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da209f89fc
Co-authored-by: juejuezi <juejuezi.git@foxmail.com> Co-authored-by: Yi Shi <96773624+singing-cat@users.noreply.github.com> Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
835 lines
37 KiB
Python
835 lines
37 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 pathlib
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import unittest
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import torch
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from datasets import load_dataset
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from parameterized import parameterized
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from transformers.testing_utils import require_peft
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from transformers.utils import is_peft_available
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from trl import RewardConfig, RewardTrainer
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from trl.trainer.reward_trainer import DataCollatorForPreference
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from .testing_utils import TrlTestCase
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if is_peft_available():
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from peft import LoraConfig, PeftModel, get_peft_model
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class TestDataCollatorForPreference(TrlTestCase):
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def test_basic_padding(self):
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"""Test basic padding functionality without completion masks."""
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self.collator = DataCollatorForPreference(pad_token_id=0)
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examples = [
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{"chosen_input_ids": [1, 2, 3], "rejected_input_ids": [4, 5]},
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{"chosen_input_ids": [6, 7], "rejected_input_ids": [8]},
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]
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result = self.collator(examples)
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torch.testing.assert_close(result["input_ids"], torch.tensor([[1, 2, 3], [6, 7, 0], [4, 5, 0], [8, 0, 0]]))
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torch.testing.assert_close(
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result["attention_mask"], torch.tensor([[1, 1, 1], [1, 1, 0], [1, 1, 0], [1, 0, 0]])
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)
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def test_pad_to_multiple_of(self):
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"""Test padding to multiple of specified value."""
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collator = DataCollatorForPreference(pad_token_id=0, pad_to_multiple_of=4)
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examples = [
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{"chosen_input_ids": [1, 2, 3], "rejected_input_ids": [4, 5]},
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{"chosen_input_ids": [6, 7], "rejected_input_ids": [8]},
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]
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result = collator(examples)
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torch.testing.assert_close(
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result["input_ids"], torch.tensor([[1, 2, 3, 0], [6, 7, 0, 0], [4, 5, 0, 0], [8, 0, 0, 0]])
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)
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torch.testing.assert_close(
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result["attention_mask"], torch.tensor([[1, 1, 1, 0], [1, 1, 0, 0], [1, 1, 0, 0], [1, 0, 0, 0]])
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)
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def test_single_example(self):
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"""Test collator with a single example."""
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self.collator = DataCollatorForPreference(pad_token_id=0)
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examples = [{"chosen_input_ids": [1, 2, 3], "rejected_input_ids": [4, 5]}]
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result = self.collator(examples)
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torch.testing.assert_close(result["input_ids"], torch.tensor([[1, 2, 3], [4, 5, 0]]))
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torch.testing.assert_close(result["attention_mask"], torch.tensor([[1, 1, 1], [1, 1, 0]]))
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def test_different_pad_token_id(self):
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"""Test with different pad token ID."""
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collator = DataCollatorForPreference(pad_token_id=999)
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examples = [
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{"chosen_input_ids": [1, 2, 3], "rejected_input_ids": [4, 5]},
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{"chosen_input_ids": [6, 7], "rejected_input_ids": [8]},
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]
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result = collator(examples)
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torch.testing.assert_close(
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result["input_ids"], torch.tensor([[1, 2, 3], [6, 7, 999], [4, 5, 999], [8, 999, 999]])
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)
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torch.testing.assert_close(
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result["attention_mask"], torch.tensor([[1, 1, 1], [1, 1, 0], [1, 1, 0], [1, 0, 0]])
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)
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def test_collate_with_margin(self):
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self.collator = DataCollatorForPreference(pad_token_id=0)
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examples = [
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{"chosen_input_ids": [1, 2, 3], "rejected_input_ids": [4, 5], "margin": 0.1},
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{"chosen_input_ids": [6, 7], "rejected_input_ids": [8], "margin": 0.2},
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]
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result = self.collator(examples)
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torch.testing.assert_close(result["input_ids"], torch.tensor([[1, 2, 3], [6, 7, 0], [4, 5, 0], [8, 0, 0]]))
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torch.testing.assert_close(
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result["attention_mask"], torch.tensor([[1, 1, 1], [1, 1, 0], [1, 1, 0], [1, 0, 0]])
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)
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torch.testing.assert_close(result["margin"], torch.tensor([0.1, 0.2]))
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class RewardTrainerTester(TrlTestCase):
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@parameterized.expand(
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[
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("trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",),
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("trl-internal-testing/tiny-Qwen3MoeForSequenceClassification",),
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("trl-internal-testing/tiny-LlamaForSequenceClassification-3.2",),
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]
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)
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def test_train(self, model_id):
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
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trainer = RewardTrainer(model=model_id, args=training_args, train_dataset=dataset)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the params 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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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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@parameterized.expand(
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[
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("standard_preference",),
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("conversational_preference",),
