Visual-language assistant with nanoLLaVA and OpenVINO#

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nanoLLaVA is a “small but mighty” 1B vision-language model designed to run efficiently on edge devices. It uses SigLIP-400m as Image Encoder and Qwen1.5-0.5B as LLM. In this tutorial, we consider how to convert and run nanoLLaVA model using OpenVINO. Additionally, we will optimize model using NNCF

Table of contents:

Prerequisites#

%pip install -q "torch>=2.1" "transformers>=4.40" "accelerate" "pillow" "gradio>=4.26" "openvino>=2024.1.0" "tqdm" "nncf>=2.10" --extra-index-url https://download.pytorch.org/whl/cpu
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.3.1+cpu which is incompatible.
mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
Note: you may need to restart the kernel to use updated packages.
from huggingface_hub import snapshot_download
from pathlib import Path

model_local_dir = Path("nanoLLaVA")

if not model_local_dir.exists():
    snapshot_download(repo_id="qnguyen3/nanoLLaVA", local_dir=model_local_dir)

modeling_file = model_local_dir / "modeling_llava_qwen2.py"
orig_modeling_file = model_local_dir / f"orig_{modeling_file.name}"


# model code depends from flash_attn package that may be problematic to load. Patch model code for avoiding import of this package
if not orig_modeling_file.exists():
    modeling_file.rename(orig_modeling_file)
with orig_modeling_file.open("r") as f:
    content = f.read()
replacement_lines = [
    ("from flash_attn import flash_attn_func, flash_attn_varlen_func", ""),
    ("from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input", ""),
    (' _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)', "pass"),
]

for replace_pair in replacement_lines:
    content = content.replace(*replace_pair)

with modeling_file.open("w") as f:
    f.write(content)
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Load PyTorch model#

For creating PyTorch model we should use from_pretrained method of AutoModelForCausalLM model class. Model weights are already downloaded from HuggingFace hub using snapshot_download function on previous step.

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings

transformers.logging.set_verbosity_error()
warnings.filterwarnings("ignore")

model = AutoModelForCausalLM.from_pretrained(model_local_dir, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_local_dir, trust_remote_code=True)
2024-06-27 01:02:36.883651: 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.
2024-06-27 01:02:36.917268: 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.
2024-06-27 01:02:37.523024: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Run PyTorch Model Inference#

import torch
import requests

prompt = "Describe this image in detail"

messages = [{"role": "user", "content": f"<image>\n{prompt}"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

text_chunks = [tokenizer(chunk).input_ids for chunk in text.split("<image>")]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/8bf7d9f2-018a-4498-bec4-55f17c273ecc"
image = Image.open(requests.get(url, stream=True).raw)
image_tensor = model.process_images([image], model.config)
print(prompt)
image
Describe this image in detail
../_images/nano-llava-multimodal-chatbot-with-output_7_1.png
from transformers import TextStreamer

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

output_ids = model.generate(input_ids, images=image_tensor, max_new_tokens=128, use_cache=True, streamer=streamer)
The image features a white lama, with its fluffy woolly body covering most of the image, including the upper part of the image. The lama's face is predominantly white, and it has a black nose and two small black eyes. It also has a small white mouth and two small black ears. The lama's ears are quite cute, and it has its tongue out, which is pink and seems to be sticking out. The lama has a small black nose and a small black mouth, and it has a small black ear.
The lama's face is adorned with pink eyes and two black noses. The eyes are

Convert and Optimize model#

Our model conversion and optimization consist of following steps: 1. Convert model to OpenVINO format and save it on disk. 2. Compress model weights using NNCF

Let’s consider each step more deeply.

Convert model to OpenVINO IR format#

Convert model to OpenVINO format using conversion helper function defined bellow. We will use OpenVINO Model Conversion API for conversion PyTorch model to OpenVINO Intermediate Representation format. ov.convert_model function accepts PyTorch model instance and example input for tracing and returns ready to use OpenVINO Model object that can be compiled on device using core.compile_model or saved on disk for next usage with help ov.save_model function. Depends from generation step, model accepts different inputs and activates different parts of pipeline. For preserving the same level of flexibility, we will split model on parts: Image Encoder, Text Embeddings, Language Model and convert each part separately.

