fix conv int8 bugs with debug log

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -173,6 +173,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
 
       dev_ctx_.SetBlob(prim_key, conv_p);
     } else {
+std::cout<<"4 is reuse = "<<is_reusing_;
       is_reusing_ = true;
     }
     return conv_p;
@@ -186,6 +187,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
     auto prim_key = key_ + "@conv_p";
     auto conv_p = std::static_pointer_cast<mkldnn::convolution_forward>(
         dev_ctx_.GetBlob(prim_key));
+    //is_reusing_ = false;
     PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false),
                    "Fail to find convolution primitive in device context");
     if (conv_p == nullptr) {
@@ -195,6 +197,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
 
       dev_ctx_.SetBlob(prim_key, conv_p);
     } else {
+std::cout<<"5 is reuse = "<<is_reusing_;
       is_reusing_ = true;
     }
     return conv_p;
@@ -376,13 +379,17 @@ std::cout<<"this is conv int8 op .............."<<std::endl;
         ctx.op().Output("Output"));
     const std::string key_conv_pd = key + "@conv_pd";
 
-    std::vector<primitive> pipeline;
+std::cout<<key_conv_pd<<std::endl;
 
+    std::vector<primitive> pipeline;
+std::cout<<"log1....."<<std::endl;
     auto user_src_md = platform::MKLDNNMemDesc(
-        {src_tz}, platform::MKLDNNGetDataType<T>(), input->format());
+        {src_tz}, platform::MKLDNNGetDataType<float>(), mkldnn::memory::format::nChw16c);
+std::cout<<"log2....."<<std::endl;
     auto user_weights_md = platform::MKLDNNMemDesc(
         {weights_tz}, platform::MKLDNNGetDataType<float>(),
         (g == 1) ? filter->format() : mkldnn::memory::format::goihw);
+std::cout<<"log3....."<<std::endl;
 
     /* create memory descriptor for convolution without specified format
      * ('any') which lets a primitive (convolution in this case) choose
@@ -391,17 +398,37 @@ std::cout<<"this is conv int8 op .............."<<std::endl;
     std::string data_format = ctx.Attr<std::string>("data_format");
     auto chosen_memory_format = 
         platform::data_format_to_memory_format(data_format);
-
+    //std::shared_ptr<mkldnn::memory::desc> src_md; 
+    //std::shared_ptr<mkldnn::memory::desc> weights_md;
+    //std::shared_ptr<mkldnn::memory::desc> dst_md;
+    std::vector<int> bias_tz; 
+
+    //if(is_INT8){
+    //    src_md.reset(new platform::MKLDNNMemDesc(
+    //        src_tz, memory::data_type::u8, chosen_memory_format));
+    //    weights_md.reset(new platform::MKLDNNMemDesc(
+    //        weights_tz, memory::data_type::s8, 
+    //        (g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw));
+    //    dst_md.reset(new platform::MKLDNNMemDesc(
+    //        dst_tz, 
+    //        fuse_relu?memory::data_type::u8:memory::data_type::s8,
+    //        chosen_memory_format));
+    //} else{
+    //    src_md.reset(new platform::MKLDNNMemDesc(
+    //        src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format));
+    //    weights_md.reset(new platform::MKLDNNMemDesc(
+    //        weights_tz, platform::MKLDNNGetDataType<T>(),
+    //        (g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw));
+    //    dst_md.reset(new platform::MKLDNNMemDesc(
+    //        dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format));
+    //}
     auto src_md = platform::MKLDNNMemDesc(
-        src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
+        src_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format);
     auto weights_md = platform::MKLDNNMemDesc(
-        weights_tz, platform::MKLDNNGetDataType<T>(),
+        weights_tz, platform::MKLDNNGetDataType<float>(),
         (g == 1) ? chosen_memory_format : mkldnn::memory::format::goihw);
-    std::vector<int> bias_tz;  // TODO(mgallus): avoid empty vector creation.
-                               // Currently used whenever bias is != nullptr.
     auto dst_md = platform::MKLDNNMemDesc(
-        dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
-
+        dst_tz, platform::MKLDNNGetDataType<float>(), chosen_memory_format);
     if(is_INT8){
         src_md = platform::MKLDNNMemDesc(
             src_tz, memory::data_type::u8, chosen_memory_format);
@@ -467,7 +494,7 @@ std::cout<<"this is conv int8 op .............."<<std::endl;
         int count = is_multi_channel? (g>1? weights_tz[1]*weights_tz[0] : weights_tz[0]) : 1;
         std::vector<float> scale_weights_data(count);
         for(int i=0; i<count; i++){
-            scale_weights_data[i] = *(scale_weights->data<T>() + i);
+            scale_weights_data[i] = *(scale_weights->data<float>() + i);
         }
         auto weights_memory_p = handler.AcquireWeightsMemoryFromPrimitive(
             user_weights_memory_p, pipeline, is_test, is_INT8, scale_weights_data, mask_reorder);
@@ -526,8 +553,8 @@ std::cout<<"input fmt = "<<input->format()<<"  output fmt = "<<output->format()<
           {bias_tz}, platform::MKLDNNGetDataType<float>(), memory::format::x);
       auto user_bias_memory_p =
           handler.AcquireBiasMemory(user_bias_md, to_void_cast<float>(bias_data));
-      auto bias_memory_p =
-          handler.AcquireBiasMemoryFromPrimitive(user_bias_memory_p, pipeline);
+      std::shared_ptr<mkldnn::memory>  bias_memory_p;// =
+          //handler.AcquireBiasMemoryFromPrimitive(user_bias_memory_p, pipeline);
       if(is_INT8){
           int mask_reorder = is_multi_channel? 0 : 1<<0;
           int count = is_multi_channel? (g>1? weights_tz[1]*weights_tz[0] : weights_tz[0]) : 1;
@@ -535,8 +562,11 @@ std::cout<<"input fmt = "<<input->format()<<"  output fmt = "<<output->format()<
           for(int i=0; i<count; i++){
               scale_bias_data[i] = (*scale_in->data<float>()) * (*(scale_weights->data<float>() + i));
           }
-          auto bias_memory_p =
+          bias_memory_p =
               handler.AcquireBiasMemoryFromPrimitive(user_bias_memory_p, pipeline, is_INT8, scale_bias_data, mask_reorder);
+      } else{
+          bias_memory_p =
+              handler.AcquireBiasMemoryFromPrimitive(user_bias_memory_p, pipeline);
       } 
       conv_p = handler.AcquireConvolution(src_memory_p, weights_memory_p,
                                           bias_memory_p, dst_memory_p);

