add python style check (#10620)

* add python style check * fix style checks * update runner * add ipex-llm-finetune-qlora-cpu-k8s to manually_build workflow * update tag to 2.1.0-SNAPSHOT
intel-analytics · Apr 2, 2024 · a10f5a1 · a10f5a1
1 parent 58b5717
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diff --git a/.github/workflows/manually_build.yml b/.github/workflows/manually_build.yml
@@ -10,13 +10,14 @@ on:
         type: choice
         options:
         - all
-        - ipex-llm-finetune-lora-cpu
-        - ipex-llm-finetune-qlora-cpu
-        - ipex-llm-finetune-qlora-xpu
-        - ipex-llm-xpu
         - ipex-llm-cpu
-        - ipex-llm-serving-xpu
+        - ipex-llm-xpu
         - ipex-llm-serving-cpu
+        - ipex-llm-serving-xpu
+        - ipex-llm-finetune-lora-cpu
+        - ipex-llm-finetune-qlora-cpu-standalone
+        - ipex-llm-finetune-qlora-cpu-k8s
+        - ipex-llm-finetune-qlora-xpu
       tag:
         description: 'docker image tag (e.g. 2.1.0-SNAPSHOT)'
         required: true
@@ -72,21 +73,21 @@ jobs:
         sudo docker push ${image}:latest
         sudo docker rmi -f ${image}:${TAG} 10.239.45.10/arda/${image}:${TAG} ${image}:latest
 
-  ipex-llm-finetune-qlora-cpu:
-    if: ${{ inputs.artifact == 'ipex-llm-finetune-qlora-cpu' || inputs.artifact == 'all' }}
+  ipex-llm-finetune-qlora-cpu-standalone:
+    if: ${{ inputs.artifact == 'ipex-llm-finetune-qlora-cpu-standalone' || inputs.artifact == 'all' }}
     runs-on: [self-hosted, Shire]
 
     steps:
       - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3
       - name: docker login
         run: |
           docker login -u ${DOCKERHUB_USERNAME} -p ${DOCKERHUB_PASSWORD}
-      - name: ipex-llm-finetune-qlora-cpu
+      - name: ipex-llm-finetune-qlora-cpu-standalone
         run: |
           echo "##############################################################"
-          echo "####### ipex-llm-finetune-qlora-cpu ########"
+          echo "####### ipex-llm-finetune-qlora-cpu-standalone ########"
           echo "##############################################################"
-          export image=intelanalytics/ipex-llm-finetune-qlora-cpu
+          export image=intelanalytics/ipex-llm-finetune-qlora-cpu-standalone
           cd docker/llm/finetune/qlora/cpu/docker
           sudo docker build \
             --no-cache=true \
@@ -102,6 +103,36 @@ jobs:
           sudo docker push ${image}:latest
           sudo docker rmi -f ${image}:${TAG} 10.239.45.10/arda/${image}:${TAG} ${image}:latest
 
+  ipex-llm-finetune-qlora-cpu-k8s:
+    if: ${{ inputs.artifact == 'ipex-llm-finetune-qlora-cpu-k8s' || inputs.artifact == 'all' }}
+    runs-on: [self-hosted, Shire]
+
+    steps:
+      - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # actions/checkout@v3
+      - name: docker login
+        run: |
+          docker login -u ${DOCKERHUB_USERNAME} -p ${DOCKERHUB_PASSWORD}
+      - name: ipex-llm-finetune-qlora-cpu-k8s
+        run: |
+          echo "##############################################################"
+          echo "####### ipex-llm-finetune-qlora-cpu-k8s ########"
+          echo "##############################################################"
+          export image=intelanalytics/ipex-llm-finetune-qlora-cpu-k8s
+          cd docker/llm/finetune/qlora/cpu/docker
+          sudo docker build \
+            --no-cache=true \
+            --build-arg http_proxy=${HTTP_PROXY} \
+            --build-arg https_proxy=${HTTPS_PROXY} \
+            --build-arg no_proxy=${NO_PROXY} \
+            -t ${image}:${TAG} -f ./Dockerfile.k8s .
+          sudo docker push ${image}:${TAG}
+          sudo docker tag ${image}:${TAG} 10.239.45.10/arda/${image}:${TAG}
+          sudo docker push 10.239.45.10/arda/${image}:${TAG}
+          # tag 'latest'
+          sudo docker tag ${image}:${TAG} ${image}:latest
+          sudo docker push ${image}:latest
+          sudo docker rmi -f ${image}:${TAG} 10.239.45.10/arda/${image}:${TAG} ${image}:latest
+
   ipex-llm-finetune-qlora-xpu:
     if: ${{ inputs.artifact == 'ipex-llm-finetune-qlora-xpu' || inputs.artifact == 'all' }}
     runs-on: [self-hosted, Shire]

