mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 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 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, unittest_name_func
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class TestTopK(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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[
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t + (p, ) for t in [
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((3, 5), 1, 1, True),
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((3, 4, 6), 2, 0, True),
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((3, 5), 1, 1, False),
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((3, 4, 6), 2, 0, False),
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((3, 4, 5000), 10, 2, True),
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] for p in [False, True] # prefer_plugin
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],
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name_func=unittest_name_func)
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def test_topk(self, input_shape, k, d, largest, prefer_plugin):
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value_dtype = 'float32'
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indices_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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input_data = torch.rand(*input_shape,
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dtype=torch.float32,
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device="cuda")
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with tensorrt_llm.net_guard(network):
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m = tensorrt_llm.functional.constant(input_data.cpu().numpy())
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topk_values, topk_indices = tensorrt_llm.functional.topk(
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m, k, d, largest=largest, prefer_plugin=prefer_plugin)
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topk_values.mark_output('output_values', value_dtype)
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topk_indices.mark_output('topk_indices', indices_dtype)
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# trt run
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session = create_session(builder, network)
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inputs = {}
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outputs = run_session(session, inputs)
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# pytorch run
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values, indices = torch.topk(input_data, k, dim=d, largest=largest)
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# compare diff
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torch.testing.assert_close(values, outputs['output_values'])
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# dtype does not match
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torch.testing.assert_close(indices.int(), outputs['topk_indices'].int())
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