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Merge pull request #4224 from tensor-tang/act

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some addition for mkldnn_act
update-doc-pybind
Tao Luo 8 years ago committed by GitHub
parent 6e3587581b eb26fdce46
commit 4400284685
6 changed files with 77 additions and 75 deletions
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5
paddle/gserver/activations/MKLDNNActivation.h
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@ -131,8 +131,9 @@ public:
fwdPD_.reset(new eltwise_fwd::primitive_desc(fwdDesc, eng));
// use inplace for forward but save input value before submit
inVal_ = val_;
if (act.grad) {
// only copy when need do backward
copyInVal_ = nullptr;
if (act.grad && algo == mkldnn::algorithm::eltwise_tanh) {
// tanh need save src input for backward
inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
CHECK(copyInVal_) << "should not be emptry";

3
paddle/gserver/layers/MKLDNNConvLayer.cpp
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@ -449,13 +449,14 @@ void MKLDNNConvLayer::resetOutGrad(
cvtOutGrad_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
outMat->setData(cpuOut->getData());
// same PrimitiveDesc with cpuInVal_
CHECK(cpuOutVal_);
cpuOutGrad_ = MKLDNNMatrix::create(cpuOut, cpuOutVal_->getPrimitiveDesc());
if (cpuOutGrad_->getPrimitiveDesc() == out->getPrimitiveDesc()) {
outMat->setData(cpuOut->getData());
out = cpuOutGrad_;
} else {
out = MKLDNNMatrix::create(nullptr, wgtPD->diff_dst_primitive_desc());
cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
CHECK(cvtOutGrad_);
}

1
paddle/gserver/layers/MKLDNNFcLayer.cpp
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@ -232,6 +232,7 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
void MKLDNNFcLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
output_.grad->setData(getOutput(device).grad->getData());
// for MKLDNN device:
// can not directly cast outputgrad to mkldnnmatrix,
// since each layer can not write the inputgrad to mkldnn inputgrad.

12
paddle/gserver/layers/MKLDNNLayer.h
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@ -141,18 +141,16 @@ public:
}
void backward(const UpdateCallback& callback) override {
/* Do derivation */ {
if (needResetBwd_) {
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
needResetBwd_ = false;
}
{
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_);
}

