mirror of
https://github.com/huggingface/trl.git
synced 2025-10-20 18:43:52 +08:00
1358 lines
52 KiB
Python
1358 lines
52 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import tempfile
|
|
import unittest
|
|
from unittest.mock import MagicMock
|
|
|
|
import numpy as np
|
|
import torch
|
|
from datasets import Dataset, features, load_dataset
|
|
from parameterized import parameterized
|
|
from transformers import (
|
|
AutoModelForCausalLM,
|
|
AutoModelForSeq2SeqLM,
|
|
AutoModelForVision2Seq,
|
|
AutoProcessor,
|
|
AutoTokenizer,
|
|
PreTrainedTokenizerBase,
|
|
is_vision_available,
|
|
)
|
|
from transformers.testing_utils import require_peft, require_torch_gpu_if_bnb_not_multi_backend_enabled, require_vision
|
|
|
|
from trl import DPOConfig, DPOTrainer, FDivergenceType
|
|
|
|
from .testing_utils import require_bitsandbytes, require_no_wandb
|
|
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
|
|
class TestTokenizeRow(unittest.TestCase):
|
|
def setUp(self):
|
|
# Set up the mock tokenizer with specific behaviors
|
|
self.tokenizer = MagicMock(spec=PreTrainedTokenizerBase)
|
|
self.tokenizer.bos_token_id = 0
|
|
self.tokenizer.eos_token_id = 2
|
|
|
|
# Define mock return values for the tokenizer's 'input_ids' for the different text inputs
|
|
self.tokenizer.return_value = {
|
|
"input_ids": {"The sky is": [464, 6766, 318], " blue": [4171], " green": [4077]}
|
|
}
|
|
|
|
# Define tokenizer behavior when called
|
|
def mock_tokenizer_call(text, add_special_tokens):
|
|
token_map = {
|
|
"The sky is": {"input_ids": [464, 6766, 318]},
|
|
" blue": {"input_ids": [4171]},
|
|
" green": {"input_ids": [4077]},
|
|
}
|
|
return token_map[text]
|
|
|
|
self.tokenizer.side_effect = mock_tokenizer_call
|
|
|
|
def test_tokenize_row_no_truncation_no_special_tokens(self):
|
|
# Define the input features
|
|
features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
|
|
|
|
# Call the method with no truncation and no special tokens
|
|
result = DPOTrainer.tokenize_row(
|
|
features=features,
|
|
processing_class=self.tokenizer,
|
|
max_prompt_length=None,
|
|
max_completion_length=None,
|
|
add_special_tokens=False,
|
|
)
|
|
|
|
# Assert the correct output without truncation or special tokens
|
|
self.assertEqual(
|
|
result,
|
|
{
|
|
"prompt_input_ids": [464, 6766, 318],
|
|
"chosen_input_ids": [4171, 2], # eos_token added
|
|
"rejected_input_ids": [4077, 2], # eos_token added
|
|
},
|
|
)
|
|
|
|
def test_tokenize_row_with_truncation(self):
|
|
# Define the input features
|
|
features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
|
|
|
|
# Call the method with truncation
|
|
result = DPOTrainer.tokenize_row(
|
|
features=features,
|
|
processing_class=self.tokenizer,
|
|
max_prompt_length=2,
|
|
max_completion_length=1,
|
|
add_special_tokens=False,
|
|
)
|
|
|
|
# Assert the correct output with truncation applied
|
|
self.assertEqual(
|
|
result,
|
|
{
|
|
"prompt_input_ids": [6766, 318], # truncated to the last 2 tokens
|
|
"chosen_input_ids": [4171], # truncated to 1 token
|
|
"rejected_input_ids": [4077], # truncated to 1 token
|
|
},
|
|
)
|
|
|
|
def test_tokenize_row_with_special_tokens(self):
|
|
# Define the input features
|
|
features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
|
|
|
|
# Call the method with special tokens
|
|
result = DPOTrainer.tokenize_row(
|
|
features=features,
|
|
processing_class=self.tokenizer,
|
|
max_prompt_length=None,
|
|
max_completion_length=None,
|
|
add_special_tokens=True,
|
|
)
|
|
|
|
# Assert the correct output with special tokens added
|
|
self.assertEqual(
|
|
result,
|
|
{
|
|
"prompt_input_ids": [0, 464, 6766, 318, 2], # bos_token and eos_token added
|
|
"chosen_input_ids": [4171, 2], # eos_token added
|
|
"rejected_input_ids": [4077, 2], # eos_token added
|
|
},
|
|
)
|
|
|
|
def test_tokenize_row_with_truncation_and_special_tokens(self):
|
|
# Define the input features
|
