Add testing for decoupled model use case #7246
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| # Copyright 2024, 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. | ||
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| import json | ||
| import queue | ||
| import time | ||
| from threading import Thread | ||
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| import numpy | ||
| import triton_python_backend_utils as pb_utils | ||
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| class WorkItem: | ||
| def __init__(self, response_sender, in_input, delay_input): | ||
| self.response_sender = response_sender | ||
| self.in_input = in_input | ||
| self.delay_input = delay_input | ||
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| class TritonPythonModel: | ||
| """This model launches a separate thread to handle the request from a queue. The thread is launched from | ||
| the `initialize` function and is terminated in the `finalize` function. This is different from the repeat | ||
| example in the Python Backend repository where a thread is launched per request and terminated after the response | ||
| is sent. | ||
| """ | ||
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| def initialize(self, args): | ||
| self.work_queue = queue.Queue() | ||
| self.running = True | ||
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| self.model_config = model_config = json.loads(args["model_config"]) | ||
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| using_decoupled = pb_utils.using_decoupled_model_transaction_policy( | ||
| model_config | ||
| ) | ||
| if not using_decoupled: | ||
| raise pb_utils.TritonModelException( | ||
| """the model `{}` can generate any number of responses per request, | ||
| enable decoupled transaction policy in model configuration to | ||
| serve this model""".format( | ||
| args["model_name"] | ||
| ) | ||
| ) | ||
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| # Get OUT configuration | ||
| out_config = pb_utils.get_output_config_by_name(model_config, "OUT") | ||
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| # Get IDX configuration | ||
| idx_config = pb_utils.get_output_config_by_name(model_config, "IDX") | ||
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| # Convert Triton types to numpy types | ||
| self.out_dtype = pb_utils.triton_string_to_numpy(out_config["data_type"]) | ||
| self.idx_dtype = pb_utils.triton_string_to_numpy(idx_config["data_type"]) | ||
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| self.sender_thread = Thread(target=self.sender_loop) | ||
| self.sender_thread.daemon = True | ||
| self.sender_thread.start() | ||
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| def sender_loop(self): | ||
| while self.running: | ||
| # Grab work from queue | ||
| work_item = self.work_queue.get() | ||
| if work_item.response_sender is None: | ||
| pb_utils.log( | ||
| pb_utils.LogLevel.INFO, | ||
| "Sender thread received dummy work item. Exiting...", | ||
| ) | ||
| self.work_queue.task_done() | ||
| break | ||
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| response_sender = work_item.response_sender | ||
| in_input = work_item.in_input | ||
| delay_input = work_item.delay_input | ||
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| idx_dtype = self.idx_dtype | ||
| out_dtype = self.out_dtype | ||
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| for idx in range(in_input.size): | ||
| in_value = in_input[idx] | ||
| delay_value = delay_input[idx] | ||
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| time.sleep(delay_value / 1000) | ||
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| idx_output = pb_utils.Tensor("IDX", numpy.array([idx], idx_dtype)) | ||
| out_output = pb_utils.Tensor("OUT", numpy.array([in_value], out_dtype)) | ||
| response = pb_utils.InferenceResponse( | ||
| output_tensors=[idx_output, out_output] | ||
| ) | ||
| response_sender.send(response) | ||
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| response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) | ||
| self.work_queue.task_done() | ||
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| def execute(self, requests): | ||
| # This model does not support batching, so 'request_count' should always | ||
| # be 1. | ||
| if len(requests) != 1: | ||
| raise pb_utils.TritonModelException( | ||
| "unsupported batch size " + len(requests) | ||
| ) | ||
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| in_input = pb_utils.get_input_tensor_by_name(requests[0], "IN").as_numpy() | ||
| delay_input = pb_utils.get_input_tensor_by_name(requests[0], "DELAY").as_numpy() | ||
| if in_input.shape != delay_input.shape: | ||
| raise pb_utils.TritonModelException( | ||
| f"expected IN and DELAY shape to match, got {list(in_input.shape)} and {list(delay_input.shape)}." | ||
| ) | ||
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| # Put work item in queue to be processed by the sender thread | ||
| self.work_queue.put( | ||
| WorkItem(requests[0].get_response_sender(), in_input, delay_input) | ||
| ) | ||
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| wait_input = pb_utils.get_input_tensor_by_name(requests[0], "WAIT").as_numpy() | ||
| time.sleep(wait_input[0] / 1000) | ||
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| return None | ||
|
There was a problem hiding this comment. I was wondering if you mean remove There was a problem hiding this comment. Not returning anything is similar to returning |
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| 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. | ||
| Here we will wait for all response threads to complete sending | ||
| responses. | ||
| """ | ||
| pb_utils.log(pb_utils.LogLevel.INFO, "Finalizing model...") | ||
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| # Pass dummy work item to the queue to terminate the sender_thread | ||
| self.work_queue.put( | ||
| WorkItem( | ||
| None, numpy.array([0], numpy.int32), numpy.array([0], numpy.uint32) | ||
| ) | ||
| ) | ||
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| self.running = False | ||
| self.sender_thread.join() | ||
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Please keep the number of characters in each line below 80.
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@dyastremsky do you know why CodeQL didn't catch this?