Running custom encoder-decoder models in onnxruntime-genai

KarelZe commented 2 months ago

background

My question is about executing encoder-decoder models with onnx genai runtime. My goal is to convert the DONUT transformer https://arxiv.org/abs/2111.15664, a sequence-to-sequence transformer for document understanding with swin encoder and mbart decoder to onnx and run it using onnxruntime-genai.

I managed to convert the individual components to onnx. Now I'm stuck at writing a genai_config.json suitable for encoder-decoder models.

steps

I started with the huggingface implementation of DONUT (https://huggingface.co/docs/transformers/v4.42.0/en/model_doc/donut#overview) and converted the encoder and merged decoder (with kv-cache) to onnx using optimum https://huggingface.co/docs/optimum/index. I converted the DONUT processor, which consists of an image processor and sentencepiece-tokenizer to onnx using onnx runtime extensions (https://github.com/microsoft/onnxruntime-extensions/blob/main/onnxruntime_extensions/tools/). I merged the image processor and swin-encoder into a single graph. I can provide the conversion scripts if needed.

My components have the following inputs/outputs shapes:

tokenizer:

encoder with image processor:

decoder: (more past keys + values)

question

However, I am stuck at manually writing/loading a suitable genai_config.json, with the hidden states from the encoder for use in the decoder's attention mechanism. I'm aware of https://onnxruntime.ai/docs/genai/reference/config.html to write configs, but it seemingly focuses on decoder-only models. I'm also aware of make_genai_config https://github.com/microsoft/onnxruntime-genai/blob/c7eba3c63a454edd6662eb007ff397d1146cc081/src/python/py/models/builder.py for auto-config generation of supported models.

I tried the following config:

{
    "model": {
        "bos_token_id": 1,
        "context_length": 1536,
        "decoder": {
            "session_options": {
                "log_id": "onnxruntime-genai",
                "provider_options": []
            },
            "filename": "decoder_model_merged.onnx",
            "head_size": 96,
            "hidden_size": 1024,
            "inputs": {
                "input_ids": "input_ids",
                "encoder_hidden_states": "encoder_hidden_states",
                "past_key_names": "past_key_values.%d.decoder.key",
                "past_value_names": "past_key_values.%d.decoder.value"
            },
            "outputs": {
                "logits": "logits",
                "present_key_names": "present.%d.decoder.key",
                "present_value_names": "present.%d.decoder.value"
            },
            "num_attention_heads": 16,
            "num_hidden_layers": 12,
            "num_key_value_heads": 32
        },
        "encoder": {
            "filename": "encoder_with_img_processor.onnx",
            "inputs": {
                "pixel_values": "pixel_values"
            },
            "outputs": {
                "encoder_hidden_states": "last_hidden_state"
            }
        },
        "eos_token_id": 2,
        "pad_token_id": 1,
        "type": "sentencepiece",
        "vocab_size": 57654
    },
    "search": {
        "diversity_penalty": 0.0,
        "do_sample": false,
        "early_stopping": true,
        "length_penalty": 1.0,
        "max_length": 1536,
        "min_length": 0,
        "no_repeat_ngram_size": 0,
        "num_beams": 1,
        "num_return_sequences": 1,
        "past_present_share_buffer": true,
        "repetition_penalty": 1.0,
        "temperature": 1.0,
        "top_k": 50,
        "top_p": 1.0
    }
}

When loading the config I receive the error RuntimeError: Error encountered while parsing 'output/genai_config.json' JSON Error: Unknown value: encoder_hidden_states at line 15 index 64. As far as I can tell from https://github.com/microsoft/onnxruntime-genai/blob/c7eba3c63a454edd6662eb007ff397d1146cc081/src/python/py/models/builder.py#L71 it's currently not possible to pass encoder hidden states to the model as inputs.

Do you have any plans of extending onnx-genai runtime for encoder-decoder models? Could you please give me a hint/advice how to work around this?

Thank you for your assistance.

yufenglee commented 2 months ago

We don't have encoder-decoder support yet. Let me take a deep into at your model and see how we can support it best.

KarelZe commented 2 months ago

@yufenglee Thanks for your update. Would be really cool to see this happen🤗

I'll provide a build script for the onnx conversion for a headstart. Please let me know if I can help with implementation/test.

KarelZe commented 2 months ago

@yufenglee Here's the build.py script I used for conversion. Please let me know, if you have any questions. 👍

"""Build script for DONUT transformer.

