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| Original file line number | Diff line number | Diff line change |
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| # Copyright (C) 2023-2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| find_package(OpenVINOGenAI REQUIRED PATHS | ||
| "${CMAKE_BINARY_DIR}" # Reuse the package from the build. | ||
| ${OpenVINO_DIR} # GenAI may be installed alogside OpenVINO. | ||
| ) | ||
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| FetchContent_Declare(cxxopts | ||
| URL https://github.com/jarro2783/cxxopts/archive/refs/tags/v3.1.1.tar.gz | ||
| URL_HASH SHA256=523175f792eb0ff04f9e653c90746c12655f10cb70f1d5e6d6d9491420298a08) | ||
| FetchContent_MakeAvailable(cxxopts) | ||
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| 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) |
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| Original file line number | Diff line number | Diff line change |
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| # LLMs benchmarking sample | ||
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| 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. | ||
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| ## Download and convert the model and tokenizers | ||
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| The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version. | ||
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| It's not required to install [../../requirements.txt](../../requirements.txt) for deployment if the model has already been exported. | ||
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| ```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 | ||
| ``` | ||
|
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| ## Usage | ||
|
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| ```sh | ||
| benchmark_vanilla_genai [OPTIONS] | ||
| ``` | ||
|
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| ### Options | ||
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| - `-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. | ||
|
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| ### Output: | ||
|
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| ``` | ||
| benchmark_vanilla_genai -m TinyLlama-1.1B-Chat-v1.0 -n 10 | ||
| ``` | ||
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| ``` | ||
| 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 | ||
| ``` | ||
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| For more information how performance metrics are calculated please follow [performance-metrics tutorial](../../../src/README.md#performance-metrics). |
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| // Copyright (C) 2023-2024 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
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| #include "openvino/genai/llm_pipeline.hpp" | ||
| #include <cxxopts.hpp> | ||
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| int main(int argc, char* argv[]) try { | ||
| cxxopts::Options options("benchmark_vanilla_genai", "Help command"); | ||
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| 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"); | ||
|
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| 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; | ||
| } | ||
|
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| if (result.count("help")) { | ||
| std::cout << options.help() << std::endl; | ||
| return EXIT_SUCCESS; | ||
| } | ||
|
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| 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>(); | ||
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| ov::genai::GenerationConfig config; | ||
| config.max_new_tokens = result["max_new_tokens"].as<size_t>(); | ||
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| ov::genai::LLMPipeline pipe(model_path, device); | ||
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| for (size_t i = 0; i < num_warmup; i++) | ||
| pipe.generate(prompt, config); | ||
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| 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; | ||
| } | ||
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| 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; | ||
|
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| 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; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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 | ||
| ``` | ||
|
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| ## Usage | ||
|
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| ```sh | ||
| python benchmark_vanilla_genai.py [OPTIONS] | ||
| ``` | ||
|
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| ### Options | ||
|
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||
| - `-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. | ||
|
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| ### Output: | ||
|
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| ``` | ||
| python benchmark_vanilla_genai.py -m TinyLlama-1.1B-Chat-v1.0 -n 10 | ||
| ``` | ||
|
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| ``` | ||
| 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 | ||
| ``` | ||
|
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| For more information on how performance metrics are calculated, see [performance metrics readme](../../../src/README.md#performance-metrics). |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,49 @@ | ||
| # Copyright (C) 2023-2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| import argparse | ||
| import openvino_genai as ov_genai | ||
|
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| 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") | ||
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| args = parser.parse_args() | ||
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| # 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 | ||
|
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| config = ov_genai.GenerationConfig() | ||
| config.max_new_tokens = args.max_new_tokens | ||
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| pipe = ov_genai.LLMPipeline(model_path, device) | ||
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| for _ in range(num_warmup): | ||
| pipe.generate(prompt, config) | ||
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| 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 | ||
|
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| 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") | ||
|
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| if __name__ == "__main__": | ||
| main() |
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