fc4e37920b
* Prepare for transformers v5 upgrade * fix hf cli * update for hf hub changes * fix tokenizer apply_chat_template args * remap include_tokens_per_second * fix tps * handle migration for warmup * use latest hf hub * Fix scan -> ls * fix import * fix for renaming of mistral common tokenizer -> backend * update for fixed tokenziation for llama * Skip phi35 tests for now * remove mistral patch fixed upstream in huggingface/transformers#41439 * use namespacing for patch * don't rely on sdist for e2e tests for now * run modal ci without waiting too * Fix dep for ci * fix imports * Fix fp8 check * fsdp2 fixes * fix version handling * update fsdp version tests for new v5 behavior * Fail multigpu tests after 3 failures * skip known v5 broken tests for now and cleanup * bump deps * unmark skipped test * re-enable test_fsdp_qlora_prequant_packed test * increase multigpu ci timeout * skip broken gemma3 test * reduce timout back to original 120min now that the hanging test is skipped * fix for un-necessary collator for pretraining with bsz=1 * fix: safe_serialization deprecated in transformers v5 rc01 (#3318) * torch_dtype deprecated * load model in float32 for consistency with tests * revert some test fixtures back * use hf cache ls instead of scan * don't strip fsdp_version more fdsp_Version fixes for v5 fix version in fsdp_config fix aliasing fix fsdp_version check check fsdp_version is 2 in both places * Transformers v5 rc2 (#3347) * bump dep * use latest fbgemm, grab model config as part of fixture, un-skip test * import AutoConfig * don't need more problematic autoconfig when specifying config.json manually * add fixtures for argilla ultrafeedback datasets * download phi4-reasoning * fix arg * update tests for phi fast tokenizer changes * use explicit model types for gemma3 --------- Co-authored-by: Wing Lian <wing@axolotl.ai> * fix: AutoModelForVision2Seq -> AutoModelForImageTextToText * chore: remove duplicate * fix: attempt fix gemma3 text mode * chore: lint * ga release of v5 * need property setter for name_or_path for mistral tokenizer * vllm not compatible with transformers v5 * setter for chat_template w mistral too --------- Co-authored-by: NanoCode012 <nano@axolotl.ai> Co-authored-by: salman <salman.mohammadi@outlook.com>
1040 lines
40 KiB
Python
1040 lines
40 KiB
Python
"""
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HF Chat Templates prompt strategy
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"""
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import json
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from collections import defaultdict
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from typing import TYPE_CHECKING, Any, Dict, List, Set, Union
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from pydantic import BaseModel
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from transformers import ProcessorMixin
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from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnalyzer
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from axolotl.prompt_tokenizers import PromptTokenizingStrategy
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from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
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from axolotl.utils.chat_templates import get_chat_template_from_config
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from axolotl.utils.dict import remove_none_values
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from axolotl.utils.logging import get_logger
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from axolotl.utils.schemas.datasets import DatasetConfig
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if TYPE_CHECKING:
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from axolotl.utils.mistral import HFMistralTokenizer
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# Configure the logger
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LOG = get_logger(__name__)
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LOG.setLevel("INFO")
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class ChatTemplatePrompter(Prompter):
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"""Prompter for HF chat templates"""
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def __init__(
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self,
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tokenizer,
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chat_template: str,
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processor=None,
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max_length=2048,
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message_property_mappings: dict[str, str] | None = None,
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message_field_training: str | None = None,
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message_field_training_detail: str | None = None,
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field_messages: str = "messages",
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field_system: str = "system",
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field_tools: str = "tools",
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field_thinking: str = "reasoning_content",
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roles: dict[str, list[str]] | None = None,
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template_thinking_key: str | None = "reasoning_content",
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chat_template_kwargs: dict[str, Any] | None = None,
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drop_system_message: bool = False,
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):
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# check if message_property_mappings is None or empty dict
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if message_property_mappings is None or (not message_property_mappings):
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message_property_mappings = {
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"role": "role",
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"content": "content",
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}
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if template_thinking_key and field_thinking:
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message_property_mappings[template_thinking_key] = field_thinking
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if roles:
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self.roles = {s: t for t, sources in roles.items() for s in sources}
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else:
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self.roles = {
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"human": "user",
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"user": "user",
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"assistant": "assistant",
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"gpt": "assistant",
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"system": "system",
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"tool": "tool",
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}
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self._chat_template_msg_variables = self.get_chat_template_msg_variables(
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chat_template, field_messages
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)
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self.message_property_mappings = message_property_mappings
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self.message_field_training = message_field_training
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self.message_field_training_detail = message_field_training_detail
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self.field_messages = field_messages
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self.field_system = field_system
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self.field_tools = field_tools
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self.field_thinking = field_thinking
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self.tokenizer = tokenizer
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self.processor: ProcessorMixin | None = processor
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self.chat_template = chat_template
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self.chat_template_kwargs = chat_template_kwargs or {}
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self.template_thinking_key: str = template_thinking_key or "reasoning_content"
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self.max_length = max_length
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self.drop_system_message = drop_system_message
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@property
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def chat_template_msg_variables(self) -> Set[str]:
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return self._chat_template_msg_variables
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def build_prompt(
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self,
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conversation: list[dict],
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add_generation_prompt=False,
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images=None,
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tools=None,
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real_last_index=None,
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):
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"""
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Build a prompt from a conversation.
