Visual-language assistant with MiniCPM-V2 and OpenVINO#
This Jupyter notebook can be launched after a local installation only.
MiniCPM-V 2 is a strong multimodal large language model for efficient end-side deployment. MiniCPM-V 2.6 is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over previous versions, and introduces new features for multi-image and video understanding.
More details about model can be found in model card and original repo.
In this tutorial we consider how to convert and optimize MiniCPM-V2 model for creating multimodal chatbot. Additionally, we demonstrate how to apply stateful transformation on LLM part and model optimization techniques like weights compression using NNCF
Table of contents:
Installation Instructions#
This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.
Prerequisites#
%pip install -q "torch>=2.1" "torchvision" "timm>=0.9.2" "transformers>=4.40" "Pillow" "gradio>=4.19" "tqdm" "sentencepiece" "peft" --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "openvino>=2024.3.0" "nncf>=2.12.0"
import requests
from pathlib import Path
if not Path("minicpm_helper.py").exists():
r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/minicpm-v-multimodal-chatbot/minicpm_helper.py")
open("ov_phi3_vision.py", "w").write(r.text)
if not Path("gradio_helper.py").exists():
r = requests.get(
url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks//minicpm-v-multimodal-chatbot//gradio_helper.py"
)
open("gradio_helper.py", "w").write(r.text)
if not Path("notebook_utils.py").exists():
r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
open("notebook_utils.py", "w").write(r.text)
Convert model to OpenVINO Intermediate Representation#
OpenVINO supports PyTorch models via conversion to OpenVINO Intermediate
Representation (IR). OpenVINO model conversion
API
should be used for these purposes. ov.convert_model
function accepts
original PyTorch model instance and example input for tracing and
returns ov.Model
representing this model in OpenVINO framework.
Converted model can be used for saving on disk using ov.save_model
function or directly loading on device using core.complie_model
.
minicpm_helper.py
script contains helper function for model
conversion, please check its content if you interested in conversion
details.
Click here for more detailed explanation of conversion steps
MiniCPM-V2.6 is autoregressive transformer generative model, it means
that each next model step depends from model output from previous step.
The generation approach is based on the assumption that the probability
distribution of a word sequence can be decomposed into the product of
conditional next word distributions. In other words, model predicts the
next token in the loop guided by previously generated tokens until the
stop-condition will be not reached (generated sequence of maximum length
or end of string token obtained). The way the next token will be
selected over predicted probabilities is driven by the selected decoding
methodology. You can find more information about the most popular
decoding methods in this
blog. The entry point
for the generation process for models from the Hugging Face Transformers
library is the generate
method. You can find more information about
its parameters and configuration in the
documentation.
To preserve flexibility in the selection decoding methodology, we will
convert only model inference for one step.
The inference flow has difference on first step and for the next. On the
first step, model accept preprocessed input instruction and image, that
transformed to the unified embedding space using input_embedding
and
image encoder
models, after that language model
, LLM-based part
of model, runs on input embeddings to predict probability of next
generated tokens. On the next step, language_model
accepts only next
token id selected based on sampling strategy and processed by
input_embedding
model and cached attention key and values. Since the
output side is auto-regressive, an output token hidden state remains the
same once computed for every further generation step. Therefore,
recomputing it every time you want to generate a new token seems
wasteful. With the cache, the model saves the hidden state once it has
been computed. The model only computes the one for the most recently
generated output token at each time step, re-using the saved ones for
hidden tokens. This reduces the generation complexity from
\(O(n^3)\) to \(O(n^2)\) for a transformer model. More details
about how it works can be found in this
article.
With increasing model size like in modern LLMs, we also can note an
increase in the number of attention blocks and size past key values
tensors respectively. The strategy for handling cache state as model
inputs and outputs in the inference cycle may become a bottleneck for
memory-bounded systems, especially with processing long input sequences,
for example in a chatbot scenario. OpenVINO suggests a transformation
that removes inputs and corresponding outputs with cache tensors from
the model keeping cache handling logic inside the model. Such models are
also called stateful. A stateful model is a model that implicitly
preserves data between two consecutive inference calls. The tensors
saved from one run are kept in an internal memory buffer called a
state
or a variable
and may be passed to the next run, while
never being exposed as model output. Hiding the cache enables storing
and updating the cache values in a more device-friendly representation.
It helps to reduce memory consumption and additionally optimize model
performance. More details about stateful models and working with state
can be found in OpenVINO
documentation.
In LLMs, input_embedding
is a part of language model, but for
multimodal case, the first step hidden state produced by this model part
should be integrated with image embeddings into common embedding space.
For ability to reuse this model part and avoid introduction of llm model
instance, we will use it separately.
image_encoder
is represented in MiniCPM-V by pretrained
SigLIP
model. Additionally, MiniCPM uses perceiver resampler
that
compresses the image representations. To preserve model ability to
process images of different size with respect aspect ratio combined in
batch, we will use image_encoder
and resampler
as separated
models.
To sum up above, model consists of 4 parts:
Image Encoder for encoding input images into embedding space. It includes SigLIP model.
Resampler for compression image representation.
Input Embedding for conversion input text tokens into embedding space.
Language Model for generation answer based on input embeddings provided by Image Encoder and Input Embedding models.
