提交 8899d422 编写于 作者: T Tomasz Patejko

MKLDNN conv residual data: primitive reuse interface used. Reorder done when formats are different

test=develop
上级 f11934cb
...@@ -59,6 +59,11 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler { ...@@ -59,6 +59,11 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
return conv_pd_->dst_primitive_desc().get_size(); return conv_pd_->dst_primitive_desc().get_size();
} }
mkldnn::memory::format GetDstFormat() const {
return static_cast<mkldnn::memory::format>(
conv_pd_->dst_primitive_desc().desc().data.format);
}
size_t GetDiffWeightsMemorySize() const { size_t GetDiffWeightsMemorySize() const {
return conv_bwd_weights_pd_->diff_weights_primitive_desc().get_size(); return conv_bwd_weights_pd_->diff_weights_primitive_desc().get_size();
} }
...@@ -115,6 +120,15 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler { ...@@ -115,6 +120,15 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
return this->AcquireMemory(md, ptr, "@user_residual_data_mem_p"); return this->AcquireMemory(md, ptr, "@user_residual_data_mem_p");
} }
std::shared_ptr<mkldnn::memory> AcquireDstMemoryFromResidualDataMemory(
const std::shared_ptr<mkldnn::memory>& user_residual_memory_p,
void* dst_ptr,
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
return this->AcquireMemory(user_residual_memory_p,
this->AcquireDstMemoryFromPrimitive(dst_ptr),
"@residual_data_mem_p", pipeline);
}
std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemoryFromDataPrimitive( std::shared_ptr<mkldnn::memory> AcquireDiffSrcMemoryFromDataPrimitive(
void* ptr) { void* ptr) {
return this->AcquireMemoryFromPrimitive( return this->AcquireMemoryFromPrimitive(
...@@ -398,10 +412,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -398,10 +412,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline); handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
auto weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive( auto weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive(
user_weights_memory_p, pipeline, is_test); user_weights_memory_p, pipeline, is_test);
auto output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize()); std::shared_ptr<mkldnn::memory> dst_memory_p;
auto dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
if (fuse_residual_conn) { if (fuse_residual_conn) {
auto residual_param = ctx.Input<Tensor>("ResidualData"); auto residual_param = ctx.Input<Tensor>("ResidualData");
...@@ -414,7 +426,9 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -414,7 +426,9 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
"Output and elementwise parameter need to have the " "Output and elementwise parameter need to have the "
"same dimension sizes"); "same dimension sizes");
if (residual_param->format() != output->format()) { if (residual_param->format() != handler.GetDstFormat()) {
auto output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
auto residual_data_tz = auto residual_data_tz =
paddle::framework::vectorize2int(residual_param->dims()); paddle::framework::vectorize2int(residual_param->dims());
auto residual_data_type = auto residual_data_type =
...@@ -424,10 +438,20 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> { ...@@ -424,10 +438,20 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
residual_data_tz, residual_data_type, residual_param->format()); residual_data_tz, residual_data_type, residual_param->format());
auto user_residual_memory_p = handler.AcquireResidualDataMemory( auto user_residual_memory_p = handler.AcquireResidualDataMemory(
user_residual_md, to_void_cast<T>(residual_param_data)); user_residual_md, to_void_cast<T>(residual_param_data));
platform::Reorder(*user_residual_memory_p, *dst_memory_p);
dst_memory_p = handler.AcquireDstMemoryFromResidualDataMemory(
user_residual_memory_p, to_void_cast<T>(output_data), pipeline);
} else { } else {
output->ShareDataWith(*residual_param); output->ShareDataWith(*residual_param);
auto output_data = output->mutable_data<T>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
} }
} else {
auto output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
} }
// create convolution op primitive // create convolution op primitive
......
...@@ -187,6 +187,29 @@ class MKLDNNHandler { ...@@ -187,6 +187,29 @@ class MKLDNNHandler {
return mem_p; return mem_p;
} }
std::shared_ptr<mkldnn::memory> AcquireMemory(
const std::shared_ptr<mkldnn::memory>& user_memory_p,
const std::shared_ptr<mkldnn::memory>& target_memory_p,
const std::string& suffix,
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
auto local_key = key_ + suffix;
auto key_reorder_p = key_ + suffix + "reorder_p";
auto stored_reorder_p = std::static_pointer_cast<mkldnn::reorder>(
dev_ctx_.GetBlob(key_reorder_p));
if (stored_reorder_p) {
pipeline.push_back(*stored_reorder_p);
} else {
auto reorder_p =
std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
dev_ctx_.SetBlob(key_reorder_p, reorder_p);
pipeline.push_back(*reorder_p);
}
return target_memory_p;
}
std::shared_ptr<mkldnn::memory> AcquireMemory( std::shared_ptr<mkldnn::memory> AcquireMemory(
mkldnn::memory::primitive_desc& mpd, // NOLINT mkldnn::memory::primitive_desc& mpd, // NOLINT
mkldnn::memory::primitive_desc& user_mpd, // NOLINT mkldnn::memory::primitive_desc& user_mpd, // NOLINT
......
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