diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc
index 41f043268ddc1d..dcbd70f51bb0c8 100644
--- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc
+++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc
@@ -1754,9 +1754,9 @@ bool FusedAttentionOpInferSymbolicShape(
     PADDLE_ENFORCE_EQ(qkv_weight_shape.size(),
                       2,
                       common::errors::InvalidArgument(
-                          "The dimensions of qkv_weight must be 2 if enable"
-                          "transpose_qkv_wb: (dim_embed, 3 * dim_embed),"
-                          "but received dimensions of"
+                          "The dimensions of qkv_weight must be 2 if enable "
+                          "transpose_qkv_wb: (dim_embed, 3 * dim_embed), "
+                          "but received dimensions of "
                           "Input is [%d]",
                           qkv_weight_shape.size()));
     PADDLE_ENFORCE_GT(num_heads_,
@@ -1779,7 +1779,7 @@ bool FusedAttentionOpInferSymbolicShape(
     PADDLE_ENFORCE_EQ(qkv_weight_shape.size(),
                       4,
                       common::errors::InvalidArgument(
-                          "The dimensions of qkv_weight must be 4 if not"
+                          "The dimensions of qkv_weight must be 4 if not "
                           "enable transpose_qkv_wb: (3, num_head, dim_head, "
                           "dim_embed), but received [%d]",
                           qkv_weight_shape.size()));
@@ -2227,16 +2227,16 @@ bool FusedMultiTransformerOpInferSymbolicShape(
       x_shape.size(),
       3,
       common::errors::InvalidArgument("The dimensions of x must be 3"
-                                      "(batch_size, seq_len, dim_embed),"
-                                      "but received dimensions of"
+                                      "(batch_size, seq_len, dim_embed), "
+                                      "but received dimensions of "
                                       "Input is [%d]",
                                       x_shape.size()));
   PADDLE_ENFORCE_EQ(
       y_shape.size(),
       4,
       common::errors::InvalidArgument("The dimensions of qkv_weight must be 4"
-                                      "(3, num_head, dim_head, dim_embed),"
-                                      "but received dimensions of"
+                                      "(3, num_head, dim_head, dim_embed), "
+                                      "but received dimensions of "
                                       "Input is [%d]",
                                       y_shape.size()));
 
@@ -3086,19 +3086,19 @@ bool MemoryEfficientAttentionOpInferSymbolicShape(
   PADDLE_ENFORCE_EQ(
       q_shape.size(),
       4,
-      common::errors::InvalidArgument("Query should be a 4-D tensor"
+      common::errors::InvalidArgument("Query should be a 4-D tensor. "
                                       "But received Query dimension(%d)",
                                       q_shape.size()));
   PADDLE_ENFORCE_EQ(
       k_shape.size(),
       4,
-      common::errors::InvalidArgument("Key should be a 4-D tensor"
+      common::errors::InvalidArgument("Key should be a 4-D tensor. "
                                       "But received Key dimension(%d)",
                                       k_shape.size()));
   PADDLE_ENFORCE_EQ(
       v_shape.size(),
       4,
-      common::errors::InvalidArgument("Value should be a 4-D tensor"
+      common::errors::InvalidArgument("Value should be a 4-D tensor. "
                                       "But received Value dimension(%d)",
                                       v_shape.size()));
 
@@ -4808,12 +4808,12 @@ bool YoloLossOpInferSymbolicShape(
   if (op->operand_source(3) != nullptr) {
     const auto &score_shape =
         infer_context->GetShapeOrDataForValue(op->operand_source(3)).shape();
-    PADDLE_ENFORCE_EQ(
-        score_shape.size(),
-        2,
-        common::errors::InvalidArgument("Input(GTScore) should be a 2-D tensor"
-                                        "But received GTScore dimension(%s)",
-                                        box_shape.size()));
+    PADDLE_ENFORCE_EQ(score_shape.size(),
+                      2,
+                      common::errors::InvalidArgument(
+                          "Input(GTScore) should be a 2-D tensor. "
+                          "But received GTScore dimension(%s)",
+                          box_shape.size()));
     infer_context->AddEqualCstr(score_shape[0], box_shape[0]);
     infer_context->AddEqualCstr(score_shape[1], box_shape[1]);
   }
diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc
index fca5f88da5e266..6750759633d0b8 100644
--- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc
+++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc
@@ -1459,7 +1459,7 @@ bool FlattenOpInferSymbolicShape(
     PADDLE_ENFORCE_GE(
         stop_axis,
         start_axis,
-        common::errors::InvalidArgument("The stop_axis should be greater"
+        common::errors::InvalidArgument("The stop_axis should be greater "
                                         "than or equal to start_axis."));
   }
 
@@ -2035,7 +2035,7 @@ bool MaxPool2dWithIndexOpInferSymbolicShape(
   PADDLE_ENFORCE_EQ(
       x_shape.size(),
       4,
-      common::errors::InvalidArgument("Pooling input should be 4-D Tensor"
+      common::errors::InvalidArgument("Pooling input should be 4-D Tensor, "
                                       "but received %dD-Tensor",
                                       x_shape.size()));
 
