wehub-resource-sync
237 lines
7.3 KiB
Python
237 lines
7.3 KiB
Python
# Copyright (c) 2023 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 os
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import unittest
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import numpy as np
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import paddle
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class TestDy2staticPir(unittest.TestCase):
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def test_basic_network(self):
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def func(x):
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out = paddle.mean(x)
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return out
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static_func = paddle.jit.to_static(func, full_graph=True)
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x = paddle.randn((3, 3))
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y = paddle.randn((3, 3))
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x.stop_gradient = False
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y.stop_gradient = False
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ans = func(x)
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out = static_func(x)
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np.testing.assert_allclose(
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out.numpy(), ans.numpy(), rtol=1e-05, atol=1e-8
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)
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def test_basic_network_backward(self):
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def func(x):
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out = paddle.mean(x)
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return out
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# ==== dygraph computation ====
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static_func = paddle.jit.to_static(func, full_graph=True)
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x = paddle.randn((3, 3))
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y = paddle.randn((3, 3))
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x.stop_gradient = False
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y.stop_gradient = False
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loss = func(x) * 2
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loss.backward()
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x_grad_ans = x.grad.numpy()
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x.clear_gradient()
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# ==== to static computation ====
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out = static_func(x)
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out = out * 2
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out.backward()
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st_grad = x.grad
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np.testing.assert_allclose(
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x_grad_ans, st_grad.numpy(), rtol=1e-05, atol=1e-8
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)
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class TestDy2staticPir2(unittest.TestCase):
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def test_basic_layer(self):
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(10, 10)
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def forward(self, x):
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return self.linear(x)
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net = SimpleNet()
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x = paddle.randn((10, 10))
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x.stop_gradient = False
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ans = net(x)
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net = paddle.jit.to_static(net, full_graph=True)
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out = net(x)
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np.testing.assert_allclose(
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out.numpy(), ans.numpy(), rtol=1e-05, atol=1e-8
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)
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class TestDy2staticPir3(unittest.TestCase):
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def test_complex_layer(self):
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def output_pure_func(x, y):
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outx = paddle.mean(x)
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outy = paddle.mean(y)
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outy.stop_gradient = True
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return paddle.add(outx, outy), outy
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def run_function(to_static=True):
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paddle.seed(2023)
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x = paddle.randn((10, 10))
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y = paddle.randn((10, 10))
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x.stop_gradient = False
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y.stop_gradient = True
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func = output_pure_func
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if to_static:
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func = paddle.jit.to_static(func, full_graph=True)
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y, y_mean = func(x, y)
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loss = y.mean()
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loss.backward()
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return (y, x.grad)
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for dy, st in zip(run_function(False), run_function(True)):
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np.testing.assert_allclose(
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dy.numpy(), st.numpy(), rtol=1e-05, atol=1e-8
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)
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class TestLossFor10Steps(unittest.TestCase):
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def test_loss_for_10_steps(self):
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# Dy2static RunProgramOp support nn.Layer's forward and backward training.
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(10, 10)
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def forward(self, x):
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return self.linear(x)
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def train_step(to_static=True):
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paddle.seed(2023)
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x = paddle.randn((10, 10), dtype='float32')
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y = paddle.randn((10, 10), dtype='float32')
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loss_fn = paddle.nn.loss.MSELoss()
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net = SimpleNet()
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=net.parameters()
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)
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if to_static:
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net = paddle.jit.to_static(net, full_graph=True)
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losses = []
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for step in range(100):
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y_pred = net(x)
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loss = loss_fn(y_pred, y)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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losses.append(loss.numpy())
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return losses
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expected_losses = train_step(True)
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losses = train_step(False)
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np.testing.assert_allclose(
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losses, expected_losses, rtol=1e-05, atol=1e-8
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)
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class TestDy2staticPir5(unittest.TestCase):
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def test_run(self):
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# Dy2static RunProgramOp support nn.Layer's forward and backward training.
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(10, 10)
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def forward(self, x, y):
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if y is True:
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return self.linear(x)
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else:
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m = self.linear(x)
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return m * m
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def train_step(to_static=True, full_graph=True):
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paddle.seed(2023)
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x = paddle.randn((10, 10), dtype='float32')
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y = paddle.randn((10, 10), dtype='float32')
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loss_fn = paddle.nn.loss.MSELoss()
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net = SimpleNet()
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=net.parameters()
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)
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if to_static:
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net = paddle.jit.to_static(net, full_graph=full_graph)
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losses = []
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for step in range(100):
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y_pred = net(x, step % 2 == 1)
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loss = loss_fn(y_pred, y)
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loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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losses.append(loss.numpy())
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return losses
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expected_losses = train_step(True)
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losses = train_step(False)
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np.testing.assert_allclose(
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losses, expected_losses, rtol=1e-05, atol=1e-8
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)
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os.environ['MIN_GRAPH_SIZE'] = '0'
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sot_losses = train_step(True, False)
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np.testing.assert_allclose(losses, sot_losses, rtol=1e-05, atol=1e-8)
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class TestDy2staticPir6(unittest.TestCase):
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# test basic-indexing __getitem__ for Value
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def test_basic_network(self):
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def func(x):
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shape = paddle.shape(x)
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out = shape[1:]
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return out
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static_func = paddle.jit.to_static(func, full_graph=True)
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x = paddle.randn((2, 3, 4))
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x.stop_gradient = False
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ans = func(x)
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out = static_func(x)
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np.testing.assert_allclose(out.numpy(), ans.numpy())
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class TestDy2staticPir7(unittest.TestCase):
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# test basic-indexing __getitem__ for Value
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def test_basic_network(self):
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def func(x):
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x = x * 2
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x = x + 1
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return 1
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static_func = paddle.jit.to_static(func, full_graph=True)
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x = paddle.randn((2, 3, 4))
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x.stop_gradient = False
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ans = func(x)
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out = static_func(x)
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np.testing.assert_allclose(out, ans)
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if __name__ == "__main__":
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unittest.main()
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