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@TaoLv
TaoLv committed Oct 27, 2018
1 parent 5412d64 commit af2f80a
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Showing 2 changed files with 47 additions and 14 deletions.
45 changes: 31 additions & 14 deletions src/operator/nn/mkldnn/mkldnn_convolution.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,11 @@ mkldnn::convolution_forward::primitive_desc GetConvFwdImpl(
attr.set_int_output_round_mode(round_nearest);
}

// MKL-DNN introduced padded formats since 0.15 which require more memory
// for computation compared with the actual tensor size. Currently, MKL-DNN
// operators are still reusing those memory from memory planning and the
// memory size may smaller than what MKL-DNN kernels require. So here we need
// select suboptimal kernel for computation according to tensor sizes.
if (param.conv_param.dilate.ndim() == 0 && bias == nullptr) {
mkldnn::convolution_forward::desc desc(prop, mkldnn::algorithm::convolution_direct,
data_md, weight_md, out_md, strides, padding, padding, mkldnn::padding_kind::zero);
Expand Down Expand Up @@ -155,6 +160,12 @@ static mkldnn::convolution_backward_data::primitive_desc GetConvBwdData(
mkldnn::memory::dims padding{0, 0};
padding[0] = param.pad[0];
padding[1] = param.pad[1];

// MKL-DNN introduced padded formats since 0.15 which require more memory
// for computation compared with the actual tensor size. Currently, MKL-DNN
// operators are still reusing those memory from memory planning and the
// memory size may smaller than what MKL-DNN kernels require. So here we need
// select suboptimal kernel for computation according to tensor sizes.
if (param.dilate.ndim() == 0) {
mkldnn::convolution_backward_data::desc desc(mkldnn::algorithm::convolution_direct,
data_md, weight_md, out_md, strides, padding, padding, mkldnn::padding_kind::zero);
Expand Down Expand Up @@ -199,6 +210,12 @@ static mkldnn::convolution_backward_weights::primitive_desc GetConvBwdWeights(
mkldnn::memory::dims padding{0, 0};
padding[0] = param.pad[0];
padding[1] = param.pad[1];

// MKL-DNN introduced padded formats since 0.15 which require more memory
// for computation compared with the actual tensor size. Currently, MKL-DNN
// operators are still reusing those memory from memory planning and the
// memory size may smaller than what MKL-DNN kernels require. So here we need
// select suboptimal kernel for computation according to tensor sizes.
if (param.dilate.ndim() == 0 && bias == nullptr) {
mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
data_md, weight_md, out_md, strides, padding, padding, mkldnn::padding_kind::zero);
Expand Down Expand Up @@ -229,26 +246,26 @@ static mkldnn::convolution_backward_weights::primitive_desc GetConvBwdWeights(
mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
data_md, weight_md, out_md, strides, dilates, padding, padding,
mkldnn::padding_kind::zero);
auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
CHECK(conv_pd.next_impl()) << "No implementation";
}
return conv_pd;
auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
CHECK(conv_pd.next_impl()) << "No implementation";
}
return conv_pd;
} else {
auto bias_md = GetMemDesc(*bias);
mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
data_md, weight_md, bias_md, out_md,
strides, dilates, padding, padding,
mkldnn::padding_kind::zero);
auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
CHECK(conv_pd.next_impl()) << "No implementation";
}
return conv_pd;
auto conv_pd = mkldnn::convolution_backward_weights::primitive_desc(desc, engine, fwd_pd);
while (conv_pd.diff_dst_primitive_desc().get_size() != GetArraySize(output) ||
conv_pd.src_primitive_desc().get_size() != GetArraySize(data) ||
conv_pd.diff_weights_primitive_desc().get_size() != GetArraySize(weights)) {
CHECK(conv_pd.next_impl()) << "No implementation";
}
return conv_pd;
}
}
}
Expand Down
16 changes: 16 additions & 0 deletions src/operator/nn/mkldnn/mkldnn_deconvolution.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -52,6 +52,11 @@ static mkldnn::convolution_forward::primitive_desc GetDeconvBwd_(
bool has_bias, const mkldnn::memory::desc &out_md,
const mkldnn::engine &engine, const mkldnn::memory::dims &strides,
const mkldnn::memory::dims &padding, const mkldnn::memory::dims &dilates) {
// MKL-DNN introduced padded formats since 0.15 which require more memory
// for computation compared with the actual tensor size. Currently, MKL-DNN
// operators are still reusing those memory from memory planning and the
// memory size may smaller than what MKL-DNN kernels require. So here we need
// select suboptimal kernel for computation according to tensor sizes.
if (!has_bias) {
mkldnn::convolution_forward::desc desc(mkldnn::prop_kind::forward_training,
mkldnn::algorithm::convolution_direct, out_md, weights_md, data_md, strides,
Expand Down Expand Up @@ -103,6 +108,11 @@ static mkldnn::convolution_backward_data::primitive_desc GetDeconvFwdImpl(
out_md, weight_md, data_md, strides, dilate, padding, padding,
mkldnn::padding_kind::zero);
auto deconv_pd = mkldnn::convolution_backward_data::primitive_desc(desc, engine, bwd_pd);
// MKL-DNN introduced padded formats since 0.15 which require more memory
// for computation compared with the actual tensor size. Currently, MKL-DNN
// operators are still reusing those memory from memory planning and the
// memory size may smaller than what MKL-DNN kernels require. So here we need
// select suboptimal kernel for computation according to tensor sizes.
while (deconv_pd.diff_dst_primitive_desc().get_size() != GetMemDescSize(data_md) ||
deconv_pd.diff_src_primitive_desc().get_size() != GetMemDescSize(out_md) ||
deconv_pd.weights_primitive_desc().get_size() != GetMemDescSize(weight_md)) {
Expand Down Expand Up @@ -155,6 +165,12 @@ GetDeconvBwdWeightsImpl(
mkldnn::memory::dims dilate{0, 0};
dilate[0] = param.dilate[0] - 1;
dilate[1] = param.dilate[1] - 1;

// MKL-DNN introduced padded formats since 0.15 which require more memory
// for computation compared with the actual tensor size. Currently, MKL-DNN
// operators are still reusing those memory from memory planning and the
// memory size may smaller than what MKL-DNN kernels require. So here we need
// select suboptimal kernel for computation according to tensor sizes.
if (!has_bias) {
mkldnn::convolution_backward_weights::desc desc(mkldnn::algorithm::convolution_direct,
out_md, weight_md, data_md, strides, dilate, padding, padding, mkldnn::padding_kind::zero);
Expand Down

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