@@ -2069,7 +2069,7 @@ bool MaxPool3dWithIndexOpInferSymbolicShape(
   PADDLE_ENFORCE_EQ(
       x_shape.size(),
       5,
-      common::errors::InvalidArgument("Pooling input should be 5-D Tensor"
+      common::errors::InvalidArgument("Pooling input should be 5-D Tensor, "
                                       "but received %dD-Tensor",
                                       x_shape.size()));
 
diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc
index c3200afd583379..483c91085e6a90 100644
--- a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc
+++ b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc
@@ -1000,7 +1000,7 @@ void FusedGemmEpilogueGradOp::Build(pir::Builder &builder,
 
   PADDLE_ENFORCE(
       attributes.find("activation_grad") != attributes.end(),
-      common::errors::NotFound("'activation_grad' Attribute is expected for"
+      common::errors::NotFound("'activation_grad' Attribute is expected for "
                                "FusedGemmEpilogueGradOp"));
   std::string activation_grad =
       attributes.at("activation_grad").dyn_cast<pir::StrAttribute>().AsString();
@@ -1071,7 +1071,7 @@ std::vector<pir::Type> FusedGemmEpilogueGradOp::InferMeta(
 
   PADDLE_ENFORCE(
       attributes.find("activation_grad") != attributes.end(),
-      common::errors::NotFound("'activation_grad' Attribute is expected for"
+      common::errors::NotFound("'activation_grad' Attribute is expected for "
                                "FusedGemmEpilogueGradOp"));
   std::string activation_grad =
       attributes.at("activation_grad").dyn_cast<pir::StrAttribute>().AsString();
diff --git a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc
index e53b1d4626a696..ef3511f2072b35 100644
--- a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc
+++ b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc
@@ -2851,9 +2851,9 @@ std::vector<pir::Value> BuildInputs(
               PADDLE_THROW(common::errors::Unimplemented(
                   "builtin.combine Input type only support "
                   "VectorType<DenseTensorType> and "
-                  "VectorType<SelectedRowsType> and"
-                  "VectorType<DenseTensorArrayType> and"
-                  "VectorType<SparseCooTensorType> and"
+                  "VectorType<SelectedRowsType> and "
+                  "VectorType<DenseTensorArrayType> and "
+                  "VectorType<SparseCooTensorType> and "
                   "VectorType<SparseCsrTensorType>"));
             }
 
@@ -2912,9 +2912,9 @@ std::vector<pir::Value> BuildInputs(
                 PADDLE_THROW(common::errors::Unimplemented(
                     "builtin.combine Input type only support "
                     "VectorType<DenseTensorType> and "
-                    "VectorType<SelectedRowsType> and"
-                    "VectorType<DenseTensorArrayType> and"
-                    "VectorType<SparseCooTensorType> and"
+                    "VectorType<SelectedRowsType> and "
+                    "VectorType<DenseTensorArrayType> and "
+                    "VectorType<SparseCooTensorType> and "
                     "VectorType<SparseCsrTensorType>"));
               }
               in_i = AddPlaceTransferOp(
diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc
index cf6d0fd09b250a..72ee552e169aaa 100644
--- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc
+++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc
@@ -1768,7 +1768,7 @@ class BilinearInterpV2Pattern
       if (scale.size() <= 1) {
         if (!op->HasAttribute("out_h") || !op->HasAttribute("out_w")) {
           VLOG(3) << "BilinearInterpV2 doesn't have scale_tensor and the scale "
-                     "size <=1 and without"
+                     "size <=1 and without "
                      "out_h / out_w, it will return false";
           return false;
         }
@@ -2895,7 +2895,7 @@ class LinearInterpOpPattern
         if (!op->HasAttribute("out_w")) {
           VLOG(3)
               << "pd_op.linear_interp doesn't have scale_tensor and the scale "
-                 "size <=1 and without"
+                 "size <=1 and without "
                  "out_w, it will return false";
           return false;
         }
diff --git a/paddle/phi/infermeta/backward.cc b/paddle/phi/infermeta/backward.cc
index 08c1fe582500ad..78850b24cf6b6c 100644
--- a/paddle/phi/infermeta/backward.cc
+++ b/paddle/phi/infermeta/backward.cc
@@ -1157,13 +1157,13 @@ void MemoryEfficientAttentionGradInferMeta(const MetaTensor& query,
   PADDLE_ENFORCE_EQ(
       output_grad.dims().size(),
       4,
-      common::errors::InvalidArgument("Key should be a 4-D tensor"
+      common::errors::InvalidArgument("Key should be a 4-D tensor. "
                                       "But received Key dimension(%s)",
                                       output_grad.dims().size()));
   PADDLE_ENFORCE_EQ(
       output.dims().size(),
       4,
-      common::errors::InvalidArgument("Key should be a 4-D tensor"
+      common::errors::InvalidArgument("Key should be a 4-D tensor. "
                                       "But received Key dimension(%s)",
                                       output_grad.dims().size()));
 