Let’s convert each model part.
from minicpm_helper import convert_minicpmv26
# uncomment the line to see model conversion code
# ??convert_minicpmv26
2024-08-19 11:46:02.978172: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2024-08-19 11:46:02.979956: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2024-08-19 11:46:03.014920: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-08-19 11:46:03.787842: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
model_id = "openbmb/MiniCPM-V-2_6"
model_dir = convert_minicpmv26(model_id)
✅ openbmb/MiniCPM-V-2_6 model already converted. You can find results in MiniCPM-V-2_6
Compress Language Model Weights to 4 bits#
For reducing memory consumption, weights compression optimization can be applied using NNCF.
Click here for more details about weight compression
Weight compression aims to reduce the memory footprint of a model. It can also lead to significant performance improvement for large memory-bound models, such as Large Language Models (LLMs). LLMs and other models, which require extensive memory to store the weights during inference, can benefit from weight compression in the following ways:
enabling the inference of exceptionally large models that cannot be accommodated in the memory of the device;
improving the inference performance of the models by reducing the latency of the memory access when computing the operations with weights, for example, Linear layers.
Neural Network Compression Framework (NNCF) provides 4-bit / 8-bit mixed weight quantization as a compression method primarily designed to optimize LLMs. The main difference between weights compression and full model quantization (post-training quantization) is that activations remain floating-point in the case of weights compression which leads to a better accuracy. Weight compression for LLMs provides a solid inference performance improvement which is on par with the performance of the full model quantization. In addition, weight compression is data-free and does not require a calibration dataset, making it easy to use.
nncf.compress_weights
function can be used for performing weights
compression. The function accepts an OpenVINO model and other
compression parameters. Compared to INT8 compression, INT4 compression
improves performance even more, but introduces a minor drop in
prediction quality.
More details about weights compression, can be found in OpenVINO documentation.
Note: weights compression process may require additional time and memory for performing. You can disable it using widget below:
from minicpm_helper import compression_widget
to_compress_weights = compression_widget()
to_compress_weights
Checkbox(value=True, description='Weights Compression')
import nncf
import gc
import openvino as ov
from minicpm_helper import llm_path, copy_llm_files
compression_configuration = {
"mode": nncf.CompressWeightsMode.INT4_SYM,
"group_size": 64,
"ratio": 0.6,
}
core = ov.Core()
llm_int4_path = Path("language_model_int4") / llm_path.name
if to_compress_weights.value and not (model_dir / llm_int4_path).exists():
ov_model = core.read_model(model_dir / llm_path)
ov_compressed_model = nncf.compress_weights(ov_model, **compression_configuration)
ov.save_model(ov_compressed_model, model_dir / llm_int4_path)
del ov_compressed_model
del ov_model
gc.collect()
copy_llm_files(model_dir, llm_int4_path.parent)
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
Prepare model inference pipeline#
As discussed, the model comprises Image Encoder and LLM (with separated
text embedding part) that generates answer. In minicpm_helper.py
we
defined LLM inference class OvModelForCausalLMWithEmb
that will
represent generation cycle, It is based on HuggingFace Transformers
GenerationMixin
and looks similar to Optimum
Intel
OVModelForCausalLM
that is used for LLM inference with only
difference that it can accept input embedding. In own turn, general
multimodal model class OvMiniCPMVModel
handles chatbot functionality
including image processing and answer generation using LLM.
from minicpm_helper import OvModelForCausalLMWithEmb, OvMiniCPMV, init_model # noqa: F401
# uncomment the line to see model inference class
# ??OVMiniCPMV
# uncomment the line to see language model inference class
# ??OvModelForCausalLMWithEmb
Run OpenVINO model inference#
Select device#
from notebook_utils import device_widget
device = device_widget(default="AUTO", exclude=["NPU"])
device
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
Select language model variant#
from minicpm_helper import lm_variant_selector
use_int4_lang_model = lm_variant_selector(model_dir / llm_int4_path)
use_int4_lang_model
Checkbox(value=True, description='INT4 language model')
ov_model = init_model(model_dir, llm_path.parent if not use_int4_lang_model.value else llm_int4_path.parent, device.value)
/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/transformers/models/auto/image_processing_auto.py:513: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use slow_image_processor_class, or fast_image_processor_class instead warnings.warn(
import requests
from PIL import Image
url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11"
image = Image.open(requests.get(url, stream=True).raw)
question = "What is unusual on this image?"
print(f"Question:\n{question}")
image
Question:
What is unusual on this image?
![../_images/minicpm-v-multimodal-chatbot-with-output_17_1.png](../_images/minicpm-v-multimodal-chatbot-with-output_17_1.png)
tokenizer = ov_model.processor.tokenizer
msgs = [{"role": "user", "content": question}]
print("Answer:")
res = ov_model.chat(image=image, msgs=msgs, context=None, tokenizer=tokenizer, sampling=True, temperature=0.7, stream=True, max_new_tokens=50)
generated_text = ""
for new_text in res:
generated_text += new_text
print(new_text, flush=True, end="")
Answer:
The unusual aspect of this image is the cat's relaxed and vulnerable position. Typically, cats avoid exposing their underbellies as it can be perceived as a sign of submission or weakness in the wild. However, in domestic settings, cats often seek out
Interactive demo#
from gradio_helper import make_demo
demo = make_demo(ov_model)
try:
demo.launch(debug=True, height=600)
except Exception:
demo.launch(debug=True, share=True, height=600)
# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs: https://gradio.app/docs/