Merge branch 'release/1.0.0' into cherrypick_13535

paddle/fluid/API.spec

@@ -49,7 +49,7 @@ paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], var
 paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0))
 paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
-paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'use_mkldnn', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, False, None, False, None))
+paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
 paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
 paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
 paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None))
@@ -62,14 +62,14 @@ paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label',
 paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None))
-paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, False, None, None))
-paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, False, None, None))
+paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
+paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
 paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn'], varargs=None, keywords=None, defaults=(None, None, False))
 paddle.fluid.layers.softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
-paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'use_mkldnn', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, False, None))
-paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'use_mkldnn', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, False, None))
-paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'use_mkldnn', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, False, None, None, None, False, False))
+paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
+paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
+paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False))
 paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
 paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
@@ -145,21 +145,31 @@ paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, key
 paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.expand ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'out', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None, None))
-paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
-paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
-paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
-paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
-paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
-paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
-paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
+paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None))
+paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
+paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
+paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
+paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
+paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
+paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
+paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None))
 paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
-paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
+paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
 paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
 paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
-paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
+paddle.fluid.layers.sum ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.logical_or ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
+paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
+paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -222,16 +232,6 @@ paddle.fluid.layers.StaticRNN.update_memory ArgSpec(args=['self', 'mem', 'var'],
 paddle.fluid.layers.reorder_lod_tensor_by_rank ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Print ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both'))
 paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords='ignored', defaults=(None,))
-paddle.fluid.layers.mean ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.mul ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.clip ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.clip_by_norm ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.exp ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
@@ -265,11 +265,11 @@ paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'asp
 paddle.fluid.layers.roi_perspective_transform ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,))
 paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True))
 paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
-paddle.fluid.layers.iou_similarity ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.box_coder ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.polygon_box_transform ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
+paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
+paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
-paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk'], varargs=None, keywords=None, defaults=('ROC', 4095, 1))
+paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
 paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
@@ -313,11 +313,11 @@ paddle.fluid.transpiler.RoundRobin.__init__ ArgSpec(args=['self', 'pserver_endpo
 paddle.fluid.transpiler.RoundRobin.dispatch ArgSpec(args=['self', 'varlist'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.RoundRobin.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ 
-paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True, False))
+paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True))
 paddle.fluid.nets.sequence_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max'))
 paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,))
 paddle.fluid.nets.scaled_dot_product_attention ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0))
-paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn', 'use_mkldnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True, False))
+paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True))
 paddle.fluid.optimizer.SGDOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.optimizer.SGDOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None))

paddle/fluid/framework/CMakeLists.txt

@@ -167,15 +167,8 @@ cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
 cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto)
 cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
 
-# cc_test(channel_test SRCS channel_test.cc)
 cc_test(tuple_test SRCS tuple_test.cc )
 
 if (NOT WIN32)
 cc_test(rw_lock_test SRCS rw_lock_test.cc)
 endif (NOT WIN32)
-
-# disable test temporarily.
-# TODO https://github.com/PaddlePaddle/Paddle/issues/11971
-# cc_test(concurrency_test SRCS concurrency_test.cc DEPS go_op channel_close_op channel_create_op
-#         channel_send_op channel_recv_op sum_op select_op elementwise_add_op compare_op
-#         conditional_block_op while_op assign_op print_op executor proto_desc)

paddle/fluid/framework/channel.h

-/* 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 <stddef.h>            // for size_t
-#include <condition_variable>  // NOLINT
-#include <typeindex>
-#include "paddle/fluid/platform/enforce.h"
-
-namespace paddle {
-namespace framework {
-
-enum class ChannelAction {
-  SEND = 0,
-  RECEIVE = 1,
-  CLOSE = 2,
-};
-
-// Channel is the abstract class of buffered and un-buffered channels.
-template <typename T>
-class Channel {
- public:
-  virtual bool CanSend() = 0;
-  virtual bool CanReceive() = 0;
-  virtual void Send(T*) = 0;
-  virtual bool Receive(T*) = 0;
-  virtual size_t Cap() = 0;
-  virtual void Lock() = 0;
-
-  virtual void Unlock() = 0;
-  virtual bool IsClosed() = 0;
-  virtual void Close() = 0;
-  virtual ~Channel() {}
-
-  virtual void AddToSendQ(const void* referrer, T* data,
-                          std::shared_ptr<std::condition_variable_any> cond,
-                          std::function<bool(ChannelAction)> cb) = 0;
-  virtual void AddToReceiveQ(const void* referrer, T* data,
-                             std::shared_ptr<std::condition_variable_any> cond,
-                             std::function<bool(ChannelAction)> cb) = 0;
-  virtual void RemoveFromSendQ(const void* referrer) = 0;
-  virtual void RemoveFromReceiveQ(const void* referrer) = 0;
-};
-
-// Forward declaration of channel implementations.
-template <typename T>
-class ChannelImpl;
-
-template <typename T>
-Channel<T>* MakeChannel(size_t buffer_size) {
-  return new ChannelImpl<T>(buffer_size);
-}
-
-template <typename T>
-void CloseChannel(Channel<T>* ch) {
-  ch->Close();
-}
-
-/*
- * The ChannelHolder class serves two main purposes:
- * 1. It acts as a unified wrapper for the different kinds of
- *    channels, i.e. Buffered and Unbuffered channels. This is
- *    similar to the ReaderHolder class.
- * 2. It also helps us in TypeHiding. This is similar to the
- *    PlaceHolder implementations in variable.h and tensor.h.
- */
-class ChannelHolder {
- public:
-  template <typename T>
-  void Reset(size_t buffer_size) {
-    holder_.reset(new PlaceholderImpl<T>(buffer_size));
-  }
-
-  template <typename T>
-  void Send(T* data) {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    PADDLE_ENFORCE_EQ(
-        holder_->Type(), std::type_index(typeid(T)),
-        "Channel type is not same as the type of the data being sent");
-    // Static cast should be safe because we have ensured that types are same
-    Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
-    PADDLE_ENFORCE_EQ(channel != nullptr, true, "Channel should not be null.");
-    channel->Send(data);
-  }
-
-  template <typename T>
-  bool Receive(T* data) {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    PADDLE_ENFORCE_EQ(
-        holder_->Type(), std::type_index(typeid(T)),
-        "Channel type is not same as the type of the data being sent");
-    Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
-    PADDLE_ENFORCE_EQ(channel != nullptr, true, "Channel should not be null.");
-    return channel->Receive(data);
-  }
-
-  bool IsClosed() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    return holder_->IsClosed();
-  }
-
-  bool CanSend() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    return holder_->CanSend();
-  }
-
-  bool CanReceive() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    return holder_->CanReceive();
-  }
-
-  void close() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    holder_->Close();
-  }
-
-  size_t Cap() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    return holder_->Cap();
-  }
-
-  void Lock() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    holder_->Lock();
-  }
-
-  void Unlock() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    holder_->Unlock();
-  }
-
-  template <typename T>
-  void AddToSendQ(const void* referrer, T* data,
-                  std::shared_ptr<std::condition_variable_any> cond,
-                  std::function<bool(ChannelAction)> cb) {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
-    if (channel != nullptr) {
-      channel->AddToSendQ(referrer, data, cond, cb);
-    }
-  }
-
-  template <typename T>
-  void AddToReceiveQ(const void* referrer, T* data,
-                     std::shared_ptr<std::condition_variable_any> cond,
-                     std::function<bool(ChannelAction)> cb) {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
-    if (channel != nullptr) {
-      channel->AddToReceiveQ(referrer, data, cond, cb);
-    }
-  }
-
-  void RemoveFromSendQ(const void* referrer) {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    holder_->RemoveFromSendQ(referrer);
-  }
-
-  void RemoveFromReceiveQ(const void* referrer) {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    holder_->RemoveFromReceiveQ(referrer);
-  }
-
-  inline bool IsInitialized() const { return holder_ != nullptr; }
-
-  inline const std::type_index Type() {
-    PADDLE_ENFORCE_EQ(IsInitialized(), true,
-                      "The Channel hasn't been initialized");
-    return holder_->Type();
-  }
-
- private:
-  /**
-   * @note    Placeholder hides type T, so it doesn't appear as a template
-   *          parameter of ChannelHolder.
-   */
-  struct Placeholder {
-    virtual ~Placeholder() {}
-    virtual const std::type_index Type() const = 0;
-    virtual void* Ptr() const = 0;
-    virtual bool IsClosed() = 0;
-    virtual bool CanSend() = 0;
-    virtual bool CanReceive() = 0;
-    virtual void RemoveFromSendQ(const void* referrer) = 0;
-    virtual void RemoveFromReceiveQ(const void* referrer) = 0;
-    virtual void Close() = 0;
-    virtual void Lock() = 0;
-    virtual void Unlock() = 0;
-    virtual size_t Cap() = 0;
-  };
-
-  template <typename T>
-  struct PlaceholderImpl : public Placeholder {
-    explicit PlaceholderImpl(size_t buffer_size)
-        : type_(std::type_index(typeid(T))) {
-      channel_.reset(MakeChannel<T>(buffer_size));
-    }
-
-    virtual const std::type_index Type() const { return type_; }
-
-    virtual void* Ptr() const { return static_cast<void*>(channel_.get()); }
-
-    virtual bool IsClosed() {
-      if (channel_) {
-        return channel_->IsClosed();
-      }
-      return false;
-    }
-
-    virtual bool CanSend() {
-      if (channel_) {
-        return channel_->CanSend();
-      }
-      return false;
-    }
-
-    virtual bool CanReceive() {
-      if (channel_) {
-        return channel_->CanReceive();
-      }
-      return false;
-    }
-
-    virtual void RemoveFromSendQ(const void* referrer) {
-      if (channel_) {
-        channel_->RemoveFromSendQ(referrer);
-      }
-    }
-
-    virtual void RemoveFromReceiveQ(const void* referrer) {
-      if (channel_) {
-        channel_->RemoveFromReceiveQ(referrer);
-      }
-    }
-
-    virtual void Close() {
-      if (channel_) channel_->Close();
-    }
-
-    virtual size_t Cap() {
-      if (channel_)
-        return channel_->Cap();
-      else
-        return -1;
-    }
-
-    virtual void Lock() {
-      if (channel_) channel_->Lock();
-    }
-
-    virtual void Unlock() {
-      if (channel_) channel_->Unlock();
-    }
-
-    std::unique_ptr<Channel<T>> channel_;
-    const std::type_index type_;
-  };
-
-  // Pointer to a PlaceholderImpl object
-  std::unique_ptr<Placeholder> holder_;
-};
-
-}  // namespace framework
-}  // namespace paddle
-
-#include "paddle/fluid/framework/channel_impl.h"

paddle/fluid/framework/channel_impl.h

-/* 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 <stddef.h>  // for size_t
-#include <atomic>
-#include <condition_variable>  // NOLINT
-#include <deque>
-#include "paddle/fluid/framework/channel.h"
-#include "paddle/fluid/platform/enforce.h"
-
-namespace paddle {
-namespace framework {
-
-template <typename T>
-class ChannelImpl : public paddle::framework::Channel<T> {
-  friend Channel<T> *paddle::framework::MakeChannel<T>(size_t);
-  friend void paddle::framework::CloseChannel<T>(Channel<T> *);
-
- public:
-  virtual bool CanSend();
-  virtual bool CanReceive();
-  virtual void Send(T *);
-  virtual bool Receive(T *);
-  virtual size_t Cap() { return cap_; }
-  virtual void Lock();
-  virtual void Unlock();
-  virtual bool IsClosed();
-  virtual void Close();
-  explicit ChannelImpl(size_t);
-  virtual ~ChannelImpl();
-
-  virtual void AddToSendQ(const void *referrer, T *data,
-                          std::shared_ptr<std::condition_variable_any> cond,
-                          std::function<bool(ChannelAction)> cb);
-  virtual void AddToReceiveQ(const void *referrer, T *data,
-                             std::shared_ptr<std::condition_variable_any> cond,
-                             std::function<bool(ChannelAction)> cb);
-
-  virtual void RemoveFromSendQ(const void *referrer);
-  virtual void RemoveFromReceiveQ(const void *referrer);
-
- private:
-  struct QueueMessage {
-    T *data;
-    std::shared_ptr<std::condition_variable_any> cond;
-    bool chan_closed = false;
-    bool completed = false;
-    const void *referrer;  // TODO(thuan): figure out better way to do this
-    std::function<bool(ChannelAction)> callback;
-
-    explicit QueueMessage(T *item)
-        : data(item), cond(std::make_shared<std::condition_variable_any>()) {}
-
-    QueueMessage(T *item, std::shared_ptr<std::condition_variable_any> cond)
-        : data(item), cond(cond) {}
-
-    void Wait(std::unique_lock<std::recursive_mutex> &lock) {
-      cond->wait(lock, [this]() { return completed; });
-    }
-
-    void Notify() {
-      completed = true;
-      cond->notify_all();
-    }
-  };
-
-  void send_return() {
-    send_ctr--;
-    destructor_cond_.notify_all();
-  }
-
-  bool recv_return(bool value) {
-    recv_ctr--;
-    destructor_cond_.notify_all();
-    return value;
-  }
-
-  std::shared_ptr<QueueMessage> get_first_message(
-      std::deque<std::shared_ptr<QueueMessage>> *queue, ChannelAction action) {
-    while (!queue->empty()) {
-      // Check whether this message was added by Select
-      // If this was added by Select then execute the callback
-      // to check if you can execute this message. The callback
-      // can return false if some other case was executed in Select.
-      // In that case just discard this QueueMessage and process next.
-      std::shared_ptr<QueueMessage> m = queue->front();
-      queue->pop_front();
-      if (m->callback == nullptr || m->callback(action)) return m;
-    }
-    return nullptr;
-  }
-
-  size_t cap_;
-  std::recursive_mutex mu_;
-  bool closed_;
-  std::deque<T> buf_;
-  std::deque<std::shared_ptr<QueueMessage>> recvq;
-  std::deque<std::shared_ptr<QueueMessage>> sendq;
-  std::atomic<unsigned> send_ctr{0};
-  std::atomic<unsigned> recv_ctr{0};
-  std::condition_variable_any destructor_cond_;
-};
-
-template <typename T>
-ChannelImpl<T>::ChannelImpl(size_t capacity)
-    : cap_(capacity), closed_(false), send_ctr(0), recv_ctr(0) {
-  PADDLE_ENFORCE_GE(capacity, 0);
-}
-
-template <typename T>
-bool ChannelImpl<T>::CanSend() {
-  std::lock_guard<std::recursive_mutex> lock{mu_};
-  return !closed_ && (!recvq.empty() || buf_.size() < cap_);
-}
-
-template <typename T>
-bool ChannelImpl<T>::CanReceive() {
-  std::lock_guard<std::recursive_mutex> lock{mu_};
-  return !(closed_ && buf_.empty()) && (!sendq.empty() || buf_.size() > 0);
-}
-
-template <typename T>
-void ChannelImpl<T>::Send(T *item) {
-  send_ctr++;
-  std::unique_lock<std::recursive_mutex> lock{mu_};
-
-  // If channel is closed, throw exception
-  if (closed_) {
-    send_return();
-    lock.unlock();
-    PADDLE_THROW("Cannot send on closed channel");
-  }
-
-  // If there is a receiver, directly pass the value we want
-  // to send to the receiver, bypassing the channel buffer if any
-  if (!recvq.empty()) {
-    std::shared_ptr<QueueMessage> m =
-        get_first_message(&recvq, ChannelAction::SEND);
-
-    if (m != nullptr) {
-      *(m->data) = std::move(*item);
-      m->Notify();
-      send_return();
-      return;
-    } else {
-      Send(item);
-      send_return();
-      return;
-    }
-  }
-
-  // Unbuffered channel will always bypass this
-  // If buffered channel has space in buffer,
-  // write the element to the buffer.
-  if (buf_.size() < cap_) {
-    // Copy to buffer
-    buf_.push_back(std::move(*item));
-    send_return();
-    return;
-  }
-
-  // Block on channel, because some receiver will complete
-  // the operation for us
-  auto m = std::make_shared<QueueMessage>(item);
-  sendq.push_back(m);
-  m->Wait(lock);
-  if (m->chan_closed) {
-    send_return();
-    lock.unlock();
-    PADDLE_THROW("Cannot send on closed channel");
-  }
-  send_return();
-}
-
-template <typename T>
-bool ChannelImpl<T>::Receive(T *item) {
-  recv_ctr++;
-  std::unique_lock<std::recursive_mutex> lock{mu_};
-
-  // If channel is closed and buffer is empty or
-  // channel is unbuffered
-  if (closed_ && buf_.empty()) return recv_return(false);
-
-  // If there is a sender, directly receive the value we want
-  // from the sender. In case of a buffered channel, read from
-  // buffer and move front of send queue to the buffer
-  if (!sendq.empty()) {
-    std::shared_ptr<QueueMessage> m =
-        get_first_message(&sendq, ChannelAction::RECEIVE);
-    if (buf_.size() > 0) {
-      // Case 1 : Channel is Buffered
-      // Do Data transfer from front of buffer
-      // and add a QueueMessage to the buffer
-      *item = std::move(buf_.front());
-      buf_.pop_front();
-      // If first message from sendq is not null
-      // add it to the buffer and notify it
-      if (m != nullptr) {
-        // Copy to buffer
-        buf_.push_back(std::move(*(m->data)));
-        m->Notify();
-      }  // Ignore if there is no first message
-    } else {
-      // Case 2: Channel is Unbuffered
-      // Do data transfer from front of SendQ
-      // If front is nullptr, then recursively call itself
-      if (m != nullptr) {
-        *item = std::move(*(m->data));
-        m->Notify();
-      } else {
-        return recv_return(Receive(item));
-      }
-    }
-    return recv_return(true);
-  }
-
-  // If this is a buffered channel and there are items in buffer
-  if (buf_.size() > 0) {
-    // Directly read from buffer
-    *item = std::move(buf_.front());
-    buf_.pop_front();
-    // return true
-    return recv_return(true);
-  }
-
-  // No sender available, block on this channel
-  // Some receiver will complete the option for us
-  auto m = std::make_shared<QueueMessage>(item);
-  recvq.push_back(m);
-  m->Wait(lock);
-
-  return recv_return(!m->chan_closed);
-}
-
-template <typename T>
-void ChannelImpl<T>::Lock() {
-  mu_.lock();
-}
-
-template <typename T>
-void ChannelImpl<T>::Unlock() {
-  mu_.unlock();
-}
-
-template <typename T>
-bool ChannelImpl<T>::IsClosed() {
-  std::lock_guard<std::recursive_mutex> lock{mu_};
-  return closed_;
-}
-
-template <typename T>
-void ChannelImpl<T>::Close() {
-  std::unique_lock<std::recursive_mutex> lock{mu_};
-
-  if (closed_) {
-    // TODO(abhinavarora): closing an already closed channel should panic
-    lock.unlock();
-    return;
-  }
-
-  closed_ = true;
-
-  // Empty the readers
-  while (!recvq.empty()) {
-    std::shared_ptr<QueueMessage> m = recvq.front();
-    recvq.pop_front();
-    m->chan_closed = true;
-
-    // Execute callback function (if any)
-    if (m->callback != nullptr) {
-      m->callback(ChannelAction::CLOSE);
-    }
-
-    m->Notify();
-  }
-
-  // Empty the senders
-  while (!sendq.empty()) {
-    std::shared_ptr<QueueMessage> m = sendq.front();
-    sendq.pop_front();
-    m->chan_closed = true;
-
-    // Execute callback function (if any)
-    if (m->callback != nullptr) {
-      m->callback(ChannelAction::CLOSE);
-    }
-
-    m->Notify();
-  }
-}
-
-template <typename T>
-void ChannelImpl<T>::AddToSendQ(
-    const void *referrer, T *data,
-    std::shared_ptr<std::condition_variable_any> cond,
-    std::function<bool(ChannelAction)> cb) {
-  std::lock_guard<std::recursive_mutex> lock{mu_};
-  auto m = std::make_shared<QueueMessage>(data, cond);
-  m->referrer = referrer;
-  m->callback = cb;
-  sendq.push_back(m);
-}
-
-template <typename T>
-void ChannelImpl<T>::AddToReceiveQ(
-    const void *referrer, T *data,
-    std::shared_ptr<std::condition_variable_any> cond,
-    std::function<bool(ChannelAction)> cb) {
-  std::lock_guard<std::recursive_mutex> lock{mu_};
-  auto m = std::make_shared<QueueMessage>(data, cond);
-  m->referrer = referrer;
-  m->callback = cb;
-  recvq.push_back(m);
-}
-
-template <typename T>
-void ChannelImpl<T>::RemoveFromSendQ(const void *referrer) {
-  std::lock_guard<std::recursive_mutex> lock{mu_};
-
-  for (auto it = sendq.begin(); it != sendq.end();) {
-    std::shared_ptr<QueueMessage> sendMsg = (std::shared_ptr<QueueMessage>)*it;
-
-    if (sendMsg->referrer == referrer) {
-      it = sendq.erase(it);
-    } else {
-      ++it;
-    }
-  }
-}
-
-template <typename T>
-void ChannelImpl<T>::RemoveFromReceiveQ(const void *referrer) {
-  std::lock_guard<std::recursive_mutex> lock{mu_};
-
-  for (auto it = recvq.begin(); it != recvq.end();) {
-    std::shared_ptr<QueueMessage> recvMsg = (std::shared_ptr<QueueMessage>)*it;
-
-    if (recvMsg->referrer == referrer) {
-      it = recvq.erase(it);
-    } else {
-      ++it;
-    }
-  }
-}
-
-template <typename T>
-ChannelImpl<T>::~ChannelImpl() {
-  Close();
-  // The destructor must wait for all readers and writers to complete their task
-  // The channel has been closed, so we will not accept new readers and writers
-  std::unique_lock<std::recursive_mutex> lock{mu_};
-  destructor_cond_.wait(lock,
-                        [this]() { return send_ctr == 0 && recv_ctr == 0; });
-}
-
-}  // namespace framework
-}  // namespace paddle

