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# Copyright (c) OpenMMLab. All rights reserved.
import json
import os.path as osp
from pathlib import Path
import numpy as np
import triton_python_backend_utils as pb_utils
# This tokenizer is `lmdeploy/turbomind/tokenizer.py`. When an LLM is served
# by triton inference server, it has to be converted first by running
# `python lmdeploy/serve/turbomind/deploy.py`. Then
# `lmdeploy/turbomind/tokenizer.py` will be copied to `tokenizer/tokenizer.py`
from .tokenizer.tokenizer import Tokenizer
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
self.model_config = model_config = json.loads(args['model_config'])
# 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'])
cur_folder = Path(__file__).parent
self.tokenizer = Tokenizer(
osp.join(
cur_folder, self.model_config['parameters']['tokenizer_path']
['string_value']))
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()
sequence_length = pb_utils.get_input_tensor_by_name(
request, 'sequence_length').as_numpy()
# Postprocessing output data.
outputs = self._postprocessing(tokens_batch.tolist(),
sequence_length)
# 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))
# 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])
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_length):
"""decode token ids into texts."""
outputs = []
for beam_tokens, beam_len in zip(tokens_batch, sequence_length):
for tokens, _len in zip(beam_tokens, beam_len):
output = self.tokenizer.decode(tokens, _len)
output = output.encode('utf8')
outputs.append(output)
return outputs
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