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


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']

        skip_special_tokens = model_config['parameters'].get(
            'skip_special_tokens')
        if skip_special_tokens is not None:
            skip_special_tokens_str = skip_special_tokens[
                'string_value'].lower()
            if skip_special_tokens_str in [
                    'true', 'false', '1', '0', 't', 'f', 'y', 'n', 'yes', 'no'
            ]:
                self.skip_special_tokens = skip_special_tokens_str in [
                    'true', '1', 't', 'y', 'yes'
                ]
            else:
                print(
                    f"[TensorRT-LLM][WARNING] Don't setup 'skip_special_tokens' correctly (set value is {skip_special_tokens['string_value']}). Set it as True by default."
                )
                self.skip_special_tokens = True
        else:
            print(
                f"[TensorRT-LLM][WARNING] Don't setup 'skip_special_tokens'. Set it as True by default."
            )
            self.skip_special_tokens = True

        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir,
                                                       legacy=False,
                                                       padding_side='left',
                                                       trust_remote_code=True)
        if not self.tokenizer.pad_token:
            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'])

    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`
        """

        tokens_batch = []
        sequence_lengths = []
        for idx, request in enumerate(requests):
            for input_tensor in request.inputs():
                if input_tensor.name() == "TOKENS_BATCH":
                    tokens_batch.append(input_tensor.as_numpy())
                elif input_tensor.name() == "SEQUENCE_LENGTH":
                    sequence_lengths.append(input_tensor.as_numpy())
                else:
                    raise ValueError(f"unknown input {input_tensor.name}")

        # batch decode
        list_of_tokens = []
        req_idx_offset = 0
        req_idx_offsets = [req_idx_offset]
        for idx, token_batch in enumerate(tokens_batch):
            for batch_idx, beam_tokens in enumerate(token_batch):
                for beam_idx, tokens in enumerate(beam_tokens):
                    seq_len = sequence_lengths[idx][batch_idx][beam_idx]
                    list_of_tokens.append(tokens[:seq_len])
                    req_idx_offset += 1

            req_idx_offsets.append(req_idx_offset)

        all_outputs = self.tokenizer.batch_decode(
            list_of_tokens, skip_special_tokens=self.skip_special_tokens)

        # construct responses
        responses = []
        for idx, request in enumerate(requests):
            req_outputs = [
                x.encode('utf8')
                for x in all_outputs[req_idx_offsets[idx]:req_idx_offsets[idx +
                                                                          1]]
            ]

            output_tensor = pb_utils.Tensor(
                'OUTPUT',
                np.array(req_outputs).astype(self.output_dtype))

            outputs = [output_tensor]

            # 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=outputs)
            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...')