From 5d98b6f217f8c59ae32f7dabefb69037d80f9cb2 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 21 Aug 2017 16:32:29 +0800
Subject: [PATCH 01/66] Adapting to the BatchNorm structure to support 3D data
---
paddle/gserver/layers/BatchNormBaseLayer.cpp | 6 ++-
paddle/gserver/layers/BatchNormBaseLayer.h | 1 +
paddle/gserver/tests/test_LayerGrad.cpp | 49 ++++++++++++++++++++
paddle/parameter/Argument.cpp | 2 +
paddle/parameter/Argument.h | 8 ++--
proto/ModelConfig.proto | 13 ++++++
6 files changed, 75 insertions(+), 4 deletions(-)
diff --git a/paddle/gserver/layers/BatchNormBaseLayer.cpp b/paddle/gserver/layers/BatchNormBaseLayer.cpp
index 1ceaaaa206..f7a80e23e1 100644
--- a/paddle/gserver/layers/BatchNormBaseLayer.cpp
+++ b/paddle/gserver/layers/BatchNormBaseLayer.cpp
@@ -62,14 +62,18 @@ void BatchNormBaseLayer::calFeatureMapSize() {
const ImageConfig& conf = config_.inputs(0).image_conf();
imageH_ = inputLayers_[0]->getOutput().getFrameHeight();
imageW_ = inputLayers_[0]->getOutput().getFrameWidth();
+ imageD_ = inputLayers_[0]->getOutput().getFrameDepth();
+
+ if (0 == imageD_) imageD_ = conf.img_size_z();
if (imageH_ == 0 && imageW_ == 0) {
imageH_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
imageW_ = conf.img_size();
} else {
getOutput().setFrameHeight(imageH_);
getOutput().setFrameWidth(imageW_);
+ getOutput().setFrameDepth(imageD_);
}
- imgPixels_ = imageH_ * imageW_;
+ imgPixels_ = imageH_ * imageW_ * imageD_;
}
} // namespace paddle
diff --git a/paddle/gserver/layers/BatchNormBaseLayer.h b/paddle/gserver/layers/BatchNormBaseLayer.h
index 230bafc31d..e721d2d267 100644
--- a/paddle/gserver/layers/BatchNormBaseLayer.h
+++ b/paddle/gserver/layers/BatchNormBaseLayer.h
@@ -80,6 +80,7 @@ protected:
/// Height or width of input image feature.
/// Both of them are 1 if the input is fully-connected layer.
+ int imageD_;
int imageH_;
int imageW_;
/// Height * Width.
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index 0f312b6ca5..6418772584 100644
--- a/paddle/gserver/tests/test_LayerGrad.cpp
+++ b/paddle/gserver/tests/test_LayerGrad.cpp
@@ -1594,6 +1594,55 @@ TEST(Layer, BatchNormalizationLayer) {
#endif
}
+void testBatchNorm3DLayer(const string& type, bool trans, bool useGpu) {
+ TestConfig config;
+ const int CHANNELS = 10;
+ const int IMG_SIZE = 16;
+ const int IMG_SIZE_Y = 8;
+ const int IMG_SIZE_Z = 8;
+ size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y * IMG_SIZE_Z;
+ config.layerConfig.set_type(type);
+ config.layerConfig.set_size(size);
+ config.layerConfig.set_active_type("sigmoid");
+ config.biasSize = CHANNELS;
+ config.inputDefs.push_back({INPUT_DATA,
+ "layer_0",
+ /* dim= */ size,
+ /* paraSize= */ CHANNELS});
+
+ config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS});
+ config.inputDefs.back().isStatic = true;
+ config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS});
+ config.inputDefs.back().isStatic = true;
+
+ LayerInputConfig* input = config.layerConfig.add_inputs();
+ config.layerConfig.add_inputs();
+ config.layerConfig.add_inputs();
+
+ ImageConfig* img_conf = input->mutable_image_conf();
+ img_conf->set_channels(CHANNELS);
+ img_conf->set_img_size(IMG_SIZE);
+ img_conf->set_img_size_y(IMG_SIZE_Y);
+ img_conf->set_img_size_z(IMG_SIZE_Z);
+
+ testLayerGrad(config,
+ "batch_norm",
+ 64,
+ /* trans= */ trans,
+ useGpu,
+ /* useWeight */ true);
+}
+
+TEST(Layer, testBatchNorm3DLayer) {
+ testBatchNorm3DLayer("batch_norm", false, false);
+#ifndef PADDLE_ONLY_CPU
+ testBatchNorm3DLayer("batch_norm", false, true);
+ if (hl_get_cudnn_lib_version() >= int(4000)) {
+ testBatchNorm3DLayer("cudnn_batch_norm", false, true);
+ }
+#endif
+}
+
void testConvOperator(bool isDeconv) {
TestConfig config;
const int NUM_FILTERS = 16;
diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp
index 0547ac93cd..77fd0c5890 100644
--- a/paddle/parameter/Argument.cpp
+++ b/paddle/parameter/Argument.cpp
@@ -186,6 +186,7 @@ void Argument::resizeAndCopyFrom(const Argument& src,
resizeAndCopy(strs, src.strs, useGpu, stream);
frameWidth = src.frameWidth;
frameHeight = src.frameHeight;
+ frameDepth = src.frameDepth;
}
int32_t Argument::resizeAndCopyFrom(const Argument& src,
@@ -206,6 +207,7 @@ int32_t Argument::resizeAndCopyFrom(const Argument& src,
dataId = src.dataId;
frameWidth = src.frameWidth;
frameHeight = src.frameHeight;
+ frameDepth = src.frameDepth;
if (!src.sequenceStartPositions) {
// non-sequence input, copy samples directly
diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h
index d8d7a4398f..ba3ad2fd4d 100644
--- a/paddle/parameter/Argument.h
+++ b/paddle/parameter/Argument.h
@@ -1,11 +1,8 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
-
http://www.apache.org/licenses/LICENSE-2.0
-
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -35,6 +32,7 @@ struct Argument {
strs(nullptr),
frameHeight(0),
frameWidth(0),
+ frameDepth(0),
sequenceStartPositions(nullptr),
subSequenceStartPositions(nullptr),
cpuSequenceDims(nullptr),
@@ -64,6 +62,7 @@ struct Argument {
allCount = argument.allCount;
frameHeight = argument.frameHeight;
frameWidth = argument.frameWidth;
+ frameDepth = argument.frameDepth;
dataId = argument.dataId;
}
@@ -76,6 +75,7 @@ struct Argument {
// A dataBatch includes batchSize frames, one frame maybe not only vector
size_t frameHeight;
size_t frameWidth;
+ size_t frameDepth;
// If NULL, each position is treated independently.
// Otherwise, its size should be #NumberOfSequences + 1.
@@ -136,8 +136,10 @@ struct Argument {
}
size_t getFrameHeight() const { return frameHeight; }
size_t getFrameWidth() const { return frameWidth; }
+ size_t getFrameDepth() const { return frameDepth; }
void setFrameHeight(size_t h) { frameHeight = h; }
void setFrameWidth(size_t w) { frameWidth = w; }
+ void setFrameDepth(size_t d) { frameDepth = d; }
int64_t getNumSequences() const {
return sequenceStartPositions ? sequenceStartPositions->getSize() - 1
diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto
index 4f3d5bf3f6..ef2b076c33 100644
--- a/proto/ModelConfig.proto
+++ b/proto/ModelConfig.proto
@@ -82,6 +82,12 @@ message ConvConfig {
// if not set, use img_size
optional uint32 img_size_y = 14;
+
+ optional uint32 filter_size_z = 15 [ default = 1 ];
+ optional uint32 padding_z = 16 [ default = 1 ];
+ optional uint32 stride_z = 17 [ default = 1 ];
+ optional uint32 output_z = 18 [ default = 1 ];
+ optional uint32 img_size_z = 19 [ default = 1 ];
}
message PoolConfig {
@@ -124,6 +130,12 @@ message PoolConfig {
// if not set, use padding
optional uint32 padding_y = 13;
+
+ optional uint32 size_z = 14 [ default = 1 ];
+ optional uint32 stride_z = 15 [ default = 1 ];
+ optional uint32 output_z = 16 [ default = 1 ];
+ optional uint32 img_size_z = 17 [ default = 1 ];
+ optional uint32 padding_z = 18 [ default = 1 ];
}
message SppConfig {
@@ -256,6 +268,7 @@ message ImageConfig {
// The size of input feature map.
required uint32 img_size = 8;
optional uint32 img_size_y = 9;
+ optional uint32 img_size_z = 10 [ default = 1 ];
}
message PriorBoxConfig {
From 46034faf97e7a135756d36391f5c4a970fed92ab Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Tue, 29 Aug 2017 16:05:10 -0700
Subject: [PATCH 02/66] Design doc: operator based parameter server.
---
doc/design/ops/dist_train.md | 82 +++++++++++++++++++++++++
doc/design/ops/src/dist-graph.graffle | Bin 0 -> 4915 bytes
doc/design/ops/src/dist-graph.png | Bin 0 -> 133866 bytes
doc/design/ops/src/local-graph.graffle | Bin 0 -> 2515 bytes
doc/design/ops/src/local-graph.png | Bin 0 -> 31493 bytes
5 files changed, 82 insertions(+)
create mode 100644 doc/design/ops/dist_train.md
create mode 100644 doc/design/ops/src/dist-graph.graffle
create mode 100644 doc/design/ops/src/dist-graph.png
create mode 100644 doc/design/ops/src/local-graph.graffle
create mode 100644 doc/design/ops/src/local-graph.png
diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md
new file mode 100644
index 0000000000..0380826b0d
--- /dev/null
+++ b/doc/design/ops/dist_train.md
@@ -0,0 +1,82 @@
+# Design Doc: Operation Graph Based Parameter Server
+
+## Abstract
+
+We propose an approach to implment the parameter server. In this
+approach, there is no fundimental difference between the trainer and
+the parameter server: they both run sub-graphs, but sub-graphs of
+different purposes.
+
+## Background
+
+The previous implementations of the parameter server does not run a
+sub-graph. parameter initialization, optimizer computation, network
+communication and checkpointing are implemented twice on both the
+trainer and the parameter server.
+
+It would be great if we can write code once and use them on both the
+trainer and the parameter server: reduces code duplication and
+improves extensibility. Given during the current refactor, we are
+representing everything as a computing graph on the
+trainer. Representing everything as a computing graph on the parameter
+server becomes a natural extension.
+
+## Design
+
+### Graph Converter
+
+The *graph converter* converts user-defined operation (OP) graph into
+sub-graphs to be scheduled on different nodes.
+
+1. The user-defined OP graph will be cut into sub-graphs of
+different purposes (e.g., trainer, parameter server) to run on
+different workers.
+
+1. OPs will be added to the subgraphs, so the subgraphs can
+communicate with each other. We will need these OPs: *send*, *recv*,
+*gradient accumulator*, *string accumulator*, *loop forever*.
+
+Below is an example of converting the user defined graph to the
+sub-graphs for the trainer and the parameter server:
+
+<img src="src/local-graph.png"/>
+
+After converting:
+
+<img src="src/dist-graph.png"/>
+
+1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
+1. Operators are added to the sub-graphs.
+ - *send* operator sends data and sender's address to the destination.
+ - *recv* operator receives data and sender's address from the
+ destination. It will block until data has been received.
+ - *gradient accumulator* operator accumulates *N* pieces of
+ gradients. N=1 in Async-SGD, N>1 in Sync-SGD.
+ - *string accumulator* accumulates *N* pieces of strings into a
+ list of strings. N=1 in Async-SGD, N>1 in Sync-SGD.
+ - *loop forever* runs itself as a target forever.
+
+### Benefits
+
+- Model parallelism become easier to implement: it's an extension to
+ the trainer - parameter server approach. we already have the
+ communication OPs, but need to extend the graph converter.
+
+- User-defined optimizer is easier to add - user can now express it as
+ a subgraph.
+
+- No more duplication logic inside the trainer and the parameter
+ server in the background section.
+
+### Challenges
+
+- It might be hard for the graph converter to cut a general graph
+ (without any hint for which sub-graph is the optimizer). We may need
+ to label which sub-graph inside the OP graph is the optimizer.
+
+- It's important to balance the parameter shards of on multiple
+ parameter server. If a single parameter is very big (some
+ word-embedding, fully connected, softmax layer), we need to
+ automatically partition the single parameter onto different
+ parameter servers when possible (only element-wise optimizer depends
+ on the parameter variable).
diff --git a/doc/design/ops/src/dist-graph.graffle b/doc/design/ops/src/dist-graph.graffle
new file mode 100644
index 0000000000000000000000000000000000000000..1e1cb18dfecd9ee956ce4fe721a9bec4a24282c2
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diff --git a/doc/design/ops/src/local-graph.graffle b/doc/design/ops/src/local-graph.graffle
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literal 0
HcmV?d00001
From 0e80f74cb54fa4fdc1a1b3170392c8d433dee87d Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Tue, 29 Aug 2017 16:07:26 -0700
Subject: [PATCH 03/66] change image size
---
doc/design/ops/dist_train.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md
index 0380826b0d..0bc350d8c0 100644
--- a/doc/design/ops/dist_train.md
+++ b/doc/design/ops/dist_train.md
@@ -39,11 +39,11 @@ communicate with each other. We will need these OPs: *send*, *recv*,
Below is an example of converting the user defined graph to the
sub-graphs for the trainer and the parameter server:
-<img src="src/local-graph.png"/>
+<img src="src/local-graph.png" width="300"/>
After converting:
-<img src="src/dist-graph.png"/>
+<img src="src/dist-graph.png" width="500"/>
1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
1. Operators are added to the sub-graphs.
From 75856ec3370358b0c182e03095032a01c8202fa7 Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Tue, 29 Aug 2017 16:17:49 -0700
Subject: [PATCH 04/66] fix typo
---
doc/design/ops/dist_train.md | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md
index 0bc350d8c0..8e92c87a59 100644
--- a/doc/design/ops/dist_train.md
+++ b/doc/design/ops/dist_train.md
@@ -2,8 +2,8 @@
## Abstract
-We propose an approach to implment the parameter server. In this
-approach, there is no fundimental difference between the trainer and
+We propose an approach to implement the parameter server. In this
+approach, there is no fundamental difference between the trainer and
the parameter server: they both run sub-graphs, but sub-graphs of
different purposes.
@@ -16,7 +16,7 @@ trainer and the parameter server.
It would be great if we can write code once and use them on both the
trainer and the parameter server: reduces code duplication and
-improves extensibility. Given during the current refactor, we are
+improves extensibility. Given that after the current refactor, we are
representing everything as a computing graph on the
trainer. Representing everything as a computing graph on the parameter
server becomes a natural extension.
@@ -25,8 +25,8 @@ server becomes a natural extension.
### Graph Converter
-The *graph converter* converts user-defined operation (OP) graph into
-sub-graphs to be scheduled on different nodes.
+The *graph converter* converts the user-defined operation (OP) graph
+into sub-graphs to be scheduled on different nodes.
1. The user-defined OP graph will be cut into sub-graphs of
different purposes (e.g., trainer, parameter server) to run on
@@ -66,7 +66,7 @@ After converting:
a subgraph.
- No more duplication logic inside the trainer and the parameter
- server in the background section.
+ server mentioned in the background section.
### Challenges
From 3b0e43aa6ab4f30ca960537b13f600cc36d6066e Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 30 Aug 2017 23:09:58 +0800
Subject: [PATCH 05/66] add config parse
---
proto/ModelConfig.proto | 2 +
python/paddle/trainer/config_parser.py | 90 +++++++++++++++++--
.../paddle/trainer_config_helpers/layers.py | 15 +++-
.../tests/configs/test_BatchNorm3D.py | 17 ++++
.../tests/layers_test.py | 2 +-
5 files changed, 112 insertions(+), 14 deletions(-)
create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto
index 95c236ad88..0525fb9dc3 100644
--- a/proto/ModelConfig.proto
+++ b/proto/ModelConfig.proto
@@ -515,6 +515,8 @@ message LayerConfig {
// for HuberRegressionLoss
optional double delta = 57 [ default = 1.0 ];
+ // for 3D data
+ optional double depth = 58 [ default = 1 ];
}
message EvaluatorConfig {
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index c11037c3c8..bc9aacaf11 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -1172,6 +1172,20 @@ def get_img_size(input_layer_name, channels):
return img_size, img_size_y
+def get_img3d_size(input_layer_name, channels):
+ input = g_layer_map[input_layer_name]
+ img_pixels = input.size / channels
+ img_size = input.width
+ img_size_y = input.height
+ img_size_z = input.depth
+
+ config_assert(
+ img_size * img_size_y * img_size_z == img_pixels,
+ "Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d"
+ % (input_layer_name, img_size, img_size_y, img_size_z, img_pixels))
+ return img_size, img_size_y, img_size_z
+
+
def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
parse_image(bilinear, input_layer_name, bilinear_conf.image_conf)
bilinear_conf.out_size_x = bilinear.out_size_x
@@ -1224,6 +1238,12 @@ def parse_image(image, input_layer_name, image_conf):
get_img_size(input_layer_name, image_conf.channels)
+def parse_image3d(image, input_layer_name, image_conf):
+ image_conf.channels = image.channels
+ image_conf.img_size, image_conf.img_size_y, image_conf.img_size_z = \
+ get_img3d_size(input_layer_name, image_conf.channels)
+
+
def parse_norm(norm, input_layer_name, norm_conf):
norm_conf.norm_type = norm.norm_type
config_assert(
@@ -1585,6 +1605,9 @@ class LayerBase(object):
self.config.height = height
self.config.width = width
+ def set_layer_depth(self, depth):
+ self.config.depth = depth
+
def set_cnn_layer(self,
input_layer_name,
height,
@@ -1788,11 +1811,19 @@ class DetectionOutputLayer(LayerBase):
@config_layer('data')
class DataLayer(LayerBase):
- def __init__(self, name, size, height=None, width=None, device=None):
+ def __init__(self,
+ name,
+ size,
+ depth=None,
+ height=None,
+ width=None,
+ device=None):
super(DataLayer, self).__init__(
name, 'data', size, inputs=[], device=device)
if height and width:
self.set_layer_height_width(height, width)
+ if depth:
+ self.set_layer_depth(depth)
'''
@@ -2077,6 +2108,7 @@ class BatchNormLayer(LayerBase):
name,
inputs,
bias=True,
+ img3D=False,
use_global_stats=True,
moving_average_fraction=0.9,
batch_norm_type=None,
@@ -2121,15 +2153,33 @@ class BatchNormLayer(LayerBase):
input_layer = self.get_input_layer(0)
image_conf = self.config.inputs[0].image_conf
- parse_image(self.inputs[0].image, input_layer.name, image_conf)
-
- # Only pass the width and height of input to batch_norm layer
- # when either of it is non-zero.
- if input_layer.width != 0 or input_layer.height != 0:
- self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
- image_conf.channels, False)
+ if img3D:
+ parse_image3d(self.inputs[0].image, input_layer.name, image_conf)
+ # Only pass the width and height of input to batch_norm layer
+ # when either of it is non-zero.
+ if input_layer.width != 0 or input_layer.height != 0:
+ self.set_cnn_layer(
+ input_layer_name=name,
+ depth=image_conf.img_size_z,
+ height=image_conf.img_size_y,
+ width=image_conf.img_size,
+ channels=image_conf.channels,
+ is_print=True)
+ else:
+ self.set_layer_size(input_layer.size)
else:
- self.set_layer_size(input_layer.size)
+ parse_image(self.inputs[0].image, input_layer.name, image_conf)
+ # Only pass the width and height of input to batch_norm layer
+ # when either of it is non-zero.
+ if input_layer.width != 0 or input_layer.height != 0:
+ self.set_cnn_layer(
+ input_layer_name=name,
+ height=image_conf.img_size_y,
+ width=image_conf.img_size,
+ channels=image_conf.channels,
+ is_print=True)
+ else:
+ self.set_layer_size(input_layer.size)
psize = self.calc_parameter_size(image_conf)
dims = [1, psize]
@@ -2139,6 +2189,28 @@ class BatchNormLayer(LayerBase):
self.create_bias_parameter(bias, psize)
+ def set_cnn_layer(self,
+ input_layer_name,
+ depth=None,
+ height=None,
+ width=None,
+ channels=None,
+ is_print=True):
+ depthIsNone = False
+ if depth is None:
+ depth = 1
+ depthIsNone = True
+ size = depth * height * width * channels
+ self.set_layer_size(size)
+ self.set_layer_height_width(height, width)
+ self.set_layer_depth(depth)
+ if is_print and depthIsNone:
+ print("output for %s: c = %d, h = %d, w = %d, size = %d" %
+ (input_layer_name, channels, height, width, size))
+ elif is_print:
+ print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" %
+ (input_layer_name, channels, depth, height, width, size))
+
def calc_parameter_size(self, image_conf):
return image_conf.channels
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index a525ce71d0..35c84ad597 100755
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -166,6 +166,7 @@ class LayerType(object):
EXCONVTRANS_LAYER = 'exconvt'
CUDNNCONV_LAYER = 'cudnn_conv'
POOL_LAYER = 'pool'
+ POOL3D_LAYER = 'pool3d'
BATCH_NORM_LAYER = 'batch_norm'
NORM_LAYER = 'norm'
SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm'
@@ -894,7 +895,8 @@ def mixed_layer(size=0,
@layer_support()
-def data_layer(name, size, height=None, width=None, layer_attr=None):
+def data_layer(name, size, depth=None, height=None, width=None,
+ layer_attr=None):
"""
Define DataLayer For NeuralNetwork.
@@ -921,15 +923,18 @@ def data_layer(name, size, height=None, width=None, layer_attr=None):
type=LayerType.DATA,
name=name,
size=size,
+ depth=depth,
height=height,
width=width,
**ExtraLayerAttribute.to_kwargs(layer_attr))
+ if depth is None:
+ depth = 1
num_filters = None
if height is not None and width is not None:
- num_filters = size / (width * height)
- assert num_filters * width * height == size, \
- "size=%s width=%s height=%s" % (size, width, height)
+ num_filters = size / (width * height * depth)
+ assert num_filters * width * height * depth == size, \
+ "size=%s width=%s height=%s depth=%s" % (size, width, height, depth)
return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters)
@@ -2799,6 +2804,7 @@ def img_cmrnorm_layer(input,
def batch_norm_layer(input,
act=None,
name=None,
+ img3D=False,
num_channels=None,
bias_attr=None,
param_attr=None,
@@ -2885,6 +2891,7 @@ def batch_norm_layer(input,
(batch_norm_type == "cudnn_batch_norm")
l = Layer(
name=name,
+ img3D=img3D,
inputs=Input(
input.name, image=Image(channels=num_channels), **param_attr.attr),
active_type=act.name,
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
new file mode 100644
index 0000000000..af694382b6
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
@@ -0,0 +1,17 @@
+from paddle.trainer_config_helpers import *
+
+settings(batch_size=1000, learning_rate=1e-4)
+
+data = data_layer(name='data', size=180, width=30, height=6)
+#
+batchNorm = batch_norm_layer(data, num_channels=1)
+#
+outputs(batchNorm)
+
+# #
+data3D = data_layer(name='data3D22', size=120 * 3, width=20, height=6, depth=3)
+#
+print(data3D)
+batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True)
+#
+outputs(batchNorm3D)
diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py
index 05902ea293..68c8e128cb 100644
--- a/python/paddle/trainer_config_helpers/tests/layers_test.py
+++ b/python/paddle/trainer_config_helpers/tests/layers_test.py
@@ -16,4 +16,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize
if __name__ == '__main__':
parse_config_and_serialize(
- 'trainer_config_helpers/tests/layers_test_config.py', '')
+ 'trainer_config_helpers/tests/configs/test_BatchNorm3D.py', '')
From 30c0df6d27198867f8c9ef0c098505eeaded1522 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Thu, 31 Aug 2017 13:49:42 +0800
Subject: [PATCH 06/66] fix layers_test.py
---
python/paddle/trainer_config_helpers/tests/configs/file_list.sh | 2 +-
python/paddle/trainer_config_helpers/tests/layers_test.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
index 1ca5c8a07e..8462f2d710 100755
--- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
+++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
@@ -9,6 +9,6 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer
-test_seq_slice_layer)
+test_seq_slice_layer test_BatchNorm3D)
export whole_configs=(test_split_datasource)
diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py
index 68c8e128cb..05902ea293 100644
--- a/python/paddle/trainer_config_helpers/tests/layers_test.py
+++ b/python/paddle/trainer_config_helpers/tests/layers_test.py
@@ -16,4 +16,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize
if __name__ == '__main__':
parse_config_and_serialize(
- 'trainer_config_helpers/tests/configs/test_BatchNorm3D.py', '')
+ 'trainer_config_helpers/tests/layers_test_config.py', '')
From 3ad748e225f7c5537338b1a5786924b93363c12d Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Thu, 31 Aug 2017 16:23:12 +0800
Subject: [PATCH 07/66] Fix img_layers.protostr,img_trans_layers.protostr. Add
test_BatchNorm3D.protostr
---
.../configs/protostr/img_layers.protostr | 1 +
.../protostr/img_trans_layers.protostr | 1 +
.../protostr/test_BatchNorm3D.protostr | 92 +++++++++++++++++++
.../tests/configs/test_BatchNorm3D.py | 14 +--
4 files changed, 98 insertions(+), 10 deletions(-)
create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr
index 1a577b8d9b..5ddf6052df 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr
@@ -62,6 +62,7 @@ layers {
moving_average_fraction: 0.9
height: 227
width: 227
+ depth: 1
}
layers {
name: "__crmnorm_0__"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
index 2818389b16..c0252b945b 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
@@ -62,6 +62,7 @@ layers {
moving_average_fraction: 0.9
height: 256
width: 256
+ depth: 1
}
layers {
name: "__crmnorm_0__"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr
new file mode 100644
index 0000000000..832ed24a31
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr
@@ -0,0 +1,92 @@
+type: "nn"
+layers {
+ name: "data3D"
+ type: "data"
+ size: 360
+ active_type: ""
+ height: 6
+ width: 20
+ depth: 3
+}
+layers {
+ name: "__batch_norm_0__"
+ type: "batch_norm"
+ size: 360
+ active_type: "relu"
+ inputs {
+ input_layer_name: "data3D"
+ input_parameter_name: "___batch_norm_0__.w0"
+ image_conf {
+ channels: 1
+ img_size: 20
+ img_size_y: 6
+ img_size_z: 3
+ }
+ }
+ inputs {
+ input_layer_name: "data3D"
+ input_parameter_name: "___batch_norm_0__.w1"
+ }
+ inputs {
+ input_layer_name: "data3D"
+ input_parameter_name: "___batch_norm_0__.w2"
+ }
+ bias_parameter_name: "___batch_norm_0__.wbias"
+ moving_average_fraction: 0.9
+ height: 6
+ width: 20
+ depth: 3
+}
+parameters {
+ name: "___batch_norm_0__.w0"
+ size: 1
+ initial_mean: 1.0
+ initial_std: 0.0
+ initial_strategy: 0
+ initial_smart: false
+}
+parameters {
+ name: "___batch_norm_0__.w1"
+ size: 1
+ initial_mean: 0.0
+ initial_std: 0.0
+ dims: 1
+ dims: 1
+ initial_strategy: 0
+ initial_smart: false
+ is_static: true
+ is_shared: true
+}
+parameters {
+ name: "___batch_norm_0__.w2"
+ size: 1
+ initial_mean: 0.0
+ initial_std: 0.0
+ dims: 1
+ dims: 1
+ initial_strategy: 0
+ initial_smart: false
+ is_static: true
+ is_shared: true
+}
+parameters {
+ name: "___batch_norm_0__.wbias"
+ size: 1
+ initial_mean: 0.0
+ initial_std: 0.0
+ dims: 1
+ dims: 1
+ initial_strategy: 0
+ initial_smart: false
+}
+input_layer_names: "data3D"
+output_layer_names: "__batch_norm_0__"
+sub_models {
+ name: "root"
+ layer_names: "data3D"
+ layer_names: "__batch_norm_0__"
+ input_layer_names: "data3D"
+ output_layer_names: "__batch_norm_0__"
+ is_recurrent_layer_group: false
+}
+
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
index af694382b6..a991b22252 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
@@ -2,16 +2,10 @@ from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-4)
-data = data_layer(name='data', size=180, width=30, height=6)
-#
-batchNorm = batch_norm_layer(data, num_channels=1)
-#
-outputs(batchNorm)
+#data = data_layer(name='data', size=180, width=30, height=6)
+#batchNorm = batch_norm_layer(data, num_channels=1)
+#outputs(batchNorm)
-# #
-data3D = data_layer(name='data3D22', size=120 * 3, width=20, height=6, depth=3)
-#
-print(data3D)
+data3D = data_layer(name='data3D', size=120 * 3, width=20, height=6, depth=3)
batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True)
-#
outputs(batchNorm3D)
From 74b22c3737834021e46430f6f74248a461f92e86 Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Thu, 31 Aug 2017 15:47:22 -0700
Subject: [PATCH 08/66] update figures
---
doc/design/ops/src/dist-graph.graffle | Bin 4915 -> 6059 bytes
doc/design/ops/src/dist-graph.png | Bin 133866 -> 228040 bytes
doc/design/ops/src/local-graph.graffle | Bin 2515 -> 2527 bytes
doc/design/ops/src/local-graph.png | Bin 31493 -> 28561 bytes
4 files changed, 0 insertions(+), 0 deletions(-)
diff --git a/doc/design/ops/src/dist-graph.graffle b/doc/design/ops/src/dist-graph.graffle
index 1e1cb18dfecd9ee956ce4fe721a9bec4a24282c2..6353f736042ed6056d4f59b0cb4072ae2f7008aa 100644
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From c2a16b5c91c5a719cecfe6f1516258c13d8295c1 Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Thu, 31 Aug 2017 16:50:51 -0700
Subject: [PATCH 09/66] update OP based parameter server design
---
doc/design/ops/dist_train.md | 65 +++++++++++++++++---------
doc/design/ops/src/dist-graph.graffle | Bin 6059 -> 6430 bytes
doc/design/ops/src/dist-graph.png | Bin 228040 -> 227532 bytes
3 files changed, 43 insertions(+), 22 deletions(-)
diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md
index 8e92c87a59..29940ac59d 100644
--- a/doc/design/ops/dist_train.md
+++ b/doc/design/ops/dist_train.md
@@ -4,13 +4,13 @@
We propose an approach to implement the parameter server. In this
approach, there is no fundamental difference between the trainer and
-the parameter server: they both run sub-graphs, but sub-graphs of
+the parameter server: they both run subgraphs, but subgraphs of
different purposes.
## Background
The previous implementations of the parameter server does not run a
-sub-graph. parameter initialization, optimizer computation, network
+subgraph. parameter initialization, optimizer computation, network
communication and checkpointing are implemented twice on both the
trainer and the parameter server.
@@ -26,35 +26,40 @@ server becomes a natural extension.
### Graph Converter
The *graph converter* converts the user-defined operation (OP) graph
-into sub-graphs to be scheduled on different nodes.
+into subgraphs to be scheduled on different nodes with the following
+steps:
-1. The user-defined OP graph will be cut into sub-graphs of
-different purposes (e.g., trainer, parameter server) to run on
-different workers.
+1. OP placement: the OPs will be placed on different nodes according
+ to heuristic that minimizes estimated total computation
+ time. Currently we will use a simple heuristic that puts parameter
+ varable on parameter server workers and everything else on trainer
+ workers.
-1. OPs will be added to the subgraphs, so the subgraphs can
-communicate with each other. We will need these OPs: *send*, *recv*,
-*gradient accumulator*, *string accumulator*, *loop forever*.
+1. Add communication OPs to enable the communication between nodes.
+
+We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
Below is an example of converting the user defined graph to the
-sub-graphs for the trainer and the parameter server:
+subgraphs for the trainer and the parameter server:
<img src="src/local-graph.png" width="300"/>
After converting:
-<img src="src/dist-graph.png" width="500"/>
+<img src="src/dist-graph.png" width="700"/>
1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
-1. Operators are added to the sub-graphs.
- - *send* operator sends data and sender's address to the destination.
- - *recv* operator receives data and sender's address from the
- destination. It will block until data has been received.
- - *gradient accumulator* operator accumulates *N* pieces of
- gradients. N=1 in Async-SGD, N>1 in Sync-SGD.
- - *string accumulator* accumulates *N* pieces of strings into a
- list of strings. N=1 in Async-SGD, N>1 in Sync-SGD.
- - *loop forever* runs itself as a target forever.
+1. Operators are added to the subgraphs.
+ - *Send* sends data to the connected *Recv* operator. The
+ scheduler on the receive node will only schedule *Recv* operator
+ to run when the *Send* operator has ran (the *Send* OP will mark
+ the *Recv* OP runnable automatically).
+ - *Enueue* enqueues the input variable, it can block until space
+ become available in the queue.
+ - *Dequeue* outputs configurable numbers of tensors from the
+ queue. It will block until the queue have the required number of
+ tensors.
+
### Benefits
@@ -71,8 +76,8 @@ After converting:
### Challenges
- It might be hard for the graph converter to cut a general graph
- (without any hint for which sub-graph is the optimizer). We may need
- to label which sub-graph inside the OP graph is the optimizer.
+ (without any hint for which subgraph is the optimizer). We may need
+ to label which subgraph inside the OP graph is the optimizer.
- It's important to balance the parameter shards of on multiple
parameter server. If a single parameter is very big (some
@@ -80,3 +85,19 @@ After converting:
automatically partition the single parameter onto different
parameter servers when possible (only element-wise optimizer depends
on the parameter variable).
+
+### Discussion
+
+- In the "Aync SGD" figure, the "W" variable on the parameter server
+ could be read and wrote concurrently, what is our locking strategy?
+
+- Does our current tensor design supports enqueue (put the input tensor
+ into the queue tensor)?
+
+- *Dequeue* OP will have variable numbers of output (depends on the
+ `min_count` attribute), does our current design support it? (similar
+ question for the *Add* OP)
+
+
+References:
+[1] (TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems)[https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf]
diff --git a/doc/design/ops/src/dist-graph.graffle b/doc/design/ops/src/dist-graph.graffle
index 6353f736042ed6056d4f59b0cb4072ae2f7008aa..941399c6ced8d5f65b6c595522b770c88259df4b 100644
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From 67eff9fa9cc36b6548d211c7f23143841b4b67d0 Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Thu, 31 Aug 2017 16:56:06 -0700
Subject: [PATCH 10/66] polish
---
doc/design/ops/dist_train.md | 13 ++++++++-----
1 file changed, 8 insertions(+), 5 deletions(-)
diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md
index 29940ac59d..fa3c5d7990 100644
--- a/doc/design/ops/dist_train.md
+++ b/doc/design/ops/dist_train.md
@@ -65,7 +65,8 @@ After converting:
- Model parallelism become easier to implement: it's an extension to
the trainer - parameter server approach. we already have the
- communication OPs, but need to extend the graph converter.
