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https://github.com/NVIDIA/TensorRT-LLM.git
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8dd9c91470
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
117 lines
4.3 KiB
Python
117 lines
4.3 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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# 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 parameterized import parameterized
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from polygraphy.backend.trt import (CreateConfig, EngineFromNetwork, Profile,
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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([('float32', ), ('float16')])
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def test_slice_1(self, dtype):
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# test data
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x_shape = (1, 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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starts_data = torch.tensor([0, 128]).int()
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sizes_data = torch.tensor([1, 1]).int()
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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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starts = Tensor(name='starts', shape=(2, ), dtype=trt.int32)
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sizes = Tensor(name='sizes', shape=(2, ), dtype=trt.int32)
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output = tensorrt_llm.functional.slice(x, starts, sizes).trt_tensor
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output.name = 'output'
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network.mark_output(output)
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# trt run
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profiles = [
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Profile().add('starts', (0, 0), (0, 128),
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(0, 256)).add('sizes', (1, 1), (1, 1), (1, 256))
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]
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build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network),
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config=CreateConfig(profiles=profiles))
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with TrtRunner(build_engine) as runner:
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outputs = runner.infer(
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feed_dict={
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'x': x_data.numpy(),
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'starts': starts_data.numpy(),
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'sizes': sizes_data.numpy(),
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})
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# pytorch run
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ref = x_data[0:1, 128:129]
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# compare diff
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np.testing.assert_allclose(ref.cpu().numpy(), outputs['output'])
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def test_slice_2(self):
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dtype = 'float32'
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x_shape = (256, )
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slice_length = 128
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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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starts = tensorrt_llm.functional.constant(
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np.array([0], dtype=np.int32))
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output_length = tensorrt_llm.functional.constant(
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np.array([0] * slice_length, dtype=np.int32))
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sizes = tensorrt_llm.functional.shape(output_length, 0)
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output = tensorrt_llm.functional.slice(x, starts,
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sizes.view([1])).trt_tensor
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output.name = 'output'
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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={'x': x_data.numpy()})
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ref = x_data[0:slice_length]
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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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