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#!/usr/bin/env python3
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Simple classifier example based on Hugging Face JAX BERT model."""
import logging
import numpy as np
from transformers import BertTokenizer, FlaxBertModel # pytype: disable=import-error
from pytriton.decorators import batch
from pytriton.model_config import ModelConfig, Tensor
from pytriton.triton import Triton
logger = logging.getLogger("examples.huggingface_bert_jax.server")
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(name)s: %(message)s")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = FlaxBertModel.from_pretrained("bert-base-uncased")
@batch
def _infer_fn(**inputs: np.ndarray):
(sequence_batch,) = inputs.values()
# need to convert dtype=object to bytes first
# end decode unicode bytes
sequence_batch = np.char.decode(sequence_batch.astype("bytes"), "utf-8")
last_hidden_states = []
for sequence_item in sequence_batch:
tokenized_sequence = tokenizer(sequence_item.item(), return_tensors="jax")
results = model(**tokenized_sequence)
last_hidden_states.append(results.last_hidden_state)
last_hidden_states = np.array(last_hidden_states, dtype=np.float32)
return [last_hidden_states]
with Triton() as triton:
logger.info("Loading BERT model.")
triton.bind(
model_name="BERT",
infer_func=_infer_fn,
inputs=[
Tensor(name="sequence", dtype=np.bytes_, shape=(1,)),
],
outputs=[
Tensor(
name="last_hidden_state",
dtype=np.float32,
shape=(-1, -1, -1),
),
],
config=ModelConfig(max_batch_size=16),
strict=True,
)
logger.info("Serving inference")
triton.serve()
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