Possible bug in reference counting with shared memory regions

[hcho3]

Description
#7567 introduced a reference counter to shared memory region to prevent users from releasing shared memory regions while any inference requests are still being processed.

I encountered an issue where the shared memory region could not be freed even when no inference request was ongoing.

Error I got:

tritonclient.utils.InferenceServerException: [StatusCode.INTERNAL] Cannot unregister shared memory region
'input_eec14889775b4c29a99d81458b0feb1a', it is currently in use

Triton Information
Triton Server 2.50.0 from NGC container 24.09

To Reproduce

1. Extract model_repository.zip into a directory named model_repository/. The directory should have the following layout after extraction:

model_repository/
model_repository/xgboost_json
model_repository/xgboost_json/config.pbtxt
model_repository/xgboost_json/1
model_repository/xgboost_json/1/xgboost.json

The example uses an XGBoost model saved as xgboost_json format and loads it using the FIL backend.
Notably, the model is configured to use the CPU for inference (instance_group [{ kind: KIND_CPU }]).

2. Launch the Triton server using the latest NGC container:

docker run -it --rm --network=host --ipc=host --pid=host --shm-size=1g \
  --gpus '"device=0"' \
  -v $(pwd)/model_repository:/model_repository \
  nvcr.io/nvidia/tritonserver:24.09-py3 \
  tritonserver --model-repository=/model_repository

3. Create the script named test.py with the following content:

Click to see the content

## test.py
from argparse import ArgumentParser
from collections import namedtuple
from uuid import uuid4

import numpy as np
import tritonclient.grpc as triton_grpc
import tritonclient.http as triton_http
import tritonclient.utils as triton_utils
import tritonclient.utils.cuda_shared_memory as shm

STANDARD_PORTS = {"http": 8000, "grpc": 8001}
TritonInput = namedtuple("TritonInput", ("name", "handle", "input"))
TritonOutput = namedtuple("TritonOutput", ("name", "handle", "output"))


class TritonMessage:
    """Adapter to read output from both GRPC and HTTP responses"""

    def __init__(self, message):
        self.message = message

    def __getattr__(self, attr):
        try:
            return getattr(self.message, attr)
        except AttributeError:
            try:
                return self.message[attr]
            except Exception:  # Re-raise AttributeError
                pass
            raise


def get_triton_client(protocol="grpc", host="localhost", port=None):
    if port is None:
        port = STANDARD_PORTS[protocol]

    if protocol == "grpc":
        client = triton_grpc.InferenceServerClient(
            url=f"{host}:{port}",
            verbose=False,
        )
    elif protocol == "http":
        client = triton_http.InferenceServerClient(
            url=f"{host}:{port}",
            verbose=False,
        )
    else:
        raise RuntimeError('Bad protocol: "{}"'.format(protocol))

    return client


def set_shared_input_data(triton_client, triton_input, data, protocol="grpc"):
    input_size = data.size * data.itemsize

    input_name = "input_{}".format(uuid4().hex)
    input_handle = shm.create_shared_memory_region(input_name, input_size, 0)
    print(f"Create CUDA shared mem: name = {input_name}")

    shm.set_shared_memory_region(input_handle, [data])

    triton_client.register_cuda_shared_memory(
        input_name, shm.get_raw_handle(input_handle), 0, input_size
    )

    triton_input.set_shared_memory(input_name, input_size)

    return TritonInput(input_name, input_handle, triton_input)


def create_output_handle(triton_client, triton_output, size, shared_mem=None):
    output_name = "output_{}".format(uuid4().hex)
    output_handle = shm.create_shared_memory_region(output_name, size, 0)
    print(f"Create CUDA shared mem: name = {output_name}")

    triton_client.register_cuda_shared_memory(
        output_name, shm.get_raw_handle(output_handle), 0, size
    )

    triton_output.set_shared_memory(output_name, size)

    return output_name, output_handle


def create_triton_input(triton_client, data, name, dtype, protocol="grpc"):
    if protocol == "grpc":
        triton_input = triton_grpc.InferInput(name, data.shape, dtype)
    else:
        triton_input = triton_http.InferInput(name, data.shape, dtype)

    return set_shared_input_data(
        triton_client,
        triton_input,
        data,
        protocol=protocol,
    )


def create_triton_output(triton_client, size, name, protocol="grpc"):
    if protocol == "grpc":
        triton_output = triton_grpc.InferRequestedOutput(name)
    else:
        triton_output = triton_http.InferRequestedOutput(name, binary_data=True)

