Fix typos(oprations, etc) (#62043)

paddle/fluid/framework/ir/fusion_group/operation.cc

@@ -152,7 +152,7 @@ void OperationMap::InsertUnaryElementwiseOperations() {
 }
 
 void OperationMap::InsertBinaryElementwiseOperations() {
-  // For binary elementwise oprations:
+  // For binary elementwise operations:
   //  ${0} - x
   //  ${1} - y
   //  ${2} - out

paddle/fluid/framework/ir/fusion_group/operation.h

@@ -55,7 +55,7 @@ struct Operation {
       return false;
     }
     if (IsGradOp() && exprs.size() != static_cast<size_t>(num_operands)) {
-      // When it is a backward opertion, it should hold a expression for each
+      // When it is a backward operation, it should hold a expression for each
       // operand.
       return false;
     }

paddle/fluid/framework/ir/fusion_group/subgraph.h

@@ -150,7 +150,7 @@ class SubGraph {
           !IsInputOfExternalOp(n)) {
         // When the outputs size is 0, it is also considered a intermidiate
         // output. It maybe an unused output or the fetching vars, so that we
-        // cannot eleiminate it directly here.
+        // cannot eliminate it directly here.
         intermediate_out_vars.push_back(n);
       }
     }

paddle/fluid/framework/ir/ipu/optimizer_extract_pass.cc

@@ -100,8 +100,8 @@ void IpuOptimizerExtractPass::ApplyImpl(ir::Graph* graph) const {
       // bool is_optimizer = is_optimizer_op(op_namescope);
       bool is_regularization = is_regularization_op(op_namescope);
 
-      VLOG(10) << "found optimizer releated op: " << op_type;
-      // initial larning_rate will be set in ipu_backend
+      VLOG(10) << "found optimizer related op: " << op_type;
+      // initial learning_rate will be set in ipu_backend
       set_ops.insert(op_type);
       if (op_type == "sgd") {
         auto type = std::string{"sgd"};
@@ -267,10 +267,10 @@ void IpuOptimizerExtractPass::ApplyImpl(ir::Graph* graph) const {
         auto value = PADDLE_GET_CONST(float, op->GetAttr("value"));
         new_op.SetAttr("clip_norm", value);
       } else if (ignored_ops.count(op_type)) {
-        VLOG(10) << "Ignore optimizer releated op: " << op_type;
+        VLOG(10) << "Ignore optimizer related op: " << op_type;
       } else {
         PADDLE_THROW(platform::errors::InvalidArgument(
-            "Unknown optimizer releated op_type: %s", op_type));
+            "Unknown optimizer related op_type: %s", op_type));
       }
     } else if (op_role == OpRole::kLoss) {
       VLOG(10) << "found loss op type: " << op->Type();
@@ -312,7 +312,7 @@ void IpuOptimizerExtractPass::ApplyImpl(ir::Graph* graph) const {
       new_op.SetAttr("weight_decay_mode", std::string{"l2_regularization"});
     }
   } else {
-    VLOG(10) << "No weight deacy setting found";
+    VLOG(10) << "No weight decay setting found";
   }
 
   // setup grad clip

paddle/fluid/framework/ir/memory_optimize_pass/buffer_shared_cross_op_memory_reuse_pass.cc

@@ -245,7 +245,7 @@ void BufferSharedCrossOpMemoryReusePass::RunOnScopeIdx(size_t idx) const {
       }
     }
 
-    // After all output args have been transversed, we should check whether
+    // After all output args have been traversed, we should check whether
     // there is new unlived var after `op` runs.
     for (auto op_iter = var_to_ops.begin(); op_iter != var_to_ops.end();) {
       // erase op from `var_to_ops` first
@@ -355,7 +355,7 @@ void BufferSharedCrossOpMemoryReusePass::BuildOpDependencyMap() const {
   // BFS to fill `preceding_ops`
   graph_view.BreadthFirstVisit([&](OpHandleBase *cur_op) {
     // All preceding ops of cur_op should be:
-    //  - preceding ops of cur_op, that is connected to cur_op directely
+    //  - preceding ops of cur_op, that is connected to cur_op directly
     //  - all preceding ops of `direct preceding ops of cur_op`
     auto &all_preceding_ops_of_cur_op = preceding_ops[cur_op];
     for (auto &preceding_op : graph_view.PrecedingOps(cur_op)) {

paddle/fluid/framework/ir/memory_optimize_pass/memory_optimization_var_info.h

@@ -89,7 +89,7 @@ class MemOptVarInfo {
    * scheduled in many threads inside ParallelExecutor, runtime_ref_cnt_
    * must be an atomic integer to guarantee the thread safety and visibility.
    *
-   * Speciallly, if ref_cnt_ is 1, we do not need to reset runtime_ref_cnt_
+   * Specially, if ref_cnt_ is 1, we do not need to reset runtime_ref_cnt_
    * after iteration ends.
    */
   size_t ref_cnt_;

paddle/fluid/framework/ir/memory_optimize_pass/share_varinfo_into_cinn_pass.cc

@@ -111,7 +111,7 @@ static void TakeVarInfoFromMainGraph(
 }
 
 // This pass will be applied on both the main graph and all cinn subgraphs,
-// and it distinguishs them according to whether the graph has the
+// and it distinguishes them according to whether the graph has the
 // kMemOptVarInfoFromMainGraph attribute or not.
 // On the main graph, it finds all cinn_launch ops and shares MemOptVarInfos
 // to their subgraphs.

paddle/fluid/framework/ir/memory_optimize_pass/while_op_eager_deletion_pass.cc

@@ -55,7 +55,7 @@ class WhileOpEagerDeletionPass : public ir::Pass {
       }
     }
     if (graph->IsConstructedByPartialProgram()) {
-      VLOG(4) << "Is Paritial Program";
+      VLOG(4) << "Is Partial Program";
       PADDLE_ENFORCE_LE(
           target_ops.size(),
           1,

paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass_tester.cc

@@ -1021,7 +1021,7 @@ TEST(CpuQuantizeSquashPass, fc_dequant_more_than_one_op_after_dequant) {
 // b->Quantize1(Scale)->c
 // c->Fc1
 // c->Fc2
-TEST(CpuQuantizeSquashPass, quatize_with_same_scale) {
+TEST(CpuQuantizeSquashPass, quantize_with_same_scale) {
   auto first_scale = 1.2345f;
   auto second_scale = 1.2345f;
   auto use_mkldnn = true;
@@ -1033,7 +1033,7 @@ TEST(CpuQuantizeSquashPass, quatize_with_same_scale) {
 }
 
 // if scales are not the same, do not fuse
-TEST(CpuQuantizeSquashPass, quatize_with_different_scale) {
+TEST(CpuQuantizeSquashPass, quantize_with_different_scale) {
   auto first_scale = 1.2345f;
   auto second_scale = 1.5432f;
   auto use_mkldnn = true;