|
features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
|
|
|
|
# Call the method with both truncation and special tokens
|
|
result = DPOTrainer.tokenize_row(
|
|
features=features,
|
|
processing_class=self.tokenizer,
|
|
max_prompt_length=4,
|
|
max_completion_length=1,
|
|
add_special_tokens=True,
|
|
)
|
|
|
|
# Assert the correct output with both truncation and special tokens
|
|
self.assertEqual(
|
|
result,
|
|
{
|
|
"prompt_input_ids": [464, 6766, 318, 2], # truncated to 4 tokens with bos_token and eos_token
|
|
"chosen_input_ids": [4171], # truncated to 1 token
|
|
"rejected_input_ids": [4077], # truncated to 1 token
|
|
},
|
|
)
|
|
|
|
|
|
class DPOTrainerTester(unittest.TestCase):
|
|
def setUp(self):
|
|
self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
self.model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
self.ref_model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
|
|
self.tokenizer.pad_token = self.tokenizer.eos_token
|
|
|
|
# get t5 as seq2seq example:
|
|
model_id = "trl-internal-testing/tiny-T5ForConditionalGeneration"
|
|
self.t5_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
|
|
self.t5_ref_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
|
|
self.t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
def test_train(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
learning_rate=9e-1,
|
|
report_to="none",
|
|
)
|
|
trainer = DPOTrainer(
|
|
model=model_id,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12))
|
|
|
|
@parameterized.expand(
|
|
[
|
|
("sigmoid",),
|
|
("hinge",),
|
|
("ipo",),
|
|
("exo_pair",),
|
|
("nca_pair",),
|
|
("robust",),
|
|
("bco_pair",),
|
|
("sppo_hard",),
|
|
("aot",),
|
|
("aot_pair",),
|
|
("discopop",),
|
|
("apo_zero",),
|
|
("apo_down",),
|
|
]
|
|
)
|
|
def test_train_loss_types(self, loss_type):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
learning_rate=9e-1,
|
|
loss_type=loss_type,
|
|
report_to="none",
|
|
)
|
|
trainer = DPOTrainer(
|
|
model=model_id,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12))
|
|
|
|
def test_dpo_trainer_with_weighting(self):
|
|
dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
learning_rate=9e-1,
|
|
use_weighting=True,
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dataset,
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12))
|
|
|
|
@parameterized.expand(
|
|
[
|
|
(None, "Test when rpo_alpha is set to None"),
|
|
(0.5, "Test when rpo_alpha is set to 0.5"),
|
|
]
|
|
)
|
|
def test_dpo_trainer_without_providing_ref_model(self, rpo_alpha, _):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
precompute_ref_log_probs=True,
|
|
rpo_alpha=rpo_alpha,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.equal(param, new_param))
|
|
|
|
def test_dpo_trainer_with_ref_model_is_model(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
with self.assertRaises(ValueError):
|
|
DPOTrainer(
|
|
model=self.model,
|
|
ref_model=self.model, # ref_model can't be the same as model
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
|
|
def test_precompute_ref_batch_size(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
precompute_ref_log_probs=True,
|
|
precompute_ref_batch_size=4,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
ref_model=self.ref_model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12))
|
|
|
|
@require_peft
|
|
def test_dpo_trainer_without_providing_ref_model_with_lora(self):
|
|
from peft import LoraConfig
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
precompute_ref_log_probs=True,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
if "lora" in n:
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.equal(param, new_param))
|
|
|
|
def test_dpo_trainer_padding_token_is_none(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
|
|
tokenizer.pad_token = None
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