+ communication OPs, but need to extend the graph converter's
+ placement functionality.
- User-defined optimizer is easier to add - user can now express it as
a subgraph.
@@ -90,14 +91,16 @@ After converting:
- In the "Aync SGD" figure, the "W" variable on the parameter server
could be read and wrote concurrently, what is our locking strategy?
+ E.g., each variable have a lock cpp method to be invoked by every
+ OP, or, have a lock OP.
-- Does our current tensor design supports enqueue (put the input tensor
- into the queue tensor)?
+- Can the Enqueue OP be implemented under our current tensor design
+ (puts the input tensor into the queue tensor)?
- *Dequeue* OP will have variable numbers of output (depends on the
`min_count` attribute), does our current design support it? (similar
question for the *Add* OP)
-References:
-[1] (TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems)[https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf]
+### References:
+[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
From eaf23cb1e8e10f0a1d9ed9dfbfe9d16b05a57dbe Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 4 Sep 2017 16:53:33 +0800
Subject: [PATCH 11/66] fix ModelConfig.proto, addto_layer and concat_layer
---
proto/ModelConfig.proto | 2 +-
python/paddle/trainer/config_parser.py | 21 ++++++++++++++++---
.../paddle/trainer_config_helpers/layers.py | 4 ++++
3 files changed, 23 insertions(+), 4 deletions(-)
diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto
index e3248b21c7..6eb12b8991 100644
--- a/proto/ModelConfig.proto
+++ b/proto/ModelConfig.proto
@@ -517,7 +517,7 @@ message LayerConfig {
optional double delta = 57 [ default = 1.0 ];
// for 3D data
- optional double depth = 58 [ default = 1 ];
+ optional uint64 depth = 58 [ default = 1 ];
}
message EvaluatorConfig {
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 43c151d6d1..b6ae11147f 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -2735,9 +2735,21 @@ class AddToLayer(LayerBase):
super(AddToLayer, self).__init__(
name, 'addto', 0, inputs=inputs, **xargs)
config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer')
- for input_index in xrange(len(self.inputs)):
- input_layer = self.get_input_layer(input_index)
- self.set_layer_size(input_layer.size)
+
+ if len(self.inputs) > 1:
+ assert len(self.inputs) == 2
+ assert self.get_input_layer(0).size == self.get_input_layer(1).size
+ assert self.get_input_layer(0).depth == self.get_input_layer(
+ 1).depth
+ assert self.get_input_layer(0).height == self.get_input_layer(
+ 1).height
+ assert self.get_input_layer(0).width == self.get_input_layer(
+ 1).width
+
+ self.set_layer_size(self.get_input_layer(0).size)
+ self.set_layer_height_width(self.get_input_layer(0).height, \
+ self.get_input_layer(0).width)
+ self.set_layer_depth(self.get_input_layer(0).depth)
self.create_bias_parameter(bias, self.config.size)
@@ -3422,6 +3434,9 @@ class ConcatenateLayer(LayerBase):
if self.config.size == 0:
size += input_layer.size
+ self.set_layer_height_width(self.get_input_layer(0).height, \
+ self.get_input_layer(0).width)
+ self.set_layer_depth(self.get_input_layer(0).depth)
self.set_layer_size(size)
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index b167a3f78a..88cb328206 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -352,6 +352,10 @@ class LayerOutput(object):
def height(self):
return cp.g_layer_map[self.full_name].height
+ @property
+ def depth(self):
+ return cp.g_layer_map[self.full_name].depth
+
def set_input(self, input):
"""
Set the input for a memory layer. Can only be used for memory layer
From 0692dd206195fd0d2e4d825b609fbb8b05a3bbf2 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 5 Sep 2017 11:06:24 +0800
Subject: [PATCH 12/66] fix test_bi_grumemory.protostr,
test_recursive_topology.protostr, util_layers.protostr and new
test_sub_nested_seq_select_layer.protostr
---
.../protostr/test_bi_grumemory.protostr | 3 +
.../protostr/test_recursive_topology.protostr | 96 +++++++++++++++++++
.../test_sub_nested_seq_select_layer.protostr | 37 +++++++
.../configs/protostr/util_layers.protostr | 6 ++
4 files changed, 142 insertions(+)
create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_sub_nested_seq_select_layer.protostr
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr
index b110e91498..8a1399efad 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr
@@ -74,6 +74,9 @@ layers {
inputs {
input_layer_name: "__bidirectional_gru_0___bw"
}
+ height: 0
+ width: 0
+ depth: 1
}
parameters {
name: "___bidirectional_gru_0___fw_transform.w0"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr
index 8133aa9c8d..046037936a 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr
@@ -16,6 +16,9 @@ layers {
inputs {
input_layer_name: "data"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_1__"
@@ -28,6 +31,9 @@ layers {
inputs {
input_layer_name: "__addto_0__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_2__"
@@ -40,6 +46,9 @@ layers {
inputs {
input_layer_name: "__addto_1__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_3__"
@@ -52,6 +61,9 @@ layers {
inputs {
input_layer_name: "__addto_2__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_4__"
@@ -64,6 +76,9 @@ layers {
inputs {
input_layer_name: "__addto_3__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_5__"
@@ -76,6 +91,9 @@ layers {
inputs {
input_layer_name: "__addto_4__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_6__"
@@ -88,6 +106,9 @@ layers {
inputs {
input_layer_name: "__addto_5__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_7__"
@@ -100,6 +121,9 @@ layers {
inputs {
input_layer_name: "__addto_6__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_8__"
@@ -112,6 +136,9 @@ layers {
inputs {
input_layer_name: "__addto_7__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_9__"
@@ -124,6 +151,9 @@ layers {
inputs {
input_layer_name: "__addto_8__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_10__"
@@ -136,6 +166,9 @@ layers {
inputs {
input_layer_name: "__addto_9__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_11__"
@@ -148,6 +181,9 @@ layers {
inputs {
input_layer_name: "__addto_10__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_12__"
@@ -160,6 +196,9 @@ layers {
inputs {
input_layer_name: "__addto_11__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_13__"
@@ -172,6 +211,9 @@ layers {
inputs {
input_layer_name: "__addto_12__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_14__"
@@ -184,6 +226,9 @@ layers {
inputs {
input_layer_name: "__addto_13__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_15__"
@@ -196,6 +241,9 @@ layers {
inputs {
input_layer_name: "__addto_14__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_16__"
@@ -208,6 +256,9 @@ layers {
inputs {
input_layer_name: "__addto_15__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_17__"
@@ -220,6 +271,9 @@ layers {
inputs {
input_layer_name: "__addto_16__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_18__"
@@ -232,6 +286,9 @@ layers {
inputs {
input_layer_name: "__addto_17__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_19__"
@@ -244,6 +301,9 @@ layers {
inputs {
input_layer_name: "__addto_18__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_20__"
@@ -256,6 +316,9 @@ layers {
inputs {
input_layer_name: "__addto_19__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_21__"
@@ -268,6 +331,9 @@ layers {
inputs {
input_layer_name: "__addto_20__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_22__"
@@ -280,6 +346,9 @@ layers {
inputs {
input_layer_name: "__addto_21__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_23__"
@@ -292,6 +361,9 @@ layers {
inputs {
input_layer_name: "__addto_22__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_24__"
@@ -304,6 +376,9 @@ layers {
inputs {
input_layer_name: "__addto_23__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_25__"
@@ -316,6 +391,9 @@ layers {
inputs {
input_layer_name: "__addto_24__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_26__"
@@ -328,6 +406,9 @@ layers {
inputs {
input_layer_name: "__addto_25__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_27__"
@@ -340,6 +421,9 @@ layers {
inputs {
input_layer_name: "__addto_26__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_28__"
@@ -352,6 +436,9 @@ layers {
inputs {
input_layer_name: "__addto_27__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_29__"
@@ -364,6 +451,9 @@ layers {
inputs {
input_layer_name: "__addto_28__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_30__"
@@ -376,6 +466,9 @@ layers {
inputs {
input_layer_name: "__addto_29__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_31__"
@@ -388,6 +481,9 @@ layers {
inputs {
input_layer_name: "__addto_30__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__fc_layer_0__"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_sub_nested_seq_select_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_sub_nested_seq_select_layer.protostr
new file mode 100644
index 0000000000..4b906b113e
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_sub_nested_seq_select_layer.protostr
@@ -0,0 +1,37 @@
+type: "nn"
+layers {
+ name: "input_seq"
+ type: "data"
+ size: 300
+ active_type: ""
+}
+layers {
+ name: "input"
+ type: "data"
+ size: 5
+ active_type: ""
+}
+layers {
+ name: "__sub_nested_seq_layer_0__"
+ type: "sub_nested_seq"
+ size: 300
+ active_type: ""
+ inputs {
+ input_layer_name: "input_seq"
+ }
+ inputs {
+ input_layer_name: "input"
+ }
+}
+input_layer_names: "input_seq"
+output_layer_names: "__sub_nested_seq_layer_0__"
+sub_models {
+ name: "root"
+ layer_names: "input_seq"
+ layer_names: "input"
+ layer_names: "__sub_nested_seq_layer_0__"
+ input_layer_names: "input_seq"
+ output_layer_names: "__sub_nested_seq_layer_0__"
+ is_recurrent_layer_group: false
+}
+
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr
index d0ad388165..7a2f3eab38 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr
@@ -22,6 +22,9 @@ layers {
inputs {
input_layer_name: "b"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__concat_0__"
@@ -34,6 +37,9 @@ layers {
inputs {
input_layer_name: "b"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__concat_1__"
From f314330c238388dd25a8be27b138a81bee2d8bfb Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Tue, 5 Sep 2017 19:32:47 +0800
Subject: [PATCH 13/66] refactor operator python test and add sum operator
---
paddle/framework/operator.h | 25 +-
paddle/operators/sum_op.cc | 69 +++++
paddle/operators/sum_op.cu | 18 ++
paddle/operators/sum_op.h | 65 +++++
paddle/pybind/pybind.cc | 9 +
python/paddle/v2/framework/op.py | 10 +-
.../paddle/v2/framework/tests/CMakeLists.txt | 1 +
python/paddle/v2/framework/tests/op_test.py | 271 ++++++++++++++++++
.../framework/tests/test_cross_entropy_op.py | 30 +-
.../v2/framework/tests/test_sigmoid_op.py | 28 +-
.../paddle/v2/framework/tests/test_sum_op.py | 26 ++
11 files changed, 513 insertions(+), 39 deletions(-)
create mode 100644 paddle/operators/sum_op.cc
create mode 100644 paddle/operators/sum_op.cu
create mode 100644 paddle/operators/sum_op.h
create mode 100644 python/paddle/v2/framework/tests/op_test.py
create mode 100644 python/paddle/v2/framework/tests/test_sum_op.py
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index da92220b04..be302669cd 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -94,6 +94,27 @@ class OperatorBase {
const VariableNameMap& Inputs() const { return inputs_; }
const VariableNameMap& Outputs() const { return outputs_; }
+
+ const std::vector<std::string> InputsNames() const {
+ std::vector<std::string> result;
+ for (auto& kv : inputs_) {
+ for (auto& name : kv.second) {
+ result.push_back(name);
+ }
+ }
+ return result;
+ }
+
+ const std::vector<std::string> OutputsNames() const {
+ std::vector<std::string> result;
+ for (auto& kv : outputs_) {
+ for (auto& name : kv.second) {
+ result.push_back(name);
+ }
+ }
+ return result;
+ }
+
//! Get a input with argument's name described in `op_proto`
std::string Input(const std::string& name) const;
//! Get a input which has multiple variables.
@@ -311,9 +332,9 @@ class InferShapeContext {
}
template <typename T>
- std::vector<const T*> MultiOutput(const std::string& name) const {
+ std::vector<T*> MultiOutput(const std::string& name) const {
auto names = op_.Outputs(name);
- std::vector<const T*> res;
+ std::vector<T*> res;
res.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
diff --git a/paddle/operators/sum_op.cc b/paddle/operators/sum_op.cc
new file mode 100644
index 0000000000..cf650787c6
--- /dev/null
+++ b/paddle/operators/sum_op.cc
@@ -0,0 +1,69 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/sum_op.h"
+#include <vector>
+
+namespace paddle {
+namespace operators {
+using framework::Tensor;
+
+class SumOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto ins = ctx.MultiInput<framework::Tensor>("X");
+ auto *out = ctx.Output<framework::Tensor>("Out");
+ int N = ins.size();
+
+ PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
+
+ auto dim_zero = ins[0]->dims();
+ out->Resize(dim_zero);
+ }
+};
+
+class SumOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "the input tensors of sum operator.").AsDuplicable();
+ AddOutput("Out", "the output tensor of sum operator.");
+ AddComment(R"DOC(
+ Sum the input tensors.
+ )DOC");
+ }
+};
+
+class SumGradOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto outputs = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
+ auto dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+ for (auto output : outputs) {
+ output->Resize(dims);
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp);
+REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(sum_grad,
+ ops::SumGradKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/sum_op.cu b/paddle/operators/sum_op.cu
new file mode 100644
index 0000000000..a465cf3659
--- /dev/null
+++ b/paddle/operators/sum_op.cu
@@ -0,0 +1,18 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/sum_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(sum, ops::SumKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(sum_grad,
+ ops::SumGradKernel<paddle::platform::GPUPlace, float>);
diff --git a/paddle/operators/sum_op.h b/paddle/operators/sum_op.h
new file mode 100644
index 0000000000..0b1e9ebaa3
--- /dev/null
+++ b/paddle/operators/sum_op.h
@@ -0,0 +1,65 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+ typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class SumKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto ins = context.MultiInput<Tensor>("X");
+ auto* out = context.Output<Tensor>("Out");
+ out->mutable_data<T>(context.GetPlace());
+
+ auto place = context.GetEigenDevice<Place>();
+ auto result = EigenVector<T>::Flatten(*out);
+
+ int N = ins.size();
+ auto in = EigenVector<T>::Flatten(*(ins[0]));
+ result.device(place) = in;
+ for (int i = 1; i < N; i++) {
+ auto in = EigenVector<T>::Flatten(*(ins[i]));
+ result.device(place) = result + in;
+ }
+ }
+};
+
+template <typename Place, typename T>
+class SumGradKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto* input = context.Input<Tensor>(framework::GradVarName("Out"));
+ auto outs = context.MultiOutput<Tensor>(framework::GradVarName("X"));
+ for (auto out : outs) {
+ out->mutable_data<T>(context.GetPlace());
+ }
+
+ auto place = context.GetEigenDevice<Place>();
+ auto in = EigenVector<T>::Flatten(*input);
+ for (auto out : outs) {
+ auto result = EigenVector<T>::Flatten(*out);
+ result.device(place) = in;
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 6896422617..a678bc4940 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -48,6 +48,7 @@ USE_NO_KERNEL_OP(identity);
USE_OP(minus);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
+USE_OP(sum);
namespace paddle {
namespace framework {
@@ -213,7 +214,15 @@ All parameter, weight, gradient are variables in Paddle.
-> std::map<std::string, std::vector<std::string>> {
return op.Outputs();
})
+ .def("outputs_names",
+ [](const OperatorBase &op) -> std::vector<std::string> {
+ return op.OutputsNames();
+ })
.def("inputs", [](const OperatorBase &op) { return op.Inputs(); })
+ .def("inputs_names",
+ [](const OperatorBase &op) -> std::vector<std::string> {
+ return op.InputsNames();
+ })
.def("__str__", &OperatorBase::DebugString)
.def("no_intermediate_outputs",
[](const OperatorBase &op) { return op.OutputVars(false); })
diff --git a/python/paddle/v2/framework/op.py b/python/paddle/v2/framework/op.py
index e7e932f6fe..431cf4b260 100644
--- a/python/paddle/v2/framework/op.py
+++ b/python/paddle/v2/framework/op.py
@@ -133,8 +133,8 @@ def create_op_creation_method(op_proto):
return OpInfo(
method=__impl__,
name=op_proto.type,
- inputs=[var.name for var in op_proto.inputs],
- outputs=[var.name for var in op_proto.outputs],
+ inputs=[(var.name, var.duplicable) for var in op_proto.inputs],
+ outputs=[(var.name, var.duplicable) for var in op_proto.outputs],
attrs=[attr.name for attr in op_proto.attrs])
@@ -168,9 +168,15 @@ class OperatorFactory(object):
return self.op_methods.get(t)
def get_op_input_names(self, type):
+ return map(lambda x: x[0], self.get_op_info(type).inputs)
+
+ def get_op_inputs(self, type):
return self.get_op_info(type).inputs
def get_op_output_names(self, type):
+ return map(lambda x: x[0], self.get_op_info(type).outputs)
+
+ def get_op_outputs(self, type):
return self.get_op_info(type).outputs
def get_op_attr_names(self, type):
diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt
index 661ebd8964..a4ce263039 100644
--- a/python/paddle/v2/framework/tests/CMakeLists.txt
+++ b/python/paddle/v2/framework/tests/CMakeLists.txt
@@ -31,4 +31,5 @@ py_test(test_sgd_op SRCS test_sgd_op.py)
py_test(test_gradient_checker SRCS test_gradient_checker.py)
py_test(test_lookup_table SRCS test_lookup_table.py)
py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)
+py_test(test_sum_op SRCS test_sum_op.py)
py_test(mnist SRCS mnist.py)
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
new file mode 100644
index 0000000000..9b2f10fdf9
--- /dev/null
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -0,0 +1,271 @@
+import unittest
+import numpy as np
+import itertools
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+
+
+def grad_var_name(var_name):
+ return var_name + "@GRAD"
+
+
+def remove_grad_var_name(var_name):
+ return var_name[0:-5]
+
+
+def create_op(scope, op_type, inputs, outputs, attrs=None):
+ kwargs = dict()
+
+ for ins in Operator.get_op_inputs(op_type):
+ in_name = ins[0]
+ in_dup = ins[1]
+ if in_name in inputs:
+ kwargs[in_name] = []
+ if in_dup:
+ sub_in = inputs[in_name]
+ for sub_in_name in sub_in:
+ var = scope.new_var(sub_in_name)
+ tensor = var.get_tensor()
+ kwargs[in_name].append(sub_in_name)
+ else:
+ var = scope.new_var(in_name)
+ tensor = var.get_tensor()
+ kwargs[in_name].append(in_name)
+
+ for outs in Operator.get_op_outputs(op_type):
+ out_name = outs[0]
+ out_dup = outs[1]
+ if out_name in outputs:
+ kwargs[out_name] = []
+ if out_dup:
+ sub_in = outputs[out_name]
+ for sun_in_name in sub_in:
+ var = scope.new_var(sun_in_name)
+ tensor = var.get_tensor()
+ kwargs[out_name].append(sun_in_name)
+ else:
+ var = scope.new_var(out_name)
+ tensor = var.get_tensor()
+ kwargs[out_name].append(out_name)
+
+ # for attr_name in Operator.get_op_attr_names(op_type):
+ # kwargs[attr_name] = attrs[attr_name]
+ return Operator(op_type, **kwargs)
+
+
+def set_input(scope, op, inputs, place):
+ for ins in Operator.get_op_inputs(op.type()):
+ in_name = ins[0]
+ in_dup = ins[1]
+ if in_name in inputs:
+ if in_dup:
+ sub_in = inputs[in_name]
+ for sub_in_name in sub_in:
+ var = scope.find_var(sub_in_name)
+ tensor = var.get_tensor()
+ arr = sub_in[sub_in_name]
+ tensor.set_dims(arr.shape)
+ tensor.set(arr, place)
+ else:
+ var = scope.find_var(in_name)
+ tensor = var.get_tensor()
+ arr = inputs[in_name]
+ tensor.set_dims(arr.shape)
+ tensor.set(arr, place)
+
+
+def set_output_grad(scope, op, outputs, place):
+ for outs in Operator.get_op_outputs(op.type()):
+ out_name = outs[0]
+ out_dup = outs[1]
+ if out_name in outputs:
+ if out_dup:
+ sub_out = outputs[out_name]
+ for sub_out_name in sub_out:
+ out_tensor = scope.find_var(sub_out_name).get_tensor()
+ grad_tensor = scope.new_var(grad_var_name(
+ sub_out_name)).get_tensor()
+ grad_tensor.set_dims(out_tensor.shape())
+ data = np.ones(out_tensor.shape(), dtype=np.float32)
+ grad_tensor.set(data, place)
+ else:
+ out_tensor = scope.find_var(out_name).get_tensor()
+ grad_tensor = scope.new_var(grad_var_name(out_name)).get_tensor(
+ )
+ grad_tensor.set_dims(out_tensor.shape())
+ data = np.ones(out_tensor.shape(), dtype=np.float32)
+ grad_tensor.set(data, place)
+
+
+def get_numeric_gradient(scope,
+ op,
+ inputs,
+ input_to_check,
+ output_name,
+ delta=0.005,
+ in_place=False):
+
+ set_input(scope, op, inputs, core.CPUPlace())
+ op.infer_shape(scope)
+
+ tensor_to_check = scope.find_var(input_to_check).get_tensor()
+
+ def product(dim):
+ return reduce(lambda a, b: a * b, dim, 1)
+
+ ctx = core.DeviceContext.create(core.CPUPlace())
+
+ def get_output():
+ op.run(scope, ctx)
+ return np.array(scope.find_var(output_name).get_tensor()).sum()
+
+ tensor_to_check = scope.find_var(input_to_check).get_tensor()
+ tensor_size = product(tensor_to_check.get_dims())
+ gradient_flat = np.zeros(shape=(tensor_size, ), dtype='float32')
+ # we only compute gradient of one element each time.
+ # we use a for loop to compute the gradient of every element.
+ for i in xrange(tensor_size):
+ if in_place:
+ set_input(op, inputs, core.CPUPlace())
+
+ # get one input element throw it's index i.
+ origin = tensor_to_check.get_float_element(i)
+ # add delta to it, run op and then get the sum of the result tensor.
+ x_pos = origin + delta
+ tensor_to_check.set_float_element(i, x_pos)
+ y_pos = get_output()
+
+ if in_place:
+ set_input(op, inputs, core.CPUPlace())
+
+ x_neg = origin - delta
+ tensor_to_check.set_float_element(i, x_neg)
+ y_neg = get_output()
+
+ tensor_to_check.set_float_element(i, origin)
+ gradient_flat[i] = (y_pos - y_neg) / delta / 2
+
+ return gradient_flat.reshape(tensor_to_check.get_dims())
+
+
+def get_backward_op(scope, op, no_grad_set):
+ backward_op = core.Operator.backward(op, no_grad_set)
+ for input in backward_op.inputs_names():
+ var = scope.new_var(input)
+ var.get_tensor()
+ for output in backward_op.outputs_names():
+ var = scope.new_var(output)
+ var.get_tensor()
+ return backward_op
+
+
+def get_gradient(scope, op, inputs, outputs, grad_name, place,
+ no_grad_set=None):
+ ctx = core.DeviceContext.create(place)
+
+ set_input(scope, op, inputs, place)
+
+ op.infer_shape(scope)
+ op.run(scope, ctx)
+
+ if no_grad_set is None:
+ no_grad_set = set()
+
+ backward_op = get_backward_op(scope, op, no_grad_set)
+ set_output_grad(scope, op, outputs, place)
+
+ backward_op.infer_shape(scope)
+ backward_op.run(scope, ctx)
+
+ out = np.array(scope.find_var(grad_name).get_tensor())
+ return out
+
+
+class OpTest(unittest.TestCase):
+ def check_output(self, place):
+ self.scope = core.Scope()
+ self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
+ if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
+ return
+ set_input(self.scope, self.op, self.inputs, place)
+ self.op.infer_shape(self.scope)
+ ctx = core.DeviceContext.create(place)
+ self.op.run(self.scope, ctx)
+
+ for outs in Operator.get_op_outputs(self.op.type()):
+ out_name = outs[0]
+ out_dup = outs[1]
+ if out_dup:
+ sub_out = self.outputs[out_name]
+ for sub_out_name in sub_out:
+ actual = np.array(
+ self.scope.find_var(sub_out_name).get_tensor())
+ expect = sub_out[sub_out_name]
+ self.assertTrue(
+ np.allclose(
+ actual, expect, atol=1e-05),
+ "output name: " + out_name + "has diff")
+ else:
+ actual = np.array(self.scope.find_var(out_name).get_tensor())
+ expect = self.outputs[out_name]
+ self.assertTrue(
+ np.allclose(
+ actual, expect, atol=1e-05),
+ "output name: " + out_name + "has diff")
+
+ def __assert_is_close(self, numeric_grads, analytic_grads, names,
+ max_relative_error, msg_prefix):
+
+ for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
+ abs_a = np.abs(a)
+ abs_a[abs_a < 1e-3] = 1
+
+ diff_mat = np.abs(a - b) / abs_a
+ max_diff = np.max(diff_mat)
+
+ def err_msg():
+ offset = np.argmax(diff_mat > max_relative_error)
+ return "%s Variable %s max gradient diff %f over limit %f, the first " \
+ "error element is %d" % (
+ msg_prefix, name, max_diff, max_relative_error, offset)
+
+ self.assertLessEqual(max_diff, max_relative_error, err_msg())
+
+ def check_grad(self,
+ inputs_to_check,
+ output_name,
+ no_grad_set=None,
+ in_place=False,
+ max_relative_error=0.005):
+ self.scope = core.Scope()
+ self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
+ if no_grad_set is None:
+ no_grad_set = set()
+
+ numeric_grads = [
+ get_numeric_gradient(
+ self.scope,
+ self.op,
+ self.inputs,
+ input_to_check,
+ output_name,
+ in_place=in_place) for input_to_check in inputs_to_check
+ ]
+ grad_names = [
+ grad_var_name(input_to_check) for input_to_check in inputs_to_check
+ ]
+
+ places = [core.CPUPlace()]
+ if core.is_compile_gpu() and op.support_gpu():
+ places.append(core.GPUPlace(0))
+
+ for place in places:
+ analytic_grads = [
+ get_gradient(self.scope, self.op, self.inputs, self.outputs,
+ grad_name, place, no_grad_set)
+ for grad_name in grad_names
+ ]
+
+ self.__assert_is_close(numeric_grads, analytic_grads, grad_names,
+ max_relative_error,
+ "Gradient Check On %s" % str(place))
diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
index d4277f2a42..20e0de3520 100644
--- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py
+++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
@@ -1,36 +1,28 @@
import unittest
import numpy
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
+import paddle.v2.framework.core as core
-class TestCrossEntropy(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestCrossEntropy(OpTest):
def setUp(self):
- self.type = "onehot_cross_entropy"
- batch_size = 30
- class_num = 10
+ self.op_type = "onehot_cross_entropy"
+ batch_size = 4
+ class_num = 4
X = numpy.random.random((batch_size, class_num)).astype("float32")
- label = 5 * numpy.ones(batch_size).astype("int32")
+ label = (class_num / 2) * numpy.ones(batch_size).astype("int32")
self.inputs = {'X': X, 'label': label}
Y = []
for i in range(0, batch_size):
Y.append(-numpy.log(X[i][label[i]]))
self.outputs = {'Y': numpy.array(Y).astype("float32")}
+ def test_check_output(self):
+ self.check_output(core.CPUPlace())
+ self.check_output(core.GPUPlace(0))
-class CrossEntropyGradOpTest(GradientChecker):
def test_check_grad(self):
- op = create_op("onehot_cross_entropy")
- batch_size = 30
- class_num = 10
- inputs = {
- "X": numpy.random.uniform(
- 0.1, 1.0, [batch_size, class_num]).astype("float32"),
- "label": (class_num / 2) * numpy.ones(batch_size).astype("int32")
- }
- self.check_grad(op, inputs, set("X"), "Y")
+ self.check_grad(["X"], "Y")
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py
index 273c2e5ab1..ff0823508f 100644
--- a/python/paddle/v2/framework/tests/test_sigmoid_op.py
+++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py
@@ -1,27 +1,23 @@
import unittest
import numpy as np
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
+import paddle.v2.framework.core as core
-class TestSigmoidOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSigmoid(OpTest):
def setUp(self):
- self.type = "sigmoid"
- self.inputs = {'X': np.random.random((15, 31)).astype("float32")}
+ self.op_type = "sigmoid"
+ self.inputs = {
+ 'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+ }
self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
+ def test_check_output(self):
+ self.check_output(core.CPUPlace())
+ self.check_output(core.GPUPlace(0))
-class TestSigmoidGradOp(GradientChecker):
- def test_grad(self):
- op = create_op("sigmoid")
- inputs = {"X": np.random.uniform(0.1, 1, [11, 17]).astype("float32")}
- # compare gpu and cpu results for backward op.
- # this test will be skiped if only compiling CPU version.
- self.compare_grad(op, inputs)
- # check gradients
- self.check_grad(op, inputs, set("X"), "Y", max_relative_error=0.007)
+ def test_check_grad(self):
+ self.check_grad(["X"], "Y", max_relative_error=0.007)
if __name__ == '__main__':
diff --git a/python/paddle/v2/framework/tests/test_sum_op.py b/python/paddle/v2/framework/tests/test_sum_op.py
new file mode 100644
index 0000000000..2a7b65ef52
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_sum_op.py
@@ -0,0 +1,26 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.v2.framework.core as core
+
+
+class TestSumOp(OpTest):
+ def setUp(self):
+ self.op_type = "sum"
+ x0 = np.random.random((3, 4)).astype('float32')
+ x1 = np.random.random((3, 4)).astype('float32')
+ x2 = np.random.random((3, 4)).astype('float32')
+ self.inputs = {"X": {"x0": x0, "x1": x1, "x2": x2}}
+ y = x0 + x1 + x2
+ self.outputs = {'Out': y}
+
+ def test_check_output(self):
+ self.check_output(core.CPUPlace())
+ self.check_output(core.GPUPlace(0))
+
+ def test_check_grad(self):
+ self.check_grad(["x0"], "Out")
+
+
+if __name__ == '__main__':
+ unittest.main()
From a22606ca0c6e4c79b357dc07290698541d505276 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Tue, 5 Sep 2017 19:36:04 +0800
Subject: [PATCH 14/66] fix bug
---
python/paddle/v2/framework/tests/op_test.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index 9b2f10fdf9..3f8e1236ff 100644
--- a/python/paddle/v2/framework/tests/op_test.py
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -256,7 +256,7 @@ class OpTest(unittest.TestCase):
]
places = [core.CPUPlace()]
- if core.is_compile_gpu() and op.support_gpu():
+ if core.is_compile_gpu() and self.op.support_gpu():
places.append(core.GPUPlace(0))
for place in places:
From 4cb2966d7b60895d33fb9c951c3707b80c891174 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 5 Sep 2017 19:16:59 +0800
Subject: [PATCH 15/66] fix config_parser.py(concat_layer and addto_layer)
---
python/paddle/trainer/config_parser.py | 21 +++++++++++++--------
1 file changed, 13 insertions(+), 8 deletions(-)
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index b6ae11147f..ffd31f48f9 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -2737,14 +2737,13 @@ class AddToLayer(LayerBase):
config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer')
if len(self.inputs) > 1:
- assert len(self.inputs) == 2
- assert self.get_input_layer(0).size == self.get_input_layer(1).size
- assert self.get_input_layer(0).depth == self.get_input_layer(
- 1).depth
- assert self.get_input_layer(0).height == self.get_input_layer(
- 1).height
- assert self.get_input_layer(0).width == self.get_input_layer(
- 1).width
+ for input_index in xrange(len(self.inputs)):
+ assert self.get_input_layer(0).height == self.get_input_layer(
+ input_index).height
+ assert self.get_input_layer(0).width == self.get_input_layer(
+ input_index).width
+ assert self.get_input_layer(0).depth == self.get_input_layer(
+ input_index).depth
self.set_layer_size(self.get_input_layer(0).size)
self.set_layer_height_width(self.get_input_layer(0).height, \
@@ -3429,6 +3428,12 @@ class ConcatenateLayer(LayerBase):
name, 'concat', 0, inputs=inputs, **xargs)
size = 0
for input_index in xrange(len(self.inputs)):
+ assert self.get_input_layer(0).height == self.get_input_layer(
+ input_index).height
+ assert self.get_input_layer(0).width == self.get_input_layer(
+ input_index).width
+ assert self.get_input_layer(0).depth == self.get_input_layer(
+ input_index).depth
input_layer = self.get_input_layer(input_index)
input = self.inputs[input_index]
if self.config.size == 0:
From 5500153a6d4fd4a70f0aa3adc773131b2d539d7a Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 6 Sep 2017 11:34:02 +0800
Subject: [PATCH 16/66] fix cudnnBatchNorm for 3D data
---
paddle/gserver/layers/CudnnBatchNormLayer.cpp | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/paddle/gserver/layers/CudnnBatchNormLayer.cpp b/paddle/gserver/layers/CudnnBatchNormLayer.cpp
index 44ba2c4b7d..49a9540c0b 100644
--- a/paddle/gserver/layers/CudnnBatchNormLayer.cpp
+++ b/paddle/gserver/layers/CudnnBatchNormLayer.cpp
@@ -37,7 +37,7 @@ bool CudnnBatchNormLayer::init(const LayerMap& layerMap,
}
void CudnnBatchNormLayer::reshape(int batchSize) {
- hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_, imageW_);
+ hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_ * imageD_, imageW_);
}
void CudnnBatchNormLayer::forward(PassType passType) {
@@ -104,7 +104,7 @@ void CudnnBatchNormLayer::forward(PassType passType) {
EPS,
batchSize,
channels_,
- imageH_,
+ imageH_ * imageD_,
imageW_);
}
}
From 12eaa22ad2d099717e6ddf2da856b67b6d887510 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Wed, 6 Sep 2017 21:25:58 -0700
Subject: [PATCH 17/66] add reshape operator
---
paddle/operators/reshape_op.cc | 84 +++++++++++++++++++
paddle/operators/reshape_op.cu | 22 +++++
paddle/operators/reshape_op.h | 60 +++++++++++++
paddle/pybind/pybind.cc | 1 +
.../paddle/v2/framework/tests/CMakeLists.txt | 1 +
.../v2/framework/tests/test_reshape_op.py | 28 +++++++
6 files changed, 196 insertions(+)
create mode 100644 paddle/operators/reshape_op.cc
create mode 100644 paddle/operators/reshape_op.cu
create mode 100644 paddle/operators/reshape_op.h
create mode 100644 python/paddle/v2/framework/tests/test_reshape_op.py
diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
new file mode 100644
index 0000000000..1b073a79bc
--- /dev/null
+++ b/paddle/operators/reshape_op.cc
@@ -0,0 +1,84 @@
+
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License. */
+
+#include "paddle/operators/reshape_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ReshapeOp : public framework::OperatorWithKernel {
+ public:
+ ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
+ const framework::VariableNameMap &outputs,
+ const framework::AttributeMap &attrs)
+ : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto *in = ctx.Input<framework::Tensor>("X");
+ auto shape = ctx.Attr<std::vector<int>>("shape");
+ PADDLE_ENFORCE_EQ((unsigned)shape.size(), in->dims().size(),
+ "The dimension of Input(X) mismatches with Attr(shape).");
+ size_t shape_size = 1;
+ for (auto dim : shape) {
+ shape_size *= dim;
+ }
+ size_t in_size = framework::product(in->dims());
+ PADDLE_ENFORCE_EQ(shape_size, in_size,
+ "The size of Input(X) mismatches with Attr(shape).");
+ }
+};
+
+class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ ReshapeOpMaker(framework::OpProto *proto,
+ framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "The input tensor of reshape operator.");
+ AddOutput("Out", "The output tensor of reshape operator.");
+ AddAttr<std::vector<int>>("shape", "Target shape of reshape operator.");
+ AddComment(R"DOC(Reshape operator
+
+The input tensor will be reshaped with Attr(shape).
