clean use_mkldnn options

Add API.spec

test=develop

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))
@@ -146,18 +146,18 @@ paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_v
 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', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None))
-paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
-paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
-paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
-paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
-paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
-paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None))
-paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, 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))
@@ -166,6 +166,10 @@ paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=
 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)
@@ -228,10 +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.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,))
@@ -265,9 +265,9 @@ 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', '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,))
@@ -318,11 +318,11 @@ 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))

python/paddle/fluid/layers/nn.py

@@ -158,7 +158,6 @@ def fc(input,
        num_flatten_dims=1,
        param_attr=None,
        bias_attr=None,
-       use_mkldnn=False,
        act=None,
        is_test=False,
        name=None):
@@ -210,8 +209,6 @@ def fc(input,
             If it is set to None, the bias is initialized zero. Default: None.
         act (str, default None): Activation to be applied to the output of this layer.
         is_test(bool): A flag indicating whether execution is in test phase.
-        use_mkldnn(bool): Use mkldnn kernel or not, it is valid only when the mkldnn
-            library is installed. Default: False
         name (str, default None): The name of this layer.
 
     Returns:
@@ -258,7 +255,7 @@ def fc(input,
             type="sum",
             inputs={"X": mul_results},
             outputs={"Out": pre_bias},
-            attrs={"use_mkldnn": use_mkldnn})
+            attrs={"use_mkldnn": False})
     # add bias
     pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
     # add activation
@@ -1422,7 +1419,6 @@ def conv2d(input,
            param_attr=None,
            bias_attr=None,
            use_cudnn=True,
-           use_mkldnn=False,
            act=None,
            name=None):
     """
@@ -1500,8 +1496,6 @@ def conv2d(input,
         bias_attr (ParamAttr): Bias parameter for the Conv2d layer. 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
         act (str): Activation type. Default: None
         name (str|None): A name for this layer(optional). If set None, the layer
             will be named automatically.
@@ -1574,7 +1568,7 @@ def conv2d(input,
             'dilations': dilation,
             'groups': groups,
             'use_cudnn': use_cudnn,
-            'use_mkldnn': use_mkldnn
+            'use_mkldnn': False
         })
 
     pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
@@ -1592,7 +1586,6 @@ def conv3d(input,
            param_attr=None,
            bias_attr=None,
            use_cudnn=True,
-           use_mkldnn=False,
            act=None,
            name=None):
     """
@@ -1666,7 +1659,6 @@ def conv3d(input,
         bias_attr (ParamAttr): Bias parameter for the Conv3d layer. 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.
         act (str): Activation type. Default: None
         name (str|None): A name for this layer(optional). If set None, the layer
             will be named automatically.
@@ -1736,7 +1728,7 @@ def conv3d(input,
             'dilations': dilation,
             'groups': groups,
             'use_cudnn': use_cudnn,
-            'use_mkldnn': use_mkldnn
+            'use_mkldnn': False
         })
 
     pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
@@ -1918,7 +1910,6 @@ def pool2d(input,
            global_pooling=False,
            use_cudnn=True,
            ceil_mode=False,
-           use_mkldnn=False,
            name=None):
     """
     ${comment}
@@ -1936,7 +1927,6 @@ def pool2d(input,
         global_pooling: ${global_pooling_comment}
         use_cudnn: ${use_cudnn_comment}
         ceil_mode: ${ceil_mode_comment}
-        use_mkldnn: ${use_mkldnn_comment}
         name (str|None): A name for this layer(optional). If set None, the
                         layer will be named automatically.
 
@@ -1996,7 +1986,7 @@ def pool2d(input,
             "paddings": pool_padding,
             "use_cudnn": use_cudnn,
             "ceil_mode": ceil_mode,
-            "use_mkldnn": use_mkldnn
+            "use_mkldnn": False
         })
 
     return pool_out
@@ -2010,7 +2000,6 @@ def pool3d(input,
            global_pooling=False,
            use_cudnn=True,
            ceil_mode=False,
-           use_mkldnn=False,
            name=None):
     """
     This function adds the operator for pooling in 3-dimensions, using the
@@ -2025,7 +2014,6 @@ def pool3d(input,
         global_pooling (bool): ${global_pooling_comment}
         use_cudnn (bool): ${use_cudnn_comment}
         ceil_mode (bool): ${ceil_mode_comment}
-        use_mkldnn (bool): ${use_mkldnn_comment}
         name (str): A name for this layer(optional). If set None, the layer
             will be named automatically.
 
