GPTQ integration (#25062)
* GTPQ integration * Add tests for gptq * support for more quantization model * fix style * typo * fix method * Update src/transformers/modeling_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add dataclass and fix quantization_method * fix doc * Update tests/quantization/gptq/test_gptq.py Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * modify dataclass * add gtpqconfig import * fix typo * fix tests * remove dataset as req arg * remove tokenizer import * add offload cpu quantization test * fix check dataset * modify dockerfile * protect trainer * style * test for config * add more log * overwrite torch_dtype * draft doc * modify quantization_config docstring * fix class name in docstring * Apply suggestions from code review Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * more warning * fix 8bit kwargs tests * peft compatibility * remove var * fix is_gptq_quantized * remove is_gptq_quantized * fix wrap * Update src/transformers/modeling_utils.py Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * add exllama * skip test * overwrite float16 * style * fix skip test * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * fix docsting formatting * add doc * better test --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
This commit is contained in:
Marc Sun
0
tests/quantization/gptq/__init__.py
Normal file
0
tests/quantization/gptq/__init__.py
Normal file
272
tests/quantization/gptq/test_gptq.py
Normal file
272
tests/quantization/gptq/test_gptq.py
Normal file
@@ -0,0 +1,272 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 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
|
||||
|
||||
import pytest
|
||||
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
|
||||
from transformers.testing_utils import (
|
||||
is_torch_available,
|
||||
require_accelerate,
|
||||
require_auto_gptq,
|
||||
require_optimum,
|
||||
require_torch_gpu,
|
||||
require_torch_multi_gpu,
|
||||
slow,
|
||||
)
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
|
||||
class GPTQConfigTest(unittest.TestCase):
|
||||
def test_bits(self):
|
||||
with self.assertRaises(ValueError):
|
||||
GPTQConfig(bits="")
|
||||
GPTQConfig(bits=1)
|
||||
GPTQConfig(bits=2)
|
||||
GPTQConfig(bits=4)
|
||||
|
||||
def test_dataset(self):
|
||||
with self.assertRaises(ValueError):
|
||||
GPTQConfig(bits=2, dataset="auto_gpt")
|
||||
GPTQConfig(bits=2, dataset="c4")
|
||||
GPTQConfig(bits=2, dataset="ptb-new")
|
||||
|
||||
def test_damp_percent(self):
|
||||
with self.assertRaises(ValueError):
|
||||
GPTQConfig(bits=2, damp_percent=10)
|
||||
GPTQConfig(bits=2, damp_percent=-1)
|
||||
GPTQConfig(bits=2, damp_percent="0")
|
||||
GPTQConfig(bits=2, damp_percent=0.01)
|
||||
|
||||
def test_to_dict(self):
|
||||
quantization_config = GPTQConfig(bits=2)
|
||||
quantization_config.to_dict()
|
||||
|
||||
def test_from_dict(self):
|
||||
dict = {"bits": 2}
|
||||
quantization_config = GPTQConfig.from_dict(dict)
|
||||
self.assertEqual(dict["bits"], quantization_config.bits)
|
||||
|
||||
@require_optimum
|
||||
def test_optimum_config(self):
|
||||
from optimum.gptq import GPTQQuantizer
|
||||
|
||||
config = GPTQConfig(bits=2)
|
||||
optimum_config = GPTQQuantizer.from_dict(config.to_dict())
|
||||
self.assertEqual(optimum_config.bits, config.bits)
|
||||
new_config = GPTQConfig.from_dict(optimum_config.to_dict())
|
||||
self.assertEqual(optimum_config.bits, new_config.bits)
|
||||
|
||||
|
||||
@slow
|
||||
@require_optimum
|
||||
@require_auto_gptq
|
||||
@require_torch_gpu
|
||||
class GPTQTest(unittest.TestCase):
|
||||
model_name = "bigscience/bloom-560m"
|
||||
|
||||
input_text = "Hello my name is"
|
||||
|
||||
EXPECTED_OUTPUTS = set()
|
||||
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I")
|
||||
EXPECTED_OUTPUTS.add("Hello my name is John and I am a very good looking man.")