diff --git a/.github/workflows/python-style-check.yml b/.github/workflows/python-style-check.yml
@@ -0,0 +1,54 @@
+name: Python Style Check
+
+# Cancel previous runs in the PR when you push new commits
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.run_id }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+# Controls when the action will run.
+on:
+  push:
+    branches: [ main ]
+    paths:
+      - 'python/**'
+      - '.github/workflows/python-style-check.yml'
+  pull_request:
+    branches: [ main ]
+    paths:
+      - 'python/**'
+      - '.github/workflows/python-style-check.yml'
+  # schedule:
+  #   - cron: '0 16 * * *'
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "build"
+  style-check:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7]
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - uses: actions/checkout@ee0669bd1cc54295c223e0bb666b733df41de1c5 # actions/checkout@v2
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: pip install pycodestyle
+             pip install pydocstyle
+             pip install mypy==0.982
+             pip install wheel
+
+      - name: LLM style checking
+        run: bash python/llm/dev/test/lint-python
+
diff --git a/docker/llm/finetune/qlora/cpu/docker/README.md b/docker/llm/finetune/qlora/cpu/docker/README.md
@@ -8,10 +8,10 @@ You can download directly from Dockerhub like:
 
 ```bash
 # For standalone
-docker pull intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
+docker pull intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.1.0-SNAPSHOT
 
 # For k8s
-docker pull intelanalytics/ipex-llm-finetune-qlora-cpu-k8s:2.5.0-SNAPSHOT
+docker pull intelanalytics/ipex-llm-finetune-qlora-cpu-k8s:2.1.0-SNAPSHOT
 ```
 
 Or build the image from source:
@@ -24,7 +24,7 @@ export HTTPS_PROXY=your_https_proxy
 docker build \
   --build-arg http_proxy=${HTTP_PROXY} \
   --build-arg https_proxy=${HTTPS_PROXY} \
-  -t intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT \
+  -t intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.1.0-SNAPSHOT \
   -f ./Dockerfile .
 
 # For k8s
@@ -34,7 +34,7 @@ export HTTPS_PROXY=your_https_proxy
 docker build \
   --build-arg http_proxy=${HTTP_PROXY} \
   --build-arg https_proxy=${HTTPS_PROXY} \
-  -t intelanalytics/ipex-llm-finetune-qlora-cpu-k8s:2.5.0-SNAPSHOT \
+  -t intelanalytics/ipex-llm-finetune-qlora-cpu-k8s:2.1.0-SNAPSHOT \
   -f ./Dockerfile.k8s .
 ```
 
@@ -55,7 +55,7 @@ docker run -itd \
    -e https_proxy=${HTTPS_PROXY} \
    -v $BASE_MODE_PATH:/ipex_llm/model \
    -v $DATA_PATH:/ipex_llm/data/alpaca-cleaned \
-   intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
+   intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.1.0-SNAPSHOT
 ```
 
 The download and mount of base model and data to a docker container demonstrates a standard fine-tuning process. You can skip this step for a quick start, and in this way, the fine-tuning codes will automatically download the needed files:
@@ -69,7 +69,7 @@ docker run -itd \
    --name=ipex-llm-fintune-qlora-cpu \
    -e http_proxy=${HTTP_PROXY} \
    -e https_proxy=${HTTPS_PROXY} \
-   intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
+   intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.1.0-SNAPSHOT
 ```
 
 However, we do recommend you to handle them manually, because the automatical download can be blocked by Internet access and Huggingface authentication etc. according to different environment, and the manual method allows you to fine-tune in a custom way (with different base model and dataset).
@@ -130,7 +130,7 @@ docker run -itd \
  -e WORKER_COUNT_DOCKER=your_worker_count \
  -v your_downloaded_base_model_path:/ipex_llm/model \
  -v your_downloaded_data_path:/ipex_llm/data/alpaca_data_cleaned_archive.json \
- intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
+ intelanalytics/ipex-llm-finetune-qlora-cpu-standalone:2.1.0-SNAPSHOT
 ```
 
 Note that `STANDALONE_DOCKER` is set to **TRUE** here.