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Args:
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conversation: A list of messages.
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add_generation_prompt: Whether to add a generation prompt.
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images: A list of images. (optional)
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tools: A list of tools. (optional)
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"""
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chat_template_kwargs = {
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"chat_template": self.chat_template,
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"add_generation_prompt": add_generation_prompt,
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**self.chat_template_kwargs,
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}
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if tools:
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chat_template_kwargs["tools"] = tools
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if real_last_index:
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chat_template_kwargs["real_last_index"] = real_last_index
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if self.processor:
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if not callable(self.processor):
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raise TypeError("Processor must be callable")
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text = self.processor.apply_chat_template(
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conversation,
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tokenize=False,
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**chat_template_kwargs,
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)
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batch = self.processor(
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text=text,
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images=images,
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return_tensors="pt",
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)
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if hasattr(batch, "to_dict"):
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batch = batch.to_dict()
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else:
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batch = dict(batch)
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# workaround since processor works in batches instead of single examples
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out = {}
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for k, val in batch.items():
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if hasattr(val, "tolist"):
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out[k] = (
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val.tolist() if k == "pixel_values" else val.squeeze(0).tolist()
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)
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else:
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out[k] = val
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return out
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return self.tokenizer.apply_chat_template(
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conversation,
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tokenize=True,
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return_dict=False,
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**chat_template_kwargs,
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)
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def get_offsets_for_train_detail(
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self, text: str, train_details: List[Dict], mask_untrainable: bool = True
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) -> List[int]:
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tokenized_output = self.tokenizer(
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text, return_offsets_mapping=True, add_special_tokens=False
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)
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tokens = tokenized_output.tokens()
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token_offsets = tokenized_output["offset_mapping"]
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LOG.debug(f"Tokenizing text: {text}")
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LOG.debug(f"Tokens: {tokens}")
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# Adjust the end offsets. For some reason by default they are set to the same value as the start offsets.
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for i in range(len(token_offsets) - 1):
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token_offsets[i] = (token_offsets[i][0], token_offsets[i + 1][0] - 1)
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# Ensure the last token's end offset is set correctly
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token_offsets[-1] = (token_offsets[-1][0], len(text) - 1)
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LOG.debug(f"Token offsets: {token_offsets}")
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# Initialize all offsets as IGNORE_TOKEN_ID (not trained)
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result = [IGNORE_TOKEN_ID] * len(token_offsets)
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# Adjust train_details to align with token boundaries
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adjusted_train_details = self.adjust_train_details(train_details, token_offsets)
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for idx, (start, end) in enumerate(token_offsets):
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for detail in adjusted_train_details:
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# Check if the token is completely within the detail's range
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if start >= detail["begin_offset"] and end <= detail["end_offset"]:
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if detail["train"] or not mask_untrainable:
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result[idx] = start
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LOG.debug(f"Token {idx} ({tokens[idx]}) marked for training")
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else:
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LOG.debug(
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f"Token {idx} ({tokens[idx]}) marked as non-trainable"
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)
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elif start < detail["end_offset"] and end > detail["begin_offset"]:
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# Token partially overlaps with detail, always mark as non-trainable
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LOG.debug(
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f"Token {idx} ({tokens[idx]}) partially overlaps detail, marked as non-trainable"
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)
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LOG.debug(f"Final result: {result}")
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return result
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def adjust_train_details(
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self, train_details: List[Dict], token_offsets: List[tuple]
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) -> List[Dict]:
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adjusted_details = []
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for detail in train_details:
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begin_offset = detail["begin_offset"]
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end_offset = detail["end_offset"]
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# Find the first token that starts after or at the begin_offset
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begin_token = next(
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(
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i
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for i, (t_start, t_end) in enumerate(token_offsets)
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if t_start >= begin_offset
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),
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len(token_offsets),
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)
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if begin_token > 0 and token_offsets[begin_token - 1][1] > begin_offset:
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begin_token -= 1
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# Find the last token that ends before or at the end_offset
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end_token = next(
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(
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i
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for i in range(len(token_offsets) - 1, -1, -1)
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if token_offsets[i][1] <= end_offset
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),
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-1,
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)
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if (
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end_token < len(token_offsets) - 1
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and token_offsets[end_token + 1][0] < end_offset
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):
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end_token += 1
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if begin_token <= end_token:
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adjusted_begin = token_offsets[begin_token][0]
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adjusted_end = token_offsets[end_token][1]
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if adjusted_begin != begin_offset or adjusted_end != end_offset:
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LOG.warning(
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f"Adjusting detail offsets: ({begin_offset}, {end_offset}) -> ({adjusted_begin}, {adjusted_end})"
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)
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adjusted_details.append(
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{
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"begin_offset": adjusted_begin,
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"end_offset": adjusted_end,
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"train": detail["train"],
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}
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)
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else:
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LOG.warning(
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f"Could not adjust detail offsets: ({begin_offset}, {end_offset}). Skipping this detail."
