enable mkldnn_batch_norm layer

Author: tensor-tang

paddle/gserver/layers/MKLDNNBatchNormLayer.cpp

@@ -0,0 +1,326 @@
+/* Copyright (c) 2017 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 "MKLDNNBatchNormLayer.h"
+
+using namespace mkldnn;  // NOLINT
+typedef memory::format format;
+
+namespace paddle {
+
+REGISTER_LAYER(mkldnn_batch_norm, MKLDNNBatchNormLayer);
+
+const real MKLDNNBatchNormLayer::EPS = 1E-5;
+
+bool MKLDNNBatchNormLayer::init(const LayerMap& layerMap,
+                                const ParameterMap& parameterMap) {
+  if (!MKLDNNLayer::init(layerMap, parameterMap)) {
+    return false;
+  }
+
+  // first one is input layer
+  // the other two are created in config_parser.py saving moving mean and var
+  CHECK_EQ(inputLayers_.size(), 3U);
+  CHECK_EQ(inputLayers_.size(), parameters_.size());
+  CHECK_EQ(inputLayers_.size(), size_t(config_.inputs_size()));
+
+  const ImageConfig& conf = config_.inputs(0).image_conf();
+  ic_ = conf.channels();
+  ih_ = inputLayers_[0]->getOutput().getFrameHeight();
+  iw_ = inputLayers_[0]->getOutput().getFrameWidth();
+  if (iw_ == 0 && ih_ == 0) {
+    iw_ = conf.img_size();
+    ih_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
+  }
+  oc_ = ic_;
+  oh_ = ih_;
+  ow_ = iw_;
+  if (config_.has_use_global_stats()) {
+    useGlobalStats_ = config_.use_global_stats();
+  }
+  movingAvgFraction_ = config_.moving_average_fraction();
+  VLOG(MKLDNN_BASE) << "--- " << (useGlobalStats_ ? "use" : "do not use")
+                    << " --- global stats";
+  VLOG(MKLDNN_BASE) << "Moving average fraction: " << movingAvgFraction_;
+
+  initWeight();
+  movingMean_.reset(new Weight(oc_, 1, parameters_[1], 0));
+  movingVar_.reset(new Weight(oc_, 1, parameters_[2], 0));
+  return true;
+}
+
+void MKLDNNBatchNormLayer::initWeight() {
+  weight_.reset(new Weight(1, oc_, parameters_[0]));
+  if (biasParameter_.get() != NULL) {
+    biases_ = std::unique_ptr<Weight>(new Weight(1, oc_, biasParameter_));
+  }
+  CHECK_EQ(weight_ != nullptr, biases_ != nullptr)
+      << "only support have both weight and bias, or neither";
+  if (weight_ && weight_->getW()) {
+    CHECK(biases_ && biases_->getW());
+    valueScaleShift_ = Matrix::create(2, oc_, false, false);
+    valueScaleShift_->zeroMem();
+    VectorPtr scale(new CpuVector(oc_, valueScaleShift_->getMemoryHandle(), 0));
+    VectorPtr shift(
+        new CpuVector(oc_, valueScaleShift_->getMemoryHandle(), oc_));
+    const VectorPtr& wgt = parameters_[0]->getBuf(PARAMETER_VALUE);
+    const VectorPtr& bias = biasParameter_->getBuf(PARAMETER_VALUE);
+    scale->copyFrom(*wgt);
+    shift->copyFrom(*bias);
+    wgt->setData(valueScaleShift_->getData());
+    bias->setData(valueScaleShift_->getData() + oc_);
+  }
+  if (weight_ && weight_->getWGrad()) {
+    CHECK(biases_ && biases_->getWGrad());
+    gradScaleShift_ = Matrix::create(2, oc_, false, false);
+    gradScaleShift_->zeroMem();
+    const VectorPtr& wgt = parameters_[0]->getBuf(PARAMETER_GRADIENT);
+    const VectorPtr& bias = biasParameter_->getBuf(PARAMETER_GRADIENT);
+    wgt->setData(gradScaleShift_->getData());
+    bias->setData(gradScaleShift_->getData() + oc_);
+  }
+}
+
+void MKLDNNBatchNormLayer::convertWeightsFromPaddle() {
+  if (hasInitedWgt_) {
+    return;
+  }
+  // prepare mean and var if necessary
+  if (useGlobalStats_) {
+    CHECK(mean_);
+    CHECK(var_);
+    mean_->copyFrom(*(movingMean_->getW()));
+    var_->copyFrom(*(movingVar_->getW()));
+  }
+  hasInitedWgt_ = true;
+}
+
+void MKLDNNBatchNormLayer::calMovingMeanAndVar() {
+  // calculating and saving moving mean and variance
+  CHECK_EQ(useGlobalStats_, false);
+  MatrixPtr movingMean = movingMean_->getW();
+  MatrixPtr movingVar = movingVar_->getW();
+  if (FLAGS_trainer_count > 1) {
+    auto mvMean = std::dynamic_pointer_cast<SharedCpuMatrix>(movingMean);
