# Copyright (c) 2018 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 numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.dygraph import to_variable from paddle.fluid.framework import ( EagerParamBase, ParamBase, _test_eager_guard, in_dygraph_mode, ) from paddle.jit import ProgramTranslator class L1(fluid.Layer): def __init__(self): super().__init__() self._param_attr = fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.1) ) self.w1 = self.create_parameter( attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False ) self.w2 = self.create_parameter( attr=self._param_attr, shape=[2, 2], dtype='float32', is_bias=False ) def forward(self): return self.w1 + self.w2 class L2(fluid.Layer): def __init__(self): super().__init__() self.layer1 = L1() self.layer2 = L1() def forward(self): return self.layer1() + self.layer2() class L3(fluid.Layer): def __init__(self): super().__init__() self.layer1 = L2() self.layer2 = L2() def forward(self): return self.layer1() + self.layer2() class TestBaseLayer(unittest.TestCase): def func_test_one_level(self): with fluid.dygraph.guard(): l = L1() ret = l() expected_names = ['l1.w1', 'l1.w2'] idx = 0 for name, _ in l.named_parameters(prefix='l1'): self.assertEqual(name, expected_names[idx]) idx += 1 np.testing.assert_allclose( ret.numpy(), 0.2 * np.ones([2, 2]), rtol=1e-05 ) def test_one_level(self): with _test_eager_guard(): self.func_test_one_level() self.func_test_one_level() def func_test_three_level(self): with fluid.dygraph.guard(): l = L3() expected_names = [ 'l3.layer1.layer1.w1', 'l3.layer1.layer1.w2', 'l3.layer1.layer2.w1', 'l3.layer1.layer2.w2', 'l3.layer2.layer1.w1', 'l3.layer2.layer1.w2', 'l3.layer2.layer2.w1', 'l3.layer2.layer2.w2', ] idx = 0 for name, _ in l.named_parameters(prefix='l3'): self.assertEqual(name, expected_names[idx]) idx += 1 ret = l() np.testing.assert_allclose( ret.numpy(), 0.8 * np.ones([2, 2]), rtol=1e-05 ) def test_three_level(self): with _test_eager_guard(): self.func_test_three_level() self.func_test_three_level() def func_test_add_parameter_with_error(self): with fluid.dygraph.guard(): net = fluid.Layer() param = net.create_parameter(shape=[1]) with self.assertRaises(TypeError): net.add_parameter(10, param) with self.assertRaises(KeyError): net.add_parameter("param.name", param) with self.assertRaises(KeyError): net.add_parameter("", param) with self.assertRaises(KeyError): net.test_param = 10 net.add_parameter("test_param", param) with self.assertRaises(TypeError): net.add_parameter("no_param", 10) load_param = net.create_parameter(shape=[1]) net._loaddict_holder[load_param.name] = load_param net.add_parameter("load_param", load_param) def test_add_parameter_with_error(self): with _test_eager_guard(): self.func_test_add_parameter_with_error() self.func_test_add_parameter_with_error() class BufferLayer(fluid.Layer): def __init__(self): super().__init__() buffer_var = to_variable(np.zeros([2, 4]).astype('int32')) self.register_buffer("layer_buffer", buffer_var) def forward(self): pass class BufferNet(fluid.Layer): def __init__(self): super().__init__() self.buffer_layer = BufferLayer() self.w1 = self.create_parameter( shape=[2, 2], dtype='float32', is_bias=False ) buffer_var = to_variable(np.ones([2, 4]).astype('int32')) self.register_buffer("net_buffer", buffer_var) self.new_buffer = to_variable(np.ones([4, 2]).astype('int32')) def forward(self): pass class TestBuffer(unittest.TestCase): def func_test_buffers_and_named_buffers(self): def names(named_buffers): return [name for name, _ in named_buffers] with fluid.dygraph.guard(): layer = BufferLayer() net = BufferNet() self.assertEqual(len(layer.buffers()), 1) self.assertEqual(names(layer.named_buffers()), ['layer_buffer']) self.assertEqual(len(net.buffers()), 3) self.assertEqual( names(net.named_buffers()), ['net_buffer', 'new_buffer', 'buffer_layer.layer_buffer'], ) self.assertEqual(len(net.buffers(include_sublayers=False)), 2) self.assertEqual( names(net.named_buffers(include_sublayers=False)), ['net_buffer', 'new_buffer'], ) def test_buffers_and_named_buffers(self): with _test_eager_guard(): self.func_test_buffers_and_named_buffers() self.func_test_buffers_and_named_buffers() def func_test_register_buffer_with_error(self): with fluid.dygraph.guard(): net = fluid.Layer() var = to_variable(np.zeros([1])) with self.assertRaisesRegexp( TypeError, "name of buffer should be a string" ): net.register_buffer(12, var) with self.assertRaisesRegexp( TypeError, "buffer should be a Paddle.Tensor" ): if in_dygraph_mode(): net.register_buffer( "buffer_name", EagerParamBase([2, 2], 'float32') ) else: net.register_buffer( "buffer_name", ParamBase([2, 2], 'float32') ) with self.assertRaisesRegexp( KeyError, "name of buffer can not contain" ): net.register_buffer("buffer.name", var) with self.assertRaisesRegexp( KeyError, "name of buffer can not be empty" ): net.register_buffer("", var) net.attr_name = 10 with self.assertRaisesRegexp(KeyError, "already exists"): net.register_buffer("attr_name", var) del net.attr_name if in_dygraph_mode(): net.attr_name = EagerParamBase([2, 2], 'float32') else: net.attr_name = ParamBase([2, 2], 'float32') with self.assertRaisesRegexp(KeyError, "already exists"): net.register_buffer("attr_name", var) def test_register_buffer_with_error(self): with _test_eager_guard(): self.func_test_register_buffer_with_error() self.func_test_register_buffer_with_error() def func_test_register_buffer_same_name(self): with fluid.dygraph.guard(): net = fluid.Layer() var1 = to_variable(np.zeros([1])) var2 = to_variable(np.zeros([2])) var3 = to_variable(np.zeros([3])) net.register_buffer("buffer_name", var1) self.assert_var_base_equal(net.buffer_name, var1) net.register_buffer("buffer_name", var2) self.assert_var_base_equal(net.buffer_name, var2) net.register_buffer("buffer_name", var3) self.assert_var_base_equal(net.buffer_name, var3) def test_register_buffer_same_name(self): with _test_eager_guard(): self.func_test_register_buffer_same_name() self.func_test_register_buffer_same_name() def func_test_buffer_not_persistable(self): with fluid.dygraph.guard(): net = fluid.Layer() var1 = to_variable(np.zeros([1])) net.register_buffer("buffer_name", var1, persistable=False) self.assertEqual(len(net.buffers()), 1) self.assertEqual(len(net.state_dict()), 0) def test_buffer_not_persistable(self): with _test_eager_guard(): self.func_test_buffer_not_persistable() self.func_test_buffer_not_persistable() def func_test_buffer_not_persistable_del(self): with fluid.dygraph.guard(): net = fluid.Layer() var1 = to_variable(np.zeros([1])) net.register_buffer("buffer_name", var1, persistable=False) del net.buffer_name self.assertEqual(len(net.buffers()), 0) def test_buffer_not_persistable_del(self): with _test_eager_guard(): self.func_test_buffer_not_persistable_del() self.func_test_buffer_not_persistable_del() def func_test_buffer_not_persistable_overwrite(self): with fluid.dygraph.guard(): net = fluid.Layer() var1 = to_variable(np.zeros([1])) var2 = to_variable(np.zeros([2])) net.register_buffer("buffer_name", var1, persistable=False) net.register_buffer("buffer_name", var2) # Allow to overwrite a non-persistable buffer with a persistable var. self.assertEqual(len(net.buffers()), 1) self.assertEqual(len(net.state_dict()), 1) net.register_buffer("buffer_name", var1, persistable=False) self.assertEqual(len(net.buffers()), 1) self.assertEqual(len(net.state_dict()), 0) def test_buffer_not_persistable_overwrite(self): with _test_eager_guard(): self.func_test_buffer_not_persistable_overwrite() self.func_test_buffer_not_persistable_overwrite() def func_test_buffer_not_persistable_assign(self): with fluid.dygraph.guard(): net = fluid.Layer() var1 = to_variable(np.zeros([1])) net.register_buffer("buffer_name", var1, persistable=False) # Assigning Nones will remove the buffer, but allow to re-assign # to remark it as buffer. net.buffer_name = None self.assertEqual(len(net.buffers()), 0) self.assertEqual(len(net.state_dict()), 0) net.buffer_name = var1 self.assertEqual(len(net.buffers()), 1) self.assertEqual(len(net.state_dict()), 0) # Re-assign a ParamBase will remove the buffer. if in_dygraph_mode(): net.buffer_name = EagerParamBase([2, 2], 'float32') else: net.buffer_name = ParamBase([2, 