Compress Model weights to 4 and 8 bits using NNCF#

For reducing memory consumption, weights compression optimization can be applied using NNCF. 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: There is no speedup for INT4 compressed models on dGPU.

Please select below whether you would like to run INT4 weight compression instead of INT8 weight compression.

import ipywidgets as widgets

compression_mode = widgets.Dropdown(
    options=["INT4", "INT8"],
    value="INT4",
    description="Compression mode:",
    disabled=False,
)

compression_mode
Dropdown(description='Compression mode:', options=('INT4', 'INT8'), value='INT4')
import gc
import warnings
import torch
import openvino as ov
import nncf
from typing import Optional, Tuple

warnings.filterwarnings("ignore")


def flattenize_inputs(inputs):
    """
    Helper function for making nested inputs flattens
    """
    flatten_inputs = []
    for input_data in inputs:
        if input_data is None:
            continue
        if isinstance(input_data, (list, tuple)):
            flatten_inputs.extend(flattenize_inputs(input_data))
        else:
            flatten_inputs.append(input_data)
    return flatten_inputs


def cleanup_torchscript_cache():
    """
    Helper for removing cached model representation
    """
    torch._C._jit_clear_class_registry()
    torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
    torch.jit._state._clear_class_state()


def postprocess_converted_model(
    ov_model,
    example_input=None,
    input_names=None,
    output_names=None,
    dynamic_shapes=None,
):
    """
    Helper function for appling postprocessing on converted model with updating input names, shapes and output names
    acording to requested specification
    """
    flatten_example_inputs = flattenize_inputs(example_input) if example_input else []

    if input_names:
        for inp_name, m_input, input_data in zip(input_names, ov_model.inputs, flatten_example_inputs):
            input_node = m_input.get_node()
            if input_node.element_type == ov.Type.dynamic:
                m_input.get_node().set_element_type(ov.Type.f32)
            shape = list(input_data.shape)
            if dynamic_shapes is not None and inp_name in dynamic_shapes:
                for k in dynamic_shapes[inp_name]:
                    shape[k] = -1
            input_node.set_partial_shape(ov.PartialShape(shape))
            m_input.get_tensor().set_names({inp_name})

    if output_names:
        for out, out_name in zip(ov_model.outputs, output_names):
            out.get_tensor().set_names({out_name})
    ov_model.validate_nodes_and_infer_types()
    return ov_model
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
if compression_mode.value == "INT4":
    ov_out_path = Path("ov_nanollava/INT4_compressed_weights")
    llava_wc_parameters = dict(mode=nncf.CompressWeightsMode.INT4_ASYM, group_size=128, ratio=0.8)
else:
    ov_out_path = Path("ov_nanollava/INT8_compressed_weights")
    llava_wc_parameters = dict(mode=nncf.CompressWeightsMode.INT8)

image_encoder_wc_parameters = dict(mode=nncf.CompressWeightsMode.INT8)

ov_out_path.mkdir(exist_ok=True, parents=True)
model.config.save_pretrained(ov_out_path)
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
    vision_tower.load_model()

image_encoder_path = ov_out_path / "image_encoder.xml"
token_embedding_model_path = ov_out_path / "token_embed.xml"
model_path = ov_out_path / "llava_with_past.xml"

model.eval()
model.config.use_cache = True
model.config.torchscript = True

Image Encoder#

Image Encoder is represented in nanoLLaVA by pretrained SigLIP model. Image encoder is responsible for encoding input images into embedding space.

if not image_encoder_path.exists():
    model.forward = model.encode_images
    with torch.no_grad():
        ov_model = ov.convert_model(
            model,
            example_input=torch.zeros((1, 3, 384, 384)),
            input=[(-1, 3, 384, 384)],
        )
    if image_encoder_wc_parameters is not None:
        print("Applying weight compression to image encoder")
        ov_model = nncf.compress_weights(ov_model, **image_encoder_wc_parameters)
    ov.save_model(ov_model, image_encoder_path)
    cleanup_torchscript_cache()
    del ov_model
    gc.collect()
    print("Image Encoder model successfully converted")
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
[ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
    - Avoid using tokenizers before the fork if possible
    - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
Applying weight compression to image encoder
INFO:nncf:Statistics of the bitwidth distribution:
┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 100% (159 / 159)            │ 100% (159 / 159)                       │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙
Output()
Image Encoder model successfully converted

