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# Copyright 2025 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, FineGrainedFP8Config, OPTForCausalLM
from transformers.testing_utils import (
backend_empty_cache,
get_device_properties,
require_accelerate,
require_read_token,
require_torch_accelerator,
require_torch_multi_accelerator,
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
@require_torch_accelerator
class FineGrainedFP8ConfigTest(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 = FineGrainedFP8Config()
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": "fp8"}
quantization_config = FineGrainedFP8Config.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_accelerate
@require_read_token
@require_torch_accelerator
class FP8QuantizerTest(unittest.TestCase):
model_name = "meta-llama/Llama-3.2-1B"
input_text = "Once upon a time"
max_new_tokens = 10
EXPECTED_OUTPUT = "Once upon a time, there was a man who was very rich."
device_map = torch_device
offload_device_map = {
"model.embed_tokens": 0,
"model.layers.0": 0,
"model.layers.1": 0,
"model.layers.2": 0,
"model.layers.3": 0,
"model.layers.4": 0,
"model.layers.5": 0,
"model.layers.6": 0,
"model.layers.7": "cpu",
"model.layers.8": "cpu",
"model.layers.9": "cpu",
"model.layers.10": "cpu",
"model.layers.11": "cpu",
"model.layers.12": "cpu",
"model.layers.13": "cpu",
"model.layers.14": "cpu",
"model.layers.15": "cpu",
"model.rotary_emb": "disk",
"model.norm": "disk",
"lm_head": 0,
}
@classmethod
def setUpClass(cls):
"""
Setup quantized model
"""
cls.quantization_config = FineGrainedFP8Config()
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_conversion(self):
"""
Simple test that checks if the quantized model has been converted properly
"""
from transformers.integrations import FP8Linear, replace_with_fp8_linear
model_id = "facebook/opt-350m"
config = AutoConfig.from_pretrained(model_id, revision="cb32f77e905cccbca1d970436fb0f5e6b58ee3c5")
quantization_config = FineGrainedFP8Config()
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_fp8_linear(model, quantization_config=quantization_config)
nb_fp8_linear = 0
for module in model.modules():
if isinstance(module, FP8Linear):
nb_fp8_linear += 1
self.assertEqual(nb_linears - 1, nb_fp8_linear)
with init_empty_weights():
model = OPTForCausalLM(config)
quantization_config = FineGrainedFP8Config(modules_to_not_convert=["fc1"])
model = replace_with_fp8_linear(model, quantization_config=quantization_config)
nb_fp8_linear = 0
for module in model.modules():
if isinstance(module, FP8Linear):
nb_fp8_linear += 1
self.assertEqual(nb_linears - 25, nb_fp8_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(self.device_map)
output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens, do_sample=False)
output_tokens = self.tokenizer.decode(output[0], skip_special_tokens=True)
self.assertEqual(output_tokens, 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(self.device_map)
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)
def test_weight_and_weight_scale_inv(self):
"""
Simple test that checks if the weight and weight_scale_inv are working properly
"""
weight = self.quantized_model.model.layers[0].self_attn.q_proj.weight
weight_scale_inv = self.quantized_model.model.layers[0].self_attn.q_proj.weight_scale_inv
self.assertEqual(weight.dtype, torch.float8_e4m3fn)
self.assertEqual(weight_scale_inv.dtype, torch.float32)
self.assertEqual(weight.shape, (weight_scale_inv.shape[0] * 128, weight_scale_inv.shape[1] * 128))
def test_block_size(self):
"""
Simple test that checks if the block size is working properly
"""
self.assertEqual(self.quantized_model.config.quantization_config.weight_block_size, (128, 128))
quantization_config = FineGrainedFP8Config(weight_block_size=(32, 32))
quantized_model = AutoModelForCausalLM.from_pretrained(
self.model_name, device_map=self.device_map, quantization_config=quantization_config
)
self.assertEqual(quantized_model.config.quantization_config.weight_block_size, (32, 32))
@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 GPUs; or set ZE_AFFINITY_MASK=0,1 if you
have more than 2 XPUs.
"""
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
quantization_config = FineGrainedFP8Config()
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, do_sample=False)
self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
@require_torch_multi_accelerator
def test_save_pretrained_multi_accelerators(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(self.device_map)
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)
def test_quantized_model_offload(self):
"""
Simple test that checks if the quantized model returns an error when loading with cpu/disk offloaded
"""
with self.assertRaisesRegex(
ValueError, "You are attempting to load an FP8 model with a device_map that contains a cpu/disk device."
):
AutoModelForCausalLM.from_pretrained(
self.model_name, device_map=self.offload_device_map, quantization_config=self.quantization_config
)
def test_save_pretrained_offload(self):
"""
Simple test that checks if the saved quantized model is working properly cpu/disk offload
"""
with tempfile.TemporaryDirectory() as tmpdirname:
self.quantized_model.save_pretrained(tmpdirname)
input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(self.device_map)
quantized_model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.offload_device_map)
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)
@require_torch_accelerator
class FP8LinearTest(unittest.TestCase):
device = torch_device
@unittest.skipIf(
get_device_properties()[0] == "cuda" and get_device_properties()[1] < 9,
"Skipping FP8LinearTest because it is not supported on GPU with capability < 9.0",
)
def test_linear_preserves_shape(self):
"""
Test that FP8Linear preserves shape when in_features == out_features.
"""
from transformers.integrations import FP8Linear
linear = FP8Linear(256, 256, block_size=(128, 128), device=self.device)
x = torch.rand((1, 5, 256)).to(self.device)
x_ = linear(x)
self.assertEqual(x_.shape, x.shape)
@unittest.skipIf(
get_device_properties()[0] == "cuda" and get_device_properties()[1] < 9,
"Skipping FP8LinearTest because it is not supported on GPU with capability < 9.0",
)
def test_linear_with_diff_feature_size_preserves_shape(self):
"""
Test that FP8Linear generates the correct shape when in_features != out_features.
"""
from transformers.integrations import FP8Linear
linear = FP8Linear(128, 256, block_size=(128, 128), device=self.device)
x = torch.rand((1, 5, 128)).to(self.device)
x_ = linear(x)
self.assertEqual(x_.shape, (1, 5, 256))
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