[FEAT]: EETQ quantizer support (#30262)

* [FEAT]: EETQ quantizer support

* Update quantization.md

* Update docs/source/en/main_classes/quantization.md
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* Update docs/source/en/quantization.md
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* Update docs/source/en/quantization.md
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* Update src/transformers/integrations/__init__.py
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* Update src/transformers/integrations/__init__.py
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* Update src/transformers/integrations/eetq.py
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* Update src/transformers/integrations/eetq.py
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* Update src/transformers/integrations/eetq.py
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* Update tests/quantization/eetq_integration/test_eetq.py
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* Update src/transformers/quantizers/auto.py
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* Update src/transformers/quantizers/auto.py
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* Update src/transformers/quantizers/auto.py
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* Update src/transformers/quantizers/quantizer_eetq.py
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* Update tests/quantization/eetq_integration/test_eetq.py
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* Update src/transformers/quantizers/quantizer_eetq.py
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* Update tests/quantization/eetq_integration/test_eetq.py
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* Update tests/quantization/eetq_integration/test_eetq.py
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* [FEAT]: EETQ quantizer support

* [FEAT]: EETQ quantizer support

* remove whitespaces

* update quantization.md

* style

* Update docs/source/en/quantization.md
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* add copyright

* Update quantization.md

* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/quantization.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Address the comments by amyeroberts

* style

---------
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -52,6 +52,9 @@ RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoA
 # Add quanto for quantization testing
 RUN python3 -m pip install --no-cache-dir quanto
 
+# Add eetq for quantization testing
+RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop
\ No newline at end of file

docs/source/en/main_classes/quantization.md

@@ -38,6 +38,9 @@ Learn how to quantize models in the [Quantization](../quantization) guide.
 
