10cfecf02e
* fix: use apply_chat_template to find turn boundaries and allow tool_calling field * fix: keys to include in turn * feat(doc): explicitly recommend setting train_on_eos and roles_to_train * fix: eos not being masked for tool due to template padding * chore: clear up docs * fix: default messages format, train_on_eos: turn, and train on all assistant msg * fix: properly warn if empty content * feat: parametrize chat_template tests to test different tokenizers * fix: set proper default for message key * fix: update defaults to match load function * fix: change defaults to use new * feat: add tool_calling dataset * feat: add tool_calling test * fix: add handling of edge case of mistral tokenizer with only system prompt * feat: refactor all test to follow source code * fix: remove unnecessary eos_token from phi35 * fix test for phi3.5 since eos was dropped from chat_template --------- Co-authored-by: Wing Lian <wing@axolotl.ai>
917 lines
33 KiB
Python
917 lines
33 KiB
Python
"""
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tests for chat_template prompt strategy
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"""
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|
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import logging
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|
import unittest
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from copy import deepcopy
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import pytest
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from datasets import Dataset
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from tokenizers import AddedToken
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from transformers import PreTrainedTokenizer
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from axolotl.prompt_strategies.chat_template import (
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ChatTemplatePrompter,
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ChatTemplateStrategy,
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)
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from axolotl.prompters import IGNORE_TOKEN_ID
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from axolotl.utils.chat_templates import get_chat_template
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logging.basicConfig(level=logging.DEBUG)
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LOG = logging.getLogger("axolotl")
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PARAMETRIZE_KEYS = "tokenizer, chat_template, chat_template_jinja, eos_token"
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PARAMETRIZE_PARAMS = [
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("llama3_tokenizer", "llama3", None, None),
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("llama3_tokenizer", "chatml", None, "<|im_end|>"),
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(
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"mistralv03_tokenizer",
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"jinja",
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"mistralv03_tokenizer_chat_template_jinja",
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"[/INST]",
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),
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(
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"gemma2_tokenizer",
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"jinja",
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"gemma2_tokenizer_chat_template_jinja",
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"<end_of_turn>",
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),
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("phi35_tokenizer", "phi_35", None, "<|end|>"),
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]
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|
|
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@pytest.mark.parametrize(
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PARAMETRIZE_KEYS,
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PARAMETRIZE_PARAMS,
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)
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class TestChatTemplateConfigurations:
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"""
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Test class for various configurations of ChatTemplateStrategy.
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"""
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@staticmethod
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def find_sublist(full_list, sub_list):
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token_count = len(sub_list)
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for index in range(len(full_list) - token_count + 1):
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if full_list[index : index + token_count] == sub_list:
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return index
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return -1
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@staticmethod
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def setup_tokenizer(
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tokenizer_name,
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chat_template,
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chat_template_jinja=None,
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eos_token=None,
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request=None,
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) -> tuple[PreTrainedTokenizer, str]:
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"""
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Helper function to set up the tokenizer and chat template for the test.
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"""
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tokenizer = deepcopy(request.getfixturevalue(tokenizer_name))
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if chat_template == "jinja":
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chat_template_jinja = request.getfixturevalue(chat_template_jinja)
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if eos_token:
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tokenizer.add_special_tokens(
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{
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"eos_token": AddedToken(
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eos_token, rstrip=False, lstrip=False, normalized=False
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)
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}
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)
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if tokenizer.__class__.__name__ in (
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"LlamaTokenizerFast",
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"CodeLlamaTokenizerFast",
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):
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tokenizer.update_post_processor()
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return tokenizer, chat_template_jinja
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def _should_skip_turn(self, tokenizer, turn, turn_idx, start_idx, end_idx):
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"""Helper method to determine if a turn should be skipped in testing.
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This is used to skip system messages for Mistral as the template does not output them without more turns.
