wehub-resource-sync
151 lines
5.0 KiB
Python
151 lines
5.0 KiB
Python
# Copyright (c) 2021 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 copy as cp
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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 IgnoreReasons, PassAutoScanTest
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from program_config import OpConfig, ProgramConfig, TensorConfig
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class DepthwiseConvONEDNNPass(PassAutoScanTest):
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r'''
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conv_input conv_weight_var(persistable)
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\ /
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conv_op
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conv_out_var
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'''
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def test(self):
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self.run_and_statistics(
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quant=False, passes=["depthwise_conv_onednn_pass"]
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)
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def sample_program_config(self, draw):
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# generate random number
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random_batch_size = draw(st.integers(min_value=1, max_value=4))
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random_channel = draw(st.integers(min_value=2, max_value=10))
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random_input_dim1 = draw(st.integers(min_value=20, max_value=50))
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random_input_dim2 = draw(st.integers(min_value=20, max_value=50))
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random_out_channel = draw(st.integers(min_value=20, max_value=25))
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random_groups = draw(st.integers(min_value=1, max_value=3))
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random_dilations = draw(
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st.lists(
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st.integers(min_value=1, max_value=3), min_size=2, max_size=2
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)
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)
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random_strides = draw(
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st.lists(
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st.integers(min_value=1, max_value=4), min_size=2, max_size=2
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)
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)
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random_paddings = draw(
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st.lists(
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st.integers(min_value=0, max_value=4), min_size=2, max_size=2
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)
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)
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random_padding_algorithm = draw(
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st.sampled_from(["EXPLICIT", "SAME", "VALID"])
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)
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random_data_layout = draw(st.sampled_from(["NCHW", "NHWC"]))
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random_filter = draw(
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st.lists(
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st.integers(min_value=1, max_value=4), min_size=2, max_size=2
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)
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)
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def generate_conv2d_Input():
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shape = [random_input_dim1, random_input_dim2]
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if random_data_layout == "NCHW":
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shape.insert(0, random_channel * random_groups)
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shape.insert(0, random_batch_size)
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else:
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shape.append(random_channel)
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shape.insert(0, random_batch_size)
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return np.random.random(shape).astype(np.float32)
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def generate_conv2d_Filter():
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shape = cp.copy(random_filter)
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shape.insert(0, random_channel)
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shape.insert(0, random_out_channel * random_groups)
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return np.random.random(shape).astype(np.float32)
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# define op
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conv2d_op = OpConfig(
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type="depthwise_conv2d",
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inputs={
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"Input": ["conv2d_Input"],
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"Filter": ["conv2d_Filter"],
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},
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outputs={
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"Output": ["conv2d_Out"],
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},
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attrs={
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'groups': random_groups,
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'dilations': random_dilations,
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'strides': random_strides,
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'paddings': random_paddings,
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'padding_algorithm': random_padding_algorithm,
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'data_format': random_data_layout,
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'use_onednn': True,
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},
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)
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# define model_net
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model_net = [conv2d_op]
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# set tensor
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program_config = ProgramConfig(
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ops=model_net,
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inputs={
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"conv2d_Input": TensorConfig(data_gen=generate_conv2d_Input),
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},
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weights={
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"conv2d_Filter": TensorConfig(data_gen=generate_conv2d_Filter),
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},
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outputs=["conv2d_Out"],
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)
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return program_config
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def sample_predictor_configs(self, program_config):
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# for onednn
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config = self.create_inference_config(use_onednn=True)
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yield config, ['conv2d'], (1e-5, 1e-5)
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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attrs = [
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program_config.ops[i].attrs for i in range(len(program_config.ops))
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]
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if attrs[0]['data_format'] == "NHWC":
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return False
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return True
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def add_ignore_pass_case(self):
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def teller1(program_config, predictor_config):
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if program_config.ops[0].attrs['data_format'] == "NHWC":
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return True
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return False
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self.add_ignore_check_case(
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teller1,
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IgnoreReasons.PASS_ACCURACY_ERROR,
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"The output format of depthwise_conv2d is wrong when data_format attribute is NHWC",
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)
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