[CHERRY-PICK] Fix for matmul_v2 oneDNN op broadcasting when inputs dims have...

[CHERRY-PICK] Fix for matmul_v2 oneDNN op broadcasting when inputs dims have different lengths (#38733)

* fix for matmul_v2 broadcasting

* resolved conflicts

paddle/fluid/operators/mkldnn/matmul_v2_mkldnn_op.cc

@@ -152,7 +152,7 @@ class MatMulV2MKLDNNKernel
       x_bd_dims[x_bd_dims.size() - 2] = x_dims[0];
     } else {
       for (size_t i = 0; i < x_dims.size(); ++i) {
-        x_bd_dims[i] = x_dims[i];
+        x_bd_dims[x_bd_dims.size() - x_dims.size() + i] = x_dims[i];
       }
     }
     if (y_dims.size() == 1) {
@@ -162,20 +162,21 @@ class MatMulV2MKLDNNKernel
       y_bd_dims[y_bd_dims.size() - 2] = y_dims[0];
     } else {
       for (size_t i = 0; i < y_dims.size(); ++i) {
-        y_bd_dims[i] = y_dims[i];
+        y_bd_dims[y_bd_dims.size() - y_dims.size() + i] = y_dims[i];
       }
     }
 
-    if ((y_dims.size() == x_dims.size()) && y_dims.size() > 2) {
-      for (size_t i = 0; i < x_dims.size() - 2; ++i) {
+    if (x_dims.size() > 2 && y_dims.size() > 2) {
+      for (size_t i = 0; i < x_bd_dims.size() - 2; ++i) {
         PADDLE_ENFORCE_EQ(
-            x_dims[i] == y_dims[i] || x_dims[i] == 1 || y_dims[i] == 1, true,
-            paddle::platform::errors::InvalidArgument(
-                "Tensor dimensions are incorrect for broadcasting."
-                "Dimensions in X and Y must be same or equal to 1, but "
-                "received x_dim[%d]=%d and y_dims[%d]= %d",
-                i, x_dims[i], i, y_dims[i]));
-        out_dims[i] = std::max(x_dims[i], y_dims[i]);
+            x_bd_dims[i] == y_bd_dims[i] || x_bd_dims[i] == 1 ||
+                y_bd_dims[i] == 1,
+            true, paddle::platform::errors::InvalidArgument(
+                      "Tensor dimensions are incorrect for broadcasting."
+                      "Dimensions in X and Y must be same or equal to 1, but "
+                      "received x_dim[%d]=%d and y_dims[%d]= %d",
+                      i, x_bd_dims[i], i, y_bd_dims[i]));
+        out_dims[i] = std::max(x_bd_dims[i], y_bd_dims[i]);
       }
       out->Resize(make_ddim(out_dims));
     }
@@ -237,11 +238,11 @@ class MatMulV2GradMKLDNNKernel : public MatMulV2MKLDNNKernel<T> {
     dy_tmp->mutable_data<T>(ctx.GetPlace());
   }
 
-  void ReduceSumForMatmulGradOutput(const ExecutionContext& ctx,
-                                    const MKLDNNDeviceContext& dev_ctx,
-                                    const mkldnn::engine onednn_engine,
-                                    const Tensor* dx_tmp, Tensor* dx,
-                                    std::vector<int64_t> dx_dims) const {
+  void ReduceSumForMatmulGradOutput(
+      const ExecutionContext& ctx, const MKLDNNDeviceContext& dev_ctx,
+      const dnnl::engine onednn_engine, const Tensor* dx_tmp, Tensor* dx,
+      std::vector<int64_t>& dx_dims,
+      const std::vector<int64_t>& squeezed_dims) const {
     paddle::platform::ReductionMKLDNNHandler<T> handler(
         dnnl::algorithm::reduction_sum, 0.0f, 0.0f, onednn_engine,
         ctx.GetPlace(), dx_tmp, dx, dx_dims);
@@ -257,6 +258,19 @@ class MatMulV2GradMKLDNNKernel : public MatMulV2MKLDNNKernel<T> {
 