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"""
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if (
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turn_idx == 0
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and turn.get("from") in ["system", "context"]
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and "mistral" in tokenizer.name_or_path.lower()
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):
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assert (
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start_idx == -1 and end_idx == -1
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), "Expected system message to be skipped"
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return True
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return False
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def test_train_on_inputs_true(
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self,
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tokenizer,
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chat_template,
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chat_template_jinja,
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eos_token,
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basic_dataset,
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request,
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|
):
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LOG.info("Testing with train_on_inputs=True")
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tokenizer, chat_template_jinja = self.setup_tokenizer(
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tokenizer, chat_template, chat_template_jinja, eos_token, request
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)
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strategy = ChatTemplateStrategy(
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ChatTemplatePrompter(
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tokenizer,
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chat_template=get_chat_template(
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chat_template, jinja_template=chat_template_jinja
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),
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message_field_role="from",
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message_field_content="value",
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),
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tokenizer=tokenizer,
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train_on_inputs=True,
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sequence_len=512,
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roles_to_train=["assistant"],
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)
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strategy.messages = "conversations"
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res = strategy.tokenize_prompt(basic_dataset[0])
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turns = strategy.get_conversation_thread(basic_dataset[0])
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labels = res["labels"]
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input_ids = res["input_ids"]
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# Verify assistant responses are labeled
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for i, turn in enumerate(basic_dataset[0]["conversations"]):
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start_idx, end_idx = strategy.find_turn(turns=turns, turn_idx=i)
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if self._should_skip_turn(tokenizer, turn, i, start_idx, end_idx):
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continue
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decoded_response = tokenizer.decode(input_ids[start_idx:end_idx])
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response = turn["value"]
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assert response in decoded_response, (
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f"Response {response} not found in index {start_idx}:{end_idx} "
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f"decoded:{decoded_response}"
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)
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assert all(
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label != IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
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), f"Expected labels for input '{response}' to be ignored, but got {labels[start_idx:end_idx]}"
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LOG.debug("Full labels: %s", labels)
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LOG.debug("Full input_ids: %s", input_ids)
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def test_train_on_inputs_false(
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self,
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tokenizer,
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chat_template,
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chat_template_jinja,
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eos_token,
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basic_dataset,
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request,
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):