diff --git a/docker/llm/finetune/qlora/cpu/kubernetes/values.yaml b/docker/llm/finetune/qlora/cpu/kubernetes/values.yaml
@@ -1,4 +1,4 @@
-imageName: intelanalytics/ipex-llm-finetune-qlora-cpu-k8s:2.5.0-SNAPSHOT
+imageName: intelanalytics/ipex-llm-finetune-qlora-cpu-k8s:2.1.0-SNAPSHOT
 trainerNum: 2
 microBatchSize: 8
 enableGradientCheckpoint: false # true will save more memory but increase latency

diff --git a/python/llm/dev/test/lint-python b/python/llm/dev/test/lint-python
@@ -21,7 +21,7 @@ SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
 PYTHON_ROOT_DIR="$SCRIPT_DIR/.."
 echo $PYTHON_ROOT_DIR
 PATHS_TO_CHECK="$SCRIPT_DIR/../../src"
-PATTERNS_TO_EXCLUDE="__init__.py,log4Error.py,$SCRIPT_DIR/../../src/bigdl/llm/langchain/*,$SCRIPT_DIR/../../src/bigdl/llm/transformers/gguf/models/model_implement/yuan2/*"
+PATTERNS_TO_EXCLUDE="__init__.py,log4Error.py,$SCRIPT_DIR/../../src/ipex_llm/langchain/*,$SCRIPT_DIR/../../src/ipex_llm/transformers/gguf/models/model_implement/yuan2/*"
 PEP8_REPORT_PATH="$PYTHON_ROOT_DIR/test/pep8-report.txt"
 PYLINT_REPORT_PATH="$PYTHON_ROOT_DIR/test/pylint-report.txt"
 PYLINT_INSTALL_INFO="$PYTHON_ROOT_DIR/test/pylint-info.txt"

diff --git a/python/llm/src/ipex_llm/transformers/low_bit_linear.py b/python/llm/src/ipex_llm/transformers/low_bit_linear.py
@@ -87,6 +87,8 @@
 #
 # Note this format cannot be used directly in IPEX-LLM's mm_int4, which expects
 # row major but packing two consecutive columns.
+
+
 def q4_0_xpu_transpose(ggml_weight, weight_shape):
     from ipex_llm.transformers.low_bit_linear import get_block_size
     Q4_0 = get_block_size("sym_int4")

diff --git a/python/llm/src/ipex_llm/transformers/model.py b/python/llm/src/ipex_llm/transformers/model.py
@@ -312,7 +312,7 @@ def from_pretrained(cls,
             cpu_embedding = kwargs.get("cpu_embedding", False)
             # for 2bit, default use embedding_quantization
             if q_k in ["gguf_iq2_xxs", "gguf_iq2_xs", "gguf_iq1_s", "q2_k"] and \
-                not cpu_embedding and embedding_qtype is None:
+               not cpu_embedding and embedding_qtype is None:
                 embedding_qtype = "q2_k"
             if imatrix_file is not None:
                 imatrix_data = load_imatrix_data(imatrix_file)

diff --git a/python/llm/src/ipex_llm/transformers/models/chatglm2.py b/python/llm/src/ipex_llm/transformers/models/chatglm2.py
@@ -227,8 +227,8 @@ def chatglm2_quantized_attention_forward_8eb45c(
             key_layer = key_layer.transpose(0, 1)
             query_layer_cur = query_layer[..., :rot_dim]
             key_layer_cur = key_layer[..., :rot_dim]
-            # ipex_llm's apply_rotary_embedding can change the origin storage, so query_layer will get
-            # the result directly.
+            # ipex_llm's apply_rotary_embedding can change the origin storage,
+            # so query_layer will get the result directly.
             torch.ops.torch_ipex.apply_rotary_embedding(query_layer_cur, sin, cos, query_layer_cur)
             torch.ops.torch_ipex.apply_rotary_embedding(key_layer_cur, sin, cos, key_layer_cur)
             query_layer = query_layer.transpose(0, 1)
@@ -367,8 +367,8 @@ def chatglm2_attention_forward_8eb45c(
             key_layer = key_layer.transpose(0, 1)
             query_layer_cur = query_layer[..., :rot_dim]
             key_layer_cur = key_layer[..., :rot_dim]
-            # ipex_llm's apply_rotary_embedding can change the origin storage, so query_layer will get
-            # the result directly.
+            # ipex_llm's apply_rotary_embedding can change the origin storage,
+            # so query_layer will get the result directly.
             torch.ops.torch_ipex.apply_rotary_embedding(query_layer_cur, sin, cos, query_layer_cur)
             torch.ops.torch_ipex.apply_rotary_embedding(key_layer_cur, sin, cos, key_layer_cur)
             query_layer = query_layer.transpose(0, 1)

diff --git a/python/llm/src/ipex_llm/transformers/models/llama.py b/python/llm/src/ipex_llm/transformers/models/llama.py
@@ -64,7 +64,7 @@
 logger = logging.get_logger(__name__)
 
