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26b7c2507e
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <gallouedec.quentin@gmail.com>
1527 lines
55 KiB
Python
1527 lines
55 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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import sys
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from unittest.mock import MagicMock
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import numpy as np
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import pytest
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import torch
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from datasets import Dataset, features, load_dataset
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from parameterized import parameterized
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from transformers import (
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AutoModelForCausalLM,
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AutoModelForImageTextToText,
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AutoModelForSeq2SeqLM,
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AutoProcessor,
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AutoTokenizer,
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PreTrainedTokenizerBase,
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is_vision_available,
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)
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from transformers.testing_utils import (
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get_device_properties,
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require_liger_kernel,
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)
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from trl import DPOConfig, DPOTrainer, FDivergenceType
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from .testing_utils import (
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TrlTestCase,
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require_bitsandbytes,
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require_no_wandb,
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require_peft,
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require_torch_gpu_if_bnb_not_multi_backend_enabled,
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require_vision,
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)
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if is_vision_available():
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from PIL import Image
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class TestTokenizeRow(TrlTestCase):
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def setup_method(self):
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# Set up the mock tokenizer with specific behaviors
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self.tokenizer = MagicMock(spec=PreTrainedTokenizerBase)
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self.tokenizer.bos_token_id = 0
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self.tokenizer.eos_token_id = 2
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# Define mock return values for the tokenizer's 'input_ids' for the different text inputs
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self.tokenizer.return_value = {
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"input_ids": {"The sky is": [464, 6766, 318], " blue": [4171], " green": [4077]}
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}
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# Define tokenizer behavior when called
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def mock_tokenizer_call(text, add_special_tokens):
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token_map = {
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"The sky is": {"input_ids": [464, 6766, 318]},
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" blue": {"input_ids": [4171]},
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" green": {"input_ids": [4077]},
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}
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return token_map[text]
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self.tokenizer.side_effect = mock_tokenizer_call
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def test_tokenize_row_no_truncation_no_special_tokens(self):
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# Define the input features
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features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
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# Call the method with no truncation and no special tokens
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result = DPOTrainer.tokenize_row(
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features=features,
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processing_class=self.tokenizer,
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max_prompt_length=None,
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max_completion_length=None,
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add_special_tokens=False,
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)
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# Assert the correct output without truncation or special tokens
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assert result == {
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"prompt_input_ids": [464, 6766, 318],
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"chosen_input_ids": [4171, 2], # eos_token added
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"rejected_input_ids": [4077, 2], # eos_token added
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}
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def test_tokenize_row_with_truncation(self):
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# Define the input features
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features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
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# Call the method with truncation
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result = DPOTrainer.tokenize_row(
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features=features,
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processing_class=self.tokenizer,
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max_prompt_length=2,
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max_completion_length=1,
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add_special_tokens=False,
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)
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# Assert the correct output with truncation applied
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assert result == {
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"prompt_input_ids": [6766, 318], # truncated to the last 2 tokens
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"chosen_input_ids": [4171], # truncated to 1 token
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"rejected_input_ids": [4077], # truncated to 1 token
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}
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def test_tokenize_row_with_special_tokens(self):
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# Define the input features
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features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
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# Call the method with special tokens
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result = DPOTrainer.tokenize_row(
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features=features,
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processing_class=self.tokenizer,
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max_prompt_length=None,
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max_completion_length=None,
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add_special_tokens=True,
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)
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# Assert the correct output with special tokens added
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assert result == {
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"prompt_input_ids": [0, 464, 6766, 318, 2], # bos_token and eos_token added
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"chosen_input_ids": [4171, 2], # eos_token added
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"rejected_input_ids": [4077, 2], # eos_token added
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}
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def test_tokenize_row_with_truncation_and_special_tokens(self):
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# Define the input features
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features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
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# Call the method with both truncation and special tokens
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result = DPOTrainer.tokenize_row(
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features=features,
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processing_class=self.tokenizer,
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max_prompt_length=4,
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max_completion_length=1,
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add_special_tokens=True,
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)
