mirror of
https://github.com/huggingface/peft.git
synced 2025-10-20 15:33:48 +08:00
564 lines
20 KiB
Python
564 lines
20 KiB
Python
# Note: These tests were copied from test_common_gpu.py and test_gpu_examples.py as they can run on CPU too.
|
|
#
|
|
# Copyright 2025-present the HuggingFace Inc. team.
|
|
#
|
|
# 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 gc
|
|
import os
|
|
import tempfile
|
|
import unittest
|
|
|
|
import pytest
|
|
import torch
|
|
from accelerate.utils.memory import clear_device_cache
|
|
from transformers import (
|
|
AutoModelForCausalLM,
|
|
AutoTokenizer,
|
|
DataCollatorForLanguageModeling,
|
|
Trainer,
|
|
TrainingArguments,
|
|
)
|
|
|
|
from peft import (
|
|
AdaLoraConfig,
|
|
LoraConfig,
|
|
OFTConfig,
|
|
PeftModel,
|
|
get_peft_model,
|
|
prepare_model_for_kbit_training,
|
|
)
|
|
from peft.tuners.lora import GPTQLoraLinear
|
|
from peft.utils import SAFETENSORS_WEIGHTS_NAME, infer_device
|
|
|
|
from .testing_utils import (
|
|
device_count,
|
|
load_dataset_english_quotes,
|
|
require_gptqmodel,
|
|
require_optimum,
|
|
require_torch_multi_accelerator,
|
|
)
|
|
|
|
|
|
@require_gptqmodel
|
|
class PeftGPTQModelCommonTests(unittest.TestCase):
|
|
r"""
|
|
A common tester to run common operations that are performed on GPU/CPU such as generation, loading in 8bit, etc.
|
|
"""
|
|
|
|
def setUp(self):
|
|
self.causal_lm_model_id = "facebook/opt-350m"
|
|
self.device = infer_device()
|
|
|
|
def tearDown(self):
|
|
r"""
|
|
Efficient mechanism to free GPU memory after each test. Based on
|
|
https://github.com/huggingface/transformers/issues/21094
|
|
"""
|
|
clear_device_cache(garbage_collection=True)
|
|
gc.collect()
|
|
|
|
def test_lora_gptq_quantization_from_pretrained_safetensors(self):
|
|
r"""
|
|
Tests that the gptqmodel quantization using LoRA works as expected with safetensors weights.
|
|
"""
|
|
from transformers import GPTQConfig
|
|
|
|
model_id = "marcsun13/opt-350m-gptq-4bit"
|
|
quantization_config = GPTQConfig(bits=4, use_exllama=False)
|
|
kwargs = {
|
|
"pretrained_model_name_or_path": model_id,
|
|
"dtype": torch.float16,
|
|
"device_map": "auto",
|
|
"quantization_config": quantization_config,
|
|
}
|
|
model = AutoModelForCausalLM.from_pretrained(**kwargs)
|
|
model = prepare_model_for_kbit_training(model)
|
|
|
|
config = LoraConfig(task_type="CAUSAL_LM")
|
|
peft_model = get_peft_model(model, config)
|
|
peft_model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(peft_model.device))
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
peft_model.save_pretrained(tmp_dir)
|
|
model = AutoModelForCausalLM.from_pretrained(**kwargs)
|
|
model = PeftModel.from_pretrained(model, tmp_dir)
|
|
model = prepare_model_for_kbit_training(model)
|
|
model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(peft_model.device))
|
|
|
|
# loading a 2nd adapter works, #1239
|
|
model.load_adapter(tmp_dir, "adapter2")
|
|
model.set_adapter("adapter2")
|
|
model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(peft_model.device))
|
|
|
|
# check that both adapters are in the same layer
|
|
assert "default" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A
|
|
assert "adapter2" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A
|
|
|
|
def test_oft_gptq_quantization_from_pretrained_safetensors(self):
|
|
r"""
|
|
Tests that the gptqmodel quantization using OFT works as expected with safetensors weights.
