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Posted to commits@singa.apache.org by wa...@apache.org on 2015/12/16 13:11:39 UTC
[07/19] incubator-singa git commit: SINGA-100 Implement layers using
CUDNN for GPU training
SINGA-100 Implement layers using CUDNN for GPU training
fix compilation errors.
pass all gtest files.
tmp commit;
1. CudnnSoftmaxloss class
ComputeFeature(): computer loss
ComputeGradient(): compute gradient
2. Add cuda math kernel functions
1) singa_gpu_softmax_loss(): computer loss kernel function
2) singa_gpu_softmax_gradient(): compute gradient kernel function
tmp commit; debug accuracy
Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/f31ba645
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/f31ba645
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/f31ba645
Branch: refs/heads/master
Commit: f31ba64589fde0d2b095804366c613988f89fc27
Parents: 8cacd83
Author: seaokcs <se...@gmail.com>
Authored: Tue Dec 1 17:54:38 2015 +0800
Committer: Wei Wang <wa...@comp.nus.edu.sg>
Committed: Fri Dec 11 11:48:23 2015 +0800
----------------------------------------------------------------------
examples/cifar10/job.conf | 7 +-
include/singa/neuralnet/loss_layer.h | 3 +-
include/singa/neuralnet/neuron_layer.h | 15 +-
include/singa/utils/math_blob.h | 37 ++--
include/singa/utils/math_kernel.h | 6 +
src/driver.cc | 2 +-
src/neuralnet/layer.cc | 4 +-
src/neuralnet/loss_layer/cudnn_softmaxloss.cu | 32 ++-
src/neuralnet/neuron_layer/cudnn_activation.cc | 100 +++++++++
src/neuralnet/neuron_layer/cudnn_activation.cu | 100 ---------
src/neuralnet/neuron_layer/cudnn_convolution.cc | 207 +++++++++++++++++++
src/neuralnet/neuron_layer/cudnn_convolution.cu | 205 ------------------
src/neuralnet/neuron_layer/cudnn_lrn.cc | 87 ++++++++
src/neuralnet/neuron_layer/cudnn_lrn.cu | 87 --------
src/neuralnet/neuron_layer/cudnn_pooling.cc | 96 +++++++++
src/neuralnet/neuron_layer/cudnn_pooling.cu | 96 ---------
src/neuralnet/neuron_layer/cudnn_softmax.cc | 75 +++++++
src/neuralnet/neuron_layer/cudnn_softmax.cu | 75 -------
src/neuralnet/neuron_layer/inner_product.cc | 6 +-
src/stub.cc | 6 +-
src/test/test_csv_input_layer.cc | 2 +-
src/test/test_math.cc | 27 ++-
src/test/test_record_input_layer.cc | 2 +-
src/utils/math_kernel.cu | 36 ++++
src/worker.cc | 4 +-
25 files changed, 702 insertions(+), 615 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/examples/cifar10/job.conf
----------------------------------------------------------------------
diff --git a/examples/cifar10/job.conf b/examples/cifar10/job.conf
index a7540a2..7e42ed8 100644
--- a/examples/cifar10/job.conf
+++ b/examples/cifar10/job.conf
@@ -1,10 +1,11 @@
name: "cifar10-convnet"
-train_steps: 1000
+train_steps: 30
test_steps: 100
-test_freq: 300
+test_freq: 0
#validate_steps: 100
#validate_freq: 300
-disp_freq: 30
+disp_freq: 10
+debug: true
#checkpoint_path: "examples/cifar10/checkpoint/step1000-worker0"
train_one_batch {
alg: kBP
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/include/singa/neuralnet/loss_layer.h
----------------------------------------------------------------------
diff --git a/include/singa/neuralnet/loss_layer.h b/include/singa/neuralnet/loss_layer.h
index 50e6c24..f78d7e0 100644
--- a/include/singa/neuralnet/loss_layer.h
+++ b/include/singa/neuralnet/loss_layer.h
@@ -61,7 +61,8 @@ class CudnnSoftmaxLossLayer : public LossLayer, public CudnnSoftmaxLayer {
const std::string ToString(bool debug, int flag) override;
private:
- int topk_;
+ float scale_;
+ int topk_, dim_;
int counter_;
float loss_, accuracy_;
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/include/singa/neuralnet/neuron_layer.h
----------------------------------------------------------------------
diff --git a/include/singa/neuralnet/neuron_layer.h b/include/singa/neuralnet/neuron_layer.h
index 830f731..9ae2738 100644
--- a/include/singa/neuralnet/neuron_layer.h
+++ b/include/singa/neuralnet/neuron_layer.h
@@ -259,19 +259,24 @@ class STanhLayer : public NeuronLayer {
class CudnnLayer : virtual public NeuronLayer {
public:
~CudnnLayer() {
- CHECK_CUDNN(cudnnDestroyTensorDescriptor(src_desc_));
- CHECK_CUDNN(cudnnDestroyTensorDescriptor(my_desc_));
- CHECK_CUDNN(cudnnDestroy(handle_));
+ if (handle_ != nullptr)
+ CHECK_CUDNN(cudnnDestroy(handle_));
+ if (src_desc_ != nullptr)
+ CHECK_CUDNN(cudnnDestroyTensorDescriptor(src_desc_));
+ if (my_desc_ != nullptr)
+ CHECK_CUDNN(cudnnDestroyTensorDescriptor(my_desc_));
}
void virtual InitCudnn() {
CHECK(!has_init_cudnn_);
CHECK_CUDNN(cudnnCreate(&handle_));
+ CHECK_CUDNN(cudnnCreateTensorDescriptor(&src_desc_));
+ CHECK_CUDNN(cudnnCreateTensorDescriptor(&my_desc_));
has_init_cudnn_ = true;
}
protected:
bool has_init_cudnn_ = false;
- cudnnHandle_t handle_;
- cudnnTensorDescriptor_t src_desc_, my_desc_;
+ cudnnHandle_t handle_ = nullptr;
+ cudnnTensorDescriptor_t src_desc_ = nullptr, my_desc_ = nullptr;
};
/**
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/include/singa/utils/math_blob.h
----------------------------------------------------------------------
diff --git a/include/singa/utils/math_blob.h b/include/singa/utils/math_blob.h
index 586554e..629839a 100644
--- a/include/singa/utils/math_blob.h
+++ b/include/singa/utils/math_blob.h
@@ -76,9 +76,10 @@ void AXPY(Dtype alpha, const Blob<Dtype> & A, Blob<Dtype> * B) {
/************* BLAS level 2 *****************/
/**
* Matrix vector multiplication, C = alpha A(.T) * B + beta C.
