& no_grad_vars);
+```
+
+The implementation behind it can be divided into two parts, **Backward Operator Creating** and **Backward Operator Building**.
+
+### Backward Operator Registry
+
+A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs, and output gradients and then calculate its input gradients.
| | forward operator | backward operator
| ---------------------- | ---------------- |------------------------- |
@@ -25,7 +36,7 @@ REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
`mul_grad` is the type of backward operator, and `MulOpGrad` is its class name.
-## Backward Opeartor Creating
+### Backward Opeartor Creating
Given a certain forward operator, we can get its corresponding backward operator by calling:
@@ -43,40 +54,47 @@ The function `BuildGradOp` will sequentially execute following processes:
4. Building backward operator with `inputs`, `outputs` and forward operator's attributes.
-## Backward Network Building
-
-A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and put them together.
+### Backward Network Building
-In our design, the network itself is also a kind of operator. So the operators contained by a big network may be some small network.
-
-given a forward network, it generates the backward network. We only care about the Gradients—`OutputGradients`, `InputGradients`.
+A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and append them together one by one. There is some corner case need to process specially.
1. Op
- when the input forward network is an Op, return its gradient Operator Immediately.
+ When the input forward network is an Op, return its gradient Operator Immediately. If all of its outputs are in no gradient set, then return a special `NOP`.
2. NetOp
- when the input forward network is a NetOp, it needs to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to the forward NetOp.
+ In our design, the network itself is also a kind of operator(**NetOp**). So the operators contained by a big network may be some small network. When the input forward network is a NetOp, it needs to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to the forward NetOp.
+
+3. RnnOp
+
+ RnnOp is a nested stepnet operator. Backward module need to recusively call `Backward` for every stepnet.
+
+4. Sharing Variables
+
+ **sharing variables**. As illustrated in the pictures, two operator's share the same variable name of W@GRAD, which will overwrite their sharing input variable.
+
+
+
- **shared variable**. As illustrated in the pictures, two operator's `Output` `Gradient` will overwrite their shared input variable.
+ pic 1. Sharing variables in operators.
-
-
+
- 1. Shared variable in operators.
+ Sharing variable between operators or same input variable used in multiple operators leads to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively and add a generic add operator to replace the overwrite links.
-
+
+
- Share variable between operators or same input variable used in multiple operators leads to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively and add a generic add operator replace the overwrite links.
+ pic 2. Replace sharing variable's gradient with `Add` operator.
-
-
+
- 2. Replace shared variable's gradient with `Add` operator.
+ Because our framework finds variables accord to their names, we need to rename the output links. We add a suffix of number to represent its position in clockwise.
-
+5. Part of Gradient is Zero.
+ In the whole graph, there is some case of that one operator's gradient is not needed, but its input's gradient is a dependency link of other operator, we need to fill a same shape gradient matrix in the position. In our implement, we insert a special `fillZeroLike` operator.
- Then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it.
+Follow these rules above, then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it.
diff --git a/paddle/framework/images/duplicate_op2.graffle b/paddle/framework/images/duplicate_op2.graffle
index ede3bca30ae17d5af52505fd94dc2f79b23b57e0..5cec3bc64dbd44dc99e348485969f29bd128ceb1 100644
Binary files a/paddle/framework/images/duplicate_op2.graffle and b/paddle/framework/images/duplicate_op2.graffle differ
diff --git a/paddle/framework/images/duplicate_op2.png b/paddle/framework/images/duplicate_op2.png
index 4e872dc2caf3b0cbd0d5176f11a14801b538dc86..21cdd5cabf1b5203e1435a75b57770d2f702fa92 100644
Binary files a/paddle/framework/images/duplicate_op2.png and b/paddle/framework/images/duplicate_op2.png differ
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 154068fef69bc96edbd85b731fe8091b3b1ff823..568f4e89819c8345d8908634f6fa56f09483a763 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -18,8 +18,10 @@
#ifndef PADDLE_ONLY_CPU
#include
#include
+#include
#endif
+#include
#include "paddle/framework/ddim.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/enforce.h"
@@ -32,7 +34,8 @@ template
using Vector = std::vector;
#else
template
-using Vector = thrust::host_vector;
+using Vector = thrust::host_vector<
+ T, thrust::system::cuda::experimental::pinned_allocator>;
#endif
using LoD = std::vector>;
diff --git a/paddle/framework/lod_tensor_test.cu b/paddle/framework/lod_tensor_test.cu
new file mode 100644
index 0000000000000000000000000000000000000000..1079a36a2e7b24f6f8a5bcbb296355567305a765
--- /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
+#include
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/platform/assert.h"
+
+#include
+
+__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{0, 2, 4, 6, 8, 10, 12, 14});
+
+ tensor.Resize({14, 16});
+ tensor.mutable_data(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/framework/tensor.h b/paddle/framework/tensor.h
index ce938b21437195fed8c1adad4329fd139f3f96ab..4b5a2ae523f2f7fde5445f0534cd99969ad9d59e 100644
--- a/paddle/framework/tensor.h
+++ b/paddle/framework/tensor.h
@@ -81,6 +81,9 @@ 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);
@@ -162,6 +165,12 @@ class Tensor {
/*! points to dimensions of memory block. */
DDim dims_;
+ /**
+ * A cache of the number of elements in a tensor.
+ * Would be 0 for an uninitialized 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 637f04ae0037bd402d855b8bcde8087bfe8328d1..642b53efc7095d25712ca324638f5fe9b8316c0c 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
inline T* Tensor::mutable_data(platform::Place place) {
static_assert(std::is_pod::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(mutable_data(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(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_;
@@ -143,11 +143,14 @@ 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_; }
+
template
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
Tensor res;
diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp
index 1b59ed60c57fe3bbfa814befa8a63408a2621715..3eea638649e8ebfdd7efa18615977a9e1344c695 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/layers/Layer.h b/paddle/gserver/layers/Layer.h
index edef36194aabdb9c122ec3423deb036169a34d7c..4002a3d0747a86ab7b495ffe52247521831b71b8 100644
--- a/paddle/gserver/layers/Layer.h
+++ b/paddle/gserver/layers/Layer.h
@@ -49,6 +49,12 @@ struct LayerState {
};
typedef std::shared_ptr 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/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp
index 8318c8c519a4cec1610eadd28320ee5ce0b4147d..f70343251ad4fbb99f9614618f6d1bff1174f15e 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();
@@ -77,111 +78,86 @@ 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(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+ reshapeInput(bs, ih, iw);
-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;
- }
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();
+ ic = iLayerSize_ / (ih * iw);
+ CHECK_EQ(size_t(ic * ih * iw), iLayerSize_) << "not divisible";
+ CHECK_EQ(size_t(oc), getSize());
- // 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() {
+void MKLDNNFcLayer::resetFwd(std::vector& 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(in);
- CHECK(inVal_) << "Input should be MKLDNNMatrix";
+ const MatrixPtr& inVal = getInputValue(0);
+ in = std::dynamic_pointer_cast(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_);
- }
- 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_);
+ const MatrixPtr& inVal = getInputValue(0, CPU_DEVICE);
+ in = MKLDNNMatrix::create(
+ inVal, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, 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(outVal_);
+ output_.value = std::dynamic_pointer_cast(out);
if (!outputIsOnlyMKLDNN()) {
- convertOutputToOtherDevice();
+ // fc cpu output value do not need create convert
+ // just share point
+ getOutput(CPU_DEVICE).value->setData(output_.value->getData());
}
// 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& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) {
+ pipeline.clear();
if (!needResetBwd_) {
return;
}
@@ -190,8 +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;
@@ -202,101 +178,66 @@ 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
- device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
- const MatrixPtr& in = getInputGrad(0, device);
- if (in == nullptr) {
+ const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
+ if (inGrad == 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());
+ 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::forward(PassType passType) {
- Layer::forward(passType);
- reshape();
-
- {
- REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
- syncInputValue();
-
- // just submit forward pipeline
- stream_->submit(pipelineFwd_);
- }
-
- /* activation */ {
- REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
- forwardActivation();
- }
+void MKLDNNFcLayer::updateInputData() {
+ 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();
-
- syncOutputGrad();
- // 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 e138a6faf181c412949218458e7ecf800a0d6a07..3119f863496df092da13c08bf733f13c42e53780 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.h
+++ b/paddle/gserver/layers/MKLDNNFcLayer.h
@@ -45,35 +45,28 @@ public:
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
- void convertWeightsFromPaddle() override;
+ void reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
- void convertWeightsToPaddle() override;
+ void resetFwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
- void forward(PassType passType) override;
+ void resetBwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
- void backward(const UpdateCallback& callback) 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 convertOutputToOtherDevice() override;
+ 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 b983b833d510b823c5d4cff0b9390173e4cefc89..169679c8297542cac4a43f5a8e1af311ad9282df 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 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 pipelineFwd_;
std::vector 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 +99,104 @@ 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());
+ 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(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
+ resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
+ 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(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
+ 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
+ * output channel can not be changed
+ */
+ 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(std::vector& 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(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) = 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 +210,38 @@ public:
virtual void convertWeightsToPaddle() {}
/**
- * convert MKLDNN output to other device.