expected_regex=r"`padding_value` is not specified in `DPOConfig`, and `pad_token_id` is missing in "
|
|
r"the `processing_class`. Please either set the `padding_value` argument in `DPOConfig`, or set "
|
|
r"`tokenizer.pad_token` \(e.g., `tokenizer.pad_token = tokenizer.eos_token`\) before instantiating "
|
|
r"the trainer.",
|
|
):
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
def test_dpo_trainer_w_dataset_num_proc(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
dataset_num_proc=2,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
def test_tr_dpo_trainer(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
precompute_ref_log_probs=False,
|
|
sync_ref_model=True,
|
|
ref_model_mixup_alpha=0.5,
|
|
ref_model_sync_steps=1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model,
|
|
ref_model=self.ref_model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# params of the ref model as its the same as the model
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.ref_model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.equal(param, new_param))
|
|
|
|
@require_no_wandb
|
|
def test_dpo_trainer_generate_during_eval_no_wandb(self):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
generate_during_eval=True,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
expected_regex="`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
|
|
" Please install `wandb` or `comet-ml` to resolve.",
|
|
):
|
|
DPOTrainer(
|
|
model=self.model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
@require_peft
|
|
def test_dpo_lora_save(self):
|
|
from peft import LoraConfig, get_peft_model
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
model_peft = get_peft_model(model, lora_config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
precompute_ref_log_probs=True,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model_peft,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
# save peft adapter
|
|
trainer.save_model()
|
|
|
|
try:
|
|
AutoModelForCausalLM.from_pretrained(tmp_dir)
|
|
except OSError:
|
|
self.fail("Loading the saved peft adapter failed")
|
|
|
|
@require_peft
|
|
@require_torch_gpu_if_bnb_not_multi_backend_enabled
|
|
def test_dpo_lora_bf16_autocast_llama(self):
|
|
# Note this test only works on compute capability > 7 GPU devices
|
|
from peft import LoraConfig
|
|
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
bf16=True,
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
# save peft adapter
|
|
trainer.save_model()
|
|
|
|
@parameterized.expand(
|
|
[
|
|
("sigmoid", False, False),
|
|
("sigmoid", False, True),
|
|
("sigmoid", True, False),
|
|
("sigmoid", True, True),
|
|
("ipo", False, False),
|
|
("ipo", False, True),
|
|
("ipo", True, False),
|
|
("ipo", True, True),
|
|
("aot_pair", False, False),
|
|
("aot_pair", False, True),
|
|
("aot_pair", True, False),
|
|
("aot_pair", True, True),
|
|
("aot", False, False),
|
|
("aot", False, True),
|
|
("aot", True, False),
|
|
("aot", True, True),
|
|
("bco_pair", False, False),
|
|
("bco_pair", False, True),
|
|
("bco_pair", True, False),
|
|
("bco_pair", True, True),
|
|
("robust", False, False),
|
|
("robust", False, True),
|
|
("robust", True, False),
|
|
("robust", True, True),
|
|
]
|
|
)
|
|
@require_bitsandbytes
|
|
@require_peft
|
|
@unittest.skip("You need a GPU with bf16 support in order to run these tests")
|
|
def test_dpo_lora_bf16_autocast(self, loss_type, pre_compute, gen_during_eval):
|
|
# Note this test only works on compute capability > 7 GPU devices
|
|
from peft import LoraConfig
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(self.model_id, load_in_4bit=True)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
bf16=True,
|
|
beta=0.1,
|
|
generate_during_eval=gen_during_eval,
|
|
loss_type=loss_type,
|
|
precompute_ref_log_probs=pre_compute,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
# save peft adapter
|
|
trainer.save_model()
|