+)DOC");
+ }
+};
+
+class ReshapeGradOp : public framework::OperatorWithKernel {
+ public:
+ ReshapeGradOp(const std::string &type,
+ const framework::VariableNameMap &inputs,
+ const framework::VariableNameMap &outputs,
+ const framework::AttributeMap &attrs)
+ : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto dims = ctx.Input<framework::Tensor>("X")->dims();
+ auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+ d_in->Resize(dims);
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad,
+ ops::ReshapeGradOp);
+REGISTER_OP_CPU_KERNEL(reshape,
+ ops::ReshapeKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+ reshape_grad, ops::ReshapeGradKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/reshape_op.cu b/paddle/operators/reshape_op.cu
new file mode 100644
index 0000000000..23dbe089d3
--- /dev/null
+++ b/paddle/operators/reshape_op.cu
@@ -0,0 +1,22 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License. */
+
+#include "paddle/operators/reshape_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+ reshape,
+ paddle::operators::ReshapeKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+ reshape_grad,
+ paddle::operators::ReshapeGradKernel<paddle::platform::GPUPlace, float>);
diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h
new file mode 100644
index 0000000000..22ede88b12
--- /dev/null
+++ b/paddle/operators/reshape_op.h
@@ -0,0 +1,60 @@
+
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename Place, typename T, typename AttrType = T>
+class ReshapeKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const {
+ auto* out = ctx.Output<Tensor>("Out");
+ auto* in = ctx.Input<Tensor>("X");
+ out->mutable_data<T>(in->place());
+
+ auto shape = ctx.Attr<std::vector<int>>("shape");
+ std::vector<int64_t> tmp;
+ for (auto dim : shape) {
+ tmp.push_back(dim);
+ }
+ auto out_dims = framework::make_ddim(tmp);
+ out->CopyFrom<T>(*in, ctx.GetPlace());
+ out->Resize(out_dims);
+ }
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class ReshapeGradKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const {
+ auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
+ auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+ d_x->mutable_data<T>(ctx.GetPlace());
+
+ auto in_dims = d_x->dims();
+
+ d_x->CopyFrom<T>(*d_out, ctx.GetPlace());
+ d_x->Resize(in_dims);
+ }
+};
+}
+}
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index c21ad3470b..bf1a321c3f 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -50,6 +50,7 @@ USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
USE_OP(squared_l2_distance);
+USE_OP(reshape);
namespace paddle {
namespace framework {
diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt
index a9c33ea163..9d41b84e57 100644
--- a/python/paddle/v2/framework/tests/CMakeLists.txt
+++ b/python/paddle/v2/framework/tests/CMakeLists.txt
@@ -34,3 +34,4 @@ py_test(test_lookup_table SRCS test_lookup_table.py)
py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)
py_test(mnist SRCS mnist.py)
py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
+py_test(test_reshape_op SRCS test_reshape_op.py)
diff --git a/python/paddle/v2/framework/tests/test_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py
new file mode 100644
index 0000000000..c101b0df9a
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_reshape_op.py
@@ -0,0 +1,28 @@
+import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+
+class TestReshapeOp(unittest.TestCase):
+ __metaclass__ = OpTestMeta
+
+ def setUp(self):
+ self.type = "reshape"
+ self.inputs = {'X': np.random.random((2, 4)).astype("float32"), }
+ print self.inputs
+ self.attrs = {'shape': [4, 2]}
+ self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
+ print self.outputs
+
+
+class ReshapeGradOpTest(GradientChecker):
+ def test_normal(self):
+ op = create_op("reshape")
+ inputs = {"X": np.random.random((2, 4)).astype("float32")}
+ attrs = {'shape': [4, 2]}
+ self.check_grad(op, inputs, attrs, set("X"), "Out")
+
+
+if __name__ == '__main__':
+ unittest.main()
From bea82122dd3c66e3a4cd69939a7ac68f7cce9524 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Thu, 7 Sep 2017 15:29:42 +0800
Subject: [PATCH 18/66] Expose LoDTensor to pybind.
---
paddle/pybind/pybind.cc | 26 ++++++++++++++++++++++++++
1 file changed, 26 insertions(+)
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index ba28b51ade..0b9d2697d2 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -17,6 +17,7 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/backward.h"
+#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
@@ -54,6 +55,7 @@ namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
+using LODTensor = framework::LODTensor;
static size_t UniqueIntegerGenerator() {
static std::atomic<size_t> generator;
@@ -113,6 +115,25 @@ PYBIND11_PLUGIN(core) {
return self.data<float>()[offset];
});
+ py::class_<LODTensor>(m, "LODTensor", R"DOC(LOD(Leval of Ddetails) Tensor.
+
+The tensor and LOD info should be created before creating the LODTensor, then
+call the set_tensor and set_lod functions to set them.
+
+)DOC")
+ .def("set_tensor",
+ [](LODTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
+ .def("set_lod",
+ [](LODTensor &self, std::vector<std::vector<size_t>> &lod) {
+ self.set_lod(lod);
+ })
+ .def("get_tensor",
+ [](LODTensor &self) -> Tensor & { return self.tensor(); },
+ py::return_value_policy::reference)
+ .def("get_lod", [](LODTensor &self) -> std::vector<std::vector<size_t>> {
+ return self.lod();
+ });
+
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
@@ -124,6 +145,11 @@ All parameter, weight, gradient are variables in Paddle.
.def("get_tensor",
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
py::return_value_policy::reference)
+ .def("get_lod_tensor",
+ [](Variable &self) -> LODTensor * {
+ return self.GetMutable<LODTensor>();
+ },
+ py::return_value_policy::reference)
.def("get_net",
[](Variable &self) -> operators::NetOp * {
return self.GetMutable<operators::NetOp>();
From 899c7d6b353c04565ebaa46d85de57348631f2e1 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Thu, 7 Sep 2017 04:16:32 -0700
Subject: [PATCH 19/66] pass unit test
---
paddle/operators/reshape_op.cc | 3 ++-
paddle/operators/reshape_op.h | 7 +++----
.../paddle/v2/framework/tests/test_reshape_op.py | 15 ++++++---------
3 files changed, 11 insertions(+), 14 deletions(-)
diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
index 1b073a79bc..d75ec76632 100644
--- a/paddle/operators/reshape_op.cc
+++ b/paddle/operators/reshape_op.cc
@@ -38,6 +38,7 @@ class ReshapeOp : public framework::OperatorWithKernel {
size_t in_size = framework::product(in->dims());
PADDLE_ENFORCE_EQ(shape_size, in_size,
"The size of Input(X) mismatches with Attr(shape).");
+ ctx.Output<framework::Tensor>("Out")->Resize(in->dims());
}
};
@@ -51,7 +52,7 @@ class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>("shape", "Target shape of reshape operator.");
AddComment(R"DOC(Reshape operator
-The input tensor will be reshaped with Attr(shape).
+Reshape Input(X) into the shape specified by Attr(shape).
)DOC");
}
};
diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h
index 22ede88b12..61d502c836 100644
--- a/paddle/operators/reshape_op.h
+++ b/paddle/operators/reshape_op.h
@@ -23,13 +23,13 @@ namespace operators {
using Tensor = framework::Tensor;
-template <typename Place, typename T, typename AttrType = T>
+template <typename Place, typename T>
class ReshapeKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* out = ctx.Output<Tensor>("Out");
auto* in = ctx.Input<Tensor>("X");
- out->mutable_data<T>(in->place());
+ out->mutable_data<T>(ctx.GetPlace());
auto shape = ctx.Attr<std::vector<int>>("shape");
std::vector<int64_t> tmp;
@@ -42,7 +42,7 @@ class ReshapeKernel : public framework::OpKernel {
}
};
-template <typename Place, typename T, typename AttrType = T>
+template <typename Place, typename T>
class ReshapeGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
@@ -51,7 +51,6 @@ class ReshapeGradKernel : public framework::OpKernel {
d_x->mutable_data<T>(ctx.GetPlace());
auto in_dims = d_x->dims();
-
d_x->CopyFrom<T>(*d_out, ctx.GetPlace());
d_x->Resize(in_dims);
}
diff --git a/python/paddle/v2/framework/tests/test_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py
index c101b0df9a..4797019435 100644
--- a/python/paddle/v2/framework/tests/test_reshape_op.py
+++ b/python/paddle/v2/framework/tests/test_reshape_op.py
@@ -1,6 +1,6 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
+from gradient_checker import GradientChecker, Operator
from op_test_util import OpTestMeta
@@ -9,19 +9,16 @@ class TestReshapeOp(unittest.TestCase):
def setUp(self):
self.type = "reshape"
- self.inputs = {'X': np.random.random((2, 4)).astype("float32"), }
- print self.inputs
- self.attrs = {'shape': [4, 2]}
+ self.inputs = {'X': np.random.random((37, 51)).astype("float32"), }
+ self.attrs = {'shape': [51, 37]}
self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
- print self.outputs
class ReshapeGradOpTest(GradientChecker):
def test_normal(self):
- op = create_op("reshape")
- inputs = {"X": np.random.random((2, 4)).astype("float32")}
- attrs = {'shape': [4, 2]}
- self.check_grad(op, inputs, attrs, set("X"), "Out")
+ op = Operator("reshape", X='X', Out='Out', shape=[5, 40])
+ inputs = {"X": np.random.random((10, 20)).astype("float32")}
+ self.check_grad(op, inputs, set("X"), "Out")
if __name__ == '__main__':
From a2a69f2a54cd7588ede6846deac758e8e8dc6b6e Mon Sep 17 00:00:00 2001
From: qingqing01 <dangqingqing@baidu.com>
Date: Thu, 7 Sep 2017 20:33:48 +0800
Subject: [PATCH 20/66] Add function to get element count from tensor.
---
paddle/framework/tensor.h | 6 ++++++
paddle/framework/tensor_impl.h | 13 ++++++++-----
paddle/operators/cos_sim_op.h | 4 ++--
paddle/operators/gaussian_random_op.cc | 2 +-
paddle/operators/gaussian_random_op.cu | 4 ++--
paddle/operators/lookup_table_op.cu | 4 ++--
paddle/operators/lookup_table_op.h | 4 ++--
paddle/operators/mean_op.h | 5 ++---
paddle/operators/minus_op.cc | 3 +--
paddle/operators/squared_l2_distance_op.cc | 6 ++----
paddle/operators/squared_l2_distance_op.h | 4 ++--
paddle/operators/uniform_random_op.cc | 2 +-
paddle/operators/uniform_random_op.cu | 4 ++--
13 files changed, 33 insertions(+), 28 deletions(-)
diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h
index 643f875491..fc54ed697f 100644
--- a/paddle/framework/tensor.h
+++ b/paddle/framework/tensor.h
@@ -78,6 +78,9 @@ class Tensor {
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
+ /*! Return the numel of the memory block. */
+ inline int64_t numel() const;
+
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
@@ -159,6 +162,9 @@ class Tensor {
/*! points to dimensions of memory block. */
DDim dims_;
+ /*! the element count of tensor. */
+ int64_t numel_;
+
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h
index 94f436294f..03678784b4 100644
--- a/paddle/framework/tensor_impl.h
+++ b/paddle/framework/tensor_impl.h
@@ -24,7 +24,7 @@ inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tenosr holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_GE(
- holder_->size(), product(dims_) * sizeof(T) + offset_,
+ holder_->size(), numel_ * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
@@ -54,11 +54,11 @@ inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
template <typename T>
inline T* Tensor::mutable_data(platform::Place place) {
static_assert(std::is_pod<T>::value, "T must be POD");
- PADDLE_ENFORCE_GT(product(dims_), 0,
+ PADDLE_ENFORCE_GT(numel_, 0,
"Tensor's numel must be larger than zero to call "
"Tensor::mutable_data. Call Tensor::set_dim first.");
/* some versions of boost::variant don't have operator!= */
- int64_t size = product(dims_) * sizeof(T);
+ int64_t size = numel_ * sizeof(T);
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
@@ -97,7 +97,7 @@ inline void Tensor::CopyFrom(const Tensor& src,
auto dst_ptr = static_cast<void*>(mutable_data<T>(dst_place));
- auto size = product(src.dims_) * sizeof(T);
+ auto size = src.numel() * sizeof(T);
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
@@ -131,7 +131,7 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
PADDLE_ENFORCE_LT(begin_idx, end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE_NE(dims_[0], 1, "Can not slice a tensor with dims_[0] = 1.");
- size_t base = product(dims_) / dims_[0];
+ size_t base = numel_ / dims_[0];
Tensor dst;
dst.holder_ = holder_;
DDim dst_dims = dims_;
@@ -143,10 +143,13 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
inline Tensor& Tensor::Resize(const DDim& dims) {
dims_ = dims;
+ numel_ = product(dims_);
return *this;
}
inline const DDim& Tensor::dims() const { return dims_; }
+inline int64_t Tensor::numel() const { return numel_; }
+
} // namespace framework
} // namespace paddle
diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index 9e2bcebe3b..0dc5099525 100644
--- a/paddle/operators/cos_sim_op.h
+++ b/paddle/operators/cos_sim_op.h
@@ -42,7 +42,7 @@ class CosSimKernel : public framework::OpKernel {
output_y_norm->mutable_data<T>(context.GetPlace());
auto dims = input_x->dims();
- int size = static_cast<int>(framework::product(dims));
+ int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix<T>::From(*input_x, new_dims);
auto y = EigenMatrix<T>::From(*input_y, new_dims);
@@ -72,7 +72,7 @@ class CosSimGradKernel : public framework::OpKernel {
auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
auto dims = input_x->dims();
- int size = static_cast<int>(framework::product(dims));
+ int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix<T>::From(*input_x, new_dims);
auto y = EigenMatrix<T>::From(*input_y, new_dims);
diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc
index 6574880c0e..3d76516405 100644
--- a/paddle/operators/gaussian_random_op.cc
+++ b/paddle/operators/gaussian_random_op.cc
@@ -31,7 +31,7 @@ class CPUGaussianRandomKernel : public framework::OpKernel {
}
engine.seed(seed);
std::normal_distribution<T> dist(mean, std);
- int64_t size = framework::product(tensor->dims());
+ int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu
index d9dbc1dcfe..2d63b30499 100644
--- a/paddle/operators/gaussian_random_op.cu
+++ b/paddle/operators/gaussian_random_op.cu
@@ -50,8 +50,8 @@ class GPUGaussianRandomKernel : public framework::OpKernel {
T mean = static_cast<T>(context.Attr<float>("mean"));
T std = static_cast<T>(context.Attr<float>("std"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
- ssize_t N = framework::product(tensor->dims());
- thrust::transform(index_sequence_begin, index_sequence_begin + N,
+ int64_t size = tensor->numel();
+ thrust::transform(index_sequence_begin, index_sequence_begin + size,
thrust::device_ptr<T>(data),
GaussianGenerator<T>(mean, std, seed));
}
diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu
index 27eee3436a..7083440467 100644
--- a/paddle/operators/lookup_table_op.cu
+++ b/paddle/operators/lookup_table_op.cu
@@ -70,7 +70,7 @@ class LookupTableCUDAKernel : public framework::OpKernel {
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
- size_t K = product(ids_t->dims());
+ size_t K = ids_t->numel();
auto ids = ids_t->data<int32_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
@@ -91,7 +91,7 @@ class LookupTableGradCUDAKernel : public framework::OpKernel {
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
- int K = product(ids_t->dims());
+ int K = ids_t->numel();
const int32_t* ids = ids_t->data<int32_t>();
const T* d_output = d_output_t->data<T>();
T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h
index 877b36cef4..a1298906dd 100644
--- a/paddle/operators/lookup_table_op.h
+++ b/paddle/operators/lookup_table_op.h
@@ -35,7 +35,7 @@ class LookupTableKernel : public framework::OpKernel {
auto ids = ids_t->data<int32_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
- for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
+ for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
@@ -61,7 +61,7 @@ class LookupTableGradKernel : public framework::OpKernel {
t.device(context.GetEigenDevice<platform::CPUPlace>()) =
t.constant(static_cast<T>(0));
- for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
+ for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
for (int j = 0; j < D; ++j) {
diff --git a/paddle/operators/mean_op.h b/paddle/operators/mean_op.h
index 9848af280b..ce31e178d8 100644
--- a/paddle/operators/mean_op.h
+++ b/paddle/operators/mean_op.h
@@ -49,12 +49,11 @@ class MeanGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto OG = context.Input<Tensor>(framework::GradVarName("Out"));
- PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
- "Mean Gradient should be scalar");
+ PADDLE_ENFORCE(OG->numel() == 1, "Mean Gradient should be scalar");
auto IG = context.Output<Tensor>(framework::GradVarName("X"));
IG->mutable_data<T>(context.GetPlace());
- T ig_size = (T)framework::product(IG->dims());
+ T ig_size = static_cast<T>(IG->numel());
Eigen::DSizes<int, 1> bcast(ig_size);
EigenVector<T>::Flatten(*IG).device(context.GetEigenDevice<Place>()) =
diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc
index 069fb5e1ab..a4876feb2e 100644
--- a/paddle/operators/minus_op.cc
+++ b/paddle/operators/minus_op.cc
@@ -31,8 +31,7 @@ class MinusOp : public framework::OperatorWithKernel {
auto *right_tensor = ctx.Input<framework::Tensor>("Y");
PADDLE_ENFORCE_EQ(
- framework::product(left_tensor->dims()),
- framework::product(right_tensor->dims()),
+ left_tensor->numel(), right_tensor->numel(),
"Minus operator must take two tensor with same num of elements");
ctx.Output<framework::Tensor>("Out")->Resize(left_tensor->dims());
}
diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc
index dc30644a5e..9f51d3efa8 100644
--- a/paddle/operators/squared_l2_distance_op.cc
+++ b/paddle/operators/squared_l2_distance_op.cc
@@ -41,8 +41,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
int rank = framework::arity(x_dims);
PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
- PADDLE_ENFORCE_EQ(framework::product(x_dims) / x_dims[0],
- framework::product(y_dims) / y_dims[0],
+ PADDLE_ENFORCE_EQ(x->numel() / x_dims[0], y->numel() / y_dims[0],
"Product of dimensions expcet the first dimension of "
"input and target must be equal.");
PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
@@ -50,8 +49,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
"or to 1.");
ctx.Output<Tensor>("sub_result")
- ->Resize({static_cast<int>(x_dims[0]),
- static_cast<int>(framework::product(x_dims) / x_dims[0])});
+ ->Resize({x_dims[0], x->numel() / x_dims[0]});
ctx.Output<Tensor>("Out")->Resize({x_dims[0], 1});
}
};
diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h
index ad3347a0b3..097ac04fc0 100644
--- a/paddle/operators/squared_l2_distance_op.h
+++ b/paddle/operators/squared_l2_distance_op.h
@@ -39,7 +39,7 @@ class SquaredL2DistanceKernel : public framework::OpKernel {
auto in0_dims = in0->dims();
auto in1_dims = in1->dims();
- int cols = framework::product(in0_dims) / in0_dims[0];
+ int cols = in0->numel() / in0_dims[0];
// reduce dimensions except the first
auto x =
EigenMatrix<T>::From(*in0, framework::make_ddim({in0_dims[0], cols}));
@@ -82,7 +82,7 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel {
auto x_dims = x_g->dims();
auto y_dims = y_g->dims();
- int cols = framework::product(x_dims) / x_dims[0];
+ int cols = x_g->numel() / x_dims[0];
// calculate gradient
auto grad_mat = 2 *
(out_grad.broadcast(Eigen::array<int, 2>({{1, cols}}))) *
diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc
index f2aeef6c31..b8fbc9b52a 100644
--- a/paddle/operators/uniform_random_op.cc
+++ b/paddle/operators/uniform_random_op.cc
@@ -35,7 +35,7 @@ class CPUUniformRandomKernel : public framework::OpKernel {
std::uniform_real_distribution<T> dist(
static_cast<T>(context.Attr<float>("min")),
static_cast<T>(context.Attr<float>("max")));
- int64_t size = framework::product(tensor->dims());
+ int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu
index c2c041b144..6614b53b3f 100644
--- a/paddle/operators/uniform_random_op.cu
+++ b/paddle/operators/uniform_random_op.cu
@@ -53,8 +53,8 @@ class GPUUniformRandomKernel : public framework::OpKernel {
T min = static_cast<T>(context.Attr<float>("min"));
T max = static_cast<T>(context.Attr<float>("max"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
- ssize_t N = framework::product(tensor->dims());
- thrust::transform(index_sequence_begin, index_sequence_begin + N,
+ int64_t size = tensor->numel();
+ thrust::transform(index_sequence_begin, index_sequence_begin + size,
thrust::device_ptr<T>(data),
UniformGenerator<T>(min, max, seed));
}
From f50e36e2855a27160750aae26458f07eaaaae4d7 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Fri, 8 Sep 2017 14:54:29 +0800
Subject: [PATCH 21/66] follow comments
---
paddle/framework/operator.cc | 9 +++
paddle/framework/operator.h | 22 +-----
paddle/operators/sum_op.cc | 10 ++-
paddle/pybind/pybind.cc | 11 +--
python/paddle/v2/framework/tests/op_test.py | 78 ++++++++++---------
.../framework/tests/test_cross_entropy_op.py | 8 +-
.../v2/framework/tests/test_sigmoid_op.py | 4 +-
.../paddle/v2/framework/tests/test_sum_op.py | 4 +-
8 files changed, 67 insertions(+), 79 deletions(-)
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 790cfc4746..e1e122091f 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -123,6 +123,15 @@ OperatorBase::OperatorBase(const std::string& type,
CheckAllInputOutputSet();
}
+std::vector<std::string> OperatorBase::InputVars() const {
+ std::vector<std::string> ret_val;
+ for (auto& o : outputs_) {
+ ret_val.reserve(ret_val.size() + o.second.size());
+ ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
+ }
+ return ret_val;
+}
+
std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
std::vector<std::string> ret_val;
if (has_intermediate) {
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index be302669cd..d0316224e9 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -95,31 +95,13 @@ class OperatorBase {
const VariableNameMap& Inputs() const { return inputs_; }
const VariableNameMap& Outputs() const { return outputs_; }
- const std::vector<std::string> InputsNames() const {
- std::vector<std::string> result;
- for (auto& kv : inputs_) {
- for (auto& name : kv.second) {
- result.push_back(name);
- }
- }
- return result;
- }
-
- const std::vector<std::string> OutputsNames() const {
- std::vector<std::string> result;
- for (auto& kv : outputs_) {
- for (auto& name : kv.second) {
- result.push_back(name);
- }
- }
- return result;
- }
-
//! Get a input with argument's name described in `op_proto`
std::string Input(const std::string& name) const;
//! Get a input which has multiple variables.
const std::vector<std::string>& Inputs(const std::string& name) const;
+ std::vector<std::string> InputVars() const;
+
//! Get a output with argument's name described in `op_proto`
std::string Output(const std::string& name) const;
//! Get an output which has multiple variables.
diff --git a/paddle/operators/sum_op.cc b/paddle/operators/sum_op.cc
index cf650787c6..5805826ee8 100644
--- a/paddle/operators/sum_op.cc
+++ b/paddle/operators/sum_op.cc
@@ -26,10 +26,14 @@ class SumOp : public framework::OperatorWithKernel {
auto *out = ctx.Output<framework::Tensor>("Out");
int N = ins.size();
- PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
+ auto in_dim = ins[0]->dims();
- auto dim_zero = ins[0]->dims();
- out->Resize(dim_zero);
+ PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
+ for (int i = 1; i < N; i++) {
+ auto dim = ins[i]->dims();
+ PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");
+ }
+ out->Resize(in_dim);
}
};
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index a678bc4940..7d8e2d8fb7 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -214,15 +214,10 @@ All parameter, weight, gradient are variables in Paddle.
-> std::map<std::string, std::vector<std::string>> {
return op.Outputs();
})
- .def("outputs_names",
- [](const OperatorBase &op) -> std::vector<std::string> {
- return op.OutputsNames();
- })
+ .def("output_vars",
+ [](const OperatorBase &op) { return op.OutputVars(true); })
.def("inputs", [](const OperatorBase &op) { return op.Inputs(); })
- .def("inputs_names",
- [](const OperatorBase &op) -> std::vector<std::string> {
- return op.InputsNames();
- })
+ .def("input_vars", [](const OperatorBase &op) { return op.InputVars(); })
.def("__str__", &OperatorBase::DebugString)
.def("no_intermediate_outputs",
[](const OperatorBase &op) { return op.OutputVars(false); })
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index 3f8e1236ff..09ee8ce385 100644
--- a/python/paddle/v2/framework/tests/op_test.py
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -9,54 +9,40 @@ def grad_var_name(var_name):
return var_name + "@GRAD"
-def remove_grad_var_name(var_name):
- return var_name[0:-5]
-
-
def create_op(scope, op_type, inputs, outputs, attrs=None):
kwargs = dict()
- for ins in Operator.get_op_inputs(op_type):
- in_name = ins[0]
- in_dup = ins[1]
+ for in_name, in_dup in Operator.get_op_inputs(op_type):
if in_name in inputs:
kwargs[in_name] = []
if in_dup:
sub_in = inputs[in_name]
for sub_in_name in sub_in:
var = scope.new_var(sub_in_name)
- tensor = var.get_tensor()
kwargs[in_name].append(sub_in_name)
else:
var = scope.new_var(in_name)
- tensor = var.get_tensor()
kwargs[in_name].append(in_name)
- for outs in Operator.get_op_outputs(op_type):
- out_name = outs[0]
- out_dup = outs[1]
+ for out_name, out_dup in Operator.get_op_outputs(op_type):
if out_name in outputs:
kwargs[out_name] = []
if out_dup:
sub_in = outputs[out_name]
for sun_in_name in sub_in:
var = scope.new_var(sun_in_name)
- tensor = var.get_tensor()
kwargs[out_name].append(sun_in_name)
else:
var = scope.new_var(out_name)
- tensor = var.get_tensor()
kwargs[out_name].append(out_name)
- # for attr_name in Operator.get_op_attr_names(op_type):
- # kwargs[attr_name] = attrs[attr_name]
+ for attr_name in Operator.get_op_attr_names(op_type):
+ kwargs[attr_name] = attrs[attr_name]
return Operator(op_type, **kwargs)
def set_input(scope, op, inputs, place):
- for ins in Operator.get_op_inputs(op.type()):
- in_name = ins[0]
- in_dup = ins[1]
+ for in_name, in_dup in Operator.get_op_inputs(op.type()):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
@@ -75,9 +61,7 @@ def set_input(scope, op, inputs, place):
def set_output_grad(scope, op, outputs, place):
- for outs in Operator.get_op_outputs(op.type()):
- out_name = outs[0]
- out_dup = outs[1]
+ for out_name, out_dup in Operator.get_op_outputs(op.type()):
if out_name in outputs:
if out_dup:
sub_out = outputs[out_name]
@@ -150,10 +134,10 @@ def get_numeric_gradient(scope,
def get_backward_op(scope, op, no_grad_set):
backward_op = core.Operator.backward(op, no_grad_set)
- for input in backward_op.inputs_names():
+ for input in backward_op.input_vars():
var = scope.new_var(input)
var.get_tensor()
- for output in backward_op.outputs_names():
+ for output in backward_op.output_vars():
var = scope.new_var(output)
var.get_tensor()
return backward_op
@@ -182,7 +166,7 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place,
class OpTest(unittest.TestCase):
- def check_output(self, place):
+ def check_output_with_place(self, place):
self.scope = core.Scope()
self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
@@ -192,9 +176,7 @@ class OpTest(unittest.TestCase):
ctx = core.DeviceContext.create(place)
self.op.run(self.scope, ctx)
- for outs in Operator.get_op_outputs(self.op.type()):
- out_name = outs[0]
- out_dup = outs[1]
+ for out_name, out_dup in Operator.get_op_outputs(self.op.type()):
if out_dup:
sub_out = self.outputs[out_name]
for sub_out_name in sub_out:
@@ -213,6 +195,13 @@ class OpTest(unittest.TestCase):
actual, expect, atol=1e-05),
"output name: " + out_name + "has diff")
+ def check_output(self):
+ places = [core.CPUPlace()]
+ if core.is_compile_gpu() and self.op.support_gpu():
+ places.append(core.GPUPlace(0))
+ for place in places:
+ self.check_output_with_place(place)
+
def __assert_is_close(self, numeric_grads, analytic_grads, names,
max_relative_error, msg_prefix):
@@ -255,17 +244,32 @@ class OpTest(unittest.TestCase):
grad_var_name(input_to_check) for input_to_check in inputs_to_check
]
- places = [core.CPUPlace()]
- if core.is_compile_gpu() and self.op.support_gpu():
- places.append(core.GPUPlace(0))
+ cpu_place = core.CPUPlace()
+ cpu_analytic_grads = [
+ get_gradient(self.scope, self.op, self.inputs, self.outputs,
+ grad_name, cpu_place, no_grad_set)
+ for grad_name in grad_names
+ ]
- for place in places:
- analytic_grads = [
+ self.__assert_is_close(numeric_grads, cpu_analytic_grads, grad_names,
+ max_relative_error,
+ "Gradient Check On %s" % str(cpu_place))
+
+ if core.is_compile_gpu() and self.op.support_gpu():
+ gpu_place = core.GPUPlace(0)
+ gpu_analytic_grads = [
get_gradient(self.scope, self.op, self.inputs, self.outputs,
- grad_name, place, no_grad_set)
+ grad_name, gpu_place, no_grad_set)
for grad_name in grad_names
]
- self.__assert_is_close(numeric_grads, analytic_grads, grad_names,
- max_relative_error,
- "Gradient Check On %s" % str(place))
+ self.__assert_is_close(numeric_grads, gpu_analytic_grads,
+ grad_names, max_relative_error,
+ "Gradient Check On %s" % str(gpu_place))
+
+ for c_grad, g_grad, name in itertools.izip(
+ cpu_analytic_grads, gpu_analytic_grads, grad_names):
+ self.assertTrue(
+ numpy.allclose(
+ c_grad, g_grad, atol=1e-4),
+ "output name: " + name + " has diff")
diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
index 20e0de3520..1956df0bb4 100644
--- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py
+++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
@@ -1,14 +1,13 @@
import unittest
import numpy
from op_test import OpTest
-import paddle.v2.framework.core as core
class TestCrossEntropy(OpTest):
def setUp(self):
self.op_type = "onehot_cross_entropy"
- batch_size = 4
- class_num = 4
+ batch_size = 30
+ class_num = 10
X = numpy.random.random((batch_size, class_num)).astype("float32")
label = (class_num / 2) * numpy.ones(batch_size).astype("int32")
self.inputs = {'X': X, 'label': label}
@@ -18,8 +17,7 @@ class TestCrossEntropy(OpTest):
self.outputs = {'Y': numpy.array(Y).astype("float32")}
def test_check_output(self):
- self.check_output(core.CPUPlace())
- self.check_output(core.GPUPlace(0))
+ self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y")
diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py
index ff0823508f..2316e49eff 100644
--- a/python/paddle/v2/framework/tests/test_sigmoid_op.py
+++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py
@@ -1,7 +1,6 @@
import unittest
import numpy as np
from op_test import OpTest
-import paddle.v2.framework.core as core
class TestSigmoid(OpTest):
@@ -13,8 +12,7 @@ class TestSigmoid(OpTest):
self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
def test_check_output(self):
- self.check_output(core.CPUPlace())
- self.check_output(core.GPUPlace(0))
+ self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y", max_relative_error=0.007)
diff --git a/python/paddle/v2/framework/tests/test_sum_op.py b/python/paddle/v2/framework/tests/test_sum_op.py
index 2a7b65ef52..66417d70e8 100644
--- a/python/paddle/v2/framework/tests/test_sum_op.py
+++ b/python/paddle/v2/framework/tests/test_sum_op.py
@@ -1,7 +1,6 @@
import unittest
import numpy as np
from op_test import OpTest
-import paddle.v2.framework.core as core
class TestSumOp(OpTest):
@@ -15,8 +14,7 @@ class TestSumOp(OpTest):
self.outputs = {'Out': y}
def test_check_output(self):
- self.check_output(core.CPUPlace())
- self.check_output(core.GPUPlace(0))
+ self.check_output()
def test_check_grad(self):
self.check_grad(["x0"], "Out")
From 15627e48d8bcec72f16d51b5e654f28f8400d5fe Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Fri, 8 Sep 2017 07:44:46 +0000
Subject: [PATCH 22/66] fix gou test bug
---
python/paddle/v2/framework/tests/op_test.py | 4 ++--
python/paddle/v2/framework/tests/test_cross_entropy_op.py | 2 +-
2 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index 09ee8ce385..3a6a5dca4c 100644
--- a/python/paddle/v2/framework/tests/op_test.py
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -197,7 +197,7 @@ class OpTest(unittest.TestCase):
def check_output(self):
places = [core.CPUPlace()]
- if core.is_compile_gpu() and self.op.support_gpu():
+ if core.is_compile_gpu():
places.append(core.GPUPlace(0))
for place in places:
self.check_output_with_place(place)
@@ -270,6 +270,6 @@ class OpTest(unittest.TestCase):
for c_grad, g_grad, name in itertools.izip(
cpu_analytic_grads, gpu_analytic_grads, grad_names):
self.assertTrue(
- numpy.allclose(
+ np.allclose(
c_grad, g_grad, atol=1e-4),
"output name: " + name + " has diff")
diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
index 1956df0bb4..e693ba0ec2 100644
--- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py
+++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
@@ -8,7 +8,7 @@ class TestCrossEntropy(OpTest):
self.op_type = "onehot_cross_entropy"
batch_size = 30
class_num = 10
- X = numpy.random.random((batch_size, class_num)).astype("float32")
+ X = numpy.random.uniform(0.1, 1.0, [batch_size, class_num]).astype("float32")
label = (class_num / 2) * numpy.ones(batch_size).astype("int32")
self.inputs = {'X': X, 'label': label}
Y = []
From c0192889ccfb947c391c08bf9827317f3769497e Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Fri, 8 Sep 2017 15:51:13 +0800
Subject: [PATCH 23/66] fix code style
---
python/paddle/v2/framework/tests/test_cross_entropy_op.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
index e693ba0ec2..fb6a440e23 100644
--- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py
+++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
@@ -8,7 +8,8 @@ class TestCrossEntropy(OpTest):
self.op_type = "onehot_cross_entropy"
batch_size = 30
class_num = 10
- X = numpy.random.uniform(0.1, 1.0, [batch_size, class_num]).astype("float32")
+ X = numpy.random.uniform(0.1, 1.0,
+ [batch_size, class_num]).astype("float32")
label = (class_num / 2) * numpy.ones(batch_size).astype("int32")
self.inputs = {'X': X, 'label': label}
Y = []
From 4c68405c95268e6f4275200e4c462b3b13f6ce85 Mon Sep 17 00:00:00 2001
From: qingqing01 <dangqingqing@baidu.com>
Date: Fri, 8 Sep 2017 17:45:30 +0800
Subject: [PATCH 24/66] Fix name in test_lookup_table
---
python/paddle/v2/framework/tests/test_lookup_table.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py
index 19eb464baa..4b7ce92c0f 100644
--- a/python/paddle/v2/framework/tests/test_lookup_table.py
+++ b/python/paddle/v2/framework/tests/test_lookup_table.py
@@ -4,7 +4,7 @@ from op_test_util import OpTestMeta
from gradient_checker import GradientChecker, create_op
-class TestSigmoidOp(unittest.TestCase):
+class TestLookupTableOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
@@ -15,7 +15,7 @@ class TestSigmoidOp(unittest.TestCase):
self.outputs = {'Out': table[ids]}
-class TestSigmoidGradOp(GradientChecker):
+class TestLookupTableGradOp(GradientChecker):
def test_grad(self):
op = create_op('lookup_table')
table = np.random.random((17, 31)).astype('float32')
From 91a0c11b194b324fb6abbbcdb0bab48720f1a8d6 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Fri, 8 Sep 2017 17:24:25 +0800
Subject: [PATCH 25/66] Adaptive data structure for SwitchOrderLayer
---
paddle/gserver/layers/SwitchOrderLayer.cpp | 7 +++++--
proto/ModelConfig.proto | 1 +
python/paddle/trainer_config_helpers/layers.py | 10 ++++++++--
3 files changed, 14 insertions(+), 4 deletions(-)
diff --git a/paddle/gserver/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp
index 92cd61cdd5..d7eee6eaf0 100644
--- a/paddle/gserver/layers/SwitchOrderLayer.cpp
+++ b/paddle/gserver/layers/SwitchOrderLayer.cpp
@@ -24,10 +24,12 @@ bool SwitchOrderLayer::init(const LayerMap& layerMap,
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
auto& img_conf = config_.inputs(0).image_conf();
+ size_t inD = img_conf.img_size_z();
size_t inH =
img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size();
size_t inW = img_conf.img_size();
size_t inC = img_conf.channels();
+ inH = inH * inD;
inDims_ = TensorShape({0, inC, inH, inW});
outDims_ = TensorShape(4);
@@ -64,9 +66,10 @@ void SwitchOrderLayer::setInDims() {
MatrixPtr input = inputLayers_[0]->getOutputValue();
size_t batchSize = input->getHeight();
inDims_.setDim(0, batchSize);
-
+ int d = inputLayers_[0]->getOutput().getFrameDepth();
+ d = (d == 0 ? 1 : d);
int h = inputLayers_[0]->getOutput().getFrameHeight();
- if (h != 0) inDims_.setDim(2, h);
+ if (h != 0) inDims_.setDim(2, h * d);
int w = inputLayers_[0]->getOutput().getFrameWidth();
if (w != 0) inDims_.setDim(3, w);
int totalCount = input->getElementCnt();
diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto
index 7d7fc23a46..2b3a8d6dcf 100644
--- a/proto/ModelConfig.proto
+++ b/proto/ModelConfig.proto
@@ -271,6 +271,7 @@ message ImageConfig {
// The size of input feature map.