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# Assert the correct output with both truncation and special tokens
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assert result == {
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"prompt_input_ids": [464, 6766, 318, 2], # truncated to 4 tokens with bos_token and eos_token
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"chosen_input_ids": [4171], # truncated to 1 token
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"rejected_input_ids": [4077], # truncated to 1 token
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}
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class TestDPOTrainer(TrlTestCase):
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def setup_method(self):
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self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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self.model = AutoModelForCausalLM.from_pretrained(self.model_id)
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self.ref_model = AutoModelForCausalLM.from_pretrained(self.model_id)
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
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self.tokenizer.pad_token = self.tokenizer.eos_token
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def test_train(self):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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learning_rate=9e-1,
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report_to="none",
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)
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trainer = DPOTrainer(
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model=model_id,
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args=training_args,
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processing_class=tokenizer,
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train_dataset=dataset,
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)
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Check that the parameters have changed
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for n, param in previous_trainable_params.items():
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new_param = trainer.model.get_parameter(n)
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if param.sum() != 0: # ignore 0 biases
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assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
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@parameterized.expand(
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[
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("sigmoid",),
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("hinge",),
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("ipo",),
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("exo_pair",),
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("nca_pair",),
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("robust",),
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("bco_pair",),
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("sppo_hard",),
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("aot",),
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("aot_pair",),
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("discopop",),
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("apo_zero",),
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("apo_down",),
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]
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)
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def test_train_loss_types(self, loss_type):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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learning_rate=9e-1,
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loss_type=loss_type,
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report_to="none",
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)
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trainer = DPOTrainer(
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model=model_id,
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args=training_args,
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processing_class=tokenizer,
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train_dataset=dataset,
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)
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Check that the parameters have changed
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for n, param in previous_trainable_params.items():
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new_param = trainer.model.get_parameter(n)
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if param.sum() != 0: # ignore 0 biases
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assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
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@require_liger_kernel
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def test_train_encoder_decoder_liger(self):
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model_id = "trl-internal-testing/tiny-BartModel"
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model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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learning_rate=9e-1,
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report_to="none",
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use_liger_loss=True,
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)
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trainer = DPOTrainer(
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model=model,
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args=training_args,
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processing_class=tokenizer,
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train_dataset=dataset,
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)
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Check that the parameters have changed
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for n, param in previous_trainable_params.items():
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new_param = trainer.model.get_parameter(n)
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if param.sum() != 0: # ignore 0 biases
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assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
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def test_dpo_trainer_with_weighting(self):
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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learning_rate=9e-1,
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use_weighting=True,
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report_to="none",
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)
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trainer = DPOTrainer(
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model=self.model,
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args=training_args,
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processing_class=self.tokenizer,
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train_dataset=dataset,
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)
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Check that the parameters have changed
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for n, param in previous_trainable_params.items():
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new_param = trainer.model.get_parameter(n)
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if param.sum() != 0: # ignore 0 biases
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assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
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def test_train_with_multiple_loss_types(self):
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"""
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Tests multi-loss combinations, loss type inference, and weight configuration. MPO combines DPO (sigmoid), BCO
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(bco_pair), and SFT (sft) losses.
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"""
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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learning_rate=9e-1,
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loss_type=["sigmoid", "bco_pair", "sft"],
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loss_weights=[0.8, 0.2, 1.0],
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report_to="none",
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)
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trainer = DPOTrainer(
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model=model_id,
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args=training_args,
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processing_class=tokenizer,
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train_dataset=dataset,
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)
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# Test that training works
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Verify SFT loss is computed in the first test too
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with torch.no_grad():
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batch = next(iter(trainer.get_train_dataloader()))
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loss, metrics = trainer.get_batch_loss_metrics(trainer.model, batch)