|
|
|
@require_peft
|
|
def test_dpo_lora_tags(self):
|
|
from peft import LoraConfig
|
|
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
for tag in ["dpo", "trl"]:
|
|
self.assertIn(tag, trainer.model.model_tags)
|
|
|
|
@require_peft
|
|
def test_dpo_tags(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
for tag in ["dpo", "trl"]:
|
|
self.assertIn(tag, trainer.model.model_tags)
|
|
|
|
@require_peft
|
|
def test_dpo_lora_force_use_ref(self):
|
|
from peft import LoraConfig, get_peft_model
|
|
|
|
lora_config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
model_peft = get_peft_model(model, lora_config)
|
|
|
|
ref_model = AutoModelForCausalLM.from_pretrained(self.model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
with self.assertRaises(ValueError):
|
|
# passing a peft_model as model and ref_model should error out,
|
|
# unless you pass `force_use_ref_model`
|
|
trainer = DPOTrainer(
|
|
model=model_peft,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
force_use_ref_model=True,
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = DPOTrainer(
|
|
model=model_peft,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=self.tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
peft_config=lora_config,
|
|
)
|
|
|
|
# train the model
|
|
trainer.train()
|
|
|
|
def test_dpo_trainer_torch_dtype(self):
|
|
# See https://github.com/huggingface/trl/issues/1751
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=1,
|
|
model_init_kwargs={"torch_dtype": "float16"},
|
|
ref_model_init_kwargs={"torch_dtype": "float16"},
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = DPOTrainer(
|
|
model=self.model_id,
|
|
ref_model=self.model_id,
|
|
processing_class=self.tokenizer,
|
|
args=training_args,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
self.assertEqual(trainer.model.config.torch_dtype, torch.float16)
|
|
self.assertEqual(trainer.ref_model.config.torch_dtype, torch.float16)
|
|
|
|
# Now test when `torch_dtype` is provided but is wrong to either the model or the ref_model
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=1,
|
|
model_init_kwargs={"torch_dtype": -1},
|
|
report_to="none",
|
|
)
|
|
|
|
with self.assertRaises(ValueError) as context:
|
|
_ = DPOTrainer(
|
|
model=self.model_id,
|
|
processing_class=self.tokenizer,
|
|
args=training_args,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
|
|
self.assertIn(
|
|
"Invalid `torch_dtype` passed to the DPOConfig. Expected a string with either `torch.dtype` or 'auto', but got -1.",
|
|
str(context.exception),
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=1,
|
|
ref_model_init_kwargs={"torch_dtype": -1},
|
|
report_to="none",
|
|
)
|
|
|
|
with self.assertRaises(ValueError) as context:
|
|
_ = DPOTrainer(
|
|
model=self.model_id,
|
|
ref_model=self.model_id,
|
|
processing_class=self.tokenizer,
|
|
args=training_args,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
|
|
self.assertIn(
|
|
"Invalid `torch_dtype` passed to the DPOConfig. Expected a string with either `torch.dtype` or 'auto', but got -1.",
|
|
str(context.exception),
|
|
)
|
|
|
|
def test_dpo_loss_alpha_div_f(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
f_divergence_type=FDivergenceType.ALPHA_DIVERGENCE.value,
|
|
f_alpha_divergence_coef=0.5,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# Fake chosen and rejected log probs
|
|
policy_chosen_logps = torch.FloatTensor([410.0, 0.1])
|
|
policy_rejected_logps = torch.FloatTensor([810.5, 0.2])
|
|
reference_chosen_logps = torch.FloatTensor([-610.0, -0.1])
|
|
reference_rejected_logps = torch.FloatTensor([110.6, 0.5])
|
|
losses, _, _ = trainer.dpo_loss(
|
|
policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
|
|
)
|
|
self.assertTrue(torch.isfinite(losses).cpu().numpy().all())
|
|
|
|
def test_dpo_loss_js_div_f(self):
|
|
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
|
|