paddle/fluid/framework/concurrency_test.cc

-/* 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 <thread>  // NOLINT
-
-#include "gtest/gtest.h"
-#include "paddle/fluid/framework/block_desc.h"
-#include "paddle/fluid/framework/channel.h"
-#include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/framework/op_registry.h"
-
-USE_NO_KERNEL_OP(go);
-USE_NO_KERNEL_OP(channel_close);
-USE_NO_KERNEL_OP(channel_create);
-USE_NO_KERNEL_OP(channel_recv);
-USE_NO_KERNEL_OP(channel_send);
-USE_NO_KERNEL_OP(elementwise_add);
-USE_NO_KERNEL_OP(select);
-USE_NO_KERNEL_OP(conditional_block);
-USE_NO_KERNEL_OP(equal);
-USE_NO_KERNEL_OP(assign);
-USE_NO_KERNEL_OP(while);
-USE_NO_KERNEL_OP(print);
-
-namespace f = paddle::framework;
-namespace p = paddle::platform;
-
-namespace paddle {
-namespace framework {
-
-template <typename T>
-LoDTensor *CreateVariable(Scope *scope, const p::CPUPlace &place,
-                          std::string name, T value) {
-  // Create LoDTensor<int> of dim [1]
-  auto var = scope->Var(name);
-  auto tensor = var->GetMutable<LoDTensor>();
-  tensor->Resize({1});
-  T *expect = tensor->mutable_data<T>(place);
-  expect[0] = value;
-  return tensor;
-}
-
-void AddOp(const std::string &type, const VariableNameMap &inputs,
-           const VariableNameMap &outputs, AttributeMap attrs,
-           BlockDesc *block) {
-  // insert op
-  auto op = block->AppendOp();
-  op->SetType(type);
-  for (auto &kv : inputs) {
-    op->SetInput(kv.first, kv.second);
-  }
-  for (auto &kv : outputs) {
-    op->SetOutput(kv.first, kv.second);
-  }
-  op->SetAttrMap(attrs);
-}
-
-void AddCase(ProgramDesc *program, Scope *scope, p::CPUPlace *place,
-             BlockDesc *casesBlock, int caseId, int caseType,
-             std::string caseChannel, std::string caseVarName,
-             std::function<void(BlockDesc *, Scope *)> func) {
-  std::string caseCondName = std::string("caseCond") + std::to_string(caseId);
-  std::string caseCondXVarName =
-      std::string("caseCondX") + std::to_string(caseId);
-
-  BlockDesc *caseBlock = program->AppendBlock(*casesBlock);
-  func(caseBlock, scope);
-
-  CreateVariable(scope, *place, caseCondName, false);
-  CreateVariable(scope, *place, caseCondXVarName, caseId);
-  CreateVariable(scope, *place, caseVarName, caseId);
-
-  scope->Var("step_scope");
-
-  AddOp("equal", {{"X", {caseCondXVarName}}, {"Y", {"caseToExecute"}}},
-        {{"Out", {caseCondName}}}, {}, casesBlock);
-
-  AddOp("conditional_block", {{"X", {caseCondName}}, {"Params", {}}},
-        {{"Out", {}}, {"Scope", {"step_scope"}}},
-        {{"sub_block", caseBlock}, {"is_scalar_condition", true}}, casesBlock);
-}
-
-void AddFibonacciSelect(Scope *scope, p::CPUPlace *place, ProgramDesc *program,
-                        BlockDesc *parentBlock, std::string dataChanName,
-                        std::string quitChanName) {
-  BlockDesc *whileBlock = program->AppendBlock(*parentBlock);
-
-  CreateVariable(scope, *place, "whileExitCond", true);
-  CreateVariable(scope, *place, "caseToExecute", -1);
-  CreateVariable(scope, *place, "case1var", 0);
-
-  CreateVariable(scope, *place, "xtemp", 0);
-
-  // TODO(thuan): Need to create fibXToSend, since channel send moves the actual
-  // data,
-  // which causes the data to be no longer accessible to do the fib calculation
-  // TODO(abhinav): Change channel send to do a copy instead of a move!
-  CreateVariable(scope, *place, "fibXToSend", 0);
-
-  CreateVariable(scope, *place, "fibX", 0);
-  CreateVariable(scope, *place, "fibY", 1);
-  CreateVariable(scope, *place, "quitVar", 0);
-
-  BlockDesc *casesBlock = program->AppendBlock(*whileBlock);
-  std::function<void(BlockDesc * caseBlock)> f = [](BlockDesc *caseBlock) {};
-
-  // TODO(thuan): Remove this once we change channel send to do a copy instead
-  // of move
-  AddOp("assign", {{"X", {"fibX"}}}, {{"Out", {"fibXToSend"}}}, {}, whileBlock);
-
-  // Case 0: Send to dataChanName
-  std::function<void(BlockDesc * caseBlock, Scope * scope)> case0Func = [&](
-      BlockDesc *caseBlock, Scope *scope) {
-    AddOp("assign", {{"X", {"fibX"}}}, {{"Out", {"xtemp"}}}, {}, caseBlock);
-    AddOp("assign", {{"X", {"fibY"}}}, {{"Out", {"fibX"}}}, {}, caseBlock);
-    AddOp("elementwise_add", {{"X", {"xtemp"}}, {"Y", {"fibY"}}},
-          {{"Out", {"fibY"}}}, {}, caseBlock);
-  };
-  AddCase(program, scope, place, casesBlock, 0, 1, dataChanName, "fibXToSend",
-          case0Func);
-  std::string case0Config =
-      std::string("0,1,") + dataChanName + std::string(",fibXToSend");
-
-  // Case 1: Receive from quitChanName
-  std::function<void(BlockDesc * caseBlock, Scope * scope)> case2Func = [&](
-      BlockDesc *caseBlock, Scope *scope) {
-    // Exit the while loop after we receive from quit channel.
-    // We assign a false to "whileExitCond" variable, which will
-    // break out of while_op loop
-    CreateVariable(scope, *place, "whileFalse", false);
-    AddOp("assign", {{"X", {"whileFalse"}}}, {{"Out", {"whileExitCond"}}}, {},
-          caseBlock);
-  };
-  AddCase(program, scope, place, casesBlock, 1, 2, quitChanName, "quitVar",
-          case2Func);
-  std::string case1Config =
-      std::string("1,2,") + quitChanName + std::string(",quitVar");
-
-  // Select block
-  AddOp("select", {{"X", {dataChanName, quitChanName}},
-                   {"case_to_execute", {"caseToExecute"}}},
-        {{"Out", {}}},
-        {{"sub_block", casesBlock},
-         {"cases", std::vector<std::string>{case0Config, case1Config}}},
-        whileBlock);
-
-  scope->Var("stepScopes");
-  AddOp("while",
-        {{"X", {dataChanName, quitChanName}}, {"Condition", {"whileExitCond"}}},
-        {{"Out", {}}, {"StepScopes", {"stepScopes"}}},
-        {{"sub_block", whileBlock}}, parentBlock);
-}
-
-TEST(Concurrency, Go_Op) {
-  Scope scope;
-  p::CPUPlace place;
-
-  // Initialize scope variables
-  p::CPUDeviceContext ctx(place);
-
-  // Create channel variable
-  scope.Var("Channel");
-
-  // Create Variables, x0 will be put into channel,
-  // result will be pulled from channel
-  CreateVariable(&scope, place, "Status", false);
-  CreateVariable(&scope, place, "x0", 99);
-  CreateVariable(&scope, place, "result", 0);
-
-  framework::Executor executor(place);
-  ProgramDesc program;
-  BlockDesc *block = program.MutableBlock(0);
-
-  // Create channel OP
-  AddOp("channel_create", {}, {{"Out", {"Channel"}}},
-        {{"capacity", 10}, {"data_type", f::proto::VarType::LOD_TENSOR}},
-        block);
-
-  // Create Go Op routine
-  BlockDesc *goOpBlock = program.AppendBlock(program.Block(0));
-  AddOp("channel_send", {{"Channel", {"Channel"}}, {"X", {"x0"}}},
-        {{"Status", {"Status"}}}, {}, goOpBlock);
-
-  // Create Go Op
-  AddOp("go", {{"X", {"Channel", "x0"}}}, {}, {{"sub_block", goOpBlock}},
-        block);
-
-  // Create Channel Receive Op
-  AddOp("channel_recv", {{"Channel", {"Channel"}}},
-        {{"Status", {"Status"}}, {"Out", {"result"}}}, {}, block);
-
-  // Create Channel Close Op
-  AddOp("channel_close", {{"Channel", {"Channel"}}}, {}, {}, block);
-
-  // Check the result tensor to make sure it is set to 0
-  const LoDTensor &tensor = (scope.FindVar("result"))->Get<LoDTensor>();
-  auto *initialData = tensor.data<int>();
-  EXPECT_EQ(initialData[0], 0);
-
-  executor.Run(program, &scope, 0, true, true);
-
-  // After we call executor.run, the Go operator should do a channel_send to
-  // set the "result" variable to 99.
-  auto *finalData = tensor.data<int>();
-  EXPECT_EQ(finalData[0], 99);
-}
-
-/**
- * This test implements the fibonacci function using go_op and select_op
- */
-TEST(Concurrency, Select) {
-  Scope scope;
-  p::CPUPlace place;
-
-  // Initialize scope variables
-  p::CPUDeviceContext ctx(place);
-
-  CreateVariable(&scope, place, "Status", false);
-  CreateVariable(&scope, place, "result", 0);
-  CreateVariable(&scope, place, "currentXFib", 0);
-
-  framework::Executor executor(place);
-  ProgramDesc program;
-  BlockDesc *block = program.MutableBlock(0);
-
-  // Create channel OP
-  std::string dataChanName = "Channel";
-  scope.Var(dataChanName);
-  AddOp("channel_create", {}, {{"Out", {dataChanName}}},
-        {{"capacity", 0}, {"data_type", f::proto::VarType::LOD_TENSOR}}, block);
-
-  std::string quitChanName = "Quit";
-  scope.Var(quitChanName);
-  AddOp("channel_create", {}, {{"Out", {quitChanName}}},
-        {{"capacity", 0}, {"data_type", f::proto::VarType::LOD_TENSOR}}, block);
-
-  // Create Go Op routine, which loops 10 times over fibonacci sequence
-  CreateVariable(&scope, place, "xReceiveVar", 0);
-
-  BlockDesc *goOpBlock = program.AppendBlock(program.Block(0));
-  for (int i = 0; i < 10; ++i) {
-    AddOp("channel_recv", {{"Channel", {dataChanName}}},
-          {{"Status", {"Status"}}, {"Out", {"currentXFib"}}}, {}, goOpBlock);
-    AddOp("print", {{"In", {"currentXFib"}}}, {{"Out", {"currentXFib"}}},
-          {{"first_n", 100},
-           {"summarize", -1},
-           {"print_tensor_name", false},
-           {"print_tensor_type", true},
-           {"print_tensor_shape", false},
-           {"print_tensor_lod", false},
-           {"print_phase", std::string("FORWARD")},
-           {"message", std::string("X: ")}},
-          goOpBlock);
-  }
-
-  CreateVariable(&scope, place, "quitSignal", 0);
-  AddOp("channel_send", {{"Channel", {quitChanName}}, {"X", {"quitSignal"}}},
-        {{"Status", {"Status"}}}, {}, goOpBlock);
-
-  // Create Go Op
-  AddOp("go", {{"X", {dataChanName, quitChanName}}}, {},
-        {{"sub_block", goOpBlock}}, block);
-
-  AddFibonacciSelect(&scope, &place, &program, block, dataChanName,
-                     quitChanName);
-
-  // Create Channel Close Op
-  AddOp("channel_close", {{"Channel", {dataChanName}}}, {}, {}, block);
-  AddOp("channel_close", {{"Channel", {quitChanName}}}, {}, {}, block);
-
-  executor.Run(program, &scope, 0, true, true);
-
-  // After we call executor.run, "result" variable should be equal to 34
-  // (which is 10 loops through fibonacci sequence)
-  const LoDTensor &tensor = (scope.FindVar("currentXFib"))->Get<LoDTensor>();
-  auto *finalData = tensor.data<int>();
-  EXPECT_EQ(finalData[0], 34);
-}
-
-}  // namespace framework
-}  // namespace paddle

paddle/fluid/framework/executor.cc

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/executor.h"
 