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("standard_implicit_prompt_preference",),
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("conversational_implicit_prompt_preference",),
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]
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)
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def test_train_dataset_types(self, config_name):
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", config_name, split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
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trainer = RewardTrainer(
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model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
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args=training_args,
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train_dataset=dataset,
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)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the params 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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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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def test_train_model(self):
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# Instantiate the model
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model = AutoModelForSequenceClassification.from_pretrained(
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"trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5"
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)
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
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trainer = RewardTrainer(model=model, args=training_args, train_dataset=dataset)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the params 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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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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def test_train_from_causal_lm(self):
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
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trainer = RewardTrainer(
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model="trl-internal-testing/tiny-Qwen3ForCausalLM", args=training_args, train_dataset=dataset
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)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the params 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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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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def test_train_model_dtype(self):
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(
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output_dir=self.tmp_dir,
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model_init_kwargs={"dtype": torch.float16},
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learning_rate=0.1,
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report_to="none",
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)
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trainer = RewardTrainer(
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model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
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args=training_args,
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train_dataset=dataset,
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)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the params have changed
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for n, param in previous_trainable_params.items():
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# For some reasonn model.layers.0.input_layernorm.weight doesn't change in GitHub Actions but does
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# locally. We ignore this parameter for now
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if "layernorm" in n:
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continue
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new_param = trainer.model.get_parameter(n)
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# Check the torch dtype
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self.assertEqual(new_param.dtype, torch.float16)
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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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@require_peft
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def test_train_dense_with_peft_config(self):
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# Get the base model parameter names
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model_id = "trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5"
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
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trainer = RewardTrainer(
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model=model_id,
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args=training_args,
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train_dataset=dataset,
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peft_config=LoraConfig(),
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)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the peft params have changed and the base model params have not 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 n in base_param_names: # We expect the base model parameters to be the same
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self.assertTrue(torch.allclose(param, new_param), f"Parameter {n} has changed")
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elif "base_layer" not in n: # We expect the peft parameters to be different (except for the base layer)
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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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@require_peft
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def test_train_moe_with_peft_config(self):
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# Get the base model parameter names
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model_id = "trl-internal-testing/tiny-Qwen3MoeForSequenceClassification"
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
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trainer = RewardTrainer(
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model=model_id,
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args=training_args,
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train_dataset=dataset,
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peft_config=LoraConfig(target_modules=["up_proj", "down_proj", "score"]),
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)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the peft params have changed and the base model params have not 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 n in base_param_names: # We expect the base model parameters to be the same
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self.assertTrue(torch.allclose(param, new_param), f"Parameter {n} has changed")
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elif "base_layer" not in n: # We expect the peft parameters to be different (except for the base layer)
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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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@require_peft
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def test_train_peft_model(self):
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# Get the base model
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model_id = "trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5"
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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# Get the base model parameter names
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base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
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# Turn the model into a peft model
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lora_config = LoraConfig()
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model = get_peft_model(model, lora_config)
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
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trainer = RewardTrainer(model=model, args=training_args, train_dataset=dataset)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the peft params have changed and the base model params have not 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 n in base_param_names: # We expect the base model parameters to be the same
