mirror of
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1270 lines
52 KiB
Python
1270 lines
52 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 copy
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import tempfile
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import unittest
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import numpy as np
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import torch
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from datasets import Dataset, Image, Sequence, load_dataset
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from transformers import (
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AutoModelForCausalLM,
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AutoProcessor,
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AutoTokenizer,
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LlavaForConditionalGeneration,
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TrainingArguments,
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is_vision_available,
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)
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from transformers.testing_utils import require_flash_attn, require_peft, require_vision
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from transformers.utils import is_peft_available
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from trl import SFTConfig, SFTTrainer
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from trl.trainer import ConstantLengthDataset, DataCollatorForCompletionOnlyLM
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from trl.trainer.sft_trainer import DataCollatorForLanguageModeling
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def formatting_prompts_func(example):
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text = f"### Question: {example['question']}\n ### Answer: {example['answer']}"
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return text
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def formatting_func_for_pretokenized(example):
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return example["input_ids"]
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if is_peft_available():
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from peft import LoraConfig, PeftModel, get_peft_model
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if is_vision_available():
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from PIL import Image as PILImage
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class TestDataCollatorForLanguageModeling(unittest.TestCase):
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def test_collate_padding(self):
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collator = DataCollatorForLanguageModeling(pad_token_id=0)
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examples = [{"input_ids": [1, 2, 3]}, {"input_ids": [4, 5]}]
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output = collator(examples)
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expected_input_ids = torch.tensor([[1, 2, 3], [4, 5, 0]])
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expected_attention_mask = torch.tensor([[1, 1, 1], [1, 1, 0]])
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expected_labels = torch.tensor([[1, 2, 3], [4, 5, -100]])
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self.assertEqual(output["input_ids"].tolist(), expected_input_ids.tolist())
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self.assertEqual(output["attention_mask"].tolist(), expected_attention_mask.tolist())
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self.assertEqual(output["labels"].tolist(), expected_labels.tolist())
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def test_collate_no_padding(self):
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collator = DataCollatorForLanguageModeling(pad_token_id=0)
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examples = [{"input_ids": [1, 2, 3]}, {"input_ids": [4, 5, 6]}]
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output = collator(examples)
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expected_input_ids = torch.tensor([[1, 2, 3], [4, 5, 6]])
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expected_attention_mask = torch.tensor([[1, 1, 1], [1, 1, 1]])
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expected_labels = torch.tensor([[1, 2, 3], [4, 5, 6]])
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self.assertEqual(output["input_ids"].tolist(), expected_input_ids.tolist())
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self.assertEqual(output["attention_mask"].tolist(), expected_attention_mask.tolist())
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self.assertEqual(output["labels"].tolist(), expected_labels.tolist())
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class SFTTrainerTester(unittest.TestCase):
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r""" """
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def setUp(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.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
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self.dummy_dataset = Dataset.from_dict(
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{
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"question": [
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"Does llamas know how to code?",
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"Does llamas know how to fly?",
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"Does llamas know how to talk?",
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"Does llamas know how to code?",
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"Does llamas know how to fly?",
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"Does llamas know how to talk?",
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"Does llamas know how to swim?",
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],
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"answer": [
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"Yes, llamas are very good at coding.",
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"No, llamas can't fly.",
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"Yes, llamas are very good at talking.",
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"Yes, llamas are very good at coding.",
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"No, llamas can't fly.",
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"Yes, llamas are very good at talking.",
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"No, llamas can't swim.",
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],
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"text": [
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"### Question: Does llamas know how to code?\n ### Answer: Yes, llamas are very good at coding.",
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"### Question: Does llamas know how to fly?\n ### Answer: No, llamas can't fly.",
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"### Question: Does llamas know how to talk?\n ### Answer: Yes, llamas are very good at talking.",
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"### Question: Does llamas know how to code?\n ### Answer: Yes, llamas are very good at coding.",
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"### Question: Does llamas know how to fly?\n ### Answer: No, llamas can't fly.",
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"### Question: Does llamas know how to talk?\n ### Answer: Yes, llamas are very good at talking.",
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"### Question: Does llamas know how to swim?\n ### Answer: No, llamas can't swim.",
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],
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}
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)
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self.dummy_tokenized_dataset = Dataset.from_dict(
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{
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"input_ids": [
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self.tokenizer.encode(
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"TRL is a library to post-train LLMs and diffusion models with methods such as Supervised Fine-tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO)."