2], 'float32') self.assertEqual(len(net.buffers()), 0) self.assertEqual(len(net.state_dict()), 1) def test_buffer_not_persistable_assign(self): with _test_eager_guard(): self.func_test_buffer_not_persistable_assign() self.func_test_buffer_not_persistable_assign() def func_test_buffer_not_persistable_load(self): with fluid.dygraph.guard(): net = fluid.Layer() var1 = to_variable(np.zeros([1])) net.register_buffer("buffer_name", var1, persistable=False) net.load_dict({}) def test_buffer_not_persistable_load(self): with _test_eager_guard(): self.func_test_buffer_not_persistable_load() self.func_test_buffer_not_persistable_load() def func_test_buffer_state_dict(self): with fluid.dygraph.guard(): net = fluid.Layer() var1 = to_variable(np.zeros([2, 3])) var2 = to_variable(np.zeros([3, 2])) net.register_buffer("buffer_var1", var1) net.register_buffer("buffer_var2", var2, persistable=False) self.assertEqual(len(net.state_dict()), 1) self.assertEqual( [name for name, _ in net.state_dict().items()], ["buffer_var1"] ) # load state_dict net_load = fluid.Layer() var = to_variable(np.ones([2, 3])) net_load.register_buffer("buffer_var1", var) net_load.load_dict(net.state_dict()) self.assert_var_base_equal(net_load.buffer_var1, var1) def test_buffer_state_dict(self): with _test_eager_guard(): self.func_test_buffer_state_dict() self.func_test_buffer_state_dict() def assert_var_base_equal(self, var1, var2): np.testing.assert_array_equal(var1.numpy(), var2.numpy()) class BufferNetWithModification(paddle.nn.Layer): def __init__(self, shape): super().__init__() self.buffer1 = paddle.zeros(shape, 'int32') self.buffer2 = paddle.zeros(shape, 'int32') @paddle.jit.to_static def forward(self, x): self.buffer1 += x self.buffer2 = self.buffer1 + x out = self.buffer1 + self.buffer2 return out class TestModifiedBuffer(unittest.TestCase): def funcsetUp(self): paddle.disable_static() self.prog_trans = ProgramTranslator() self.shape = [10, 16] def _run(self, to_static=False): self.prog_trans.enable(to_static) x = paddle.ones([1], 'int32') net = BufferNetWithModification(self.shape) out = net(x) return out, net.buffer1, net.buffer2 def func_test_modified(self): self.funcsetUp() dy_outs = self._run(False) st_outs = self._run(True) for i in range(len(dy_outs)): np.testing.assert_array_equal( dy_outs[i].numpy(), st_outs[i].numpy() ) def test_modified(self): with _test_eager_guard(): self.func_test_modified() self.func_test_modified() class TestLayerTo(unittest.TestCase): def funcsetUp(self): paddle.disable_static() self.linear = paddle.nn.Linear(2, 2) self.new_grad = np.random.random([2, 2]) self.linear.weight._set_grad_ivar(paddle.to_tensor(self.new_grad)) buffer = paddle.to_tensor([0.0], dtype='float32') self.linear.register_buffer("buf_name", buffer, persistable=True) sublayer = paddle.nn.Conv1D(3, 2, 3) self.linear.add_sublayer("1", sublayer) def func_test_to_api(self): self.linear.to(dtype='double') self.assertEqual( self.linear.weight.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) self.assertEqual( self.linear.buf_name.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) np.testing.assert_allclose( self.linear.weight.grad.numpy(), self.new_grad, rtol=1e-05 ) self.assertEqual( self.linear.weight._grad_ivar().dtype, paddle.fluid.core.VarDesc.VarType.FP64, ) self.linear.to() self.assertEqual( self.linear.weight.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) self.assertEqual( self.linear.buf_name.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) np.testing.assert_allclose( self.linear.weight.grad.numpy(), self.new_grad, rtol=1e-05 ) self.assertEqual( self.linear.weight._grad_ivar().dtype, paddle.fluid.core.VarDesc.VarType.FP64, ) for p in self.linear.parameters(): if in_dygraph_mode(): self.assertTrue( isinstance(p, paddle.fluid.framework.EagerParamBase) ) else: self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase)) if paddle.fluid.is_compiled_with_cuda(): self.linear.to(device=paddle.CUDAPlace(0)) self.assertTrue(self.linear.weight.place.is_gpu_place()) self.assertEqual(self.linear.weight.place.gpu_device_id(), 