# lora model
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=4,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
f_divergence_type=FDivergenceType.JS_DIVERGENCE.value,
|
|
f_alpha_divergence_coef=0.5,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# Fake chosen and rejected log probs
|
|
policy_chosen_logps = torch.FloatTensor([410.0, 0.1])
|
|
policy_rejected_logps = torch.FloatTensor([95.5, 0.2])
|
|
reference_chosen_logps = torch.FloatTensor([-610.0, -0.1])
|
|
reference_rejected_logps = torch.FloatTensor([5.5, 0.5])
|
|
losses, _, _ = trainer.dpo_loss(
|
|
policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps
|
|
)
|
|
self.assertTrue(torch.isfinite(losses).cpu().numpy().all())
|
|
|
|
def test_dpo_trainer_use_logits_to_keep(self):
|
|
model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
max_steps=3,
|
|
remove_unused_columns=False,
|
|
gradient_accumulation_steps=1,
|
|
learning_rate=9e-1,
|
|
eval_strategy="steps",
|
|
beta=0.1,
|
|
use_logits_to_keep=True,
|
|
rpo_alpha=0.5,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
# dpo train lora model with a lora config
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
training_args.use_logits_to_keep = False
|
|
trainer2 = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
|
|
# Fake batch
|
|
prompt_input_ids = torch.randint(1, 1000, (2, 10))
|
|
chosen_input_ids = torch.randint(1, 1000, (2, 5))
|
|
rejected_input_ids = torch.randint(1, 1000, (2, 7))
|
|
prompt_attention_mask = torch.ones_like(prompt_input_ids)
|
|
chosen_attention_mask = torch.ones_like(chosen_input_ids)
|
|
rejected_attention_mask = torch.ones_like(rejected_input_ids)
|
|
|
|
batch = {
|
|
"prompt_input_ids": prompt_input_ids.to(model.device),
|
|
"chosen_input_ids": chosen_input_ids.to(model.device),
|
|
"rejected_input_ids": rejected_input_ids.to(model.device),
|
|
"prompt_attention_mask": prompt_attention_mask.to(model.device),
|
|
"chosen_attention_mask": chosen_attention_mask.to(model.device),
|
|
"rejected_attention_mask": rejected_attention_mask.to(model.device),
|
|
}
|
|
|
|
output = trainer.concatenated_forward(model, batch)
|
|
output2 = trainer2.concatenated_forward(model, batch)
|
|
|
|
np.testing.assert_allclose(output["nll_loss"].item(), output2["nll_loss"].item(), atol=1e-5)
|
|
np.testing.assert_allclose(
|
|
output["mean_chosen_logits"].item(), output2["mean_chosen_logits"].item(), atol=1e-5
|
|
)
|
|
np.testing.assert_allclose(
|
|
output["mean_rejected_logits"].item(), output2["mean_rejected_logits"].item(), atol=1e-5
|
|
)
|
|
|
|
for i in range(output["chosen_logps"].shape[0]):
|
|
np.testing.assert_allclose(
|
|
output["chosen_logps"][i].item(), output2["chosen_logps"][i].item(), atol=1e-5
|
|
)
|
|
np.testing.assert_allclose(
|
|
output["rejected_logps"][i].item(), output2["rejected_logps"][i].item(), atol=1e-5
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
def test_dpo_trainer_with_tools(self):
|
|
model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
# Define dummy test tools
|
|
def get_current_temperature(location: str):
|
|
"""
|
|
Gets the temperature at a given location.
|
|
|
|
Args:
|
|
location: The location to get the temperature for
|
|
"""
|
|
return 22.0
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
tools=[get_current_temperature],
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "conversational_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=None,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
)
|
|
# We don't run the training, but at this stage, the dataset is supposed to be pre-processed. When
|
|
# pre-processing, we expect the available tools to be explicitly mentioned in the system prompt. That's
|
|
# what we're checking here
|
|
self.assertIn("get_current_temperature", tokenizer.decode(trainer.train_dataset["prompt_input_ids"][0]))
|
|
|
|
def test_padding_free(self):
|
|
model_id = "trl-internal-testing/tiny-LlamaForCausalLM-3.2"