- * Strict shape checking:
- * - dim of A ==2
- * columsn of A(.T) == B.count()
+ * Loose shape checking:
+ * - dim of A >=2
+ * - row of A is shape(0) (no transpose)
+ * - column of A(.T) == B.count()
* - rows of A(.T) == C.count()
*
* @param[in] alpha
@@ -90,10 +91,10 @@ void AXPY(Dtype alpha, const Blob<Dtype> & A, Blob<Dtype> * B) {
template<typename Dtype>
void GEMV(Dtype alpha, Dtype beta, const Blob<Dtype>& A,
const Blob<Dtype>& B, Blob<Dtype>* C) {
- CHECK_EQ(A.shape().size(), 2) << "A must be a matrix";
+ CHECK_EQ(A.shape().size(), 2);
int a1, a2, m, n;
- a1 = A.transpose() ? A.shape(1) : A.shape(0);
- a2 = A.transpose() ? A.shape(0) : A.shape(1);
+ a1 = A.transpose() ? A.count() / A.shape(0) : A.shape(0);
+ a2 = A.transpose() ? A.shape(0) : A.count() / A.shape(0);
m = B.count();
n = C->count();
CHECK_EQ(a2, m) << "# columns of A(.T) must = length of B";
@@ -134,8 +135,8 @@ void MVDot(const Blob<Dtype>& A, const Blob<Dtype>& B,
* Matrix multiplication, C = alpha A*B + beta C, A, B and C are matrix.
*
* Tranpose is considered for A and B.
- * Strict shape checking:
- * - all are matrix
+ * Loose shape checking:
+ * - the first dimension is row (no transpose) or col (with transpose) size
* - shapes match for matrix multiplication
*
* @param[in] alpha
@@ -147,17 +148,17 @@ void MVDot(const Blob<Dtype>& A, const Blob<Dtype>& B,
template <typename Dtype>
void GEMM( Dtype alpha, Dtype beta, const Blob<Dtype>& A,
const Blob<Dtype> & B, Blob<Dtype> * C) {
- CHECK_EQ(A.shape().size(), 2);
- CHECK_EQ(B.shape().size(), 2);
- CHECK_EQ(C->shape().size(), 2);
+ CHECK_GE(A.shape().size(), 2);
+ CHECK_GE(B.shape().size(), 2);
+ CHECK_GE(C->shape().size(), 2);
int a1, a2, b1, b2, m, n;
CHECK(!C->transpose());
- a1 = A.transpose() ? A.shape(1) : A.shape(0);
- a2 = A.transpose() ? A.shape(0) : A.shape(1);
- b1 = B.transpose() ? B.shape(1) : B.shape(0);
- b2 = B.transpose() ? B.shape(0) : B.shape(1);
+ a1 = A.transpose() ? A.count() / A.shape(0) : A.shape(0);
+ a2 = A.transpose() ? A.shape(0) : A.count() / A.shape(0);
+ b1 = B.transpose() ? B.count() /B.shape(0) : B.shape(0);
+ b2 = B.transpose() ? B.shape(0) : B.count() / B.shape(0);
m = C->shape(0);
- n = C->shape(1);
+ n = C->count() / C->shape(0);
CHECK_EQ(a2, b1);
CHECK_EQ(a1, m);
CHECK_EQ(b2, n);
@@ -560,7 +561,7 @@ void MVSumCol(Dtype alpha, Dtype beta, const Blob<Dtype> & A, Blob<Dtype> * B) {
A.transpose(), false, B->mutable_cpu_data());
} else {
#ifdef USE_GPU
- singa_gpu_sum_col(A.gpu_data(), B->gpu_data(), m, n, n);
+ singa_gpu_sum_by_col(A.gpu_data(), B->gpu_data(), m, n, n);
// gpu part (TODO check transpose case)
#endif // USE_GPU
}
@@ -585,7 +586,7 @@ void MVSumRow(Dtype alpha, Dtype beta, const Blob<Dtype> & A, Blob<Dtype> * B) {
false, B->mutable_cpu_data());
} else {
#ifdef USE_GPU
- singa_gpu_sum_vec(A.gpu_data(), B->gpu_data(), m, n, n);
+ singa_gpu_sum_by_row(A.gpu_data(), B->gpu_data(), m, n, n);
// gpu part (TODO check transpose case)
#endif // USE_GPU
}
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/include/singa/utils/math_kernel.h
----------------------------------------------------------------------
diff --git a/include/singa/utils/math_kernel.h b/include/singa/utils/math_kernel.h
index 37a9356..8cfa562 100644
--- a/include/singa/utils/math_kernel.h
+++ b/include/singa/utils/math_kernel.h
@@ -24,6 +24,12 @@
namespace singa {
extern "C" {
+ void singa_gpu_softmax_loss(const float *prob, const int *label,
+ float *loss, int n, int dim);
+
+ void singa_gpu_softmax_gradient(float *grad, const int *label ,
+ int n, int dim, float scale);
+
void singa_gpu_sum_vec(float *data, float *sum , int n);
void singa_gpu_sum_by_col(const float *src_mat_data, float *dst_vec_data,
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/driver.cc
----------------------------------------------------------------------
diff --git a/src/driver.cc b/src/driver.cc
index 1ae6d9f..b963912 100644
--- a/src/driver.cc
+++ b/src/driver.cc
@@ -216,7 +216,7 @@ void Driver::Train(const JobProto& job_conf) {
threads.push_back(std::thread(&Server::Run, server));
int gpu = 0;
auto context = Singleton<Context>::Instance();
- CHECK_LE(workers.size(), job_conf.gpu_size());
+ // CHECK_LE(workers.size(), job_conf.gpu_size());
for (auto worker : workers) {
threads.push_back(std::thread(&Worker::Run, worker));
if (gpu < job_conf.gpu_size()) {
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/layer.cc
----------------------------------------------------------------------
diff --git a/src/neuralnet/layer.cc b/src/neuralnet/layer.cc
index 9414948..df77239 100644
--- a/src/neuralnet/layer.cc
+++ b/src/neuralnet/layer.cc
@@ -49,8 +49,8 @@ const std::string Layer::ToString(bool debug, int flag) {
string ret = StringPrintf("Layer %10s ", name().c_str());
if ((flag & kForward) == kForward && data_.count() !=0) {
ret += StringPrintf("data norm1 %13.9f", Asum(data_));
- } else if ((flag & kBackward) == kBackward) {
- if (grad_.count() != 0)
+ }
+ if ((flag & kBackward) == kBackward && grad_.count() != 0) {
ret += StringPrintf("grad norm1 %13.9f\n", Asum(grad_));
}
if ((flag & kTrain) == kTrain) {
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/loss_layer/cudnn_softmaxloss.cu
----------------------------------------------------------------------
diff --git a/src/neuralnet/loss_layer/cudnn_softmaxloss.cu b/src/neuralnet/loss_layer/cudnn_softmaxloss.cu
index e0af05f..53420d3 100644
--- a/src/neuralnet/loss_layer/cudnn_softmaxloss.cu
+++ b/src/neuralnet/loss_layer/cudnn_softmaxloss.cu
@@ -20,6 +20,8 @@
*************************************************************/
#include "singa/neuralnet/loss_layer.h"
+#include "singa/utils/blob.h"
+#include "singa/utils/math_kernel.h"
namespace singa {
void CudnnSoftmaxLossLayer::Setup(const LayerProto& conf,
@@ -33,13 +35,41 @@ void CudnnSoftmaxLossLayer::ComputeFeature(int flag,
const vector<Layer*>& srclayers) {
CudnnSoftmaxLayer::ComputeFeature(flag, srclayers);
// compute loss
+ float *prob = data_.mutable_gpu_data();
+ Blob<int> label(batchsize_);
+ int *labelptr = label.mutable_cpu_data();
+
+ //aux_data: vector<int>, convert vector to int array.
