Decouple the computational batch size and minibatch size by accumulating gradients

include/caffe/solver.hpp

@@ -81,6 +81,7 @@ class SGDSolver : public Solver<Dtype> {
   void PreSolve();
   Dtype GetLearningRate();
   virtual void ApplyUpdate();
+  virtual void Normalize(int param_id);
   virtual void Regularize(int param_id);
   virtual void ComputeUpdateValue(int param_id, Dtype rate);
   virtual void ClipGradients();

include/caffe/test/test_gradient_check_util.hpp

@@ -80,11 +80,14 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
       CHECK_EQ(top_count, bottom[blob_id]->count());
     }
   }
-  // First, figure out what blobs we need to check against.
+  // First, figure out what blobs we need to check against, and zero init
+  // parameter blobs.
   vector<Blob<Dtype>*> blobs_to_check;
   vector<bool> propagate_down(bottom.size(), check_bottom < 0);
   for (int i = 0; i < layer->blobs().size(); ++i) {
-    blobs_to_check.push_back(layer->blobs()[i].get());
+    Blob<Dtype>* blob = layer->blobs()[i].get();
+    caffe_set(blob->count(), static_cast<Dtype>(0), blob->mutable_cpu_diff());
+    blobs_to_check.push_back(blob);
   }
   if (check_bottom < 0) {
     for (int i = 0; i < bottom.size(); ++i) {

src/caffe/layers/conv_layer.cpp

@@ -39,13 +39,6 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_cpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
     const Dtype* bottom_data = bottom[i]->cpu_data();

src/caffe/layers/conv_layer.cu

@@ -31,13 +31,6 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_gpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     // Bias gradient, if necessary.

src/caffe/layers/cudnn_conv_layer.cu

@@ -101,12 +101,10 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (this->param_propagate_down_[0]) {
     weight = this->blobs_[0]->gpu_data();
     weight_diff = this->blobs_[0]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
   if (this->bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();

src/caffe/layers/deconv_layer.cpp

@@ -39,13 +39,6 @@ void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_cpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
     const Dtype* bottom_data = bottom[i]->cpu_data();

src/caffe/layers/deconv_layer.cu

@@ -31,13 +31,6 @@ void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_gpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     const Dtype* bottom_data = bottom[i]->gpu_data();

src/caffe/layers/inner_product_layer.cpp

@@ -101,13 +101,13 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* bottom_data = bottom[0]->cpu_data();
     // Gradient with respect to weight
     caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
+        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.cpu_data(), (Dtype)0.,
+        bias_multiplier_.cpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_cpu_diff());
   }
   if (propagate_down[0]) {

src/caffe/layers/inner_product_layer.cu

@@ -33,13 +33,13 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* bottom_data = bottom[0]->gpu_data();
     // Gradient with respect to weight
     caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
+        top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.gpu_data(), (Dtype)0.,
+        bias_multiplier_.gpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_gpu_diff());
   }
   if (propagate_down[0]) {

src/caffe/proto/caffe.proto

@@ -96,7 +96,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 36 (last added: clip_gradients)
+// SolverParameter next available ID: 37 (last added: iter_size)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -149,6 +149,8 @@ message SolverParameter {
   // Display the loss averaged over the last average_loss iterations
   optional int32 average_loss = 33 [default = 1];
   optional int32 max_iter = 7; // the maximum number of iterations
+  // accumulate gradients over `iter_size` x `batch_size` instances
+  optional int32 iter_size = 36 [default = 1];
   optional string lr_policy = 8; // The learning rate decay policy.
   optional float gamma = 9; // The parameter to compute the learning rate.
   optional float power = 10; // The parameter to compute the learning rate.

src/caffe/solver.cpp

@@ -168,14 +168,39 @@ void Solver<Dtype>::Step(int iters) {
   Dtype smoothed_loss = 0;
 
   while (iter_ < stop_iter) {
+    // zero-init the params
+    for (int i = 0; i < net_->params().size(); ++i) {
+      shared_ptr<Blob<Dtype> > blob = net_->params()[i];
+      switch (Caffe::mode()) {
+      case Caffe::CPU:
+        caffe_set(blob->count(), static_cast<Dtype>(0),
+            blob->mutable_cpu_diff());
+        break;
+      case Caffe::GPU:
+#ifndef CPU_ONLY
+        caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
+            blob->mutable_gpu_diff());
+#else
+        NO_GPU;
+#endif
+        break;
+      }
+    }
+
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
         && (iter_ > 0 || param_.test_initialization())) {
       TestAll();
     }
 
