model.py

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import json

import jax.numpy as jnp
import numpy as np

# triton_python_backend_utils is available in every Triton Python model. You
# need to use this module to create inference requests and responses. It also
# contains some utility functions for extracting information from model_config
# and converting Triton input/output types to numpy types.
import triton_python_backend_utils as pb_utils


def AddSub(input_0, input_1):
    """
    Simple AddSub operations in JAX. This outputs the sum and subtraction of
    the inputs.
    JAX API: https://jax.readthedocs.io/en/latest/jax.html
    """
    output_0 = jnp.add(input_0, input_1)
    output_1 = jnp.subtract(input_0, input_1)
    return [output_0, output_1]


class TritonPythonModel:
    """Your Python model must use the same class name. Every Python model
    that is created must have "TritonPythonModel" as the class name.
    """

    def initialize(self, args):
        """`initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
        the model to initialize any state associated with this model.

        Parameters
        ----------
        args : dict
          Both keys and values are strings. The dictionary keys and values are:
          * model_config: A JSON string containing the model configuration
          * model_instance_kind: A string containing model instance kind
          * model_instance_device_id: A string containing model instance device ID
          * model_repository: Absolute model repository path
          * model_version: Model version
          * model_name: Model name
        """

        # You must parse model_config. JSON string is not parsed here
        self.model_config = model_config = json.loads(args["model_config"])

        # Get OUTPUT0 configuration
        output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT0")

        # Get OUTPUT1 configuration
        output1_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT1")

        # Convert Triton types to numpy types
        self.output0_dtype = pb_utils.triton_string_to_numpy(
            output0_config["data_type"]
        )
        self.output1_dtype = pb_utils.triton_string_to_numpy(
            output1_config["data_type"]
        )

    def execute(self, requests):
        """`execute` must be implemented in every Python model. `execute`
        function receives a list of pb_utils.InferenceRequest as the only
        argument. This function is called when an inference is requested
        for this model. Depending on the batching configuration (e.g. Dynamic
        Batching) used, `requests` may contain multiple requests. Every
        Python model, must create one pb_utils.InferenceResponse for every
        pb_utils.InferenceRequest in `requests`. If there is an error, you can
        set the error argument when creating a pb_utils.InferenceResponse.

        Parameters
        ----------
        requests : list
          A list of pb_utils.InferenceRequest

        Returns
        -------
        list
          A list of pb_utils.InferenceResponse. The length of this list must
          be the same as `requests`
        """

        output0_dtype = self.output0_dtype
        output1_dtype = self.output1_dtype

        responses = []

        # Every Python backend must iterate over every one of the requests and
        # create a pb_utils.InferenceResponse for each of them.
        for request in requests:
            # Get INPUT0
            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
            # Get INPUT1
            in_1 = pb_utils.get_input_tensor_by_name(request, "INPUT1")

            out_0, out_1 = AddSub(in_0.as_numpy(), in_1.as_numpy())

            # Create output tensors. You need pb_utils.Tensor
            # objects to create pb_utils.InferenceResponse.
            out_tensor_0 = pb_utils.Tensor(
                "OUTPUT0", np.array(out_0).astype(output0_dtype)
            )
            out_tensor_1 = pb_utils.Tensor(
                "OUTPUT1", np.array(out_1).astype(output1_dtype)
            )

            # Create InferenceResponse. You can set an error here in case
            # there was a problem with handling this inference request.
            # Below is an example of how you can set errors in inference
            # response:
            #
            # pb_utils.InferenceResponse(
            #    output_tensors=..., TritonError("An error occurred"))
            inference_response = pb_utils.InferenceResponse(
                output_tensors=[out_tensor_0, out_tensor_1]
            )
            responses.append(inference_response)

        # You should return a list of pb_utils.InferenceResponse. Length
        # of this list must match the length of `requests` list.
        return responses

    def finalize(self):
        """`finalize` is called only once when the model is being unloaded.
        Implementing `finalize` function is optional. This function allows
        the model to perform any necessary clean ups before exit.
        """
        print("Cleaning up...")