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LOG.info("Testing with train_on_inputs=False, on assistant only")
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tokenizer, chat_template_jinja = self.setup_tokenizer(
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tokenizer, chat_template, chat_template_jinja, eos_token, request
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)
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strategy = ChatTemplateStrategy(
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ChatTemplatePrompter(
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tokenizer,
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chat_template=get_chat_template(
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chat_template, jinja_template=chat_template_jinja
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),
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message_field_role="from",
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message_field_content="value",
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),
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tokenizer=tokenizer,
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train_on_inputs=False,
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sequence_len=512,
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roles_to_train=["assistant"],
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)
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strategy.messages = "conversations"
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res = strategy.tokenize_prompt(basic_dataset[0])
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turns = strategy.get_conversation_thread(basic_dataset[0])
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labels = res["labels"]
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input_ids = res["input_ids"]
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# Process all turns and verify correct labeling based on role
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for i, turn in enumerate(basic_dataset[0]["conversations"]):
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start_idx, end_idx = strategy.find_turn(turns=turns, turn_idx=i)
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if self._should_skip_turn(tokenizer, turn, i, start_idx, end_idx):
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continue
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decoded_response = tokenizer.decode(input_ids[start_idx:end_idx])
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response = turn["value"]
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assert response in decoded_response, (
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f"Response {response} not found in index {start_idx}:{end_idx} "
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f"decoded:{decoded_response}"
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)
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# Verify that assistant responses are labeled and other inputs are not
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is_assistant = turn["from"] == "assistant"
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if is_assistant:
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assert all(
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label != IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
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), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:end_idx]}"
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else:
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assert all(
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label == IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
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), f"Expected labels for human input '{response}' to be IGNORE_TOKEN_ID, but got {labels[start_idx:end_idx]}"
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def test_roles_to_train_human_assistant_only(
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self,
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tokenizer,
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chat_template,
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chat_template_jinja,
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eos_token,
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basic_dataset,
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request,
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):
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LOG.info("Testing roles_to_train with human assistant only")
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tokenizer, chat_template_jinja = self.setup_tokenizer(
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tokenizer, chat_template, chat_template_jinja, eos_token, request
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)
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strategy = ChatTemplateStrategy(
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ChatTemplatePrompter(
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tokenizer,
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chat_template=get_chat_template(
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chat_template, jinja_template=chat_template_jinja
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),