@@ -1254,13 +1254,13 @@ void MoeCombineGradInferMeta(const MetaTensor& x,
   PADDLE_ENFORCE_EQ(
       x_dim.size(),
       2,
-      errors::InvalidArgument("The input X should have 2 dimensions"
+      errors::InvalidArgument("The input X should have 2 dimensions. "
                               "But received X's dimension = %d",
                               x_dim.size()));
   PADDLE_ENFORCE_EQ(
       (scatter_index.dtype() == phi::DataType::INT32),
       true,
-      errors::InvalidArgument("The input scatter_index type should be int32"
+      errors::InvalidArgument("The input scatter_index type should be int32. "
                               "But received scatter_index type = %s",
                               scatter_index.dtype()));
   grad_x->set_dims(common::make_ddim({x_dim[0], x_dim[1]}));
diff --git a/paddle/phi/infermeta/binary.cc b/paddle/phi/infermeta/binary.cc
index c6a1ab135f2a7d..581725aabc6835 100644
--- a/paddle/phi/infermeta/binary.cc
+++ b/paddle/phi/infermeta/binary.cc
@@ -1095,8 +1095,7 @@ void CrossEntropyInferMeta(const MetaTensor& x,
             "shall have the same dimensions. But received: the dimensions of "
             "Input(X) is [%d],"
             "the shape of Input(X) is [%s], the dimensions of Input(Label) "
-            "is "
-            "[%d], the shape of"
+            "is [%d], the shape of "
             "Input(Label) is [%s]",
             rank,
             x_dims,
@@ -1109,11 +1108,11 @@ void CrossEntropyInferMeta(const MetaTensor& x,
           label_dims[rank - 1],
           common::errors::InvalidArgument(
               "If Attr(soft_label) == true, the last dimension of "
-              "Input(X) and Input(Label) should be equal. But received: the"
+              "Input(X) and Input(Label) should be equal. But received: the "
               "last dimension of Input(X) is [%d], the shape of Input(X) is "
-              "[%s],"
+              "[%s], "
               "the last dimension of Input(Label) is [%d], the shape of "
-              "Input(Label)"
+              "Input(Label) "
               "is [%s], the last dimension is [%d].",
               x_dims[rank - 1],
               x_dims,
@@ -2707,9 +2706,9 @@ void LogLossInferMeta(const MetaTensor& input,
         pred_dims,
         label_dims,
         common::errors::InvalidArgument(
-            "The dimensions of Input(Predicted) must be equal to the"
+            "The dimensions of Input(Predicted) must be equal to the "
             "dimensions of Input(Labels), but received dimensions of "
-            "Input(Predicted)"
+            "Input(Predicted) "
             "is [%s], received dimensions of Input(Labels) is [%s].",
             pred_dims,
             label_dims));
@@ -3332,7 +3331,7 @@ void MvInferMeta(const MetaTensor& x, const MetaTensor& vec, MetaTensor* out) {
                     dim_vec[0],
                     common::errors::InvalidArgument(
                         "X's second dimension is expected to be equal to "
-                        "Vec's first dimension"
+                        "Vec's first dimension, "
                         "but received X'shape = [%s], Vec's shape = [%s]",
                         dim_x,
                         dim_vec));
@@ -4535,9 +4534,9 @@ void UnpoolInferMeta(const MetaTensor& x,
   PADDLE_ENFORCE_EQ(in_x_dims,
                     in_y_dims,
                     common::errors::InvalidArgument(
-                        "The dimensions of Input(X) must equal to be"
-                        "the dimensions of Input(Indices), but received"
-                        "dimensions of Input(X) is [%d], received dimensions"
+                        "The dimensions of Input(X) must equal to be "
+                        "the dimensions of Input(Indices), but received "
+                        "dimensions of Input(X) is [%d], received dimensions "
                         "of Input(Indices) is [%d]",
                         in_x_dims,
                         in_y_dims));
@@ -4581,9 +4580,9 @@ void Unpool3dInferMeta(const MetaTensor& x,
   PADDLE_ENFORCE_EQ(in_x_dims,
                     in_y_dims,
                     common::errors::InvalidArgument(
-                        "The dimensions of Input(X) must equal to be"
-                        "the dimensions of Input(Indices), but received"
-                        "dimensions of Input(X) is [%d], received dimensions"
+                        "The dimensions of Input(X) must equal to be "
+                        "the dimensions of Input(Indices), but received "
+                        "dimensions of Input(X) is [%d], received dimensions "
                         "of Input(Indices) is [%d]",
                         in_x_dims,
                         in_y_dims));
@@ -4656,7 +4655,7 @@ void WeightDequantizeInferMeta(const MetaTensor& x,
         (x.dims()[1] + (group_size - 1)) / group_size,
         errors::InvalidArgument("The input(weight_scale) dim[0] must be equal "
                                 "to (Input(weight).dim[1] + (group_size -1))"
-                                " / group_size"
+                                " / group_size. "
                                 "But receive %d and %d",
                                 dim_scale[0],
                                 (x.dims()[1] + (group_size - 1)) / group_size));
diff --git a/paddle/phi/infermeta/fusion.cc b/paddle/phi/infermeta/fusion.cc
index f4a16df2ca0930..0152ddcebc90bf 100644
--- a/paddle/phi/infermeta/fusion.cc
+++ b/paddle/phi/infermeta/fusion.cc
@@ -187,8 +187,8 @@ void FusedMultiTransformerInferMeta(
       x_dim.size(),
       3,
       common::errors::InvalidArgument("The dimensions of x must be 3"
-                                      "(batch_size, seq_len, dim_embed),"
-                                      "but received dimensions of"
+                                      "(batch_size, seq_len, dim_embed), "
+                                      "but received dimensions of "
                                       "Input is [%d]",
                                       x_dim.size()));
 