|
|
"""
|
|
from transformers import GPTQConfig
|
|
|
|
model_id = "marcsun13/opt-350m-gptq-4bit"
|
|
quantization_config = GPTQConfig(bits=4, use_exllama=False)
|
|
kwargs = {
|
|
"pretrained_model_name_or_path": model_id,
|
|
"dtype": torch.float16,
|
|
"device_map": "auto",
|
|
"quantization_config": quantization_config,
|
|
}
|
|
model = AutoModelForCausalLM.from_pretrained(**kwargs)
|
|
model = prepare_model_for_kbit_training(model)
|
|
|
|
config = OFTConfig(task_type="CAUSAL_LM")
|
|
peft_model = get_peft_model(model, config)
|
|
peft_model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(peft_model.device))
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
peft_model.save_pretrained(tmp_dir)
|
|
model = AutoModelForCausalLM.from_pretrained(**kwargs)
|
|
model = PeftModel.from_pretrained(model, tmp_dir)
|
|
model = prepare_model_for_kbit_training(model)
|
|
model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(peft_model.device))
|
|
|
|
# loading a 2nd adapter works, #1239
|
|
model.load_adapter(tmp_dir, "adapter2")
|
|
model.set_adapter("adapter2")
|
|
model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(peft_model.device))
|
|
|
|
# check that both adapters are in the same layer
|
|
assert "default" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.oft_R
|
|
assert "adapter2" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.oft_R
|
|
|
|
|
|
@require_gptqmodel
|
|
@require_optimum
|
|
class PeftGPTQModelTests(unittest.TestCase):
|
|
r"""
|
|
GPTQ + peft tests
|
|
"""
|
|
|
|
def setUp(self):
|
|
from transformers import GPTQConfig
|
|
|
|
self.causal_lm_model_id = "marcsun13/opt-350m-gptq-4bit"
|
|
self.quantization_config = GPTQConfig(bits=4, backend="auto_trainable")
|
|
self.tokenizer = AutoTokenizer.from_pretrained(self.causal_lm_model_id)
|
|
|
|
def tearDown(self):
|
|
r"""
|
|
Efficient mechanism to free GPU memory after each test. Based on
|
|
https://github.com/huggingface/transformers/issues/21094
|
|
"""
|
|
clear_device_cache(garbage_collection=True)
|
|
|
|
def _check_inference_finite(self, model, batch):
|
|
# try inference without Trainer class
|
|
training = model.training
|
|
model.eval()
|
|
output = model(**batch.to(model.device))
|
|
assert torch.isfinite(output.logits).all()
|
|
model.train(training)
|
|
|
|
def test_causal_lm_training(self):
|
|
r"""
|
|
Test the CausalLM training on a single GPU device. The test would simply fail if the adapters are not set
|
|
correctly.
|
|
"""
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
|
|
model = prepare_model_for_kbit_training(model)
|
|
config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
target_modules=["q_proj", "v_proj"],
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
model = get_peft_model(model, config)
|
|
|
|
data = load_dataset_english_quotes()
|
|
data = data.map(lambda samples: self.tokenizer(samples["quote"]), batched=True)
|
|
|
|
trainer = Trainer(
|
|
model=model,
|
|
train_dataset=data["train"],
|
|
args=TrainingArguments(
|
|
per_device_train_batch_size=4,
|
|
gradient_accumulation_steps=4,
|
|
warmup_steps=2,
|
|
max_steps=3,
|
|
learning_rate=2e-4,
|
|
fp16=True,
|
|
logging_steps=1,
|
|
output_dir=tmp_dir,
|
|
),
|
|
data_collator=DataCollatorForLanguageModeling(self.tokenizer, mlm=False),
|
|
)
|
|
model.config.use_cache = False
|
|
trainer.train()
|
|
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
assert "adapter_config.json" in os.listdir(tmp_dir)
|
|
assert SAFETENSORS_WEIGHTS_NAME in os.listdir(tmp_dir)
|
|
|
|
# assert loss is not None
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
def test_oft_causal_lm_training(self):
|
|
r"""
|
|
Test the CausalLM training on a single GPU device. The test would simply fail if the adapters are not set
|
|
correctly.
