add python api

src/python/openvino_genai/__init__.py

@@ -76,6 +76,8 @@
     Generator,
     CppStdGenerator,
     TorchGenerator,
+    ImageGenerationPerfMetrics,
+    RawImageGenerationPerfMetrics,
 )
 
 # Continuous batching

src/python/openvino_genai/__init__.pyi

@@ -21,9 +21,11 @@ from openvino_genai.py_openvino_genai import GenerationResult
 from openvino_genai.py_openvino_genai import Generator
 from openvino_genai.py_openvino_genai import Image2ImagePipeline
 from openvino_genai.py_openvino_genai import ImageGenerationConfig
+from openvino_genai.py_openvino_genai import ImageGenerationPerfMetrics
 from openvino_genai.py_openvino_genai import InpaintingPipeline
 from openvino_genai.py_openvino_genai import LLMPipeline
 from openvino_genai.py_openvino_genai import PerfMetrics
+from openvino_genai.py_openvino_genai import RawImageGenerationPerfMetrics
 from openvino_genai.py_openvino_genai import RawPerfMetrics
 from openvino_genai.py_openvino_genai import SD3Transformer2DModel
 from openvino_genai.py_openvino_genai import Scheduler
@@ -45,5 +47,5 @@ from openvino_genai.py_openvino_genai import draft_model
 from openvino_genai.py_openvino_genai import get_version
 import os as os
 from . import py_openvino_genai
-__all__ = ['Adapter', 'AdapterConfig', 'AggregationMode', 'AutoencoderKL', 'CLIPTextModel', 'CLIPTextModelWithProjection', 'CacheEvictionConfig', 'ChunkStreamerBase', 'ContinuousBatchingPipeline', 'CppStdGenerator', 'DecodedResults', 'EncodedResults', 'FluxTransformer2DModel', 'GenerationConfig', 'GenerationResult', 'Generator', 'Image2ImagePipeline', 'ImageGenerationConfig', 'InpaintingPipeline', 'LLMPipeline', 'PerfMetrics', 'RawPerfMetrics', 'SD3Transformer2DModel', 'Scheduler', 'SchedulerConfig', 'StopCriteria', 'StreamerBase', 'T5EncoderModel', 'Text2ImagePipeline', 'TokenizedInputs', 'Tokenizer', 'TorchGenerator', 'UNet2DConditionModel', 'VLMPipeline', 'WhisperGenerationConfig', 'WhisperPerfMetrics', 'WhisperPipeline', 'WhisperRawPerfMetrics', 'draft_model', 'get_version', 'openvino', 'os', 'py_openvino_genai']
+__all__ = ['Adapter', 'AdapterConfig', 'AggregationMode', 'AutoencoderKL', 'CLIPTextModel', 'CLIPTextModelWithProjection', 'CacheEvictionConfig', 'ChunkStreamerBase', 'ContinuousBatchingPipeline', 'CppStdGenerator', 'DecodedResults', 'EncodedResults', 'FluxTransformer2DModel', 'GenerationConfig', 'GenerationResult', 'Generator', 'Image2ImagePipeline', 'ImageGenerationConfig', 'ImageGenerationPerfMetrics', 'InpaintingPipeline', 'LLMPipeline', 'PerfMetrics', 'RawImageGenerationPerfMetrics', 'RawPerfMetrics', 'SD3Transformer2DModel', 'Scheduler', 'SchedulerConfig', 'StopCriteria', 'StreamerBase', 'T5EncoderModel', 'Text2ImagePipeline', 'TokenizedInputs', 'Tokenizer', 'TorchGenerator', 'UNet2DConditionModel', 'VLMPipeline', 'WhisperGenerationConfig', 'WhisperPerfMetrics', 'WhisperPipeline', 'WhisperRawPerfMetrics', 'ImageGenerationPerfMetrics', 'RawImageGenerationPerfMetrics', 'draft_model', 'get_version', 'openvino', 'os', 'py_openvino_genai']
 __version__: str