|
||||
EXPECTED_OUTPUTS.add("Hello my name is Alyson and I am a professional photographer")
|
||||
|
||||
# this seems a little small considering that we are doing 4bit quant but we have a small model and ww don't quantize the embeddings
|
||||
EXPECTED_RELATIVE_DIFFERENCE = 1.664253062
|
||||
|
||||
bits = 4
|
||||
group_size = 128
|
||||
desc_act = False
|
||||
disable_exllama = True
|
||||
|
||||
dataset = [
|
||||
"auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
|
||||
]
|
||||
|
||||
device_map = None
|
||||
|
||||
# called only once for all test in this class
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
"""
|
||||
Setup quantized model
|
||||
"""
|
||||
cls.model_fp16 = AutoModelForCausalLM.from_pretrained(
|
||||
cls.model_name, torch_dtype=torch.float16, device_map=cls.device_map
|
||||
)
|
||||
cls.mem_fp16 = cls.model_fp16.get_memory_footprint()
|
||||
|
||||
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name, use_fast=True)
|
||||
|
||||
quantization_config = GPTQConfig(
|
||||
bits=cls.bits,
|
||||
dataset=cls.dataset,
|
||||
tokenizer=cls.tokenizer,
|
||||
group_size=cls.group_size,
|
||||
desc_act=cls.desc_act,
|
||||
disable_exllama=cls.disable_exllama,
|
||||
)
|
||||
|
||||
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
|
||||
cls.model_name,
|
||||
torch_dtype=torch.float16,
|
||||
device_map=cls.device_map,
|
||||
quantization_config=quantization_config,
|
||||
)
|
||||
|
||||
def test_memory_footprint(self):
|
||||
r"""
|
||||
A simple test to check if the model conversion has been done correctly by checking on the
|
||||
memory footprint of the converted model
|
||||
"""
|
||||
|
||||
mem_quantized = self.quantized_model.get_memory_footprint()
|
||||
|
||||
self.assertAlmostEqual(self.mem_fp16 / mem_quantized, self.EXPECTED_RELATIVE_DIFFERENCE)
|
||||
|
||||
def test_quantized_layers_class(self):
|
||||
"""
|
||||
Simple test to check if the model conversion has been done correctly by checking on
|
||||
the class type of the linear layers of the converted models
|
||||
"""
|
||||
from auto_gptq.utils.import_utils import dynamically_import_QuantLinear
|
||||
|
||||
QuantLinear = dynamically_import_QuantLinear(
|
||||
use_triton=False,
|
||||
desc_act=self.desc_act,
|
||||
group_size=self.group_size,
|
||||
bits=self.bits,
|
||||
disable_exllama=self.disable_exllama,
|
||||
)
|
||||
self.assertTrue(self.quantized_model.transformer.h[0].mlp.dense_4h_to_h.__class__ == QuantLinear)
|
||||
|
||||
def check_inference_correctness(self, model):
|
||||
r"""
|
||||
Test the generation quality of the quantized model and see that we are matching the expected output.