 [[autodoc]] AwqConfig
 
+## EetqConfig
+[[autodoc]] EetqConfig
+
 ## GPTQConfig
 
 [[autodoc]] GPTQConfig

docs/source/en/quantization.md

@@ -642,6 +642,37 @@ double_quant_config = BitsAndBytesConfig(
 model_double_quant = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b", quantization_config=double_quant_config)
 ```
 
+## EETQ
+The [EETQ](https://github.com/NetEase-FuXi/EETQ) library supports int8 per-channel weight-only quantization for NVIDIA GPUS. The high-performance GEMM and GEMV kernels are from FasterTransformer and TensorRT-LLM. It requires no calibration dataset and does not need to pre-quantize your model. Moreover, the accuracy degradation is negligible owing to the per-channel quantization. 
+
+Make sure you have eetq installed from the [relase page](https://github.com/NetEase-FuXi/EETQ/releases)
+```
+pip install --no-cache-dir https://github.com/NetEase-FuXi/EETQ/releases/download/v1.0.0/EETQ-1.0.0+cu121+torch2.1.2-cp310-cp310-linux_x86_64.whl
+```
+or via the source code https://github.com/NetEase-FuXi/EETQ. EETQ requires CUDA capability <= 8.9 and >= 7.0
+```
+git clone https://github.com/NetEase-FuXi/EETQ.git
+cd EETQ/
+git submodule update --init --recursive
+pip install .
+```
+
+An unquantized model can be quantized via "from_pretrained".
+```py
+from transformers import AutoModelForCausalLM, EetqConfig
+path = "/path/to/model"
+quantization_config = EetqConfig("int8")
+model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", quantization_config=quantization_config)
+```
+
+A quantized model can be saved via "saved_pretrained" and be reused again via the "from_pretrained".
+
+```py
+quant_path = "/path/to/save/quantized/model"
+model.save_pretrained(quant_path)
+model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
+```
+
 ## Optimum
 
 The [Optimum](https://huggingface.co/docs/optimum/index) library supports quantization for Intel, Furiosa, ONNX Runtime, GPTQ, and lower-level PyTorch quantization functions. Consider using Optimum for quantization if you're using specific and optimized hardware like Intel CPUs, Furiosa NPUs or a model accelerator like ONNX Runtime.

src/transformers/__init__.py

@@ -1126,7 +1126,14 @@ _import_structure = {
         "is_vision_available",
         "logging",
     ],
-    "utils.quantization_config": ["AqlmConfig", "AwqConfig", "BitsAndBytesConfig", "GPTQConfig", "QuantoConfig"],
+    "utils.quantization_config": [
+        "AqlmConfig",
+        "AwqConfig",
+        "BitsAndBytesConfig",
+        "EetqConfig",
+        "GPTQConfig",
+        "QuantoConfig",
+    ],
 }
 
 # sentencepiece-backed objects
@@ -6071,7 +6078,14 @@ if TYPE_CHECKING:
     )
 
     # bitsandbytes config
-    from .utils.quantization_config import AqlmConfig, AwqConfig, BitsAndBytesConfig, GPTQConfig, QuantoConfig
+    from .utils.quantization_config import (
+        AqlmConfig,
+        AwqConfig,
+        BitsAndBytesConfig,
+        EetqConfig,
+        GPTQConfig,
+        QuantoConfig,
+    )
 
     try:
         if not is_sentencepiece_available():

src/transformers/integrations/__init__.py

@@ -42,6 +42,7 @@ _import_structure = {
         "set_hf_deepspeed_config",
         "unset_hf_deepspeed_config",
     ],
+    "eetq": ["replace_with_eetq_linear"],
     "integration_utils": [
         "INTEGRATION_TO_CALLBACK",
         "AzureMLCallback",
@@ -111,6 +112,7 @@ if TYPE_CHECKING:
         set_hf_deepspeed_config,
         unset_hf_deepspeed_config,
     )
+    from .eetq import replace_with_eetq_linear
     from .integration_utils import (
         INTEGRATION_TO_CALLBACK,
         AzureMLCallback,

src/transformers/integrations/eetq.py

+# coding=utf-8
+# Copyright 2024 NetEase, Inc. and the HuggingFace Inc. 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.
+from ..utils import is_accelerate_available, is_eetq_available, logging
+
+
+if is_eetq_available():
+    import eetq
+    import torch.nn as nn
+
+if is_accelerate_available():
+    from accelerate import init_empty_weights
+
+logger = logging.get_logger(__name__)
+
+
+def _replace_with_eetq_linear(
+    model,
+    modules_to_not_convert=None,
+    current_key_name=None,
+    quantization_config=None,
+    has_been_replaced=False,
+    pre_quantized=False,
+):
+    """
+    Private method that wraps the recursion for module replacement.
+
+    Returns the converted model and a boolean that indicates if the conversion has been successfull or not.
+    """
+    if current_key_name is None:
+        current_key_name = []
+
+    for name, module in model.named_children():
+        current_key_name.append(name)
+
+        if (isinstance(module, nn.Linear)) and name not in modules_to_not_convert:
+            # Check if the current key is not in the `modules_to_not_convert`
+            current_key_name_str = ".".join(current_key_name)
+            if not any(
+                (key + "." in current_key_name_str) or (key == current_key_name_str) for key in modules_to_not_convert