0) self.assertTrue(self.linear.buf_name.place.is_gpu_place()) self.assertEqual(self.linear.buf_name.place.gpu_device_id(), 0) self.assertTrue( self.linear.weight._grad_ivar().place.is_gpu_place() ) self.assertEqual( self.linear.weight._grad_ivar().place.gpu_device_id(), 0 ) self.linear.to(device='gpu:0') self.assertTrue(self.linear.weight.place.is_gpu_place()) self.assertEqual(self.linear.weight.place.gpu_device_id(), 0) self.assertTrue(self.linear.buf_name.place.is_gpu_place()) self.assertEqual(self.linear.buf_name.place.gpu_device_id(), 0) self.assertTrue( self.linear.weight._grad_ivar().place.is_gpu_place() ) self.assertEqual( self.linear.weight._grad_ivar().place.gpu_device_id(), 0 ) for p in self.linear.parameters(): if in_dygraph_mode(): self.assertTrue( isinstance(p, paddle.fluid.framework.EagerParamBase) ) else: self.assertTrue( isinstance(p, paddle.fluid.framework.ParamBase) ) self.linear.to(device=paddle.CPUPlace()) self.assertTrue(self.linear.weight.place.is_cpu_place()) self.assertTrue(self.linear.buf_name.place.is_cpu_place()) self.assertTrue(self.linear.weight._grad_ivar().place.is_cpu_place()) self.linear.to(device='cpu') self.assertTrue(self.linear.weight.place.is_cpu_place()) self.assertTrue(self.linear.buf_name.place.is_cpu_place()) self.assertTrue(self.linear.weight._grad_ivar().place.is_cpu_place()) self.assertRaises(ValueError, self.linear.to, device=1) self.assertRaises(AssertionError, self.linear.to, blocking=1) def func_test_to_api_paddle_dtype(self): self.linear.to(dtype=paddle.float64) self.assertEqual( self.linear.weight.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) self.assertEqual( self.linear.buf_name.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) np.testing.assert_allclose( self.linear.weight.grad.numpy(), self.new_grad, rtol=1e-05 ) self.assertEqual( self.linear.weight._grad_ivar().dtype, paddle.fluid.core.VarDesc.VarType.FP64, ) self.linear.to() self.assertEqual( self.linear.weight.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) self.assertEqual( self.linear.buf_name.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) np.testing.assert_allclose( self.linear.weight.grad.numpy(), self.new_grad, rtol=1e-05 ) self.assertEqual( self.linear.weight._grad_ivar().dtype, paddle.fluid.core.VarDesc.VarType.FP64, ) for p in self.linear.parameters(): if in_dygraph_mode(): self.assertTrue( isinstance(p, paddle.fluid.framework.EagerParamBase) ) else: self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase)) def func_test_to_api_numpy_dtype(self): self.linear.to(dtype=np.float64) self.assertEqual( self.linear.weight.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) self.assertEqual( self.linear.buf_name.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) np.testing.assert_allclose( self.linear.weight.grad.numpy(), self.new_grad, rtol=1e-05 ) self.assertEqual( self.linear.weight._grad_ivar().dtype, paddle.fluid.core.VarDesc.VarType.FP64, ) self.linear.to() self.assertEqual( self.linear.weight.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) self.assertEqual( self.linear.buf_name.dtype, paddle.fluid.core.VarDesc.VarType.FP64 ) np.testing.assert_allclose( self.linear.weight.grad.numpy(), self.new_grad, rtol=1e-05 ) self.assertEqual( self.linear.weight._grad_ivar().dtype, paddle.fluid.core.VarDesc.VarType.FP64, ) for p in self.linear.parameters(): if in_dygraph_mode(): self.assertTrue( isinstance(p, paddle.fluid.framework.EagerParamBase) ) else: self.assertTrue(isinstance(p, paddle.fluid.framework.ParamBase)) def func_test_to_api_none_buffer(self): model = paddle.nn.Linear(2, 4) buffer = None model.register_buffer("buf_name", buffer, persistable=True) model.to(dtype='float64') self.assertIsNone(model._buffers['buf_name']) def test_main(self): with _test_eager_guard(): self.funcsetUp() self.func_test_to_api() self.func_test_to_api_paddle_dtype() self.func_test_to_api_numpy_dtype() self.func_test_to_api_none_buffer() self.funcsetUp() self.func_test_to_api() self.func_test_to_api_paddle_dtype() self.func_test_to_api_numpy_dtype() self.func_test_to_api_none_buffer() if __name__ == '__main__': paddle.enable_static() unittest.main()