wehub-resource-sync
272 lines
9.5 KiB
Python
272 lines
9.5 KiB
Python
# Copyright (c) 2020 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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import numpy as np
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from op_test import get_device_place, is_custom_device
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core
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paddle.enable_static()
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"Paddle core is not compiled with CUDA",
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)
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class TestFusedBnAddActAPI(unittest.TestCase):
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def setUp(self):
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self.conv_param_attr1 = base.ParamAttr(
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name='conv2d_1.weight',
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initializer=paddle.nn.initializer.XavierNormal(),
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learning_rate=0.001,
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)
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self.conv_param_attr2 = base.ParamAttr(
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name='conv2d_2.weight',
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initializer=paddle.nn.initializer.XavierNormal(),
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learning_rate=0.001,
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)
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self.bn_param_attr1 = base.ParamAttr(
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name='batch_norm_w_1',
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initializer=paddle.nn.initializer.Constant(value=1.0),
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)
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self.bn_bias_attr1 = base.ParamAttr(
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name='batch_norm_b_1',
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initializer=paddle.nn.initializer.Constant(value=0.0),
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)
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self.bn_param_attr2 = base.ParamAttr(
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name='batch_norm_w_2',
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initializer=paddle.nn.initializer.Constant(value=1.0),
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)
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self.bn_bias_attr2 = base.ParamAttr(
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name='batch_norm_b_2',
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initializer=paddle.nn.initializer.Constant(value=0.0),
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)
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self.fc_param_attr = base.ParamAttr(
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name='fc.weight',
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initializer=paddle.nn.initializer.XavierNormal(),
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)
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def build_fused_program(
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self, main_program, startup_program, use_cuda, seed=1
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):
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with base.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name='x', shape=[-1, 1, 28, 28], dtype='float32'
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)
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y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
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conv1_1 = paddle.static.nn.conv2d(
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input=x,
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filter_size=3,
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num_filters=32,
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stride=1,
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padding=1,
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act=None,
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param_attr=self.conv_param_attr1,
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bias_attr=False,
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data_format='NHWC',
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)
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conv1_2 = paddle.static.nn.conv2d(
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input=x,
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filter_size=3,
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num_filters=32,
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stride=1,
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padding=1,
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act=None,
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param_attr=self.conv_param_attr2,
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bias_attr=False,
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data_format='NHWC',
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)
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bn = paddle.static.nn.batch_norm(
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input=conv1_1,
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param_attr=self.bn_param_attr1,
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bias_attr=self.bn_bias_attr1,
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act=None,
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data_layout='NHWC',
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)
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fused_bn_add_act = paddle.incubate.layers.nn.fused_bn_add_act(
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conv1_2,
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bn,
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param_attr=self.bn_param_attr2,
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bias_attr=self.bn_bias_attr2,
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)
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prediction = paddle.static.nn.fc(
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x=fused_bn_add_act,
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size=10,
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activation='softmax',
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weight_attr=self.fc_param_attr,
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)
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loss = paddle.nn.functional.cross_entropy(
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input=prediction, label=y, reduction='none', use_softmax=False
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)
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loss = paddle.mean(loss)
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sgd = paddle.optimizer.SGD(learning_rate=0.001)
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sgd = paddle.static.amp.decorate(
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sgd, use_dynamic_loss_scaling=True, init_loss_scaling=128.0
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)
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sgd.minimize(loss)
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return x, y, loss
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def build_origin_program(
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self, main_program, startup_program, use_cuda, seed=1
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):
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with base.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name='x', shape=[-1, 1, 28, 28], dtype='float32'
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)
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y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
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conv1_1 = paddle.static.nn.conv2d(
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input=x,
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filter_size=3,
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num_filters=32,
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stride=1,
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padding=1,
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act=None,
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param_attr=self.conv_param_attr1,
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bias_attr=False,
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data_format='NHWC',
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)
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bn1 = paddle.static.nn.batch_norm(
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input=conv1_1,
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param_attr=self.bn_param_attr1,
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bias_attr=self.bn_bias_attr1,