-#include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/framework/lod_rank_table.h"
 #include "paddle/fluid/framework/lod_tensor_array.h"
@@ -76,15 +75,13 @@ void InitializeVariable(Variable* var, proto::VarType::Type var_type) {
     var->GetMutable<platform::PlaceList>();
   } else if (var_type == proto::VarType::READER) {
     var->GetMutable<ReaderHolder>();
-  } else if (var_type == proto::VarType::CHANNEL) {
-    var->GetMutable<ChannelHolder>();
   } else if (var_type == proto::VarType::RAW) {
     // GetMutable will be called in operator
   } else {
     PADDLE_THROW(
         "Variable type %d is not in "
         "[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
-        "LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL, RAW]",
+        "LOD_RANK_TABLE, PLACE_LIST, READER, RAW]",
         var_type);
   }
 }

paddle/fluid/framework/framework.proto

@@ -126,7 +126,6 @@ message VarType {
     LOD_TENSOR_ARRAY = 13;
     PLACE_LIST = 14;
     READER = 15;
-    CHANNEL = 16;
     // Any runtime decided variable type is raw
     // raw variables should manage their own allocations
     // in operators like nccl_op
@@ -158,12 +157,6 @@ message VarType {
   message ReaderDesc { repeated LoDTensorDesc lod_tensor = 1; }
   optional ReaderDesc reader = 5;
 
-  message ChannelDesc {
-    required Type data_type = 1;
-    required int64 capacity = 2;
-  }
-  optional ChannelDesc channel = 6;
-
   message Tuple { repeated Type element_type = 1; }
   optional Tuple tuple = 7;
 }

paddle/fluid/framework/selected_rows_test.cc

@@ -27,8 +27,11 @@ class SelectedRowsTester : public ::testing::Test {
     selected_rows_.reset(new SelectedRows(rows, height));
 
     Tensor* value = selected_rows_->mutable_value();
-    value->mutable_data<float>(
+    auto* data = value->mutable_data<float>(
         make_ddim({static_cast<int64_t>(rows.size()), row_numel}), place_);
+    for (int64_t i = 0; i < value->numel(); ++i) {
+      data[i] = static_cast<float>(i);
+    }
   }
 
  protected:
@@ -60,6 +63,10 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
   ASSERT_EQ(selected_rows_->height(), dst_tensor.height());
   ASSERT_EQ(selected_rows_->value().dims(), dst_tensor.value().dims());
   ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
+  auto* dst_data = dst_tensor.value().data<float>();
+  for (int64_t i = 0; i < dst_tensor.value().numel(); ++i) {
+    ASSERT_EQ(dst_data[i], static_cast<float>(i));
+  }
 }
 
 TEST(SelectedRows, SparseTable) {

paddle/fluid/framework/tuple.h

@@ -17,7 +17,6 @@ limitations under the License. */
 #include <stdexcept>
 #include <string>
 #include <vector>
-#include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/framework/var_desc.h"

paddle/fluid/framework/var_desc.cc

@@ -88,13 +88,7 @@ std::vector<std::vector<int64_t>> VarDesc::GetShapes() const {
 }
 
 void VarDesc::SetDataType(proto::VarType::Type data_type) {
-  switch (desc_.type().type()) {
-    case proto::VarType::CHANNEL:
-      mutable_channel_desc()->set_data_type(data_type);
-      break;
-    default:
-      mutable_tensor_desc()->set_data_type(data_type);
-  }
+  mutable_tensor_desc()->set_data_type(data_type);
 }
 
 void VarDesc::SetDataTypes(
@@ -115,13 +109,7 @@ void VarDesc::SetDataTypes(
 }
 
 proto::VarType::Type VarDesc::GetDataType() const {
-  switch (desc_.type().type()) {
-    case proto::VarType::CHANNEL:
-      return channel_desc().data_type();
-      break;
-    default:
-      return tensor_desc().data_type();
-  }
+  return tensor_desc().data_type();
 }
 
 std::vector<proto::VarType::Type> VarDesc::GetDataTypes() const {
@@ -134,17 +122,6 @@ std::vector<proto::VarType::Type> VarDesc::GetDataTypes() const {
   return res;
 }
 
-void VarDesc::SetCapacity(int64_t capacity) {
-  switch (desc_.type().type()) {
-    case proto::VarType::CHANNEL:
-      desc_.mutable_type()->mutable_channel()->set_capacity(capacity);
-      break;
-    default:
-      PADDLE_THROW("Setting 'capacity' is not supported by the type of var %s.",
-                   this->Name());
-  }
-}
-
 void VarDesc::SetLoDLevel(int32_t lod_level) {
   switch (desc_.type().type()) {
     case proto::VarType::LOD_TENSOR:
@@ -214,19 +191,6 @@ std::vector<int32_t> VarDesc::GetLoDLevels() const {
   }
 }
 
-const proto::VarType::ChannelDesc &VarDesc::channel_desc() const {
-  PADDLE_ENFORCE(desc_.has_type(), "The var's type hasn't been set.");
-  PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");
-  switch (desc_.type().type()) {
-    case proto::VarType::CHANNEL:
-      return desc_.type().channel();
-    default:
-      PADDLE_THROW(
-          "Getting 'channel_desc' is not supported by the type of var %s.",
-          this->Name());
-  }
-}
-
 const proto::VarType::TensorDesc &VarDesc::tensor_desc() const {
   PADDLE_ENFORCE(desc_.has_type(), "The var's type hasn't been set.");
   PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");
@@ -262,20 +226,6 @@ std::vector<proto::VarType::TensorDesc> VarDesc::tensor_descs() const {
   }
 }
 
-proto::VarType::ChannelDesc *VarDesc::mutable_channel_desc() {
-  PADDLE_ENFORCE(desc_.has_type(), "The var type hasn't been set.");
-  PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");
-  switch (desc_.type().type()) {
-    case proto::VarType::CHANNEL:
-      return desc_.mutable_type()->mutable_channel();
-    default:
-      PADDLE_THROW(
-          "Getting 'mutable_channel_desc' is not supported by the type of var "
-          "%s.",
-          this->Name());
-  }
-}
-
 proto::VarType::TensorDesc *VarDesc::mutable_tensor_desc() {
   PADDLE_ENFORCE(desc_.has_type(), "The var type hasn't been set.");
   PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");

paddle/fluid/framework/var_desc.h

@@ -87,8 +87,6 @@ class VarDesc {
   void SetDataTypes(
       const std::vector<proto::VarType::Type> &multiple_data_type);
 
-  void SetCapacity(int64_t capacity);
-
   proto::VarType::Type GetDataType() const;
 
   std::vector<proto::VarType::Type> GetDataTypes() const;
@@ -110,10 +108,8 @@ class VarDesc {
   void SetPersistable(bool persistable) { desc_.set_persistable(persistable); }
 
  private:
-  const proto::VarType::ChannelDesc &channel_desc() const;
   const proto::VarType::TensorDesc &tensor_desc() const;
   std::vector<proto::VarType::TensorDesc> tensor_descs() const;
-  proto::VarType::ChannelDesc *mutable_channel_desc();
   proto::VarType::TensorDesc *mutable_tensor_desc();
   std::vector<proto::VarType::TensorDesc *> mutable_tensor_descs();
 

paddle/fluid/framework/var_type.h

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/framework/framework.pb.h"
 #include "paddle/fluid/framework/lod_rank_table.h"
 #include "paddle/fluid/framework/lod_tensor.h"
@@ -41,8 +40,6 @@ inline proto::VarType::Type ToVarType(std::type_index type) {
     return proto::VarType_Type_SELECTED_ROWS;
   } else if (IsType<ReaderHolder>(type)) {
     return proto::VarType_Type_READER;
-  } else if (IsType<ChannelHolder>(type)) {
-    return proto::VarType_Type_CHANNEL;
   } else {
     PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
   }
@@ -66,9 +63,6 @@ inline void VisitVarType(const framework::Variable& var, Visitor visitor) {
     case proto::VarType_Type_READER:
       visitor(var.Get<ReaderHolder>());
       return;
-    case proto::VarType_Type_CHANNEL:
-      visitor(var.Get<ChannelHolder>());
-      return;
     default:
       PADDLE_THROW("Not supported visit type, %d", ToVarType(var.Type()));
   }

paddle/fluid/inference/analysis/analysis_pass.h

@@ -41,12 +41,6 @@ class AnalysisPass {
   // all passes have run.
   virtual bool Finalize() { return false; }
 
-  // Get a Pass appropriate to print the Node this pass operates on.
-  virtual AnalysisPass *CreatePrinterPass(std::ostream &os,
-                                          const std::string &banner) const {
-    return nullptr;
-  }
-
   // Create a debugger Pass that draw the DFG by graphviz toolkit.
   virtual AnalysisPass *CreateGraphvizDebugerPass() const { return nullptr; }
 

paddle/fluid/inference/api/api_impl_tester.cc

@@ -21,6 +21,12 @@ limitations under the License. */
 #include "paddle/fluid/inference/api/api_impl.h"
 #include "paddle/fluid/inference/tests/test_helper.h"
 
+#ifdef __clang__
+#define ACC_DIFF 4e-3
+#else
+#define ACC_DIFF 1e-3
+#endif
+
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
 namespace paddle {
@@ -99,8 +105,8 @@ void MainWord2Vec(bool use_gpu) {
 
   float* lod_data = output1.data<float>();
   for (int i = 0; i < output1.numel(); ++i) {
-    EXPECT_LT(lod_data[i] - data[i], 1e-3);
-    EXPECT_GT(lod_data[i] - data[i], -1e-3);
+    EXPECT_LT(lod_data[i] - data[i], ACC_DIFF);
+    EXPECT_GT(lod_data[i] - data[i], -ACC_DIFF);
   }
 }
 
@@ -144,7 +150,7 @@ void MainImageClassification(bool use_gpu) {
   float* data = static_cast<float*>(outputs[0].data.data());
   float* lod_data = output1.data<float>();
   for (size_t j = 0; j < len / sizeof(float); ++j) {
-    EXPECT_NEAR(lod_data[j], data[j], 1e-3);
+    EXPECT_NEAR(lod_data[j], data[j], ACC_DIFF);
   }
 }
 
@@ -199,7 +205,7 @@ void MainThreadsWord2Vec(bool use_gpu) {
       float* ref_data = refs[tid].data<float>();
       EXPECT_EQ(refs[tid].numel(), static_cast<int64_t>(len / sizeof(float)));
       for (int i = 0; i < refs[tid].numel(); ++i) {
-        EXPECT_NEAR(ref_data[i], data[i], 1e-3);
+        EXPECT_NEAR(ref_data[i], data[i], ACC_DIFF);
       }
     });
   }
@@ -251,7 +257,7 @@ void MainThreadsImageClassification(bool use_gpu) {
       float* ref_data = refs[tid].data<float>();
       EXPECT_EQ((size_t)refs[tid].numel(), len / sizeof(float));
       for (int i = 0; i < refs[tid].numel(); ++i) {
-        EXPECT_NEAR(ref_data[i], data[i], 1e-3);
+        EXPECT_NEAR(ref_data[i], data[i], ACC_DIFF);
       }
     });
   }

paddle/fluid/operators/CMakeLists.txt

@@ -313,11 +313,6 @@ op_library(save_combine_op DEPS lod_tensor)
 op_library(load_combine_op DEPS lod_tensor)
 op_library(concat_op DEPS concat)
 
-# FIXME(thuan): Move CSP operators to paddle/fluid/framework/operators/concurrency
-add_subdirectory(concurrency)
-op_library(channel_send_op DEPS concurrency)
-op_library(channel_recv_op DEPS concurrency)
-
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 
 foreach(src ${GENERAL_OPS})

paddle/fluid/operators/auc_op.cc

@@ -36,11 +36,16 @@ class AucOp : public framework::OperatorWithKernel {
                       "Out and Label should have same height.");
 
     int num_pred_buckets = ctx->Attrs().Get<int>("num_thresholds") + 1;
+    int slide_steps = ctx->Attrs().Get<int>("slide_steps");
+
+    PADDLE_ENFORCE_GE(num_pred_buckets, 1, "num_thresholds must larger than 1");
+    PADDLE_ENFORCE_GE(slide_steps, 0, "slide_steps must be natural number");
 
     ctx->SetOutputDim("AUC", {1});
-    ctx->SetOutputDim("BatchAUC", {1});
-    ctx->SetOutputDim("StatPosOut", {num_pred_buckets});
-    ctx->SetOutputDim("StatNegOut", {num_pred_buckets});
+
+    slide_steps = slide_steps == 0 ? 1 : slide_steps;
+    ctx->SetOutputDim("StatPosOut", {slide_steps, num_pred_buckets});
+    ctx->SetOutputDim("StatNegOut", {slide_steps, num_pred_buckets});
   }
 
  protected:
@@ -62,6 +67,7 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Label",
              "A 2D int tensor indicating the label of the training data. "
              "shape: [batch_size, 1]");
+
     // TODO(typhoonzero): support weight input
     AddInput("StatPos", "Statistic value when label = 1");
     AddInput("StatNeg", "Statistic value when label = 0");
@@ -69,18 +75,19 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("AUC",
               "A scalar representing the "
               "current area-under-the-curve.");
-    AddOutput("BatchAUC", "The AUC for current batch");
+
     AddOutput("StatPosOut", "Statistic value when label = 1");
     AddOutput("StatNegOut", "Statistic value when label = 0");
 
     AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
         .SetDefault("ROC");
 
-    AddAttr<int>("num_thresholds",
-                 "The number of thresholds to use when discretizing the"
-                 " roc curve.")
+    AddAttr<int>(
+        "num_thresholds",
+        "The number of thresholds to use when discretizing the roc curve.")
         .SetDefault((2 << 12) - 1);
-
+    AddAttr<int>("slide_steps", "Use slide steps to calc batch auc.")
+        .SetDefault(1);
     AddComment(R"DOC(
 Area Under The Curve (AUC) Operator.
 

paddle/fluid/operators/auc_op.h

@@ -32,7 +32,9 @@ class AucKernel : public framework::OpKernel<T> {
 
     std::string curve = ctx.Attr<std::string>("curve");
     int num_thresholds = ctx.Attr<int>("num_thresholds");
+    // buckets contain numbers from 0 to num_thresholds
     int num_pred_buckets = num_thresholds + 1;
+    int slide_steps = ctx.Attr<int>("slide_steps");
 
     // Only use output var for now, make sure it's persistable and
     // not cleaned up for each batch.
@@ -40,16 +42,19 @@ class AucKernel : public framework::OpKernel<T> {
     auto *stat_pos = ctx.Output<Tensor>("StatPosOut");
     auto *stat_neg = ctx.Output<Tensor>("StatNegOut");
 
-    auto *stat_pos_data = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
-    auto *stat_neg_data = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
-    calcAuc(ctx, label, predict, stat_pos_data, stat_neg_data, num_thresholds,
-            auc);
+    auto *origin_stat_pos = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
+    auto *origin_stat_neg = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
 
-    auto *batch_auc = ctx.Output<Tensor>("BatchAUC");
-    std::vector<int64_t> stat_pos_batch(num_pred_buckets, 0);
-    std::vector<int64_t> stat_neg_batch(num_pred_buckets, 0);
-    calcAuc(ctx, label, predict, stat_pos_batch.data(), stat_neg_batch.data(),
-            num_thresholds, batch_auc);
+    std::vector<int64_t> stat_pos_data(num_pred_buckets, 0);
+    std::vector<int64_t> stat_neg_data(num_pred_buckets, 0);
+
+    auto stat_pos_calc = stat_pos_data.data();
+    auto stat_neg_calc = stat_neg_data.data();
+
+    statAuc(label, predict, num_pred_buckets, num_thresholds, slide_steps,
+            origin_stat_pos, origin_stat_neg, &stat_pos_calc, &stat_neg_calc);
+
+    calcAuc(ctx, stat_pos_calc, stat_neg_calc, num_thresholds, auc);
   }
 
  private:
@@ -58,29 +63,76 @@ class AucKernel : public framework::OpKernel<T> {
     return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0;
   }
 
-  inline static void calcAuc(const framework::ExecutionContext &ctx,
-                             const framework::Tensor *label,
+  inline static void statAuc(const framework::Tensor *label,
                              const framework::Tensor *predict,
-                             int64_t *stat_pos, int64_t *stat_neg,
-                             int num_thresholds,
-                             framework::Tensor *auc_tensor) {
+                             const int num_pred_buckets,
+                             const int num_thresholds, const int slide_steps,
+                             int64_t *origin_stat_pos, int64_t *origin_stat_neg,
+                             int64_t **stat_pos, int64_t **stat_neg) {
     size_t batch_size = predict->dims()[0];
     size_t inference_width = predict->dims()[1];
     const T *inference_data = predict->data<T>();
     const auto *label_data = label->data<int64_t>();
 