+    auto mvVar = std::dynamic_pointer_cast<SharedCpuMatrix>(movingVar);
+    CHECK(mvMean && mvVar);
+    mvMean->add(*mean_, movingAvgFraction_, 1.0 - movingAvgFraction_);
+    mvVar->add(*var_, movingAvgFraction_, 1.0 - movingAvgFraction_);
+  } else {
+    movingMean->add(*mean_, movingAvgFraction_, 1.0 - movingAvgFraction_);
+    // here var is v^2
+    movingVar->add(*var_, movingAvgFraction_, 1.0 - movingAvgFraction_);
+  }
+}
+
+void MKLDNNBatchNormLayer::reshape(
+    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+  reshapeInput(bs, ih, iw);
+  oh = ih;
+  ow = ow;
+  // ic_ and oc can not be changed
+  CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
+      << "Input channel can not be changed";
+  reshapeOutput(oh, ow);
+  resizeOutput(bs, oc * oh * ow);
+  printSizeInfo();
+}
+
+void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
+                                    MKLDNNMatrixPtr& in,
+                                    MKLDNNMatrixPtr& wgt,
+                                    MKLDNNMatrixPtr& bias,
+                                    MKLDNNMatrixPtr& out) {
+  // in training always calculate mean and var, so useGlobalStats must be false
+  // in test depends on useGlobalStats
+  if (passType_ != PASS_TEST && useGlobalStats_ == true) {
+    LOG(WARNING) << "use_global_stats is invalid setting in training phase";
+    useGlobalStats_ = false;
+  }
+
+  resetFwdBuffers(in, wgt, out);
+
+  resetFwdPD(fwdPD_, in, wgt, out);
+
+  resetFwdPipeline(pipeline, fwdPD_, in, wgt, out);
+}
+
+void MKLDNNBatchNormLayer::resetBwd(std::vector<primitive>& pipeline,
+                                    MKLDNNMatrixPtr& in,
+                                    MKLDNNMatrixPtr& wgt,
+                                    MKLDNNMatrixPtr& bias,
+                                    MKLDNNMatrixPtr& out) {
+  std::shared_ptr<bn_bwd::primitive_desc> pd;
+
+  resetBwdBuffers(in, wgt, out);
+
+  resetBwdPD(pd, in, wgt, out);
+
+  resetBwdPipeline(pipeline, pd, in, wgt, out);
+}
+
+void MKLDNNBatchNormLayer::forward(PassType passType) {
+  MKLDNNLayer::forward(passType);
+
+  // calculating and saving moving mean and variance
+  if (passType_ != PASS_TEST) {
+    calMovingMeanAndVar();
+  }
+}
+
+void MKLDNNBatchNormLayer::updateWeights(const UpdateCallback& callback) {
+  weight_->getParameterPtr()->incUpdate(callback);
+  if (biases_ && biases_->getWGrad()) {
+    biases_->getParameterPtr()->incUpdate(callback);
+  }
+}
+
+void MKLDNNBatchNormLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
+                                           MKLDNNMatrixPtr& wgt,
+                                           MKLDNNMatrixPtr& out) {
+  resetInValue(in);
+
+  memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
+  CHECK(in);
+  auto outPD =
+      MKLDNNMatrix::createPrimitiveDesc(outDims, in->getFormat(), engine_);
+  resetOutValue(out, outPD);
+
+  if (valueScaleShift_) {
+    auto pd = MKLDNNMatrix::createPrimitiveDesc({2, oc_}, format::nc, engine_);
+    resetWithMatrix(wgt, valueScaleShift_, pd);
+  }
+  if (passType_ != PASS_TEST || useGlobalStats_) {
+    auto pd = MKLDNNMatrix::createPrimitiveDesc({oc_}, format::x, engine_);
+    mean_ = MKLDNNMatrix::create(pd);
+    var_ = MKLDNNMatrix::create(pd);
+  }
+}
+
+void MKLDNNBatchNormLayer::resetFwdPD(
+    std::shared_ptr<bn_fwd::primitive_desc>& pd,
+    MKLDNNMatrixPtr in,
+    MKLDNNMatrixPtr wgt,
+    MKLDNNMatrixPtr out) {
+  flags_ = 0u;
+  prop_kind pk = passType_ == PASS_TEST ? prop_kind::forward_scoring
+                                        : prop_kind::forward_training;
+  if (useGlobalStats_) {
+    flags_ = (flags_ | batch_normalization_flag::use_global_stats);
+  }
+  if (wgt) {
+    flags_ = (flags_ | batch_normalization_flag::use_scale_shift);
+  }
