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未验证 提交 022dfed4 编写于 作者: J Jiabin Yang 提交者： GitHub
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Add optimizer save and load (#16986) 

* save optimizer related vars in dygraph

* test=develop, add optimizer save and load

* test=develop, add optimizer save and load

* test=develop, merge code and add multi-optimizer save and load

* test=develop, fix test_imperative_checkpoint

* test=develop, fix include error

* test=develop, fix include error

* test=develop, renew api spec

* test=develop, refine code

* test=develop, set default value for checkpoint

* test=develop, fix ci error

* test=develop, change API.spec and make api more readable

* test=develop, refine version and time stamp

* test=develop, add example code and refine code

* test=develop, refine doc

* test=develop, change version
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paddle/fluid/API.spec
浏览文件 @ 022dfed4
......@@ -450,54 +450,63 @@ paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'para
paddle.fluid.optimizer.SGDOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.SGDOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.SGDOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.SGDOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.MomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.MomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.MomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.MomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.MomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.MomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.AdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.AdamOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdamOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdamOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdamOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.AdamaxOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdamaxOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdamaxOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdamaxOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamaxOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.FtrlOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.FtrlOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.FtrlOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.FtrlOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.FtrlOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.FtrlOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.RMSPropOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.RMSPropOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.RMSPropOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.RMSPropOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.RMSPropOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.AdadeltaOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdadeltaOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdadeltaOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdadeltaOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '648010d0ac1fa707dac0b89f74b0e35c'))
......@@ -505,6 +514,7 @@ paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'para
paddle.fluid.optimizer.ModelAverage.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.ModelAverage.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '5f14ea4adda2791e1c3b37ff327f6a83'))
paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......@@ -512,18 +522,21 @@ paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['sel
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.LarsMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.LarsMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LarsMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.DGCMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'rampup_begin_step', 'rampup_step', 'sparsity', 'use_nesterov', 'local_grad_clip_norm', 'num_trainers', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1, [0.999], False, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DGCMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.DGCMomentumOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.DGCMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.DGCMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DGCMomentumOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.LambOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'lamb_weight_decay', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.01, 0.9, 0.999, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LambOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.LambOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
