mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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109 lines
3.6 KiB
Python
109 lines
3.6 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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# isort: off
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import torch
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import tensorrt as trt
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# isort: on
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from utils.util import create_session, run_session
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import tensorrt_llm
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from tensorrt_llm import Tensor
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class TestView(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_view_static(self):
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# test data
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dtype = 'float32'
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input_shape = (4, 3)
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output_shape = (12, 1)
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input_data = torch.rand(
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input_shape,
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
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device="cuda")
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# construct trt network
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builder = tensorrt_llm.Builder()
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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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input = Tensor(name='input',
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shape=input_shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.view(input=input,
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shape=output_shape)
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output.mark_output('output')
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# trt run
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session = create_session(builder, network, precision=dtype)
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inputs = {
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'input': input_data,
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}
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outputs = run_session(session, inputs)
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# pytorch run
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ref = input_data.view(output_shape)
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# compare diff
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torch.testing.assert_close(ref, outputs['output'])
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def test_view_dynamic(self):
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# test data
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dtype = 'float32'
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input_shape = (4, 3)
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output_shape = (2, 6)
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input_data = torch.rand(
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input_shape,
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
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device="cuda")
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shape_data = torch.tensor(output_shape).int()
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# construct trt network
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builder = tensorrt_llm.Builder()
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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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input = Tensor(name='input',
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shape=input_shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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shape = Tensor(name='shape',
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shape=(len(input_shape), ),
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dtype=trt.int32)
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output = tensorrt_llm.functional.view(input=input, shape=shape)
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output.mark_output('output')
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# trt run
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profile = builder.trt_builder.create_optimization_profile()
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profile.set_shape_input('shape', output_shape, output_shape,
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output_shape)
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session = create_session(builder,
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network,
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precision=dtype,
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optimization_profiles=[profile])
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inputs = {'input': input_data, 'shape': shape_data}
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outputs = run_session(session, inputs)
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# pytorch run
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ref = input_data.view(output_shape)
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# compare diff
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torch.testing.assert_close(ref, outputs['output'])
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