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act=None,
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data_layout='NHWC',
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)
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conv1_2 = paddle.static.nn.conv2d(
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input=conv1_1,
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filter_size=1,
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num_filters=32,
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stride=1,
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act=None,
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param_attr=self.conv_param_attr2,
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bias_attr=False,
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data_format='NHWC',
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)
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bn2 = paddle.static.nn.batch_norm(
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input=conv1_1,
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param_attr=self.bn_param_attr2,
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bias_attr=self.bn_bias_attr2,
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act=None,
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data_layout='NHWC',
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)
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out = bn1 + bn2
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out = F.relu(out)
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prediction = paddle.static.nn.fc(
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x=out,
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size=10,
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activation='softmax',
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weight_attr=self.fc_param_attr,
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)
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loss = paddle.nn.functional.cross_entropy(
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input=prediction, label=y, reduction='none', use_softmax=False
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)
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loss = paddle.mean(loss)
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sgd = paddle.optimizer.SGD(learning_rate=0.001)
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sgd = paddle.static.amp.decorate(
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sgd, use_dynamic_loss_scaling=True, init_loss_scaling=128.0
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)
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sgd.minimize(loss)
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return loss
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def check(self, place, use_cuda):
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paddle.seed(1)
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paddle.framework.random._manual_program_seed(1)
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iters = 5
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batch_size = 16
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# build_fused_program: turn on fuse_bn_add_act_ops
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main_program = base.Program()
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startup_program = base.Program()
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loss = self.build_origin_program(
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main_program, startup_program, use_cuda
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)
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build_strategy_fused = base.BuildStrategy()
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build_strategy_fused.fuse_bn_add_act_ops = True
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binary_fused = base.CompiledProgram(
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main_program, build_strategy=build_strategy_fused
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)
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exe = base.Executor(place)
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loss_vals_fused = []
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x_data = []
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y_data = []
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scope = base.Scope()
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with base.scope_guard(scope):
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exe.run(startup_program)
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for _ in range(iters):
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x = np.random.random((batch_size, 1, 28, 28)).astype("float32")
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y = np.random.random((batch_size, 1)).astype("int64")
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x_data.append(x)
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y_data.append(y)
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loss_v = exe.run(
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binary_fused, feed={"x": x, "y": y}, fetch_list=[loss]
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)
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loss_vals_fused.append(loss_v[0])
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# build_origin_program: turn off fused_bn_act_ops
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build_strategy = base.BuildStrategy()
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build_strategy.fuse_bn_add_act_ops = False
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binary = base.CompiledProgram(
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main_program, build_strategy=build_strategy_fused
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)
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loss_vals = []
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scope = base.Scope()
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with base.scope_guard(scope):
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exe.run(startup_program)
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for i in range(iters):
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loss_v = exe.run(
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binary,
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feed={"x": x_data[i], "y": y_data[i]},
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fetch_list=[loss],
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)
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loss_vals.append(loss_v[0])
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# check loss
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for i in range(iters):
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self.assertAlmostEqual(loss_vals[i], loss_vals_fused[i], delta=1e-5)
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def test_fuse_bn_add_act(self):
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with paddle.pir_utils.OldIrGuard():
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place = get_device_place()
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self.check(place, use_cuda=True)
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def test_fuse_bn_add_act_API(self):
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with paddle.pir_utils.OldIrGuard():
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# build_fused_program: use fused_bn_add_act python API
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main_program = base.Program()
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startup_program = base.Program()
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place = get_device_place()
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x, y, loss = self.build_fused_program(
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main_program, startup_program, use_cuda=True
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)
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exe = base.Executor(place)
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scope = base.Scope()
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with base.scope_guard(scope):
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exe.run(startup_program)
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for _ in range(5):
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x = np.random.random((4, 1, 28, 28)).astype("float32")
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y = np.random.random((4, 1)).astype("int64")
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loss_v = exe.run(
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main_program, feed={"x": x, "y": y}, fetch_list=[loss]
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)
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if __name__ == '__main__':
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unittest.main()
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