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
# Normally, we need `attn_implementation="flash_attention_2"` to that the model returns correct logits.
|
|
# Without it, the logits may be incorrect, but that's fine here. This test focuses only on the inner logic
|
|
# of padding_free.
|
|
model = AutoModelForCausalLM.from_pretrained(model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
learning_rate=9e-1,
|
|
per_device_train_batch_size=2,
|
|
padding_free=True,
|
|
report_to="none",
|
|
)
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
)
|
|
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
# Check that the parameters have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12))
|
|
|
|
def test_compute_metrics(self):
|
|
model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
|
|
ref_model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
|
|
tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
|
|
|
|
def dummy_compute_metrics(*args, **kwargs):
|
|
return {"test": 0.0}
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
do_eval=True,
|
|
eval_strategy="steps",
|
|
eval_steps=3,
|
|
per_device_eval_batch_size=2,
|
|
report_to="none",
|
|
)
|
|
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=tokenizer,
|
|
train_dataset=dummy_dataset["train"],
|
|
eval_dataset=dummy_dataset["test"],
|
|
compute_metrics=dummy_compute_metrics,
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
self.assertEqual(trainer.state.log_history[-2]["eval_test"], 0.0)
|
|
|
|
|
|
@require_vision
|
|
class DPOVisionTrainerTester(unittest.TestCase):
|
|
@parameterized.expand(
|
|
[
|
|
("trl-internal-testing/tiny-Idefics2ForConditionalGeneration",),
|
|
# ("trl-internal-testing/tiny-PaliGemmaForConditionalGeneration",),
|
|
("trl-internal-testing/tiny-LlavaForConditionalGeneration",),
|
|
("trl-internal-testing/tiny-LlavaNextForConditionalGeneration",),
|
|
]
|
|
)
|
|
def test_vdpo_trainer(self, model_id):
|
|
# fmt: off
|
|
dataset_dict = {
|
|
"prompt": [
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe the image in great detail."}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Is this bus in the USA?"}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Give a thorough description of the image."}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Who are the people in the image?"}]}],
|
|
[{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What is written?"}]}],
|
|
],
|
|
"chosen": [
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a modern, multi-colored train."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "Yes, it can be assumed that this bus is in the USA."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a forest path."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "There are two individuals, possibly girls or women."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": '"ccpb".'}]}],
|
|
],
|
|
"rejected": [
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a modern, colorful train."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "No, it's not in the USA."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "The image features a forest path surrounded by trees."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": "In the image, there are two individuals."}]}],
|
|
[{"role": "assistant", "content": [{"type": "text", "text": '"ccpb".'}]}],
|
|
],
|
|
"images": [
|
|
[Image.fromarray(np.random.randint(0, 255, (92, 33, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (64, 48, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (80, 152, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (57, 24, 3), dtype=np.uint8))],
|
|
[Image.fromarray(np.random.randint(0, 255, (102, 48, 3), dtype=np.uint8))],
|
|
],
|
|
}
|
|
# fmt: on
|
|
dataset = Dataset.from_dict(dataset_dict)
|
|
dataset = dataset.cast_column("images", features.Sequence(features.Image()))
|
|
|
|
# Instantiate the model and processor
|
|
model = AutoModelForVision2Seq.from_pretrained(model_id)
|
|
ref_model = AutoModelForVision2Seq.from_pretrained(model_id)
|
|
processor = AutoProcessor.from_pretrained(model_id)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
training_args = DPOConfig(
|
|
output_dir=tmp_dir,
|
|
per_device_train_batch_size=2,
|
|
remove_unused_columns=False,
|
|
learning_rate=0.01, # increase learning rate to speed up test
|
|
max_prompt_length=None, # don't truncate to avoid issues with patch tokens
|
|
max_length=None,
|
|
report_to="none",
|
|
)
|
|
trainer = DPOTrainer(
|
|
model=model,
|
|
ref_model=ref_model,
|
|
args=training_args,
|
|
processing_class=processor,
|
|
train_dataset=dataset,
|
|
eval_dataset=dataset,
|
|
)
|
|
|
|
# Save the initial weights, so we can check if they have changed after training
|
|
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
|
|
|
|
trainer.train()
|
|
|
|
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
|
|
|
|
# Check that the trainable params have changed
|
|
for n, param in previous_trainable_params.items():
|
|
new_param = trainer.model.get_parameter(n)
|
|
if param.sum() != 0: # ignore 0 biases
|
|
if model_id in [
|
|
"trl-internal-testing/tiny-LlavaForConditionalGeneration",
|
|
"trl-internal-testing/tiny-LlavaNextForConditionalGeneration",
|
|
] and (
|
|
n.startswith("vision_tower.vision_model.encoder.layers.1")
|
|
or n == "vision_tower.vision_model.post_layernorm.weight"
|
|
):
|
|
# For some reason, these params are not updated. This is probably not related to TRL, but to
|
|
# the model itself. We should investigate this further, but for now we just skip these params.
|
|
continue
|
|
self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|