add generation time metrics (#613)

- Added performance metrics and updated Readme with description how to
use them
- Added cpp and python sample for benchmarking

Sample to calculate and visualize performance metrics.
```
import openvino_genai as ov_genai
import tqdm
import pandas as pd
import matplotlib.pylab as pl

pipe = ov_genai.LLMPipeline('TinyLlama-1.1B-Chat-v1.0/')
config = ov_genai.GenerationConfig(max_new_tokens=15)
metrics_df = pd.DataFrame(columns=['batch_size', 'throughput', 'ttft', 'tpot', 'std_throughput', 'std_ttft', 'std_tpot'])

num_iter = 3
for batch_size in tqdm.tqdm([1, 2, 4, 16, 32, 64, 128]):
    prompts = ["The Sky is blue because"] * batch_size
    res = pipe.generate(prompts, config)
    metrics = res.perf_metrics
    
    for _ in range(num_iter - 1):
        res = pipe.generate(prompts, config)
        metrics += res.perf_metrics
    metrics_df = metrics_df._append({
        'throughput': metrics.get_throughput().mean, 'ttft': metrics.get_ttft().mean, 'tpot': metrics.get_tpot().mean,
        'std_throughput': metrics.get_throughput().std, 'std_ttft': metrics.get_ttft().std, 'std_tpot': metrics.get_tpot().std,
        'batch_size': batch_size, 
    }, ignore_index=True)

fig, axes = pl.subplots(nrows=3, ncols=1, figsize=(6, 8), sharex=True)

axes[0].plot(metrics_df['batch_size'], metrics_df['throughput'], '-o')
axes[1].plot(metrics_df['batch_size'], metrics_df['ttft'], '-o', )
axes[2].plot(metrics_df['batch_size'], metrics_df['tpot'], '-o')

axes[0].set_ylabel('Throughput'), axes[1].set_ylabel('TTFT'), axes[2].set_ylabel('TPOT')
axes[2].set_xlabel('Batch Size')
axes[0].grid(True), axes[1].grid(True), axes[2].grid(True)
pl.tight_layout()
```


![image](https://github.com/user-attachments/assets/021a94b4-fc75-4b5f-90e6-60db471a3810)

ticket: CVS-132859

Author: andrei-kochin

samples/CMakeLists.txt

@@ -10,6 +10,7 @@ add_subdirectory(cpp/greedy_causal_lm)
 add_subdirectory(cpp/multinomial_causal_lm)
 add_subdirectory(cpp/prompt_lookup_decoding_lm)
 add_subdirectory(cpp/speculative_decoding_lm)
+add_subdirectory(cpp/benchmark_genai)
 
 install(FILES requirements.txt DESTINATION samples
         COMPONENT cpp_samples_genai)

samples/cpp/benchmark_genai/CMakeLists.txt

@@ -0,0 +1,24 @@
+# Copyright (C) 2023-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+
+find_package(OpenVINOGenAI REQUIRED PATHS
+    "${CMAKE_BINARY_DIR}"  # Reuse the package from the build.
+    ${OpenVINO_DIR}  # GenAI may be installed alogside OpenVINO.
+)
+
+FetchContent_Declare(cxxopts
+    URL https://github.com/jarro2783/cxxopts/archive/refs/tags/v3.1.1.tar.gz
+    URL_HASH SHA256=523175f792eb0ff04f9e653c90746c12655f10cb70f1d5e6d6d9491420298a08)
+FetchContent_MakeAvailable(cxxopts)
+
+add_executable(benchmark_genai benchmark_genai.cpp)
+target_link_libraries(benchmark_genai PRIVATE openvino::genai cxxopts::cxxopts)
+set_target_properties(benchmark_genai PROPERTIES
+    COMPILE_PDB_NAME benchmark_genai
+    # Ensure out of box LC_RPATH on macOS with SIP
+    INSTALL_RPATH_USE_LINK_PATH ON)
+install(TARGETS benchmark_genai
+    RUNTIME DESTINATION samples_bin/
+    COMPONENT samples_bin
+    EXCLUDE_FROM_ALL)