required uint32 img_size = 8;
optional uint32 img_size_y = 9;
+ optional uint32 img_size_z = 10 [ default = 1 ];
}
message PriorBoxConfig {
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index cba45bd3af..9a43cb1709 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -6410,7 +6410,7 @@ def gated_unit_layer(input,
@wrap_name_default('switch_order')
def switch_order_layer(input,
name=None,
- reshape=None,
+ reshape_axis=None,
act=None,
layer_attr=None):
"""
@@ -6421,8 +6421,9 @@ def switch_order_layer(input,
The example usage is:
.. code-block:: python
+ reshape_axis = 3
+ switch = switch_order(input=layer, name='switch', reshape_axis=reshape_axis)
reshape = {'height':[ 0, 1, 2], 'width':[3]}
- switch = switch_order(input=layer, name='switch', reshape=reshape)
:param input: The input layer.
:type input: LayerOutput
@@ -6434,6 +6435,11 @@ def switch_order_layer(input,
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
+ assert reshape_axis != None and (reshape_axis > 0 and reshape_axis < 4)
+ height = [ele for ele in xrange(reshape_axis)]
+ width = [ele for ele in range(reshape_axis, 4)]
+ reshape = {'height': height, 'width': width}
+
l = Layer(
name=name,
inputs=input.name,
From 26638e9cab3e4287b0c90dea3aa5f5d4a81f603d Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 9 Sep 2017 10:50:57 +0800
Subject: [PATCH 26/66] fix batch_norm parameter share
---
python/paddle/trainer/config_parser.py | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 11dc84ae20..12a629a031 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -2421,6 +2421,11 @@ class BatchNormLayer(LayerBase):
psize = self.calc_parameter_size(image_conf)
dims = [1, psize]
+
+ self.inputs[1].parameter_name = self.inputs[0].parameter_name.split('.')[0] + '.' + \
+ self.inputs[1].parameter_name.split('.')[1]
+ self.inputs[2].parameter_name = self.inputs[0].parameter_name.split('.')[0] + '.' + \
+ self.inputs[2].parameter_name.split('.')[1]
self.create_input_parameter(0, psize)
self.create_input_parameter(1, psize, dims)
self.create_input_parameter(2, psize, dims)
From aea05b6e95fe934039bd3d1a7c64bdec5abf5462 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 9 Sep 2017 11:35:49 +0800
Subject: [PATCH 27/66] add param mean_var_names
---
python/paddle/trainer/config_parser.py | 9 +++++----
python/paddle/trainer_config_helpers/layers.py | 4 ++++
2 files changed, 9 insertions(+), 4 deletions(-)
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 12a629a031..9c765b32d3 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -2366,6 +2366,7 @@ class BatchNormLayer(LayerBase):
inputs,
bias=True,
use_global_stats=True,
+ mean_var_names=None,
moving_average_fraction=0.9,
batch_norm_type=None,
**xargs):
@@ -2421,11 +2422,11 @@ class BatchNormLayer(LayerBase):
psize = self.calc_parameter_size(image_conf)
dims = [1, psize]
+ if mean_var_names is not None:
+ assert len(mean_var_names) == 2
+ self.inputs[1].parameter_name = mean_var_names[0]
+ self.inputs[2].parameter_name = mean_var_names[1]
- self.inputs[1].parameter_name = self.inputs[0].parameter_name.split('.')[0] + '.' + \
- self.inputs[1].parameter_name.split('.')[1]
- self.inputs[2].parameter_name = self.inputs[0].parameter_name.split('.')[0] + '.' + \
- self.inputs[2].parameter_name.split('.')[1]
self.create_input_parameter(0, psize)
self.create_input_parameter(1, psize, dims)
self.create_input_parameter(2, psize, dims)
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index cba45bd3af..4749f38c38 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -2957,6 +2957,7 @@ def batch_norm_layer(input,
bias_attr=None,
param_attr=None,
layer_attr=None,
+ mean_var_names=None,
batch_norm_type=None,
moving_average_fraction=0.9,
use_global_stats=None):
@@ -3014,6 +3015,8 @@ def batch_norm_layer(input,
:type param_attr: ParameterAttribute
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
+ :param mean_var_names: [mean name, variance name]
+ :type mean_var_names: string list
:param use_global_stats: whether use moving mean/variance statistics
during testing peroid. If None or True,
it will use moving mean/variance statistics during
@@ -3044,6 +3047,7 @@ def batch_norm_layer(input,
active_type=act.name,
type=LayerType.BATCH_NORM_LAYER,
batch_norm_type=batch_norm_type,
+ mean_var_names=mean_var_names,
bias=ParamAttr.to_bias(bias_attr),
moving_average_fraction=moving_average_fraction,
use_global_stats=use_global_stats,
From 80a8e91fb8c267ef5b2b726fb7110e4d12c96c71 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 9 Sep 2017 11:54:38 +0800
Subject: [PATCH 28/66] fix order
---
python/paddle/trainer/config_parser.py | 2 +-
python/paddle/trainer_config_helpers/layers.py | 10 +++++-----
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 9c765b32d3..2a6b6d5e2b 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -2366,9 +2366,9 @@ class BatchNormLayer(LayerBase):
inputs,
bias=True,
use_global_stats=True,
- mean_var_names=None,
moving_average_fraction=0.9,
batch_norm_type=None,
+ mean_var_names=None,
**xargs):
if inputs is None:
inputs = []
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 4749f38c38..e1703c158a 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -2957,10 +2957,10 @@ def batch_norm_layer(input,
bias_attr=None,
param_attr=None,
layer_attr=None,
- mean_var_names=None,
batch_norm_type=None,
moving_average_fraction=0.9,
- use_global_stats=None):
+ use_global_stats=None,
+ mean_var_names=None):
"""
Batch Normalization Layer. The notation of this layer as follow.
@@ -3015,8 +3015,6 @@ def batch_norm_layer(input,
:type param_attr: ParameterAttribute
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
- :param mean_var_names: [mean name, variance name]
- :type mean_var_names: string list
:param use_global_stats: whether use moving mean/variance statistics
during testing peroid. If None or True,
it will use moving mean/variance statistics during
@@ -3029,6 +3027,8 @@ def batch_norm_layer(input,
:math:`runningMean = newMean*(1-factor)
+ runningMean*factor`
:type moving_average_fraction: float.
+ :param mean_var_names: [mean name, variance name]
+ :type mean_var_names: string list
:return: LayerOutput object.
:rtype: LayerOutput
"""
@@ -3047,10 +3047,10 @@ def batch_norm_layer(input,
active_type=act.name,
type=LayerType.BATCH_NORM_LAYER,
batch_norm_type=batch_norm_type,
- mean_var_names=mean_var_names,
bias=ParamAttr.to_bias(bias_attr),
moving_average_fraction=moving_average_fraction,
use_global_stats=use_global_stats,
+ mean_var_names=mean_var_names,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
From fa5cb7104d7a8bce56a446eb352037302f93ae70 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 9 Sep 2017 17:40:38 +0800
Subject: [PATCH 29/66] fix switchOrderLayer
---
python/paddle/trainer/config_parser.py | 4 ++--
python/paddle/trainer_config_helpers/layers.py | 1 +
2 files changed, 3 insertions(+), 2 deletions(-)
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 7e9112b43b..356e1d8b6f 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -3748,8 +3748,8 @@ class SwitchOrderLayer(LayerBase):
def __init__(self, name, inputs, reshape, **xargs):
super(SwitchOrderLayer, self).__init__(
name, 'switch_order', 0, inputs=inputs, **xargs)
- self.config.reshape_conf.heightAxis.extend(reshape['height'])
- self.config.reshape_conf.widthAxis.extend(reshape['width'])
+ self.config.reshape_conf.height_axis.extend(reshape['height'])
+ self.config.reshape_conf.width_axis.extend(reshape['width'])
# Deprecated, use a new layer specific class instead
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index dc68c213da..c103edf237 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -6460,6 +6460,7 @@ def switch_order_layer(input,
return LayerOutput(
name=name,
layer_type=LayerType.SWITCH_ORDER_LAYER,
+ activation=act,
parents=input,
size=l.config.size)
From a5103770240ceebb5e98f2d99c97f9a6038818b9 Mon Sep 17 00:00:00 2001
From: wenshilei <wenshilei@baidu.com>
Date: Sun, 10 Sep 2017 02:37:34 +0800
Subject: [PATCH 30/66] Fix ssd bugs.
---
paddle/gserver/layers/DetectionOutputLayer.cpp | 1 +
paddle/gserver/layers/DetectionUtil.cpp | 4 +++-
paddle/gserver/layers/DetectionUtil.h | 2 +-
3 files changed, 5 insertions(+), 2 deletions(-)
diff --git a/paddle/gserver/layers/DetectionOutputLayer.cpp b/paddle/gserver/layers/DetectionOutputLayer.cpp
index 8ab838e191..a1036ea866 100644
--- a/paddle/gserver/layers/DetectionOutputLayer.cpp
+++ b/paddle/gserver/layers/DetectionOutputLayer.cpp
@@ -139,6 +139,7 @@ void DetectionOutputLayer::forward(PassType passType) {
allDecodedBBoxes,
&allIndices);
+ numKept = numKept > 0 ? numKept : 1;
resetOutput(numKept, 7);
MatrixPtr outV = getOutputValue();
getDetectionOutput(confBuffer_->getData(),
diff --git a/paddle/gserver/layers/DetectionUtil.cpp b/paddle/gserver/layers/DetectionUtil.cpp
index 3e61adc66e..92c6193035 100644
--- a/paddle/gserver/layers/DetectionUtil.cpp
+++ b/paddle/gserver/layers/DetectionUtil.cpp
@@ -469,7 +469,7 @@ size_t getDetectionIndices(
const size_t numClasses,
const size_t backgroundId,
const size_t batchSize,
- const size_t confThreshold,
+ const real confThreshold,
const size_t nmsTopK,
const real nmsThreshold,
const size_t keepTopK,
@@ -536,6 +536,8 @@ void getDetectionOutput(const real* confData,
MatrixPtr outBuffer;
Matrix::resizeOrCreate(outBuffer, numKept, 7, false, false);
real* bufferData = outBuffer->getData();
+ for (size_t i = 0; i < 7; i++)
+ bufferData[i] = -1;
size_t count = 0;
for (size_t n = 0; n < batchSize; ++n) {
for (map<size_t, vector<size_t>>::const_iterator it = allIndices[n].begin();
diff --git a/paddle/gserver/layers/DetectionUtil.h b/paddle/gserver/layers/DetectionUtil.h
index fe4f9f075e..641ed873b4 100644
--- a/paddle/gserver/layers/DetectionUtil.h
+++ b/paddle/gserver/layers/DetectionUtil.h
@@ -275,7 +275,7 @@ size_t getDetectionIndices(
const size_t numClasses,
const size_t backgroundId,
const size_t batchSize,
- const size_t confThreshold,
+ const real confThreshold,
const size_t nmsTopK,
const real nmsThreshold,
const size_t keepTopK,
From dade4272c7104653587ec516ce77a4ac6dee23da Mon Sep 17 00:00:00 2001
From: Yuan Gao <yuan.gao.gavin@gmail.com>
Date: Sun, 10 Sep 2017 02:41:43 +0800
Subject: [PATCH 31/66] Update DetectionUtil.cpp
---
paddle/gserver/layers/DetectionUtil.cpp | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/paddle/gserver/layers/DetectionUtil.cpp b/paddle/gserver/layers/DetectionUtil.cpp
index 92c6193035..7c18295385 100644
--- a/paddle/gserver/layers/DetectionUtil.cpp
+++ b/paddle/gserver/layers/DetectionUtil.cpp
@@ -537,7 +537,7 @@ void getDetectionOutput(const real* confData,
Matrix::resizeOrCreate(outBuffer, numKept, 7, false, false);
real* bufferData = outBuffer->getData();
for (size_t i = 0; i < 7; i++)
- bufferData[i] = -1;
+ bufferData[i] = -1;
size_t count = 0;
for (size_t n = 0; n < batchSize; ++n) {
for (map<size_t, vector<size_t>>::const_iterator it = allIndices[n].begin();
From 4343be3309cd966026fcbd16ff60dd3474c7cf14 Mon Sep 17 00:00:00 2001
From: Yuan Gao <yuan.gao.gavin@gmail.com>
Date: Sun, 10 Sep 2017 10:16:24 +0800
Subject: [PATCH 32/66] Update DetectionUtil.cpp
---
paddle/gserver/layers/DetectionUtil.cpp | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/paddle/gserver/layers/DetectionUtil.cpp b/paddle/gserver/layers/DetectionUtil.cpp
index 7c18295385..070bc0bce6 100644
--- a/paddle/gserver/layers/DetectionUtil.cpp
+++ b/paddle/gserver/layers/DetectionUtil.cpp
@@ -536,8 +536,7 @@ void getDetectionOutput(const real* confData,
MatrixPtr outBuffer;
Matrix::resizeOrCreate(outBuffer, numKept, 7, false, false);
real* bufferData = outBuffer->getData();
- for (size_t i = 0; i < 7; i++)
- bufferData[i] = -1;
+ for (size_t i = 0; i < 7; i++) bufferData[i] = -1;
size_t count = 0;
for (size_t n = 0; n < batchSize; ++n) {
for (map<size_t, vector<size_t>>::const_iterator it = allIndices[n].begin();
From 5bd3fbeef8c369b2d0cb968947853314db3ffd49 Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Mon, 11 Sep 2017 10:23:11 +0800
Subject: [PATCH 33/66] Fix switch layer attribute name
---
python/paddle/trainer/config_parser.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 11dc84ae20..14a1b38079 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -3675,8 +3675,8 @@ class SwitchOrderLayer(LayerBase):
def __init__(self, name, inputs, reshape, **xargs):
super(SwitchOrderLayer, self).__init__(
name, 'switch_order', 0, inputs=inputs, **xargs)
- self.config.reshape_conf.heightAxis.extend(reshape['height'])
- self.config.reshape_conf.widthAxis.extend(reshape['width'])
+ self.config.reshape_conf.height_axis.extend(reshape['height'])
+ self.config.reshape_conf.width_axis.extend(reshape['width'])
# Deprecated, use a new layer specific class instead
From a251956d7dddb2302b26b7342132d2bb0d33233b Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Mon, 11 Sep 2017 10:37:27 +0800
Subject: [PATCH 34/66] Add activation to output of switch layer
---
python/paddle/trainer_config_helpers/layers.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index dc68c213da..c103edf237 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -6460,6 +6460,7 @@ def switch_order_layer(input,
return LayerOutput(
name=name,
layer_type=LayerType.SWITCH_ORDER_LAYER,
+ activation=act,
parents=input,
size=l.config.size)
From 38e7ad799cb96ff2b7430fc0c8c7d9e57470131a Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Mon, 11 Sep 2017 11:19:36 +0800
Subject: [PATCH 35/66] Set detection output to NULL
---
paddle/gserver/layers/DetectionOutputLayer.cpp | 9 +++++++--
paddle/gserver/layers/DetectionUtil.cpp | 1 -
2 files changed, 7 insertions(+), 3 deletions(-)
diff --git a/paddle/gserver/layers/DetectionOutputLayer.cpp b/paddle/gserver/layers/DetectionOutputLayer.cpp
index a1036ea866..0cf0a92bf4 100644
--- a/paddle/gserver/layers/DetectionOutputLayer.cpp
+++ b/paddle/gserver/layers/DetectionOutputLayer.cpp
@@ -139,8 +139,13 @@ void DetectionOutputLayer::forward(PassType passType) {
allDecodedBBoxes,
&allIndices);
- numKept = numKept > 0 ? numKept : 1;
- resetOutput(numKept, 7);
+ if (numKept > 0) {
+ resetOutput(numKept, 7);
+ } else {
+ MatrixPtr outV = getOutputValue();
+ outV = NULL;
+ return;
+ }
MatrixPtr outV = getOutputValue();
getDetectionOutput(confBuffer_->getData(),
numKept,
diff --git a/paddle/gserver/layers/DetectionUtil.cpp b/paddle/gserver/layers/DetectionUtil.cpp
index 070bc0bce6..d83674f45a 100644
--- a/paddle/gserver/layers/DetectionUtil.cpp
+++ b/paddle/gserver/layers/DetectionUtil.cpp
@@ -536,7 +536,6 @@ void getDetectionOutput(const real* confData,
MatrixPtr outBuffer;
Matrix::resizeOrCreate(outBuffer, numKept, 7, false, false);
real* bufferData = outBuffer->getData();
- for (size_t i = 0; i < 7; i++) bufferData[i] = -1;
size_t count = 0;
for (size_t n = 0; n < batchSize; ++n) {
for (map<size_t, vector<size_t>>::const_iterator it = allIndices[n].begin();
From 1eb5e56f3844f5b4e2bac1be68e1f316e5eeddda Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Mon, 11 Sep 2017 11:56:22 +0800
Subject: [PATCH 36/66] Add comment to the PythonAPI
---
python/paddle/trainer_config_helpers/layers.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index dc68c213da..fb1af2d8e8 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -1223,7 +1223,8 @@ def detection_output_layer(input_loc,
name=None):
"""
Apply the NMS to the output of network and compute the predict bounding
- box location.
+ box location. The output of this layer could be None if there is no valid
+ bounding box.
:param name: The Layer Name.
:type name: basestring
From d0dbc0610fd41d10ebb5abc133b25976e53484db Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Mon, 11 Sep 2017 12:10:01 +0800
Subject: [PATCH 37/66] Correctly use host_vector in LoDTensor and expose
LoDTensor to Python.
---
paddle/framework/CMakeLists.txt | 1 +
paddle/framework/lod_tensor.h | 17 ++++-
paddle/framework/lod_tensor_test.cu | 52 +++++++++++++++
paddle/operators/math/im2col_test.cc | 4 +-
paddle/pybind/pybind.cc | 43 ++++++++++---
.../paddle/v2/framework/tests/test_tensor.py | 63 ++++++++++++++++++-
6 files changed, 164 insertions(+), 16 deletions(-)
create mode 100644 paddle/framework/lod_tensor_test.cu
diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index c0838d9b75..3371962c63 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -9,6 +9,7 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
+nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
cc_test(variable_test SRCS variable_test.cc)
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 154068fef6..bbddd6de9d 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -18,8 +18,10 @@
#ifndef PADDLE_ONLY_CPU
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
+#include <thrust/system/cuda/experimental/pinned_allocator.h>
#endif
+#include <glog/logging.h>
#include "paddle/framework/ddim.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/enforce.h"
@@ -32,7 +34,8 @@ template <typename T>
using Vector = std::vector<T>;
#else
template <typename T>
-using Vector = thrust::host_vector<T>;
+using Vector = thrust::host_vector<
+ T, thrust::system::cuda::experimental::pinned_allocator<T>>;
#endif
using LoD = std::vector<Vector<size_t>>;
@@ -53,7 +56,17 @@ class LoDTensor {
LoDTensor() {}
LoDTensor(const LoD& lod, Tensor* t) : lod_(lod), tensor_(t) {}
- void set_lod(const LoD& lod) { lod_ = lod; }
+ void set_lod(const LoD& lod) {
+ lod_ = lod;
+ LOG(INFO) << lod_[0][0];
+ }
+
+#ifdef PADDLE_ONLY_CPU
+ void set_lod(const std::vector<std::vector<size_t>>& lod) {
+ lod_ = lod;
+ LOG(INFO) << lod_[0][0];
+ }
+#endif
void set_tensor(Tensor* tensor) { tensor_ = tensor; }
diff --git a/paddle/framework/lod_tensor_test.cu b/paddle/framework/lod_tensor_test.cu
new file mode 100644
index 0000000000..1079a36a2e
--- /dev/null
+++ b/paddle/framework/lod_tensor_test.cu
@@ -0,0 +1,52 @@
+/*
+ Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+ http://www.apache.org/licenses/LICENSE-2.0
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+*/
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/platform/assert.h"
+
+#include <gtest/gtest.h>
+
+__global__ void test(size_t* a, int size) {
+ for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size;
+ i += blockDim.x * gridDim.x) {
+ a[i] *= 2;
+ }
+}
+
+TEST(LoDTensor, LoDInGPU) {
+ paddle::framework::Tensor tensor;
+ paddle::framework::LoDTensor lod_tensor;
+ paddle::platform::GPUPlace place(0);
+
+ paddle::framework::LoD src_lod;
+ src_lod.push_back(std::vector<size_t>{0, 2, 4, 6, 8, 10, 12, 14});
+
+ tensor.Resize({14, 16});
+ tensor.mutable_data<float>(place);
+
+ lod_tensor.set_lod(src_lod);
+ lod_tensor.set_tensor(&tensor);
+ CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
+ CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
+
+ auto lod = lod_tensor.lod();
+
+ test<<<1, 8>>>(lod[0].data(), lod[0].size());
+ cudaDeviceSynchronize();
+
+ for (size_t i = 0; i < src_lod[0].size(); ++i) {
+ CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
+ }
+}
diff --git a/paddle/operators/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc
index ee5fb98acd..f905600bb3 100644
--- a/paddle/operators/math/im2col_test.cc
+++ b/paddle/operators/math/im2col_test.cc
@@ -71,8 +71,10 @@ void testIm2col() {
context =
new paddle::platform::CPUDeviceContext(paddle::platform::CPUPlace());
} else {
+#ifndef PADDLE_ONLY_CPU
context =
new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace());
+#endif
}
im2col(input, output_cfo, stride, stride, padding, padding, context);
im2col_ocf(input, output_ocf, stride, stride, padding, padding, context);
@@ -115,4 +117,4 @@ TEST(math, im2col) {
#ifndef PADDLE_ONLY_CPU
testIm2col<paddle::platform::GPUPlace>();
#endif
-}
\ No newline at end of file
+}
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 30189d538b..73fb7186ae 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -56,7 +56,8 @@ namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
-using LODTensor = framework::LODTensor;
+using LoDTensor = framework::LoDTensor;
+using LoD = framework::LoD;
static size_t UniqueIntegerGenerator() {
static std::atomic<size_t> generator;
@@ -116,23 +117,45 @@ PYBIND11_PLUGIN(core) {
return self.data<float>()[offset];
});
- py::class_<LODTensor>(m, "LODTensor", R"DOC(LOD(Leval of Ddetails) Tensor.
+ py::class_<LoDTensor>(m, "LoDTensor", R"DOC(LoD(Leval of Ddetails) Tensor.
-The tensor and LOD info should be created before creating the LODTensor, then
+The tensor and LoD info should be created before creating the LoDTensor, then
call the set_tensor and set_lod functions to set them.
)DOC")
.def("set_tensor",
- [](LODTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
+ [](LoDTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
.def("set_lod",
- [](LODTensor &self, std::vector<std::vector<size_t>> &lod) {
+ [](LoDTensor &self, std::vector<std::vector<size_t>> &lod) {
+#ifdef PADDLE_ONLY_CPU
self.set_lod(lod);
+#else
+ paddle::framework::LoD new_lod;
+ new_lod.reserve(lod.size());
+ std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+ self.set_lod(new_lod);
+#endif
})
- .def("get_tensor",
- [](LODTensor &self) -> Tensor & { return self.tensor(); },
+ .def("tensor",
+ [](LoDTensor &self) -> Tensor & { return self.tensor(); },
py::return_value_policy::reference)
- .def("get_lod", [](LODTensor &self) -> std::vector<std::vector<size_t>> {
+ .def("lod", [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
+#ifdef PADDLE_ONLY_CPU
return self.lod();
+#else
+ auto lod = self.lod();
+ std::vector<std::vector<size_t>> new_lod;
+ new_lod.reserve(lod.size());
+ std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod),
+ [](paddle::framework::Vector<size_t> item) ->
+ std::vector<size_t> {
+ std::vector<size_t> v;
+ v.reserve(item.size());
+ std::copy(item.begin(), item.end(), std::back_inserter(v));
+ return v;
+ });
+ return new_lod;
+#endif
});
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
@@ -147,8 +170,8 @@ All parameter, weight, gradient are variables in Paddle.
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
py::return_value_policy::reference)
.def("get_lod_tensor",
- [](Variable &self) -> LODTensor * {
- return self.GetMutable<LODTensor>();
+ [](Variable &self) -> LoDTensor * {
+ return self.GetMutable<LoDTensor>();
},
py::return_value_policy::reference)
.def("get_net",
diff --git a/python/paddle/v2/framework/tests/test_tensor.py b/python/paddle/v2/framework/tests/test_tensor.py
index 1af39818a3..1bfe1370e2 100644
--- a/python/paddle/v2/framework/tests/test_tensor.py
+++ b/python/paddle/v2/framework/tests/test_tensor.py
@@ -3,7 +3,7 @@ import unittest
import numpy
-class TestScope(unittest.TestCase):
+class TestTensor(unittest.TestCase):
def test_int_tensor(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
@@ -20,8 +20,8 @@ class TestScope(unittest.TestCase):
tensor.set(tensor_array, place)
tensor_array_2 = numpy.array(tensor)
- self.assertEqual(1.0, tensor_array_2[3, 9])
- self.assertEqual(2.0, tensor_array_2[19, 11])
+ self.assertEqual(1, tensor_array_2[3, 9])
+ self.assertEqual(2, tensor_array_2[19, 11])
def test_float_tensor(self):
scope = core.Scope()
@@ -43,6 +43,63 @@ class TestScope(unittest.TestCase):
self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
+ def test_int_lod_tensor(self):
+ scope = core.Scope()
+ var = scope.new_var("test_tensor")
+ var_lod = scope.new_var("test_lod_tensor")
+ place = core.CPUPlace()
+
+ tensor = var.get_tensor()
+ lod_tensor = var_lod.get_lod_tensor()
+
+ tensor.set_dims([4, 4, 6])
+ tensor.alloc_int(place)
+ array = numpy.array(tensor)
+ array[0, 0, 0] = 3
+ array[3, 3, 5] = 10
+ tensor.set(array, place)
+
+ lod_tensor.set_tensor(tensor)
+ lod_tensor.set_lod([[0, 2, 4]])
+
+ lod_v = numpy.array(lod_tensor.tensor())
+ self.assertTrue(numpy.alltrue(array == lod_v))
+
+ lod = lod_tensor.lod()
+ self.assertEqual(0, lod[0][0])
+ self.assertEqual(2, lod[0][1])
+ self.assertEqual(4, lod[0][2])
+
+ def test_float_lod_tensor(self):
+ scope = core.Scope()
+ var = scope.new_var("test_tensor")
+ var_lod = scope.new_var("test_lod_tensor")
+ place = core.CPUPlace()
+
+ tensor = var.get_tensor()
+ lod_tensor = var_lod.get_lod_tensor()
+
+ tensor.set_dims([5, 2, 3, 4])
+ tensor.alloc_float(place)
+
+ tensor_array = numpy.array(tensor)
+ self.assertEqual((5, 2, 3, 4), tensor_array.shape)
+ tensor_array[0, 0, 0, 0] = 1.0
+ tensor_array[0, 0, 0, 1] = 2.0
+ tensor.set(tensor_array, place)
+
+ lod_tensor.set_tensor(tensor)
+
+ lod_v = numpy.array(lod_tensor.tensor())
+ self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
+ self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
+ self.assertEqual(len(lod_tensor.lod()), 0)
+
+ lod_py = [[0, 2, 5], [0, 2, 4, 5]]
+ lod_tensor.set_lod(lod_py)
+ lod = lod_tensor.lod()
+ self.assertListEqual(lod_py, lod)
+
if __name__ == '__main__':
unittest.main()
From 74de8c0c763f7f42e0fb0c8a23bb7b289af4c06a Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 11 Sep 2017 11:42:08 +0800
Subject: [PATCH 38/66] make docker can configure with_mkldnn and with_mklml
---
paddle/scripts/docker/build.sh | 4 ++++
1 file changed, 4 insertions(+)
diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index e57f793ac4..2ac455d771 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -30,6 +30,8 @@ Configuring cmake in /paddle/build ...
-DCMAKE_BUILD_TYPE=Release
-DWITH_DOC=OFF
-DWITH_GPU=${WITH_GPU:-OFF}
+ -DWITH_MKLDNN=${WITH_MKLDNN:-ON}
+ -DWITH_MKLML=${WITH_MKLML:-ON}
-DWITH_AVX=${WITH_AVX:-OFF}
-DWITH_GOLANG=${WITH_GOLANG:-ON}
-DWITH_SWIG_PY=ON
@@ -50,6 +52,8 @@ cmake .. \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_DOC=OFF \
-DWITH_GPU=${WITH_GPU:-OFF} \
+ -DWITH_MKLDNN=${WITH_MKLDNN:-ON} \
+ -DWITH_MKLML=${WITH_MKLML:-ON} \
-DWITH_AVX=${WITH_AVX:-OFF} \
-DWITH_GOLANG=${WITH_GOLANG:-ON} \
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \
From dad5421afebbe025898ffe45eceb4e2669827ae8 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Sun, 10 Sep 2017 21:25:40 -0700
Subject: [PATCH 39/66] Remove enforce demangle
It is buggy in some Linux because the unique_ptr will be free however
the std::string trying to use that char*.
Moreover, it is no need to demangle for error log by Paddle.
Just use `c++filt` or other shell utilities to do this.
