# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import paddle.fluid as fluid import numpy as np from paddle.fluid.framework import _test_eager_guard class AutoPruneLayer0(fluid.Layer): def __init__(self, input_size): super(AutoPruneLayer0, self).__init__() self.linear1 = fluid.dygraph.Linear( input_size, 5, param_attr=fluid.initializer.ConstantInitializer(value=2), bias_attr=False) self.linear2 = fluid.dygraph.Linear( 5, 5, param_attr=fluid.initializer.ConstantInitializer(value=2), bias_attr=False) def forward(self, x, y): a = self.linear1(x) b = self.linear2(y) c = fluid.layers.mul(a, b) d = fluid.layers.reduce_mean(c) return d class AutoPruneLayer1(fluid.Layer): def __init__(self, input_size): super(AutoPruneLayer1, self).__init__() self.linear1 = fluid.dygraph.Linear( input_size, 5, param_attr=fluid.initializer.ConstantInitializer(value=2), bias_attr=False) self.linear2 = fluid.dygraph.Linear( 5, 5, param_attr=fluid.initializer.ConstantInitializer(value=2), bias_attr=False) def forward(self, x, y): a = self.linear1(x) b = self.linear2(y) b.stop_gradient = True c = fluid.layers.mul(a, b) d = fluid.layers.reduce_mean(c) return d class AutoPruneLayer2(fluid.Layer): def __init__(self, input_size): super(AutoPruneLayer2, self).__init__() self.linear = fluid.dygraph.Linear(input_size, 10, act=None) self.linear2 = fluid.dygraph.Linear(1, 1, act=None) def forward(self, x, label): feature = self.linear(x) label = self.linear2(label) label = fluid.layers.cast(label, dtype="float32") label = fluid.layers.cast(label, dtype='int64') # Note that the label is not persistable in fluid.layers.cross_entropy. loss = fluid.layers.cross_entropy(input=feature, label=label) loss = fluid.layers.mean(loss) return loss class AutoPruneLayer3(fluid.Layer): def __init__(self, input_size): super(AutoPruneLayer3, self).__init__() self.linear = fluid.dygraph.Linear(input_size, 20, act=None) def forward(self, x, label, test_num): feature = self.linear(x) part1, part2 = fluid.layers.split( feature, num_or_sections=[10, 10], dim=1) # Note that: part2 is not used. loss = fluid.layers.cross_entropy(input=part1, label=label) loss = fluid.layers.mean(loss) if test_num == 1: return loss, part2 else: return loss, part1, part2 class MyLayer(fluid.Layer): def __init__(self, input_size, vocab_size, size, dtype="float32"): super(MyLayer, self).__init__(dtype=dtype) self.embed0 = fluid.Embedding(size=(vocab_size, size)) self.embed1 = fluid.Embedding(size=(vocab_size, size)) self.linear_0 = fluid.Linear(input_size, size, dtype=dtype) self.linear_1 = fluid.Linear(input_size, size, dtype=dtype) def forward(self, x): # this method involves only the linear layers loss = fluid.layers.reduce_mean(self.linear_0(x) + self.linear_1(x)) return loss def linear0(self, x): loss = fluid.layers.reduce_mean(self.linear_0(x)) return loss def embed_linear0(self, x): loss = fluid.layers.reduce_mean(self.linear_0(self.embed0(x))) return loss class MyLayer2(fluid.Layer): def __init__(self, input_size, vocab_size, size, dtype="float32"): super(MyLayer2, self).__init__(dtype=dtype) self.embed0 = fluid.Embedding(size=(vocab_size, size)) self.embed1 = fluid.Embedding(size=(vocab_size, size)) self.linear_0 = fluid.Linear(input_size, size, dtype=dtype) self.linear_1 = fluid.Linear(input_size, size, dtype=dtype) def forward(self, indices): # mind the difference with MyLayer # In this example, the forward method involes all params loss = fluid.layers.reduce_mean( self.linear_0(self.embed0(indices)) + self.linear_1( self.embed1(indices))) return loss def linear0(self, x): loss = fluid.layers.reduce_mean(self.linear_0(x)) return