+            ):
+                with init_empty_weights():
+                    in_features = module.in_features
+                    out_features = module.out_features
+                    model._modules[name] = eetq.EetqLinear(
+                        in_features, out_features, module.bias is not None, module.weight.device
+                    )
+                    if pre_quantized:
+                        model._modules[name].register_scale(module.weight.device)
+                    has_been_replaced = True
+
+                    # Force requires grad to False to avoid unexpected errors
+                    model._modules[name].requires_grad_(False)
+        if len(list(module.children())) > 0:
+            _, has_been_replaced = _replace_with_eetq_linear(
+                module,
+                modules_to_not_convert,
+                current_key_name,
+                quantization_config,
+                has_been_replaced=has_been_replaced,
+                pre_quantized=pre_quantized,
+            )
+        # Remove the last key for recursion
+        current_key_name.pop(-1)
+    return model, has_been_replaced
+
+
+def replace_with_eetq_linear(
+    model, modules_to_not_convert=None, current_key_name=None, quantization_config=None, pre_quantized=False
+):
+    """
+    A helper function to replace all `torch.nn.Linear` modules by `eetq.EetqLinear` modules from the `eetq`
+    library. This will enable running your models using high performance int8 weight-only gemm kerner from
+    FasterTransformer and TensorRT-LLM. Make sure `eetq` compiled with the correct CUDA
+    version of your hardware is installed before running this function. EETQ shall be installed via the source
+    'https://github.com/NetEase-FuXi/EETQ'
+
+    The function will be run recursively and replace all `torch.nn.Linear` modules except for the `lm_head` that should
+    be kept as a `torch.nn.Linear` module. The replacement is done under `init_empty_weights` context manager so no
+    CPU/GPU memory is required to run this function. Each weight will be quantized along the channel.
+
+    Parameters:
+        model (`torch.nn.Module`):
+            Input model or `torch.nn.Module` as the function is run recursively.
+        modules_to_not_convert (`List[`str`]`, *optional*, defaults to `["lm_head"]`):
+            Names of the modules to not convert in `EetqLinear`. In practice we keep the `lm_head` in full precision
+            for numerical stability reasons.
+        current_key_name (`List[`str`]`, *optional*):
+            An array to track the current key of the recursion. This is used to check whether the current key (part of
+            it) is not in the list of modules to not convert (for instances modules that are offloaded to `cpu` or
+            `disk`).
+    """
+
+    modules_to_not_convert = ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
+
+    if quantization_config.modules_to_not_convert is not None:
+        modules_to_not_convert.extend(quantization_config.modules_to_not_convert)
+    modules_to_not_convert = list(set(modules_to_not_convert))
+    model, has_been_replaced = _replace_with_eetq_linear(
+        model, modules_to_not_convert, current_key_name, quantization_config, pre_quantized=pre_quantized
+    )
+
+    if not has_been_replaced:
+        logger.warning(
+            "You are loading your model using eetq but no linear modules were found in your model."
+            " Please double check your model architecture, or submit an issue on github if you think this is"
+            " a bug."
+        )
+
+    return model

src/transformers/quantizers/auto.py

@@ -19,6 +19,7 @@ from ..utils.quantization_config import (
     AqlmConfig,
     AwqConfig,
     BitsAndBytesConfig,
+    EetqConfig,
     GPTQConfig,
     QuantizationConfigMixin,
     QuantizationMethod,
@@ -28,6 +29,7 @@ from .quantizer_aqlm import AqlmHfQuantizer
 from .quantizer_awq import AwqQuantizer
 from .quantizer_bnb_4bit import Bnb4BitHfQuantizer
 from .quantizer_bnb_8bit import Bnb8BitHfQuantizer
+from .quantizer_eetq import EetqHfQuantizer
 from .quantizer_gptq import GptqHfQuantizer
 from .quantizer_quanto import QuantoHfQuantizer
 
@@ -39,12 +41,14 @@ AUTO_QUANTIZER_MAPPING = {
     "gptq": GptqHfQuantizer,
     "aqlm": AqlmHfQuantizer,
     "quanto": QuantoHfQuantizer,
+    "eetq": EetqHfQuantizer,
 }
 
 AUTO_QUANTIZATION_CONFIG_MAPPING = {
     "awq": AwqConfig,
     "bitsandbytes_4bit": BitsAndBytesConfig,
     "bitsandbytes_8bit": BitsAndBytesConfig,
+    "eetq": EetqConfig,
     "gptq": GPTQConfig,
     "aqlm": AqlmConfig,
     "quanto": QuantoConfig,

src/transformers/quantizers/quantizer_eetq.py

+# Copyright 2024 The HuggingFace Inc. 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.
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from .base import HfQuantizer
+
+
+if TYPE_CHECKING:
