.. _prompt_templates_usage_label:
================
Prompt Templates
================
Prompt templates are structured text templates which are used to format user prompts
to optimize model performance on specific tasks. They can serve many purposes:
1. Model-specific templates that are required whenever the model is prompted, such as the [INST]
tags in the instruct-tuned Llama2 and Mistral models. These models were pre-trained with these tags and using them
in inference can help ensure optimal performance.
2. Task-specific templates to gear models for a particular task that it will expect after training.
Example include grammar correction (:class:`~torchtune.data.GrammarErrorCorrectionTemplate`),
summarization (:class:`~torchtune.data.SummarizeTemplate`), question answering (:class:`~torchtune.data.QuestionAnswerTemplate`),
and more.
3. Community standardized templates, such as :class:`~torchtune.data.ChatMLTemplate`
For example, if I wanted to fine-tune a model to perform a grammar correction task, I could use the :class:`~torchtune.data.GrammarErrorCorrectionTemplate`
to add the text "Correct this to standard English: {prompt} --- Corrected: {response}" to all my data samples.
.. code-block:: python
from torchtune.data import GrammarErrorCorrectionTemplate, Message
sample = {
"incorrect": "This are a cat",
"correct": "This is a cat.",
}
msgs = [
Message(role="user", content=sample["incorrect"]),
Message(role="assistant", content=sample["correct"]),
]
gec_template = GrammarErrorCorrectionTemplate()
templated_msgs = gec_template(msgs)
for msg in templated_msgs:
print(msg.text_content)
# Correct this to standard English: This are a cat
# ---
# Corrected:
# This is a cat.
The added text is different from special tokens that are added by the model tokenizer. For an extended
discussion on the different between prompt templates and special tokens, see :ref:`prompt_template_vs_special_tokens`.
.. _using_prompt_templates:
Using prompt templates
----------------------
Prompt templates are passed into the tokenizer and will be automatically applied for the dataset you are fine-tuning on. You can pass it in two ways:
- A string dotpath to a prompt template class, i.e., "torchtune.models.mistral.MistralChatTemplate" or "path.to.my.CustomPromptTemplate"
- A dictionary that maps role to a tuple of strings indicating the text to add before and after the message content
Defining via dotpath string
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
# In code
from torchtune.models.mistral import mistral_tokenizer
m_tokenizer = mistral_tokenizer(
path="/tmp/Mistral-7B-v0.1/tokenizer.model"
prompt_template="torchtune.models.mistral.MistralChatTemplate"
)
.. code-block:: yaml
# In config
tokenizer:
_component_: torchtune.models.mistral.mistral_tokenizer
path: /tmp/Mistral-7B-v0.1/tokenizer.model
prompt_template: torchtune.models.mistral.MistralChatTemplate
Defining via dictionary
^^^^^^^^^^^^^^^^^^^^^^^
For example to achieve the following prompt template:
.. code-block:: text
System: {content}\\n
User: {content}\\n
Assistant: {content}\\n
Tool: {content}\\n
You need to pass in a tuple for each role, where ``PREPEND_TAG`` is the string
added before the text content and ``APPEND_TAG`` is the string added after.
.. code-block:: python
template = {role: (PREPEND_TAG, APPEND_TAG)}
Thus, the template would be defined as follows:
.. code-block:: python
template = {
"system": ("System: ", "\\n"),
"user": ("User: ", "\\n"),
"assistant": ("Assistant: ", "\\n"),
"ipython": ("Tool: ", "\\n"),
}
Now we can pass it into the tokenizer as a dictionary:
.. code-block:: python
# In code
from torchtune.models.mistral import mistral_tokenizer
template = {
"system": ("System: ", "\\n"),
"user": ("User: ", "\\n"),
"assistant": ("Assistant: ", "\\n"),
"ipython": ("Tool: ", "\\n"),
}
m_tokenizer = mistral_tokenizer(
path="/tmp/Mistral-7B-v0.1/tokenizer.model"
prompt_template=template,
)
.. code-block:: yaml
# In config
tokenizer:
_component_: torchtune.models.mistral.mistral_tokenizer
path: /tmp/Mistral-7B-v0.1/tokenizer.model
prompt_template:
system:
- "System: "
- "\\n"
user:
- "User: "
- "\\n"
assistant:
- "Assistant: "
- "\\n"
ipython:
- "Tool: "
- "\\n"
If you don't want to add a prepend/append tag to a role, you can just pass in an empty string "" where needed.
