wehub-resource-sync
279 lines
9.3 KiB
Python
279 lines
9.3 KiB
Python
# Copyright (c) 2018 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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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.autograd.backward_utils import ValueDict
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from paddle.base import core
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from paddle.nn import Linear
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SEED = 123123111
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class SimpleImgConvPool(paddle.nn.Layer):
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def __init__(
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self,
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num_channels,
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num_filters,
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filter_size,
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pool_size,
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pool_stride,
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pool_padding=0,
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pool_type='max',
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global_pooling=False,
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conv_stride=1,
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conv_padding=0,
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conv_dilation=1,
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conv_groups=1,
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act=None,
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use_cudnn=False,
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param_attr=None,
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bias_attr=None,
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):
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super().__init__()
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self._conv2d = paddle.nn.Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=conv_stride,
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padding=conv_padding,
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dilation=conv_dilation,
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groups=conv_groups,
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weight_attr=None,
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bias_attr=None,
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)
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self._pool2d = paddle.nn.MaxPool2D(
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kernel_size=pool_size,
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stride=pool_stride,
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padding=pool_padding,
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)
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def forward(self, inputs):
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x = self._conv2d(inputs)
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x = self._pool2d(x)
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return x
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class MNIST(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._simple_img_conv_pool_1 = SimpleImgConvPool(
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1, 20, 5, 2, 2, act="relu"
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)
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self._simple_img_conv_pool_2 = SimpleImgConvPool(
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20, 50, 5, 2, 2, act="relu"
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)
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self.pool_2_shape = 50 * 4 * 4
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SIZE = 100 # 10
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scale = (2.0 / (self.pool_2_shape**2 * SIZE)) ** 0.5
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self._fc = Linear(
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self.pool_2_shape,
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SIZE,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Normal(mean=0.0, std=scale)
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),
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)
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def forward(self, inputs):
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x = self._simple_img_conv_pool_1(inputs)
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x = self._simple_img_conv_pool_2(x)
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x = paddle.reshape(x, shape=[-1, self.pool_2_shape])
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x = self._fc(x)
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x = paddle.nn.functional.softmax(x)
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return x
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def create_parameter_mapping(startup_program, main_program):
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startup_params = {}
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main_params = {}
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parameter_mapping = ValueDict()
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for op in startup_program.global_block().ops:
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if op.name() == "builtin.set_parameter":
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name = op.attrs()["parameter_name"]
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param = op.operand(0).source()
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startup_params[name] = param
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for op in main_program.global_block().ops:
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if op.name() == "builtin.parameter":
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name = op.attrs()["parameter_name"]
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param = op.result(0)
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main_params[name] = param
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assert len(startup_params) == len(main_params)
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for name, startup_param in startup_params.items():
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assert name in main_params
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main_param = main_params[name]
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parameter_mapping[main_param] = startup_param
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return parameter_mapping
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class TestDygraphMultiForward(unittest.TestCase):
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def test_mnist_forward_float32(self):
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epoch_num = 1
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with base.dygraph.guard():
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paddle.seed(SEED)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(SEED)
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paddle.framework.random._manual_program_seed(SEED)
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else:
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paddle.framework.random._manual_program_seed(SEED)
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mnist = MNIST()
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sgd = paddle.optimizer.SGD(
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learning_rate=1e-3, parameters=mnist.parameters()
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)
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=128, drop_last=True
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)
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dy_param_init_value = {}
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mnist.eval()
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for epoch in range(epoch_num):
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for batch_id, data in enumerate(train_reader()):
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dy_x_data = np.array(
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[x[0].reshape(1, 28, 28) for x in data]
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).astype('float32')
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y_data = (
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np.array([x[1] for x in data])
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.astype('int64')
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.reshape(128, 1)
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)
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img = paddle.to_tensor(dy_x_data)
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label = paddle.to_tensor(y_data)
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label.stop_gradient = True
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cost = mnist(img)
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loss = paddle.nn.functional.cross_entropy(
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cost, label, reduction='none', use_softmax=False
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)
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avg_loss = paddle.mean(loss)
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dy_out = avg_loss.numpy()
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if epoch == 0 and batch_id == 0:
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for param in mnist.parameters():
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dy_param_init_value[param.name] = param.numpy()
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with new_program_scope():
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paddle.seed(SEED)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(SEED)
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paddle.framework.random._manual_program_seed(SEED)
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else:
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paddle.framework.random._manual_program_seed(SEED)
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if core.is_compiled_with_cuda() or is_custom_device():
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exe = base.Executor(get_device_place())
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elif core.is_compiled_with_xpu():
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exe = base.Executor(base.XPUPlace(0))
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else:
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exe = base.Executor(base.CPUPlace())
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mnist = MNIST()
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sgd = paddle.optimizer.SGD(learning_rate=1e-3)
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=128, drop_last=True
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)
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img = paddle.static.data(
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name='pixel', shape=[-1, 1, 28, 28], dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=[-1, 1], dtype='int64'
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)
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cost = mnist(img)
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loss = paddle.nn.functional.cross_entropy(
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cost, label, reduction='none', use_softmax=False
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)
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avg_loss = paddle.mean(loss)
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# initialize params and fetch them
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static_param_init_value = {}
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static_param_name_list = []
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static_params = []
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for param in mnist.parameters():
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static_param_name_list.append(param.name)
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static_params.append(param)
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if paddle.framework.use_pir_api():
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parameter_mapping = create_parameter_mapping(
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paddle.static.default_startup_program(),
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paddle.static.default_main_program(),
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)
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startup_params = [
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parameter_mapping[param] for param in static_params
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]
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else:
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startup_params = static_params
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out = exe.run(
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paddle.static.default_startup_program(),
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fetch_list=startup_params,
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)
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for i in range(len(static_params)):
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param_name = static_param_name_list[i]
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static_param_init_value[param_name] = out[i]
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for epoch in range(epoch_num):
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for batch_id, data in enumerate(train_reader()):
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static_x_data = np.array(
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[x[0].reshape(1, 28, 28) for x in data]
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).astype('float32')
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y_data = (
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np.array([x[1] for x in data])
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.astype('int64')
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.reshape([128, 1])
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)
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fetch_list = [avg_loss]
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out = exe.run(
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base.default_main_program(),
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feed={"pixel": static_x_data, "label": y_data},
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fetch_list=fetch_list,
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)
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static_out = out[0]
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np.testing.assert_allclose(
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dy_x_data.all(), static_x_data.all(), rtol=1e-05
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)
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for key, value in static_param_init_value.items():
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np.testing.assert_allclose(
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value, dy_param_init_value[key], rtol=1e-05
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)
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np.testing.assert_allclose(static_out, dy_out, rtol=1e-05)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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