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message_field_role="from",
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message_field_content="value",
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),
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tokenizer=tokenizer,
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train_on_inputs=False,
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sequence_len=512,
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roles_to_train=["assistant", "human"],
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)
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strategy.messages = "conversations"
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res = strategy.tokenize_prompt(basic_dataset[0])
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labels = res["labels"]
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input_ids = res["input_ids"]
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strategy.messages = "conversations"
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res = strategy.tokenize_prompt(basic_dataset[0])
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turns = strategy.get_conversation_thread(basic_dataset[0])
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labels = res["labels"]
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input_ids = res["input_ids"]
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# Process all turns and verify correct labeling based on role
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for i, turn in enumerate(basic_dataset[0]["conversations"]):
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start_idx, end_idx = strategy.find_turn(turns=turns, turn_idx=i)
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if self._should_skip_turn(tokenizer, turn, i, start_idx, end_idx):
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continue
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decoded_response = tokenizer.decode(input_ids[start_idx:end_idx])
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response = turn["value"]
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assert response in decoded_response, (
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f"Response {response} not found in index {start_idx}:{end_idx} "
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f"decoded:{decoded_response}"
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)
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# Verify that non-system responses are labeled and system are not
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should_be_labelled = turn["from"] != "system"
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if should_be_labelled:
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assert all(
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label != IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
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), f"Expected labels for assistant response '{response}' to be set, but got {labels[start_idx:end_idx]}"
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else:
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assert all(
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label == IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
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), f"Expected labels for human input '{response}' to be IGNORE_TOKEN_ID, but got {labels[start_idx:end_idx]}"
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def test_roles_to_train_all(
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self,
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|
tokenizer,
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chat_template,
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|
chat_template_jinja,
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|
eos_token,
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basic_dataset,
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|
request,
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|
):
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|
LOG.info("Testing roles_to_train with all roles")
|
|
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|
tokenizer, chat_template_jinja = self.setup_tokenizer(
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tokenizer, chat_template, chat_template_jinja, eos_token, request
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)
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|
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|
strategy = ChatTemplateStrategy(
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ChatTemplatePrompter(
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tokenizer,
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chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
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|
message_field_role="from",
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|
message_field_content="value",
|
|
),
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|
tokenizer=tokenizer,
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|
train_on_inputs=True,
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sequence_len=512,
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|
roles_to_train=["human", "assistant"],
|
|
)
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|
strategy.messages = "conversations"
