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Paddle
“1006383b8485c3409a9a7e09d9623df7e03f7364”上不存在“paddle/phi/kernels/bilinear_kernel.h”
提交 072d95d8 编写于 作者: S sneaxiy
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Merge develop 

test=develop
上级 a93a9eef f9061796
隐藏空白更改
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Showing with 5493 addition and 1467 deletion
paddle/fluid/API.spec
浏览文件 @ 072d95d8
......@@ -255,6 +255,7 @@ paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=
paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '8f8c0306117ea441f20dcbbdba1f0ecc'))
paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '2e53e83127dbfd86e7098bdfe9a549e8'))
paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292'))
paddle.fluid.layers.range (ArgSpec(args=['start', 'end', 'step', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '2ec937ede953ded2fdff2675883900bb'))
paddle.fluid.layers.While.__init__ (ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.While.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.Switch.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......@@ -376,23 +377,9 @@ paddle.fluid.contrib.Calibrator.__init__ (ArgSpec(args=['self'], varargs='args',
paddle.fluid.contrib.Calibrator.sample_data (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3b8c85ca1e2cf753cc8c90a6c6992958'))
paddle.fluid.contrib.Calibrator.save_int8_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.reader.ctr_reader.ctr_reader (ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2ebf3de2a6ef1af2c3b88d2db7591ab'))
paddle.fluid.contrib.build_compressor (ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.CompressPass.__init__ (ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.CompressPass.add_strategy (ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None), ('document', '3bf6010b6f47d3c86df0ec8957be95e0'))
paddle.fluid.contrib.CompressPass.apply (ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None), ('document', 'a92bf85d4b59bd4f2ac1706d7c4899a6'))
paddle.fluid.contrib.ImitationGraph.__init__ (ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.ImitationGraph.all_parameters (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.__init__ (ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.MagnitudePruner.__init__ (ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.MagnitudePruner.prune (ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.RatioPruner.__init__ (ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e7a81a325b296a9ca502ee5adb4fc85d'))
paddle.fluid.contrib.RatioPruner.prune (ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,)), ('document', '358cbf2978c91028fb96a195a9884645'))
paddle.fluid.contrib.Compressor.__init__ (ArgSpec(args=['self', 'place', 'scope', 'train_program', 'train_reader', 'train_feed_list', 'train_fetch_list', 'eval_program', 'eval_reader', 'eval_feed_list', 'eval_fetch_list', 'teacher_programs', 'checkpoint_path', 'train_optimizer', 'distiller_optimizer'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, [], './checkpoints', None, None)), ('document', '31ae143830c9bf6b43547dd546c5ba80'))
paddle.fluid.contrib.Compressor.config (ArgSpec(args=['self', 'config_file'], varargs=None, keywords=None, defaults=None), ('document', '780d9c007276ccbb95b292400d7807b0'))
paddle.fluid.contrib.Compressor.run (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'c6e43d6a078d307672283c1f36e04fe9'))
paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '2ab36d4f7a564f5f65e455807ad06c67'))
paddle.fluid.contrib.load_persistables_for_inference (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None), ('document', '59066bac9db0ac6ce414d05780b7333f'))
paddle.fluid.contrib.convert_dist_to_sparse_program (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '74c39c595dc70d6be2f16d8e462d282b'))
......@@ -432,48 +419,59 @@ paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'poo
paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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.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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
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', '46234a5470590feb336346f70a3db715'))
paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '18db9c70be9c4dd466f9844457b21bfe'))
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'))
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
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.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '1a79bd7d10ae54ca763ec81bca36ba24'))
paddle.fluid.regularizer.L1DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 072d95d8
......@@ -51,9 +51,7 @@ else()
cc_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
endif()
cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_base scope lod_tensor)
cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
if(WITH_GPU)
cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper gpu_info)
......@@ -74,7 +72,7 @@ cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS grap
cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_helper pass)
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle fused_broadcast_op_handle)
cc_library(fuse_all_reduce_op_pass SRCS fuse_all_reduce_op_pass.cc DEPS graph graph_helper fused_all_reduce_op_handle)
......
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 072d95d8
......@@ -11,9 +11,8 @@
// 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.
#include <algorithm>
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include <algorithm>
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
......@@ -56,6 +55,7 @@ void AllReduceOpHandle::RunImpl() {
platform::RecordEvent record_event(Name());
WaitInputVarGenerated();
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(
......
paddle/fluid/framework/details/broadcast_op_handle.h
浏览文件 @ 072d95d8
......@@ -57,7 +57,7 @@ struct BroadcastOpHandle : public OpHandleBase {
std::string Name() const override;
bool IsMultiDeviceTransfer() override { return false; };
bool IsMultiDeviceTransfer() override { return true; };
protected:
void RunImpl() override;
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 072d95d8
......@@ -147,6 +147,10 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Verify that the graph is correct for multi-device executor.
AppendPass("multi_devices_check_pass");
if (VLOG_IS_ON(2)) {
AppendPass("all_reduce_deps_pass");
}
if (SeqOnlyAllReduceOps(strategy)) {
VLOG(10) << "Add all_reduce_deps_pass";
AppendPass("all_reduce_deps_pass");
......
paddle/fluid/framework/details/data_balance_op_handle.cc 已删除 100644 → 0
浏览文件 @ a93a9eef
// 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.
#include "paddle/fluid/framework/details/data_balance_op_handle.h"
#include <algorithm>
#include "paddle/fluid/framework/details/container_cast.h"
namespace paddle {
namespace framework {
namespace details {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
DataBalanceOpHandle::DataBalanceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs)
: OpHandleBase(node), local_scopes_(local_scopes), places_(places) {
if (ctxs) {
for (auto &p : places_) {
this->SetDeviceContext(p, ctxs->DevCtx(p));
}
}
}
#else
DataBalanceOpHandle::DataBalanceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places)
: OpHandleBase(node), local_scopes_(local_scopes), places_(places) {}
#endif
std::string DataBalanceOpHandle::Name() const { return "data balance"; }
std::vector<std::array<int, 3>> DataBalanceOpHandle::GetBalancePlan(
const std::vector<int> &device_sizes) {
int device_num = device_sizes.size();
int total_size = 0;
int empty_num = 0;
std::vector<std::array<int, 2>> size_device_vec;
size_device_vec.reserve(device_num);
for (int i = 0; i < device_num; ++i) {
if (device_sizes[i] == 0) {
++empty_num;
}
total_size += device_sizes[i];
size_device_vec.push_back({{device_sizes[i], i}});
}
std::vector<std::array<int, 3>> res;
if (empty_num == 0) {
// No need to do data balance.
return res;
}
if (total_size < device_num) {
// No enough data.
PADDLE_THROW_EOF();
}
std::sort(size_device_vec.begin(), size_device_vec.end(),
[](const std::array<int, 2> &a, const std::array<int, 2> &b) {
return a[0] > b[0];
});
int expected_device_size = total_size / device_num;
int src_idx = 0;
for (int dst_idx = device_num - empty_num; dst_idx < device_num; ++dst_idx) {
if (size_device_vec[src_idx][0] <= expected_device_size) {
++src_idx;
PADDLE_ENFORCE_LT(
src_idx, device_num - empty_num,
"In current srategy an empty tensor should not be copy source.");
}
size_device_vec[src_idx][0] -= expected_device_size;
size_device_vec[dst_idx][0] += expected_device_size;
res.push_back({{size_device_vec[src_idx][1], size_device_vec[dst_idx][1],
expected_device_size}});
}
return res;
}
void DataBalanceOpHandle::RunImpl() {
PADDLE_ENFORCE_GT(places_.size(), 1UL,
"Data balance can only be enabled when the number of "
"places to run larger than 1.");
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE(in_var_handles.size() % places_.size() == 0);
PADDLE_ENFORCE_EQ(
in_var_handles.size(), out_var_handles.size(),
"The NoDummyInputSize and NoDummyOutputSize should be equal.");
int data_num = in_var_handles.size() / places_.size();
WaitInputVarGenerated();
std::vector<std::vector<LoDTensor *>> lod_tensors(data_num);
std::vector<int> device_sizes;
for (int i = 0; i < static_cast<int>(in_var_handles.size()); ++i) {
PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
"The name of input and output should be equal.");
int place_idx = i / data_num;
int data_idx = i % data_num;
auto *local_scope =
local_scopes_[place_idx]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto *tensor_var = local_scope->FindVar(in_var_handles[i]->name());
PADDLE_ENFORCE(tensor_var->IsType<LoDTensor>());
auto *tensor = tensor_var->GetMutable<LoDTensor>();
lod_tensors[data_idx].push_back(tensor);
int ins_size =
tensor->lod().empty() ? tensor->dims()[0] : tensor->NumElements();
if (data_idx == 0) {
device_sizes.emplace_back(ins_size);
} else {
PADDLE_ENFORCE_EQ(
ins_size, device_sizes.at(place_idx),
"All data on the same device shall have the same batch size.");
}
}
const auto &balance_plan = GetBalancePlan(device_sizes);
for (const auto &trans : balance_plan) {
for (int data_idx = 0; data_idx < data_num; ++data_idx) {
LoDTensor *src_tensor = lod_tensors[data_idx][trans[0]];
LoDTensor *dst_tensor = lod_tensors[data_idx][trans[1]];
int trans_ins_size = trans[2];
LoD src_lod = src_tensor->lod();
int src_ins_size =
src_lod.empty() ? src_tensor->dims()[0] : src_tensor->NumElements();
int cut_point = src_ins_size - trans_ins_size;
if (!src_lod.empty()) {
for (auto &level : src_lod) {
cut_point = level[cut_point];
}
}
TensorCopySync(src_tensor->Slice(cut_point, src_tensor->dims()[0]),
dst_tensor->place(), dst_tensor);
src_tensor->ShareDataWith(src_tensor->Slice(0, cut_point));
if (!src_lod.empty()) {
dst_tensor->set_lod(SliceInLevel(
src_lod, 0, src_ins_size - trans_ins_size, src_ins_size));
src_tensor->set_lod(
SliceInLevel(src_lod, 0, 0, src_ins_size - trans_ins_size));
}
}
}
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/data_balance_op_handle.h 已删除 100644 → 0
浏览文件 @ a93a9eef
// 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.
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
namespace details {
struct DataBalanceOpHandle : public OpHandleBase {
public:
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
DataBalanceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs);
#else
DataBalanceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places);
#endif
std::string Name() const override;
bool IsMultiDeviceTransfer() override { return false; };
protected:
void RunImpl() override;
private:
// std::vector<(src_dev_id, dst_dev_id, trans_size)>
std::vector<std::array<int, 3>> GetBalancePlan(
const std::vector<int> &batch_size_per_device);
const std::vector<Scope *> local_scopes_;
const std::vector<platform::Place> places_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fetch_op_handle.cc
浏览文件 @ 072d95d8
......@@ -82,6 +82,8 @@ void FetchOpHandle::WaitInputVarGenerated(const platform::Place &place) {
}
}
bool FetchOpHandle::IsMultiDeviceTransfer() { return true; }
std::string FetchOpHandle::Name() const { return "Fetch"; }
} // namespace details
......
paddle/fluid/framework/details/fetch_op_handle.h
浏览文件 @ 072d95d8
......@@ -39,6 +39,8 @@ struct FetchOpHandle : public OpHandleBase {
std::string Name() const override;
bool IsMultiDeviceTransfer() override;
protected:
void RunImpl() override;
......
paddle/fluid/framework/details/fuse_vars_op_handle.cc 已删除 100644 → 0
浏览文件 @ a93a9eef
// 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.
#include "paddle/fluid/framework/details/fuse_vars_op_handle.h"
namespace paddle {
namespace framework {
namespace details {
void FuseVarsOpHandle::RunImpl() {
WaitInputVarGenerated(place_);
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(in_var_handles.size(), 0UL);
PADDLE_ENFORCE_EQ(out_var_handles.size() - 1, inputs_numel_.size(), "");
auto scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto out_var_handle = out_var_handles[0];
auto out_var = scope->Var(out_var_handle->name());
auto out_tensor = out_var->GetMutable<LoDTensor>();
out_tensor->Resize({total_numel_}).mutable_data(this->place_, type_);
int64_t s = 0;
for (size_t i = 1; i < out_var_handles.size(); ++i) {
auto out_name = out_var_handles[i]->name();
auto out_t = scope->Var(out_name)->GetMutable<LoDTensor>();
auto numel = this->inputs_numel_.at(out_name);
out_t->ShareDataWith(out_tensor->Slice(s, s + numel));
s += numel;
}
this->RunAndRecordEvent([] {});
}
std::string FuseVarsOpHandle::Name() const { return "fuse vars"; }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fuse_vars_op_handle.h 已删除 100644 → 0
浏览文件 @ a93a9eef
// 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.
#pragma once
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
namespace details {
struct FuseVarsOpHandle : public OpHandleBase {
public:
FuseVarsOpHandle(ir::Node *node, Scope *local_scope,
const platform::Place &place,
const std::unordered_map<std::string, int64_t> &inputs_numel,
const proto::VarType::Type var_type)
: OpHandleBase(node),
local_scope_(local_scope),
place_(place),
inputs_numel_(inputs_numel),
type_(var_type) {
total_numel_ = 0;
for (auto in_numel : inputs_numel) {
PADDLE_ENFORCE_GT(in_numel.second, 0);
total_numel_ += in_numel.second;
}
}
std::string Name() const override;
bool IsMultiDeviceTransfer() override { return false; };
protected:
void RunImpl() override;
private:
Scope *local_scope_;
const platform::Place place_;
const std::unordered_map<std::string, int64_t> inputs_numel_;
const proto::VarType::Type type_;
int64_t total_numel_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fused_all_reduce_op_handle.cc
浏览文件 @ 072d95d8
......@@ -112,19 +112,20 @@ void FusedAllReduceOpHandle::RunImpl() {
});
for (size_t k = 1; k < g_tensor.size(); ++k) {
const void *pre_address = g_tensor.at(k - 1).second->data<void>();
const void *cur_address = g_tensor.at(k - 1).second->data<void>();
int64_t len = g_tensor.at(k - 1).second->numel();
auto offset = len * framework::SizeOfType(dtype);
void *next_address = reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(pre_address) + offset);
const void *cur_address = g_tensor.at(k).second->data<void>();
VLOG(10) << k << ", "
<< " pre_address(" << g_tensor.at(k - 1).first
<< "): " << pre_address << ", cur_address("
<< g_tensor.at(k).first << "): " << cur_address
<< ", offset:" << offset << ", " << next_address << ", "
<< cur_address;
PADDLE_ENFORCE_EQ(next_address, cur_address);
void *infer_next_address = reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(cur_address) + offset);
const void *next_address = g_tensor.at(k).second->data<void>();
VLOG(10) << string::Sprintf(
"Input[%d](%s) address: 0X%02x, Input[%d](%s) address: 0X%02x, Infer "
"input[%d] address: 0X%02x. The offset: %d",
k - 1, g_tensor.at(k - 1).first, cur_address, g_tensor.at(k).first, k,
next_address, k, infer_next_address, offset);
PADDLE_ENFORCE_EQ(infer_next_address, next_address,
"The address is not consistent.");
}
}
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 072d95d8
......@@ -14,13 +14,15 @@
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include <algorithm>
#include <fstream>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/data_balance_op_handle.h"
#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/rpc_op_handle.h"
......
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ 072d95d8
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/framework/details/op_handle_base.h"
#include <map>
#include <unordered_set>
namespace paddle {
namespace framework {
......@@ -41,15 +42,42 @@ OpHandleBase::~OpHandleBase() {
void OpHandleBase::Run(bool use_cuda) {
#ifdef PADDLE_WITH_CUDA
if (events_.empty() && use_cuda) {
if (events_.empty() && use_cuda && dev_ctxes_.size() > 0) {
for (auto &p : dev_ctxes_) {
int dev_id = boost::get<platform::CUDAPlace>(p.first).device;
PADDLE_ENFORCE(cudaSetDevice(dev_id));
PADDLE_ENFORCE(
cudaEventCreateWithFlags(&events_[dev_id], cudaEventDisableTiming));
}
if (IsMultiDeviceTransfer() && dev_ctxes_.size() > 0) {
for (auto &out_var : outputs_) {
auto *out_var_handle = dynamic_cast<VarHandle *>(out_var);
if (out_var_handle) {
int dev_id =
boost::get<platform::CUDAPlace>(out_var_handle->place()).device;
out_var_handle->SetGenerateEvent(events_[dev_id]);
}
}
} else {
PADDLE_ENFORCE_EQ(dev_ctxes_.size(), 1UL,
"%s should have only one dev_ctx.", Name());
auto &place = dev_ctxes_.begin()->first;
int dev_id = boost::get<platform::CUDAPlace>(place).device;
for (auto &out_var : outputs_) {
auto *out_var_handle = dynamic_cast<VarHandle *>(out_var);
if (out_var_handle) {
PADDLE_ENFORCE(
platform::is_same_place(place, out_var_handle->place()),
"The place of input(%s) is not consistent with the "
"place of current op(%s).",
out_var_handle->Name(), Name());
out_var_handle->SetGenerateEvent(events_[dev_id]);
}
}
}
}
#else
PADDLE_ENFORCE(!use_cuda);
#endif
......@@ -93,17 +121,48 @@ void OpHandleBase::AddOutput(VarHandleBase *out) {
void OpHandleBase::WaitInputVarGenerated() {
for (auto in_var : inputs_) {
if (NeedWait(in_var)) {
for (auto &pair : dev_ctxes_) {
in_var->GeneratedOp()->RecordWaitEventOnCtx(pair.second);
// Dummy Variable is used to represent dependencies between operators, so
// there doesn't add event for it.
auto *in_var_handle = dynamic_cast<VarHandle *>(in_var);
if (in_var_handle) {
auto &place = in_var_handle->place();
if (platform::is_gpu_place(place)) {
#ifdef PADDLE_WITH_CUDA
auto stream =
static_cast<platform::CUDADeviceContext *>(dev_ctxes_.at(place))
->stream();
PADDLE_ENFORCE(
cudaStreamWaitEvent(stream, in_var_handle->GetEvent(), 0));
#else
PADDLE_THROW("Doesn't compile the GPU.");
#endif
}
// There are nothing to do when the place is CPUPlace.
}
}
}
}
void OpHandleBase::WaitInputVarGenerated(const platform::Place &place) {
for (auto *in : inputs_) {
if (NeedWait(in)) {
in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(place));
for (auto in_var : inputs_) {
if (NeedWait(in_var)) {
// Dummy Variable is used to represent dependencies between operators, so
// there doesn't add event for it.
auto *in_var_handle = dynamic_cast<VarHandle *>(in_var);
if (in_var_handle) {
if (platform::is_gpu_place(in_var_handle->place())) {
#ifdef PADDLE_WITH_CUDA
auto stream = static_cast<platform::CUDADeviceContext *>(
dev_ctxes_.at(in_var_handle->place()))
->stream();
PADDLE_ENFORCE(
cudaStreamWaitEvent(stream, in_var_handle->GetEvent(), 0));
#else
PADDLE_THROW("Doesn't compile the GPU.");
#endif
}
// There are nothing to do when the place is CPUPlace.
}
}
}
}
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 072d95d8
......@@ -14,7 +14,6 @@
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -27,62 +26,49 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
: graph_(graph),
pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
: nullptr),
prepare_pool_(1),
local_scopes_(local_scopes),
places_(places),
fetch_ctxs_(places),
running_ops_(0),
strategy_(strategy) {}
strategy_(strategy) {
PrepareOpDeps();
CopyOpDeps();
}
FeedFetchList ThreadedSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
std::unique_ptr<platform::RecordEvent> event(
new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare"));
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
auto ready_vars = std::make_shared<BlockingQueue<VarHandleBase *>>();
std::unordered_set<OpHandleBase *> ready_ops;
std::unique_ptr<OpDependentData> op_deps = op_deps_futures_.get();
CopyOpDeps();
VLOG(10) << "ThreadedSSAGraphExecutor::Run";
std::shared_ptr<BlockingQueue<VarHandleBase *>> ready_vars(
new BlockingQueue<VarHandleBase *>);
auto &pending_ops = op_deps->pending_ops_;
auto &pending_vars = op_deps->pending_vars_;
auto &ready_ops = op_deps->ready_ops_;
// For ops (e.g. nccl_all_reduce) that need to coordinate multiple
// streams from multiple GPUs, it's faster to buffer them and schedule
// together since we currently cannot overlap computation and memcpy streams.
// Should revisit it if overlapping is available.
std::unordered_set<OpHandleBase *> delayed_ops;
// Transform SSAGraph to pending_ops & pending_vars
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
InsertPendingVar(&pending_vars, ready_vars.get(), version_pair);
}
}
}
for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
InsertPendingVar(&pending_vars, ready_vars.get(), var);
}
for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
if (op->Inputs().empty()) { // Special case, Op has no input.
ready_ops.insert(op);
} else {
InsertPendingOp(&pending_ops, op);
}
}
// Step 2. Insert FetchOps
std::vector<FetchOpHandle *> fetch_ops;
std::unordered_set<VarHandleBase *> fetch_dependencies;
FeedFetchList fetch_data(fetch_tensors.size());
InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &pending_ops,
&pending_vars, ready_vars.get(), &fetch_data);
InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &ready_ops,
&pending_ops, &pending_vars, &fetch_data);
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
running_ops_++;
RunOp(ready_vars, op);
}
set.clear();
};
auto run_all_op = [&](OpHandleBase *op) { RunOp(ready_vars, op); };
// Clean run context
run_op_futures_.clear();
exception_holder_.Clear();
......@@ -91,19 +77,11 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
while (!pending_vars.empty()) {
// 1. Run All Ready ops
// Keep loop until all vars are ready.
//
// NOTE: DelayedOps have a lower priority. It will be scheduled after all
// ready_ops have been performed.
if (ready_ops.empty() && strategy_.allow_op_delay_ && running_ops_ == 0) {
run_all_ops(delayed_ops);
} else {
run_all_ops(ready_ops);
}
run_all_ops(ready_ops);
// 2. Find ready variable
bool timeout;
auto cur_ready_vars = ready_vars->PopAll(1, &timeout);
if (timeout) {
if (exception_holder_.IsCaught()) {
for (auto &run_op_future : run_op_futures_) {
......@@ -115,6 +93,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
continue;
}
}
// 3. Remove the dependency of ready_var.
