Fix/snippets/dynamism/loop emitters

.github/workflows/job_build_linux.yml

@@ -69,7 +69,7 @@ jobs:
 
       # Ticket: 139627
       - name: Checkout the latest OneDNN for GPU in nightly
-        if: ${{ inputs.event-name == 'schedule' }}
+        if: ${{ inputs.event-name == 'schedule' && inputs.os == 'ubuntu_20_04' }} # GPU tests are enabled only on U20
         working-directory: ${{ env.OPENVINO_REPO }}/src/plugins/intel_gpu/thirdparty/onednn_gpu
         run: |
           git fetch origin

docs/articles_en/learn-openvino/llm_inference_guide/genai-guide-npu.rst

@@ -8,60 +8,56 @@ This guide will give you extra details on how to utilize NPU with the GenAI flav
 :doc:`See the installation guide <../../get-started/install-openvino/install-openvino-genai>`
 for information on how to start.
 
-Export an LLM model via Hugging Face Optimum-Intel
-##################################################
+Prerequisites
+#############
 
-1. Create a python virtual environment and install the correct components for exporting a model:
+Install required dependencies:
 
-   .. code-block:: console
+.. code-block:: console
 
-      python -m venv export-npu-env
-      export-npu-env\Scripts\activate
-      pip install transformers>=4.42.4 openvino==2024.2.0 openvino-tokenizers==2024.2.0 nncf==2.11.0 onnx==1.16.1 optimum-intel@git+https://github.com/huggingface/optimum-intel.git
+   python -m venv npu-env
+   npu-env\Scripts\activate
+   pip install optimum-intel nncf==2.11 onnx==1.16.1
+   pip install --pre openvino openvino-tokenizers openvino-genai --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+
+Export an LLM model via Hugging Face Optimum-Intel
+##################################################
 
-2. A chat-tuned TinyLlama model is used in this example. The following conversion & optimization settings are recommended when using the NPU:
+A chat-tuned TinyLlama model is used in this example. The following conversion & optimization settings are recommended when using the NPU:
 
-   .. code-block:: python
+.. code-block:: python
 
-      optimum-cli export openvino -m TinyLlama/TinyLlama-1.1B-Chat-v1.0 --weight-format int4 --sym --group-size 128 --ratio 1.0 TinyLlama
+   optimum-cli export openvino -m TinyLlama/TinyLlama-1.1B-Chat-v1.0 --weight-format int4 --sym --group-size 128 --ratio 1.0 TinyLlama
 
 Run generation using OpenVINO GenAI
-##########################################
-
-1. Create a python virtual environment and install the correct components for running the model on the NPU via OpenVINO GenAI:
-
-   .. code-block:: console
-
-      python -m venv run-npu-env
-      run-npu-env\Scripts\activate
-      pip install openvino>=2024.3.1 openvino-tokenizers>=2024.3.1 openvino-genai>=2024.3.1
+###################################
 
-2. Perform generation using the new GenAI API
+Use the following code snippet to perform generation with OpenVINO GenAI API:
 
-   .. tab-set::
+.. tab-set::
 
-      .. tab-item:: Python
-         :sync: py
+   .. tab-item:: Python
+      :sync: py
 
-         .. code-block:: python
+      .. code-block:: python
 
-            import openvino_genai as ov_genai
-            pipe = ov_genai.LLMPipeline(model_path, "NPU")
-            print(pipe.generate("The Sun is yellow because", max_new_tokens=100))
+         import openvino_genai as ov_genai
+         pipe = ov_genai.LLMPipeline(model_path, "NPU")
+         print(pipe.generate("The Sun is yellow because", max_new_tokens=100))
 
-      .. tab-item:: C++
-         :sync: cpp
+   .. tab-item:: C++
+      :sync: cpp
 
-         .. code-block:: cpp
+      .. code-block:: cpp
 
-            #include "openvino/genai/llm_pipeline.hpp"
-            #include <iostream>
+         #include "openvino/genai/llm_pipeline.hpp"
+         #include <iostream>
 
-            int main(int argc, char* argv[]) {
-               std::string model_path = argv[1];
-               ov::genai::LLMPipeline pipe(model_path, "NPU");
-               std::cout << pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(100));
-            }
+         int main(int argc, char* argv[]) {
+            std::string model_path = argv[1];
+            ov::genai::LLMPipeline pipe(model_path, "NPU");
+            std::cout << pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(100));
+         }
 
 Additional configuration options
 ################################

src/bindings/js/node/.gitignore

@@ -3,6 +3,7 @@ dist
 build
 types
 ov_runtime
+tests/unit/test_models
 
 
 *.exp

src/bindings/js/node/package.json

@@ -18,7 +18,8 @@
     "build": "npm run tsc",
     "prepare": "npm run build",
     "lint": "eslint .",
-    "test": "node --test ./tests/unit/*.test.js",
+    "test_setup": "node ./tests/unit/setup.js",
+    "test": "npm run test_setup && node --test ./tests/unit/*.test.js",
     "test:e2e": "mocha ./tests/e2e/electron-app.test.js",
     "tsc": "tsc",
     "postinstall": "npm run install_runtime",

src/bindings/js/node/scripts/download_runtime.js

@@ -61,7 +61,7 @@ class RuntimeExistsError extends Error {
 async function downloadRuntime(destinationPath, config = { force: false, ignoreIfExists: true, proxy: null }) {
   const { version } = packageJson;
   const osInfo = await getOsInfo();
-  const isRuntimeDirectoryExists = await checkIfDirectoryExists(destinationPath);
+  const isRuntimeDirectoryExists = await checkIfPathExists(destinationPath);
 
   if (isRuntimeDirectoryExists && !config.force) {
     if (config.ignoreIfExists) {
@@ -87,7 +87,7 @@ async function downloadRuntime(destinationPath, config = { force: false, ignoreI
     await fs.mkdir(tempDirectoryPath);
 
     console.log('Downloading OpenVINO runtime archive...');
-    await downloadFile(runtimeArchiveUrl, filename, tempDirectoryPath, config.proxy);
+    await downloadFile(runtimeArchiveUrl, tempDirectoryPath, filename, config.proxy);
     console.log('OpenVINO runtime archive downloaded.');
 
     await removeDirectory(destinationPath);
@@ -139,16 +139,16 @@ async function getOsInfo() {
 }
 
 /**
- * Check if directory exists.
+ * Check if path exists.
  *
  * @async
- * @function checkIfDirectoryExists
- * @param {string} directoryPath - The directory path.
+ * @function checkIfPathExists
+ * @param {string} path - The path to directory or file.
  * @returns {Promise<boolean>}
  */
-async function checkIfDirectoryExists(directoryPath) {
+async function checkIfPathExists(path) {
   try {
-    await fs.access(directoryPath);
+    await fs.access(path);
     return true;
   } catch (error) {
     if (error.code === codeENOENT) {
@@ -210,7 +210,7 @@ async function removeDirectory(path) {
  * @param {string} [proxy=null] - (Optional) The proxy URL.
  * @returns {Promise<void>}
  */
-function downloadFile(url, filename, destination, proxy = null) {
+function downloadFile(url, destination, filename, proxy = null) {
   const timeout = 5000;
   const fullPath = path.resolve(destination, filename);
   const file = createWriteStream(fullPath);
@@ -281,4 +281,4 @@ function unarchive(tarFilePath, dest) {
   });
 }
 
-module.exports = { downloadRuntime };
+module.exports = { downloadRuntime, downloadFile, checkIfPathExists };