Partly adapted from:
https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda/blob/main/onnx/builder.py
"""

import logging
import shutil
import subprocess
import sys
from pathlib import Path

import numpy as np
import onnx
import onnxruntime as ort
from onnxruntime_extensions import gen_processing_models, get_library_path
from onnxruntime_extensions.tools.pre_post_processing import (
    ChannelsLastToChannelsFirst,
    ImageBytesToFloat,
    LetterBox,
    Normalize,
    PrePostProcessor,
    Resize,
    Unsqueeze,
    create_named_value,
)
from PIL import Image
from transformers import AutoConfig, DonutProcessor, VisionEncoderDecoderModel

logger = logging.getLogger(__name__)

output_dir = Path("output/")
cache_dir = Path("cache_dir/")
path_model = Path(
    "/path/to/model/"
)
path_test_img = Path("/path/to/test/img.png")

precision = "fp16"
# pipeline in onnx extension requires at least 16 better 18.
opset = 18

def export_model():
    """Export encoder + decoder.

    see:
    https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model
    """
    subprocess.run([
        "optimum-cli",
        "export",
        "onnx",
        "--model",
        path_model,
        output_dir / "model_init_export",
        "--task",
        "image-to-text-with-past",
        "--framework",
        "pt",
        "--opset",
        str(opset),
    ])

def optimize_encoder():
    """Optimize encoder."""
    filename = "encoder_model.onnx"
    temp_folder_1 = output_dir / "model_init_export"
    fpath_1 = temp_folder_1 / filename

    onnx.checker.check_model(fpath_1)
    onnx.shape_inference.infer_shapes_path(fpath_1)
    onnx_model = onnx.load_model(fpath_1, load_external_data=True)

    temp_folder_2 = output_dir / "encoder_after_export"
    temp_folder_2.mkdir(exist_ok=True)
    fpath_2 = temp_folder_2 / filename

    onnx.save_model(
        onnx_model,
        fpath_2,
        save_as_external_data=True,
        all_tensors_to_one_file=True,
        location=f"{filename}.data",
        size_threshold=0,
        convert_attribute=False,
    )

    temp_folder_3 = output_dir / "encoder_after_opt"
    temp_folder_3.mkdir(exist_ok=True)
    fpath_3 = temp_folder_3 / filename

    subprocess.run([
        f"{sys.executable}",
        "-m",
        "onnxruntime.transformers.optimizer",
        "--input",
        fpath_2,
        "--output",
        fpath_3,
        "--model_type",
        "swin",
        "--num_heads",
        str(0),  # In config 4 8 16 32  --> Use 0 is auto-discover from graph
        "--hidden_size",
        str(0),  # 0 = auto-discover
        "--use_external_data_format",
        "--opt_level",
        str(0),
    ])
    shutil.rmtree(temp_folder_2)

    fpath_4 = output_dir / filename
    cmd = [
        f"{sys.executable}",
        "-m",
        "onnxruntime.quantization.matmul_4bits_quantizer",
        "--input_model",
        fpath_3,
        "--output_model",
        fpath_4,
        "--block_size",
        str(32),
    ]
    if precision == "fp32":
        cmd.extend(["--accuracy_level", str(4)])

    subprocess.run(cmd)

    shutil.rmtree(temp_folder_3)

def optimize_decoder():
    """Optimize decoder.

    Adapted from:
    https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda/blob/main/onnx/builder.py
    """
    filename = "decoder_model_merged.onnx"
    temp_folder_1 = output_dir / "model_init_export"
    fpath_1 = temp_folder_1 / filename

    onnx.checker.check_model(fpath_1)
    onnx.shape_inference.infer_shapes_path(fpath_1)
    onnx_model = onnx.load_model(fpath_1, load_external_data=True)

    temp_folder_2 = output_dir / "decoder_after_export"
    temp_folder_2.mkdir(exist_ok=True)
    fpath_2 = temp_folder_2 / filename

    onnx.save_model(
        onnx_model,
        fpath_2,
        save_as_external_data=True,
        all_tensors_to_one_file=True,
        location=f"{filename}.data",
        size_threshold=0,
        convert_attribute=False,
    )

    temp_folder_3 = output_dir / "decoder_after_opt"
    temp_folder_3.mkdir(exist_ok=True)
    fpath_3 = temp_folder_3 / filename