-    auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
-
     for (size_t i = 0; i < batch_size; i++) {
       uint32_t binIdx = static_cast<uint32_t>(
           inference_data[i * inference_width + 1] * num_thresholds);
       if (label_data[i]) {
-        stat_pos[binIdx] += 1.0;
+        (*stat_pos)[binIdx] += 1.0;
       } else {
-        stat_neg[binIdx] += 1.0;
+        (*stat_neg)[binIdx] += 1.0;
       }
     }
 
+    int bucket_length = num_pred_buckets * sizeof(int64_t);
+
+    // will stat auc unlimited.
+    if (slide_steps == 0) {
+      for (int slide = 0; slide < num_pred_buckets; ++slide) {
+        origin_stat_pos[slide] += (*stat_pos)[slide];
+        origin_stat_neg[slide] += (*stat_neg)[slide];
+      }
+
+      *stat_pos = origin_stat_pos;
+      *stat_neg = origin_stat_neg;
+
+    } else {
+      for (int slide = 1; slide < slide_steps; ++slide) {
+        int dst_idx = (slide - 1) * num_pred_buckets;
+        int src_inx = slide * num_pred_buckets;
+        std::memcpy(origin_stat_pos + dst_idx, origin_stat_pos + src_inx,
+                    bucket_length);
+        std::memcpy(origin_stat_neg + dst_idx, origin_stat_neg + src_inx,
+                    bucket_length);
+      }
+
+      std::memcpy(origin_stat_pos + (slide_steps - 1) * num_pred_buckets,
+                  *stat_pos, bucket_length);
+      std::memcpy(origin_stat_neg + (slide_steps - 1) * num_pred_buckets,
+                  *stat_neg, bucket_length);
+
+      std::memset(*stat_pos, 0, bucket_length);
+      std::memset(*stat_neg, 0, bucket_length);
+
+      for (int slide = 0; slide < num_pred_buckets; ++slide) {
+        int stat_pos_steps = 0;
+        int stat_neg_steps = 0;
+        for (int step = 0; step < slide_steps; ++step) {
+          stat_pos_steps += origin_stat_pos[slide + step * num_pred_buckets];
+          stat_neg_steps += origin_stat_neg[slide + step * num_pred_buckets];
+        }
+        (*stat_pos)[slide] += stat_pos_steps;
+        (*stat_neg)[slide] += stat_neg_steps;
+      }
+    }
+  }
+
+  inline static void calcAuc(const framework::ExecutionContext &ctx,
+                             int64_t *stat_pos, int64_t *stat_neg,
+                             int num_thresholds,
+                             framework::Tensor *auc_tensor) {
+    auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
+
     *auc = 0.0f;
 
     double totPos = 0.0;
@@ -96,7 +148,6 @@ class AucKernel : public framework::OpKernel<T> {
       totPos += stat_pos[idx];
       totNeg += stat_neg[idx];
       *auc += trapezoidArea(totNeg, totNegPrev, totPos, totPosPrev);
-
       --idx;
     }
 

paddle/fluid/operators/channel_close_op.cc

-/* 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/channel.h"
-#include "paddle/fluid/framework/op_registry.h"
-
-namespace pf = paddle::framework;
-static constexpr char kChannel[] = "Channel";
-
-namespace paddle {
-namespace operators {
-
-class ChannelCloseOp : public framework::OperatorBase {
- public:
-  ChannelCloseOp(const std::string &type,
-                 const framework::VariableNameMap &inputs,
-                 const framework::VariableNameMap &outputs,
-                 const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &dev_place) const override {
-    auto &inp = *scope.FindVar(Input(kChannel));
-
-    // Get the mutable version of the channel variable and closes it.
-    pf::ChannelHolder *ch = inp.GetMutable<framework::ChannelHolder>();
-    ch->close();
-  }
-};
-
-class ChannelCloseOpOpInferShape : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext *context) const override {
-    PADDLE_ENFORCE(context->HasInput("Channel"),
-                   "The input of ChannelClose op must be set");
-  }
-};
-
-class ChannelCloseOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(kChannel,
-             "The Channel Variable that should be closed by"
-             " the ChannelClose Op.");
-    AddComment(R"DOC(
-Channel Close Operator.
-
-This operator closes an open channel.
-)DOC");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(channel_close, paddle::operators::ChannelCloseOp,
-                  paddle::framework::EmptyGradOpMaker,
-                  paddle::operators::ChannelCloseOpMaker);

paddle/fluid/operators/channel_create_op.cc

-/* 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/channel.h"
-#include "paddle/fluid/framework/lod_rank_table.h"
-#include "paddle/fluid/framework/lod_tensor_array.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/reader.h"
-
-namespace pf = paddle::framework;
-
-static constexpr char kOutput[] = "Out";
-
-namespace paddle {
-namespace operators {
-
-class ChannelCreateOp : public framework::OperatorBase {
- public:
-  ChannelCreateOp(const std::string &type,
-                  const framework::VariableNameMap &inputs,
-                  const framework::VariableNameMap &outputs,
-                  const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &dev_place) const override {
-    auto &out = *scope.FindVar(Output(kOutput));
-
-    // Determine the datatype and capacity of the channel to be created
-    // from the attributes provided.
-    auto dtype =
-        static_cast<framework::proto::VarType::Type>(Attr<int>("data_type"));
-    auto capacity = Attr<int>("capacity");
-
-    // Based on the datatype, create a new channel holder initialized with
-    // the given capacity. When capacity is 0, an unbuffered channel is
-    // created.
-    pf::ChannelHolder *ch = out.GetMutable<framework::ChannelHolder>();
-    if (dtype == framework::proto::VarType::LOD_TENSOR) {
-      ch->Reset<pf::LoDTensor>(capacity);
-    } else if (dtype == framework::proto::VarType::SELECTED_ROWS) {
-      ch->Reset<pf::SelectedRows>(capacity);
-    } else if (dtype == framework::proto::VarType::LOD_RANK_TABLE) {
-      ch->Reset<pf::LoDRankTable>(capacity);
-    } else if (dtype == framework::proto::VarType::LOD_TENSOR_ARRAY) {
-      ch->Reset<pf::LoDTensorArray>(capacity);
-    } else if (dtype == framework::proto::VarType::READER) {
-      ch->Reset<pf::ReaderHolder>(capacity);
-    } else if (dtype == framework::proto::VarType::CHANNEL) {
-      ch->Reset<pf::ChannelHolder>(capacity);
-    } else if (dtype == framework::proto::VarType::BOOL) {
-      ch->Reset<bool>(capacity);
-    } else if (dtype == framework::proto::VarType::INT32) {
-      ch->Reset<int>(capacity);
-    } else if (dtype == framework::proto::VarType::INT64) {
-      ch->Reset<int64_t>(capacity);
-    } else if (dtype == framework::proto::VarType::FP32) {
-      ch->Reset<float>(capacity);
-    } else if (dtype == framework::proto::VarType::FP64) {
-      ch->Reset<double>(capacity);
-    } else {
-      PADDLE_THROW(
-          "Data type %d is not in "
-          "[LOD_TENSOR, SELECTED_ROWS, LOD_RANK_TABLE, LOD_TENSOR_ARRAY, "
-          "READER, CHANNEL, BOOL, INT32, INT64, FP32, FP64]",
-          dtype);
-    }
-  }
-};
-
-class ChannelCreateOpOpInferShape : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext *context) const override {
-    PADDLE_ENFORCE(context->HasOutput(kOutput),
-                   "The output of ChannelCreate op must be set");
-    context->SetOutputDim(kOutput, {1});
-  }
-};
-
-class ChannelCreateOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddOutput(kOutput,
-              "The object of a Channel type created by ChannelCreate Op.");
-    AddAttr<int>("capacity", "The size of the buffer of Channel.")
-        .SetDefault(0);
-    AddAttr<int>("data_type", "The data type of elements inside the Channel.");
-    AddComment(R"DOC(
-Channel Create Operator.
-
-This operator creates an object of the VarType Channel and returns it.
-)DOC");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(channel_create, paddle::operators::ChannelCreateOp,
-                  paddle::framework::EmptyGradOpMaker,
-                  paddle::operators::ChannelCreateOpMaker);

paddle/fluid/operators/channel_recv_op.cc

-/* 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/channel.h"
-#include <paddle/fluid/framework/lod_rank_table.h>
-#include <paddle/fluid/framework/lod_tensor_array.h>
-#include <paddle/fluid/framework/reader.h>
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/var_type.h"
-#include "paddle/fluid/operators/concurrency/channel_util.h"
-#include "paddle/fluid/operators/math/math_function.h"
-
-static constexpr char Channel[] = "Channel";
-static constexpr char Status[] = "Status";
-static constexpr char Out[] = "Out";
-
-namespace paddle {
-namespace operators {
-
-void SetReceiveStatus(const platform::Place &dev_place,
-                      framework::Variable *status_var, bool status) {
-  auto cpu = platform::CPUPlace();
-  auto status_tensor =
-      status_var->GetMutable<framework::LoDTensor>()->mutable_data<bool>({1},
-                                                                         cpu);
-  status_tensor[0] = status;
-}
-
-class ChannelRecvOp : public framework::OperatorBase {
- public:
-  ChannelRecvOp(const std::string &type,
-                const framework::VariableNameMap &inputs,
-                const framework::VariableNameMap &outputs,
-                const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
-  void InferShape(framework::InferShapeContext *ctx) const {
-    PADDLE_ENFORCE(ctx->HasInput(Channel),
-                   "Input(Channel) of ChannelRecvOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput(Out),
-                   "Input(Channel) of ChannelRecvOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput(Status),
-                   "Output(Status) of ChannelRecvOp should not be null.");
-    ctx->SetOutputDim("Status", {1});
-  }
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &dev_place) const override {
-    // Get the channel holder created by channel_create op, passed as input.
-    framework::ChannelHolder *ch =
-        scope.FindVar(Input(Channel))->GetMutable<framework::ChannelHolder>();
-    auto output_var = scope.FindVar(Output(Out));
-    // Receive the data from the channel.
-    bool ok = concurrency::ChannelReceive(ch, output_var);
-
-    // Set the status output of the `ChannelReceive` call.
-    SetReceiveStatus(dev_place, scope.FindVar(Output(Status)), ok);
-  }
-};
-
-class ChannelRecvOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(Channel,
-             "(Channel) A variable which \"receives\" the a value sent"
-             "to it by a channel_send op.")
-        .AsDuplicable();
-    AddOutput(Out,
-              "(Variable) Output Variable that will hold the data received"
-              " from the Channel")
-        .AsDuplicable();
-    AddOutput(Status,
-              "(Tensor) An LoD Tensor that returns a boolean status of the"
-              "result of the receive operation.")
-        .AsDuplicable();
-    AddComment(R"DOC(
-)DOC");
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(channel_recv, paddle::operators::ChannelRecvOp,
-                  paddle::framework::EmptyGradOpMaker,
-                  paddle::operators::ChannelRecvOpMaker);

paddle/fluid/operators/channel_send_op.cc

-/* 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/channel.h"
-#include <paddle/fluid/framework/lod_rank_table.h>
-#include <paddle/fluid/framework/lod_tensor_array.h>
-#include <paddle/fluid/framework/reader.h>
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/var_type.h"
-#include "paddle/fluid/operators/concurrency/channel_util.h"
-#include "paddle/fluid/operators/math/math_function.h"
-
-static constexpr char Channel[] = "Channel";
-static constexpr char X[] = "X";
-
-namespace paddle {
-namespace operators {
-
-class ChannelSendOp : public framework::OperatorBase {
- public:
-  ChannelSendOp(const std::string &type,
-                const framework::VariableNameMap &inputs,
-                const framework::VariableNameMap &outputs,
-                const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
-  void InferShape(framework::InferShapeContext *ctx) const {
-    PADDLE_ENFORCE(ctx->HasInput(Channel),
-                   "Input(Channel) of ChannelSendOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput(X),
-                   "Input(X) of ChannelSendOp should not be null.");
-  }
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &dev_place) const override {
-    // Get the channel holder created by channel_create op, passed as input.
-    framework::ChannelHolder *ch =
-        scope.FindVar(Input(Channel))->GetMutable<framework::ChannelHolder>();
-    auto input_var = scope.FindVar(Input(X));
-
-    // Send the input data through the channel.
-    concurrency::ChannelSend(ch, input_var);
-  }
-};
-
-class ChannelSendOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(Channel,
-             "(Channel) A variable which \"sends\" the passed in value to "
-             "a listening receiver.")
-        .AsDuplicable();
-    AddInput(X, "(Variable) The value which gets sent by the channel.")
-        .AsDuplicable();
-    AddComment(R"DOC(
-)DOC");
-  }
-};
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(channel_send, paddle::operators::ChannelSendOp,
-                  paddle::framework::EmptyGradOpMaker,
-                  paddle::operators::ChannelSendOpMaker);

paddle/fluid/operators/concurrency/CMakeLists.txt

-cc_library(concurrency SRCS channel_util.cc DEPS device_context framework_proto boost eigen3)

paddle/fluid/operators/concurrency/channel_util.cc

-/* 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/concurrency/channel_util.h"
-#include "paddle/fluid/framework/var_type.h"
-
-namespace poc = paddle::operators::concurrency;
-
-void poc::ChannelSend(framework::ChannelHolder *ch, framework::Variable *var) {
-  auto type = framework::ToVarType(var->Type());
-  if (type == framework::proto::VarType_Type_LOD_TENSOR)
-    ch->Send(var->GetMutable<framework::LoDTensor>());
-  else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE)
-    ch->Send(var->GetMutable<framework::LoDRankTable>());
-  else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY)
-    ch->Send(var->GetMutable<framework::LoDTensorArray>());
-  else if (type == framework::proto::VarType_Type_SELECTED_ROWS)
-    ch->Send(var->GetMutable<framework::SelectedRows>());
-  else if (type == framework::proto::VarType_Type_READER)
-    ch->Send(var->GetMutable<framework::ReaderHolder>());
-  else if (type == framework::proto::VarType_Type_CHANNEL)
-    ch->Send(var->GetMutable<framework::ChannelHolder>());
-  else
-    PADDLE_THROW("ChannelSend:Unsupported type");
-}
-
-bool poc::ChannelReceive(framework::ChannelHolder *ch,
-                         framework::Variable *var) {
-  // Get type of channel and use that to call mutable data for Variable
-  auto type = framework::ToVarType(ch->Type());
-  if (type == framework::proto::VarType_Type_LOD_TENSOR)
-    return ch->Receive(var->GetMutable<framework::LoDTensor>());
-  else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE)
-    return ch->Receive(var->GetMutable<framework::LoDRankTable>());
-  else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY)
-    return ch->Receive(var->GetMutable<framework::LoDTensorArray>());
-  else if (type == framework::proto::VarType_Type_SELECTED_ROWS)
-    return ch->Receive(var->GetMutable<framework::SelectedRows>());
-  else if (type == framework::proto::VarType_Type_READER)
-    return ch->Receive(var->GetMutable<framework::ReaderHolder>());
-  else if (type == framework::proto::VarType_Type_CHANNEL)
-    return ch->Receive(var->GetMutable<framework::ChannelHolder>());
-  else
-    PADDLE_THROW("ChannelReceive:Unsupported type");
-}
-
-void poc::ChannelAddToSendQ(framework::ChannelHolder *ch, const void *referrer,
-                            framework::Variable *var,
-                            std::shared_ptr<std::condition_variable_any> cond,
-                            std::function<bool(framework::ChannelAction)> cb) {
-  auto type = framework::ToVarType(var->Type());
-  if (type == framework::proto::VarType_Type_LOD_TENSOR) {
-    ch->AddToSendQ(referrer, var->GetMutable<framework::LoDTensor>(), cond, cb);
-  } else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE) {
-    ch->AddToSendQ(referrer, var->GetMutable<framework::LoDRankTable>(), cond,
-                   cb);
-  } else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY) {
-    ch->AddToSendQ(referrer, var->GetMutable<framework::LoDTensorArray>(), cond,
-                   cb);
-  } else if (type == framework::proto::VarType_Type_SELECTED_ROWS) {
-    ch->AddToSendQ(referrer, var->GetMutable<framework::SelectedRows>(), cond,
-                   cb);
-  } else if (type == framework::proto::VarType_Type_READER) {
-    ch->AddToSendQ(referrer, var->GetMutable<framework::ReaderHolder>(), cond,
-                   cb);
-  } else if (type == framework::proto::VarType_Type_CHANNEL) {
-    ch->AddToSendQ(referrer, var->GetMutable<framework::ChannelHolder>(), cond,
-                   cb);
-  } else {
-    PADDLE_THROW("ChannelAddToSendQ:Unsupported type");
-  }
-}
-
-void poc::ChannelAddToReceiveQ(
-    framework::ChannelHolder *ch, const void *referrer,
-    framework::Variable *var, std::shared_ptr<std::condition_variable_any> cond,
-    std::function<bool(framework::ChannelAction)> cb) {
-  auto type = framework::ToVarType(var->Type());
-  if (type == framework::proto::VarType_Type_LOD_TENSOR) {
-    ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDTensor>(), cond,
-                      cb);
-  } else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE) {
-    ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDRankTable>(),
-                      cond, cb);
-  } else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY) {
-    ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDTensorArray>(),
-                      cond, cb);
-  } else if (type == framework::proto::VarType_Type_SELECTED_ROWS) {
-    ch->AddToReceiveQ(referrer, var->GetMutable<framework::SelectedRows>(),
-                      cond, cb);
-  } else if (type == framework::proto::VarType_Type_READER) {
-    ch->AddToReceiveQ(referrer, var->GetMutable<framework::ReaderHolder>(),
-                      cond, cb);
-  } else if (type == framework::proto::VarType_Type_CHANNEL) {
-    ch->AddToReceiveQ(referrer, var->GetMutable<framework::ChannelHolder>(),
-                      cond, cb);
-  } else {
-    PADDLE_THROW("ChannelAddToReceiveQ:Unsupported type");
-  }
-}

paddle/fluid/operators/concurrency/channel_util.h

-/* 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 "paddle/fluid/framework/channel.h"
-#include "paddle/fluid/framework/variable.h"
-
-namespace paddle {
-namespace operators {
-namespace concurrency {
-
-void ChannelSend(framework::ChannelHolder *ch, framework::Variable *var);
-bool ChannelReceive(framework::ChannelHolder *ch, framework::Variable *var);
-
-void ChannelAddToSendQ(framework::ChannelHolder *ch, const void *referrer,
-                       framework::Variable *var,
-                       std::shared_ptr<std::condition_variable_any> cond,
-                       std::function<bool(framework::ChannelAction)> cb);
-void ChannelAddToReceiveQ(framework::ChannelHolder *ch, const void *referrer,
-                          framework::Variable *var,
-                          std::shared_ptr<std::condition_variable_any> cond,
-                          std::function<bool(framework::ChannelAction)> cb);
-
-}  // namespace concurrency
-}  // namespace operators
-}  // namespace paddle

paddle/fluid/operators/distributed/grpc_client.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include <time.h>
+#include <atomic>
 