// Find the ready_ops after the ready_var.
for (auto ready_var : cur_ready_vars) {
......@@ -123,11 +102,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
auto &deps = pending_ops[op];
--deps;
if (deps == 0) {
if (op->IsMultiDeviceTransfer() && strategy_.allow_op_delay_) {
delayed_ops.insert(op);
} else {
ready_ops.insert(op);
}
run_all_op(op);
}
}
}
......@@ -143,16 +118,17 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
const std::vector<std::string> &fetch_tensors,
std::vector<FetchOpHandle *> *fetch_ops,
std::unordered_set<VarHandleBase *> *fetch_dependencies,
std::unordered_set<OpHandleBase *> *ready_ops,
std::unordered_map<OpHandleBase *, size_t> *pending_ops,
std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars, FeedFetchList *fetch_data) {
FeedFetchList *fetch_data) {
std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
std::unordered_set<VarHandleBase *> local_ready_vars;
for (auto &fetch_var_name : fetch_tensors) {
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
auto it = var_map.find(fetch_var_name);
if (it != var_map.end()) {
fetched_vars[fetch_var_name].push_back(*it->second.rbegin());
fetched_vars[fetch_var_name].emplace_back(*it->second.rbegin());
}
}
}
......@@ -161,8 +137,9 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
auto &var_name = fetch_tensors[i];
auto fetched_var_it = fetched_vars.find(var_name);
PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
"Cannot find fetched variable.(Perhaps the main_program "
"is not set to ParallelExecutor)");
"Cannot find fetched variable(%s).(Perhaps the main_program "
"is not set to ParallelExecutor)",
var_name);
auto &vars = fetched_var_it->second;
......@@ -184,9 +161,23 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
auto *fetch_dummy = new DummyVarHandle(fetch_var);
op->AddOutput(fetch_dummy);
fetch_dependencies->emplace(fetch_dummy);
this->InsertPendingVar(pending_vars, ready_vars, fetch_dummy);
this->InsertPendingOp(pending_ops, op);
this->InsertPendingVar(pending_vars, &local_ready_vars, fetch_dummy);
size_t wait_input_num = 0;
std::unordered_set<VarHandleBase *> input_set(vars.begin(), vars.end());
for (auto *var : input_set) {
if (pending_vars->count(var)) {
++wait_input_num;
}
}
if (wait_input_num) {
pending_ops->insert({op, wait_input_num});
} else {
ready_ops->insert(static_cast<OpHandleBase *>(op));
}
}
PADDLE_ENFORCE_EQ(local_ready_vars.size(), 0);
}
void ThreadedSSAGraphExecutor::InsertPendingOp(
......@@ -197,11 +188,63 @@ void ThreadedSSAGraphExecutor::InsertPendingOp(
void ThreadedSSAGraphExecutor::InsertPendingVar(
std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars, VarHandleBase *var) const {
std::unordered_set<VarHandleBase *> *ready_vars, VarHandleBase *var) const {
pending_vars->insert(var);
if (var->GeneratedOp() == nullptr) {
ready_vars->Push(var);
ready_vars->insert(var);
}
}
void ThreadedSSAGraphExecutor::PrepareOpDeps() {
op_deps_.reset(new OpDependentData());
std::unordered_map<OpHandleBase *, size_t> &pending_ops =
op_deps_->pending_ops_;
std::unordered_set<VarHandleBase *> &pending_vars = op_deps_->pending_vars_;
std::unordered_set<OpHandleBase *> &ready_ops = op_deps_->ready_ops_;
std::unordered_set<VarHandleBase *> ready_vars;
// Transform SSAGraph to pending_ops & pending_vars
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
InsertPendingVar(&pending_vars, &ready_vars, version_pair);
}
}
}
for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
InsertPendingVar(&pending_vars, &ready_vars, var);
}
for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
if (op->Inputs().empty()) { // Special case, Op has no input.
ready_ops.insert(op);
} else {
InsertPendingOp(&pending_ops, op);
}
}
for (auto ready_var : ready_vars) {
pending_vars.erase(ready_var);
for (auto *op : ready_var->PendingOps()) {
auto &deps = pending_ops[op];
--deps;
if (deps == 0) {
ready_ops.insert(op);
}
}
}
}
void ThreadedSSAGraphExecutor::CopyOpDeps() {
op_deps_futures_ = prepare_pool_.enqueue([&] {
auto *op_deps = new OpDependentData();
op_deps->pending_ops_.insert(op_deps_->pending_ops_.begin(),
op_deps_->pending_ops_.end());
op_deps->pending_vars_.insert(op_deps_->pending_vars_.begin(),
op_deps_->pending_vars_.end());
op_deps->ready_ops_.insert(op_deps_->ready_ops_.begin(),
op_deps_->ready_ops_.end());
return std::unique_ptr<OpDependentData>(op_deps);
});
}
void ThreadedSSAGraphExecutor::RunOp(
......@@ -216,7 +259,6 @@ void ThreadedSSAGraphExecutor::RunOp(
op->Run(strategy_.use_cuda_);
}
VLOG(10) << op << " " << op->Name() << " Done ";
running_ops_--;
ready_var_q->Extend(op->Outputs());
VLOG(10) << op << " " << op->Name() << " Signal posted";
} catch (...) {
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ 072d95d8
......@@ -15,18 +15,20 @@
#pragma once
#include <deque>
#include <functional>
#include <list>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <functional>
#include "ThreadPool.h" // ThreadPool in thrird party
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/exception_holder.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
#include "paddle/fluid/framework/ir/graph.h"
......@@ -36,6 +38,12 @@ class Scope;
namespace details {
struct OpDependentData {
std::unordered_map<OpHandleBase *, size_t> pending_ops_;
std::unordered_set<VarHandleBase *> pending_vars_;
std::unordered_set<OpHandleBase *> ready_ops_;
};
class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
public:
ThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
......@@ -57,29 +65,35 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
private:
ir::Graph *graph_;
std::unique_ptr<::ThreadPool> pool_;
::ThreadPool prepare_pool_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
platform::DeviceContextPool fetch_ctxs_;
ExceptionHolder exception_holder_;
std::atomic<int> running_ops_;
void InsertPendingOp(std::unordered_map<OpHandleBase *, size_t> *pending_ops,
OpHandleBase *op_instance) const;
void InsertPendingVar(std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars,
std::unordered_set<VarHandleBase *> *ready_vars,
VarHandleBase *var) const;
void InsertFetchOps(const std::vector<std::string> &fetch_tensors,
std::vector<FetchOpHandle *> *fetch_ops,
std::unordered_set<VarHandleBase *> *fetch_dependencies,
std::unordered_set<OpHandleBase *> *ready_ops,
std::unordered_map<OpHandleBase *, size_t> *pending_ops,
std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars,
FeedFetchList *fetch_data);
void PrepareOpDeps();
void CopyOpDeps();
private:
std::future<std::unique_ptr<OpDependentData>> op_deps_futures_;
ExecutionStrategy strategy_;
std::unique_ptr<OpDependentData> op_deps_;
// use std::list because clear(), push_back, and for_each are O(1)
std::list<std::future<void>> run_op_futures_;
};
......
paddle/fluid/framework/details/var_handle.h
浏览文件 @ 072d95d8
......@@ -43,6 +43,7 @@ struct VarHandleBase {
virtual ~VarHandleBase();
virtual std::string DebugString() const = 0;
virtual const std::string& Name() const = 0;
void AddInput(OpHandleBase* in, ir::Node* node) {
node_->inputs.clear();
......@@ -95,8 +96,6 @@ struct VarHandleBase {
//
// NOTE: runtime variables have place.
struct VarHandle : public VarHandleBase {
explicit VarHandle(ir::Node* node) : VarHandleBase(node) {}
virtual ~VarHandle();
std::string DebugString() const override;
......@@ -109,6 +108,20 @@ struct VarHandle : public VarHandleBase {
name_(std::move(name)),
place_(std::move(place)) {}
#ifdef PADDLE_WITH_CUDA
bool HasEvent() { return has_event_; }
const cudaEvent_t& GetEvent() {
PADDLE_ENFORCE(HasEvent(), "The event is not set.");
return event_;
}
void SetGenerateEvent(const cudaEvent_t& event) {
has_event_ = true;
event_ = event;
}
#endif
// version field currently is not used, however, just store the version to
// debug easily.
private:
......@@ -116,6 +129,11 @@ struct VarHandle : public VarHandleBase {
size_t scope_idx_;
std::string name_;
platform::Place place_;
#ifdef PADDLE_WITH_CUDA
// Only when this event is triggered, var is generated.
cudaEvent_t event_;
bool has_event_{false};
#endif
public:
bool IsTheSameVar(const VarHandle& o) const {
......@@ -125,6 +143,7 @@ struct VarHandle : public VarHandleBase {
size_t version() const { return version_; }
size_t scope_idx() const { return scope_idx_; }
const std::string& Name() const override { return name_; }
const std::string& name() const { return name_; }
const platform::Place& place() const { return place_; }
};
......@@ -136,6 +155,10 @@ struct DummyVarHandle : public VarHandleBase {
virtual ~DummyVarHandle();
std::string DebugString() const override;
public:
const std::string& Name() const override { return name_; }
std::string name_{"DummyVar"};
};
} // namespace details
......
paddle/fluid/operators/concat_op.cc
浏览文件 @ 072d95d8
......@@ -57,7 +57,7 @@ class ConcatOp : public framework::OperatorWithKernel {
"elements except the specify axis.");
} else {
// not check -1 with other in compile time
if (out_dims[j] != -1 && ins[i][j] != -1) {
if (out_dims[j] > 0 && ins[i][j] > 0) {
PADDLE_ENFORCE_EQ(out_dims[j], ins[i][j],
"Input tensors should have the same "
"elements except the specify axis.");
......
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#include "paddle/fluid/operators/elementwise/elementwise_floordiv_op.h"
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
namespace paddle {
namespace operators {
class ElementwiseFloorDivOpMaker : public ElementwiseOpMaker {
protected:
std::string GetName() const override { return "FloorDiv"; }
std::string GetEquation() const override { return "Out = X // Y"; }
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(elementwise_floordiv, ops::ElementwiseOp,
ops::ElementwiseFloorDivOpMaker);
REGISTER_OP_CPU_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext,
int64_t>);
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#include "paddle/fluid/operators/elementwise/elementwise_floordiv_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int64_t>);
paddle/fluid/operators/elementwise/elementwise_floordiv_op.h 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
namespace operators {
template <typename T>
struct FloorDivFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a / b; }
};
template <typename DeviceContext, typename T>
void elementwise_floor_div(const framework::ExecutionContext &ctx,
const framework::Tensor *x,
const framework::Tensor *y, framework::Tensor *z) {
int axis = ctx.Attr<int>("axis");
ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, FloorDivFunctor<T>(), z);
}
template <typename DeviceContext, typename T>
class ElementwiseFloorDivKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<framework::LoDTensor>("X");
auto *y = ctx.Input<framework::LoDTensor>("Y");
auto *z = ctx.Output<framework::LoDTensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
// dtype of x and y is int64 or int32
elementwise_floor_div<DeviceContext, T>(ctx, x, y, z);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/elementwise/elementwise_mod_op.cc 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h"
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
namespace paddle {
namespace operators {
class ElementwiseModOpMaker : public ElementwiseOpMaker {
protected:
std::string GetName() const override { return "Mod"; }
std::string GetEquation() const override { return "Out = X % Y"; }
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(elementwise_mod, ops::ElementwiseOp,
ops::ElementwiseModOpMaker);
REGISTER_OP_CPU_KERNEL(
elementwise_mod,
ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/elementwise/elementwise_mod_op.cu 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_mod, ops::ElementwiseModKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseModKernel<plat::CUDADeviceContext, int64_t>);
paddle/fluid/operators/elementwise/elementwise_mod_op.h 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
namespace operators {
template <typename T>
struct ModFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a % b; }
};
template <typename DeviceContext, typename T>
void elementwise_mod(const framework::ExecutionContext &ctx,
const framework::Tensor *x, const framework::Tensor *y,
framework::Tensor *z) {
int axis = ctx.Attr<int>("axis");
ElementwiseComputeEx<ModFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
ModFunctor<T>(), z);
}
template <typename DeviceContext, typename T>
class ElementwiseModKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<framework::LoDTensor>("X");
auto *y = ctx.Input<framework::LoDTensor>("Y");
auto *z = ctx.Output<framework::LoDTensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
// dtype of x and y is int64 or int32
elementwise_mod<DeviceContext, T>(ctx, x, y, z);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/fake_dequantize_op.cc
浏览文件 @ 072d95d8
......@@ -33,8 +33,51 @@ struct DequantizeFunctor<platform::CPUDeviceContext, T> {
}
};
template <typename T>
struct ChannelDequantizeFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor** scales,
const int scale_num, T max_range, framework::Tensor* out) {
if (scale_num == 1) {
const int channel = in->dims()[0];
const T* scale_factor = scales[0]->data<T>();
for (int i = 0; i < channel; i++) {
T s = scale_factor[i];
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
auto in_e = framework::EigenVector<T>::Flatten(one_channel_in);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
auto& dev = *dev_ctx.eigen_device();
out_e.device(dev) = (s / max_range) * in_e;
}
} else if (scale_num == 2) {
int batch_size = in->dims()[0];
int channel = in->dims()[1];
const T* scale_one = scales[0]->data<T>();
const T* scale_two = scales[1]->data<T>();
for (int i = 0; i < batch_size; i++) {
framework::Tensor one_batch_in = in->Slice(i, i + 1).Resize(
framework::slice_ddim(in->dims(), 1, in->dims().size()));
framework::Tensor one_batch_out = out->Slice(i, i + 1).Resize(
framework::slice_ddim(out->dims(), 1, out->dims().size()));
for (int j = 0; j < channel; j++) {
T s = scale_one[j];
framework::Tensor one_channel_in = one_batch_in.Slice(j, j + 1);
framework::Tensor one_channel_out = one_batch_out.Slice(j, j + 1);
auto in_e = framework::EigenVector<T>::Flatten(one_channel_in);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
auto& dev = *dev_ctx.eigen_device();
out_e.device(dev) = (s * scale_two[0] / max_range) * in_e;
}
}
}
}
};
template struct DequantizeFunctor<platform::CPUDeviceContext, float>;
template struct DequantizeFunctor<platform::CPUDeviceContext, double>;
template struct ChannelDequantizeFunctor<platform::CPUDeviceContext, float>;
template struct ChannelDequantizeFunctor<platform::CPUDeviceContext, double>;
class FakeDequantizeMaxAbsOp : public framework::OperatorWithKernel {
public:
......
paddle/fluid/operators/fake_dequantize_op.cu
浏览文件 @ 072d95d8
......@@ -44,8 +44,66 @@ struct DequantizeFunctor<platform::CUDADeviceContext, T> {
}
};
template <typename T>
__global__ void DequantizeOneScale(const T* in, const T* scale, T max_range,
int num, int channel, T* out) {
int tid = threadIdx.x;
int channel_size = num / channel;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
for (int i = tid; i < channel_size; i += blockDim.x) {
out_c[i] = in_c[i] * scale[blockIdx.x] / max_range;
}
}
template <typename T>
__global__ void DequantizeTwoScale(const T* in, const T* scale_one,
const T* scale_two, T max_range, int num,
int batch_size, int channel, T* out) {
int tid = threadIdx.x;
int channel_size = num / (batch_size * channel);
int scale_index = blockIdx.x % channel;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
for (int i = tid; i < channel_size; i += blockDim.x) {
out_c[i] = in_c[i] * scale_one[scale_index] * scale_two[0] / max_range;
}
}
template <typename T>
struct ChannelDequantizeFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor** scales,
const int scale_num, T max_range, framework::Tensor* out) {
const T* in_data = in->data<T>();
T* out_data = out->mutable_data<T>(dev_ctx.GetPlace());
if (scale_num == 1) {
int num = in->numel();
int channel = in->dims()[0];
const T* scale_factor = scales[0]->data<T>();
int block = 1024;
int grid = channel;
DequantizeOneScale<T><<<grid, block, 0, dev_ctx.stream()>>>(
in_data, scale_factor, max_range, num, channel, out_data);
} else if (scale_num == 2) {
int num = in->numel();
int batch_size = in->dims()[0];
int channel = in->dims()[1];
const T* scale_one = scales[0]->data<T>();
const T* scale_two = scales[1]->data<T>();
int block = 1024;
int grid = batch_size * channel;
DequantizeTwoScale<T><<<grid, block, 0, dev_ctx.stream()>>>(
in_data, scale_one, scale_two, max_range, num, batch_size, channel,
out_data);
}
}
};
template struct DequantizeFunctor<platform::CUDADeviceContext, float>;
template struct DequantizeFunctor<platform::CUDADeviceContext, double>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, float>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, double>;
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/fake_dequantize_op.h
浏览文件 @ 072d95d8
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -28,6 +29,13 @@ struct DequantizeFunctor {
framework::Tensor* out);
};
template <typename DeviceContext, typename T>
struct ChannelDequantizeFunctor {
void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
const framework::Tensor** scales, const int scale_num,
T max_range, framework::Tensor* out);
};
template <typename DeviceContext, typename T>
class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
public:
......@@ -54,32 +62,33 @@ class FakeChannelWiseDequantizeMaxAbsKernel : public framework::OpKernel<T> {
auto scales = ctx.MultiInput<framework::Tensor>("Scales");
auto* out = ctx.Output<framework::Tensor>("Out");
PADDLE_ENFORCE_EQ(scales[0]->numel(), in->dims()[0],
"The number of first scale values must be the same with "
"first dimension value of Input(X).");
auto quant_bits = ctx.Attr<std::vector<int>>("quant_bits");
int max_range = std::pow(2, quant_bits[0] - 1) - 1;
int max_range = 1;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
out->mutable_data<T>(dev_ctx.GetPlace());
auto dequant = DequantizeFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
framework::Tensor one_channel_scale = scales[0]->Slice(i, i + 1);
dequant(dev_ctx, &one_channel_in, &one_channel_scale,
static_cast<T>(max_range), &one_channel_out);
}
if (scales.size() == 2) {
int scale_num = scales.size();
if (scale_num == 1) {
PADDLE_ENFORCE_EQ(
scales[0]->numel(), in->dims()[0],
"The number of first scale values must be the same with "
"first dimension value of Input(X) when the `Scales` has only one "
"element.");
max_range *= (std::pow(2, quant_bits[0] - 1) - 1);
} else if (scale_num == 2) {
PADDLE_ENFORCE_EQ(
scales[0]->numel(), in->dims()[1],
"The number of first scale values must be the same with "
"second dimension value of Input(X) when the `Scales` has two "
"elements.");
PADDLE_ENFORCE_EQ(
scales[1]->numel(), 1,
"The second scale tensor should only have one value at now.");
max_range = std::pow(2, quant_bits[1] - 1) - 1;
dequant(dev_ctx, out, scales[1], static_cast<T>(max_range), out);
max_range *= (std::pow(2, quant_bits[0] - 1) - 1) *
(std::pow(2, quant_bits[1] - 1) - 1);
}
ChannelDequantizeFunctor<DeviceContext, T>()(
dev_ctx, in, scales.data(), scale_num, static_cast<T>(max_range), out);
}
};
......
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @ 072d95d8
......@@ -37,6 +37,21 @@ struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
template struct FindAbsMaxFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx, const T* in,
const int num, const int channel, T* out) {
const int channel_size = num / channel;
for (int i = 0; i < channel; i++) {
auto* start = in + i * channel_size;
auto* end = in + (i + 1) * channel_size;
out[i] = std::abs(*(std::max_element(start, end, Compare<T>())));
}
}
};
template struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
......@@ -53,6 +68,36 @@ struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
template struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, const int channel,
framework::Tensor* out) {
auto* scale_data = scale.data<T>();
auto* in_data = in.data<T>();
auto* out_data = out->mutable_data<T>(ctx.GetPlace());
const int channel_size = in.numel() / channel;
platform::Transform<platform::CPUDeviceContext> trans;
for (int i = 0; i < channel; i++) {
T s = scale_data[i];
auto* start = in_data + i * channel_size;
auto* end = in_data + (i + 1) * channel_size;
trans(ctx, start, end, out_data + i * channel_size,
ClipFunctor<T>(-s, s));
}
for (int i = 0; i < channel; i++) {
T s = scale_data[i];
framework::Tensor one_channel_out = out->Slice(i, i + 1);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
out_e.device(*ctx.eigen_device()) = (bin_cnt / s * out_e).round();
}
}
};
template struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext,
float>;
template <typename T>
struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
......@@ -169,10 +214,10 @@ class FakeChannelWiseQuantizeAbsMaxOp : public framework::OperatorWithKernel {
ctx->HasOutput("Out"),
"Output(Out) of FakeChannelWiseQuantizeOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("OutScales"),
"Output(Scales) of FakeChannelWiseQuantizeOp should not be null.");
ctx->HasOutput("OutScale"),
"Output(Scale) of FakeChannelWiseQuantizeOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->SetOutputDim("OutScales", {ctx->GetInputDim("X")[0]});
ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[0]});
ctx->ShareLoD("X", /*->*/ "Out");
}
......@@ -192,7 +237,7 @@ class FakeChannelWiseQuantizeAbsMaxOpMaker
AddOutput("Out",
"(Tensor) Output of quantized low level tensor, "
"but also saved as float data type.");
AddOutput("OutScales", "(Tensor) Current channel wise scale");
AddOutput("OutScale", "(Tensor) Current channel wise scale");
AddAttr<int>("bit_length", "(int, default 8)")
.SetDefault(8)
.AddCustomChecker([](const int& bit_length) {
......
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ 072d95d8
......@@ -74,6 +74,45 @@ struct FindAbsMaxFunctor<platform::CUDADeviceContext, T> {
template struct FindAbsMaxFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void FindChannelAbsMaxKernel(const T* in, const int n, const int c,
T* out) {
int tid = threadIdx.x;
int channel_size = n / c;
const T* in_c = in + blockIdx.x * channel_size;
extern __shared__ T shared_max_data[];
shared_max_data[tid] = T(0);
for (int i = tid; i < channel_size; i += blockDim.x) {
T tmp = fabs(in_c[i]);
if (tmp > shared_max_data[tid]) {
shared_max_data[tid] = tmp;
}
}
__syncthreads();
for (int i = blockDim.x / 2; i > 0; i >>= 1) {
if (tid < i && (shared_max_data[tid] < shared_max_data[tid + i])) {
shared_max_data[tid] = shared_max_data[tid + i];
}
__syncthreads();
}
if (tid == 0) {
out[blockIdx.x] = shared_max_data[0];
}
}
template <typename T>
struct FindChannelAbsMaxFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx, const T* in,
const int num, const int channel, T* out) {
int block = 1024;
int grid = channel;
FindChannelAbsMaxKernel<T><<<grid, block, 1024 * sizeof(T), ctx.stream()>>>(
in, num, channel, out);
}
};
template struct FindChannelAbsMaxFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void ClipAndQuantKernel(const T* in, const T* scale,
const int bin_cnt, const int n, T* out) {
......@@ -82,14 +121,76 @@ __global__ void ClipAndQuantKernel(const T* in, const T* scale,
T s = scale[0];
for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
T x = in[bid];
T x = in[i];
T v = x > s ? s : x;
v = v < -s ? -s : v;
v = bin_cnt / s * v;
out[bid] = round(v);
out[i] = round(v);
}
}
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = (block - 1 + num) / block;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, out_data);
}
};
template struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void ChannelClipAndQuantKernel(const T* in, const T* scale,
const int bin_cnt, const int n,
const int c, T* out) {
int tid = threadIdx.x;
int channel_size = n / c;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
T s = scale[blockIdx.x];
for (int i = tid; i < channel_size; i += blockDim.x) {
T x = in_c[i];
T v = x > s ? s : x;
v = v < -s ? -s : v;
v = bin_cnt / s * v;
out_c[i] = round(v);
}
}
template <typename T>
struct ChannelClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, const int channel,
framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = channel;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ChannelClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, channel, out_data);
}
};
template struct ChannelClipAndFakeQuantFunctor<platform::CUDADeviceContext,
float>;
template <typename T>
__global__ void FindRangeAbsMaxAndFillArray(const T* cur_scale,
const T* last_scale,
......@@ -182,26 +283,6 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
template struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext,
float>;
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = (block - 1 + num) / block;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, out_data);
}
};
template struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, float>;
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/fake_quantize_op.h
浏览文件 @ 072d95d8
......@@ -42,6 +42,19 @@ struct FindRangeAbsMaxFunctor {
framework::Tensor* scales_arr, framework::Tensor* out_scale);
};
template <typename DeviceContext, typename T>
struct FindChannelAbsMaxFunctor {
void operator()(const DeviceContext& ctx, const T* in, const int num,
const int channel, T* out);
};
template <typename DeviceContext, typename T>
struct ChannelClipAndFakeQuantFunctor {
void operator()(const DeviceContext& ctx, const framework::Tensor& in,
const framework::Tensor& scale, const int bin_cnt,
const int channel, framework::Tensor* out);
};
template <typename DeviceContext, typename T>
struct FindMovingAverageAbsMaxFunctor {
void operator()(const DeviceContext& ctx, const framework::Tensor& in_accum,
......@@ -78,29 +91,18 @@ class FakeChannelWiseQuantizeAbsMaxKernel : public framework::OpKernel<T> {
auto* in = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
auto* out_scales = context.Output<framework::Tensor>("OutScales");
T* out_scales_data = out_scales->mutable_data<T>(context.GetPlace());
auto* out_scale = context.Output<framework::Tensor>("OutScale");
T* out_scale_data = out_scale->mutable_data<T>(context.GetPlace());
out->mutable_data<T>(context.GetPlace());
int bit_length = context.Attr<int>("bit_length");
int bin_cnt = std::pow(2, bit_length - 1) - 1;
auto& dev_ctx = context.template device_context<DeviceContext>();
auto find_abs_max = FindAbsMaxFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel = in->Slice(i, i + 1);
const T* one_channel_data = one_channel.data<T>();
find_abs_max(dev_ctx, one_channel_data, one_channel.numel(),
&out_scales_data[i]);
}
auto clip_quant = ClipAndFakeQuantFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
framework::Tensor one_channel_scale = out_scales->Slice(i, i + 1);
clip_quant(dev_ctx, one_channel_in, one_channel_scale, bin_cnt,
&one_channel_out);
}
FindChannelAbsMaxFunctor<DeviceContext, T>()(
dev_ctx, in->data<T>(), in->numel(), in->dims()[0], out_scale_data);
ChannelClipAndFakeQuantFunctor<DeviceContext, T>()(
dev_ctx, *in, *out_scale, bin_cnt, in->dims()[0], out);
}
};
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ 072d95d8
......@@ -11,89 +11,27 @@ 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. */
#include <fstream>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include <string>
#include <vector>
#include "paddle/fluid/operators/load_combine_op.h"
namespace paddle {
namespace operators {
class LoadCombineOp : public framework::OperatorBase {
class LoadCombineOp : public framework::OperatorWithKernel {
public:
LoadCombineOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto load_as_fp16 = Attr<bool>("load_as_fp16");
auto model_from_memory = Attr<bool>("model_from_memory");
auto out_var_names = Outputs("Out");
PADDLE_ENFORCE_GT(
static_cast<int>(out_var_names.size()), 0,
"The number of output variables should be greater than 0.");
if (!model_from_memory) {
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin),
"Cannot open file %s for load_combine op", filename);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
} else {
PADDLE_ENFORCE(!filename.empty(), "Cannot load file from memory");
std::stringstream fin(filename, std::ios::in | std::ios::binary);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
}
}
void LoadParamsFromBuffer(
const framework::Scope &scope, const platform::Place &place,
std::istream *buffer, bool load_as_fp16,
const std::vector<std::string> &out_var_names) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
for (size_t i = 0; i < out_var_names.size(); i++) {
auto *out_var = scope.FindVar(out_var_names[i]);
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
out_var_names[i]);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, dev_ctx);
auto in_dtype = tensor->type();
auto out_dtype =
load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
out_var->Clear();
tensor = out_var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
buffer->peek();
PADDLE_ENFORCE(buffer->eof(),
"You are not allowed to load partial data via "
"load_combine_op, use load_op instead.");
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
framework::proto::VarType::FP32, ctx.GetPlace());
return kt;
}
};
......@@ -124,21 +62,30 @@ class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
LoadCombine Operator.
