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396 lines
16 KiB
Python
396 lines
16 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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from datasets import load_dataset
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from parameterized import parameterized
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from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
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from trl import (
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BCOConfig,
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BCOTrainer,
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CPOConfig,
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CPOTrainer,
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DPOConfig,
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DPOTrainer,
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FDivergenceType,
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KTOConfig,
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KTOTrainer,
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NashMDConfig,
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NashMDTrainer,
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OnlineDPOConfig,
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OnlineDPOTrainer,
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ORPOConfig,
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ORPOTrainer,
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RewardConfig,
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RewardTrainer,
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SFTConfig,
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SFTTrainer,
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XPOConfig,
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XPOTrainer,
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)
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from .testing_utils import TrlTestCase, require_sklearn
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class TestTrainerArg(TrlTestCase):
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@require_sklearn
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def test_bco(self):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
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training_args = BCOConfig(
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self.tmp_dir,
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max_length=256,
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max_prompt_length=64,
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max_completion_length=64,
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beta=0.5,
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label_pad_token_id=-99,
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padding_value=-99,
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truncation_mode="keep_start",
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# generate_during_eval=True, # ignore this one, it requires wandb
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is_encoder_decoder=True,
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precompute_ref_log_probs=True,
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model_init_kwargs={"trust_remote_code": True},
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ref_model_init_kwargs={"trust_remote_code": True},
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dataset_num_proc=4,
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prompt_sample_size=512,
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min_density_ratio=0.2,
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max_density_ratio=20.0,
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)
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trainer = BCOTrainer(
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model=model_id,
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ref_model=model_id,
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args=training_args,
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train_dataset=dataset,
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processing_class=tokenizer,
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)
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assert trainer.args.max_length == 256
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assert trainer.args.max_prompt_length == 64
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assert trainer.args.max_completion_length == 64
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assert trainer.args.beta == 0.5
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assert trainer.args.label_pad_token_id == -99
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assert trainer.args.padding_value == -99
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assert trainer.args.truncation_mode == "keep_start"
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# self.assertEqual(trainer.args.generate_during_eval, True)
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assert trainer.args.is_encoder_decoder
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assert trainer.args.precompute_ref_log_probs
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assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
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assert trainer.args.ref_model_init_kwargs == {"trust_remote_code": True}
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assert trainer.args.dataset_num_proc == 4
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assert trainer.args.prompt_sample_size == 512
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assert trainer.args.min_density_ratio == 0.2
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assert trainer.args.max_density_ratio == 20.0
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def test_cpo(self):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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training_args = CPOConfig(
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self.tmp_dir,
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max_length=256,
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max_prompt_length=64,
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max_completion_length=64,
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beta=0.5,
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label_smoothing=0.5,
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loss_type="hinge",
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disable_dropout=False,
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cpo_alpha=0.5,
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simpo_gamma=0.2,
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label_pad_token_id=-99,
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padding_value=-99,
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truncation_mode="keep_start",
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# generate_during_eval=True, # ignore this one, it requires wandb
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is_encoder_decoder=True,
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model_init_kwargs={"trust_remote_code": True},
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dataset_num_proc=4,
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)
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trainer = CPOTrainer(model=model_id, args=training_args, train_dataset=dataset, processing_class=tokenizer)
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assert trainer.args.max_length == 256
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assert trainer.args.max_prompt_length == 64
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assert trainer.args.max_completion_length == 64
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assert trainer.args.beta == 0.5
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assert trainer.args.label_smoothing == 0.5
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assert trainer.args.loss_type == "hinge"
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assert not trainer.args.disable_dropout
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assert trainer.args.cpo_alpha == 0.5
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assert trainer.args.simpo_gamma == 0.2
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assert trainer.args.label_pad_token_id == -99
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assert trainer.args.padding_value == -99
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assert trainer.args.truncation_mode == "keep_start"
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# self.assertEqual(trainer.args.generate_during_eval, True)
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assert trainer.args.is_encoder_decoder
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assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
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assert trainer.args.dataset_num_proc == 4
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def test_dpo(self):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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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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self.tmp_dir,
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beta=0.5,
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label_smoothing=0.5,
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loss_type="hinge",
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label_pad_token_id=-99,
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pad_token=".",
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truncation_mode="keep_start",
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max_length=256,
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max_prompt_length=64,
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max_completion_length=64,
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disable_dropout=False,
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# generate_during_eval=True, # ignore this one, it requires wandb
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precompute_ref_log_probs=True,
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dataset_num_proc=4,
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model_init_kwargs={"trust_remote_code": True},
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ref_model_init_kwargs={"trust_remote_code": True},
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model_adapter_name="dummy_adapter",
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ref_adapter_name="dummy_adapter",
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reference_free=True,
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force_use_ref_model=True,
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f_divergence_type="js_divergence",
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f_alpha_divergence_coef=0.5,
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# sync_ref_model=True, # cannot be True when precompute_ref_log_probs=True. Don't test this.