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)
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return adjusted_details
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def get_chat_template_msg_variables(
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self, chat_template: str, field_messages: str
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) -> Set[str]:
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template_analyzer = JinjaTemplateAnalyzer(chat_template)
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return template_analyzer.get_message_vars(field_messages)
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|
|
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class ChatTemplateStrategy(PromptTokenizingStrategy):
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"""
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Tokenizing strategy for instruction-based prompts.
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"""
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def __init__(
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self,
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prompter: "ChatTemplatePrompter",
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tokenizer,
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train_on_inputs: bool,
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sequence_len: int,
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roles_to_train: list[str] | None = None,
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train_on_eos: str | None = None,
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train_on_eot: str | None = None,
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eot_tokens: list[str] | None = None,
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split_thinking: bool | None = False,
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):
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super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
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self.prompter: ChatTemplatePrompter = prompter
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self.roles_to_train = []
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if roles_to_train:
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# map roles if exist in prompter.roles else use the role as is
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self.roles_to_train = [
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prompter.roles.get(role, role) for role in roles_to_train
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]
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self.train_on_eos = train_on_eos
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# Backward compatibility, load from train_on_eos
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self.train_on_eot = train_on_eot if train_on_eot is not None else train_on_eos
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# Default to eos_token if eot_tokens not provided
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self.eot_tokens = []
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if eot_tokens is not None:
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self.eot_tokens = eot_tokens
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elif (
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hasattr(self.tokenizer, "eos_token")
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and self.tokenizer.eos_token is not None
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):
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self.eot_tokens = [self.tokenizer.eos_token]
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self.split_thinking = split_thinking
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self.images = "images"
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LOG.debug(
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f"The chat template uses the following properites on the message: {self.prompter.chat_template_msg_variables}"
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)
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self._validate_eot_and_eos_tokens()
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def _validate_eot_and_eos_tokens(self):
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"""
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- Validates that EOT tokens (or eos_token) are in the chat_template
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- Checks if EOT tokens are encoded as multiple tokens in the tokenizer.
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- Checks for potential conflicts between train_on_eos and train_on_eot.
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"""
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if self.prompter.chat_template is None:
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# Usually this should not happen
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LOG.warning(
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"No chat template provided, skipping EOT and EOS token validation"
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)
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return
|
|
|
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# If the EOT token is the same as the EOS token, we need to check differently
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if len(self.eot_tokens) == 1 and self.eot_tokens[0] == self.tokenizer.eos_token:
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# Check if the eos_token is in the chat_template or as a variable `eos_token`
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# Note: we check for `eos_token` in the string, but it could possibly not be a variable
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if (
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self.tokenizer.eos_token not in self.prompter.chat_template
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and "eos_token" not in self.prompter.chat_template
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):
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LOG.warning(
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f"EOS token '{self.tokenizer.eos_token}' not found in chat_template. Please check if your template/EOS token is correct."
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)
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return
|
|
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# Create a new list to store tokens that should be kept
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valid_eot_tokens = []
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for token in self.eot_tokens:
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# Check if EOT token is in the chat_template
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if token not in self.prompter.chat_template:
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LOG.warning(f"EOT token '{token}' not found in chat_template.")
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# Don't add to the valid tokens list
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continue
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valid_eot_tokens.append(token)
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|
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# Replace the original list with the filtered one
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self.eot_tokens = valid_eot_tokens
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|
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for token in self.eot_tokens:
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# If token in template, check if EOT token is in tokenizer and not encoded as multiple tokens
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token_ids = self.tokenizer.encode(token, add_special_tokens=False)
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if not token_ids:
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raise ValueError(
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"EOT token encoding failed. Please check if the token is valid and can be encoded."
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)
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if token_ids and len(token_ids) > 1:
|
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raise ValueError(
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f"EOT token '{token}' is encoded as multiple tokens: {token_ids}. Please add it under `tokens: ` in the config "
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"or (recommended) override unused added_tokens via `added_tokens_overrides: `."
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)
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|
|
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# If eos_token is in eot_tokens and conflict between train_on_eos and train_on_eot, raise an error
|
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if (
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self.tokenizer.eos_token in self.eot_tokens
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and self.train_on_eos != self.train_on_eot
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):
|
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raise ValueError(
|
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"Conflict between train_on_eos and train_on_eot. eos_token is in eot_tokens and train_on_eos != train_on_eot"
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f"train_on_eos: {self.train_on_eos}, train_on_eot: {self.train_on_eot}"
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f"eot_tokens: {self.eot_tokens}"
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f"eos_token: {self.tokenizer.eos_token}"
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|
)
|
|
|
|
@property
|
|
def supports_batched(self) -> bool:
|
|
# Let calling code know we can handle lists of examples
|
|
return True
|
|
|
|
def is_prompt_batched(self, prompt: dict[str, Any]) -> bool:
|
|
try:
|
|
return all(isinstance(v, list) for v in prompt.values()) and all(
|
|
isinstance(v, list) for v in prompt[self.prompter.field_messages]
|
|
)
|
|
except KeyError:
|
|
return False
|
|
|
|
def tokenize_prompt(self, prompt: dict[str, Any]):
|
|
"""
|
|
Public method that can handle either a single prompt or a batch of prompts.
