Fix auto-round install & bump into 0.4.4 (#387)

Signed-off-by: Sun, Xuehao <xuehao.sun@intel.com>
Co-authored-by: Yi Liu <yi4.liu@intel.com>

.azure-pipelines/scripts/codeScan/pylint/pylint.sh

@@ -15,7 +15,9 @@ RESET="echo -en \\E[0m \\n" # close color
 log_dir="/auto-round/.azure-pipelines/scripts/codeScan/scanLog"
 mkdir -p $log_dir
 
+pip install torch --index-url https://download.pytorch.org/whl/cpu
 pip install -r /auto-round/requirements.txt
+pip install -r /auto-round/requirements-cpu.txt
 
 echo "[DEBUG] list pipdeptree..."
 pip install pipdeptree

.azure-pipelines/template/docker-template.yml

@@ -74,7 +74,7 @@ steps:
 
   - ${{ if eq(parameters.imageSource, 'pull') }}:
       - script: |
-            docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+            docker pull vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.5.1:latest
         displayName: "Pull habana docker image"
 
   - script: |
@@ -90,12 +90,13 @@ steps:
           script: |
             if [[ "${{ parameters.imageSource }}" == "build" ]]; then
                 docker run -dit --disable-content-trust --privileged --name=${{ parameters.containerName }} --shm-size="2g" \
-                -v ${BUILD_SOURCESDIRECTORY}:/auto-round -v /tf_dataset:/tf_dataset -v /tf_dataset2:/tf_dataset2 \
-                ${{ parameters.repoName }}:${{ parameters.repoTag }}
+                  -v ${BUILD_SOURCESDIRECTORY}:/auto-round -v /tf_dataset:/tf_dataset -v /tf_dataset2:/tf_dataset2 \
+                  ${{ parameters.repoName }}:${{ parameters.repoTag }}
             else
                 docker run -dit --disable-content-trust --privileged --name=${{ parameters.containerName }} --shm-size="2g" \
-                --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host \
-                -v ${BUILD_SOURCESDIRECTORY}:/auto-round vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+                  --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host \
+                  -v ${BUILD_SOURCESDIRECTORY}:/auto-round vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.5.1:latest
+                docker exec ${{ parameters.containerName }} bash -c "ln -sf \$(which python3) /usr/bin/python"
             fi
             echo "Show the container list after docker run ... "
             docker ps -a

.azure-pipelines/template/ut-template.yml

@@ -35,14 +35,16 @@ steps:
   - ${{ if eq(parameters.imageSource, 'build') }}:
     - script: |
         docker exec ${{ parameters.utContainerName }} bash -c "cd /auto-round \
-          && pip install -vvv --no-build-isolation . \
+          && pip install torch --index-url https://download.pytorch.org/whl/cpu \
+          && pip install .[cpu] \
           && pip list"
       displayName: "Env Setup"
 
   - ${{ if eq(parameters.imageSource, 'pull') }}:
     - script: |
         docker exec ${{ parameters.utContainerName }} bash -c "cd /auto-round \
-          && pip install -vvv --no-build-isolation . \
+          && python setup.py bdist_wheel lib \
+          && pip install dist/*.whl \
           && pip list"
       displayName: "HPU Env Setup"
 

MANIFEST.in

@@ -1,3 +1,4 @@
 include requirements.txt
 include requirements-cpu.txt
 include requirements-hpu.txt
+include requirements-lib.txt

README.md

@@ -5,7 +5,7 @@ AutoRound
 <h3> Advanced Quantization Algorithm for LLMs</h3>
 
 [![python](https://img.shields.io/badge/python-3.9%2B-blue)](https://github.com/intel/auto-round)
-[![version](https://img.shields.io/badge/release-0.4.3-green)](https://github.com/intel/auto-round)
+[![version](https://img.shields.io/badge/release-0.4.4-green)](https://github.com/intel/auto-round)
 [![license](https://img.shields.io/badge/license-Apache%202-blue)](https://github.com/intel/auto-round/blob/main/LICENSE)
 ---
 <div align="left">
@@ -57,14 +57,28 @@ details and quantized models in several Hugging Face Spaces, e.g. [OPEA](https:/
 ### Install from pypi
 
 ```bash
-pip install auto-round
+# GPU
+pip install auto-round[gpu]
+
+# CPU
+pip install auto-round[cpu]
+
+# HPU
+pip install auto-round-lib
 ```
 
 <details>
   <summary>Build from Source</summary>
 
   ```bash
-  pip install -vvv --no-build-isolation .
+  # GPU
+  pip install .[gpu]
+
+  # CPU
+  pip install .[cpu]
+
+  # HPU
+  python setup.py install lib
   ```
 
 </details>

auto_round/auto_quantizer.py

@@ -548,7 +548,7 @@ def remove_device_str(s, device_str):
                 )
             if "gptq" in layer_backend and "exllamav2" in layer_backend:
                 try:
-                    from exllamav2_kernels import gemm_half_q_half, make_q_matrix  # pylint: disable=E0611
+                    from exllamav2_kernels import gemm_half_q_half, make_q_matrix  # pylint: disable=E0611, E0401
                 except:
                     logger.warning_once(
                         "For better inference performance, please install exllamav2 kernel "

auto_round/version.py

@@ -14,4 +14,4 @@
 """Intel® auto-round: An open-source Python library
 supporting popular model weight only compression based on signround."""
 
