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# coding=utf-8 | |
# Copyright 2024 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 gc | |
import tempfile | |
import unittest | |
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, EetqConfig, OPTForCausalLM | |
from transformers.testing_utils import ( | |
require_accelerate, | |
require_eetq, | |
require_torch_gpu, | |
require_torch_multi_gpu, | |
slow, | |
torch_device, | |
) | |
from transformers.utils import is_accelerate_available, is_torch_available | |
if is_torch_available(): | |
import torch | |
if is_accelerate_available(): | |
from accelerate import init_empty_weights | |
class EetqConfigTest(unittest.TestCase): | |
def test_to_dict(self): | |
""" | |
Simple test that checks if one uses a config and converts it to a dict, the dict is the same as the config object | |
""" | |
quantization_config = EetqConfig() | |
config_to_dict = quantization_config.to_dict() | |
for key in config_to_dict: | |
self.assertEqual(getattr(quantization_config, key), config_to_dict[key]) | |
def test_from_dict(self): | |
""" | |
Simple test that checks if one uses a dict and converts it to a config object, the config object is the same as the dict | |
""" | |
dict = {"modules_to_not_convert": ["lm_head.weight"], "quant_method": "eetq", "weights": "int8"} | |
quantization_config = EetqConfig.from_dict(dict) | |
self.assertEqual(dict["modules_to_not_convert"], quantization_config.modules_to_not_convert) | |
self.assertEqual(dict["quant_method"], quantization_config.quant_method) | |
self.assertEqual(dict["weights"], quantization_config.weights) | |
class EetqTest(unittest.TestCase): | |
model_name = "facebook/opt-350m" | |
input_text = "What are we having for dinner?" | |
max_new_tokens = 9 | |
EXPECTED_OUTPUT = "What are we having for dinner?\nI'm having a steak and a salad" | |
device_map = "cuda" | |
# called only once for all test in this class | |
def setUpClass(cls): | |
""" | |
Setup quantized model | |
""" | |
quantization_config = EetqConfig(weights="int8") | |
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name) | |
cls.quantized_model = AutoModelForCausalLM.from_pretrained( | |
cls.model_name, device_map=cls.device_map, quantization_config=quantization_config | |
) | |
def tearDown(self): | |
gc.collect() | |
torch.cuda.empty_cache() | |
gc.collect() | |
def test_quantized_model_conversion(self): | |
""" | |
Simple test that checks if the quantized model has been converted properly | |
""" | |
from eetq import EetqLinear | |
from transformers.integrations import replace_with_eetq_linear | |
model_id = "facebook/opt-350m" | |
config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5") | |
quantization_config = EetqConfig(weights="int8") | |
with init_empty_weights(): | |
model = OPTForCausalLM(config) | |
nb_linears = 0 | |
for module in model.modules(): | |
if isinstance(module, torch.nn.Linear): | |
nb_linears += 1 | |
model = replace_with_eetq_linear(model, quantization_config=quantization_config) | |
nb_eetq_linear = 0 | |
for module in model.modules(): | |
if isinstance(module, EetqLinear): | |
nb_eetq_linear += 1 | |
self.assertEqual(nb_linears - 1, nb_eetq_linear) | |
# Try with `linear_weights_not_to_quantize` | |
with init_empty_weights(): | |
model = OPTForCausalLM(config) | |
quantization_config = EetqConfig(modules_to_not_convert=["fc1"]) | |
model = replace_with_eetq_linear(model, quantization_config=quantization_config) | |
nb_eetq_linear = 0 | |
for module in model.modules(): | |
if isinstance(module, EetqLinear): | |
nb_eetq_linear += 1 | |
self.assertEqual(nb_linears - 25, nb_eetq_linear) | |
def test_quantized_model(self): | |
""" | |
Simple test that checks if the quantized model is working properly | |
""" | |
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device) | |
output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens) | |
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT) | |
def test_save_pretrained(self): | |
""" | |
Simple test that checks if the quantized model is working properly after being saved and loaded | |
""" | |
with tempfile.TemporaryDirectory() as tmpdirname: | |
self.quantized_model.save_pretrained(tmpdirname) | |
model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.device_map) | |
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device) | |
output = model.generate(**input_ids, max_new_tokens=self.max_new_tokens) | |
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT) | |
def test_quantized_model_multi_gpu(self): | |
""" | |
Simple test that checks if the quantized model is working properly with multiple GPUs | |
set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 GPUS | |
""" | |
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device) | |
quantization_config = EetqConfig() | |
quantized_model = AutoModelForCausalLM.from_pretrained( | |
self.model_name, device_map="auto", quantization_config=quantization_config | |
) | |
self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1}) | |
output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens) | |
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT) | |