LoadCombine operator loads LoDTensor variables from a file, which could be
loaded in memory already. The file should contain one or more LoDTensors
LoadCombine operator loads LoDTensor variables from a file, which could be
loaded in memory already. The file should contain one or more LoDTensors
serialized using the SaveCombine operator. The
LoadCombine operator applies a deserialization strategy to appropriately load
the LodTensors, and this strategy complements the serialization strategy used
LoadCombine operator applies a deserialization strategy to appropriately load
the LodTensors, and this strategy complements the serialization strategy used
in the SaveCombine operator. Hence, the LoadCombine operator is tightly coupled
with the SaveCombine operator, and can only deserialize one or more LoDTensors
with the SaveCombine operator, and can only deserialize one or more LoDTensors
that were saved using the SaveCombine operator.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(load_combine, ops::LoadCombineOp,
ops::LoadCombineOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
load_combine,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/load_combine_op.cu 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#include "paddle/fluid/operators/load_combine_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
load_combine,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/load_combine_op.h 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#pragma once
#include <fstream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class LoadCombineOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto filename = ctx.Attr<std::string>("file_path");
auto load_as_fp16 = ctx.Attr<bool>("load_as_fp16");
auto model_from_memory = ctx.Attr<bool>("model_from_memory");
auto &out_var_names = ctx.Outputs("Out");
PADDLE_ENFORCE_GT(
static_cast<int>(out_var_names.size()), 0,
"The number of output variables should be greater than 0.");
if (!model_from_memory) {
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin),
"Cannot open file %s for load_combine op", filename);
LoadParamsFromBuffer(ctx, place, &fin, load_as_fp16, out_var_names);
} else {
PADDLE_ENFORCE(!filename.empty(), "Cannot load file from memory");
std::stringstream fin(filename, std::ios::in | std::ios::binary);
LoadParamsFromBuffer(ctx, place, &fin, load_as_fp16, out_var_names);
}
}
void LoadParamsFromBuffer(
const framework::ExecutionContext &context, const platform::Place &place,
std::istream *buffer, bool load_as_fp16,
const std::vector<std::string> &out_var_names) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto out_vars = context.MultiOutputVar("Out");
for (size_t i = 0; i < out_var_names.size(); i++) {
PADDLE_ENFORCE(out_vars[i] != nullptr,
"Output variable %s cannot be found", out_var_names[i]);
auto *tensor = out_vars[i]->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, dev_ctx);
auto in_dtype = tensor->type();
auto out_dtype =
load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
out_vars[i]->Clear();
tensor = out_vars[i]->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
buffer->peek();
PADDLE_ENFORCE(buffer->eof(),
"You are not allowed to load partial data via "
"load_combine_op, use load_op instead.");
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/load_op.cc
浏览文件 @ 072d95d8
......@@ -11,89 +11,26 @@ 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. */
#include <fstream>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/profiler.h"
#include <string>
#include "paddle/fluid/operators/load_op.h"
namespace paddle {
namespace operators {
class LoadOp : public framework::OperatorBase {
class LoadOp : public framework::OperatorWithKernel {
public:
LoadOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = Attr<std::string>("file_path");
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
filename);
using framework::OperatorWithKernel::OperatorWithKernel;
auto out_var_name = Output("Out");
auto *out_var = scope.FindVar(out_var_name);
PADDLE_ENFORCE(out_var != nullptr,
"Output variable %s cannot be found in scope %p",
out_var_name, &scope);
void InferShape(framework::InferShapeContext *ctx) const override {}
if (out_var->IsType<framework::LoDTensor>()) {
LoadLodTensor(fin, place, out_var);
} else if (out_var->IsType<framework::SelectedRows>()) {
LoadSelectedRows(fin, place, out_var);
} else {
PADDLE_ENFORCE(
false,
"Load only support LoDTensor and SelectedRows, %s has wrong type",
out_var_name);
}
}
void LoadLodTensor(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto *tensor = var->GetMutable<framework::LoDTensor>();
DeserializeFromStream(fin, tensor, dev_ctx);
auto load_as_fp16 = Attr<bool>("load_as_fp16");
auto in_dtype = tensor->type();
auto out_dtype = load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
var->Clear();
tensor = var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
void LoadSelectedRows(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
auto *selectedRows = var->GetMutable<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::DeserializeFromStream(fin, selectedRows, dev_ctx);
selectedRows->SyncIndex();
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
framework::proto::VarType::FP32, platform::CPUPlace());
return kt;
}
};
......@@ -116,8 +53,15 @@ class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"file.");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(load, ops::LoadOp, ops::LoadOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
load, ops::LoadOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/load_op.cu 0 → 100644
浏览文件 @ 072d95d8
/* Copyright (c) 2016 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. */
#include "paddle/fluid/operators/load_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
load, ops::LoadOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/load_op.h 0 → 100644
浏览文件 @ 072d95d8
/* Copyright (c) 2016 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. */
#pragma once
#include <fstream>
#include <string>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class LoadOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = ctx.Attr<std::string>("file_path");
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
filename);
auto out_var_name = ctx.Outputs("Out").data();
auto *out_var = ctx.OutputVar("Out");
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found ",
out_var_name);
PADDLE_ENFORCE(out_var != nullptr, "Output variable cannot be found ");
if (out_var->IsType<framework::LoDTensor>()) {
LoadLodTensor(fin, place, out_var, ctx);
} else if (out_var->IsType<framework::SelectedRows>()) {
LoadSelectedRows(fin, place, out_var);
} else {
PADDLE_ENFORCE(
false,
"Load only support LoDTensor and SelectedRows, %s has wrong type",
out_var_name);
}
}
void LoadLodTensor(std::istream &fin, const platform::Place &place,
framework::Variable *var,
const framework::ExecutionContext &ctx) const {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto *tensor = var->GetMutable<framework::LoDTensor>();
DeserializeFromStream(fin, tensor, dev_ctx);
auto load_as_fp16 = ctx.Attr<bool>("load_as_fp16");
auto in_dtype = tensor->type();
auto out_dtype = load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
var->Clear();
tensor = var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
void LoadSelectedRows(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
auto *selectedRows = var->GetMutable<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::DeserializeFromStream(fin, selectedRows, dev_ctx);
selectedRows->SyncIndex();
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/sequence_padding.cu
浏览文件 @ 072d95d8
......@@ -78,12 +78,6 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
"The numel of 'pad_value' can only be 1 or be equal to the "
"'step_width'.");
if (!norm_by_times && seq_num == 1UL && pad_seq_len == max_seq_len) {
TensorCopy(seq_tensor, context.GetPlace(), context, pad_tensor);
pad_tensor->Resize(pad_tensor_dims);
return;
}
const int kBlockSize = 512;
/* At least use 32 threads to copy sequence_width elements,
......@@ -129,12 +123,13 @@ class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
step_width, layout);
/*
if (!norm_by_times && seq_num == 1UL && pad_seq_len == max_seq_len) {
TensorCopy(pad_tensor, context.GetPlace(), context, seq_tensor);
seq_tensor->Resize(seq_tensor_dims);
return;
}
*/
const int kBlockSize = 512;
......
paddle/fluid/operators/matmul_op.cc
浏览文件 @ 072d95d8
......@@ -290,8 +290,10 @@ class MatMulOp : public framework::OperatorWithKernel {
context->Attrs().Get<bool>("transpose_Y"));
PADDLE_ENFORCE_EQ(mat_dim_x.width_, mat_dim_y.height_);
PADDLE_ENFORCE(mat_dim_x.batch_size_ == mat_dim_y.batch_size_ ||
mat_dim_x.batch_size_ == 0 || mat_dim_y.batch_size_ == 0);
if (context->IsRuntime()) {
PADDLE_ENFORCE(mat_dim_x.batch_size_ == mat_dim_y.batch_size_ ||
mat_dim_x.batch_size_ == 0 || mat_dim_y.batch_size_ == 0);
}
std::vector<int64_t> dim_out;
if (mat_dim_x.batch_size_ != 0) {
dim_out = framework::vectorize(dim_x);
......
paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc
浏览文件 @ 072d95d8
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <memory>
#include "paddle/fluid/operators/concat_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace paddle {
namespace operators {
......@@ -38,15 +39,20 @@ static void EnforceLayouts(const std::vector<const Tensor*> inputs) {
}
static memory::primitive_desc CreateMemPrimDesc(const Tensor& input,
const mkldnn::engine& engine) {
constexpr auto data_type = mkldnn::memory::f32;
const mkldnn::engine& engine,
const memory::data_type& dt) {
const auto dims = paddle::framework::vectorize2int(input.dims());
const auto format = input.format();
auto description = memory::desc(dims, data_type, format);
auto description = memory::desc(dims, dt, format);
auto mem_prim_desc = memory::primitive_desc(description, engine);
return mem_prim_desc;
}
static mkldnn::memory::format GetDstMemFormat(
const concat::primitive_desc& concat_pd) {
return (memory::format)concat_pd.dst_primitive_desc().desc().data.format;
}
static platform::CPUPlace GetCpuPlace(
const paddle::framework::ExecutionContext& ctx) {
auto place = ctx.GetPlace();
......@@ -61,14 +67,30 @@ static const mkldnn::engine& GetMKLDNNEngine(
return dev_ctx.GetEngine();
}
std::string CreateKey(const paddle::framework::ExecutionContext& ctx,
const std::vector<const Tensor*> multi_input,
const int64_t& concat_axis, const memory::data_type& dt) {
std::string key;
key.reserve(platform::MKLDNNHandler::MaxKeyLength);
for (size_t i = 0; i < multi_input.size(); i++) {
platform::MKLDNNHandler::AppendKeyDims(
&key, paddle::framework::vectorize2int(multi_input[i]->dims()));
}
platform::MKLDNNHandler::AppendKey(&key, std::to_string(concat_axis));
platform::MKLDNNHandler::AppendKey(&key, ctx.op().Output("Out"));
platform::MKLDNNHandler::AppendKey(&key, std::to_string(dt));
return key;
}
template <typename T>
class ConcatPrimitiveFactory {
public:
concat::primitive_desc CreateConcatPrimDescriptor(
const std::vector<const Tensor*> multi_input, Tensor* output,
int concat_axis, const mkldnn::engine& mkldnn_engine) {
CreateSourcesDescriptors(multi_input, mkldnn_engine);
auto dst_desc = CreateDstMemDescriptor(output);
int concat_axis, const mkldnn::engine& mkldnn_engine,
const memory::data_type& dt = memory::data_type::f32) {
CreateSourcesDescriptors(multi_input, mkldnn_engine, dt);
auto dst_desc = CreateDstMemDescriptor(output, dt);
return concat::primitive_desc(dst_desc, concat_axis, srcs_pd);
}
......@@ -79,23 +101,39 @@ class ConcatPrimitiveFactory {
return concat(concat_pd, inputs, dst_mem.get());
}
void SetSrcDataHandleByIndex(const std::vector<memory>& srcs, const size_t& i,
void* handler) {
srcs[i].set_data_handle(handler);
}
void SetDstDataHandle(const memory& dst_mem, void* handler) {
dst_mem.set_data_handle(handler);
}
std::vector<memory> GetSrcs() { return srcs; }
memory GetDst() { return dst_mem.get(); }
private:
memory::desc CreateDstMemDescriptor(Tensor* output) {
memory::desc CreateDstMemDescriptor(Tensor* output,
const memory::data_type& dt) {
auto dst_dims = paddle::framework::vectorize2int(output->dims());
return memory::desc(dst_dims, platform::MKLDNNGetDataType<T>(),
memory::format::any);
return memory::desc(dst_dims, dt, memory::format::any);
}
mkldnn::memory CreateDstMemory(const concat::primitive_desc& concat_pd,
Tensor* output, platform::CPUPlace place) {
Tensor* output,
const platform::CPUPlace& place) {
return memory(concat_pd.dst_primitive_desc(),
output->mutable_data<T>(place));
}
void CreateSourcesDescriptors(const std::vector<const Tensor*> multi_input,
const mkldnn::engine& mkldnn_engine) {
const mkldnn::engine& mkldnn_engine,
const memory::data_type& dt) {
for (size_t i = 0; i < multi_input.size(); i++) {
auto mem_prim_desc = CreateMemPrimDesc(*multi_input[i], mkldnn_engine);
auto mem_prim_desc =
CreateMemPrimDesc(*multi_input[i], mkldnn_engine, dt);
srcs_pd.push_back(mem_prim_desc);
srcs.push_back(
memory(mem_prim_desc, to_void_cast(multi_input[i]->data<T>())));
......@@ -120,21 +158,59 @@ template <typename T>
class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
auto place = GetCpuPlace(ctx);
const auto& mkldnn_engine = GetMKLDNNEngine(ctx);
auto multi_input = ctx.MultiInput<Tensor>("X");
EnforceLayouts(multi_input);
Tensor* output = ctx.Output<Tensor>("Out");
int64_t concat_axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
auto& dev_ctx =
ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
auto place = GetCpuPlace(ctx);
memory::data_type dt =
paddle::framework::ToMKLDNNDataType(multi_input[0]->type());
ConcatPrimitiveFactory<T> prim_creator;
auto concat_pd = prim_creator.CreateConcatPrimDescriptor(
multi_input, output, static_cast<int>(concat_axis), mkldnn_engine);
auto concat = prim_creator.CreateConcatPrimitive(concat_pd, output, place);
stream(stream::kind::eager).submit({concat}).wait();
std::string key = CreateKey(ctx, multi_input, concat_axis, dt);
const std::string key_prim = key + "@concat_p";
const std::string key_concat_pd = key + "@concat_pd";
const std::string key_srcs = key + "@concat_srcs";
const std::string key_dst = key + "@concat_dst";
std::shared_ptr<concat::primitive_desc> concat_pd;
std::shared_ptr<std::vector<memory>> srcs;
std::shared_ptr<memory> dst_mem;
auto concat_p = std::static_pointer_cast<concat>(dev_ctx.GetBlob(key_prim));
if (concat_p == nullptr) {
const auto& mkldnn_engine = dev_ctx.GetEngine();
concat_pd = std::make_shared<concat::primitive_desc>(
prim_creator.CreateConcatPrimDescriptor(multi_input, output,
static_cast<int>(concat_axis),
mkldnn_engine, dt));
concat_p = std::make_shared<concat>(
prim_creator.CreateConcatPrimitive(*concat_pd, output, place));
srcs = std::make_shared<std::vector<memory>>(prim_creator.GetSrcs());
dst_mem = std::make_shared<memory>(prim_creator.GetDst());
dev_ctx.SetBlob(key_prim, concat_p);
dev_ctx.SetBlob(key_concat_pd, concat_pd);
dev_ctx.SetBlob(key_srcs, srcs);
dev_ctx.SetBlob(key_dst, dst_mem);
} else {
srcs = std::static_pointer_cast<std::vector<memory>>(
dev_ctx.GetBlob(key_srcs));
dst_mem = std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_dst));
concat_pd = std::static_pointer_cast<concat::primitive_desc>(
dev_ctx.GetBlob(key_concat_pd));
for (size_t i = 0; i < multi_input.size(); i++) {
prim_creator.SetSrcDataHandleByIndex(
*srcs, i, to_void_cast<T>(multi_input[i]->data<T>()));
}
prim_creator.SetDstDataHandle(*dst_mem, output->mutable_data<T>(place));
}
stream(stream::kind::eager).submit({*concat_p}).wait();
output->set_mkldnn_prim_desc(concat_pd.dst_primitive_desc());
output->set_mkldnn_prim_desc(concat_pd->dst_primitive_desc());
}
};
} // namespace operators
......@@ -143,4 +219,6 @@ class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(concat, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConcatMKLDNNOpKernel<float>)
ops::ConcatMKLDNNOpKernel<float>,
ops::ConcatMKLDNNOpKernel<int8_t>,
ops::ConcatMKLDNNOpKernel<uint8_t>);
paddle/fluid/operators/range_op.cc 0 → 100644
浏览文件 @ 072d95d8
/* Copyright (c) 2016 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. */
#include "paddle/fluid/operators/range_op.h"
namespace paddle {
namespace operators {
class RangeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
if (ctx->HasInput("Start")) {
auto s_dims = ctx->GetInputDim("Start");
PADDLE_ENFORCE((s_dims.size() == 1) && (s_dims[0] == 1),
"The shape of Input(Start) should be [1].");
}
if (ctx->HasInput("End")) {
auto e_dims = ctx->GetInputDim("End");
PADDLE_ENFORCE((e_dims.size() == 1) && (e_dims[0] == 1),
"The shape of Input(End) should be [1].");
}
if (ctx->HasInput("Step")) {
auto step_dims = ctx->GetInputDim("Step");
PADDLE_ENFORCE((step_dims.size() == 1) && (step_dims[0] == 1),
"The shape of Input(Step) should be [1].");
}
ctx->SetOutputDim("Out", {-1});
}
};
class RangeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Start",
"Start of interval. The interval includes this value. It is a "
"tensor with shape=[1].");
AddInput("End",
"End of interval. The interval does not include this value, "
"except in some cases where step is not an integer and floating "
"point round-off affects the length of out. It is a tensor with "
"shape=[1].");
AddInput("Step", "Spacing between values. It is a tensor with shape=[1].");
AddOutput("Out", "A sequence of numbers.");
AddComment(R"DOC(
Return evenly spaced values within a given interval. Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). Like arange function of numpy.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(range, ops::RangeOp, ops::RangeOpMaker);
REGISTER_OP_CPU_KERNEL(range, ops::CPURangeKernel<int>,
ops::CPURangeKernel<float>, ops::CPURangeKernel<double>,
ops::CPURangeKernel<int64_t>);
paddle/fluid/operators/range_op.cu 0 → 100644
浏览文件 @ 072d95d8
/* Copyright (c) 2016 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. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/range_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace paddle {
namespace operators {
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
template <typename T>
__global__ void RangeKernel(T start, T step, int64_t size, T* out) {
CUDA_1D_KERNEL_LOOP(index, size) { out[index] = start + step * index; }
}
template <typename T>
class CUDARangeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* start_t = context.Input<framework::Tensor>("Start");
auto* end_t = context.Input<framework::Tensor>("End");
auto* step_t = context.Input<framework::Tensor>("Step");
auto* out = context.Output<framework::Tensor>("Out");
framework::Tensor n;
framework::TensorCopy(*start_t, platform::CPUPlace(), &n);
T start = n.data<T>()[0];
framework::TensorCopy(*end_t, platform::CPUPlace(), &n);
T end = n.data<T>()[0];
framework::TensorCopy(*step_t, platform::CPUPlace(), &n);
T step = n.data<T>()[0];
int64_t size = 0;
GetSize(start, end, step, &size);
out->Resize(framework::make_ddim({size}));
T* out_data = out->mutable_data<T>(context.GetPlace());
auto stream = context.cuda_device_context().stream();
int block = 512;
int grid = (size + block - 1) / block;
RangeKernel<T><<<grid, block, 0, stream>>>(start, step, size, out_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(range, ops::CUDARangeKernel<int>,
ops::CUDARangeKernel<int64_t>,
ops::CUDARangeKernel<float>,
ops::CUDARangeKernel<double>);
paddle/fluid/operators/range_op.h 0 → 100644
浏览文件 @ 072d95d8
/* Copyright (c) 2016 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. */
#pragma once
#include <functional>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
template <typename T>
void GetSize(T start, T end, T step, int64_t* size) {
PADDLE_ENFORCE(!std::equal_to<T>()(step, 0),
"The step of range op should not be 0.");
PADDLE_ENFORCE(((start < end) && (step > 0)) || ((start > end) && (step < 0)),
"The step should be greater than 0 while start < end. And the "
"step should be less than 0 while start > end.");
*size = std::is_integral<T>::value
? ((std::abs(end - start) + std::abs(step) - 1) / std::abs(step))
: std::ceil(std::abs((end - start) / step));
}
template <typename T>
class CPURangeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
T start = context.Input<framework::Tensor>("Start")->data<T>()[0];
T end = context.Input<framework::Tensor>("End")->data<T>()[0];
T step = context.Input<framework::Tensor>("Step")->data<T>()[0];
auto* out = context.Output<framework::Tensor>("Out");
int64_t size = 0;
GetSize(start, end, step, &size);
out->Resize(framework::make_ddim({size}));
T* out_data = out->mutable_data<T>(context.GetPlace());
T value = start;
for (int64_t i = 0; i < size; ++i) {
out_data[i] = value;
value += step;
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/save_combine_op.cc
浏览文件 @ 072d95d8
......@@ -12,87 +12,18 @@ 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. */
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <sstream>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
#include <string>
#include "paddle/fluid/operators/save_combine_op.h"
namespace paddle {
namespace operators {
class SaveCombineOp : public framework::OperatorBase {
class SaveCombineOp : public framework::OperatorWithKernel {
public:
SaveCombineOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
auto save_as_fp16 = Attr<bool>("save_as_fp16");
bool is_present = FileExists(filename);
if (is_present && !overwrite) {
PADDLE_THROW("%s exists!, cannot save_combine to it when overwrite=false",
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto inp_var_names = Inputs("X");
PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
"The number of input variables should be greater than 0");
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
using framework::OperatorWithKernel::OperatorWithKernel;
for (size_t i = 0; i < inp_var_names.size(); i++) {
auto *var = scope.FindVar(inp_var_names[i]);
PADDLE_ENFORCE(var != nullptr,
"Cannot find variable %s for save_combine_op",
inp_var_names[i]);
PADDLE_ENFORCE(var->IsType<framework::LoDTensor>(),
"SaveCombineOp only supports LoDTensor, %s has wrong type",
inp_var_names[i]);
auto &tensor = var->Get<framework::LoDTensor>();
// Serialize tensors one by one
// Check types to see if a fp16 transformation is required
auto in_dtype = tensor.type();
auto out_dtype =
save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
}
fout.close();
}
void InferShape(framework::InferShapeContext *ctx) const override {}
};
class SaveCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
......@@ -105,7 +36,7 @@ class SaveCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
SaveCombine operator
This operator will serialize and write a list of input LoDTensor variables
This operator will serialize and write a list of input LoDTensor variables
to a file on disk.
)DOC");
AddAttr<bool>("overwrite",
......@@ -134,3 +65,10 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(save_combine, ops::SaveCombineOp,
ops::SaveCombineOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
save_combine,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/save_combine_op.cu 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#include "paddle/fluid/operators/save_combine_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
save_combine,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/save_combine_op.h 0 → 100644
浏览文件 @ 072d95d8
/* 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. */
#pragma once
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <sstream>
#include <string>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SaveCombineOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto filename = ctx.Attr<std::string>("file_path");
auto overwrite = ctx.Attr<bool>("overwrite");
auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
bool is_present = FileExists(filename);
if (is_present && !overwrite) {
PADDLE_THROW("%s exists!, cannot save_combine to it when overwrite=false",
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto &inp_var_names = ctx.Inputs("X");
auto &inp_vars = ctx.MultiInputVar("X");
PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
"The number of input variables should be greater than 0");
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
for (size_t i = 0; i < inp_var_names.size(); i++) {
PADDLE_ENFORCE(inp_vars[i] != nullptr,
"Cannot find variable %s for save_combine_op",
inp_var_names[i]);
PADDLE_ENFORCE(inp_vars[i]->IsType<framework::LoDTensor>(),
"SaveCombineOp only supports LoDTensor, %s has wrong type",
inp_var_names[i]);
auto &tensor = inp_vars[i]->Get<framework::LoDTensor>();
// Serialize tensors one by one
// Check types to see if a fp16 transformation is required
auto in_dtype = tensor.type();
auto out_dtype =
save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
}
fout.close();
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/save_load_combine_op_test.cc
浏览文件 @ 072d95d8
......@@ -19,8 +19,8 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
USE_NO_KERNEL_OP(save_combine);
USE_NO_KERNEL_OP(load_combine);
USE_CPU_ONLY_OP(save_combine);
USE_CPU_ONLY_OP(load_combine);
template <typename T, typename U>
T* CreateForSaveCombineOp(int x, int y, const std::vector<int>& lod_info,
......
paddle/fluid/operators/save_load_op_test.cc
浏览文件 @ 072d95d8
......@@ -16,8 +16,8 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
USE_NO_KERNEL_OP(save);
USE_NO_KERNEL_OP(load);
USE_CPU_ONLY_OP(save);
USE_CPU_ONLY_OP(load);
TEST(SaveLoadOp, CPU) {
paddle::framework::Scope scope;
......
paddle/fluid/operators/save_op.cc
浏览文件 @ 072d95d8
......@@ -15,118 +15,24 @@ limitations under the License. */
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
#include "paddle/fluid/operators/save_op.h"
namespace paddle {
namespace operators {
// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";
class SaveOp : public framework::OperatorBase {
class SaveOp : public framework::OperatorWithKernel {
public:
SaveOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto iname = Input("X");
auto *var = scope.FindVar(iname);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s for save_op",
iname);
if (var->IsType<framework::LoDTensor>()) {
SaveLodTensor(place, var);
} else if (var->IsType<framework::SelectedRows>()) {
SaveSelectedRows(scope, place, var);
} else {
PADDLE_ENFORCE(
false,
"SaveOp only support LoDTensor and SelectedRows, %s has wrong type",
iname);
}
}
using framework::OperatorWithKernel::OperatorWithKernel;
void SaveLodTensor(const platform::Place &place,
framework::Variable *var) const {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
if (FileExists(filename) && !overwrite) {
PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
auto &tensor = var->Get<framework::LoDTensor>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto save_as_fp16 = Attr<bool>("save_as_fp16");
auto in_dtype = tensor.type();
auto out_dtype = save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
fout.close();
}
void InferShape(framework::InferShapeContext *ctx) const override {}
void SaveSelectedRows(const framework::Scope &scope,
const platform::Place &place,
framework::Variable *var) const {
auto *lt_var = scope.FindVar(LOOKUP_TABLE_PATH)->GetMutable<std::string>();
PADDLE_ENFORCE(
lt_var != nullptr,
"Can not find variable kLookupTablePath for SaveSelectedRows");
std::string filename = lt_var->data();
VLOG(4) << "SaveSelectedRows get File name: " << filename;
MkDirRecursively(DirName(filename).c_str());
auto &selectedRows = var->Get<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
framework::SerializeToStream(fout, selectedRows, dev_ctx);
fout.close();
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
ctx.GetPlace());
}
};
......@@ -154,14 +60,20 @@ This operator will serialize and write LoDTensor / SelectedRows variable to file
"The \"file_path\" where the variable will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddOutput(LOOKUP_TABLE_PATH,
"(string)"
"for pserver: The \"kLookupTablePath\" where checkpoint notify "
"to save lookup table variables"
" to directory specified.")
