support SelectedRows in dygraph, test=develop (#21078)
* support SelectedRows in dygraph, test=develop * fix bug of _grad_ivar interface, test=develop * add optest for support seletedrows, test=develop * fix bug for gradient_accumulator in GPU mode, test=develop * fix error when Selectedrows addto LodTensor in sorted_gradient mdoe in dygraph, test=develop * refine and simplify gradient accumulator code, test=develop * add optest, test=develop * add optest and simplify code, test=develop * fix bug for test_imperative_selected_rows, test=develop * add optest for Coverage, test=develop * fix gradient interface and simplify code, test=develop * update api for gradient, test=develop * fix ShareDim's bug in DygraphExecutionContext class, test=develop * add optest, test=developpaddle_tiny_install
zhongpu
5 years ago
committed by
hong
parent
70eb397677
commit
6ebf0f47b8
@ -0,0 +1,201 @@
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# Copyright (c) 2019 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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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.dygraph.nn import Embedding
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import paddle.fluid.framework as framework
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from paddle.fluid.optimizer import SGDOptimizer
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from paddle.fluid.dygraph.base import to_variable
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from test_imperative_base import new_program_scope
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import numpy as np
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import six
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from utils import DyGraphProgramDescTracerTestHelper
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class SimpleNet(fluid.Layer):
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def __init__(self,
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name_scope,
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hidden_size,
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vocab_size,
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num_steps=20,
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init_scale=0.1,
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is_sparse=False,
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dtype='float32'):
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super(SimpleNet, self).__init__(name_scope)
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.init_scale = init_scale
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self.num_steps = num_steps
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self.embedding = Embedding(
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self.full_name(),
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size=[vocab_size, hidden_size],
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dtype=dtype,
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is_sparse=is_sparse,
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param_attr=fluid.ParamAttr(
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name='embedding_para',
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initializer=fluid.initializer.UniformInitializer(
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low=-init_scale, high=init_scale)))
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self.softmax_bias = self.create_parameter(
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attr=fluid.ParamAttr(),
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shape=[self.vocab_size],
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dtype=dtype,
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self.init_scale, high=self.init_scale))
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def forward(self, input, label):
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x_emb = self.embedding(input)
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projection = fluid.layers.matmul(
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x_emb, fluid.layers.transpose(
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self.embedding._w, perm=[1, 0]))
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projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
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projection = fluid.layers.reshape(
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projection, shape=[-1, self.vocab_size])
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loss = fluid.layers.softmax_with_cross_entropy(
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logits=projection, label=label, soft_label=False)
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loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
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loss = fluid.layers.reduce_mean(loss, dim=[0])
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loss = fluid.layers.reduce_sum(loss)
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loss.permissions = True
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return loss
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class TestDygraphSimpleNet(unittest.TestCase):
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def test_simple_net(self):
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for is_sparse in [True, False]:
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for dtype in ["float32", "float64"]:
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self.simple_net_float32(is_sparse, dtype)
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def simple_net_float32(self, is_sparse, dtype):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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seed = 90
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hidden_size = 10
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vocab_size = 1000
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num_steps = 3
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init_scale = 0.1
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batch_size = 4
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batch_num = 200
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for is_sort_sum_gradient in [True, False]:
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with fluid.dygraph.guard(place):
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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simple_net = SimpleNet(
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"simple_net",
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_steps=num_steps,
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init_scale=init_scale,
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is_sparse=is_sparse,
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dtype=dtype)
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sgd = SGDOptimizer(learning_rate=1e-3)
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dy_param_updated = dict()
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dy_param_init = dict()
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dy_loss = None
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helper = DyGraphProgramDescTracerTestHelper(self)
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backward_strategy = fluid.dygraph.BackwardStrategy()
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backward_strategy.sort_sum_gradient = is_sort_sum_gradient
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for i in range(batch_num):
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x_data = np.arange(12).reshape(4, 3).astype('int64')