---
paddle/platform/enforce.h | 19 +------------------
1 file changed, 1 insertion(+), 18 deletions(-)
diff --git a/paddle/platform/enforce.h b/paddle/platform/enforce.h
index 81448897e9..64fcbd93b6 100644
--- a/paddle/platform/enforce.h
+++ b/paddle/platform/enforce.h
@@ -25,10 +25,6 @@ limitations under the License. */
#include "paddle/string/printf.h"
#include "paddle/string/to_string.h"
-#ifdef __GNUC__
-#include <cxxabi.h> // for __cxa_demangle
-#endif
-
#ifndef PADDLE_ONLY_CPU
#include "paddle/platform/dynload/cublas.h"
@@ -46,19 +42,6 @@ limitations under the License. */
namespace paddle {
namespace platform {
-namespace {
-#ifdef __GNUC__
-inline std::string demangle(std::string name) {
- int status = -4; // some arbitrary value to eliminate the compiler warning
- std::unique_ptr<char, void (*)(void*)> res{
- abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free};
- return (status == 0) ? res.get() : name;
-}
-#else
-inline std::string demangle(std::string name) { return name; }
-#endif
-}
-
struct EnforceNotMet : public std::exception {
std::exception_ptr exp_;
std::string err_str_;
@@ -79,7 +62,7 @@ struct EnforceNotMet : public std::exception {
Dl_info info;
for (int i = 0; i < size; ++i) {
if (dladdr(call_stack[i], &info)) {
- auto demangled = demangle(info.dli_sname);
+ auto demangled = info.dli_sname;
auto addr_offset = static_cast<char*>(call_stack[i]) -
static_cast<char*>(info.dli_saddr);
sout << string::Sprintf("%-3d %*0p %s + %zd\n", i,
From e75aab3a392a7dd692c0aafa36e12fbf864f1134 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Mon, 11 Sep 2017 12:57:00 +0800
Subject: [PATCH 40/66] Remove redundant code in lod_tensor.h
---
paddle/framework/lod_tensor.h | 12 +-----------
1 file changed, 1 insertion(+), 11 deletions(-)
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index bbddd6de9d..568f4e8981 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -56,17 +56,7 @@ class LoDTensor {
LoDTensor() {}
LoDTensor(const LoD& lod, Tensor* t) : lod_(lod), tensor_(t) {}
- void set_lod(const LoD& lod) {
- lod_ = lod;
- LOG(INFO) << lod_[0][0];
- }
-
-#ifdef PADDLE_ONLY_CPU
- void set_lod(const std::vector<std::vector<size_t>>& lod) {
- lod_ = lod;
- LOG(INFO) << lod_[0][0];
- }
-#endif
+ void set_lod(const LoD& lod) { lod_ = lod; }
void set_tensor(Tensor* tensor) { tensor_ = tensor; }
From d34516fb662c2fe9727989dd2885de0b9ad9cf8f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=AD=A6=E6=AF=85?= <typhoonzero1986@gmail.com>
Date: Mon, 11 Sep 2017 13:00:44 +0800
Subject: [PATCH 41/66] Get output when training (#3978)
* get output when training
* follow comments
---
python/paddle/v2/event.py | 10 ++++++++--
python/paddle/v2/trainer.py | 9 +++++++--
2 files changed, 15 insertions(+), 4 deletions(-)
diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py
index 7589cc9917..e66bf67d79 100644
--- a/python/paddle/v2/event.py
+++ b/python/paddle/v2/event.py
@@ -53,10 +53,13 @@ class BeginPass(object):
class EndPass(WithMetric):
"""
Event On One Pass Training Complete.
+ To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')"
+ in your event_handler call back
"""
- def __init__(self, pass_id, evaluator):
+ def __init__(self, pass_id, evaluator, gm):
self.pass_id = pass_id
+ self.gm = gm
WithMetric.__init__(self, evaluator)
@@ -73,10 +76,13 @@ class BeginIteration(object):
class EndIteration(WithMetric):
"""
Event On One Batch Training Complete.
+ To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')"
+ in your event_handler call back
"""
- def __init__(self, pass_id, batch_id, cost, evaluator):
+ def __init__(self, pass_id, batch_id, cost, evaluator, gm):
self.pass_id = pass_id
self.batch_id = batch_id
self.cost = cost
+ self.gm = gm
WithMetric.__init__(self, evaluator)
diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py
index 0654a30104..ca95ef13bd 100644
--- a/python/paddle/v2/trainer.py
+++ b/python/paddle/v2/trainer.py
@@ -174,13 +174,18 @@ class SGD(object):
pass_id=pass_id,
batch_id=batch_id,
cost=cost,
- evaluator=batch_evaluator))
+ evaluator=batch_evaluator,
+ gm=self.__gradient_machine__))
self.__parameter_updater__.finishBatch(cost)
batch_evaluator.finish()
self.__parameter_updater__.finishPass()
pass_evaluator.finish()
- event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
+ event_handler(
+ v2_event.EndPass(
+ pass_id,
+ evaluator=pass_evaluator,
+ gm=self.__gradient_machine__))
self.__gradient_machine__.finish()
def test(self, reader, feeding=None):
From d74fe780402747baf6bd5564b8584bf06e9fb099 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Mon, 11 Sep 2017 13:56:07 +0800
Subject: [PATCH 42/66] refine MKLDNNMatrix, solid data handle, rename
updateData to setData
---
paddle/gserver/layers/MKLDNNLayer.h | 4 ++--
paddle/math/MKLDNNMatrix.cpp | 10 ++++-----
paddle/math/MKLDNNMatrix.h | 35 +++++++++++++++++++++++------
3 files changed, 34 insertions(+), 15 deletions(-)
diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h
index b983b833d5..ed1ad7c0bd 100644
--- a/paddle/gserver/layers/MKLDNNLayer.h
+++ b/paddle/gserver/layers/MKLDNNLayer.h
@@ -203,7 +203,7 @@ protected:
real* iData = getInputValue(0, CPU_DEVICE)->getData();
// update input data
// since it might be changed if this is after data layer
- inVal_->updateData(iData);
+ inVal_->setData(iData);
}
/**
@@ -216,7 +216,7 @@ protected:
// update diff
real* oDiff = getOutput(CPU_DEVICE).grad->getData();
- outGrad_->updateData(oDiff);
+ outGrad_->setData(oDiff);
}
/**
diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp
index 0a355e2644..c4063e5069 100644
--- a/paddle/math/MKLDNNMatrix.cpp
+++ b/paddle/math/MKLDNNMatrix.cpp
@@ -33,14 +33,12 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) {
size_t width = cnts / dims[0];
m = Matrix::create(height, width, false, false);
}
-
CHECK(m) << " Matrix should not be empty";
+
CpuMatrixPtr cpuMatrix = std::dynamic_pointer_cast<CpuMatrix>(m);
CHECK(cpuMatrix) << "Only support create from CPU matrix yet";
-
- CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match";
- return std::make_shared<MKLDNNMatrix>(
- m->getData(), m->getHeight(), m->getWidth(), pd);
+ CHECK_EQ(cpuMatrix->getElementCnt(), cnts) << "Count size does not match";
+ return std::make_shared<MKLDNNMatrix>(cpuMatrix, pd);
}
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m,
@@ -138,7 +136,7 @@ void MKLDNNMatrix::downSpatial() {
mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr),
"could not create a memory primitive");
reset(result);
- set_data_handle(getData());
+ set_data_handle(data_);
}
} // namespace paddle
diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h
index e50f698b49..eef3b429e6 100644
--- a/paddle/math/MKLDNNMatrix.h
+++ b/paddle/math/MKLDNNMatrix.h
@@ -30,11 +30,10 @@ typedef std::shared_ptr<MKLDNNMatrix> MKLDNNMatrixPtr;
*/
class MKLDNNMatrix : public CpuMatrix, public mkldnn::memory {
public:
- MKLDNNMatrix(real* data,
- size_t height,
- size_t width,
- mkldnn::memory::primitive_desc pd)
- : CpuMatrix(data, height, width, false), mkldnn::memory(pd, data) {}
+ MKLDNNMatrix(CpuMatrixPtr m, mkldnn::memory::primitive_desc pd)
+ : CpuMatrix(m->getData(), m->getHeight(), m->getWidth(), false),
+ mkldnn::memory(pd, m->getData()),
+ m_(m) {}
~MKLDNNMatrix() {}
@@ -81,11 +80,29 @@ public:
void downSpatial();
/**
- * Update the memory data handle.
+ * set the memory data handle.
* Caution: This will not check the buffer size of the data,
* it should be coverd by user.
*/
- void updateData(void* data) { set_data_handle(data); }
+ void setData(real* data) {
+ set_data_handle(data);
+ CpuMatrix::setData(data);
+ m_.reset();
+ }
+
+ /**
+ * override Matrix::getData
+ * check data before return
+ */
+ real* getData() override {
+ CHECK_EQ((void*)data_, get_data_handle());
+ return data_;
+ }
+
+ const real* getData() const override {
+ CHECK_EQ((void*)data_, get_data_handle());
+ return data_;
+ }
/**
* Get primitive descriptor.
@@ -143,6 +160,10 @@ protected:
memory::format srcFmt,
memory::format dstFmt,
memory::dims dm);
+
+private:
+ // save the CpuMatrixPtr in case the buffer released outside
+ CpuMatrixPtr m_;
};
} // namespace paddle
From d4c0734840420314298e4a330ddd4f10d957e8e7 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Mon, 11 Sep 2017 14:08:13 +0800
Subject: [PATCH 43/66] remove convertOutputToOtherDevice
---
paddle/gserver/layers/MKLDNNFcLayer.cpp | 23 ++++-------------------
paddle/gserver/layers/MKLDNNFcLayer.h | 2 --
paddle/gserver/layers/MKLDNNLayer.h | 13 +++++++------
3 files changed, 11 insertions(+), 27 deletions(-)
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp
index 8318c8c519..f4deb351f2 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp
@@ -77,24 +77,6 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
-void MKLDNNFcLayer::convertOutputToOtherDevice() {
- copyOutputInfoToOtherDevice();
- // find other cpu device and reorder output to cpu device
- int cnt = 0;
- for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
- if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
- // fc cpu output value do not need convert
- // just share point
- outputOtherDevice_[i].value = output_.value;
- ++cnt;
- }
- }
-
- if (cnt > 1) {
- LOG(WARNING) << "should not have more than one CPU devie";
- }
-}
-
void MKLDNNFcLayer::reshape() {
const Argument& input = getInput(0, getPrev(0)->getDeviceId());
int batchSize = input.getBatchSize();
@@ -155,7 +137,10 @@ void MKLDNNFcLayer::resetFwd() {
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
if (!outputIsOnlyMKLDNN()) {
- convertOutputToOtherDevice();
+ copyOutputInfoToOtherDevice();
+ // fc cpu output value do not need create convert
+ // just share point
+ getOutput(CPU_DEVICE).value->setData(output_.value->getData());
}
// create forward handle
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h
index e138a6faf1..e2657a8d5e 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.h
+++ b/paddle/gserver/layers/MKLDNNFcLayer.h
@@ -72,8 +72,6 @@ protected:
* only would be called when needed
*/
void resetBwd();
-
- void convertOutputToOtherDevice() override;
};
} // namespace paddle
diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h
index ed1ad7c0bd..1a3e949fb9 100644
--- a/paddle/gserver/layers/MKLDNNLayer.h
+++ b/paddle/gserver/layers/MKLDNNLayer.h
@@ -113,12 +113,6 @@ public:
*/
virtual void convertWeightsToPaddle() {}
- /**
- * convert MKLDNN output to other device.
- * only support CPU device yet
- */
- virtual void convertOutputToOtherDevice() {}
-
/**
* print info about sizes
*/
@@ -155,6 +149,7 @@ protected:
* copy base info and do not copy data value
*/
void copyOutputInfoToOtherDevice() {
+ int cnt = 0;
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].setFrameHeight(output_.getFrameHeight());
outputOtherDevice_[i].setFrameWidth(output_.getFrameWidth());
@@ -163,6 +158,12 @@ protected:
outputOtherDevice_[i].subSequenceStartPositions =
output_.subSequenceStartPositions;
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
+ if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
+ ++cnt;
+ }
+ }
+ if (cnt > 1) {
+ LOG(WARNING) << "should not have more than one CPU devie";
}
}
From f40d5f580de3731e071bb9cca3c98a6537955e25 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Mon, 11 Sep 2017 14:19:10 +0800
Subject: [PATCH 44/66] remove syncOutputGrad, rename syncInputValue to
updateInputData
---
paddle/gserver/layers/MKLDNNFcLayer.cpp | 18 +++++++++-----
paddle/gserver/layers/MKLDNNFcLayer.h | 2 ++
paddle/gserver/layers/MKLDNNLayer.h | 32 +++++--------------------
3 files changed, 20 insertions(+), 32 deletions(-)
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp
index f4deb351f2..53433cef35 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp
@@ -220,13 +220,12 @@ void MKLDNNFcLayer::resetBwd() {
pipelineBwd_.push_back(*bwdWgt_);
/// backward data
- device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
- const MatrixPtr& in = getInputGrad(0, device);
+ const MatrixPtr& in = inputLayers_[0]->getOutput().grad;
if (in == nullptr) {
return;
}
- if (getInput(0, device).getAllCount() > 1) {
- // TODO(TJ): use outputMaps_ ways when merge outgrad done
+ if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) {
+ // TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
} else {
inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc());
}
@@ -243,13 +242,21 @@ void MKLDNNFcLayer::resetBwd() {
pipelineBwd_.push_back(*bwdData_);
}
+void MKLDNNFcLayer::updateInputData() {
+ if (inputLayers_[0]->getType() != "data") {
+ return;
+ }
+ real* iData = getInputValue(0, CPU_DEVICE)->getData();
+ inVal_->setData(iData);
+}
+
void MKLDNNFcLayer::forward(PassType passType) {
Layer::forward(passType);
reshape();
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
- syncInputValue();
+ updateInputData();
// just submit forward pipeline
stream_->submit(pipelineFwd_);
@@ -271,7 +278,6 @@ void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
resetBwd();
- syncOutputGrad();
// just sumbmit backward pipeline
stream_->submit(pipelineBwd_);
}
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h
index e2657a8d5e..4ad67a16e0 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.h
+++ b/paddle/gserver/layers/MKLDNNFcLayer.h
@@ -53,6 +53,8 @@ public:
void backward(const UpdateCallback& callback) override;
+ void updateInputData() override;
+
protected:
/**
* reshape the input image sizes
diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h
index 1a3e949fb9..543364edce 100644
--- a/paddle/gserver/layers/MKLDNNLayer.h
+++ b/paddle/gserver/layers/MKLDNNLayer.h
@@ -113,6 +113,12 @@ public:
*/
virtual void convertWeightsToPaddle() {}
+ /**
+ * Update input value data when input layer is "data" type.
+ * Since the input value data address might be changed.
+ */
+ virtual void updateInputData() {}
+
/**
* print info about sizes
*/
@@ -194,32 +200,6 @@ protected:
return outputOtherDevice_.size() == 0;
}
- /**
- * Sync input value data
- */
- void syncInputValue() {
- if (inputIsOnlyMKLDNN()) {
- return;
- }
- real* iData = getInputValue(0, CPU_DEVICE)->getData();
- // update input data
- // since it might be changed if this is after data layer
- inVal_->setData(iData);
- }
-
- /**
- * Sync output grad data
- */
- void syncOutputGrad() {
- if (outputIsOnlyMKLDNN()) {
- return;
- }
-
- // update diff
- real* oDiff = getOutput(CPU_DEVICE).grad->getData();
- outGrad_->setData(oDiff);
- }
-
/**
* Set deviceId of this layer.
*/
From dd64349a9213b419c6a50c81e06e2d6a8fa9ebd5 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Mon, 11 Sep 2017 00:06:06 -0700
Subject: [PATCH 45/66] refine reshape operator
---
paddle/operators/reshape_op.cc | 15 +++++++++------
paddle/operators/reshape_op.h | 10 ++++------
.../paddle/v2/framework/tests/test_reshape_op.py | 16 ++++++++++++++--
3 files changed, 27 insertions(+), 14 deletions(-)
diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
index d75ec76632..37cbecbf25 100644
--- a/paddle/operators/reshape_op.cc
+++ b/paddle/operators/reshape_op.cc
@@ -29,14 +29,17 @@ class ReshapeOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext &ctx) const override {
auto *in = ctx.Input<framework::Tensor>("X");
auto shape = ctx.Attr<std::vector<int>>("shape");
- PADDLE_ENFORCE_EQ((unsigned)shape.size(), in->dims().size(),
- "The dimension of Input(X) mismatches with Attr(shape).");
- size_t shape_size = 1;
+ int64_t capacity = -1;
for (auto dim : shape) {
- shape_size *= dim;
+ PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
+ if (capacity < 0) {
+ capacity = dim;
+ } else {
+ capacity *= dim;
+ }
}
- size_t in_size = framework::product(in->dims());
- PADDLE_ENFORCE_EQ(shape_size, in_size,
+ int64_t in_size = framework::product(in->dims());
+ PADDLE_ENFORCE_EQ(capacity, in_size,
"The size of Input(X) mismatches with Attr(shape).");
ctx.Output<framework::Tensor>("Out")->Resize(in->dims());
}
diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h
index 61d502c836..0e920329d9 100644
--- a/paddle/operators/reshape_op.h
+++ b/paddle/operators/reshape_op.h
@@ -21,14 +21,12 @@
namespace paddle {
namespace operators {
-using Tensor = framework::Tensor;
-
template <typename Place, typename T>
class ReshapeKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
- auto* out = ctx.Output<Tensor>("Out");
- auto* in = ctx.Input<Tensor>("X");
+ auto* out = ctx.Output<framework::Tensor>("Out");
+ auto* in = ctx.Input<framework::Tensor>("X");
out->mutable_data<T>(ctx.GetPlace());
auto shape = ctx.Attr<std::vector<int>>("shape");
@@ -46,8 +44,8 @@ template <typename Place, typename T>
class ReshapeGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
- auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
- auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+ auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+ auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
d_x->mutable_data<T>(ctx.GetPlace());
auto in_dims = d_x->dims();
diff --git a/python/paddle/v2/framework/tests/test_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py
index 4797019435..df7d913ba4 100644
--- a/python/paddle/v2/framework/tests/test_reshape_op.py
+++ b/python/paddle/v2/framework/tests/test_reshape_op.py
@@ -10,15 +10,27 @@ class TestReshapeOp(unittest.TestCase):
def setUp(self):
self.type = "reshape"
self.inputs = {'X': np.random.random((37, 51)).astype("float32"), }
- self.attrs = {'shape': [51, 37]}
+ self.attrs = {'shape': [51 * 37]}
self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
-class ReshapeGradOpTest(GradientChecker):
+class TestReshapeGradOp(GradientChecker):
+ """
def test_normal(self):
op = Operator("reshape", X='X', Out='Out', shape=[5, 40])
inputs = {"X": np.random.random((10, 20)).astype("float32")}
self.check_grad(op, inputs, set("X"), "Out")
+ """
+
+ def setUp(self):
+ self.op = Operator("reshape", X='X', Out='Out', shape=[5, 40])
+ self.inputs = {"X": np.random.random((10, 20)).astype("float32")}
+
+ def test_normal(self):
+ self.check_grad(self.op, self.inputs, ["X"], "Out")
+
+ def test_dev_compare(self):
+ self.compare_grad(self.op, self.inputs)
if __name__ == '__main__':
From 7bd517129ae50850979c7a7c6cc8fce22e2131a8 Mon Sep 17 00:00:00 2001
From: Yancey <yancey1989@gmail.com>
Date: Mon, 11 Sep 2017 15:21:14 +0800
Subject: [PATCH 46/66] Add Concat operator with CPU kernel (#3775)
add concat op with CPU kernel
---
paddle/operators/concat_op.cc | 79 +++++++++++++++++++
paddle/operators/concat_op.cu | 19 +++++
paddle/operators/concat_op.h | 64 +++++++++++++++
paddle/pybind/pybind.cc | 1 +
python/paddle/v2/framework/op.py | 1 -
.../paddle/v2/framework/tests/CMakeLists.txt | 1 +
.../v2/framework/tests/gradient_checker.py | 5 +-
.../paddle/v2/framework/tests/op_test_util.py | 32 +++++---
.../v2/framework/tests/test_concat_op.py | 22 ++++++
9 files changed, 211 insertions(+), 13 deletions(-)
create mode 100644 paddle/operators/concat_op.cc
create mode 100644 paddle/operators/concat_op.cu
create mode 100644 paddle/operators/concat_op.h
create mode 100644 python/paddle/v2/framework/tests/test_concat_op.py
diff --git a/paddle/operators/concat_op.cc b/paddle/operators/concat_op.cc
new file mode 100644
index 0000000000..0ebefbab26
--- /dev/null
+++ b/paddle/operators/concat_op.cc
@@ -0,0 +1,79 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/concat_op.h"
+#include <vector>
+
+namespace paddle {
+namespace operators {
+using framework::Tensor;
+
+class ConcatOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto ins = ctx.MultiInput<framework::Tensor>("X");
+ auto *out = ctx.Output<framework::Tensor>("Out");
+ size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
+ size_t n = ins.size();
+
+ PADDLE_ENFORCE_GT(n, 1, "Input tensors count should > 1.");
+
+ auto out_dims = ins[0]->dims();
+ size_t in_zero_dims_size = out_dims.size();
+ for (size_t i = 1; i < n; i++) {
+ for (size_t j = 0; j < in_zero_dims_size; j++) {
+ if (j == axis) {
+ out_dims[axis] += ins[i]->dims()[j];
+ continue;
+ }
+ PADDLE_ENFORCE_EQ(out_dims[j], ins[i]->dims()[j],
+ "Input tensors should have the same "
+ "elements except the specify axis.")
+ }
+ }
+ out->Resize(out_dims);
+ }
+};
+
+class ConcatOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ ConcatOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "the input tensors of concat operator.").AsDuplicable();
+ AddOutput("Out", "the output tensor of concat operator.");
+ AddComment(R"DOC(
+ Join the input tensors along with the axis.
+ Examples:
+ Input[0] = [[1,2],[3,4]]
+ Input[1] = [[5,6]]
+ axis = 0
+ Output = [[1,2],
+ [3,4],
+ [5,6]]
+ )DOC");
+ AddAttr<int>("axis", "The axis which the inputs will be joined with.")
+ .SetDefault(0);
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(concat, ops::ConcatOp, ops::ConcatOpMaker)
+REGISTER_OP_CPU_KERNEL(concat,
+ ops::ConcatKernel<paddle::platform::CPUPlace, float>)
diff --git a/paddle/operators/concat_op.cu b/paddle/operators/concat_op.cu
new file mode 100644
index 0000000000..38fee7473d
--- /dev/null
+++ b/paddle/operators/concat_op.cu
@@ -0,0 +1,19 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/concat_op.h"
+
+namespace ops = paddle::operators;
+// TODO(Yancey1989) Add GPU kernel
diff --git a/paddle/operators/concat_op.h b/paddle/operators/concat_op.h
new file mode 100644
index 0000000000..f977054fdf
--- /dev/null
+++ b/paddle/operators/concat_op.h
@@ -0,0 +1,64 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <vector>
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class ConcatKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const override {
+ auto ins = ctx.MultiInput<framework::Tensor>("X");
+ auto* out = ctx.Output<framework::Tensor>("Out");
+ int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
+ size_t n = ins.size();
+ size_t output_axis_dim = 0;
+ size_t before = 1, after = 1;
+ for (size_t i = 0; i < n; i++) {
+ output_axis_dim += ins[i]->dims()[axis];
+ }
+ auto& input_zero = ins[0];
+ for (int64_t i = 0; i < input_zero->dims().size(); i++) {
+ if (i == axis) {
+ continue;
+ }
+ if (i < axis) {
+ before *= input_zero->dims()[i];
+ } else {
+ after *= input_zero->dims()[i];
+ }
+ }
+ size_t output_offset = 0;
+ for (size_t i = 0; i < n; i++) {
+ auto& in = ins[i];
+ auto axis_dim = in->dims()[axis];
+ for (size_t j = 0; j < before; j++) {
+ size_t len = axis_dim * after * sizeof(T);
+ const T* src = in->data<T>() + axis_dim * after * j;
+ T* out_data = out->mutable_data<T>(platform::CPUPlace());
+ T* dest = out_data + output_offset + output_axis_dim * after * j;
+ memcpy(dest, src, len);
+ }
+ output_offset += axis_dim * after;
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index db701a2a30..227b75aff8 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -49,6 +49,7 @@ USE_OP(minus);
USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
+USE_CPU_ONLY_OP(concat);
USE_OP(top_k);
USE_OP(squared_l2_distance);
USE_OP(sum);
diff --git a/python/paddle/v2/framework/op.py b/python/paddle/v2/framework/op.py
index 4e91924a50..9e665adad2 100644
--- a/python/paddle/v2/framework/op.py
+++ b/python/paddle/v2/framework/op.py
@@ -43,7 +43,6 @@ class OpDescCreationMethod(object):
if len(args) != 0:
raise ValueError("Only keyword arguments are supported.")
op_desc = framework_pb2.OpDesc()
-
for input_parameter in self.__op_proto__.inputs:
input_arguments = kwargs.get(input_parameter.name, [])
if is_str(input_arguments):
diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt
index 2117fdf0d5..2f6be105b6 100644
--- a/python/paddle/v2/framework/tests/CMakeLists.txt
+++ b/python/paddle/v2/framework/tests/CMakeLists.txt
@@ -35,4 +35,5 @@ py_test(test_lookup_table SRCS test_lookup_table.py)
py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)
py_test(test_sum_op SRCS test_sum_op.py)
py_test(mnist SRCS mnist.py)
+py_test(test_concat_op SRCS test_concat_op.py)
py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
index fdb06b7988..51a98284bd 100644
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ b/python/paddle/v2/framework/tests/gradient_checker.py
@@ -11,11 +11,10 @@ __all__ = ['get_numeric_gradient']
def create_op(op_type):
# TODO need to set attrs
kwargs = dict()
- for in_name in Operator.get_op_input_names(op_type):
+ for in_name, _ in Operator.get_op_input_names(op_type):
kwargs[in_name] = in_name
- for out_name in Operator.get_op_output_names(op_type):
+ for out_name, _ in Operator.get_op_output_names(op_type):
kwargs[out_name] = out_name
-
return Operator(op_type, **kwargs)
diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py
index 370f27eaf6..54fe5da440 100644
--- a/python/paddle/v2/framework/tests/op_test_util.py
+++ b/python/paddle/v2/framework/tests/op_test_util.py
@@ -27,17 +27,30 @@ class OpTestMeta(type):
places.append(core.GPUPlace(0))
for place in places:
- for in_name in Operator.get_op_input_names(self.type):
- if hasattr(self, "inputs") and in_name in self.inputs:
- kwargs[in_name] = in_name
- var = scope.new_var(in_name).get_tensor()
- arr = self.inputs[in_name]
- var.set_dims(arr.shape)
- var.set(arr, place)
+ for ins in Operator.get_op_input_names(self.type):
+ in_name = ins[0]
+ in_dup = ins[1]
+ if hasattr(self, 'inputs') and in_name in self.inputs:
+ kwargs[in_name] = []
+ if in_dup:
+ arrays = self.inputs[in_name]
+ for index, arr in enumerate(arrays):
+ var = scope.new_var(in_name + str(index))
+ tensor = var.get_tensor()
+ tensor.set_dims(arr.shape)
+ tensor.set(arr, place)
+ kwargs[in_name].append(in_name + str(index))
+ else:
+ kwargs[in_name] = in_name
+ var = scope.new_var(in_name).get_tensor()
+ arr = self.inputs[in_name]
+ var.set_dims(arr.shape)
+ var.set(arr, place)
else:
kwargs[in_name] = "@EMPTY@"
- for out_name in Operator.get_op_output_names(self.type):
+ for out_name, out_dup in Operator.get_op_output_names(
+ self.type):
if not hasattr(self, "outputs"):
raise ValueError(
"The test op must set self.outputs dict.")
@@ -60,7 +73,8 @@ class OpTestMeta(type):
ctx = core.DeviceContext.create(place)
op.run(scope, ctx)
- for out_name in Operator.get_op_output_names(self.type):
+ for out_name, out_dup in Operator.get_op_output_names(
+ self.type):
actual = numpy.array(scope.find_var(out_name).get_tensor())
expect = self.outputs[out_name]
self.assertTrue(
diff --git a/python/paddle/v2/framework/tests/test_concat_op.py b/python/paddle/v2/framework/tests/test_concat_op.py
new file mode 100644
index 0000000000..6bd4c30974
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_concat_op.py
@@ -0,0 +1,22 @@
+import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+
+class TestConcatOp(unittest.TestCase):
+ __metaclass__ = OpTestMeta
+
+ def setUp(self):
+ self.type = "concat"
+ x0 = np.random.random((2, 3, 2, 5)).astype('float32')
+ x1 = np.random.random((2, 3, 3, 5)).astype('float32')
+ x2 = np.random.random((2, 3, 4, 5)).astype('float32')
+ axis = 2
+ self.inputs = {'X': [x0, x1, x2]}
+ self.attrs = {'axis': axis}
+ self.outputs = {'Out': np.concatenate((x0, x1, x2), axis=axis)}
+
+
+if __name__ == '__main__':
+ unittest.main()
From 2d807f2b4c586656b760e31030a08655f7d298b1 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Mon, 11 Sep 2017 15:31:33 +0800
Subject: [PATCH 47/66] init refine op python tests
---
.../paddle/v2/framework/tests/CMakeLists.txt | 2 -
python/paddle/v2/framework/tests/op_test.py | 28 ++++--
.../v2/framework/tests/test_add_two_op.py | 23 ++---
.../v2/framework/tests/test_cos_sim_op.py | 49 +++-------
.../framework/tests/test_cross_entropy_op.py | 2 +-
.../tests/test_fill_zeros_like_op.py | 19 ++--
.../v2/framework/tests/test_gather_op.py | 32 +++---
.../tests/test_gaussian_random_op.py | 8 +-
.../framework/tests/test_gradient_checker.py | 42 ++++----
.../v2/framework/tests/test_lookup_table.py | 29 ++----
.../paddle/v2/framework/tests/test_mean_op.py | 24 ++---
.../v2/framework/tests/test_minus_op.py | 23 ++---
.../paddle/v2/framework/tests/test_mul_op.py | 98 +++++--------------
python/paddle/v2/framework/tests/test_net.py | 2 +-
.../v2/framework/tests/test_rowwise_add_op.py | 73 ++++++--------
.../tests/test_scale_and_identity_op.py | 41 +++-----
.../v2/framework/tests/test_scatter_op.py | 37 +++----
.../paddle/v2/framework/tests/test_sgd_op.py | 17 ++--
.../v2/framework/tests/test_softmax_op.py | 28 ++----
.../tests/test_squared_l2_distance_op.py | 76 ++++++--------
.../paddle/v2/framework/tests/test_sum_op.py | 12 +--
.../v2/framework/tests/test_top_k_op.py | 17 ++--
.../framework/tests/test_uniform_random_op.py | 8 +-
23 files changed, 267 insertions(+), 423 deletions(-)
diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt
index 2117fdf0d5..07997e201a 100644
--- a/python/paddle/v2/framework/tests/CMakeLists.txt
+++ b/python/paddle/v2/framework/tests/CMakeLists.txt
@@ -19,8 +19,6 @@ py_test(test_scatter_op SRCS test_scatter_op.py)
py_test(test_fill_zeros_like_op SRCS test_fill_zeros_like_op.py)
py_test(test_top_k_op SRCS test_top_k_op.py)
-py_test(gradient_checker SRCS gradient_checker.py)
-
py_test(test_rowwise_add_op SRCS test_rowwise_add_op.py)
py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index 3a6a5dca4c..fe094df8e5 100644
--- a/python/paddle/v2/framework/tests/op_test.py
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -9,7 +9,7 @@ def grad_var_name(var_name):
return var_name + "@GRAD"
-def create_op(scope, op_type, inputs, outputs, attrs=None):
+def create_op(scope, op_type, inputs, outputs, attrs):
kwargs = dict()
for in_name, in_dup in Operator.get_op_inputs(op_type):
@@ -29,15 +29,16 @@ def create_op(scope, op_type, inputs, outputs, attrs=None):
kwargs[out_name] = []
if out_dup:
sub_in = outputs[out_name]
- for sun_in_name in sub_in:
- var = scope.new_var(sun_in_name)
- kwargs[out_name].append(sun_in_name)
+ for sub_in_name in sub_in:
+ var = scope.new_var(sub_in_name)
+ kwargs[out_name].append(sub_in_name)
else:
var = scope.new_var(out_name)
kwargs[out_name].append(out_name)
for attr_name in Operator.get_op_attr_names(op_type):
- kwargs[attr_name] = attrs[attr_name]
+ if attr_name in attrs:
+ kwargs[attr_name] = attrs[attr_name]
return Operator(op_type, **kwargs)
@@ -89,6 +90,7 @@ def get_numeric_gradient(scope,
delta=0.005,
in_place=False):
+ print "before set input"
set_input(scope, op, inputs, core.CPUPlace())
op.infer_shape(scope)
@@ -110,7 +112,7 @@ def get_numeric_gradient(scope,
# we use a for loop to compute the gradient of every element.
for i in xrange(tensor_size):
if in_place:
- set_input(op, inputs, core.CPUPlace())
+ set_input(scope, op, inputs, core.CPUPlace())
