mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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67 lines
2.1 KiB
Python
67 lines
2.1 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 torch
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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 TestConv2D(unittest.TestCase):
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def setUp(self):
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# Disable TF32 because accuracy is bad
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torch.backends.cudnn.allow_tf32 = False
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tensorrt_llm.logger.set_level('error')
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def test_conv2d(self):
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# test data
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dtype = 'float32'
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x_data = torch.randn(8, 4, 5, 5, device="cuda")
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weight_data = torch.randn(8, 4, 3, 3, device="cuda")
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padding = (1, 1)
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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_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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weight = tensorrt_llm.constant(weight_data.cpu().numpy())
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output = tensorrt_llm.functional.conv2d(x, weight, padding=padding)
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output.mark_output('output', dtype)
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# trt run
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session = create_session(builder, network, precision=dtype)
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inputs = {
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'x': x_data,
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}
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outputs = run_session(session, inputs)
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# pytorch run
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ref = torch.nn.functional.conv2d(x_data, weight_data, padding=padding)
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# compare diff
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torch.testing.assert_close(ref, outputs['output'])
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