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提交 53545a37 编写于 作者: Z Zhang, Guoming
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Merge branch 'prv-calibration' of... 

Merge branch 'prv-calibration' of ssh://git-ccr-1.devtools.intel.com:29418/intelpaddle-shanghai into prv-calibration
上级 2be274ae 93c4ee01
隐藏空白更改
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Showing with 132 addition and 115 deletion
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ 53545a37
......@@ -401,11 +401,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
int count = is_multi_channel? (g>1? weights_tz[1]*weights_tz[0] : weights_tz[0]) : 1;
scale_in_data = {*(scale_in->data<float>())};
scale_weights_data.resize(count);
#pragma omp parallel for if (count > 1)
for(int i=0; i<count; i++){
scale_weights_data[i] =*(scale_weights->data<float>() + i);
}
scale_out_data = {*(scale_out->data<float>())};
output_shift_scale.resize(count);
#pragma omp parallel for if (count > 1)
for(int i=0; i<count; i++){
if(scale_weights_data[i] == 0.0)
output_shift_scale[i] = scale_out_data[0];
......@@ -454,61 +456,56 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto chosen_memory_format =
platform::data_format_to_memory_format(data_format);
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format);
auto weights_md = platform::MKLDNNMemDesc(
weights_tz, platform::MKLDNNGetDataType<float>(),
(g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw);
auto dst_md = platform::MKLDNNMemDesc(
dst_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format);
std::vector<int> bias_tz;
std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd;
auto bias_tz = paddle::framework::vectorize2int(bias->dims());
if(is_INT8){
src_md = platform::MKLDNNMemDesc(
auto src_md = platform::MKLDNNMemDesc(
src_tz, memory::data_type::u8, chosen_memory_format);
weights_md = platform::MKLDNNMemDesc(
auto weights_md = platform::MKLDNNMemDesc(
weights_tz, memory::data_type::s8,
(g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw);
auto dst_dt = fuse_relu? paddle::framework::ToMKLDNNDataType(std::type_index(typeid(unsigned char))) : paddle::framework::ToMKLDNNDataType(std::type_index(typeid(signed char)));
if(fuse_residual_conn){
auto residual = ctx.Input<Tensor>("ResidualData");
auto residual_dt = paddle::framework::ToMKLDNNDataType(residual->type());
if(dst_dt != residual_dt)
if(dst_dt != residual_dt)
dst_dt = residual_dt;
}
dst_md = platform::MKLDNNMemDesc(dst_tz, dst_dt, chosen_memory_format);
}
auto dst_md = platform::MKLDNNMemDesc(dst_tz, dst_dt, chosen_memory_format);
// create a conv primitive descriptor and save it for usage in backward
std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd;
if (bias) {
bias_tz = paddle::framework::vectorize2int(bias->dims());
auto bias_md = platform::MKLDNNMemDesc(
bias_tz, platform::MKLDNNGetDataType<float>(), memory::format::x);
if(is_INT8){
bias_md = platform::MKLDNNMemDesc(
// create a conv primitive descriptor and save it for usage in backward
if (bias) {
auto bias_md = platform::MKLDNNMemDesc(
bias_tz, memory::data_type::s32, memory::format::x);
}
if(is_INT8){
conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md,
strides, paddings, mkldnn_engine,
fuse_relu, fuse_residual_conn,
output_shift_scale, sum_scale[0]);
} else{
conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md,
strides, paddings, mkldnn_engine,
fuse_relu, fuse_residual_conn);
}
} else {
if(is_INT8){
fuse_relu, fuse_residual_conn,
output_shift_scale, sum_scale[0], is_test);
} else {
conv_pd =
ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings,
mkldnn_engine, fuse_relu, fuse_residual_conn,
output_shift_scale, sum_scale[0]);
} else{
conv_pd =
ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings,
mkldnn_engine, fuse_relu, fuse_residual_conn);
output_shift_scale, sum_scale[0], is_test);
}
} else{
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format);
auto weights_md = platform::MKLDNNMemDesc(
weights_tz, platform::MKLDNNGetDataType<float>(),
(g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw);
auto dst_md = platform::MKLDNNMemDesc(
dst_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format);
// create a conv primitive descriptor and save it for usage in backward
if (bias) {
auto bias_md = platform::MKLDNNMemDesc(
