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# 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 AutoConfig, AutoModelForCausalLM, AutoTokenizer, GPTQConfig
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_gptq,
require_optimum,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.utils import is_auto_gptq_available, is_gptqmodel_available, is_ipex_available
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")
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_optimum())
self.assertEqual(optimum_config.bits, config.bits)
new_config = GPTQConfig.from_dict_optimum(optimum_config.to_dict())
self.assertEqual(optimum_config.bits, new_config.bits)
@slow
@require_optimum
@require_gptq
class GPTQTest(unittest.TestCase):
model_name = "bigscience/bloom-560m"
input_text = "Hello my name is"
EXPECTED_OUTPUTS = set()
# flaky test: gptqmodel and auto-gptq are not output equivalent nor is string compare deterministic even between transformer/torch versions
EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I")
EXPECTED_OUTPUTS.add("Hello my name is John, I am a professional photographer and I")
EXPECTED_OUTPUTS.add("Hello my name is John, I am a student in the University of")
EXPECTED_OUTPUTS.add("Hello my name is John and I am a very good looking man.")
EXPECTED_OUTPUTS.add("Hello my name is Alyson, I am a student in the")
EXPECTED_OUTPUTS.add("Hello my name is Alyson and I am a very sweet,")
EXPECTED_OUTPUTS.add("Hello my name is Aiden, I am a student at the University")
EXPECTED_OUTPUTS.add("Hello my name is Nate and I am a member of the N")
EXPECTED_OUTPUTS.add("Hello my name is Nellie and I am a student at the")
EXPECTED_OUTPUTS.add("Hello my name is Nate and I am a new member of the")
# 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
sym = True
group_size = 128
desc_act = False
use_exllama = False
dataset = [
"auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
]
device_map = "cpu" if is_gptqmodel_available() else 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, 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)
cls.config = AutoConfig.from_pretrained(cls.model_name)
cls.quantization_config = GPTQConfig(
bits=cls.bits,
dataset=cls.dataset,
tokenizer=cls.tokenizer,
group_size=cls.group_size,
desc_act=cls.desc_act,
sym=cls.sym,
use_exllama=cls.use_exllama,
)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name,
dtype=torch.float16,
device_map=cls.device_map,
quantization_config=cls.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, places=4)
def test_device_and_dtype_assignment(self):
r"""
Test whether trying to cast (or assigning a device to) a model after quantization will throw an error.
Checks also if other models are casted correctly.
"""
# This should work
if self.device_map in (None, "cpu"):
_ = self.quantized_model.to(0)
with self.assertRaises(ValueError):
# Tries with a `dtype``
self.quantized_model.to(torch.float16)
def test_original_dtype(self):
r"""
A simple test to check if the model successfully stores the original dtype
"""
self.assertTrue(hasattr(self.quantized_model.config, "_pre_quantization_dtype"))
self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
self.assertTrue(self.quantized_model.config._pre_quantization_dtype == torch.float16)
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
"""
if is_gptqmodel_available():
from gptqmodel.utils.importer import hf_select_quant_linear
if hasattr(self.config, "quantization_config"):
checkpoint_format = self.config.quantization_config.get("checkpoint_format")
meta = self.config.quantization_config.get("meta")
else:
checkpoint_format = "gptq"
meta = None
QuantLinear = hf_select_quant_linear(
bits=self.bits,
group_size=self.group_size,
desc_act=self.desc_act,
sym=self.sym,
device_map=self.device_map,
checkpoint_format=checkpoint_format,
meta=meta,
backend=self.quantization_config.backend,
)
elif is_auto_gptq_available():
from auto_gptq.utils.import_utils import dynamically_import_QuantLinear as hf_select_quant_linear
QuantLinear = hf_select_quant_linear(
use_triton=False,
desc_act=self.desc_act,
group_size=self.group_size,
bits=self.bits,
disable_exllama=not self.use_exllama,
disable_exllamav2=True,
)
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(model.device), max_new_tokens=10)
# Get the generation
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
def check_quantized_layers_type(self, model, value):
self.assertTrue(model.transformer.h[0].mlp.dense_4h_to_h.QUANT_TYPE == value)
def test_generate_quality(self):
"""
Simple test to check the quality of the model by comparing the generated tokens with the expected tokens
"""
if self.device_map is None:
self.check_inference_correctness(self.quantized_model.to(0))
else:
if self.device_map == "cpu" and self.quantized_model.device.type != "cpu":
self.quantized_model.to("cpu")
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 is_auto_gptq_available() and not is_gptqmodel_available():
quant_type = "cuda-old" if not self.use_exllama else "exllama"
if not self.use_exllama:
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
tmpdirname, quantization_config=GPTQConfig(use_exllama=False, bits=4)
)
if self.device_map != "cpu":
quantized_model_from_saved = quantized_model_from_saved.to(0)
else:
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
tmpdirname, device_map=self.device_map
)
else:
if self.device_map == "cpu":
quant_type = "ipex" if is_ipex_available() else "torch"
else:
# We expect tritonv2 to be used here, because exllama backend doesn't support packing https://github.com/ModelCloud/GPTQModel/issues/1354
# TODO: Remove this once GPTQModel exllama kernels supports packing
quant_type = "tritonv2"
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(
tmpdirname, device_map=self.device_map
)
self.check_quantized_layers_type(quantized_model_from_saved, quant_type)
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)
device_map = self.device_map or "auto"
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=device_map)
self.check_inference_correctness(quantized_model_from_saved)
@require_torch_gpu
class GPTQTestCUDA(GPTQTest):
device_map = {"": 0}
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 is_auto_gptq_available() and not is_gptqmodel_available() and not self.use_exllama:
self.check_quantized_layers_type(self.quantized_model, "cuda-old")
# 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(use_exllama=True, bits=4), device_map=self.device_map
)
self.assertEqual(quantized_model_from_saved.config.quantization_config.bits, self.bits)
self.check_quantized_layers_type(quantized_model_from_saved, "exllama")
self.check_inference_correctness(quantized_model_from_saved)
@require_accelerate
@require_torch_multi_gpu
class GPTQTestDeviceMap(GPTQTestCUDA):
device_map = "auto"
@require_accelerate
@require_torch_multi_gpu
class GPTQTestDeviceMapExllama(GPTQTestCUDA):
device_map = "auto"
use_exllama = True
@slow
@require_optimum
@require_gptq
@require_torch_gpu
@require_accelerate
class GPTQTestActOrderExllama(unittest.TestCase):
"""
Test GPTQ model with exllama kernel and desc_act=True (also known as act-order).