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)
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]
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* 10
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}
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)
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self.conversational_lm_dataset = load_dataset("trl-internal-testing/zen", "conversational_language_modeling")
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self.standard_prompt_completion_dataset = load_dataset(
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"trl-internal-testing/zen", "standard_prompt_completion"
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)
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if is_vision_available():
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self.dummy_vsft_instruction_dataset = Dataset.from_dict(
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{
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"messages": [
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[
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{
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"role": "user",
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"content": [{"type": "text", "text": "What is in this image?"}, {"type": "image"}],
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "text": "It is random noise."}],
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},
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{
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"role": "user",
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"content": [{"type": "text", "text": "Oh ye, you are right, what is 1+1"}],
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "text": "2"}],
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},
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],
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[
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{
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"role": "user",
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"content": [{"type": "text", "text": "What is in this image?"}, {"type": "image"}],
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "text": "It is random noise."}],
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},
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],
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],
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"images": [
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[PILImage.fromarray((np.random.rand(40, 50, 3) * 255).astype("uint8")).convert("RGBA")],
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[PILImage.fromarray((np.random.rand(50, 60, 3) * 255).astype("uint8")).convert("RGBA")],
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],
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}
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)
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self.dummy_vsft_instruction_dataset.cast_column("images", Sequence(Image()))
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self.dummy_vsft_instruction_dataset = self.dummy_vsft_instruction_dataset.cast_column(
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"images", Sequence(Image())
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)
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self.train_dataset = ConstantLengthDataset(
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self.tokenizer,
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self.dummy_dataset,
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formatting_func=formatting_prompts_func,
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seq_length=16,
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num_of_sequences=16,
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)
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self.eval_dataset = ConstantLengthDataset(
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self.tokenizer,
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self.dummy_dataset,
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formatting_func=formatting_prompts_func,
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seq_length=16,
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num_of_sequences=16,
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)
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self.train_dataset_from_pretokenized = ConstantLengthDataset(
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self.tokenizer,
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self.dummy_tokenized_dataset,
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seq_length=16,
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num_of_sequences=16,
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formatting_func=formatting_func_for_pretokenized,
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)
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self.eval_dataset_from_pretokenized = ConstantLengthDataset(
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self.tokenizer,
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self.dummy_tokenized_dataset,
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seq_length=16,
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num_of_sequences=16,
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formatting_func=formatting_func_for_pretokenized,
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)
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def test_constant_length_dataset_with_pretokenized_data(self):
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constant_len_dataset = ConstantLengthDataset(
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self.tokenizer,
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self.dummy_tokenized_dataset,
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formatting_func=formatting_func_for_pretokenized,
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)
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assert len(constant_len_dataset) == len(self.dummy_tokenized_dataset)
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assert len(constant_len_dataset) > 0
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for example in constant_len_dataset:
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assert "input_ids" in example
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assert "labels" in example
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assert len(example["input_ids"]) == constant_len_dataset.seq_length
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assert len(example["labels"]) == constant_len_dataset.seq_length
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decoded_text = self.tokenizer.decode(example["input_ids"])
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assert ("TRL" in decoded_text) and ("(DPO)" in decoded_text)
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def test_constant_length_dataset(self):
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formatted_dataset = ConstantLengthDataset(
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self.tokenizer,
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self.dummy_dataset,
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formatting_func=formatting_prompts_func,
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)
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self.assertEqual(len(formatted_dataset), len(self.dummy_dataset))
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self.assertGreater(len(formatted_dataset), 0)
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for example in formatted_dataset:
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self.assertIn("input_ids", example)
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self.assertIn("labels", example)
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self.assertEqual(len(example["input_ids"]), formatted_dataset.seq_length)
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self.assertEqual(len(example["labels"]), formatted_dataset.seq_length)
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decoded_text = self.tokenizer.decode(example["input_ids"])
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self.assertTrue(("Question" in decoded_text) and ("Answer" in decoded_text))
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def test_backward_compatibility(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = TrainingArguments(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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hub_token="not_a_real_token",
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report_to="none",
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)
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trainer = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.train_dataset,
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formatting_func=formatting_prompts_func,
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)
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self.assertEqual(trainer.args.hub_token, training_args.hub_token)
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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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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# Check that 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.equal(param, new_param), f"Parameter {n} has not changed.")