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ref_model_mixup_alpha=0.5,
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ref_model_sync_steps=32,
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rpo_alpha=0.5,
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discopop_tau=0.1,
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)
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trainer = DPOTrainer(
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model=model_id,
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ref_model=model_id,
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args=training_args,
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train_dataset=dataset,
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processing_class=tokenizer,
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)
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assert trainer.args.beta == 0.5
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assert trainer.args.label_smoothing == 0.5
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assert trainer.args.loss_type == "hinge"
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assert trainer.args.label_pad_token_id == -99
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assert trainer.args.pad_token == "."
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assert trainer.args.truncation_mode == "keep_start"
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assert trainer.args.max_length == 256
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assert trainer.args.max_prompt_length == 64
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assert trainer.args.max_completion_length == 64
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assert not trainer.args.disable_dropout
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# self.assertEqual(trainer.args.generate_during_eval, True)
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assert trainer.args.precompute_ref_log_probs
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assert trainer.args.dataset_num_proc == 4
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assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
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assert trainer.args.ref_model_init_kwargs == {"trust_remote_code": True}
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assert trainer.args.model_adapter_name == "dummy_adapter"
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assert trainer.args.ref_adapter_name == "dummy_adapter"
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assert trainer.args.reference_free
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assert trainer.args.force_use_ref_model
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assert trainer.args.f_divergence_type == FDivergenceType.JS_DIVERGENCE
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assert trainer.args.f_alpha_divergence_coef == 0.5
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# self.assertEqual(trainer.args.sync_ref_model, True)
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assert trainer.args.ref_model_mixup_alpha == 0.5
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assert trainer.args.ref_model_sync_steps == 32
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assert trainer.args.rpo_alpha == 0.5
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assert trainer.args.discopop_tau == 0.1
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def test_kto(self):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
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training_args = KTOConfig(
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self.tmp_dir,
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max_length=256,
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max_prompt_length=64,
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max_completion_length=64,
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beta=0.5,
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desirable_weight=0.5,
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undesirable_weight=0.5,
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label_pad_token_id=-99,
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padding_value=-99,
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truncation_mode="keep_start",
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# generate_during_eval=True, # ignore this one, it requires wandb
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is_encoder_decoder=True,
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precompute_ref_log_probs=True,
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model_init_kwargs={"trust_remote_code": True},
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ref_model_init_kwargs={"trust_remote_code": True},
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dataset_num_proc=4,
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)
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trainer = KTOTrainer(
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model=model_id,
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ref_model=model_id,
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args=training_args,
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train_dataset=dataset,
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processing_class=tokenizer,
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)
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assert trainer.args.max_length == 256
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assert trainer.args.max_prompt_length == 64
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assert trainer.args.max_completion_length == 64
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assert trainer.args.beta == 0.5
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assert trainer.args.desirable_weight == 0.5
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assert trainer.args.undesirable_weight == 0.5
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assert trainer.args.label_pad_token_id == -99
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assert trainer.args.padding_value == -99
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assert trainer.args.truncation_mode == "keep_start"
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# self.assertEqual(trainer.args.generate_during_eval, True)
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assert trainer.args.is_encoder_decoder
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assert trainer.args.precompute_ref_log_probs
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assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
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assert trainer.args.ref_model_init_kwargs == {"trust_remote_code": True}
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assert trainer.args.dataset_num_proc == 4
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@parameterized.expand([(False,), (True,)])
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def test_nash_md(self, mixtures_coef_list):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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ref_model = AutoModelForCausalLM.from_pretrained(model_id)
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reward_model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1)
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dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
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training_args = NashMDConfig(
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self.tmp_dir,
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mixture_coef=0.5 if not mixtures_coef_list else [0.5, 0.6],
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)
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trainer = NashMDTrainer(
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args=training_args,
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processing_class=tokenizer,
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model=model,
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ref_model=ref_model,
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reward_funcs=reward_model,
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train_dataset=dataset,
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)
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assert trainer.args.mixture_coef == (0.5 if not mixtures_coef_list else [0.5, 0.6])
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@parameterized.expand([(False,), (True,)])
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def test_online_dpo(self, beta_list):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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ref_model = AutoModelForCausalLM.from_pretrained(model_id)