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self.assertTrue(torch.allclose(param, new_param), f"Parameter {n} has changed")
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elif "base_layer" not in n: # We expect the peft parameters to be different (except for the base layer)
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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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@require_peft
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def test_train_dense_with_peft_config_and_gradient_checkpointing(self):
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# Get the base model parameter names
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model_id = "trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5"
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, gradient_checkpointing=True, report_to="none")
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trainer = RewardTrainer(
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model=model_id,
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args=training_args,
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train_dataset=dataset,
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peft_config=LoraConfig(),
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)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
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# Train the model
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trainer.train()
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# Check that the training loss is not None
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check the peft params have changed and the base model params have not 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 n in base_param_names: # We expect the base model parameters to be the same
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self.assertTrue(torch.allclose(param, new_param), f"Parameter {n} has changed")
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elif "base_layer" not in n: # We expect the peft parameters to be different (except for the base layer)
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self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
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@require_peft
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def test_train_moe_with_peft_config_and_gradient_checkpointing(self):
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# Get the base model parameter names
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model_id = "trl-internal-testing/tiny-Qwen3MoeForSequenceClassification"
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
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# Get the dataset
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dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
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# Initialize the trainer
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training_args = RewardConfig(output_dir=self.tmp_dir, gradient_checkpointing=True, report_to="none")
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trainer = RewardTrainer(
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model=model_id,
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args=training_args,
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train_dataset=dataset,
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peft_config=LoraConfig(target_modules=["up_proj", "down_proj", "score"]),
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)
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# Save the initial parameters to compare them later
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previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the peft params have changed and the base model params have not changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if n in base_param_names: # We expect the base model parameters to be the same
|
|
self.assertTrue(torch.allclose(param, new_param), f"Parameter {n} has changed")
|
|
elif "base_layer" not in n: # We expect the peft parameters to be different (except for the base layer)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
@require_peft
|
|
def test_train_with_peft_model_and_gradient_checkpointing(self):
|
|
# Get the base model parameter names
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5"
|
|
model = AutoModelForSequenceClassification.from_pretrained(model_id)
|
|
base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
|
|
model = get_peft_model(model, LoraConfig())
|
|
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, gradient_checkpointing=True, report_to="none")
|
|
|
|
trainer = RewardTrainer(model=model, args=training_args, train_dataset=dataset)
|
|
|
|
# Verify model is a PeftModel
|
|
self.assertIsInstance(trainer.model, PeftModel)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the peft params have changed and the base model params have not changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if n in base_param_names: # We expect the base model parameters to be the same
|
|
self.assertTrue(torch.allclose(param, new_param), f"Parameter {n} has changed")
|
|
elif "base_layer" not in n: # We expect the peft parameters to be different (except for the base layer)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_with_pretokenized_data(self):
|
|
# Get the dataset
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
def tokenize_example(example):
|
|
return {
|
|
"chosen_input_ids": tokenizer(example["chosen"]).input_ids,
|
|
"rejected_input_ids": tokenizer(example["rejected"]).input_ids,
|
|
}
|
|
|
|
# Apply tokenization
|
|
tokenized_dataset = dataset.map(tokenize_example, remove_columns=["chosen", "rejected"])
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
|
|
trainer = RewardTrainer(model=model_id, args=training_args, train_dataset=tokenized_dataset)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_with_iterable_dataset(self):
|
|
# Get the dataset
|
|
dataset = load_dataset(
|
|
"trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train", streaming=True
|
|
)
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, max_steps=3, report_to="none")
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_with_chat_template_kwargs(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5")
|
|
# The following template is a simplified version of the Qwen chat template, where an additional argument
|
|
# `role_capital` is used to control the capitalization of roles.
|
|
tokenizer.chat_template = '{%- if messages[0]["role"] == "system" -%} {{ "<|im_start|>" + ("SYSTEM" if role_capital else "system") + "\\n" + messages[0]["content"] + "<|im_end|>\\n" }}{%- else -%} {{ "<|im_start|>" + ("SYSTEM" if role_capital else "system") + "\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n" }}{%- endif -%}{%- for message in messages -%} {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) -%} {{ "<|im_start|>" + (message.role.upper() if role_capital else message.role) + "\\n" + message.content + "<|im_end|>\\n" }} {%- elif message.role == "assistant" -%} {{ "<|im_start|>" + ("ASSISTANT" if role_capital else "assistant") }} {%- if message.content -%} {{ "\\n" + message.content }} {%- endif -%} {{ "<|im_end|>\\n" }} {%- elif message.role == "tool" -%} {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") -%} {{ "<|im_start|>" + ("USER" if role_capital else "user") }} {%- endif -%} {{ "\\n<tool_response>\\n" + message.content + "\\n</tool_response>" }} {%- if loop.last or (messages[loop.index0 + 1].role != "tool") -%} {{ "<|im_end|>\\n" }} {%- endif -%} {%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%} {{ "<|im_start|>" + ("ASSISTANT" if role_capital else "assistant") + "\\n" }}{%- endif -%}'
|
|
|
|
dataset.add_column("chat_template_kwargs", [{"role_capital": bool(i % 2)} for i in range(len(dataset))])
|
|
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_with_set_chat_template_from_model(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "conversational_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, chat_template_path="Qwen/Qwen3-4B", report_to="none")
|
|
# trl-internal-testing/tiny-GPTNeoXForSequenceClassification doesn't have a chat template set by default
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-GPTNeoXForSequenceClassification",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