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y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
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x_data = x_data.reshape((-1, num_steps, 1))
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y_data = y_data.reshape((-1, 1))
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x = to_variable(x_data)
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y = to_variable(y_data)
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outs = simple_net(x, y)
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dy_loss = outs
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if i == 0:
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for param in simple_net.parameters():
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dy_param_init[param.name] = param.numpy()
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dy_loss.backward(backward_strategy)
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sgd.minimize(dy_loss)
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simple_net.clear_gradients()
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if i == batch_num - 1:
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for param in simple_net.parameters():
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dy_param_updated[param.name] = param.numpy()
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dy_loss_value = dy_loss.numpy()
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with new_program_scope():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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simple_net = SimpleNet(
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"simple_net",
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_steps=num_steps,
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is_sparse=is_sparse,
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dtype=dtype)
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exe = fluid.Executor(place)
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sgd = SGDOptimizer(learning_rate=1e-3)
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x = fluid.layers.data(
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name="x", shape=[-1, num_steps, 1], dtype='int64')
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y = fluid.layers.data(name="y", shape=[-1, 1], dtype=dtype)
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static_loss = simple_net(x, y)
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sgd.minimize(static_loss)
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static_param_updated = dict()
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static_param_init = dict()
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static_param_name_list = list()
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for param in simple_net.parameters():
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static_param_name_list.append(param.name)
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out = exe.run(fluid.default_startup_program(),
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fetch_list=static_param_name_list)
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for i in range(len(static_param_name_list)):
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static_param_init[static_param_name_list[i]] = out[i]
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static_loss_value = None
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for i in range(batch_num):
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x_data = np.arange(12).reshape(4, 3).astype('int64')
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y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
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x_data = x_data.reshape((-1, num_steps, 1))
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y_data = y_data.reshape((-1, 1))
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fetch_list = [static_loss]
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fetch_list.extend(static_param_name_list)
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out = exe.run(fluid.default_main_program(),
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feed={"x": x_data,
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"y": y_data},
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fetch_list=fetch_list)
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static_loss_value = out[0]
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if i == batch_num - 1:
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for k in range(3, len(out)):
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static_param_updated[static_param_name_list[
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k - 1]] = out[k]
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self.assertTrue(
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np.array_equal(static_loss_value, dy_loss_value))
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for key, value in six.iteritems(static_param_init):
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self.assertTrue(np.array_equal(value, dy_param_init[key]))
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for key, value in six.iteritems(static_param_updated):
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self.assertTrue(
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np.array_equal(value, dy_param_updated[key]))
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if __name__ == '__main__':
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unittest.main()
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# Copyright (c) 2019 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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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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from paddle.fluid.dygraph.base import to_variable
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from paddle.fluid.dygraph.nn import Embedding
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from paddle.fluid.optimizer import SGDOptimizer
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import numpy as np
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import paddle.fluid.core as core
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class SimpleNet(fluid.Layer):
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def __init__(self, name_scope, vocab_size, hidden_size, dtype):
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super(SimpleNet, self).__init__(name_scope)
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self.emb = fluid.dygraph.Embedding(
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self.full_name(),
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size=[vocab_size, hidden_size],
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dtype=dtype,
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param_attr='emb.w',
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is_sparse=True)
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def forward(self, input):
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input_emb = self.emb(input)
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return input_emb, self.emb
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class TestSimpleNet(unittest.TestCase):
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def test_selectedrows_gradient1(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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for dtype in ["float32", "float64"]:
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for sort_sum_gradient in [True, False]:
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with fluid.dygraph.guard(place):
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backward_strategy = fluid.dygraph.BackwardStrategy()
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backward_strategy.sort_sum_gradient = sort_sum_gradient
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adam = SGDOptimizer(learning_rate=0.001)
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# grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(5.0)
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input_word = np.array(
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[[[1], [2]], [[2], [1]]]).astype('int64')
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input = to_variable(input_word)
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simplenet = SimpleNet("SimpleNet", 20, 32, dtype)
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input_emb, emb = simplenet(input)
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try:
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emb._w.gradient()
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except ValueError as e:
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pass
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try:
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input_emb.gradient()
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except ValueError as e:
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pass
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input_emb.backward(backward_strategy)
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adam.minimize(input_emb) # grad_clip=grad_clip
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emb._w.gradient()
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emb.clear_gradients()
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try:
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emb._w.gradient()
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except ValueError as e:
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pass
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input_emb.clear_gradient()
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try:
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input_emb.gradient()
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except ValueError as e:
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pass
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def test_selectedrows_gradient2(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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for sort_sum_gradient in [True, False]:
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with fluid.dygraph.guard(place):
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backward_strategy = fluid.dygraph.BackwardStrategy()
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backward_strategy.sort_sum_gradient = sort_sum_gradient
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adam = SGDOptimizer(learning_rate=0.001)
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grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
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5.0)
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input_word = np.array(
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[[[1], [2]], [[2], [1]]]).astype('int64')
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input = to_variable(input_word)
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simplenet = SimpleNet("SimpleNet", 20, 32, "float32")
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input_emb, emb = simplenet(input)
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try:
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emb._w.gradient()
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except ValueError as e:
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pass
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try:
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input_emb.gradient()
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except ValueError as e:
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pass
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input_emb.backward(backward_strategy)
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adam.minimize(input_emb, grad_clip=grad_clip)
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emb._w.gradient()
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emb.clear_gradients()
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try:
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emb._w.gradient()
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except ValueError as e:
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pass
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input_emb.clear_gradient()
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try:
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input_emb.gradient()
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except ValueError as e:
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pass
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if __name__ == '__main__':
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unittest.main()
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# Copyright (c) 2019 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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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.dygraph.nn import Embedding
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import paddle.fluid.framework as framework
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from paddle.fluid.optimizer import SGDOptimizer
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from paddle.fluid.dygraph.base import to_variable
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from test_imperative_base import new_program_scope
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import numpy as np
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import six
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from utils import DyGraphProgramDescTracerTestHelper, is_equal_program
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from paddle.fluid.dygraph.jit import TracedLayer
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class SimpleNet(fluid.Layer):
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def __init__(self,
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name_scope,
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hidden_size,
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vocab_size,
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num_steps=20,
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init_scale=0.1,
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is_sparse=False,
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dtype='float32'):
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super(SimpleNet, self).__init__(name_scope)
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.init_scale = init_scale
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self.num_steps = num_steps
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self.embedding = Embedding(
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self.full_name(),
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size=[vocab_size, hidden_size],
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dtype=dtype,
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is_sparse=is_sparse,