+ for(int i = 0; i < batchsize_; ++i) {
+ labelptr[i] = srclayers[1]->aux_data(this)[i];
+ }
+
+ Blob<float> loss(batchsize_);
+
+ singa_gpu_softmax_loss(prob , label.mutable_gpu_data() , loss.mutable_gpu_data(),
+ batchsize_, dim_);
+
counter_++;
- // add loss and accuracy
+ // TODO add loss and accuracy
}
void CudnnSoftmaxLossLayer::ComputeGradient(int flag,
const vector<Layer*>& srclayers) {
// compute gradient
+ Blob<float>* gsrcblob = srclayers[0]->mutable_grad(this);
+ gsrcblob->CopyFrom(data_);
+ float* gsrcptr = gsrcblob->mutable_gpu_data();
+
+ Blob<int> label(batchsize_);
+ int *labelptr = label.mutable_cpu_data();
+
+ //aux_data: vector<int>, convert vector to int array.
+ for(int i = 0; i < batchsize_; ++i) {
+ labelptr[i] = srclayers[1]->aux_data(this)[i];
+ }
+
+ singa_gpu_softmax_gradient(gsrcptr, label.mutable_gpu_data(), batchsize_, dim_, scale_);
+
}
const std::string CudnnSoftmaxLossLayer::ToString(bool debug, int flag) {
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_activation.cc
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_activation.cc b/src/neuralnet/neuron_layer/cudnn_activation.cc
new file mode 100644
index 0000000..e8a7b41
--- /dev/null
+++ b/src/neuralnet/neuron_layer/cudnn_activation.cc
@@ -0,0 +1,100 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you 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 "singa/neuralnet/neuron_layer.h"
+
+namespace singa {
+
+void CudnnActivationLayer::InitCudnn() {
+ CudnnLayer::InitCudnn();
+
+ // TODO(wangwei) make the mode case insensitive
+ if (layer_conf_.activation_conf().type() == SIGMOID)
+ mode_ = CUDNN_ACTIVATION_SIGMOID;
+ else if (layer_conf_.activation_conf().type() == TANH)
+ mode_ = CUDNN_ACTIVATION_TANH;
+ else if (layer_conf_.activation_conf().type() == RELU)
+ mode_ = CUDNN_ACTIVATION_RELU;
+ else {
+ LOG(FATAL) << "Unkown activation: " << layer_conf_.activation_conf().type();
+ }
+
+ const auto& shape = data_.shape();
+ CHECK_GT(shape.size(), 0);
+ // size of each dimension
+ int* sdim= new int[shape.size()];
+ int* stride = new int[shape.size()];
+ stride[shape.size() -1] = 1;
+ int i = shape.size() - 1;
+ sdim[i] = shape[i];
+ stride[i] = 1;
+ for (--i; i >= 0; i--) {
+ sdim[i] = shape[i];
+ stride[i] = shape[i + 1] * stride[i + 1];
+ }
+ CHECK_CUDNN(cudnnSetTensorNdDescriptor(src_desc_,
+ CUDNN_DATA_FLOAT,
+ shape.size(),
+ sdim,
+ stride));
+ CHECK_CUDNN(cudnnSetTensorNdDescriptor(my_desc_,
+ CUDNN_DATA_FLOAT,
+ shape.size(),
+ sdim,
+ stride));
+ delete[] sdim;
+ delete[] stride;
+}
+
+void CudnnActivationLayer::ComputeFeature(int flag,
+ const vector<Layer*>& srclayers) {
+ if (!has_init_cudnn_)
+ InitCudnn();
+ float alpha = 1.0f, beta = 0.0f;
+ // currently only consider single src layer
+ CHECK_EQ(srclayers.size(), 1);
+ CHECK_CUDNN(cudnnActivationForward(handle_,
+ mode_,
+ &alpha,
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ &beta,
+ my_desc_,
+ data_.mutable_gpu_data()));
+}
+
+void CudnnActivationLayer::ComputeGradient(int flag,
+ const vector<Layer*>& srclayers) {
+ float alpha = 1.0f, beta = 0.0f;
+ CHECK_CUDNN(cudnnActivationBackward(handle_,
+ mode_,
+ &alpha,
+ my_desc_,
+ data_.gpu_data(),
+ my_desc_,
+ grad_.gpu_data(),
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ &beta,
+ src_desc_,
+ srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
+}
+} // namespace singa
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_activation.cu
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_activation.cu b/src/neuralnet/neuron_layer/cudnn_activation.cu
deleted file mode 100644
index e8a7b41..0000000
--- a/src/neuralnet/neuron_layer/cudnn_activation.cu
+++ /dev/null
@@ -1,100 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you 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 "singa/neuralnet/neuron_layer.h"
-
-namespace singa {
-
-void CudnnActivationLayer::InitCudnn() {
- CudnnLayer::InitCudnn();
-
- // TODO(wangwei) make the mode case insensitive
- if (layer_conf_.activation_conf().type() == SIGMOID)
- mode_ = CUDNN_ACTIVATION_SIGMOID;
- else if (layer_conf_.activation_conf().type() == TANH)
- mode_ = CUDNN_ACTIVATION_TANH;
- else if (layer_conf_.activation_conf().type() == RELU)
- mode_ = CUDNN_ACTIVATION_RELU;
- else {
- LOG(FATAL) << "Unkown activation: " << layer_conf_.activation_conf().type();
- }
-
- const auto& shape = data_.shape();
- CHECK_GT(shape.size(), 0);
- // size of each dimension
- int* sdim= new int[shape.size()];
- int* stride = new int[shape.size()];
- stride[shape.size() -1] = 1;
- int i = shape.size() - 1;
- sdim[i] = shape[i];
- stride[i] = 1;
- for (--i; i >= 0; i--) {
- sdim[i] = shape[i];
- stride[i] = shape[i + 1] * stride[i + 1];
- }
- CHECK_CUDNN(cudnnSetTensorNdDescriptor(src_desc_,
- CUDNN_DATA_FLOAT,
- shape.size(),
- sdim,
- stride));
- CHECK_CUDNN(cudnnSetTensorNdDescriptor(my_desc_,
- CUDNN_DATA_FLOAT,
- shape.size(),
- sdim,
- stride));
- delete[] sdim;
- delete[] stride;
-}
-
-void CudnnActivationLayer::ComputeFeature(int flag,
- const vector<Layer*>& srclayers) {
- if (!has_init_cudnn_)
- InitCudnn();
- float alpha = 1.0f, beta = 0.0f;
- // currently only consider single src layer
- CHECK_EQ(srclayers.size(), 1);
- CHECK_CUDNN(cudnnActivationForward(handle_,
- mode_,
- &alpha,
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- &beta,
- my_desc_,
- data_.mutable_gpu_data()));
-}
-
-void CudnnActivationLayer::ComputeGradient(int flag,
- const vector<Layer*>& srclayers) {
- float alpha = 1.0f, beta = 0.0f;
- CHECK_CUDNN(cudnnActivationBackward(handle_,
- mode_,
- &alpha,
- my_desc_,
- data_.gpu_data(),
- my_desc_,
- grad_.gpu_data(),