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assert "nll_loss" in metrics # SFT loss should be computed
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def test_wrong_loss_weights_length(self):
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with pytest.raises(ValueError, match="Length of loss_weights list"):
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DPOConfig(
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output_dir=self.tmp_dir,
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loss_type=["sigmoid", "bco_pair"],
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loss_weights=[1.0, 0.5, 0.1], # Wrong length
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)
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@parameterized.expand([(None,), (0.5,)])
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def test_dpo_trainer_without_providing_ref_model(self, rpo_alpha):
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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max_steps=3,
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remove_unused_columns=False,
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gradient_accumulation_steps=4,
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learning_rate=9e-1,
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eval_strategy="steps",
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beta=0.1,
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precompute_ref_log_probs=True,
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rpo_alpha=rpo_alpha,
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report_to="none",
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)
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dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
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trainer = DPOTrainer(
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model=self.model,
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ref_model=None,
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args=training_args,
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processing_class=self.tokenizer,
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train_dataset=dummy_dataset["train"],
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eval_dataset=dummy_dataset["test"],
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)
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Check that the parameters have changed
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for n, param in previous_trainable_params.items():
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new_param = trainer.model.get_parameter(n)
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if param.sum() != 0: # ignore 0 biases
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assert not torch.equal(param, new_param)
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def test_dpo_trainer_with_ref_model_is_model(self):
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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max_steps=3,
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report_to="none",
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)
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dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
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with pytest.raises(ValueError):
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DPOTrainer(
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model=self.model,
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ref_model=self.model, # ref_model can't be the same as model
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args=training_args,
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processing_class=self.tokenizer,
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train_dataset=dummy_dataset["train"],
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)
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def test_precompute_ref_batch_size(self):
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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precompute_ref_log_probs=True,
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precompute_ref_batch_size=4,
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report_to="none",
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)
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dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
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trainer = DPOTrainer(
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model=self.model,
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ref_model=self.ref_model,
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args=training_args,
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processing_class=self.tokenizer,
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train_dataset=dummy_dataset["train"],
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eval_dataset=dummy_dataset["test"],
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)
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Check that the parameters have changed
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for n, param in previous_trainable_params.items():
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new_param = trainer.model.get_parameter(n)
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if param.sum() != 0: # ignore 0 biases
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assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
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@require_peft
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def test_dpo_trainer_without_providing_ref_model_with_lora(self):
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from peft import LoraConfig
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lora_config = LoraConfig(
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r=16,
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lora_alpha=32,
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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max_steps=3,
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remove_unused_columns=False,
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gradient_accumulation_steps=4,
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learning_rate=9e-1,
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eval_strategy="steps",
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beta=0.1,
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precompute_ref_log_probs=True,
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report_to="none",
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)
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dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
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trainer = DPOTrainer(
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model=self.model,
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ref_model=None,
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args=training_args,
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processing_class=self.tokenizer,
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train_dataset=dummy_dataset["train"],
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eval_dataset=dummy_dataset["test"],
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peft_config=lora_config,
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)
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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trainer.train()
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assert trainer.state.log_history[-1]["train_loss"] is not None
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# Check that the parameters have changed
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for n, param in previous_trainable_params.items():
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if "lora" in n:
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new_param = trainer.model.get_parameter(n)
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if param.sum() != 0: # ignore 0 biases
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assert not torch.equal(param, new_param)
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def test_dpo_trainer_w_dataset_num_proc(self):
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training_args = DPOConfig(
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output_dir=self.tmp_dir,
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per_device_train_batch_size=2,
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max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
dataset_num_proc=2,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
def test_tr_dpo_trainer(self):
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
precompute_ref_log_probs=False,
|
|
sync_ref_model=True,
|
|
ref_model_mixup_alpha=0.5,
|
|
ref_model_sync_steps=1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
ref_model=self.ref_model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# params of the ref model as its the same as the model
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.ref_model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
assert not torch.equal(param, new_param)
|
|
|
|
@require_no_wandb
|
|
def test_dpo_trainer_generate_during_eval_no_wandb(self):
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
generate_during_eval=True,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="`generate_during_eval=True` requires Weights and Biases, MLFlow or Comet to be installed."