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reward_model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1)
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dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
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training_args = OnlineDPOConfig(
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self.tmp_dir,
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max_new_tokens=42,
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temperature=0.5,
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missing_eos_penalty=0.33,
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beta=0.6 if not beta_list else [0.6, 0.7],
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loss_type="hinge",
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)
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trainer = OnlineDPOTrainer(
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model=model,
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ref_model=ref_model,
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reward_funcs=reward_model,
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args=training_args,
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train_dataset=dataset,
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processing_class=tokenizer,
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reward_processing_classes=tokenizer,
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)
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assert trainer.args.max_new_tokens == 42
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assert trainer.args.temperature == 0.5
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assert trainer.args.missing_eos_penalty == 0.33
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assert trainer.args.beta == (0.6 if not beta_list else [0.6, 0.7])
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assert trainer.args.loss_type == "hinge"
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def test_orpo(self):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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training_args = ORPOConfig(
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self.tmp_dir,
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max_length=256,
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max_prompt_length=64,
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max_completion_length=64,
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beta=0.5,
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disable_dropout=False,
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label_pad_token_id=-99,
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padding_value=-99,
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truncation_mode="keep_start",
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# generate_during_eval=True, # ignore this one, it requires wandb
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is_encoder_decoder=True,
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model_init_kwargs={"trust_remote_code": True},
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dataset_num_proc=4,
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)
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trainer = ORPOTrainer(model=model_id, args=training_args, train_dataset=dataset, processing_class=tokenizer)
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assert trainer.args.max_length == 256
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assert trainer.args.max_prompt_length == 64
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assert trainer.args.max_completion_length == 64
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assert trainer.args.beta == 0.5
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assert not trainer.args.disable_dropout
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assert trainer.args.label_pad_token_id == -99
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def test_reward(self):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
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training_args = RewardConfig(
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self.tmp_dir,
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max_length=256,
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dataset_num_proc=4,
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center_rewards_coefficient=0.1,
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)
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trainer = RewardTrainer(
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model=model,
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args=training_args,
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train_dataset=dataset,
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processing_class=tokenizer,
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)
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assert trainer.args.max_length == 256
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assert trainer.args.dataset_num_proc == 4
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assert trainer.args.center_rewards_coefficient == 0.1
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def test_sft(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_language_modeling", split="train")
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training_args = SFTConfig(
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self.tmp_dir,
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dataset_text_field="dummy_text_field",
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packing=True,
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max_length=256,
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dataset_num_proc=4,
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neftune_noise_alpha=0.1,
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model_init_kwargs={"trust_remote_code": True},
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dataset_kwargs={"append_concat_token": True, "skip_prepare_dataset": True},
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eval_packing=True,
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)
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trainer = SFTTrainer(model_id, args=training_args, train_dataset=dataset)
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assert trainer.args.dataset_text_field == "dummy_text_field"
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assert trainer.args.packing
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assert trainer.args.max_length == 256
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assert trainer.args.dataset_num_proc == 4
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assert trainer.args.neftune_noise_alpha == 0.1
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assert trainer.args.model_init_kwargs == {"trust_remote_code": True}
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assert "append_concat_token" in trainer.args.dataset_kwargs
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assert trainer.args.dataset_kwargs["append_concat_token"]
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assert trainer.args.eval_packing
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@parameterized.expand([(False,), (True,)])
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def test_xpo(self, alpha_list):
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model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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ref_model = AutoModelForCausalLM.from_pretrained(model_id)
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reward_model = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=1)
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dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
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training_args = XPOConfig(
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self.tmp_dir,
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alpha=0.5 if not alpha_list else [0.5, 0.6],
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)
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trainer = XPOTrainer(
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args=training_args,
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processing_class=tokenizer,
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model=model,
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ref_model=ref_model,
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reward_funcs=reward_model,
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train_dataset=dataset,
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)
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assert trainer.args.alpha == (0.5 if not alpha_list else [0.5, 0.6])
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