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def test_with_pretokenized_data_packing(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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packing=True,
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report_to="none",
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)
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trainer = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.train_dataset_from_pretokenized,
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)
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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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def test_uncorrect_data(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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# Shoud work as SFTTrainer natively supports conversational lm dataset
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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max_length=32, # make sure there is at least 1 packed sequence
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packing=True,
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report_to="none",
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)
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_ = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.conversational_lm_dataset["train"],
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)
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# Same, but without packing
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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packing=False,
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report_to="none",
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)
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_ = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.conversational_lm_dataset["train"],
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)
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# Same, but with packing with `max_length`
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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max_length=16, # make sure there is at least 1 packed sequence
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packing=True,
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report_to="none",
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)
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_ = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.standard_prompt_completion_dataset["train"],
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)
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# Same but with prompt completion dataset
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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packing=False,
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report_to="none",
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)
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_ = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.standard_prompt_completion_dataset["train"],
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)
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# Should work as dummy dataset are supported with a formatting function
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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max_length=32, # make sure there is at least 1 packed sequence
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packing=True,
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report_to="none",
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)
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_ = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.dummy_dataset,
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formatting_func=formatting_prompts_func,
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)
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def test_sft_trainer_with_model_num_train_epochs(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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num_train_epochs=2,
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per_device_train_batch_size=2,
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packing=True,
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report_to="none",
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)
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trainer = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.train_dataset,
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)
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trainer.train()
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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num_train_epochs=2,
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max_length=16,
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packing=True,
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report_to="none",
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)
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trainer = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.dummy_dataset,
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)
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trainer.train()
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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num_train_epochs=2,
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per_device_train_batch_size=2,
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max_length=16,
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report_to="none",
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)
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trainer = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.dummy_dataset,
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)
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trainer.train()
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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def test_with_model_(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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max_length=16,
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packing=True,
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report_to="none",
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)
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trainer = SFTTrainer(
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model=self.model,
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args=training_args,
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train_dataset=self.dummy_dataset,
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)
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trainer.train()
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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# with formatting_func + packed
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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max_length=16,
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packing=True,
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report_to="none",
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)
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trainer = SFTTrainer(
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model=self.model,
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args=training_args,
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train_dataset=self.dummy_dataset,
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formatting_func=formatting_prompts_func,
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)
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trainer.train()
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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max_length=16,
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report_to="none",
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)
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trainer = SFTTrainer(
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model=self.model,
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args=training_args,
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train_dataset=self.dummy_dataset,
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)
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trainer.train()
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self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
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def test_with_multiple_eval_datasets(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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training_args = SFTConfig(
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output_dir=tmp_dir,
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per_device_train_batch_size=2,
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eval_strategy="steps",
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eval_steps=3,
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report_to="none",
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)
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trainer = SFTTrainer(
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model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
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args=training_args,
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train_dataset=self.train_dataset,
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eval_dataset={
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"data1": self.eval_dataset,
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"data2": self.eval_dataset,
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|
},
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
self.assertIsNotNone(trainer.state.log_history[0]["eval_data1_loss"])
|
|
self.assertIsNotNone(trainer.state.log_history[1]["eval_data2_loss"])
|
|
|
|
def test_data_collator_completion_lm(self):
|
|
response_template = "### Response:\n"
|
|
data_collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=self.tokenizer, mlm=False)
|
|
|
|
text = """\n\n### Instructions:\nHello all this should be masked\n\n### Response:\nI have not been masked correctly."""
|
|
encoded_text = self.tokenizer(text)
|
|
|
|
examples = [encoded_text]
|
|
|
|
batch = data_collator(examples)
|
|
labels = batch["labels"]
|
|
last_pad_idx = np.where(labels == -100)[1][-1]
|
|
result_text = self.tokenizer.decode(batch["input_ids"][0, last_pad_idx + 1 :])
|
|
self.assertEqual(result_text, "I have not been masked correctly.")