@@ -197,8 +197,8 @@ void FusedMultiTransformerInferMeta(
                       3,
                       common::errors::InvalidArgument(
                           "The dimensions of qkv_weight when use gqa must be 3"
-                          "(num_head + 2 * kv_num_heads, dim_head, dim_embed),"
-                          "but received dimensions of"
+                          "(num_head + 2 * kv_num_heads, dim_head, dim_embed), "
+                          "but received dimensions of "
                           "Input is [%d]",
                           y_dim.size()));
   } else {
@@ -206,8 +206,8 @@ void FusedMultiTransformerInferMeta(
         y_dim.size(),
         4,
         common::errors::InvalidArgument("The dimensions of qkv_weight must be 4"
-                                        "(3, num_head, dim_head, dim_embed),"
-                                        "but received dimensions of"
+                                        "(3, num_head, dim_head, dim_embed), "
+                                        "but received dimensions of "
                                         "Input is [%d]",
                                         y_dim.size()));
   }
@@ -264,7 +264,7 @@ void FusedMultiTransformerInferMeta(
       PADDLE_ENFORCE_EQ(c_dim[2],
                         gqa_group_size,
                         common::errors::InvalidArgument(
-                            "The third dim of CacheKV must be equal with num"
+                            "The third dim of CacheKV must be equal with num "
                             "head %d, but got %d",
                             gqa_group_size,
                             c_dim[2]));  // num_head
@@ -280,7 +280,7 @@ void FusedMultiTransformerInferMeta(
       PADDLE_ENFORCE_EQ(c_dim[2],
                         trans_qkvw ? y_dim[1] : y_dim[2],
                         common::errors::InvalidArgument(
-                            "The third dim of CacheKV must be equal with num"
+                            "The third dim of CacheKV must be equal with num "
                             "head %d, but got %d",
                             trans_qkvw ? y_dim[1] : y_dim[2],
                             c_dim[2]));  // num_head
@@ -956,9 +956,9 @@ void FusedAttentionInferMeta(const MetaTensor& x,
     PADDLE_ENFORCE_EQ(y_dim.size(),
                       2,
                       common::errors::InvalidArgument(
-                          "The dimensions of qkv_weight must be 2 if enable"
-                          "transpose_qkv_wb: (dim_embed, 3 * dim_embed),"
-                          "but received dimensions of"
+                          "The dimensions of qkv_weight must be 2 if enable "
+                          "transpose_qkv_wb: (dim_embed, 3 * dim_embed), "
+                          "but received dimensions of "
                           "Input is [%d]",
                           y_dim.size()));
     PADDLE_ENFORCE_GT(num_heads,
@@ -991,7 +991,7 @@ void FusedAttentionInferMeta(const MetaTensor& x,
     PADDLE_ENFORCE_EQ(y_dim.size(),
                       4,
                       common::errors::InvalidArgument(
-                          "The dimensions of qkv_weight must be 4 if not"
+                          "The dimensions of qkv_weight must be 4 if not "
                           "enable transpose_qkv_wb: (3, num_head, dim_head, "
                           "dim_embed), but received [%d]",
                           y_dim.size()));
@@ -1019,8 +1019,8 @@ void FusedAttentionInferMeta(const MetaTensor& x,
       x_dim.size(),
       3,
       common::errors::InvalidArgument("The dimensions of x must be 3"
-                                      "(batch_size, seq_len, dim_embed),"
-                                      "but received dimensions of"
+                                      "(batch_size, seq_len, dim_embed), "
+                                      "but received dimensions of "
                                       "Input is [%d]",
                                       x_dim.size()));
 
@@ -1423,8 +1423,8 @@ void FusedDotProductAttentionInferMeta(const MetaTensor& q,
   PADDLE_ENFORCE(q_dim.size() == 4 && k_dim.size() == 4 && v_dim.size() == 4,
                  common::errors::InvalidArgument(
                      "The dimensions of q, k, v must be 4"
-                     "(batch_size, seq_len, num_heads, head_size),"
-                     "but received dimensions of"
+                     "(batch_size, seq_len, num_heads, head_size), "
+                     "but received dimensions of "
                      "Input is [%d], [%d], [%d]",
                      q_dim.size(),
                      k_dim.size(),
@@ -1432,8 +1432,8 @@ void FusedDotProductAttentionInferMeta(const MetaTensor& q,
 
   PADDLE_ENFORCE(q_dim[0] == k_dim[0] && k_dim[0] == v_dim[0],
                  common::errors::InvalidArgument(
-                     "The first dimension of q, k, v must be equal"
-                     "but received dimensions of"
+                     "The first dimension of q, k, v must be equal, "
+                     "but received dimensions of "
                      "Input is [%d], [%d], [%d]",
                      q_dim[0],
                      k_dim[0],
@@ -1662,7 +1662,7 @@ void FusedElemwiseAddActivationInferMeta(
       true,
       common::errors::InvalidArgument(
           "When the FusedElemwiseAddActivationOp Is used in fused pass, the "
-          "elementwise_add Op must be"
+          "elementwise_add Op must be "
           "detected and used, Please check the fuse pass pattern"));
 }
 