samples/cpp/benchmark_genai/README.md

@@ -0,0 +1,47 @@
+# LLMs benchmarking sample
+
+This sample script demonstrates how to benchmark an LLMs in OpenVINO GenAI. The script includes functionality for warm-up iterations, generating text, and calculating various performance metrics.
+
+## Download and convert the model and tokenizers
+
+The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version.
+
+It's not required to install [../../requirements.txt](../../requirements.txt) for deployment if the model has already been exported.
+
+```sh
+pip install --upgrade-strategy eager -r ../../requirements.txt
+optimum-cli export openvino --trust-remote-code --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 TinyLlama-1.1B-Chat-v1.0
+```
+
+## Usage
+
+```sh
+benchmark_vanilla_genai [OPTIONS]
+```
+
+### Options
+
+- `-m, --model`: Path to the model and tokenizers base directory.
+- `-p, --prompt` (default: `"The Sky is blue because"`): The prompt to generate text.
+- `-nw, --num_warmup` (default: `1`): Number of warmup iterations.
+- `-mt, --max_new_tokens` (default: `20`): Number of warmup iterations.
+- `-n, --num_iter` (default: `3`): Number of iterations.
+- `-d, --device` (default: `"CPU"`): Device to run the model on.
+
+### Output:
+
+```
+benchmark_vanilla_genai -m TinyLlama-1.1B-Chat-v1.0 -n 10
+```
+
+```
+Load time: 3405.69 ms
+Generate time: 1430.77 ± 3.04 ms
+Tokenization time: 0.51 ± 0.02 ms
+Detokenization time: 0.37 ± 0.01 ms
+TTFT: 81.60 ± 0.54 ms
+TPOT: 71.52 ± 2.72 ms
+Throughput tokens/s: 13.98 ± 0.53
+```
+
+For more information how performance metrics are calculated please follow [performance-metrics tutorial](../../../src/README.md#performance-metrics).

samples/cpp/benchmark_genai/benchmark_genai.cpp

@@ -0,0 +1,70 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+
+#include "openvino/genai/llm_pipeline.hpp"
+#include <cxxopts.hpp>
+
+int main(int argc, char* argv[]) try {
+    cxxopts::Options options("benchmark_vanilla_genai", "Help command");
+
+    options.add_options()
+    ("m,model", "Path to model and tokenizers base directory", cxxopts::value<std::string>()->default_value("."))
+    ("p,prompt", "Prompt", cxxopts::value<std::string>()->default_value("The Sky is blue because"))
+    ("nw,num_warmup", "Number of warmup iterations", cxxopts::value<size_t>()->default_value(std::to_string(1)))
+    ("n,num_iter", "Number of iterations", cxxopts::value<size_t>()->default_value(std::to_string(3)))
+    ("mt,max_new_tokens", "Maximal number of new tokens", cxxopts::value<size_t>()->default_value(std::to_string(20)))
+    ("d,device", "device", cxxopts::value<std::string>()->default_value("CPU"))
+    ("h,help", "Print usage");
+
+    cxxopts::ParseResult result;
+    try {
+        result = options.parse(argc, argv);
+    } catch (const cxxopts::exceptions::exception& e) {
+        std::cout << e.what() << "\n\n";
+        std::cout << options.help() << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    if (result.count("help")) {
+        std::cout << options.help() << std::endl;
+        return EXIT_SUCCESS;
+    }
+
+    std::string prompt = result["prompt"].as<std::string>();
+    const std::string model_path = result["model"].as<std::string>();
+    std::string device = result["device"].as<std::string>();
+    size_t num_warmup = result["num_warmup"].as<size_t>();
+    size_t num_iter = result["num_iter"].as<size_t>();
+  
+    ov::genai::GenerationConfig config;
+    config.max_new_tokens = result["max_new_tokens"].as<size_t>();
+
+    ov::genai::LLMPipeline pipe(model_path, device);
+    
+    for (size_t i = 0; i < num_warmup; i++)
+        pipe.generate(prompt, config);
+    
+    ov::genai::DecodedResults res = pipe.generate(prompt, config);
+    ov::genai::PerfMetrics metrics = res.perf_metrics;
+    for (size_t i = 0; i < num_iter - 1; i++) {
+        res = pipe.generate(prompt, config);
+        metrics = metrics + res.perf_metrics;
+    }
+
+    std::cout << std::fixed << std::setprecision(2);
+    std::cout << "Load time: " << metrics.get_load_time() << " ms" << std::endl;
+    std::cout << "Generate time: " << metrics.get_generate_duration().mean << " ± " << metrics.get_generate_duration().std << " ms" << std::endl;
+    std::cout << "Tokenization time: " << metrics.get_tokenization_duration().mean << " ± " << metrics.get_tokenization_duration().std << " ms" << std::endl;
+    std::cout << "Detokenization time: " << metrics.get_detokenization_duration().mean << " ± " << metrics.get_detokenization_duration().std << " ms" << std::endl;
+    std::cout << "TTFT: " << metrics.get_ttft().mean  << " ± " << metrics.get_ttft().std << " ms" << std::endl;
+    std::cout << "TPOT: " << metrics.get_tpot().mean  << " ± " << metrics.get_tpot().std << " ms/token " << std::endl;
+    std::cout << "Throughput: " << metrics.get_throughput().mean  << " ± " << metrics.get_throughput().std << " tokens/s" << std::endl;
+
+    return 0;
+} catch (const std::exception& error) {
+    std::cerr << error.what() << '\n';
+    return EXIT_FAILURE;
+} catch (...) {
+    std::cerr << "Non-exception object thrown\n";
+    return EXIT_FAILURE;
+}