     reduction_p->execute(astream, reduction_args);
     astream.wait();
+
+    dx->set_format(paddle::platform::GetMKLDNNFormat(
+        dst_memory_p->get_desc().reshape(squeezed_dims)));
+  }
+
+  std::vector<int64_t> ExtendDimsWithOnes(const std::vector<int64_t>& dims,
+                                          int new_size) const {
+    std::vector<int64_t> new_dims(new_size, 1);
+    for (size_t i = 0; i < dims.size(); ++i) {
+      new_dims[new_size - dims.size() + i] = dims[i];
+    }
+
+    return new_dims;
   }
 
   void RunKernel(const ExecutionContext& ctx) const {
@@ -295,8 +309,14 @@ class MatMulV2GradMKLDNNKernel : public MatMulV2MKLDNNKernel<T> {
     bool trans_y = ctx.Attr<bool>("trans_y");
     auto dout_dims = vectorize(dout->dims());
 
-    int ndims = std::max(x->dims().size(), y->dims().size());
-    ndims = std::max(ndims, 3);
+    size_t ndims = std::max(x->dims().size(), y->dims().size());
+    ndims = std::max<size_t>(ndims, 3);
+
+    if (x_dims.size() != ndims) {
+      x_dims = ExtendDimsWithOnes(x_dims, ndims);
+    } else if (y_dims.size() != ndims) {
+      y_dims = ExtendDimsWithOnes(y_dims, ndims);
+    }
 
     // in broadcasting scenario new memory is required because
     // reduce sum must be calculated upon broadcasted dims
@@ -340,21 +360,21 @@ class MatMulV2GradMKLDNNKernel : public MatMulV2MKLDNNKernel<T> {
 
     if (x_dims != dx_bd_dims) {
       ReduceSumForMatmulGradOutput(ctx, dev_ctx, onednn_engine, &dx_tmp, dx,
-                                   x_dims);
+                                   x_dims,
+                                   paddle::framework::vectorize(x->dims()));
     } else {
       *dx = std::move(dx_tmp);
     }
     if (y_dims != dy_bd_dims) {
       ReduceSumForMatmulGradOutput(ctx, dev_ctx, onednn_engine, &dy_tmp, dy,
-                                   y_dims);
+                                   y_dims,
+                                   paddle::framework::vectorize(y->dims()));
     } else {
       *dy = std::move(dy_tmp);
     }
 
-    dx->set_layout(paddle::framework::DataLayout::kMKLDNN);
-    dx->set_format(x->format());
-    dy->set_layout(paddle::framework::DataLayout::kMKLDNN);
-    dy->set_format(y->format());
+    dx->Resize(x->dims());
+    dy->Resize(y->dims());
   }
 };
 }  // anonymous namespace

python/paddle/fluid/tests/unittests/mkldnn/test_matmul_v2_mkldnn_op.py

@@ -251,6 +251,41 @@ class TestMatMulV2MatrixXMatrix2Dx5DOneDNNOp(TestMatMulV2VectorXVectorOneDNNOp):
         self.trans_y = False
 
 
+class TestMatMulV2MatrixXMatrix4Dx3DTransposeXOneDNNOp(
+        TestMatMulV2VectorXVectorOneDNNOp):
+    def config(self):
+        self.x_shape = (5, 4, 15, 10)
+        self.y_shape = (1, 15, 20)
+        self.trans_x = True
+        self.trans_y = False
+
+
+class TestMatMulV2MatrixXMatrix3Dx4DTransposeYOneDNNOp(
+        TestMatMulV2VectorXVectorOneDNNOp):
+    def config(self):
+        self.x_shape = (2, 10, 15)
+        self.y_shape = (4, 2, 20, 15)
+        self.trans_x = False
+        self.trans_y = True
+
+
+class TestMatMulV2MatrixXMatrix5Dx3DTransposeXTransposeYOneDNNOp(
+        TestMatMulV2VectorXVectorOneDNNOp):
+    def config(self):
+        self.x_shape = (4, 3, 2, 15, 10)
+        self.y_shape = (1, 20, 15)
+        self.trans_x = True
+        self.trans_y = True
+
+
+class TestMatMulV2MatrixXMatrix3Dx4DOneDNNOp(TestMatMulV2VectorXVectorOneDNNOp):
+    def config(self):
+        self.x_shape = (1, 1, 32, 16)
+        self.y_shape = (16, 16, 16)
+        self.trans_x = False
+        self.trans_y = False
+
+
 #   BF16 TESTS
 def create_bf16_test_class(parent):
     @OpTestTool.skip_if_not_cpu_bf16()