PaddlePaddle
diff --git a/‎paddle/phi/api/yaml/ops.yaml
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+10Lines changed: 10 additions & 0 deletions b/‎paddle/phi/api/yaml/ops.yaml
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+10Lines changed: 10 additions & 0 deletions
diff --git a/‎paddle/phi/infermeta/binary.cc
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+32Lines changed: 32 additions & 0 deletions b/‎paddle/phi/infermeta/binary.cc
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+32Lines changed: 32 additions & 0 deletions
diff --git a/‎paddle/phi/infermeta/binary.h
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+7Lines changed: 7 additions & 0 deletions b/‎paddle/phi/infermeta/binary.h
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+7Lines changed: 7 additions & 0 deletions
@@ -1994,6 +1994,16 @@
     data_type : x
   backward : reverse_grad
 
+- op : rms_norm
+  args : (Tensor x, Tensor weight, Tensor bias, float epsilon, int begin_norm_axis)
+  output : Tensor(out)
+  infer_meta :
+    func : RmsNormInferMeta
+  kernel :
+    func : rms_norm
+    data_type : x
+  optional : bias
+
 - op : rmsprop_
   args : (Tensor param, Tensor mean_square, Tensor grad, Tensor moment, Tensor learning_rate, Tensor mean_grad, Tensor master_param, float epsilon = 1.0e-10f, float decay = 0.9f, float momentum = 0.0f, bool centered = false, bool multi_precision = false)
   output : Tensor(param_out), Tensor(moment_out), Tensor(mean_square_out), Tensor(mean_grad_out), Tensor(master_param_outs)
 
@@ -3137,6 +3137,38 @@ void Unpool3dInferMeta(const MetaTensor& x,
   }
 }
 
+void RmsNormInferMeta(const MetaTensor& x,
+                      const MetaTensor& weight,
+                      const MetaTensor& bias,
+                      const float epsilon,
+                      const int begin_norm_axis,
+                      MetaTensor* out) {
+  std::vector<int64_t> x_dims_vec = phi::vectorize(x.dims());
+  auto x_dims_size = x_dims_vec.size();
+
+  size_t normalized_dims = 1;
+  for (size_t i = begin_norm_axis; i < x_dims_size; ++i) {
+    normalized_dims *= x_dims_vec[i];
+  }
+
+  PADDLE_ENFORCE_EQ(normalized_dims,
+                    weight.dims()[0],
+                    phi::errors::InvalidArgument(
+                        "The normalized size of Input(X) must equal to be"
+                        "the size of Weight, but received"
+                        "normalized size of Input(X) is [%d], received size"
+                        "of Weight is [%d]",
+                        normalized_dims,
+                        weight.dims()[0]));
+
+  auto out_dims = phi::make_ddim(x_dims_vec);
+
+  out->set_dims(out_dims);
+  out->set_dtype(x.dtype());
+  out->set_layout(x.layout());
+  out->share_lod(x);
+}
+
 }  // namespace phi
 
 PD_REGISTER_INFER_META_FN(add_raw, phi::ElementwiseRawInferMeta);
@@ -479,4 +479,11 @@ void Unpool3dInferMeta(const MetaTensor& x,
                        MetaTensor* out,
                        MetaConfig config = MetaConfig());
 
+void RmsNormInferMeta(const MetaTensor& x,
+                      const MetaTensor& weight,
+                      const MetaTensor& bias,
+                      const float epsilon,
+                      const int begin_norm_axis,
+                      MetaTensor* out);
+
 }  // namespace phi