# get one input element throw it's index i.
origin = tensor_to_check.get_float_element(i)
@@ -120,7 +122,7 @@ def get_numeric_gradient(scope,
y_pos = get_output()
if in_place:
- set_input(op, inputs, core.CPUPlace())
+ set_input(scope, op, inputs, core.CPUPlace())
x_neg = origin - delta
tensor_to_check.set_float_element(i, x_neg)
@@ -168,7 +170,11 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place,
class OpTest(unittest.TestCase):
def check_output_with_place(self, place):
self.scope = core.Scope()
- self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
+ op_inputs = self.inputs if hasattr(self, "inputs") else dict()
+ op_outputs = self.outputs if hasattr(self, "outputs") else dict()
+ op_attrs = self.attrs if hasattr(self, "attrs") else dict()
+ self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
+ op_attrs)
if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
return
set_input(self.scope, self.op, self.inputs, place)
@@ -227,7 +233,11 @@ class OpTest(unittest.TestCase):
in_place=False,
max_relative_error=0.005):
self.scope = core.Scope()
- self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
+ op_inputs = self.inputs if hasattr(self, "inputs") else dict()
+ op_outputs = self.outputs if hasattr(self, "outputs") else dict()
+ op_attrs = self.attrs if hasattr(self, "attrs") else dict()
+ self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
+ op_attrs)
if no_grad_set is None:
no_grad_set = set()
diff --git a/python/paddle/v2/framework/tests/test_add_two_op.py b/python/paddle/v2/framework/tests/test_add_two_op.py
index a578e74eca..3ca34d9b9f 100644
--- a/python/paddle/v2/framework/tests/test_add_two_op.py
+++ b/python/paddle/v2/framework/tests/test_add_two_op.py
@@ -1,23 +1,20 @@
import unittest
+import numpy as np
+from op_test import OpTest
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-
-from op_test_util import OpTestMeta
-
-
-class TestAddOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
+class TestAddOp(OpTest):
def setUp(self):
- self.type = "add"
+ self.op_type = "add"
self.inputs = {
- 'X': numpy.random.random((102, 105)).astype("float32"),
- 'Y': numpy.random.random((102, 105)).astype("float32")
+ 'X': np.random.random((102, 105)).astype("float32"),
+ 'Y': np.random.random((102, 105)).astype("float32")
}
self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
+ def test_check_output(self):
+ self.check_output()
+
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_cos_sim_op.py b/python/paddle/v2/framework/tests/test_cos_sim_op.py
index 32013a7999..797cbd8cc5 100644
--- a/python/paddle/v2/framework/tests/test_cos_sim_op.py
+++ b/python/paddle/v2/framework/tests/test_cos_sim_op.py
@@ -1,17 +1,14 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
-class TestCosSimOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestCosSimOp(OpTest):
def setUp(self):
- self.type = "cos_sim"
+ self.op_type = "cos_sim"
self.inputs = {
- 'X': np.random.random((32, 64)).astype("float32"),
- 'Y': np.random.random((32, 64)).astype("float32")
+ 'X': np.random.random((10, 5)).astype("float32"),
+ 'Y': np.random.random((10, 5)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
@@ -23,38 +20,20 @@ class TestCosSimOp(unittest.TestCase):
'Out': np.expand_dims(expect_out, 1)
}
+ def test_check_output(self):
+ self.check_output()
-class TestCosSimGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("cos_sim")
- self.inputs = {
- 'X': np.random.random((10, 5)).astype("float32"),
- 'Y': np.random.random((10, 5)).astype("float32")
- }
-
- def test_cpu_gpu_compare(self):
- self.compare_grad(self.op, self.inputs)
-
- def test_normal(self):
- self.check_grad(
- self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.05)
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05)
- def test_ignore_x(self):
+ def test_check_grad_ingore_x(self):
self.check_grad(
- self.op,
- self.inputs, ["Y"],
- "Out",
- max_relative_error=0.05,
- no_grad_set={"X"})
+ ['Y'], 'Out', max_relative_error=0.05, no_grad_set=set('X'))
- def test_ignore_y(self):
+ def test_check_grad_ignore_y(self):
self.check_grad(
- self.op,
- self.inputs, ["X"],
- "Out",
- max_relative_error=0.05,
- no_grad_set={"Y"})
+ ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
index fb6a440e23..c2fc102a8b 100644
--- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py
+++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
@@ -21,7 +21,7 @@ class TestCrossEntropy(OpTest):
self.check_output()
def test_check_grad(self):
- self.check_grad(["X"], "Y")
+ self.check_grad(['X'], 'Y')
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py b/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
index e5c862605f..2473daaba2 100644
--- a/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
+++ b/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
@@ -1,16 +1,17 @@
import unittest
-from op_test_util import OpTestMeta
-import numpy
+import numpy as np
+from op_test import OpTest
-class TestFillZerosLikeOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestFillZerosLikeOp(OpTest):
def setUp(self):
- self.type = "fill_zeros_like"
- self.inputs = {'Src': numpy.random.random((219, 232)).astype("float32")}
- self.outputs = {'Dst': numpy.zeros_like(self.inputs['Src'])}
+ self.op_type = "fill_zeros_like"
+ self.inputs = {'Src': np.random.random((219, 232)).astype("float32")}
+ self.outputs = {'Dst': np.zeros_like(self.inputs["Src"])}
+
+ def test_check_output(self):
+ self.check_output()
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_gather_op.py b/python/paddle/v2/framework/tests/test_gather_op.py
index e3de3fd0a1..b0ab429ef1 100644
--- a/python/paddle/v2/framework/tests/test_gather_op.py
+++ b/python/paddle/v2/framework/tests/test_gather_op.py
@@ -1,30 +1,20 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
+import numpy as np
+from op_test import OpTest
-class TestGatherOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestGatherOp(OpTest):
def setUp(self):
- self.type = "gather"
- xnp = numpy.random.random((10, 20)).astype("float32")
- self.inputs = {
- 'X': xnp,
- 'Index': numpy.array([1, 3, 5]).astype("int32")
- }
- self.outputs = {'Out': self.inputs['X'][self.inputs['Index']]}
+ self.op_type = "gather"
+ xnp = np.random.random((10, 20)).astype("float32")
+ self.inputs = {'X': xnp, 'Index': np.array([1, 3, 5]).astype("int32")}
+ self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
+ def test_check_output(self):
+ self.check_output()
-class TestGatherGradOp(GradientChecker):
- def test_gather_grad(self):
- op = create_op("gather")
- xnp = numpy.random.random((10, 20)).astype("float32")
- inputs = {'X': xnp, 'Index': numpy.array([1, 3, 5]).astype("int32")}
- self.check_grad(op, inputs, set("X"), "Out")
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Out')
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_gaussian_random_op.py b/python/paddle/v2/framework/tests/test_gaussian_random_op.py
index f95ed70b58..1f9e4db783 100644
--- a/python/paddle/v2/framework/tests/test_gaussian_random_op.py
+++ b/python/paddle/v2/framework/tests/test_gaussian_random_op.py
@@ -14,11 +14,11 @@ class GaussianRandomTest(unittest.TestCase):
def gaussian_random_test(self, place):
scope = core.Scope()
- scope.new_var("Out").get_tensor()
+ scope.new_var('Out').get_tensor()
op = Operator(
"gaussian_random",
- Out="Out",
+ Out='Out',
dims=[1000, 784],
mean=.0,
std=1.,
@@ -27,10 +27,10 @@ class GaussianRandomTest(unittest.TestCase):
op.infer_shape(scope)
context = core.DeviceContext.create(place)
op.run(scope, context)
- tensor = numpy.array(scope.find_var("Out").get_tensor())
+ tensor = numpy.array(scope.find_var('Out').get_tensor())
self.assertAlmostEqual(numpy.mean(tensor), .0, delta=0.1)
self.assertAlmostEqual(numpy.std(tensor), 1., delta=0.1)
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py
index e8a7f848df..abeb01cb34 100644
--- a/python/paddle/v2/framework/tests/test_gradient_checker.py
+++ b/python/paddle/v2/framework/tests/test_gradient_checker.py
@@ -1,42 +1,44 @@
import unittest
-import numpy
-from paddle.v2.framework.op import Operator
-from gradient_checker import GradientChecker
-from gradient_checker import get_numeric_gradient
+import numpy as np
+import paddle.v2.framework.core as core
+from op_test import get_numeric_gradient
+from op_test import create_op
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
- add_op = Operator("add", X="X", Y="Y", Out="Z")
- x = numpy.random.random((10, 1)).astype("float32")
- y = numpy.random.random((10, 1)).astype("float32")
-
- arr = get_numeric_gradient(add_op, {"X": x, "Y": y}, "Z", "X")
+ x = np.random.random((10, 1)).astype("float32")
+ y = np.random.random((10, 1)).astype("float32")
+ z = x + y
+ scope = core.Scope()
+ add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict())
+ arr = get_numeric_gradient(scope, add_op, {'X': x, 'Y': y}, 'X', 'Out')
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
def test_softmax_op(self):
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
- shiftx = x - numpy.max(x)
- exps = numpy.exp(shiftx)
- return exps / numpy.sum(exps)
+ shiftx = x - np.max(x)
+ exps = np.exp(shiftx)
+ return exps / np.sum(exps)
def label_softmax_grad(Y, dY):
dX = Y * 0.0
for i in range(Y.shape[0]):
- d = numpy.dot(Y[i, :], dY[i, :])
+ d = np.dot(Y[i, :], dY[i, :])
dX[i, :] = Y[i, :] * (dY[i, :] - d)
return dX
- softmax_op = Operator("softmax", X="X", Y="Y")
-
- X = numpy.random.random((2, 2)).astype("float32")
- Y = numpy.apply_along_axis(stable_softmax, 1, X)
- dY = numpy.ones(Y.shape)
+ X = np.random.random((2, 2)).astype("float32")
+ Y = np.apply_along_axis(stable_softmax, 1, X)
+ dY = np.ones(Y.shape)
dX = label_softmax_grad(Y, dY)
- arr = get_numeric_gradient(softmax_op, {"X": X}, "Y", "X")
- numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
+ scope = core.Scope()
+ softmax_op = create_op(scope, "softmax", {"X": X}, {"Y": Y}, dict())
+
+ arr = get_numeric_gradient(scope, softmax_op, {"X": X}, "X", "Y")
+ np.testing.assert_almost_equal(arr, dX, decimal=1e-2)
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py
index 4b7ce92c0f..b259bb67e8 100644
--- a/python/paddle/v2/framework/tests/test_lookup_table.py
+++ b/python/paddle/v2/framework/tests/test_lookup_table.py
@@ -1,31 +1,22 @@
import unittest
import numpy as np
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
-class TestLookupTableOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestLookupTableOp(OpTest):
def setUp(self):
- self.type = 'lookup_table'
- table = np.random.random((17, 31)).astype('float32')
- ids = np.random.randint(0, 17, 4).astype('int32')
+ self.op_type = "lookup_table"
+ table = np.random.random((17, 31)).astype("float32")
+ ids = np.random.randint(0, 17, 4).astype("int32")
self.inputs = {'W': table, 'Ids': ids}
self.outputs = {'Out': table[ids]}
+ def test_check_output(self):
+ self.check_output()
-class TestLookupTableGradOp(GradientChecker):
- def test_grad(self):
- op = create_op('lookup_table')
- table = np.random.random((17, 31)).astype('float32')
- ids = np.random.randint(0, 17, 4).astype('int32')
- inputs = {'W': table, 'Ids': ids}
- # comapre gradients
- self.compare_grad(op, inputs, set(['Ids']))
- # check gradients
- self.check_grad(op, inputs, set('W'), 'Out')
+ def test_check_grad(self):
+ self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_mean_op.py b/python/paddle/v2/framework/tests/test_mean_op.py
index f32b3160d6..7823abd8f8 100644
--- a/python/paddle/v2/framework/tests/test_mean_op.py
+++ b/python/paddle/v2/framework/tests/test_mean_op.py
@@ -1,24 +1,20 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
import numpy as np
+from op_test import OpTest
-class TestMeanOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestMeanOp(OpTest):
def setUp(self):
- self.type = "mean"
- self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
- self.outputs = {'Out': np.mean(self.inputs['X'])}
+ self.op_type = "mean"
+ self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
+ self.outputs = {'Out': np.mean(self.inputs["X"])}
+ def test_check_output(self):
+ self.check_output()
-class MeanGradOpTest(GradientChecker):
- def test_normal(self):
- op = create_op("mean")
- inputs = {"X": np.random.random((10, 10)).astype("float32")}
- self.check_grad(op, inputs, set("X"), "Out")
+ def test_checkout_grad(self):
+ self.check_grad(['X'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_minus_op.py b/python/paddle/v2/framework/tests/test_minus_op.py
index 5abdd4a69b..dea797a1fe 100644
--- a/python/paddle/v2/framework/tests/test_minus_op.py
+++ b/python/paddle/v2/framework/tests/test_minus_op.py
@@ -1,30 +1,23 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
-class MinusOpTest(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class MinusOpTest(OpTest):
def setUp(self):
- self.type = "minus"
+ self.op_type = "minus"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((32, 84)).astype("float32")
}
self.outputs = {'Out': (self.inputs['X'] - self.inputs['Y'])}
+ def test_check_output(self):
+ self.check_output()
-class MinusGradTest(GradientChecker):
- def test_left(self):
- op = create_op("minus")
- inputs = {
- "X": np.random.random((10, 10)).astype("float32"),
- "Y": np.random.random((10, 10)).astype("float32")
- }
- self.check_grad(op, inputs, ["X", 'Y'], "Out")
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py
index 8c827e242e..b3d95a56b8 100644
--- a/python/paddle/v2/framework/tests/test_mul_op.py
+++ b/python/paddle/v2/framework/tests/test_mul_op.py
@@ -1,27 +1,35 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
-from paddle.v2.framework.op import Operator
+from op_test import OpTest
-class TestMulOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestMulOp(OpTest):
def setUp(self):
- self.type = "mul"
+ self.op_type = "mul"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
-class TestMulOp2(unittest.TestCase):
- __metaclass__ = OpTestMeta
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
+
+
+class TestMulOp2(OpTest):
def setUp(self):
- self.type = "mul"
+ self.op_type = "mul"
self.inputs = {
'X': np.random.random((15, 4, 12, 10)).astype("float32"),
'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32")
@@ -32,72 +40,20 @@ class TestMulOp2(unittest.TestCase):
self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9))
}
+ def test_check_output(self):
+ self.check_output()
-class TestMulGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("mul")
- self.inputs = {
- 'X': np.random.random((32, 84)).astype("float32"),
- 'Y': np.random.random((84, 100)).astype("float32")
- }
-
- def test_cpu_gpu_compare(self):
- self.compare_grad(self.op, self.inputs)
-
- def test_normal(self):
- # mul op will enlarge the relative error
- self.check_grad(
- self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
-
- def test_ignore_x(self):
- self.check_grad(
- self.op,
- self.inputs, ["Y"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"X"})
-
- def test_ignore_y(self):
- self.check_grad(
- self.op,
- self.inputs, ["X"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"Y"})
-
-
-class TestMulGradTest2(GradientChecker):
- def setUp(self):
- self.op = Operator(
- "mul", X="X", Y="Y", Out="Out", x_num_col_dims=2, y_num_col_dims=2)
- self.inputs = {
- "X": np.random.random((15, 4, 12, 10)).astype("float32"),
- "Y": np.random.random((4, 30, 8, 2, 9)).astype("float32")
- }
-
- def test_cpu_gpu_compare(self):
- self.compare_grad(self.op, self.inputs)
-
- def test_normal(self):
- self.check_grad(
- self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
- def test_ignore_x(self):
+ def test_check_grad_ingore_x(self):
self.check_grad(
- self.op,
- self.inputs, ["Y"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"X"})
+ ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set('X'))
- def test_ignore_y(self):
+ def test_check_grad_ignore_y(self):
self.check_grad(
- self.op,
- self.inputs, ["X"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"Y"})
+ ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py
index e4b7cd480c..50cfb855f2 100644
--- a/python/paddle/v2/framework/tests/test_net.py
+++ b/python/paddle/v2/framework/tests/test_net.py
@@ -35,5 +35,5 @@ Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}
self.assertEqual(expected, "\n" + str(net))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py
index 8378c1cd21..336645bd99 100644
--- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py
+++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py
@@ -1,68 +1,51 @@
import unittest
import numpy as np
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
-class TestRowwiseAddOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
- def setUp(self):
- self.type = "rowwise_add"
- self.inputs = {
- 'X': np.random.random((32, 84)).astype("float32"),
- 'b': np.random.random(84).astype("float32")
- }
- self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
-
-
-class TestRowwiseAddOp2(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestRowwiseAddOp(OpTest):
def setUp(self):
- self.type = "rowwise_add"
+ self.op_type = "rowwise_add"
self.inputs = {
- 'X': np.random.random((13, 6, 7, 8)).astype("float32"),
- 'b': np.random.random((7, 8)).astype("float32")
+ 'X': np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
+ 'b': np.random.uniform(0.1, 1, [10]).astype("float32")
}
self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
+ def test_check_output(self):
+ self.check_output()
-class TestRowwiseAddGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("rowwise_add")
- self.inputs = {
- "X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
- "b": np.random.uniform(0.1, 1, [10]).astype("float32")
- }
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'b'], 'Out')
- def test_normal(self):
- self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
+ def test_check_grad_ingore_b(self):
+ self.check_grad(['X'], 'Out', no_grad_set=set('b'))
- def test_ignore_b(self):
- self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
+ def test_check_grad_ingore_x(self):
+ self.check_grad(['b'], 'Out', no_grad_set=set('X'))
- def test_ignore_x(self):
- self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
-
-class TestRowwiseAddGradOp2(GradientChecker):
+class TestRowwiseAddOp2(OpTest):
def setUp(self):
- self.op = create_op("rowwise_add")
+ self.op_type = "rowwise_add"
self.inputs = {
- "X": np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"),
- "b": np.random.uniform(0.1, 1, [2, 5]).astype("float32")
+ 'X': np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"),
+ 'b': np.random.uniform(0.1, 1, [2, 5]).astype("float32")
}
+ self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
+
+ def test_check_output(self):
+ self.check_output()
- def test_normal(self):
- self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'b'], 'Out')
- def test_ignore_b(self):
- self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
+ def test_check_grad_ignore_b(self):
+ self.check_grad(['X'], 'Out', no_grad_set=set('b'))
- def test_ignore_x(self):
- self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
+ def test_check_grad_ignore_x(self):
+ self.check_grad(['b'], 'Out', no_grad_set=set('X'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_scale_and_identity_op.py b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py
index 69b301c376..05d76d4282 100644
--- a/python/paddle/v2/framework/tests/test_scale_and_identity_op.py
+++ b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py
@@ -1,43 +1,34 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
import numpy as np
-from paddle.v2.framework.op import Operator
+from op_test import OpTest
-class IdentityTest(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class IdentityTest(OpTest):
def setUp(self):
- self.type = "identity"
- self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+ self.op_type = "identity"
+ self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.outputs = {'Out': self.inputs['X']}
+ def test_check_output(self):
+ self.check_output()
-class IdentityGradOpTest(GradientChecker):
- def test_normal(self):
- op = create_op("identity")
- inputs = {"X": np.random.random((10, 10)).astype("float32")}
- self.check_grad(op, inputs, set("X"), "Out")
-
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Out')
-class ScaleTest(unittest.TestCase):
- __metaclass__ = OpTestMeta
+class ScaleTest(OpTest):
def setUp(self):
- self.type = "scale"
- self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+ self.op_type = "scale"
+ self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.attrs = {'scale': -2.3}
self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']}
+ def test_check_output(self):
+ self.check_output()
-class ScaleGradTest(GradientChecker):
- def test_normal(self):
- op = Operator("scale", X="X", Out="Out", scale=3.2)
- self.check_grad(op,
- {"X": np.random.random((10, 10)).astype("float32")},
- set("X"), "Out")
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py
index c1f9444889..33c73c5263 100644
--- a/python/paddle/v2/framework/tests/test_scatter_op.py
+++ b/python/paddle/v2/framework/tests/test_scatter_op.py
@@ -1,37 +1,24 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
+import numpy as np
+from op_test import OpTest
-class TestScatterOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestScatterOp(OpTest):
def setUp(self):
- self.type = "scatter"
- ref_np = numpy.ones((3, 3)).astype("float32")
- index_np = numpy.array([1, 2]).astype("int32")
- updates_np = numpy.random.random((2, 3)).astype("float32")
- output_np = numpy.copy(ref_np)
+ self.op_type = "scatter"
+ ref_np = np.ones((3, 3)).astype("float32")
+ index_np = np.array([1, 2]).astype("int32")
+ updates_np = np.random.random((2, 3)).astype("float32")
+ output_np = np.copy(ref_np)
output_np[index_np] += updates_np
self.inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
+ def test_check_output(self):
+ self.check_output()
-class TestScatterGradOp(GradientChecker):
- def test_scatter_grad(self):
- op = create_op("scatter")
- # test data setup
- ref_np = numpy.ones((3, 10)).astype("float32")
- index_np = numpy.array([1, 2]).astype("int32")
- updates_np = numpy.random.random((2, 10)).astype("float32")
- output_np = numpy.copy(ref_np)
- output_np[index_np] += updates_np
- inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np}
- self.check_grad(
- op, inputs, set(["Updates", "Ref"]), "Out", in_place=True)
+ def test_check_grad(self):
+ self.check_grad(['Updates', 'Ref'], 'Out', in_place=True)
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_sgd_op.py b/python/paddle/v2/framework/tests/test_sgd_op.py
index e5f9ef865e..557cf15ace 100644
--- a/python/paddle/v2/framework/tests/test_sgd_op.py
+++ b/python/paddle/v2/framework/tests/test_sgd_op.py
@@ -1,21 +1,22 @@
import unittest
-import numpy
-from op_test_util import OpTestMeta
+import numpy as np
+from op_test import OpTest
-class TestSGD(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSGD(OpTest):
def setUp(self):
- self.type = "sgd"
- w = numpy.random.random((102, 105)).astype("float32")
- g = numpy.random.random((102, 105)).astype("float32")
+ self.op_type = "sgd"
+ w = np.random.random((102, 105)).astype("float32")
+ g = np.random.random((102, 105)).astype("float32")
lr = 0.1
self.inputs = {'param': w, 'grad': g}
self.attrs = {'learning_rate': lr}
self.outputs = {'param_out': w - lr * g}
+ def test_check_output(self):
+ self.check_output()
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_softmax_op.py b/python/paddle/v2/framework/tests/test_softmax_op.py
index 0d590fa706..1c5802dfd5 100644
--- a/python/paddle/v2/framework/tests/test_softmax_op.py
+++ b/python/paddle/v2/framework/tests/test_softmax_op.py
@@ -1,9 +1,6 @@
import unittest
-
import numpy as np
-
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
def stable_softmax(x):
@@ -13,26 +10,21 @@ def stable_softmax(x):
return exps / np.sum(exps)
-class TestSoftmaxOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSoftmaxOp(OpTest):
def setUp(self):
self.type = "softmax"
- self.inputs = {"X": np.random.random((10, 10)).astype("float32")}
+ self.inputs = {
+ 'X': np.random.uniform(0.1, 1, [10, 10]).astype("float32")
+ }
self.outputs = {
- "Y": np.apply_along_axis(stable_softmax, 1, self.inputs["X"])
+ 'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
}
+ def test_check_output(self):
+ self.check_output()
-class TestSoftmaxGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("softmax")
- self.inputs = {
- "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32")
- }
-
- def test_softmax_grad(self):
- self.check_grad(self.op, self.inputs, ["X"], "Y")
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Y')
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
index 2bcdf37df4..dc6ebf5d30 100644
--- a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
+++ b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
@@ -1,17 +1,14 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
import numpy as np
+from op_test import OpTest
-class TestSquaredL2DistanceOp_f0(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSquaredL2DistanceOp_f0(OpTest):
def setUp(self):
- self.type = 'squared_l2_distance'
+ self.op_type = "squared_l2_distance"
self.inputs = {
- 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'),
- 'Y': np.random.uniform(0.1, 1., (32, 64)).astype('float32')
+ 'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"),
+ 'Y': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32")
}
sub_res = self.inputs['X'] - self.inputs['Y']
output = sub_res * sub_res
@@ -20,15 +17,19 @@ class TestSquaredL2DistanceOp_f0(unittest.TestCase):
'Out': np.expand_dims(output.sum(1), 1)
}
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
-class TestSquaredL2DistanceOp_f1(unittest.TestCase):
- __metaclass__ = OpTestMeta
+class TestSquaredL2DistanceOp_f1(OpTest):
def setUp(self):
- self.type = 'squared_l2_distance'
+ self.op_type = "squared_l2_distance"
self.inputs = {
- 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'),
- 'Y': np.random.uniform(0.1, 1., (1, 64)).astype('float32')
+ 'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"),
+ 'Y': np.random.uniform(0.1, 0.6, (1, 3)).astype("float32")
}
sub_res = self.inputs['X'] - self.inputs['Y']
output = sub_res * sub_res
@@ -37,53 +38,34 @@ class TestSquaredL2DistanceOp_f1(unittest.TestCase):
'Out': np.expand_dims(output.sum(1), 1)
}
+ def test_check_output(self):
+ self.check_output()
-class TestSquaredL2DistanceOp_f2(unittest.TestCase):
- __metaclass__ = OpTestMeta
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
+
+class TestSquaredL2DistanceOp_f2(OpTest):
def setUp(self):
- self.type = 'squared_l2_distance'
+ self.op_type = "squared_l2_distance"
self.inputs = {
- 'X': np.random.uniform(0.1, 1., (32, 64, 128)).astype('float32'),
- 'Y': np.random.uniform(0.1, 1., (1, 64, 128)).astype('float32')
+ 'X': np.random.uniform(0.1, 0.6, (2, 3, 4)).astype("float32"),
+ 'Y': np.random.uniform(0.1, 0.6, (1, 3, 4)).astype("float32")
}
sub_res = self.inputs['X'] - self.inputs['Y']
- sub_res = sub_res.reshape((32, 64 * 128))
+ sub_res = sub_res.reshape((2, 3 * 4))
output = sub_res * sub_res
self.outputs = {
'sub_result': sub_res,
'Out': np.expand_dims(output.sum(1), 1)
}
+ def test_check_output(self):
+ self.check_output()
-class TestSquaredL2DistanceGradOp(GradientChecker):
- def test_squared_l2_distance_b0(self):
- op = create_op("squared_l2_distance")
- inputs = {
- 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'),
- 'Y': np.random.uniform(0.1, .6, (2, 3)).astype('float32')
- }
- self.compare_grad(op, inputs)
- self.check_grad(op, inputs, set(["X", "Y"]), "Out")
-
- def test_squared_l2_distance_b1(self):
- op = create_op("squared_l2_distance")
- inputs = {
- 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'),
- 'Y': np.random.uniform(0.1, .6, (1, 3)).astype('float32')
- }
- self.compare_grad(op, inputs)
- self.check_grad(op, inputs, set(["X", "Y"]), "Out")
-
- def test_squared_l2_distance_b2(self):
- op = create_op("squared_l2_distance")
- inputs = {
- 'X': np.random.uniform(0.1, .6, (2, 3, 4)).astype('float32'),
- 'Y': np.random.uniform(0.1, .6, (1, 3, 4)).astype('float32')
- }
- self.compare_grad(op, inputs)
- self.check_grad(op, inputs, set(["X", "Y"]), "Out")
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_sum_op.py b/python/paddle/v2/framework/tests/test_sum_op.py
index 66417d70e8..f8eb34c076 100644
--- a/python/paddle/v2/framework/tests/test_sum_op.py
+++ b/python/paddle/v2/framework/tests/test_sum_op.py
@@ -6,10 +6,10 @@ from op_test import OpTest
class TestSumOp(OpTest):
def setUp(self):
self.op_type = "sum"
- x0 = np.random.random((3, 4)).astype('float32')
- x1 = np.random.random((3, 4)).astype('float32')
- x2 = np.random.random((3, 4)).astype('float32')
- self.inputs = {"X": {"x0": x0, "x1": x1, "x2": x2}}
+ x0 = np.random.random((3, 4)).astype("float32")
+ x1 = np.random.random((3, 4)).astype("float32")
+ x2 = np.random.random((3, 4)).astype("float32")
+ self.inputs = {'X': {'x0': x0, 'x1': x1, 'x2': x2}}
y = x0 + x1 + x2
self.outputs = {'Out': y}
@@ -17,8 +17,8 @@ class TestSumOp(OpTest):
self.check_output()
def test_check_grad(self):
- self.check_grad(["x0"], "Out")
+ self.check_grad(['x0'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_top_k_op.py b/python/paddle/v2/framework/tests/test_top_k_op.py
index e841d96d26..cab799256d 100644
--- a/python/paddle/v2/framework/tests/test_top_k_op.py
+++ b/python/paddle/v2/framework/tests/test_top_k_op.py
@@ -1,14 +1,11 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
-class TestTopkOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestTopkOp(OpTest):
def setUp(self):
- self.type = "top_k"
+ self.op_type = "top_k"
k = 1
input = np.random.random((32, 84)).astype("float32")
output = np.ndarray((32, k))
@@ -25,11 +22,9 @@ class TestTopkOp(unittest.TestCase):
self.outputs = {'Out': output, 'Indices': indices}
-class TestTopkOp3d(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestTopkOp3d(OpTest):
def setUp(self):
- self.type = "top_k"
+ self.op_type = "top_k"
k = 1
input = np.random.random((32, 2, 84)).astype("float32")
input_flat_2d = input.reshape(64, 84)
@@ -48,5 +43,5 @@ class TestTopkOp3d(unittest.TestCase):
self.outputs = {'Out': output, 'Indices': indices}
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_uniform_random_op.py b/python/paddle/v2/framework/tests/test_uniform_random_op.py
index c3d2bb44da..76a5e36e56 100644
--- a/python/paddle/v2/framework/tests/test_uniform_random_op.py
+++ b/python/paddle/v2/framework/tests/test_uniform_random_op.py
@@ -14,11 +14,11 @@ class UniformRandomTest(unittest.TestCase):
def uniform_random_test(self, place):
scope = core.Scope()
- scope.new_var("X").get_tensor()
+ scope.new_var('X').get_tensor()
op = Operator(
"uniform_random",
- Out="X",
+ Out='X',
dims=[1000, 784],
min=-5.0,
max=10.0,
@@ -27,9 +27,9 @@ class UniformRandomTest(unittest.TestCase):
op.infer_shape(scope)
ctx = core.DeviceContext.create(place)
op.run(scope, ctx)
- tensor = numpy.array(scope.find_var("X").get_tensor())
+ tensor = numpy.array(scope.find_var('X').get_tensor())
self.assertAlmostEqual(tensor.mean(), 2.5, delta=0.1)
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
From 7ae72f752d1dcf3a818b9e9a3bef001fa8344b8e Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Mon, 11 Sep 2017 01:09:20 -0700
Subject: [PATCH 48/66] remove unused code in test
---
python/paddle/v2/framework/tests/test_reshape_op.py | 7 -------
1 file changed, 7 deletions(-)
diff --git a/python/paddle/v2/framework/tests/test_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py
index df7d913ba4..50653f58ee 100644
--- a/python/paddle/v2/framework/tests/test_reshape_op.py
+++ b/python/paddle/v2/framework/tests/test_reshape_op.py
@@ -15,13 +15,6 @@ class TestReshapeOp(unittest.TestCase):
class TestReshapeGradOp(GradientChecker):
- """
- def test_normal(self):
- op = Operator("reshape", X='X', Out='Out', shape=[5, 40])
- inputs = {"X": np.random.random((10, 20)).astype("float32")}
- self.check_grad(op, inputs, set("X"), "Out")
- """
-
def setUp(self):
self.op = Operator("reshape", X='X', Out='Out', shape=[5, 40])
self.inputs = {"X": np.random.random((10, 20)).astype("float32")}
From 94ea8ee0e5e9f079cffb87f756d3274f522066e9 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Mon, 11 Sep 2017 16:23:20 +0800
Subject: [PATCH 49/66] refine MKLDNNLayer logical: move forward and backward
to MKLDNNLayer and remove copyOutputInfoToOtherDevice
---
paddle/gserver/layers/MKLDNNFcLayer.cpp | 81 ++--------
paddle/gserver/layers/MKLDNNFcLayer.h | 32 +---
paddle/gserver/layers/MKLDNNLayer.h | 190 ++++++++++++++++++++----
3 files changed, 178 insertions(+), 125 deletions(-)
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp
index 53433cef35..a47967b3d3 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp
@@ -14,7 +14,6 @@ limitations under the License. */
#include "MKLDNNFcLayer.h"
#include "paddle/utils/Logging.h"
-#include "paddle/utils/Stat.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
@@ -40,6 +39,8 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap,
oc_ = getSize();
oh_ = 1;
ow_ = 1;
+ ih_ = 1;
+ iw_ = 1;
// input size can not change in FC
iLayerSize_ = inputLayers_[0]->getSize();
@@ -78,36 +79,17 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
}
void MKLDNNFcLayer::reshape() {
- const Argument& input = getInput(0, getPrev(0)->getDeviceId());
- int batchSize = input.getBatchSize();
- if (bs_ == batchSize) {
- return;
- }
- bs_ = batchSize;
- ih_ = input.getFrameHeight();
- iw_ = input.getFrameWidth();
- if (ih_ == 0) {
- ih_ = 1;
- }
- if (iw_ == 0) {
- iw_ = 1;
- }
+ reshapeInput();
+
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
ic_ = iLayerSize_ / (ih_ * iw_);
CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
CHECK_EQ(size_t(oc_), getSize());
- printSizeInfo();
- // reset output
- output_.setFrameHeight(oh_);
- output_.setFrameWidth(ow_);
- resetOutput(bs_, oc_);
+ reshapeOutput(oh_, ow_);
+ resizeOutput(bs_, oc_);
- // reset mkldnn forward
- resetFwd();
- needResetBwd_ = true;
-
- convertWeightsFromPaddle();
+ printSizeInfo();
}
void MKLDNNFcLayer::resetFwd() {
@@ -137,7 +119,6 @@ void MKLDNNFcLayer::resetFwd() {
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
if (!outputIsOnlyMKLDNN()) {
- copyOutputInfoToOtherDevice();
// fc cpu output value do not need create convert
// just share point
getOutput(CPU_DEVICE).value->setData(output_.value->getData());
@@ -243,51 +224,13 @@ void MKLDNNFcLayer::resetBwd() {
}
void MKLDNNFcLayer::updateInputData() {
- if (inputLayers_[0]->getType() != "data") {
- return;
- }
- real* iData = getInputValue(0, CPU_DEVICE)->getData();
- inVal_->setData(iData);
-}
-
-void MKLDNNFcLayer::forward(PassType passType) {
- Layer::forward(passType);
- reshape();
-
- {
- REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
- updateInputData();
-
- // just submit forward pipeline
- stream_->submit(pipelineFwd_);
- }
-
- /* activation */ {
- REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
- forwardActivation();
- }
+ inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
-void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
- /* Do derivation */ {
- REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
- backwardActivation();
- }
-
- {
- REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
- resetBwd();
-
- // just sumbmit backward pipeline
- stream_->submit(pipelineBwd_);
- }
-
- {
- REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
- weight_->getParameterPtr()->incUpdate(callback);
- if (biases_ && biases_->getWGrad()) {
- biases_->getParameterPtr()->incUpdate(callback);
- }
+void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
+ weight_->getParameterPtr()->incUpdate(callback);
+ if (biases_ && biases_->getWGrad()) {
+ biases_->getParameterPtr()->incUpdate(callback);
}
}
} // namespace paddle
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h
index 4ad67a16e0..add8ac9991 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.h
+++ b/paddle/gserver/layers/MKLDNNFcLayer.h
@@ -45,35 +45,19 @@ public:
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
- void convertWeightsFromPaddle() override;
-
- void convertWeightsToPaddle() override;
+ void reshape() override;
- void forward(PassType passType) override;
+ void resetFwd() override;
- void backward(const UpdateCallback& callback) override;
+ void resetBwd() override;
void updateInputData() override;
-protected:
- /**
- * reshape the input image sizes
- * and reset output buffer size
- * and reset mkldnn forward
- */
- void reshape();
-
- /**
- * reset the forward primitve and memory
- * only would be called when input size changes
- */
- void resetFwd();
-
- /**
- * reset the backward primitve and memory for mkldnn fc
- * only would be called when needed
- */
- void resetBwd();
+ void updateWeights(const UpdateCallback& callback) override;
+
+ void convertWeightsFromPaddle() override;
+
+ void convertWeightsToPaddle() override;
};
} // namespace paddle
diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h
index 543364edce..c10f2fec2f 100644
--- a/paddle/gserver/layers/MKLDNNLayer.h
+++ b/paddle/gserver/layers/MKLDNNLayer.h
@@ -19,6 +19,7 @@ limitations under the License. */
#include "MKLDNNBase.h"
#include "mkldnn.hpp"
#include "paddle/math/MKLDNNMatrix.h"
+#include "paddle/utils/Stat.h"
DECLARE_bool(use_mkldnn);
@@ -33,6 +34,8 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
*/
class MKLDNNLayer : public Layer {
protected:
+ // input value element count
+ size_t inputElemenCnt_;
// batch size
int bs_;
// input image channel, height and width
@@ -52,7 +55,7 @@ protected:
std::vector<mkldnn::primitive> pipelineFwd_;
std::vector<mkldnn::primitive> pipelineBwd_;
- // MKLDNNMatrixPtr
+ // MKLDNNMatrixPtr with internal format
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
MKLDNNMatrixPtr outVal_;
@@ -65,6 +68,7 @@ protected:
public:
explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config),
+ inputElemenCnt_(0),
bs_(0),
ic_(0),
ih_(0),
@@ -95,12 +99,93 @@ public:
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
+ checkCPUOutputsNumber();
stream_.reset(new MKLDNNStream());
engine_ = CPUEngine::Instance().getEngine();
return true;
}
+ void forward(PassType passType) override {
+ passType_ = passType;
+
+ {
+ REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
+ copySeqInfoToOutputs();
+ CHECK(!inputLayers_.empty());
+ size_t elemenCnt = inputLayers_[0]->getOutput().value->getElementCnt();
+ if (inputElemenCnt_ != elemenCnt) {
+ inputElemenCnt_ = elemenCnt;
+ reshape();
+ resetFwd();
+ convertWeightsFromPaddle();
+ needResetBwd_ = true;
+ }
+
+ if (inputLayers_[0]->getType() == "data") {
+ updateInputData();
+ }
+
+ stream_->submit(pipelineFwd_);
+ }
+
+ /* activation */ {
+ REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
+ forwardActivation();
+ }
+ }
+
+ void backward(const UpdateCallback& callback) override {
+ /* Do derivation */ {
+ REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
+ backwardActivation();
+ }
+
+ {
+ REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
+ if (needResetBwd_) {
+ resetBwd();
+ needResetBwd_ = false;
+ }
+
+ stream_->submit(pipelineBwd_);
+ }
+
+ {
+ REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
+ updateWeights(callback);
+ }
+ }
+
+ /**
+ * reshape the input image sizes
+ * and reset output image and buffer size
+ */
+ virtual void reshape() = 0;
+
+ /**
+ * reset the mkldnn forward primitve and memory
+ * only would be called when input size changes
+ */
+ virtual void resetFwd() = 0;
+
+ /**
+ * reset the mkldnn backward primitve and memory for mkldnn fc
+ * only would be called when needed
+ */
+ virtual void resetBwd() = 0;
+
+ /**
+ * Update input value data when input layer is "data" type.