Text Embeddings#

In LLMs, input embedding is a part of language model, but for LLaVA 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 extra llm model instance, we will use it separately.

if not token_embedding_model_path.exists():
    with torch.no_grad():
        ov_model = ov.convert_model(model.get_model().embed_tokens, example_input=torch.ones((1, 10), dtype=torch.long))
    ov.save_model(ov_model, token_embedding_model_path)
    cleanup_torchscript_cache()
    del ov_model
    gc.collect()
    print("Token Embedding model successfully converted")
Token Embedding model successfully converted

Language Model#

Language Model is responsible for generation answer in LLaVA. This part is very similar to standard LLM for text generation. Our model uses Qwen/Qwen1.5-0.5B as base LLM. To optimize the generation process and use memory more efficiently, HuggingFace transformers API provides a mechanism for caching model state externally using use_cache=True parameter and past_key_values argument in inputs and outputs. 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. With this option, the model gets the previous step’s hidden states (cached attention keys and values) as input and additionally provides hidden states for the current step as output. It means for all next iterations, it is enough to provide only a new token obtained from the previous step and cached key values to get the next token prediction.

if not model_path.exists():
    model.forward = super(type(model), model).forward
    example_input = {"attention_mask": torch.ones([2, 10], dtype=torch.int64), "position_ids": torch.tensor([[8, 9], [8, 9]], dtype=torch.int64)}

    dynamic_shapes = {
        "input_embeds": {0: "batch_size", 1: "seq_len"},
        "attention_mask": {0: "batch_size", 1: "prev_seq_len + seq_len"},
        "position_ids": {0: "batch_size", 1: "seq_len"},
    }
    input_embeds = torch.zeros((2, 2, model.config.hidden_size))

    input_names = ["attention_mask", "position_ids"]
    output_names = ["logits"]

    past_key_values = []
    for i in range(model.config.num_hidden_layers):
        kv = [torch.randn([2, model.config.num_key_value_heads, 8, model.config.hidden_size // model.config.num_attention_heads]) for _ in range(2)]
        past_key_values.append(kv)
        input_names.extend([f"past_key_values.{i}.key", f"past_key_values.{i}.value"])
        output_names.extend([f"present.{i}.key", f"present.{i}.value"])
        dynamic_shapes[input_names[-2]] = {0: "batch_size", 2: "seq_len"}
        dynamic_shapes[input_names[-1]] = {0: "batch_size", 2: "seq_len"}

    example_input["past_key_values"] = past_key_values
    example_input["inputs_embeds"] = input_embeds
    input_names.append("inputs_embeds")
    dynamic_shapes["inputs_embeds"] = {0: "batch_size", 1: "seq_len"}
    ov_model = ov.convert_model(model, example_input=example_input)
    ov_model = postprocess_converted_model(
        ov_model, example_input=example_input.values(), input_names=input_names, output_names=output_names, dynamic_shapes=dynamic_shapes
    )

    if llava_wc_parameters is not None:
        print("Applying weight compression to second stage LLava model")
        ov_model = nncf.compress_weights(ov_model, **llava_wc_parameters)
    ov.save_model(ov_model, model_path)
    cleanup_torchscript_cache()
    del ov_model
    gc.collect()

    print("LLaVA model successfully converted")
del model
gc.collect();
Applying weight compression to second stage LLava model
Output()
INFO:nncf:Statistics of the bitwidth distribution:
┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 47% (48 / 169)              │ 20% (47 / 168)                         │
├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
│              4 │ 53% (121 / 169)             │ 80% (121 / 168)                        │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙
Output()
LLaVA model successfully converted

Prepare model inference pipeline#

OVLlavaQwen2ForCausalLM class provides ease-to-use interface for using model in generation scenario. It is based on transformers.generation.GenerationMixin that gives us opportunity to reuse all reach capabilities for generation implemented in HuggingFace Transformers library. More details about this interface can be found in HuggingFace documentation.

