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feat: add config for optional parameters in a chat message (#2260)

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* feat: add config for optional parameters in a chat message

* chore: cleanup

* chore: fix nits and add light docs

* docs: update docs/dataset-formats/conversation.qmd

Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>

* feat: configurable message mappings, jinja template analyzer

* chore: handle bradley terry

* docs: update docs

* refactor: change order of mappings, improve message transform

* refactor: make chat awware of property mappings

* chore: remove .python-version

* chore: revert change

* chore: add dataset validation to tests where appropriate

* chore: add dataset validation to tests where appropriate

* chore: clean up handling of ds_cfg

* chore: recursively serialize config

* make sure to use the return value from validate_config

* DefaultDict pickle/unpickle fix

* fix super call for override

* refactor: message fields

* chore: empty commit

* tests: validate config before using

* chore: add config validation to all e2e tests

* chore: add unneeded logging

* chore: add missed config validation

* chore: pass field_messages to prompter

* test: fix borked test

* chore: remove uninteded file

* chore: add deprecation warning and update chat_datasets script

* chore: lint

* refactor: message fields

* feat: update axolotlinputconfig and test_models

- add configdict import in axolotl/utils/config/models/input/v0_4_1/__init__.py
- remove unnecessary line breaks in sftdataset, dpodataset, ktodataset, stepwisesuperviseddataset classes
- update model_dump method in axolotlinputconfig to exclude none values
- correct typo in test_models.py comment

* feat: simplify dpodataset and ktodataset classes in config models

removed several optional fields from dpodataset and ktodataset classes in axolotl/utils/config/models/input/v0_4_1. this simplifies the configuration subsets for these datasets.

* feat: improve readability and structure in dataset configuration models

this commit enhances the readability and structure of the dataset configuration models in the `axolotl/utils/config/models/input/v0_4_1` module. it removes unused `configdict` import and adds line breaks to separate class definitions for better clarity. additionally, a minor documentation fix is included to ensure a newline at the end of the `stepwise_supervised.qmd` file.

* feat: change log level from info to debug in chattemplatestrategy

* feat(prompt_strategies): refactor chattemplateprompter and chattemplatestrategy

- Make `chat_template` a required parameter in `ChatTemplatePrompter` constructor
- Add default value for `message_property_mappings` in `ChatTemplatePrompter` constructor
- Add `messages_array_name` property to `ChatTemplatePrompter`
- Change `processor` type to Optional in `ChatTemplatePrompter`
- Add TypeError check for `processor` in `ChatTemplatePrompter.build_prompt`
- Remove `_messages` property from `ChatTemplateStrategy`
- Make `prompter` a required parameter and add type hint in `ChatTemplateStrategy` constructor
- Remove `messages` getter and setter from `ChatTemplateStrategy`
- Use `prompter.messages_array_name` in `ChatTemplateStrategy.get_conversation_thread`
- Remove condition to set `messages` field in `load` function

* feat(tests/utils): ignore type check in load_model call in test_models.py

* feat: improve type handling and test structure in chat templates

- Add return type hint for `get_chat_template` function in `chat_templates.py`
- Remove unnecessary assignment of `strategy.messages` in several test cases
- Add `messages_array_name` parameter to various test configurations in `test_chat_templates.py` and `test_chat_templates_advanced.py`
- Remove redundant `strategy.messages` assignment in `test_chat_templates_advanced.py`

* feat(axolotl): enhance chat strategy with datasetconfig support

This commit introduces support for DatasetConfig in the ChatTemplateStrategy. It also refines the strategy loader to handle different types of ds_cfg inputs and improves the clarity of the code by formatting and reordering. The key changes include:

- Importing Union from typing and BaseModel from pydantic.
- Adding DatasetConfig as an optional type for ds_cfg in StrategyLoader.
- Adjusting the handling of ds_cfg in StrategyLoader to account for BaseModel instances.
- Refactoring the prompter_params and strategy_params for better readability.
- Changing the reference from prompt[self.messages] to prompt[self.prompter.messages_array_name] in the is_prompt_batched method.