samples/python/benchmark_genai/README.md

@@ -0,0 +1,47 @@
+# LLMs benchmarking sample
+
+This sample script demonstrates how to benchmark an LLMs in OpenVINO GenAI. The script includes functionality for warm-up iterations, generating text, and calculating various performance metrics.
+
+## Download and convert the model and tokenizers
+
+The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version.
+
+It's not required to install [../../requirements.txt](../../requirements.txt) for deployment if the model has already been exported.
+
+```sh
+pip install --upgrade-strategy eager -r ../../requirements.txt
+optimum-cli export openvino --trust-remote-code --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 TinyLlama-1.1B-Chat-v1.0
+```
+
+## Usage
+
+```sh
+python benchmark_vanilla_genai.py [OPTIONS]
+```
+
+### Options
+
+- `-m, --model`: Path to the model and tokenizers base directory.
+- `-p, --prompt` (default: `"The Sky is blue because"`): The prompt to generate text.
+- `-nw, --num_warmup` (default: `1`): Number of warmup iterations.
+- `-n, --num_iter` (default: `3`): Number of iterations.
+- `-mt, --max_new_tokens` (default: `20`): Number of warmup iterations.
+- `-d, --device` (default: `"CPU"`): Device to run the model on.
+
+### Output:
+
+```
+python benchmark_vanilla_genai.py -m TinyLlama-1.1B-Chat-v1.0 -n 10
+```
+
+```
+Load time: 3405.69 ms
+Generate time: 1430.77 ± 3.04 ms
+Tokenization time: 0.51 ± 0.02 ms
+Detokenization time: 0.37 ± 0.01 ms
+TTFT: 81.60 ± 0.54 ms
+TPOT: 71.52 ± 2.72 ms
+Throughput tokens/s: 13.98 ± 0.53
+```
+
+For more information on how performance metrics are calculated, see [performance metrics readme](../../../src/README.md#performance-metrics).