 #include <chrono>              // NOLINT
 #include <condition_variable>  // NOLINT

paddle/fluid/operators/distributed/request_handler.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include <time.h>
+#include <condition_variable>  // NOLINT
 
 #include <functional>
 #include <string>

paddle/fluid/operators/distributed/rpc_server.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include <atomic>
 #include <set>
 #include <string>
 #include <thread>  // NOLINT

paddle/fluid/operators/scale_op.cc

@@ -77,8 +77,10 @@ class ScaleOpVarTypeInference : public framework::VarTypeInference {
     auto out_var_name = op_desc.Output("Out").front();
     auto *out_var = block->FindVarRecursive(out_var_name);
 
-    out_var->SetType(in_var.GetType());
-    out_var->SetDataType(in_var.GetDataType());
+    if (in_var_name != out_var_name) {
+      out_var->SetType(in_var.GetType());
+      out_var->SetDataType(in_var.GetDataType());
+    }
   }
 };
 

paddle/fluid/operators/select_op.cc

-/* 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 <memory>
-#include <thread>  // NOLINT
-#include <vector>
-#include "paddle/fluid/framework/channel.h"
-#include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/operators/concurrency/channel_util.h"
-
-#include <boost/tokenizer.hpp>
-
-namespace paddle {
-namespace operators {
-
-static constexpr char kX[] = "X";
-static constexpr char kCaseToExecute[] = "case_to_execute";
-static constexpr char kOutputs[] = "Out";
-
-static constexpr char kCases[] = "cases";
-static constexpr char kCasesBlock[] = "sub_block";
-
-class SelectOp : public framework::OperatorBase {
- public:
-  SelectOp(const std::string &type, const framework::VariableNameMap &inputs,
-           const framework::VariableNameMap &outputs,
-           const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  enum class SelectOpCaseType {
-    DEFAULT = 0,
-    SEND = 1,
-    RECEIVE = 2,
-  };
-
-  struct SelectOpCase {
-    int caseIndex;
-    SelectOpCaseType caseType;
-    std::string channelName;
-    std::string varName;
-
-    SelectOpCase() {}
-
-    SelectOpCase(int caseIndex, SelectOpCaseType caseType,
-                 std::string channelName, std::string varName)
-        : caseIndex(caseIndex),
-          caseType(caseType),
-          channelName(channelName),
-          varName(varName) {}
-  };
-
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &dev_place) const override {
-    std::vector<std::string> casesConfigs =
-        Attr<std::vector<std::string>>(kCases);
-
-    framework::BlockDesc *casesBlock =
-        Attr<framework::BlockDesc *>(kCasesBlock);
-
-    framework::Scope &casesBlockScope = scope.NewScope();
-
-    std::string caseToExecuteVarName = Input(kCaseToExecute);
-    framework::Variable *caseToExecuteVar =
-        casesBlockScope.FindVar(caseToExecuteVarName);
-
-    // Construct cases from "conditional_block_op"(s) in the casesBlock
-    std::vector<std::shared_ptr<SelectOpCase>> cases =
-        ParseAndShuffleCases(&casesConfigs);
-
-    // Get all unique channels involved in select
-    std::set<framework::ChannelHolder *> channelsSet;
-    for (auto c : cases) {
-      if (!c->channelName.empty()) {
-        auto channelVar = scope.FindVar(c->channelName);
-        framework::ChannelHolder *ch =
-            channelVar->GetMutable<framework::ChannelHolder>();
-
-        if (channelsSet.find(ch) == channelsSet.end()) {
-          channelsSet.insert(ch);
-        }
-      }
-    }
-
-    // Order all channels by their pointer address
-    std::vector<framework::ChannelHolder *> channels(channelsSet.begin(),
-                                                     channelsSet.end());
-    std::sort(channels.begin(), channels.end());
-
-    // Poll all cases
-    int32_t caseToExecute = pollCases(&scope, &cases, channels);
-
-    // At this point, the case to execute has already been determined,
-    // so we can proceed with executing the cases block
-    framework::LoDTensor *caseToExecuteTensor =
-        caseToExecuteVar->GetMutable<framework::LoDTensor>();
-    caseToExecuteTensor->data<int32_t>()[0] = caseToExecute;
-
-    // Execute the cases block, only one case will be executed since we set the
-    // case_to_execute value to the index of the case we want to execute
-    framework::Executor executor(dev_place);
-    framework::ProgramDesc *program = casesBlock->Program();
-    executor.Run(*program, &casesBlockScope, casesBlock->ID(),
-                 false /*create_local_scope*/);
-  }
-
-  /**
-   * Goes through all operators in the casesConfigs and processes
-   * "conditional_block" operators.  These operators are mapped to our
-   * SelectOpCase objects.  We randomize the case orders, and set the
-   * default case (if any exists) as the last case)
-   * @param casesBlock
-   * @return
-   */
-  std::vector<std::shared_ptr<SelectOpCase>> ParseAndShuffleCases(
-      std::vector<std::string> *casesConfigs) const {
-    std::vector<std::shared_ptr<SelectOpCase>> cases;
-    std::shared_ptr<SelectOpCase> defaultCase;
-
-    if (casesConfigs != nullptr) {
-      boost::char_delimiters_separator<char> sep(false, ",", "");
-      for (std::vector<std::string>::iterator itr = casesConfigs->begin();
-           itr < casesConfigs->end(); ++itr) {
-        std::string caseConfig = *itr;
-        boost::tokenizer<> tokens(caseConfig, sep);
-
-        boost::tokenizer<>::iterator tok_iter = tokens.begin();
-        PADDLE_ENFORCE(tok_iter != tokens.end(), "Cannot get case index");
-        std::string caseIndexString = *tok_iter;
-        int caseIndex = std::stoi(caseIndexString);
-
-        ++tok_iter;
-        PADDLE_ENFORCE(tok_iter != tokens.end(), "Cannot get case type");
-        std::string caseTypeString = *tok_iter;
-        SelectOpCaseType caseType = (SelectOpCaseType)std::stoi(caseTypeString);
-
-        std::string caseChannel;
-        std::string caseChannelVar;
-
-        ++tok_iter;
-        if (caseType != SelectOpCaseType::DEFAULT) {
-          PADDLE_ENFORCE(tok_iter != tokens.end(), "Cannot get case channel");
-          caseChannel = *tok_iter;
-
-          ++tok_iter;
-          PADDLE_ENFORCE(tok_iter != tokens.end(),
-                         "Cannot get case channel variable");
-          caseChannelVar = *tok_iter;
-        }
-
-        auto c = std::make_shared<SelectOpCase>(caseIndex, caseType,
-                                                caseChannel, caseChannelVar);
-
-        if (caseType == SelectOpCaseType::DEFAULT) {
-          PADDLE_ENFORCE(defaultCase == nullptr,
-                         "Select can only contain one default case.");
-          defaultCase = c;
-        } else {
-          cases.push_back(c);
-        }
-      }
-    }
-
-    // Randomly sort cases, with default case being last
-    std::random_shuffle(cases.begin(), cases.end());
-    if (defaultCase != nullptr) {
-      cases.push_back(defaultCase);
-    }
-
-    return cases;
-  }
-
-  /**
-   * This method will recursively poll the cases and determines if any case
-   * condition is true.
-   * If none of the cases conditions are true (and there is no default case),
-   * then block
-   * the thread.  The thread may be woken up by a channel operation, at which
-   * point we
-   * execute the case.
-   * @param scope
-   * @param cases
-   * @param channels
-   * @return
-   */
-  int32_t pollCases(const framework::Scope *scope,
-                    std::vector<std::shared_ptr<SelectOpCase>> *cases,
-                    std::vector<framework::ChannelHolder *> channels) const {
-    // Lock all involved channels
-    lockChannels(channels);
-
-    std::atomic<int> caseToExecute(-1);
-
-    std::vector<std::shared_ptr<SelectOpCase>>::iterator it = cases->begin();
-    while (it != cases->end()) {
-      std::shared_ptr<SelectOpCase> c = *it;
-
-      auto chVar = scope->FindVar(c->channelName);
-      framework::ChannelHolder *ch =
-          chVar->GetMutable<framework::ChannelHolder>();
-
-      switch (c->caseType) {
-        case SelectOpCaseType::SEND:
-          PADDLE_ENFORCE(!ch->IsClosed(), "Cannot send to a closed channel");
-          if (ch->CanSend()) {
-            // We can send to channel directly, send the data to channel
-            // and execute case
-            auto chVar = scope->FindVar(c->varName);
-            concurrency::ChannelSend(ch, chVar);
-            caseToExecute = c->caseIndex;
-          }
-          break;
-        case SelectOpCaseType::RECEIVE:
-          if (ch->CanReceive()) {
-            // We can receive from channel directly, send the data to channel
-            // and execute case
-            auto chVar = scope->FindVar(c->varName);
-            concurrency::ChannelReceive(ch, chVar);
-            caseToExecute = c->caseIndex;
-          }
-          break;
-        case SelectOpCaseType::DEFAULT:
-          caseToExecute = c->caseIndex;
-          break;
-      }
-
-      if (caseToExecute != -1) {
-        // We found a case to execute, stop looking at other case statements
-        break;
-      }
-
-      ++it;
-    }
-
-    if (caseToExecute == -1) {
-      // None of the cases are eligible to execute, enqueue current thread
-      // into all the sending/receiving queue of each involved channel
-      std::atomic<bool> completed(false);
-      std::recursive_mutex mutex;
-      std::unique_lock<std::recursive_mutex> lock{mutex};
-      // std::condition_variable_any selectCond;
-      auto selectCond = std::make_shared<std::condition_variable_any>();
-
-      std::recursive_mutex callbackMutex;
-      pushThreadOnChannelQueues(scope, cases, selectCond, &caseToExecute,
-                                &completed, &callbackMutex);
-
-      // TODO(thuan): Atomically unlock all channels and sleep current thread
-      unlockChannels(channels);
-      selectCond->wait(lock, [&completed]() { return completed.load(); });
-
-      // Select has been woken up by case operation
-      lockChannels(channels);
-      removeThreadOnChannelQueues(scope, cases);
-
-      if (caseToExecute == -1) {
-        // Recursively poll cases, since we were woken up by a channel close
-        // TODO(thuan): Need to test if this is a valid case
-        unlockChannels(channels);
-        return pollCases(scope, cases, channels);
-      }
-    }
-
-    // At this point, caseToExecute != -1, and we can proceed with executing
-    // the case block
-    unlockChannels(channels);
-
-    return caseToExecute;
-  }
-
-  void lockChannels(std::vector<framework::ChannelHolder *> chs) const {
-    std::vector<framework::ChannelHolder *>::iterator it = chs.begin();
-    while (it != chs.end()) {
-      framework::ChannelHolder *ch = *it;
-      ch->Lock();
-      ++it;
-    }
-  }
-
-  void unlockChannels(std::vector<framework::ChannelHolder *> chs) const {
-    std::vector<framework::ChannelHolder *>::reverse_iterator it = chs.rbegin();
-    while (it != chs.rend()) {
-      framework::ChannelHolder *ch = *it;
-      ch->Unlock();
-      ++it;
-    }
-  }
-
-  void pushThreadOnChannelQueues(
-      const framework::Scope *scope,
-      std::vector<std::shared_ptr<SelectOpCase>> *cases,
-      std::shared_ptr<std::condition_variable_any> rCond,
-      std::atomic<int> *caseToExecute, std::atomic<bool> *completed,
-      std::recursive_mutex *callbackMutex) const {
-    std::vector<std::shared_ptr<SelectOpCase>>::iterator it = cases->begin();
-    while (it != cases->end()) {
-      std::shared_ptr<SelectOpCase> c = *it;
-
-      auto chVar = scope->FindVar(c->channelName);
-      framework::ChannelHolder *ch =
-          chVar->GetMutable<framework::ChannelHolder>();
-
-      std::function<bool(framework::ChannelAction channelAction)> cb =
-          [&caseToExecute, &completed, &callbackMutex,
-           c](framework::ChannelAction channelAction) {
-            std::lock_guard<std::recursive_mutex> lock{*callbackMutex};
-
-            bool canProcess = false;
-            if (!(*completed)) {
-              // If the channel wasn't closed, we set the caseToExecute index
-              // as this current case
-              if (channelAction != framework::ChannelAction::CLOSE) {
-                *caseToExecute = c->caseIndex;
-              }
-              // This will allow our conditional variable to break out of wait
-              *completed = true;
-              canProcess = true;
-            }
-
-            return canProcess;
-          };
-
-      switch (c->caseType) {
-        case SelectOpCaseType::SEND: {
-          auto chOutputVar = scope->FindVar(c->varName);
-          concurrency::ChannelAddToSendQ(ch, this, chOutputVar, rCond, cb);
-          break;
-        }
-        case SelectOpCaseType::RECEIVE: {
-          auto chOutputVar = scope->FindVar(c->varName);
-          concurrency::ChannelAddToReceiveQ(ch, this, chOutputVar, rCond, cb);
-          break;
-        }
-        default:
-          break;
-      }
-      ++it;
-    }
-  }
-
-  void removeThreadOnChannelQueues(
-      const framework::Scope *scope,
-      std::vector<std::shared_ptr<SelectOpCase>> *cases) const {
-    std::vector<std::shared_ptr<SelectOpCase>>::iterator it = cases->begin();
-    while (it != cases->end()) {
-      std::shared_ptr<SelectOpCase> c = *it;
-
-      auto chVar = scope->FindVar(c->channelName);
-      framework::ChannelHolder *ch =
-          chVar->GetMutable<framework::ChannelHolder>();
-      switch (c->caseType) {
-        case SelectOpCaseType::SEND: {
-          ch->RemoveFromSendQ(this);
-          break;
-        }
-        case SelectOpCaseType::RECEIVE: {
-          ch->RemoveFromReceiveQ(this);
-          break;
-        }
-        default:
-          break;
-      }
-      ++it;
-    }
-  }
-};
-
-class SelectOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(kX,
-             "A set of variables, which are required by operators inside the "
-             "cases of Select Op")
-        .AsDuplicable();
-    AddInput(kCaseToExecute,
-             "(Int) The variable the sets the index of the case to execute, "
-             "after evaluating the channels being sent to and received from")
-        .AsDuplicable();
-    AddOutput(kOutputs,
-              "A set of variables, which will be assigned with values "
-              "generated by the operators inside the cases of Select Op.")
-        .AsDuplicable();
-    AddAttr<std::vector<std::string>>(kCases,
-                                      "(String vector) Serialized list of"
-                                      "all cases in the select op. Each"
-                                      "case is serialized as: "
-                                      "'<index>,<type>,<channel>,<value>'"
-                                      "where type is 0 for default, 1 for"
-                                      "send, and 2 for receive"
-                                      "No channel and values are needed for"
-                                      "default cases.");
-    AddAttr<framework::BlockDesc *>(kCasesBlock,
-                                    "The cases block inside select_op");
-    AddComment(R"DOC(
-)DOC");
-  }
-};
-
-// TODO(thuan): Implement Gradient Operator for SELECT_OP
-
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(select, paddle::operators::SelectOp,
-                  paddle::framework::EmptyGradOpMaker,
-                  paddle::operators::SelectOpMaker);

paddle/fluid/operators/sum_op.h

@@ -32,7 +32,7 @@ class SumKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &context) const override {
     auto in_vars = context.MultiInputVar("X");
-    int N = in_vars.size();
+    size_t in_num = in_vars.size();
     auto out_var = context.OutputVar("Out");
 
     bool in_place = out_var == in_vars[0];
@@ -53,7 +53,7 @@ class SumKernel : public framework::OpKernel<T> {
       auto &place =
           *context.template device_context<DeviceContext>().eigen_device();
       // If in_place, just skip the first tensor
-      for (int i = in_place ? 1 : 0; i < N; i++) {
+      for (size_t i = in_place ? 1 : 0; i < in_num; i++) {
         if (in_vars[i]->IsType<framework::LoDTensor>()) {
           auto &in_t = in_vars[i]->Get<framework::LoDTensor>();
           if (in_t.numel() == 0) {
@@ -101,13 +101,13 @@ class SumKernel : public framework::OpKernel<T> {
 