from transformers.generation import GenerationConfig, GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers import AutoConfig
from transformers.image_processing_utils import BatchFeature, get_size_dict
from transformers.image_transforms import (
    convert_to_rgb,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from transformers.image_utils import (
    ChannelDimension,
    PILImageResampling,
    to_numpy_array,
)
import numpy as np
import torch
from typing import Dict
from functools import partial, reduce

IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200


class ImageProcessor:
    def __init__(
        self,
        image_mean=(0.5, 0.5, 0.5),
        image_std=(0.5, 0.5, 0.5),
        size=(384, 384),
        crop_size: Dict[str, int] = None,
        resample=PILImageResampling.BICUBIC,
        rescale_factor=1 / 255,
        data_format=ChannelDimension.FIRST,
    ):
        crop_size = crop_size if crop_size is not None else {"height": 384, "width": 384}
        crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")

        self.image_mean = image_mean
        self.image_std = image_std
        self.size = size
        self.resample = resample
        self.rescale_factor = rescale_factor
        self.data_format = data_format
        self.crop_size = crop_size

    def preprocess(self, images, return_tensors):
        if isinstance(images, Image.Image):
            images = [images]
        else:
            assert isinstance(images, list)

        transforms = [
            convert_to_rgb,
            to_numpy_array,
            partial(resize, size=self.size, resample=self.resample, data_format=self.data_format),
            partial(rescale, scale=self.rescale_factor, data_format=self.data_format),
            partial(normalize, mean=self.image_mean, std=self.image_std, data_format=self.data_format),
            partial(to_channel_dimension_format, channel_dim=self.data_format, input_channel_dim=self.data_format),
        ]

        images = reduce(lambda x, f: [*map(f, x)], transforms, images)
        data = {"pixel_values": images}

        return BatchFeature(data=data, tensor_type=return_tensors)


class OVLlavaQwen2ForCausalLM(GenerationMixin):
    def __init__(self, core, model_dir, device):
        self.image_encoder = core.compile_model(model_dir / "image_encoder.xml", device)
        self.embed_tokens = core.compile_model(model_dir / "token_embed.xml", device)
        self.model = core.read_model(model_dir / "llava_with_past.xml")
        self.input_names = {key.get_any_name(): idx for idx, key in enumerate(self.model.inputs)}
        self.output_names = {key.get_any_name(): idx for idx, key in enumerate(self.model.outputs)}
        self.key_value_input_names = [key for key in self.input_names if "key_values" in key]
        self.key_value_output_names = [key for key in self.output_names if "present" in key]
        compiled_model = core.compile_model(self.model, device)
        self.request = compiled_model.create_infer_request()
        self.config = AutoConfig.from_pretrained(model_dir)
        self.generation_config = GenerationConfig.from_model_config(self.config)
        self.main_input_name = "input_ids"
        self.device = torch.device("cpu")
        self.num_pkv = 2
        self.image_processor = ImageProcessor()

    def can_generate(self):
        """Returns True to validate the check that the model using `GenerationMixin.generate()` can indeed generate."""
        return True

    def __call__(
        self,
        input_ids: torch.LongTensor,
        images: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        **kwargs,
    ) -> CausalLMOutputWithPast:
        return self.forward(input_ids, images, attention_mask, position_ids, past_key_values)

    def forward(
        self,
        input_ids: torch.LongTensor,
        images: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        **kwargs,
    ) -> CausalLMOutputWithPast:
        """General inference method"""
        inputs = self.prepare_inputs_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, images)

        # Run inference
        self.request.start_async(inputs, share_inputs=True)
        self.request.wait()

        logits = torch.from_numpy(self.request.get_tensor("logits").data)

