mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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# isort: off
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import torch
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# isort: on
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from parameterized import parameterized
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from polygraphy.backend.trt import (CreateConfig, EngineFromNetwork, Profile,
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TrtRunner)
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import tensorrt_llm
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from tensorrt_llm import Tensor
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class TestFunctional(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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@parameterized.expand([
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((4, ), ),
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((4, 2), ),
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((0, 4, 2), ),
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])
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def test_nonzero(self, x_shape):
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# test data
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# x_shape = (4, 4)
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x_shape_last = list(x_shape[1:])
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x_data = torch.randint(2, size=x_shape, dtype=torch.int32).bool()
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print(x_data)
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# construct trt network
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builder = tensorrt_llm.Builder()
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net = builder.create_network()
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with tensorrt_llm.net_guard(net):
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network = tensorrt_llm.default_trtnet()
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x = Tensor(name='x',
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shape=[-1] + x_shape_last,
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dtype=tensorrt_llm.torch_dtype_to_trt(x_data.dtype))
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output = tensorrt_llm.functional.nonzero(x).trt_tensor
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output.name = 'output'
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network.mark_output(output)
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# trt run
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# needs profile for dynamic shape
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profiles = Profile().add('x', [0] + x_shape_last, [2] + x_shape_last,
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[32] + x_shape_last)
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build_engine = EngineFromNetwork(
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(builder.trt_builder, net.trt_network),
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config=CreateConfig(profiles=[profiles]))
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with TrtRunner(build_engine) as runner:
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outputs = runner.infer(feed_dict={
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'x': x_data.numpy(),
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})
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print(outputs['output'].transpose())
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# pytorch run
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# print(x_data.nonzero())
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ref = x_data.nonzero().transpose(0, 1)
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# compare diff
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np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])
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return
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