loss def embed_linear0(self, x): loss = fluid.layers.reduce_mean(self.linear_0(self.embed0(x))) return loss class TestImperativeAutoPrune(unittest.TestCase): def func_auto_prune(self): with fluid.dygraph.guard(): case1 = AutoPruneLayer0(input_size=5) value1 = np.arange(25).reshape(5, 5).astype("float32") value2 = np.arange(25).reshape(5, 5).astype("float32") v1 = fluid.dygraph.to_variable(value1) v2 = fluid.dygraph.to_variable(value2) loss = case1(v1, v2) loss.backward() self.assertTrue(case1.linear2.weight._grad_ivar() is not None) self.assertTrue(case1.linear1.weight._grad_ivar() is not None) def test_auto_prune(self): with _test_eager_guard(): self.func_auto_prune() self.func_auto_prune() def func_auto_prune2(self): with fluid.dygraph.guard(): case2 = AutoPruneLayer1(input_size=5) value1 = np.arange(25).reshape(5, 5).astype("float32") value2 = np.arange(25).reshape(5, 5).astype("float32") v1 = fluid.dygraph.to_variable(value1) v2 = fluid.dygraph.to_variable(value2) loss = case2(v1, v2) loss.backward() self.assertTrue(case2.linear2.weight._grad_ivar() is None) self.assertTrue(case2.linear1.weight._grad_ivar() is not None) def test_auto_prune2(self): with _test_eager_guard(): self.func_auto_prune2() self.func_auto_prune2() # TODO(jiabin): Support this when we support better split tensor def func_auto_prune3(self): with fluid.dygraph.guard(): case3 = AutoPruneLayer3(input_size=784) value1 = np.arange(784).reshape(1, 784).astype("float32") value2 = np.arange(1).reshape(1, 1).astype("int64") v1 = fluid.dygraph.to_variable(value1) v2 = fluid.dygraph.to_variable(value2) loss, part2 = case3(v1, v2, 1) loss.backward() self.assertTrue(case3.linear.weight._grad_ivar() is not None) self.assertTrue((part2.gradient() == 0).all()) def test_auto_prune3(self): with _test_eager_guard(): self.func_auto_prune3() self.func_auto_prune3() def func_auto_prune4(self): with fluid.dygraph.guard(): case4 = AutoPruneLayer3(input_size=784) value1 = np.arange(784).reshape(1, 784).astype("float32") value2 = np.arange(1).reshape(1, 1).astype("int64") v1 = fluid.dygraph.to_variable(value1) v2 = fluid.dygraph.to_variable(value2) loss, part2 = case4(v1, v2, 1) part2.backward() self.assertTrue(case4.linear.weight._grad_ivar() is not None) self.assertTrue((part2.gradient() == 1).all()) def test_auto_prune4(self): with _test_eager_guard(): self.func_auto_prune4() self.func_auto_prune4() def func_auto_prune5(self): with fluid.dygraph.guard(): case4 = AutoPruneLayer3(input_size=784) value1 = np.arange(784).reshape(1, 784).astype("float32") value2 = np.arange(1).reshape(1, 1).astype("int64") v1 = fluid.dygraph.to_variable(value1) v2 = fluid.dygraph.to_variable(value2) loss, part1, part2 = case4(v1, v2, 2) part1.backward() self.assertTrue(case4.linear.weight._grad_ivar() is not None) self.assertTrue((part2.gradient() == 0).all()) def test_auto_prune5(self): with _test_eager_guard(): self.func_auto_prune5() self.func_auto_prune5() def func_auto_prune6(self): with fluid.dygraph.guard(): value0 = np.arange(26).reshape(2, 13).astype("float32") value1 = np.arange(6).reshape(2, 3).astype("float32") value2 = np.arange(10).reshape(2, 5).astype("float32") linear = fluid.Linear(13, 5, dtype="float32") linear2 = fluid.Linear(3, 3, dtype="float32") a = fluid.dygraph.to_variable(value0) b = fluid.dygraph.to_variable(value1) c = fluid.dygraph.to_variable(value2) out1 = linear(a) out2 = linear2(b) out1.stop_gradient = True out = fluid.layers.concat(input=[out1, out2, c], axis=1) out.backward() self.assertTrue(linear.weight.gradient() is None) self.assertTrue(out1.gradient() is