Attempting to run T5 ORT model in Triton inference server

[samiur]

Hi there,

Thanks again for this library!

We're trying to convert a fine-tuned T5 model to ONNX and run it in Triton. We've managed to convert the model to ONNX and use the T5 notebook guide to run the model just fine in python.

But trying to get it to run in Triton has been a challenge. In particular, we're not sure how to get past_key_values to be passed through in Triton. We have the decoder config as follows:

name: "t5-dec-if-node_onnx_model"
max_batch_size: 0
platform: "onnxruntime_onnx"
default_model_filename: "model.bin"
input [
    {
        name: "input_ids"
        data_type: TYPE_INT32
        dims: [ -1, -1 ]
    },
    {
        name: "encoder_hidden_states"
        data_type: TYPE_FP32
        dims: [ -1, -1, 2048 ]
    },
    {
        name: "enable_cache"
        data_type: TYPE_BOOL
        dims: [ 1 ]
    },

        {
            name: "past_key_values.0.decoder.key"
            data_type: TYPE_FP32
            dims: [-1, 32, -1, 64]
        },
        {
            name: "past_key_values.0.decoder.value"
            data_type: TYPE_FP32
            dims: [-1, 32, -1, 64]
        },
        {
            name: "past_key_values.0.encoder.key"
            data_type: TYPE_FP32
            dims: [-1, 32, -1, 64]
        },
        {
            name: "past_key_values.0.encoder.value"
            data_type: TYPE_FP32
            dims: [-1, 32, -1, 64]
        }
     ...
]
output [
    {
        name: "logits"
        data_type: TYPE_FP32
        dims: [ -1, -1, 32128 ]
    }
]
instance_group [
    {
      count: 1
      kind: KIND_GPU
    }
]

And when we do the following:

input_1 = tritonclient.http.InferInput(name="input_ids", shape=(1, 24), datatype="INT32")
input_2 = tritonclient.http.InferInput(name="encoder_hidden_states", shape=(1, 24, 2048), datatype="FP32")
input_3 = tritonclient.http.InferInput(name="enable_cache", shape=(1, ), datatype="BOOL")

input_1.set_data_from_numpy(input_ids)
input_2.set_data_from_numpy(encoder_hidden_states)
input_3.set_data_from_numpy(np.asarray([True]))

result = triton_client.infer(
    model_name='t5-dec-if-node_onnx_model', 
    inputs=[input_1, input_2, input_3], 
    outputs=[tritonclient.http.InferRequestedOutput(name="logits", binary_data=False)]
)

We get this error:

InferenceServerException: [request id: <id_unknown>] expected 99 inputs but got 3 inputs for model 't5-dec-if-node_onnx_model'

Any idea how we can fix this?

[ayoub-louati]

Hello, Thanks for trying our library, We are actually working on adding T5 officialy in the convert script so that you can do conversion with one line command, It will be added very soon (especially onnx conversion maybe in less than a week), but if you want I can help you with the triton configuration (it is a little bit complicated).