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param_attr=fluid.ParamAttr(
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name='embedding_para',
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initializer=fluid.initializer.UniformInitializer(
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low=-init_scale, high=init_scale)))
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self.softmax_weight = self.create_parameter(
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attr=fluid.ParamAttr(),
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shape=[self.hidden_size, self.hidden_size],
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dtype=dtype,
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self.init_scale, high=self.init_scale))
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self.softmax_bias = self.create_parameter(
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attr=fluid.ParamAttr(),
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shape=[self.hidden_size],
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dtype=dtype,
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self.init_scale, high=self.init_scale))
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def forward(self, input, label):
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x_emb = self.embedding(input)
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fc = fluid.layers.matmul(x_emb, self.softmax_weight)
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fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
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projection = fluid.layers.matmul(
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fc, fluid.layers.transpose(
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self.embedding._w, perm=[1, 0]))
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projection = fluid.layers.reshape(
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projection, shape=[-1, self.vocab_size])
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loss = fluid.layers.softmax_with_cross_entropy(
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logits=projection, label=label, soft_label=False)
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loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
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loss = fluid.layers.reduce_mean(loss, dim=[0])
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loss = fluid.layers.reduce_sum(loss)
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loss.permissions = True
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return loss
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class TestDygraphSimpleNet(unittest.TestCase):
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def test_simple_net(self):
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for is_sparse in [True, False]:
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for dtype in ["float32", "float64"]:
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self.simple_net_float(is_sparse, dtype)
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def simple_net_float(self, is_sparse, dtype):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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seed = 90
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hidden_size = 10
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vocab_size = 1000
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num_steps = 3
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init_scale = 0.1
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batch_size = 4
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batch_num = 200
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for is_sort_sum_gradient in [True, False]:
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traced_layer = None
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with fluid.dygraph.guard(place):
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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simple_net = SimpleNet(
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"simple_net",
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hidden_size=hidden_size,
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||||
vocab_size=vocab_size,
|
||||
num_steps=num_steps,
|
||||
init_scale=init_scale,
|
||||
is_sparse=is_sparse,
|
||||
dtype=dtype)
|
||||
|
||||
sgd = SGDOptimizer(learning_rate=1e-3)
|
||||
dy_param_updated = dict()
|
||||
dy_param_init = dict()
|
||||
dy_loss = None
|
||||
|
||||
helper = DyGraphProgramDescTracerTestHelper(self)
|
||||
program = None
|
||||
backward_strategy = fluid.dygraph.BackwardStrategy()
|
||||
backward_strategy.sort_sum_gradient = is_sort_sum_gradient
|
||||
|
||||
for i in range(batch_num):
|
||||
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
||||
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
||||
x_data = x_data.reshape((-1, num_steps, 1))
|
||||
y_data = y_data.reshape((-1, 1))
|
||||
|
||||
x = to_variable(x_data)
|
||||
y = to_variable(y_data)
|
||||
outs = simple_net(x, y)
|
||||
dy_loss = outs
|
||||
if i == 0:
|
||||
for param in simple_net.parameters():
|
||||
dy_param_init[param.name] = param.numpy()
|
||||
dy_loss.backward(backward_strategy)
|
||||
sgd.minimize(dy_loss)
|
||||
simple_net.clear_gradients()
|
||||
if i == batch_num - 1:
|
||||
for param in simple_net.parameters():
|
||||
dy_param_updated[param.name] = param.numpy()
|
||||
dy_loss_value = dy_loss.numpy()
|
||||
|
||||
with new_program_scope():
|
||||
fluid.default_startup_program().random_seed = seed
|
||||
fluid.default_main_program().random_seed = seed
|
||||
|
||||
simple_net = SimpleNet(
|
||||
"simple_net",
|
||||
hidden_size=hidden_size,
|
||||
vocab_size=vocab_size,
|
||||
num_steps=num_steps,
|
||||
is_sparse=is_sparse,
|
||||
dtype=dtype)
|
||||
|
||||
exe = fluid.Executor(place)
|
||||
sgd = SGDOptimizer(learning_rate=1e-3)
|
||||
x = fluid.layers.data(
|
||||
name="x", shape=[-1, num_steps, 1], dtype='int64')
|
||||
y = fluid.layers.data(name="y", shape=[-1, 1], dtype=dtype)
|
||||
|
||||
static_loss = simple_net(x, y)
|
||||
sgd.minimize(static_loss)
|
||||
static_param_updated = dict()
|
||||
static_param_init = dict()
|
||||
static_param_name_list = list()
|
||||
for param in simple_net.parameters():
|
||||
static_param_name_list.append(param.name)
|
||||
|
||||
out = exe.run(framework.default_startup_program(),
|
||||
fetch_list=static_param_name_list)
|
||||
for i in range(len(static_param_name_list)):
|
||||
static_param_init[static_param_name_list[i]] = out[i]
|
||||
static_loss_value = None
|
||||
for i in range(batch_num):
|
||||
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
||||
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
||||
x_data = x_data.reshape((-1, num_steps, 1))
|
||||
y_data = y_data.reshape((-1, 1))
|
||||
fetch_list = [static_loss]
|
||||
fetch_list.extend(static_param_name_list)
|
||||
out = exe.run(fluid.default_main_program(),
|
||||
feed={"x": x_data,
|
||||
"y": y_data},
|
||||
fetch_list=fetch_list)
|
||||
static_loss_value = out[0]
|
||||
|
||||
if i == batch_num - 1:
|
||||
for k in range(3, len(out)):
|
||||
static_param_updated[static_param_name_list[
|
||||
k - 1]] = out[k]
|
||||
|
||||
self.assertTrue(
|
||||
np.array_equal(static_loss_value, dy_loss_value))
|
||||
for key, value in six.iteritems(static_param_init):
|
||||
self.assertTrue(np.array_equal(value, dy_param_init[key]))
|
||||
for key, value in six.iteritems(static_param_updated):
|
||||
self.assertTrue(
|
||||
np.array_equal(value, dy_param_updated[key]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue