wehub-resource-sync
150 lines
4.8 KiB
Python
150 lines
4.8 KiB
Python
# Copyright (c) 2023 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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import unittest
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from functools import partial
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import hypothesis.strategies as st
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import numpy as np
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from auto_scan_test import PassAutoScanTest
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from program_config import OpConfig, ProgramConfig, TensorConfig
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from paddle.base import core
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@unittest.skipIf(
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core.get_xpu_device_version(0) == core.XPUVersion.XPU3,
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"Unsupported on XPU3",
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)
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class TestConvTransposeXPUFusePass(PassAutoScanTest):
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def sample_predictor_configs(self, program_config):
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config = self.create_inference_config(use_xpu=True)
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yield config, ["conv2d_transpose_xpu"], (3e-3, 3e-3)
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def sample_program_config(self, draw):
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x_shape = draw(
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st.lists(
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st.integers(min_value=4, max_value=16), min_size=4, max_size=4
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)
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)
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oc = draw(st.integers(min_value=2, max_value=16))
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weight_shape = [x_shape[1], oc, 4, 4]
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y_shape = [oc]
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has_bn = draw(st.booleans())
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has_add = draw(st.booleans())
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has_relu = draw(st.booleans())
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def generate_data(shape):
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return 0.1 * np.random.random(shape).astype(np.float32)
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deconv_op = OpConfig(
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"conv2d_transpose",
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inputs={"Input": ["input_x"], "Filter": ["weight_x"]},
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outputs={"Output": ["output_x"]},
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data_format="NCHW",
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dilations=[1, 1],
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groups=1,
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paddings=[0, 0],
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padding_algorithm="EXPLICIT",
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strides=[4, 4],
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fuse_relu=False,
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)
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input_name_op = "output_x"
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ops = [deconv_op]
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if has_add:
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add_op = OpConfig(
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"elementwise_add",
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inputs={"X": [input_name_op], "Y": ["bias"]},
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outputs={"Out": ["add_out"]},
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axis=1,
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)
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input_name_op = "add_out"
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ops.append(add_op)
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if has_bn:
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bn_op = OpConfig(
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"batch_norm",
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inputs={
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"X": [input_name_op],
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"Bias": ["bn_bias"],
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"Mean": ["bn_mean"],
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"Scale": ["bn_scale"],
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"Variance": ["bn_var"],
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},
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outputs={
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"Y": ["bn_y"],
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"MeanOut": ["bn_mean"],
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"SavedMean": ["bn_mean_save"],
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"SavedVariance": ["bn_save_var"],
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"VarianceOut": ["bn_var"],
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},
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data_layout="NCHW",
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epsilon=0.000009999999747378752,
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momentum=0.89999,
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is_test=True,
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use_global_stats=True,
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)
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input_name_op = "bn_y"
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ops.append(bn_op)
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if has_relu:
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relu_op = OpConfig(
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"relu",
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inputs={"X": [input_name_op]},
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outputs={"Out": ["relu_out"]},
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)
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input_name_op = "relu_out"
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ops.append(relu_op)
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program_config = ProgramConfig(
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ops=ops,
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weights={
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"weight_x": TensorConfig(
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data_gen=partial(generate_data, weight_shape)
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),
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"bias": TensorConfig(data_gen=partial(generate_data, y_shape)),
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"bn_bias": TensorConfig(
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data_gen=partial(generate_data, y_shape)
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),
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"bn_mean": TensorConfig(
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data_gen=partial(generate_data, y_shape)
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),
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"bn_scale": TensorConfig(
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data_gen=partial(generate_data, y_shape)
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),
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"bn_var": TensorConfig(
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data_gen=partial(generate_data, y_shape)
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),
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},
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inputs={
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"input_x": TensorConfig(
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data_gen=partial(generate_data, x_shape)
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),
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},
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outputs=[input_name_op],
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)
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return program_config
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def test(self):
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self.run_and_statistics(
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quant=False,
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max_examples=100,
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passes=["conv2d_transpose_xpu_fuse_pass"],
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)
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if __name__ == "__main__":
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unittest.main()
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