None) def test_auto_prune6(self): with _test_eager_guard(): self.func_auto_prune6() self.func_auto_prune6() def func_auto_prune7(self): with fluid.dygraph.guard(): value0 = np.arange(26).reshape(2, 13).astype("float32") value1 = np.arange(6).reshape(2, 3).astype("float32") value2 = np.arange(10).reshape(2, 5).astype("float32") linear = fluid.Linear(13, 5, dtype="float32") linear2 = fluid.Linear(3, 3, dtype="float32") a = fluid.dygraph.to_variable(value0) b = fluid.dygraph.to_variable(value1) c = fluid.dygraph.to_variable(value2) out1 = linear(a) out2 = linear2(b) out1.stop_gradient = True out = fluid.layers.concat(input=[out1, out2, c], axis=1) out.backward() self.assertTrue(linear.weight.gradient() is None) self.assertTrue(out1.gradient() is None) def test_auto_prune7(self): with _test_eager_guard(): self.func_auto_prune7() self.func_auto_prune7() def func_auto_prune8(self): with fluid.dygraph.guard(): value0 = np.arange(26).reshape(2, 13).astype("float32") value1 = np.arange(6).reshape(2, 3).astype("float32") value2 = np.arange(10).reshape(2, 5).astype("float32") linear = fluid.Linear(13, 5, dtype="float32") linear2 = fluid.Linear(5, 3, dtype="float32") a = fluid.dygraph.to_variable(value0) b = fluid.dygraph.to_variable(value1) c = fluid.dygraph.to_variable(value2) out1 = linear(a) linear_origin = linear.weight.numpy() out2 = linear2(out1) linear2_origin = linear2.weight.numpy() linear2.weight.stop_gradient = True out2.backward() optimizer = fluid.optimizer.SGD( learning_rate=0.003, parameter_list=(linear.parameters() + linear2.parameters())) optimizer.minimize(out2) self.assertTrue( np.array_equal(linear2_origin, linear2.weight.numpy())) self.assertFalse( np.array_equal(linear_origin, linear.weight.numpy())) def test_auto_prune8(self): with _test_eager_guard(): self.func_auto_prune8() self.func_auto_prune8() def func_auto_prune9(self): with fluid.dygraph.guard(): value0 = np.arange(26).reshape(2, 13).astype("float32") value1 = np.arange(6).reshape(2, 3).astype("float32") value2 = np.arange(10).reshape(2, 5).astype("float32") linear = fluid.Linear(13, 5, dtype="float32") linear2 = fluid.Linear(5, 3, dtype="float32") a = fluid.dygraph.to_variable(value0) b = fluid.dygraph.to_variable(value1) c = fluid.dygraph.to_variable(value2) out1 = linear(a) linear_origin = linear.weight.numpy() out2 = linear2(out1) linear2_origin = linear2.weight.numpy() out2.stop_gradient = True out2.backward() optimizer = fluid.optimizer.SGD( learning_rate=0.003, parameter_list=(linear.parameters() + linear2.parameters())) optimizer.minimize(out2) self.assertTrue( np.array_equal(linear2_origin, linear2.weight.numpy())) self.assertTrue( np.array_equal(linear_origin, linear.weight.numpy())) try: linear2.weight.gradient() except ValueError as e: assert type(e) == ValueError def test_auto_prune9(self): with _test_eager_guard(): self.func_auto_prune9() self.func_auto_prune9() def func_auto_prune10(self): with fluid.dygraph.guard(): value0 = np.arange(26).reshape(2, 13).astype("float32") value1 = np.arange(6).reshape(2, 3).astype("float32") value2 = np.arange(10).reshape(2, 5).astype("float32") linear = fluid.Linear(13, 5, dtype="float32") linear2 = fluid.Linear(3, 3, dtype="float32") a = fluid.dygraph.to_variable(value0) b = fluid.dygraph.to_variable(value1) c = fluid.dygraph.to_variable(value2) out1 = linear(a) out2 = linear2(b) out1.stop_gradient = True out = fluid.layers.concat(input=[out1, out2, c], axis=1) #TODO(jiabin): In Eager Mode we don't actually need sort_sum_gradient, this test should be removed when we don't support fluid