multi_modal_client.py

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import argparse
import queue
import warnings
from functools import partial

import numpy as np
import tritonclient.grpc as grpcclient
from tritonclient.utils import *

warnings.filterwarnings("ignore")


class UserData:
    def __init__(self):
        self._completed_requests = queue.Queue()


def callback(user_data, result, error):
    if error:
        user_data._completed_requests.put(error)
    else:
        user_data._completed_requests.put(result)


def prepare_tensor(name, input):
    t = grpcclient.InferInput(name, input.shape, np_to_triton_dtype(input.dtype))
    t.set_data_from_numpy(input)
    return t


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name",
        type=str,
        required=False,
        default="llava-1.5",
        help="Model name",
    )
    parser.add_argument(
        "--image_url",
        type=str,
        required=False,
        default="http://images.cocodataset.org/test2017/000000557146.jpg",
        help="Image URL. Default is:\
                            http://images.cocodataset.org/test2017/000000557146.jpg",
    )
    parser.add_argument(
        "--prompt",
        type=str,
        required=False,
        default="What is shown on the picture?",
        help="Prompt. Default is:\
                            What is shown on the picture?",
    )
    parser.add_argument(
        "--max-tokens",
        type=int,
        required=False,
        default=50,
        help="Max amount of tokens in the output. Default is 50.",
    )
    parser.add_argument(
        "--temperature",
        type=float,
        required=False,
        default=0.9,
        help="Temperatue. Default is 0.9.",
    )
    parser.add_argument(
        "--top-k",
        type=int,
        required=False,
        default=1,
        help="Top K. Default is 1.",
    )
    parser.add_argument(
        "--frequency-penalty",
        type=float,
        required=False,
        default=0.9,
        help="Frequency penalty. Default is 0.9.",
    )
    parser.add_argument(
        "--seed",
        type=int,
        required=False,
        default=10,
        help="Random seed. Default is 10.",
    )
    parser.add_argument(
        "--url",
        type=str,
        required=False,
        default="localhost:8000",
        help="Inference server URL. Default is localhost:8000.",
    )
    parser.add_argument(
        "-v",
        "--verbose",
        action="store_true",
        required=False,
        default=False,
        help="Enable verbose output",
    )

    args = parser.parse_args()
    user_data = UserData()

    input_text = "USER: <image>\nQuestion:" + args.prompt + " Answer:"
    image_url = np.array([args.image_url.encode("utf-8")], dtype=np.object_)
    prompt_data = np.array([input_text.encode("utf-8")], dtype=np.object_)
    max_tokens = np.array([args.max_tokens], dtype=np.int32)
    temperature = np.array([args.temperature], dtype=np.float32)
    top_k = np.array([args.top_k], dtype=np.int32)
    frequency_penalty = np.array([args.frequency_penalty], dtype=np.float32)
    seed = np.array([args.seed], dtype=np.uint64)
    inputs = [
        prepare_tensor("image", image_url),
        prepare_tensor("prompt", prompt_data),
        prepare_tensor("max_tokens", max_tokens),
        prepare_tensor("temperature", temperature),
        prepare_tensor("top_k", top_k),
        prepare_tensor("frequency_penalty", frequency_penalty),
        prepare_tensor("seed", seed),
    ]
    outputs = []
    for output_name in [
        "text",
        "finish_reason",
        "prompt_tokens",
        "completion_tokens",
        "total_tokens",
    ]:
        outputs.append(grpcclient.InferRequestedOutput(output_name))
    output_text = ""

    with grpcclient.InferenceServerClient(url="localhost:8001") as client:
        client.start_stream(partial(callback, user_data))
        client.async_stream_infer(
            args.model_name,
            inputs,
            outputs=outputs,
        )
        expected_responses = 1
        processed_count = 0
        while processed_count < expected_responses:
            try:
                result = user_data._completed_requests.get()
                print("Got completed request", flush=True)
            except Exception:
                break

            if type(result) == InferenceServerException:
                if result.status() == "StatusCode.CANCELLED":
                    print("Request is cancelled")
                else:
                    print("Received an error from server:")
                    print(result)
                    raise result
            else:
                output_text = result.as_numpy("text")
                print(output_text[0].decode("utf-8"))

            processed_count = processed_count + 1

            client.stop_stream()