        # Tuple of length equal to : number of layer * number of past_key_value per decoder layer (2 corresponds to the self-attention layer)
        past_key_values = tuple(self.request.get_tensor(key).data for key in self.key_value_output_names)
        # Tuple of tuple of length `n_layers`, with each tuple of length equal to 2 (k/v of self-attention)

        past_key_values = tuple(past_key_values[i : i + self.num_pkv] for i in range(0, len(past_key_values), self.num_pkv))
        return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)

    def prepare_inputs_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, images):
        inputs = {}
        if past_key_values is None:
            past_key_values = self._dummy_past_key_values(input_ids.shape[0])
        else:
            past_key_values = tuple(past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer)
        inputs.update(zip(self.key_value_input_names, past_key_values))

        if images is None or input_ids.shape[1] == 1:
            target_shape = past_key_values[-1][-1].shape[-2] + 1 if past_key_values is not None else input_ids.shape[1]
            attention_mask = torch.cat(
                (
                    attention_mask,
                    torch.ones((attention_mask.shape[0], target_shape - attention_mask.shape[1]), dtype=attention_mask.dtype, device=attention_mask.device),
                ),
                dim=1,
            )
            position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
            inputs_embeds = self.embed_tokens(input_ids)[0]
            inputs["attention_mask"] = attention_mask.numpy()
            inputs["position_ids"] = position_ids.numpy()
            inputs["inputs_embeds"] = inputs_embeds

            return inputs

        if type(images) is list or images.ndim == 5:
            concat_images = torch.cat([image for image in images], dim=0)
            image_features = self.encode_images(concat_images)
            split_sizes = [image.shape[0] for image in images]
            image_features = torch.split(image_features, split_sizes, dim=0)
            image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
        else:
            image_features = self.encode_images(images).to(self.device)

        # Let's just add dummy tensors if they do not exist,
        # it is a headache to deal with None all the time.
        # But it is not ideal, and if you have a better idea,
        # please open an issue / submit a PR, thanks.
        labels = None
        _attention_mask = attention_mask
        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
        else:
            attention_mask = attention_mask.bool()
        if position_ids is None:
            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
        if labels is None:
            labels = torch.full_like(input_ids, IGNORE_INDEX)

        # remove the padding using attention_mask -- TODO: double check
        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]

        new_input_embeds = []
        new_labels = []
        cur_image_idx = 0
        for batch_idx, cur_input_ids in enumerate(input_ids):
            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
            if num_images == 0:
                cur_image_features = image_features[cur_image_idx]
                cur_input_embeds_1 = self.embed_tokens(cur_input_ids)
                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
                new_input_embeds.append(cur_input_embeds)
                new_labels.append(labels[batch_idx])
                cur_image_idx += 1
                continue

            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
            cur_input_ids_noim = []
            cur_labels = labels[batch_idx]
            cur_labels_noim = []
            for i in range(len(image_token_indices) - 1):
                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1 : image_token_indices[i + 1]])
                cur_labels_noim.append(cur_labels[image_token_indices[i] + 1 : image_token_indices[i + 1]])
            split_sizes = [x.shape[0] for x in cur_labels_noim]
            cur_input_embeds = torch.from_numpy(self.embed_tokens(torch.cat(cur_input_ids_noim).unsqueeze(0))[0])[0]
            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
            cur_new_input_embeds = []
            cur_new_labels = []

            for i in range(num_images + 1):
                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
                cur_new_labels.append(cur_labels_noim[i])
                if i < num_images:
                    cur_image_features = image_features[cur_image_idx]
                    cur_image_idx += 1
                    cur_new_input_embeds.append(cur_image_features)
                    cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))

            cur_new_input_embeds = torch.cat(cur_new_input_embeds)
            cur_new_labels = torch.cat(cur_new_labels)

            new_input_embeds.append(cur_new_input_embeds)
            new_labels.append(cur_new_labels)

        # Truncate sequences to max length as image embeddings can make the sequence longer
        tokenizer_model_max_length = getattr(self.config, "tokenizer_model_max_length", None)
        if tokenizer_model_max_length is not None:
            new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
            new_labels = [x[:tokenizer_model_max_length] for x in new_labels]

        # Combine them
        max_len = max(x.shape[0] for x in new_input_embeds)
        batch_size = len(new_input_embeds)

        new_input_embeds_padded = []
        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
        position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)

        for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
            cur_len = cur_new_embed.shape[0]
            if getattr(self.config, "tokenizer_padding_side", "right") == "left":
                new_input_embeds_padded.append(
                    torch.cat(
                        (torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device), cur_new_embed), dim=0
                    )
                )
                if cur_len > 0:
                    new_labels_padded[i, -cur_len:] = cur_new_labels
                    attention_mask[i, -cur_len:] = True
                    position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
            else:
                new_input_embeds_padded.append(
                    torch.cat(
                        (cur_new_embed, torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0
                    )
                )
                if cur_len > 0:
                    new_labels_padded[i, :cur_len] = cur_new_labels
                    attention_mask[i, :cur_len] = True
                    position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)