.AsDispensable();
}
};
class SaveOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(framework::InferVarTypeContext *ctx) const override {
auto out_var_name = ctx->Output(LOOKUP_TABLE_PATH).front();
ctx->SetType(out_var_name, framework::proto::VarType::RAW);
auto var_type = framework::proto::VarType::RAW;
ctx->SetType(LOOKUP_TABLE_PATH, var_type);
}
};
......@@ -169,11 +81,18 @@ class SaveOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(save, ops::SaveOp, paddle::framework::EmptyGradOpMaker,
ops::SaveOpProtoMaker, ops::SaveOpVarTypeInference,
ops::SaveOpShapeInference);
REGISTER_OPERATOR(save, ops::SaveOp, ops::SaveOpProtoMaker,
ops::SaveOpVarTypeInference, ops::SaveOpShapeInference);
REGISTER_OP_CPU_KERNEL(
save, ops::SaveOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int8_t>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/save_op.cu 0 → 100644
浏览文件 @ 072d95d8
/* Copyright (c) 2016 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. */
#include "paddle/fluid/operators/save_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
save, ops::SaveOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>);
paddle/fluid/operators/save_op.h 0 → 100644
浏览文件 @ 072d95d8
/* Copyright (c) 2016 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. */
#pragma once
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
namespace paddle {
namespace operators {
// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";
template <typename DeviceContext, typename T>
class SaveOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto *input_var = ctx.InputVar("X");
auto iname = ctx.Inputs("X").data();
PADDLE_ENFORCE(input_var != nullptr, "Cannot find variable %s for save_op",
iname);
if (input_var->IsType<framework::LoDTensor>()) {
SaveLodTensor(ctx, place, input_var);
} else if (input_var->IsType<framework::SelectedRows>()) {
SaveSelectedRows(ctx, place, input_var);
} else {
PADDLE_ENFORCE(
false,
"SaveOp only support LoDTensor and SelectedRows, %s has wrong type",
iname);
}
}
void SaveLodTensor(const framework::ExecutionContext &ctx,
const platform::Place &place,
const framework::Variable *var) const {
auto filename = ctx.Attr<std::string>("file_path");
auto overwrite = ctx.Attr<bool>("overwrite");
if (FileExists(filename) && !overwrite) {
PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
auto &tensor = var->Get<framework::LoDTensor>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
auto in_dtype = tensor.type();
auto out_dtype = save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
fout.close();
}
void SaveSelectedRows(const framework::ExecutionContext &ctx,
const platform::Place &place,
const framework::Variable *var) const {
framework::Variable *out_put_var = ctx.OutputVar(LOOKUP_TABLE_PATH);
PADDLE_ENFORCE(
out_put_var != nullptr,
"Can not find variable kLookupTablePath for SaveSelectedRows");
auto *lt_var = out_put_var->GetMutable<std::string>();
std::string filename = lt_var->data();
VLOG(4) << "SaveSelectedRows get File name: " << filename;
MkDirRecursively(DirName(filename).c_str());
auto &selectedRows = var->Get<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
framework::SerializeToStream(fout, selectedRows, dev_ctx);
fout.close();
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/squeeze_op.cc
浏览文件 @ 072d95d8
......@@ -40,7 +40,7 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
"tensor's rank.");
}
auto out_dims = GetOutputShape(axes, x_dims);
auto out_dims = GetOutputShape(axes, x_dims, false);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
......@@ -50,7 +50,8 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
}
static framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
const framework::DDim &in_dims) {
const framework::DDim &in_dims,
bool is_runtime) {
size_t num_squeeze_dims = squeeze_dims.size();
int cnt_squeezed_dims = 0;
bool should_squeeze[9] = {false};
......@@ -71,9 +72,12 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
// Check current index, the upper limit has beed checked in line 36.
PADDLE_ENFORCE(current >= 0,
"Invalid axis, the negative axis is out of range.");
PADDLE_ENFORCE(in_dims[current] == 1,
"Invalid axis index, the axis that will be squeezed "
"should be equal to 1.");
if (is_runtime) {
PADDLE_ENFORCE(in_dims[current] == 1,
"Invalid axis index, the axis that will be squeezed "
"should be equal to 1.");
}
if (!(should_squeeze[current])) {
++cnt_squeezed_dims;
......@@ -104,7 +108,7 @@ class SqueezeOp : public framework::OperatorBase {
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = SqueezeOpInferShape::GetOutputShape(axes, x_dims);
auto out_dims = SqueezeOpInferShape::GetOutputShape(axes, x_dims, true);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
......@@ -224,7 +228,7 @@ class Squeeze2Op : public framework::OperatorBase {
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = Squeeze2OpInferShape::GetOutputShape(axes, x_dims);
auto out_dims = Squeeze2OpInferShape::GetOutputShape(axes, x_dims, true);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 072d95d8
......@@ -34,8 +34,11 @@ class TopkOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GE(k, 1, "k must >= 1");
PADDLE_ENFORCE_GE(input_dims.size(), 1, "input must have >= 1d shape");
PADDLE_ENFORCE_GE(input_dims[input_dims.size() - 1], k,
"input must have >= k columns");
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_GE(input_dims[input_dims.size() - 1], k,
"input must have >= k columns");
}
framework::DDim dims = input_dims;
dims[dims.size() - 1] = k;
......
python/paddle/fluid/contrib/slim/__init__.py
浏览文件 @ 072d95d8
......@@ -13,13 +13,4 @@
# limitations under the License.
from .core import *
from .graph import *
from .prune import *
__all__ = [
'build_compressor',
'CompressPass',
'ImitationGraph',
'SensitivePruneStrategy',
'MagnitudePruner',
'RatioPruner',
]
__all__ = ['Compressor', ]
python/paddle/fluid/contrib/slim/core/__init__.py
浏览文件 @ 072d95d8
......@@ -14,11 +14,9 @@
from . import config
from .config import *
from . import compress_pass
from .compress_pass import *
from . import compressor
from .compressor import *
from . import strategy
from .strategy import *
from . import pass_builder
from .pass_builder import *
__all__ = config.__all__ + compress_pass.__all__ + strategy.__all__ + pass_builder.__all__
__all__ = config.__all__ + compressor.__all__ + strategy.__all__
python/paddle/fluid/contrib/slim/core/compress_pass.py 已删除 100644 → 0
浏览文件 @ a93a9eef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 ....core import CPUPlace
from ..graph import get_executor
__all__ = ['Context', 'CompressPass']
class Context(object):
"""
The context in the process of compression.
Args:
exe: The executor used to execute graph.
graph: The graph to be compressed.
scope: The scope used to execute graph.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self, exe, graph, scope, program_exe=None):
# The total number of epoches to be trained.
self.epoch = 0
# Current epoch
self.epoch_id = 0
# Current batch
self.batch_id = 0
self.exe = exe
self.graph = graph
self.scope = scope
self.program_exe = program_exe
class CompressPass(object):
"""
The pass used to compress model.
Args:
place: The device used in compression.
data_reader: The data_reader used to run graph.
data_feeder: The data_feeder used to run graph.
scope: The scope used to run graph.
metrics: The metrics for evaluating model.
epoch: The total epoches of trainning in compression.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self,
place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
program_exe=None):
self.strategies = []
self.place = CPUPlace() if place is None else place
self.data_reader = data_reader
self.data_feeder = data_feeder
self.scope = scope
self.metrics = metrics
self.epoch = epoch
self.program_exe = program_exe
def add_strategy(self, strategy):
"""
Add a strategy to current compress pass.
Args:
strategy: The strategy to be added into current compress pass.
"""
self.strategies.append(strategy)
self.epoch = max(strategy.end_epoch, self.epoch)
def apply(self, graph):
"""
Compress a model.
Args:
graph: The target graph to be compressed.
"""
self.executor = get_executor(graph, self.place)
context = Context(
self.executor, graph, self.scope, program_exe=self.program_exe)
for strategy in self.strategies:
strategy.on_compress_begin(context)
for epoch in range(self.epoch):
for strategy in self.strategies:
strategy.on_epoch_begin(context)
for data in self.data_reader():
for strategy in self.strategies:
strategy.on_batch_begin(context)
fetches = None
if self.metrics:
fetches = self.metrics.values()
feed = None
if self.data_feeder:
feed = self.data_feeder.feed(data)
results = self.executor.run(graph,
fetches=fetches,
scope=self.scope,
feed=feed)
if results:
print("results: {}".format(
zip(self.metrics.keys(), results)))
for strategy in self.strategies:
strategy.on_batch_end(context)
context.batch_id += 1
for strategy in self.strategies:
strategy.on_epoch_end(context)
context.epoch_id += 1
for strategy in self.strategies:
strategy.on_compress_end(context)
python/paddle/fluid/contrib/slim/core/compressor.py 0 → 100644
浏览文件 @ 072d95d8
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 ....core import CPUPlace
from .... import compiler
from .... import io
from .... import profiler
from .... import scope_guard
from ....data_feeder import DataFeeder
from ..graph import *
from .config import ConfigFactory
import numpy as np
from collections import Iterable
import time
import os
import logging
import sys
import pickle
import functools
__all__ = ['Context', 'Compressor']
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.INFO)
def cached_reader(reader, sampled_rate, cache_path, cached_id):
"""
Sample partial data from reader and cache them into local file system.
Args:
reader: Iterative data source.
sampled_rate(float): The sampled rate used to sample partial data for evaluation. None means using all data in eval_reader. default: None.
cache_path(str): The path to cache the sampled data.
cached_id(int): The id of dataset sampled. Evaluations with same cached_id use the same sampled dataset. default: 0.
"""
np.random.seed(cached_id)
cache_path = os.path.join(cache_path, str(cached_id))
_logger.debug('read data from: {}'.format(cache_path))
def s_reader():
if os.path.isdir(cache_path):
for file_name in open(os.path.join(cache_path, "list")):
yield np.load(os.path.join(cache_path, file_name.strip()))
else:
os.makedirs(cache_path)
list_file = open(os.path.join(cache_path, "list"), 'w')
batch = 0
dtype = None
for data in reader():
if batch == 0 or (np.random.uniform() < sampled_rate):
np.save(
os.path.join(cache_path, 'batch' + str(batch)), data)
list_file.write('batch' + str(batch) + '.npy\n')
batch += 1
yield data
return s_reader
class Context(object):
"""
The context in the process of compression.
"""
def __init__(self,
place,
scope,
train_graph=None,
train_reader=None,
eval_graph=None,
eval_reader=None,
teacher_graphs=None,
train_optimizer=None,
distiller_optimizer=None):
"""
Args:
place: The device place where the compression job running.
scope: The scope used in compression job.
train_graph: The graph with loss as output node.
eval_graph: The graph used for evaluation.
eval_reader: The data reader used for evaluation.
teacher_graphs: The teacher graphs used in distillation strategies.
train_optimizer: The optimizer used to append backward ops and
optimization ops into train_graph.
distiller_optimizer: The optimizer used by distillation strategies.
"""
# The total number of epoches to be trained.
self.epoch = 0
# Current epoch
self.epoch_id = 0
# Current batch
self.batch_id = 0
self.k_v = {}
self.place = place
self.scope = scope
self.train_graph = train_graph
self.train_reader = train_reader
self.eval_graph = eval_graph
self.eval_reader = eval_reader
self.executor = None
self.teacher_graphs = teacher_graphs
self.train_optimizer = train_optimizer
self.distiller_optimizer = distiller_optimizer
self.optimize_graph = None
self.cache_path = './eval_cache'
self.eval_results = {}
def to_file(self, file_name):
"""
Save the context into file.
"""
data = {}
data['epoch_id'] = self.epoch_id
data['eval_results'] = self.eval_results
with open(file_name, 'wb') as context_file:
pickle.dump(data, context_file)
def from_file(self, file_name):
"""
Load the context from file.
"""
with open(file_name) as context_file:
if sys.version_info < (3, 0):
data = pickle.load(context_file)
else:
data = pickle.load(context_file, encoding='bytes')
self.epoch_id = data['epoch_id']
self.eval_results = data['eval_results']
def eval_converged(self, metric_name, delta=0.001):
"""
Check whether the training has been converged.
Args:
metric_name(str): The metric used to check convergence.
delta(float): '(metric[k] - metric[k-1] / metric[k-1]) < delta'
means that the training has been converged.
Returns:
bool: True means the training has been converged.
"""
# TODO(wanghaoshuang@baidu.com): enhence this method.
if (metric_name not in self.eval_results
) or len(self.eval_results[metric_name]) < 2:
return False
results = self.eval_results[metric_name][-2:]
_logger.info('Latest evaluations: {}'.format(results))
return abs(results[1] - results[0]) / results[0] < delta
def run_eval_graph(self, sampled_rate=None, cached_id=0):
"""
Evaluate the current mode in context.
Args:
sampled_rate(float): The sampled rate used to sample partial data
for evaluation. None means using all data in eval_reader. default: None.
cached_id(int): The id of dataset sampled. Evaluations with same
cached_id use the same sampled dataset. default: 0.
"""
_logger.info('Running evaluation')
assert self.eval_graph is not None
assert self.eval_reader is not None
eval_graph = self.eval_graph.clone(for_test=True)
executor = SlimGraphExecutor(self.place)
results = []
batch_id = 0
s_time = time.time()
reader = self.eval_reader
if sampled_rate:
reader = cached_reader(reader, sampled_rate, self.cache_path,
cached_id)
for data in reader():
result = executor.run(eval_graph, self.scope, data=data)
result = [np.mean(r) for r in result]
results.append(result)
if batch_id % 20 == 0:
_logger.info("batch-{}; {}={}".format(
batch_id, eval_graph.out_nodes.keys(), result))
batch_id += 1
result = np.mean(np.array(results), axis=0)
_logger.info("Final eval result: {}={}".format(
eval_graph.out_nodes.keys(), result))
if not isinstance(result, Iterable):
result = [result]
_logger.info('Finish evaluation')
return result, eval_graph.out_nodes.keys()
def put(self, key, value):
self.k_v[key] = value
def get(self, key):
return self.k_v.get(key)
class Compressor(object):
"""
The pass used to compress model.
"""
def __init__(self,
place,
scope,
train_program,
train_reader=None,
train_feed_list=None,
train_fetch_list=None,
eval_program=None,
eval_reader=None,
eval_feed_list=None,
eval_fetch_list=None,
teacher_programs=[],
checkpoint_path='./checkpoints',
train_optimizer=None,
distiller_optimizer=None):
"""
Args:
place(fluid.Place): The device place where the compression job running.
scope(fluid.core.Scope): The scope used to run graph.
train_program(Program): The main program to be compressed. It must have loss op.
train_reader: The data reader used for training.
train_feed_list(dict): A dict to indicate the input variable of the training program.
The key is user-defined and human-readable name.
The value is the name of Variable.
train_fetch_list(dict): A dict to indicate the output variable of the training program.
The key is user-defined and human-readable name.
The value is the name of Variable.
eval_program(Program): The program used for evaluation.
eval_reader: The data reader used for evaluation.
eval_feed_list(dict): A dict to indicate the input variable of the evaluation program.
The key is user-defined and human-readable name.
The value is the name of Variable.
eval_fetch_list(dict): A dict to indicate the output variable of the evaluation program.
The key is user-defined and human-readable name.
The value is the name of Variable.
teacher_programs: The teacher graphs used in distillation strategies.
train_optimizer: The optimizer used to append backward ops and
optimization ops into train_graph.
distiller_optimizer: The optimizer used by distillation strategies. In distillation strategy,
this optimizer is used to minimize the combined loss of student-net and
teacher-net while train_optimizer is used to minimize loss of
student-net in fine-tune stage.
"""
assert isinstance(
train_feed_list, list
), "train_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]"
assert isinstance(
eval_feed_list, list
), "eval_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]"
self.strategies = []
self.epoch = 0
self.place = CPUPlace() if place is None else place
self.scope = scope
self.train_graph = GraphWrapper(
train_program, in_nodes=train_feed_list, out_nodes=train_fetch_list)
self.eval_graph = GraphWrapper(
eval_program, in_nodes=eval_feed_list, out_nodes=eval_fetch_list)
self.train_reader = train_reader
self.eval_reader = eval_reader
self.teacher_graphs = []
for teacher in teacher_programs:
self.teacher_graphs.append(ImitationGraph(teacher, scope=scope))
self.checkpoint = None
self.checkpoint_path = checkpoint_path
self.eval_epoch = 1
self.train_optimizer = train_optimizer
self.distiller_optimizer = distiller_optimizer
self.init_model = None
def _add_strategy(self, strategy):
"""
Add a strategy to current compress pass.
Args:
strategy: The strategy to be added into current compress pass.
"""
self.strategies.append(strategy)
self.epoch = max(strategy.end_epoch, self.epoch)
def config(self, config_file):
"""
Configure the compress pass from file with yaml format.
Args:
config_file(str): The config file in local file system.
"""
factory = ConfigFactory(config_file)
self.epoch = factory.compressor['epoch']
for strategy in factory.compressor['strategies']:
self._add_strategy(strategy)
if 'checkpoint_path' in factory.compressor:
self.checkpoint_path = factory.compressor['checkpoint_path']
if 'init_model' in factory.compressor:
self.init_model = factory.compressor['init_model']
def _init_model(self, context):
"""
Load model that has been compressed.
"""
if self.init_model and os.path.exists(self.init_model):
exe = SlimGraphExecutor(context.place)
with scope_guard(context.scope):
context.train_graph.load_persistables(self.init_model, exe)
flops = context.eval_graph.flops()
conv_flops = context.eval_graph.flops(only_conv=True)
context.eval_graph.update_param_shape(context.scope)
context.eval_graph.update_groups_of_conv()
_logger.info("conv flops: -{}".format(1 - float(
context.eval_graph.flops(only_conv=True)) / conv_flops))
_logger.info("total flops: -{}".format(1 - float(
context.eval_graph.flops()) / flops))
context.train_graph.update_param_shape(context.scope)
context.train_graph.update_groups_of_conv()
context.train_graph.infer_shape()
_logger.info("Init model from: {}".format(self.init_model))
def _load_checkpoint(self, context):
"""
Load checkpoints from file.
"""
_logger.debug('_load_checkpoint')
strategies = self.strategies
if self.checkpoint_path:
if not os.path.exists(self.checkpoint_path):
_logger.warning("Checkpints path doesn't exist: [{}]".format(
self.checkpoint_path))
return context, strategies
checkpoints = [
dir for dir in os.listdir(self.checkpoint_path)
if os.path.isdir(os.path.join(self.checkpoint_path, dir))
]
_logger.debug('self.checkpoint_path: {}'.format(
self.checkpoint_path))
_logger.info('checkpoints: {}'.format(checkpoints))
if len(checkpoints) > 0:
latest = max([int(ck) for ck in checkpoints])
latest_ck_path = os.path.join(self.checkpoint_path, str(latest))
model_path = os.path.join(latest_ck_path, 'model')
context_path = os.path.join(latest_ck_path, 'context')
strategy_path = os.path.join(latest_ck_path, 'strategies')
if os.path.exists(context_path):
context.from_file(context_path)
context.epoch_id += 1
if os.path.exists(strategy_path):
with open(strategy_path, 'rb') as strategy_file:
if sys.version_info < (3, 0):
strategies = pickle.load(strategy_file)
else:
strategies = pickle.load(
strategy_file, encoding='bytes')
if os.path.exists(model_path):
exe = SlimGraphExecutor(context.place)
with scope_guard(context.scope):
context.optimize_graph.load_persistables(model_path,
exe)
context.optimize_graph.update_param_shape(context.scope)
context.optimize_graph.update_groups_of_conv()
context.eval_graph.update_param_shape(context.scope)
context.eval_graph.update_groups_of_conv()
_logger.info("Loaded params from: {}".format(model_path))
return context, strategies
def _save_checkpoint(self, context):
"""
Save checkpoints to file.
"""
if context.epoch_id % 1 == 0 and self.checkpoint_path:
checkpoint_path = os.path.join(self.checkpoint_path,
str(context.epoch_id))
model_path = os.path.join(checkpoint_path, 'model')
context_path = os.path.join(checkpoint_path, 'context')
strategy_path = os.path.join(checkpoint_path, 'strategies')
if not os.path.isdir(model_path):
os.makedirs(model_path)
exe = SlimGraphExecutor(context.place)
with scope_guard(context.scope):
context.optimize_graph.save_persistables(model_path, exe)
context.to_file(context_path)
with open(strategy_path, 'wb') as strategy_file:
pickle.dump(self.strategies, strategy_file)
_logger.info('Saved checkpoint to: {}'.format(checkpoint_path))
def _train_one_epoch(self, context):
"""
Train one epoch.
"""
executor = SlimGraphExecutor(self.place)
if context.optimize_graph.compiled_graph is None:
context.optimize_graph.compiled_graph = compiler.CompiledProgram(
context.optimize_graph.program).with_data_parallel(
loss_name=context.optimize_graph.out_nodes['loss'])
for data in context.train_reader():
for strategy in self.strategies:
strategy.on_batch_begin(context)
results = executor.run(context.optimize_graph,
context.scope,
data=data)
results = [float(np.mean(result)) for result in results]
if context.batch_id % 20 == 0:
_logger.info("epoch:{}; batch_id:{}; {} = {}".format(
context.epoch_id, context.batch_id,
context.optimize_graph.out_nodes.keys(
), [round(r, 3) for r in results]))
for strategy in self.strategies:
strategy.on_batch_end(context)
context.batch_id += 1
context.batch_id = 0
def _eval(self, context):
"""
Runing evaluation.
"""
results, names = context.run_eval_graph()
for name, result in zip(names, results):
if name not in context.eval_results:
context.eval_results[name] = []
context.eval_results[name].append(result)
def run(self):
"""
Execute compressiong pass.
"""
context = Context(
place=self.place,
scope=self.scope,
train_graph=self.train_graph,
train_reader=self.train_reader,
eval_graph=self.eval_graph,
eval_reader=self.eval_reader,
teacher_graphs=self.teacher_graphs,
train_optimizer=self.train_optimizer,
distiller_optimizer=self.distiller_optimizer)
self.context = context
if self.teacher_graphs:
context.put('teachers', self.teacher_graphs)
self._init_model(context)
if not context.optimize_graph:
if context.train_optimizer:
context.train_optimizer._name = 'train_opt'
context.optimize_graph = context.train_graph.get_optimize_graph(
context.train_optimizer, context.place, context.scope)
else:
context.optimize_graph = context.train_graph
context, self.strategies = self._load_checkpoint(context)
for strategy in self.strategies:
strategy.on_compression_begin(context)
start = context.epoch_id
self._eval(context)
for epoch in range(start, self.epoch):
context.epoch_id = epoch
for strategy in self.strategies:
strategy.on_epoch_begin(context)
self._train_one_epoch(context)
for strategy in self.strategies:
strategy.on_epoch_end(context)
if self.eval_epoch and epoch % self.eval_epoch == 0:
self._eval(context)
self._save_checkpoint(context)
for strategy in self.strategies:
strategy.on_compression_end(context)
return context.eval_graph
python/paddle/fluid/contrib/slim/core/config.py
浏览文件 @ 072d95d8
......@@ -17,7 +17,7 @@ import funcsigs
import yaml
from collections import OrderedDict
from ..prune import *
from .compress_pass import *
from ..quantization import *
from .strategy import *
__all__ = ['ConfigFactory']
......@@ -29,15 +29,10 @@ class ConfigFactory(object):
def __init__(self, config):
"""Init a factory from configure file."""
self.instances = {}
self.compressor = {}
self.version = None
self._parse_config(config)
def get_compress_pass(self):
"""
Get compress pass from factory.
"""
return self.instance('compress_pass')
def instance(self, name):
"""
Get instance from factory.
......@@ -59,8 +54,16 @@ class ConfigFactory(object):
args = {}
for key in keys:
value = attrs[key]
if isinstance(value, str) and value.lower() == 'none':
value = None
if isinstance(value, str) and value in self.instances:
value = self.instances[value]
if isinstance(value, list):
for i in range(len(value)):
if isinstance(value[i],
str) and value[i] in self.instances:
value[i] = self.instances[value[i]]
args[key] = value
self.instances[name] = class_(**args)
return self.instances.get(name)
......@@ -76,16 +79,23 @@ class ConfigFactory(object):
assert self.version == int(key_values['version'])
# parse pruners
if key == 'pruners' or key == 'strategies':
if key == 'distillers' or key == 'pruners' or key == 'quantizers' or key == 'strategies':
instances = key_values[key]
for name in instances:
self._new_instance(name, instances[name])
if key == 'compress_pass':
compress_pass = self._new_instance(key, key_values[key])
for name in key_values[key]['strategies']:
strategy = self.instance(name)
compress_pass.add_strategy(strategy)
if key == 'compressor':
self.compressor['strategies'] = []
self.compressor['epoch'] = key_values[key]['epoch']
if 'init_model' in key_values[key]:
self.compressor['init_model'] = key_values[key][
'init_model']
self.compressor['checkpoint_path'] = key_values[key][
'checkpoint_path']
if 'strategies' in key_values[key]:
for name in key_values[key]['strategies']:
strategy = self.instance(name)
self.compressor['strategies'].append(strategy)
if key == 'include':
for config_file in key_values[key]:
......
python/paddle/fluid/contrib/slim/core/pass_builder.py 已删除 100644 → 0
浏览文件 @ a93a9eef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 .compress_pass import CompressPass
from .config import ConfigFactory
__all__ = ['build_compressor']
def build_compressor(place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
config=None):
if config is not None:
factory = ConfigFactory(config)
comp_pass = factory.get_compress_pass()
else:
comp_pass = CompressPass()
comp_pass.place = place
comp_pass.data_reader = data_reader
comp_pass.data_feeder = data_feeder
comp_pass.scope = scope
comp_pass.metrics = metrics
comp_pass.epoch = epoch
return comp_pass
python/paddle/fluid/contrib/slim/core/strategy.py
浏览文件 @ 072d95d8
......@@ -20,7 +20,7 @@ class Strategy(object):
Base class for all strategies.
