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# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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
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