       // Runtime InferShape
       size_t first_dim = 0;
-      for (int i = 0; i < N; i++) {
+      for (size_t i = 0; i < in_num; i++) {
         auto &sel_row = get_selected_row(i);
         first_dim += sel_row.rows().size();
       }
 
       std::vector<int64_t> in_dim;
-      for (int i = 0; i < N; i++) {
+      for (size_t i = 0; i < in_num; i++) {
         auto &sel_row = get_selected_row(i);
         if (sel_row.rows().size() > 0) {
           in_dim = framework::vectorize(sel_row.value().dims());
@@ -116,7 +116,8 @@ class SumKernel : public framework::OpKernel<T> {
       }
       if (in_dim.empty()) {
         VLOG(3) << "WARNING: all the inputs are empty";
-        in_dim = framework::vectorize(get_selected_row(N - 1).value().dims());
+        in_dim =
+            framework::vectorize(get_selected_row(in_num - 1).value().dims());
       } else {
         in_dim[0] = static_cast<int64_t>(first_dim);
       }
@@ -133,7 +134,7 @@ class SumKernel : public framework::OpKernel<T> {
       math::SelectedRowsAddTo<DeviceContext, T> functor;
 
       int64_t offset = 0;
-      for (int i = 0; i < N; i++) {
+      for (size_t i = 0; i < in_num; i++) {
         auto &sel_row = get_selected_row(i);
         if (sel_row.rows().size() == 0) {
           continue;

paddle/fluid/pybind/protobuf.cc

@@ -214,7 +214,6 @@ void BindVarDsec(pybind11::module *m) {
       .def("set_shapes", &pd::VarDesc::SetShapes)
       .def("set_dtype", &pd::VarDesc::SetDataType)
       .def("set_dtypes", &pd::VarDesc::SetDataTypes)
-      .def("set_capacity", &pd::VarDesc::SetCapacity)
       .def("shape", &pd::VarDesc::GetShape,
            pybind11::return_value_policy::reference)
       .def("shapes", &pd::VarDesc::GetShapes,
@@ -251,7 +250,6 @@ void BindVarDsec(pybind11::module *m) {
       .value("STEP_SCOPES", pd::proto::VarType::STEP_SCOPES)
       .value("LOD_RANK_TABLE", pd::proto::VarType::LOD_RANK_TABLE)
       .value("LOD_TENSOR_ARRAY", pd::proto::VarType::LOD_TENSOR_ARRAY)
-      .value("CHANNEL", pd::proto::VarType::CHANNEL)
       .value("PLACE_LIST", pd::proto::VarType::PLACE_LIST)
       .value("READER", pd::proto::VarType::READER)
       .value("RAW", pd::proto::VarType::RAW);

paddle/fluid/pybind/pybind.cc

@@ -21,7 +21,6 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 
-#include "paddle/fluid/framework/channel.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/framework/framework.pb.h"

paddle/legacy/trainer/tests/CMakeLists.txt

@@ -16,7 +16,11 @@ endfunction()
 trainer_test(test_Compare)
 trainer_test(test_PyDataProviderWrapper)
 trainer_test(test_recurrent_machine_generation)
-trainer_test(test_Trainer)
+if(NOT APPLE)
+  trainer_test(test_Trainer)
+else()
+  message(WARNING "These tests has been disabled in OSX for random fail: \n test_Trainer") 
+endif()
 
 ############### test_TrainerOnePass ##########################
 if(WITH_PYTHON)

paddle/scripts/paddle_build.sh

@@ -70,8 +70,8 @@ function cmake_gen() {
     PYTHON_FLAGS=""
     SYSTEM=`uname -s`
     if [ "$SYSTEM" == "Darwin" ]; then
+        echo "using python abi: $1"
         if [[ "$1" == "cp27-cp27m" ]] || [[ "$1" == "" ]]; then
-            echo "using python abi: $1"
             if [ -d "/Library/Frameworks/Python.framework/Versions/2.7" ]; then
                 export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
                 export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
@@ -82,7 +82,18 @@ function cmake_gen() {
             else
                 exit 1
             fi
-        # TODO: qiyang add python3 part here 
+        elif [ "$1" == "cp35-cp35m" ]; then
+            if [ -d "/Library/Frameworks/Python.framework/Versions/3.5" ]; then
+                export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
+                export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
+                export PATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/:${PATH}
+                PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/python3
+            -DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.5/include/python3.5m/
+            -DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/libpython3.5m.dylib"
+                WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
+            else
+                exit 1
+            fi
         fi
     else 
         if [ "$1" != "" ]; then
@@ -384,7 +395,7 @@ EOF
         ctest --output-on-failure -j $1     
         # make install should also be test when unittest 
         make install -j 8
-        pip install /usr/local/opt/paddle/share/wheels/*.whl
+        pip install ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
         if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
             paddle version
         fi

python/paddle/dataset/common.py

@@ -77,13 +77,14 @@ def download(url, module_name, md5sum, save_name=None):
     retry_limit = 3
     while not (os.path.exists(filename) and md5file(filename) == md5sum):
         if os.path.exists(filename):
-            print("file md5", md5file(filename), md5sum)
+            sys.stderr.write("file %s  md5 %s" % (md5file(filename), md5sum))
         if retry < retry_limit:
             retry += 1
         else:
             raise RuntimeError("Cannot download {0} within retry limit {1}".
                                format(url, retry_limit))
-        print("Cache file %s not found, downloading %s" % (filename, url))
+        sys.stderr.write("Cache file %s not found, downloading %s" %
+                         (filename, url))
         r = requests.get(url, stream=True)
         total_length = r.headers.get('content-length')
 
@@ -100,10 +101,11 @@ def download(url, module_name, md5sum, save_name=None):
                     dl += len(data)
                     f.write(data)
                     done = int(50 * dl / total_length)
-                    sys.stdout.write("\r[%s%s]" % ('=' * done,
+                    sys.stderr.write("\r[%s%s]" % ('=' * done,
                                                    ' ' * (50 - done)))
                     sys.stdout.flush()
-
+    sys.stderr.write("\n")
+    sys.stdout.flush()
     return filename
 
 

python/paddle/fluid/clip.py

@@ -271,7 +271,8 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
                     "All parameters' 'clip_norm' of a same group should be the same"
                 )
 
-        local_norm_var = layers.reduce_sum(input=layers.pow(x=grad, factor=2.0))
+        square = grad * grad
+        local_norm_var = layers.cast(layers.reduce_sum(input=square), 'float64')
         context[self.group_name].append(local_norm_var)
 
         self.context = context
@@ -281,6 +282,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         if group_scale_name not in self.context:
             group_norm_var = layers.sums(input=self.context[self.group_name])
             group_norm_var = layers.sqrt(x=group_norm_var)
+            group_norm_var = layers.cast(group_norm_var, 'float32')
             clip_var = self.context[self.group_name + "_clip"]
             group_scale_var = layers.elementwise_div(
                 x=clip_var,

python/paddle/fluid/concurrency.py

-#   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
-
-from .layers.control_flow import BlockGuard, equal
-from .framework import Operator
-from .layer_helper import LayerHelper, unique_name
-from .layers import fill_constant
-from . import core
-
-__all__ = [
-    'make_channel', 'channel_send', 'channel_recv', 'channel_close', 'Select'
-]
-
-
-class Go(BlockGuard):
-    def __init__(self, name=None):
-        self.helper = LayerHelper("go", name=name)
-        super(Go, self).__init__(self.helper.main_program)
-
-    def __enter__(self):
-        super(Go, self).__enter__()
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if exc_type is not None:
-            return False
-        self._construct_go_op()
-        return super(Go, self).__exit__(exc_type, exc_val, exc_tb)
-
-    def _construct_go_op(self):
-        main_program = self.helper.main_program
-        go_block = main_program.current_block()
-        parent_block = main_program.block(main_program.current_block()
-                                          .parent_idx)
-
-        inner_outputs = set()
-        x_name_list = set()
-        for op in go_block.ops:
-            # Iterate over all operators, get all the inputs
-            # and add as input to the Go operator.
-            for iname in op.input_names:
-                for in_var_name in op.input(iname):
-                    if in_var_name not in inner_outputs:
-                        x_name_list.add(in_var_name)
-
-            for oname in op.output_names:
-                for out_var_name in op.output(oname):
-                    inner_outputs.add(out_var_name)
-
-        # Iterate over all operators , get all the outputs
-        # add to the output list of Go operator only if
-        # they exist in the parent block.
-        out_vars = []
-        for inner_out_name in inner_outputs:
-            if inner_out_name in parent_block.vars:
-                out_vars.append(parent_block.var(inner_out_name))
-
-        parent_block.append_op(
-            type='go',
-            inputs={
-                'X': [
-                    parent_block._var_recursive(x_name)
-                    for x_name in x_name_list
-                ]
-            },
-            outputs={},
-            attrs={'sub_block': go_block})
-
-
-class SelectCase(object):
-    DEFAULT = 0
-    SEND = 1
-    RECEIVE = 2
-
-    def __init__(self,
-                 select,
-                 case_idx,
-                 case_to_execute,
-                 channel_action_fn=None,
-                 channel=None,
-                 value=None,
-                 is_copy=False):
-        self.select = select
-        self.helper = LayerHelper('conditional_block')
-        self.main_program = self.helper.main_program
-        self.is_scalar_condition = True
-
-        self.case_to_execute = case_to_execute
-        self.idx = case_idx
-
-        # Since we aren't going to use the `channel_send` or `channel_recv`
-        # functions directly, we just need to capture the name.
-        self.action = (self.SEND
-                       if channel_action_fn.__name__ == ('channel_send') else
-                       self.RECEIVE) if channel_action_fn else self.DEFAULT
-
-        X = value
-        if self.action == self.SEND and is_copy:
-            # We create of copy of the data we want to send
-            copied_X = self.select.parent_block.create_var(
-                name=unique_name.generate(value.name + '_copy'),
-                type=value.type,
-                dtype=value.dtype,
-                shape=value.shape,
-                lod_level=value.lod_level,
-                capacity=value.capacity
-                if hasattr(value, 'capacity') else None, )
-
-            self.select.parent_block.append_op(
-                type="assign", inputs={"X": value}, outputs={"Out": copied_X})
-            X = copied_X
-
-        self.value = X
-        self.channel = channel
-
-    def __enter__(self):
-        self.block = self.main_program._create_block()
-
-    def construct_op(self):
-        main_program = self.helper.main_program
-        cases_block = main_program.current_block()
-
-        inner_outputs = set()
-        input_set = set()
-        params = set()
-
-        for op in self.block.ops:
-            # Iterate over all operators, get all the inputs
-            # and add as input to the SelectCase operator.
-            for iname in op.input_names:
-                for in_var_name in op.input(iname):
-                    if in_var_name not in inner_outputs:
-                        input_set.add(in_var_name)
-
-            for oname in op.output_names:
-                for out_var_name in op.output(oname):
-                    inner_outputs.add(out_var_name)
-
-        param_list = [
-            cases_block.var(each_name) for each_name in params
-            if each_name not in input_set
-        ]
-
-        # Iterate over all operators, get all the outputs
-        # add to the output list of SelectCase operator only if
-        # they exist in the parent block.
-        out_vars = []
-        for inner_out_name in inner_outputs:
-            if inner_out_name in cases_block.vars:
-                out_vars.append(cases_block.var(inner_out_name))
-
-        # First, create an op that will determine whether or not this is the
-        # conditional variable to execute.
-        should_execute_block = equal(
-            fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.INT32, value=self.idx),
-            self.case_to_execute)
-
-        step_scope = cases_block.create_var(
-            type=core.VarDesc.VarType.STEP_SCOPES)
-
-        cases_block.append_op(
-            type='conditional_block',
-            inputs={'X': [should_execute_block],
-                    'Params': param_list},
-            outputs={'Out': out_vars,
-                     'Scope': [step_scope]},
-            attrs={
-                'sub_block': self.block,
-                'is_scalar_condition': self.is_scalar_condition
-            })
-
-        return '%s,%s,%s,%s' % (self.idx, self.action, self.channel.name
-                                if self.channel else '', self.value.name
-                                if self.value else '')
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.main_program._rollback()
-        if exc_type is not None:
-            return False  # re-raise exception
-        return True
-
-
-class Select(BlockGuard):
-    def __init__(self, name=None):
-        self.helper = LayerHelper('select', name=name)
-        self.parent_block = self.helper.main_program.current_block()
-        self.cases = []
-
-        super(Select, self).__init__(self.helper.main_program)
-        self.case_to_execute = fill_constant(
-            shape=[1], dtype=core.VarDesc.VarType.INT32, value=-1)
-
-    def __enter__(self):
-        super(Select, self).__enter__()
-        return self
-
-    def case(self, channel_action_fn, channel, value, is_copy=False):
-        """Create a new block for this condition.
-        """
-        select_case = SelectCase(self,
-                                 len(self.cases), self.case_to_execute,
-                                 channel_action_fn, channel, value, is_copy)
-
-        self.cases.append(select_case)
-
-        return select_case
-
-    def default(self):
-        """Create a default case block for this condition.
-        """
-        default_case = SelectCase(self, len(self.cases), self.case_to_execute)
-
-        self.cases.append(default_case)
-
-        return default_case
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if exc_type is not None:
-            return False
-
-        # Create a select op and another block to wrap its
-        # case blocks.
-        select_block = self.helper.main_program.current_block()
-        parent_block = self.helper.main_program.block(select_block.parent_idx)
-
-        # Construct each case op, inside the newly created select block.
-        serialized_cases = []
-        for case in self.cases:
-            serialized_cases.append(case.construct_op())
-
-        intermediate = set()
-        params = set()
-
-        for case_block in select_block.ops:
-            if case_block.attrs and 'sub_block' in case_block.attrs:
-                for each_op in case_block.attrs['sub_block'].ops:
-                    assert isinstance(each_op, Operator)
-                    for iname in each_op.input_names:
-                        for in_var_name in each_op.input(iname):
-                            if in_var_name not in intermediate:
-                                params.add(in_var_name)
-
-                    for oname in each_op.output_names:
-                        for out_var_name in each_op.output(oname):
-                            intermediate.add(out_var_name)
-
-        out_list = [
-            parent_block.var(var_name) for var_name in parent_block.vars
-            if var_name in intermediate
-        ]
-
-        X = [select_block._var_recursive(x_name) for x_name in params]
-
-        # Needs to be used by `equal` inside the cases block.
-        X.append(self.case_to_execute)
-
-        # Construct the select op.
-        parent_block.append_op(
-            type='select',
-            inputs={'X': X,
-                    'case_to_execute': self.case_to_execute},
-            attrs={'sub_block': select_block,
-                   'cases': serialized_cases},
-            outputs={'Out': out_list})
-
-        return super(Select, self).__exit__(exc_type, exc_val, exc_tb)
-
-
-def make_channel(dtype, capacity=0):
-    """
-    Helps implementation of a concurrent program by creating a "channel" of
-    a defined data type. Channels allow for the passing of data in
-    concurrent scenarios - such as when using threads to divide computation.
-    Channels can be used to "send" and "receive" such data concurrently.
-
-    There are two kinds of channels: unbuffered and buffered. Unbuffered
-    channels have no capacity - and thus, block on send and only unblock only
-    once what they have sent has been received.
-
-    On the other hand, buffered channels are initialized with a capacity -
-    and do not block on sends.
-
-    Use this method in combination with `channel_send`, `channel_recv`,
-    `channel_close`, and `Go` to design a concurrent Paddle program.
-
-    Args:
-        dtype (ParamAttr|string): Data type of the data sent in the channel.
-        This data type should be the string name of a numpy data type.
-        capacity (ParamAttr|int): Size of the channel. Defaults to 0 for
-        to create an unbuffered channel.
-
-    Returns:
-        Variable: The channel variable that can be used to send an receive data
-                  of the defined dtype.
-
-    Examples:
-        .. code-block:: python
-
-          ch = fluid.make_channel(dtype='int32', capacity=10)
-          ...
-          # Code to execute in a Go block, which receives the channel data.
-          fluid.channel_send(ch, 100)
-          fluid.channel_close(ch)
-    """
-    helper = LayerHelper('channel_create', **locals())
-    main_program = helper.main_program
-    make_channel_block = main_program.current_block()
-
-    # Make a channel variable (using the channel data type) and make sure it
-    # persists into the global scope.
-    channel = helper.create_variable(
-        name=unique_name.generate('channel'),
-        type=core.VarDesc.VarType.CHANNEL,
-        persistable=True)
-
-    create_channel_op = make_channel_block.append_op(
-        type="channel_create",
-        outputs={"Out": channel},
-        attrs={"data_type": dtype,
-               "capacity": capacity})
-
-    return channel
-
-
-def channel_send(channel, value, is_copy=False):
-    """
-    Sends a value through a channel variable. Used by an unbuffered or buffered
-    channel to pass data from within or to a concurrent Go block, where
-    `channel_recv` to used to get the passed value.
-
-    Args:
-        channel (Variable|Channel): Channel variable created using
-        `make_channel`.
-        value (Variable): Value to send to channel
-        is_copy (bool): Copy data while channel send. If False, then data
-        is moved. The input cannot be used after move. (default False)
-    Returns:
-        Variable: The boolean status on whether or not the channel
-                  successfully sent the passed value.
-
-    Examples:
-        .. code-block:: python
-
-          ch = fluid.make_channel(dtype='int32', capacity=10)
-          ...
-          # Code to execute in a Go block, which receives the channel data.
-          fluid.channel_send(ch, 100)
-    """
-    helper = LayerHelper('channel_send', **locals())
-    main_program = helper.main_program
-    channel_send_block = main_program.current_block()
-
-    X = value
-
-    if is_copy:
-        copied_X = helper.create_variable(
-            name=unique_name.generate(value.name + '_copy'),
-            type=value.type,
-            dtype=value.dtype,
-            shape=value.shape,
-            lod_level=value.lod_level,
-            capacity=value.capacity if hasattr(value, 'capacity') else None)
-
-        assign_op = channel_send_block.append_op(
-            type="assign", inputs={"X": value}, outputs={"Out": copied_X})
-        X = copied_X
-
-    channel_send_block.append_op(
-        type="channel_send", inputs={
-            "Channel": channel,
-            "X": X,
-        })
-
-
-def channel_recv(channel, return_value):
-    """
-    Receives a value through a channel variable. Used by an unbuffered or
-    buffered channel within a concurrent Go block to get data from originally
-    sent using `channel_send`, or from outside such a block where
-    `channel_send` is used to send the value.
-
-    Args:
-        channel (Variable|Channel): Channel variable created using
-        `make_channel`.
-        return_value (Variable): Variable to set as a result of running channel_recv_op
-
-    Returns:
-        Variable: The received value from the channel.
-        Variable: The boolean status on whether or not the channel
-                  successfully received the passed value.
-
-    Examples:
-        .. code-block:: python
-
-          ch = fluid.make_channel(dtype='int32', capacity=10)
-          with fluid.Go():
-            returned_value, return_status = fluid.channel_recv(ch, 'int32')
-
-          # Code to send data through the channel.
-    """
-    helper = LayerHelper('channel_recv', **locals())
-    main_program = helper.main_program
-    channel_recv_block = main_program.current_block()
-
-    status = helper.create_variable(
-        name=unique_name.generate('status'),
-        type=core.VarDesc.VarType.LOD_TENSOR,
-        dtype=core.VarDesc.VarType.BOOL)
-
-    channel_recv_op = channel_recv_block.append_op(
-        type="channel_recv",
-        inputs={"Channel": channel},
-        outputs={"Out": return_value,
-                 "Status": status})
-
-    return return_value, status
-
-
-def channel_close(channel):
-    """
-    Closes a channel created using `make_channel`.
-
-    Args:
-        channel (Variable|Channel): Channel variable created using
-        `make_channel`.
-
-    Examples:
-        .. code-block:: python
-
-          ch = fluid.make_channel(dtype='int32', capacity=10)
-          ...
-          # Code to receive and send data through a channel
-          ...
-          fluid.channel_close(ch)
-    """
-    helper = LayerHelper('channel_close', **locals())
-    main_program = helper.main_program
-    channel_close_block = main_program.current_block()
-
-    channel_close_op = channel_close_block.append_op(
-        type="channel_close", inputs={"Channel": channel})