"""
def __init__(self, start_epoch=0, end_epoch=10):
def __init__(self, start_epoch=0, end_epoch=0):
"""
Args:
start_epoch: The first epoch to apply the strategy.
......@@ -29,7 +29,7 @@ class Strategy(object):
self.start_epoch = start_epoch
self.end_epoch = end_epoch
def on_compress_begin(self, context):
def on_compression_begin(self, context):
pass
def on_epoch_begin(self, context):
......@@ -44,5 +44,5 @@ class Strategy(object):
def on_batch_end(self, context):
pass
def on_compress_end(self, context):
def on_compression_end(self, context):
pass
python/paddle/fluid/contrib/slim/demo/filter_prune/config.yaml 已删除 100644 → 0
浏览文件 @ a93a9eef
version: 1.0
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/demo/filter_prune/demo.py 已删除 100644 → 0
浏览文件 @ a93a9eef
# 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 paddle.fluid as fluid
import paddle
import os
import sys
from paddle.fluid.contrib.slim import CompressPass
from paddle.fluid.contrib.slim import build_compressor
from paddle.fluid.contrib.slim import ImitationGraph
class LinearModel(object):
def __init__(slef):
pass
def train(self):
train_program = fluid.Program()
startup_program = fluid.Program()
startup_program.random_seed = 10
with fluid.program_guard(train_program, startup_program):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=predict, label=y)
avg_cost = fluid.layers.mean(cost)
eval_program = train_program.clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
eval_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=1)
place = fluid.CPUPlace()
train_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
eval_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(startup_program)
train_metrics = {"loss": avg_cost.name}
eval_metrics = {"loss": avg_cost.name}
graph = ImitationGraph(train_program)
config = './config.yaml'
comp_pass = build_compressor(
place,
data_reader=train_reader,
data_feeder=train_feeder,
scope=fluid.global_scope(),
metrics=train_metrics,
epoch=1,
config=config)
comp_pass.apply(graph)
if __name__ == "__main__":
model = LinearModel()
model.train()
python/paddle/fluid/contrib/slim/graph/__init__.py
浏览文件 @ 072d95d8
......@@ -14,10 +14,7 @@
from . import executor
from .executor import *
from . import graph
from .graph import *
from . import graph_pass
from .graph_pass import *
from . import graph_wrapper
from .graph_wrapper import *
__all__ = executor.__all__
__all__ += graph.__all__
__all__ += graph_pass.__all__
__all__ += graph_wrapper.__all__
python/paddle/fluid/contrib/slim/graph/executor.py
浏览文件 @ 072d95d8
......@@ -12,51 +12,46 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
from abc import abstractmethod
from ....compiler import CompiledProgram
from ....data_feeder import DataFeeder
from .... import executor
from .graph import IRGraph, ImitationGraph
from .graph_wrapper import GraphWrapper
__all__ = ['get_executor']
__all__ = ['SlimGraphExecutor']
class GraphExecutor(object):
__metaclass__ = abc.ABCMeta
class SlimGraphExecutor(object):
"""
Wrapper of executor used to run GraphWrapper.
"""
def __init__(self, place):
self.place = place
@abstractmethod
def run(self, graph, feches=None, feed=None):
pass
class IRGraphExecutor(GraphExecutor):
def run(self, grah, fetches, feed=None):
pass
class ImitationGraphExecutor(GraphExecutor):
def __init__(self, place):
super(ImitationGraphExecutor, self).__init__(place)
self.exe = executor.Executor(place)
self.place = place
def run(self, graph, scope=None, fetches=None, feed=None):
assert isinstance(graph, ImitationGraph)
fetch_list = None
if fetches:
fetch_list = [
graph.program.global_block().var(name) for name in fetches
]
results = self.exe.run(graph.program,
def run(self, graph, scope, data=None):
"""
Runing a graph with a batch of data.
Args:
graph(GraphWrapper): The graph to be executed.
scope(fluid.core.Scope): The scope to be used.
data(list<tuple>): A batch of data. Each tuple in this list is a sample.
It will feed the items of tuple to the in_nodes of graph.
Returns:
results(list): A list of result with the same order indicated by graph.out_nodes.
"""
assert isinstance(graph, GraphWrapper)
if data is not None:
feeder = DataFeeder(
feed_list=graph.in_nodes.values(),
place=self.place,
program=graph.program)
feed = feeder.feed(data)
fetch_list = graph.out_nodes.values()
program = graph.compiled_graph if graph.compiled_graph else graph.program
results = self.exe.run(program,
scope=scope,
fetch_list=fetch_list,
feed=feed)
return results
def get_executor(graph, place):
if isinstance(graph, ImitationGraph):
return ImitationGraphExecutor(place)
if isinstance(graph, IRGraph):
return IRGraphExecutor(place)
python/paddle/fluid/contrib/slim/graph/graph_wrapper.py 0 → 100644
浏览文件 @ 072d95d8
# 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 collections import OrderedDict
from .... import io
from .... import compiler
from ....framework import Program
from ....framework import program_guard
from ....framework import Parameter
from ....framework import Variable
from ....executor import Executor
import copy
from collections import Iterable
from ....io import save_inference_model, load_inference_model, save_persistables
import numpy as np
import pickle
import os
__all__ = ['GraphWrapper', 'VarWrapper', 'OpWrapper']
OPTIMIZER_OPS = [
'momentum',
'lars_momentum',
'adagrad',
'adam',
'adamax',
'decayed_adagrad',
'adadelta',
'rmsprop',
]
class VarWrapper(object):
def __init__(self, var, graph):
assert isinstance(var, Variable)
assert isinstance(graph, GraphWrapper)
self._var = var
self._graph = graph
def __eq__(self, v):
"""
Overwrite this function for ...in... syntax in python.
"""
return self._var.name == v._var.name
def name(self):
"""
Get the name of the variable.
"""
return self._var.name
def shape(self):
"""
Get the shape of the varibale.
"""
return self._var.shape
def set_shape(self, shape):
"""
Set the shape of the variable.
"""
self._var.desc.set_shape(shape)
def inputs(self):
"""
Get all the operators that use this variable as output.
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for op in self._graph.ops():
if self in op.all_inputs():
ops.append(op)
return ops
def outputs(self):
"""
Get all the operators that use this variable as input.
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for op in self._graph.ops():
if self in op.all_outputs():
ops.append(op)
return ops
class OpWrapper(object):
def __init__(self, op, graph):
assert isinstance(graph, GraphWrapper)
self._op = op
self._graph = graph
def __eq__(self, op):
"""
Overwrite this function for ...in... syntax in python.
"""
return self.idx() == op.idx()
def all_inputs(self):
"""
Get all the input variables of this operator.
"""
return [
self._graph.var(var_name) for var_name in self._op.input_arg_names
]
def all_outputs(self):
"""
Get all the output variables of this operator.
"""
return [
self._graph.var(var_name) for var_name in self._op.output_arg_names
]
def idx(self):
"""
Get the id of this operator.
"""
return self._op.idx
def type(self):
"""
Get the type of this operator.
"""
return self._op.type
def is_bwd_op(self):
"""
Whether this operator is backward op.
"""
return self.type().endswith('_grad')
def is_opt_op(self):
"""
Whether this operator is optimizer op.
"""
return self.type() in OPTIMIZER_OPS
def inputs(self, name):
"""
Get all the varibales by the input name.
"""
return [self._graph.var(var_name) for var_name in self._op.input(name)]
def outputs(self, name):
"""
Get all the varibales by the output name.
"""
return [self._graph.var(var_name) for var_name in self._op.output(name)]
def set_attr(self, key, value):
"""
Set the value of attribute by attribute's name.
Args:
key(str): the attribute name.
value(bool|int|str|float|list): the value of the attribute.
"""
self._op._set_attr(key, value)
def attr(self, name):
"""
Get the attribute by name.
Args:
name(str): the attribute name.
Returns:
bool|int|str|float|list: The attribute value. The return value
can be any valid attribute type.
"""
return self._op.attr(name)
class GraphWrapper(object):
"""
It is a wrapper of paddle.fluid.framework.IrGraph with some special functions
for paddle slim framework.
"""
def __init__(self, program=None, in_nodes=[], out_nodes=[]):
"""
Args:
program(framework.Program): A program with
in_nodes(dict): A dict to indicate the input nodes of the graph.
The key is user-defined and human-readable name.
The value is the name of Variable.
out_nodes(dict): A dict to indicate the input nodes of the graph.
The key is user-defined and human-readable name.
The value is the name of Variable.
"""
super(GraphWrapper, self).__init__()
self.program = Program() if program is None else program
self.compiled_graph = None
self.in_nodes = OrderedDict(in_nodes)
self.out_nodes = OrderedDict(out_nodes)
self._attrs = OrderedDict()
def all_parameters(self):
"""
Get all the parameters in this graph.
Returns:
list<VarWrapper>: A list of VarWrapper instances.
"""
params = []
for block in self.program.blocks:
for param in block.all_parameters():
params.append(VarWrapper(param, self))
return params
def is_parameter(self, var):
"""
Whether the given variable is parameter.
Args:
var(VarWrapper): The given varibale.
"""
return isinstance(var._var, Parameter)
def is_persistable(self, var):
"""
Whether the given variable is persistable.
Args:
var(VarWrapper): The given varibale.
"""
return var._var.persistable
def compile(self, for_parallel=True, for_test=False):
"""
Compile the program in this wrapper to framework.CompiledProgram for next running.
This function must be called if the program is modified.
Args:
for_parallel(bool): Whether the program to run in data parallel way. default: True.
for_test(bool): Whether the compiled program is used for test.
"""
target = self.program
if for_test:
loss = None
else:
loss = self.out_nodes['loss']
if for_parallel:
# disable memory optimize for stable training
build_strategy = compiler.BuildStrategy()
build_strategy.enable_inplace = False
build_strategy.memory_optimize = False
self.compiled_graph = compiler.CompiledProgram(
target).with_data_parallel(
loss_name=loss, build_strategy=build_strategy)
else:
self.compiled_graph = compiler.CompiledProgram(target)
def ops(self):
"""
Return all operator nodes included in the graph as a set.
"""
ops = []
for block in self.program.blocks:
for op in block.ops:
ops.append(OpWrapper(op, self))
return ops
def vars(self):
"""
Get all the variables.
"""
return [VarWrapper(var, self) for var in self.program.list_vars()]
def var(self, name):
"""
Get the variable by variable name.
"""
return VarWrapper(self.program.global_block().var(name), self)
def clone(self, for_test=False):
"""
Clone a new graph from current graph.
Returns:
(GraphWrapper): The wrapper of a new graph.
"""
return GraphWrapper(
self.program.clone(for_test),
copy.deepcopy(self.in_nodes), copy.deepcopy(self.out_nodes))
def merge(self, graph):
"""
Merge a graph into current graph.
Args:
graph(GraphWrapper): The graph to be merged by current graph.
"""
for var in graph.program.list_vars():
self.program.global_block()._clone_variable(var)
# TODO: parameters should be cloned
for op in graph.ops():
op = op._op
inputs = {}
outputs = {}
attrs = {}
for input_name in op.input_names:
inputs[input_name] = [
self.var(in_var_name)
for in_var_name in op.inputs(input_name)
]
for output_name in op.output_names:
outputs[output_name] = [
self.var(out_var_name)
for out_var_name in op.output(output_name)
]
for attr_name in op.attr_names:
attrs[attr_name] = op.attr(attr_name)
self.program.global_block().append_op(
type=op.type, inputs=inputs, outputs=outputs, attrs=attrs)
def program(self):
"""
Get the program in current wrapper.
"""
return self.program
def pre_ops(self, op):
"""
Get all the previous operators of target operator.
Args:
op(OpWrapper): Target operator..
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for p in self.ops():
for in_var in op.all_inputs():
if in_var in p.all_outputs():
ops.append(p)
return ops
def next_ops(self, op):
"""
Get all the next operators of target operator.
Args:
op(OpWrapper): Target operator..
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for p in self.ops():
for out_var in op.all_outputs():
if out_var in p.all_inputs():
ops.append(p)
return ops
def get_param_by_op(self, op):
"""
Get the parameters used by target operator.
"""
assert isinstance(op, OpWrapper)
params = []
for var in op.all_inputs():
if isinstance(var._var, Parameter):
params.append(var)
assert len(params) > 0
return params
def numel_params(self):
"""
Get the number of elements in all parameters.
"""
ret = 0
for param in self.all_parameters():
ret += np.product(param.shape())
return ret
def get_optimize_graph(self, optimizer, place, scope, no_grad_var_names=[]):
"""
Get a new graph for training by appending some backward operators and optimization operators.
Args:
optimizer: The optimzier used to generate training graph.
place: The place to run the graph.
scope: The scope used to run the graph. Some new variable will be added into this scope.
no_grad_var_names(list<str>): Names of variables that should be ignored while computing gradients. default: [].
Returns:
(GraphWrapper): The wrapper of new graph with backward ops and optimization ops.
"""
graph = self.clone()
startup_program = Program()
with program_guard(
main_program=graph.program, startup_program=startup_program):
target_name = None
if 'loss' in graph.out_nodes:
target_name = graph.out_nodes['loss']
elif 'cost' in graph.out_nodes:
target_name = graph.out_nodes['cost']
target = graph.var(target_name)._var
optimizer.minimize(target, no_grad_set=no_grad_var_names)
exe = Executor(place)
exe.run(program=startup_program, scope=scope)
return graph
def flops(self, only_conv=False):
"""
Get the flops of current graph.
Args:
only_conv: Only calculating the conv layers. default: False.
Returns:
int: The flops of current graph.
"""
flops = 0
for op in self.ops():
if op.type() in ['conv2d', 'depthwise_conv2d']:
filter_shape = op.inputs("Filter")[0].shape()
input_shape = op.inputs("Input")[0].shape()
output_shape = op.outputs("Output")[0].shape()
c_out, c_in, k_h, k_w = filter_shape
_, _, h_out, w_out = output_shape
groups = op.attr("groups")
kernel_ops = k_h * k_w * (c_in / groups)
if len(op.inputs("Bias")) > 0:
with_bias = 1
else:
with_bias = 0
flops += 2 * h_out * w_out * c_out * (kernel_ops + with_bias)
elif op.type() == 'pool2d' and not only_conv:
input_shape = op.inputs("X")[0].shape()
output_shape = op.outputs("Out")[0].shape()
_, c_out, h_out, w_out = output_shape
k_size = op.attr("ksize")
flops += h_out * w_out * c_out * (k_size[0]**2)
elif op.type() == 'mul' and not only_conv:
x_shape = list(op.inputs("X")[0].shape())
y_shape = op.inputs("Y")[0].shape()
if x_shape[0] == -1:
x_shape[0] = 1
flops += 2 * x_shape[0] * x_shape[1] * y_shape[1]
elif op.type() in ['relu', 'sigmoid', 'batch_norm'
] and not only_conv:
input_shape = list(op.inputs("X")[0].shape())
if input_shape[0] == -1:
input_shape[0] = 1
flops += np.product(input_shape)
return flops
def save_persistables(self, path, exe):
"""
Save all the persistable variables into file.
Args:
path(str): The path to save the persistables.
exe(framework.Executor): The executor used to save the persistables.
"""
io.save_persistables(exe.exe, path, main_program=self.program)
def load_persistables(self, path, exe):
"""
Load the persistable variables from file.
Args:
path(str): The path to load the persistables.
exe(framework.Executor): The executor used to load the persistables.
"""
def if_exist(var):
return os.path.exists(os.path.join(path, var.name))
io.load_vars(
exe.exe, path, main_program=self.program, predicate=if_exist)
def update_param_shape(self, scope):
"""
Update the shape of parameters in the graph according to tensors in scope.
It is used after loading pruned parameters from file.
"""
for param in self.all_parameters():
tensor_shape = np.array(scope.find_var(param.name()).get_tensor(
)).shape
param.set_shape(tensor_shape)
def infer_shape(self):
"""
Update the groups of convolution layer according to current filters.
It is used after loading pruned parameters from file.
"""
for op in self.ops():
if op.type() != 'conditional_block':
op._op.desc.infer_shape(op._op.block.desc)
def update_groups_of_conv(self):
for op in self.ops():
if op.type() == 'depthwise_conv2d':
op.set_attr('groups', op.inputs('Filter')[0].shape()[0])
python/paddle/fluid/contrib/slim/prune/prune_strategy.py
浏览文件 @ 072d95d8
......@@ -13,54 +13,919 @@
# limitations under the License.
from ..core.strategy import Strategy
from ....framework import Program, program_guard
from ..graph import VarWrapper, OpWrapper, GraphWrapper
from ....framework import Program, program_guard, Parameter
from .... import layers
import prettytable as pt
import numpy as np
from scipy.optimize import leastsq
import copy
import re
import os
import pickle
import logging
import sys
__all__ = ['SensitivePruneStrategy', 'PruneStrategy']
__all__ = ['SensitivePruneStrategy', 'UniformPruneStrategy']
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.INFO)
class PruneStrategy(Strategy):
"""
The base class of all pruning strategies.
"""
class SensitivePruneStrategy(Strategy):
def __init__(self,
pruner=None,
start_epoch=0,
end_epoch=10,
delta_rate=0.20,
acc_loss_threshold=0.2,
sensitivities=None):
super(SensitivePruneStrategy, self).__init__(start_epoch, end_epoch)
end_epoch=0,
target_ratio=0.5,
metric_name=None,
pruned_params='conv.*_weights'):
"""
Args:
pruner(slim.Pruner): The pruner used to prune the parameters.
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
target_ratio(float): The flops ratio to be pruned from current model.
metric_name(str): The metric used to evaluate the model.
It should be one of keys in out_nodes of graph wrapper.
pruned_params(str): The pattern str to match the parameter names to be pruned.
"""
super(PruneStrategy, self).__init__(start_epoch, end_epoch)
self.pruner = pruner
self.delta_rate = delta_rate
self.acc_loss_threshold = acc_loss_threshold
self.sensitivities = sensitivities
self.target_ratio = target_ratio
self.metric_name = metric_name
self.pruned_params = pruned_params
self.pruned_list = []
self.backup = {}
self.param_shape_backup = {}
def _eval_graph(self, context, sampled_rate=None, cached_id=0):
"""
Evaluate the current mode in context.
Args:
context(slim.core.Context): The context storing all information used to evaluate the current model.
sampled_rate(float): The sampled rate used to sample partial data for evaluation. None means using all data in eval_reader. default: None.
cached_id(int): The id of dataset sampled. Evaluations with same cached_id use the same sampled dataset. default: 0.
"""
results, names = context.run_eval_graph(sampled_rate, cached_id)
metric = np.mean(results[list(names).index(self.metric_name)])
return metric
class PruneStrategy(Strategy):
def _prune_filters_by_ratio(self,
scope,
params,
ratio,
place,
lazy=False,
only_graph=False):
"""
Pruning filters by given ratio.
Args:
scope(fluid.core.Scope): The scope used to pruning filters.
params(list<VarWrapper>): A list of filter parameters.
ratio(float): The ratio to be pruned.
place(fluid.Place): The device place of filter parameters.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
if params[0].name() in self.pruned_list[0]:
return
param_t = scope.find_var(params[0].name()).get_tensor()
pruned_idx = self.pruner.cal_pruned_idx(
params[0].name(), np.array(param_t), ratio, axis=0)
for param in params:
assert isinstance(param, VarWrapper)
param_t = scope.find_var(param.name()).get_tensor()
if lazy:
self.backup[param.name()] = copy.deepcopy(np.array(param_t))
pruned_param = self.pruner.prune_tensor(
np.array(param_t), pruned_idx, pruned_axis=0, lazy=lazy)
if not only_graph:
param_t.set(pruned_param, place)
ori_shape = param.shape()
if param.name() not in self.param_shape_backup:
self.param_shape_backup[param.name()] = copy.deepcopy(
param.shape())
new_shape = list(param.shape())
new_shape[0] = pruned_param.shape[0]
param.set_shape(new_shape)
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
_logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format(
str(param.name()), str(0), str(ori_shape), str(param.shape())))
self.pruned_list[0].append(param.name())
return pruned_idx
def _prune_parameter_by_idx(self,
scope,
params,
pruned_idx,
pruned_axis,
place,
lazy=False,
only_graph=False):
"""
Pruning parameters in given axis.
Args:
scope(fluid.core.Scope): The scope storing paramaters to be pruned.
params(VarWrapper): The parameter to be pruned.
pruned_idx(list): The index of elements to be pruned.
pruned_axis(int): The pruning axis.
place(fluid.Place): The device place of filter parameters.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
if params[0].name() in self.pruned_list[pruned_axis]:
return
for param in params:
assert isinstance(param, VarWrapper)
param_t = scope.find_var(param.name()).get_tensor()
if lazy:
self.backup[param.name()] = copy.deepcopy(np.array(param_t))
pruned_param = self.pruner.prune_tensor(
np.array(param_t), pruned_idx, pruned_axis, lazy=lazy)
if not only_graph:
param_t.set(pruned_param, place)
ori_shape = param.shape()
if param.name() not in self.param_shape_backup:
self.param_shape_backup[param.name()] = copy.deepcopy(
param.shape())
new_shape = list(param.shape())
new_shape[pruned_axis] = pruned_param.shape[pruned_axis]
param.set_shape(new_shape)
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
_logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format(
str(param.name()),
str(pruned_axis), str(ori_shape), str(param.shape())))
self.pruned_list[pruned_axis].append(param.name())
def _forward_search_related_op(self, graph, param):
"""
Forward search operators that will be affected by pruning of param.
Args:
graph(GraphWrapper): The graph to be searched.
param(VarWrapper): The current pruned parameter.
Returns:
list<OpWrapper>: A list of operators.
"""
assert isinstance(param, VarWrapper)
visited = {}
for op in graph.ops():
visited[op.idx()] = False
stack = []
for op in graph.ops():
if (not op.is_bwd_op()) and (param in op.all_inputs()):
stack.append(op)
visit_path = []
while len(stack) > 0:
top_op = stack[len(stack) - 1]
if visited[top_op.idx()] == False:
visit_path.append(top_op)
visited[top_op.idx()] = True
next_ops = None
if top_op.type() == "conv2d" and param not in top_op.all_inputs():
next_ops = None
elif top_op.type() == "mul":
next_ops = None
else:
next_ops = self._get_next_unvisited_op(graph, visited, top_op)
if next_ops == None:
stack.pop()
else:
stack += next_ops
return visit_path
def _get_next_unvisited_op(self, graph, visited, top_op):
"""
Get next unvisited adjacent operators of given operators.
Args:
graph(GraphWrapper): The graph used to search.
visited(list): The ids of operators that has been visited.
top_op: The given operator.
Returns:
list<OpWrapper>: A list of operators.
"""
assert isinstance(top_op, OpWrapper)
next_ops = []
for op in graph.next_ops(top_op):
if (visited[op.idx()] == False) and (not op.is_bwd_op()):
next_ops.append(op)
return next_ops if len(next_ops) > 0 else None
def _get_accumulator(self, graph, param):
"""
Get accumulators of given parameter. The accumulator was created by optimizer.
Args:
graph(GraphWrapper): The graph used to search.
param(VarWrapper): The given parameter.
Returns:
list<VarWrapper>: A list of accumulators which are variables.
"""
assert isinstance(param, VarWrapper)
params = []
for op in param.outputs():
if op.is_opt_op():
for out_var in op.all_outputs():
if graph.is_persistable(out_var) and out_var.name(
) != param.name():
params.append(out_var)
return params
def _forward_pruning_ralated_params(self,
graph,
scope,
param,
place,
ratio=None,
pruned_idxs=None,
lazy=False,
only_graph=False):
"""
Pruning all the parameters affected by the pruning of given parameter.
