wehub-resource-sync
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
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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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"""Test HIP BF16 convolution kernel registration.
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This test keeps coverage focused on minimal convolution forward passes so the
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suite validates kernel registration without pulling in unrelated BF16 operator
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chains.
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"""
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import unittest
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import numpy as np
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import paddle
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from paddle.base import core
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@unittest.skipIf(not core.is_compiled_with_rocm(), "HIP/ROCm is not available")
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class TestHIPBF16Conv2dKernel(unittest.TestCase):
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"""Test that conv2d kernel is registered for BF16 on HIP."""
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def test_conv2d_bf16_forward(self):
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"""Test conv2d BF16 forward pass on HIP."""
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paddle.set_device("gpu")
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# Create BF16 input tensor
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input_np = np.random.randn(1, 3, 64, 64).astype(np.float32)
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filter_np = np.random.randn(8, 3, 3, 3).astype(np.float32)
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input_tensor = paddle.to_tensor(input_np).astype("bfloat16")
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filter_tensor = paddle.to_tensor(filter_np).astype("bfloat16")
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# This should not raise "kernel not registered" error
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output = paddle.nn.functional.conv2d(input_tensor, filter_tensor)
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self.assertEqual(output.dtype, paddle.bfloat16)
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self.assertEqual(output.shape, [1, 8, 62, 62])
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# Verify output is not NaN
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self.assertFalse(paddle.isnan(output).any())
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def test_conv2d_bf16_with_groups(self):
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"""Test conv2d BF16 with groups (depthwise-like) on HIP."""
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paddle.set_device("gpu")
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input_np = np.random.randn(1, 8, 16, 16).astype(np.float32)
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filter_np = np.random.randn(8, 1, 3, 3).astype(np.float32)
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input_tensor = paddle.to_tensor(input_np).astype("bfloat16")
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filter_tensor = paddle.to_tensor(filter_np).astype("bfloat16")
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output = paddle.nn.functional.conv2d(
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input_tensor, filter_tensor, groups=8
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)
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self.assertEqual(output.dtype, paddle.bfloat16)
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self.assertEqual(output.shape, [1, 8, 14, 14])
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if __name__ == "__main__":
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unittest.main()
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