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|
res = strategy.tokenize_prompt(basic_dataset[0])
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|
turns = strategy.get_conversation_thread(basic_dataset[0])
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|
labels = res["labels"]
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|
input_ids = res["input_ids"]
|
|
|
|
# Verify that all responses are labeled (except for special tokens)
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|
for i, turn in enumerate(basic_dataset[0]["conversations"]):
|
|
response = turn["value"]
|
|
|
|
start_idx, end_idx = strategy.find_turn(turns=turns, turn_idx=i)
|
|
|
|
if self._should_skip_turn(tokenizer, turn, i, start_idx, end_idx):
|
|
continue
|
|
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|
decoded_response = tokenizer.decode(input_ids[start_idx:end_idx])
|
|
assert (
|
|
response in decoded_response
|
|
), f"Response {response} not found in index {start_idx}:{end_idx} decoded:{decoded_response}"
|
|
|
|
assert all(
|
|
label != IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
|
|
), f"Expected labels for response '{response}' to be set, but got {labels[start_idx:end_idx]}"
|
|
|
|
def test_empty_roles_to_train(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
basic_dataset,
|
|
request,
|
|
):
|
|
LOG.info("Testing with empty roles_to_train")
|
|
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
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tokenizer,
|
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chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=[],
|
|
train_on_eos="none", # Add this line
|
|
)
|
|
strategy.messages = "conversations"
|
|
res = strategy.tokenize_prompt(basic_dataset[0])
|
|
labels = res["labels"]
|
|
|
|
# Verify that no labels are set when roles_to_train is empty
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|
LOG.debug("Full labels: %s", labels)
|
|
assert all(
|
|
label == IGNORE_TOKEN_ID for label in labels
|
|
), "Expected all labels to be IGNORE_TOKEN_ID when roles_to_train is empty"
|
|
|
|
def test_train_on_eos_all(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
basic_dataset,
|
|
request,
|
|
):
|
|
LOG.info("Testing with train_on_eos='all'")
|
|
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
|
tokenizer,
|
|
chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=["assistant"],
|
|
train_on_eos="all",
|
|
)
|
|
strategy.messages = "conversations"
|
|
res = strategy.tokenize_prompt(basic_dataset[0])
|
|
labels = res["labels"]
|
|
input_ids = res["input_ids"]
|
|
|
|
eos_token_id = tokenizer.eos_token_id
|
|
eos_indices = [
|
|
i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
|
|
]
|
|
|
|
assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
|
|
for eos_idx in eos_indices:
|
|
assert (
|
|
labels[eos_idx] != IGNORE_TOKEN_ID
|
|
), f"Expected EOS token at index {eos_idx} to be labeled"
|
|
|
|
def test_train_on_eos_turn(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
basic_dataset,
|
|
request,
|
|
):
|
|
LOG.info("Testing with train_on_eos='turn'")
|
|
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
|
tokenizer,
|
|
chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=["assistant"],
|
|
train_on_eos="turn",
|
|
)
|
|
strategy.messages = "conversations"
|
|
res = strategy.tokenize_prompt(basic_dataset[0])
|
|
turns = strategy.get_conversation_thread(basic_dataset[0])
|
|
labels = res["labels"]
|
|
input_ids = res["input_ids"]
|
|
|
|
eos_token_id = tokenizer.eos_token_id
|
|
# Process all turns and verify EOS token labeling
|
|
for i, turn in enumerate(basic_dataset[0]["conversations"]):
|
|
start_idx, end_idx = strategy.find_turn(turns=turns, turn_idx=i)
|
|
|
|
if self._should_skip_turn(tokenizer, turn, i, start_idx, end_idx):
|
|
continue
|
|
|
|
decoded_response = tokenizer.decode(input_ids[start_idx:end_idx])
|
|
response = turn["value"]
|
|
|
|
assert response in decoded_response, (
|
|
f"Response {response} not found in index {start_idx}:{end_idx} "
|
|
f"decoded:{decoded_response}"
|
|
)
|
|
|
|
# Find the EOS token after this turn
|
|
eos_idx = end_idx
|
|
while eos_idx < len(input_ids) and input_ids[eos_idx] != eos_token_id:
|
|
eos_idx += 1
|
|
|
|
assert eos_idx < len(
|
|
input_ids
|
|
), f"Could not find EOS token after '{response}'"
|
|
|
|
LOG.debug(
|
|
f"Turn {i}: role={turn['from']}, content='{turn['value']}', start_idx={start_idx}, end_idx={end_idx}, eos_idx={eos_idx}"
|
|
)
|
|
|
|
LOG.debug(
|
|
f"Labels for turn {i}: {labels[start_idx:end_idx]}, EOS label: {labels[eos_idx]}"
|
|
)
|
|
|
|
# Verify EOS token labeling based on role
|
|
is_assistant = turn["from"] == "assistant"
|
|
if is_assistant:
|
|
assert (
|
|
labels[eos_idx] != IGNORE_TOKEN_ID
|
|
), f"Expected EOS token after assistant response '{response}' to be labeled"
|
|
else:
|
|
assert (
|
|
labels[eos_idx] == IGNORE_TOKEN_ID
|
|
), f"Expected EOS token after non-assistant input '{response}' to not be labeled"
|
|
|
|
def test_train_on_eos_last(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
basic_dataset,
|
|
request,
|
|
):
|
|
LOG.info("Testing with train_on_eos='last'")
|
|
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
|
tokenizer,
|
|
chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=["assistant"],
|
|
train_on_eos="last",
|
|
)
|
|
strategy.messages = "conversations"
|
|
res = strategy.tokenize_prompt(basic_dataset[0])
|
|