        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
        attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
        inputs["inputs_embeds"] = new_input_embeds.numpy()
        inputs["attention_mask"] = attention_mask.numpy()
        inputs["position_ids"] = position_ids.numpy()

        return inputs

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
        """
        This function is used during running GenerationMixin.generate for preparing model specific inputs for
        each generation step
        """
        past_len = 0
        if past_key_values is not None:
            input_ids = input_ids[:, -1].unsqueeze(-1)
            past_len = past_key_values[-1][-1].shape[-2]
        attention_mask = kwargs.get(
            "attention_mask",
            torch.ones(input_ids.shape[0], input_ids.shape[1] + past_len),
        )
        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "position_ids": kwargs.get("position_ids", None),
            "past_key_values": past_key_values,
            "images": kwargs.get("images", None),
        }

    def _reorder_cache(self, past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called.
        This is required to match `past_key_values` with the correct beam_idx at every generation step.
        """

        # from transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel._reorder_cache
        return tuple(tuple(np.take(past_state, beam_idx, 0) for past_state in layer_past) for layer_past in past_key_values)

    def _dummy_past_key_values(self, batch_size):
        pkv = []
        for input_name in self.key_value_input_names:
            input_t = self.model.input(input_name)
            input_shape = self.model.input(input_name).get_partial_shape()
            input_shape[0] = batch_size
            input_shape[2] = 0
            pkv.append(ov.Tensor(input_t.get_element_type(), input_shape.get_shape()))

        return pkv

    def encode_images(self, images):
        return torch.from_numpy(self.image_encoder(images)[0])

    def expand2square(self, pil_img, background_color):
        width, height = pil_img.size
        if width == height:
            return pil_img
        elif width > height:
            result = Image.new(pil_img.mode, (width, width), background_color)
            result.paste(pil_img, (0, (width - height) // 2))
            return result
        else:
            result = Image.new(pil_img.mode, (height, height), background_color)
            result.paste(pil_img, ((height - width) // 2, 0))
            return result

    def process_images(self, images, model_cfg):
        image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
        new_images = []
        if image_aspect_ratio == "pad":
            for image in images:
                image = self.expand2square(image, tuple(int(x * 255) for x in self.image_processor.image_mean))
                image = self.image_processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
                new_images.append(image)
        else:
            return self.image_processor(images, return_tensors="pt")["pixel_values"]
        if all(x.shape == new_images[0].shape for x in new_images):
            new_images = torch.stack(new_images, dim=0)
        return new_images

Run OpenVINO Model Inference#

import ipywidgets as widgets

core = ov.Core()

support_devices = core.available_devices
if "NPU" in support_devices:
    support_devices.remove("NPU")

device = widgets.Dropdown(
    options=support_devices + ["AUTO"],
    value="AUTO",
    description="Device:",
    disabled=False,
)

device
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
ov_model = OVLlavaQwen2ForCausalLM(core, ov_out_path, device.value)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

output_ids = ov_model.generate(input_ids, images=image_tensor, max_new_tokens=128, use_cache=True, streamer=streamer)
The image features a white, fluffy lamb with a playful, cheerful expression. The lamb is positioned in the center of the image, and it appears to be in motion, as if it's running. The lamb's face is white and it has a cute, adorable expression. It has a pair of bright, pink eyes that are wide open, and it has a small, black nose. The lamb's ears are also white and pink, and they are quite large. The lamb's legs are white and fluffy, and it has a black hoof. The lamb's tail is also white and fluffy, and it appears to be curled up.

