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#
# 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 AutoModelForCausalLM, AutoTokenizer, FPQuantConfig
from transformers.testing_utils import (
backend_empty_cache,
require_accelerate,
require_fp_quant,
require_qutlass,
require_torch_accelerator,
require_torch_multi_accelerator,
slow,
torch_device,
)
@require_torch_accelerator
class FPQuantConfigTest(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 = FPQuantConfig()
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": ["embed_tokens", "lm_head"], "quant_method": "fp_quant"}
quantization_config = FPQuantConfig.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)
@slow
@require_torch_accelerator
@require_fp_quant
@require_accelerate
class FPQuantBaseTest(unittest.TestCase):
model_name = "unsloth/Llama-3.2-1B"
input_text = "1 2 3 4"
max_new_tokens = 4
EXPECTED_OUTPUT = "1 2 3 4 5 6"
device_map = torch_device
@classmethod
def getQuantizationConfig(cls):
unittest.skip("Subclass must implement this method")
# called only once for all test in this class
@classmethod
def setUpClass(cls):
"""
Setup quantized model
"""
cls.quantization_config = cls.getQuantizationConfig()
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.quantized_model = AutoModelForCausalLM.from_pretrained(
cls.model_name, device_map=cls.device_map, quantization_config=cls.quantization_config
)
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)
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)
@require_torch_multi_accelerator
def test_quantized_model_multi_accelerator(self):
"""
Simple test that checks if the quantized model is working properly with multiple accelerators.
Set CUDA_VISIBLE_DEVICES=0,1 if you have more than 2 CUDA GPUs. Or set ZE_AFFINITY_MASK=0,1
if you have more than 2 Intel XPUs.
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)
quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name, device_map="auto", quantization_config=self.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)
@require_torch_multi_accelerator
def test_save_pretrained_multi_accelerator(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="auto")
self.assertTrue(set(model.hf_device_map.values()) == {0, 1})
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)
class FPQuantMXFP4PseudoquantTest(FPQuantBaseTest):
@classmethod
def getQuantizationConfig(cls):
return FPQuantConfig(forward_dtype="mxfp4", pseudoquantization=True)
class FPQuantNVFP4PseudoquantTest(FPQuantBaseTest):
@classmethod
def getQuantizationConfig(cls):
return FPQuantConfig(forward_dtype="nvfp4", pseudoquantization=True)
@require_qutlass
class FPQuantMXFP4Test(FPQuantBaseTest):
@classmethod
def getQuantizationConfig(cls):
return FPQuantConfig(forward_dtype="mxfp4", pseudoquantization=False)
@require_qutlass
class FPQuantNVFP4Test(FPQuantBaseTest):
@classmethod
def getQuantizationConfig(cls):
return FPQuantConfig(forward_dtype="nvfp4", pseudoquantization=False)
@require_qutlass
class FPQuantMXFP4GS128Test(FPQuantBaseTest):
@classmethod
def getQuantizationConfig(cls):
return FPQuantConfig(forward_dtype="mxfp4", pseudoquantization=False, hadamard_group_size=128)
@require_qutlass
class FPQuantNVFP4GS128Test(FPQuantBaseTest):
@classmethod
def getQuantizationConfig(cls):
return FPQuantConfig(forward_dtype="nvfp4", pseudoquantization=False, hadamard_group_size=128)
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