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Enable gptqmodel (#35012)

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* gptqmodel

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* update readme

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* gptqmodel need use checkpoint_format (#1)

* gptqmodel need use checkpoint_format

* fix quantize

* Update quantization_config.py

* Update quantization_config.py

* Update quantization_config.py

---------

Co-authored-by: ZX-ModelCloud <zx@modelcloud.ai>
Co-authored-by: Qubitium-ModelCloud <qubitium@modelcloud.ai>

* Revert quantizer_gptq.py (#2)

* revert quantizer_gptq.py change

* pass **kwargs

* limit gptqmodel and optimum version

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix warning

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix version check

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* revert unrelated changes

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* enable gptqmodel tests

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix requires gptq

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* Fix Transformer compat (#3)

* revert quantizer_gptq.py change

* pass **kwargs

* add meta info

* cleanup

* cleanup

* Update quantization_config.py

* hf_select_quant_linear pass checkpoint_format and meta

* fix GPTQTestCUDA

* Update test_gptq.py

* gptqmodel.hf_select_quant_linear() now does not select ExllamaV2

* cleanup

* add backend

* cleanup

* cleanup

* no need check exllama version

* Update quantization_config.py

* lower checkpoint_format and backend

* check none

* cleanup

* Update quantization_config.py

* fix self.use_exllama == False

* spell

* fix unittest

* fix unittest

---------

Co-authored-by: LRL <lrl@lbx.dev>
Co-authored-by: Qubitium-ModelCloud <qubitium@modelcloud.ai>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format again

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* update gptqmodel version (#6)

* update gptqmodel version

* update gptqmodel version

* fix unit test (#5)

* update gptqmodel version

* update gptqmodel version

* "not self.use_exllama" is not equivalent to "self.use_exllama==False"

* fix unittest

* update gptqmodel version

* backend is loading_attibutes (#7)

* fix format and tests

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix memory check

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix device mismatch

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix result check

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* Update src/transformers/quantizers/quantizer_gptq.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* Update src/transformers/quantizers/quantizer_gptq.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* Update src/transformers/quantizers/quantizer_gptq.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* update tests

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* review: update docs (#10)

* review: update docs (#12)

* review: update docs

* fix typo

* update tests for gptqmodel

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* update document (#9)

* update overview.md

* cleanup

* Update overview.md

* Update overview.md

* Update overview.md

* update gptq.md

* Update gptq.md

* Update gptq.md

* Update gptq.md

* Update gptq.md

* Update gptq.md

* Update gptq.md

---------

Co-authored-by: Qubitium-ModelCloud <qubitium@modelcloud.ai>

* typo

* doc note for asymmetric quant

* typo with apple silicon(e)

* typo for marlin

* column name revert: review

* doc rocm support

* Update docs/source/en/quantization/gptq.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/quantization/gptq.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/quantization/gptq.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/quantization/gptq.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/quantization/overview.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update docs/source/en/quantization/overview.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

