[TESTS] Use FP32 inference precision, FP16 KV cache precision for all…

… pipelines

samples/export-requirements.txt

@@ -2,7 +2,7 @@
 --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/pre-release
 --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
 openvino-tokenizers~=2025.0.0.0.dev
-optimum-intel @ git+https://github.com/huggingface/optimum-intel.git
+optimum-intel @ git+https://github.com/huggingface/optimum-intel.git@753f84db6e0966580eb9eaa74a808213be730631
 numpy<2.0.0; sys_platform == 'darwin'
 einops==0.8.0  # For Qwen
 transformers_stream_generator==0.0.5  # For Qwen

src/python/openvino_genai/py_openvino_genai.pyi

@@ -364,7 +364,7 @@ class ContinuousBatchingPipeline:
     def __init__(self, models_path: os.PathLike, scheduler_config: SchedulerConfig, device: str, properties: dict[str, typing.Any] = {}, tokenizer_properties: dict[str, typing.Any] = {}) -> None:
         ...
     @typing.overload
-    def __init__(self, models_path: os.PathLike, tokenizer: Tokenizer, scheduler_config: SchedulerConfig, device: str, properties: dict[str, typing.Any] = {}) -> None:
+    def __init__(self, models_path: os.PathLike, tokenizer: Tokenizer, scheduler_config: SchedulerConfig, device: str, **kwargs) -> None:
         ...
     @typing.overload
     def add_request(self, request_id: int, input_ids: openvino._pyopenvino.Tensor, generation_config: GenerationConfig) -> GenerationHandle:

src/python/py_continuous_batching_pipeline.cpp

@@ -223,15 +223,14 @@ void init_continuous_batching_pipeline(py::module_& m) {
         py::arg("properties") = ov::AnyMap({}),
         py::arg("tokenizer_properties") = ov::AnyMap({}))
 
-        .def(py::init([](const std::filesystem::path& models_path, const ov::genai::Tokenizer& tokenizer, const SchedulerConfig& scheduler_config, const std::string& device, const std::map<std::string, py::object>& plugin_config) {
+        .def(py::init([](const std::filesystem::path& models_path, const ov::genai::Tokenizer& tokenizer, const SchedulerConfig& scheduler_config, const std::string& device, const py::kwargs& kwargs) {
             ScopedVar env_manager(pyutils::ov_tokenizers_module_path());
-            return std::make_unique<ContinuousBatchingPipeline>(models_path, tokenizer, scheduler_config, device, pyutils::properties_to_any_map(plugin_config));
+            return std::make_unique<ContinuousBatchingPipeline>(models_path, tokenizer, scheduler_config, device, pyutils::kwargs_to_any_map(kwargs));
         }),
         py::arg("models_path"),
         py::arg("tokenizer"),
         py::arg("scheduler_config"),
-        py::arg("device"),
-        py::arg("properties") = ov::AnyMap({}))
+        py::arg("device"))
 
         .def("get_tokenizer", &ContinuousBatchingPipeline::get_tokenizer)
         .def("get_config", &ContinuousBatchingPipeline::get_config)

tests/python_tests/common.py

@@ -306,7 +306,7 @@ def run_continuous_batching(
     if type(generation_configs) is not list:
         generation_configs = [generation_configs] * len(prompts)
 
-    cb_pipe = ContinuousBatchingPipeline(models_path, scheduler_config=scheduler_config, device='CPU')
+    cb_pipe = ContinuousBatchingPipeline(models_path, scheduler_config=scheduler_config, device='CPU', tokenizer_properties={}, properties=get_default_properties())
     output = cb_pipe.generate(prompts, generation_configs)
 
     del cb_pipe

tests/python_tests/ov_genai_test_utils.py

@@ -14,7 +14,7 @@
 import json
 
 import openvino_genai as ov_genai
-
+from common import get_default_properties
 
 def get_models_list():
     precommit_models = [
@@ -92,7 +92,7 @@ def read_model(params, **tokenizer_kwargs):
 
     if (models_path / "openvino_model.xml").exists():
         opt_model = OVModelForCausalLM.from_pretrained(models_path, trust_remote_code=True,
-                                                       compile=False, device='CPU')
+                                                       compile=False, device='CPU', ov_config=get_default_properties())
     else:
         ov_tokenizer, ov_detokenizer = openvino_tokenizers.convert_tokenizer(hf_tokenizer,
                                                                              with_detokenizer=True,
@@ -104,7 +104,7 @@ def read_model(params, **tokenizer_kwargs):
         hf_tokenizer.save_pretrained(models_path)
 
         opt_model = OVModelForCausalLM.from_pretrained(model_id, export=True, trust_remote_code=True,
-                                                       compile=False, device='CPU', load_in_8bit=False)
+                                                       compile=False, device='CPU', load_in_8bit=False, ov_config=get_default_properties())
         opt_model.generation_config.save_pretrained(models_path)
         opt_model.config.save_pretrained(models_path)
         opt_model.save_pretrained(models_path)
@@ -114,7 +114,7 @@ def read_model(params, **tokenizer_kwargs):
         models_path,
         hf_tokenizer,
         opt_model,
-        ov_genai.LLMPipeline(models_path, 'CPU', ENABLE_MMAP=False),
+        ov_genai.LLMPipeline(models_path, 'CPU', ENABLE_MMAP=False, **get_default_properties()),
     )
 
 
@@ -178,7 +178,7 @@ def load_genai_pipe_with_configs(configs: List[Tuple], temp_path):
         with (temp_path / config_name).open('w') as f:
             json.dump(config_json, f)
 