|
|
"""
|
|
|
|
prompt = remove_none_values(prompt)
|
|
|
|
if not self.is_prompt_batched(prompt) or not self.supports_batched:
|
|
return self._tokenize_single_prompt(prompt)
|
|
|
|
res = defaultdict(lambda: [])
|
|
feature_names = list(prompt.keys())
|
|
|
|
# Process each prompt individually
|
|
for row in zip(*prompt.values(), strict=False):
|
|
tokenized_prompt = self._tokenize_single_prompt(
|
|
dict(zip(feature_names, row, strict=False))
|
|
)
|
|
for key, val in tokenized_prompt.items():
|
|
res[key].append(val)
|
|
|
|
# If there are no examples left, return an empty dictionary
|
|
if not res:
|
|
return {}
|
|
|
|
return dict(res)
|
|
|
|
def _tokenize_single_prompt(self, prompt: dict) -> Dict[str, List[int]]:
|
|
# Old simple legacy behavior that works reliably.
|
|
if (
|
|
not self.roles_to_train
|
|
and not self.train_on_eos
|
|
and not self.train_on_eot
|
|
and not self.prompter.message_field_training # type: ignore
|
|
and not self.prompter.message_field_training_detail # type: ignore
|
|
):
|
|
turns = self.get_conversation_thread(prompt)
|
|
images = self._get_images(prompt)
|
|
prompt_ids = self.prompter.build_prompt( # type: ignore
|
|
turns[:-1],
|
|
add_generation_prompt=True,
|
|
images=images,
|
|
)
|
|
tokenized_res = self.prompter.build_prompt(turns, images=images) # type: ignore
|
|
tokenized_prompt = {}
|
|
if isinstance(tokenized_res, list):
|
|
input_ids = prompt_ids + tokenized_res[len(prompt_ids) :]
|
|
tokenized_prompt["input_ids"] = input_ids
|
|
tokenized_prompt["attention_mask"] = [1] * len(input_ids)
|
|
else:
|
|
input_ids = tokenized_res["input_ids"]
|
|
tokenized_prompt = dict(tokenized_res)
|
|
|
|
if not self.train_on_inputs:
|
|
if isinstance(prompt_ids, dict):
|
|
user_prompt_len = len(prompt_ids["input_ids"])
|
|
else:
|
|
user_prompt_len = len(prompt_ids)
|
|
labels = [-100] * user_prompt_len + input_ids[user_prompt_len:]
|
|
else:
|
|
labels = input_ids
|
|
|
|
tokenized_prompt["labels"] = labels
|
|
|
|
return tokenized_prompt
|
|
|
|
turns = self.get_conversation_thread(prompt)
|
|
tools = self._get_tools(prompt)
|
|
input_ids = self.prompter.build_prompt(turns, tools=tools) # type: ignore
|
|
labels = [IGNORE_TOKEN_ID] * len(input_ids)
|
|
|
|
last_eos_idx = -1
|
|
last_eot_idx = -1
|
|
for index, turn in enumerate(turns):
|
|
role = turn.get("role")
|
|
content = turn.get("content")
|
|
train_turn = turn.get("training")
|
|
train_detail = turn.get("training_detail")
|
|
|
|
LOG.debug(
|
|
f"Processing turn {index}: role={role}, content={content}, train_turn={train_turn}, train_detail={train_detail}"
|
|
)
|
|
|
|
should_train = None
|
|
if train_turn is not None:
|
|
should_train = train_turn
|
|
elif train_detail is not None:
|
|
should_train = bool(train_detail)
|
|
else:
|
|
should_train = self.train_on_inputs or role in self.roles_to_train
|
|
|
|
LOG.debug(f"Should train: {should_train}")
|
|
|
|
# turn not trainable, skip having to find the turn indices
|
|
# unless last turn and train_on_eos/train_on_eot is all
|
|
if not should_train and (
|
|
self.train_on_eos != "all" and self.train_on_eot != "all"
|
|
):
|
|
if index == len(turns) - 1:
|
|
LOG.warning(
|
|
"Last turn is not trainable, skipping having to find the turn indices. "
|
|
"This may cause incorrect last EOT/EOS token to be unmasked."
|
|
"This is likely a dataset design issue. Please ensure last turn is trainable."
|
|
)
|
|
|
|
continue
|
|
|
|
turn_start_idx, turn_end_idx = self.find_turn(
|
|
turns=turns, turn_idx=index, tools=tools
|
|
)
|
|
|
|
LOG.debug(f"Turn indices: start={turn_start_idx}, end={turn_end_idx}")
|
|
|
|
if should_train and turn_start_idx != -1 and turn_end_idx != -1:
|
|
if train_detail:
|
|
# Block multi-content for now
|
|
if not isinstance(content, str):
|
|
raise ValueError(
|
|
"`train_detail` is not supported when `content` is not a string."