- * only support CPU device yet
+ * add this interface as public for unit test
+ */
+ void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
+
+protected:
+ /**
+ * reshape the input image sizes and input batchsize
*/
- virtual void convertOutputToOtherDevice() {}
+ virtual void reshapeInput(int& batchsize, int& height, int& width) {
+ const Argument& input = inputLayers_[0]->getOutput();
+ batchsize = input.getBatchSize();
+ int h = input.getFrameHeight();
+ int w = input.getFrameWidth();
+ if (h != 0) {
+ height = h;
+ }
+ if (w != 0) {
+ width = w;
+ }
+ }
+
+ /**
+ * 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 +257,8 @@ public:
*/
virtual void printValueFormatFlow() {
if (inVal_ && outVal_) {
- VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat()
- << " >>> " << outVal_->getFormat();
+ VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>> "
+ << outVal_->getFormat();
}
}
@@ -143,29 +267,12 @@ 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() {
- 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 input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
@@ -193,37 +300,12 @@ 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_->updateData(iData);
- }
-
- /**
- * Sync output grad data
- */
- void syncOutputGrad() {
- if (outputIsOnlyMKLDNN()) {
- return;
- }
-
- // update diff
- real* oDiff = getOutput(CPU_DEVICE).grad->getData();
- outGrad_->updateData(oDiff);
- }
-
/**
* Set deviceId of this layer.
*/
void setDevice(int id) { deviceId_ = id; }
+private:
/**
* Set deviceId of the params used in this layer.
*/
@@ -247,6 +329,42 @@ 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
diff --git a/paddle/gserver/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp
index d7eee6eaf078dab8d48adc4c7ee758a433672ac6..e97809141a93106f9e6ebaf40c7e8aa9c6010557 100644
--- a/paddle/gserver/layers/SwitchOrderLayer.cpp
+++ b/paddle/gserver/layers/SwitchOrderLayer.cpp
@@ -83,8 +83,7 @@ void SwitchOrderLayer::forward(PassType passType) {
setOutDims();
resetOutput(outDims_[0], outDims_[1] * outDims_[2] * outDims_[3]);
if (heightAxis_.size() > 0) {
- getOutputValue()->reshape(reshapeHeight_, reshapeWidth_);
- getOutputGrad()->reshape(reshapeHeight_, reshapeWidth_);
+ resetOutput(reshapeHeight_, reshapeWidth_);
}
// switch NCHW to NHWC
diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp
index de1635be2af37cd0ba49010199a417090865b0e4..2f48e5b2d3ffc9337ed1314f6db6549e56263fdd 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(testLayers_[DNN]);
+ CHECK(dnnLayer_);
+ // for comparison with Paddle reference results,
+ // need manually add cpu device output for test
+ dnnLayer_->addOutputArgument(CPU_DEVICE);
EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
@@ -109,20 +113,22 @@ void MKLDNNTester::randomBotDatas() {
void MKLDNNTester::randomTopDiffs() {
refLayer_->getOutputGrad()->randomizeUniform();
- dnnLayer_->getOutputGrad()->copyFrom(*(refLayer_->getOutputGrad()));
- VLOG(lvl_) << "Random dom Backward Input, TopDiff: ";
+ dnnLayer_->getOutput(CPU_DEVICE)
+ .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 +150,12 @@ void MKLDNNTester::checkBackwardData() {
}
void MKLDNNTester::checkBackwardWgts() {
+ VLOG(MKLDNN_ALL) << "Check Backward Weight";
CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
vector dnnWgts; // used to temply save mkldnn weights
saveWgt(parameters_[DNN], dnnWgts);
- const MKLDNNLayerPtr dnnlayer =
- std::dynamic_pointer_cast(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 +193,38 @@ void MKLDNNTester::restoreWgt(const vector& 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 +304,24 @@ 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 e55e4493ffdfe45b8cfdee423febd1878b8b3d8a..5ac885638cde7693a0c847733e7a6149c1b7e6c2 100644
--- a/paddle/gserver/tests/MKLDNNTester.h
+++ b/paddle/gserver/tests/MKLDNNTester.h
@@ -18,6 +18,7 @@ limitations under the License. */
#include
#include "LayerGradUtil.h"
#include "paddle/gserver/layers/MKLDNNBase.h"
+#include "paddle/gserver/layers/MKLDNNLayer.h"
namespace paddle {
@@ -40,7 +41,8 @@ protected:
vector layerMaps_;
vector> parameters_;
vector 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);
diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index 0e6be2df9ef5f0fae8ed2b0c65ac6c032fe45ab1..090bde7b203652e3ffb1662b8f5b8937885d2608 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(
diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp
index 0a355e2644cce572ce90ecf5c9d2a5b7b395bc61..c4063e5069854242d9f93886b66580385557ca73 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(m);
CHECK(cpuMatrix) << "Only support create from CPU matrix yet";
-
- CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match";
- return std::make_shared(
- m->getData(), m->getHeight(), m->getWidth(), pd);
+ CHECK_EQ(cpuMatrix->getElementCnt(), cnts) << "Count size does not match";
+ return std::make_shared(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 e50f698b495713e6f15ab7a12a7ee7487662040f..eef3b429e6fa0087aeac3f5aed9dff983b06e826 100644
--- a/paddle/math/MKLDNNMatrix.h
+++ b/paddle/math/MKLDNNMatrix.h
@@ -30,11 +30,10 @@ typedef std::shared_ptr 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
diff --git a/paddle/operators/concat_op.cc b/paddle/operators/concat_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..0ebefbab26ec8fdf316f852fbb7f6d9f3bbc48eb
--- /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
+
+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("X");
+ auto *out = ctx.Output("Out");
+ size_t axis = static_cast(ctx.Attr("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("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)
diff --git a/paddle/operators/concat_op.cu b/paddle/operators/concat_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..38fee7473dbb2ba97fe95b6632db7a1749cf3bbe
--- /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 0000000000000000000000000000000000000000..f977054fdf8aa0164db726b94a21c57f770dd674
--- /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
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template
+class ConcatKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const override {
+ auto ins = ctx.MultiInput("X");
+ auto* out = ctx.Output("Out");
+ int64_t axis = static_cast(ctx.Attr("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() + axis_dim * after * j;
+ T* out_data = out->mutable_data(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/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index 9e2bcebe3b5432c157fac895a9bbab5164193dbb..0dc509952578497671a128374f77ce616a520909 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(context.GetPlace());
auto dims = input_x->dims();
- int size = static_cast(framework::product(dims));
+ int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix::From(*input_x, new_dims);
auto y = EigenMatrix::From(*input_y, new_dims);
@@ -72,7 +72,7 @@ class CosSimGradKernel : public framework::OpKernel {
auto* input_grad_z = context.Input(framework::GradVarName("Out"));
auto dims = input_x->dims();
- int size = static_cast(framework::product(dims));
+ int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix::From(*input_x, new_dims);
auto y = EigenMatrix::From(*input_y, new_dims);
diff --git a/paddle/operators/elementwise_mul_op.cc b/paddle/operators/elementwise_mul_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..1742925545d29df5d7df719faaea3b754680ab61
--- /dev/null
+++ b/paddle/operators/elementwise_mul_op.cc
@@ -0,0 +1,109 @@
+/* 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/elementwise_mul_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class ElementWiseMulOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+ auto x_dim = ctx.Input("X")->dims();
+ auto y_dim = ctx.Input("Y")->dims();
+ PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
+ "Rank of first input must >= rank of second input.")