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- &beta,
- src_desc_,
- srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
-}
-} // namespace singa
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_convolution.cc
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_convolution.cc b/src/neuralnet/neuron_layer/cudnn_convolution.cc
new file mode 100644
index 0000000..e08b57a
--- /dev/null
+++ b/src/neuralnet/neuron_layer/cudnn_convolution.cc
@@ -0,0 +1,207 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you 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 "singa/neuralnet/neuron_layer.h"
+
+namespace singa {
+
+CudnnConvLayer::~CudnnConvLayer() {
+ if (has_init_cudnn_) {
+ CHECK_CUDNN(cudnnDestroyTensorDescriptor(bias_desc_));
+ CHECK_CUDNN(cudnnDestroyFilterDescriptor(filter_desc_));
+ CHECK_CUDNN(cudnnDestroyConvolutionDescriptor(conv_desc_));
+ }
+}
+
+void CudnnConvLayer::InitCudnn() {
+ CudnnLayer::InitCudnn();
+ // convert MB to bytes
+ workspace_byte_limit_ = layer_conf_.convolution_conf().workspace_byte_limit() << 20;
+
+ CHECK_CUDNN(cudnnCreateTensorDescriptor(&bias_desc_));
+ CHECK_CUDNN(cudnnCreateFilterDescriptor(&filter_desc_));
+ CHECK_CUDNN(cudnnCreateConvolutionDescriptor(&conv_desc_));
+
+ CHECK_CUDNN(cudnnSetConvolution2dDescriptor(conv_desc_,
+ pad_y_,
+ pad_x_,
+ stride_y_,
+ stride_x_,
+ 1,
+ 1,
+ CUDNN_CROSS_CORRELATION));
+ CHECK_CUDNN(cudnnSetFilter4dDescriptor(filter_desc_,
+ CUDNN_DATA_FLOAT,
+ num_filters_,
+ channels_,
+ kernel_y_,
+ kernel_x_));
+ if (bias_) {
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(bias_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ 1,
+ num_filters_,
+ 1,
+ 1));
+ }
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ channels_,
+ height_,
+ width_));
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ num_filters_,
+ conv_height_,
+ conv_width_));
+
+ CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithm(handle_,
+ src_desc_,
+ filter_desc_,
+ conv_desc_,
+ my_desc_,
+ CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
+ workspace_byte_limit_,
+ &fp_alg_));
+
+ CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(handle_,
+ src_desc_,
+ my_desc_,
+ conv_desc_,
+ filter_desc_,
+ CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
+ workspace_byte_limit_,
+ &bp_filter_alg_));
+ CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(handle_,
+ filter_desc_,
+ my_desc_,
+ conv_desc_,
+ src_desc_,
+ CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
+ workspace_byte_limit_,
+ &bp_data_alg_));
+
+ size_t fp_byte, bp_data_byte, bp_filter_byte;
+ CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(handle_,
+ src_desc_,
+ filter_desc_,
+ conv_desc_,
+ my_desc_,
+ fp_alg_,
+ &fp_byte));
+ CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_,
+ filter_desc_,
+ my_desc_,
+ conv_desc_,
+ src_desc_,
+ bp_data_alg_,
+ &bp_data_byte));
+ CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_,
+ src_desc_,
+ my_desc_,
+ conv_desc_,
+ filter_desc_,
+ bp_filter_alg_,
+ &bp_filter_byte));
+ workspace_count_ = std::max(std::max(fp_byte, bp_data_byte), bp_filter_byte)
+ / sizeof(float) + 1;
+}
+
+void CudnnConvLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
+ if (!has_init_cudnn_)
+ InitCudnn();
+ float alpha = 1.f, beta = 0.f;
+ Blob<float> workspace(vector<int>{static_cast<int>(workspace_count_)});
+ CHECK_CUDNN(cudnnConvolutionForward(handle_,
+ &alpha,
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ filter_desc_,
+ weight_->data().gpu_data(),
+ conv_desc_,
+ fp_alg_,
+ workspace.mutable_gpu_data(),
+ workspace_count_ * sizeof(float),
+ &beta,
+ my_desc_,
+ data_.mutable_gpu_data()));
+
+ if (bias_) {
+ beta = 1.f;
+ CHECK_CUDNN(cudnnAddTensor(handle_,
+ CUDNN_ADD_SAME_C,
+ &alpha,
+ bias_desc_,
+ bias_->data().gpu_data(),
+ &beta,
+ my_desc_,
+ data_.mutable_gpu_data()));
+ }
+}
+
+void CudnnConvLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers)
+{
+ float alpha = 1.f, beta = 0.f;
+ Blob<float> workspace(vector<int>{static_cast<int>(workspace_count_)});
+ if (bias_) {
+ CHECK_CUDNN(cudnnConvolutionBackwardBias(handle_,
+ &alpha,
+ my_desc_,
+ grad_.gpu_data(),
+ &beta,
+ bias_desc_,
+ bias_->mutable_grad()->mutable_gpu_data()));
+ }
+ CHECK_CUDNN(cudnnConvolutionBackwardFilter_v3(handle_,
+ &alpha,
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ my_desc_,
+ grad_.gpu_data(),
+ conv_desc_,
+ bp_filter_alg_,
+ workspace.mutable_gpu_data(),
+ workspace_count_ * sizeof(float),
+ &beta,
+ filter_desc_,
+ weight_->mutable_grad()->mutable_gpu_data()));
+ if (srclayers[0]->mutable_grad(this) != nullptr) {
+ CHECK_CUDNN(cudnnConvolutionBackwardData_v3(handle_,
+ &alpha,
+ filter_desc_,
+ weight_->data().gpu_data(),
+ my_desc_,
+ grad_.gpu_data(),
+ conv_desc_,
+ bp_data_alg_,
+ workspace.mutable_gpu_data(),
+ workspace_count_ * sizeof(float),
+ &beta,
+ src_desc_,
+ srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
+ }
+}
+} /* singa */
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_convolution.cu
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_convolution.cu b/src/neuralnet/neuron_layer/cudnn_convolution.cu
deleted file mode 100644
index 13e9f65..0000000
--- a/src/neuralnet/neuron_layer/cudnn_convolution.cu
+++ /dev/null
@@ -1,205 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you 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 "singa/neuralnet/neuron_layer.h"
-
-namespace singa {
-
-CudnnConvLayer::~CudnnConvLayer() {
- if (has_init_cudnn_) {
- CHECK_CUDNN(cudnnDestroyTensorDescriptor(bias_desc_));
- CHECK_CUDNN(cudnnDestroyFilterDescriptor(filter_desc_));