labels = res["labels"]
|
|
input_ids = res["input_ids"]
|
|
|
|
eos_token_id = tokenizer.eos_token_id
|
|
eos_indices = [
|
|
i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
|
|
]
|
|
|
|
assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
|
|
last_eos_idx = eos_indices[-1]
|
|
|
|
# Check that only the last EOS token is labeled
|
|
for idx in eos_indices[:-1]:
|
|
assert (
|
|
labels[idx] == IGNORE_TOKEN_ID
|
|
), f"Expected EOS token at index {idx} to not be labeled"
|
|
assert (
|
|
labels[last_eos_idx] != IGNORE_TOKEN_ID
|
|
), f"Expected last EOS token at index {last_eos_idx} to be labeled"
|
|
|
|
def test_train_on_eos_none(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
basic_dataset,
|
|
request,
|
|
):
|
|
LOG.info("Testing with train_on_eos='none'")
|
|
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
|
tokenizer,
|
|
chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=["assistant"],
|
|
train_on_eos="none",
|
|
)
|
|
strategy.messages = "conversations"
|
|
res = strategy.tokenize_prompt(basic_dataset[0])
|
|
labels = res["labels"]
|
|
input_ids = res["input_ids"]
|
|
|
|
eos_token_id = tokenizer.eos_token_id
|
|
eos_indices = [
|
|
i for i, token_id in enumerate(input_ids) if token_id == eos_token_id
|
|
]
|
|
|
|
assert len(eos_indices) > 0, "Expected at least one EOS token in the input"
|
|
for eos_idx in eos_indices:
|
|
assert (
|
|
labels[eos_idx] == IGNORE_TOKEN_ID
|
|
), f"Expected EOS token at index {eos_idx} to not be labeled"
|
|
|
|
def test_drop_system_message(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
basic_dataset,
|
|
request,
|
|
):
|
|
LOG.info("Testing with drop_system_message=True")
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
|
tokenizer,
|
|
chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
drop_system_message=True,
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=["assistant"],
|
|
)
|
|
strategy.messages = "conversations"
|
|
res = strategy.tokenize_prompt(basic_dataset[0])
|
|
input_ids = res["input_ids"]
|
|
|
|
# Check if system message is not present in input_ids
|
|
system_message = "You are an AI assistant."
|
|
decoded_message = tokenizer.decode(input_ids)
|
|
assert (
|
|
system_message not in decoded_message
|
|
), "Expected system message to be dropped"
|
|
|
|
def test_custom_roles(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
request,
|
|
):
|
|
LOG.info("Testing with custom roles mapping")
|
|
custom_roles = {
|
|
"user": ["human", "user"],
|
|
"assistant": ["ai", "assistant"],
|
|
"system": ["context"],
|
|
}
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
|
tokenizer,
|
|
chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
roles=custom_roles,
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=["ai"],
|
|
)
|
|
|
|
# Create a new dataset with modified role names
|
|
modified_conversations = [
|
|
{"from": "context", "value": "You are an AI assistant."},
|
|
{"from": "human", "value": "Hello"},
|
|
{"from": "ai", "value": "Hi there!"},
|
|
{"from": "human", "value": "How are you?"},
|
|
{"from": "ai", "value": "I'm doing well, thank you!"},
|
|
]
|
|
|
|
modified_dataset = Dataset.from_dict({"messages": [modified_conversations]})
|
|
|
|
res = strategy.tokenize_prompt(modified_dataset[0])
|
|
turns = strategy.get_conversation_thread(modified_dataset[0])
|
|
labels = res["labels"]
|
|
input_ids = res["input_ids"]
|
|
|
|
# Process all turns and verify labeling
|
|
for i, turn in enumerate(modified_dataset[0]["messages"]):
|
|
start_idx, end_idx = strategy.find_turn(turns=turns, turn_idx=i)
|
|
|
|
if self._should_skip_turn(tokenizer, turn, i, start_idx, end_idx):
|
|
continue
|
|
|
|
decoded_response = tokenizer.decode(input_ids[start_idx:end_idx])
|
|
response = turn["value"]
|
|
|
|
assert response in decoded_response, (
|
|
f"Response {response} not found in index {start_idx}:{end_idx} "
|
|
f"decoded:{decoded_response}"
|
|
)
|
|
|
|
# Check if responses are labeled correctly based on role
|
|
is_ai = turn["from"] == "ai"
|
|
if is_ai:
|
|
assert all(
|
|
label != IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
|
|
), f"Expected labels for AI response '{response}' to be set"
|
|
else:
|
|
assert all(
|
|
label == IGNORE_TOKEN_ID for label in labels[start_idx:end_idx]
|
|
), f"Expected labels for non-AI message '{response}' to be IGNORE_TOKEN_ID"
|
|
|
|
def test_message_field_training(
|
|
self,
|
|
tokenizer,
|
|
chat_template,
|
|
chat_template_jinja,
|
|
eos_token,
|
|
request,
|
|
):
|
|
LOG.info("Testing with message_field_training")
|
|
|
|
tokenizer, chat_template_jinja = self.setup_tokenizer(
|
|
tokenizer, chat_template, chat_template_jinja, eos_token, request
|
|
)
|
|
|
|
strategy = ChatTemplateStrategy(
|
|
ChatTemplatePrompter(
|
|
tokenizer,
|
|
chat_template=get_chat_template(
|
|
chat_template, jinja_template=chat_template_jinja
|
|
),
|
|
message_field_training="train",
|
|
message_field_training_detail="train_detail",
|
|
message_field_role="from",
|
|
message_field_content="value",
|
|
),
|
|
tokenizer=tokenizer,
|
|
train_on_inputs=False,
|
|
sequence_len=512,
|
|
roles_to_train=[],
|
|
)
|
|
|
|
# Create a new dataset with the train and train_detail fields
|
|
modified_conversation = [
|
|
{"from": "system", "value": "You are an AI assistant.", "train": False},
|
|
{"from": "human", "value": "Hello", "train": False},
|
|
{"from": "assistant", "value": "Hello", "train": True},
|
|
{"from": "human", "value": "How are you?", "train": True},
