mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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86 lines
3.0 KiB
Python
86 lines
3.0 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 numpy as np
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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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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([
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('float32', 0, 4),
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('float32', 1, 4),
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('float32', -1, 4),
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('float32', -2, 4),
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('float16', 0, 4),
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('float16', 1, 4),
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('float16', -1, 4),
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('float16', -2, 4),
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('float32', 0, [2, 100, 26]),
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('float32', 1, [2, 100, 100, 52, 2]),
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('float32', -1, [2, 100, 100, 52, 2]),
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('float32', -2, [2, 100, 26]),
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('float16', 0, [2, 100, 26]),
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('float16', 1, [2, 100, 100, 52, 2]),
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('float16', -1, [2, 100, 100, 52, 2]),
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('float16', -2, [2, 100, 26]),
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])
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def test_split(self, dtype, dim, split_size_or_sections):
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# test data
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x_shape = (128, 256)
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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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# 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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outputs = tensorrt_llm.functional.split(x, split_size_or_sections,
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dim)
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for i in range(len(outputs)):
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output = outputs[i].trt_tensor
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output.name = f'output_{i}'
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network.mark_output(output)
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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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'x': x_data.numpy(),
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})
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# pytorch run
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ref_outputs = torch.split(x_data, split_size_or_sections, dim)
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# compare diff
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assert len(outputs.keys()) == len(ref_outputs)
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for i in range(len(ref_outputs)):
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np.testing.assert_allclose(ref_outputs[i].cpu().numpy(),
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outputs[f'output_{i}'])
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