src/python/openvino_genai/py_openvino_genai.pyi

@@ -5,7 +5,7 @@ from __future__ import annotations
 import openvino._pyopenvino
 import os
 import typing
-__all__ = ['Adapter', 'AdapterConfig', 'AggregationMode', 'AutoencoderKL', 'CLIPTextModel', 'CLIPTextModelWithProjection', 'CacheEvictionConfig', 'ChunkStreamerBase', 'ContinuousBatchingPipeline', 'CppStdGenerator', 'DecodedResults', 'EncodedGenerationResult', 'EncodedResults', 'FluxTransformer2DModel', 'GenerationConfig', 'GenerationFinishReason', 'GenerationHandle', 'GenerationOutput', 'GenerationResult', 'GenerationStatus', 'Generator', 'Image2ImagePipeline', 'ImageGenerationConfig', 'InpaintingPipeline', 'LLMPipeline', 'MeanStdPair', 'PerfMetrics', 'PipelineMetrics', 'RawPerfMetrics', 'SD3Transformer2DModel', 'Scheduler', 'SchedulerConfig', 'StopCriteria', 'StreamerBase', 'T5EncoderModel', 'Text2ImagePipeline', 'TokenizedInputs', 'Tokenizer', 'TorchGenerator', 'UNet2DConditionModel', 'VLMDecodedResults', 'VLMPerfMetrics', 'VLMPipeline', 'VLMRawPerfMetrics', 'WhisperDecodedResultChunk', 'WhisperDecodedResults', 'WhisperGenerationConfig', 'WhisperPerfMetrics', 'WhisperPipeline', 'WhisperRawPerfMetrics', 'draft_model', 'get_version']
+__all__ = ['Adapter', 'AdapterConfig', 'AggregationMode', 'AutoencoderKL', 'CLIPTextModel', 'CLIPTextModelWithProjection', 'CacheEvictionConfig', 'ChunkStreamerBase', 'ContinuousBatchingPipeline', 'CppStdGenerator', 'DecodedResults', 'EncodedGenerationResult', 'EncodedResults', 'FluxTransformer2DModel', 'GenerationConfig', 'GenerationFinishReason', 'GenerationHandle', 'GenerationOutput', 'GenerationResult', 'GenerationStatus', 'Generator', 'Image2ImagePipeline', 'ImageGenerationConfig', 'ImageGenerationPerfMetrics', 'InpaintingPipeline', 'LLMPipeline', 'MeanStdPair', 'PerfMetrics', 'PipelineMetrics', 'RawImageGenerationPerfMetrics', 'RawPerfMetrics', 'SD3Transformer2DModel', 'Scheduler', 'SchedulerConfig', 'StopCriteria', 'StreamerBase', 'T5EncoderModel', 'Text2ImagePipeline', 'TokenizedInputs', 'Tokenizer', 'TorchGenerator', 'UNet2DConditionModel', 'VLMDecodedResults', 'VLMPerfMetrics', 'VLMPipeline', 'VLMRawPerfMetrics', 'WhisperDecodedResultChunk', 'WhisperDecodedResults', 'WhisperGenerationConfig', 'WhisperPerfMetrics', 'WhisperPipeline', 'WhisperRawPerfMetrics', 'draft_model', 'get_version']
 class Adapter:
     """
     Immutable LoRA Adapter that carries the adaptation matrices and serves as unique adapter identifier.
@@ -2200,6 +2200,101 @@ class WhisperRawPerfMetrics:
     @property
     def features_extraction_durations(self) -> list[float]:
         ...
+class RawImageGenerationPerfMetrics:
+    """
+
+        Structure with raw performance metrics for each generation before any statistics are calculated.
+
+        :param unet_inference_durations: Inference time for each unet inference in microseconds.
+        :type unet_inference_durations: List[float]
+
+        :param transformer_inference_durations: Inference time for each transformer inference in microseconds.
+        :type transformer_inference_durations: List[float]
+
+        :param iteration_durations: durations for each step iteration in microseconds.
+        :type iteration_durations: List[float]
+    """
+    def __init__(self) -> None:
+        ...
+    @property
+    def unet_inference_durations(self) -> list[float]:
+        ...
+    @property
+    def transformer_inference_durations(self) -> list[float]:
+        ...
+    @property
+    def iteration_durations(self) -> list[float]:
+        ...
+class ImageGenerationPerfMetrics:
+    """
+
+        Holds performance metrics for each image generate call.
+
+        PerfMetrics holds fields with mean and standard deviations for the following metrics:
+        - one generation iteration, ms
+        - inference duration for unet model, ms
+        - inference duration for transformer model, ms
+
+        Additional fields include:
+        - Load time, ms
+        - total duration of image generation, ms
+        - inference duration of vae_encoder model, ms
+        - inference duration of vae_decoder model, ms
+        - inference duration of each encoder model, ms
+
+        Preferable way to access values is via get functions. Getters calculate mean and std values from raw_metrics and return pairs.
+        If mean and std were already calculated, getters return cached values.
+
+        :param get_load_time: Returns the load time in milliseconds.
+        :type get_load_time: float
+
+        :param get_unet_inference_duration: Returns the mean and standard deviation of unet inference in millionseconds.
+        :type get_unet_inference_duration: MeanStdPair
+
+        :param get_transformer_inference_duration: Returns the mean and standard deviation of transformer inference in millionseconds.
+        :type get_transformer_inference_duration: MeanStdPair
+
+        :param get_iteration_duration: Returns the mean and standard deviation of one generation iteration in millionseconds.
+        :type get_iteration_duration: MeanStdPair
+
+        :param get_inference_total_duration: Returns all inference duration including encoder, decoder and transformer/unet inference.
+        :type get_inference_total_duration: float
+
+        :param get_generate_duration: Returns generate duration in millionseconds.
+        :type get_generate_duration: float
+
+        :param raw_metrics: A structure of RawImageGenerationPerfMetrics type that holds raw metrics.
+        :type raw_metrics: RawImageGenerationPerfMetrics
+    """
+    def __init__(self) -> None:
+        ...
+    @property
+    def load_time(self) -> float:
+        ...
+    @property
+    def generate_duration(self) -> float:
+        ...
+    @property
+    def vae_encoder_inference_duration(self) -> float:
+        ...
+    @property
+    def vae_decoder_inference_duration(self) -> float:
+        ...
+    @property
+    def encoder_inference_duration(self) -> dict[str, float]:
+        ...
+    def get_unet_inference_duration(self) -> MeanStdPair:
+        ...
+    def get_transformer_inference_duration(self) -> MeanStdPair:
+        ...
+    def get_iteration_duration(self) -> MeanStdPair:
+        ...
+    def get_inference_total_duration(self) -> float:
+        ...
+    def get_load_time(self) -> float:
+        ...
+    def get_generate_duration(self) -> float:
+        ...
 def draft_model(models_path: os.PathLike, device: str = '', **kwargs) -> openvino._pyopenvino.OVAny:
     """
     device on which inference will be performed