|
|
" Please install `wandb`, `mlflow` or `comet-ml` to resolve.",
|
|
):
|
|
DPOTrainer(
|
|
model=self.model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
@require_peft
|
|
def test_dpo_lora_save(self):
|
|
from peft import LoraConfig, get_peft_model
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
model_peft = get_peft_model(model, lora_config)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
precompute_ref_log_probs=True,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model_peft,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
# save peft adapter
|
|
trainer.save_model()
|
|
|
|
try:
|
|
AutoModelForCausalLM.from_pretrained(self.tmp_dir)
|
|
except OSError:
|
|
pytest.fail("Loading the saved peft adapter failed")
|
|
|
|
@require_peft
|
|
@require_torch_gpu_if_bnb_not_multi_backend_enabled
|
|
def test_dpo_lora_bf16_autocast_llama(self):
|
|
# Note this test only works on compute capability > 7 GPU devices
|
|
from peft import LoraConfig
|
|
from transformers import BitsAndBytesConfig
|
|
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_id, quantization_config=BitsAndBytesConfig(load_in_4bit=True)
|
|
)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
bf16=True,
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
# save peft adapter
|
|
trainer.save_model()
|
|
|
|
@parameterized.expand(
|
|
[
|
|
("sigmoid", False, False),
|
|
("sigmoid", False, True),
|
|
("sigmoid", True, False),
|
|
("sigmoid", True, True),
|
|
("ipo", False, False),
|
|
("ipo", False, True),
|
|
("ipo", True, False),
|
|
("ipo", True, True),
|
|
("aot_pair", False, False),
|
|
("aot_pair", False, True),
|
|
("aot_pair", True, False),
|
|
("aot_pair", True, True),
|
|
("aot", False, False),
|
|
("aot", False, True),
|
|
("aot", True, False),
|
|
("aot", True, True),
|
|
("bco_pair", False, False),
|
|
("bco_pair", False, True),
|
|
("bco_pair", True, False),
|
|
("bco_pair", True, True),
|
|
("robust", False, False),
|
|
("robust", False, True),
|
|
("robust", True, False),
|
|
("robust", True, True),
|
|
]
|
|
)
|
|
@require_bitsandbytes
|
|
@require_peft
|
|
@pytest.mark.skipif(
|
|
get_device_properties()[0] == "cuda" and get_device_properties()[1] < 8,
|
|
reason="Skipping because bf16 not supported on CUDA GPU with capability < 8.0",
|
|
)
|
|
def test_dpo_lora_bf16_autocast(self, loss_type, pre_compute, gen_during_eval):
|
|
from peft import LoraConfig
|
|
from transformers import BitsAndBytesConfig
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.model_id, quantization_config=BitsAndBytesConfig(load_in_4bit=True)
|
|
)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
bf16=True,
|
|
beta=0.1,
|
|
generate_during_eval=gen_during_eval,
|
|
loss_type=loss_type,
|
|
precompute_ref_log_probs=pre_compute,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
# save peft adapter
|
|
trainer.save_model()
|
|
|
|
@require_peft
|
|
def test_dpo_lora_tags(self):
|
|
from peft import LoraConfig
|
|
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
for tag in ["dpo", "trl"]:
|
|
assert tag in trainer.model.model_tags
|
|
|
|
@require_peft
|
|
def test_dpo_tags(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
for tag in ["dpo", "trl"]:
|
|
assert tag in trainer.model.model_tags
|
|
|
|
@require_peft
|
|
def test_dpo_lora_force_use_ref(self):
|
|
from peft import LoraConfig, get_peft_model
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
model_peft = get_peft_model(model, lora_config)
|
|
|
|
ref_model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
with pytest.raises(ValueError):
|
|
# passing a peft_model as model and ref_model should error out,
|
|
# unless you pass `force_use_ref_model`
|
|
trainer = DPOTrainer(
|
|
model=model_peft,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
force_use_ref_model=True,
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = DPOTrainer(
|
|
model=model_peft,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
def test_dpo_trainer_dtype(self):
|
|
# See https://github.com/huggingface/trl/issues/1751
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=1,
|
|
model_init_kwargs={"dtype": "float16"},
|
|
ref_model_init_kwargs={"dtype": "float16"},
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model_id,
|
|
ref_model=self.model_id,
|
|
processing_class=self.tokenizer,
|
|
args=training_args,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
assert trainer.model.config.dtype == torch.float16
|
|
assert trainer.ref_model.config.dtype == torch.float16
|
|
|
|
# Now test when `dtype` is provided but is wrong to either the model or the ref_model
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=1,
|
|
model_init_kwargs={"dtype": -1},
|
|
report_to="none",
|
|
)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=re.escape(
|
|
"Invalid `dtype` passed to the config. Expected either 'auto' or a string representing a valid `torch.dtype` (e.g., 'float32'), but got -1."