    output_name, output_handle = create_output_handle(
        triton_client, triton_output, size
    )

    return TritonOutput(name=output_name, handle=output_handle, output=triton_output)


def get_response_data(response, output_handle, output_name):
    if output_handle is None:
        return response.as_numpy(output_name)
    else:
        network_result = TritonMessage(response.get_output(output_name))
        return shm.get_contents_as_numpy(
            output_handle,
            triton_utils.triton_to_np_dtype(network_result.datatype),
            network_result.shape,
        )


def release_shared_memory(triton_client, shm_objs):
    for io_ in shm_objs:
        if io_.name is not None:
            # if 'name' field is None, no shared mem is used
            print(f"Free CUDA shared mem: name = {io_.name}")
            triton_client.unregister_cuda_shared_memory(name=io_.name)
            shm.destroy_shared_memory_region(io_.handle)


def infer(protocol, host):
    triton_client = get_triton_client(protocol=protocol, host=host)
    arr = np.zeros((1, 500), dtype=np.float32)

    inputs = [
        create_triton_input(
            triton_client,
            arr,
            name="input__0",
            dtype="FP32",
            protocol=protocol,
        )
    ]
    outputs = {
        "output__0": create_triton_output(
            triton_client,
            size=2 * 4,
            name="output__0",
            protocol=protocol,
        )
    }
    response = triton_client.infer(
        "xgboost_json",
        model_version="1",
        inputs=[input_.input for input_ in inputs],
        outputs=[output_.output for output_ in outputs.values()],
    )
    result = {
        name: get_response_data(response, handle, name)
        for name, (_, handle, _) in outputs.items()
    }
    release_shared_memory(triton_client, inputs)
    release_shared_memory(triton_client, outputs.values())


def main(args):
    for i in range(args.n_infer):
        infer(protocol=args.protocol, host=args.host)
        print("")


if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument(
        "--n_infer",
        type=int,
        default=1000,
        help="Number of times to run inference (default 1000)",
    )
    parser.add_argument(
        "--protocol",
        type=str,
        choices=["grpc", "http"],
        required=True,
        help="Protocol to use to connect to the Triton server",
    )
    parser.add_argument("--host", type=str, default="localhost")
    parsed_args = parser.parse_args()
    main(parsed_args)

4. Using the same machine as the server, run the test script:

docker run -it --rm --network=host --ipc=host --pid=host --shm-size=1g \
  --gpus '"device=0"' \
  -v $(pwd):/workspace \
  nvcr.io/nvidia/tritonserver:24.09-py3-sdk \
  python /workspace/test.py --protocol grpc

The script will crash, producing a stack trace that looks like the following:

Create CUDA shared mem: name = input_660453d16483422bb3839a093360fd55
Create CUDA shared mem: name = output_012419c8da8443da9100011d565930cf
Free CUDA shared mem: name = input_660453d16483422bb3839a093360fd55
Traceback (most recent call last):
  File "/workspace/test.py", line 216, in <module>
    main(parsed_args)
  File "/workspace/test.py", line 195, in main
    infer(protocol=args.protocol, host=args.host, shared_mem="cuda")
  File "/workspace/test.py", line 189, in infer
    release_shared_memory(triton_client, inputs)
  File "/workspace/test.py", line 152, in release_shared_memory
    triton_client.unregister_cuda_shared_memory(name=io_.name)
  File "/usr/local/lib/python3.10/dist-packages/tritonclient/grpc/_client.py", line 1443, in unregister_cuda_shared_memory
    raise_error_grpc(rpc_error)
  File "/usr/local/lib/python3.10/dist-packages/tritonclient/grpc/_utils.py", line 77, in raise_error_grpc
    raise get_error_grpc(rpc_error) from None
tritonclient.utils.InferenceServerException: [StatusCode.INTERNAL] Cannot unregister shared memory region 'input_660453d16483422bb3839a093360fd55', it is currently in use.

If the script does not crash, run it a few more times.

Expected behavior
The test script should complete without crashing.

Some observations

• The test script does not crash if the number of inference is reduced to under 100. On the other hand, increasing the number of inference to 1000-5000 reliably triggers the error.
• The test script does not crash if the HTTP protocol is used instead of gRPC. (Replace argument --protocol grpc with --protocol http.)
• The test script does not crash if the model is configured to use the GPU for inference (instance_group [{ kind: KIND_GPU }]).

[pskiran1]

@hcho3, We have implemented a new feature, #7743, to fix the issue, and it has been merged.
Can we close this issue now? Thank you.