125
paddle/gserver/tests/test_MKLDNN.cpp
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@ -26,17 +26,26 @@ DECLARE_bool(thread_local_rand_use_global_seed);
DECLARE_bool(use_gpu);
DECLARE_bool(use_mkldnn);
struct testFCDesc {
#define RUN_MKLDNN_TEST(DNN_CONFIG, REF_CONFIG, DESC) \
MKLDNNTester tester; \
for (auto bs : {DESC.bs, 1}) { \
tester.run(DNN_CONFIG, REF_CONFIG, bs, DESC.ih, DESC.iw); \
}
#define RUN_MKLDNN_TEST_LAYER(DNN_CONFIG, REF_TYPE, DESC) \
TestConfig ref = DNN_CONFIG; \
ref.layerConfig.set_type(REF_TYPE); \
RUN_MKLDNN_TEST(DNN_CONFIG, ref, DESC)
struct testFcDesc {
int bs;
int ic;
int oc;
int ih, iw; // oh == ow == 1
};
void testFcLayer(const testFCDesc& pm) {
const std::string compareTypes[] = {"mkldnn_fc", "fc"};
TestConfig cfg;
cfg.layerConfig.set_type(compareTypes[0]);
static void getMKLDNNFcConfig(TestConfig& cfg, const testFcDesc& pm) {
cfg.layerConfig.set_type("mkldnn_fc");
cfg.layerConfig.set_size(pm.oc);
cfg.inputDefs.push_back(
{INPUT_DATA,
@ -44,25 +53,25 @@ void testFcLayer(const testFCDesc& pm) {
/* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
/* size of weight= */ size_t(pm.oc * pm.ic * pm.ih * pm.iw)});
cfg.layerConfig.add_inputs();
}
MKLDNNTester tester;
void testFcLayer(const testFcDesc& pm) {
TestConfig dnnConfig;
getMKLDNNFcConfig(dnnConfig, pm);
for (auto biasSize : {pm.oc, 0}) {
cfg.biasSize = biasSize;
TestConfig ref = cfg;
ref.layerConfig.set_type(compareTypes[1]);
for (auto bs : {pm.bs, 1}) {
tester.run(cfg, ref, bs, pm.ih, pm.iw);
}
dnnConfig.biasSize = biasSize;
RUN_MKLDNN_TEST_LAYER(dnnConfig, "fc", pm)
}
}
TEST(MKLDNNLayer, FcLayer) {
testFcLayer({/*bs*/ 2, /*ic*/ 2, /*oc*/ 3, /*ih*/ 1, /*iw*/ 1});
testFcLayer({/*bs*/ 3, /*ic*/ 7, /*oc*/ 19, /*ih*/ 1, /*iw*/ 1});
testFcLayer({/*bs*/ 8, /*ic*/ 16, /*oc*/ 32, /*ih*/ 13, /*iw*/ 13});
testFcLayer({/*bs*/ 4, /*ic*/ 12, /*oc*/ 18, /*ih*/ 13, /*iw*/ 11});
testFcLayer({/*bs*/ 2, /*ic*/ 64, /*oc*/ 32, /*ih*/ 16, /*iw*/ 16});
testFcLayer({/*bs*/ 15, /*ic*/ 3, /*oc*/ 6, /*ih*/ 16, /*iw*/ 16});
/* bs, ic, ih, iw, oc */
testFcLayer({2, 2, 1, 1, 3});
testFcLayer({3, 7, 1, 1, 19});
testFcLayer({8, 16, 13, 13, 32});
testFcLayer({4, 12, 13, 13, 18});
testFcLayer({2, 64, 16, 16, 32});
testFcLayer({15, 3, 16, 16, 6});
}
struct testConvDesc {
@ -75,13 +84,10 @@ struct testConvDesc {
int dh, dw;
};
void testConvLayer(const testConvDesc& pm) {
const std::string compareTypes[] = {"mkldnn_conv", "exconv"};
TestConfig cfg;
cfg.layerConfig.set_type(compareTypes[0]);
static void getMKLDNNConvConfig(TestConfig& cfg, const testConvDesc& pm) {
cfg.layerConfig.set_type("mkldnn_conv");
cfg.layerConfig.set_num_filters(pm.oc);
cfg.layerConfig.set_size(pm.oc * pm.oh * pm.ow);
// cfg.layerConfig.set_partial_sum(1); // TODO: check it
cfg.layerConfig.set_shared_biases(true);
cfg.inputDefs.push_back(
{INPUT_DATA,
@ -115,15 +121,14 @@ void testConvLayer(const testConvDesc& pm) {
int oh = outputSize(pm.ih, fh, pm.ph, pm.sh, true);
CHECK_EQ(ow, pm.ow) << "output size check failed";
CHECK_EQ(oh, pm.oh) << "output size check failed";
}
MKLDNNTester tester;
void testConvLayer(const testConvDesc& pm) {
TestConfig dnnConfig;
getMKLDNNConvConfig(dnnConfig, pm);
for (auto biasSize : {pm.oc, 0}) {
cfg.biasSize = biasSize;
TestConfig ref = cfg;
ref.layerConfig.set_type(compareTypes[1]);
for (auto bs : {pm.bs, 1}) {
tester.run(cfg, ref, bs, pm.ih, pm.iw);
}
dnnConfig.biasSize = biasSize;
RUN_MKLDNN_TEST_LAYER(dnnConfig, "exconv", pm)
}
}
@ -143,7 +148,7 @@ TEST(MKLDNNLayer, ConvLayer) {
}
struct testPoolDesc {
int bs, ch; // input channel and output channel are the same
int bs, ic; // input channel and output channel are the same
int ih, iw;
int oh, ow;
int fh, fw;
@ -151,19 +156,18 @@ struct testPoolDesc {
int sh, sw;
};