|
|
),
|
|
):
|
|
_ = DPOTrainer(
|
|
model=self.model_id,
|
|
processing_class=self.tokenizer,
|
|
args=training_args,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=1,
|
|
ref_model_init_kwargs={"dtype": -1},
|
|
report_to="none",
|
|
)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match=re.escape(
|
|
"Invalid `dtype` passed to the config. Expected either 'auto' or a string representing a valid `torch.dtype` (e.g., 'float32'), but got -1."
|
|
),
|
|
):
|
|
_ = DPOTrainer(
|
|
model=self.model_id,
|
|
ref_model=self.model_id,
|
|
processing_class=self.tokenizer,
|
|
args=training_args,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
|
|
def test_dpo_loss_alpha_div_f(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
f_divergence_type=FDivergenceType.ALPHA_DIVERGENCE.value,
|
|
f_alpha_divergence_coef=0.5,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# Fake chosen and rejected log probs
|
|
policy_chosen_logps = torch.FloatTensor([410.0, 0.1])
|
|
policy_rejected_logps = torch.FloatTensor([810.5, 0.2])
|
|
reference_chosen_logps = torch.FloatTensor([-610.0, -0.1])
|
|
reference_rejected_logps = torch.FloatTensor([110.6, 0.5])
|
|
losses, _, _ = trainer.dpo_loss(
|
|
policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
|
|
)
|
|
assert torch.isfinite(losses).cpu().numpy().all()
|
|
|
|
def test_dpo_loss_js_div_f(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
f_divergence_type=FDivergenceType.JS_DIVERGENCE.value,
|
|
f_alpha_divergence_coef=0.5,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# Fake chosen and rejected log probs
|
|
policy_chosen_logps = torch.FloatTensor([410.0, 0.1])
|
|
policy_rejected_logps = torch.FloatTensor([95.5, 0.2])
|
|
reference_chosen_logps = torch.FloatTensor([-610.0, -0.1])
|
|
reference_rejected_logps = torch.FloatTensor([5.5, 0.5])
|
|
losses, _, _ = trainer.dpo_loss(
|
|
policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
|
|
)
|
|
assert torch.isfinite(losses).cpu().numpy().all()
|
|
|
|
def test_dpo_trainer_use_logits_to_keep(self):
|
|
model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
use_logits_to_keep=True,
|
|
rpo_alpha=0.5,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
training_args.use_logits_to_keep = False
|
|
trainer2 = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# Fake batch
|
|
prompt_input_ids = torch.randint(1, 1000, (2, 10))
|
|
chosen_input_ids = torch.randint(1, 1000, (2, 5))
|
|
rejected_input_ids = torch.randint(1, 1000, (2, 7))
|
|
prompt_attention_mask = torch.ones_like(prompt_input_ids)
|
|
chosen_attention_mask = torch.ones_like(chosen_input_ids)
|
|
rejected_attention_mask = torch.ones_like(rejected_input_ids)
|
|
|
|
batch = {
|
|
"prompt_input_ids": prompt_input_ids.to(model.device),
|
|
"chosen_input_ids": chosen_input_ids.to(model.device),
|
|
"rejected_input_ids": rejected_input_ids.to(model.device),
|
|
"prompt_attention_mask": prompt_attention_mask.to(model.device),
|
|
"chosen_attention_mask": chosen_attention_mask.to(model.device),
|
|
"rejected_attention_mask": rejected_attention_mask.to(model.device),
|
|
}
|
|
|
|
output = trainer.concatenated_forward(model, batch)
|
|
output2 = trainer2.concatenated_forward(model, batch)
|
|
|
|
np.testing.assert_allclose(output["nll_loss"].item(), output2["nll_loss"].item(), atol=1e-5)
|
|
np.testing.assert_allclose(
|
|
output["mean_chosen_logits"].item(), output2["mean_chosen_logits"].item(), atol=1e-5
|
|
)
|
|
np.testing.assert_allclose(
|
|
output["mean_rejected_logits"].item(), output2["mean_rejected_logits"].item(), atol=1e-5
|
|
)
|
|
|
|
for i in range(output["chosen_logps"].shape[0]):
|
|
np.testing.assert_allclose(output["chosen_logps"][i].item(), output2["chosen_logps"][i].item(), atol=1e-5)
|
|
np.testing.assert_allclose(
|
|
output["rejected_logps"][i].item(), output2["rejected_logps"][i].item(), atol=1e-5
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
def test_dpo_trainer_with_tools(self):
|
|
model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
# Define dummy test tools
|
|
def get_current_temperature(location: str):
|
|
"""
|
|
Gets the temperature at a given location.
