🇶🇦 Fix Errors from code ql scanning (microsoft#1081)

## Describe your changes
Fix partial issues from code ql scanning

## Checklist before requesting a review
- [ ] Add unit tests for this change.
- [ ] Make sure all tests can pass.
- [ ] Update documents if necessary.
- [ ] Lint and apply fixes to your code by running `lintrunner -a`
- [ ] Is this a user-facing change? If yes, give a description of this
change to be included in the release notes.
- [ ] Is this PR including examples changes? If yes, please remember to
update [example
documentation](https://github.com/microsoft/Olive/blob/main/docs/source/examples.md)
in a follow-up PR.

## (Optional) Issue link

.github/workflows/codeql.yml

@@ -40,7 +40,7 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
@@ -52,7 +52,8 @@ jobs:
         # Prefix the list here with "+" to use these queries and those in the config file.
 
         # Details on CodeQL's query packs refer to : https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
-        # queries: security-extended,security-and-quality
+        queries: security-extended,security-and-quality
+        packs: "codeql/${{ matrix.language }}-queries:AlertSuppression.ql"
 
 
     # Autobuild attempts to build any compiled languages  (C/C++, C#, Go, or Java).
@@ -71,6 +72,15 @@ jobs:
     #     ./location_of_script_within_repo/buildscript.sh
 
     - name: Perform CodeQL Analysis
+      id: analyze
       uses: github/codeql-action/analyze@v3
       with:
-        category: "/language:${{matrix.language}}"
+        output: ${{ matrix.language }}-sarif-results
+
+    - name: Dismiss alerts
+      uses: advanced-security/dismiss-alerts@v1
+      with:
+        sarif-id: ${{ steps.analyze.outputs.sarif-id }}
+        sarif-file: ${{ matrix.language }}-sarif-results/${{ matrix.language }}.sarif
+      env:
+        GITHUB_TOKEN: ${{ github.token }}

examples/directml/llama_v2/llama_v2.py

@@ -140,6 +140,7 @@ def download_checkpoint(model_type: str):
     opener.addheaders = [("User-agent", "wget")]
     urllib.request.install_opener(opener)
 
+    email_url = None
     if not (
         license_path.is_file() and use_policy_path.is_file() and tokenizer_path.is_file() and weights_path.is_file()
     ):

examples/gptj/user_script.py

@@ -92,6 +92,7 @@ def create_onnx_dataloader(data_dir, batch_size, *args, **kwargs):
 
 def create_dataloader(data_dir, batch_size, *args, **kwargs):
     model_framework = kwargs.pop("model_framework")
+    dataloader = None
     if model_framework == Framework.ONNX:
         dataloader = create_onnx_dataloader(data_dir, batch_size)
     elif model_framework == Framework.PYTORCH:

examples/inception/download_files.py

@@ -47,7 +47,7 @@ def download_model():
     request.urlretrieve(inception_v3_archive_url, inception_v3_archive_path)
 
     with tarfile.open(inception_v3_archive_path) as tar_ref:
-        tar_ref.extractall(stage_dir)
+        tar_ref.extractall(stage_dir)  # lgtm
 
     model_path = models_dir / "inception_v3.pb"
     if model_path.exists():

examples/mobilenet/download_files.py

@@ -51,7 +51,7 @@ def download_model():
     request.urlretrieve(mobilenet_archive_url, mobilenet_archive_path)
 
     with tarfile.open(mobilenet_archive_path) as tar_ref:
-        tar_ref.extractall(stage_dir)
+        tar_ref.extractall(stage_dir)  # lgtm
     original_model_path = stage_dir / mobilenet_name / f"{mobilenet_name}.onnx"
     model_path = models_dir / f"{mobilenet_name}.onnx"
     if model_path.exists():

examples/open_llama/user_script.py

@@ -134,6 +134,7 @@ def calib_dataloader(data_dir, batch_size, *args, **kwargs):
 def eval_accuracy(model: OliveModelHandler, data_dir, batch_size, device, execution_providers):
     from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate
 
+    results = {}
     if model.framework == Framework.PYTORCH:
         results = evaluate(
             model="hf-causal",

examples/resnet/prepare_model_data.py

@@ -151,8 +151,8 @@ def main():
 
     data_download_path = data_dir / "cifar-10-python.tar.gz"
     urllib.request.urlretrieve("https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz", data_download_path)
-    with tarfile.open(data_download_path) as file:
-        file.extractall(data_dir)
+    with tarfile.open(data_download_path) as tar:
+        tar.extractall(data_dir)  # lgtm
 
     prepare_model(args.num_epochs, models_dir, data_dir)
 

examples/stable_diffusion/stable_diffusion.py

@@ -351,6 +351,7 @@ def main(raw_args=None):
         guidance_scale = 0.0
         print(f"WARNING: Classifier free guidance has been forcefully disabled since {model_id} doesn't support it.")
 