Interactive demo#

import gradio as gr
import time
from transformers import TextIteratorStreamer, StoppingCriteria
from threading import Thread
import requests

example_image_urls = [
    (
        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/1d6a0188-5613-418d-a1fd-4560aae1d907",
        "bee.jpg",
    ),
    (
        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/6cc7feeb-0721-4b5d-8791-2576ed9d2863",
        "baklava.png",
    ),
    ("https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/dd5105d6-6a64-4935-8a34-3058a82c8d5d", "small.png"),
    ("https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/1221e2a8-a6da-413a-9af6-f04d56af3754", "chart.png"),
]
for url, file_name in example_image_urls:
    if not Path(file_name).exists():
        Image.open(requests.get(url, stream=True).raw).save(file_name)


class KeywordsStoppingCriteria(StoppingCriteria):
    def __init__(self, keywords, tokenizer, input_ids):
        self.keywords = keywords
        self.keyword_ids = []
        self.max_keyword_len = 0
        for keyword in keywords:
            cur_keyword_ids = tokenizer(keyword).input_ids
            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
                cur_keyword_ids = cur_keyword_ids[1:]
            if len(cur_keyword_ids) > self.max_keyword_len:
                self.max_keyword_len = len(cur_keyword_ids)
            self.keyword_ids.append(torch.tensor(cur_keyword_ids))
        self.tokenizer = tokenizer
        self.start_len = input_ids.shape[1]

    def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
        for keyword_id in self.keyword_ids:
            truncated_output_ids = output_ids[0, -keyword_id.shape[0] :]
            if torch.equal(truncated_output_ids, keyword_id):
                return True
        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
        for keyword in self.keywords:
            if keyword in outputs:
                return True
        return False

    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        outputs = []
        for i in range(output_ids.shape[0]):
            outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores))
        return all(outputs)


def bot_streaming(message, history):
    messages = []
    if message["files"]:
        image = message["files"][-1]["path"] if isinstance(message["files"][-1], dict) else message["files"][-1]
    else:
        for _, hist in enumerate(history):
            if isinstance(hist[0], tuple):
                image = hist[0][0]

    if len(history) > 0 and image is not None:
        messages.append({"role": "user", "content": f"<image>\n{history[1][0]}"})
        messages.append({"role": "assistant", "content": history[1][1]})
        for human, assistant in history[2:]:
            if assistant is None:
                continue
            messages.append({"role": "user", "content": human})
            messages.append({"role": "assistant", "content": assistant})
        messages.append({"role": "user", "content": message["text"]})
    elif len(history) > 0 and image is None:
        for human, assistant in history:
            if assistant is None:
                continue
            messages.append({"role": "user", "content": human})
            messages.append({"role": "assistant", "content": assistant})
        messages.append({"role": "user", "content": message["text"]})
    elif len(history) == 0 and image is not None:
        messages.append({"role": "user", "content": f"<image>\n{message['text']}"})
    elif len(history) == 0 and image is None:
        messages.append({"role": "user", "content": message["text"]})

    print(messages)
    image = Image.open(image).convert("RGB")
    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    text_chunks = [tokenizer(chunk).input_ids for chunk in text.split("<image>")]
    input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
    stop_str = "<|im_end|>"
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    image_tensor = ov_model.process_images([image], ov_model.config)
    generation_kwargs = dict(
        input_ids=input_ids, images=image_tensor, streamer=streamer, max_new_tokens=128, stopping_criteria=[stopping_criteria], temperature=0.01
    )
    thread = Thread(target=ov_model.generate, kwargs=generation_kwargs)
    thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        generated_text_without_prompt = buffer[:]
        time.sleep(0.04)
        yield generated_text_without_prompt


demo = gr.ChatInterface(
    fn=bot_streaming,
    title="🚀nanoLLaVA",
    examples=[
        {"text": "What is on the flower?", "files": ["./bee.jpg"]},
        {"text": "How to make this pastry?", "files": ["./baklava.png"]},
        {"text": "What is the text saying?", "files": ["./small.png"]},
        {"text": "What does the chart display?", "files": ["./chart.png"]},
    ],
    description="Try [nanoLLaVA](https://huggingface.co/qnguyen3/nanoLLaVA) using OpenVINO in this demo. Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
    stop_btn="Stop Generation",
    multimodal=True,
)

# 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/
try:
    demo.launch(debug=False)
except Exception:
    demo.launch(share=True, debug=False)
Running on local URL:  http://127.0.0.1:7860

To create a public link, set share=True in launch().