- CHECK_CUDNN(cudnnDestroyConvolutionDescriptor(conv_desc_));
- }
-}
-
-void CudnnConvLayer::InitCudnn() {
- CudnnLayer::InitCudnn();
- // convert MB to bytes
- workspace_byte_limit_ = layer_conf_.convolution_conf().workspace_byte_limit() << 20;
-
- CHECK_CUDNN(cudnnCreateTensorDescriptor(&bias_desc_));
- CHECK_CUDNN(cudnnCreateFilterDescriptor(&filter_desc_));
- CHECK_CUDNN(cudnnCreateConvolutionDescriptor(&conv_desc_));
-
- CHECK_CUDNN(cudnnSetConvolution2dDescriptor(conv_desc_,
- pad_y_,
- pad_x_,
- stride_y_,
- stride_x_,
- 1,
- 1,
- CUDNN_CROSS_CORRELATION));
- CHECK_CUDNN(cudnnSetFilter4dDescriptor(filter_desc_,
- CUDNN_DATA_FLOAT,
- num_filters_,
- channels_,
- kernel_y_,
- kernel_x_));
- if (bias_) {
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(bias_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- 1,
- num_filters_,
- 1,
- 1));
- }
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- channels_,
- height_,
- width_));
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- num_filters_,
- conv_height_,
- conv_width_));
-
- CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithm(handle_,
- src_desc_,
- filter_desc_,
- conv_desc_,
- my_desc_,
- CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
- workspace_byte_limit_,
- &fp_alg_));
-
- CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(handle_,
- src_desc_,
- my_desc_,
- conv_desc_,
- filter_desc_,
- CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
- workspace_byte_limit_,
- &bp_filter_alg_));
- CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(handle_,
- filter_desc_,
- my_desc_,
- conv_desc_,
- src_desc_,
- CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
- workspace_byte_limit_,
- &bp_data_alg_));
-
- size_t fp_byte, bp_data_byte, bp_filter_byte;
- CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(handle_,
- src_desc_,
- filter_desc_,
- conv_desc_,
- my_desc_,
- fp_alg_,
- &fp_byte));
- CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_,
- filter_desc_,
- my_desc_,
- conv_desc_,
- src_desc_,
- bp_data_alg_,
- &bp_data_byte));
- CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_,
- src_desc_,
- my_desc_,
- conv_desc_,
- filter_desc_,
- bp_filter_alg_,
- &bp_filter_byte));
- workspace_count_ = std::max(std::max(fp_byte, bp_data_byte), bp_filter_byte)
- / sizeof(float) + 1;
-}
-
-void CudnnConvLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
- if (!has_init_cudnn_)
- InitCudnn();
- float alpha = 1.f, beta = 0.f;
- Blob<float> workspace(vector<int>{static_cast<int>(workspace_count_)});
- CHECK_CUDNN(cudnnConvolutionForward(handle_,
- &alpha,
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- filter_desc_,
- weight_->data().gpu_data(),
- conv_desc_,
- fp_alg_,
- workspace.mutable_gpu_data(),
- workspace_count_ * sizeof(float),
- &beta,
- my_desc_,
- data_.mutable_gpu_data()));
-
- if (bias_) {
- beta = 1.f;
- CHECK_CUDNN(cudnnAddTensor(handle_,
- CUDNN_ADD_SAME_C,
- &alpha,
- bias_desc_,
- bias_->data().gpu_data(),
- &beta,
- my_desc_,
- data_.mutable_gpu_data()));
- }
-}
-
-void CudnnConvLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers)
-{
- float alpha = 1.f, beta = 0.f;
- Blob<float> workspace(vector<int>{static_cast<int>(workspace_count_)});
- if (bias_) {
- CHECK_CUDNN(cudnnConvolutionBackwardBias(handle_,
- &alpha,
- my_desc_,
- grad_.gpu_data(),
- &beta,
- bias_desc_,
- bias_->mutable_grad()->mutable_gpu_data()));
- }
- CHECK_CUDNN(cudnnConvolutionBackwardFilter_v3(handle_,
- &alpha,
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- my_desc_,
- grad_.gpu_data(),
- conv_desc_,
- bp_filter_alg_,
- workspace.mutable_gpu_data(),
- workspace_count_ * sizeof(float),
- &beta,
- filter_desc_,
- weight_->mutable_grad()->mutable_gpu_data()));
- CHECK_CUDNN(cudnnConvolutionBackwardData_v3(handle_,
- &alpha,
- filter_desc_,
- weight_->data().gpu_data(),
- my_desc_,
- grad_.gpu_data(),
- conv_desc_,
- bp_data_alg_,
- workspace.mutable_gpu_data(),
- workspace_count_ * sizeof(float),
- &beta,
- src_desc_,
- srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
-}
-} /* singa */
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_lrn.cc
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_lrn.cc b/src/neuralnet/neuron_layer/cudnn_lrn.cc
new file mode 100644
index 0000000..4a2b695
--- /dev/null
+++ b/src/neuralnet/neuron_layer/cudnn_lrn.cc
@@ -0,0 +1,87 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you 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 "singa/neuralnet/neuron_layer.h"
+
+namespace singa {
+CudnnLRNLayer::~CudnnLRNLayer() {
+ if (has_init_cudnn_) {
+ cudnnDestroyLRNDescriptor(norm_desc_);
+ }
+}
+
+void CudnnLRNLayer::InitCudnn() {
+ mode_ = CUDNN_LRN_CROSS_CHANNEL_DIM1;
+ CudnnLayer::InitCudnn();
+ CHECK_CUDNN(cudnnCreateLRNDescriptor(&norm_desc_));
+ CHECK_CUDNN(cudnnSetLRNDescriptor(norm_desc_,
+ lsize_,
+ alpha_,
+ beta_,
+ knorm_));
+ CHECK_CUDNN(cudnnCreateTensorDescriptor(&src_desc_));
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ channels_,
+ height_,
+ width_));
+ CHECK_CUDNN(cudnnCreateTensorDescriptor(&my_desc_));
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ channels_,
+ height_,
+ width_));
+}
+void CudnnLRNLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
+ if (!has_init_cudnn_)
+ InitCudnn();
+ CHECK_CUDNN(cudnnLRNCrossChannelForward(handle_,
+ norm_desc_,
+ mode_,
+ &alpha_,
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ &beta_,
+ my_desc_,
+ data_.mutable_gpu_data()));
+}
+void CudnnLRNLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers) {
+ CHECK_CUDNN(cudnnLRNCrossChannelBackward(handle_,
+ norm_desc_,
+ mode_,
+ &alpha_,
+ my_desc_, // ???