-    ov_pipe = ov_genai.LLMPipeline(temp_path, 'CPU')
+    ov_pipe = ov_genai.LLMPipeline(temp_path, 'CPU', **get_default_properties())
 
     for _, config_name in configs:
         os.remove(temp_path / config_name)
@@ -188,4 +188,4 @@ def load_genai_pipe_with_configs(configs: List[Tuple], temp_path):
 
 @functools.lru_cache(1)
 def get_continuous_batching(path):
-    return ov_genai.LLMPipeline(path, 'CPU', scheduler_config=ov_genai.SchedulerConfig())
+    return ov_genai.LLMPipeline(path, 'CPU', scheduler_config=ov_genai.SchedulerConfig(), **get_default_properties())

tests/python_tests/requirements.txt

@@ -1,6 +1,6 @@
 --extra-index-url https://download.pytorch.org/whl/cpu
 diffusers==0.32.1
-optimum-intel @ git+https://github.com/huggingface/optimum-intel.git
+optimum-intel @ git+https://github.com/huggingface/optimum-intel.git@753f84db6e0966580eb9eaa74a808213be730631
 numpy<2.0.0; platform_system == "Darwin" and platform_machine == "x86_64"
 onnx==1.17.0
 pytest

tests/python_tests/test_continuous_batching.py

@@ -9,7 +9,7 @@
 from pathlib import Path
 from openvino_genai import ContinuousBatchingPipeline, GenerationConfig, Tokenizer
 
-from common import get_hugging_face_models, convert_models, generate_and_compare_with_reference_text, \
+from common import get_default_properties, get_hugging_face_models, convert_models, generate_and_compare_with_reference_text, \
     get_scheduler_config, get_greedy, run_cb_pipeline_with_ref, get_beam_search, get_greedy, \
     get_multinomial_all_parameters, get_multinomial_temperature_and_num_return_sequence, \
     get_multinomial_temperature_and_top_k, get_multinomial_temperature, get_multinomial_temperature_and_top_p
@@ -155,7 +155,7 @@ def test_post_oom_health(tmp_path, sampling_config):
     models_path : Path = tmp_path / model_id
     convert_models(opt_model, hf_tokenizer, models_path)
 
-    cb_pipe = ContinuousBatchingPipeline(models_path, Tokenizer(models_path), scheduler_config, "CPU")
+    cb_pipe = ContinuousBatchingPipeline(models_path, Tokenizer(models_path), scheduler_config, "CPU", **get_default_properties())
 
     # First run should return incomplete response
     output = cb_pipe.generate(["What is OpenVINO?"], [generation_config])

tests/python_tests/test_kv_cache_eviction.py

@@ -15,7 +15,7 @@
 from openvino import serialize
 from transformers import AutoTokenizer
 
-from common import TESTS_ROOT, run_cb_pipeline_with_ref
+from common import TESTS_ROOT, run_cb_pipeline_with_ref, get_default_properties
 
 
 def load_prompts_dataset(file_name : str) -> Dict[str, List[str]]:
@@ -42,7 +42,7 @@ class ConvertedModel:
 @pytest.fixture(scope='module')
 def converted_model(tmp_path_factory):
     model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-    model = OVModelForCausalLM.from_pretrained(model_id, export=True, trust_remote_code=True, load_in_8bit=False, compile=False)
+    model = OVModelForCausalLM.from_pretrained(model_id, export=True, trust_remote_code=True, load_in_8bit=False, compile=False, ov_config=get_default_properties())
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     models_path = tmp_path_factory.mktemp("cacheopt_test_models") / model_id
     model.save_pretrained(models_path)
@@ -112,8 +112,8 @@ def test_cache_optimized_generation_is_similar_to_unoptimized(converted_model, t
     scheduler_config_opt.enable_prefix_caching = enable_prefix_caching
 
     models_path = converted_model.models_path
-    model_cb_noopt = ContinuousBatchingPipeline(models_path, scheduler_config, "CPU")
-    model_cb_opt = ContinuousBatchingPipeline(models_path, scheduler_config_opt, "CPU")
+    model_cb_noopt = ContinuousBatchingPipeline(models_path, scheduler_config, "CPU", {}, get_default_properties())
+    model_cb_opt = ContinuousBatchingPipeline(models_path, scheduler_config_opt, "CPU", {}, get_default_properties())
 
     tokenizer = converted_model.tokenizer
 

tests/python_tests/test_vlm_pipeline.py

@@ -7,7 +7,7 @@
 import transformers
 from optimum.intel.openvino import OVModelForVisualCausalLM
 from openvino_genai import VLMPipeline, GenerationConfig
-from common import get_image_by_link, get_beam_search, get_multinomial_all_parameters
+from common import get_image_by_link, get_beam_search, get_multinomial_all_parameters, get_default_properties
 
 def get_ov_model(cache):
     model_dir = cache.mkdir("tiny-random-minicpmv-2_6")
@@ -19,7 +19,7 @@ def get_ov_model(cache):
     ov_tokenizer, ov_detokenizer = openvino_tokenizers.convert_tokenizer(processor.tokenizer, with_detokenizer=True)
     openvino.save_model(ov_tokenizer, model_dir / "openvino_tokenizer.xml")
     openvino.save_model(ov_detokenizer, model_dir / "openvino_detokenizer.xml")
-    model = OVModelForVisualCausalLM.from_pretrained(model_id, compile=False, device="CPU", export=True, load_in_8bit=False, trust_remote_code=True)
+    model = OVModelForVisualCausalLM.from_pretrained(model_id, compile=False, device="CPU", export=True, load_in_8bit=False, trust_remote_code=True, ov_config=get_default_properties())
     processor.save_pretrained(model_dir)
     model.save_pretrained(model_dir)
     return model_dir