@@ -2066,7 +2054,7 @@ def pool3d(input,
             "paddings": pool_padding,
             "use_cudnn": use_cudnn,
             "ceil_mode": ceil_mode,
-            "use_mkldnn": use_mkldnn
+            "use_mkldnn": False
         })
 
     return pool_out
@@ -2081,7 +2069,6 @@ def batch_norm(input,
                bias_attr=None,
                data_layout='NCHW',
                in_place=False,
-               use_mkldnn=False,
                name=None,
                moving_mean_name=None,
                moving_variance_name=None,
@@ -2123,7 +2110,6 @@ def batch_norm(input,
         bias_attr(ParamAttr): The parameter attribute for Parameter `bias`.
         data_layout(string, default NCHW): NCHW|NHWC
         in_place(bool, Default False): Make the input and output of batch norm reuse memory.
-        use_mkldnn(bool, Default false): ${use_mkldnn_comment}
         name(string, Default None): A name for this layer(optional). If set None, the layer
             will be named automatically.
         moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
@@ -2215,7 +2201,7 @@ def batch_norm(input,
             "momentum": momentum,
             "epsilon": epsilon,
             "is_test": is_test,
-            "use_mkldnn": use_mkldnn,
+            "use_mkldnn": False,
             "fuse_with_relu": fuse_with_relu
         })
 
@@ -6530,12 +6516,7 @@ def uniform_random_batch_size_like(input,
 
 
 @templatedoc()
-def gaussian_random(shape,
-                    mean=0.0,
-                    std=1.0,
-                    seed=0,
-                    dtype='float32',
-                    use_mkldnn=False):
+def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
     """
     ${comment}
 
@@ -6545,7 +6526,6 @@ def gaussian_random(shape,
         std (Float): ${std_comment}
         seed (Int): ${seed_comment}
         dtype(np.dtype|core.VarDesc.VarType|str): Output data type.
-        use_mkldnn (Bool): Only used in mkldnn kernel.
 
     Returns:
         out (Variable): ${out_comment}
@@ -6564,7 +6544,7 @@ def gaussian_random(shape,
             'std': std,
             'seed': seed,
             'dtype': c_dtype,
-            'use_mkldnn': use_mkldnn
+            'use_mkldnn': False
         })
 
     return out
@@ -6647,13 +6627,12 @@ def gaussian_random_batch_size_like(input,
 
 
 @templatedoc()
-def sum(x, use_mkldnn=False):
+def sum(x):
     """
     ${comment}
 
     Args:
         x (Variable): ${x_comment}
-        use_mkldnn (Bool): ${use_mkldnn_comment}
 
     Returns:
         out (Variable): ${out_comment}
@@ -6665,7 +6644,7 @@ def sum(x, use_mkldnn=False):
         type='sum',
         inputs={'X': x},
         outputs={'Out': out},
-        attrs={'use_mkldnn': use_mkldnn})
+        attrs={'use_mkldnn': False})
 
     return out
 
@@ -6781,31 +6760,31 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
     return helper.append_activation(out)
 
 
-def elementwise_add(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
+def elementwise_add(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_add', **locals()))
 
 
-def elementwise_div(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
+def elementwise_div(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_div', **locals()))
 
 
-def elementwise_sub(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
+def elementwise_sub(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_sub', **locals()))
 
 
-def elementwise_mul(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
+def elementwise_mul(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_mul', **locals()))
 
 
-def elementwise_max(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
+def elementwise_max(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_max', **locals()))
 
 
-def elementwise_min(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
+def elementwise_min(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_min', **locals()))
 
 
-def elementwise_pow(x, y, axis=-1, use_mkldnn=False, act=None, name=None):
+def elementwise_pow(x, y, axis=-1, act=None, name=None):
     return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
 
 

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