@@ -1746,7 +1746,7 @@ void FusedElemwiseAddActivationGradInferMeta(
       true,
       common::errors::InvalidArgument(
           "When the FusedElemwiseAddActivationOpGrad Is used in fused pass, "
-          "the elementwise_add_grad Op must be"
+          "the elementwise_add_grad Op must be "
           "detected and used, Please check the fuse pass pattern"));
 }
 
@@ -2028,7 +2028,7 @@ void FusedGemmEpilogueGradInferMeta(const MetaTensor& x,
       dout_mat_dims[1],
       trans_y ? y_dims[0] : y_dims[1],
       common::errors::InvalidArgument(
-          "The last dimension of DOut should be equal with Y's last"
+          "The last dimension of DOut should be equal with Y's last "
           "dimension. But received DOut[-1] = [%d], Y[1] = [%d].",
           dout_mat_dims[1],
           y_dims[1]));
@@ -2328,16 +2328,16 @@ void FusedMultiTransformerInt8InferMeta(
       x_dim.size(),
       3,
       common::errors::InvalidArgument("The dimensions of x must be 3"
-                                      "(batch_size, seq_len, dim_embed),"
-                                      "but received dimensions of"
+                                      "(batch_size, seq_len, dim_embed), "
+                                      "but received dimensions of "
                                       "Input is [%d]",
                                       x_dim.size()));
   PADDLE_ENFORCE_EQ(
       y_dim.size(),
       4,
       common::errors::InvalidArgument("The dimensions of qkv_weight must be 4"
-                                      "(3, num_head, dim_head, dim_embed),"
-                                      "but received dimensions of"
+                                      "(3, num_head, dim_head, dim_embed), "
+                                      "but received dimensions of "
                                       "Input is [%d]",
                                       y_dim.size()));
   PADDLE_ENFORCE_EQ(
@@ -3905,7 +3905,7 @@ void FusionGRUInferMeta(const MetaTensor& x,
       wx_dims[0],
       x_mat_dims[1],
       common::errors::InvalidArgument(
-          "The first dimension of flattened WeightX"
+          "The first dimension of flattened WeightX "
           "should equal to last dimension of flattened input X, but "
           "received fattened WeightX dimension is:%d, flattened X dimension "
           "is:%d",
@@ -4444,19 +4444,19 @@ void VariableLengthMemoryEfficientAttentionInferMeta(
   PADDLE_ENFORCE_EQ(
       query.dims().size(),
       4,
-      common::errors::InvalidArgument("Query should be a 4-D tensor"
+      common::errors::InvalidArgument("Query should be a 4-D tensor. "
                                       "But received Query dimension(%s)",
                                       query.dims().size()));
   PADDLE_ENFORCE_EQ(
       key.dims().size(),
       4,
-      common::errors::InvalidArgument("Key should be a 4-D tensor"
+      common::errors::InvalidArgument("Key should be a 4-D tensor. "
                                       "But received Key dimension(%s)",
                                       key.dims().size()));
   PADDLE_ENFORCE_EQ(
       value.dims().size(),
       4,
-      common::errors::InvalidArgument("Value should be a 4-D tensor"
+      common::errors::InvalidArgument("Value should be a 4-D tensor. "
                                       "But received Value dimension(%s)",
                                       value.dims().size()));
 
@@ -4512,7 +4512,7 @@ void VariableLengthMemoryEfficientAttentionInferMeta(
     PADDLE_ENFORCE_EQ(
         mask.dims().size(),
         4,
-        common::errors::InvalidArgument("Mask should be a 4-D tensor"
+        common::errors::InvalidArgument("Mask should be a 4-D tensor. "
                                         "But received Value dimension(%s)",
                                         mask.dims().size()));
     const int64_t mask_batch_size = mask.dims()[0];
@@ -4822,7 +4822,7 @@ void MultiGruInferMeta(
         weight_x[i]->dims()[0],
         x_mat_dims[1],
         common::errors::InvalidArgument(
-            "The first dimension of flattened WeightX #%d"
+            "The first dimension of flattened WeightX #%d "
             "should equal to last dimension of flattened input X, but "
             "received fattened WeightX dimension is:%d, flattened X "
             "dimension "
@@ -5353,39 +5353,39 @@ void FusedTokenPruneInferMeta(const MetaTensor& attn,
   PADDLE_ENFORCE_EQ(mask_dim[0],
                     attn_dim[0],
                     common::errors::InvalidArgument(
-                        "The first dim of mask and attn should be the same"
+                        "The first dim of mask and attn should be the same "
                         "which is batch size"));
   PADDLE_ENFORCE_EQ(mask_dim[1],
                     attn_dim[1],
                     common::errors::InvalidArgument(
-                        "The second dim of mask and attn should be the same"
+                        "The second dim of mask and attn should be the same "
                         "which is nb_head"));
   PADDLE_ENFORCE_EQ(mask_dim[0],
                     x_dim[0],
                     common::errors::InvalidArgument(
-                        "The first dim of mask and x should be the same"
+                        "The first dim of mask and x should be the same "
                         "which is batch size"));
   PADDLE_ENFORCE_EQ(
       mask_dim[2],
       mask_dim[3],
       common::errors::InvalidArgument(
-          "The third dim and the fourth dim of mask should be the same"
+          "The third dim and the fourth dim of mask should be the same "
           "which is max seq len"));
   PADDLE_ENFORCE_EQ(
       attn_dim[2],
       attn_dim[3],
       common::errors::InvalidArgument(
-          "The third dim and the fourth dim of mask should be the same"
+          "The third dim and the fourth dim of mask should be the same "
           "which is max seq len"));
   PADDLE_ENFORCE_EQ(attn_dim[2],
                     mask_dim[2],
                     common::errors::InvalidArgument(
-                        "The third dim of mask and attn should be the same"
+                        "The third dim of mask and attn should be the same "
                         "which is max seq len"));
   PADDLE_ENFORCE_EQ(attn_dim[2],
                     x_dim[1],
                     common::errors::InvalidArgument(
-                        "The third dim of mask and the second dim of attn"
+                        "The third dim of mask and the second dim of attn "
                         "should be the same which is max seq len"));
 
