wehub-resource-sync
433 lines
16 KiB
Python
433 lines
16 KiB
Python
# 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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import unittest
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import numpy as np
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import paddle
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from paddle import base
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from paddle.nn import Embedding
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from paddle.tensor import random
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class AutoPruneLayer0(paddle.nn.Layer):
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def __init__(self, input_size):
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super().__init__()
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self.linear1 = paddle.nn.Linear(
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input_size,
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5,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=2)
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),
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bias_attr=False,
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)
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self.linear2 = paddle.nn.Linear(
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5,
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5,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=2)
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),
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bias_attr=False,
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)
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def forward(self, x, y):
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a = self.linear1(x)
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b = self.linear2(y)
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c = paddle.matmul(a, b)
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d = paddle.mean(c)
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return d
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class AutoPruneLayer1(paddle.nn.Layer):
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def __init__(self, input_size):
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super().__init__()
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self.linear1 = paddle.nn.Linear(
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input_size,
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5,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=2)
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),
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bias_attr=False,
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)
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self.linear2 = paddle.nn.Linear(
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5,
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5,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=2)
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),
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bias_attr=False,
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)
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def forward(self, x, y):
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a = self.linear1(x)
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b = self.linear2(y)
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b.stop_gradient = True
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c = paddle.matmul(a, b)
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d = paddle.mean(c)
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return d
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class AutoPruneLayer2(paddle.nn.Layer):
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def __init__(self, input_size):
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super().__init__()
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self.linear = paddle.nn.Linear(input_size, 10)
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self.linear2 = paddle.nn.Linear(1, 1)
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def forward(self, x, label):
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feature = self.linear(x)
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label = self.linear2(label)
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label = paddle.cast(label, dtype="float32")
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label = paddle.cast(label, dtype='int64')
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# Note that the label is not persistable in paddle.nn.functional.cross_entropy.
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loss = paddle.nn.functional.cross_entropy(
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input=feature, label=label, reduction='none', use_softmax=False
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)
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loss = paddle.mean(loss)
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return loss
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class AutoPruneLayer3(paddle.nn.Layer):
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def __init__(self, input_size):
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super().__init__()
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self.linear = paddle.nn.Linear(input_size, 20)
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def forward(self, x, label, test_num):
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feature = self.linear(x)
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part1, part2 = paddle.split(feature, num_or_sections=[10, 10], axis=1)
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# Note that: part2 is not used.
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loss = paddle.nn.functional.cross_entropy(
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input=part1, label=label, reduction='none', use_softmax=False
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)
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loss = paddle.mean(loss)
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if test_num == 1:
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return loss, part2
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else:
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return loss, part1, part2
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class MyLayer(paddle.nn.Layer):
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def __init__(self, input_size, vocab_size, size, dtype="float32"):
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super().__init__(dtype=dtype)
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self.embed0 = Embedding(vocab_size, size)
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self.embed1 = Embedding(vocab_size, size)
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self.linear_0 = paddle.nn.Linear(input_size, size)
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self.linear_1 = paddle.nn.Linear(input_size, size)
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def forward(self, x):
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# this method involves only the linear layers
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loss = paddle.mean(self.linear_0(x) + self.linear_1(x))
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return loss
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def linear0(self, x):
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loss = paddle.mean(self.linear_0(x))
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return loss
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def embed_linear0(self, x):
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loss = paddle.mean(self.linear_0(self.embed0(x)))
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return loss
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class MyLayer2(paddle.nn.Layer):
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def __init__(self, input_size, vocab_size, size, dtype="float32"):
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super().__init__(dtype=dtype)
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self.embed0 = Embedding(vocab_size, size)
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self.embed1 = Embedding(vocab_size, size)
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self.linear_0 = paddle.nn.Linear(input_size, size)
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self.linear_1 = paddle.nn.Linear(input_size, size)
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def forward(self, indices):
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# mind the difference with MyLayer
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# In this example, the forward method involves all params
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loss = paddle.mean(
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self.linear_0(self.embed0(indices))
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+ self.linear_1(self.embed1(indices))
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)
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return loss
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def linear0(self, x):
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loss = paddle.mean(self.linear_0(x))
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return loss
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def embed_linear0(self, x):
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loss = paddle.mean(self.linear_0(self.embed0(x)))
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return loss
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class TestImperativeAutoPrune(unittest.TestCase):
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def test_auto_prune(self):
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with base.dygraph.guard():
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case1 = AutoPruneLayer0(input_size=5)
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value1 = np.arange(25).reshape(5, 5).astype("float32")
