Merge branch 'master' into guozhong/add_test_case_for_native_pytorch

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/beam_search_causal_lm/README.md

@@ -17,8 +17,20 @@ optimum-cli export openvino --trust-remote-code --model TinyLlama/TinyLlama-1.1B
 
 `beam_search_causal_lm.py TinyLlama-1.1B-Chat-v1.0 "Why is the Sun yellow?"`
 
-To enable Unicode characters for Windows cmd open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
 
 Discrete GPUs (dGPUs) usually provide better performance compared to CPUs. It is recommended to run larger models on a dGPU with 32GB+ RAM. For example, the model meta-llama/Llama-2-13b-chat-hf can benefit from being run on a dGPU. Modify the source code to change the device for inference to the GPU.
 
 See https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md#supported-models for the list of supported models.
+
+### Troubleshooting
+
+#### Unicode characters encoding error on Windows
+
+Example error:
+```
+UnicodeEncodeError: 'charmap' codec can't encode character '\u25aa' in position 0: character maps to <undefined>
+```
+
+If you encounter the error described in the example when sample is printing output to the Windows console, it is likely due to the default Windows encoding not supporting certain Unicode characters. To resolve this:
+1. Enable Unicode characters for Windows cmd - open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
+2. Enable UTF-8 mode by setting environment variable `PYTHONIOENCODING="utf8"`.

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_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_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()

samples/python/chat_sample/README.md

@@ -17,15 +17,25 @@ optimum-cli export openvino --trust-remote-code --model TinyLlama/TinyLlama-1.1B
 
 `chat_sample.py TinyLlama-1.1B-Chat-v1.0`
 
-To enable Unicode characters for Windows cmd open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
 
 Discrete GPUs (dGPUs) usually provide better performance compared to CPUs. It is recommended to run larger models on a dGPU with 32GB+ RAM. For example, the model meta-llama/Llama-2-13b-chat-hf can benefit from being run on a dGPU. Modify the source code to change the device for inference to the GPU.
 
 See https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md#supported-models for the list of supported models.
 
+### Troubleshooting
 
-## Troubleshooting
-### Missing chat template
+#### Unicode characters encoding error on Windows
+
+Example error:
+```
+UnicodeEncodeError: 'charmap' codec can't encode character '\u25aa' in position 0: character maps to <undefined>
+```
+
+If you encounter the error described in the example when sample is printing output to the Windows console, it is likely due to the default Windows encoding not supporting certain Unicode characters. To resolve this:
+1. Enable Unicode characters for Windows cmd - open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
+2. Enable UTF-8 mode by setting environment variable `PYTHONIOENCODING="utf8"`.
+
+#### Missing chat template
 
 If you encounter an exception indicating a missing "chat template" when launching the `ov::genai::LLMPipeline` in chat mode, it likely means the model was not tuned for chat functionality. To work this around, manually add the chat template to tokenizer_config.json of your model.
 The following template can be used as a default, but it may not work properly with every model:

samples/python/greedy_causal_lm/README.md

@@ -17,8 +17,20 @@ optimum-cli export openvino --trust-remote-code --model TinyLlama/TinyLlama-1.1B
 
 `greedy_causal_lm.py TinyLlama-1.1B-Chat-v1.0 "Why is the Sun yellow?"`
 
-To enable Unicode characters for Windows cmd open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
 
 Discrete GPUs (dGPUs) usually provide better performance compared to CPUs. It is recommended to run larger models on a dGPU with 32GB+ RAM. For example, the model meta-llama/Llama-2-13b-chat-hf can benefit from being run on a dGPU. Modify the source code to change the device for inference to the GPU.
 
 See https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md#supported-models for the list of supported models.
+
+### Troubleshooting
+
+#### Unicode characters encoding error on Windows
+
+Example error:
+```
+UnicodeEncodeError: 'charmap' codec can't encode character '\u25aa' in position 0: character maps to <undefined>
+```
+
+If you encounter the error described in the example when sample is printing output to the Windows console, it is likely due to the default Windows encoding not supporting certain Unicode characters. To resolve this:
+1. Enable Unicode characters for Windows cmd - open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
+2. Enable UTF-8 mode by setting environment variable `PYTHONIOENCODING="utf8"`.

samples/python/multinomial_causal_lm/README.md

@@ -17,8 +17,20 @@ optimum-cli export openvino --trust-remote-code --model TinyLlama/TinyLlama-1.1B
 
 `multinomial_causal_lm.py TinyLlama-1.1B-Chat-v1.0 "Why is the Sun yellow?"`
 
-To enable Unicode characters for Windows cmd open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
 
 Discrete GPUs (dGPUs) usually provide better performance compared to CPUs. It is recommended to run larger models on a dGPU with 32GB+ RAM. For example, the model meta-llama/Llama-2-13b-chat-hf can benefit from being run on a dGPU. Modify the source code to change the device for inference to the GPU.
 
 See https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md#supported-models for the list of supported models.
+
+### Troubleshooting
+
+#### Unicode characters encoding error on Windows
+
+Example error:
+```
+UnicodeEncodeError: 'charmap' codec can't encode character '\u25aa' in position 0: character maps to <undefined>
+```
+
+If you encounter the error described in the example when sample is printing output to the Windows console, it is likely due to the default Windows encoding not supporting certain Unicode characters. To resolve this:
+1. Enable Unicode characters for Windows cmd - open `Region` settings from `Control panel`. `Administrative`->`Change system locale`->`Beta: Use Unicode UTF-8 for worldwide language support`->`OK`. Reboot.
+2. Enable UTF-8 mode by setting environment variable `PYTHONIOENCODING="utf8"`.