+    from ..modeling_utils import PreTrainedModel
+
+from ..utils import is_accelerate_available, is_eetq_available, is_torch_available, logging
+from .quantizers_utils import get_module_from_name
+
+
+if is_torch_available():
+    import torch
+
+
+logger = logging.get_logger(__name__)
+
+
+class EetqHfQuantizer(HfQuantizer):
+    """
+    8-bit quantization from EETQ quantization method:
+        before loading: converts transformer layers into W8A16Linear during loading: load 16bit weight and pass to the
+        layer object after: quantizes individual weights in Linear8bitLt into 8bit at first .cuda() call
+    """
+
+    requires_parameters_quantization = True
+    requires_calibration = False
+
+    required_packages = ["eetq", "accelerate"]
+
+    def __init__(self, quantization_config, **kwargs):
+        super().__init__(quantization_config, **kwargs)
+        self.quantization_config = quantization_config
+
+    def validate_environment(self, *args, **kwargs):
+        if not is_eetq_available():
+            raise ImportError(
+                "Using `eetq` 8-bit quantization requires eetq."
+                "Please install the latest version of eetq from : https://github.com/NetEase-FuXi/EETQ"
+            )
+
+        if not is_accelerate_available():
+            raise ImportError("Loading an EETQ quantized model requires accelerate (`pip install accelerate`)")
+
+        if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
+            raise ValueError(
+                "Converting into 8-bit weights from tf/flax weights is currently not supported, please make"
+                " sure the weights are in PyTorch format."
+            )
+
+        if not torch.cuda.is_available():
+            raise RuntimeError("No GPU found. A GPU is needed for quantization.")
+
+        device_map = kwargs.get("device_map", None)
+        if device_map is None:
+            logger.warning_once(
+                "You have loaded an EETQ model on CPU and have a CUDA device available, make sure to set "
+                "your model on a GPU device in order to run your model."
+            )
+        elif device_map is not None:
+            if isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()):
+                raise ValueError(
+                    "You are attempting to load an EETQ model with a device_map that contains a CPU or disk device."
+                    " This is not supported. Please remove the CPU or disk device from the device_map."
+                )
+
+    def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
+        if torch_dtype is None:
+            torch_dtype = torch.float16
+            logger.info(
+                "Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
+                "requirements of `eetq` to enable model loading in 8-bit. "
+                "Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
+                " torch_dtype=torch.float16 to remove this warning.",
+                torch_dtype,
+            )
+        elif torch_dtype != torch.float16:
+            logger.info("We suggest you to set `torch_dtype=torch.float16` for better efficiency with EETQ.")
+        return torch_dtype
+
+    def check_quantized_param(
+        self,
+        model: "PreTrainedModel",
+        param_value: "torch.Tensor",
+        param_name: str,
+        state_dict: Dict[str, Any],
+        **kwargs,
+    ):
+        from eetq import EetqLinear
+
+        module, tensor_name = get_module_from_name(model, param_name)
+
+        if isinstance(module, EetqLinear):
+            if self.pre_quantized or tensor_name == "bias":
+                if tensor_name == "weight" and param_value.dtype != torch.int8:
+                    raise ValueError("Expect quantized weights but got an unquantized weight")
+                return False
+            else:
+                if tensor_name == "weight_scale":
+                    raise ValueError("Expect unquantized weights but got a quantized weight_scale")
+                return True
+        return False
+
+    def create_quantized_param(
+        self,
+        model: "PreTrainedModel",
+        param_value: "torch.Tensor",
+        param_name: str,
+        target_device: "torch.device",
+        state_dict: Dict[str, Any],
+        unexpected_keys: Optional[List[str]] = None,
+    ):
+        """
+        quantizes weights into qweight and weight_scales
+        """
+        from eetq import quantize_and_preprocess_weights
+
+        module, tensor_name = get_module_from_name(model, param_name)
+        new_value, weight_scale = quantize_and_preprocess_weights(param_value)
+
+        module._buffers[tensor_name] = new_value.to(target_device)
+        module.register("weight_scales", weight_scale.to(target_device))
+
+    def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
+        return model
+
+    def _process_model_before_weight_loading(
+        self,
