[CodeStyle][CINN] fix cinn cpplint codestyle about `[readability/todo]`,...

[CodeStyle][CINN] fix cinn cpplint codestyle about `[readability/todo]`, `[readability/check]` (#55022)

paddle/cinn/auto_schedule/search_space/auto_gen_rule/multi_level_tiling.cc

@@ -153,7 +153,7 @@ void MultiLevelTiling::ApplyTiling(ir::IRSchedule* ir_schedule,
       idx = &r_indices_;
     } else {
       idx = &s_indices_;
-    }  // TODO: support more iterator variable types
+    }  // TODO(zhhsplendid): support more iterator variable types
 
     int extent = ir_for->extent.as_int32();  // maybe int64?
 

paddle/cinn/auto_schedule/search_space/auto_gen_rule/multi_level_tiling_test.cc

@@ -148,7 +148,7 @@ TEST(MultiLevelTile, SimpleLoops) {
   test_func(&new_states[0]->ir_schedule);
 }
 
-// TODO: fix in future
+// TODO(SunNy820828449): fix in future
 /*
 TEST(MulitLevelTile, MatrixMultiply) {
   srand(0);

paddle/cinn/common/cas.cc

@@ -2190,7 +2190,7 @@ Expr CasSimplifyMutator::SimplifyFracOp(Expr expr) {
   };
 
   {
-    // TODO : fix in future.
+    // TODO(SunNy820828449): fix in future.
     // std::vector<Expr> a_args, b_args;
     // if (ap)
     //   a_args = ap->operands();

paddle/cinn/frontend/op_mappers/paddle/argsort.cc

@@ -45,10 +45,10 @@ void ArgsortOpMapper(const paddle::cpp::OpDesc& op_desc,
   ctx.AddVar(indices_name, idx);
   ctx.AddVarModelToProgram(indices_name, idx->id);
 
-  // TODO: return the sorted tensor here. Now out[1] is a temporary tensor.
-  // this is because output 'Out' is never uesd in Paddle API, but CINN need to
-  // return 2 output vars to meet the op defination, this should be resolved
-  // after sort op restructured.
+  // TODO(lanxianghit): return the sorted tensor here. Now out[1] is a temporary
+  // tensor. this is because output 'Out' is never uesd in Paddle API, but CINN
+  // need to return 2 output vars to meet the op defination, this should be
+  // resolved after sort op restructured.
   ctx.AddVar(out_name, out[1]);
   ctx.AddVarModelToProgram(out_name, out[1]->id);
 }

paddle/cinn/hlir/op/contrib/argmax.cc

@@ -157,9 +157,9 @@ std::shared_ptr<framework::OpStrategy> StrategyForArgmax(
     ir::IRSchedule ir_sch(mod_expr);
     ir_sch.MergeExprs();
     auto blocks = ir_sch.GetAllBlocks();
-    // TODO: It needs to be rewritten according to the reduction_max operator to
-    // improve performance. Do not use local variables, because the size will
-    // exceed the limit.
+    // TODO(zhhsplendid): It needs to be rewritten according to the
+    // reduction_max operator to improve performance. Do not use local
+    // variables, because the size will exceed the limit.
     ir_sch.SetBuffer(blocks[0], "local");
     ir_sch.SetBuffer(blocks[1], "local");
 
@@ -184,7 +184,7 @@ std::shared_ptr<framework::OpStrategy> StrategyForArgmax(
 std::vector<shape_t> InferShapeForArgmax(
     const std::vector<shape_t> &inputs_shape,
     const framework::AttrMapType &attrs) {
-  CHECK(inputs_shape.size() == 1UL);
+  CHECK_EQ(inputs_shape.size(), 1UL);
   auto ndim = inputs_shape[0].size();
   CHECK_GT(ndim, 0) << "tensor's dim must be more than 0";
   int axis;