samples/python/benchmark_genai/benchmark_genai.py

@@ -0,0 +1,49 @@
+# Copyright (C) 2023-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import openvino_genai as ov_genai
+
+def main():
+    parser = argparse.ArgumentParser(description="Help command")
+    parser.add_argument("-m", "--model", type=str, help="Path to model and tokenizers base directory")
+    parser.add_argument("-p", "--prompt", type=str, default="The Sky is blue because", help="Prompt")
+    parser.add_argument("-nw", "--num_warmup", type=int, default=1, help="Number of warmup iterations")
+    parser.add_argument("-n", "--num_iter", type=int, default=2, help="Number of iterations")
+    parser.add_argument("-mt", "--max_new_tokens", type=int, default=20, help="Maximal number of new tokens")
+    parser.add_argument("-d", "--device", type=str, default="CPU", help="Device")
+    
+    args = parser.parse_args()
+
+    # Perf metrics is stored in DecodedResults. 
+    # In order to get DecodedResults instead of a string input should be a list.
+    prompt = [args.prompt]
+    model_path = args.model
+    device = args.device
+    num_warmup = args.num_warmup
+    num_iter = args.num_iter
+    
+    config = ov_genai.GenerationConfig()
+    config.max_new_tokens = args.max_new_tokens
+
+    pipe = ov_genai.LLMPipeline(model_path, device)
+    
+    for _ in range(num_warmup):
+        pipe.generate(prompt, config)
+    
+    res = pipe.generate(prompt, config)
+    perf_metrics = res.perf_metrics
+    for _ in range(num_iter - 1):
+        res = pipe.generate(prompt, config)
+        perf_metrics += res.perf_metrics
+    
+    print(f"Load time: {perf_metrics.get_load_time():.2f} ms")
+    print(f"Generate time: {perf_metrics.get_generate_duration().mean:.2f} ± {perf_metrics.get_generate_duration().std:.2f} ms")
+    print(f"Tokenization time: {perf_metrics.get_tokenization_duration().mean:.2f} ± {perf_metrics.get_tokenization_duration().std:.2f} ms")
+    print(f"Detokenization time: {perf_metrics.get_detokenization_duration().mean:.2f} ± {perf_metrics.get_detokenization_duration().std:.2f} ms")
+    print(f"TTFT: {perf_metrics.get_ttft().mean:.2f} ± {perf_metrics.get_ttft().std:.2f} ms")
+    print(f"TPOT: {perf_metrics.get_tpot().mean:.2f} ± {perf_metrics.get_tpot().std:.2f} ms")
+    print(f"Throughput : {perf_metrics.get_throughput().mean:.2f} ± {perf_metrics.get_throughput().std:.2f} tokens/s")
+
+if __name__ == "__main__":
+    main()