Args:
graph(GraphWrapper): The graph to be searched.
scope(fluid.core.Scope): The scope storing paramaters to be pruned.
param(VarWrapper): The given parameter.
place(fluid.Place): The device place of filter parameters.
ratio(float): The target ratio to be pruned.
pruned_idx(list): The index of elements to be pruned.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
assert isinstance(
graph,
GraphWrapper), "graph must be instance of slim.core.GraphWrapper"
assert isinstance(
param, VarWrapper), "param must be instance of slim.core.VarWrapper"
if param.name() in self.pruned_list[0]:
return
related_ops = self._forward_search_related_op(graph, param)
if ratio is None:
assert pruned_idxs is not None
self._prune_parameter_by_idx(
scope, [param] + self._get_accumulator(graph, param),
pruned_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
else:
pruned_idxs = self._prune_filters_by_ratio(
scope, [param] + self._get_accumulator(graph, param),
ratio,
place,
lazy=lazy,
only_graph=only_graph)
corrected_idxs = pruned_idxs[:]
for idx, op in enumerate(related_ops):
if op.type() == "conv2d" and (param not in op.all_inputs()):
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
conv_param = in_var
self._prune_parameter_by_idx(
scope, [conv_param] + self._get_accumulator(
graph, conv_param),
corrected_idxs,
pruned_axis=1,
place=place,
lazy=lazy,
only_graph=only_graph)
if op.type() == "depthwise_conv2d":
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
conv_param = in_var
self._prune_parameter_by_idx(
scope, [conv_param] + self._get_accumulator(
graph, conv_param),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
elif op.type() == "elementwise_add":
# pruning bias
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
bias_param = in_var
self._prune_parameter_by_idx(
scope, [bias_param] + self._get_accumulator(
graph, bias_param),
pruned_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
elif op.type() == "mul": # pruning fc layer
fc_input = None
fc_param = None
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
fc_param = in_var
else:
fc_input = in_var
idx = []
feature_map_size = fc_input.shape()[2] * fc_input.shape()[3]
range_idx = np.array(range(feature_map_size))
for i in corrected_idxs:
idx += list(range_idx + i * feature_map_size)
corrected_idxs = idx
self._prune_parameter_by_idx(
scope, [fc_param] + self._get_accumulator(graph, fc_param),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
elif op.type() == "concat":
concat_inputs = op.all_inputs()
last_op = related_ops[idx - 1]
for out_var in last_op.all_outputs():
if out_var in concat_inputs:
concat_idx = concat_inputs.index(out_var)
offset = 0
for ci in range(concat_idx):
offset += concat_inputs[ci].shape()[1]
corrected_idxs = [x + offset for x in pruned_idxs]
elif op.type() == "batch_norm":
bn_inputs = op.all_inputs()
mean = bn_inputs[2]
variance = bn_inputs[3]
alpha = bn_inputs[0]
beta = bn_inputs[1]
self._prune_parameter_by_idx(
scope, [mean] + self._get_accumulator(graph, mean),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
self._prune_parameter_by_idx(
scope, [variance] + self._get_accumulator(graph, variance),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
self._prune_parameter_by_idx(
scope, [alpha] + self._get_accumulator(graph, alpha),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
self._prune_parameter_by_idx(
scope, [beta] + self._get_accumulator(graph, beta),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
def _prune_parameters(self,
graph,
scope,
params,
ratios,
place,
lazy=False,
only_graph=False):
"""
Pruning the given parameters.
Args:
graph(GraphWrapper): The graph to be searched.
scope(fluid.core.Scope): The scope storing paramaters to be pruned.
params(list<str>): A list of parameter names to be pruned.
ratios(list<float>): A list of ratios to be used to pruning parameters.
place(fluid.Place): The device place of filter parameters.
pruned_idx(list): The index of elements to be pruned.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
_logger.debug('\n################################')
_logger.debug('# pruning parameters #')
_logger.debug('################################\n')
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
_logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format('parameter', 'axis',
'from', 'to'))
assert len(params) == len(ratios)
self.pruned_list = [[], []]
for param, ratio in zip(params, ratios):
assert isinstance(param, str) or isinstance(param, unicode)
param = graph.var(param)
self._forward_pruning_ralated_params(
graph,
scope,
param,
place,
ratio=ratio,
lazy=lazy,
only_graph=only_graph)
ops = param.outputs()
for op in ops:
if op.type() == 'conv2d':
brother_ops = self._search_brother_ops(graph, op)
for broher in brother_ops:
for p in graph.get_param_by_op(broher):
self._forward_pruning_ralated_params(
graph,
scope,
p,
place,
ratio=ratio,
lazy=lazy,
only_graph=only_graph)
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
def _search_brother_ops(self, graph, op_node):
"""
Search brother operators that was affected by pruning of given operator.
Args:
graph(GraphWrapper): The graph to be searched.
op_node(OpWrapper): The start node for searching.
Returns:
list<VarWrapper>: A list of operators.
"""
visited = [op_node.idx()]
stack = []
brothers = []
for op in graph.next_ops(op_node):
if (op.type() != 'conv2d') and (op.type() != 'fc') and (
not op._is_bwd_op()):
stack.append(op)
visited.append(op.idx())
while len(stack) > 0:
top_op = stack.pop()
for parent in graph.pre_ops(top_op):
if parent.idx() not in visited and (not parent._is_bwd_op()):
if ((parent.type == 'conv2d') or (parent.type == 'fc')):
brothers.append(parent)
else:
stack.append(parent)
visited.append(parent.idx())
for child in graph.next_ops(top_op):
if (child.type != 'conv2d') and (child.type != 'fc') and (
child.idx() not in visited) and (
not child._is_bwd_op()):
stack.append(child)
visited.append(child.idx())
return brothers
def _prune_graph(self, graph, target_graph):
"""
Pruning parameters of graph according to target graph.
Args:
graph(GraphWrapper): The graph to be pruned.
target_graph(GraphWrapper): The reference graph.
Return: None
"""
count = 1
_logger.debug(
'|----+----------------------------------------+------------------------------+------------------------------|'
)
_logger.debug('|{:^4}|{:^40}|{:^30}|{:^30}|'.format('id', 'parammeter',
'from', 'to'))
for param in target_graph.all_parameters():
var = graph.var(param.name())
ori_shape = var.shape()
var.set_shape(param.shape())
_logger.debug(
'|----+----------------------------------------+------------------------------+------------------------------|'
)
_logger.debug('|{:^4}|{:^40}|{:^30}|{:^30}|'.format(
str(count),
str(param.name()), str(ori_shape), str(param.shape())))
count += 1
_logger.debug(
'|----+----------------------------------------+------------------------------+------------------------------|'
)
class UniformPruneStrategy(PruneStrategy):
"""
The strategy that pruning weights by threshold or ratio iteratively.
The uniform pruning strategy. The parameters will be pruned by uniform ratio.
"""
def __init__(self,
pruner,
mini_batch_pruning_frequency=1,
pruner=None,
start_epoch=0,
end_epoch=10):
super(PruneStrategy, self).__init__(start_epoch, end_epoch)
self.pruner = pruner
self.mini_batch_pruning_frequency = mini_batch_pruning_frequency
def _triger(self, context):
return (context.batch_id % self.mini_batch_pruning_frequency == 0 and
self.start_epoch <= context.epoch_id < self.end_epoch)
def on_batch_end(self, context):
if self._triger(context):
prune_program = Program()
with program_guard(prune_program):
for param in context.graph.all_parameters():
prune_program.global_block().clone_variable(param)
p = prune_program.global_block().var(param.name)
zeros_mask = self.pruner.prune(p)
pruned_param = p * zeros_mask
layers.assign(input=pruned_param, output=param)
context.program_exe.run(prune_program, scope=context.scope)
end_epoch=0,
target_ratio=0.5,
metric_name=None,
pruned_params='conv.*_weights'):
"""
Args:
pruner(slim.Pruner): The pruner used to prune the parameters.
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
target_ratio(float): The flops ratio to be pruned from current model.
metric_name(str): The metric used to evaluate the model.
It should be one of keys in out_nodes of graph wrapper.
pruned_params(str): The pattern str to match the parameter names to be pruned.
"""
super(UniformPruneStrategy, self).__init__(pruner, start_epoch,
end_epoch, target_ratio,
metric_name, pruned_params)
def _get_best_ratios(self, context):
"""
Search a group of ratios for pruning target flops.
"""
_logger.info('_get_best_ratios')
pruned_params = []
for param in context.eval_graph.all_parameters():
if re.match(self.pruned_params, param.name()):
pruned_params.append(param.name())
min_ratio = 0.
max_ratio = 1.
flops = context.eval_graph.flops()
model_size = context.eval_graph.numel_params()
while min_ratio < max_ratio:
ratio = (max_ratio + min_ratio) / 2
_logger.debug(
'-----------Try pruning ratio: {:.2f}-----------'.format(ratio))
ratios = [ratio] * len(pruned_params)
self._prune_parameters(
context.eval_graph,
context.scope,
pruned_params,
ratios,
context.place,
only_graph=True)
pruned_flops = 1 - (float(context.eval_graph.flops()) / flops)
pruned_size = 1 - (float(context.eval_graph.numel_params()) /
model_size)
_logger.debug('Pruned flops: {:.2f}'.format(pruned_flops))
_logger.debug('Pruned model size: {:.2f}'.format(pruned_size))
for param in self.param_shape_backup.keys():
context.eval_graph.var(param).set_shape(self.param_shape_backup[
param])
self.param_shape_backup = {}
if abs(pruned_flops - self.target_ratio) < 1e-2:
break
if pruned_flops > self.target_ratio:
max_ratio = ratio
else:
min_ratio = ratio
_logger.info('Get ratios: {}'.format([round(r, 2) for r in ratios]))
return pruned_params, ratios
def on_epoch_begin(self, context):
if context.epoch_id == self.start_epoch:
params, ratios = self._get_best_ratios(context)
self._prune_parameters(context.optimize_graph, context.scope,
params, ratios, context.place)
model_size = context.eval_graph.numel_params()
flops = context.eval_graph.flops()
_logger.debug('\n################################')
_logger.debug('# pruning eval graph #')
_logger.debug('################################\n')
self._prune_graph(context.eval_graph, context.optimize_graph)
context.optimize_graph.update_groups_of_conv()
context.eval_graph.update_groups_of_conv()
_logger.info(
'------------------finish pruning--------------------------------'
)
_logger.info('Pruned size: {:.2f}'.format(1 - (float(
context.eval_graph.numel_params()) / model_size)))
_logger.info('Pruned flops: {:.2f}'.format(1 - (float(
context.eval_graph.flops()) / flops)))
# metric = self._eval_graph(context)
# _logger.info('Metric after pruning: {:.2f}'.format(metric))
_logger.info(
'------------------UniformPruneStrategy.on_compression_begin finish--------------------------------'
)
class SensitivePruneStrategy(PruneStrategy):
"""
Sensitive pruning strategy. Different pruned ratio was applied on each layer.
"""
def __init__(self,
pruner=None,
start_epoch=0,
end_epoch=0,
delta_rate=0.20,
target_ratio=0.5,
metric_name='top1_acc',
pruned_params='conv.*_weights',
sensitivities_file='./sensitivities.data',
sensitivities={},
num_steps=1,
eval_rate=None):
"""
Args:
pruner(slim.Pruner): The pruner used to prune the parameters.
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0.
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 10.
delta_rate(float): The delta used to generate ratios when calculating sensitivities. default: 0.2
target_ratio(float): The flops ratio to be pruned from current model. default: 0.5
metric_name(str): The metric used to evaluate the model.
It should be one of keys in out_nodes of graph wrapper. default: 'top1_acc'
pruned_params(str): The pattern str to match the parameter names to be pruned. default: 'conv.*_weights'.
sensitivities_file(str): The sensitivities file. default: './sensitivities.data'
sensitivities(dict): The user-defined sensitivities. default: {}.
num_steps(int): The number of pruning steps. default: 1.
eval_rate(float): The rate of sampled data used to calculate sensitivities.
None means using all the data. default: None.
"""
super(SensitivePruneStrategy, self).__init__(pruner, start_epoch,
end_epoch, target_ratio,
metric_name, pruned_params)
self.delta_rate = delta_rate
self.pruned_list = []
self.sensitivities = sensitivities
self.sensitivities_file = sensitivities_file
self.backup = {}
self.param_shape_backup = {}
self.num_steps = num_steps
self.eval_rate = eval_rate
self.pruning_step = 1 - pow((1 - target_ratio), 1.0 / self.num_steps)
def _save_sensitivities(self, sensitivities, sensitivities_file):
"""
Save sensitivities into file.
"""
with open(sensitivities_file, 'wb') as f:
pickle.dump(sensitivities, f)
def _load_sensitivities(self, sensitivities_file):
"""
Load sensitivities from file.
"""
sensitivities = {}
if sensitivities_file and os.path.exists(sensitivities_file):
with open(sensitivities_file, 'rb') as f:
if sys.version_info < (3, 0):
sensitivities = pickle.load(f)
else:
sensitivities = pickle.load(f, encoding='bytes')
for param in sensitivities:
sensitivities[param]['pruned_percent'] = [
round(p, 2) for p in sensitivities[param]['pruned_percent']
]
self._format_sensitivities(sensitivities)
return sensitivities
def _format_sensitivities(self, sensitivities):
"""
Print formated sensitivities in debug log level.
"""
tb = pt.PrettyTable()
tb.field_names = ["parameter", "size"] + [
str(round(i, 2))
for i in np.arange(self.delta_rate, 1, self.delta_rate)
]
for param in sensitivities:
if len(sensitivities[param]['loss']) == (len(tb.field_names) - 2):
tb.add_row([param, sensitivities[param]['size']] + [
round(loss, 2) for loss in sensitivities[param]['loss']
])
_logger.debug('\n################################')
_logger.debug('# sensitivities table #')
_logger.debug('################################\n')
_logger.debug(tb)
def _compute_sensitivities(self, context):
"""
Computing the sensitivities of all parameters.
"""
_logger.info("calling _compute_sensitivities.")
self.param_shape_backup = {}
self.backup = {}
cached_id = np.random.randint(1000)
if self.start_epoch == context.epoch_id:
sensitivities_file = self.sensitivities_file
else:
sensitivities_file = self.sensitivities_file + ".epoch" + str(
context.epoch_id)
sensitivities = self._load_sensitivities(sensitivities_file)
for param in context.eval_graph.all_parameters():
if not re.match(self.pruned_params, param.name()):
continue
if param.name() not in sensitivities:
sensitivities[param.name()] = {
'pruned_percent': [],
'loss': [],
'size': param.shape()[0]
}
metric = None
for param in sensitivities.keys():
ratio = self.delta_rate
while ratio < 1:
ratio = round(ratio, 2)
if ratio in sensitivities[param]['pruned_percent']:
_logger.debug('{}, {} has computed.'.format(param, ratio))
ratio += self.delta_rate
continue
if metric is None:
metric = self._eval_graph(context, self.eval_rate,
cached_id)
# prune parameter by ratio
self._prune_parameters(
context.eval_graph,
context.scope, [param], [ratio],
context.place,
lazy=True)
self.pruned_list[0]
# get accuracy after pruning and update self.sensitivities
pruned_metric = self._eval_graph(context, self.eval_rate,
cached_id)
loss = metric - pruned_metric
_logger.info("pruned param: {}; {}; loss={}".format(
param, ratio, loss))
for brother in self.pruned_list[0]:
if re.match(self.pruned_params, brother):
if brother not in sensitivities:
sensitivities[brother] = {
'pruned_percent': [],
'loss': []
}
sensitivities[brother]['pruned_percent'].append(ratio)
sensitivities[brother]['loss'].append(loss)
self._save_sensitivities(sensitivities, sensitivities_file)
# restore pruned parameters
for param_name in self.backup.keys():
param_t = context.scope.find_var(param_name).get_tensor()
param_t.set(self.backup[param_name], context.place)
# pruned_metric = self._eval_graph(context)
self.backup = {}
ratio += self.delta_rate
return sensitivities
def _get_best_ratios(self, context, sensitivities, target_ratio):
"""
Search a group of ratios for pruning target flops.
"""
_logger.info('_get_best_ratios for pruning ratie: {}'.format(
target_ratio))
self.param_shape_backup = {}
self.backup = {}
def func(params, x):
a, b, c, d = params
return a * x * x * x + b * x * x + c * x + d
def error(params, x, y):
return func(params, x) - y
def slove_coefficient(x, y):
init_coefficient = [10, 10, 10, 10]
coefficient, loss = leastsq(error, init_coefficient, args=(x, y))
return coefficient
min_loss = 0.
max_loss = 0.
# step 1: fit curve by sensitivities
coefficients = {}
for param in sensitivities:
losses = np.array([0] * 5 + sensitivities[param]['loss'])
precents = np.array([0] * 5 + sensitivities[param][
'pruned_percent'])
coefficients[param] = slove_coefficient(precents, losses)
loss = np.max(losses)
max_loss = np.max([max_loss, loss])
# step 2: Find a group of ratios by binary searching.
flops = context.eval_graph.flops()
model_size = context.eval_graph.numel_params()
ratios = []
while min_loss < max_loss:
loss = (max_loss + min_loss) / 2
_logger.info(
'-----------Try pruned ratios while acc loss={:.4f}-----------'.
format(loss))
ratios = []
# step 2.1: Get ratios according to current loss
for param in sensitivities:
coefficient = copy.deepcopy(coefficients[param])
coefficient[-1] = coefficient[-1] - loss
roots = np.roots(coefficient)
for root in roots:
min_root = 1
if np.isreal(root) and root > 0 and root < 1:
selected_root = min(root.real, min_root)
ratios.append(selected_root)
_logger.info('Pruned ratios={}'.format(
[round(ratio, 3) for ratio in ratios]))
# step 2.2: Pruning by current ratios
self._prune_parameters(
context.eval_graph,
context.scope,
sensitivities.keys(),
ratios,
context.place,
only_graph=True)
pruned_flops = 1 - (float(context.eval_graph.flops()) / flops)
pruned_size = 1 - (float(context.eval_graph.numel_params()) /
model_size)
_logger.info('Pruned flops: {:.4f}'.format(pruned_flops))
_logger.info('Pruned model size: {:.4f}'.format(pruned_size))
for param in self.param_shape_backup.keys():
context.eval_graph.var(param).set_shape(self.param_shape_backup[
param])
self.param_shape_backup = {}
# step 2.3: Check whether current ratios is enough
if abs(pruned_flops - target_ratio) < 0.015:
break
if pruned_flops > target_ratio:
max_loss = loss
else:
min_loss = loss
return sensitivities.keys(), ratios
def _current_pruning_target(self, context):
'''
Get the target pruning rate in current epoch.
'''
_logger.info('Left number of pruning steps: {}'.format(self.num_steps))
if self.num_steps <= 0:
return None
if (self.start_epoch == context.epoch_id) or context.eval_converged(
self.metric_name, 0.005):
self.num_steps -= 1
return self.pruning_step
def on_epoch_begin(self, context):
current_ratio = self._current_pruning_target(context)
if current_ratio is not None:
sensitivities = self._compute_sensitivities(context)
params, ratios = self._get_best_ratios(context, sensitivities,
current_ratio)
self._prune_parameters(context.optimize_graph, context.scope,
params, ratios, context.place)
self.param_shape_backup = {}
self.backup = {}
model_size = context.eval_graph.numel_params()
flops = context.eval_graph.flops()
_logger.debug('################################')
_logger.debug('# pruning eval graph #')
_logger.debug('################################')
self._prune_graph(context.eval_graph, context.optimize_graph)
context.optimize_graph.update_groups_of_conv()
context.eval_graph.update_groups_of_conv()
context.optimize_graph.compile() # to update the compiled program
context.eval_graph.compile(
for_parallel=False,
for_test=True) # to update the compiled program
_logger.info(
'------------------finish pruning--------------------------------'
)
_logger.info('Pruned size: {:.3f}'.format(1 - (float(
context.eval_graph.numel_params()) / model_size)))
_logger.info('Pruned flops: {:.3f}'.format(1 - (float(
context.eval_graph.flops()) / flops)))
metric = self._eval_graph(context)
_logger.info('Metric after pruning: {:.2f}'.format(metric))
_logger.info(
'------------------SensitivePruneStrategy.on_epoch_begin finish--------------------------------'
)
python/paddle/fluid/contrib/slim/prune/pruner.py
浏览文件 @ 072d95d8
......@@ -13,9 +13,10 @@
# limitations under the License.
import numpy as np
import collections
from .... import layers
__all__ = ['Pruner', 'MagnitudePruner', 'RatioPruner']
__all__ = ['Pruner', 'StructurePruner']
class Pruner(object):
......@@ -30,54 +31,77 @@ class Pruner(object):
pass
class MagnitudePruner(Pruner):
class StructurePruner(Pruner):
"""
Pruner used to pruning a parameter by threshold.
Pruner used to pruning parameters by groups.
"""
def __init__(self, threshold):
self.threshold = threshold
def prune(self, param, threshold=None):
if threshold is None:
thres = layers.fill_constant(
shape=[1], dtype='float32', value=self.threshold)
else:
thres = threshold
zeros_mask = layers.less_than(x=param, y=thres)
return zeros_mask
class RatioPruner(Pruner):
"""
Pruner used to pruning a parameter by ratio.
"""
def __init__(self, pruning_axis, criterions):
"""
Args:
pruning_axis(dict): The key is the name of parameter to be pruned,
'*' means all the parameters.
The value is the axis to be used. Given a parameter
with shape [3, 4], the result of pruning 50% on aixs 1
is a parameter with shape [3, 2].
criterions(dict): The key is the name of parameter to be pruned,
'*' means all the parameters.
The value is the criterion used to sort groups for pruning.
It only supports 'l1_norm' currently.
"""
self.pruning_axis = pruning_axis
self.criterions = criterions
def __init__(self, ratios=None):
def cal_pruned_idx(self, name, param, ratio, axis=None):
"""
Calculate the index to be pruned on axis by given pruning ratio.
Args:
ratios: dict with pair (paramer_name, pruned_ratio).
name(str): The name of parameter to be pruned.
param(np.array): The data of parameter to be pruned.
ratio(float): The ratio to be pruned.
axis(int): The axis to be used for pruning given parameter.
If it is None, the value in self.pruning_axis will be used.
default: None.
Returns:
list<int>: The indexes to be pruned on axis.
"""
self.ratios = ratios
criterion = self.criterions[
name] if name in self.criterions else self.criterions['*']
if axis is None:
assert self.pruning_axis is not None, "pruning_axis should set if axis is None."
axis = self.pruning_axis[
name] if name in self.pruning_axis else self.pruning_axis['*']
prune_num = int(round(param.shape[axis] * ratio))
reduce_dims = [i for i in range(len(param.shape)) if i != axis]
if criterion == 'l1_norm':
criterions = np.sum(np.abs(param), axis=tuple(reduce_dims))
pruned_idx = criterions.argsort()[:prune_num]
return pruned_idx
def prune(self, param, ratio=None):
def prune_tensor(self, tensor, pruned_idx, pruned_axis, lazy=False):
"""
Pruning a array by indexes on given axis.
Args:
ratio: `ratio=40%` means pruning (1 - 40%) weights to zero.
tensor(numpy.array): The target array to be pruned.
pruned_idx(list<int>): The indexes to be pruned.
pruned_axis(int): The axis of given array to be pruned on.
lazy(bool): True means setting the pruned elements to zero.
False means remove the pruned elements from memory.
default: False.
Returns:
numpy.array: The pruned array.
"""
if ratio is None:
rat = self.ratios[
param.name] if param.name in self.ratios else self.ratios['*']
else:
rat = ratio
if rat < 1.0:
k = max(int(rat * np.prod(param.shape)), 1)
param_vec = layers.reshape(x=param, shape=[1, -1])
param_topk, _ = layers.topk(param_vec, k=k)
threshold = layers.slice(
param_topk, axes=[1], starts=[-1], ends=[k])
threshold = layers.reshape(x=threshold, shape=[1])
zeros_mask = layers.less_than(x=param, y=threshold)
mask = np.zeros(tensor.shape[pruned_axis], dtype=bool)
mask[pruned_idx] = True
def func(data):
return data[~mask]
def lazy_func(data):
data[mask] = 0
return data
if lazy:
return np.apply_along_axis(lazy_func, pruned_axis, tensor)
else:
zeros_mask = layers.ones(param.shape)
return zeros_mask
return np.apply_along_axis(func, pruned_axis, tensor)
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @ 072d95d8
......@@ -22,6 +22,7 @@ from ....framework import IrGraph
from ....framework import IrNode
from ....framework import Program
from ....initializer import Constant
from ....initializer import NumpyArrayInitializer
from .... import unique_name
__all__ = [
......@@ -54,14 +55,15 @@ class QuantizationTransformPass(object):
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
activation_quantize_type (str): quantization type for activation,
now support 'abs_max', 'range_abs_max'. If use 'abs_max' mode,
the quantization scale will be calculated dynamically each step
in both training and testing period. If use 'range_abs_max',
a static quantization scale will be calculated during training
and used in inference.
now support 'abs_max', 'range_abs_max' and 'moving_average_abs_max'.
If use 'abs_max' mode, the quantization scale will be calculated
dynamically each step in both training and testing period. If use
'range_abs_max', a static quantization scale will be calculated
during training and used in inference.
weight_quantize_type (str): quantization type for weights,
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
support 'abs_max' and 'channel_wise_abs_max'. The 'range_abs_max'
usually is not used for weight, since weights are fixed once the
model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
......@@ -84,7 +86,11 @@ class QuantizationTransformPass(object):
self._weight_bits = weight_bits
self._activation_bits = activation_bits
quant_type = ['abs_max', 'range_abs_max', 'moving_average_abs_max']
quant_type = [
'abs_max', 'channel_wise_abs_max', 'range_abs_max',
'moving_average_abs_max'
]
assert activation_quantize_type != 'channel_wise_abs_max', "The activation quantization type does not support 'channel_wise_abs_max'."
if activation_quantize_type not in quant_type:
raise ValueError(
"Unknown activation_quantize_type : '%s'. It can only be ",
......@@ -93,7 +99,7 @@ class QuantizationTransformPass(object):
if weight_quantize_type not in quant_type:
raise ValueError(
"Unknown weight_quantize_type: '%s'. It can only be ",
"'abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
"'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
str(weight_quantize_type))
self._activation_quantize_type = activation_quantize_type
......@@ -103,6 +109,7 @@ class QuantizationTransformPass(object):
self._need_initialized = collections.OrderedDict()
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._conv_ops = ['conv2d', 'depthwise_conv2d']
self._quantizable_grad_ops = [
'%s_grad' % (op) for op in self._quantizable_ops
]
......@@ -135,10 +142,26 @@ class QuantizationTransformPass(object):
else self._activation_bits
quant_type = self._weight_quantize_type if var_node.name() \
in persistable_vars else self._activation_quantize_type
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node, quant_bits)
if quant_type == 'channel_wise_abs_max':
assert var_node.name(
) in persistable_vars, "'channel_wise_abs_max' can only be applied on weights."
if op.name() in self._conv_ops:
quant_var_node, scale_var_node = self._insert_channel_quant_op(
graph, var_node, quant_bits)
dequant_var_node = self._insert_channel_dequant_op(
graph, quant_var_node, [scale_var_node],
[quant_bits])
else:
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, 'abs_max')
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node,
quant_bits)
else:
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node, quant_bits)
dequantized_vars[var_node.name()] = dequant_var_node
graph.update_input_link(var_node, dequant_var_node, op)
......@@ -244,7 +267,7 @@ class QuantizationTransformPass(object):
scale_var_node = graph.create_var_node(
name=self._quantized_scale_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
shape=[1],
var_dtype=var_node.dtype())
quant_op_node = graph.create_op_node(
op_type='fake_quantize_abs_max',
......@@ -384,6 +407,36 @@ class QuantizationTransformPass(object):
return quant_var_node, scale_out_node
def _insert_channel_quant_op(self, graph, var_node, quant_bits):
"""
Insert fake_channel_wise_quantize_abs_max op in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
quant_var_node = graph.create_var_node(
name=self._quantized_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=var_node.dtype())
scale_var_node = graph.create_var_node(
name=self._quantized_scale_name(var_node.name()),
var_type=var_node.type(),
shape=[var_node.shape()[0]],
var_dtype=var_node.dtype())
quant_op_node = graph.create_op_node(
op_type='fake_channel_wise_quantize_abs_max',
attrs={
'bit_length': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node},
outputs={'Out': quant_var_node,
'OutScale': scale_var_node})
graph.link_to(var_node, quant_op_node)
graph.link_to(quant_op_node, quant_var_node)
graph.link_to(quant_op_node, scale_var_node)
return quant_var_node, scale_var_node
def _insert_dequant_op(self, graph, var_node, scale_var_node, quant_bits):
"""
Insert fake_dequantize_op in the graph.
