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5955b8afba
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
79 lines
2.6 KiB
Python
79 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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# 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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import tensorrt_llm
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class MyAddModule(tensorrt_llm.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, y):
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return x + y
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class TestSession(unittest.TestCase):
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def test_session_debug_run(self):
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tensorrt_llm.logger.set_level('verbose')
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builder = tensorrt_llm.Builder()
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builder_config = builder.create_builder_config("test", "llmTimingCache")
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model = MyAddModule()
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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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x = tensorrt_llm.Tensor(name='x', dtype=trt.float32, shape=[1, 1])
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y = tensorrt_llm.Tensor(name='y', dtype=trt.float32, shape=[1, 1])
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# Prepare
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network.set_named_parameters(model.named_parameters())
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# Forward
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z = model(x, y)
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z.mark_output('z', trt.float32)
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### Addtionl debug tensor
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debug_tensor = x * y
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debug_tensor.mark_output('debug_tensor', trt.float32)
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engine = builder.build_engine(network, builder_config)
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assert engine is not None
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# Show to _debug_run can be used
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# You need to mark "z" and "debug_tensor" as output, and then use Session._debug_run
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# to run inference and get the output
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session = tensorrt_llm.runtime.Session.from_serialized_engine(engine)
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inputs = {
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'x': torch.rand([1, 1], device='cuda'),
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'y': torch.rand([1, 1], device='cuda')
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}
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outputs = session._debug_run(inputs)
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assert 'z' in outputs and 'debug_tensor' in outputs
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expected_debug_tensor = inputs['x'] * inputs['y']
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expected_z = inputs['x'] + inputs['y']
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self.assertTrue(
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torch.allclose(outputs['debug_tensor'], expected_debug_tensor))
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self.assertTrue(torch.allclose(outputs['z'], expected_z))
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if __name__ == '__main__':
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unittest.main()
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