src/python/py_image_generation_pipelines.cpp

@@ -285,6 +285,28 @@ void init_image_generation_pipelines(py::module_& m) {
             config.update_generation_config(pyutils::kwargs_to_any_map(kwargs));
         });
 
+    py::class_<RawImageGenerationPerfMetrics>(m, "RawImageGenerationPerfMetrics", raw_image_generation_perf_metrics_docstring)
+        .def(py::init<>())
+        .def_property_readonly("unet_inference_durations", [](const RawImageGenerationPerfMetrics &rw) {
+            return pyutils::get_ms(rw, &RawImageGenerationPerfMetrics::unet_inference_durations);
+        })
+        .def_property_readonly("transformer_inference_durations", [](const RawImageGenerationPerfMetrics &rw) { 
+            return pyutils::get_ms(rw, &RawImageGenerationPerfMetrics::transformer_inference_durations);
+        })
+        .def_property_readonly("iteration_durations", [](const RawImageGenerationPerfMetrics &rw) { 
+            return pyutils::get_ms(rw, &RawImageGenerationPerfMetrics::iteration_durations); 
+        });
+
+    py::class_<ImageGenerationPerfMetrics>(m, "ImageGenerationPerfMetrics", image_generation_perf_metrics_docstring)
+        .def(py::init<>())
+        .def("get_load_time", &ImageGenerationPerfMetrics::get_load_time)
+        .def("get_generate_duration", &ImageGenerationPerfMetrics::get_generate_duration)
+        .def("get_unet_inference_duration", &ImageGenerationPerfMetrics::get_unet_inference_duration)
+        .def("get_transformer_inference_duration", &ImageGenerationPerfMetrics::get_transformer_inference_duration)
+        .def("get_iteration_duration", &ImageGenerationPerfMetrics::get_iteration_duration)
+        .def("get_inference_total_duration", &ImageGenerationPerfMetrics::get_inference_total_duration)
+        .def_readonly("raw_metrics", &ImageGenerationPerfMetrics::raw_metrics);
+
     auto text2image_pipeline = py::class_<ov::genai::Text2ImagePipeline>(m, "Text2ImagePipeline", "This class is used for generation with text-to-image models.")
         .def(py::init([](const std::filesystem::path& models_path) {
             ScopedVar env_manager(pyutils::ov_tokenizers_module_path());
@@ -350,7 +372,8 @@ void init_image_generation_pipelines(py::module_& m) {
             },
             py::arg("prompt"), "Input string",
             (text2image_generate_docstring + std::string(" \n ")).c_str())
-        .def("decode", &ov::genai::Text2ImagePipeline::decode, py::arg("latent"));
+        .def("decode", &ov::genai::Text2ImagePipeline::decode, py::arg("latent"))
+        .def("get_perfomance_metrics", &ov::genai::Text2ImagePipeline::get_perfomance_metrics);
 
 
     auto image2image_pipeline = py::class_<ov::genai::Image2ImagePipeline>(m, "Image2ImagePipeline", "This class is used for generation with image-to-image models.")
@@ -413,7 +436,8 @@ void init_image_generation_pipelines(py::module_& m) {
             py::arg("prompt"), "Input string",
             py::arg("image"), "Initial image",
             (text2image_generate_docstring + std::string(" \n ")).c_str())
-        .def("decode", &ov::genai::Image2ImagePipeline::decode, py::arg("latent"));
+        .def("decode", &ov::genai::Image2ImagePipeline::decode, py::arg("latent"))
+        .def("get_perfomance_metrics", &ov::genai::Image2ImagePipeline::get_perfomance_metrics);
 
 
     auto inpainting_pipeline = py::class_<ov::genai::InpaintingPipeline>(m, "InpaintingPipeline", "This class is used for generation with inpainting models.")
@@ -478,7 +502,8 @@ void init_image_generation_pipelines(py::module_& m) {
             py::arg("image"), "Initial image",
             py::arg("mask_image"), "Mask image",
             (text2image_generate_docstring + std::string(" \n ")).c_str())
-        .def("decode", &ov::genai::InpaintingPipeline::decode, py::arg("latent"));
+        .def("decode", &ov::genai::InpaintingPipeline::decode, py::arg("latent"))
+        .def("get_perfomance_metrics", &ov::genai::InpaintingPipeline::get_perfomance_metrics);
 
     // define constructors to create one pipeline from another
     // NOTE: needs to be defined once all pipelines are created