|
|
)
|
|
|
|
token_offsets = self.prompter.get_offsets_for_train_detail( # type: ignore
|
|
content, train_detail
|
|
)
|
|
LOG.debug(f"Token offsets: {token_offsets}")
|
|
for i, offset in enumerate(token_offsets):
|
|
if offset != IGNORE_TOKEN_ID and turn_start_idx + i < len(
|
|
input_ids
|
|
):
|
|
labels[turn_start_idx + i] = input_ids[turn_start_idx + i]
|
|
LOG.debug(
|
|
f"Label set at index {turn_start_idx + i}: {input_ids[turn_start_idx + i]}"
|
|
)
|
|
else:
|
|
labels[turn_start_idx:turn_end_idx] = input_ids[
|
|
turn_start_idx:turn_end_idx
|
|
]
|
|
LOG.debug(
|
|
f"Set labels for training from {turn_start_idx} to {turn_end_idx}"
|
|
)
|
|
|
|
LOG.debug(f"Labels after processing turn {index}: {labels}")
|
|
|
|
# Handle special tokens (EOT and EOS)
|
|
for token_type, find_func, train_option in [
|
|
("EOT", self.find_first_eot_token, self.train_on_eot),
|
|
("EOS", self.find_first_eos_token, self.train_on_eos),
|
|
]:
|
|
token_idx = find_func(input_ids, start_idx=turn_end_idx)
|
|
|
|
if (
|
|
token_idx != -1 and abs(token_idx - turn_end_idx) <= 3
|
|
): # Allow for some template padding
|
|
# Update the last token index
|
|
if token_type == "EOT": # nosec B105
|
|
last_eot_idx = token_idx
|
|
else:
|
|
last_eos_idx = token_idx
|
|
|
|
# Set labels if needed for this turn
|
|
if train_option == "all" or (
|
|
train_option == "turn" and should_train
|
|
):
|
|
labels[token_idx] = input_ids[token_idx]
|
|
LOG.debug(
|
|
f"{token_type} token set for training at index {token_idx}"
|
|
)
|
|
else:
|
|
LOG.debug(
|
|
f"{token_type} token missing after turn {turn}. {token_type.lower()}_idx: {token_idx}, turn_end_idx: {turn_end_idx}"
|
|
)
|
|
|
|
# Handle 'last' option for special tokens
|
|
for token_type, last_idx, train_option in [
|
|
("EOT", last_eot_idx, self.train_on_eot),
|
|
("EOS", last_eos_idx, self.train_on_eos),
|
|
]:
|
|
if train_option == "last" and last_idx != -1:
|
|
labels[last_idx] = input_ids[last_idx]
|
|
LOG.debug(
|
|
f"Last {token_type} token set for training at index {last_idx}"
|
|
)
|
|
|
|
LOG.debug(f"Final labels: {labels}")
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
"labels": labels,
|
|
"attention_mask": [1] * len(input_ids),
|
|
}
|
|
|
|
def find_first_eos_token(self, input_ids, start_idx):
|
|
eos_token_id = self.tokenizer.eos_token_id
|
|
for i in range(start_idx, len(input_ids)):
|
|
if input_ids[i] == eos_token_id:
|
|
return i
|
|
return -1
|
|
|
|
def find_first_eot_token(self, input_ids, start_idx):
|
|
"""Find the first EOT token in the input_ids starting from start_idx."""
|
|
# Get token IDs for all EOT tokens
|
|
eot_token_ids = []
|
|
for token in self.eot_tokens:
|
|
token_ids = self.tokenizer.encode(token, add_special_tokens=False)
|
|
if len(token_ids) != 1:
|
|
raise ValueError(
|
|
f"EOT token '{token}' is encoded as multiple tokens: {token_ids}. Please add it under `tokens: ` in the config."
|
|
)
|
|
|
|
eot_token_ids.append(token_ids[0]) # Use the last token ID if multiple
|
|
|
|
# Search for any of the EOT token IDs
|
|
for i in range(start_idx, len(input_ids)):
|
|
if input_ids[i] in eot_token_ids:
|
|
return i
|
|
return -1
|
|
|
|
def find_turn(
|
|
self, turns: list[dict], turn_idx: int, tools: list[dict] | None = None
|
|
):
|
|
"""
|
|
Locate the starting and ending indices of the specified turn in a conversation.