void testPoolLayer(const testPoolDesc& pm) {
const std::string compareTypes[] = {"mkldnn_pool", "pool"};
TestConfig cfg;
cfg.layerConfig.set_type(compareTypes[0]);
cfg.layerConfig.set_size(pm.ch * pm.oh * pm.ow);
static void getMKLDNNPoolConfig(TestConfig& cfg, const testPoolDesc& pm) {
cfg.layerConfig.set_type("mkldnn_pool");
cfg.layerConfig.set_size(pm.ic * pm.oh * pm.ow);
cfg.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
/* size of input layer= */ size_t(pm.ch * pm.ih * pm.iw),
/* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
0});
LayerInputConfig* input = cfg.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
pool->set_channels(pm.ch);
pool->set_pool_type("avg-projection");
pool->set_channels(pm.ic);
pool->set_img_size(pm.iw);
pool->set_img_size_y(pm.ih);
pool->set_output_x(pm.ow);
@ -179,15 +183,16 @@ void testPoolLayer(const testPoolDesc& pm) {
int ow = outputSize(pm.iw, pm.fw, pm.pw, pm.sw, false);
CHECK_EQ(ow, pm.ow) << "output size check failed";
CHECK_EQ(oh, pm.oh) << "output size check failed";
}
MKLDNNTester tester;
void testPoolLayer(const testPoolDesc& pm) {
TestConfig dnnConfig;
getMKLDNNPoolConfig(dnnConfig, pm);
LayerInputConfig* input = dnnConfig.layerConfig.mutable_inputs(0);
PoolConfig* pool = input->mutable_pool_conf();
for (auto type : {"max-projection", "avg-projection"}) {
pool->set_pool_type(type);
TestConfig ref = cfg;
ref.layerConfig.set_type(compareTypes[1]);
for (auto bs : {pm.bs, 1}) {
tester.run(cfg, ref, bs, pm.ih, pm.iw);
}
RUN_MKLDNN_TEST_LAYER(dnnConfig, "pool", pm)
}
}
@ -204,44 +209,36 @@ TEST(MKLDNNLayer, PoolLayer) {
}
struct testActDesc {
int bs, ch;
int ih, iw;
int bs, ic, ih, iw;
};
static void getAddtoConfig(TestConfig& cfg, const testActDesc& pm) {
cfg.biasSize = 0;
cfg.layerConfig.set_type("addto");
cfg.layerConfig.set_size(pm.ch * pm.ih * pm.iw);
cfg.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
/* size of input layer= */ size_t(pm.ch * pm.ih * pm.iw),
0});
size_t layerSize = pm.ih * pm.ih * pm.iw;
cfg.layerConfig.set_size(layerSize);
cfg.inputDefs.push_back({INPUT_DATA, "layer_0", layerSize, 0});
cfg.layerConfig.add_inputs();
}
void testActivation(std::string& type, const testActDesc& pm) {
const std::string compareTypes[] = {type, type.erase(0, 7)};
void testActivation(std::string& actType, const testActDesc& pm) {
// TODO(TJ): mkldnn_softmax not implemented, paddle do not have elu activation
if (actType == "mkldnn_softmax" || actType == "mkldnn_elu") {
return;
}
const std::string compareTypes[] = {actType, actType.erase(0, 7)};
TestConfig cfg;
getAddtoConfig(cfg, pm);
TestConfig ref = cfg;
cfg.layerConfig.set_active_type(compareTypes[0]);
ref.layerConfig.set_active_type(compareTypes[1]);
MKLDNNTester tester;
for (auto bs : {pm.bs, 1}) {
tester.run(cfg, ref, bs, pm.ih, pm.iw);
}
RUN_MKLDNN_TEST(cfg, ref, pm)
}
TEST(MKLDNNActivation, Activations) {
auto types = MKLDNNActivation::getAllRegisteredTypes();
// TODO(TJ): mkldnn_softmax not implemented, paddle do not have elu activation
std::set<string> excluded{"mkldnn_softmax", "mkldnn_elu"};
for (auto type : types) {
if (excluded.count(type)) {
continue;
}
/* bs, c, h, w*/
testActivation(type, {16, 64, 32, 32});
}
}

4
python/paddle/trainer/config_parser.py
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@ -1565,6 +1565,10 @@ class LayerBase(object):
self.config = g_config.model_config.layers.add()
assert isinstance(self.config, LayerConfig)
use_mkldnn = bool(int(g_command_config_args.get("use_mkldnn", 0)))
mkldnn_acts = ['relu', 'tanh']
if use_mkldnn and active_type in mkldnn_acts:
active_type = "mkldnn_" + active_type
self.config.name = name
self.config.type = type
self.config.active_type = active_type

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