paddle.fluid.optimizer.LambOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.LambOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LambOptimizer.load (ArgSpec(args=['self', 'stat_dict'], varargs=None, keywords=None, defaults=None), ('document', '649a92cf7f1ea28666fd00c4ea01acde'))
paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'b15cffad0903fc81af77a0580ceb2a9b'))
paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '30f494752ac8921dc5835a63637f453a'))
......
python/paddle/fluid/dygraph/base.py
浏览文件 @ 022dfed4
......@@ -92,3 +92,6 @@ def to_variable(value, block=None, name=None):
return py_var
elif isinstance(value, framework.Variable):
return value
else:
raise TypeError(
"to_variable only accepts 'ndarray' and 'Variable' as value's input")
python/paddle/fluid/dygraph/checkpoint.py
浏览文件 @ 022dfed4
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -16,13 +16,18 @@ from __future__ import print_function
import os
import collections
from .. import core
from ..framework import Variable, default_main_program
import pickle
from . import learning_rate_scheduler
import warnings
__all__ = ['save_persistables', 'load_persistables']
def save_persistables(vardict, dirname, filename=None):
def save_persistables(model_dict,
optimizer=None,
dirname='save_dir',
filename=None):
"""
This function filters out all variables in layer.parameters from the
give `layer` and then trys to load these variables from the folder
......@@ -34,12 +39,12 @@ def save_persistables(vardict, dirname, filename=None):
the file name.
Args:
vardict(dict of Parameters): The parameters will
model_dict(dict of Parameters): The parameters will
be saved. If it is None, nothing
will be deal.
dirname(str): The directory path.
filename(str|None): The file which saved all variables. If variables were
saved in differnet files, set it to None.
saved in different files, set it to None.
Default: None
Returns:
......@@ -71,11 +76,11 @@ def save_persistables(vardict, dirname, filename=None):
fluid.dygraph.save_persistables(ptb_model.state_dict(), dirname=param_path,
layer=ptb_model)
"""
if isinstance(vardict, collections.OrderedDict):
_save_var_to_file(vardict, dirname, filename)
if isinstance(model_dict, collections.OrderedDict):
_save_var_to_file(model_dict, optimizer, dirname, filename)
def load_persistables(dirname):
def load_persistables(dirname='save_dir'):
"""
This function trys to load persistable variables from the folder
`dirname` or the file `filename`.
......@@ -86,7 +91,8 @@ def load_persistables(dirname):
the file name.
Args:
dirname(str): The directory path.
dirname(str): The directory path. default is save_dir
optimizer(Optimizer): Optimizer to be saved
Returns:
dict: The parameter-dict resumed from file
......@@ -103,7 +109,7 @@ def load_persistables(dirname):
return _load_var_from_file(dirname)
def _save_var_to_file(stat_dict, file_dir, file_name):
def _save_var_to_file(stat_dict, optimizers, file_dir, file_name):
save_block = default_main_program().global_block()
save_var_map = {}
for var_key, each_var in stat_dict.items():
......@@ -117,6 +123,32 @@ def _save_var_to_file(stat_dict, file_dir, file_name):
'file_path': os.path.join(file_dir,
os.path.normpath(each_var.name))
})
if isinstance(optimizers, (list, tuple)):
optimizers = optimizers
else:
optimizers = [optimizers]
if os.path.exists(os.path.join(file_dir, os.path.normpath("optimizers"))):
pass
else:
os.mkdir(os.path.join(file_dir, os.path.normpath("optimizers")))
for optimizer in optimizers:
if isinstance(optimizer._learning_rate,
learning_rate_scheduler.LearningRateDecay):
try:
f = open(
os.path.join(file_dir, "optimizers",
os.path.normpath(str(optimizer._name))), "wb")
pickle.dump(optimizer._learning_rate, f, 2)
f.close()
except ():
raise IOError("Can't load %s",
os.path.join(
file_dir, "optimizers",
os.path.normpath(str(optimizer._name))))
else:
warnings.warn(
"Optimizer not saved, Only optimizer with 'LearningRateDecay' under DyGraph mode need to be saved"
)
if file_name is not None:
save_var_list = []
......@@ -138,6 +170,8 @@ def _load_var_from_file(file_dir):
var_name_list = []
if os.path.exists(base_path):
for dirpath, dirnames, filenames in os.walk(base_path):
if "optimizers" in dirpath:
continue
pt = dirpath.replace(base_path, "", 1)
if pt.startswith("/") or pt.startswith("\\"):
pt = pt[1:]
......@@ -152,6 +186,7 @@ def _load_var_from_file(file_dir):
load_block = default_main_program().global_block()
load_var_map = {}
load_optimizer_map = {}
file_var_list = walk_filename(file_dir)
for var_name in file_var_list:
new_var = Variable(block=load_block, name=var_name)
......@@ -165,8 +200,22 @@ def _load_var_from_file(file_dir):
})
load_var_map[new_var.name] = new_var
return load_var_map
opt_path = os.path.join(file_dir, "optimizers")