+  auto fwdDesc = bn_fwd::desc(pk, in->getMemoryDesc(), EPS, flags_);
+  pd.reset(new bn_fwd::primitive_desc(fwdDesc, engine_));
+  // TODO(TJ): use check macro
+  CHECK(out);
+  CHECK(out->getPrimitiveDesc() == pd->dst_primitive_desc());
+  if (wgt) {
+    CHECK(wgt->getPrimitiveDesc() == pd->weights_primitive_desc());
+  }
+  if (passType_ != PASS_TEST || useGlobalStats_) {
+    CHECK(mean_);
+    CHECK(mean_->getPrimitiveDesc() == pd->mean_primitive_desc());
+    CHECK(var_);
+    CHECK(var_->getPrimitiveDesc() == pd->variance_primitive_desc());
+  }
+}
+
+void MKLDNNBatchNormLayer::resetFwdPipeline(
+    std::vector<primitive>& pipeline,
+    std::shared_ptr<bn_fwd::primitive_desc>& pd,
+    MKLDNNMatrixPtr& in,
+    MKLDNNMatrixPtr& wgt,
+    MKLDNNMatrixPtr& out) {
+  if (passType_ == PASS_TEST) {
+    if (useGlobalStats_) {
+      fwd_.reset(wgt != nullptr ? new bn_fwd(*pd,
+                                             *in,
+                                             (const primitive::at)(*mean_),
+                                             (const primitive::at)(*var_),
+                                             *wgt,
+                                             *out)
+                                : new bn_fwd(*pd,
+                                             *in,
+                                             (const primitive::at)(*mean_),
+                                             (const primitive::at)(*var_),
+                                             *out));
+    } else {
+      fwd_.reset(wgt != nullptr ? new bn_fwd(*pd, *in, *wgt, *out)
+                                : new bn_fwd(*pd, *in, *out));
+    }
+  } else {
+    CHECK_EQ(useGlobalStats_, false)
+        << "useGlobalStats should be false in training";
+    fwd_.reset(wgt != nullptr ? new bn_fwd(*pd, *in, *wgt, *out, *mean_, *var_)
+                              : new bn_fwd(*pd, *in, *out, *mean_, *var_));
+  }
+  pipeline.push_back(*fwd_);
+}
+
+void MKLDNNBatchNormLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
+                                           MKLDNNMatrixPtr& wgt,
+                                           MKLDNNMatrixPtr& out) {
+  CHECK(inVal_ && outVal_);
+  resetOutGrad(out, outVal_->getPrimitiveDesc());
+  resetInGrad(in, inVal_->getPrimitiveDesc());
+  if (gradScaleShift_) {
+    CHECK(wgtVal_);
+    resetWithMatrix(wgt, gradScaleShift_, wgtVal_->getPrimitiveDesc());
+  }
+}
+
+void MKLDNNBatchNormLayer::resetBwdPD(
+    std::shared_ptr<bn_bwd::primitive_desc>& pd,
+    MKLDNNMatrixPtr& in,
+    MKLDNNMatrixPtr& wgt,
+    MKLDNNMatrixPtr& out) {
+  pd = nullptr;
+  if (in == nullptr) {
+    return;
+  }
+  CHECK(out);
+  CHECK(out->getPrimitiveDesc() == in->getPrimitiveDesc());
+  auto md = in->getMemoryDesc();
+  auto bwdDesc = bn_bwd::desc(prop_kind::backward, md, md, EPS, flags_);
+  pd.reset(new bn_bwd::primitive_desc(bwdDesc, engine_, *fwdPD_));
+  // TODO(TJ): use check macro
+  CHECK(wgt);
+  CHECK(wgt->getPrimitiveDesc() == pd->diff_weights_primitive_desc());
+  CHECK(pd->weights_primitive_desc() == fwdPD_->weights_primitive_desc());
+  CHECK(mean_);
+  CHECK(mean_->getPrimitiveDesc() == pd->mean_primitive_desc());
+  CHECK(var_);
+  CHECK(var_->getPrimitiveDesc() == pd->variance_primitive_desc());
+}
+
+void MKLDNNBatchNormLayer::resetBwdPipeline(
+    std::vector<primitive>& pipeline,
+    std::shared_ptr<bn_bwd::primitive_desc>& pd,
+    MKLDNNMatrixPtr& in,
+    MKLDNNMatrixPtr& wgt,
+    MKLDNNMatrixPtr& out) {
+  if (pd == nullptr) {
+    return;
+  }
+  CHECK(inVal_);
+  bwdData_.reset(
+      wgt && wgtVal_
+          ? new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *wgtVal_, *in, *wgt)
+          : new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *in));
+  pipeline.push_back(*bwdData_);
+}
+
+}  // namespace paddle