+ data_.gpu_data(),
+ my_desc_,
+ grad_.gpu_data(),
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ &beta_,
+ src_desc_,
+ srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
+}
+
+
+} /* singa */
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_lrn.cu
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_lrn.cu b/src/neuralnet/neuron_layer/cudnn_lrn.cu
deleted file mode 100644
index 4a2b695..0000000
--- a/src/neuralnet/neuron_layer/cudnn_lrn.cu
+++ /dev/null
@@ -1,87 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you 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 "singa/neuralnet/neuron_layer.h"
-
-namespace singa {
-CudnnLRNLayer::~CudnnLRNLayer() {
- if (has_init_cudnn_) {
- cudnnDestroyLRNDescriptor(norm_desc_);
- }
-}
-
-void CudnnLRNLayer::InitCudnn() {
- mode_ = CUDNN_LRN_CROSS_CHANNEL_DIM1;
- CudnnLayer::InitCudnn();
- CHECK_CUDNN(cudnnCreateLRNDescriptor(&norm_desc_));
- CHECK_CUDNN(cudnnSetLRNDescriptor(norm_desc_,
- lsize_,
- alpha_,
- beta_,
- knorm_));
- CHECK_CUDNN(cudnnCreateTensorDescriptor(&src_desc_));
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- channels_,
- height_,
- width_));
- CHECK_CUDNN(cudnnCreateTensorDescriptor(&my_desc_));
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- channels_,
- height_,
- width_));
-}
-void CudnnLRNLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
- if (!has_init_cudnn_)
- InitCudnn();
- CHECK_CUDNN(cudnnLRNCrossChannelForward(handle_,
- norm_desc_,
- mode_,
- &alpha_,
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- &beta_,
- my_desc_,
- data_.mutable_gpu_data()));
-}
-void CudnnLRNLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers) {
- CHECK_CUDNN(cudnnLRNCrossChannelBackward(handle_,
- norm_desc_,
- mode_,
- &alpha_,
- my_desc_, // ???
- data_.gpu_data(),
- my_desc_,
- grad_.gpu_data(),
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- &beta_,
- src_desc_,
- srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
-}
-
-
-} /* singa */
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_pooling.cc
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_pooling.cc b/src/neuralnet/neuron_layer/cudnn_pooling.cc
new file mode 100644
index 0000000..ffdfb3b
--- /dev/null
+++ b/src/neuralnet/neuron_layer/cudnn_pooling.cc
@@ -0,0 +1,96 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you 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 "singa/neuralnet/neuron_layer.h"
+
+namespace singa {
+
+CudnnPoolLayer::~CudnnPoolLayer() {
+ if (has_init_cudnn_) {
+ CHECK_CUDNN(cudnnDestroyPoolingDescriptor(pool_desc_));
+ }
+}
+
+void CudnnPoolLayer::InitCudnn() {
+ CudnnLayer::InitCudnn();
+ CHECK_CUDNN(cudnnCreatePoolingDescriptor(&pool_desc_));
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ channels_,
+ height_,
+ width_));
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ channels_,
+ pooled_height_,
+ pooled_width_));
+ auto pool_method = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
+ if (pool_ == PoolingProto_PoolMethod_MAX)
+ pool_method = CUDNN_POOLING_MAX;
+ CHECK_CUDNN(cudnnSetPooling2dDescriptor(pool_desc_,
+ pool_method,
+ kernel_y_,
+ kernel_x_,
+ pad_y_,
+ pad_x_,
+ stride_y_,
+ stride_x_));
+
+}
+
+void CudnnPoolLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
+ if (!has_init_cudnn_)
+ InitCudnn();
+ float alpha = 1.0f, beta = 0.0f;
+ // currently only consider single src layer
+ CHECK_EQ(srclayers.size(), 1);
+ CHECK_CUDNN(cudnnPoolingForward(handle_,
+ pool_desc_,
+ &alpha,
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ &beta,
+ my_desc_,
+ data_.mutable_gpu_data()));
+}
+
+void CudnnPoolLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers)
+{
+ float alpha = 1.0f, beta = 0.0f;
+ CHECK_CUDNN(cudnnPoolingBackward(handle_,
+ pool_desc_,
+ &alpha,
+ my_desc_,
+ data_.gpu_data(),
+ my_desc_,
+ grad_.gpu_data(),
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ &beta,
+ src_desc_,
+ srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
+}
+} /* singa */
+
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_pooling.cu
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_pooling.cu b/src/neuralnet/neuron_layer/cudnn_pooling.cu
deleted file mode 100644
index ffdfb3b..0000000
--- a/src/neuralnet/neuron_layer/cudnn_pooling.cu
+++ /dev/null
@@ -1,96 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you 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 "singa/neuralnet/neuron_layer.h"
-
-namespace singa {
-
-CudnnPoolLayer::~CudnnPoolLayer() {
- if (has_init_cudnn_) {
- CHECK_CUDNN(cudnnDestroyPoolingDescriptor(pool_desc_));
- }
-}
-
-void CudnnPoolLayer::InitCudnn() {
- CudnnLayer::InitCudnn();
- CHECK_CUDNN(cudnnCreatePoolingDescriptor(&pool_desc_));
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- channels_,
- height_,
- width_));
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- channels_,
- pooled_height_,
- pooled_width_));
- auto pool_method = CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
- if (pool_ == PoolingProto_PoolMethod_MAX)
- pool_method = CUDNN_POOLING_MAX;
- CHECK_CUDNN(cudnnSetPooling2dDescriptor(pool_desc_,
- pool_method,
- kernel_y_,
- kernel_x_,
- pad_y_,
- pad_x_,
- stride_y_,
- stride_x_));
-
-}
-
-void CudnnPoolLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
- if (!has_init_cudnn_)
- InitCudnn();
- float alpha = 1.0f, beta = 0.0f;
- // currently only consider single src layer
- CHECK_EQ(srclayers.size(), 1);
- CHECK_CUDNN(cudnnPoolingForward(handle_,
- pool_desc_,
- &alpha,
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- &beta,
- my_desc_,
- data_.mutable_gpu_data()));
-}
-
-void CudnnPoolLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers)
-{
- float alpha = 1.0f, beta = 0.0f;
- CHECK_CUDNN(cudnnPoolingBackward(handle_,
- pool_desc_,
- &alpha,
- my_desc_,
- data_.gpu_data(),
- my_desc_,
- grad_.gpu_data(),
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- &beta,
- src_desc_,
- srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
-}
-} /* singa */
-
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_softmax.cc
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_softmax.cc b/src/neuralnet/neuron_layer/cudnn_softmax.cc
new file mode 100644
index 0000000..7fade3e
--- /dev/null
+++ b/src/neuralnet/neuron_layer/cudnn_softmax.cc
@@ -0,0 +1,75 @@
+/************************************************************