|
|
"""
|
|
|
|
if turn_idx >= len(turns):
|
|
raise ValueError(f"Turn index {turn_idx} out of range")
|
|
|
|
# mistral/gemma3 does not output message if it contains only system message
|
|
if (
|
|
turn_idx == 0
|
|
and turns[0].get("role") == "system"
|
|
and ("mistral" in self.tokenizer.name_or_path.lower())
|
|
):
|
|
return -1, -1
|
|
|
|
empty_turn = {
|
|
"role": turns[turn_idx].get("role"),
|
|
"content": "[[dummy_message]]",
|
|
}
|
|
|
|
# Create conversation versions
|
|
turns_with_empty = turns[:turn_idx] + [empty_turn]
|
|
turns_with_content = turns[: turn_idx + 1]
|
|
|
|
real_last_index = len(turns) - 1
|
|
|
|
# Generate the conversation up to the turn, with final turn replaced with dummy content
|
|
dummy_ids = self.prompter.build_prompt(
|
|
turns_with_empty, tools=tools, real_last_index=real_last_index
|
|
) # type: ignore
|
|
|
|
# Generate the conversation up to the turn, with final turn included
|
|
full_ids = self.prompter.build_prompt(
|
|
turns_with_content, tools=tools, real_last_index=real_last_index
|
|
) # type: ignore
|
|
|
|
if not full_ids or not dummy_ids:
|
|
LOG.warning(f"Empty template generated for turn {turn_idx}")
|
|
return -1, -1
|
|
|
|
# Find first difference (start of content)
|
|
start_idx = None
|
|
min_len = min(len(dummy_ids), len(full_ids))
|
|
for i in range(min_len):
|
|
if dummy_ids[i] != full_ids[i]:
|
|
start_idx = i
|
|
break
|
|
|
|
if start_idx is None:
|
|
LOG.warning(f"Could not find content start boundary for turn {turn_idx}")
|
|
return -1, -1
|
|
|
|
# Find last difference (end of content)
|
|
end_idx = None
|
|
for i in range(min_len):
|
|
dummy_pos = len(dummy_ids) - 1 - i
|
|
full_pos = len(full_ids) - 1 - i
|
|
if dummy_ids[dummy_pos] != full_ids[full_pos]:
|
|
end_idx = full_pos + 1 # Add one to include the last token when slice
|
|
break
|
|
|
|
if end_idx is None:
|
|
LOG.warning(f"Could not find content end boundary for turn {turn_idx}")
|
|
return -1, -1
|
|
|
|
if end_idx < start_idx:
|
|
LOG.warning(
|
|
f"Content end boundary is before start boundary for turn {turn_idx}"
|
|
)
|
|
return -1, -1
|
|
|
|
if end_idx == start_idx:
|
|
LOG.warning(
|
|
f"Content end boundary is the same as start boundary for turn {turn_idx}. This is likely an empty turn."
|
|
)
|
|
return -1, -1
|
|
|
|
LOG.debug(f"Content boundaries: {start_idx}, {end_idx}")
|
|
LOG.debug(
|
|
f"Content tokens: {self.tokenizer.convert_ids_to_tokens(full_ids[start_idx:end_idx])}"
|
|
)
|
|
|
|
return start_idx, end_idx
|
|
|
|
def get_conversation_thread(self, prompt):
|
|
turns = []
|
|
|
|
messages = self._get_messages(prompt)
|
|
|
|
possible_sys_turn = self.transform_message(messages[0])
|
|
|
|
if (
|
|
possible_sys_turn["role"] != "system"
|
|
and self.prompter.field_system in prompt
|
|
):
|
|
turn = {"role": "system", "content": prompt[self.prompter.field_system]}
|
|
turns.append(turn)
|
|
|
|
for message in messages:
|
|
transformed_message = self.transform_message(message)
|
|
|
|
turn = transformed_message
|
|
|
|
training = message.get(self.prompter.message_field_training)
|
|
training_detail = message.get(self.prompter.message_field_training_detail)
|
|
if training is not None:
|
|
turn["training"] = training
|
|
if training_detail is not None:
|
|
turn["training_detail"] = training_detail
|
|
|
|
turns.append(turn)
|
|
|
|
if self.prompter.drop_system_message and turns[0]["role"] == "system":
|
|
turns = turns[1:]
|
|
|
|
return turns
|
|
|
|
def transform_message(self, message: dict) -> dict:
|
|
# Build the initial transformed message from the mappings
|
|
transformed_message = {}
|
|
for key, value in self.prompter.message_property_mappings.items():
|
|
if message.get(value) is not None:
|
|
transformed_message[key] = message[value]
|
|
else:
|
|
LOG.debug(
|
|
f"Could not find value for property {value} in message: {message}"
|
|
)
|
|
|
|
# Map the role if necessary
|
|
if "role" in transformed_message:
|
|
transformed_message["role"] = self.prompter.roles.get(
|
|
transformed_message["role"], transformed_message["role"]
|
|
)
|
|
|
|
# TODO handle reasoning_content with split_thinking
|
|
# if the role is assistant that we want to use reasoning_content
|
|
if self.split_thinking and transformed_message["role"] == "assistant":
|
|
content = transformed_message["content"]
|
|
thinking_pairs = [
|
|
("<think>", "</think>"),
|
|
("<reasoning>", "</reasoning>"),
|
|
("<|begin_of_thought|>", "<|end_of_thought|>"),
|
|
]
|
|
content_pairs = [("<|begin_of_solution|>", "<|end_of_solution|>")]
|
|
for tpair in thinking_pairs:
|
|
# check if the thinking pair is in the content
|
|
if tpair[0] in content and tpair[1] in content:
|
|
# find the start and end index of the thinking pair
|
|
t_start_idx = content.find(tpair[0])
|
|
t_end_idx = content.find(tpair[1])
|
|
|
|
# get the thinking content
|
|
thinking_content = content[t_start_idx + len(tpair[0]) : t_end_idx]
|
|
transformed_message[self.prompter.template_thinking_key] = (
|
|
thinking_content.strip()
|
|
)
|
|
|
|
# take remainder of the content
|
|
# strip whitespace from beginning of the remainder (thinking tokens)
|
|