python/paddle/fluid/framework.py

@@ -537,8 +537,7 @@ class Operator(object):
         'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
         'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
         'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine',
-        'ncclInit', 'channel_create', 'channel_close', 'channel_send',
-        'channel_recv', 'select', 'checkpoint_notify', 'gen_nccl_id'
+        'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
     }
 
     def __init__(self,

python/paddle/fluid/layers/control_flow.py

@@ -21,7 +21,7 @@ from .. import core
 from ..framework import Program, Variable, Operator
 from ..layer_helper import LayerHelper, unique_name
 from ..initializer import force_init_on_cpu
-from .ops import logical_and, logical_not, logical_or
+from .nn import logical_and, logical_not, logical_or
 import numpy
 import warnings
 import six

python/paddle/fluid/layers/detection.py

@@ -42,19 +42,11 @@ __all__ = [
     'roi_perspective_transform',
     'generate_proposal_labels',
     'generate_proposals',
-]
-
-__auto__ = [
     'iou_similarity',
     'box_coder',
     'polygon_box_transform',
 ]
 
-__all__ += __auto__
-
-for _OP in set(__auto__):
-    globals()[_OP] = generate_layer_fn(_OP)
-
 
 def rpn_target_assign(bbox_pred,
                       cls_logits,
@@ -308,6 +300,101 @@ def detection_output(loc,
     return nmsed_outs
 
 
+@templatedoc()
+def iou_similarity(x, y, name=None):
+    """
+    ${comment}
+
+    Args:
+        x(${x_type}): ${x_comment}
+        y(${y_type}): ${y_comment}
+
+    Returns:
+        out(${out_type}): ${out_comment}
+    """
+    helper = LayerHelper("iou_similarity", **locals())
+    if name is None:
+        out = helper.create_tmp_variable(dtype=x.dtype)
+    else:
+        out = helper.create_variable(
+            name=name, dtype=x.dtype, persistable=False)
+
+    helper.append_op(
+        type="iou_similarity",
+        inputs={"X": x,
+                "Y": y},
+        attrs={},
+        outputs={"Out": out})
+    return out
+
+
+@templatedoc()
+def box_coder(prior_box,
+              prior_box_var,
+              target_box,
+              code_type="encode_center_size",
+              box_normalized=True,
+              name=None):
+    """
+    ${comment}
+
+    Args:
+        prior_box(${prior_box_type}): ${prior_box_comment}
+        prior_box_var(${prior_box_var_type}): ${prior_box_var_comment}
+        target_box(${target_box_type}): ${target_box_comment}
+        code_type(${code_type_type}): ${code_type_comment}
+        box_normalized(${box_normalized_type}): ${box_normalized_comment}
+
+    Returns:
+        output_box(${output_box_type}): ${output_box_comment}
+    """
+    helper = LayerHelper("box_coder", **locals())
+
+    if name is None:
+        output_box = helper.create_tmp_variable(dtype=prior_box.dtype)
+    else:
+        output_box = helper.create_variable(
+            name=name, dtype=prior_box.dtype, persistable=False)
+
+    helper.append_op(
+        type="box_coder",
+        inputs={
+            "PriorBox": prior_box,
+            "PriorBoxVar": prior_box_var,
+            "TargetBox": target_box
+        },
+        attrs={"code_type": code_type,
+               "box_normalized": box_normalized},
+        outputs={"OutputBox": output_box})
+    return output_box
+
+
+@templatedoc()
+def polygon_box_transform(input, name=None):
+    """
+    ${comment}
+
+    Args:
+        input(${input_type}): ${input_comment}
+
+    Returns:
+        output(${output_type}): ${output_comment}
+    """
+    helper = LayerHelper("polygon_box_transform", **locals())
+    if name is None:
+        output = helper.create_tmp_variable(dtype=input.dtype)
+    else:
+        output = helper.create_variable(
+            name=name, dtype=prior_box.input, persistable=False)
+
+    helper.append_op(
+        type="polygon_box_transform",
+        inputs={"Input": input},
+        attrs={},
+        outputs={"Output": output})
+    return output
+
+
 @templatedoc()
 def detection_map(detect_res,
                   label,

python/paddle/fluid/layers/metric_op.py

@@ -78,7 +78,12 @@ def accuracy(input, label, k=1, correct=None, total=None):
     return acc_out
 
 
-def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
+def auc(input,
+        label,
+        curve='ROC',
+        num_thresholds=2**12 - 1,
+        topk=1,
+        slide_steps=1):
     """
     **Area Under the Curve (AUC) Layer**
 
@@ -105,6 +110,8 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
         num_thresholds(int): The number of thresholds to use when discretizing
                              the roc curve. Default 200.
         topk(int): only topk number of prediction output will be used for auc.
+        slide_steps: when calc batch auc, we can not only use step currently but the previous steps can be used. slide_steps=1 means use the current step, slide_steps=3 means use current step and the previous second steps, slide_steps=0 use all of the steps.
+
 
     Returns:
         Variable: A scalar representing the current AUC.
@@ -120,16 +127,48 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
     auc_out = helper.create_tmp_variable(dtype="float64")
     batch_auc_out = helper.create_tmp_variable(dtype="float64")
     # make tp, tn, fp, fn persistable, so that can accumulate all batches.
+
+    # for batch auc
+    batch_stat_pos = helper.create_global_variable(
+        persistable=True,
+        dtype='int64',
+        shape=[slide_steps, num_thresholds + 1])
+    batch_stat_neg = helper.create_global_variable(
+        persistable=True,
+        dtype='int64',
+        shape=[slide_steps, num_thresholds + 1])
+
+    # for global auc
     stat_pos = helper.create_global_variable(
-        persistable=True, dtype='int64', shape=[num_thresholds + 1])
+        persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
     stat_neg = helper.create_global_variable(
-        persistable=True, dtype='int64', shape=[num_thresholds + 1])
+        persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
 
-    for var in [stat_pos, stat_neg]:
+    for var in [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg]:
         helper.set_variable_initializer(
             var, Constant(
                 value=0.0, force_cpu=True))
 
+    # Batch AUC
+    helper.append_op(
+        type="auc",
+        inputs={
+            "Predict": [input],
+            "Label": [label],
+            "StatPos": [batch_stat_pos],
+            "StatNeg": [batch_stat_neg]
+        },
+        attrs={
+            "curve": curve,
+            "num_thresholds": num_thresholds,
+            "slide_steps": slide_steps
+        },
+        outputs={
+            "AUC": [batch_auc_out],
+            "StatPosOut": [batch_stat_pos],
+            "StatNegOut": [batch_stat_neg]
+        })
+    # Global AUC
     helper.append_op(
         type="auc",
         inputs={
@@ -138,12 +177,16 @@ def auc(input, label, curve='ROC', num_thresholds=2**12 - 1, topk=1):
             "StatPos": [stat_pos],
             "StatNeg": [stat_neg]
         },
-        attrs={"curve": curve,
-               "num_thresholds": num_thresholds},
+        attrs={
+            "curve": curve,
+            "num_thresholds": num_thresholds,
+            "slide_steps": 0
+        },
         outputs={
             "AUC": [auc_out],
-            "BatchAUC": [batch_auc_out],
             "StatPosOut": [stat_pos],
             "StatNegOut": [stat_neg]
         })
-    return auc_out, batch_auc_out, [stat_pos, stat_neg]
+    return auc_out, batch_auc_out, [
+        batch_stat_pos, batch_stat_neg, stat_pos, stat_neg
+    ]

python/paddle/fluid/layers/ops.py

@@ -35,18 +35,7 @@ __activations_noattr__ = [
     'softsign',
 ]
 
-__all__ = [
-    'mean',
-    'mul',
-    'sigmoid_cross_entropy_with_logits',
-    'clip',
-    'clip_by_norm',
-    'logical_and',
-    'logical_or',
-    'logical_xor',
-    'logical_not',
-    'maxout',
-]
+__all__ = []
 
 for _OP in set(__all__):
     globals()[_OP] = generate_layer_fn(_OP)
@@ -56,6 +45,8 @@ for _OP in set(__all__):
 # e.g.: test_program_code.py, test_dist_train.py
 globals()['_scale'] = generate_layer_fn('scale')
 
+globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
+
 __all__ += __activations_noattr__
 
 for _OP in set(__activations_noattr__):

python/paddle/fluid/nets.py

@@ -40,8 +40,7 @@ def simple_img_conv_pool(input,
                          param_attr=None,
                          bias_attr=None,
                          act=None,
-                         use_cudnn=True,
-                         use_mkldnn=False):
+                         use_cudnn=True):
     """
     The simple_img_conv_pool is composed with one Convolution2d and one Pool2d.
 
@@ -84,8 +83,6 @@ def simple_img_conv_pool(input,
         act (str): Activation type for Conv2d. Default: None
         use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
             library is installed. Default: True
-        use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
-            with mkldnn library. Default: False
 
     Return:
         Variable: The result of input after Convolution2d and Pool2d.
@@ -112,8 +109,7 @@ def simple_img_conv_pool(input,
         param_attr=param_attr,
         bias_attr=bias_attr,
         act=act,
-        use_cudnn=use_cudnn,
-        use_mkldnn=use_mkldnn)
+        use_cudnn=use_cudnn)
 
     pool_out = layers.pool2d(
         input=conv_out,
@@ -122,8 +118,7 @@ def simple_img_conv_pool(input,
         pool_stride=pool_stride,
         pool_padding=pool_padding,
         global_pooling=global_pooling,
-        use_cudnn=use_cudnn,
-        use_mkldnn=use_mkldnn)
+        use_cudnn=use_cudnn)
     return pool_out
 
 
@@ -138,8 +133,7 @@ def img_conv_group(input,
                    conv_batchnorm_drop_rate=0.0,
                    pool_stride=1,
                    pool_type="max",
-                   use_cudnn=True,
-                   use_mkldnn=False):
+                   use_cudnn=True):
     """
     The Image Convolution Group is composed of Convolution2d, BatchNorm, DropOut,
     and Pool2d. According to the input arguments, img_conv_group will do serials of
@@ -177,8 +171,6 @@ def img_conv_group(input,
             average-pooling. Default :math:`max`.
         use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
             library is installed. Default: True
-        use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
-            with mkldnn library. Default: False
 
     Return:
         Variable: The final result after serial computation using Convolution2d,
@@ -226,8 +218,7 @@ def img_conv_group(input,
             padding=conv_padding[i],
             param_attr=param_attr[i],
             act=local_conv_act,
-            use_cudnn=use_cudnn,
-            use_mkldnn=use_mkldnn)
+            use_cudnn=use_cudnn)
 
         if conv_with_batchnorm[i]:
             tmp = layers.batch_norm(input=tmp, act=conv_act, in_place=True)
@@ -240,8 +231,7 @@ def img_conv_group(input,
         pool_size=pool_size,
         pool_type=pool_type,
         pool_stride=pool_stride,
-        use_cudnn=use_cudnn,
-        use_mkldnn=use_mkldnn)
+        use_cudnn=use_cudnn)
     return pool_out
 
 

python/paddle/fluid/optimizer.py

@@ -26,6 +26,7 @@ from .layer_helper import LayerHelper
 from .regularizer import append_regularization_ops
 from .clip import append_gradient_clip_ops, error_clip_callback
 from contextlib import contextmanager
+from .layers import ops
 