|
|
"""
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
|
|
model = prepare_model_for_kbit_training(model)
|
|
config = OFTConfig(
|
|
r=0,
|
|
oft_block_size=8,
|
|
target_modules=["q_proj", "v_proj"],
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
model = get_peft_model(model, config)
|
|
|
|
data = load_dataset_english_quotes()
|
|
data = data.map(lambda samples: self.tokenizer(samples["quote"]), batched=True)
|
|
|
|
trainer = Trainer(
|
|
model=model,
|
|
train_dataset=data["train"],
|
|
args=TrainingArguments(
|
|
per_device_train_batch_size=4,
|
|
gradient_accumulation_steps=4,
|
|
warmup_steps=2,
|
|
max_steps=3,
|
|
learning_rate=2e-4,
|
|
fp16=True,
|
|
logging_steps=1,
|
|
output_dir=tmp_dir,
|
|
),
|
|
data_collator=DataCollatorForLanguageModeling(self.tokenizer, mlm=False),
|
|
)
|
|
model.config.use_cache = False
|
|
trainer.train()
|
|
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
assert "adapter_config.json" in os.listdir(tmp_dir)
|
|
assert SAFETENSORS_WEIGHTS_NAME in os.listdir(tmp_dir)
|
|
|
|
# assert loss is not None
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
@pytest.mark.single_gpu_tests
|
|
def test_adalora_causalLM(self):
|
|
r"""
|
|
Tests the gptq training with adalora
|
|
"""
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(self.causal_lm_model_id)
|
|
model = prepare_model_for_kbit_training(model)
|
|
|
|
peft_config = AdaLoraConfig(
|
|
total_step=40,
|
|
init_r=6,
|
|
target_r=4,
|
|
tinit=10,
|
|
tfinal=20,
|
|
deltaT=5,
|
|
beta1=0.3,
|
|
beta2=0.3,
|
|
orth_reg_weight=0.2,
|
|
lora_alpha=32,
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
model = get_peft_model(model, peft_config)
|
|
|
|
data = load_dataset_english_quotes()
|
|
data = data.map(lambda samples: self.tokenizer(samples["quote"]), batched=True)
|
|
batch = tokenizer(data["train"][:3]["quote"], return_tensors="pt", padding=True)
|
|
self._check_inference_finite(model, batch)
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
trainer = Trainer(
|
|
model=model,
|
|
train_dataset=data["train"],
|
|
args=TrainingArguments(
|
|
per_device_train_batch_size=4,
|
|
gradient_accumulation_steps=4,
|
|
warmup_steps=2,
|
|
max_steps=3,
|
|
learning_rate=2e-4,
|
|
fp16=True,
|
|
logging_steps=1,
|
|
output_dir=tmp_dir,
|
|
),
|
|
data_collator=DataCollatorForLanguageModeling(self.tokenizer, mlm=False),
|
|
)
|
|
model.config.use_cache = False
|
|
trainer.train()
|
|
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
assert "adapter_config.json" in os.listdir(tmp_dir)
|
|
assert SAFETENSORS_WEIGHTS_NAME in os.listdir(tmp_dir)
|
|
|
|
# assert loss is not None
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
@pytest.mark.multi_gpu_tests
|
|
@require_torch_multi_accelerator
|
|
def test_causal_lm_training_multi_accelerator(self):
|
|
r"""
|
|
Test the CausalLM training on a multi-accelerator device. The test would simply fail if the adapters are not
|
|
set correctly.
|
|
"""
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
|
|
assert set(model.hf_device_map.values()) == set(range(device_count))
|
|
|
|
model = prepare_model_for_kbit_training(model)
|
|
|
|
setattr(model, "model_parallel", True)
|
|
setattr(model, "is_parallelizable", True)
|
|
|
|
config = LoraConfig(
|
|
r=16,
|
|
lora_alpha=32,
|
|
target_modules=["q_proj", "v_proj"],
|
|
lora_dropout=0.05,
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
model = get_peft_model(model, config)
|
|
|
|
data = load_dataset_english_quotes()
|
|
data = data.map(lambda samples: self.tokenizer(samples["quote"]), batched=True)
|
|
|
|
trainer = Trainer(
|
|
model=model,
|
|
train_dataset=data["train"],
|
|
args=TrainingArguments(
|
|
per_device_train_batch_size=4,
|
|
gradient_accumulation_steps=4,
|
|
warmup_steps=2,
|
|
max_steps=3,
|
|
learning_rate=2e-4,
|
|
fp16=True,
|
|
logging_steps=1,
|
|
output_dir=tmp_dir,
|
|
),
|
|
data_collator=DataCollatorForLanguageModeling(self.tokenizer, mlm=False),
|
|
)
|
|
model.config.use_cache = False
|
|
trainer.train()
|
|
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
assert "adapter_config.json" in os.listdir(tmp_dir)
|
|
assert SAFETENSORS_WEIGHTS_NAME in os.listdir(tmp_dir)
|
|
|
|
# assert loss is not None
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
@pytest.mark.multi_gpu_tests
|
|
@require_torch_multi_accelerator
|
|
def test_oft_causal_lm_training_multi_accelerator(self):
|
|
r"""
|
|
Test the CausalLM training on a multi-accelerator device. The test would simply fail if the adapters are not
|
|
set correctly.