......@@ -410,6 +463,33 @@ class QuantizationTransformPass(object):
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _insert_channel_dequant_op(self, graph, var_node, scale_var_nodes,
quant_bits):
"""
Insert fake_channel_wise_dequantize_max_abs in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=var_node.dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_channel_wise_dequantize_max_abs',
attrs={
'quant_bits': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node,
'Scales': scale_var_nodes},
outputs={'Out': dequant_var_node})
graph.link_to(var_node, dequant_op_node)
for scale_n in scale_var_nodes:
graph.link_to(scale_n, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _quantized_var_name(self, var_name):
"""
Return quantized variable name for the input `var_name`.
......@@ -442,7 +522,7 @@ class QuantizationFreezePass(object):
place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the weight tensors.
weight_bits (int): quantization bit number for weights.
activation_bits (int): quantization bit number for activation.
weight_quantize_type (str): quantization type for weights, support 'abs_max'.
weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'.
The 'range_abs_max' usually is not used for weight, since weights are fixed once the
model is well trained.
"""
......@@ -463,11 +543,15 @@ class QuantizationFreezePass(object):
self._activation_bits = activation_bits
self._weight_quantize_type = weight_quantize_type
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._conv_ops = ['conv2d', 'depthwise_conv2d']
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
'fake_quantize_moving_average_abs_max'
'fake_quantize_moving_average_abs_max',
'fake_channel_wise_quantize_abs_max'
]
self._fake_dequant_op_names = [
'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
self._op_input_rename_map = collections.OrderedDict()
self._op_output_rename_map = collections.OrderedDict()
self._var_scale_map = collections.OrderedDict()
......@@ -489,20 +573,27 @@ class QuantizationFreezePass(object):
if self._weight_quantize_type == 'abs_max':
param = self._load_var(input_arg_name)
scale_v = np.max(np.abs(param))
elif self._weight_quantize_type == 'channel_wise_abs_max':
param = self._load_var(input_arg_name)
if len(param.shape) == 4: # conv2d or depthwise_conv2d
scale_v = []
for i in range(param.shape[0]):
scale_v.append(np.max(np.abs(param[i])))
else:
scale_v = np.max(np.abs(param))
else:
scale_v = self._load_var(
op_node.output('OutScale')[0])[0]
self._var_scale_map[input_arg_name] = scale_v
else:
scale_v = graph.var_node(op_node.output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
if input_arg_name in persistable_vars:
self._remove_fake_quant_and_dequant_op(graph, op_node)
# quantize weight and restore
param_v = self._load_var(input_arg_name)
quantized_param_v = self._quant(param_v, scale_v,
self._weight_bits)
self._restore_var(input_arg_name, quantized_param_v)
else:
scale_v = graph.var_node(op_node.output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
ops = graph.all_op_nodes()
for op_node in ops:
......@@ -514,7 +605,10 @@ class QuantizationFreezePass(object):
for op_node in ops:
op_name = op_node.name()
if op_name in self._quantizable_ops:
self._insert_post_dequant_op(graph, op_node)
if self._weight_quantize_type == 'channel_wise_abs_max' and op_name in self._conv_ops:
self._insert_post_channel_dequant_op(graph, op_node)
else:
self._insert_post_dequant_op(graph, op_node)
for op_node in ops:
# insert dequant_op after fc/conv, need to rename inputs of the followed ops
......@@ -538,9 +632,73 @@ class QuantizationFreezePass(object):
self._op_input_rename_map[k] = self._op_input_rename_map[v]
graph.safe_remove_nodes(op_node)
def _insert_post_channel_dequant_op(self, graph, op_node):
persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
for var_node in op_node.inputs:
name = var_node.name()
if name in self._op_input_rename_map:
old_in = graph.var_node(name)
new_in = graph.var_node(self._op_input_rename_map[name])
new_in.clear_outputs()
graph.update_input_link(old_in, new_in, op_node)
original_var_name = self._original_var_name(name)
scale_v = self._var_scale_map[original_var_name]
if original_var_name in persistable_vars:
assert isinstance(
scale_v,
list), 'The scale of parameter %s is not a list.' % (
original_var_name)
channel_scale = np.array(scale_v)
else:
assert isinstance(scale_v, IrNode)
scale_var_node = self._var_scale_map[original_var_name]
if len(op_node.outputs) != 1:
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op_node.name()))
output_var_node = op_node.outputs[0]
weight_scale_node = graph.create_persistable_node(
name=unique_name.generate('channel_scale'),
var_type=core.VarDesc.VarType.LOD_TENSOR,
shape=[channel_scale.shape[0]],
var_dtype=output_var_node.dtype())
init_program = Program()
weight_scale_var = init_program.global_block().create_var(
name=weight_scale_node.name(),
shape=weight_scale_node.shape(),
dtype=weight_scale_node.dtype(),
type=weight_scale_node.type(),
lod_level=weight_scale_node.var().lod_level(),
persistable=weight_scale_node.persistable())
initializer = NumpyArrayInitializer(value=channel_scale)
initializer(weight_scale_var, init_program.global_block())
exe = Executor(self._place)
exe.run(program=init_program, scope=self._scope)
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(output_var_node.name()),
var_type=output_var_node.type(),
shape=output_var_node.shape(),
var_dtype=output_var_node.dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_channel_wise_dequantize_max_abs',
attrs={
'quant_bits': [self._weight_bits, self._activation_bits],
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={
'X': output_var_node,
'Scales': [weight_scale_node, scale_var_node]
},
outputs={'Out': dequant_var_node})
graph.link_to(output_var_node, dequant_op_node)
graph.link_to(scale_var_node, dequant_op_node)
graph.link_to(weight_scale_node, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
self._op_output_rename_map[output_var_node.name()] = dequant_var_node
return dequant_var_node
def _insert_post_dequant_op(self, graph, op_node):
max_range = None
scale_var_node = None
persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
for var_node in op_node.inputs:
name = var_node.name()
......@@ -637,7 +795,12 @@ class QuantizationFreezePass(object):
or isinstance(v, np.float64)
def _quant(self, x, scale, num_bits):
return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
if isinstance(scale, list):
for i, s in enumerate(scale):
x[i] = np.round(x[i] / s * ((1 << (num_bits - 1)) - 1))
return x
else:
return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
class ConvertToInt8Pass(object):
......@@ -731,9 +894,13 @@ class TransformForMobilePass(object):
def __init__(self):
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
'fake_quantize_moving_average_abs_max',
'fake_channel_wise_quantize_abs_max'
]
self._fake_dequant_op_names = [
'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
def apply(self, graph):
"""
......
python/paddle/fluid/contrib/slim/tests/configs/config.yaml 已删除 100644 → 0
浏览文件 @ a93a9eef
version: 1.0
include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_2'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/tests/configs/filter_pruning.yaml 0 → 100644
浏览文件 @ 072d95d8
#start_epoch: The 'on_epoch_begin' function will be called in start_epoch. default: 0.
#end_epoch: The 'on_epoch_end' function will be called in end_epoch. default: 10.
#delta_rate: The delta used to generate ratios when calculating sensitivities.
#target_ratio: The flops ratio to be pruned from current model.
#metric_name: The metric used to evaluate the model.
#pruned_params: The pattern str to match the parameter names to be pruned.
#sensitivities_file: The sensitivities file.
#num_steps: The number of pruning steps.
#eval_rate: The rate of sampled data used to calculate sensitivities.
version: 1.0
pruners:
pruner_1:
class: 'StructurePruner'
pruning_axis:
'*': 0
criterions:
'*': 'l1_norm'
strategies:
sensitive_pruning_strategy:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 0
delta_rate: 0.1
target_ratio: 0.3
num_steps: 1
eval_rate: 0.5
pruned_params: '.*_sep_weights'
sensitivities_file: 'mobilenet_acc_top1_sensitive.data'
metric_name: 'acc_top1'
compressor:
epoch: 120
checkpoint_path: './checkpoints/'
strategies:
- sensitive_pruning_strategy
python/paddle/fluid/contrib/slim/tests/configs/pruners.yaml 已删除 100644 → 0
浏览文件 @ a93a9eef
version: 1.0
pruners:
pruner_2:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/tests/configs/pruners_0.yaml 已删除 100644 → 0
浏览文件 @ a93a9eef
version: 1.0
pruners:
pruner_3:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/graph/graph_pass.py → python/paddle/fluid/contrib/slim/tests/filter_pruning/__init__.py
浏览文件 @ 072d95d8
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# 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.
......@@ -11,32 +11,3 @@
# 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.
__all__ = ['GraphPass', 'PruneParameterPass']
class GraphPass(object):
"""
Base class for all graph pass.
"""
def __init__(self):
pass
def apply(self, graph):
pass
class PruneParameterPass(GraphPass):
"""
Generate a graph for pruning parameters from target graph.
"""
def __init__(self, pruned_params, thresholds):
super(PruneParameterPass, self).__init__()
self.pruned_params = pruned_params
self.thresholds = thresholds
self.default_threshold = thresholds['*']
def apply(self, graph):
pass
python/paddle/fluid/contrib/slim/tests/filter_pruning/compress.yaml 0 → 100644
浏览文件 @ 072d95d8
#start_epoch: The 'on_epoch_begin' function will be called in start_epoch. default: 0.
#end_epoch: The 'on_epoch_end' function will be called in end_epoch. default: 10.
#delta_rate: The delta used to generate ratios when calculating sensitivities.
#target_ratio: The flops ratio to be pruned from current model.
#metric_name: The metric used to evaluate the model.
#pruned_params: The pattern str to match the parameter names to be pruned.
#sensitivities_file: The sensitivities file.
#num_steps: The number of pruning steps.
#eval_rate: The rate of sampled data used to calculate sensitivities.
version: 1.0
pruners:
pruner_1:
class: 'StructurePruner'
pruning_axis:
'*': 0
criterions:
'*': 'l1_norm'
strategies:
sensitive_pruning_strategy:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 1
delta_rate: 0.2
target_ratio: 0.08
num_steps: 1
eval_rate: 0.5
pruned_params: 'conv6_sep_weights'
sensitivities_file: 'mobilenet_acc_top1_sensitive.data'
metric_name: 'acc_top1'
compressor:
epoch: 2
checkpoint_path: './checkpoints/'
strategies:
- sensitive_pruning_strategy
python/paddle/fluid/contrib/slim/tests/filter_pruning/mobilenet.py 0 → 100644
浏览文件 @ 072d95d8
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
__all__ = ['MobileNet']
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class MobileNet():
def __init__(self):
self.params = train_parameters
def net(self, input, class_dim=1000, scale=1.0):
# conv1: 112x112
input = self.conv_bn_layer(
input,
filter_size=3,
channels=3,
num_filters=int(32 * scale),
stride=2,
padding=1,
name="conv1")
# 56x56
input = self.depthwise_separable(
input,
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1,
scale=scale,
name="conv2_1")
input = self.depthwise_separable(
input,
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2,
scale=scale,
name="conv2_2")
# 28x28
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1,
scale=scale,
name="conv3_1")
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2,
scale=scale,
name="conv3_2")
# 14x14
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1,
scale=scale,
name="conv4_1")
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2,
scale=scale,
name="conv4_2")
# 14x14
for i in range(5):
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1,
scale=scale,
name="conv5" + "_" + str(i + 1))
# 7x7
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2,
scale=scale,
name="conv5_6")
input = self.depthwise_separable(
input,
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1,
scale=scale,
name="conv6")
input = fluid.layers.pool2d(
input=input,
pool_size=0,
pool_stride=1,
pool_type='avg',
global_pooling=True)
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=MSRA(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
return output
def conv_bn_layer(self,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
act='relu',
use_cudnn=True,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(
initializer=MSRA(), name=name + "_weights"),
bias_attr=False)
bn_name = name + "_bn"
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def depthwise_separable(self,
input,
num_filters1,
num_filters2,
num_groups,
stride,
scale,
name=None):
depthwise_conv = self.conv_bn_layer(
input=input,
filter_size=3,
num_filters=int(num_filters1 * scale),
stride=stride,
padding=1,
num_groups=int(num_groups * scale),
use_cudnn=False,
name=name + "_dw")
pointwise_conv = self.conv_bn_layer(
input=depthwise_conv,
filter_size=1,
num_filters=int(num_filters2 * scale),
stride=1,
padding=0,
name=name + "_sep")
return pointwise_conv
python/paddle/fluid/contrib/slim/tests/test_factory.py
浏览文件 @ 072d95d8
......@@ -12,29 +12,25 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.fluid.contrib.slim import ConfigFactory
from paddle.fluid.contrib.slim.core import ConfigFactory
import unittest
class TestFactory(unittest.TestCase):
def test_parse(self):
factory = ConfigFactory('./configs/config.yaml')
def test_parse_pruning(self):
factory = ConfigFactory('./configs/filter_pruning.yaml')
pruner = factory.instance('pruner_1')
self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)
pruner_1 = factory.instance('pruner_1')
self.assertEquals(pruner_1.pruning_axis['*'], 0)
self.assertEquals(pruner_1.criterions['*'], 'l1_norm')
pruner = factory.instance('pruner_2')
self.assertEquals(pruner.ratios['*'], 0.7)
strategy = factory.instance('sensitive_pruning_strategy')
pruner_1 = strategy.pruner
self.assertEquals(pruner_1.criterions['*'], 'l1_norm')
strategy = factory.instance('strategy_1')
pruner = strategy.pruner
self.assertEquals(pruner.ratios['*'], 0.7)
compress_pass = factory.get_compress_pass()
self.assertEquals(compress_pass.epoch, 100)
strategy = compress_pass.strategies[0]
self.assertEquals(strategy.delta_rate, 0.2)
self.assertEquals(strategy.start_epoch, 0)
self.assertEquals(strategy.sensitivities_file,
'mobilenet_acc_top1_sensitive.data')
if __name__ == '__main__':
......
python/paddle/fluid/contrib/slim/tests/test_filter_pruning.py 0 → 100644
浏览文件 @ 072d95d8
# 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.
import paddle
import unittest
import paddle.fluid as fluid
from filter_pruning.mobilenet import MobileNet
from paddle.fluid.contrib.slim.core import Compressor
from paddle.fluid.contrib.slim.graph import GraphWrapper
class TestFilterPruning(unittest.TestCase):
def test_compression(self):
"""
Model: mobilenet_v1
data: mnist
step1: Training one epoch
step2: pruning flops
step3: fine-tune one epoch
step4: check top1_acc.
"""
if not fluid.core.is_compiled_with_cuda():
return
class_dim = 10
image_shape = [1, 28, 28]
image = fluid.layers.data(
name='image', shape=image_shape, dtype='float32')
image.stop_gradient = False
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
out = MobileNet().net(input=image, class_dim=class_dim)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
val_program = fluid.default_main_program().clone(for_test=False)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5))
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
val_feed_list = [('img', image.name), ('label', label.name)]
val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5',
acc_top5.name)]
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
train_feed_list = [('img', image.name), ('label', label.name)]
train_fetch_list = [('loss', avg_cost.name)]
com_pass = Compressor(
place,
fluid.global_scope(),
fluid.default_main_program(),
train_reader=train_reader,
train_feed_list=train_feed_list,
train_fetch_list=train_fetch_list,
eval_program=val_program,
eval_reader=val_reader,
eval_feed_list=val_feed_list,
eval_fetch_list=val_fetch_list,
train_optimizer=optimizer)
com_pass.config('./filter_pruning/compress.yaml')
eval_graph = com_pass.run()
self.assertTrue(
abs((com_pass.context.eval_results['acc_top1'][-1] - 0.969) / 0.969)
< 0.02)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/tests/test_graph_wrapper.py 0 → 100644
浏览文件 @ 072d95d8
# 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 paddle.fluid as fluid
import six
import numpy as np
from paddle.fluid.contrib.slim.graph import GraphWrapper
from paddle.fluid import core
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 8, 8], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
data.stop_gradinet = False
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return data, label, loss
class TestGraphWrapper(unittest.TestCase):
def build_program(self):
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
image, label, self.loss = residual_block(2)
eval_program = main.clone()
opt = fluid.optimizer.SGD(learning_rate=0.001)
opt.minimize(self.loss)
self.scope = core.Scope()
exe = fluid.Executor(place)
exe.run(startup, scope=self.scope)
self.eval_graph = GraphWrapper(
program=eval_program,
in_nodes={'image': image.name,
'label': label.name},
out_nodes={'loss': self.loss.name})
self.train_graph = GraphWrapper(
program=main,
in_nodes={'image': image.name,
'label': label.name},
out_nodes={'loss': self.loss.name})
def test_all_parameters(self):
self.build_program()
self.assertEquals(len(self.train_graph.all_parameters()), 24)
def test_all_vars(self):
self.build_program()
self.assertEquals(len(self.train_graph.vars()), 90)
def test_numel_params(self):
self.build_program()
self.assertEquals(self.train_graph.numel_params(), 13258)
def test_compile(self):
self.build_program()
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
self.train_graph.compile()
exe.run(self.train_graph.compiled_graph,
scope=self.scope,
feed={
'image':
np.random.randint(0, 40, [16, 1, 8, 8]).astype('float32'),
'label': np.random.randint(0, 10, [16, 1]).astype('int64')
})
def test_pre_and_next_ops(self):
self.build_program()
for op in self.train_graph.ops():
for next_op in self.train_graph.next_ops(op):
self.assertTrue(op in self.train_graph.pre_ops(next_op))
def test_get_optimize_graph(self):
self.build_program()
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
opt = fluid.optimizer.SGD(learning_rate=0.001)
train_graph = self.eval_graph.get_optimize_graph(
opt, place, self.scope, no_grad_var_names=['image'])
self.assertEquals(len(self.train_graph.ops()), len(train_graph.ops()))
exe = fluid.Executor(place)
train_graph.compile()
image = np.random.randint(0, 225, [16, 1, 8, 8]).astype('float32')
label = np.random.randint(0, 10, [16, 1]).astype('int64')
exe.run(train_graph.compiled_graph,
scope=self.scope,
feed={'image': image,
'label': label})
def test_flops(self):
self.build_program()
self.assertEquals(self.train_graph.flops(), 354624)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ 072d95d8
......@@ -127,7 +127,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
arg_name.endswith('.quantized.dequantized'))
self.assertTrue(arg_name in quantized_ops)
def linear_fc_quant(self, quant_type, for_ci=False):
def linear_fc_quant(self, activation_quant_type, for_ci=False):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -140,14 +140,15 @@ class TestQuantizationTransformPass(unittest.TestCase):
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=quant_type)
activation_quantize_type=activation_quant_type)
transform_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
graph.draw('.', 'quantize_fc_' + activation_quant_type,
marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
......@@ -156,7 +157,8 @@ class TestQuantizationTransformPass(unittest.TestCase):
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes)
val_graph.draw('.', 'val_fc_' + activation_quant_type,
val_marked_nodes)
def test_linear_fc_quant_abs_max(self):
self.linear_fc_quant('abs_max', for_ci=True)
......@@ -167,7 +169,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
def test_linear_fc_quant_moving_average_abs_max(self):
self.linear_fc_quant('moving_average_abs_max', for_ci=True)
def residual_block_quant(self, quant_type, for_ci=False):
def residual_block_quant(self, activation_quant_type, for_ci=False):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -180,14 +182,15 @@ class TestQuantizationTransformPass(unittest.TestCase):
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=quant_type)
activation_quantize_type=activation_quant_type)
transform_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
graph.draw('.', 'quantize_residual_' + activation_quant_type,
marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
......@@ -196,7 +199,8 @@ class TestQuantizationTransformPass(unittest.TestCase):
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes)
val_graph.draw('.', 'val_residual_' + activation_quant_type,
val_marked_nodes)
def test_residual_block_abs_max(self):
self.residual_block_quant('abs_max', for_ci=True)
......@@ -209,7 +213,12 @@ class TestQuantizationTransformPass(unittest.TestCase):
class TestQuantizationFreezePass(unittest.TestCase):
def freeze_graph(self, use_cuda, seed, quant_type, for_ci=False):
def freeze_graph(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
......@@ -243,7 +252,12 @@ class TestQuantizationFreezePass(unittest.TestCase):
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope, place=place, activation_quantize_type=quant_type)
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
#transform_pass = QuantizationTransformPass(
# scope=scope, place=place, activation_quantize_type=activation_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
......@@ -252,12 +266,14 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
main_graph.draw('.', 'main' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
test_graph.draw('.', 'test' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
......@@ -282,8 +298,9 @@ class TestQuantizationFreezePass(unittest.TestCase):
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name + quant_type,
loss_v))
print('{}: {}'.format('loss' + dev_name +
activation_quant_type + '_' +
weight_quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
......@@ -296,14 +313,17 @@ class TestQuantizationFreezePass(unittest.TestCase):
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
#freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
test_graph.draw('.', 'test_freeze' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
server_program = test_graph.to_program()
......@@ -313,18 +333,20 @@ class TestQuantizationFreezePass(unittest.TestCase):
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
if not for_ci:
print('{}: {}'.format('test_loss1' + dev_name + quant_type,
test_loss1))
print('{}: {}'.format('test_loss2' + dev_name + quant_type,
test_loss2))
print(
'{}: {}'.format('test_loss1' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss1))
print(
'{}: {}'.format('test_loss2' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
if not for_ci:
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + quant_type,
np.sum(w_quant)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
......@@ -334,26 +356,28 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + quant_type,
marked_nodes)
test_graph.draw('.', 'test_int8' + dev_name + activation_quant_type
+ '_' + weight_quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
server_program_int8)
fluid.io.save_inference_model(
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + quant_type, exe)
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
if not for_ci:
print('{}: {}'.format('w_8bit' + dev_name + quant_type,
np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
np.sum(w_freeze)))
print('{}: {}'.format('w_8bit' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
......@@ -362,42 +386,103 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
test_graph.draw('.', 'test_mobile' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
mobile_program)
fluid.io.save_inference_model(
'mobile_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
mobile_program)
def test_freeze_graph_cuda_dynamic(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(
True, seed=1, quant_type='abs_max', for_ci=True)
True,
seed=1,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=True)
with fluid.unique_name.guard():
self.freeze_graph(
True,
seed=1,
activation_quant_type='abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cpu_dynamic(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='abs_max', for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cuda_static(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(
True, seed=1, quant_type='range_abs_max', for_ci=True)
True,
seed=1,
activation_quant_type='range_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
activation_quant_type='moving_average_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
quant_type='moving_average_abs_max',
activation_quant_type='range_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
activation_quant_type='moving_average_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cpu_static(self):
with fluid.unique_name.guard():
self.freeze_graph(
False, seed=2, quant_type='range_abs_max', for_ci=True)
False,
seed=2,
activation_quant_type='range_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='moving_average_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='range_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
self.freeze_graph(
False, seed=2, quant_type='moving_average_abs_max', for_ci=True)
False,
seed=2,
activation_quant_type='moving_average_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
if __name__ == '__main__':
......
python/paddle/fluid/contrib/tests/test_calibration.py
浏览文件 @ 072d95d8
......@@ -290,7 +290,7 @@ class TestCalibrationForResnet50(unittest.TestCase):
self.model, self.infer_iterations)
(int8_throughput, int8_latency,
int8_acc1) = self.run_program("calibration_out")
delta_value = np.abs(fp32_acc1 - int8_acc1)
delta_value = fp32_acc1 - int8_acc1
self.assertLess(delta_value, 0.01)
print(
"FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
......
python/paddle/fluid/framework.py
浏览文件 @ 072d95d8
......@@ -644,10 +644,9 @@ class Operator(object):
outputs={"Out": [var1]})
"""
OP_WITHOUT_KERNEL_SET = {
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'save_combine', 'load_combine', 'ncclInit', 'select',
'checkpoint_notify', 'gen_nccl_id'
'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
}
def __init__(self,
......
python/paddle/fluid/imperative/__init__.py
浏览文件 @ 072d95d8
......@@ -29,9 +29,13 @@ from .tracer import *
from . import profiler
from .profiler import *
from . import checkpoint
from .checkpoint import *
__all__ = []
__all__ += layers.__all__
__all__ += base.__all__
__all__ += nn.__all__
__all__ += tracer.__all__
__all__ += profiler.__all__
__all__ += checkpoint.__all__
python/paddle/fluid/imperative/checkpoint.py 0 → 100644
浏览文件 @ 072d95d8
# 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.
from __future__ import print_function
import os
import collections
from .. import core
from ..framework import Variable, default_main_program
__all__ = ['save_persistables', 'load_persistables']
def save_persistables(vardict, dirname, 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
`dirname` or the file `filename`.
