added backward handle caching and hash modification (tensorflow#12)

tensorflow/core/kernels/xsmm_conv2d.cc

@@ -136,6 +136,18 @@ class libxsmm_dnn_conv_desc_wrap{
 
 struct HashFunction{
   std::size_t operator()(const libxsmm_dnn_conv_desc_wrap & w) const{
+
+
+
+
+    //unsigned char ptr[sizeof(&w.d)];
+
+
+    //memcpy(ptr, (unsigned char *)&w.d, sizeof(&w.d))
+
+
+    //
+    /*
     std::ostringstream N,C,H,W,K,R,S,u,v,padh,padw;
  
     N << w.d.N; C << w.d.C;
@@ -144,28 +156,28 @@ struct HashFunction{
     S << w.d.S; u << w.d.u;
     v << w.d.v; padh << w.d.pad_h_in;
     padw << w.d.pad_w_in;
-
-
+ 
+ 
     std::string out_ =   N.str() + C.str()\
                        + H.str() + W.str()\
                        + K.str() + R.str()\
                        + S.str() + u.str()\
                        + v.str() + padh.str()\
                        + padw.str();
-
-    return ( std::hash<std::string>()(out_));
+    //
+    //
+    */
+    return ( std::hash<unsigned long long>()((unsigned long long)&(w.d)));
   }
 };
-
+ 
 class handles{
   public:
     libxsmm_dnn_layer* find( const libxsmm_dnn_conv_desc_wrap &w) {
-      std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*,
-                         HashFunction>::iterator i = libxsmm_handles.find(w);
+      std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*, HashFunction>::iterator i = libxsmm_handles.find(w);
       if (i == libxsmm_handles.end()){
         libxsmm_dnn_err_t status;
-        libxsmm_dnn_layer* libxsmm_handle =
-            libxsmm_dnn_create_conv_layer(w.d, &status);
+        libxsmm_dnn_layer* libxsmm_handle = libxsmm_dnn_create_conv_layer(w.d, &status);
         chk_libxsmm_err(status, "Create handle");
         libxsmm_handles.insert(std::make_pair(w, libxsmm_handle));
         return libxsmm_handle;
@@ -174,14 +186,15 @@ class handles{
         return i->second;
     }
    ~handles(){
-     std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*,
-                        HashFunction>::iterator i;
+    std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*, HashFunction>::iterator i;
     for (i= libxsmm_handles.begin(); i != libxsmm_handles.end(); i++)
       chk_libxsmm_err(libxsmm_dnn_destroy_conv_layer(i->second),
                     "Destroy handle");
     }
   private:
+
     std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*, HashFunction> libxsmm_handles;
+
 };
 
 static handles libxsmm_handles;
@@ -198,12 +211,12 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
   libxsmm_dnn_conv_desc_wrap w(desc);
   void* scratch;
 
-  if (kind == LIBXSMM_DNN_COMPUTE_KIND_FWD)
-    libxsmm_handle = libxsmm_handles.find(w);
-  else {
-    libxsmm_handle = libxsmm_dnn_create_conv_layer(desc, &status);
-    chk_libxsmm_err(status, "Create handle");
-  }
+  //if(kind == LIBXSMM_DNN_COMPUTE_KIND_FWD)
+  libxsmm_handle = libxsmm_handles.find(w);
+  //else{
+  //  libxsmm_handle = libxsmm_dnn_create_conv_layer(desc, &status);
+  //  chk_libxsmm_err(status, "Create handle");
+  //}
 
   status = libxsmm_dnn_get_codegen_success(libxsmm_handle, kind);
   if (status == LIBXSMM_DNN_WARN_FALLBACK) {
@@ -216,23 +229,23 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
   libxsmm_dnn_buffer* libxsmm_input;
   libxsmm_dnn_buffer* libxsmm_output;
   libxsmm_dnn_filter* libxsmm_filter;
-
- /*
+  
+ /* 
   const DeviceBase::CpuWorkerThreads* worker_threads =
       ctx->device()->tensorflow_cpu_worker_threads();
-
+ 
   int num_threads = worker_threads->num_threads;
 */
 
   int ifmblock = (libxsmm_handle->ifmblock);
-  int ofmblock = (libxsmm_handle->ofmblock);
+  int ofmblock = (libxsmm_handle->ofmblock); 
 
-  int blocksifm = desc.C%ifmblock ==0 ? desc.C/ifmblock :desc.C/ifmblock + 1;
+  int blocksifm = desc.C%ifmblock ==0 ? desc.C/ifmblock :desc.C/ifmblock + 1;           
   int blocksofm = desc.K%ofmblock ==0 ? desc.K/ofmblock :desc.K/ofmblock + 1;
-  float *native_filter = (float*)libxsmm_aligned_scratch(
-      blocksofm*blocksifm*desc.R*desc.S*ifmblock*ofmblock*sizeof(float),
-      2097152);
+  float *native_filter = (float*)libxsmm_aligned_scratch( blocksofm*blocksifm*desc.R*desc.S*ifmblock*ofmblock*sizeof(float), 2097152);
+
 
+
   const DeviceBase::CpuWorkerThreads* worker_threads =
       ctx->device()->tensorflow_cpu_worker_threads();
 
@@ -301,9 +314,9 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
   }
 
   /* bind scratch */
-  scratch = (void*)libxsmm_aligned_scratch( libxsmm_dnn_get_scratch_size( libxsmm_handle, kind, &status ), 2097152);
+  scratch = (void*)libxsmm_aligned_scratch( libxsmm_dnn_get_scratch_size( libxsmm_handle, LIBXSMM_DNN_COMPUTE_KIND_ALL, &status ), 2097152);
   chk_libxsmm_err( status, "scratch allocation" );
-  chk_libxsmm_err( libxsmm_dnn_bind_scratch( libxsmm_handle, kind, scratch ), "binding scratch" );
+  chk_libxsmm_err( libxsmm_dnn_bind_scratch( libxsmm_handle, LIBXSMM_DNN_COMPUTE_KIND_ALL, scratch ), "binding scratch" );
 
   if (kind == LIBXSMM_DNN_COMPUTE_KIND_BWD) {
     libxsmm_dnn_transpose_filter(libxsmm_handle, LIBXSMM_DNN_FILTER);
@@ -335,9 +348,9 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
   chk_libxsmm_err(libxsmm_dnn_destroy_buffer(libxsmm_output), "Destroy output");
   chk_libxsmm_err(libxsmm_dnn_destroy_filter(libxsmm_filter), "Destroy filter");
 
-  if(kind != LIBXSMM_DNN_COMPUTE_KIND_FWD)
-    chk_libxsmm_err(libxsmm_dnn_destroy_conv_layer(libxsmm_handle),
-                  "Destroy handle");
+  //if(kind != LIBXSMM_DNN_COMPUTE_KIND_FWD)
+  //chk_libxsmm_err(libxsmm_dnn_destroy_conv_layer(libxsmm_handle),
+  //               "Destroy handle");
 
   libxsmm_free(native_filter);
   libxsmm_free(scratch);