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value2 = np.arange(25).reshape(5, 5).astype("float32")
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v1 = paddle.to_tensor(value1)
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v2 = paddle.to_tensor(value2)
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loss = case1(v1, v2)
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loss.backward()
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self.assertIsNotNone(case1.linear2.weight._grad_ivar())
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self.assertIsNotNone(case1.linear1.weight._grad_ivar())
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def test_auto_prune2(self):
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with base.dygraph.guard():
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case2 = AutoPruneLayer1(input_size=5)
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value1 = np.arange(25).reshape(5, 5).astype("float32")
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value2 = np.arange(25).reshape(5, 5).astype("float32")
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v1 = paddle.to_tensor(value1)
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v2 = paddle.to_tensor(value2)
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loss = case2(v1, v2)
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loss.backward()
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self.assertIsNone(case2.linear2.weight._grad_ivar())
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self.assertIsNotNone(case2.linear1.weight._grad_ivar())
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# TODO(jiabin): Support this when we support better split tensor
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def test_auto_prune3(self):
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with base.dygraph.guard():
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case3 = AutoPruneLayer3(input_size=784)
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value1 = np.arange(784).reshape(1, 784).astype("float32")
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value2 = np.arange(1).reshape(1, 1).astype("int64")
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v1 = paddle.to_tensor(value1)
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v2 = paddle.to_tensor(value2)
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loss, part2 = case3(v1, v2, 1)
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part2.retain_grads()
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loss.backward()
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self.assertIsNotNone(case3.linear.weight._grad_ivar())
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self.assertTrue((part2.gradient() == 0).all())
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def test_auto_prune4(self):
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with base.dygraph.guard():
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case4 = AutoPruneLayer3(input_size=784)
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value1 = np.arange(784).reshape(1, 784).astype("float32")
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value2 = np.arange(1).reshape(1, 1).astype("int64")
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v1 = paddle.to_tensor(value1)
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v2 = paddle.to_tensor(value2)
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loss, part2 = case4(v1, v2, 1)
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part2.retain_grads()
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part2.backward()
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self.assertIsNotNone(case4.linear.weight._grad_ivar())
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self.assertTrue((part2.gradient() == 1).all())
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def test_auto_prune5(self):
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with base.dygraph.guard():
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case4 = AutoPruneLayer3(input_size=784)
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value1 = np.arange(784).reshape(1, 784).astype("float32")
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value2 = np.arange(1).reshape(1, 1).astype("int64")
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v1 = paddle.to_tensor(value1)
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v2 = paddle.to_tensor(value2)
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loss, part1, part2 = case4(v1, v2, 2)
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part2.retain_grads()
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part1.backward()
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self.assertIsNotNone(case4.linear.weight._grad_ivar())
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self.assertTrue((part2.gradient() == 0).all())
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def test_auto_prune6(self):
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with base.dygraph.guard():
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value0 = np.arange(26).reshape(2, 13).astype("float32")
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value1 = np.arange(6).reshape(2, 3).astype("float32")
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value2 = np.arange(10).reshape(2, 5).astype("float32")
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linear = paddle.nn.Linear(13, 5)
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linear2 = paddle.nn.Linear(3, 3)
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a = paddle.to_tensor(value0)
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b = paddle.to_tensor(value1)
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c = paddle.to_tensor(value2)
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out1 = linear(a)
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out2 = linear2(b)
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out1.stop_gradient = True
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out = paddle.concat([out1, out2, c], axis=1)
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out.backward()
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self.assertIsNone(linear.weight.gradient())
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self.assertIsNone(out1.gradient())
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def test_auto_prune7(self):
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with base.dygraph.guard():
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value0 = np.arange(26).reshape(2, 13).astype("float32")
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value1 = np.arange(6).reshape(2, 3).astype("float32")
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value2 = np.arange(10).reshape(2, 5).astype("float32")
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linear = paddle.nn.Linear(13, 5)
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linear2 = paddle.nn.Linear(3, 3)
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a = paddle.to_tensor(value0)
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b = paddle.to_tensor(value1)
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c = paddle.to_tensor(value2)
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out1 = linear(a)
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out2 = linear2(b)
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out1.stop_gradient = True
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out = paddle.concat([out1, out2, c], axis=1)
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out.backward()
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self.assertIsNone(linear.weight.gradient())
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self.assertIsNone(out1.gradient())
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def test_auto_prune8(self):
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with base.dygraph.guard():
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value0 = np.arange(26).reshape(2, 13).astype("float32")
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value1 = np.arange(6).reshape(2, 3).astype("float32")
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value2 = np.arange(10).reshape(2, 5).astype("float32")
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linear = paddle.nn.Linear(13, 5)
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linear2 = paddle.nn.Linear(5, 3)
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a = paddle.to_tensor(value0)
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b = paddle.to_tensor(value1)
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c = paddle.to_tensor(value2)
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out1 = linear(a)
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linear_origin = linear.weight.numpy()
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out2 = linear2(out1)
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linear2_origin = linear2.weight.numpy()
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linear2.weight.stop_gradient = True
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out2.backward()
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.003,
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parameters=(linear.parameters() + linear2.parameters()),
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)
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optimizer.minimize(out2)
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np.testing.assert_array_equal(
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linear2_origin, linear2.weight.numpy()
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)
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self.assertFalse(
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np.array_equal(linear_origin, linear.weight.numpy())
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)
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def test_auto_prune9(self):
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with base.dygraph.guard():
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value0 = np.arange(26).reshape(2, 13).astype("float32")
