Added workaround for elementwise oneDNN kernel (#47080)

* return proper state

* fix for dims

* fix

paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h

@@ -78,6 +78,7 @@ class EltwiseMKLDNNKernel : public framework::OpKernel<T> {
                                              scale_x,
                                              scale_y,
                                              scale_o,
+                                             true,
                                              get_post_ops(ctx));
 
     // oneDNN's binary is optimized for broadcasting y into x, so in other case
@@ -126,7 +127,14 @@ class EltwiseMKLDNNKernel : public framework::OpKernel<T> {
     binary_prim->execute(astream, args);
     astream.wait();
 
-    z->set_mem_desc(dst_memory->get_desc());
+    if (handler.use_broadcasting_hack == false) {
+      z->set_mem_desc(dst_memory->get_desc());
+    } else {
+      auto dims = dst_memory->get_desc().dims();
+      dims.insert(dims.begin(), x->dims()[0]);
+      dims[1] /= dims[0];
+      z->set_mem_desc(dst_memory->get_desc().reshape(dims));
+    }
   }
 };
 
@@ -210,7 +218,8 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
                                                         dx,
                                                         1.0f,
                                                         1.0f,
-                                                        1.0f);
+                                                        1.0f,
+                                                        false);
 
         const auto src_dout_memory = binary_handler.AcquireSrcMemory(dout);
         const auto src_y_memory = binary_handler.AcquireSecondSrcMemory(y);
@@ -276,7 +285,8 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
             nullptr,
             1.0f,
             1.0f,
-            1.0f);
+            1.0f,
+            false);
 
         src_1_memory = binary_handler.AcquireSecondSrcMemory(x);
 
@@ -291,7 +301,8 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
               nullptr,
               1.0f,
               1.0f,
-              1.0f);
+              1.0f,
+              false);
 
           post_op_memory = post_op_binary_handler.AcquireSrcMemory(y);
 
@@ -310,6 +321,7 @@ class EltwiseMKLDNNGradKernel : public ElemwiseGradKernel<T> {
                                                -1.0f,
                                                1.0f,
                                                1.0f,
+                                               false,
                                                po);
 
           src_1_memory = binary_handler.AcquireSecondSrcMemory(out);

paddle/phi/backends/onednn/onednn_reuse.h

@@ -825,6 +825,7 @@ class ReorderOneDNNHandler {
 template <typename T>
 class BinaryOneDNNHandler : public OneDNNHandlerNoCachingT<T, dnnl::binary> {
  public:
+  bool use_broadcasting_hack;
   BinaryOneDNNHandler(const dnnl::algorithm algo,
                       const int axis,
                       const dnnl::engine engine,
@@ -835,15 +836,17 @@ class BinaryOneDNNHandler : public OneDNNHandlerNoCachingT<T, dnnl::binary> {
                       float scale_x,
                       float scale_y,
                       float scale_out,
+                      bool allow_hack,
                       const dnnl::post_ops& post_ops = dnnl::post_ops{})
       : OneDNNHandlerNoCachingT<T, dnnl::binary>(engine, cpu_place) {
+    use_broadcasting_hack = false;
     const auto src_x_tz = vectorize(x->dims());
     const auto src_y_tz = vectorize(y->dims());
     // if output tensor(z) is nullptr then we are computing into oneDNN
     // managed buffer
     auto rankdiff = x->dims().size() - y->dims().size();
-    const auto dst_tz = (out == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
-                                         : vectorize(out->dims());
+    auto dst_tz = (out == nullptr) ? (rankdiff > 0 ? src_x_tz : src_y_tz)
+                                   : vectorize(out->dims());
 
     auto src0_md = x->mem_desc();
     auto src1_md = y->mem_desc();
@@ -870,12 +873,48 @@ class BinaryOneDNNHandler : public OneDNNHandlerNoCachingT<T, dnnl::binary> {
       }
       src0_md = src0_md.reshape(dims0_ex);
     }
-    const auto dst_md =
-        memory::desc(dst_tz, OneDNNGetDataType<T>(), OneDNNMemoryFormat::any);
 
     auto attributes =
         CreateAttributes(algo, scale_x, scale_y, scale_out, post_ops);
 
+    // Workaround for U2++ model which deletes first tensor dimensions to enable
+    // optimized oneDNNs broadcasting. Output tensor is reshaped back afterwards
+    // at the end of the kernel, after the computation
+    if (allow_hack && dst_tz.size() == 4 &&
+        src0_md.dims()[2] != src1_md.dims()[2]) {
+      auto are_strides_plain = [](int64_t* strides, int ndims) {
+        for (int i = 0; i < ndims - 1; ++i) {
+          if (strides[i] < strides[i + 1]) {
+            return false;
+          }
+        }
+        return true;
+      };
+
+      auto src0_strides = src0_md.data.format_desc.blocking.strides;
+      auto src1_strides = src1_md.data.format_desc.blocking.strides;
+      auto src0_dims = src0_md.dims();
+      auto src1_dims = src1_md.dims();
+
+      bool can_squeeze = src0_dims[0] == src1_dims[0] &&
+                         src0_dims[1] == src1_dims[1] &&
+                         src0_dims[3] == src1_dims[3];
+
+      if (can_squeeze && are_strides_plain(src0_strides, 4) &&
+          are_strides_plain(src1_strides, 4)) {
+        src0_dims[1] *= dst_tz[0];
+        src1_dims[1] *= dst_tz[0];
+        dst_tz[1] *= dst_tz[0];
+        dst_tz.erase(dst_tz.begin());
+        src0_md = src0_md.reshape({src0_dims.begin() + 1, src0_dims.end()});
+        src1_md = src1_md.reshape({src1_dims.begin() + 1, src1_dims.end()});
+        use_broadcasting_hack = true;
+      }
+    }
+
+    auto dst_md =
+        memory::desc(dst_tz, OneDNNGetDataType<T>(), OneDNNMemoryFormat::any);
+
     if (x->numel() < y->numel()) {
       if (algo == dnnl::algorithm::binary_sub) {
         attributes = CreateAttributes(