|
|
"""
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
|
|
assert set(model.hf_device_map.values()) == set(range(device_count))
|
|
|
|
model = prepare_model_for_kbit_training(model)
|
|
|
|
setattr(model, "model_parallel", True)
|
|
setattr(model, "is_parallelizable", True)
|
|
|
|
config = OFTConfig(
|
|
r=0,
|
|
oft_block_size=8,
|
|
target_modules=["q_proj", "v_proj"],
|
|
bias="none",
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
model = get_peft_model(model, config)
|
|
|
|
data = load_dataset_english_quotes()
|
|
data = data.map(lambda samples: self.tokenizer(samples["quote"]), batched=True)
|
|
|
|
trainer = Trainer(
|
|
model=model,
|
|
train_dataset=data["train"],
|
|
args=TrainingArguments(
|
|
per_device_train_batch_size=4,
|
|
gradient_accumulation_steps=4,
|
|
warmup_steps=2,
|
|
max_steps=3,
|
|
learning_rate=2e-4,
|
|
fp16=True,
|
|
logging_steps=1,
|
|
output_dir=tmp_dir,
|
|
),
|
|
data_collator=DataCollatorForLanguageModeling(self.tokenizer, mlm=False),
|
|
)
|
|
model.config.use_cache = False
|
|
trainer.train()
|
|
|
|
model.cpu().save_pretrained(tmp_dir)
|
|
|
|
assert "adapter_config.json" in os.listdir(tmp_dir)
|
|
assert SAFETENSORS_WEIGHTS_NAME in os.listdir(tmp_dir)
|
|
|
|
# assert loss is not None
|
|
assert trainer.state.log_history[-1]["train_loss"] is not None
|
|
|
|
def test_non_default_adapter_name(self):
|
|
# See issue 1346
|
|
config = LoraConfig(
|
|
r=16,
|
|
target_modules=["q_proj", "v_proj"],
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# default adapter name
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
model = prepare_model_for_kbit_training(model)
|
|
model = get_peft_model(model, config)
|
|
n_trainable_default, n_total_default = model.get_nb_trainable_parameters()
|
|
|
|
# other adapter name
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
model = prepare_model_for_kbit_training(model)
|
|
model = get_peft_model(model, config, adapter_name="other")
|
|
n_trainable_other, n_total_other = model.get_nb_trainable_parameters()
|
|
|
|
assert n_trainable_other > 0
|
|
# sanity check
|
|
assert n_trainable_default == n_trainable_other
|
|
assert n_total_default == n_total_other
|
|
|
|
def test_oft_non_default_adapter_name(self):
|
|
# See issue 1346
|
|
config = OFTConfig(
|
|
r=0,
|
|
oft_block_size=8,
|
|
target_modules=["q_proj", "v_proj"],
|
|
task_type="CAUSAL_LM",
|
|
)
|
|
|
|
# default adapter name
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
model = prepare_model_for_kbit_training(model)
|
|
model = get_peft_model(model, config)
|
|
n_trainable_default, n_total_default = model.get_nb_trainable_parameters()
|
|
|
|
# other adapter name
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
self.causal_lm_model_id,
|
|
dtype=torch.float16,
|
|
device_map="auto",
|
|
quantization_config=self.quantization_config,
|
|
)
|
|
model = prepare_model_for_kbit_training(model)
|
|
model = get_peft_model(model, config, adapter_name="other")
|
|
n_trainable_other, n_total_other = model.get_nb_trainable_parameters()
|
|
|
|
assert n_trainable_other > 0
|
|
# sanity check
|
|
assert n_trainable_default == n_trainable_other
|
|
assert n_total_default == n_total_other
|
|
|
|
def test_load_lora(self):
|
|
model_id = "ModelCloud/Llama-3.2-1B-gptqmodel-ci-4bit"
|
|
adapter_id = "ModelCloud/Llama-3.2-1B-gptqmodel-ci-4bit-lora"
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
|
|
model.load_adapter(adapter_id)
|
|
|
|
# assert dynamic rank
|
|
v_proj_module = model.model.layers[5].self_attn.v_proj
|
|
assert isinstance(v_proj_module, GPTQLoraLinear)
|
|
assert v_proj_module.lora_A["default"].weight.data.shape[0] == 128
|
|
assert v_proj_module.lora_B["default"].weight.data.shape[1] == 128
|
|
gate_proj_module = model.model.layers[5].mlp.gate_proj
|
|
assert isinstance(gate_proj_module, GPTQLoraLinear)
|
|
assert gate_proj_module.lora_A["default"].weight.data.shape[0] == 256
|
|
assert gate_proj_module.lora_B["default"].weight.data.shape[1] == 256
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
inp = tokenizer("Capital of France is", return_tensors="pt").to(model.device)
|
|
tokens = model.generate(**inp)[0]
|
|
result = tokenizer.decode(tokens)
|
|
|
|
assert "paris" in result.lower()
|