mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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106 lines
3.6 KiB
Python
106 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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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 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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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_unbind_1(self):
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# test data
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dtype = 'float32'
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input_shape = (1, 2, 3)
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unbind_dim = 0
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input_data = torch.rand(
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input_shape, 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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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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outputs = input.unbind(unbind_dim)
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for i in range(len(outputs)):
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outputs[i].name = f'output_{i}'
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network.mark_output(outputs[i].trt_tensor)
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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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outputs = runner.infer(feed_dict={
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'input': input_data.numpy(),
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})
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# pytorch run
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refs = input_data.unbind(unbind_dim)
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# compare diff
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# compare diff
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for idx, ref in enumerate(refs):
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np.testing.assert_allclose(ref.cpu().numpy(),
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outputs[f'output_{idx}'])
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def test_unbind_1(self):
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# test data
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dtype = 'float32'
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input_shape = (1, 2, 3)
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unbind_dim = 1
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input_data = torch.rand(
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input_shape, 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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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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outputs = input.unbind(unbind_dim)
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for i in range(len(outputs)):
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outputs[i].name = f'output_{i}'
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network.mark_output(outputs[i].trt_tensor)
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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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outputs = runner.infer(feed_dict={
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'input': input_data.numpy(),
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})
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# pytorch run
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refs = input_data.unbind(unbind_dim)
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# compare diff
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# compare diff
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for idx, ref in enumerate(refs):
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np.testing.assert_allclose(ref.cpu().numpy(),
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outputs[f'output_{idx}'])
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