+ * Since the input value data address might be changed.
+ */
+ virtual void updateInputData() {}
+
+ /**
+ * Update weights and biases if necessary.
+ */
+ virtual void updateWeights(const UpdateCallback& callback) {}
+
/**
* convert weight from paddle format to mkldnn format
* weight_ will be override
@@ -114,10 +199,38 @@ public:
virtual void convertWeightsToPaddle() {}
/**
- * Update input value data when input layer is "data" type.
- * Since the input value data address might be changed.
+ * add this interface as public for unit test
*/
- virtual void updateInputData() {}
+ void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
+
+protected:
+ /**
+ * reshape the input image sizes and input batchsize
+ */
+ virtual void reshapeInput() {
+ const Argument& input = inputLayers_[0]->getOutput();
+ bs_ = input.getBatchSize();
+ int height = input.getFrameHeight();
+ int width = input.getFrameWidth();
+ if (height != 0) {
+ ih_ = height;
+ }
+ if (width != 0) {
+ iw_ = width;
+ }
+ }
+
+ /**
+ * reshape output image sizes
+ */
+ virtual void reshapeOutput(size_t height, size_t width) {
+ output_.setFrameHeight(height);
+ output_.setFrameWidth(width);
+ for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
+ outputOtherDevice_[i].setFrameHeight(height);
+ outputOtherDevice_[i].setFrameWidth(width);
+ }
+ }
/**
* print info about sizes
@@ -133,8 +246,8 @@ public:
*/
virtual void printValueFormatFlow() {
if (inVal_ && outVal_) {
- VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat()
- << " >>> " << outVal_->getFormat();
+ VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>> "
+ << outVal_->getFormat();
}
}
@@ -143,36 +256,12 @@ public:
*/
virtual void printGradFormatFlow() {
if (inGrad_ && outGrad_) {
- VLOG(MKLDNN_FMTS) << "grad format flow --- " << inGrad_->getFormat()
- << " <<< " << outGrad_->getFormat();
+ VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<< "
+ << outGrad_->getFormat();
}
}
protected:
- /**
- * copy image size and sequence info to other device
- * @note: can not directly use Layer::copyOutputToOtherDevice since here only
- * copy base info and do not copy data value
- */
- void copyOutputInfoToOtherDevice() {
- int cnt = 0;
- for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
- outputOtherDevice_[i].setFrameHeight(output_.getFrameHeight());
- outputOtherDevice_[i].setFrameWidth(output_.getFrameWidth());
- outputOtherDevice_[i].sequenceStartPositions =
- output_.sequenceStartPositions;
- outputOtherDevice_[i].subSequenceStartPositions =
- output_.subSequenceStartPositions;
- outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
- if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
- ++cnt;
- }
- }
- if (cnt > 1) {
- LOG(WARNING) << "should not have more than one CPU devie";
- }
- }
-
/**
* If input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
@@ -205,6 +294,7 @@ protected:
*/
void setDevice(int id) { deviceId_ = id; }
+private:
/**
* Set deviceId of the params used in this layer.
*/
@@ -228,6 +318,42 @@ protected:
parameter->setDevice(id);
}
}
+
+ /**
+ * Check the cpu device number of outputOtherDevice_.
+ * should have only one at most.
+ */
+ void checkCPUOutputsNumber(int max = 1) {
+ int cnt = 0;
+ for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
+ if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
+ ++cnt;
+ }
+ }
+ CHECK_LE(cnt, max) << "too much CPU devies";
+ }
+
+ /**
+ * copy SeqInfo from input layer to this output and other output devices.
+ * @note: do not use getInput(0) since it used this deviceId_,
+ * use "inputLayers_[0]->getOutput()" instead.
+ */
+ void copySeqInfoToOutputs() {
+ if (inputLayers_.empty() || !needSequenceInfo_) {
+ return;
+ }
+ const Argument& input = inputLayers_[0]->getOutput();
+ output_.sequenceStartPositions = input.sequenceStartPositions;
+ output_.subSequenceStartPositions = input.subSequenceStartPositions;
+ output_.cpuSequenceDims = input.cpuSequenceDims;
+ for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
+ outputOtherDevice_[i].sequenceStartPositions =
+ output_.sequenceStartPositions;
+ outputOtherDevice_[i].subSequenceStartPositions =
+ output_.subSequenceStartPositions;
+ outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
+ }
+ }
};
} // namespace paddle
From f3bb7b99dddf98b1217e6d906ccbe069e2e1e309 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Mon, 11 Sep 2017 16:24:58 +0800
Subject: [PATCH 50/66] refine MKLDNNTester add UpdateCallback for test
---
paddle/gserver/tests/MKLDNNTester.cpp | 77 +++++++++++++++------------
paddle/gserver/tests/MKLDNNTester.h | 12 +++--
2 files changed, 51 insertions(+), 38 deletions(-)
diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp
index de1635be2a..11e8527910 100644
--- a/paddle/gserver/tests/MKLDNNTester.cpp
+++ b/paddle/gserver/tests/MKLDNNTester.cpp
@@ -63,8 +63,12 @@ void MKLDNNTester::reset(const TestConfig& dnn,
initTestLayer(
configs_[i], &(layerMaps_[i]), &(parameters_[i]), &(testLayers_[i]));
}
- dnnLayer_ = testLayers_[DNN];
refLayer_ = testLayers_[REF];
+ dnnLayer_ = std::dynamic_pointer_cast<MKLDNNLayer>(testLayers_[DNN]);
+ CHECK(dnnLayer_);
+ // for comparison with Paddle reference results,
+ // need manually add cpu device output for test
+ dnnLayer_->addOutputArgument(-1);
EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
@@ -109,20 +113,21 @@ void MKLDNNTester::randomBotDatas() {
void MKLDNNTester::randomTopDiffs() {
refLayer_->getOutputGrad()->randomizeUniform();
- dnnLayer_->getOutputGrad()->copyFrom(*(refLayer_->getOutputGrad()));
- VLOG(lvl_) << "Random dom Backward Input, TopDiff: ";
+ dnnLayer_->getOutput(-1).grad->copyFrom(*(refLayer_->getOutputGrad()));
+ VLOG(lvl_) << "Random Backward Input, TopDiff: ";
printMatrix(refLayer_->getOutputGrad());
}
void MKLDNNTester::checkForward() {
- printTopDatas();
- double delta = compareMatrix(testLayers_[DNN]->getOutputValue(),
- testLayers_[REF]->getOutputValue());
VLOG(MKLDNN_ALL) << "Check Forward";
+ printTopDatas();
+ double delta = compareMatrix(dnnLayer_->getOutput(-1).value,
+ refLayer_->getOutputValue());
EXPECT_LE(fabs(delta), eps_);
}
void MKLDNNTester::checkBackwardData() {
+ VLOG(MKLDNN_ALL) << "Check Backward Data";
// TODO(TJ): uncomment me when batch norm ready
// const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
@@ -144,14 +149,12 @@ void MKLDNNTester::checkBackwardData() {
}
void MKLDNNTester::checkBackwardWgts() {
+ VLOG(MKLDNN_ALL) << "Check Backward Weight";
CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
vector<VectorPtr> dnnWgts; // used to temply save mkldnn weights
saveWgt(parameters_[DNN], dnnWgts);
- const MKLDNNLayerPtr dnnlayer =
- std::dynamic_pointer_cast<MKLDNNLayer>(dnnLayer_);
- CHECK(dnnlayer);
- dnnlayer->convertWeightsToPaddle();
+ dnnLayer_->convertWeightsToPaddle();
for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
@@ -189,38 +192,38 @@ void MKLDNNTester::restoreWgt(const vector<VectorPtr>& from,
}
// clear parameters grad
-void MKLDNNTester::clearWgtDiffs() {
+void MKLDNNTester::clearWgtDiffs(size_t id) {
+ CHECK_LE(id, parameters_.size());
for (size_t n = 0; n < parameters_.size(); ++n) {
- for (size_t i = 0; i < parameters_[n].size(); ++i) {
- const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
- if (grad) {
- grad->zeroMem();
+ if (id == n || id == parameters_.size()) {
+ for (size_t i = 0; i < parameters_[n].size(); ++i) {
+ const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
+ if (grad) {
+ grad->zeroMem();
+ }
}
}
}
}
-void MKLDNNTester::clearBotDiffs() {
- // dnn and ref
+void MKLDNNTester::clearBotDiffs(size_t id) {
+ CHECK_LE(id, dataLayers_.size());
for (size_t n = 0; n < dataLayers_.size(); ++n) {
- // all inputs layers
- for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
- dataLayers_[n][i]->getOutputGrad()->zeroMem();
+ if (id == n || id == dataLayers_.size()) {
+ // clear inputs layers of this specific layer
+ for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+ dataLayers_[n][i]->getOutputGrad()->zeroMem();
+ }
}
}
}
-void MKLDNNTester::clearBotDiffs(int n) {
- CHECK_LT(n, NUM);
- // all inputs layers
- for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
- dataLayers_[n][i]->getOutputGrad()->zeroMem();
- }
-}
-
-void MKLDNNTester::clearTopDatas() {
+void MKLDNNTester::clearTopDatas(size_t id) {
+ CHECK_LE(id, testLayers_.size());
for (size_t i = 0; i < testLayers_.size(); ++i) {
- testLayers_[i]->getOutputValue()->zeroMem();
+ if (id == i || id == testLayers_.size()) {
+ testLayers_[i]->getOutputValue()->zeroMem();
+ }
}
}
@@ -300,16 +303,24 @@ void MKLDNNTester::runOnce() {
checkForward();
// test backward
+ // simple updater
+ UpdateCallback updateCallback = [](Parameter* para) {
+ auto& grad = para->getBuf(PARAMETER_GRADIENT);
+ auto& value = para->getBuf(PARAMETER_VALUE);
+ real lr = 1e-3;
+ value->add(*grad, lr);
+ };
randomTopDiffs();
- dnnLayer_->backward(nullptr);
- refLayer_->backward(nullptr);
+ dnnLayer_->backward(updateCallback);
+ refLayer_->backward(updateCallback);
checkBackwardData();
checkBackwardWgts();
// clear buffers
// ref code will addto the diff, dnn code will writeto it
- // and clearTopDatas() and clearWgtDiffs() should be coverd by test layers
+ // and clearTopDatas(REF) should be coverd by ref layers
clearBotDiffs(REF);
+ clearWgtDiffs(REF);
}
void MKLDNNTester::run(const TestConfig& dnn,
diff --git a/paddle/gserver/tests/MKLDNNTester.h b/paddle/gserver/tests/MKLDNNTester.h
index e55e4493ff..5ac885638c 100644
--- a/paddle/gserver/tests/MKLDNNTester.h
+++ b/paddle/gserver/tests/MKLDNNTester.h
@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#include "LayerGradUtil.h"
#include "paddle/gserver/layers/MKLDNNBase.h"
+#include "paddle/gserver/layers/MKLDNNLayer.h"
namespace paddle {
@@ -40,7 +41,8 @@ protected:
vector<LayerMap> layerMaps_;
vector<vector<ParameterPtr>> parameters_;
vector<LayerPtr> testLayers_;
- LayerPtr dnnLayer_, refLayer_;
+ LayerPtr refLayer_;
+ MKLDNNLayerPtr dnnLayer_;
/// run some iterations, all the result should pass
size_t iter_;
@@ -88,10 +90,10 @@ private:
void checkBackwardData();
void checkBackwardWgts();
- void clearWgtDiffs();
- void clearBotDiffs();
- void clearBotDiffs(int n); // clear specific layer
- void clearTopDatas();
+ // clear specific layer, clear all when id equals NUM
+ void clearWgtDiffs(size_t id = NUM);
+ void clearBotDiffs(size_t id = NUM);
+ void clearTopDatas(size_t id = NUM);
void printTopDatas();
void printMatrix(const MatrixPtr& m);
From 9d46f443fede2edba2e8041b2b30d9513852820b Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Mon, 11 Sep 2017 16:43:17 +0800
Subject: [PATCH 51/66] fix attr bug in op_test and ensure order in duplicate
inputs/outputs
---
python/paddle/v2/framework/tests/op_test.py | 34 ++++++++++++-------
.../paddle/v2/framework/tests/test_sum_op.py | 2 +-
2 files changed, 22 insertions(+), 14 deletions(-)
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index 3a6a5dca4c..1daa6fa277 100644
--- a/python/paddle/v2/framework/tests/op_test.py
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -9,7 +9,7 @@ def grad_var_name(var_name):
return var_name + "@GRAD"
-def create_op(scope, op_type, inputs, outputs, attrs=None):
+def create_op(scope, op_type, inputs, outputs, attrs):
kwargs = dict()
for in_name, in_dup in Operator.get_op_inputs(op_type):
@@ -17,7 +17,7 @@ def create_op(scope, op_type, inputs, outputs, attrs=None):
kwargs[in_name] = []
if in_dup:
sub_in = inputs[in_name]
- for sub_in_name in sub_in:
+ for sub_in_name, arr in sub_in:
var = scope.new_var(sub_in_name)
kwargs[in_name].append(sub_in_name)
else:
@@ -29,15 +29,16 @@ def create_op(scope, op_type, inputs, outputs, attrs=None):
kwargs[out_name] = []
if out_dup:
sub_in = outputs[out_name]
- for sun_in_name in sub_in:
- var = scope.new_var(sun_in_name)
- kwargs[out_name].append(sun_in_name)
+ for sub_in_name, arr in sub_in:
+ var = scope.new_var(sub_in_name)
+ kwargs[out_name].append(sub_in_name)
else:
var = scope.new_var(out_name)
kwargs[out_name].append(out_name)
for attr_name in Operator.get_op_attr_names(op_type):
- kwargs[attr_name] = attrs[attr_name]
+ if attr_name in attrs:
+ kwargs[attr_name] = attrs[attr_name]
return Operator(op_type, **kwargs)
@@ -46,10 +47,9 @@ def set_input(scope, op, inputs, place):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
- for sub_in_name in sub_in:
+ for sub_in_name, arr in sub_in:
var = scope.find_var(sub_in_name)
tensor = var.get_tensor()
- arr = sub_in[sub_in_name]
tensor.set_dims(arr.shape)
tensor.set(arr, place)
else:
@@ -65,7 +65,7 @@ def set_output_grad(scope, op, outputs, place):
if out_name in outputs:
if out_dup:
sub_out = outputs[out_name]
- for sub_out_name in sub_out:
+ for sub_out_name, arr in sub_out:
out_tensor = scope.find_var(sub_out_name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(
sub_out_name)).get_tensor()
@@ -110,7 +110,7 @@ def get_numeric_gradient(scope,
# we use a for loop to compute the gradient of every element.
for i in xrange(tensor_size):
if in_place:
- set_input(op, inputs, core.CPUPlace())
+ set_input(scope, op, inputs, core.CPUPlace())
# get one input element throw it's index i.
origin = tensor_to_check.get_float_element(i)
@@ -120,7 +120,7 @@ def get_numeric_gradient(scope,
y_pos = get_output()
if in_place:
- set_input(op, inputs, core.CPUPlace())
+ set_input(scope, op, inputs, core.CPUPlace())
x_neg = origin - delta
tensor_to_check.set_float_element(i, x_neg)
@@ -168,7 +168,11 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place,
class OpTest(unittest.TestCase):
def check_output_with_place(self, place):
self.scope = core.Scope()
- self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
+ op_inputs = self.inputs if hasattr(self, "inputs") else dict()
+ op_outputs = self.outputs if hasattr(self, "outputs") else dict()
+ op_attrs = self.attrs if hasattr(self, "attrs") else dict()
+ self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
+ op_attrs)
if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
return
set_input(self.scope, self.op, self.inputs, place)
@@ -227,7 +231,11 @@ class OpTest(unittest.TestCase):
in_place=False,
max_relative_error=0.005):
self.scope = core.Scope()
- self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs)
+ op_inputs = self.inputs if hasattr(self, "inputs") else dict()
+ op_outputs = self.outputs if hasattr(self, "outputs") else dict()
+ op_attrs = self.attrs if hasattr(self, "attrs") else dict()
+ self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
+ op_attrs)
if no_grad_set is None:
no_grad_set = set()
diff --git a/python/paddle/v2/framework/tests/test_sum_op.py b/python/paddle/v2/framework/tests/test_sum_op.py
index 66417d70e8..2ad1cc2610 100644
--- a/python/paddle/v2/framework/tests/test_sum_op.py
+++ b/python/paddle/v2/framework/tests/test_sum_op.py
@@ -9,7 +9,7 @@ class TestSumOp(OpTest):
x0 = np.random.random((3, 4)).astype('float32')
x1 = np.random.random((3, 4)).astype('float32')
x2 = np.random.random((3, 4)).astype('float32')
- self.inputs = {"X": {"x0": x0, "x1": x1, "x2": x2}}
+ self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
y = x0 + x1 + x2
self.outputs = {'Out': y}
From 1eb18c5715c845f64ac70b770f053c90bcbb272c Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 11 Sep 2017 16:48:31 +0800
Subject: [PATCH 52/66] update write_docs_cn.rst
---
doc/howto/dev/write_docs_cn.rst | 64 +++++++++++++--------------------
1 file changed, 25 insertions(+), 39 deletions(-)
diff --git a/doc/howto/dev/write_docs_cn.rst b/doc/howto/dev/write_docs_cn.rst
index 36e5d420c9..731a63f945 100644
--- a/doc/howto/dev/write_docs_cn.rst
+++ b/doc/howto/dev/write_docs_cn.rst
@@ -5,15 +5,13 @@
PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。
-如何构建PaddlePaddle的文档
-==========================
+如何构建文档
+============
-PaddlePaddle的文档构建有直接构建和基于Docker构建两种方式，我们提供了一个构建脚本build_docs.sh来进行构建。
-PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使用基于Docker来构建PaddlePaddle的文档。
+PaddlePaddle的文档构建有两种方式。
-
-使用Docker构建PaddlePaddle的文档
---------------------------------
+使用Docker构建
+--------------
使用Docker构建PaddlePaddle的文档，需要在系统里先安装好Docker工具包。Docker安装请参考 `Docker的官网 <https://docs.docker.com/>`_ 。安装好Docker之后可以使用源码目录下的脚本构建文档，即
@@ -21,58 +19,46 @@ PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使
cd TO_YOUR_PADDLE_CLONE_PATH
cd paddle/scripts/tools/build_docs
- bash build_docs.sh with_docker
-
-编译完成后，会在当前目录生成两个子目录\:
-
-* doc 英文文档目录
-* doc_cn 中文文档目录
+ sh build_docs.sh
+编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。
打开浏览器访问对应目录下的index.html即可访问本地文档。
-
-
-直接构建PaddlePaddle的文档
---------------------------
-
-因为PaddlePaddle的v2 api文档生成过程依赖于py_paddle Python包，用户需要首先确认py_paddle包已经安装。
-
-.. code-block:: bash
-
- python -c "import py_paddle"
-
-如果提示错误，那么用户需要在本地编译安装PaddlePaddle，请参考 `源码编译文档 <http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html>`_ 。
-注意，用户在首次编译安装PaddlePaddle时，请将WITH_DOC选项关闭。在编译安装正确之后，请再次确认py_paddle包已经安装，即可进行下一步操作。
+直接构建
+--------
如果提示正确，可以执行以下命令编译生成文档，即
.. code-block:: bash
cd TO_YOUR_PADDLE_CLONE_PATH
- cd paddle/scripts/tools/build_docs
- bash build_docs.sh local
-
-编译完成之后，会在当前目录生成两个子目录\:
-
-* doc 英文文档目录
-* doc_cn 中文文档目录
+ mkdir -p build
+ cd build
+ cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
+ make gen_proto_py
+ make paddle_docs paddle_docs_cn
+编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。
打开浏览器访问对应目录下的index.html即可访问本地文档。
-如何书写PaddlePaddle的文档
-==========================
+如何书写文档
+============
PaddlePaddle文档使用 `sphinx`_ 自动生成，用户可以参考sphinx教程进行书写。
-如何更新www.paddlepaddle.org文档
-================================
+如何更新文档主题
+================
+
+PaddlePaddle文档主题在 `TO_YOUR_PADDLE_CLONE_PATH/doc_theme` 文件夹下，包含所有和前端网页设计相关的文件。
-开发者给PaddlePaddle代码增加的注释以PR的形式提交到github中，提交方式可参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
+如何更新doc.paddlepaddle.org
+============================
+
+更新的文档以PR的形式提交到github中，提交方式参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的，用户可以分别查看最新的 `中文文档 <http://doc.paddlepaddle.org/develop/doc_cn/>`_ 和
`英文文档 <http://doc.paddlepaddle.org/develop/doc/>`_ 。
-
.. _cmake: https://cmake.org/
.. _sphinx: http://www.sphinx-doc.org/en/1.4.8/
From 76a70d10db35a812654d3d2b2351b1ce867d511d Mon Sep 17 00:00:00 2001
From: Yancey1989 <yancey1989@gmail.com>
Date: Mon, 11 Sep 2017 17:27:49 +0800
Subject: [PATCH 53/66] fix unit test error
---
python/paddle/v2/framework/tests/gradient_checker.py | 4 ++--
python/paddle/v2/framework/tests/op_test_util.py | 10 +++-------
2 files changed, 5 insertions(+), 9 deletions(-)
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
index 51a98284bd..ed838b5979 100644
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ b/python/paddle/v2/framework/tests/gradient_checker.py
@@ -11,9 +11,9 @@ __all__ = ['get_numeric_gradient']
def create_op(op_type):
# TODO need to set attrs
kwargs = dict()
- for in_name, _ in Operator.get_op_input_names(op_type):
+ for in_name in Operator.get_op_input_names(op_type):
kwargs[in_name] = in_name
- for out_name, _ in Operator.get_op_output_names(op_type):
+ for out_name in Operator.get_op_output_names(op_type):
kwargs[out_name] = out_name
return Operator(op_type, **kwargs)
diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py
index 54fe5da440..99a114e45f 100644
--- a/python/paddle/v2/framework/tests/op_test_util.py
+++ b/python/paddle/v2/framework/tests/op_test_util.py
@@ -27,9 +27,7 @@ class OpTestMeta(type):
places.append(core.GPUPlace(0))
for place in places:
- for ins in Operator.get_op_input_names(self.type):
- in_name = ins[0]
- in_dup = ins[1]
+ for in_name, in_dup in Operator.get_op_inputs(self.type):
if hasattr(self, 'inputs') and in_name in self.inputs:
kwargs[in_name] = []
if in_dup:
@@ -49,8 +47,7 @@ class OpTestMeta(type):
else:
kwargs[in_name] = "@EMPTY@"
- for out_name, out_dup in Operator.get_op_output_names(
- self.type):
+ for out_name, out_dup in Operator.get_op_outputs(self.type):
if not hasattr(self, "outputs"):
raise ValueError(
"The test op must set self.outputs dict.")
@@ -73,8 +70,7 @@ class OpTestMeta(type):
ctx = core.DeviceContext.create(place)
op.run(scope, ctx)
- for out_name, out_dup in Operator.get_op_output_names(
- self.type):
+ for out_name, out_dup in Operator.get_op_outputs(self.type):
actual = numpy.array(scope.find_var(out_name).get_tensor())
expect = self.outputs[out_name]
self.assertTrue(
From 477b23c3f5c123b446cec48321105e1a471c1212 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Mon, 11 Sep 2017 18:37:19 +0800
Subject: [PATCH 54/66] follow comments
---
python/paddle/v2/framework/tests/op_test.py | 18 ++++++++----------
1 file changed, 8 insertions(+), 10 deletions(-)
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index 1daa6fa277..489358ba85 100644
--- a/python/paddle/v2/framework/tests/op_test.py
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -17,7 +17,7 @@ def create_op(scope, op_type, inputs, outputs, attrs):
kwargs[in_name] = []
if in_dup:
sub_in = inputs[in_name]
- for sub_in_name, arr in sub_in:
+ for sub_in_name, _ in sub_in:
var = scope.new_var(sub_in_name)
kwargs[in_name].append(sub_in_name)
else:
@@ -29,7 +29,7 @@ def create_op(scope, op_type, inputs, outputs, attrs):
kwargs[out_name] = []
if out_dup:
sub_in = outputs[out_name]
- for sub_in_name, arr in sub_in:
+ for sub_in_name, _ in sub_in:
var = scope.new_var(sub_in_name)
kwargs[out_name].append(sub_in_name)
else:
@@ -47,11 +47,11 @@ def set_input(scope, op, inputs, place):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
- for sub_in_name, arr in sub_in:
+ for sub_in_name, sub_in_array in sub_in:
var = scope.find_var(sub_in_name)
tensor = var.get_tensor()
- tensor.set_dims(arr.shape)
- tensor.set(arr, place)
+ tensor.set_dims(sub_in_array.shape)
+ tensor.set(sub_in_array, place)
else:
var = scope.find_var(in_name)
tensor = var.get_tensor()
@@ -65,7 +65,7 @@ def set_output_grad(scope, op, outputs, place):
if out_name in outputs:
if out_dup:
sub_out = outputs[out_name]
- for sub_out_name, arr in sub_out:
+ for sub_out_name, sub_out_grad in sub_out:
out_tensor = scope.find_var(sub_out_name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(
sub_out_name)).get_tensor()
@@ -169,9 +169,8 @@ class OpTest(unittest.TestCase):
def check_output_with_place(self, place):
self.scope = core.Scope()
op_inputs = self.inputs if hasattr(self, "inputs") else dict()
- op_outputs = self.outputs if hasattr(self, "outputs") else dict()
op_attrs = self.attrs if hasattr(self, "attrs") else dict()
- self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
+ self.op = create_op(self.scope, self.op_type, op_inputs, self.outputs,
op_attrs)
if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
return
@@ -232,9 +231,8 @@ class OpTest(unittest.TestCase):
max_relative_error=0.005):
self.scope = core.Scope()
op_inputs = self.inputs if hasattr(self, "inputs") else dict()
- op_outputs = self.outputs if hasattr(self, "outputs") else dict()
op_attrs = self.attrs if hasattr(self, "attrs") else dict()
- self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
+ self.op = create_op(self.scope, self.op_type, op_inputs, self.outputs,
op_attrs)
if no_grad_set is None:
no_grad_set = set()
From 68943f59aa58b3f4f18c7647305dcc3683c17b7a Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Mon, 11 Sep 2017 18:38:07 +0800
Subject: [PATCH 55/66] Add construction function for LoDTensor in pybind.
---
paddle/pybind/pybind.cc | 15 +++-
.../paddle/v2/framework/tests/test_tensor.py | 89 ++++++++++++-------
2 files changed, 69 insertions(+), 35 deletions(-)
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 12eb14cf20..fe4af2c99e 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -125,10 +125,23 @@ The tensor and LoD info should be created before creating the LoDTensor, then
call the set_tensor and set_lod functions to set them.
