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https://github.com/NVIDIA/TensorRT-LLM.git
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9bd15f1937
* TensorRT-LLM Release 0.10.0 --------- Co-authored-by: Loki <lokravi@amazon.com> Co-authored-by: meghagarwal <16129366+megha95@users.noreply.github.com>
69 lines
2.4 KiB
Python
69 lines
2.4 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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import torch
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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._utils import str_dtype_to_torch
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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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def test_squeeze(self,
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input_data=[[[-3.0, -2.0, -1.0, 10.0, -25.0]],
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[[0.0, 1.0, 2.0, -2.0, -1.0]]],
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dim=1):
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dtype = 'float32'
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torch_dtype = str_dtype_to_torch(dtype)
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input_data = input_data if isinstance(
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input_data, torch.Tensor) else torch.tensor(input_data)
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input_data = input_data.to(torch_dtype)
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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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input_t = Tensor(name='input',
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shape=input_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.squeeze(input_t, dim=dim)
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output = output.trt_tensor
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output.name = 'output'
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network.mark_output(output)
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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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outputs = runner.infer(feed_dict={
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'input': input_data.numpy(),
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})
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ref = torch.squeeze(input_data, dim=dim)
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np.testing.assert_allclose(ref.cpu().numpy(),
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outputs['output'],
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atol=1e-5)
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# print(ref)
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# print(outputs['output'])
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return
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