|
|
{
|
|
"from": "assistant",
|
|
"value": "I'm doing very well, thank you!",
|
|
"train_detail": [
|
|
{"begin_offset": 0, "end_offset": 8, "train": False},
|
|
{"begin_offset": 9, "end_offset": 18, "train": True},
|
|
{"begin_offset": 19, "end_offset": 30, "train": False},
|
|
],
|
|
},
|
|
{
|
|
"from": "human",
|
|
"value": "I'm doing very well, thank you!",
|
|
"train": False,
|
|
},
|
|
{"from": "assistant", "value": "Hi there!", "train": True},
|
|
]
|
|
|
|
modified_dataset = Dataset.from_dict({"messages": [modified_conversation]})
|
|
|
|
res = strategy.tokenize_prompt(modified_dataset[0])
|
|
turns = strategy.get_conversation_thread(modified_dataset[0])
|
|
labels = res["labels"]
|
|
input_ids = res["input_ids"]
|
|
|
|
def verify_labels(labels_span, should_train, context_message):
|
|
"""Helper to verify if a span of labels matches expected training state"""
|
|
if should_train:
|
|
assert all(
|
|
label != IGNORE_TOKEN_ID for label in labels_span
|
|
), f"Expected all labels for {context_message} to be set, but got {labels_span}"
|
|
else:
|
|
assert all(
|
|
label == IGNORE_TOKEN_ID for label in labels_span
|
|
), f"Expected all labels for {context_message} to be {IGNORE_TOKEN_ID}, but got {labels_span}"
|
|
|
|
# Process all turns and verify labeling
|
|
for i, turn in enumerate(modified_dataset[0]["messages"]):
|
|
start_idx, end_idx = strategy.find_turn(turns=turns, turn_idx=i)
|
|
|
|
if self._should_skip_turn(tokenizer, turn, i, start_idx, end_idx):
|
|
continue
|
|
|
|
decoded_response = tokenizer.decode(input_ids[start_idx:end_idx])
|
|
response = turn["value"]
|
|
|
|
assert response in decoded_response, (
|
|
f"Response {response} not found in index {start_idx}:{end_idx} "
|
|
f"decoded:{decoded_response}"
|
|
)
|
|
|
|
LOG.debug(
|
|
f"Processing turn {i}: role={turn['from']}, content='{turn['value']}', "
|
|
f"start_idx={start_idx}, end_idx={end_idx}"
|
|
)
|
|
|
|
if turn.get("train_detail", None) is not None:
|
|
# Handle detailed token-level training control
|
|
tokenized_output = tokenizer(
|
|
turn["value"], return_offsets_mapping=True, add_special_tokens=False
|
|
)
|
|
assert tokenized_output["input_ids"] == input_ids[start_idx:end_idx], (
|
|
f"Tokenized input mismatch for turn: {turn['value']}\n"
|
|
f"Expected: {input_ids[start_idx:end_idx]}\nActual: {tokenized_output['input_ids']}\n"
|
|
f"This will likely be a mismatch between template content and encoded content"
|
|
)
|
|
|
|
token_offsets = tokenized_output["offset_mapping"]
|
|
|
|
# Adjust token offsets
|
|
for j in range(len(token_offsets) - 1):
|
|
token_offsets[j] = (
|
|
token_offsets[j][0],
|
|
token_offsets[j + 1][0] - 1,
|
|
)
|
|
token_offsets[-1] = (token_offsets[-1][0], len(turn["value"]) - 1)
|
|
|
|
adjusted_train_details = strategy.prompter.adjust_train_details(
|
|
turn["train_detail"], token_offsets
|
|
)
|
|
|
|
LOG.debug(f"Original train_details: {turn['train_detail']}")
|
|
LOG.debug(f"Adjusted train_details: {adjusted_train_details}")
|
|
|
|
# Get and verify token offsets
|
|
turn_tokens = input_ids[start_idx:end_idx]
|
|
token_offsets_unmasked = strategy.prompter.get_offsets_for_train_detail(
|
|
text=turn["value"],
|
|
train_details=adjusted_train_details,
|
|
mask_untrainable=False,
|
|
)
|
|
|
|
for i, offset in enumerate(token_offsets_unmasked):
|
|
assert token_offsets[i][0] == offset, (
|
|
f"Token start offsets mismatch for turn: {turn['value']}\n"
|
|
f"Expected: {token_offsets[i][0]}\nActual: {offset}"
|
|
)
|
|
|
|
token_offsets_masked = strategy.prompter.get_offsets_for_train_detail(
|
|
text=turn["value"],
|
|
train_details=adjusted_train_details,
|
|
mask_untrainable=True,
|
|
)
|
|
LOG.debug(f"Token offsets: {token_offsets_masked}")
|
|
|
|
# Verify expected labels against actual labels
|
|
expected_labels = [IGNORE_TOKEN_ID] * len(turn_tokens)
|
|
for i, offset in enumerate(token_offsets_masked):
|
|
if offset != IGNORE_TOKEN_ID:
|
|
expected_labels[i] = turn_tokens[i]
|
|
actual_labels = labels[
|
|
start_idx : start_idx + len(token_offsets_masked)
|
|
]
|
|
assert (
|
|
actual_labels == expected_labels
|
|
), f"Labels mismatch for turn: {turn['value']}\nExpected: {expected_labels}\nActual: {actual_labels}"
|
|
|
|
# Verify each detail section
|
|
for detail in adjusted_train_details:
|
|
detail_start = start_idx + next(
|
|
j
|
|
for j, offset in enumerate(token_offsets_unmasked)
|
|
if offset >= detail["begin_offset"]
|
|
)
|
|
detail_end = start_idx + next(
|
|
(
|
|
j
|
|
for j, offset in enumerate(token_offsets_unmasked)
|
|
if offset > detail["end_offset"]
|
|
),
|
|
len(token_offsets),
|
|
)
|
|
|
|
detail_text = turn["value"][
|
|
detail["begin_offset"] : detail["end_offset"] + 1
|
|
]
|
|
detail_labels = labels[detail_start:detail_end]
|
|
|
|
context = (
|
|
f"detail (ind {detail_start}:{detail_end}): '{detail_text}'\n"
|
|
f"decoded: '{tokenizer.decode(input_ids[detail_start:detail_end])}')"
|
|
)
|
|
verify_labels(detail_labels, detail["train"], context)
|
|
else:
|
|
# Handle regular turn-level training control
|
|
should_train = turn.get("train", False)
|
|
turn_labels = labels[start_idx:end_idx]
|
|
context = (
|
|
f"turn (ind {start_idx}:{end_idx}): '{turn['value']}'\n"
|
|
f"decoded: '{decoded_response}')"
|
|
)
|
|
verify_labels(turn_labels, should_train, context)
|
|
|
|
LOG.debug(f"Final labels: {labels}")
|
|
LOG.debug(f"Final input_ids: {input_ids}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|