---------

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
Co-authored-by: LRL-ModelCloud <165116337+LRL-ModelCloud@users.noreply.github.com>
Co-authored-by: ZX-ModelCloud <zx@modelcloud.ai>
Co-authored-by: Qubitium-ModelCloud <qubitium@modelcloud.ai>
Co-authored-by: ZX-ModelCloud <165115237+ZX-ModelCloud@users.noreply.github.com>
Co-authored-by: LRL <lrl@lbx.dev>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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docs/source/en/quantization/gptq.md
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@@ -22,15 +22,42 @@ Try GPTQ quantization with PEFT in this [notebook](https://colab.research.google
</Tip>
The [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) library implements the GPTQ algorithm, a post-training quantization technique where each row of the weight matrix is quantized independently to find a version of the weights that minimizes the error. These weights are quantized to int4, but they're restored to fp16 on the fly during inference. This can save your memory-usage by 4x because the int4 weights are dequantized in a fused kernel rather than a GPU's global memory, and you can also expect a speedup in inference because using a lower bitwidth takes less time to communicate.
Both [GPTQModel](https://github.com/ModelCloud/GPTQModel) and [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) libraries implement the GPTQ algorithm, a post-training quantization technique where each row of the weight matrix is quantized independently to find a version of the weights that minimizes error. These weights are quantized to int4, stored as int32 (int4 x 8) and dequantized (restored) to fp16 on the fly during inference. This can save memory by almost 4x because the int4 weights are often dequantized in a fused kernel. You can also expect a substantial speedup in inference due to lower bandwidth requirements for lower bitwidth.
Before you begin, make sure the following libraries are installed:
[GPTQModel](https://github.com/ModelCloud/GPTQModel) started as a maintained fork of AutoGPTQ but has since differentiated itself with the following major differences.
* Model support: GPTQModel continues to support all of the latest LLM models.
* Multimodal support: GPTQModel supports accurate quantization of Qwen 2-VL and Ovis 1.6-VL image-to-text models.
* Platform support: Linux, macOS (Apple Silicon), and Windows 11.
* Hardware support: NVIDIA CUDA, AMD ROCm, Apple Silicon M1/MPS /CPU, Intel/AMD CPU, and Intel Datacenter Max/Arc GPUs.
* Asymmetric support: Asymmetric quantization can potentially introduce lower quantization errors compared to symmetric quantization. However, it is not backward compatible with AutoGPTQ, and not all kernels, such as Marlin, support asymmetric quantization.
* IPEX kernel for Intel/AMD accelerated CPU and Intel GPU (Datacenter Max/Arc GPUs) support.
* Updated Marlin kernel from Neural Magic optimized for A100 (Ampere).
* Updated kernels with auto-padding for legacy model support and models with non-uniform in/out-features.
* Faster quantization, lower memory usage, and more accurate default quantization via GPTQModel quantization APIs.
* User and developer friendly APIs.
[AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) will likely be deprecated in the future due the lack of continued support for new models and features.
Before you begin, make sure the following libraries are installed and updated to the latest release:
```bash
pip install auto-gptq
pip install --upgrade accelerate optimum transformers
```
Then install either GPTQModel or AutoGPTQ.
```bash
pip install gptqmodel --no-build-isolation
```
or
```bash
pip install auto-gptq --no-build-isolation
```
To quantize a model (currently only supported for text models), you need to create a [`GPTQConfig`] class and set the number of bits to quantize to, a dataset to calibrate the weights for quantization, and a tokenizer to prepare the dataset.
```py
@@ -92,9 +119,22 @@ from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto")
```
## Marlin
[Marlin](https://github.com/IST-DASLab/marlin) is a 4-bit only CUDA GPTQ kernel, highly optimized for the NVIDIA A100 GPU (Ampere) architecture. Loading, dequantization, and execution of post-dequantized weights are highly parallelized, offering a substantial inference improvement versus the original CUDA GPTQ kernel. Marlin is only available for quantized inference and does not support model quantization.
Marlin inference can be activated with the `backend` parameter in [`GPTQConfig`].
```py
from transformers import AutoModelForCausalLM, GPTQConfig
model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto", quantization_config=GPTQConfig(bits=4, backend="marlin"))
```
## ExLlama
[ExLlama](https://github.com/turboderp/exllama) is a Python/C++/CUDA implementation of the [Llama](model_doc/llama) model that is designed for faster inference with 4-bit GPTQ weights (check out these [benchmarks](https://github.com/huggingface/optimum/tree/main/tests/benchmark#gptq-benchmark)). The ExLlama kernel is activated by default when you create a [`GPTQConfig`] object. To boost inference speed even further, use the [ExLlamaV2](https://github.com/turboderp/exllamav2) kernels by configuring the `exllama_config` parameter:
[ExLlama](https://github.com/turboderp/exllama) is a CUDA implementation of the [Llama](model_doc/llama) model that is designed for faster inference with 4-bit GPTQ weights (check out these [benchmarks](https://github.com/huggingface/optimum/tree/main/tests/benchmark#gptq-benchmark)). The ExLlama kernel is activated by default when you create a [`GPTQConfig`] object. To boost inference speed even further, use the [ExLlamaV2](https://github.com/turboderp/exllamav2) kernels by configuring the `exllama_config` parameter:
```py
import torch
@@ -110,11 +150,11 @@ Only 4-bit models are supported, and we recommend deactivating the ExLlama kerne
</Tip>
The ExLlama kernels are only supported when the entire model is on the GPU. If you're doing inference on a CPU with AutoGPTQ (version > 0.4.2), then you'll need to disable the ExLlama kernel. This overwrites the attributes related to the ExLlama kernels in the quantization config of the config.json file.
The ExLlama kernels are only supported when the entire model is on the GPU. If you're doing inference on a CPU with AutoGPTQ or GPTQModel, then you'll need to disable the ExLlama kernel. This overwrites the attributes related to the ExLlama kernels in the quantization config of the config.json file.
```py
import torch
from transformers import AutoModelForCausalLM, GPTQConfig
gptq_config = GPTQConfig(bits=4, use_exllama=False)
model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="cpu", quantization_config=gptq_config)
```
```