|
|
|
|
def test_data_collator_completion_lm_with_multiple_text(self):
|
|
tokenizer = copy.deepcopy(self.tokenizer)
|
|
tokenizer.padding_side = "left"
|
|
|
|
response_template = "### Response:\n"
|
|
data_collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer, mlm=False)
|
|
|
|
text1 = """\n\n### Instructions:\nHello all this should be masked\n\n### Response:\nI have not been masked correctly."""
|
|
text2 = """\n\n### Instructions:\nThis is another longer text that should also be masked. This text is significantly longer than the previous one.\n\n### Response:\nI have not been masked correctly."""
|
|
|
|
encoded_text1 = tokenizer(text1)
|
|
encoded_text2 = tokenizer(text2)
|
|
|
|
examples = [encoded_text1, encoded_text2]
|
|
|
|
batch = data_collator(examples)
|
|
|
|
for i in range(2):
|
|
labels = batch["labels"][i]
|
|
last_pad_idx = np.where(labels == -100)[0][-1]
|
|
result_text = tokenizer.decode(batch["input_ids"][i, last_pad_idx + 1 :])
|
|
self.assertEqual(result_text, "I have not been masked correctly.")
|
|
|
|
def test_data_collator_chat_completion_lm(self):
|
|
instruction_template = "### Human:"
|
|
assistant_template = "### Assistant:"
|
|
data_collator = DataCollatorForCompletionOnlyLM(
|
|
response_template=assistant_template,
|
|
instruction_template=instruction_template,
|
|
tokenizer=self.tokenizer,
|
|
mlm=False,
|
|
)
|
|
|
|
text = """### Human: Hello all this should be masked.### Assistant: I should not be masked.### Human: All this should be masked too.### Assistant: I should not be masked too."""
|
|
encoded_text = self.tokenizer(text)
|
|
|
|
examples = [encoded_text]
|
|
|
|
batch = data_collator(examples)
|
|
labels = batch["labels"]
|
|
non_masked_tokens = batch["input_ids"][labels != -100]
|
|
result_text = self.tokenizer.decode(non_masked_tokens)
|
|
self.assertEqual(result_text, " I should not be masked. I should not be masked too.")
|
|
|
|
def test_data_collator_chat_completion_lm_with_multiple_text(self):
|
|
tokenizer = copy.deepcopy(self.tokenizer)
|
|
tokenizer.padding_side = "left"
|
|
|
|
instruction_template = "### Human:"
|
|
assistant_template = "### Assistant:"
|
|
data_collator = DataCollatorForCompletionOnlyLM(
|
|
response_template=assistant_template,
|
|
instruction_template=instruction_template,
|
|
tokenizer=tokenizer,
|
|
mlm=False,
|
|
)
|
|
|
|
text1 = """### Human: Hello all this should be masked.### Assistant: I should not be masked."""
|
|
text2 = """### Human: Hello all this should be masked.### Assistant: I should not be masked.### Human: All this should be masked too.### Assistant: I should not be masked too."""
|
|
encoded_text1 = tokenizer(text1)
|
|
encoded_text2 = tokenizer(text2)
|
|
|
|
examples = [encoded_text1, encoded_text2]
|
|
|
|
batch = data_collator(examples)
|
|
labels = batch["labels"]
|
|
input_ids = batch["input_ids"]
|
|
|
|
non_masked_tokens1 = input_ids[0][labels[0] != -100]
|
|
result_text1 = tokenizer.decode(non_masked_tokens1)
|
|
self.assertEqual(result_text1, " I should not be masked.")
|
|
|
|
non_masked_tokens2 = input_ids[1][labels[1] != -100]
|
|
result_text2 = tokenizer.decode(non_masked_tokens2)
|
|
self.assertEqual(result_text2, " I should not be masked. I should not be masked too.")