remainder = content[t_end_idx + len(tpair[1]) :].lstrip()
|
|
|
|
# check if the content pair is in the remainder
|
|
cpair_found = False
|
|
for cpair in content_pairs:
|
|
if cpair[0] in remainder and cpair[1] in remainder:
|
|
# find the start and end index of the content pair
|
|
c_start_idx = remainder.find(cpair[0])
|
|
c_end_idx = remainder.find(cpair[1])
|
|
|
|
# get the content content
|
|
content_content = remainder[
|
|
c_start_idx + len(cpair[0]) : c_end_idx
|
|
]
|
|
transformed_message["content"] = content_content.strip()
|
|
cpair_found = True
|
|
break
|
|
|
|
# else, the content is the remainder
|
|
if not cpair_found:
|
|
transformed_message["content"] = remainder
|
|
break
|
|
|
|
# Determine which keys in the original message were not mapped
|
|
mapped_values = set(self.prompter.message_property_mappings.values())
|
|
remaining_keys = set(message) - mapped_values
|
|
|
|
# Keep only the properties defined in the chat template
|
|
# and not already mapped
|
|
for key in self.prompter.chat_template_msg_variables:
|
|
if key in remaining_keys:
|
|
val = message.get(key)
|
|
if val is not None:
|
|
transformed_message[key] = val
|
|
|
|
if "tool_calls" in transformed_message and transformed_message["tool_calls"]:
|
|
for tool_call in transformed_message["tool_calls"]:
|
|
if "function" in tool_call and "arguments" in tool_call["function"]:
|
|
args = tool_call["function"]["arguments"]
|
|
if isinstance(args, str):
|
|
try:
|
|
tool_call["function"]["arguments"] = json.loads(args)
|
|
except json.JSONDecodeError as e:
|
|
LOG.error(
|
|
f"Error parsing tool_calls arguments as JSON. "
|
|
f"Function: {tool_call.get('function', {}).get('name', 'unknown')}, "
|
|
f"Arguments string: {args!r}, "
|
|
f"Error: {e}"
|
|
)
|
|
raise
|
|
|
|
return transformed_message
|
|
|
|
def _get_images(self, prompt):
|
|
return prompt.get(self.images, None)
|
|
|
|
def _get_tools(self, prompt) -> list[dict] | None:
|
|
"""Get tools from prompt if available."""
|
|
tools = prompt.get(self.prompter.field_tools, None)
|
|
if tools is None:
|
|
return None
|
|
|
|
if isinstance(tools, list):
|
|
# Process each tool to handle JSON string parameters
|
|
for tool in tools:
|
|
if isinstance(tool, dict) and "function" in tool:
|
|
function = tool["function"]
|
|
if "parameters" in function:
|
|
params = function["parameters"]
|
|
if isinstance(params, str):
|
|
try:
|
|
function["parameters"] = json.loads(params)
|
|
except json.JSONDecodeError as e:
|
|
LOG.error(
|
|
f"Error parsing tool parameters as JSON. "
|
|
f"Function: {function.get('name', 'unknown')}, "
|
|
f"Parameters string: {params!r}, "
|
|
f"Error: {e}"
|
|
)
|
|
raise
|
|
return tools
|
|
|
|
raise ValueError(
|
|
"Unknown tools format. Please convert it into a list[dict].\n"
|
|
f"Current format: {type(tools)}"
|
|
)
|
|
|
|
def _get_messages(self, prompt):
|
|
messages = prompt.get(self.prompter.field_messages, None)
|
|
if messages is None:
|
|
raise ValueError("Messages is null. Please check `field_messages`.")
|
|
|
|
if isinstance(messages, list):
|
|
return messages
|
|
|
|
raise ValueError(
|
|
"Unknown messages format. Please convert it into a list[dict].\n"
|
|
f"Current format: {type(messages)}"
|
|
)
|
|
|
|
|
|
class MistralStrategy(ChatTemplateStrategy):
|
|
"""
|
|
Mistral strategy for chat template.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
prompter: "ChatTemplatePrompter",
|
|
tokenizer: "HFMistralTokenizer",
|
|
train_on_inputs: bool,
|
|
sequence_len: int,
|
|
roles_to_train: list[str] | None = None,
|
|
train_on_eos: str | None = None,
|
|
train_on_eot: str | None = None,
|
|
eot_tokens: list[str] | None = None,
|
|
split_thinking: bool | None = False,
|
|
):
|
|
# Call the parent's parent __init__ (PromptTokenizingStrategy) to skip ChatTemplateStrategy's validation
|
|
|
|
PromptTokenizingStrategy.__init__(
|
|
self, prompter, tokenizer, train_on_inputs, sequence_len
|
|
)
|
|
self.prompter: ChatTemplatePrompter = prompter
|
|
|
|
self.roles_to_train = []
|
|
if roles_to_train:
|
|
# map roles if exist in prompter.roles else use the role as is
|
|
self.roles_to_train = [
|
|
prompter.roles.get(role, role) for role in roles_to_train
|
|
]
|
|
|
|
self.train_on_eos = train_on_eos
|
|
# Backward compatibility, load from train_on_eos
|
|
self.train_on_eot = train_on_eot if train_on_eot is not None else train_on_eos
|
|
|
|
# Default to eos_token if eot_tokens not provided
|
|
self.eot_tokens = []
|
|
if eot_tokens is not None:
|
|
self.eot_tokens = eot_tokens
|
|
else:
|
|
# set eot_tokens to the eos_token
|
|
self.eot_tokens = [self.tokenizer.eos_token]
|
|
|
|
self.split_thinking = split_thinking
|
|
|
|
self.images = "images"
|
|
|
|
LOG.debug(
|
|
f"The chat template uses the following properites on the message: {self.prompter.chat_template_msg_variables}"
|
|
)
|
|
|
|
# Skip the validation that ChatTemplateStrategy calls
|
|
# TODO: address this in the future with mistral-specific checks
|
|
# self._validate_eot_and_eos_tokens()
|
|
|
|
def find_first_eot_token(self, input_ids, start_idx):
|
|
"""Find the first EOT token in the input_ids starting from start_idx."""