* feat: update message handling in btchattemplatestrategy

* Replace `self.messages` with direct string references to "chosen_messages" and "rejected_messages"
* Append system, user, and assistant content directly to "chosen_messages" and "rejected_messages"
* Add a new attribute "messages_array_name" to the `load` function parameters
* Remove the conditional attribute assignment for "field_messages" in the `load` function

* feat: add config validation in test_kd.py

- Import `validate_config` from `axolotl.utils.config`
- Validate the configuration in `test_llama_kd` and another function in `TestKnowledgeDistillation` class

* feat: enhance config validation and capabilities handling

* Import `EnvCapabilities` and `GPUCapabilities` from `axolotl.utils.config.models.internals`
* Update `validate_config` function to create `KTODataset` and `SFTDataset` instances using `dict(ds_cfg)`
* Replace `capabilities` and `env_capabilities` with instances of `GPUCapabilities` and `EnvCapabilities` respectively in `AxolotlConfigWCapabilities` model dump

* feat: update config validation in axolotl utils

- Remove import of `EnvCapabilities` and `GPUCapabilities` from `axolotl.utils.config.models.internals`
- Update `validate_config` function to use `capabilities` and `env_capabilities` directly instead of creating new instances of `GPUCapabilities` and `EnvCapabilities`

* feat: refactor strategyloader in chat_template.py

- Extracted the creation of strategy parameters into a separate function, `_get_strategy_params(cfg, dataset_config)`
- Created a new function, `_get_strategy_cls()`, to obtain the strategy class
- Replaced `ChatTemplateStrategy` with `strategy_cls` for strategy instantiation

* trigger CI

* chore: revert dataset config changes for kto/dpo

* subject: refactor: rename 'messages_array_name' to 'field_messages'

Body:
- Renamed 'messages_array_name' to 'field_messages' in 'ChatTemplatePrompter' class and its usages in 'chat_template.py'
- Updated 'load' function in 'bradley_terry/chat_template.py' to reflect the change
- Adjusted 'get_chat_template_msg_variables' and 'get_message_vars' methods in 'jinja_template_analyzer.py' to use the new variable name
- Modified 'StrategyLoader' in 'chat_template.py' to use 'field_messages'
- Updated tests in 'test_chat_templates.py' and 'test_chat_templates_advanced.py' to use 'field_messages' instead of 'messages_array_name'

* feat: refactor prompt strategies and update config models

* Remove redundant 'return None' in `axolotl/prompt_strategies/__init__.py`
* Simplify message handling in `axolotl/prompt_strategies/bradley_terry/chat_template.py` by using a single 'messages' list instead of separate 'chosen_messages' and 'rejected_messages' lists
* Update default 'message_property_mappings' in `axolotl/prompt_strategies/bradley_terry/chat_template.py`
* Add 'field_messages' field to `axolotl/utils/config/models/input/v0_4_1/__init__.py` configuration model

* chore: remove unused input

* chore: remove redundant type ignore

* fix: remove old configs and update examples

* fix: type check

* fix: remove loading old config in ChatMessage

* fix: update faq with potential new undefinederror

* fix: add debug if property mapped is not found

* chore: improve explanation for unmapped properties

* fix: update docs with new config

* chore: add note for deprecation config and del old config from dict

---------

Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
This commit is contained in:
NJordan72
2025-02-17 21:59:27 -05:00
committed by GitHub
parent 3aac3b1da9
commit b194e17c28
51 changed files with 1190 additions and 230 deletions
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17
docs/config.qmd
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@@ -142,10 +142,19 @@ datasets:
# Key containing the messages (default: "messages")
field_messages: messages
# Key for role in each message (default: "role")
message_field_role: role
# Key for content in each message (default: "content")
message_field_content: content
# Mapping of properties from the input dataset to the chat template.
# (default: message_property_mappings={'role':'role', 'content':'content'})
# If a property exists in the template but not in this mapping, the system will attempt
# to load it directly from the message using the property name as the key.
# Example: In the mapping below, 'from' is loaded from input dataset and used as 'role',
# while 'value' is loaded and used as 'content' in the chat template.
message_property_mappings:
role: from
content: value
# ...
message_property_mappings:
# Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
roles:

15
docs/dataset-formats/conversation.qmd
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@@ -42,8 +42,9 @@ datasets:
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
message_property_mappings:
role: from
content: value
# new (if setting a new chat_template like chatml, gemma, etc)
chat_template: chatml
@@ -52,8 +53,9 @@ datasets:
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
message_property_mappings:
role: from
content: value
```
We recommend checking the below examples for other usecases.
@@ -138,8 +140,9 @@ datasets:
type: chat_template
chat_template: tokenizer_default
field_messages: conversations
message_field_role: from
message_field_content: value
message_property_mappings:
role: from
content: value
roles_to_train: []
train_on_eos: turn
message_field_training: train

24
docs/dataset-formats/index.qmd
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@@ -114,7 +114,7 @@ A flow chart is as follows:
4. Is your dataset in an "instruct" format, containing `{ instruction, response }`? If yes, check [Instruction Dataset](#instruction-dataset)
If you went through the flow chart and did not find one that matches, it is recommended to preprocess your dataset into one of the above or create a Github Discussion.
If you went through the flow chart and did not find one that matches, it is recommended to preprocess your dataset into one of the above or create a thread on Github Discussion.
::: {.callout-tip}
You can mix and match within each approach or across approaches to train a model on a variety of datasets.
@@ -289,9 +289,10 @@ If your dataset format is different, here are the keys you should check (with th
```yaml
datasets:
...
field_messages: messages
message_field_role: role
message_field_content: content
field_messages: messages # this should point to the key containing the list of conversations
message_property_mappings: # this is a mapping from keys in your dataset to keys in chat_template
role: role
content: content
```
In some `chat_templates` (e.g. [Gemma](https://huggingface.co/google/gemma-2b-it/blob/main/tokenizer_config.json#L1507)), the roles are hardcoded to `user` and `assistant`. Consequently, you may find it necessary to map the roles in your dataset to these above. We currently have some defaults that should work for common datasets, but if you get a `KeyError`, it would be necessary to add mapping for your roles. Here is an example of how it would look like:
@@ -348,13 +349,14 @@ datasets:
- path: A.jsonl
type: chat_template
# step 1
# step 1
chat_template: chatml
# step 2
field_messages: messages
message_field_role: role
message_field_content: content
# step 2
field_messages: messages
message_property_mappings:
role: role
content: content
roles:
assistant:
@@ -365,8 +367,8 @@ datasets:
- human
- user
# step 3
roles_to_train: ["assistant"]
# step 3
roles_to_train: ["assistant"]
train_on_eos: "turn"
special_tokens:

4
docs/faq.qmd
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@@ -23,3 +23,7 @@ description: Frequently asked questions
**Q: The codes is stuck on saving preprocessed datasets.**
> A: This is usually an issue with the GPU. This can be resolved through setting the os environment variable `CUDA_VISIBLE_DEVICES=0`. If you are on runpod, this is usually a pod issue. Starting a new pod should take care of it.
**Q: `jinja2.exceptions.UndefinedError: 'dict object' has no attribute 'content' / 'role' / ____`**
> A: This means that the property mapping for the stated attribute does not exist when building `chat_template` prompt. For example, if `no attribute 'content'`, please check you have added the correct mapping for `content` under `message_property_mappings`.

5
docs/rlhf.qmd
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@@ -229,8 +229,9 @@ datasets:
field_messages: "messages"
field_chosen: "chosen"
field_rejected: "rejected"
message_field_role: "role"
message_field_content: "content"
message_property_mappings:
role: role
content: content
roles:
user: ["user"]
assistant: ["assistant"]