 __all__ = [
     'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
@@ -1301,7 +1302,7 @@ class ModelAverage(Optimizer):
             x=tmp, dtype='float32' if self._dtype == None else self._dtype)
         sum = layers.cast(
             x=sum, dtype='float32' if self._dtype == None else self._dtype)
-        layers.elementwise_div(x=sum, y=tmp, out=param)
+        ops._elementwise_div(x=sum, y=tmp, out=param)
 
     def _add_average_restore_op(self, block, param_grad):
         param = block._clone_variable(param_grad[0])

python/paddle/fluid/tests/book/high-level-api/recognize_digits/CMakeLists.txt

@@ -2,6 +2,16 @@ file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
 # default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()
+if(NOT APPLE)
+    foreach(src ${TEST_OPS})
+        py_test(${src} SRCS ${src}.py)
+    endforeach()
+else()
+    foreach(src ${TEST_OPS})
+        if(${src} STREQUAL "test_recognize_digits_conv")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        else()
+            py_test(${src} SRCS ${src}.py)
+        endif()
+    endforeach()
+endif()

python/paddle/fluid/tests/no_test_concurrency.py

-#   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 unittest
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-from paddle.fluid import framework, unique_name, layer_helper
-from paddle.fluid.executor import Executor
-from paddle.fluid.layers import fill_constant, assign, While, elementwise_add, Print
-
-
-class TestRoutineOp(unittest.TestCase):
-    def test_simple_routine(self):
-        ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
-
-        # Create LOD_TENSOR<INT64> and put it into the scope.  This placeholder
-        # variable will be filled in and returned by fluid.channel_recv
-        result = self._create_tensor('return_value',
-                                     core.VarDesc.VarType.LOD_TENSOR,
-                                     core.VarDesc.VarType.INT64)
-
-        with fluid.Go():
-            input_value = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.FP64, value=1234)
-            fluid.channel_send(ch, input_value)
-
-        result, status = fluid.channel_recv(ch, result)
-        fluid.channel_close(ch)
-
-        cpu = core.CPUPlace()
-        exe = Executor(cpu)
-
-        outs = exe.run(fetch_list=[result])
-        self.assertEqual(outs[0], 1234)
-
-    def test_daisy_chain(self):
-        '''
-        Mimics classic Daisy-chain test:  https://talks.golang.org/2012/concurrency.slide#39
-        '''
-        n = 100
-
-        leftmost = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
-        left = leftmost
-
-        # TODO(thuan): Use fluid.While() after scope capture is implemented.
-        # https://github.com/PaddlePaddle/Paddle/issues/8502
-        for i in range(n):
-            right = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
-            with fluid.Go():
-                one_tensor = self._create_one_dim_tensor(1)
-                result = self._create_tensor('return_value',
-                                             core.VarDesc.VarType.LOD_TENSOR,
-                                             core.VarDesc.VarType.INT64)
-
-                result, status = fluid.channel_recv(right, result)
-                one_added = fluid.layers.elementwise_add(x=one_tensor, y=result)
-                fluid.channel_send(left, one_added)
-            left = right
-
-        # Trigger the channel propagation by sending a "1" to rightmost channel
-        with fluid.Go():
-            one_tensor = self._create_one_dim_tensor(1)
-            fluid.channel_send(right, one_tensor)
-
-        leftmost_result = self._create_tensor('return_value',
-                                              core.VarDesc.VarType.LOD_TENSOR,
-                                              core.VarDesc.VarType.INT64)
-        leftmost_result, status = fluid.channel_recv(leftmost, leftmost_result)
-
-        cpu = core.CPUPlace()
-        exe = Executor(cpu)
-        leftmost_data = exe.run(fetch_list=[leftmost_result])
-
-        # The leftmost_data should be equal to the number of channels + 1
-        self.assertEqual(leftmost_data[0][0], n + 1)
-
-    def _create_one_dim_tensor(self, value):
-        one_dim_tensor = fill_constant(shape=[1], dtype='int', value=value)
-        one_dim_tensor.stop_gradient = True
-        return one_dim_tensor
-
-    def _create_tensor(self, name, type, dtype):
-        return framework.default_main_program().current_block().create_var(
-            name=unique_name.generate(name), type=type, dtype=dtype)
-
-    def _create_persistable_tensor(self, name, type, dtype):
-        return framework.default_main_program().current_block().create_var(
-            name=unique_name.generate(name),
-            type=type,
-            dtype=dtype,
-            persistable=True)
-
-    def test_select(self):
-        with framework.program_guard(framework.Program()):
-            ch1 = fluid.make_channel(
-                dtype=core.VarDesc.VarType.LOD_TENSOR, capacity=1)
-
-            result1 = self._create_tensor('return_value',
-                                          core.VarDesc.VarType.LOD_TENSOR,
-                                          core.VarDesc.VarType.FP64)
-
-            input_value = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.FP64, value=10)
-
-            with fluid.Select() as select:
-                with select.case(fluid.channel_send, ch1, input_value):
-                    # Execute something.
-                    pass
-
-                with select.default():
-                    pass
-
-            # This should not block because we are using a buffered channel.
-            result1, status = fluid.channel_recv(ch1, result1)
-            fluid.channel_close(ch1)
-
-            cpu = core.CPUPlace()
-            exe = Executor(cpu)
-
-            result = exe.run(fetch_list=[result1])
-            self.assertEqual(result[0][0], 10)
-
-    def test_fibonacci(self):
-        """
-        Mimics Fibonacci Go example: https://tour.golang.org/concurrency/5
-        """
-        with framework.program_guard(framework.Program()):
-            quit_ch_input_var = self._create_persistable_tensor(
-                'quit_ch_input', core.VarDesc.VarType.LOD_TENSOR,
-                core.VarDesc.VarType.INT32)
-            quit_ch_input = fill_constant(
-                shape=[1],
-                dtype=core.VarDesc.VarType.INT32,
-                value=0,
-                out=quit_ch_input_var)
-
-            result = self._create_persistable_tensor(
-                'result', core.VarDesc.VarType.LOD_TENSOR,
-                core.VarDesc.VarType.INT32)
-            fill_constant(
-                shape=[1],
-                dtype=core.VarDesc.VarType.INT32,
-                value=0,
-                out=result)
-
-            x = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
-            y = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.INT32, value=1)
-
-            while_cond = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.BOOL, value=True)
-
-            while_false = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.BOOL, value=False)
-
-            x_tmp = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
-
-            def fibonacci(channel, quit_channel):
-                while_op = While(cond=while_cond)
-                with while_op.block():
-                    result2 = fill_constant(
-                        shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
-
-                    with fluid.Select() as select:
-                        with select.case(
-                                fluid.channel_send, channel, x, is_copy=True):
-                            assign(input=x, output=x_tmp)
-                            assign(input=y, output=x)
-                            assign(elementwise_add(x=x_tmp, y=y), output=y)
-
-                        with select.case(fluid.channel_recv, quit_channel,
-                                         result2):
-                            # Quit
-                            helper = layer_helper.LayerHelper('assign')
-                            helper.append_op(
-                                type='assign',
-                                inputs={'X': [while_false]},
-                                outputs={'Out': [while_cond]})
-
-            ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
-            quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
-
-            with fluid.Go():
-                for i in range(10):
-                    fluid.channel_recv(ch1, result)
-                    Print(result)
-
-                fluid.channel_send(quit_ch, quit_ch_input)
-
-            fibonacci(ch1, quit_ch)
-
-            fluid.channel_close(ch1)
-            fluid.channel_close(quit_ch)
-
-            cpu = core.CPUPlace()
-            exe = Executor(cpu)
-
-            exe_result = exe.run(fetch_list=[result])
-            self.assertEqual(exe_result[0][0], 34)
-
-    def test_ping_pong(self):
-        """
-        Mimics Ping Pong example: https://gobyexample.com/channel-directions
-        """
-        with framework.program_guard(framework.Program()):
-            result = self._create_tensor('return_value',
-                                         core.VarDesc.VarType.LOD_TENSOR,
-                                         core.VarDesc.VarType.FP64)
-
-            ping_result = self._create_tensor('ping_return_value',
-                                              core.VarDesc.VarType.LOD_TENSOR,
-                                              core.VarDesc.VarType.FP64)
-
-            def ping(ch, message):
-                fluid.channel_send(ch, message, is_copy=True)
-
-            def pong(ch1, ch2):
-                fluid.channel_recv(ch1, ping_result)
-                fluid.channel_send(ch2, ping_result, is_copy=True)
-
-            pings = fluid.make_channel(
-                dtype=core.VarDesc.VarType.LOD_TENSOR, capacity=1)
-            pongs = fluid.make_channel(
-                dtype=core.VarDesc.VarType.LOD_TENSOR, capacity=1)
-
-            msg = fill_constant(
-                shape=[1], dtype=core.VarDesc.VarType.FP64, value=9)
-
-            ping(pings, msg)
-            pong(pings, pongs)
-
-            fluid.channel_recv(pongs, result)
-
-            fluid.channel_close(pings)
-            fluid.channel_close(pongs)
-
-            cpu = core.CPUPlace()
-            exe = Executor(cpu)
-
-            exe_result = exe.run(fetch_list=[result])
-            self.assertEqual(exe_result[0][0], 9)
-
-
-if __name__ == '__main__':
-    unittest.main()

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -17,6 +17,9 @@ if(NOT WITH_DISTRIBUTE)
     list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op)
     LIST(REMOVE_ITEM TEST_OPS test_dist_mnist)
     LIST(REMOVE_ITEM TEST_OPS test_dist_word2vec)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_ctr)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_simnet_bow)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
 endif(NOT WITH_DISTRIBUTE)
 
 list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290

python/paddle/fluid/tests/unittests/dist_ctr.py

+#   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 paddle
+import paddle.fluid as fluid
+
+import dist_ctr_reader
+from test_dist_base import TestDistRunnerBase, runtime_main
+
+IS_SPARSE = True
+
+# Fix seed for test
+fluid.default_startup_program().random_seed = 1
+fluid.default_main_program().random_seed = 1
+
+
+class TestDistCTR2x2(TestDistRunnerBase):
+    def get_model(self, batch_size=2):
+        dnn_input_dim, lr_input_dim = dist_ctr_reader.load_data_meta()
+        """ network definition """
+        dnn_data = fluid.layers.data(
+            name="dnn_data",
+            shape=[-1, 1],
+            dtype="int64",
+            lod_level=1,
+            append_batch_size=False)
+        lr_data = fluid.layers.data(
+            name="lr_data",
+            shape=[-1, 1],
+            dtype="int64",
+            lod_level=1,
+            append_batch_size=False)
+        label = fluid.layers.data(
+            name="click",
+            shape=[-1, 1],
+            dtype="int64",
+            lod_level=0,
+            append_batch_size=False)
+
+        # build dnn model
+        dnn_layer_dims = [128, 64, 32, 1]
+        dnn_embedding = fluid.layers.embedding(
+            is_distributed=False,
+            input=dnn_data,
+            size=[dnn_input_dim, dnn_layer_dims[0]],
+            param_attr=fluid.ParamAttr(
+                name="deep_embedding",
+                initializer=fluid.initializer.Constant(value=0.01)),
+            is_sparse=IS_SPARSE)
+        dnn_pool = fluid.layers.sequence_pool(
+            input=dnn_embedding, pool_type="sum")
+        dnn_out = dnn_pool
+        for i, dim in enumerate(dnn_layer_dims[1:]):
+            fc = fluid.layers.fc(
+                input=dnn_out,
+                size=dim,
+                act="relu",
+                param_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.Constant(value=0.01)),
+                name='dnn-fc-%d' % i)
+            dnn_out = fc
+
+        # build lr model
+        lr_embbding = fluid.layers.embedding(
+            is_distributed=False,
+            input=lr_data,
+            size=[lr_input_dim, 1],
+            param_attr=fluid.ParamAttr(
+                name="wide_embedding",
+                initializer=fluid.initializer.Constant(value=0.01)),
+            is_sparse=IS_SPARSE)
+        lr_pool = fluid.layers.sequence_pool(input=lr_embbding, pool_type="sum")
+
+        merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)
+
+        predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
+        acc = fluid.layers.accuracy(input=predict, label=label)
+        auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
+                                                              label=label)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+
+        inference_program = paddle.fluid.default_main_program().clone()
+
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
+        sgd_optimizer.minimize(avg_cost)
+
+        dataset = dist_ctr_reader.Dataset()
+        train_reader = paddle.batch(dataset.train(), batch_size=batch_size)
+        test_reader = paddle.batch(dataset.test(), batch_size=batch_size)
+
+        return inference_program, avg_cost, train_reader, test_reader, None, predict
+
+
+if __name__ == "__main__":
+    runtime_main(TestDistCTR2x2)

python/paddle/fluid/tests/unittests/dist_mnist.py

@@ -47,7 +47,7 @@ def cnn_model(data):
         pool_stride=2,
         act="relu",
         param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
-            value=0.3)))
+            value=0.01)))
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
@@ -56,7 +56,7 @@ def cnn_model(data):
         pool_stride=2,
         act="relu",
         param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
-            value=0.2)))
+            value=0.01)))
 
     SIZE = 10
     input_shape = conv_pool_2.shape
@@ -68,7 +68,7 @@ def cnn_model(data):
         size=SIZE,
         act="softmax",
         param_attr=fluid.param_attr.ParamAttr(
-            initializer=fluid.initializer.Constant(value=0.1)))
+            initializer=fluid.initializer.Constant(value=0.01)))
     return predict
 
 

python/paddle/fluid/tests/unittests/dist_se_resnext.py

@@ -247,7 +247,7 @@ class DistSeResneXt2x2(TestDistRunnerBase):
 
         # Reader
         train_reader = paddle.batch(
-            paddle.dataset.flowers.train(), batch_size=batch_size)
+            paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size)
         test_reader = paddle.batch(
             paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size)
 

python/paddle/fluid/tests/unittests/dist_transformer.py

@@ -1699,10 +1699,9 @@ class DistTransformer2x2(TestDistRunnerBase):
         exe.run(startup_prog)
         exe.run(pserver_prog)
 
-    def run_trainer(self, use_cuda, args):
-        place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-        TrainTaskConfig.use_gpu = use_cuda
-        sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
+    def run_trainer(self, args):
+        TrainTaskConfig.use_gpu = args.use_cuda
+        sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
             args.is_dist, not args.sync_mode)
 
         if args.is_dist:
@@ -1718,6 +1717,11 @@ class DistTransformer2x2(TestDistRunnerBase):
             TrainTaskConfig.batch_size = 20
             trainer_prog = fluid.default_main_program()
 
+        if args.use_cuda:
+            place = fluid.CUDAPlace(0)
+        else:
+            place = fluid.CPUPlace()
+
         startup_exe = fluid.Executor(place)
 
         TrainTaskConfig.local = not args.is_dist

python/paddle/fluid/tests/unittests/dist_word2vec.py

@@ -122,4 +122,7 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
 
 
 if __name__ == "__main__":
+    import os
+    os.environ['CPU_NUM'] = '1'
+    os.environ['USE_CUDA'] = "FALSE"
     runtime_main(TestDistWord2vec2x2)

python/paddle/fluid/tests/unittests/test_auc_op.py

@@ -36,7 +36,11 @@ class TestAucOp(OpTest):
             "StatPos": stat_pos,
             "StatNeg": stat_neg
         }
-        self.attrs = {'curve': 'ROC', 'num_thresholds': num_thresholds}
+        self.attrs = {
+            'curve': 'ROC',
+            'num_thresholds': num_thresholds,
+            "slide_steps": 1
+        }
 
         python_auc = metrics.Auc(name="auc",
                                  curve='ROC',
@@ -45,7 +49,6 @@ class TestAucOp(OpTest):
 
         self.outputs = {
             'AUC': np.array(python_auc.eval()),
-            'BatchAUC': np.array(python_auc.eval()),
             'StatPosOut': np.array(python_auc._stat_pos),
             'StatNegOut': np.array(python_auc._stat_neg)
         }

python/paddle/fluid/tests/notest_concurrency.py → python/paddle/fluid/tests/unittests/test_dist_ctr.py

@@ -11,31 +11,22 @@
 # 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 unittest
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-from paddle.fluid.executor import Executor
-
+from test_dist_base import TestDistBase
 
-class TestRoutineOp(unittest.TestCase):
-    def test_simple_routine(self):
-        ch = fluid.make_channel(
-            dtype=core.VarDesc.VarType.BOOL, name="CreateChannel")
-        with fluid.Go():
-            fluid.channel_send(ch, True)
 
-        result = fluid.channel_recv(ch)
-        fluid.channel_close(ch)
+class TestDistCTR2x2(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._enforce_place = "CPU"
 
-        cpu = core.CPUPlace()
-        exe = Executor(cpu)
 
-        outs = exe.run(fetch_list=[result])
-        self.assertEqual(outs[0], True)
+def test_dist_ctr(self):
+    self.check_with_place("dist_ctr.py", delta=1e-7)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_dist_mnist.py

@@ -23,7 +23,7 @@ class TestDistMnist2x2(TestDistBase):
         self._use_reduce = False
 
     def test_dist_train(self):
-        self.check_with_place("dist_mnist.py", delta=1e-7)
+        self.check_with_place("dist_mnist.py", delta=1e-5)
 
 
 class TestDistMnist2x2WithMemopt(TestDistBase):
@@ -32,7 +32,7 @@ class TestDistMnist2x2WithMemopt(TestDistBase):
         self._mem_opt = True
 
     def test_dist_train(self):
-        self.check_with_place("dist_mnist.py", delta=1e-7)
+        self.check_with_place("dist_mnist.py", delta=1e-5)
 
 
 class TestDistMnistAsync(TestDistBase):

python/paddle/fluid/tests/unittests/test_dist_word2vec.py

@@ -39,7 +39,7 @@ class TestDistW2V2x2Async(TestDistBase):
         self._sync_mode = False
 
     def test_dist_train(self):
-        self.check_with_place("dist_word2vec.py", delta=1)
+        self.check_with_place("dist_word2vec.py", delta=100)
 
 
 if __name__ == "__main__":