+*
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you 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 "singa/neuralnet/neuron_layer.h"
+
+namespace singa {
+
+void CudnnSoftmaxLayer::InitCudnn() {
+ CudnnLayer::InitCudnn();
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ num_softmax_per_instance_,
+ count_per_softmax_,
+ 1));
+ CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
+ CUDNN_TENSOR_NCHW,
+ CUDNN_DATA_FLOAT,
+ batchsize_,
+ num_softmax_per_instance_,
+ count_per_softmax_,
+ 1));
+}
+
+void CudnnSoftmaxLayer::ComputeFeature(int flag,
+ const vector<Layer*>& srclayers) {
+ if (!has_init_cudnn_)
+ InitCudnn();
+ const float alpha = 1.0f, beta = 0.0f;
+ CHECK_CUDNN(cudnnSoftmaxForward(handle_,
+ CUDNN_SOFTMAX_ACCURATE,
+ CUDNN_SOFTMAX_MODE_CHANNEL,
+ &alpha,
+ src_desc_,
+ srclayers[0]->data(this).gpu_data(),
+ &beta,
+ my_desc_,
+ data_.mutable_gpu_data()));
+}
+
+void CudnnSoftmaxLayer::ComputeGradient(int flag,
+ const vector<Layer*>& srclayers) {
+ const float alpha = 1.f, beta = 0.f;
+ CHECK_CUDNN(cudnnSoftmaxBackward(handle_,
+ CUDNN_SOFTMAX_ACCURATE,
+ CUDNN_SOFTMAX_MODE_CHANNEL,
+ &alpha,
+ my_desc_,
+ data_.gpu_data(),
+ my_desc_,
+ grad_.gpu_data(),
+ &beta,
+ src_desc_,
+ srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
+}
+} /* singa */
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/cudnn_softmax.cu
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/cudnn_softmax.cu b/src/neuralnet/neuron_layer/cudnn_softmax.cu
deleted file mode 100644
index 7fade3e..0000000
--- a/src/neuralnet/neuron_layer/cudnn_softmax.cu
+++ /dev/null
@@ -1,75 +0,0 @@
-/************************************************************
-*
-* Licensed to the Apache Software Foundation (ASF) under one
-* or more contributor license agreements. See the NOTICE file
-* distributed with this work for additional information
-* regarding copyright ownership. The ASF licenses this file
-* to you 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 "singa/neuralnet/neuron_layer.h"
-
-namespace singa {
-
-void CudnnSoftmaxLayer::InitCudnn() {
- CudnnLayer::InitCudnn();
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- num_softmax_per_instance_,
- count_per_softmax_,
- 1));
- CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
- CUDNN_TENSOR_NCHW,
- CUDNN_DATA_FLOAT,
- batchsize_,
- num_softmax_per_instance_,
- count_per_softmax_,
- 1));
-}
-
-void CudnnSoftmaxLayer::ComputeFeature(int flag,
- const vector<Layer*>& srclayers) {
- if (!has_init_cudnn_)
- InitCudnn();
- const float alpha = 1.0f, beta = 0.0f;
- CHECK_CUDNN(cudnnSoftmaxForward(handle_,
- CUDNN_SOFTMAX_ACCURATE,
- CUDNN_SOFTMAX_MODE_CHANNEL,
- &alpha,
- src_desc_,
- srclayers[0]->data(this).gpu_data(),
- &beta,
- my_desc_,
- data_.mutable_gpu_data()));
-}
-
-void CudnnSoftmaxLayer::ComputeGradient(int flag,
- const vector<Layer*>& srclayers) {
- const float alpha = 1.f, beta = 0.f;
- CHECK_CUDNN(cudnnSoftmaxBackward(handle_,
- CUDNN_SOFTMAX_ACCURATE,
- CUDNN_SOFTMAX_MODE_CHANNEL,
- &alpha,
- my_desc_,
- data_.gpu_data(),
- my_desc_,
- grad_.gpu_data(),
- &beta,
- src_desc_,
- srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
-}
-} /* singa */
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/neuralnet/neuron_layer/inner_product.cc
----------------------------------------------------------------------
diff --git a/src/neuralnet/neuron_layer/inner_product.cc b/src/neuralnet/neuron_layer/inner_product.cc
index 3b18cd7..6b5ec36 100644
--- a/src/neuralnet/neuron_layer/inner_product.cc
+++ b/src/neuralnet/neuron_layer/inner_product.cc
@@ -58,7 +58,7 @@ void InnerProductLayer::Setup(const LayerProto& conf,
void InnerProductLayer::ComputeFeature(int flag,
const vector<Layer*>& srclayers) {
- MMDot(srclayers[0]->data(this), weight_->data(), &data_);
+ MMDot(srclayers[0]->data(this), weight_->data().T(), &data_);
MVAddRow(bias_->data(), &data_);
}
@@ -66,9 +66,9 @@ void InnerProductLayer::ComputeGradient(int flag,
const vector<Layer*>& srclayers) {
MVSumRow(1.0f, 0.0f, grad_, bias_->mutable_grad());
- MVDot(grad_.T(), srclayers[0]->data(this), weight_->mutable_grad());
+ MMDot(grad_.T(), srclayers[0]->data(this), weight_->mutable_grad());
if (srclayers[0]->mutable_grad(this) != nullptr) {
- MVDot(grad_, weight_->data(), srclayers[0]->mutable_grad(this));
+ MMDot(grad_, weight_->data(), srclayers[0]->mutable_grad(this));
}
}
} // namespace singa
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/stub.cc
----------------------------------------------------------------------
diff --git a/src/stub.cc b/src/stub.cc
index 3f7c59d..a3605f7 100644
--- a/src/stub.cc
+++ b/src/stub.cc
@@ -207,12 +207,12 @@ void Stub::GenMsgs(int type, int version, ParamEntry* entry, Msg* msg,
Msg* new_msg = nullptr;
if (type == kPut) {
CHECK_GT(entry->num_total, 0);
- new_msg = param->GenPutMsg(dst_procs != procs_id, idx);
+ new_msg = param->GenPutMsg(dst_procs == procs_id, idx);
new_msg->AddFormatFrame("i", entry->num_total);
} else if (type == kGet) {
- new_msg = param->GenGetMsg(dst_procs != procs_id, idx);
+ new_msg = param->GenGetMsg(dst_procs == procs_id, idx);
} else if (type == kUpdate) {
- new_msg = param->GenUpdateMsg(dst_procs != procs_id, idx);
+ new_msg = param->GenUpdateMsg(dst_procs == procs_id, idx);
new_msg->AddFormatFrame("i", entry->num_local);
} else {
LOG(FATAL) << "Wrong type";
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/test/test_csv_input_layer.cc
----------------------------------------------------------------------
diff --git a/src/test/test_csv_input_layer.cc b/src/test/test_csv_input_layer.cc
index ce5847d..6613d87 100644
--- a/src/test/test_csv_input_layer.cc
+++ b/src/test/test_csv_input_layer.cc
@@ -23,7 +23,7 @@
#include <fstream>
#include "gtest/gtest.h"
-#include "singa/neuralnet/input_layer/csv.h"
+#include "singa/neuralnet/input_layer.h"
#include "singa/proto/job.pb.h"
class CSVInputLayerTest : public ::testing::Test {
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/test/test_math.cc
----------------------------------------------------------------------
diff --git a/src/test/test_math.cc b/src/test/test_math.cc
index 39ec2a0..c2730a4 100644
--- a/src/test/test_math.cc
+++ b/src/test/test_math.cc
@@ -7,9 +7,9 @@
* to you 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
@@ -18,11 +18,13 @@
* under the License.