Use the `dirname` to specify the folder where persistable variables were
saved. If variables were saved in separate files, set `filename` None;
if all variables were saved in a single file, use `filename` to specify
the file name.
Args:
vardict(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.
Default: None
Returns:
Examples:
.. code-block:: python
ptb_model = PtbModel(
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale)
x_data = np.arange(12).reshape(4, 3).astype('int64')
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, 1))
init_hidden_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
init_cell_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
init_cell)
param_path = "./my_paddle_model"
fluid.imperative.checkpoint.save_persistables(ptb_model.state_dict(), dirname=param_path,
layer=ptb_model)
"""
if isinstance(vardict, collections.OrderedDict):
_save_var_to_file(vardict, dirname, filename)
def load_persistables(vardict, dirname, filename=None):
"""
This function trys to load persistable variables from the folder
`dirname` or the file `filename`.
Use the `dirname` to specify the folder where persistable variables were
saved. If variables were saved in separate files, set `filename` None;
if all variables were saved in a single file, use `filename` to specify
the file name.
Args:
vardict(dict of Parameters): The parameters will be loaded.
dirname(str): The directory path.
filename(str|None): The file which saved all variables, this file path should be end with '.npz'. If variables were
saved in differnet files, set it to None.
Default: None
Returns:
dict: The parameter-dict resumed from file
Examples:
.. code-block:: python
my_layer = layer(fluid.imperative.Layer)
param_path = "./my_paddle_model"
param_dict = fluid.imperative.checkpoint.load_persistables(my_layer.parameters(), param_path)
param_1 = param_dict['PtbModel_0.w_1']
or:
my_layer = layer(fluid.imperative.Layer)
param_path = "./my_paddle_model"
filename = "model.file"
param_dict = fluid.imperative.checkpoint.load_persistables(my_layer.state_dict(), param_path,
filename=filename)
param_1 = param_dict['PtbModel_0.w_1']
"""
if isinstance(vardict, collections.OrderedDict):
return _load_var_from_file(vardict, dirname, filename)
return {}
def _save_var_to_file(stat_dict, file_dir, file_name):
save_block = default_main_program().global_block()
save_var_map = {}
for each_var in stat_dict.items():
save_var_map[each_var.name] = each_var
if file_name is None:
save_block.append_op(
type='save',
inputs={'X': [each_var]},
outputs={},
attrs={'file_path': os.path.join(file_dir, each_var.name)})
if file_name is not None:
save_var_list = []
for name in sorted(save_var_map.keys()):
save_var_list.append(save_var_map[name])
save_block.append_op(
type='save_combine',
inputs={'X': save_var_list},
outputs={},
attrs={'file_path': os.path.join(file_dir, file_name)})
def _load_var_from_file(stat_dict, file_dir, file_name):
load_block = default_main_program().global_block()
load_var_map = {}
for each_var in stat_dict.items():
assert isinstance(each_var, Variable)
if each_var.type == core.VarDesc.VarType.RAW:
continue
new_var = _clone_var_in_block_(load_block, each_var)
if file_name is None:
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [new_var]},
attrs={'file_path': os.path.join(file_dir, each_var.name)})
load_var_map[new_var.name] = new_var
if file_name is not None:
load_var_list = []
for name in sorted(load_var_map.keys()):
load_var_list.append(load_var_map[name])
load_block.append_op(
type='load_combine',
inputs={},
outputs={"Out": load_var_list},
attrs={'file_path': os.path.join(file_dir, file_name)})
for res_var in load_var_list:
load_var_map[res_var.name] = res_var
return load_var_map
def _clone_var_in_block_(block, var):
assert isinstance(var, Variable)
return block.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
python/paddle/fluid/imperative/layers.py
浏览文件 @ 072d95d8
......@@ -212,6 +212,34 @@ class Layer(core.Layer):
else:
object.__delattr__(self, name)
def state_dict(self, destination=None, prefix='', include_sublayers=True):
if destination is None:
destination = collections.OrderedDict()
for name, data in self._parameters.items():
if data is not None:
destination[prefix + name] = data
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
destination_temp = destination.copy()
destination_temp.update(
layer_item.state_dict(destination_temp, prefix +
layer_name + ".",
include_sublayers))
destination = destination_temp
return destination
def load_dict(self, stat_dict, include_sublayers=True):
for name, item in self.__dict__.get('_parameters', None).items():
if item.name in stat_dict:
self.__setattr__(name, stat_dict[item.name])
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
layer_item.load_dict(stat_dict)
class PyLayer(core.PyLayer):
"""Layers composed of user-defined python codes."""
......
python/paddle/fluid/layers/math_op_patch.py
浏览文件 @ 072d95d8
......@@ -174,6 +174,8 @@ def monkey_patch_variable():
("__rtruediv__", "elementwise_div", True),
("__pow__", "elementwise_pow", False),
("__rpow__", "elementwise_pow", True),
("__floordiv__", "elementwise_floordiv", False),
("__mod__", "elementwise_mod", False),
# for logical compare
("__eq__", "equal", False),
("__ne__", "not_equal", False),
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 072d95d8
......@@ -4901,6 +4901,9 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
if len(y_shape) > 2 and len(x_shape) > 2:
for i, dim_x in enumerate(x_shape[:-2]):
# don't check neg shape
if dim_x < 0 or y_shape[i] < 0:
continue
if dim_x != y_shape[i]:
raise ValueError("Invalid inputs for matmul. x(%s), y(%s)" %
(x.shape, y.shape))
......@@ -9228,9 +9231,24 @@ def elementwise_pow(x, y, axis=-1, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
def elementwise_mod(x, y, axis=-1, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_floordiv', **locals()))
for func in [
elementwise_add, elementwise_div, elementwise_sub, elementwise_mul,
elementwise_max, elementwise_min, elementwise_pow
elementwise_add,
elementwise_div,
elementwise_sub,
elementwise_mul,
elementwise_max,
elementwise_min,
elementwise_pow,
elementwise_mod,
elementwise_floordiv,
]:
op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
func.__doc__ = _generate_doc_string_(
......@@ -9750,7 +9768,7 @@ def affine_channel(x,
'Bias': bias},
attrs={"data_layout": data_layout},
outputs={"Out": out})
return helper.append_activation(pre_activation)
return helper.append_activation(out)
def similarity_focus(input, axis, indexes, name=None):
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 072d95d8
......@@ -25,10 +25,26 @@ from .layer_function_generator import templatedoc
import numpy
__all__ = [
'create_tensor', 'create_parameter', 'create_global_var', 'cast',
'tensor_array_to_tensor', 'concat', 'sums', 'assign',
'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite'
'create_tensor',
'create_parameter',
'create_global_var',
'cast',
'tensor_array_to_tensor',
'concat',
'sums',
'assign',
'fill_constant_batch_size_like',
'fill_constant',
'argmin',
'argmax',
'argsort',
'ones',
'zeros',
'reverse',
'has_inf',
'has_nan',
'isfinite',
'range',
]
......@@ -764,3 +780,50 @@ def isfinite(x):
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
return out
def range(start, end, step, dtype):
"""
Return evenly spaced values within a given interval.
Values are generated within the half-open interval [start, stop) (in other words,
the interval including start but excluding stop).
args:
start(int|float|Variable): Start of interval. The interval includes this value.
end(int|float|Variable): End of interval. The interval does not include this
value, except in some cases where step is not an integer
and floating point round-off affects the length of out.
step(int|float|Variable): Spacing between values. For any output out, this is the
distance between two adjacent values, out[i+1] - out[i].
The default step size is 1.
dtype(string): 'float32'|'int32'|..., the data type of the output tensor.
returns:
Evenly spaced values within a given interval.
examples:
.. code-block:: python
data = fluid.layers.range(0, 10, 2, 'int32')
"""
helper = LayerHelper("range", **locals())
if not isinstance(start, Variable):
start = fill_constant([1], dtype, start)
if not isinstance(end, Variable):
end = fill_constant([1], dtype, end)
if not isinstance(step, Variable):
step = fill_constant([1], dtype, step)
out = helper.create_variable_for_type_inference(dtype=start.dtype)
helper.append_op(
type='range',
inputs={'Start': start,
'End': end,
'Step': step},
outputs={'Out': [out]})
return out
python/paddle/fluid/optimizer.py
浏览文件 @ 072d95d8
......@@ -70,6 +70,10 @@ class Optimizer(object):
# {accum_name : { paramter_name : accumulator_for_parameter, ...}, ...}
self._accumulators = defaultdict(lambda: dict())
self.helper = None
self._opti_name_list = []
def get_opti_var_name_list(self):
return self._opti_name_list
def _create_global_learning_rate(self):
lr = self._global_learning_rate()
......@@ -166,8 +170,13 @@ class Optimizer(object):
if shape == None:
shape = param.shape
assert isinstance(self.helper, LayerHelper)
var_name = param.name + "_" + name
var_name = unique_name.generate(var_name)
self._opti_name_list.append(var_name)
var = self.helper.create_global_variable(
name=unique_name.generate(name),
name=var_name,
persistable=True,
dtype=dtype or param.dtype,
type=param.type,
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 072d95d8
......@@ -105,7 +105,7 @@ if(WITH_DISTRIBUTE)
# set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
set_tests_properties(test_dist_ctr test_dist_mnist test_dist_mnist_batch_merge test_dist_save_load test_dist_se_resnext test_dist_simnet_bow test_dist_text_classification test_dist_train test_dist_word2vec PROPERTIES RUN_SERIAL TRUE)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
# py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
endif()
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
......@@ -118,8 +118,8 @@ if(NOT APPLE)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
endif()
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
# change the timeout from 600 to 1200, because in debug mode, this test need more time.
set_tests_properties(test_parallel_executor_seresnext PROPERTIES TIMEOUT 1200)
# change the timeout from 600 to 2200, because in debug mode, this test need more time.
set_tests_properties(test_parallel_executor_seresnext PROPERTIES TIMEOUT 2200)
endif()
if (WITH_NGRAPH)
......
python/paddle/fluid/tests/unittests/mkldnn/test_concat_int8_mkldnn_op.py 0 → 100644
浏览文件 @ 072d95d8
# 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
from paddle.fluid.tests.unittests.op_test import OpTest
class TestConcatOp(OpTest):
def setUp(self):
self.op_type = "concat"
self.use_mkldnn = True
self._cpu_only = True
self.init_axis()
self.init_shape()
self.init_test_data()
self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
self.attrs = {'axis': self.axis, 'use_mkldnn': True}
self.output = np.concatenate(
(self.x0, self.x1, self.x2), axis=self.axis).astype('int')
self.outputs = {'Out': self.output}
def test_check_output(self):
self.check_output()
#--------------------test concat s8 in with axis 0--------------------
def init_test_data(self):
self.x0 = (np.random.randint(0, 100, self.x0_shape) - 50).astype('int8')
self.x1 = (np.random.randint(0, 80, self.x1_shape) - 30).astype('int8')
self.x2 = (np.random.randint(0, 110, self.x2_shape) - 80).astype('int8')
def init_axis(self):
self.axis = 0
def init_shape(self):
self.x0_shape = [2, 2, 1, 2]
self.x1_shape = [1, 2, 1, 2]
self.x2_shape = [3, 2, 1, 2]
#--------------------test concat u8 in with axis 0--------------------
class TestConcatOp2(TestConcatOp):
def init_test_data(self):
self.x0 = (np.random.randint(0, 100, self.x0_shape)).astype('uint8')
self.x1 = (np.random.randint(0, 50, self.x1_shape)).astype('uint8')
self.x2 = (np.random.randint(0, 80, self.x2_shape)).astype('uint8')
def init_axis(self):
self.axis = 0
def init_shape(self):
self.x0_shape = [2, 1, 5, 5]
self.x1_shape = [1, 1, 5, 5]
self.x2_shape = [3, 1, 5, 5]
def create_test_int8_class(parent):
#--------------------test concat s8/u8 in with axis 1--------------------
class TestAxis1Case(parent):
def init_axis(self):
self.axis = 1
def init_shape(self):
self.x0_shape = [1, 1, 5, 5]
self.x1_shape = [1, 2, 5, 5]
self.x2_shape = [1, 3, 5, 5]
#--------------------test concat s8/u8 in with axis 2--------------------
class TestAxis2Case(parent):
def init_axis(self):
self.axis = 2
def init_shape(self):
self.x0_shape = [2, 3, 4, 5]
self.x1_shape = [2, 3, 5, 5]
self.x2_shape = [2, 3, 6, 5]
#--------------------test concat s8/u8 in with axis 3--------------------
class TestAxis3Case(parent):
def init_axis(self):
self.axis = 3
def init_shape(self):
self.x0_shape = [2, 3, 5, 5]
self.x1_shape = [2, 3, 5, 6]
self.x2_shape = [2, 3, 5, 7]
cls_name_1 = "{0}_axis_{1}".format(parent.__name__, "1")
cls_name_2 = "{0}_axis_{1}".format(parent.__name__, "2")
cls_name_3 = "{0}_axis_{1}".format(parent.__name__, "3")
TestAxis1Case.__name__ = cls_name_1
TestAxis2Case.__name__ = cls_name_2
TestAxis3Case.__name__ = cls_name_3
globals()[cls_name_1] = TestAxis1Case
globals()[cls_name_2] = TestAxis2Case
globals()[cls_name_3] = TestAxis3Case
create_test_int8_class(TestConcatOp)
create_test_int8_class(TestConcatOp2)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py 0 → 100644
浏览文件 @ 072d95d8
# 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.fluid.core as core
from op_test import OpTest
import random
class TestElementwiseModOp(OpTest):
def init_kernel_type(self):
self.use_mkldnn = False
def setUp(self):
self.op_type = "elementwise_floordiv"
self.dtype = np.int32
self.axis = -1
self.init_dtype()
self.init_input_output()
self.init_kernel_type()
self.init_axis()
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(self.x),
'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
}
self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': self.out}
def test_check_output(self):
self.check_output()
def init_input_output(self):
self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
self.out = np.floor_divide(self.x, self.y)
def init_dtype(self):
pass
def init_axis(self):
pass
class TestElementwiseModOp_scalar(TestElementwiseModOp):
def init_input_output(self):
scale_x = random.randint(0, 100000000)
scale_y = random.randint(1, 100000000)
self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
self.out = np.floor_divide(self.x, self.y)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_mod_op.py 0 → 100644
浏览文件 @ 072d95d8
# 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.fluid.core as core
from op_test import OpTest
import random
class TestElementwiseModOp(OpTest):
def init_kernel_type(self):
self.use_mkldnn = False
def setUp(self):
self.op_type = "elementwise_mod"
self.dtype = np.int32
self.axis = -1
self.init_dtype()
self.init_input_output()
self.init_kernel_type()
self.init_axis()
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(self.x),
'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
}
self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': self.out}
def test_check_output(self):
self.check_output()
def init_input_output(self):
self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
self.out = np.mod(self.x, self.y)
def init_dtype(self):
pass
def init_axis(self):
pass
class TestElementwiseModOp_scalar(TestElementwiseModOp):
def init_input_output(self):
scale_x = random.randint(0, 100000000)
scale_y = random.randint(1, 100000000)
self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
self.out = np.mod(self.x, self.y)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py
浏览文件 @ 072d95d8
......@@ -31,15 +31,27 @@ def dequantize_max_abs(x, scale, max_range):
return y
def channel_wise_quantize_max_abs(x, quant_bit=8):
def channel_wise_quantize_max_abs(x, quant_bit=8, use_second_dim=False):
scales = []
for i in range(x.shape[0]):
scales.append(np.max(np.abs(x[i])).astype("float32"))
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i, scale in enumerate(scales):
y[i] = np.round(y[i] / scale * max_range)
if not use_second_dim:
for i in range(x.shape[0]):
scales.append(np.max(np.abs(x[i])).astype("float32"))
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i, scale in enumerate(scales):
y[i] = np.round(x[i] / scale * max_range)
else:
for i in range(x.shape[0]):
s = []
for j in range(x.shape[1]):
s.append(np.max(np.abs(x[i][j])).astype("float32"))
scales.append(s)
scales = np.amax(np.array(scales), axis=0)
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i in range(x.shape[0]):
for j, scale in enumerate(scales):
y[i][j] = np.round(x[i][j] / scale * max_range)
return y, scales
......@@ -47,10 +59,16 @@ def channel_wise_dequantize_max_abs(x,
scales,
quant_bits,
activation_scale=None):
y = x.copy()
for i in range(x.shape[0]):
y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * y[i]
if activation_scale is not None:
if activation_scale is None:
y = x.copy()
for i in range(x.shape[0]):
y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * x[i]
else:
y = x.copy()
for i in range(x.shape[0]):
for j in range(x.shape[1]):
y[i][j] = (scales[j] /
(math.pow(2, quant_bits[0] - 1) - 1)) * x[i][j]
y *= activation_scale / (math.pow(2, quant_bits[1] - 1) - 1)
return y
......@@ -65,7 +83,8 @@ class TestFakeChannelWiseDequantizeMaxAbsOpTwoScales(OpTest):
self.set_args()
self.op_type = "fake_channel_wise_dequantize_max_abs"
x = np.random.randn(4, 3, 64, 64).astype(self.data_type)
yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0])
yq, scales = channel_wise_quantize_max_abs(
x, self.quant_bits[0], use_second_dim=True)
ydq = channel_wise_dequantize_max_abs(yq, scales, self.quant_bits,
self.activation_scale)
......
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
浏览文件 @ 072d95d8
......@@ -53,7 +53,7 @@ class TestFakeChannelWiseQuantizeOp(OpTest):
self.outputs = {
'Out': outputs,
'OutScales': np.array(scales).astype("float32"),
'OutScale': np.array(scales).astype("float32"),
}
def test_check_output(self):
......
python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py 0 → 100644
浏览文件 @ 072d95d8
# 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.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
from paddle.fluid.imperative.base import to_variable
class SimpleImgConvPool(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
pool_size,
pool_stride,
pool_padding=0,
pool_type='max',
global_pooling=False,
conv_stride=1,
conv_padding=0,
conv_dilation=1,
conv_groups=1,
act=None,
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
padding=conv_padding,
dilation=conv_dilation,
groups=conv_groups,
param_attr=None,
bias_attr=None,
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
pool_padding=pool_padding,
global_pooling=global_pooling,
use_cudnn=use_cudnn)
def forward(self, inputs):
x = self._conv2d(inputs)
x = self._pool2d(x)
return x
class MNIST(fluid.imperative.Layer):
def __init__(self, name_scope):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(), 1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(), 20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
self._fc = FC(self.full_name(),
10,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)),
act="softmax")
def forward(self, inputs):
x = self._simple_img_conv_pool_1(inputs)
x = self._simple_img_conv_pool_2(x)
x = self._fc(x)
return x
class TestImperativeCheckpoint(unittest.TestCase):
def save_load_persistables(self):
seed = 90
epoch_num = 1
with fluid.imperative.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
dy_param_init_value = {}
step = 0
for epoch in range(epoch_num):
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 = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy()
avg_loss._backward()
sgd.minimize(avg_loss)
fluid.imperative.save_persistables(mnist, "save_dir")
mnist.clear_gradients()
for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy()
mnist.load_dict(
fluid.imperative.load_persistables(mnist, "save_dir"))
restore = mnist.parameters()
self.assertEqual(len(dy_param_init_value), len(restore))
for value in restore:
self.assertTrue(
np.allclose(value, dy_param_init_value[value.name]))
self.assertTrue(np.isfinite(value.all()))
self.assertFalse(np.isnan(value.any()))
step += 1
if step > 20:
break
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 072d95d8
......@@ -1240,6 +1240,14 @@ class TestBook(unittest.TestCase):
print(str(program))
def test_range(self):
program = Program()
with program_guard(program):
layers.range(0, 10, 2, 'int32')
layers.range(0.1, 10.0, 0.2, 'float32')
print(str(program))
def test_spectral_norm(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py
浏览文件 @ 072d95d8
......@@ -13,6 +13,9 @@
# limitations under the License.
from __future__ import print_function
import os
os.environ['FLAGS_fuse_parameter_memory_size'] = "131072"
os.environ['FLAGS_fuse_parameter_groups_size'] = "3"
import paddle.fluid as fluid
import paddle.fluid.layers.ops as ops
......@@ -22,7 +25,6 @@ import paddle.fluid.core as core
from parallel_executor_test_base import TestParallelExecutorBase
import unittest
import math
import os
import numpy as np
# FIXME(zcd): If the neural net has dropout_op, the output of ParallelExecutor
......@@ -312,17 +314,59 @@ class TestResnet(TestParallelExecutorBase):
self.assertAlmostEquals(
np.mean(parallel_last_loss), single_last_loss[0], delta=delta2)
def _compare_with_fused_all_reduce(self,
model,
use_cuda,
iter=20,
delta2=1e-5):
if use_cuda and not core.is_compiled_with_cuda():
return
global remove_bn
remove_bn = True
img, label = self._init_data(batch_size=batch_size)
all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
iter=iter,
batch_size=batch_size,
use_cuda=use_cuda,
fuse_all_reduce_ops=False,
optimizer=optimizer)
reduce_first_loss, reduce_last_loss = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
iter=iter,
batch_size=batch_size,
use_cuda=use_cuda,
fuse_all_reduce_ops=True,
optimizer=optimizer)
for loss in zip(all_reduce_first_loss, reduce_first_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
def test_seresnext_with_learning_rate_decay(self):
self._check_resnet_convergence(model=SE_ResNeXt50Small, use_cuda=True)
self._check_resnet_convergence(
model=SE_ResNeXt50Small, use_cuda=False, iter=2, delta2=1e-3)
def test_seresnext_with_new_strategy(self):
def test_seresnext_with_reduce(self):
self._compare_reduce_and_allreduce(
model=SE_ResNeXt50Small, use_cuda=True, delta2=1e-2)
self._compare_reduce_and_allreduce(
model=SE_ResNeXt50Small, use_cuda=False, iter=5)
def test_seresnext_with_fused_all_reduce(self):
self._compare_with_fused_all_reduce(
model=SE_ResNeXt50Small, use_cuda=True, delta2=1e-3)
self._compare_with_fused_all_reduce(
model=SE_ResNeXt50Small, use_cuda=False, iter=2, delta2=1e-3)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/graph/graph.py → python/paddle/fluid/tests/unittests/test_range.py
浏览文件 @ 072d95d8
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -11,39 +11,60 @@
# 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 os
import subprocess
from ....framework import Program
from ....framework import Block
from .... import core
__all__ = ['Graph', 'ImitationGraph', 'IRGraph']
import unittest
import numpy as np
from op_test import OpTest
class TestRangeOp(OpTest):
def setUp(self):
self.op_type = "range"
self.init_config()
self.inputs = {
'Start': np.array([self.case[0]]).astype(self.dtype),
'End': np.array([self.case[1]]).astype(self.dtype),
'Step': np.array([self.case[2]]).astype(self.dtype)
}
self.outputs = {
'Out': np.arange(self.case[0], self.case[1],
self.case[2]).astype(self.dtype)
}
def init_config(self):
self.dtype = np.float32
self.case = (0, 1, 0.2)
def test_check_output(self):
self.check_output()
class TestFloatRangeOpCase0(TestRangeOp):
def init_config(self):
self.dtype = np.float32
self.case = (0, 5, 1)
class Graph(object):
"""
Base class for all graph.
"""
def __init__(self):
pass
class TestInt32RangeOpCase0(TestRangeOp):
def init_config(self):
self.dtype = np.int32
self.case = (0, 5, 2)
def all_parameters(self):
"""
Return all the parameters in current graph.
"""
pass
class TestInt32RangeOpCase1(TestRangeOp):
def init_config(self):
self.dtype = np.int32
self.case = (10, 1, -2)
class ImitationGraph(Graph):
def __init__(self, program=None):
super(ImitationGraph, self).__init__()
self.program = Program() if program is None else program
def all_parameters(self):
return self.program.global_block().all_parameters()
class TestInt32RangeOpCase2(TestRangeOp):
def init_config(self):
self.dtype = np.int32
self.case = (-1, -10, -2)
class IRGraph(Graph):
pass
if __name__ == "__main__":
unittest.main()
python/requirements.txt
浏览文件 @ 072d95d8
......@@ -12,3 +12,4 @@ six
funcsigs
pyyaml
decorator
prettytable
tools/manylinux1/Dockerfile.x64
浏览文件 @ 072d95d8
......@@ -52,7 +52,7 @@ RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /o
LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
RUN wget -O /opt/swig-2.0.12.tar.gz https://cytranet.dl.sourceforge.net/project/swig/swig/swig-2.0.12/swig-2.0.12.tar.gz && \
RUN wget -O /opt/swig-2.0.12.tar.gz https://sourceforge.net/projects/swig/files/swig/swig-2.0.12/swig-2.0.12.tar.gz/download && \
cd /opt && tar xzf swig-2.0.12.tar.gz && cd /opt/swig-2.0.12 && ./configure && make && make install && cd /opt && rm swig-2.0.12.tar.gz
CMD ["bash", "/paddle/paddle/scripts/docker/build.sh"]
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