|
|
|
|
def test_with_model_neftune(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
neftune_noise_alpha=5,
|
|
packing=True,
|
|
report_to="none",
|
|
)
|
|
trainer = SFTTrainer(
|
|
model=self.model,
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
)
|
|
|
|
trainer.model = trainer._activate_neftune(trainer.model)
|
|
|
|
device = trainer.model.get_input_embeddings().weight.device
|
|
trainer.model.train()
|
|
|
|
torch.random.manual_seed(42)
|
|
embeds_neftune = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
|
|
|
|
torch.random.manual_seed(24)
|
|
embeds_neftune_2 = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
|
|
|
|
self.assertFalse(torch.allclose(embeds_neftune, embeds_neftune_2))
|
|
self.assertGreater(len(trainer.model.get_input_embeddings()._forward_hooks), 0)
|
|
|
|
trainer.neftune_hook_handle.remove()
|
|
|
|
trainer.train()
|
|
|
|
# Make sure forward pass works fine
|
|
_ = trainer.model(torch.LongTensor([[1, 0, 1]]).to(device))
|
|
self.assertEqual(len(trainer.model.get_input_embeddings()._forward_hooks), 0)
|
|
|
|
@require_peft
|
|
def test_peft_str(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
peft_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
training_args = SFTConfig(
|
|
packing=True,
|
|
output_dir=tmp_dir,
|
|
report_to="none",
|
|
)
|
|
|
|
_ = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
peft_config=peft_config,
|
|
)
|
|
|
|
@require_peft
|
|
def test_peft_sft_trainer(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
packing=True,
|
|
report_to="none",
|
|
)
|
|
|
|
peft_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
peft_config=peft_config,
|
|
)
|
|
|
|
self.assertTrue(isinstance(trainer.model, PeftModel))
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
@require_peft
|
|
def test_peft_and_gradient_checkpointing(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
gradient_checkpointing=True,
|
|
report_to="none",
|
|
)
|
|
|
|
peft_config = LoraConfig(r=16, lora_alpha=32, lora_dropout=0.05, task_type="CAUSAL_LM")
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
peft_config=peft_config,
|
|
)
|
|
|
|
self.assertIsInstance(trainer.model, PeftModel)
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
@require_peft
|
|
def test_peft_neftune(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
neftune_noise_alpha=5,
|
|
packing=True,
|
|
report_to="none",
|
|
)
|
|
|
|
peft_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
peft_config=peft_config,
|
|
)
|
|
|
|
trainer.model = trainer._activate_neftune(trainer.model)
|
|
|
|
self.assertIsInstance(trainer.model, PeftModel)
|
|
|
|
device = trainer.model.get_input_embeddings().weight.device
|
|
trainer.model.train()
|
|
|
|
torch.random.manual_seed(42)
|
|
embeds_neftune = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
|
|
|
|
torch.random.manual_seed(24)
|
|
embeds_neftune_2 = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
|
|
|
|
self.assertFalse(torch.allclose(embeds_neftune, embeds_neftune_2))
|
|
self.assertGreater(len(trainer.model.get_input_embeddings()._forward_hooks), 0)
|
|
|
|
trainer.neftune_hook_handle.remove()
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Make sure forward pass works fine to check if embeddings forward is not broken.