|
|
|
|
Args:
|
|
location: The location to get the temperature for
|
|
"""
|
|
return 22.0
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
tools=[get_current_temperature],
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "conversational_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
# We don't run the training, but at this stage, the dataset is supposed to be pre-processed. When
|
|
# pre-processing, we expect the available tools to be explicitly mentioned in the system prompt. That's
|
|
# what we're checking here
|
|
assert "get_current_temperature" in tokenizer.decode(trainer.train_dataset["prompt_input_ids"][0])
|
|
|
|
def test_padding_free(self):
|
|
model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
# Normally, we need `attn_implementation="flash_attention_2"` to that the model returns correct logits.
|
|
# Without it, the logits may be incorrect, but that's fine here. This test focuses only on the inner logic
|
|
# of padding_free.
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
learning_rate=9e-1,
|
|
per_device_train_batch_size=2,
|
|
padding_free=True,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
|
|
|
|
def test_compute_metrics(self):
|
|
model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
|
|
ref_model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
|
|
tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
def dummy_compute_metrics(*args, **kwargs):
|
|
return {"test": 0.0}
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
do_eval=True,
|
|
eval_strategy="steps",
|
|
eval_steps=3,
|
|
per_device_eval_batch_size=2,
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
compute_metrics=dummy_compute_metrics,
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
assert trainer.state.log_history[-2]["eval_test"] == 0.0
|
|
|
|
def test_train_with_length_desensitization(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
learning_rate=9e-1,
|
|
ld_alpha=0.5,
|
|
report_to="none",
|
|
)
|
|
trainer = DPOTrainer(
|
|
model=model_id,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
(0.1, "sigmoid"),
|
|
(0.1, "apo_zero"),
|
|
(0.1, "apo_down"),
|
|
(0.1, "sppo_hard"),
|
|
(0.1, "nca_pair"),
|
|
(0.5, "sigmoid"),
|
|
(0.5, "apo_zero"),
|
|
(0.5, "apo_down"),
|
|
(0.5, "sppo_hard"),
|
|
(0.5, "nca_pair"),
|
|
]
|
|
)
|
|
@require_liger_kernel
|
|
@pytest.mark.skipif(not (sys.version_info >= (3, 10)), reason="Liger kernel is not supported on Python 3.9")
|
|
def test_dpo_trainer_with_liger(self, beta, loss_type):
|
|
"""Test DPO trainer with Liger loss enabled across supported loss types.
|
|
|
|
This test verifies that:
|
|
1. Training runs successfully with Liger loss
|
|
2. Model parameters update as expected
|
|
3. Loss values are reasonable and finite
|
|
4. Training works with both default and custom beta values
|
|
"""
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
do_eval=True,
|
|
eval_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=beta,
|
|
use_liger_loss=True, # Enable Liger loss
|
|
loss_type=loss_type,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
ref_model=self.ref_model, # Add reference model
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# Store initial parameters
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
train_output = trainer.train()
|
|
|
|
# Verify training completed successfully
|
|
assert train_output is not None
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
# Verify loss is finite
|
|
assert np.isfinite(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check parameters have been updated
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
# Only check non-zero parameters
|
|
if param.sum() != 0:
|
|
assert not torch.equal(param, new_param)
|
|
# Verify new parameters are finite
|
|
assert torch.isfinite(new_param).all()
|
|
|
|
# Verify model can still do forward pass after training
|
|
dummy_batch = next(iter(trainer.get_train_dataloader()))
|
|
model_inputs = {
|
|
"input_ids": dummy_batch["prompt_input_ids"],
|
|
"attention_mask": dummy_batch["prompt_attention_mask"],
|
|
}
|
|
with torch.no_grad():
|
|
output = trainer.model(**model_inputs)
|
|
assert output is not None
|
|
assert "loss" not in output.keys()
|
|
|
|
def test_train_with_iterable_dataset(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
dataset = load_dataset(
|
|
"trl-internal-testing/zen",
|
|
"standard_preference",
|
|
split="train",
|
|
streaming=True,
|
|
)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
max_steps=3,
|
|
report_to="none",
|
|
)
|
|
trainer = DPOTrainer(
|
|
model=model_id,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12)
|
|
|
|
|
|
@require_vision
|
|