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
-    Dtype loss = net_->ForwardBackward(bottom_vec);
+    // accumulate the loss and gradient
+    Dtype loss = 0;
+    for (int i = 0; i < param_.iter_size(); ++i) {
+      loss += net_->ForwardBackward(bottom_vec);
+    }
+    loss /= param_.iter_size();
+    // average the loss across iterations for smoothed reporting
     if (losses.size() < average_loss) {
       losses.push_back(loss);
       int size = losses.size();
@@ -462,12 +487,39 @@ void SGDSolver<Dtype>::ApplyUpdate() {
   }
   ClipGradients();
   for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) {
+    Normalize(param_id);
     Regularize(param_id);
     ComputeUpdateValue(param_id, rate);
   }
   this->net_->Update();
 }
 
+template <typename Dtype>
+void SGDSolver<Dtype>::Normalize(int param_id) {
+  if (this->param_.iter_size() == 1) { return; }
+  // Scale gradient to counterbalance accumulation.
+  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
+  const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
 template <typename Dtype>
 void SGDSolver<Dtype>::Regularize(int param_id) {
   const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();

src/caffe/test/test_gradient_based_solver.cpp

@@ -23,7 +23,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   GradientBasedSolverTest() :
-      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+      seed_(1701), num_(4), channels_(3), height_(10), width_(10) {}
 
   shared_ptr<SGDSolver<Dtype> > solver_;
   int seed_;
@@ -56,26 +56,32 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   }
 
   void RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+      const Dtype weight_decay, const Dtype momentum, const int num_iters,
+      const int iter_size = 1) {
     ostringstream proto;
     proto <<
        "max_iter: " << num_iters << " "
        "base_lr: " << learning_rate << " "
        "lr_policy: 'fixed' "
+       "iter_size: " << iter_size << " "
        "net_param { "
        "  name: 'TestNetwork' "
        "  layer { "
        "    name: 'data' "
        "    type: 'DummyData' "
        "    dummy_data_param { "
-       "      num: " << num_ << " "
+       "      num: " << num_ / iter_size << " "
        "      channels: " << channels_ << " "
        "      height: " << height_ << " "
        "      width: " << width_ << " "
        "      channels: 1 "
        "      height: 1 "
        "      width: 1 "
        "      data_filler { "
+       "        type: 'constant' "
+       "        value: 1.0 "
+       "      } "
+       "      data_filler { "
        "        type: 'gaussian' "
        "        std: 1.0 "
        "      } "
@@ -270,6 +276,45 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
+  void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay,
+      const Dtype kMomentum, const int kNumIters, const int kIterSize) {
+    const double kPrecision = 1e-2;
+    const double kMinPrecision = 1e-7;
+    // Solve without accumulation and save parameters.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters);
+    // Save parameters for comparison.
+    Net<Dtype>& net = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    vector<shared_ptr<Blob<Dtype> > > noaccum_params(param_blobs.size());
+    for (int i = 0; i < param_blobs.size(); ++i) {
+      noaccum_params[i].reset(new Blob<Dtype>());
+      noaccum_params[i]->CopyFrom(*param_blobs[i], false, true);
+    }
+    // Solve by equivalent accumulation of gradients over divided batches.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters, kIterSize);
+    Net<Dtype>& net_accum = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& accum_params =
+        net_accum.layer_by_name("innerprod")->blobs();
+    // Compare accumulated parameters against no accumulation standard.
+    const int D = this->channels_ * this->height_ * this->width_;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_param = noaccum_params[0]->cpu_data()[i];
+      const Dtype accum_param = accum_params[0]->cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_param), fabs(accum_param)));
+      EXPECT_NEAR(expected_param, accum_param, error_margin);
+    }
+    ASSERT_EQ(1, accum_params[1]->count());
+    const Dtype expected_bias = noaccum_params[1]->cpu_data()[0];
+    const Dtype accum_bias = accum_params[1]->cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+        std::min(fabs(expected_bias), fabs(accum_bias)));
+    EXPECT_NEAR(expected_bias, accum_bias, error_margin);
+  }
+
   // Test that the correct update is computed for a regularized least squares
   // problem:
   //
@@ -372,6 +417,16 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
 
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -416,6 +471,16 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
 
 template <typename TypeParam>
 class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -482,4 +547,15 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
   }
 }
 
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
 }  // namespace caffe