 
-def llama_decoding_fast_path_qtype_check(proj): 
+def llama_decoding_fast_path_qtype_check(proj):
     # IQ2_XXS only can be used in Llama-like model
     qtype = getattr(proj, "qtype", None)
     return qtype in [SYM_INT4, FP8E5, IQ2_XXS, FP4]

diff --git a/python/llm/src/ipex_llm/transformers/models/qwen.py b/python/llm/src/ipex_llm/transformers/models/qwen.py
@@ -136,7 +136,7 @@ def qwen_attention_forward_original(
     device = hidden_states.device
     # for flash attention
     original_dtype = hidden_states.dtype
-    position_ids = rotary_pos_emb_list[-1] # the last one is posisiton_ids
+    position_ids = rotary_pos_emb_list[-1]  # the last one is posisiton_ids
     rotary_pos_emb_list = rotary_pos_emb_list[:-1]
 
     use_fuse_rope = should_use_fuse_rope(self, hidden_states)
@@ -332,7 +332,7 @@ def qwen_attention_forward_quantized(
 
     bsz, q_len, _ = hidden_states.size()
     device = hidden_states.device
-    position_ids = rotary_pos_emb_list[-1] # the last one is posisiton_ids
+    position_ids = rotary_pos_emb_list[-1]  # the last one is posisiton_ids
     rotary_pos_emb_list = rotary_pos_emb_list[:-1]
 
     use_fuse_rope = should_use_fuse_rope(self, hidden_states)

diff --git a/python/llm/src/ipex_llm/transformers/models/utils.py b/python/llm/src/ipex_llm/transformers/models/utils.py
@@ -29,7 +29,7 @@
 GELU = 1
 
 
-def decoding_fast_path_qtype_check(proj): 
+def decoding_fast_path_qtype_check(proj):
     qtype = getattr(proj, "qtype", None)
     return qtype in [SYM_INT4, FP8E5, FP4]
 
@@ -91,7 +91,7 @@ def use_quantize_kv_cache(linear: torch.nn.Module, x: torch.Tensor) -> bool:
 
 def kv_cache_device_check(x: torch.Tensor) -> bool:
     return get_xpu_device_type(x) == "mtl" or \
-        ((get_xpu_device_type(x) == "arc" or get_xpu_device_type(x) == "flex") and \
+        ((get_xpu_device_type(x) == "arc" or get_xpu_device_type(x) == "flex") and
             1 < x.size(0) and x.size(0) <= 8)
 
 
@@ -330,8 +330,8 @@ def use_esimd_sdp(q_len, k_len, head_dim, query_states, attention_mask=None):
 
     device_name = torch.xpu.get_device_name(query_states.device.index)
     if device_name.startswith("Intel(R) Arc(TM) A") or \
-              device_name.startswith("Intel(R) Data Center GPU Flex") or \
-              device_name.startswith("Intel(R) Data Center GPU Max"):
+       device_name.startswith("Intel(R) Data Center GPU Flex") or \
+       device_name.startswith("Intel(R) Data Center GPU Max"):
         import linear_fp16_esimd
         if not hasattr(linear_fp16_esimd, "sdp_forward"):
             return False

diff --git a/python/llm/src/ipex_llm/vllm/core/scheduler.py b/python/llm/src/ipex_llm/vllm/core/scheduler.py
@@ -43,7 +43,7 @@
 from ipex_llm.vllm.logger import init_logger
 from ipex_llm.vllm.sequence import SequenceData, SequenceStatus
 from ipex_llm.vllm.sequence import (Sequence, SequenceGroup,
-                                     SequenceGroupMetadata)
+                                    SequenceGroupMetadata)
 from ipex_llm.utils.common import invalidInputError
 
 logger = init_logger(__name__)

diff --git a/python/llm/src/ipex_llm/vllm/model_executor/layers/bigdl_sampler.py b/python/llm/src/ipex_llm/vllm/model_executor/layers/bigdl_sampler.py
@@ -43,7 +43,7 @@
 
 from ipex_llm.vllm.sampling_params import SamplingParams, SamplingType
 from ipex_llm.vllm.sequence import (SamplerOutput, SequenceGroupMetadata,
-                                     SequenceData, SequenceOutputs)
+                                    SequenceData, SequenceOutputs)
 
 import time