   auto bsz = mask_dim[0];
diff --git a/paddle/phi/infermeta/multiary.cc b/paddle/phi/infermeta/multiary.cc
index eaddd75c097c7d..3e1edcddd48d92 100644
--- a/paddle/phi/infermeta/multiary.cc
+++ b/paddle/phi/infermeta/multiary.cc
@@ -1348,7 +1348,7 @@ void ConcatInferMeta(const std::vector<const MetaTensor*>& x,
   PADDLE_ENFORCE_GE(x.size(),
                     0UL,
                     common::errors::InvalidArgument(
-                        "The size of input meta vector should be greater"
+                        "The size of input meta vector should be greater "
                         "than 0."));
   if (axis_scalar.FromTensor() && !config.is_runtime) {
     auto out_dims =
@@ -2652,15 +2652,16 @@ void FusedLayerNormInferMeta(const MetaTensor& x,
   }
   if (config.is_runtime) {
     if (norm_weight) {
-      PADDLE_ENFORCE_EQ(normalized_dims,
-                        norm_weight.dims()[0],
-                        common::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,
-                            norm_weight.dims()[0]));
+      PADDLE_ENFORCE_EQ(
+          normalized_dims,
+          norm_weight.dims()[0],
+          common::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,
+              norm_weight.dims()[0]));
     }
   }
 
@@ -3243,7 +3244,7 @@ static void Interpolate1DInferShapeCheck(
   PADDLE_ENFORCE_EQ("linear",
                     interp_method,
                     common::errors::InvalidArgument(
-                        "Interpolation method can only be \"linear\" when"
+                        "Interpolation method can only be \"linear\" when "
                         "Input(X) dimension is 3, but got method = %s .",
                         interp_method));
   const DataLayout data_layout = common::StringToDataLayout(data_layout_str);
@@ -4147,19 +4148,19 @@ void MemoryEfficientAttentionInferMeta(const MetaTensor& query,
   PADDLE_ENFORCE_EQ(
       query.dims().size(),
       4,
-      common::errors::InvalidArgument("Query should be a 4-D tensor"
+      common::errors::InvalidArgument("Query should be a 4-D tensor. "
                                       "But received Query dimension(%s)",
                                       query.dims().size()));
   PADDLE_ENFORCE_EQ(
       key.dims().size(),
       4,
-      common::errors::InvalidArgument("Key should be a 4-D tensor"
+      common::errors::InvalidArgument("Key should be a 4-D tensor. "
                                       "But received Key dimension(%s)",
                                       key.dims().size()));
   PADDLE_ENFORCE_EQ(
       value.dims().size(),
       4,
-      common::errors::InvalidArgument("Value should be a 4-D tensor"
+      common::errors::InvalidArgument("Value should be a 4-D tensor. "
                                       "But received Value dimension(%s)",
                                       value.dims().size()));
 
@@ -5573,7 +5574,7 @@ void UnchangedMultiInferMeta(const std::vector<const MetaTensor*>& x,
       x.size(),
       out.size(),
       common::errors::InvalidArgument(
-          "Input's size should be equal to the output's size"
+          "Input's size should be equal to the output's size, "
           "but received input size: (%d) does not equals output_size: (%d)",
           x.size(),
           out.size()));
@@ -5793,7 +5794,7 @@ void WeightOnlyLinearInferMeta(const MetaTensor& x,
         weight_scale_dims[0],
         (w_dims[1] + (group_size - 1)) / group_size,
         errors::InvalidArgument("The input(weight_scale) dim[0] must be equal "
-                                "to Input(weight) dim[1] / group_size"
+                                "to Input(weight) dim[1] / group_size. "
                                 "But receive %d and %d",
                                 weight_scale_dims[0],
                                 (w_dims[1] + (group_size - 1)) / group_size));
@@ -6000,17 +6001,17 @@ void YoloLossInferMeta(const MetaTensor& x,
 