+ ctx.Output("Out")->Resize(x_dim);
+ }
+};
+
+class ElementWiseMulOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ ElementWiseMulOpMaker(framework::OpProto *proto,
+ framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "The first input of elementwise mul op");
+ AddInput("Y", "The second input of elementwise mul op");
+ AddAttr("axis",
+ R"DOC(
+When shape(Y) does not equal shape(X),Y will be broadcasted
+to match the shape of X and axis should be dimension index Y in X
+ )DOC")
+ .SetDefault(-1)
+ .EqualGreaterThan(-1);
+
+ AddOutput("Out", "The output of elementwise mul op");
+ AddComment(R"DOC(
+Limited elementwise multiple operator.The equation is: Out = X ⊙ Y.
+1. The shape of Y should be same with X or
+2. Y's shape is a subset of X.
+ Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
+ example:
+ shape(X) = (2, 3, 4, 5), shape(Y) = (,)
+ shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
+ shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
+ shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+ shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
+)DOC");
+ }
+};
+
+class ElementWiseMulOpGrad : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+ "Input(Out@GRAD) should not be null");
+
+ auto x_dims = ctx.Input("X")->dims();
+ auto y_dims = ctx.Input("Y")->dims();
+ auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims();
+ auto *x_grad = ctx.Output(framework::GradVarName("X"));
+ auto *y_grad = ctx.Output(framework::GradVarName("Y"));
+
+ PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+ "Rank of first input must >= rank of second input.")
+
+ if (x_grad) {
+ x_grad->Resize(x_dims);
+ }
+
+ if (y_grad) {
+ y_grad->Resize(y_dims);
+ }
+ }
+};
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(elementwise_mul, ops::ElementWiseMulOp, ops::ElementWiseMulOpMaker,
+ elementwise_mul_grad, ops::ElementWiseMulOpGrad);
+REGISTER_OP_CPU_KERNEL(
+ elementwise_mul,
+ ops::ElementWiseMulKernel);
+REGISTER_OP_CPU_KERNEL(
+ elementwise_mul_grad,
+ ops::ElementWiseMulGradKernel);
diff --git a/paddle/operators/elementwise_mul_op.cu b/paddle/operators/elementwise_mul_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..56f2087c22c6c599a3c5aef36eb0fe3eac295bef
--- /dev/null
+++ b/paddle/operators/elementwise_mul_op.cu
@@ -0,0 +1,25 @@
+/* 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/elementwise_mul_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+ elementwise_mul,
+ ops::ElementWiseMulKernel);
+REGISTER_OP_GPU_KERNEL(
+ elementwise_mul_grad,
+ ops::ElementWiseMulGradKernel);
diff --git a/paddle/operators/elementwise_mul_op.h b/paddle/operators/elementwise_mul_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..e9ed6791799240039f9af42c1a4339be7126ee65
--- /dev/null
+++ b/paddle/operators/elementwise_mul_op.h
@@ -0,0 +1,185 @@
+/* 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
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+/*
+ * Out = X ⊙ Y
+ * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
+ * pre=2, n=3*4, post=5
+ * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
+ * pre=2*3, n=4*5, post=1
+ */
+
+inline void get_mid_dims(const framework::DDim& x_dims,
+ const framework::DDim& y_dims, const int axis,
+ int& pre, int& n, int& post) {
+ pre = 1;
+ n = 1;
+ post = 1;
+ for (int i = 0; i < axis; ++i) {
+ pre *= x_dims[i];
+ }
+
+ for (int i = 0; i < y_dims.size(); ++i) {
+ PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i],
+ "Broadcast dimension mismatch.");
+ n *= y_dims[i];
+ }
+
+ for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
+ post *= x_dims[i];
+ }
+}
+
+template
+class ElementWiseMulKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const override {
+ using Tensor = framework::Tensor;
+
+ auto* x = ctx.Input("X");
+ auto* y = ctx.Input("Y");
+ auto* z = ctx.Output("Out");
+ z->mutable_data(ctx.GetPlace());
+
+ auto x_e = framework::EigenVector::Flatten(*x);
+ auto y_e = framework::EigenVector::Flatten(*y);
+ auto z_e = framework::EigenVector::Flatten(*z);
+
+ auto x_dims = x->dims();
+ auto y_dims = y->dims();
+ PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+ "Rank of first input must >= rank of second input.")
+
+ if (x_dims == y_dims || product(y_dims) == 1) {
+ z_e.device(ctx.GetEigenDevice()) = x_e * y_e;
+ return;
+ }
+
+ int axis = ctx.Attr("axis");
+ axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+ PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
+ "Axis should be in range [0, x_dims)");
+
+ int pre, n, post;
+ get_mid_dims(x_dims, y_dims, axis, pre, n, post);
+ if (post == 1) {
+ auto y_bcast = y_e.reshape(Eigen::DSizes(1, n))
+ .broadcast(Eigen::DSizes(pre, 1))
+ .reshape(Eigen::DSizes(x_e.size()));
+ z_e.device(ctx.GetEigenDevice()) = x_e * y_bcast;
+ return;
+ } else {
+ auto y_bcast = y_e.reshape(Eigen::DSizes(1, n, 1))
+ .broadcast(Eigen::DSizes(pre, 1, post))
+ .reshape(Eigen::DSizes(x_e.size()));
+ z_e.device(ctx.GetEigenDevice()) = x_e * y_bcast;
+ return;
+ }
+ }
+};
+
+template
+class ElementWiseMulGradKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const override {
+ using Tensor = framework::Tensor;
+
+ auto* x = ctx.Input("X");
+ auto* y = ctx.Input("Y");
+ auto* dout = ctx.Input(framework::GradVarName("Out"));
+
+ auto x_e = framework::EigenVector::Flatten(*x);
+ auto y_e = framework::EigenVector::Flatten(*y);
+ auto dout_e = framework::EigenVector::Flatten(*dout);
+
+ auto x_dims = x->dims();
+ auto y_dims = y->dims();
+
+ auto* dx = ctx.Output(framework::GradVarName("X"));
+ auto* dy = ctx.Output(framework::GradVarName("Y"));
+ if (dx) {
+ dx->mutable_data(ctx.GetPlace());
+ }
+ if (dy) {
+ dy->mutable_data(ctx.GetPlace());
+ }
+
+ if (x_dims == y_dims || product(y_dims) == 1) {
+ if (dx) {
+ auto dx_e = framework::EigenVector::Flatten(*dx);
+ dx_e.device(ctx.GetEigenDevice()) = dout_e * y_e;
+ }
+
+ if (dy) {
+ auto dy_e = framework::EigenVector::Flatten(*dy);
+ dy_e.device(ctx.GetEigenDevice()) = x_e * dout_e;
+ }
+ return;
+ }
+
+ int axis = ctx.Attr("axis");
+ axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+
+ int pre, n, post;
+ get_mid_dims(x_dims, y_dims, axis, pre, n, post);
+
+ // TODO(gongweibao): wrap reshape to a function.