Using the :class:`~torchtune.data.PromptTemplate` class
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A template dictionary can also be passed into :class:`~torchtune.data.PromptTemplate` so you can use it as a standalone custom
prompt template class.
.. code-block:: python
from torchtune.data import PromptTemplate
def my_custom_template() -> PromptTemplate:
return PromptTemplate(
template={
"user": ("User: ", "\\n"),
"assistant": ("Assistant: ", "\\n"),
},
)
template = my_custom_template()
msgs = [
Message(role="user", content="Hello world!"),
Message(role="assistant", content="Is AI overhyped?"),
]
templated_msgs = template(msgs)
for msg in templated_msgs:
print(msg.role, msg.text_content)
# user, User: Hello world!
#
# assistant, Assistant: Is AI overhyped?
#
.. TODO (RdoubleA) add a section on how to define prompt templates for inference once generate script is finalized
Custom prompt templates
-----------------------
For more advanced configuration that doesn't neatly fall into the ``PREPEND_TAG content APPEND_TAG``
pattern, you can create a new class that inherits from :class:`~torchtune.data.PromptTemplateInterface`
and implements the ``__call__`` method.
.. code-block:: python
from torchtune.data import Message
class PromptTemplateInterface(Protocol):
def __call__(
self,
messages: List[Message],
inference: bool = False,
) -> List[Message]:
"""
Format each role's message(s) according to the prompt template
Args:
messages (List[Message]): a single conversation, structured as a list
of :class:`~torchtune.data.Message` objects
inference (bool): Whether the template is being used for inference or not.
Returns:
The formatted list of messages
"""
pass
# Contrived example - make all assistant prompts say "Eureka!"
class EurekaTemplate(PromptTemplateInterface):
def __call__(
self,
messages: List[Message],
inference: bool = False,
) -> List[Message]:
formatted_dialogue = []
for message in messages:
if message.role == "assistant":
content = "Eureka!"
else:
content = message.content
formatted_dialogue.append(
Message(
role=message.role,
content=content,
masked=message.masked,
ipython=message.ipython,
eot=message.eot,
),
)
return formatted_dialogue
template = EurekaTemplate()
msgs = [
Message(role="user", content="Hello world!"),
Message(role="assistant", content="Is AI overhyped?"),
]
templated_msgs = template(msgs)
for msg in templated_msgs:
print(msg.role, msg.text_content)
# user, Hello world!
# assistant, Eureka!
For more examples, you can look at :class:`~torchtune.models.mistral.MistralChatTemplate` or
:class:`~torchtune.models.llama2.Llama2ChatTemplate`.
To use this custom template in the tokenizer, you can pass it in via dotpath string:
.. code-block:: python
# In code
from torchtune.models.mistral import mistral_tokenizer
m_tokenizer = mistral_tokenizer(
path="/tmp/Mistral-7B-v0.1/tokenizer.model",
prompt_template="path.to.template.EurekaTemplate",
)
.. code-block:: yaml
# In config
tokenizer:
_component_: torchtune.models.mistral.mistral_tokenizer
path: /tmp/Mistral-7B-v0.1/tokenizer.model
prompt_template: path.to.template.EurekaTemplate
Built-in prompt templates
-------------------------
- :class:`torchtune.data.GrammarErrorCorrectionTemplate`
- :class:`torchtune.data.SummarizeTemplate`
- :class:`torchtune.data.QuestionAnswerTemplate`
- :class:`torchtune.data.ChatMLTemplate`