|
|
trainer.model(torch.LongTensor([[1, 0, 1]]).to(device))
|
|
self.assertEqual(len(trainer.model.get_input_embeddings()._forward_hooks), 0)
|
|
|
|
@require_peft
|
|
def test_peft_tag(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
gradient_checkpointing=True,
|
|
packing=True,
|
|
report_to="none",
|
|
)
|
|
|
|
peft_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
peft_config=peft_config,
|
|
)
|
|
|
|
for tag in ["sft", "trl"]:
|
|
self.assertIn(tag, trainer.model.model_tags)
|
|
|
|
@require_peft
|
|
def test_tag(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
gradient_checkpointing=True,
|
|
packing=True,
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
)
|
|
|
|
for tag in ["sft", "trl"]:
|
|
self.assertIn(tag, trainer.model.model_tags)
|
|
|
|
def test_only_train_packing(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
gradient_checkpointing=True,
|
|
packing=True,
|
|
max_length=16, # make sure there is at least 1 packed sequence
|
|
eval_packing=False,
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.conversational_lm_dataset["train"],
|
|
eval_dataset=self.conversational_lm_dataset["test"],
|
|
)
|
|
|
|
self.assertEqual(len(trainer.train_dataset["input_ids"]), 47) # w/ this dataset, we end up with 46 seqs
|
|
self.assertEqual(len(trainer.eval_dataset["input_ids"]), len(self.conversational_lm_dataset["test"]))
|
|
|
|
def test_eval_packing(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_length=16, # make sure there is at least 1 packed sequence
|
|
packing=True,
|
|
report_to="none",
|
|
)
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.conversational_lm_dataset["train"],
|
|
eval_dataset=self.conversational_lm_dataset["test"],
|
|
)
|
|
|
|
self.assertEqual(len(trainer.train_dataset["input_ids"]), 47) # w/ this dataset, we end up with 47 seqs
|
|
self.assertEqual(len(trainer.eval_dataset["input_ids"]), 7) # w/ this dataset, we end up with 7 seqs
|
|
|
|
def test_no_packing(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_length=16, # make sure there is at least 1 packed sequence
|
|
packing=False,
|
|
report_to="none",
|
|
)
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.conversational_lm_dataset["train"],
|
|
eval_dataset=self.conversational_lm_dataset["test"],
|
|
)
|
|
|
|
self.assertEqual(len(trainer.train_dataset["input_ids"]), len(self.conversational_lm_dataset["train"]))
|
|
self.assertEqual(len(trainer.eval_dataset["input_ids"]), len(self.conversational_lm_dataset["test"]))
|
|
|
|
@require_vision
|
|
def test_skip_prepare_dataset(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
remove_unused_columns=False,
|
|
dataset_kwargs={"skip_prepare_dataset": True},
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.dummy_vsft_instruction_dataset,
|
|
)
|
|
self.assertEqual(trainer.train_dataset.features, self.dummy_vsft_instruction_dataset.features)
|
|
|
|
def test_skip_prepare_dataset_with_no_packing(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
remove_unused_columns=False,
|
|
packing=False,
|
|
dataset_kwargs={"skip_prepare_dataset": True},
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.dummy_dataset,
|
|
)
|
|
self.assertEqual(trainer.train_dataset.features, self.dummy_dataset.features)
|
|
|
|
@require_vision
|
|
def test_llava(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
remove_unused_columns=False,
|
|
dataset_kwargs={"skip_prepare_dataset": True},
|
|
report_to="none",
|
|
)
|
|
tiny_llava = LlavaForConditionalGeneration.from_pretrained(
|
|
"trl-internal-testing/tiny-LlavaForConditionalGeneration"
|
|
)
|
|
processor = AutoProcessor.from_pretrained("trl-internal-testing/tiny-LlavaForConditionalGeneration")
|
|
|
|
processor.chat_template = """{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. {% for message in messages %}{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<image>{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}{% if add_generation_prompt %}ASSISTANT: {% endif %}"""
|
|
|
|
def collate_fn(examples):
|
|
# Get the texts and images, and apply the chat template
|
|
texts = [processor.apply_chat_template(example["messages"], tokenize=False) for example in examples]
|
|
images = [example["images"][0] for example in examples]
|
|
|
|
# Tokenize the texts and process the images
|
|
batch = processor(texts, images, return_tensors="pt", padding=True)
|
|
|
|
# The labels are the input_ids, and we mask the padding tokens in the loss computation
|
|
labels = batch["input_ids"].clone()
|
|
labels[labels == processor.tokenizer.pad_token_id] = -100
|
|
batch["labels"] = labels
|
|
|
|
return batch
|
|
|
|
trainer = SFTTrainer(
|
|
model=tiny_llava,
|
|
args=training_args,
|
|
data_collator=collate_fn,
|
|
train_dataset=self.dummy_vsft_instruction_dataset,
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
def test_torch_dtype(self):
|
|
# See https://github.com/huggingface/trl/issues/1751
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
model_init_kwargs={"torch_dtype": torch.float16},
|
|
report_to="none",
|
|
)
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
formatting_func=formatting_prompts_func,
|
|
)
|
|
self.assertEqual(trainer.model.config.torch_dtype, torch.float16)
|
|
|
|
