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https://github.com/NVIDIA/TensorRT-LLM.git
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b777bd6475
* Update TensorRT-LLM --------- Co-authored-by: RunningLeon <mnsheng@yeah.net> Co-authored-by: Tlntin <TlntinDeng01@Gmail.com> Co-authored-by: ZHENG, Zhen <zhengzhen.z@qq.com> Co-authored-by: Pham Van Ngoan <ngoanpham1196@gmail.com> Co-authored-by: Nathan Price <nathan@abridge.com> Co-authored-by: Tushar Goel <tushar.goel.ml@gmail.com> Co-authored-by: Mati <132419219+matichon-vultureprime@users.noreply.github.com>
89 lines
2.8 KiB
Python
89 lines
2.8 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 os
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import sys
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import unittest
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import numpy as np
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import torch
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import tensorrt_llm
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import create_session, run_session
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class TestArange(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_arange_int(self):
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# test data
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start = 0
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end = 128
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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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output = tensorrt_llm.functional.arange(start=start,
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end=end,
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dtype="int32")
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output.mark_output('output', "int32")
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# trt run
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inputs = {}
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session = create_session(builder, network, precision="float32")
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outputs = run_session(session, inputs)
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ref = torch.arange(start, end).int().cuda()
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torch.testing.assert_close(outputs['output'], ref)
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def test_arange_tensor(self):
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# test data
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s = 0
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e = 128
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dtype = 'int32'
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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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start = tensorrt_llm.functional.constant(np.array(s,
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dtype=np.int32))
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end_tensor = tensorrt_llm.functional.constant(
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np.array([0] * e, dtype=np.int32))
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output = tensorrt_llm.functional.arange(
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start=start,
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end=tensorrt_llm.functional.shape(end_tensor, 0),
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dtype=dtype)
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output.mark_output('output', dtype)
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# trt run
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inputs = {}
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session = create_session(builder, network, precision="float32")
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outputs = run_session(session, inputs)
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# pytorch run
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ref = torch.arange(s, e).int().cuda()
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# compare diff
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torch.testing.assert_close(outputs['output'], ref)
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