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value1 = np.arange(6).reshape(2, 3).astype("float32")
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value2 = np.arange(10).reshape(2, 5).astype("float32")
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linear = paddle.nn.Linear(13, 5)
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linear2 = paddle.nn.Linear(5, 3)
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a = paddle.to_tensor(value0)
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b = paddle.to_tensor(value1)
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c = paddle.to_tensor(value2)
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out1 = linear(a)
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linear_origin = linear.weight.numpy()
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out2 = linear2(out1)
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linear2_origin = linear2.weight.numpy()
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out2.stop_gradient = True
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out2.backward()
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.003,
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parameters=(linear.parameters() + linear2.parameters()),
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)
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optimizer.minimize(out2)
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np.testing.assert_array_equal(
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linear2_origin, linear2.weight.numpy()
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)
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np.testing.assert_array_equal(linear_origin, linear.weight.numpy())
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try:
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linear2.weight.gradient()
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except ValueError as e:
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assert type(e) == ValueError
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def test_auto_prune10(self):
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with base.dygraph.guard():
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value0 = np.arange(26).reshape(2, 13).astype("float32")
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value1 = np.arange(6).reshape(2, 3).astype("float32")
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value2 = np.arange(10).reshape(2, 5).astype("float32")
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linear = paddle.nn.Linear(13, 5)
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linear2 = paddle.nn.Linear(3, 3)
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a = paddle.to_tensor(value0)
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b = paddle.to_tensor(value1)
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c = paddle.to_tensor(value2)
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out1 = linear(a)
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out2 = linear2(b)
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out1.stop_gradient = True
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out = paddle.concat([out1, out2, c], axis=1)
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# TODO(jiabin): In Eager Mode we don't actually need sort_sum_gradient, this test should be removed when we don't support base anymore.
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base.set_flags({'FLAGS_sort_sum_gradient': True})
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out.backward()
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self.assertIsNone(linear.weight.gradient())
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self.assertIsNone(out1.gradient())
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def test_auto_prune_with_optimizer(self):
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vocab_size = 100
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size = 20
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batch_size = 16
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indices = np.random.randint(
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low=0, high=100, size=(batch_size, 1)
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).astype("int64")
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embed = np.random.randn(batch_size, size).astype("float32")
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place = base.CPUPlace()
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with base.dygraph.guard(place):
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model = MyLayer(size, vocab_size, size)
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grad_clip = paddle.nn.ClipGradByGlobalNorm(0.001)
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optimizer = paddle.optimizer.Adam(
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0.001, parameters=model.parameters(), grad_clip=grad_clip
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)
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indices = paddle.to_tensor(indices)
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embed = paddle.to_tensor(embed)
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dummy_loss = model(embed)
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loss = model.embed_linear0(indices)
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loss.backward()
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_, params_grads = optimizer.minimize(loss)
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for items_0, *items_len in params_grads:
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assert items_0.name is not model.embed1.weight.name
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assert items_0.name is not model.linear_1.weight.name
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assert model.embed1.weight._grad_ivar() is None
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assert model.linear_1.weight._grad_ivar() is None
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with base.dygraph.guard(place):
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model = MyLayer2(size, vocab_size, size)
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grad_clip = paddle.nn.ClipGradByGlobalNorm(0.001)
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optimizer = paddle.optimizer.Adam(
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0.001, parameters=model.parameters(), grad_clip=grad_clip
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)
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indices = paddle.to_tensor(indices)
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emebd = paddle.to_tensor(embed)
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dummy_loss = model(indices)
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loss = model.embed_linear0(indices)
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loss.backward()
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optimizer.minimize(loss)
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for items in params_grads:
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assert items[0].name is not model.embed1.weight.name
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assert items[0].name is not model.linear_1.weight.name
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assert model.embed1.weight._grad_ivar() is None
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assert model.linear_1.weight._grad_ivar() is None
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def test_case2_prune_no_grad_branch(self):
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with base.dygraph.guard():
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value1 = np.arange(784).reshape(1, 784)
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value2 = np.arange(1).reshape(1, 1)
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v1 = paddle.to_tensor(value1).astype("float32")
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v2 = paddle.to_tensor(value2).astype("float32")
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case3 = AutoPruneLayer2(input_size=784)
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loss = case3(v1, v2)
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loss.backward()
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self.assertIsNone(case3.linear2.weight._grad_ivar())
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self.assertIsNotNone(case3.linear.weight._grad_ivar())
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def test_case3_prune_no_grad_branch2(self):
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with base.dygraph.guard():
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value1 = np.arange(1).reshape(1, 1)
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linear = paddle.nn.Linear(1, 1)
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label = paddle.to_tensor(value1).astype("float32")
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label = linear(label)
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label = paddle.cast(label, dtype="float32")
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label = paddle.cast(label, dtype='int64')
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out = paddle.nn.functional.one_hot(label, 100)
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loss = paddle.mean(out)
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loss.backward()
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self.assertIsNone(linear.weight._grad_ivar())
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def test_case4_with_no_grad_op_maker(self):
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with base.dygraph.guard():
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out = random.gaussian(shape=[20, 30])
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loss = paddle.mean(out)
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loss.backward()
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self.assertIsNone(out._grad_ivar())
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if __name__ == '__main__':
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unittest.main()
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