# Copyright (c) 2020 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 os import tempfile import unittest import numpy as np from dygraph_to_static_utils import ( Dy2StTestBase, enable_to_static_guard, test_ast_only, ) from test_fetch_feed import Linear import paddle import paddle.nn.functional as F from paddle import base, nn from paddle.base import core from paddle.nn import BatchNorm from paddle.optimizer import Adam np.random.seed(2020) place = base.CUDAPlace(0) if base.is_compiled_with_cuda() else base.CPUPlace() class PrimeNet(paddle.nn.Layer): def __init__(self, data_layout='NCHW'): super().__init__() self.conv = nn.Conv2D(2, 4, (3, 3), bias_attr=False) self.bn = BatchNorm(4, act="relu", data_layout=data_layout) def forward(self, x): y = self.conv(x) out = self.bn(y) res = F.max_pool2d(out, kernel_size=2, stride=2, padding=0) return res def apply_to_static(net): return paddle.jit.to_static(net, backend=None) def forward_post_hook_for_prim_net(layer, input, output): return output * 2 class TestDyToStaticSaveLoad(Dy2StTestBase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() self.model_path = os.path.join( self.temp_dir.name, "test_dy2stat_save_load" ) def tearDown(self): self.temp_dir.cleanup() def test_save_load_same_result(self): x_data = np.random.randn(30, 10, 32).astype('float32') batch_num = 3 x = paddle.to_tensor(x_data) net = Linear(32, 64) adam = Adam(learning_rate=0.1, parameters=net.parameters()) for i in range(batch_num): static_out, static_loss = net(x) # Update parameters static_loss.backward() adam.minimize(static_loss) net.clear_gradients() # Save parameters paddle.save(net.state_dict(), self.model_path + '.pdparams') # minimize() will update parameter, call net() to get output and avg_loss. # Switch into eval mode. net.eval() static_out, static_loss = net(x) # load parameters into dygraph dygraph_net = Linear(32, 64) # Load parameters model_dict = paddle.load(self.model_path + '.pdparams') dygraph_net.set_dict(model_dict) # Switch into eval mode. dygraph_net.eval() x = paddle.to_tensor(x_data) # predict output with enable_to_static_guard(False): dygraph_out, dygraph_loss = dygraph_net(x) np.testing.assert_allclose( dygraph_out.numpy(), static_out.numpy(), rtol=1e-05 ) np.testing.assert_allclose( dygraph_loss.numpy(), static_loss.numpy(), rtol=1e-05 ) def _compute_op_num(self, composite_program): comp_op_type_list = [ op.name() for op in composite_program.program.global_block().ops ] return comp_op_type_list @test_ast_only def test_save_load_prim(self): with base.dygraph.guard(place): self.x = paddle.randn([4, 2, 6, 6], dtype="float32") self.x.stop_gradient = False net = PrimeNet(data_layout="NCHW") core._set_prim_all_enabled(True) net.eval() static_net = apply_to_static(net) res = static_net(self.x) composite_program = static_net.forward.get_concrete_program(self.x)[ 1 ].train_program comp_op_type_list = self._compute_op_num(composite_program) self.assertNotIn("pd_op.batch_norm_", comp_op_type_list) self.assertNotIn("pd_op.relu", comp_op_type_list) self.assertNotIn("pd_op.pow", comp_op_type_list) self.assertNotIn("pd_op.expand_v2", comp_op_type_list) self.assertNotIn("pd_op.unsqueeze2", comp_op_type_list) self.assertNotIn("pd_op.reduce_mean", comp_op_type_list) self.assertNotIn("pd_op.batch_norm_grad", comp_op_type_list) self.assertNotIn("pd_op.relu_grad", comp_op_type_list) self.assertNotIn("pd_op.pow_grad", comp_op_type_list) self.assertNotIn("pd_op.expand_v2_grad", comp_op_type_list) self.assertNotIn("pd_op.unsqueeze2_grad", comp_op_type_list) self.assertNotIn("pd_op.reduce_mean_grad", comp_op_type_list) paddle.jit.save(static_net, self.model_path) load_func = paddle.jit.load(self.model_path) load_program = load_func.program() load_op_type_list = [ op.name() for op in load_program.global_block().ops ] new_res = load_func(self.x) self.assertIn("pd_op.conv2d", load_op_type_list) self.assertIn("pd_op.batch_norm_", load_op_type_list) self.assertIn("pd_op.relu", load_op_type_list) self.assertIn("pd_op.pool2d", load_op_type_list) np.testing.assert_allclose(res.numpy(), new_res.numpy(), rtol=1e-05) @test_ast_only def test_save_load_prim_with_hook(self): with base.dygraph.guard(place): self.x = paddle.randn([4, 2, 6, 6], dtype="float32") self.x.stop_gradient = False net = PrimeNet(data_layout="NCHW") net.register_forward_post_hook(forward_post_hook_for_prim_net) core._set_prim_all_enabled(True) net.eval() static_net = apply_to_static(net) res = static_net(self.x) composite_program = static_net.forward.get_concrete_program(self.x)[ 1 ].train_program comp_op_type_list = self._compute_op_num(composite_program) self.assertNotIn("pd_op.batch_norm_", comp_op_type_list) self.assertNotIn("pd_op.relu", comp_op_type_list) self.assertNotIn("pd_op.pow", comp_op_type_list) self.assertNotIn("pd_op.expand_v2", comp_op_type_list) self.assertNotIn("pd_op.unsqueeze2", comp_op_type_list) self.assertNotIn("pd_op.reduce_mean", comp_op_type_list) self.assertNotIn("pd_op.batch_norm_grad", comp_op_type_list) self.assertNotIn("pd_op.relu_grad", comp_op_type_list) self.assertNotIn("pd_op.pow_grad", comp_op_type_list) self.assertNotIn("pd_op.expand_v2_grad", comp_op_type_list) self.assertNotIn("pd_op.unsqueeze2_grad", comp_op_type_list) self.assertNotIn("pd_op.reduce_mean_grad", comp_op_type_list) self.assertNotIn("pd_op.multiply_grad", comp_op_type_list) paddle.jit.save(static_net, self.model_path) load_func = paddle.jit.load(self.model_path) load_program = load_func.program() load_op_type_list = [ op.name() for op in load_program.global_block().ops ] new_res = load_func(self.x) self.assertIn("pd_op.conv2d", load_op_type_list) self.assertIn("pd_op.batch_norm_", load_op_type_list) self.assertIn("pd_op.relu", load_op_type_list) self.assertIn("pd_op.pool2d", load_op_type_list) np.testing.assert_allclose(res.numpy(), new_res.numpy(), rtol=1e-05) if __name__ == '__main__': unittest.main()