remove md recreate for int8

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -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, 
+                                           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){
+        } 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);
+        }
+    } 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);
+        } else {
+                conv_pd =
+                    ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings,
+                                         mkldnn_engine, fuse_relu, fuse_residual_conn);
         }
     }
     // Save conv_pd/src_memory/weights_memory for backward pass
@@ -634,6 +631,7 @@ 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 +720,7 @@ 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);
       }
@@ -774,7 +772,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       memory::dims padding_dims = {paddings[0], paddings[1]};
   
       auto conv_desc = mkldnn::convolution_forward::desc(
-          mkldnn::prop_kind::forward, mkldnn::convolution_direct, src, weights,
+          mkldnn::prop_kind::forward_scoring, mkldnn::convolution_direct, src, weights,
           dst, stride_dims, padding_dims, padding_dims,
           mkldnn::padding_kind::zero);
   
@@ -824,7 +822,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       memory::dims padding_dims = {paddings[0], paddings[1]};
 
       auto conv_desc = mkldnn::convolution_forward::desc(
-          mkldnn::prop_kind::forward, mkldnn::convolution_direct, src, weights,
+          mkldnn::prop_kind::forward_scoring, mkldnn::convolution_direct, src, weights,
           bias, dst, stride_dims, padding_dims, padding_dims,
           mkldnn::padding_kind::zero);