# Now test when `torch_dtype` is provided but is wrong
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
model_init_kwargs={"torch_dtype": -1},
|
|
report_to="none",
|
|
)
|
|
with self.assertRaises(ValueError) as context:
|
|
_ = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
|
|
args=training_args,
|
|
train_dataset=self.train_dataset,
|
|
)
|
|
|
|
self.assertIn(
|
|
"Invalid `torch_dtype` passed to `SFTConfig`. Expected either 'auto' or a string representing "
|
|
"a `torch.dtype` (e.g., 'float32'), but got -1.",
|
|
str(context.exception),
|
|
)
|
|
|
|
|
|
# This new tester aims to replace the first one at some point
|
|
class SFTTrainerTester2(unittest.TestCase):
|
|
def test_train(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, report_to="none")
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-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_model(self):
|
|
# Instantiate the model
|
|
model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
|
|
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, report_to="none")
|
|
trainer = SFTTrainer(model=model, 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_model_torch_dtype(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir, model_init_kwargs={"torch_dtype": torch.float16}, report_to="none"
|
|
)
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-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)
|
|
# Check the torch dtype
|
|
self.assertEqual(new_param.dtype, torch.float16)
|
|
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
|
|
|
|
def test_train_model_wrong_torch_dtype(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, model_init_kwargs={"torch_dtype": -1}, report_to="none")
|
|
with self.assertRaises(ValueError) as context:
|
|
SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5", args=training_args, train_dataset=dataset
|
|
)
|
|
self.assertIn(
|
|
"Invalid `torch_dtype` passed to `SFTConfig`. Expected either 'auto' or a string representing "
|
|
"a `torch.dtype` (e.g., 'float32'), but got -1.",
|
|
str(context.exception),
|
|
)
|
|
|
|
@require_peft
|
|
def test_train_peft_model(self):
|
|
# Get the base model
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
# Get the base model parameter names
|
|
base_param_names = [f"base_model.model.{n}" for n, _ in model.named_parameters()]
|
|
|
|
# Turn the model into a peft model
|
|
lora_config = LoraConfig()
|
|
model = get_peft_model(model, lora_config)
|
|
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, report_to="none")
|
|
trainer = SFTTrainer(model=model, 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 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_non_chatml_conversational_data(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "conversational_language_modeling", split="train")
|
|
|
|
# Rename role/content to from/value to ensure SFT works with non-chatML conversational data
|
|
def rename_fields(example: list[dict]):
|
|
return {"conversations": [{"from": m["role"], "value": m["content"]} for m in example["messages"]]}
|
|
|
|
dataset = dataset.map(rename_fields, remove_columns="messages")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, report_to="none")
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-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_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_language_modeling", split="train")
|
|
|
|
def tokenize_example(example):
|
|
return tokenizer(example["text"])
|
|
|
|
# Apply tokenization
|
|
tokenized_dataset = dataset.map(tokenize_example, remove_columns=["text"])
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, report_to="none")
|
|
trainer = SFTTrainer(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_language_modeling", split="train", streaming=True)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, max_steps=3, report_to="none")
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-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_data_collator_for_completion_only_and_padding_free(self):
|
|
# Get the dataset
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
dataset = load_dataset("trl-internal-testing/zen", "conversational_prompt_completion", split="train")
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
response_template = "<|im_start|>assistant\n"
|
|
collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer, padding_free=True)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(output_dir=tmp_dir, report_to="none")
|
|
trainer = SFTTrainer(model=model_id, args=training_args, train_dataset=dataset, data_collator=collator)
|
|
|
|
# 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")
|
|
|
|
@require_flash_attn
|
|
def test_train_padding_free(self):
|
|
# Get the dataset
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_language_modeling", split="train")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
# Initialize the trainer
|
|
training_args = SFTConfig(
|
|
output_dir=tmp_dir,
|
|
padding_free=True,
|
|
model_init_kwargs={"attn_implementation": "flash_attention_2"},
|
|
bf16=True, # flash_attention_2 only supports bf16 and fp16
|
|
report_to="none",
|
|
)
|
|
trainer = SFTTrainer(
|
|
model="trl-internal-testing/tiny-Qwen2ForCausalLM-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")
|