|
|
# mistral-common tokenizer does not support eot_tokens
|
|
return self.find_first_eos_token(input_ids, start_idx)
|
|
|
|
|
|
class MistralPrompter(ChatTemplatePrompter):
|
|
"""
|
|
Mistral prompter for chat template.
|
|
"""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
self._chat_template_msg_variables = set(["tool_call_id", "name", "tool_calls"])
|
|
|
|
|
|
class StrategyLoader:
|
|
"""
|
|
Load chat template strategy based on configuration.
|
|
"""
|
|
|
|
def _get_strategy_cls(self, cfg):
|
|
if cfg.tokenizer_use_mistral_common:
|
|
return MistralStrategy
|
|
|
|
return ChatTemplateStrategy
|
|
|
|
def _get_prompter_cls(self, cfg):
|
|
if cfg.tokenizer_use_mistral_common:
|
|
return MistralPrompter
|
|
|
|
return ChatTemplatePrompter
|
|
|
|
def _get_strategy_params(self, cfg, ds_cfg: Dict[str, Any]):
|
|
return {
|
|
"train_on_inputs": cfg.train_on_inputs,
|
|
"sequence_len": cfg.sequence_len,
|
|
"roles_to_train": ds_cfg.get("roles_to_train", ["assistant"]),
|
|
"train_on_eos": ds_cfg.get("train_on_eos", "turn"),
|
|
"train_on_eot": ds_cfg.get("train_on_eot", None),
|
|
"eot_tokens": cfg.get("eot_tokens", None), # loads from cfg, not ds_cfg
|
|
"split_thinking": ds_cfg.get("split_thinking", False),
|
|
}
|
|
|
|
def __call__(
|
|
self,
|
|
tokenizer,
|
|
cfg,
|
|
ds_cfg: Union[Dict[str, Any], DatasetConfig] | None = None,
|
|
processor=None,
|
|
):
|
|
if ds_cfg is None:
|
|
dataset_config = {}
|
|
elif isinstance(ds_cfg, BaseModel):
|
|
dataset_config = ds_cfg.model_dump()
|
|
else:
|
|
dataset_config = ds_cfg
|
|
|
|
if cfg.tokenizer_use_mistral_common:
|
|
# mistral-common does not use this, so we pass an empty string
|
|
chat_template_string = ""
|
|
else:
|
|
chat_template_string = get_chat_template_from_config(
|
|
cfg=cfg, ds_cfg=dataset_config, tokenizer=tokenizer
|
|
)
|
|
|
|
LOG.info(f"Using chat template:\n---\n{chat_template_string!s}\n---")
|
|
|
|
prompter_params = {
|
|
"tokenizer": tokenizer,
|
|
"chat_template": chat_template_string,
|
|
"chat_template_kwargs": cfg.get("chat_template_kwargs", {}),
|
|
"message_property_mappings": dataset_config.get(
|
|
"message_property_mappings", {}
|
|
),
|
|
"message_field_training": dataset_config.get(
|
|
"message_field_training", None
|
|
),
|
|
"message_field_training_detail": dataset_config.get(
|
|
"message_field_training_detail",
|
|
None,
|
|
),
|
|
"field_messages": dataset_config.get("field_messages", "messages"),
|
|
"field_thinking": dataset_config.get("field_thinking", "reasoning_content"),
|
|
"template_thinking_key": dataset_config.get(
|
|
"template_thinking_key", "reasoning_content"
|
|
),
|
|
"roles": dataset_config.get("roles"),
|
|
"drop_system_message": dataset_config.get("drop_system_message", False),
|
|
# we need to add one for detecting sequences with exceeding the `sequence_len` limit.
|
|
"max_length": cfg.sequence_len + 1,
|
|
"processor": processor,
|
|
}
|
|
|
|
strategy_params = self._get_strategy_params(cfg, dataset_config)
|
|
strategy_cls = self._get_strategy_cls(cfg)
|
|
prompter_cls = self._get_prompter_cls(cfg)
|
|
|
|
strategy = strategy_cls(
|
|
prompter_cls(**prompter_params),
|
|
tokenizer=tokenizer,
|
|
**strategy_params,
|
|
)
|
|
|
|
return strategy
|
|
|
|
|
|
load = StrategyLoader()
|