|
||||
Given that we are operating on small numbers + the testing model is relatively small, we might not get
|
||||
the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
|
||||
"""
|
||||
# Check that inference pass works on the model
|
||||
encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
|
||||
|
||||
# Check the exactness of the results
|
||||
output_sequences = model.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
|
||||
|
||||
# Get the generation
|
||||
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
|
||||
|
||||
def test_generate_quality(self):
|
||||
"""
|
||||
Simple test to check the quality of the model by comapring the the generated tokens with the expected tokens
|
||||
"""
|
||||
if self.device_map is None:
|
||||
self.check_inference_correctness(self.quantized_model.to(0))
|
||||
else:
|
||||
self.check_inference_correctness(self.quantized_model)
|
||||
|
||||
def test_serialization(self):
|
||||
"""
|
||||
Test the serialization of the model and the loading of the quantized weights works
|
||||
"""
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.quantized_model.save_pretrained(tmpdirname)
|
||||
if self.disable_exllama:
|
||||
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname).to(0)
|
||||
else:
|
||||
# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
|
||||
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map={"": 0})
|
||||
self.check_inference_correctness(quantized_model_from_saved)
|
||||
|
||||
@require_accelerate
|
||||
def test_serialization_big_model_inference(self):
|
||||
"""
|
||||
Test the serialization of the model and the loading of the quantized weights with big model inference
|
||||
"""
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.quantized_model.save_pretrained(tmpdirname)
|
||||
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map="auto")
|
||||
self.check_inference_correctness(quantized_model_from_saved)
|
||||
|
||||
def test_change_loading_attributes(self):
|
||||
"""
|
||||
Test the serialization of the model and the loading of the quantized weights works with another config file
|
||||
"""
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
self.quantized_model.save_pretrained(tmpdirname)
|
||||
if self.disable_exllama:
|
||||
self.assertEqual(self.quantized_model.config.quantization_config.disable_exllama, True)
|
||||
# we need to put it directly to the gpu. Otherwise, we won't be able to initialize the exllama kernel
|
||||
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
|
||||
tmpdirname, quantization_config=GPTQConfig(disable_exllama=False, bits=6), device_map={"": 0}
|
||||
)
|
||||
self.assertEqual(quantized_model_from_saved.config.quantization_config.disable_exllama, False)
|
||||
self.assertEqual(quantized_model_from_saved.config.quantization_config.bits, self.bits)
|
||||
self.check_inference_correctness(quantized_model_from_saved)
|
||||
|
||||
|
||||
@require_accelerate
|
||||
@require_torch_multi_gpu
|
||||
class GPTQTestDeviceMap(GPTQTest):
|
||||
device_map = "auto"
|
||||
|
||||
|
||||
@require_accelerate
|
||||
@require_torch_multi_gpu
|
||||
class GPTQTestDeviceMapExllama(GPTQTest):
|
||||
device_map = "auto"
|
||||
disable_exllama = False
|
||||
|
||||
|
||||
# fail when run all together
|
||||
@pytest.mark.skip
|
||||
@require_accelerate
|
||||
@require_torch_multi_gpu
|
||||
class GPTQTestDeviceMapCPUOffload(GPTQTest):
|
||||
device_map = {
|
||||
"transformer.word_embeddings": 0,
|
||||
"transformer.word_embeddings_layernorm": 0,
|
||||
"lm_head": 0,
|
||||
"transformer.h.0": 0,
|
||||
"transformer.h.1": 0,
|
||||
"transformer.h.2": 0,
|
||||
"transformer.h.3": 0,
|
||||
"transformer.h.4": 0,
|
||||
"transformer.h.5": 0,
|
||||
"transformer.h.6": 0,
|
||||
"transformer.h.7": 0,
|
||||
"transformer.h.8": 0,
|
||||
"transformer.h.9": 0,
|
||||
"transformer.h.10": 1,
|
||||
"transformer.h.11": 1,
|
||||
"transformer.h.12": 1,
|
||||
"transformer.h.13": 1,
|
||||
"transformer.h.14": 1,
|
||||
"transformer.h.15": 1,
|
||||
"transformer.h.16": 1,
|
||||
"transformer.h.17": 0,
|
||||
"transformer.h.18": "cpu",
|
||||
"transformer.h.19": "cpu",
|
||||
"transformer.h.20": "cpu",
|
||||
"transformer.h.21": "cpu",
|
||||
"transformer.h.22": "cpu",
|
||||
"transformer.h.23": 1,
|
||||
"transformer.ln_f": 0,
|
||||
}
|
||||
Reference in New Issue
Block a user