+ if (post == 1) {
+ auto y_e_bcast = y_e.reshape(Eigen::DSizes(1, n))
+ .broadcast(Eigen::DSizes(pre, 1))
+ .reshape(Eigen::DSizes(x_e.size()));
+ if (dx) {
+ auto dx_e = framework::EigenVector::Flatten(*dx);
+ dx_e.device(ctx.GetEigenDevice()) = dout_e * y_e_bcast;
+ }
+
+ if (dy) {
+ auto dy_e = framework::EigenVector::Flatten(*dy);
+ dy_e.device(ctx.GetEigenDevice()) =
+ (x_e * dout_e)
+ .reshape(Eigen::DSizes(pre, n))
+ .sum(Eigen::array{{0}});
+ }
+ return;
+ } else {
+ auto y_e_bcast = y_e.reshape(Eigen::DSizes(1, n, 1))
+ .broadcast(Eigen::DSizes(pre, 1, post))
+ .reshape(Eigen::DSizes(x_e.size()));
+ if (dx) {
+ auto dx_e = framework::EigenVector::Flatten(*dx);
+ dx_e.device(ctx.GetEigenDevice()) = dout_e * y_e_bcast;
+ }
+
+ if (dy) {
+ auto dy_e = framework::EigenVector::Flatten(*dy);
+ dy_e.device(ctx.GetEigenDevice()) =
+ (x_e * dout_e)
+ .reshape(Eigen::DSizes(pre, n, post))
+ .sum(Eigen::array{{0, 2}});
+ }
+ return;
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc
index 6574880c0eb6324b2dd175e39a364d2ef46e735e..3d76516405960c502a46997108049b2db5cab6bf 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 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 d9dbc1dcfe6a6676938d64be93c879ea69148018..2d63b3049988cfc3135a87a57dad56b970df3eab 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(context.Attr("mean"));
T std = static_cast(context.Attr("std"));
thrust::counting_iterator 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(data),
GaussianGenerator(mean, std, seed));
}
diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu
index 27eee3436af8107cef2aa3577ea238be49edf1af..708344046760691aa2da562eb1ee3d8b130c5f18 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();
auto table = table_t->data();
auto output = output_t->mutable_data(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();
const T* d_output = d_output_t->data();
T* d_table = d_table_t->mutable_data(context.GetPlace());
diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h
index 877b36cef4ea9cdaaaf37c97d5e5bfce55b91436..a1298906dd4b4209644fe06584f70169519de01c 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();
auto table = table_t->data();
auto output = output_t->mutable_data(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()) =
t.constant(static_cast(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/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc
index 186a33edcec88bd5e51091a524a778eeb27ad526..4f380388b108dc173d847f027ba5c9db387a87f8 100644
--- a/paddle/operators/math/im2col_test.cc
+++ b/paddle/operators/math/im2col_test.cc
@@ -119,4 +119,4 @@ TEST(math, im2col) {
#ifndef PADDLE_ONLY_CPU
testIm2col();
#endif
-}
\ No newline at end of file
+}
diff --git a/paddle/operators/mean_op.h b/paddle/operators/mean_op.h
index 9848af280b62729bef9243052ceae0b7d8f4c6f5..ce31e178d8e375dc59be80a6c05133201308da70 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(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(framework::GradVarName("X"));
IG->mutable_data(context.GetPlace());
- T ig_size = (T)framework::product(IG->dims());
+ T ig_size = static_cast(IG->numel());
Eigen::DSizes bcast(ig_size);
EigenVector::Flatten(*IG).device(context.GetEigenDevice()) =
diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc
index 1d7276a19438dea8cf6c64f5b022e965467bcd5d..c98f445dc908d02c9f8794824e3d2146e5a8fd31 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("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("Out")->Resize(left_tensor->dims());
}
diff --git a/paddle/operators/name_convention.md b/paddle/operators/name_convention.md
new file mode 100644
index 0000000000000000000000000000000000000000..a090e0b5450509affdd739f63df618595f204f97
--- /dev/null
+++ b/paddle/operators/name_convention.md
@@ -0,0 +1,59 @@
+## Operator's Parameter Name Convention
+
+To make the operator document itself more clear, we recommend operator names obey the listing conventions.
+
+### OpProtoMaker names
+
+When defining an operator in Paddle, a corresponding [OpProtoMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L170) (TODO: OpProtoMaker Doc)need to be defined. All the Input/Output and Attributes will write into the [OpProto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L61) , and will be used in client language to create operator.
+
+- Input/Output.
+ - Input/Output names follow the **CamelCase**. e.g. `X`, `Y`, `Matrix`, `LastAxisInMatrix`. Input/Output much more like Variables, we prefer to meaningful English words.
+ - If an operator's Input/Output are tensors in math, not match to any meaningful words, input name should starts from `X`. e.g. `X`, `Y`, and output name should starts from `Out`. e.g. `Out`. This rule intends making operators which have few inputs/outputs unified.
+
+- Attribute.
+ - Attribute name follows the **camelCase**. e.g. `x`, `y`, `axis`, `rowwiseMatrix`. Also, attribute name prefers to meaningful English words.
+
+- Comments.
+ - Input/Output/Attr comment follow the format of **(type,default value) usage**, corresponding to which type it can be and how it will be used in the operator. e.g. Attribute in Accumulator`"gamma" `,`(float, default 1.0) Accumulation multiplier`.
+ - Operator comment format of` R"DOC(your comment here)DOC"`. You should explain the input/output of the operator first. If there is math calculation in this operator, you should write the equation in the comment. e.g. `Out = X + Y`.
+
+- Order.
+ - Follow the order of Input/Output, then Attribute, then Comments. See the example in best practice.
+
+### Best Practice
+
+Here we give some examples to show how these rules will be used.
+
+- The operator has one input, one output. e.g.`relu`, inputs: `X`, outputs: `Out`.
+
+- The operator has two input, one output. e.g. `rowwise_add`, inputs : `X`, `Y`, outputs : `Out`.
+
+- The operator contains attribute. e.g. `cosine`, inputs : `X`, `axis`, outputs : `Out`.
+
+ We give a full example of Accumulator Operator.
+
+```c++
+class AccumulateOpMaker : public framework::OpProtoAndCheckerMaker {
+public:
+ AccumulateOpMaker(framework::OpProto *proto,
+ framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor. If the output size is not the same as input size, the output tensor is first reshaped and initialized to zero, and only then, accumulation is done.");
+ AddOutput("Out", "(Tensor) Accumulated output tensor");
+ AddAttr("gamma", "(float, default 1.0) Accumulation multiplier");
+ AddComment(R"DOC(
+Accumulate operator accumulates the input tensor to the output tensor. If the
+output tensor already has the right size, we add to it; otherwise, we first
+initialize the output tensor to all zeros, and then do accumulation. Any
+further calls to the operator, given that no one else fiddles with the output
+in the interim, will do simple accumulations.
+Accumulation is done as shown:
+
+Out = 1*X + gamma*Out
+
+where X is the input tensor, Y is the output tensor and gamma is the multiplier
+argument.