for _, _, optimizers in os.walk(opt_path):
for optimizer in optimizers:
try:
f = open(os.path.join(opt_path, optimizer), "rb")
load_optimizer_map[optimizer] = pickle.load(f)
f.close()
except IOError:
raise IOError("Can't load %s",
os.path.join(
file_dir, "optimizers",
os.path.normpath(str(optimizer._name))))
if len(load_optimizer_map) == 0:
warnings.warn("No optimizer loaded")
return load_var_map, load_optimizer_map
def _clone_var_in_block_(block, var):
......
python/paddle/fluid/dygraph/learning_rate_scheduler.py
浏览文件 @ 022dfed4
......@@ -63,13 +63,13 @@ class PiecewiseDecay(LearningRateDecay):
self.vars = []
for value in values:
self.vars.append(self.create_lr_var(value))
self.vars.append(value)
def step(self):
for i in range(len(self.boundaries)):
if self.step_num < self.boundaries[i]:
return self.vars[i]
return self.vars[len(self.values) - 1]
return self.create_lr_var(self.vars[len(self.values) - 1])
class NaturalExpDecay(LearningRateDecay):
......
python/paddle/fluid/optimizer.py
浏览文件 @ 022dfed4
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -16,25 +16,25 @@ from __future__ import print_function
import numpy as np
from collections import defaultdict
from functools import reduce
from paddle.fluid import core
from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
from paddle.fluid.framework import Program, Variable, name_scope, default_main_program, default_startup_program
from paddle.fluid.layers import tensor
from . import framework
from . import layers
from . import unique_name
from .backward import append_backward
from .clip import append_gradient_clip_ops, error_clip_callback
from .dygraph import base as imperative_base
from .dygraph.learning_rate_scheduler import LearningRateDecay
from .framework import program_guard
from .initializer import Constant
from .layer_helper import LayerHelper
from .layers import ops
from .regularizer import append_regularization_ops
from .dygraph import base as imperative_base
from .dygraph.learning_rate_scheduler import LearningRateDecay
from paddle.fluid import core
from paddle.fluid.layers import tensor
from functools import reduce
from .wrapped_decorator import signature_safe_contextmanager
__all__ = [
......@@ -63,14 +63,18 @@ class Optimizer(object):
raise TypeError(
"learning rate should be float or LearningRateDecay, got %s here"
% type(learning_rate))
if name is not None:
self._name = unique_name.generate(name)
else:
self._name = unique_name.generate(self.__class__.__name__)
else:
if not isinstance(learning_rate, float) and \
not isinstance(learning_rate, framework.Variable):
raise TypeError(
"learning rate should be float or Variable, got %s here" %
type(learning_rate))
self._name = name
self._name = name
self.regularization = regularization
self._learning_rate = learning_rate
# the learning rate type should be inferenced from loss
......@@ -89,6 +93,90 @@ class Optimizer(object):
self.helper = None
self._opti_name_list = []
def load(self, stat_dict):
"""
load optimizer with learning rate decay in dygraph mode
:return: None
Args:
stat_dict: the dict load by load_persistable method
Examples:
.. code-block:: python
from __future__ import print_function
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import FC
from paddle.fluid.dygraph.base import to_variable
class MLP(fluid.Layer):
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), 10)
self._fc2 = FC(self.full_name(), 10)
def forward(self, inputs):
y = self._fc1(inputs)
y = self._fc2(y)
return y
with fluid.dygraph.guard():
mlp = MLP('mlp')
optimizer2 = SGDOptimizer(
learning_rate=fluid.layers.natural_exp_decay(
learning_rate=0.1,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array(
[x[0].reshape(1, 28, 28) for x in data]).astype('float32')
y_data = np.array([x[1] for x in data]).astype('int64').reshape(
128, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost)
avg_loss.backward()
optimizer.minimize(avg_loss)
mlp.clear_gradients()
fluid.dygraph.save_persistables(
mlp.state_dict(), [optimizer, optimizer2], "save_dir_2")
if batch_id == 2:
break
with fluid.dygraph.guard():
mlp_load = MLP('mlp')
optimizer_load2 = SGDOptimizer(
learning_rate=fluid.layers.natural_exp_decay(
learning_rate=0.1,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
parameters, optimizers = fluid.dygraph.load_persistables(
"save_dir_2")
mlp_load.load_dict(parameters)
optimizer_load2.load(optimizers)
self.assertTrue(optimizer2._learning_rate.__dict__ == optimizer_load2._learning_rate.__dict__)
"""
if framework.in_dygraph_mode():
self._learning_rate = stat_dict[self._name]
else:
raise TypeError("load can only be used under DyGraph mode")
def get_opti_var_name_list(self):
return self._opti_name_list
......