*
*************************************************************/
-
+#include <thread>
#include "gtest/gtest.h"
#include "singa/utils/math_addr.h"
#include "singa/utils/math_kernel.h"
#include "singa/utils/singa_op.h"
+#include "singa/utils/context.h"
+#include "singa/utils/singleton.h"
#ifdef USE_GPU
#include <cuda_runtime.h>
@@ -82,6 +84,7 @@ TEST(MathTest, TestGemvCPU) {
}
+/*
TEST(MathTest, TestAxpyCPU) {
float A[4][3] = {};
float C[4][3] = {};
@@ -109,7 +112,6 @@ TEST(MathTest, TestAxpyCPU) {
}
}
-/*
TEST(MathTest, TestEopCPU) {
float A[10] = {};
@@ -154,8 +156,9 @@ TEST(MathTest, TestGemmGPU) {
cudaMemcpy(A_gpu, A, 3*2*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(B_gpu, B, 3*2*sizeof(float), cudaMemcpyHostToDevice);
-
- gpu_gemm<float>(A_gpu, B_gpu, 2, 2, 3 , 1, 0, true, false, C_gpu);
+ auto context = Singleton<Context>::Instance();
+ context->SetupDevice(std::this_thread::get_id(), 0);
+ gpu_gemm<float>(context->cublas_handle(0), A_gpu, B_gpu, 2, 2, 3 , 1, 0, true, false, C_gpu);
cudaMemcpy(C, C_gpu, 2*2*sizeof(float), cudaMemcpyDeviceToHost);
@@ -207,8 +210,9 @@ TEST(MathTest, TestGemvGPU) {
cudaMemcpy(A_gpu, A, 4*3*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(B_gpu, B, 4*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(C_gpu, C, 3*sizeof(float), cudaMemcpyHostToDevice);
-
- gpu_gemv<float>(A_gpu, B_gpu, 4, 3, 1, 1, true, C_gpu);
+ auto context = Singleton<Context>::Instance();
+ context->SetupDevice(std::this_thread::get_id(), 0);
+ gpu_gemv<float>(context->cublas_handle(0), A_gpu, B_gpu, 4, 3, 1.0f, 1.0f, true, C_gpu);
cudaMemcpy(C, C_gpu, 3*sizeof(float), cudaMemcpyDeviceToHost);
@@ -294,7 +298,9 @@ TEST(MathTest, TestDotGPU) {
cudaMemcpy(A_gpu, A, 12*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(B_gpu, B, 12*sizeof(float), cudaMemcpyHostToDevice);
- float gpu_ret = gpu_dot<float>(A_gpu, B_gpu, 12);
+ auto context = Singleton<Context>::Instance();
+ context->SetupDevice(std::this_thread::get_id(), 0);
+ float gpu_ret = gpu_dot<float>(context->cublas_handle(0), A_gpu, B_gpu, 12);
float cpu_ret = 0.0f;
for (int i = 0; i < 12; i++) {
@@ -329,8 +335,7 @@ TEST(MathTest, TestSingaSumColGPU) {
cudaMalloc(reinterpret_cast<void**>(&A_gpu), 12*sizeof(float));
cudaMalloc(reinterpret_cast<void**>(&B_gpu), 4*sizeof(float));
cudaMemcpy(A_gpu, A, 12*sizeof(float), cudaMemcpyHostToDevice);
-
- singa_gpu_sum_col(A_gpu, B_gpu, 3, 4, 4);
+ singa_gpu_sum_by_col(A_gpu, B_gpu, 3, 4, 4);
cudaMemcpy(B, B_gpu, 4*sizeof(float), cudaMemcpyDeviceToHost);
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/test/test_record_input_layer.cc
----------------------------------------------------------------------
diff --git a/src/test/test_record_input_layer.cc b/src/test/test_record_input_layer.cc
index 78e6047..9c953c1 100644
--- a/src/test/test_record_input_layer.cc
+++ b/src/test/test_record_input_layer.cc
@@ -22,7 +22,7 @@
#include <vector>
#include "gtest/gtest.h"
-#include "singa/neuralnet/input_layer/record.h"
+#include "singa/neuralnet/input_layer.h"
#include "singa/proto/job.pb.h"
#include "singa/proto/common.pb.h"
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/utils/math_kernel.cu
----------------------------------------------------------------------
diff --git a/src/utils/math_kernel.cu b/src/utils/math_kernel.cu
index 12501fd..a4cd513 100644
--- a/src/utils/math_kernel.cu
+++ b/src/utils/math_kernel.cu
@@ -21,6 +21,7 @@
#include <cmath>
#include <algorithm>
#include "singa/utils/math_kernel.h"
+#include "mshadow/tensor.h" //FLT_MIN?
#define CU2DBLOCK_X 32
#define CU2DBLOCK_Y 32
@@ -31,6 +32,29 @@
// Cuda Kernel Functions
__global__
+void kernel_softmax_loss(const float *prob, const int *label ,
+ float *loss, int n, int dim) {
+ int index = blockIdx.x * blockDim.x + threadIdx.x;
+ int num_threads = blockDim.x * gridDim.x;
+ for (; index < n; index += num_threads) {
+ const int label_value = static_cast<int>(label[index]);
+ float prob_of_truth = prob[index * dim + label_value];
+ loss[index] -= log(max(prob_of_truth, FLT_MIN));
+ }
+}
+
+__global__
+void kernel_softmax_gradient(float *grad, const int *label ,
+ int n, int dim, float scale) {
+ int index = blockIdx.x * blockDim.x + threadIdx.x;
+ int num_threads = blockDim.x * gridDim.x;
+ for (; index < n; index += num_threads) {
+ int pos = index * dim + static_cast<int>(label[index]);
+ grad[pos] = (grad[pos] - 1.0f) * scale / (1.0 * n);
+ }
+}
+
+__global__
void kernel_sum_vec(float *data, float *sum , int n) {
int THREADS = blockDim.x;
@@ -293,6 +317,18 @@ void kernel_threshold(const float *src_data, float *des_data,
//
namespace singa {
+void singa_gpu_softmax_loss(const float *prob, const int *label,
+ float *loss, int n, int dim) {
+ kernel_softmax_loss<<<ceil(n/CU1DBLOCKF), CU1DBLOCKF>>>
+ (prob, label, loss, n, dim);
+}
+
+void singa_gpu_softmax_gradient(float *grad, const int *label,
+ int n, int dim, float scale) {
+ kernel_softmax_gradient<<<ceil(n/CU1DBLOCKF), CU1DBLOCKF>>>
+ (grad, label, n, dim, scale);
+}
+
void singa_gpu_sum_vec(float *data, float *sum , int n) {
int threads_per_block = n > CU1DBLOCK ? CU1DBLOCK : n;
// here, we only need one block
http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/f31ba645/src/worker.cc
----------------------------------------------------------------------
diff --git a/src/worker.cc b/src/worker.cc
index 8c1d950..d9380b6 100644
--- a/src/worker.cc
+++ b/src/worker.cc
@@ -91,7 +91,7 @@ void Worker::Run() {
}
TrainOneBatch(step_, train_net_);
if (DisplayNow(step_) && grp_id_ == 0 && id_ == 0)
- Display(kTrain, "Train @ step " + std::to_string(step_), train_net_);
+ Display(kTrain | kForward | kBackward, "Train @ step " + std::to_string(step_), train_net_);
step_++;
}
@@ -297,7 +297,7 @@ void Worker::Display(int flag, const std::string& prefix, NeuralNet* net) {
if (job_conf_.debug()) {
const string& info = layer->ToString(true, flag);
if (info.length()) {
- LOG(INFO) << prefix << info;
+ LOG(INFO) << prefix << info;
}
}
}