+)DOC");
+ }
+};
+```
diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..b7061153d2bf13982f14f233e87a87daeeebf5fd
--- /dev/null
+++ b/paddle/operators/reshape_op.cc
@@ -0,0 +1,107 @@
+
+/* 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 {
+ // input check
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null");
+ auto shape = ctx.Attr>("shape");
+ 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.");
+ }
+ // capacity check
+ int64_t capacity =
+ std::accumulate(shape.begin(), shape.end(), 1, std::multiplies());
+ auto *in = ctx.Input("X");
+ int64_t in_size = framework::product(in->dims());
+ PADDLE_ENFORCE_EQ(capacity, in_size,
+ "The size of Input(X) mismatches with Attr(shape).");
+ // resize output
+ std::vector shape_int64(shape.size(), 0);
+ std::transform(shape.begin(), shape.end(), shape_int64.begin(),
+ [](int a) { return static_cast(a); });
+ auto out_dims = framework::make_ddim(shape_int64);
+ ctx.Output("Out")->Resize(out_dims);
+ }
+};
+
+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>("shape", "Target shape of reshape operator.");
+ 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 transform
+the tensor X into a 1-D tensor:
+
+ [1, 2, 3, 4]
+
+)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 {
+ 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("X")->dims();
+ auto *d_in = ctx.Output(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);
+REGISTER_OP_CPU_KERNEL(
+ reshape_grad, ops::ReshapeGradKernel);
diff --git a/paddle/operators/reshape_op.cu b/paddle/operators/reshape_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..23dbe089d3b37aabedf9ef166f7bbfbf67da7e0a
--- /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);
+REGISTER_OP_GPU_KERNEL(
+ reshape_grad,
+ paddle::operators::ReshapeGradKernel);
diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..873acf30782d390cdca5e7e864c76e1f743f9a7c
--- /dev/null
+++ b/paddle/operators/reshape_op.h
@@ -0,0 +1,55 @@
+/* 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 {
+
+template
+class ReshapeKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const {
+ auto* out = ctx.Output("Out");
+ auto* in = ctx.Input("X");
+ out->mutable_data(ctx.GetPlace());
+
+ auto shape = ctx.Attr>("shape");
+ std::vector shape_int64(shape.size(), 0);
+ std::transform(shape.begin(), shape.end(), shape_int64.begin(),
+ [](int a) { return static_cast(a); });
+ auto out_dims = framework::make_ddim(shape_int64);
+ out->CopyFrom(*in, ctx.GetPlace());
+ out->Resize(out_dims);
+ }
+};
+
+template
+class ReshapeGradKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const {
+ auto* d_out = ctx.Input(framework::GradVarName("Out"));
+ auto* d_x = ctx.Output(framework::GradVarName("X"));
+ d_x->mutable_data(ctx.GetPlace());
+
+ auto in_dims = d_x->dims();
+ d_x->CopyFrom(*d_out, ctx.GetPlace());
+ d_x->Resize(in_dims);
+ }
+};
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc
index dc30644a5e7e33d4289e48cac093aa5fde7e75e7..9f51d3efa8ecba894a1023b9de2df451ca85916c 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("sub_result")
- ->Resize({static_cast(x_dims[0]),
- static_cast(framework::product(x_dims) / x_dims[0])});
+ ->Resize({x_dims[0], x->numel() / x_dims[0]});
ctx.Output("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 ad3347a0b35f3385c5adbcd7ceaa94fe134105e3..097ac04fc09a10b3b624f491a847e281e41a802c 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::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({{1, cols}}))) *
diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc
index f2aeef6c310df8535e67fa3906301a87f8ec4694..b8fbc9b52aecdb5c8d985b5de9bcd7cb85835b60 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 dist(
static_cast(context.Attr("min")),
static_cast(context.Attr("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 c2c041b144b6ca1f019f972e1301b756ec1c9301..6614b53b3f990d10c82633f3c1f079acea0cd827 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(context.Attr("min"));
T max = static_cast(context.Attr("max"));
thrust::counting_iterator 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(data),
UniformGenerator(min, max, seed));
}
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index c85fffb559568eaba8a434b16aa35f0a33b13ce8..e553a0d168e0a58c75a07b34c97c3fea517aca8b 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -17,6 +17,7 @@ limitations under the License. */
#include
#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"
@@ -34,6 +35,7 @@ USE_OP(add);
USE_OP(onehot_cross_entropy);
USE_OP(sgd);
USE_OP(mul);
+USE_OP(elementwise_mul);
USE_OP(mean);
USE_OP(sigmoid);
USE_OP(softmax);
@@ -50,14 +52,18 @@ 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);
+USE_OP(reshape);
namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using LoD = framework::LoD;
static size_t UniqueIntegerGenerator() {
static std::atomic generator;
@@ -117,6 +123,60 @@ PYBIND11_PLUGIN(core) {
return self.data()[offset];
});
+ py::class_(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("__init__",
+ [](LoDTensor &instance,
+ const std::vector> &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, const std::vector> &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("tensor",
+ [](LoDTensor &self) -> Tensor & { return self.tensor(); },
+ py::return_value_policy::reference)
+ .def("lod", [](LoDTensor &self) -> std::vector> {
+#ifdef PADDLE_ONLY_CPU
+ return self.lod();
+#else
+ auto lod = self.lod();
+ std::vector> new_lod;
+ new_lod.reserve(lod.size());
+ std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod),
+ [](paddle::framework::Vector item) ->
+ std::vector {
+ std::vector v;
+ v.reserve(item.size());
+ std::copy(item.begin(), item.end(), std::back_inserter(v));
+ return v;
+ });
+ return new_lod;
+#endif
+ });
+
py::class_(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
@@ -128,6 +188,11 @@ All parameter, weight, gradient are variables in Paddle.
.def("get_tensor",
[](Variable &self) -> Tensor * { return self.GetMutable(); },
py::return_value_policy::reference)
+ .def("get_lod_tensor",
+ [](Variable &self) -> LoDTensor * {
+ return self.GetMutable();
+ },
+ py::return_value_policy::reference)
.def("get_net",
[](Variable &self) -> operators::NetOp * {
return self.GetMutable();
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 356e1d8b6fa9173db33a340744afd8d513a83a96..4f68a8953446ffa0510df65c5b214d09b913cff8 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 64d227565f2b21ff43d4391c682ca90c0f47908e..94ad56cab063df9e6a11bb1c293727fb9dec810f 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 2e3057f323db22ffc3911cce30ec2e8bb95e3dbe..aae90fab32db78a70c2169ed8fafb930433f4136 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)
diff --git a/python/paddle/v2/framework/op.py b/python/paddle/v2/framework/op.py
index 4e91924a50cf6401d4002510e940ddc84edbe61a..9e665adad2d3ad91d183c6815fbd7135ac4e8965 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 20be26cbddc6d590deb7081e33714b6fc81feb4d..4d7664469e481344cf9eea84688f068b4fb99dee 100644
--- a/python/paddle/v2/framework/tests/CMakeLists.txt
+++ b/python/paddle/v2/framework/tests/CMakeLists.txt
@@ -1,40 +1,5 @@
-py_test(test_net SRCS test_net.py)
-
-py_test(test_scope SRCS test_scope.py)
-
-py_test(test_tensor SRCS test_tensor.py)
-py_test(test_mul_op SRCS test_mul_op.py)
-py_test(test_cos_sim_op SRCS test_cos_sim_op.py)
-
-py_test(test_mean_op SRCS test_mean_op.py)
-
-py_test(test_protobuf SRCS test_protobuf.py)
-
-py_test(test_add_two_op SRCS test_add_two_op.py)
-py_test(test_sigmoid_op SRCS test_sigmoid_op.py)
-py_test(test_softmax_op SRCS test_softmax_op.py)
-py_test(test_cross_entropy_op SRCS test_cross_entropy_op.py)
-py_test(test_gather_op SRCS test_gather_op.py)
-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_fc_op SRCS test_fc_op.py)
-py_test(test_minus_op SRCS test_minus_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)
-
-py_test(test_operator SRCS test_operator.py)
-py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
-py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
-py_test(test_recurrent_op SRCS test_recurrent_op.py)
-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)
-py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+foreach(src ${TEST_OPS})
+ py_test(${src} SRCS ${src}.py)
+endforeach()
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
deleted file mode 100644
index 0607275a4e773b5b01fb3920fb002e096f1eae1f..0000000000000000000000000000000000000000
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ /dev/null
@@ -1,307 +0,0 @@
-import unittest
-
-import numpy
-import itertools
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-
-__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):
- kwargs[in_name] = in_name
- for out_name in Operator.get_op_output_names(op_type):
- kwargs[out_name] = out_name
-
- return Operator(op_type, **kwargs)
-
-
-def grad_var_name(var_name):
- return var_name + "@GRAD"
-
-
-def empty_var_name():
- return "@EMPTY@"
-
-
-def get_numeric_gradient(op,
- input_values,
- output_name,
- input_to_check,
- delta=0.005,
- local_scope=None,
- in_place=False):
- """
- Get Numeric Gradient for an operator's input.
