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']

        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')
        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

        self._init_token_map()

        # 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")

        # Convert Triton types to numpy types
        self.output_lens_dtype = pb_utils.triton_string_to_numpy(
            output_lens_config['data_type'])

    def _init_token_map(self):
        v = self.tokenizer.get_vocab()
        self.token_map = [None] * len(v)
        for k, val in v.items():
            self.token_map[val] = k

        for i in range(len(v)):
            if self.token_map[i] is None:
                print("error %s" % i)

    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()

            # 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)

            # 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))
            output_lens_tensor = pb_utils.Tensor(
                'OUTPUT_LENS',
                np.array(output_lens).astype(self.output_lens_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=[output_tensor, output_lens_tensor])
            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 _single_token_decode(self, token):
        st = self.token_map[token]
        if st[0] == '▁':
            return " " + st[1:]
        else:
            return self.tokenizer.decode([token])

    def _postprocessing(self, tokens_batch):
        outputs = []
        output_lens = []
        for beam_tokens in tokens_batch:
            total_len = 0
            for tokens in beam_tokens:
                if len(tokens) == 1:
                    output = self._single_token_decode(tokens[0])
                else:
                    output = self.tokenizer.decode(tokens)
                outputs.append(output.encode('utf8'))
                total_len += len(tokens)
            output_lens.append(total_len)
        return outputs, output_lens