# RewardTrainer uses a mean-free loss that cancels uniform shifts in output scores. Since GPT-NeoX models
|
|
# include a final LayerNorm, its bias consistently receives zero gradient and remains unchanged, so we skip
|
|
# this parameter.
|
|
if n == "gpt_neox.final_layer_norm.bias":
|
|
continue
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_with_set_chat_template_from_path(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "conversational_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(
|
|
output_dir=self.tmp_dir,
|
|
chat_template_path=str(pathlib.Path(__file__).parent / "data" / "template.jinja"),
|
|
report_to="none",
|
|
)
|
|
# trl-internal-testing/tiny-GPTNeoXForSequenceClassification doesn't have a chat template set by default
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-GPTNeoXForSequenceClassification",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
# RewardTrainer uses a mean-free loss that cancels uniform shifts in output scores. Since GPT-NeoX models
|
|
# include a final LayerNorm, its bias consistently receives zero gradient and remains unchanged, so we skip
|
|
# this parameter.
|
|
if n == "gpt_neox.final_layer_norm.bias":
|
|
continue
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
# Check that the template saved in the output directory is the same as the one used for training
|
|
template_path = pathlib.Path(self.tmp_dir) / "checkpoint-9" / "chat_template.jinja"
|
|
self.assertTrue(template_path.exists(), f"Chat template not found at {template_path}")
|
|
|
|
with open(template_path) as f:
|
|
template_content = f.read()
|
|
with open(training_args.chat_template_path) as f:
|
|
original_template_content = f.read()
|
|
self.assertEqual(
|
|
template_content, original_template_content, "Chat template content does not match the original"
|
|
)
|
|
|
|
@unittest.skip("Skipping until we have a dataset with tool calls")
|
|
def test_train_toolcall_data(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/toolcall", split="train")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_with_eval(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, eval_strategy="steps", eval_steps=3, report_to="none")
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset["train"],
|
|
eval_dataset=dataset["test"],
|
|
)
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the eval loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
|
|
|
|
def test_train_with_multiple_eval_dataset(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, eval_strategy="steps", eval_steps=3, report_to="none")
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset["train"],
|
|
eval_dataset={"data1": dataset["test"], "data2": dataset["test"]},
|
|
)
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the eval losses are not None
|
|
self.assertIsNotNone(trainer.state.log_history[-3]["eval_data1_loss"])
|
|
self.assertIsNotNone(trainer.state.log_history[-2]["eval_data2_loss"])
|
|
|
|
def test_train_with_gradient_checkpointing(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, gradient_checkpointing=True, report_to="none")
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_tag_added(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
for tag in ["reward-trainer", "trl"]:
|
|
self.assertIn(tag, trainer.model.model_tags)
|
|
|
|
@require_peft
|
|
def test_tag_added_peft(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
train_dataset=dataset,
|
|
peft_config=LoraConfig(),
|
|
)
|
|
|
|
for tag in ["reward-trainer", "trl"]:
|
|
self.assertIn(tag, trainer.model.model_tags)
|
|
|
|
def test_train_with_margin(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
def add_margin(example):
|
|
# dummy margin based on the length of the chosen summary
|
|
return {"margin": len(example["chosen"])}
|
|
|
|
dataset = dataset.map(add_margin)
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, report_to="none")
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_with_center_rewards_coefficient(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_implicit_prompt_preference", split="train")
|
|
|
|
# Initialize the trainer
|
|
training_args = RewardConfig(output_dir=self.tmp_dir, center_rewards_coefficient=0.01, report_to="none")
|
|
trainer = RewardTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
|
|
args=training_args,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial parameters to compare them later
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
# Train the model
|
|
trainer.train()
|
|
|
|
# Check that the training loss is not None
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check the params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|