+        model: "PreTrainedModel",
+        device_map,
+        keep_in_fp32_modules: List[str] = [],
+        **kwargs,
+    ):
+        from ..integrations import get_keys_to_not_convert, replace_with_eetq_linear
+
+        self.modules_to_not_convert = get_keys_to_not_convert(model)
+
+        if self.quantization_config.modules_to_not_convert is not None:
+            self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
+
+        model = replace_with_eetq_linear(
+            model,
+            modules_to_not_convert=self.modules_to_not_convert,
+            quantization_config=self.quantization_config,
+            pre_quantized=self.pre_quantized,
+        )
+
+        model.config.quantization_config = self.quantization_config
+
+    @property
+    def is_serializable(self):
+        return True
+
+    @property
+    def is_trainable(self) -> bool:
+        return False

src/transformers/testing_utils.py

@@ -65,6 +65,7 @@ from .utils import (
     is_cython_available,
     is_decord_available,
     is_detectron2_available,
+    is_eetq_available,
     is_essentia_available,
     is_faiss_available,
     is_flash_attn_2_available,
@@ -1014,6 +1015,13 @@ def require_aqlm(test_case):
     return unittest.skipUnless(is_aqlm_available(), "test requires aqlm")(test_case)
 
 
+def require_eetq(test_case):
+    """
+    Decorator marking a test that requires eetq
+    """
+    return unittest.skipUnless(is_eetq_available(), "test requires eetq")(test_case)
+
+
 def require_av(test_case):
     """
     Decorator marking a test that requires av

src/transformers/utils/__init__.py

@@ -119,6 +119,7 @@ from .import_utils import (
     is_datasets_available,
     is_decord_available,
     is_detectron2_available,
+    is_eetq_available,
     is_essentia_available,
     is_faiss_available,
     is_flash_attn_2_available,

src/transformers/utils/import_utils.py

@@ -97,6 +97,7 @@ _apex_available = _is_package_available("apex")
 _aqlm_available = _is_package_available("aqlm")
 _av_available = importlib.util.find_spec("av") is not None
 _bitsandbytes_available = _is_package_available("bitsandbytes")
+_eetq_available = _is_package_available("eetq")
 _galore_torch_available = _is_package_available("galore_torch")
 # `importlib.metadata.version` doesn't work with `bs4` but `beautifulsoup4`. For `importlib.util.find_spec`, reversed.
 _bs4_available = importlib.util.find_spec("bs4") is not None
@@ -829,6 +830,10 @@ def is_auto_gptq_available():
     return _auto_gptq_available
 
 
+def is_eetq_available():
+    return _eetq_available
+
+
 def is_levenshtein_available():
     return _levenshtein_available
 

src/transformers/utils/quantization_config.py

@@ -40,6 +40,7 @@ class QuantizationMethod(str, Enum):
     AWQ = "awq"
     AQLM = "aqlm"
     QUANTO = "quanto"
+    EETQ = "eetq"
 
 
 class AWQLinearVersion(str, Enum):
@@ -893,3 +894,37 @@ class QuantoConfig(QuantizationConfigMixin):
             raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights}")
         if self.activations not in accepted_activations:
             raise ValueError(f"Only support weights in {accepted_activations} but found {self.activations}")
+
+
+@dataclass
+class EetqConfig(QuantizationConfigMixin):
+    """
+    This is a wrapper class about all possible attributes and features that you can play with a model that has been
+    loaded using `eetq`.
+
+    Args:
+        weights (`str`, *optional*, defaults to `"int8"`):
+            The target dtype for the weights. Supported value is only "int8"
+        modules_to_not_convert (`list`, *optional*, default to `None`):
+            The list of modules to not quantize, useful for quantizing models that explicitly require to have
+            some modules left in their original precision.
+    """
+
+    def __init__(
+        self,
+        weights: str = "int8",
+        modules_to_not_convert: Optional[List] = None,
+        **kwargs,
+    ):
+        self.quant_method = QuantizationMethod.EETQ
+        self.weights = weights
+        self.modules_to_not_convert = modules_to_not_convert
+        self.post_init()
+
+    def post_init(self):
+        r"""
+        Safety checker that arguments are correct
+        """
+        accepted_weights = ["int8"]
+        if self.weights not in accepted_weights:
+            raise ValueError(f"Only support weights in {accepted_weights} but found {self.weights}")

tests/quantization/eetq_integration/test_eetq.py

+# 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
+
+
+@require_torch_gpu
+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)
+
+
+@slow
+@require_torch_gpu
+@require_eetq
+@require_accelerate
+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
+    @classmethod
+    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)
+
+    @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
+        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)