Skip to content

Commit

Permalink
Adding info on VLMS and Speculative Decoding
Browse files Browse the repository at this point in the history
Signed-off-by: sgolebiewski-intel <[email protected]>
  • Loading branch information
sgolebiewski-intel committed Nov 27, 2024
1 parent 4ca1437 commit c900453
Show file tree
Hide file tree
Showing 2 changed files with 251 additions and 6 deletions.
108 changes: 108 additions & 0 deletions docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst
Original file line number Diff line number Diff line change
Expand Up @@ -218,6 +218,114 @@ Specify generation_config to use grouped beam search:
cout << pipe.generate("The Sun is yellow because", config);
}
Efficient Text Generation via Speculative Decoding
##################################################

Speculative decoding (or assisted-generation) enables faster token generation
when an additional smaller draft model is used alongside the main model.
The draft model predicts the next K tokens one by one in an autoregressive manner,
while the main model validates these predictions and corrects them if necessary.

Each predicted token is compared, and when there is a difference between the draft and
main model, the last token predicted by the main model is kept. Then, the draft
model acquires this token and tries prediction of the next K tokens,
thus repeating the cycle.

This method eliminates the need for multiple infer requests to the main model,
which results in increased performance. Its implementation in the pipeline is
shown in the code samples below:

.. tab-set::

.. tab-item:: Python
:sync: py

.. code-block:: python
import openvino_genai
import queue
import threading
def streamer(subword):
print(subword, end='', flush=True)
return False
def infer(model_dir: str, draft_model_dir: str, prompt: str):
main_device = 'CPU' # GPU can be used as well.
draft_device = 'CPU'
scheduler_config = openvino_genai.SchedulerConfig()
scheduler_config.cache_size = 2
draft_model = openvino_genai.draft_model(draft_model_dir, draft_device)
pipe = openvino_genai.LLMPipeline(model_dir, main_device, scheduler_config=scheduler_config, draft_model=draft_model)
config = openvino_genai.GenerationConfig()
config.max_new_tokens = 100
config.num_assistant_tokens = 5
pipe.generate(prompt, config, streamer)
For more information, refer to the
`Python sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/speculative_decoding_lm/>`__.


.. tab-item:: C++
:sync: cpp

.. code-block:: cpp
#include <openvino/openvino.hpp>
#include "openvino/genai/llm_pipeline.hpp"
int main(int argc, char* argv[]) try {
if (4 != argc) {
throw std::runtime_error(std::string{"Usage: "} + argv[0] + " <MODEL_DIR> <DRAFT_MODEL_DIR> '<PROMPT>'");
}
ov::genai::GenerationConfig config;
config.max_new_tokens = 100;
config.num_assistant_tokens = 5;
std::string main_model_path = argv[1];
std::string draft_model_path = argv[2];
std::string prompt = argv[3];
std::string main_device = "CPU", draft_device = "CPU";
ov::genai::SchedulerConfig scheduler_config;
scheduler_config.cache_size = 5;
ov::genai::LLMPipeline pipe(
main_model_path,
main_device,
ov::genai::draft_model(draft_model_path, draft_device),
ov::genai::scheduler_config(scheduler_config));
auto streamer = [](std::string subword) {
std::cout << subword << std::flush;
return false;
};
pipe.generate(prompt, config, streamer);
} catch (const std::exception& error) {
try {
std::cerr << error.what() << '\n';
} catch (const std::ios_base::failure&) {}
return EXIT_FAILURE;
} catch (...) {
try {
std::cerr << "Non-exception object thrown\n";
} catch (const std::ios_base::failure&) {}
return EXIT_FAILURE;
}
For more information, refer to the
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/speculative_decoding_lm/>`__

Comparing with Hugging Face Results
#######################################
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ sample shows basic usage of the ``Text2ImagePipeline`` pipeline.
image_write("baseline.bmp", image)
For more information, refer to the
`Python sample <https://github.com/openvinotoolkit/openvino.genai/blob/master/samples/python/text2image/README.md>`__
`Python sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/text2image/>`__

.. tab-item:: C++
:sync: cpp
Expand Down Expand Up @@ -218,7 +218,7 @@ sample shows basic usage of the ``Text2ImagePipeline`` pipeline.
For more information, refer to the
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/blob/master/samples/cpp/text2image/README.md>`__
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/text2image/>`__



Expand Down Expand Up @@ -269,7 +269,7 @@ and use audio files in WAV format at a sampling rate of 16 kHz as input.
For more information, refer to the
`Python sample <https://github.com/openvinotoolkit/openvino.genai/blob/master/samples/python/whisper_speech_recognition/README.md>`__.
`Python sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/whisper_speech_recognition/>`__.

.. tab-item:: C++
:sync: cpp
Expand Down Expand Up @@ -322,7 +322,7 @@ and use audio files in WAV format at a sampling rate of 16 kHz as input.
For more information, refer to the
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/blob/master/samples/cpp/whisper_speech_recognition/README.md>`__.
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/whisper_speech_recognition/>`__.