-
- :param op: C++ operator instance, could be an network
- :param input_values: The input variables. Should be an dictionary, key is
- variable name. Value is numpy array.
- :param output_name: The final output variable name.
- :param input_to_check: The input variable need to get gradient.
- :param delta: The perturbation value for numeric gradient method. The
- smaller delta is, the more accurate result will get. But if that delta is
- too small, it could occur numerical stability problem.
- :param local_scope: The local scope used for get_numeric_gradient.
- :return: The gradient array in numpy format.
- """
- if local_scope is None:
- local_scope = core.Scope()
-
- # Create all input variable in local_scope
- for var_name in input_values:
- var = local_scope.new_var(var_name)
- tensor = var.get_tensor()
- tensor.set_dims(input_values[var_name].shape)
- tensor.alloc_float(core.CPUPlace())
- tensor.set(input_values[var_name], core.CPUPlace())
-
- # Create all output variable in local_scope
- opts = op.outputs()
- for key in opts:
- for output in opts[key]:
- if local_scope.find_var(output) is None:
- local_scope.new_var(output).get_tensor()
- op.infer_shape(local_scope)
-
- # allocate output memory
- for key in opts:
- for output in opts[key]:
- local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace(
- ))
-
- cpu_ctx = core.DeviceContext.create(core.CPUPlace())
-
- def get_output():
- op.run(local_scope, cpu_ctx)
- return numpy.array(local_scope.find_var(output_name).get_tensor()).sum()
-
- def product(dim):
- return reduce(lambda a, b: a * b, dim, 1)
-
- def restore_inputs():
- for var_name in input_values:
- tensor_ = local_scope.find_var(var_name).get_tensor()
- tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace())
-
- # get the input tensor that we want to get it's numeric gradient.
- tensor_to_check = local_scope.find_var(input_to_check).get_tensor()
- tensor_size = product(tensor_to_check.get_dims())
- # prepare a numpy array to store the gradient.
- gradient_flat = numpy.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:
- restore_inputs()
- # 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()
-
- # plus delta to this element, run op and get the sum of the result tensor.
- if in_place:
- restore_inputs()
- x_neg = origin - delta
- tensor_to_check.set_float_element(i, x_neg)
- y_neg = get_output()
-
- # restore old value
- tensor_to_check.set_float_element(i, origin)
-
- # compute the gradient of this element and store it into a numpy array.
- gradient_flat[i] = (y_pos - y_neg) / delta / 2
-
- # reshape the gradient result to the shape of the source tensor.
- return gradient_flat.reshape(tensor_to_check.get_dims())
-
-
-class GradientChecker(unittest.TestCase):
- def __get_gradient(self, forward_op, backward_op, input_value, grad_names,
- place):
- """Get the input gradients after running forward and backward operators
- on the given places.
-
- :param forward_op: forward operator
- :type forward_op: Operator
- :param backward_op: backward operator
- :type backward_op: Operator
- :param input_value: input values.
- :type input_value: dict{string:numpy.array}
- :param grad_names: the names of returned input gradients.
- :type input_value: a list of string
- :param place: the device type.
- :type place: CPUPlace or GPUPlace
- :return: the input grdients of given grad_names.
- :rtype: a list of numpy.array
- """
- scope = core.Scope()
- ctx = core.DeviceContext.create(place)
-
- inputs = forward_op.inputs()
- in_names = [item for k in inputs for item in inputs[k]]
- outputs = forward_op.outputs()
- out_names = [item for k in outputs for item in outputs[k]]
-
- # create input var and set value
- for name, value in input_value.iteritems():
- if name not in in_names:
- raise ValueError(name + "does not exist in Op's inputs.")
- var = scope.new_var(name).get_tensor()
- var.set_dims(value.shape)
- var.set(value, place)
-
- # run forward op
- for out_name in out_names:
- scope.new_var(out_name)
- forward_op.infer_shape(scope)
- forward_op.run(scope, ctx)
-
- # set output var's shape
- # set output grad to ones
- for name in out_names:
- out_tensor = scope.find_var(name).get_tensor()
- grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
- grad_tensor.set_dims(out_tensor.shape())
- data = numpy.ones(out_tensor.shape(), dtype=numpy.float32)
- grad_tensor.set(data, place)
-
- # run backward op
- backward_outs = backward_op.outputs()
- backward_names = [
- item for key in backward_outs for item in backward_outs[key]
- ]
- for name in backward_names:
- scope.new_var(name)
-
- backward_op.infer_shape(scope)
- backward_op.run(scope, ctx)
-
- outs = [
- numpy.array(scope.find_var(name).get_tensor())
- for name in grad_names
- ]
- return outs
-
- def compare_grad(self, forward_op, input_value, no_grad_set=None):
- """ Compare the input gradients between CPU and GPU for the given forward
- operator.
-
- :param forward_op: forward operator
- :type forward_op: Operator
- :param input_value: input values.
- :type input_value: dict{string:numpy.array}
- :param no_grad_set: the set of variables names without gradients.
- :type no_grad_set: a set of string
- :raises: AssertionError, there is different gradient value.
- """
- if no_grad_set is None:
- no_grad_set = set()
- backward_op = core.Operator.backward(forward_op, no_grad_set)
- # return if not compile with GPU or not implementing GPU kernel
- if not (core.is_compile_gpu() and backward_op.support_gpu()):
- return
-
- outputs = backward_op.outputs()
- out_names = [item for k in outputs for item in outputs[k]]
- out_names = filter(lambda x: x != empty_var_name(), out_names)
- cpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
- out_names, core.CPUPlace())
- gpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
- out_names, core.GPUPlace(0))
-
- for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads,
- out_names):
- self.assertTrue(
- numpy.allclose(
- c_grad, g_grad, atol=1e-4),
- "output name: " + name + " has diff")
-
- def __assert_is_close(self, numeric_grads, analytic_grads, names,
- max_relative_error, msg_prefix):
- """Use relative error for the comparison.
-
- :param numeric_grads: the numerical graidents.
- :type numeric_grads: a list of numpy.array
- :param analytic_grads: the analytical graidents.
- :type analytic_grads: a list of numpy.array
- :param name: the names of gradients, used to print for debug.
- :type names: a list of string
- :param msg_prefix: string info, used to print for debug.
- :type msf_prefix: string
- """
- for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
- abs_a = numpy.abs(a)
- # if abs_a is nearly zero, then use abs error for a, not relative
- # error.
- abs_a[abs_a < 1e-3] = 1
-
- diff_mat = numpy.abs(a - b) / abs_a
- max_diff = numpy.max(diff_mat)
-
- def err_msg():
- offset = numpy.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,
- forward_op,
- input_vars,
- inputs_to_check,
- output_name,
- no_grad_set=None,
- only_cpu=False,
- in_place=False,
- max_relative_error=0.005):
- """
- :param forward_op: used to create backward_op
- :param input_vars: numpy value of input variable. The following
- computation will use these variables.
- :param inputs_to_check: inputs var names that should check gradient.
- :param output_name: the output variable name of forward network.
- :param max_relative_error: The relative tolerance parameter.
- :param no_grad_set: used when create backward ops
- :param only_cpu: only compute and check gradient on cpu kernel.