    subprocess.run([
        f"{sys.executable}",
        "-m",
        "onnxruntime.transformers.optimizer",
        "--input",
        fpath_2,
        "--output",
        fpath_3,
        "--model_type",
        "bart",
        "--num_heads",
        str(config.decoder.decoder_attention_heads),
        "--hidden_size",
        str(config.decoder.d_model),
        "--use_external_data_format",
        "--opt_level",
        str(0),
    ])
    shutil.rmtree(temp_folder_2)

    fpath_4 = output_dir / filename
    cmd = [
        f"{sys.executable}",
        "-m",
        "onnxruntime.quantization.matmul_4bits_quantizer",
        "--input_model",
        fpath_3,
        "--output_model",
        fpath_4,
        "--block_size",
        str(32),
    ]
    if precision == "fp32":
        cmd.extend(["--accuracy_level", str(4)])

    subprocess.run(cmd)

    shutil.rmtree(temp_folder_3)

def build_tokenizer() -> None:
    """Get sentence piece tokenizer from DONUT processor."""
    onnx_tokenizer = gen_processing_models(tokenizer, opset=opset, pre_kwargs={})[0]
    fpath_tokenizer = output_dir / "tokenizer.onnx"
    with fpath_tokenizer.open(mode="wb") as f:
        f.write(onnx_tokenizer.SerializeToString())

def build_img_preprocessor() -> None:
    """Build image processor.

    Adapted from:
    https://github.com/microsoft/onnxruntime-extensions/pull/478/files#diff-8f875d92e23f555946efe7bec0ccdefde80c06a4b74b595071961ec4e0f84f5d

    For operations and their order see:
    https://github.com/huggingface/transformers/blob/v4.42.0/src/transformers/models/donut/image_processing_donut.py#L54

    Raises
    ------
        NotImplementedError: do_thumbnail not yet implemented
        NotImplementedError: do_align_long_axis not yet implemented
        NotImplementedError: resizing methods !=2 not implemented

    """
    pixel_values_in = [
        create_named_value("pixel_values", onnx.TensorProto.UINT8, ["h", "w", 3])
    ]
    pipeline = PrePostProcessor(pixel_values_in, onnx_opset=opset)

    steps = []

    size = (
        image_processor_config["size"]["height"],
        image_processor_config["size"]["width"],
    )

    if image_processor_config["do_align_long_axis"]:
        logger.warning(
            "'do_align_long_axis' is not yet implemented. This will lead to a performance degradation for rotated images."
        )

    if image_processor_config["do_resize"]:
        # 2 = BILINEAR
        # see: https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Resampling.BICUBIC
        if image_processor_config["resample"] != 2:
            logger.warning(
                "resampling method '%s' not supported. Resampling with 2=BILINEAR.",
                image_processor_config["resample"],
            )

        steps.append(
            Resize(
                size,
                layout="HWC",
                name="do_resize",
                policy="not_larger",
            )
        )

    if image_processor_config["do_thumbnail"]:
        logger.warning("'do_thumbnail' is not yet implemented.")
    if image_processor_config["do_pad"]:
        steps.append(
            LetterBox(target_shape=size, fill_value=0, name="do_pad", layout="HWC")
        )

    if image_processor_config["do_rescale"]:
        steps.append(
            ImageBytesToFloat(
                image_processor_config["rescale_factor"], name="do_rescale"
            )
        )

    if image_processor_config["do_normalize"]:
        mean_std = list(
            zip(
                image_processor_config["image_mean"],
                image_processor_config["image_std"],
            )
        )
        steps.append(Normalize(mean_std, layout="HWC", name="do_normalize"))

    steps.extend([
        ChannelsLastToChannelsFirst(name="RGBImageCHW"),  # HWC to CHW
        Unsqueeze([0], name="unsqueeze"),  # add batch dimension, CHW --> 1CHW
    ])
    pipeline.add_pre_processing(steps)

    pixel_values_out = [
        onnx.helper.make_tensor_value_info(
            "pixel_values_out", onnx.TensorProto.FLOAT, [1, 3, *size]
        )
    ]

    g = onnx.helper.make_graph(
        [onnx.helper.make_node("Identity", ["pixel_values"], ["pixel_values_out"])],
        "empty",
        pixel_values_in,
        pixel_values_out,
    )
    onnx_import = onnx.helper.make_operatorsetid("", opset)
    ir_version = onnx.helper.find_min_ir_version_for([onnx_import])
    model = onnx.helper.make_model_gen_version(
        g, opset_imports=[onnx_import], ir_version=ir_version
    )

