postprocessing/1/model.py

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import json

import numpy as np
import triton_python_backend_utils as pb_utils
from transformers import AutoTokenizer, LlamaTokenizer, T5Tokenizer


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: Model repository path
          * model_version: Model version
          * model_name: Model name
        """
        # Parse model configs
        model_config = json.loads(args['model_config'])
        tokenizer_dir = model_config['parameters']['tokenizer_dir'][
            'string_value']
        tokenizer_type = model_config['parameters']['tokenizer_type'][
            'string_value']
        self.skip_special_tokens = model_config['parameters'].get(
            'skip_special_tokens',
            {'string_value': "true"})['string_value'].lower() in [
                'true', '1', 't', 'y', 'yes'
            ]

        if tokenizer_type == 't5':
            self.tokenizer = T5Tokenizer(vocab_file=tokenizer_dir,
                                         padding_side='left')
        elif tokenizer_type == 'auto':
            self.tokenizer = AutoTokenizer.from_pretrained(
                tokenizer_dir, padding_side='left', trust_remote_code=True)
        elif tokenizer_type == 'llama':
            self.tokenizer = LlamaTokenizer.from_pretrained(
                tokenizer_dir, legacy=False, padding_side='left')
        else:
            raise AttributeError(
                f'Unexpected tokenizer type: {tokenizer_type}')
        self.tokenizer.pad_token = self.tokenizer.eos_token

        # Parse model output configs
        output_config = pb_utils.get_output_config_by_name(
            model_config, "OUTPUT")

        # Convert Triton types to numpy types
        self.output_dtype = pb_utils.triton_string_to_numpy(
            output_config['data_type'])
        output_lens_config = pb_utils.get_output_config_by_name(
            model_config, "OUTPUT_LENS")

    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`
        """

        responses = []

        # Every Python backend must iterate over everyone of the requests
        # and create a pb_utils.InferenceResponse for each of them.
        for idx, request in enumerate(requests):
            # Get input tensors
            tokens_batch = pb_utils.get_input_tensor_by_name(
                request, 'TOKENS_BATCH').as_numpy()

            # Get sequence length
            sequence_lengths = pb_utils.get_input_tensor_by_name(
                request, 'SEQUENCE_LENGTH').as_numpy()

            # Get cum log probs
            cum_log_probs = pb_utils.get_input_tensor_by_name(
                request, 'CUM_LOG_PROBS').as_numpy()

            # Get sequence length
            output_log_probs = pb_utils.get_input_tensor_by_name(
                request, 'OUTPUT_LOG_PROBS').as_numpy()

            # Reshape Input
            # tokens_batch = tokens_batch.reshape([-1, tokens_batch.shape[0]])
            # tokens_batch = tokens_batch.T

            # Postprocessing output data.
            outputs, output_lens = self._postprocessing(tokens_batch, sequence_lengths)


            # Create output tensors. You need pb_utils.Tensor
            # objects to create pb_utils.InferenceResponse.
            output_tensor = pb_utils.Tensor(
                'OUTPUT',
                np.array(outputs).astype(self.output_dtype))

            out_cum_log_probs = pb_utils.Tensor('OUT_CUM_LOG_PROBS',
                                                cum_log_probs)

            out_output_log_probs = pb_utils.Tensor('OUT_OUTPUT_LOG_PROBS',
                                                   output_log_probs)

            # 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=[
                output_tensor, out_cum_log_probs, out_output_log_probs
            ])
            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...')

    def _postprocessing(self, tokens_batch, sequence_lengths):
        outputs = []
        for batch_idx, beam_tokens in enumerate(tokens_batch):
            for beam_idx, tokens in enumerate(beam_tokens):
                seq_len = sequence_lengths[batch_idx][beam_idx]
                output = self.tokenizer.decode(
                    tokens[:seq_len],
                    skip_special_tokens=self.skip_special_tokens)
                outputs.append(output.encode('utf8'))
        return outputs