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# 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, VptqConfig
from transformers.testing_utils import (
backend_empty_cache,
require_accelerate,
require_torch_gpu,
require_torch_multi_gpu,
require_vptq,
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 VptqConfigTest(unittest.TestCase):
def test_to_dict(self):
"""
Makes sure the config format is properly set
"""
quantization_config = VptqConfig()
vptq_orig_config = quantization_config.to_dict()
self.assertEqual(vptq_orig_config["quant_method"], quantization_config.quant_method)
@slow
@require_torch_gpu
@require_vptq
@require_accelerate
class VptqTest(unittest.TestCase):
model_name = "VPTQ-community/Meta-Llama-3.1-8B-Instruct-v12-k65536-4096-woft"
input_text = "Hello my name is"
max_new_tokens = 32
EXPECTED_OUTPUT = "Hello my name is Sarah and I am a 25 year old woman from the United States. I am a college graduate and I am currently working as a marketing specialist for a small"
device_map = "cuda"
# called only once for all test in this class
@classmethod
def setUpClass(cls):
"""
Setup quantized model
"""
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name,
device_map=cls.device_map,
)
def tearDown(self):
gc.collect()
backend_empty_cache(torch_device)
gc.collect()
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, do_sample=False)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
def test_raise_if_non_quantized(self):
model_id = "facebook/opt-125m"
quantization_config = VptqConfig()
with self.assertRaises(ValueError):
_ = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
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, do_sample=False)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
@require_torch_multi_gpu
def test_quantized_model_multi_gpu(self):
"""
Simple test that checks if the quantized model is working properly with multiple GPUs
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
quantized_model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map="auto")
self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1})
output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
def test_quantized_model_conversion(self):
"""
Simple test that checks if the quantized model has been converted properly
"""
from vptq import VQuantLinear
from transformers.integrations import replace_with_vptq_linear
model_id = "facebook/opt-350m"
config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5")
modules_to_not_convert = ["lm_head"]
names = [
"q_proj",
"k_proj",
"v_proj",
"out_proj",
"fc1",
"fc2",
]
value = {
"enable_norm": True,
"enable_perm": True,
"group_num": 1,
"group_size": 128,
"indices_as_float": False,
"num_centroids": [-1, 128],
"num_res_centroids": [-1, 128],
"outlier_size": 0,
"vector_lens": [-1, 12],
}
shared_layer_config = {}
for name in names:
shared_layer_config[name] = value
for i in range(24):
modules_to_not_convert.append(f"model.decoder.layers.{i}.fc1")
layer_configs = {}
layer_configs["model.decoder.project_out"] = value
layer_configs["model.decoder.project_in"] = value
quantization_config = VptqConfig(config_for_layers=layer_configs, shared_layer_config=shared_layer_config)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config)
nb_linears = 0
for module in model.modules():
if isinstance(module, torch.nn.Linear):
nb_linears += 1
model, _ = replace_with_vptq_linear(model, quantization_config=quantization_config)
nb_vptq_linear = 0
for module in model.modules():
if isinstance(module, VQuantLinear):
nb_vptq_linear += 1
self.assertEqual(nb_linears - 1, nb_vptq_linear)
# Try with `linear_weights_not_to_quantize`
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config)
quantization_config = VptqConfig(config_for_layers=layer_configs, shared_layer_config=shared_layer_config)
model, _ = replace_with_vptq_linear(
model, quantization_config=quantization_config, modules_to_not_convert=modules_to_not_convert
)
nb_vptq_linear = 0
for module in model.modules():
if isinstance(module, VQuantLinear):
nb_vptq_linear += 1
# 25 comes from 24 decoder.layers.{layer_idx}.fc1
# and the last lm_head
self.assertEqual(nb_linears - 25, nb_vptq_linear)
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