Using GenAI in Chat Scenario
Expand Down Expand Up @@ -367,7 +367,7 @@ mark a conversation session, as shown in the samples below:
For more information, refer to the
`Python sample <https://github.com/openvinotoolkit/openvino.genai/blob/master/samples/python/chat_sample/README.md>`__.
`Python sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/chat_sample/>`__.

.. tab-item:: C++
:sync: cpp
Expand Down Expand Up @@ -415,12 +415,149 @@ mark a conversation session, as shown in the samples below:
For more information, refer to the
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/blob/master/samples/cpp/chat_sample/README.md>`__
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/chat_sample/>`__


Using GenAI with Vision Language Models
#######################################

OpenVINO GenAI introduces the ``openvino_genai.VLMPipeline`` pipeline for
inference of multimodal text-generation Vision Language Models (VLMs).
With a text prompt and an image as input, VLMPipeline can generate text using
models such as LLava or MiniCPM-V. See the chat scenario presented
in the samples below:

.. tab-set::

.. tab-item:: Python
:sync: py

.. code-block:: python
import numpy as np
import openvino_genai
from PIL import Image
from openvino import Tensor
from pathlib import Path
def streamer(subword: str) -> bool:
print(subword, end='', flush=True)
def read_image(path: str) -> Tensor:
pic = Image.open(path).convert("RGB")
image_data = np.array(pic.getdata()).reshape(1, pic.size[1], pic.size[0], 3).astype(np.uint8)
return Tensor(image_data)
def read_images(path: str) -> list[Tensor]:
entry = Path(path)
if entry.is_dir():
return [read_image(str(file)) for file in sorted(entry.iterdir())]
return [read_image(path)]
def infer(model_dir: str, image_dir: str):
rgbs = read_images(image_dir)
device = 'CPU' # GPU can be used as well.
enable_compile_cache = dict()
if "GPU" == device:
enable_compile_cache["CACHE_DIR"] = "vlm_cache"
pipe = openvino_genai.VLMPipeline(model_dir, device, **enable_compile_cache)
config = openvino_genai.GenerationConfig()
config.max_new_tokens = 100
pipe.start_chat()
prompt = input('question:\n')
pipe.generate(prompt, images=rgbs, generation_config=config, streamer=streamer)
while True:
try:
prompt = input("\n----------\n"
"question:\n")
except EOFError:
break
pipe.generate(prompt, generation_config=config, streamer=streamer)
pipe.finish_chat()
For more information, refer to the
`Python sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/visual_language_chat>`__.

.. tab-item:: C++
:sync: cpp

.. code-block:: cpp
#include "load_image.hpp"
#include <openvino/genai/visual_language/pipeline.hpp>
#include <filesystem>
bool print_subword(std::string&& subword) {
return !(std::cout << subword << std::flush);
}
int main(int argc, char* argv[]) try {
if (3 != argc) {
throw std::runtime_error(std::string{"Usage "} + argv[0] + " <MODEL_DIR> <IMAGE_FILE OR DIR_WITH_IMAGES>");
}
std::vector<ov::Tensor> rgbs = utils::load_images(argv[2]);
std::string device = "CPU"; // GPU can be used as well.
ov::AnyMap enable_compile_cache;
if ("GPU" == device) {
enable_compile_cache.insert({ov::cache_dir("vlm_cache")});
}
ov::genai::VLMPipeline pipe(argv[1], device, enable_compile_cache);
ov::genai::GenerationConfig generation_config;
generation_config.max_new_tokens = 100;
std::string prompt;
pipe.start_chat();
std::cout << "question:\n";
std::getline(std::cin, prompt);
pipe.generate(prompt,
ov::genai::images(rgbs),
ov::genai::generation_config(generation_config),
ov::genai::streamer(print_subword));
std::cout << "\n----------\n"
"question:\n";
while (std::getline(std::cin, prompt)) {
pipe.generate(prompt,
ov::genai::generation_config(generation_config),
ov::genai::streamer(print_subword));
std::cout << "\n----------\n"
"question:\n";
}
pipe.finish_chat();
} catch (const std::exception& error) {
try {
std::cerr << error.what() << '\n';
} catch (const std::ios_base::failure&) {}
return EXIT_FAILURE;
} catch (...) {
try {
std::cerr << "Non-exception object thrown\n";
} catch (const std::ios_base::failure&) {}
return EXIT_FAILURE;
}
For more information, refer to the
`C++ sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/visual_language_chat/>`__

Additional Resources
#####################

* :doc:`Install OpenVINO GenAI <../../../get-started/install-openvino/install-openvino-genai>`
* `OpenVINO GenAI Repo <https://github.com/openvinotoolkit/openvino.genai>`__
* `OpenVINO GenAI Samples <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples>`__
* A Jupyter notebook demonstrating
`Visual-language assistant with MiniCPM-V2 and OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/minicpm-v-multimodal-chatbot>`__
* `OpenVINO Tokenizers <https://github.com/openvinotoolkit/openvino_tokenizers>`__

0 comments on commit c900453

Please sign in to comment.