paddle/fluid/platform/mkldnn_helper.h

@@ -70,6 +70,7 @@ inline mkldnn::memory::desc MKLDNNMemDesc(const std::vector<int>& dims,
                                           mkldnn::memory::data_type data_type,
                                           mkldnn::memory::format format) {
   mkldnn::memory::dims tz = dims;
+  std::cout<<"this is MKLDNNMemDesc"<<"   data_type"<<data_type<<"   format"<<format<<std::endl;
   return mkldnn::memory::desc({tz}, data_type, format);
 }
 
@@ -163,6 +164,7 @@ std::cout<<"mem_p == null"<<std::endl;
       mem_p->set_data_handle(ptr);
       // Mark that reusing happenned. All primitives from operator instance
       // should be reused or none of them. So we check consistency
+std::cout<<"1 is reuse = "<<is_reusing_;
       is_reusing_ = true;
     }
 std::cout<<"mdp fmt = "<<mdp.desc().data.format<<"   mem_p fmt = "<<mem_p->get_primitive_desc().desc().data.format<<std::endl;
@@ -188,6 +190,7 @@ std::cout<<"mem_p == null"<<std::endl;
       mem_p->set_data_handle(ptr);
       // Mark that reusing happenned. All primitives from operator instance
       // should be reused or none of them. So we check consistency
+std::cout<<"2 is reuse = "<<is_reusing_;
       is_reusing_ = true;
     }
 std::cout<<"md fmt = "<<md.data.format<<"   mem_p fmt = "<<mem_p->get_primitive_desc().desc().data.format<<std::endl;
@@ -239,6 +242,7 @@ std::cout<<"md fmt = "<<md.data.format<<"   mem_p fmt = "<<mem_p->get_primitive_
       if (reorder_p != nullptr) {
         pipeline.push_back(*reorder_p);
       }
+std::cout<<"3 is reuse = "<<is_reusing_;
       is_reusing_ = true;
     }
     return target_memory_p;