Fix

paddle/cinn/common/simplify_special_pattern.cc

@@ -262,8 +262,8 @@ std::optional<ir::IndexExpr> DivMulAddModDivCase(const ir::IndexExpr& lhs,
       // Check if the pattern is matched
       if (CheckPattern(cand, pattern, &map) &&
           map.at("c") == map.at("a") * map.at("b")) {
-        ir::IndexExpr simplied = map.at("f") / map.at("b");
-        res = res.defined() ? res + simplied : simplied;
+        ir::IndexExpr simplified = map.at("f") / map.at("b");
+        res = res.defined() ? res + simplified : simplified;
         find = true;
         continue;
       }

paddle/cinn/runtime/cuda/cuda_util.cc

@@ -1667,11 +1667,11 @@ void GemmStridedBatched(const cublasHandle_t &cublas,
   PADDLE_ENFORCE_EQ(
       lhs_bs,
       rhs_bs,
-      ::common::errors::InvalidArgument("bs of lhs and rhs dismatch."));
+      ::common::errors::InvalidArgument("bs of lhs and rhs mismatch."));
   PADDLE_ENFORCE_EQ(
       lhs_bs,
       output_bs,
-      ::common::errors::InvalidArgument("bs of lhs and output dismatch."));
+      ::common::errors::InvalidArgument("bs of lhs and output mismatch."));
 
   // copy values of bias_data to the output_data
   if (bias_data != nullptr) {
@@ -1858,7 +1858,7 @@ void cinn_call_triangular_solve_nvgpu(void *v_args,
   PADDLE_ENFORCE_EQ(input1->type.bits,
                     input2->type.bits,
                     ::common::errors::InvalidArgument(
-                        "input1 and ipnput2's type bits is dismatch."));
+                        "input1 and input2's type bits is mismatch."));
   uint8_t bits = input1->type.bits;
   uint8_t bytes = bits / 8;
   PADDLE_ENFORCE_EQ(
@@ -2035,11 +2035,11 @@ void cinn_gpu_cublas_gemm(const std::vector<int> &attrs,
   PADDLE_ENFORCE_EQ(lhs_dim_size,
                     rhs_dim_size,
                     ::common::errors::InvalidArgument(
-                        "dimension dismatch between lhs and rhs."));
+                        "dimension mismatch between lhs and rhs."));
   PADDLE_ENFORCE_EQ(lhs_dim_size,
                     out_dim_size,
                     ::common::errors::InvalidArgument(
-                        "dimension dismatch between lhs and out."));
+                        "dimension mismatch between lhs and out."));
   PADDLE_ENFORCE_EQ(
       (lhs_dim_size == 2 || lhs_dim_size == 3),
       true,

paddle/fluid/framework/ir/auto_mixed_precision_pass.cc

@@ -902,7 +902,7 @@ void AutoMixedPrecisionPass::SetVarPrecision() const {
     }
   }
 
-  // This code used to precess vars with the same name. Vars with the same
+  // This code used to process vars with the same name. Vars with the same
   // name should have the same data type.
   for (auto* subgraph : subgraphes_) {
     for (auto* var_node : subgraph->Nodes()) {

paddle/fluid/framework/new_executor/instruction/cinn_jit_instruction.cc

@@ -333,7 +333,7 @@ void CinnJitInstruction::Run() {
     dev_ctx_->Alloc(tensor_args_[i], tensor_args_[i]->dtype());
   }
 
-  // 2. exexute kernel
+  // 2. execute kernel
   fn_ptr_impl_->Run(tensor_args_, running_stream, is_gpu);
 
   // 3. release resource

paddle/fluid/inference/tensorrt/engine.h

@@ -359,7 +359,7 @@ class TensorRTEngine {
     }
   }
 
-  // NOTE: The func bellow was modified to adapt the dynamic shape.
+  // NOTE: The func below was modified to adapt the dynamic shape.
   // Initialize the inference network, so that TensorRT layers can add to this
   // network.
   void InitNetwork();

paddle/fluid/pir/transforms/onednn/cpu_bfloat16_placement_pass.cc

@@ -156,7 +156,7 @@ class OneDNNBf16PlacementPattern : public pir::RewritePattern {
              idx++) {
           auto input_type =
               vector_type[idx].isa<paddle::dialect::DenseTensorType>();
-          // We don't precess nested VectorType
+          // We don't process nested VectorType
           if (!input_type) return false;
           pir::Type input_dtype =
               vector_type[idx]

paddle/phi/kernels/gpu/edit_distance_kernel.cu

@@ -163,7 +163,7 @@ void EditDistanceKernel(const Context& ctx,
                         0,
                         stream>>>(dist, n);
 
-      // Compute the elements of distance matrix in the anti-diagonal diretion
+      // Compute the elements of distance matrix in the anti-diagonal direction
       for (int64_t slice = 2; slice < m + n + 1; ++slice) {
         int z_m = slice < m + 1 ? 0 : slice - m;
         int z_n = slice < n + 1 ? 0 : slice - n;

python/paddle/jit/pir_dy2static/parameter_recorder.py

@@ -110,11 +110,11 @@ def restore_checkpoint(self, checkpoint):
         self.params_dict = checkpoint
 
     def save_checkpoint(self):
-        ckeckpoint = {}
+        checkpoint = {}
         for program_id, params in self.params_dict.items():
             new_params = dict(params.items())
-            ckeckpoint[program_id] = new_params
-        return ckeckpoint
+            checkpoint[program_id] = new_params
+        return checkpoint
 
 
 _global_parameter_recorder = ParametersRecorder()

python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py

@@ -1667,7 +1667,7 @@ def UNPACK_EX(self, instr: Instruction):
 
         if instr.argval >= 256:
             # NOTE: If the number of unpacked variables exceeds 256, python will report an error like:
-            # SyntaxError: too many expressions in star-unpacking assignmen,
+            # SyntaxError: too many expressions in star-unpacking assignment,
             # so if the number of unpacked variables exceeds 256, it will be treated as the following case.
             # a, b, *c, d = e
             front_nums = instr.arg & 0xFF