anymore. fluid.set_flags({'FLAGS_sort_sum_gradient': True}) out.backward() self.assertTrue(linear.weight.gradient() is None) self.assertTrue(out1.gradient() is None) def test_auto_prune10(self): with _test_eager_guard(): self.func_auto_prune10() self.func_auto_prune10() def func_auto_prune_with_optimizer(self): vocab_size = 100 size = 20 batch_size = 16 indices = np.random.randint( low=0, high=100, size=(batch_size, 1)).astype("int64") embed = np.random.randn(batch_size, size).astype("float32") place = fluid.CPUPlace() with fluid.dygraph.guard(place): model = MyLayer(size, vocab_size, size) grad_clip = fluid.clip.GradientClipByGlobalNorm(0.001) optimizer = fluid.optimizer.AdamOptimizer( 0.001, parameter_list=model.parameters(), grad_clip=grad_clip) indices = fluid.dygraph.to_variable(indices) embed = fluid.dygraph.to_variable(embed) dummy_loss = model(embed) loss = model.embed_linear0(indices) loss.backward() _, params_grads = optimizer.minimize(loss) for items in params_grads: assert items[0].name is not model.embed1.weight.name assert items[0].name is not model.linear_1.weight.name assert model.embed1.weight._grad_ivar() is None assert model.linear_1.weight._grad_ivar() is None with fluid.dygraph.guard(place): model = MyLayer2(size, vocab_size, size) grad_clip = fluid.clip.GradientClipByGlobalNorm(0.001) optimizer = fluid.optimizer.AdamOptimizer( 0.001, parameter_list=model.parameters(), grad_clip=grad_clip) indices = fluid.dygraph.to_variable(indices) emebd = fluid.dygraph.to_variable(embed) dummy_loss = model(indices) loss = model.embed_linear0(indices) loss.backward() optimizer.minimize(loss) for items in params_grads: assert items[0].name is not model.embed1.weight.name assert items[0].name is not model.linear_1.weight.name assert model.embed1.weight._grad_ivar() is None assert model.linear_1.weight._grad_ivar() is None def test_auto_prune_with_optimizer(self): with _test_eager_guard(): self.func_auto_prune_with_optimizer() self.func_auto_prune_with_optimizer() def func_case2_prune_no_grad_branch(self): with fluid.dygraph.guard(): value1 = np.arange(784).reshape(1, 784) value2 = np.arange(1).reshape(1, 1) v1 = fluid.dygraph.to_variable(value1).astype("float32") v2 = fluid.dygraph.to_variable(value2).astype("float32") case3 = AutoPruneLayer2(input_size=784) loss = case3(v1, v2) loss.backward() self.assertTrue(case3.linear2.weight._grad_ivar() is None) self.assertTrue(case3.linear.weight._grad_ivar() is not None) def test_case2_prune_no_grad_branch(self): with _test_eager_guard(): self.func_case2_prune_no_grad_branch() self.func_case2_prune_no_grad_branch() def func_case3_prune_no_grad_branch2(self): with fluid.dygraph.guard(): value1 = np.arange(1).reshape(1, 1) linear = fluid.dygraph.Linear(1, 1, act=None) label = fluid.dygraph.to_variable(value1).astype("float32") label = linear(label) label = fluid.layers.cast(label, dtype="float32") label = fluid.layers.cast(label, dtype='int64') out = fluid.layers.one_hot(input=label, depth=100) loss = fluid.layers.mean(out) loss.backward() self.assertTrue(linear.weight._grad_ivar() is None) def test_case3_prune_no_grad_branch2(self): with _test_eager_guard(): self.func_case3_prune_no_grad_branch2() self.func_case3_prune_no_grad_branch2() def func_case4_with_no_grad_op_maker(self): with fluid.dygraph.guard(): out = fluid.layers.gaussian_random(shape=[20, 30]) loss = fluid.layers.mean(out) loss.backward() self.assertTrue(out._grad_ivar() is None) def test_case4_with_no_grad_op_maker(self): with _test_eager_guard(): self.func_case4_with_no_grad_op_maker() self.func_case4_with_no_grad_op_maker() if __name__ == '__main__': unittest.main()