mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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 torch
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from parameterized import parameterized
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from polygraphy.backend.trt import EngineFromNetwork, TrtRunner
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import tensorrt_llm
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from tensorrt_llm import Tensor
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from tensorrt_llm.functional import shape
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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([('float32', )])
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def test_assertion(self, dtype):
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# test data
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x_shape = (2, 4, 8)
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y_shape = (4, 4, 4)
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x_data = torch.rand(x_shape,
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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y_data = torch.rand(y_shape,
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
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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=x_shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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y = Tensor(name='y',
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shape=y_shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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tensorrt_llm.functional.assertion(shape(x, 1) == shape(y, 1))
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output = tensorrt_llm.functional.identity(x).trt_tensor
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output.name = 'output'
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network.mark_output(output)
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output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)
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# trt run
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
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with TrtRunner(build_engine) as runner:
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runner.infer(feed_dict={'x': x_data.numpy(), 'y': y_data.numpy()})
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