mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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2a115dae84
Co-authored-by: DreamGenX <x@dreamgen.com> Co-authored-by: Ace-RR <78812427+Ace-RR@users.noreply.github.com> Co-authored-by: bprus <39293131+bprus@users.noreply.github.com> Co-authored-by: janpetrov <janpetrov@icloud.com>
102 lines
3.3 KiB
Python
102 lines
3.3 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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from itertools import product
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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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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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@parameterized.expand(
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list(
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product(['int32', 'int64'], ['int32', 'int64'],
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['int32', 'int64', 'float32', 'float16'])))
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def test_arange_tensor(self,
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s_dtype='int32',
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e_dtype='int32',
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r_dtype='int32'):
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# test data
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s = 0
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e = 128
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s_np_dtype = tensorrt_llm._utils.str_dtype_to_np(s_dtype)
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e_np_dtype = tensorrt_llm._utils.str_dtype_to_np(e_dtype)
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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(
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np.array(s, dtype=s_np_dtype))
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end = tensorrt_llm.functional.constant(
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np.array([e], dtype=e_np_dtype))
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output = tensorrt_llm.functional.arange(start=start,
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end=end,
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dtype=r_dtype)
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output.mark_output('output', r_dtype)
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# trt run
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inputs = {}
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session = create_session(
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builder,
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network,
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precision="float32" if r_dtype != 'float16' else 'float16')
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outputs = run_session(session, inputs)
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# pytorch run
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ref = torch.arange(
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s, e, dtype=tensorrt_llm.str_dtype_to_torch(r_dtype)).cuda()
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# compare diff
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torch.testing.assert_close(outputs['output'], ref)
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