bias_tz, platform::MKLDNNGetDataType<float>(), memory::format::x);
conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md,
strides, paddings, mkldnn_engine,
fuse_relu, fuse_residual_conn, is_test);
} else {
conv_pd =
ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings,
mkldnn_engine, fuse_relu, fuse_residual_conn, is_test);
}
}
// Save conv_pd/src_memory/weights_memory for backward pass
......@@ -538,27 +535,80 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::shared_ptr<mkldnn::memory> dst_memory_p;
bool need_s8_to_u8 = false;
if(is_INT8){
if (fuse_residual_conn) {
auto residual_param = ctx.Input<Tensor>("ResidualData");
PADDLE_ENFORCE_EQ(output->dims(), residual_param->dims(),
"Output and elementwise parameter need to have the "
"same dimension sizes");
output->ShareDataWith(*residual_param);
auto residual_dt = paddle::framework::ToMKLDNNDataType(residual_param->type());
if(residual_dt == mkldnn::memory::data_type::u8){
uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<uint8_t>(output_data));
} else{
int8_t* output_data = output->mutable_data<int8_t>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<int8_t>(output_data));
if(fuse_relu)
need_s8_to_u8 = true;
}
if(fuse_residual_conn) {
auto residual_param = ctx.Input<Tensor>("ResidualData");
PADDLE_ENFORCE_EQ(output->dims(), residual_param->dims(),
"Output and elementwise parameter need to have the "
"same dimension sizes");
auto residual_dt = paddle::framework::ToMKLDNNDataType(residual_param->type());
if(residual_param->format() != handler.GetDstFormat()) {
auto residual_data_tz =
paddle::framework::vectorize2int(residual_param->dims());
auto residual_data_type =
paddle::framework::ToMKLDNNDataType(residual_param->type());
auto user_residual_md = platform::MKLDNNMemDesc(
residual_data_tz, residual_data_type, residual_param->format());
if(is_INT8){
if(residual_dt == mkldnn::memory::data_type::u8){
auto residual_param_data = residual_param->data<uint8_t>();
auto user_residual_memory_p = handler.AcquireResidualDataMemory(
user_residual_md, to_void_cast<uint8_t>(residual_param_data));
PADDLE_ENFORCE(
residual_param_data != nullptr,
"Provide data if you want MKLDNN conv+elementwise_add fusion");
uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromResidualDataMemory(
user_residual_memory_p, to_void_cast<uint8_t>(output_data), pipeline);
} else{
auto residual_param_data = residual_param->data<int8_t>();
auto user_residual_memory_p = handler.AcquireResidualDataMemory(
user_residual_md, to_void_cast<int8_t>(residual_param_data));
PADDLE_ENFORCE(
residual_param_data != nullptr,
"Provide data if you want MKLDNN conv+elementwise_add fusion");
int8_t* output_data = output->mutable_data<int8_t>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromResidualDataMemory(
user_residual_memory_p, to_void_cast<int8_t>(output_data), pipeline);
if(fuse_relu)
need_s8_to_u8 = true;
}
} else{
auto residual_param_data = residual_param->data<T>();
auto user_residual_memory_p = handler.AcquireResidualDataMemory(
user_residual_md, to_void_cast<T>(residual_param_data));
PADDLE_ENFORCE(
residual_param_data != nullptr,
"Provide data if you want MKLDNN conv+elementwise_add fusion");
auto output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
dst_memory_p = handler.AcquireDstMemoryFromResidualDataMemory(
user_residual_memory_p, to_void_cast<T>(output_data), pipeline);
}
} else {
output->ShareDataWith(*residual_param);
if(is_INT8){
if(residual_dt == mkldnn::memory::data_type::u8){
uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<uint8_t>(output_data));
} else{
int8_t* output_data = output->mutable_data<int8_t>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<int8_t>(output_data));
if(fuse_relu)
need_s8_to_u8 = true;
}
} else{
auto output_data = output->mutable_data<T>(ctx.GetPlace());
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
}
}
} else {
if(is_INT8){
if(fuse_relu){
uint8_t* output_data = output->mutable_data<uint8_t>(ctx.GetPlace(), handler.GetDstMemorySize());
dst_memory_p =
......@@ -568,54 +618,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
dst_memory_p =
handler.AcquireDstMemoryFromPrimitive(to_void_cast<int8_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));
}
} else{
// create reorder primitive if the input format is not the preferred one
// auto src_memory_p =
// handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
// auto weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive(
// user_weights_memory_p, pipeline, is_test);
// std::shared_ptr<mkldnn::memory> dst_memory_p;
if (fuse_residual_conn) {
auto residual_param = ctx.Input<Tensor>("ResidualData");
auto residual_param_data = residual_param->data<T>();
PADDLE_ENFORCE(
residual_param_data != nullptr,
"Provide data if you want MKLDNN conv+elementwise_add fusion");
PADDLE_ENFORCE_EQ(output->dims(), residual_param->dims(),
"Output and elementwise parameter need to have the "
"same dimension sizes");
if (residual_param->format() != handler.GetDstFormat()) {
auto output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
auto residual_data_tz =
paddle::framework::vectorize2int(residual_param->dims());
auto residual_data_type =
paddle::framework::ToMKLDNNDataType(residual_param->type());
auto user_residual_md = platform::MKLDNNMemDesc(
residual_data_tz, residual_data_type, residual_param->format());
auto user_residual_memory_p = handler.AcquireResidualDataMemory(
user_residual_md, to_void_cast<T>(residual_param_data));
dst_memory_p = handler.AcquireDstMemoryFromResidualDataMemory(
user_residual_memory_p, to_void_cast<T>(output_data), pipeline);
} else {
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));
}
// dst_memory_p =
// handler.AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
}
// create convolution op primitive
......@@ -634,6 +642,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
if(scale_reuse){
int count = is_multi_channel? (g>1? weights_tz[1]*weights_tz[0] : weights_tz[0]) : 1;
scale_bias_data.resize(count);
#pragma omp parallel for if (count > 1)
for(int i=0; i<count; i++){
scale_bias_data[i] = scale_in_data[0] * scale_weights_data[i];
}
......@@ -722,7 +731,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
// the scale parameter. It is assumed that when fuse_residual_conn is true, the
// Output tensor contains the data coming from residual connection. The
// result of this post_op is: Output = scale * Output + Conv_Out.
conv_attr.set_output_scales(0, {1.0f});
if (fuse_residual_conn) {
post_operations.append_sum(1.0f);
}
......@@ -745,12 +754,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_residual_conn,
const std::vector<float> output_shift_scale, const float sum_scale) const {
const std::vector<float> output_shift_scale, const float sum_scale, bool is_test) const {
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;
auto conv_desc = mkldnn::convolution_forward::desc(
mkldnn::prop_kind::forward_scoring, mkldnn::convolution_direct, src, weights,
propagation, mkldnn::convolution_direct, src, weights,
dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
......@@ -769,12 +780,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_residual_conn) const{
const bool fuse_residual_conn, bool is_test) const{
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;
auto conv_desc = mkldnn::convolution_forward::desc(
mkldnn::prop_kind::forward, mkldnn::convolution_direct, src, weights,
propagation, mkldnn::convolution_direct, src, weights,
dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
......@@ -794,12 +807,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_residual_conn,
const std::vector<float> output_shift_scale, const float sum_scale) const {
const std::vector<float> output_shift_scale, const float sum_scale, bool is_test) const {
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;
auto conv_desc = mkldnn::convolution_forward::desc(
mkldnn::prop_kind::forward_scoring, mkldnn::convolution_direct, src, weights,
propagation, mkldnn::convolution_direct, src, weights,
bias, dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
......@@ -819,12 +834,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_residual_conn) const{
const bool fuse_residual_conn, bool is_test) const{
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
auto propagation = is_test ? mkldnn::prop_kind::forward_scoring : mkldnn::prop_kind::forward_training;
auto conv_desc = mkldnn::convolution_forward::desc(
mkldnn::prop_kind::forward, mkldnn::convolution_direct, src, weights,
propagation, mkldnn::convolution_direct, src, weights,
bias, dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
......
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