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ci/L0_vllm_additional_outputs/additional_outputs_test.py
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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. | ||
|
||
import json | ||
import unittest | ||
|
||
import numpy as np | ||
import tritonclient.grpc as grpcclient | ||
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||
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class InferTest(unittest.TestCase): | ||
_grpc_url = "localhost:8001" | ||
_model_name = "vllm_opt" | ||
_sampling_parameters = {"temperature": "0", "top_p": "1"} | ||
_prompt = "In this example," | ||
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def _get_inputs( | ||
self, | ||
prompt, | ||
stream=True, | ||
sampling_parameters=None, | ||
output_finish_reason=None, | ||
output_cumulative_logprob=None, | ||
output_num_token_ids=None, | ||
): | ||
inputs = [] | ||
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inputs.append(grpcclient.InferInput("text_input", [1], "BYTES")) | ||
inputs[-1].set_data_from_numpy( | ||
np.array([prompt.encode("utf-8")], dtype=np.object_) | ||
) | ||
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inputs.append(grpcclient.InferInput("stream", [1], "BOOL")) | ||
inputs[-1].set_data_from_numpy(np.array([stream], dtype=bool)) | ||
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if sampling_parameters is not None: | ||
inputs.append(grpcclient.InferInput("sampling_parameters", [1], "BYTES")) | ||
inputs[-1].set_data_from_numpy( | ||
np.array( | ||
[json.dumps(sampling_parameters).encode("utf-8")], dtype=np.object_ | ||
) | ||
) | ||
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if output_finish_reason is not None: | ||
inputs.append(grpcclient.InferInput("output_finish_reason", [1], "BOOL")) | ||
inputs[-1].set_data_from_numpy(np.array([output_finish_reason], dtype=bool)) | ||
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if output_cumulative_logprob is not None: | ||
inputs.append( | ||
grpcclient.InferInput("output_cumulative_logprob", [1], "BOOL") | ||
) | ||
inputs[-1].set_data_from_numpy( | ||
np.array([output_cumulative_logprob], dtype=bool) | ||
) | ||
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if output_num_token_ids is not None: | ||
inputs.append(grpcclient.InferInput("output_num_token_ids", [1], "BOOL")) | ||
inputs[-1].set_data_from_numpy(np.array([output_num_token_ids], dtype=bool)) | ||
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return inputs | ||
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def _callback(self, result, error): | ||
self._responses.append({"result": result, "error": error}) | ||
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def _llm_infer(self, inputs): | ||
self._responses = [] | ||
with grpcclient.InferenceServerClient(self._grpc_url) as client: | ||
client.start_stream(self._callback) | ||
client.async_stream_infer( | ||
self._model_name, inputs=inputs, parameters=self._sampling_parameters | ||
) | ||
client.stop_stream() | ||
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def _assert_text_output_valid(self): | ||
text_output = "" | ||
for response in self._responses: | ||
result, error = response["result"], response["error"] | ||
self.assertIsNone(error) | ||
text_output += result.as_numpy(name="text_output")[0].decode("utf-8") | ||
self.assertGreater(len(text_output), 0, "output is empty") | ||
self.assertGreater(text_output.count(" "), 4, "output is not a sentence") | ||
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def _assert_finish_reason(self, output_finish_reason): | ||
for i in range(len(self._responses)): | ||
result, error = self._responses[i]["result"], self._responses[i]["error"] | ||
self.assertIsNone(error) | ||
finish_reason_np = result.as_numpy(name="finish_reason") | ||
if output_finish_reason is None or output_finish_reason == False: | ||
self.assertIsNone(finish_reason_np) | ||
continue | ||
finish_reason = finish_reason_np[0].decode("utf-8") | ||
if i < len(self._responses) - 1: | ||
self.assertEqual(finish_reason, "None") | ||
else: | ||
self.assertEqual(finish_reason, "length") | ||
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def _assert_cumulative_logprob(self, output_cumulative_logprob): | ||
prev_cumulative_logprob = 0.0 | ||
for response in self._responses: | ||
result, error = response["result"], response["error"] | ||
self.assertIsNone(error) | ||
cumulative_logprob_np = result.as_numpy(name="cumulative_logprob") | ||
if output_cumulative_logprob is None or output_cumulative_logprob == False: | ||
self.assertIsNone(cumulative_logprob_np) | ||
continue | ||
cumulative_logprob = cumulative_logprob_np[0].astype(float) | ||
self.assertNotEqual(cumulative_logprob, prev_cumulative_logprob) | ||
prev_cumulative_logprob = cumulative_logprob | ||
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def _assert_num_token_ids(self, output_num_token_ids): | ||
for response in self._responses: | ||
result, error = response["result"], response["error"] | ||
self.assertIsNone(error) | ||
num_token_ids_np = result.as_numpy(name="num_token_ids") | ||
if output_num_token_ids is None or output_num_token_ids == False: | ||
self.assertIsNone(num_token_ids_np) | ||
continue | ||
num_token_ids = num_token_ids_np[0].astype(int) | ||
self.assertGreater(num_token_ids, 0) | ||
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def _assert_additional_outputs_valid( | ||
self, | ||
stream, | ||
output_finish_reason, | ||
output_cumulative_logprob, | ||
output_num_token_ids, | ||
): | ||
inputs = self._get_inputs( | ||
self._prompt, | ||
stream=stream, | ||
sampling_parameters=self._sampling_parameters, | ||
output_finish_reason=output_finish_reason, | ||
output_cumulative_logprob=output_cumulative_logprob, | ||
output_num_token_ids=output_num_token_ids, | ||
) | ||
self._llm_infer(inputs) | ||
self._assert_text_output_valid() | ||
self._assert_finish_reason(output_finish_reason) | ||
self._assert_cumulative_logprob(output_cumulative_logprob) | ||
self._assert_num_token_ids(output_num_token_ids) | ||
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def test_additional_outputs(self): | ||
for stream in [True, False]: | ||
choices = [None, False, True] | ||
for output_finish_reason in choices: | ||
for output_cumulative_logprob in choices: | ||
for output_num_token_ids in choices: | ||
self._assert_additional_outputs_valid( | ||
stream, | ||
output_finish_reason, | ||
output_cumulative_logprob, | ||
output_num_token_ids, | ||
) | ||
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if __name__ == "__main__": | ||
unittest.main() |
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#!/bin/bash | ||
# 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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export CUDA_VISIBLE_DEVICES=0 | ||
source ../common/util.sh | ||
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pip3 install tritonclient[grpc] | ||
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# Prepare Model | ||
rm -rf models vllm_baseline_output.pkl && mkdir -p models | ||
SAMPLE_MODELS_REPO="../../samples/model_repository" | ||
cp -r $SAMPLE_MODELS_REPO/vllm_model models/vllm_opt | ||
sed -i 's/"gpu_memory_utilization": 0.5/"gpu_memory_utilization": 0.3/' models/vllm_opt/1/model.json | ||
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RET=0 | ||
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# Infer Test | ||
CLIENT_LOG="vllm_opt.log" | ||
SERVER_LOG="vllm_opt.server.log" | ||
SERVER_ARGS="--model-repository=models" | ||
run_server | ||
if [ "$SERVER_PID" == "0" ]; then | ||
echo -e "\n***\n*** Failed to start $SERVER\n***" | ||
cat $SERVER_LOG | ||
exit 1 | ||
fi | ||
set +e | ||
python3 additional_outputs_test.py > $CLIENT_LOG 2>&1 | ||
if [ $? -ne 0 ]; then | ||
cat $CLIENT_LOG | ||
echo -e "\n***\n*** additional_outputs_test FAILED. \n***" | ||
RET=1 | ||
fi | ||
set -e | ||
kill $SERVER_PID | ||
wait $SERVER_PID | ||
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if [ $RET -eq 0 ]; then | ||
echo -e "\n***\n*** Test Passed\n***" | ||
else | ||
echo -e "\n***\n*** Test FAILED\n***" | ||
fi | ||
exit $RET |
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