class TestDPOVisionTrainer(TrlTestCase):
|
|
@parameterized.expand(
|
|
[
|
|
# ("trl-internal-testing/tiny-Idefics2ForConditionalGeneration",), device issue from transformers, see https://github.com/huggingface/transformers/pull/39975
|
|
# ("trl-internal-testing/tiny-PaliGemmaForConditionalGeneration",),
|
|
("trl-internal-testing/tiny-LlavaForConditionalGeneration",),
|
|
("trl-internal-testing/tiny-LlavaNextForConditionalGeneration",),
|
|
("trl-internal-testing/tiny-Gemma3ForConditionalGeneration",),
|
|
]
|
|
)
|
|
def test_vdpo_trainer(self, model_id):
|
|
# fmt: off
|
|
dataset_dict = {
|
|
"prompt": [
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe the image in great detail."}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Is this bus in the USA?"}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Give a thorough description of the image."}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Who are the people in the image?"}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What is written?"}]}],
|
|
],
|
|
"chosen": [
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a modern, multi-colored train."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "Yes, it can be assumed that this bus is in the USA."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a forest path."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "There are two individuals, possibly girls or women."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": '"ccpb".'}]}],
|
|
],
|
|
"rejected": [
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a modern, colorful train."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "No, it's not in the USA."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a forest path surrounded by trees."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "In the image, there are two individuals."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": '"ccpb".'}]}],
|
|
],
|
|
"images": [
|
|
[Image.fromarray(np.random.randint(0, 255, (92, 33, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (64, 48, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (80, 152, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (57, 24, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (102, 48, 3), dtype=np.uint8))],
|
|
],
|
|
}
|
|
# fmt: on
|
|
dataset = Dataset.from_dict(dataset_dict)
|
|
dataset = dataset.cast_column("images", features.Sequence(features.Image()))
|
|
|
|
# Instantiate the model and processor
|
|
model = AutoModelForImageTextToText.from_pretrained(model_id)
|
|
ref_model = AutoModelForImageTextToText.from_pretrained(model_id)
|
|
processor = AutoProcessor.from_pretrained(model_id)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
remove_unused_columns=False,
|
|
learning_rate=0.01, # increase learning rate to speed up test
|
|
max_prompt_length=None, # don't truncate to avoid issues with patch tokens
|
|
max_length=None,
|
|
report_to="none",
|
|
)
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=processor,
|
|
train_dataset=dataset,
|
|
eval_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial weights, so we can check if they have changed after training
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
# Check that the trainable params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
if model_id in [
|
|
"trl-internal-testing/tiny-LlavaForConditionalGeneration",
|
|
"trl-internal-testing/tiny-LlavaNextForConditionalGeneration",
|
|
] and (
|
|
"vision_tower.vision_model.encoder.layers.1" in n
|
|
or "vision_tower.vision_model.post_layernorm.weight" in n
|
|
):
|
|
# For some reason, these params are not updated. This is probably not related to TRL, but to
|
|
# the model itself. We should investigate this further, but for now we just skip these params.
|
|
continue
|
|
assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12), f"Param {n} is not updated"
|
|
|
|
|
|
class TestDPOConfig(TrlTestCase):
|
|
@parameterized.expand([(f_div_type, as_str) for f_div_type in list(FDivergenceType) for as_str in [False, True]])
|
|
def test_f_divergence_type(self, f_divergence_type, as_string: bool):
|
|
training_args = DPOConfig(
|
|
output_dir=self.tmp_dir,
|
|
report_to="none",
|
|
f_divergence_type=f_divergence_type.value if as_string else f_divergence_type,
|
|
)
|
|
|
|
# Internal normalization: keep Enum member
|
|
assert isinstance(training_args.f_divergence_type, FDivergenceType)
|
|
assert training_args.f_divergence_type == f_divergence_type
|
|
|
|
# Serialization: TrainingArguments.to_dict should yield the enum's string value
|
|
configparser_dict = training_args.to_dict()
|
|
assert configparser_dict["f_divergence_type"] == f_divergence_type.value
|