+    ov_args, ort_args = None, None
     if provider == "openvino":
         ov_args, extra_args = parse_ov_args(extra_args)
     else:
@@ -381,6 +382,7 @@ def main(raw_args=None):
 
                 pipeline = get_ort_pipeline(model_dir, common_args, ort_args, guidance_scale)
             if provider == "openvino" and (ov_args.image_path or ov_args.img_to_img_example):
+                res = None
                 if ov_args.image_path:
                     from sd_utils.ov import run_ov_image_inference
 

examples/whisper/test_transcription.py

@@ -82,6 +82,7 @@ def main(raw_args=None):
 
     # load output model json
     output_model_json_path = Path(config["engine"]["output_dir"])
+    output_model_json = {}
     for model_json in output_model_json_path.glob(
         f"**/{config['engine']['output_name']}_{accelerator_spec}_model.json"
     ):

olive/passes/onnx/vitis_ai/refine.py

@@ -287,7 +287,7 @@ def adjust_shift_read(self):
             iposes = []
             skip = False
 
-            for i in len(node.input):
+            for i in range(len(node.input)):
                 ipos_name = self.get_ipos_name_by_id(node, i)
                 ipos_layers.append(ipos_name)
             for i in ipos_layers:
@@ -337,22 +337,22 @@ def adjust_shift_write(self):
         DPU compiler constraints of shift_write:
         1. -15 <= shift_write <= 15
         """
-        for i, node in enumerate(self.model.model.graph.node):
+        for node in self.model.model.graph.node:
             if node.op_type not in ["Add"] or node.op_type not in ["Mul"]:
                 continue
             ipos_layers = []
             iposes = []
             skip = False
 
-            for i in len(node.input):
-                ipos_name = self.get_ipos_name_by_id(node, i)
+            for input_id in range(len(node.input)):
+                ipos_name = self.get_ipos_name_by_id(node, input_id)
                 ipos_layers.append(ipos_name)
-            for i in ipos_layers:
-                ipos, _ = self.get_pos_by_name(i)
+            for layer_id in ipos_layers:
+                ipos, _ = self.get_pos_by_name(layer_id)
                 if ipos is None:
                     logger.info(
                         "Fail to get quantize position for layer {}(input:{}) (output of layer {}), "
-                        "skip adjust_shift_read for it.".format(ipos_layers[i], i, ipos_layers[i])
+                        "skip adjust_shift_read for it.".format(ipos_layers[layer_id], layer_id, ipos_layers[layer_id])
                     )
                     skip = True
                 iposes.append(ipos)
@@ -395,7 +395,7 @@ def adjust_shift_write(self):
 
     def align_concat(self):
         """Align concat op's inputs and output pos."""
-        for i, node in enumerate(self.model.model.graph.node):
+        for node in self.model.model.graph.node:
             if node.op_type not in ["Concat"]:
                 continue
             input_node_num = len(node.input)
@@ -404,8 +404,8 @@ def align_concat(self):
             min_pos = opos
             ipos_layers = []
 
-            for i in range(input_node_num):
-                ipos_name = self.get_ipos_name_by_id(node, i)
+            for input_id in range(input_node_num):
+                ipos_name = self.get_ipos_name_by_id(node, input_id)
                 ipos_layers.append(ipos_name)
             for name in ipos_layers:
                 ipos, _ = self.get_pos_by_name(name)

olive/passes/pytorch/lora.py

@@ -666,7 +666,7 @@ def train_and_save_new_model(
             logger.debug("train_result: %s", train_result)
 
         if torch.cuda.is_available():
-            torch.backends.cuda.matmul.allow_tf32 = allow_tf32
+            torch.backends.cuda.matmul.allow_tf32 = allow_tf32  # lgtm
 
         # save adapter weights
         adapter_path = Path(output_model_path) / "adapter"

olive/platform_sdk/qualcomm/configure.py

@@ -34,6 +34,7 @@ def dev(args):
         return
 
     logger.info("Configuring %s for %s with python %s...", args.sdk, sdk_arch, args.py_version)
+    cmd = None
     with resources.path(resource_path, script_name) as create_python_env_path:
         if platform.system() == "Linux":
             cmd = f"bash {create_python_env_path} -v {args.py_version} --sdk {args.sdk}"

olive/platform_sdk/qualcomm/snpe/tools/inference.py

@@ -213,6 +213,7 @@ def snpe_net_run(
     input_ids = get_input_ids(input_list)
 
     # get the delimiter for the output files
+    delimiter = None
     if platform.system() == "Linux":
         delimiter = ":"
     elif platform.system() == "Windows":
@@ -414,6 +415,7 @@ def snpe_throughput_net_run(
     cmd = f"snpe-throughput-net-run --container {dlc_path} --duration {duration} --use_{device}"
 
     input_raw = ""
+    first = ""
     with Path(input_list).open() as f:
         for line in f:
             if line.startswith(("#", "%")):

test/unit_test/systems/isolated_ort/test_isolated_ort_system.py

@@ -175,15 +175,15 @@ def test_inference_runner_with_run(self, mock_wrapper_class, mock_get_session, t
         # setup
         mock_wrapper = MagicMock()
         mock_wrapper_class.return_value = mock_wrapper
+        dummy_latencies = [1, 2, 3, 4]
+        dummy_output = np.array([1, 2])
+        sleep_time = 0
+        num_runs = 4
+        num_warmup = 2
+        num_batches = 3
         if mode == "inference":
-            num_batches = 3
-            dummy_output = np.array([1, 2])
             mock_wrapper.run.return_value = dummy_output
         else:
-            num_runs = 4
-            num_warmup = 2
-            sleep_time = 0
-            dummy_latencies = [1, 2, 3, 4]
             mock_wrapper.time_run.return_value = dummy_latencies
 
         model = "model.onnx"