src/README.md

@@ -196,6 +196,97 @@ int main(int argc, char* argv[]) {
 }
 ```
 
+### Performance Metrics
+
+`openvino_genai.PerfMetrics` (referred as `PerfMetrics` for simplicity) is a structure that holds performance metrics for each generate call. `PerfMetrics` holds fields with mean and standard deviations for the following metrics:
+- Time To the First Token (TTFT), ms
+- Time per Output Token (TPOT), ms/token
+- Generate total duration, ms
+- Tokenization duration, ms
+- Detokenization duration, ms
+- Throughput, tokens/s
+
+and:
+- Load time, ms
+- Number of generated tokens
+- Number of tokens in the input prompt
+
+Performance metrics are stored either in the `DecodedResults` or `EncodedResults` `perf_metric` field. Additionally to the fields mentioned above, `PerfMetrics` has a member `raw_metrics` of type `openvino_genai.RawPerfMetrics` (referred to as `RawPerfMetrics` for simplicity) that contains raw values for the durations of each batch of new token generation, tokenization durations, detokenization durations, and more. These raw metrics are accessible if you wish to calculate your own statistical values such as median or percentiles. However, since mean and standard deviation values are usually sufficient, we will focus on `PerfMetrics`.
+
+```python
+import openvino_genai as ov_genai
+pipe = ov_genai.LLMPipeline(model_path, "CPU")
+result = pipe.generate(["The Sun is yellow because"], max_new_tokens=20)
+perf_metrics = result.perf_metrics
+
+print(f'Generate duration: {perf_metrics.get_generate_duration().mean:.2f}')
+print(f'TTFT: {perf_metrics.get_ttft().mean:.2f} ms')
+print(f'TPOT: {perf_metrics.get_tpot().mean:.2f} ms/token')
+print(f'Throughput: {perf_metrics.get_throughput()get_.mean():.2f} tokens/s')
+```
+
+```cpp
+#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, "CPU");
+    auto result = pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(20));
+    auto perf_metrics = result.perf_metrics;
+    
+    std::cout << std::fixed << std::setprecision(2);
+    std::cout << "Generate duration: " << perf_metrics.get_generate_duration().mean << " ms" << std::endl;
+    std::cout << "TTFT: " << metrics.get_ttft().mean  << " ms" << std::endl;
+    std::cout << "TPOT: " << metrics.get_tpot().mean  << " ms/token " << std::endl;
+    std::cout << "Throughput: " << metrics.get_throughput().mean  << " tokens/s" << std::endl;
+}
+```
+output:
+```sh
+mean_generate_duration: 76.28
+mean_ttft: 42.58
+mean_tpot 3.80
+```
+
+>**Note**: If the input prompt is just a string, the generate function returns only a string without perf_metrics. To obtain perf_metrics, provide the prompt as a list with at least one element or call generate with encoded inputs.
+
+Several `perf_metrics` can be added to each other. In that case `raw_metrics` are concatenated and mean/std values are recalculated. This accumulates statistics from several `generate()` calls
+
+```cpp
+#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, "CPU");
+    auto result_1 = pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(20));
+    auto result_2 = pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(20));
+    auto perf_metrics = result_1.perf_metrics + result_2.perf_metrics
+    
+    std::cout << std::fixed << std::setprecision(2);
+    std::cout << "Generate duration: " << perf_metrics.get_generate_duration().mean << " ms" << std::endl;
+    std::cout << "TTFT: " << metrics.get_ttft().mean  << " ms" << std::endl;
+    std::cout << "TPOT: " << metrics.get_tpot().mean  << " ms/token " << std::endl;
+    std::cout << "Throughput: " << metrics.get_throughput().mean  << " tokens/s" << std::endl;
+}
+```
+
+```python
+import openvino_genai as ov_genai
+pipe = ov_genai.LLMPipeline(model_path, "CPU")
+res_1 = pipe.generate(["The Sun is yellow because"], max_new_tokens=20)
+res_2 = pipe.generate(["Why Sky is blue because"], max_new_tokens=20)
+perf_metrics = res_1.perf_metrics + res_2.perf_metrics
+
+print(f'Generate duration: {perf_metrics.get_generate_duration().mean:.2f}')
+print(f'TTFT: {perf_metrics.get_ttft().mean:.2f} ms')
+print(f'TPOT: {perf_metrics.get_tpot().mean:.2f} ms/token')
+print(f'Throughput: {perf_metrics.get_throughput().mean:.2f} tokens/s')
+```
+
+For more examples of how metrics are used, please refer to the Python [benchmark_genai.py](https://github.com/openvinotoolkit/openvino.genai/tree/releases/2024/3/samples/python/benchmark_genai/README.md) and C++ [benchmark_genai](https://github.com/openvinotoolkit/openvino.genai/tree/releases/2024/3/samples/cpp/benchmark_genai/README.md) samples.
+
 ## How It Works
 
 For information on how OpenVINO™ GenAI works, refer to the [How It Works Section](https://github.com/openvinotoolkit/openvino.genai/tree/releases/2024/2/src/docs/HOW_IT_WORKS.md).

src/cpp/include/openvino/genai/llm_pipeline.hpp

@@ -5,11 +5,13 @@
 
 #include <optional>
 #include <variant>
+#include <chrono>
 
 #include "openvino/core/any.hpp"
 #include "openvino/genai/generation_config.hpp"
 #include "openvino/genai/tokenizer.hpp"
 #include "openvino/genai/streamer_base.hpp"
+#include "openvino/genai/perf_metrics.hpp"
 
 namespace ov {
 namespace genai {
@@ -29,11 +31,13 @@ using StringInputs = std::variant<std::string, std::vector<std::string>>;
 *
 * @param tokens sequence of resulting tokens
 * @param scores sum of logarithmic probabilities of all tokens in the sequence
+* @param metrics performance metrics with tpot, ttft, etc. of type ov::genai::PerfMetrics
 */
 class EncodedResults {
 public:
     std::vector<std::vector<int64_t>> tokens;
     std::vector<float> scores;
+    PerfMetrics perf_metrics;
 };
 
 /**
@@ -42,11 +46,13 @@ class EncodedResults {
 *
 * @param texts vector of resulting sequences
 * @param scores scores for each sequence
+* @param metrics performance metrics with tpot, ttft, etc. of type ov::genai::PerfMetrics
 */
 class DecodedResults {
 public:
     std::vector<std::string> texts;
     std::vector<float> scores;
+    PerfMetrics perf_metrics;
 
     // @brief Convert DecodedResults to a string.
     operator std::string() const {