remove the bos token from dpo outputs (#1733) [skip ci]
* remove the bos token from dpo outputs * don't forget to fix prompt_input_ids too * use processing_class instead of tokenizer * fix for processing class
This commit is contained in:
Wing Lian
GitHub
67
examples/qwen2/dpo.yaml
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67
examples/qwen2/dpo.yaml
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@@ -0,0 +1,67 @@
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base_model: Qwen/Qwen2.5-0.5B
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strict: false
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chat_template: qwen_25
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rl: dpo
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datasets:
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- path: fozziethebeat/alpaca_messages_2k_dpo_test
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type: chat_template.default
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field_messages: conversation
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field_chosen: chosen
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field_rejected: rejected
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message_field_role: role
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message_field_content: content
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roles:
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system:
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- system
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user:
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- user
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assistant:
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- assistant
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dataset_prepared_path:
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val_set_size: 0.0
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output_dir: ./outputs/dpo-out
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sequence_len: 2048
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sample_packing: false
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pad_to_sequence_len: true
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 4
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 10
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evals_per_epoch: 4
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eval_table_size:
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eval_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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@@ -1038,24 +1038,37 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
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return super().push_to_hub(*args, **kwargs)
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@staticmethod
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def tokenize_row(
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self,
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features,
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processing_class,
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max_prompt_length,
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max_completion_length,
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add_special_tokens,
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) -> Dict:
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res = super().tokenize_row(
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res = DPOTrainer.tokenize_row(
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features,
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processing_class,
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max_prompt_length,
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max_completion_length,
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add_special_tokens,
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)
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if processing_class.bos_token_id is None and res["prompt_input_ids"][0] is None:
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# fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
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if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
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for key in res.keys():
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res[key] = res[key][1:]
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if processing_class.bos_token and processing_class.bos_token_id is not None:
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# dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
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if res["chosen_input_ids"][0] == processing_class.bos_token_id:
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res["chosen_input_ids"] = res["chosen_input_ids"][1:]
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res["chosen_labels"] = res["chosen_labels"][1:]
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res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
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if res["rejected_input_ids"][0] == processing_class.bos_token_id:
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res["rejected_input_ids"] = res["rejected_input_ids"][1:]
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res["rejected_labels"] = res["rejected_labels"][1:]
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res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
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return res
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def training_step(
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85
tests/e2e/test_qwen.py
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85
tests/e2e/test_qwen.py
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"""
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E2E tests for qwen
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"""
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import logging
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import os
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from pathlib import Path
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import pytest
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import yaml
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from accelerate.test_utils import execute_subprocess_async
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from transformers.testing_utils import get_torch_dist_unique_port
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from axolotl.utils.dict import DictDefault
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LOG = logging.getLogger("axolotl.tests.qwen")
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os.environ["WANDB_DISABLED"] = "true"
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class TestE2eQwen:
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"""
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Test cases for qwen models
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"""
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@pytest.mark.parametrize("base_model", ["Qwen/Qwen2-0.5B", "Qwen/Qwen2.5-0.5B"])
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def test_dpo(self, base_model, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": base_model,
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"rl": "dpo",
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"chat_template": "qwen_25",
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"sequence_len": 2048,
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"val_set_size": 0.0,
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"datasets": [
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{
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"path": "fozziethebeat/alpaca_messages_2k_dpo_test",
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"split": "train",
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"type": "chat_template.default",
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"field_messages": "conversation",
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"field_chosen": "chosen",
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"field_rejected": "rejected",
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"message_field_role": "role",
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"message_field_content": "content",
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"roles": {
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"system": ["system"],
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"user": ["user"],
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"assistant": ["assistant"],
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},
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},
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],
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"num_epochs": 1,
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"max_steps": 5,
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"warmup_steps": 20,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_bnb_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"bf16": "auto",
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"tf32": True,
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"gradient_checkpointing": True,
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}
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)
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# write cfg to yaml file
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Path(temp_dir).mkdir(parents=True, exist_ok=True)
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with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
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fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
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execute_subprocess_async(
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[
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"accelerate",
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"launch",
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"--num-processes",
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"2",
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"--main_process_port",
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f"{get_torch_dist_unique_port()}",
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"-m",
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"axolotl.cli.train",
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str(Path(temp_dir) / "config.yaml"),
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]
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)
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