mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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68 lines
2.2 KiB
Python
68 lines
2.2 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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# isort: off
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import torch
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# isort: on
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from parameterized import parameterized
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import tensorrt_llm
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from tensorrt_llm import Tensor
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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 TestArgmax(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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list(product(['float32', 'float16'], [False, True], [0, 1, 2])))
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def test_argmax(self, dtype, keep_dim, dim):
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# test data
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x_shape = (4, 12, 32)
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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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device="cuda")
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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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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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output = tensorrt_llm.functional.argmax(x, dim, keepdim=keep_dim)
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output.mark_output('output')
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# trt run
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inputs = {'x': x_data}
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session = create_session(builder, network, precision=dtype)
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outputs = run_session(session, inputs)
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# pytorch run
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ref = x_data.argmax(dim=dim, keepdim=keep_dim)
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# compare diff
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# ref is torch.int64, outputs is torch.int32
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torch.testing.assert_close(ref.int(), outputs['output'].int())
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