromptTemplate` class
-    due to this custom logic.
-
-    Args:
-        train_on_input (bool): Whether the model is trained on the user prompt or not.
-            Default is True.
-        column_map (Optional[Dict[str, str]]): a mapping to change the expected "instruction", "input",
-            and "output" column names to the actual column names in the dataset. Default is None,
-            keeping the default column names.
-    """
-
-    def __init__(
-        self, train_on_input: bool = True, column_map: Optional[Dict[str, str]] = None
-    ):
-        self.train_on_input = train_on_input
-        self.column_map = column_map
-        self.template = {
-            "prompt_input": (
-                "Below is an instruction that describes a task, paired with an input that provides further context. "
-                "Write a response that appropriately completes the request.\n\n"
-                "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
-            ),
-            "prompt_no_input": (
-                "Below is an instruction that describes a task. "
-                "Write a response that appropriately completes the request.\n\n"
-                "### Instruction:\n{instruction}\n\n### Response:\n"
-            ),
-        }
-
-    def __call__(self, sample: Mapping[str, Any]) -> Mapping[str, Any]:
-        column_map = self.column_map or {}
-        key_input = column_map.get("input", "input")
-        key_instruction = column_map.get("instruction", "instruction")
-        key_output = column_map.get("output", "output")
-
-        if key_input in sample and sample[key_input]:
-            prompt = self.template["prompt_input"].format(
-                instruction=sample[key_instruction], input=sample[key_input]
-            )
-        else:
-            prompt = self.template["prompt_no_input"].format(
-                instruction=sample[key_instruction]
-            )
-
-        messages = [
-            Message(
-                role="user",
-                content=prompt,
-                masked=not self.train_on_input,
-                eot=True,
-            ),
-            Message(
-                role="assistant",
-                content=sample[key_output],
-                masked=False,
-                eot=True,
-            ),
-        ]
-        return {"messages": messages}
 
 
 def alpaca_dataset(