- :return:
- """
- if no_grad_set is None:
- no_grad_set = set()
-
- inputs = forward_op.inputs()
- in_names = [item for k in inputs for item in inputs[k]]
- for no_grad in no_grad_set:
- if no_grad not in in_names:
- raise ValueError("no_grad should be in in_names")
- if no_grad in inputs_to_check:
- raise ValueError("no_grad should not be in inputs_to_check")
-
- backward_op = core.Operator.backward(forward_op, no_grad_set)
-
- places = [core.CPUPlace()]
- if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu():
- places.append(core.GPUPlace(0))
-
- # get numerical gradients
- numeric_grads = [
- get_numeric_gradient(
- forward_op, input_vars, output_name, name, in_place=in_place)
- for name in inputs_to_check
- ]
-
- check_names = [grad_var_name(name) for name in inputs_to_check]
- for place in places:
- analytic_grads = self.__get_gradient(forward_op, backward_op,
- input_vars, check_names, place)
- self.__assert_is_close(numeric_grads, analytic_grads, check_names,
- max_relative_error,
- "Gradient Check On %s" % str(place))
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
index b524f88551f7547a4787d7e1b589a0b9143582dc..c6e4c59881e4166de4afc7516bd0bee62040b87f 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, _ in sub_in:
var = scope.new_var(sub_in_name)
kwargs[in_name].append(sub_in_name)
else:
@@ -28,16 +28,18 @@ def create_op(scope, op_type, inputs, outputs, attrs=None):
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)
- kwargs[out_name].append(sun_in_name)
+ sub_out = outputs[out_name]
+ for sub_out_name, _ in sub_out:
+ var = scope.new_var(sub_out_name)
+ kwargs[out_name].append(sub_out_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]
+ for attr_name in Operator.get_op_attr_names(op_type):
+ if attr_name in attrs:
+ kwargs[attr_name] = attrs[attr_name]
+
return Operator(op_type, **kwargs)
@@ -46,12 +48,11 @@ def set_input(scope, op, inputs, place):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
- for sub_in_name in sub_in:
+ for sub_in_name, sub_in_array 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)
+ 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 +66,7 @@ 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, _ 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 +111,7 @@ 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 +121,7 @@ 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 +169,11 @@ 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)
@@ -179,10 +184,10 @@ class OpTest(unittest.TestCase):
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:
+ for sub_out_name, sub_out_array in sub_out:
actual = np.array(
self.scope.find_var(sub_out_name).get_tensor())
- expect = sub_out[sub_out_name]
+ expect = sub_out_array
self.assertTrue(
np.allclose(
actual, expect, atol=1e-05),
@@ -227,7 +232,11 @@ 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/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py
deleted file mode 100644
index 370f27eaf658dadbf7e82262c118140a10d15c41..0000000000000000000000000000000000000000
--- a/python/paddle/v2/framework/tests/op_test_util.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-
-
-class OpTestMeta(type):
- """
- Operator Test ClassMeta.
-
- It injects `test_all` method into user's OperatorTest class, to make Python
- unittest module run that method.
-
- The `test_all` read what value is stored in `self`. It use self's values to
- create and run a operator, and check whether that op is OK or not.
-
- See `test_add_two_op` for example usage.
- """
-
- def __new__(cls, name, bases, attrs):
- obj = super(OpTestMeta, cls).__new__(cls, name, bases, attrs)
-
- def test_all(self):
- scope = core.Scope()
- kwargs = dict()
- places = [core.CPUPlace()]
- if core.is_compile_gpu():
- 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)
- else:
- kwargs[in_name] = "@EMPTY@"
-
- for out_name in Operator.get_op_output_names(self.type):
- if not hasattr(self, "outputs"):
- raise ValueError(
- "The test op must set self.outputs dict.")
- if out_name not in self.outputs:
- raise ValueError("The %s is not in self.outputs dict." %
- (out_name))
- kwargs[out_name] = out_name
- scope.new_var(out_name).get_tensor()
-
- for attr_name in Operator.get_op_attr_names(self.type):
- if hasattr(self, "attrs") and attr_name in self.attrs:
- kwargs[attr_name] = self.attrs[attr_name]
-
- op = Operator(self.type, **kwargs)
- if isinstance(place, core.GPUPlace) and not op.support_gpu():
- return
-
- op.infer_shape(scope)
-
- ctx = core.DeviceContext.create(place)
- op.run(scope, ctx)
-
- for out_name 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(
- numpy.allclose(
- actual, expect, atol=1e-05),
- "output name: " + out_name + " has diff")
-
- obj.test_all = test_all
- return obj
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 a578e74eca9a3c4327a4881f853028e2347c98ad..3ca34d9b9fc2b7b54cc25ca0e0d1a08a71e37c52 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_concat_op.py b/python/paddle/v2/framework/tests/test_concat_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..656563f96e52df30951ec0ec7042ad9c530e90b2
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_concat_op.py
@@ -0,0 +1,22 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestConcatOp(OpTest):
+ def setUp(self):
+ self.op_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', x0), ('x1', x1), ('x2', x2)]}
+ self.attrs = {'axis': axis}
+ self.outputs = {'Out': np.concatenate((x0, x1, x2), axis=axis)}
+
+ def test_check_output(self):
+ self.check_output()
+
+
+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 32013a7999a4be42e5974b9ac751d5d911730994..797cbd8cc5cf7f73d58ca713d02667731d5c8a0e 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 fb6a440e23c26d1766bdf1fc5f24217afe1150f8..c2fc102a8b8de82da5c3fc5fee273790325908f8 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_elementwise_mul_op.py b/python/paddle/v2/framework/tests/test_elementwise_mul_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..e268cfddb26721a35ddd2d2cc18f526ff7b2f6d9
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_elementwise_mul_op.py
@@ -0,0 +1,157 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseMulOp_Matrix(OpTest):
+ def setUp(self):
+ self.op_type = "elementwise_mul"
+ """ Warning
+ CPU gradient check error!