   if (gt_score) {
     auto dim_gtscore = gt_score.dims();
-    PADDLE_ENFORCE_EQ(
-        dim_gtscore.size(),
-        2,
-        common::errors::InvalidArgument("Input(GTScore) should be a 2-D tensor"
-                                        "But received GTScore dimension(%s)",
-                                        dim_gtbox.size()));
+    PADDLE_ENFORCE_EQ(dim_gtscore.size(),
+                      2,
+                      common::errors::InvalidArgument(
+                          "Input(GTScore) should be a 2-D tensor. "
+                          "But received GTScore dimension(%s)",
+                          dim_gtbox.size()));
     PADDLE_ENFORCE_EQ(
         dim_gtscore[0],
         dim_gtbox[0],
         common::errors::InvalidArgument(
-            "Input(GTBox) and Input(GTScore) dim[0] should be same"
+            "Input(GTBox) and Input(GTScore) dim[0] should be same. "
             "But received GTBox dim[0](%s) != GTScore dim[0](%s)",
             dim_gtbox[0],
             dim_gtscore[0]));
@@ -6018,7 +6019,7 @@ void YoloLossInferMeta(const MetaTensor& x,
         dim_gtscore[1],
         dim_gtbox[1],
         common::errors::InvalidArgument(
-            "Input(GTBox) and Input(GTScore) dim[1] should be same"
+            "Input(GTBox) and Input(GTScore) dim[1] should be same. "
             "But received GTBox dim[1](%s) != GTScore dim[1](%s)",
             dim_gtscore[1],
             dim_gtbox[1]));
diff --git a/paddle/phi/infermeta/ternary.cc b/paddle/phi/infermeta/ternary.cc
index b7ca2257970295..7625ded6824a5f 100644
--- a/paddle/phi/infermeta/ternary.cc
+++ b/paddle/phi/infermeta/ternary.cc
@@ -372,7 +372,7 @@ void BoxCoderInferMeta(const MetaTensor& prior_box,
           prior_box_dims,
           prior_box_var_dims,
           common::errors::InvalidArgument(
-              "The dimension of Input(PriorBoxVar) should be equal to"
+              "The dimension of Input(PriorBoxVar) should be equal to "
               "the dimension of Input(PriorBox) in BoxCoder operator "
               "when the rank is 2."));
     }
@@ -1272,7 +1272,7 @@ void LayerNormInferMeta(const MetaTensor& x,
   PADDLE_ENFORCE_GT(begin_norm_axis,
                     0,
                     common::errors::InvalidArgument(
-                        "'begin_norm_axis' in Op(LayerNorm) should be"
+                        "'begin_norm_axis' in Op(LayerNorm) should be "
                         "greater than zero. But received [%d].",
                         begin_norm_axis));
   PADDLE_ENFORCE_LT(
@@ -1296,7 +1296,7 @@ void LayerNormInferMeta(const MetaTensor& x,
                       1,
                       common::errors::InvalidArgument(
                           "The dimensions of Input(Scale) must be 1, but "
-                          "received dimensions of"
+                          "received dimensions of "
                           "Input(Scale) is [%d]",
                           scale.dims().size()));
   }
@@ -1306,10 +1306,10 @@ void LayerNormInferMeta(const MetaTensor& x,
         scale.dims()[0],
         right,
         common::errors::InvalidArgument(
-            "The first dimension value of Input(Scale) must equal to be the"
-            "second dimension value of the flattened 2D matrix of Input(X),"
-            "But received the first dimension value of Input(Scale) is"
-            "[%d], the second dimension value of the flattened 2D matrix of"
+            "The first dimension value of Input(Scale) must equal to be the "
+            "second dimension value of the flattened 2D matrix of Input(X), "
+            "But received the first dimension value of Input(Scale) is "
+            "[%d], the second dimension value of the flattened 2D matrix of "
             " Input(Scale) is [%d].",
             scale.dims()[0],
             right));
@@ -1319,7 +1319,7 @@ void LayerNormInferMeta(const MetaTensor& x,
                       1,
                       common::errors::InvalidArgument(
                           "The dimensions of Input(Bias) must be 1, but "
-                          "received dimensions of"
+                          "received dimensions of "
                           "Input(Bias) is [%d]",
                           bias.dims().size()));
   }
@@ -1328,10 +1328,10 @@ void LayerNormInferMeta(const MetaTensor& x,
         bias.dims()[0],
         right,
         common::errors::InvalidArgument(
-            "The first dimension value of Input(Bias) must equal to be the"
-            "second dimension value of the flattened 2D matrix of Input(X),"
-            "But received the first dimension value of Input(Bias) is"
-            "[%d], the second dimension value of the flattened 2D matrix of"
+            "The first dimension value of Input(Bias) must equal to be the "
+            "second dimension value of the flattened 2D matrix of Input(X), "
+            "But received the first dimension value of Input(Bias) is "
+            "[%d], the second dimension value of the flattened 2D matrix of "
             " Input(Bias) is [%d].",
             bias.dims()[0],
             right));
@@ -1627,7 +1627,7 @@ void MoeCombineInferMeta(const MetaTensor& x,
                     2,
                     common::errors::InvalidArgument(
                         "The dimensions of Input(x) must be 1, but "
-                        "received dimensions of"
+                        "received dimensions of "
                         "Input(x) is [%d]",
                         x_dim.size()));
   // maybe there is more conditions here....
@@ -1696,7 +1696,7 @@ void MoeGateDispatchPartialNoSoftmaxTopKInferMeta(
                     2,
                     common::errors::InvalidArgument(
                         "The dimensions of Input(x) must be 2, but "
-                        "received dimensions of"
+                        "received dimensions of "
                         "Input(x) is [%d]",
                         x_dims.size()));
   auto combine_weights_dims = combine_weights.dims();
@@ -1705,7 +1705,7 @@ void MoeGateDispatchPartialNoSoftmaxTopKInferMeta(
       2,
       common::errors::InvalidArgument(
           "The dimensions of Input(combine_weights) must be 2, but "
-          "received dimensions of"
+          "received dimensions of "
           "Input(combine_weights) is [%d]",
           combine_weights_dims.size()));
   PADDLE_ENFORCE_EQ(combine_weights_dims[0],
@@ -1804,7 +1804,7 @@ void MoeGateDispatchPermuteInferMeta(const MetaTensor& x,
                     2,
                     common::errors::InvalidArgument(
                         "The dimensions of Input(x) must be 2, but "
-                        "received dimensions of"
+                        "received dimensions of "
                         "Input(x) is [%d]",
                         x_dims.size()));
   auto gate_logits_dims = gate_logits.dims();
@@ -1812,7 +1812,7 @@ void MoeGateDispatchPermuteInferMeta(const MetaTensor& x,
                     2,
                     common::errors::InvalidArgument(
                         "The dimensions of Input(gate_logits) must be 2, but "
-                        "received dimensions of"
+                        "received dimensions of "
                         "Input(gate_logits) is [%d]",
                         gate_logits_dims.size()));
   PADDLE_ENFORCE_EQ(gate_logits_dims[0],
@@ -2199,7 +2199,7 @@ void RoiAlignInferMeta(const MetaTensor& x,
   PADDLE_ENFORCE_EQ(input_dims.size(),
                     4,
                     common::errors::InvalidArgument(
-                        "The format of Input(x) in"
+                        "The format of Input(x) in "
                         "RoiAlignOp is NCHW. And the rank of input must be 4. "
                         "But received rank = %d",
                         input_dims.size()));
diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc
index 89135880485c18..a30e9fd2f035e4 100644
--- a/paddle/phi/infermeta/unary.cc
+++ b/paddle/phi/infermeta/unary.cc
@@ -1162,8 +1162,8 @@ void EighInferMeta(const MetaTensor& x,
       input_dim[rank - 2],
       input_dim[rank - 1],
       common::errors::InvalidArgument(
-          "Eigh op is designed for square matrix, consequently"
-          "inner-most 2 dimensions of Input(X) should be symmetric."
+          "Eigh op is designed for square matrix, consequently "
+          "inner-most 2 dimensions of Input(X) should be symmetric. "
           "But received X's shape[-2] = %d and shape[-1] = %d.",
           input_dim[rank - 2],
           input_dim[rank - 1]));
@@ -1230,8 +1230,8 @@ void EigvalshInferMeta(const MetaTensor& x,
       input_dim[rank - 2],
       input_dim[rank - 1],
       errors::InvalidArgument(
-          "Eigvalsh op is designed for square matrix, consequently"
-          "inner-most 2 dimensions of Input(X) should be symmetric."
+          "Eigvalsh op is designed for square matrix, consequently "
+          "inner-most 2 dimensions of Input(X) should be symmetric. "
           "But received X's shape[-2] = %d and shape[-1] = %d.",
           input_dim[rank - 2],
           input_dim[rank - 1]));
@@ -1395,7 +1395,7 @@ void ExpandModalityExpertIdInferMeta(const MetaTensor& expert_id,
           expert_id.dtype() == DataType::INT64,
       true,
       common::errors::InvalidArgument(
-          "The dtype of expert_id should be INT32 or INT64. But received"
+          "The dtype of expert_id should be INT32 or INT64. But received "
           "dtype=%s.",
           DataTypeToString(expert_id.dtype())));
 