)DOC")
+ .def("__init__",
+ [](LoDTensor &instance,
+ const std::vector<std::vector<size_t>> &lod,
+ Tensor *t) {
+#ifdef PADDLE_ONLY_CPU
+ new (&instance) LoDTensor(lod, t);
+#else
+ paddle::framework::LoD new_lod;
+ new_lod.reserve(lod.size());
+ std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+ new (&instance) LoDTensor(new_lod, t);
+#endif
+ })
.def("set_tensor",
[](LoDTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
.def("set_lod",
- [](LoDTensor &self, std::vector<std::vector<size_t>> &lod) {
+ [](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
#ifdef PADDLE_ONLY_CPU
self.set_lod(lod);
#else
diff --git a/python/paddle/v2/framework/tests/test_tensor.py b/python/paddle/v2/framework/tests/test_tensor.py
index 1bfe1370e2..f26ed4964c 100644
--- a/python/paddle/v2/framework/tests/test_tensor.py
+++ b/python/paddle/v2/framework/tests/test_tensor.py
@@ -44,61 +44,82 @@ class TestTensor(unittest.TestCase):
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
def test_int_lod_tensor(self):
- scope = core.Scope()
- var = scope.new_var("test_tensor")
- var_lod = scope.new_var("test_lod_tensor")
- place = core.CPUPlace()
+ places = [core.CPUPlace(), core.GPUPlace(0)]
+ for place in places:
+ scope = core.Scope()
+ var = scope.new_var("test_tensor")
+ var_lod = scope.new_var("test_lod_tensor")
- tensor = var.get_tensor()
- lod_tensor = var_lod.get_lod_tensor()
+ tensor = var.get_tensor()
+ lod_tensor = var_lod.get_lod_tensor()
- tensor.set_dims([4, 4, 6])
- tensor.alloc_int(place)
- array = numpy.array(tensor)
- array[0, 0, 0] = 3
- array[3, 3, 5] = 10
- tensor.set(array, place)
+ tensor.set_dims([4, 4, 6])
+ tensor.alloc_int(place)
+ array = numpy.array(tensor)
+ array[0, 0, 0] = 3
+ array[3, 3, 5] = 10
+ tensor.set(array, place)
- lod_tensor.set_tensor(tensor)
- lod_tensor.set_lod([[0, 2, 4]])
+ lod_tensor.set_tensor(tensor)
+ lod_tensor.set_lod([[0, 2, 4]])
- lod_v = numpy.array(lod_tensor.tensor())
- self.assertTrue(numpy.alltrue(array == lod_v))
+ lod_v = numpy.array(lod_tensor.tensor())
+ self.assertTrue(numpy.alltrue(array == lod_v))
- lod = lod_tensor.lod()
- self.assertEqual(0, lod[0][0])
- self.assertEqual(2, lod[0][1])
- self.assertEqual(4, lod[0][2])
+ lod = lod_tensor.lod()
+ self.assertEqual(0, lod[0][0])
+ self.assertEqual(2, lod[0][1])
+ self.assertEqual(4, lod[0][2])
def test_float_lod_tensor(self):
+ places = [core.CPUPlace(), core.GPUPlace(0)]
+ for place in places:
+ scope = core.Scope()
+ var = scope.new_var("test_tensor")
+ var_lod = scope.new_var("test_lod_tensor")
+
+ tensor = var.get_tensor()
+ lod_tensor = var_lod.get_lod_tensor()
+
+ tensor.set_dims([5, 2, 3, 4])
+ tensor.alloc_float(place)
+
+ tensor_array = numpy.array(tensor)
+ self.assertEqual((5, 2, 3, 4), tensor_array.shape)
+ tensor_array[0, 0, 0, 0] = 1.0
+ tensor_array[0, 0, 0, 1] = 2.0
+ tensor.set(tensor_array, place)
+
+ lod_tensor.set_tensor(tensor)
+
+ lod_v = numpy.array(lod_tensor.tensor())
+ self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
+ self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
+ self.assertEqual(len(lod_tensor.lod()), 0)
+
+ lod_py = [[0, 2, 5], [0, 2, 4, 5]]
+ lod_tensor.set_lod(lod_py)
+ lod = lod_tensor.lod()
+ self.assertListEqual(lod_py, lod)
+
+ def test_lod_tensor_init(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
- var_lod = scope.new_var("test_lod_tensor")
place = core.CPUPlace()
-
tensor = var.get_tensor()
- lod_tensor = var_lod.get_lod_tensor()
-
tensor.set_dims([5, 2, 3, 4])
tensor.alloc_float(place)
-
tensor_array = numpy.array(tensor)
- self.assertEqual((5, 2, 3, 4), tensor_array.shape)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
tensor.set(tensor_array, place)
+ lod_py = [[0, 2, 5], [0, 2, 4, 5]]
- lod_tensor.set_tensor(tensor)
-
+ lod_tensor = core.LoDTensor(lod_py, tensor)
lod_v = numpy.array(lod_tensor.tensor())
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
- self.assertEqual(len(lod_tensor.lod()), 0)
-
- lod_py = [[0, 2, 5], [0, 2, 4, 5]]
- lod_tensor.set_lod(lod_py)
- lod = lod_tensor.lod()
- self.assertListEqual(lod_py, lod)
+ self.assertListEqual(lod_py, lod_tensor.lod())
if __name__ == '__main__':
From e9a1f2ad8b2c1e78d0f3fcb50d2e026030bfcb03 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 11 Sep 2017 17:51:09 +0800
Subject: [PATCH 56/66] fix DeConv3D switch(imgSize*_, output*_)
---
paddle/gserver/layers/DeConv3DLayer.cpp | 40 ++++++++++++-------------
paddle/gserver/tests/test_LayerGrad.cpp | 33 ++++++++++----------
2 files changed, 37 insertions(+), 36 deletions(-)
diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp
index 1b59ed60c5..3eea638649 100644
--- a/paddle/gserver/layers/DeConv3DLayer.cpp
+++ b/paddle/gserver/layers/DeConv3DLayer.cpp
@@ -53,27 +53,27 @@ bool DeConv3DLayer::init(const LayerMap &layerMap,
size_t DeConv3DLayer::getSize() {
CHECK_NE(inputLayers_.size(), 0UL);
- outputH_.clear();
- outputW_.clear();
- outputD_.clear();
+ imgSizeW_.clear();
+ imgSizeH_.clear();
+ imgSizeD_.clear();
N_.clear();
NOut_.clear();
size_t layerSize = 0;
for (size_t i = 0; i < inputLayers_.size(); ++i) {
- outputW_.push_back(
- imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true));
- outputH_.push_back(imageSize(
- imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
- outputD_.push_back(imageSize(
- imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
- NOut_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
- N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
+ imgSizeW_.push_back(
+ imageSize(outputW_[i], filterSize_[i], padding_[i], stride_[i], true));
+ imgSizeH_.push_back(imageSize(
+ outputH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
+ imgSizeD_.push_back(imageSize(
+ outputD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
+ NOut_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
+ N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
layerSize += NOut_[i] * numFilters_;
}
- getOutput().setFrameHeight(outputH_[0]);
- getOutput().setFrameWidth(outputW_[0]);
- getOutput().setFrameDepth(outputD_[0]);
+ getOutput().setFrameHeight(imgSizeH_[0]);
+ getOutput().setFrameWidth(imgSizeW_[0]);
+ getOutput().setFrameDepth(imgSizeD_[0]);
return layerSize;
}
@@ -103,9 +103,9 @@ void DeConv3DLayer::forward(PassType passType) {
}
colBuf_->col2Vol(outMat->getData() + n * outMat->getStride(),
numFilters_,
- outputD_[i],
- outputH_[i],
- outputW_[i],
+ imgSizeD_[i],
+ imgSizeH_[i],
+ imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
@@ -144,9 +144,9 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) {
colBuf_->vol2Col(
getOutputGrad()->getData() + n * getOutputGrad()->getStride(),
numFilters_,
- outputD_[i],
- outputH_[i],
- outputW_[i],
+ imgSizeD_[i],
+ imgSizeH_[i],
+ imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index 0e6be2df9e..090bde7b20 100644
--- a/paddle/gserver/tests/test_LayerGrad.cpp
+++ b/paddle/gserver/tests/test_LayerGrad.cpp
@@ -2302,26 +2302,27 @@ void test3DDeConvLayer(const string& type, bool trans, bool useGpu) {
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_stride_z(2);
- conv->set_img_size(IMAGE_SIZE);
- conv->set_img_size_y(IMAGE_SIZE_Y);
- conv->set_img_size_z(IMAGE_SIZE_Z);
- conv->set_output_x(imageSize(conv->img_size(),
+ conv->set_output_x(IMAGE_SIZE);
+ conv->set_output_y(IMAGE_SIZE_Y);
+ conv->set_output_z(IMAGE_SIZE_Z);
+
+ conv->set_img_size(imageSize(conv->output_x(),
conv->filter_size(),
conv->padding(),
conv->stride(),
true));
- conv->set_output_y(imageSize(conv->img_size_y(),
- conv->filter_size_y(),
- conv->padding_y(),
- conv->stride_y(),
- true));
- conv->set_output_z(imageSize(conv->img_size_z(),
- conv->filter_size_z(),
- conv->padding_z(),
- conv->stride_z(),
- true));
- config.layerConfig.set_size(conv->output_x() * conv->output_y() *
- conv->output_z() * NUM_FILTERS);
+ conv->set_img_size_y(imageSize(conv->output_y(),
+ conv->filter_size_y(),
+ conv->padding_y(),
+ conv->stride_y(),
+ true));
+ conv->set_img_size_z(imageSize(conv->output_z(),
+ conv->filter_size_z(),
+ conv->padding_z(),
+ conv->stride_z(),
+ true));
+ config.layerConfig.set_size(conv->img_size() * conv->img_size_y() *
+ conv->img_size_z() * NUM_FILTERS);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
config.inputDefs.push_back(
From a3ec652110291c539eca9234561f2ca433fbe2a1 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Mon, 11 Sep 2017 21:39:43 +0800
Subject: [PATCH 57/66] fix bug
---
python/paddle/v2/framework/tests/op_test.py | 1 -
python/paddle/v2/framework/tests/test_softmax_op.py | 2 +-
2 files changed, 1 insertion(+), 2 deletions(-)
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index ddeb2aabc6..4fec4c9109 100644
--- a/python/paddle/v2/framework/tests/op_test.py
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -89,7 +89,6 @@ def get_numeric_gradient(scope,
delta=0.005,
in_place=False):
- print "before set input"
set_input(scope, op, inputs, core.CPUPlace())
op.infer_shape(scope)
diff --git a/python/paddle/v2/framework/tests/test_softmax_op.py b/python/paddle/v2/framework/tests/test_softmax_op.py
index 1c5802dfd5..1b948f252f 100644
--- a/python/paddle/v2/framework/tests/test_softmax_op.py
+++ b/python/paddle/v2/framework/tests/test_softmax_op.py
@@ -12,7 +12,7 @@ def stable_softmax(x):
class TestSoftmaxOp(OpTest):
def setUp(self):
- self.type = "softmax"
+ self.op_type = "softmax"
self.inputs = {
'X': np.random.uniform(0.1, 1, [10, 10]).astype("float32")
}
From 4f0869bef36a349506467160b67596ac251bf78b Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Mon, 11 Sep 2017 22:28:41 +0800
Subject: [PATCH 58/66] add some necessary params for reset functions
---
paddle/gserver/layers/MKLDNNFcLayer.cpp | 135 +++++++++++++-----------
paddle/gserver/layers/MKLDNNFcLayer.h | 19 +++-
paddle/gserver/layers/MKLDNNLayer.h | 41 ++++---
3 files changed, 111 insertions(+), 84 deletions(-)
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp
index a47967b3d3..f70343251a 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp
@@ -78,46 +78,52 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
-void MKLDNNFcLayer::reshape() {
- reshapeInput();
+void MKLDNNFcLayer::reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+ reshapeInput(bs, ih, iw);
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
- ic_ = iLayerSize_ / (ih_ * iw_);
- CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
- CHECK_EQ(size_t(oc_), getSize());
+ ic = iLayerSize_ / (ih * iw);
+ CHECK_EQ(size_t(ic * ih * iw), iLayerSize_) << "not divisible";
+ CHECK_EQ(size_t(oc), getSize());
- reshapeOutput(oh_, ow_);
- resizeOutput(bs_, oc_);
+ reshapeOutput(oh, ow);
+ resizeOutput(bs, oc);
printSizeInfo();
}
-void MKLDNNFcLayer::resetFwd() {
+void MKLDNNFcLayer::resetFwd(std::vector<mkldnn::primitive>& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) {
+ pipeline.clear();
bool hasBias = biases_ && biases_->getW();
- const MatrixPtr& wgt = weight_->getW();
- const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr;
- const MatrixPtr& out = output_.value;
+ const MatrixPtr& wgtVal = weight_->getW();
+ const MatrixPtr& biasVal = hasBias ? biases_->getW() : nullptr;
+ const MatrixPtr& outVal = output_.value;
if (inputIsOnlyMKLDNN()) {
- const MatrixPtr& in = getInputValue(0);
- inVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(in);
- CHECK(inVal_) << "Input should be MKLDNNMatrix";
+ const MatrixPtr& inVal = getInputValue(0);
+ in = std::dynamic_pointer_cast<MKLDNNMatrix>(inVal);
+ CHECK(in) << "Input should be MKLDNNMatrix";
} else {
CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
- const MatrixPtr& in = getInputValue(0, CPU_DEVICE);
- inVal_ = MKLDNNMatrix::create(
- in, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
+ const MatrixPtr& inVal = getInputValue(0, CPU_DEVICE);
+ in = MKLDNNMatrix::create(
+ inVal, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
}
- inVal_->downSpatial();
- wgtVal_ = MKLDNNMatrix::create(
- wgt, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
- wgtVal_->downSpatial();
- biasVal_ =
- hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr;
- outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_);
+ in->downSpatial();
+ wgt = MKLDNNMatrix::create(
+ wgtVal, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
+ wgt->downSpatial();
+ bias = hasBias ? MKLDNNMatrix::create(biasVal, {oc_}, format::x, engine_)
+ : nullptr;
+ out = MKLDNNMatrix::create(outVal, {bs_, oc_}, format::nc, engine_);
// change original output value to mkldnn output value
- output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
+ output_.value = std::dynamic_pointer_cast<Matrix>(out);
if (!outputIsOnlyMKLDNN()) {
// fc cpu output value do not need create convert
// just share point
@@ -127,27 +133,31 @@ void MKLDNNFcLayer::resetFwd() {
// create forward handle
prop_kind pk = prop_kind::forward;
fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk,
- inVal_->getMemoryDesc(),
- wgtVal_->getMemoryDesc(),
- biasVal_->getMemoryDesc(),
- outVal_->getMemoryDesc())
+ in->getMemoryDesc(),
+ wgt->getMemoryDesc(),
+ bias->getMemoryDesc(),
+ out->getMemoryDesc())
: fc_fwd::desc(pk,
- inVal_->getMemoryDesc(),
- wgtVal_->getMemoryDesc(),
- outVal_->getMemoryDesc());
+ in->getMemoryDesc(),
+ wgt->getMemoryDesc(),
+ out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
if (hasBias) {
- fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
+ fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *bias, *out));
} else {
- fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
+ fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *out));
}
printValueFormatFlow();
- pipelineFwd_.clear();
- pipelineFwd_.push_back(*fwd_);
+ pipeline.push_back(*fwd_);
}
-void MKLDNNFcLayer::resetBwd() {
+void MKLDNNFcLayer::resetBwd(std::vector<mkldnn::primitive>& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) {
+ pipeline.clear();
if (!needResetBwd_) {
return;
}
@@ -156,8 +166,8 @@ void MKLDNNFcLayer::resetBwd() {
/// backward weight
CHECK(inVal_) << "Should have input value";
- const MatrixPtr& wgt = weight_->getWGrad();
- const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr;
+ const MatrixPtr& wgtGrad = weight_->getWGrad();
+ const MatrixPtr& biasGrad = hasBias ? biases_->getWGrad() : nullptr;
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
@@ -168,59 +178,56 @@ void MKLDNNFcLayer::resetBwd() {
// for CPU device:
// fc do not need to convert from cpu device since output is always nc format
// only need create from cpu device
- const MatrixPtr& out = getOutput(device).grad;
- outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc());
- wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc());
- biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc())
- : nullptr;
+ const MatrixPtr& outGrad = getOutput(device).grad;
+ out = MKLDNNMatrix::create(outGrad, outVal_->getPrimitiveDesc());
+ wgt = MKLDNNMatrix::create(wgtGrad, wgtVal_->getPrimitiveDesc());
+ bias = hasBias ? MKLDNNMatrix::create(biasGrad, biasVal_->getPrimitiveDesc())
+ : nullptr;
// create memory primitive desc
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward,
inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc());
+ wgt->getMemoryDesc(),
+ out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
fc_bwdWgt::desc bwdWgtDesc = hasBias
? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- biasGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc())
+ wgt->getMemoryDesc(),
+ bias->getMemoryDesc(),
+ out->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc());
+ wgt->getMemoryDesc(),
+ out->getMemoryDesc());
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
if (hasBias) {
- bwdWgt_.reset(
- new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
+ bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt, *bias));
} else {
- bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_));
+ bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt));
}
- pipelineBwd_.clear();
- pipelineBwd_.push_back(*bwdWgt_);
+ pipeline.push_back(*bwdWgt_);
/// backward data
- const MatrixPtr& in = inputLayers_[0]->getOutput().grad;
- if (in == nullptr) {
+ const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
+ if (inGrad == nullptr) {
return;
}
if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) {
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
} else {
- inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc());
+ in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
}
- fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc());
+ fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(
+ inVal_->getMemoryDesc(), wgt->getMemoryDesc(), out->getMemoryDesc());
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
CHECK(wgtVal_) << "Should have weight memory";
- bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
+ bwdData_.reset(new fc_bwdData(bwdDataPD, *out, *wgtVal_, *in));
printGradFormatFlow();
- pipelineBwd_.push_back(*bwdData_);
+ pipeline.push_back(*bwdData_);
}
void MKLDNNFcLayer::updateInputData() {
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h
index add8ac9991..3119f86349 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.h
+++ b/paddle/gserver/layers/MKLDNNFcLayer.h
@@ -45,11 +45,20 @@ public:
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
- void reshape() override;
-
- void resetFwd() override;
-
- void resetBwd() override;
+ void reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+
+ void resetFwd(std::vector<mkldnn::primitive>& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
+
+ void resetBwd(std::vector<mkldnn::primitive>& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
void updateInputData() override;
diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h
index c10f2fec2f..169679c829 100644
--- a/paddle/gserver/layers/MKLDNNLayer.h
+++ b/paddle/gserver/layers/MKLDNNLayer.h
@@ -111,13 +111,14 @@ public:
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
- copySeqInfoToOutputs();
CHECK(!inputLayers_.empty());
+ copySeqInfoToOutputs();
size_t elemenCnt = inputLayers_[0]->getOutput().value->getElementCnt();
if (inputElemenCnt_ != elemenCnt) {
+ // reset when input total sizes changed, not only the batchsize
inputElemenCnt_ = elemenCnt;
- reshape();
- resetFwd();
+ reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
+ resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
convertWeightsFromPaddle();
needResetBwd_ = true;
}
@@ -144,7 +145,7 @@ public:
{
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
if (needResetBwd_) {
- resetBwd();
+ resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
needResetBwd_ = false;
}
@@ -160,20 +161,30 @@ public:
/**
* reshape the input image sizes
* and reset output image and buffer size
+ * output channel can not be changed
*/
- virtual void reshape() = 0;
+ virtual void reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0;
/**
* reset the mkldnn forward primitve and memory
* only would be called when input size changes
*/
- virtual void resetFwd() = 0;
+ virtual void resetFwd(std::vector<mkldnn::primitive>& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) = 0;
/**
* reset the mkldnn backward primitve and memory for mkldnn fc
* only would be called when needed
*/
- virtual void resetBwd() = 0;
+ virtual void resetBwd(std::vector<mkldnn::primitive>& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) = 0;
/**
* Update input value data when input layer is "data" type.
@@ -207,16 +218,16 @@ protected:
/**
* reshape the input image sizes and input batchsize
*/
- virtual void reshapeInput() {
+ virtual void reshapeInput(int& batchsize, int& height, int& width) {
const Argument& input = inputLayers_[0]->getOutput();
- bs_ = input.getBatchSize();
- int height = input.getFrameHeight();
- int width = input.getFrameWidth();
- if (height != 0) {
- ih_ = height;
+ batchsize = input.getBatchSize();
+ int h = input.getFrameHeight();
+ int w = input.getFrameWidth();
+ if (h != 0) {
+ height = h;
}
- if (width != 0) {
- iw_ = width;
+ if (w != 0) {
+ width = w;
}
}
From fb32106e246695fc91a63186fb22a68c66f98a33 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Mon, 11 Sep 2017 17:14:01 -0700
Subject: [PATCH 59/66] Make paddle.v2.inference can direct load protobuf
---
python/paddle/v2/inference.py | 16 +++++++++++-----
1 file changed, 11 insertions(+), 5 deletions(-)
diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py
index 8acea6155c..19624a704f 100644
--- a/python/paddle/v2/inference.py
+++ b/python/paddle/v2/inference.py
@@ -25,11 +25,18 @@ class Inference(object):
:type parameters: paddle.v2.parameters.Parameters
"""
- def __init__(self, output_layer, parameters):
+ def __init__(self, output_layer, parameters, data_types=None):
import py_paddle.swig_paddle as api
- topo = topology.Topology(output_layer)
- gm = api.GradientMachine.createFromConfigProto(
- topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
+ if isinstance(output_layer, str):
+ gm = api.GradientMachine.createByConfigProtoStr(output_layer)
+ if data_types is None:
+ raise ValueError("data_types != None when using protobuf bin")
+ self.__data_types__ = data_types
+ else:
+ topo = topology.Topology(output_layer)
+ gm = api.GradientMachine.createFromConfigProto(
+ topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
+ self.__data_types__ = topo.data_type()
for param in gm.getParameters():
val = param.getBuf(api.PARAMETER_VALUE)
name = param.getName()
@@ -43,7 +50,6 @@ class Inference(object):
# called here, but it's better to call this function in one place.
param.setValueUpdated()
self.__gradient_machine__ = gm
- self.__data_types__ = topo.data_type()
def iter_infer(self, input, feeding=None):
from data_feeder import DataFeeder
From 2b352212c27ccdccb94a2878d823b2150d74bf00 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Mon, 11 Sep 2017 17:42:20 -0700
Subject: [PATCH 60/66] Add serialize to file for topology and read file obj
for inference
---
python/paddle/v2/inference.py | 20 +++++++++++++-------
python/paddle/v2/topology.py | 9 +++++++++
2 files changed, 22 insertions(+), 7 deletions(-)
diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py
index 19624a704f..e80456d9bb 100644
--- a/python/paddle/v2/inference.py
+++ b/python/paddle/v2/inference.py
@@ -2,6 +2,7 @@ import numpy
import collections
import topology
import minibatch
+import cPickle
__all__ = ['infer', 'Inference']
@@ -25,18 +26,23 @@ class Inference(object):
:type parameters: paddle.v2.parameters.Parameters
"""
- def __init__(self, output_layer, parameters, data_types=None):
+ def __init__(self, parameters, output_layer=None, fileobj=None):
import py_paddle.swig_paddle as api
- if isinstance(output_layer, str):
- gm = api.GradientMachine.createByConfigProtoStr(output_layer)
- if data_types is None:
- raise ValueError("data_types != None when using protobuf bin")
- self.__data_types__ = data_types
- else:
+
+ if output_layer is not None:
topo = topology.Topology(output_layer)
gm = api.GradientMachine.createFromConfigProto(
topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
self.__data_types__ = topo.data_type()
+ elif fileobj is not None:
+ tmp = cPickle.load(fileobj)
+ gm = api.GradientMachine.createByConfigProtoStr(
+ tmp['protobin'], api.CREATE_MODE_TESTING,
+ [api.PARAMETER_VALUE])
+ self.__data_types__ = tmp['data_type']
+ else:
+ raise ValueError("Either output_layer or fileobj must be set")
+
for param in gm.getParameters():
val = param.getBuf(api.PARAMETER_VALUE)
name = param.getName()
diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py
index a20e878d08..2db66be250 100644
--- a/python/paddle/v2/topology.py
+++ b/python/paddle/v2/topology.py
@@ -18,6 +18,7 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig
import paddle.trainer_config_helpers as conf_helps
import layer as v2_layer
import config_base
+import cPickle
__all__ = ['Topology']
@@ -100,6 +101,14 @@ class Topology(object):
return layer
return None
+ def serialize_for_inference(self, stream):
+ protobin = self.proto().SerializeToString()
+ data_type = self.data_type()
+ cPickle.dump({
+ 'protobin': protobin,
+ 'data_type': data_type
+ }, stream, cPickle.HIGHEST_PROTOCOL)
+
def __check_layer_type__(layer):
if not isinstance(layer, config_base.Layer):
From 7f7fa325287a2ef434aca8a38bed4af2496429a2 Mon Sep 17 00:00:00 2001
From: tensor-tang <jian.j.tang@intel.com>
Date: Mon, 11 Sep 2017 22:49:11 +0800
Subject: [PATCH 61/66] use CPU_DEVICE instead of magic number
---
paddle/gserver/layers/Layer.h | 11 ++++++-----
paddle/gserver/tests/MKLDNNTester.cpp | 5 +++--
2 files changed, 9 insertions(+), 7 deletions(-)
diff --git a/paddle/gserver/layers/Layer.h b/paddle/gserver/layers/Layer.h
index edef36194a..4002a3d074 100644
--- a/paddle/gserver/layers/Layer.h
+++ b/paddle/gserver/layers/Layer.h
@@ -49,6 +49,12 @@ struct LayerState {
};
typedef std::shared_ptr<LayerState> LayerStatePtr;
+/// Paddle device ID, MKLDNN is -2, CPU is -1
+enum PADDLE_DEVICE_ID {
+ MKLDNN_DEVICE = -2,
+ CPU_DEVICE = -1,
+};
+
/**
* @brief Base class for layer.
* Define necessary variables and functions for every layer.
@@ -59,11 +65,6 @@ protected:
LayerConfig config_;
/// whether to use GPU
bool useGpu_;
- /// Paddle device ID, MKLDNN is -2, CPU is -1
- enum PADDLE_DEVICE_ID {
- MKLDNN_DEVICE = -2,
- CPU_DEVICE = -1,
- };
/// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ...
int deviceId_;
/// Input layers
diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp
index 11e8527910..2f48e5b2d3 100644
--- a/paddle/gserver/tests/MKLDNNTester.cpp
+++ b/paddle/gserver/tests/MKLDNNTester.cpp
@@ -68,7 +68,7 @@ void MKLDNNTester::reset(const TestConfig& dnn,
CHECK(dnnLayer_);
// for comparison with Paddle reference results,
// need manually add cpu device output for test
- dnnLayer_->addOutputArgument(-1);
+ dnnLayer_->addOutputArgument(CPU_DEVICE);
EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
@@ -113,7 +113,8 @@ void MKLDNNTester::randomBotDatas() {
void MKLDNNTester::randomTopDiffs() {
refLayer_->getOutputGrad()->randomizeUniform();
- dnnLayer_->getOutput(-1).grad->copyFrom(*(refLayer_->getOutputGrad()));
+ dnnLayer_->getOutput(CPU_DEVICE)
+ .grad->copyFrom(*(refLayer_->getOutputGrad()));
VLOG(lvl_) << "Random Backward Input, TopDiff: ";
printMatrix(refLayer_->getOutputGrad());
}
From ad64ca5da20e696d66cfcf9011d16a81e8ef8ff8 Mon Sep 17 00:00:00 2001
From: qingqing01 <dangqingqing@baidu.com>
Date: Tue, 12 Sep 2017 10:45:11 +0800
Subject: [PATCH 62/66] Call Tensor::numel() everywhere.
---
paddle/framework/tensor.h | 5 ++++-
paddle/framework/tensor_impl.h | 8 ++++----
2 files changed, 8 insertions(+), 5 deletions(-)
diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h
index fc54ed697f..19051db539 100644
--- a/paddle/framework/tensor.h
+++ b/paddle/framework/tensor.h
@@ -162,7 +162,10 @@ class Tensor {
/*! points to dimensions of memory block. */
DDim dims_;
- /*! the element count of tensor. */
+ /**
+ * A cache of the number of elements in a tensor.
+ * Would be 0 for an uninitialized tensor.
+ */
int64_t numel_;
/**
diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h
index 03678784b4..5e32bfcac6 100644
--- a/paddle/framework/tensor_impl.h
+++ b/paddle/framework/tensor_impl.h
@@ -24,7 +24,7 @@ inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tenosr holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_GE(
- holder_->size(), numel_ * sizeof(T) + offset_,
+ holder_->size(), numel() * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
@@ -54,11 +54,11 @@ inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
template <typename T>
inline T* Tensor::mutable_data(platform::Place place) {
static_assert(std::is_pod<T>::value, "T must be POD");
- PADDLE_ENFORCE_GT(numel_, 0,
+ PADDLE_ENFORCE_GT(numel(), 0,
"Tensor's numel must be larger than zero to call "
"Tensor::mutable_data. Call Tensor::set_dim first.");
/* some versions of boost::variant don't have operator!= */
- int64_t size = numel_ * sizeof(T);
+ int64_t size = numel() * sizeof(T);
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
@@ -131,7 +131,7 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
PADDLE_ENFORCE_LT(begin_idx, end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE_NE(dims_[0], 1, "Can not slice a tensor with dims_[0] = 1.");
- size_t base = numel_ / dims_[0];
+ size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
DDim dst_dims = dims_;
From dd926498e7e61b25250c8f59d91afe57ab24098a Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Mon, 11 Sep 2017 20:09:21 -0700
Subject: [PATCH 63/66] adapt to the new test framework
---
.../v2/framework/tests/test_reshape_op.py | 27 +++++++------------
1 file changed, 9 insertions(+), 18 deletions(-)
diff --git a/python/paddle/v2/framework/tests/test_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py
index 50653f58ee..16bb6bb2af 100644
--- a/python/paddle/v2/framework/tests/test_reshape_op.py
+++ b/python/paddle/v2/framework/tests/test_reshape_op.py
@@ -1,29 +1,20 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, Operator
-from op_test_util import OpTestMeta
+from op_test import OpTest
-class TestReshapeOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestReshapeOp(OpTest):
def setUp(self):
- self.type = "reshape"
- self.inputs = {'X': np.random.random((37, 51)).astype("float32"), }
- self.attrs = {'shape': [51 * 37]}
+ self.op_type = "reshape"
+ self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
+ self.attrs = {'shape': [10 * 20]}
self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
+ def test_check_output(self):
+ self.check_output()
-class TestReshapeGradOp(GradientChecker):
- def setUp(self):
- self.op = Operator("reshape", X='X', Out='Out', shape=[5, 40])
- self.inputs = {"X": np.random.random((10, 20)).astype("float32")}
-
- def test_normal(self):
- self.check_grad(self.op, self.inputs, ["X"], "Out")
-
- def test_dev_compare(self):
- self.compare_grad(self.op, self.inputs)
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
if __name__ == '__main__':
From 0289a0091f094c75190698df7e450d8e1a70bbaa Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Mon, 11 Sep 2017 22:15:29 -0700
Subject: [PATCH 64/66] follow comments to cleanup code
---
paddle/operators/reshape_op.cc | 35 ++++++++++++++++++++++++++--------
paddle/operators/reshape_op.h | 9 ++++-----
2 files changed, 31 insertions(+), 13 deletions(-)
diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
index 37cbecbf25..da29c89150 100644
--- a/paddle/operators/reshape_op.cc
+++ b/paddle/operators/reshape_op.cc
@@ -27,21 +27,26 @@ class ReshapeOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
- auto *in = ctx.Input<framework::Tensor>("X");
+ // input check
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null");
auto shape = ctx.Attr<std::vector<int>>("shape");
- int64_t capacity = -1;
+ PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
for (auto dim : shape) {
PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
- if (capacity < 0) {
- capacity = dim;
- } else {
- capacity *= dim;
- }
}
+ // capacity check
+ int64_t capacity =
+ std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
+ auto *in = ctx.Input<framework::Tensor>("X");
int64_t in_size = framework::product(in->dims());
PADDLE_ENFORCE_EQ(capacity, in_size,
"The size of Input(X) mismatches with Attr(shape).");
- ctx.Output<framework::Tensor>("Out")->Resize(in->dims());
+ // resize output
+ std::vector<int64_t> shape_int64(shape.size(), 0);
+ std::transform(shape.begin(), shape.end(), shape_int64.begin(),
+ [](int a) { return static_cast<int64_t>(a); });
+ auto out_dims = framework::make_ddim(shape_int64);
+ ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
}
};
@@ -56,6 +61,17 @@ class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(Reshape operator
Reshape Input(X) into the shape specified by Attr(shape).
+
+An example:
+Given a 2-D tensor X with 2 rows and 2 columns
+
+ [[1, 2], [3, 4]]
+
+with target shape = [1, 4], the reshape operator will tansform
+the tensor X into a 1-D tensor:
+
+ [1, 2, 3, 4]
+
)DOC");
}
};
@@ -70,6 +86,9 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+ "Input(Out@GRAD) shouldn't be null.");
auto dims = ctx.Input<framework::Tensor>("X")->dims();
auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
d_in->Resize(dims);
diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h
index 0e920329d9..26708e72dc 100644
--- a/paddle/operators/reshape_op.h
+++ b/paddle/operators/reshape_op.h
@@ -30,11 +30,10 @@ class ReshapeKernel : public framework::OpKernel {
out->mutable_data<T>(ctx.GetPlace());
auto shape = ctx.Attr<std::vector<int>>("shape");
- std::vector<int64_t> tmp;
- for (auto dim : shape) {
- tmp.push_back(dim);
- }
- auto out_dims = framework::make_ddim(tmp);
+ std::vector<int64_t> shape_int64(shape.size(), 0);
+ std::transform(shape.begin(), shape.end(), shape_int64.begin(),
+ [](int a) { return static_cast<int64_t>(a); });
+ auto out_dims = framework::make_ddim(shape_int64);
out->CopyFrom<T>(*in, ctx.GetPlace());
out->Resize(out_dims);
}
From 5915138c791c7a2d6fd40c0ae6c942ca870033c8 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Mon, 11 Sep 2017 22:22:43 -0700
Subject: [PATCH 65/66] fix a typo
---
paddle/operators/reshape_op.cc | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
index da29c89150..b7061153d2 100644
--- a/paddle/operators/reshape_op.cc
+++ b/paddle/operators/reshape_op.cc
@@ -67,7 +67,7 @@ Given a 2-D tensor X with 2 rows and 2 columns
[[1, 2], [3, 4]]
-with target shape = [1, 4], the reshape operator will tansform
+with target shape = [1, 4], the reshape operator will transform
the tensor X into a 1-D tensor:
[1, 2, 3, 4]
From 1f839a6618db31b9be26f5d2604d98ef4fd2f46e Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Tue, 12 Sep 2017 17:03:17 +0800
Subject: [PATCH 66/66] fix bug in prelu parsing.
---
python/paddle/trainer/config_parser.py | 1 +
.../protostr/test_prelu_layer.protostr | 45 ++++++++++++++++++-
.../tests/configs/test_prelu_layer.py | 2 +
3 files changed, 46 insertions(+), 2 deletions(-)
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 356e1d8b6f..4f68a89534 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -2034,6 +2034,7 @@ class ParameterReluLayer(LayerBase):
config_assert(input_layer.size % partial_sum == 0,
"a wrong setting for partial_sum")
self.set_layer_size(input_layer.size)
+ self.config.partial_sum = partial_sum
self.create_input_parameter(0, input_layer.size / partial_sum)
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
index 64d227565f..94ad56cab0 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
@@ -14,6 +14,29 @@ layers {
input_layer_name: "input"
input_parameter_name: "___prelu_layer_0__.w0"
}
+ partial_sum: 1
+}
+layers {
+ name: "__prelu_layer_1__"
+ type: "prelu"
+ size: 300
+ active_type: ""
+ inputs {
+ input_layer_name: "input"
+ input_parameter_name: "___prelu_layer_1__.w0"
+ }
+ partial_sum: 1
+}
+layers {
+ name: "__prelu_layer_2__"
+ type: "prelu"
+ size: 300
+ active_type: ""
+ inputs {
+ input_layer_name: "input"
+ input_parameter_name: "___prelu_layer_2__.w0"
+ }
+ partial_sum: 5
}
parameters {
name: "___prelu_layer_0__.w0"
@@ -23,14 +46,32 @@ parameters {
initial_strategy: 0
initial_smart: true
}
+parameters {
+ name: "___prelu_layer_1__.w0"
+ size: 300
+ initial_mean: 0.0
+ initial_std: 0.057735026919
+ initial_strategy: 0
+ initial_smart: true
+}
+parameters {
+ name: "___prelu_layer_2__.w0"
+ size: 60
+ initial_mean: 0.0
+ initial_std: 0.129099444874
+ initial_strategy: 0
+ initial_smart: true
+}
input_layer_names: "input"
-output_layer_names: "__prelu_layer_0__"
+output_layer_names: "__prelu_layer_2__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__prelu_layer_0__"
+ layer_names: "__prelu_layer_1__"
+ layer_names: "__prelu_layer_2__"
input_layer_names: "input"
- output_layer_names: "__prelu_layer_0__"
+ output_layer_names: "__prelu_layer_2__"
is_recurrent_layer_group: false
}
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
index 2e3057f323..aae90fab32 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
@@ -2,5 +2,7 @@ from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300)
prelu = prelu_layer(input=data)
+prelu = prelu_layer(input=data, partial_sum=1)
+prelu = prelu_layer(input=data, partial_sum=5)
outputs(prelu)