+ 'X': np.random.random((32,84)).astype("float32"),
+ 'Y': np.random.random((32,84)).astype("float32")
+ """
+ self.inputs = {
+ 'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
+ 'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+ }
+ self.outputs = {'Out': np.multiply(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=0.1)
+
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_Vector(OpTest):
+ def setUp(self):
+ self.op_type = "elementwise_mul"
+ self.inputs = {
+ 'X': np.random.random((32, )).astype("float32"),
+ 'Y': np.random.random((32, )).astype("float32")
+ }
+ self.outputs = {'Out': np.multiply(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=0.1)
+
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_0(OpTest):
+ def setUp(self):
+ self.op_type = "elementwise_mul"
+ self.inputs = {
+ 'X': np.random.rand(2, 3, 4).astype(np.float32),
+ 'Y': np.random.rand(2).astype(np.float32)
+ }
+
+ self.attrs = {'axis': 0}
+ self.outputs = {
+ 'Out': self.inputs['X'] * self.inputs['Y'].reshape(2, 1, 1)
+ }
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_1(OpTest):
+ def setUp(self):
+ self.op_type = "elementwise_mul"
+ self.inputs = {
+ 'X': np.random.rand(2, 3, 4).astype(np.float32),
+ 'Y': np.random.rand(3).astype(np.float32)
+ }
+
+ self.attrs = {'axis': 1}
+ self.outputs = {
+ 'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 1)
+ }
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_2(OpTest):
+ def setUp(self):
+ self.op_type = "elementwise_mul"
+ self.inputs = {
+ 'X': np.random.rand(2, 3, 4).astype(np.float32),
+ 'Y': np.random.rand(4).astype(np.float32)
+ }
+
+ self.outputs = {
+ 'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 1, 4)
+ }
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1)
+
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X"))
+
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y'))
+
+
+class TestElementwiseMulOp_broadcast_3(OpTest):
+ def setUp(self):
+ self.op_type = "elementwise_mul"
+ self.inputs = {
+ 'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
+ 'Y': np.random.rand(3, 4).astype(np.float32)
+ }
+
+ self.attrs = {'axis': 1}
+ self.outputs = {
+ 'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 3, 4, 1)
+ }
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_fc_op.py b/python/paddle/v2/framework/tests/test_fc_op.py
index 4355191223a228b85a5b537786c94b8bae135a82..00c487099710a702541fcdface9f71308724abc7 100644
--- a/python/paddle/v2/framework/tests/test_fc_op.py
+++ b/python/paddle/v2/framework/tests/test_fc_op.py
@@ -6,18 +6,16 @@ from op_test import OpTest
class TestFCOp1(OpTest):
def setUp(self):
self.op_type = "fc"
- x1 = np.random.random((16, 32)).astype("float32")
- w1 = np.random.random((32, 10)).astype("float32")
+ x0 = np.random.random((16, 32)).astype("float32")
+ w0 = np.random.random((32, 10)).astype("float32")
b = np.random.random(10).astype("float32")
- self.inputs = {"X": {"X1": x1}, "W": {"W1": w1}, "b": b}
- mul_out1 = np.dot(x1, w1)
- sum_out = mul_out1
+ self.inputs = {"X": [("X0", x0)], "W": [("W0", w0)], "b": b}
+ mul_out0 = np.dot(x0, w0)
+ sum_out = mul_out0
add_out = sum_out + b
identity_out = add_out
self.outputs = {
- "mul_out": {
- "mul_out1": mul_out1,
- },
+ "mul_out": [("mul_out0", mul_out0)],
"sum_out": sum_out,
"add_out": add_out,
"Y": identity_out
@@ -27,40 +25,30 @@ class TestFCOp1(OpTest):
self.check_output()
def test_check_grad(self):
- self.check_grad(["X1", "W1", "b"], "Y", max_relative_error=0.05)
+ self.check_grad(["X0", "W0", "b"], "Y", max_relative_error=0.01)
class TestFCOp2(OpTest):
def setUp(self):
self.op_type = "fc"
+ x0 = np.random.random((16, 32)).astype("float32")
x1 = np.random.random((16, 32)).astype("float32")
- x2 = np.random.random((16, 32)).astype("float32")
+ w0 = np.random.random((32, 10)).astype("float32")
w1 = np.random.random((32, 10)).astype("float32")
- w2 = np.random.random((32, 10)).astype("float32")
b = np.random.random(10).astype("float32")
self.inputs = {
- "X": {
- "X1": x1,
- "X2": x2
- },
- "W": {
- "W1": w1,
- "W2": w2
- },
+ "X": [("X0", x0), ("X1", x1)],
+ "W": [("W0", w0), ("W1", w1)],
"b": b
}
- #self.attrs = {"activation": "sigmoid"}
+ self.attrs = {"activation": "sigmoid"}
+ mul_out0 = np.dot(x0, w0)
mul_out1 = np.dot(x1, w1)
- mul_out2 = np.dot(x2, w2)
- sum_out = mul_out1 + mul_out2
+ sum_out = mul_out0 + mul_out1
add_out = np.add(sum_out, b)
- #sigmoid_out = 1 / (1 + np.exp(-add_out))
- sigmoid_out = add_out
+ sigmoid_out = 1 / (1 + np.exp(-add_out))
self.outputs = {
- "mul_out": {
- "mul_out0": mul_out1,
- "mul_out1": mul_out2
- },
+ "mul_out": [("mul_out0", mul_out0), ("mul_out1", mul_out1)],
"sum_out": sum_out,
"add_out": add_out,
"Y": sigmoid_out
@@ -71,7 +59,7 @@ class TestFCOp2(OpTest):
def test_check_grad(self):
self.check_grad(
- ["X1", "X2", "W1", "W2", "b"], "Y", max_relative_error=0.05)
+ ["X0", "X1", "W0", "W1", "b"], "Y", max_relative_error=0.01)
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 e5c862605fb11a5ea1426cf8f9054589dc377ff1..2473daaba24438819f3f55ccc40fe1c64ee59960 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 e3de3fd0a1dddb3edb0de5987bd71d8a176d97ef..b0ab429ef1b53640dfb696f6ea2f7b745564b874 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 f95ed70b58d611b3233a21d3f2a34c864ae4d1b3..1f9e4db783c9907a22db72c8a6ff06c7ca0735da 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 e8a7f848dffa0529c8cb0d6599286ce0e228d180..abeb01cb34158a43b5dcce5e39efc0e21e9fe638 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 4b7ce92c0f0492a73c158378299933a0b329948b..b259bb67e832adcb31b0ab4e992738be2b85f884 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 f32b3160d651a290823223c46c45bb3b6950a505..7823abd8f813aad6462c98a9ace9a13dc286a157 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 aa05d87baa195490ecec478d56eaac6fc1ae231e..dea797a1fea34265d0a32e097f413f421abf2521 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 TestMinusOp(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 TestMinusGrad(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/mnist.py b/python/paddle/v2/framework/tests/test_mnist.py
similarity index 100%
rename from python/paddle/v2/framework/tests/mnist.py
rename to python/paddle/v2/framework/tests/test_mnist.py
diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py
index 8c827e242e866b267e0fc4b73c31bafa0ccc7c48..b3d95a56b88e510734da54f36ff21ccd7e1baabb 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 e4b7cd480cb36249bb64ba3cab9a4b220d812346..50cfb855f2b01d8fd32342855d46716da7e07856 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_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..16bb6bb2af67f7d32a2fafc1cb37412084ec0829
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_reshape_op.py
@@ -0,0 +1,21 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestReshapeOp(OpTest):
+ def setUp(self):
+ 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()
+
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
+
+
+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 8378c1cd21c21fd31da9b82d2cdaaff332f291d7..336645bd993ff743cbe20bb5cae5cd278db57ce7 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 4c1d48499126f042bb9dc7cb853eebeea87a57ab..42b4d20ac9cfb88b9532e5cab4f9b06f3a9832df 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 = {'Y': 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"), "Y")
-
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Y')
-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 c1f9444889372104e39ded78fc7207a59b80a293..33c73c52631a09ea0fefdeb9467991ae9c04321c 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 e5f9ef865e84f1a78e28884ad7e2e758f9ca8054..557cf15ace63e336462c7dcdbbc10f30aeedc6f4 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 0d590fa7065bdd2df0e3f2aea5464f0524d70670..1b948f252fa631e9886840b377de2996e110dc91 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.op_type = "softmax"
+ 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 2bcdf37df434c9a089d75438d876114156261a5c..dc6ebf5d30369231b4918a168bbdf25c7096c808 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 66417d70e81186465e6f59a17fb62255afeddea5..60254291e2ab9215e2bc37c12d5e2e1ca6d33d5d 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}
@@ -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_tensor.py b/python/paddle/v2/framework/tests/test_tensor.py
index 1af39818a305215b45219b8c5f0a10630fd64279..f26ed4964c521be1cd839b39d7244f96c653cb1a 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 +43,84 @@ 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):
+ 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([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):
+ 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")
+ place = core.CPUPlace()
+ tensor = var.get_tensor()
+ tensor.set_dims([5, 2, 3, 4])
+ tensor.alloc_float(place)
+ tensor_array = numpy.array(tensor)
+ 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 = 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.assertListEqual(lod_py, lod_tensor.lod())
+
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 e841d96d26bba13b2780c41ea7a209fd470cad3b..cab799256d791889c295aa7f9048080f5caaf2dc 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 c3d2bb44da3977c0899b2609a8efe15b7e1789f2..76a5e36e56ab08230bdc2597d209fcf5d1d2acb0 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()
diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py
index 8acea6155c588f2e8e5ad009cd8f0a0c09afb92b..e80456d9bbeb3c34ac9eab873a84dbf8f06e34df 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']
@@ -25,11 +26,23 @@ class Inference(object):
:type parameters: paddle.v2.parameters.Parameters
"""
- def __init__(self, output_layer, parameters):
+ def __init__(self, parameters, output_layer=None, fileobj=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 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()
@@ -43,7 +56,6 @@ 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
diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py
index a20e878d0817d0a75e9c47a44f8765deca99225c..2db66be2505dde38a501edf45984e1f36beb351d 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):