-__version__ = "0.4.3"
+__version__ = "0.4.4"

requirements-cpu.txt

@@ -1,16 +1,2 @@
-accelerate
-datasets
-py-cpuinfo
-sentencepiece
-torch
-transformers>=4.38
-numpy < 2.0
-threadpoolctl
-lm-eval>=0.4.2,<0.5
-tqdm
-packaging
-pillow
-numba
-tbb
 intel-extension-for-pytorch
 intel-extension-for-transformers

requirements-gpu.txt

@@ -0,0 +1 @@
+auto-gptq>=0.7.1

requirements-hpu.txt → requirements-lib.txt

@@ -2,10 +2,10 @@ accelerate
 datasets
 py-cpuinfo
 sentencepiece
-transformers
 numpy < 2.0
 tqdm
 packaging
 pillow
 numba
-tbb
+tbb
+transformers

requirements.txt

@@ -2,14 +2,13 @@ accelerate
 datasets
 py-cpuinfo
 sentencepiece
-torch
-transformers>=4.38
 numpy < 2.0
-threadpoolctl
-lm-eval>=0.4.2,<0.5
 tqdm
 packaging
-auto-gptq>=0.7.1
 pillow
 numba
 tbb
+torch
+transformers>=4.38
+threadpoolctl
+lm-eval>=0.4.2,<0.5

setup.py

@@ -25,7 +25,6 @@
 
 def is_cuda_available():
     try:
-        os.system("pip install torch")
         import torch
 
         return torch.cuda.is_available()
@@ -112,15 +111,6 @@ def detect_local_sm_architectures():
     return arch_list
 
 
-def detect_hardware():
-    if is_hpu_available():
-        return "requirements-hpu.txt"
-    elif is_cuda_available():
-        return "requirements.txt"
-    else:
-        return "requirements-cpu.txt"
-
-
 UNSUPPORTED_COMPUTE_CAPABILITIES = ['3.5', '3.7', '5.0', '5.2', '5.3']
 
 if BUILD_CUDA_EXT:
@@ -229,23 +219,60 @@ def detect_hardware():
             "auto_round_extension.*",
         ],
     ),
-    "install_requires": fetch_requirements(detect_hardware()),
+    "install_requires": fetch_requirements("requirements.txt"),
+    "extras_require": {
+        "gpu": fetch_requirements("requirements-gpu.txt"),
+        "cpu": fetch_requirements("requirements-cpu.txt"),
+    },
+}
+
+###############################################################################
+# Configuration for auto_round_lib
+# From pip:
+# pip install auto-round-lib
+# From source:
+# python setup.py lib install
+###############################################################################
+
+
+LIB_REQUIREMENTS_FILE = "requirements-lib.txt"
+LIB_INSTALL_CFG = {
+    "include_packages": find_packages(
+        include=[
+            "auto_round",
+            "auto_round.*",
+            "auto_round_extension",
+            "auto_round_extension.*",
+        ],
+    ),
+    "install_requires": fetch_requirements(LIB_REQUIREMENTS_FILE),
 }
 
 if __name__ == "__main__":
     # There are two ways to install hpu-only package:
-    # 1. pip install -vvv --no-build-isolation -e .[hpu]
-    # 2. Within the gaudi docker where the HPU is available, we install the hpu package by default.
+    # 1. python setup.py lib install
+    # 2. Within the gaudi docker where the HPU is available, we install the auto_round_lib by default.
+    is_user_requesting_library_build = "lib" in sys.argv
+    if is_user_requesting_library_build:
+        sys.argv.remove("lib")
+    should_build_library = is_user_requesting_library_build or BUILD_HPU_ONLY
+
+    if should_build_library:
+        package_name = "auto_round_lib"
+        INSTALL_CFG = LIB_INSTALL_CFG
+    else:
+        package_name = "auto_round"
+        INSTALL_CFG = PKG_INSTALL_CFG
 
-    include_packages = PKG_INSTALL_CFG.get("include_packages", {})
-    install_requires = PKG_INSTALL_CFG.get("install_requires", [])
-    extras_require = PKG_INSTALL_CFG.get("extras_require", {})
+    include_packages = INSTALL_CFG.get("include_packages", {})
+    install_requires = INSTALL_CFG.get("install_requires", [])
+    extras_require = INSTALL_CFG.get("extras_require", {})
 
     setup(
-        name="auto_round",
+        name=package_name,
         author="Intel AIPT Team",
         version=version,
-        author_email="wenhua.cheng@intel.com, weiwei1.zhang@intel.com",
+        author_email="wenhua.cheng@intel.com, weiwei1.zhang@intel.com, heng.guo@intel.com",
         description="Repository of AutoRound: Advanced Weight-Only Quantization Algorithm for LLMs",
         long_description=open("README.md", "r", encoding="utf-8").read(),
         long_description_content_type="text/markdown",