@@ -1652,7 +1652,7 @@ void FlattenInferMeta(const MetaTensor& x,
     PADDLE_ENFORCE_GE(
         stop_axis,
         start_axis,
-        common::errors::InvalidArgument("The stop_axis should be greater"
+        common::errors::InvalidArgument("The stop_axis should be greater "
                                         "than or equal to start_axis."));
   }
 
@@ -1985,7 +1985,7 @@ void FoldInferMeta(const MetaTensor& x,
       in_dims[1] % (kernel_sizes[0] * kernel_sizes[1]),
       0,
       common::errors::InvalidArgument(
-          "Expected size of input's dimension 1 to be divisible by the"
+          "Expected size of input's dimension 1 to be divisible by the "
           "product of kernel_size, but got input.size(1)=%d and "
           "kernel_size=( %d"
           ", %d).",
@@ -6063,7 +6063,7 @@ void UniqueConsecutiveInferMeta(const MetaTensor& x,
     PADDLE_ENFORCE_EQ(axis.empty(),
                       true,
                       common::errors::InvalidArgument(
-                          "The Input(X) with 0-D Tensor, axis must be None"
+                          "The Input(X) with 0-D Tensor, axis must be None. "
                           "But now the axis is %d.",
                           axis[0]));
   }
@@ -6164,7 +6164,7 @@ void UniqueRawInferMeta(const MetaTensor& x,
     PADDLE_ENFORCE_EQ(axis.empty(),
                       true,
                       common::errors::InvalidArgument(
-                          "The Input(X) with 0-D Tensor, axis must be None"
+                          "The Input(X) with 0-D Tensor, axis must be None. "
                           "But now the axis is %d.",
                           axis[0]));
   }