improve tool handling roles (#1587)
Browse files
src/axolotl/prompt_strategies/sharegpt.py
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"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
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import logging
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from typing import Any, Dict, Optional
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from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
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@@ -39,76 +39,40 @@ def register_chatml_template(system_message=None):
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def
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)
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cfg.train_on_inputs,
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cfg.sequence_len,
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)
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if ds_cfg and "strict" in ds_cfg:
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strategy.strict = ds_cfg["strict"]
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return strategy
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def load_role(tokenizer, cfg):
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return SimpleRoleShareGPTPromptTokenizingStrategy(
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ShareGPTPrompterV2(),
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tokenizer,
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cfg.train_on_inputs,
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cfg.sequence_len,
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)
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def load_guanaco(tokenizer, cfg):
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return GuanacoShareGPTPromptTokenizingStrategy(
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ShareGPTPrompterV2(),
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tokenizer,
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cfg.train_on_inputs,
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cfg.sequence_len,
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)
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def load_glaive(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
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conversation = (
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ds_cfg["conversation"]
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if ds_cfg and "conversation" in ds_cfg
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else "chatml_glaive"
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)
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return GlaiveShareGPTPromptTokenizingStrategy(
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ShareGPTPrompterV2(conversation=conversation),
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tokenizer,
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cfg.train_on_inputs,
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cfg.sequence_len,
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)
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class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
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@@ -158,7 +122,9 @@ class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
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return turns
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class SimpleRoleShareGPTPromptTokenizingStrategy(
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"""
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basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
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"""
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@@ -209,3 +175,16 @@ class GlaiveShareGPTPromptTokenizingStrategy(SimpleShareGPTPromptTokenizingStrat
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conversation = merge_consecutive_messages(conversation)
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return conversation
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"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
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import logging
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from typing import Any, Dict, Optional, Type
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from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
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)
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def build_loader(
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tokenization_strategy_cls: Type["ShareGPTPromptTokenizingStrategy"],
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prompter_cls: Type["ShareGPTPrompterV2"],
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default_conversation: Optional[str] = None,
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):
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def _load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
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conversation = (
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ds_cfg["conversation"]
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if ds_cfg and "conversation" in ds_cfg
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else default_conversation
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)
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field_human = (
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ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
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)
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field_model = (
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ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
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)
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roles = ds_cfg["roles"].to_dict() if ds_cfg and "roles" in ds_cfg else None
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strategy = tokenization_strategy_cls(
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prompter_cls(
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conversation=conversation,
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role_key_model=field_model,
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role_key_human=field_human,
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roles=roles,
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),
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tokenizer,
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cfg.train_on_inputs,
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cfg.sequence_len,
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)
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if ds_cfg and "strict" in ds_cfg and hasattr(strategy, "strict"):
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strategy.strict = ds_cfg["strict"]
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return strategy
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return _load
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class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
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return turns
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class SimpleRoleShareGPTPromptTokenizingStrategy(
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SimpleShareGPTPromptTokenizingStrategy
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):
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"""
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basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
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"""
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conversation = merge_consecutive_messages(conversation)
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return conversation
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load = build_loader(SimpleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
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load_role = build_loader(SimpleRoleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
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load_ultrachat = build_loader(
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UltrachatShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2
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)
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load_guanaco = build_loader(GuanacoShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
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load_glaive = build_loader(
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GlaiveShareGPTPromptTokenizingStrategy,
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ShareGPTPrompterV2,
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default_conversation="chatml_glaive",
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)
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src/axolotl/prompters.py
CHANGED
@@ -348,7 +348,10 @@ class ShareGPTPrompter(Prompter): # pylint: disable=too-few-public-methods
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if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
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conv.append_message(role, sentence["value"])
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)
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if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
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if (
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role != "assistant"
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): # back to back assistant calls may be okay for tool calls
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LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
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conv.append_message(role, sentence["value"])
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