python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py
浏览文件 @ 022dfed4
......@@ -144,14 +144,14 @@ class TestDygraphCheckpoint(unittest.TestCase):
avg_loss.backward()
sgd.minimize(avg_loss)
fluid.dygraph.save_persistables(mnist.state_dict(),
fluid.dygraph.save_persistables(mnist.state_dict(), [sgd],
"save_dir")
mnist.clear_gradients()
for param in mnist.parameters():
dy_param_init_value[param.name] = param.numpy()
restore = fluid.dygraph.load_persistables("save_dir")
restore, _ = fluid.dygraph.load_persistables("save_dir")
mnist.load_dict(restore)
self.assertEqual(len(dy_param_init_value), len(restore))
......
python/paddle/fluid/tests/unittests/test_imperative_save_load_optimizer.py 0 → 100644
浏览文件 @ 022dfed4
# 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer, Adam
from paddle.fluid.dygraph.nn import FC
from paddle.fluid.dygraph.base import to_variable
class MLP(fluid.Layer):
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), 10)
self._fc2 = FC(self.full_name(), 10)
def forward(self, inputs):
y = self._fc1(inputs)
y = self._fc2(y)
return y
class TestImperativeOptimizerBase(unittest.TestCase):
def setUp(self):
self.batch_num = 20
def get_optimizer(self):
raise NotImplementedError()
def _check_mlp(self):
seed = 90
with fluid.dygraph.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mlp = MLP('mlp')
optimizer = self.get_optimizer()
optimizer2 = SGDOptimizer(
learning_rate=fluid.layers.natural_exp_decay(
learning_rate=0.1,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array(
[x[0].reshape(1, 28, 28) for x in data]).astype('float32')
y_data = np.array([x[1] for x in data]).astype('int64').reshape(
128, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
cost = mlp(img)
avg_loss = fluid.layers.reduce_mean(cost)
avg_loss.backward()
optimizer.minimize(avg_loss)
optimizer2.minimize(avg_loss)
mlp.clear_gradients()
fluid.dygraph.save_persistables(
mlp.state_dict(), [optimizer, optimizer2], "save_dir_2")
if batch_id == 2:
break
with fluid.dygraph.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mlp_load = MLP('mlp')
optimizer_load1 = self.get_optimizer()
optimizer_load2 = SGDOptimizer(
learning_rate=fluid.layers.natural_exp_decay(
learning_rate=0.1,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
parameters, optimizers = fluid.dygraph.load_persistables(
"save_dir_2")
mlp_load.load_dict(parameters)
optimizer_load1.load(optimizers)
optimizer_load2.load(optimizers)
self.assertTrue(optimizer._learning_rate.__dict__ ==
optimizer_load1._learning_rate.__dict__)
self.assertTrue(optimizer2._learning_rate.__dict__ ==
optimizer_load2._learning_rate.__dict__)
class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
def get_optimizer(self):
bd = [3, 6, 9]
optimizer = SGDOptimizer(learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=[0.1 * (0.1**i) for i in range(len(bd) + 1)]))
return optimizer
def test_sgd(self):
self._check_mlp()
class TestImperativeOptimizerNaturalExpDecay(TestImperativeOptimizerBase):
def get_optimizer(self):
optimizer = SGDOptimizer(learning_rate=fluid.layers.natural_exp_decay(
learning_rate=0.1,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
return optimizer
def test_sgd(self):
self._check_mlp()
class TestImperativeOptimizerExponentialDecay(TestImperativeOptimizerBase):
def get_optimizer(self):
optimizer = SGDOptimizer(learning_rate=fluid.layers.exponential_decay(
learning_rate=0.1,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
return optimizer
def test_sgd(self):
self._check_mlp()
class TestImperativeOptimizerInverseTimeDecay(TestImperativeOptimizerBase):
def get_optimizer(self):
optimizer = Adam(learning_rate=fluid.layers.inverse_time_decay(
learning_rate=0.1,
decay_steps=10000,
decay_rate=0.5,
staircase=True))
return optimizer
def test_adam(self):
self._check_mlp()
class TestImperativeOptimizerPolynomialDecay(TestImperativeOptimizerBase):
def get_optimizer(self):
optimizer = SGDOptimizer(learning_rate=fluid.layers.polynomial_decay(
learning_rate=0.1, decay_steps=5, cycle=self.cycle))
return optimizer
def test_sgd_cycle(self):
self.cycle = True
self._check_mlp()
def test_sgd(self):
self.cycle = False
self._check_mlp()
class TestImperativeOptimizerCosineDecay(TestImperativeOptimizerBase):
def get_optimizer(self):
optimizer = SGDOptimizer(learning_rate=fluid.layers.cosine_decay(
learning_rate=0.1, step_each_epoch=10000, epochs=120))
return optimizer
def test_sgd(self):
self._check_mlp()
class TestImperativeOptimizerNoamDecay(TestImperativeOptimizerBase):
def get_optimizer(self):
optimizer = SGDOptimizer(learning_rate=fluid.layers.noam_decay(
d_model=512, warmup_steps=8000))
return optimizer
def test_sgd(self):
self._check_mlp()
if __name__ == '__main__':
unittest.main()
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