More information on those arguments here:
https://huggingface.co/docs/transformers/main_classes/quantization#transformers.GPTQConfig
"""
EXPECTED_OUTPUTS = set()
# flaky test: gptqmodel and auto-gptq are not output equivalent nor is string compare deterministic even between transformer/torch versions
EXPECTED_OUTPUTS.add("Hello, how are you ? I'm doing good, thanks for asking.")
# 4bit + act_order + 128g
model_name = "hf-internal-testing/TinyLlama-1.1B-Chat-v0.3-GPTQ"
input_text = "Hello, how are you ?"
@classmethod
def setUpClass(cls):
"""
Setup quantized model
"""
cls.quantization_config = GPTQConfig(bits=4, max_input_length=4028)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name,
dtype=torch.float16,
device_map={"": 0},
quantization_config=cls.quantization_config,
)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name, use_fast=True)
def check_inference_correctness(self, model):
"""
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_quantized_layers_type(self):
self.assertTrue(self.quantized_model.model.layers[0].self_attn.k_proj.QUANT_TYPE == "exllama")
def test_generate_quality(self):
"""
Simple test to check the quality of the model by comparing the generated tokens with the expected tokens
"""
self.check_inference_correctness(self.quantized_model)
def test_max_input_length(self):
"""
Test if the max_input_length works. It modifies the maximum input length that of the model that runs with exllama backend.
"""
prompt = "I am in Paris and" * 1000
inp = self.tokenizer(prompt, return_tensors="pt").to(0)
self.assertTrue(inp["input_ids"].shape[1] > 4028)
with self.assertRaises(RuntimeError) as cm:
self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3)
self.assertTrue("temp_state buffer is too small" in str(cm.exception))
prompt = "I am in Paris and"
inp = self.tokenizer(prompt, return_tensors="pt").to(0)
self.assertTrue(inp["input_ids"].shape[1] < 4028)
self.quantized_model.generate(**inp, num_beams=1, min_new_tokens=3, max_new_tokens=3)
@slow
@require_optimum
@require_gptq
@require_torch_gpu
@require_accelerate
class GPTQTestExllamaV2(unittest.TestCase):
"""
Test GPTQ model with exllamav2 kernel and desc_act=True (also known as act-order).
More information on those arguments here:
https://huggingface.co/docs/transformers/main_classes/quantization#transformers.GPTQConfig
"""
EXPECTED_OUTPUTS = set()
# flaky test: gptqmodel and auto-gptq are not output equivalent nor is string compare deterministic even between transformer/torch versions
EXPECTED_OUTPUTS.add("Hello, how are you ? I'm doing good, thanks for asking.")
# 4bit + act_order + 128g
model_name = "hf-internal-testing/TinyLlama-1.1B-Chat-v0.3-GPTQ"
input_text = "Hello, how are you ?"
@classmethod
def setUpClass(cls):
"""
Setup quantized model
"""
cls.quantization_config = GPTQConfig(bits=4, exllama_config={"version": 2})
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name,
dtype=torch.float16,
device_map={"": 0},
quantization_config=cls.quantization_config,
)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name, use_fast=True)
def test_quantized_layers_type(self):
if is_auto_gptq_available() and not is_gptqmodel_available():
self.assertEqual(
self.quantized_model.model.layers[0].self_attn.k_proj.QUANT_TYPE,
"exllamav2",
)
else:
# We expect tritonv2 to be used here, because exllama backend doesn't support packing https://github.com/ModelCloud/GPTQModel/issues/1354
# TODO: Remove this once GPTQModel exllama kernels supports packing
self.assertEqual(
self.quantized_model.model.layers[0].self_attn.k_proj.QUANT_TYPE,
"tritonv2",
)
def check_inference_correctness(self, model):
"""
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 comparing the generated tokens with the expected tokens
"""
self.check_inference_correctness(self.quantized_model)
# 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,
}