    new_model = pipeline.run(model)
    new_model.doc_string = "Donut-like image pre-processor."
    new_model.graph.doc_string = ""

    temp_folder_1 = output_dir / "img_processor_after_export"
    temp_folder_1.mkdir(exist_ok=True)

    filename = "preprocessor.onnx"

    fpath_1 = temp_folder_1 / filename
    onnx.save_model(new_model, fpath_1)
    onnx.checker.check_model(fpath_1)
    onnx.shape_inference.infer_shapes_path(fpath_1)

    subprocess.run([
        f"{sys.executable}",
        "-m",
        "onnxoptimizer",
        fpath_1,
        output_dir / filename,
    ])

def test_components():
    """Test tokenizer, image processor, and fused image processor with encoder."""
    input_text = "<s_name>YOUR_NAME</s_name>"

    sess_options = ort.SessionOptions()
    sess_options.register_custom_ops_library(get_library_path())
    session = ort.InferenceSession(
        output_dir / "tokenizer.onnx",
        sess_options=sess_options,
        providers=["CPUExecutionProvider"],
    )
    input_feed = {"inputs": np.asarray([input_text])}

    outputs = session.run(["token_indices"], input_feed)
    print("token_ids", outputs[0])
    print(tokenizer(input_text))

    session = ort.InferenceSession(
        output_dir / "preprocessor.onnx",
        sess_options,
        providers=["CPUExecutionProvider"],
    )
    outputs = session.run(
        ["pixel_values_out"], {"pixel_values": np.array(Image.open(path_test_img))}
    )
    # remove batch dim, CHW -> HWC
    # [-1, 1] -> [0, 1] * 255 for visualization
    img = np.squeeze(outputs[0]).transpose((1, 2, 0))
    img = np.uint8((img - img.min()) / (img.max() - img.min()) * 255)
    Image.fromarray(img).save(
        output_dir / "test_img_from_onnx_processor.png", format="PNG"
    )
    session = ort.InferenceSession(
        output_dir / "encoder_with_img_processor.onnx",
        sess_options,
        providers=["CPUExecutionProvider"],
    )
    outputs = session.run(
        ["last_hidden_state"], {"pixel_values": np.array(Image.open(path_test_img))}
    )
    print("last_hidden_state", outputs[0])
    print("last_hidden-state (shape)", outputs[0].shape)

def merge_img_processor_encoder():
    """Merge image processor and encoder into a single graph."""
    model_preprocessor = onnx.load(output_dir / "preprocessor.onnx")
    model_encoder = onnx.load(output_dir / "encoder_model.onnx")

    output_name_from_img_processor = model_preprocessor.graph.output[0].name
    input_name_of_encoder = model_encoder.graph.input[0].name

    merged_model = onnx.compose.merge_models(
        model_preprocessor,
        model_encoder,
        io_map=[(output_name_from_img_processor, input_name_of_encoder)],
    )

    filename = "encoder_with_img_processor.onnx"

    onnx.save(
        merged_model,
        output_dir / filename,
        save_as_external_data=True,
        all_tensors_to_one_file=True,
        location=f"{filename}.data",
        size_threshold=0,
        convert_attribute=False,
    )

if __name__ == "__main__":
    config = AutoConfig.from_pretrained(path_model)

    processor = DonutProcessor.from_pretrained(path_model)
    image_processor = processor.image_processor
    image_processor_config = image_processor.to_dict()
    tokenizer = processor.tokenizer
    model = VisionEncoderDecoderModel.from_pretrained(path_model)
    config = AutoConfig.from_pretrained(path_model)

    export_model()
    build_tokenizer()
    optimize_encoder()
    optimize_decoder()
    build_img_preprocessor()
    merge_img_processor_encoder()
    test_components()

KarelZe commented 1 month ago

@yufenglee Is there any update? Could you give me any guidance, how I could help to make this feature happen?

martinkorelic commented 3 weeks ago

Would it be not possible for your case to load the input beforehand by using something like this: onnxruntime_genai.GeneratorParams.set_model_input(name: str, value: []) and not including the actual input name in the generation config?

I had a custom model made by myself which loaded some of the inputs before starting the generation, however if you have a model which needs to continously load and update the inputs, this might not be supported as of yet. Perhaps maybe consider splitting the model in more parts.

microsoft / onnxruntime-genai