985819d89b
* Implementing a basic chat_template strategy for DPO datasets This mimics the sft chat_template strategy such that users can: * Specify the messages field * Specify the per message role and content fields * speicfy the chosen and rejected fields * Let the tokenizer construct the raw prompt * Ensure the chosen and rejected fields don't have any prefix tokens * Adding additional dpo chat template unittests * Rename test class
157 lines
4.9 KiB
Python
157 lines
4.9 KiB
Python
"""
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tests for chat_template prompt strategy
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"""
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import unittest
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import pytest
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from datasets import Dataset
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from transformers import AutoTokenizer
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from axolotl.prompt_strategies.dpo.chat_template import default
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from axolotl.utils.dict import DictDefault
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@pytest.fixture(name="assistant_dataset")
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def fixture_assistant_dataset():
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# pylint: disable=duplicate-code
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return Dataset.from_list(
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[
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{
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"messages": [
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{
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"role": "user",
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"content": "hello",
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},
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{
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"role": "assistant",
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"content": "hello",
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},
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{
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"role": "user",
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"content": "goodbye",
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},
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],
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"chosen": {
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"role": "assistant",
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"content": "goodbye",
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},
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"rejected": {
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"role": "assistant",
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"content": "party on",
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},
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}
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]
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)
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@pytest.fixture(name="custom_assistant_dataset")
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def fixture_custom_assistant_dataset():
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# pylint: disable=duplicate-code
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return Dataset.from_list(
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[
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{
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"conversation": [
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{
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"speaker": "human",
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"text": "hello",
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},
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{
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"speaker": "agent",
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"text": "hello",
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},
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{
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"speaker": "human",
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"text": "goodbye",
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},
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],
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"better": {
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"speaker": "agent",
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"text": "goodbye",
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},
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"worse": {
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"speaker": "agent",
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"text": "party on",
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},
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}
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]
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)
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@pytest.fixture(name="llama3_tokenizer")
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def fixture_llama3_tokenizer():
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tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B")
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tokenizer.eos_token = "<|eot_id|>"
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return tokenizer
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class TestAssistantDPOChatTemplateLlama3:
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"""
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Test class for assistant style datasets with llama-3 prompts using the chat_template strategy.
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"""
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def test_llama3_defaults(self, llama3_tokenizer, assistant_dataset):
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# pylint: disable=duplicate-code
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transform_fn = default(
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DictDefault(
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{
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"chat_template": "llama3",
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"datasets": [
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{
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"chat_template": "llama3",
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}
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],
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}
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)
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)
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result = transform_fn(assistant_dataset[0], tokenizer=llama3_tokenizer)
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assert result["prompt"] == (
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"<|begin_of_text|>"
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+ "<|start_header_id|>user<|end_header_id|>\n\nhello<|eot_id|>"
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+ "<|start_header_id|>assistant<|end_header_id|>\n\nhello<|eot_id|>"
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+ "<|start_header_id|>user<|end_header_id|>\n\ngoodbye<|eot_id|>"
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+ "<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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assert result["chosen"] == "goodbye<|eot_id|>"
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assert result["rejected"] == "party on<|eot_id|>"
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def test_llama3_configured(self, llama3_tokenizer, custom_assistant_dataset):
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# pylint: disable=duplicate-code
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transform_fn = default(
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DictDefault(
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{
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"chat_template": "llama3",
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"datasets": [
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{
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"chat_template": "llama3",
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"field_messages": "conversation",
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"field_chosen": "better",
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"field_rejected": "worse",
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"message_field_role": "speaker",
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"message_field_content": "text",
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"roles": {
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"user": ["human"],
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"assistant": ["agent"],
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"system": ["sys"],
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},
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}
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],
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}
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)
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)
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result = transform_fn(custom_assistant_dataset[0], tokenizer=llama3_tokenizer)
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assert result["prompt"] == (
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"<|begin_of_text|>"
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+ "<|start_header_id|>user<|end_header_id|>\n\nhello<|eot_id|>"
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+ "<|start_header_id|>assistant<|end_header_id|>\n\nhello<|eot_id|>"
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+ "<|start_header_id|>user<|end_header_id|>\n\ngoodbye<|eot_id|>"
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+ "<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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assert result["chosen"] == "goodbye<|eot_id|>"
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assert result["rejected"] == "party on<|eot_id|>"
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if __name__ == "__main__":
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unittest.main()
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