paddle/cinn/hlir/op/contrib/argmin.cc

@@ -155,9 +155,9 @@ std::shared_ptr<framework::OpStrategy> StrategyForArgmin(
     ir::IRSchedule ir_sch(mod_expr);
     ir_sch.MergeExprs();
     auto blocks = ir_sch.GetAllBlocks();
-    // TODO: It needs to be rewritten according to the reduction_min operator to
-    // improve performance. Do not use local variables, because the size will
-    // exceed the limit.
+    // TODO(zhhsplendid): It needs to be rewritten according to the
+    // reduction_min operator to improve performance. Do not use local
+    // variables, because the size will exceed the limit.
     ir_sch.SetBuffer(blocks[0], "local");
     ir_sch.SetBuffer(blocks[1], "local");
     int64_t prod_size = std::accumulate(output_shapes[0].begin(),
@@ -181,7 +181,7 @@ std::shared_ptr<framework::OpStrategy> StrategyForArgmin(
 std::vector<shape_t> InferShapeForArgmin(
     const std::vector<shape_t> &inputs_shape,
     const framework::AttrMapType &attrs) {
-  CHECK(inputs_shape.size() == 1UL);
+  CHECK_EQ(inputs_shape.size(), 1UL);
   auto ndim = inputs_shape[0].size();
   CHECK_GT(ndim, 0) << "tensor's dim must be more than 0";
   int axis;

paddle/cinn/hlir/op/contrib/sort.cc

@@ -213,8 +213,8 @@ std::shared_ptr<framework::OpStrategy> StrategyForSort(
       ir::IRSchedule ir_sch(mod_expr);
       ir_sch.MergeExprs();
       auto blocks = ir_sch.GetAllBlocks();
-      // TODO: remove external calls, do not use local variables, because
-      // the size will exceed the limit.
+      // TODO(Shixiaowei02): remove external calls, do not use local variables,
+      // because the size will exceed the limit.
       ir_sch.SetBuffer(blocks[0], "local");
       ir_sch.SetBuffer(blocks[1], "local");
 
@@ -307,10 +307,11 @@ std::shared_ptr<framework::OpStrategy> StrategyForArgSort(
       ir::IRSchedule ir_sch(mod_expr);
       ir_sch.MergeExprs();
       auto blocks = ir_sch.GetAllBlocks();
-      // TODO: remove external calls, do not use local variables, because
-      // the size will exceed the limit.
-      // TODO: There is a bug, setting buffer to "local" here will cause the var
-      // declared twice at CodeGen. ir_sch.SetBuffer(blocks[0], "local");
+      // TODO(Shixiaowei02): remove external calls, do not use local variables,
+      // because the size will exceed the limit.
+      // TODO(lanxianghit): There is a bug, setting buffer to "local" here will
+      // cause the var declared twice at CodeGen. ir_sch.SetBuffer(blocks[0],
+      // "local");
       int64_t prod_size = std::accumulate(output_shapes[0].begin(),
                                           output_shapes[0].end(),
                                           1,

paddle/cinn/hlir/op/nn.cc

@@ -1716,7 +1716,7 @@ std::shared_ptr<OpStrategy> StrategyForPool2d(
       *ret = CINNValuePack{res};
     } else {
       CINNValuePack arg_pack = args[0];
-      CHECK(arg_pack.size() == 3UL);
+      CHECK_EQ(arg_pack.size(), 3UL);
       Expr out = arg_pack[0];
       Expr reduce = arg_pack[1];
       CHECK(out.as_tensor() && reduce.as_tensor());
@@ -1834,7 +1834,8 @@ std::shared_ptr<OpStrategy> StrategyForPool2d(
   bool use_warp_reduce = false;
   if (global_pooling && data_format == "NCHW" &&
       target.arch == Target::Arch::NVGPU) {
-    // TODO 32 may not be the exact number, try also 16 or 8 or other number
+    // TODO(hp03): 32 may not be the exact number, try also 16 or 8 or other
+    // number
     //      we choose 32 to make sure all the threads in a warp has work to do,
     if ((A_tensor->shape[2].as_int32() * A_tensor->shape[3].as_int32()) >= 32) {
       use_warp_reduce = true;

paddle/cinn/hlir/pass/reduce_split_pass.cc

@@ -103,7 +103,7 @@ class ReduceSplitPass {
         auto in_shape = shape_dict.at(in->id());
         auto out_shape = shape_dict.at(out->id());
         // all preceding reduced
-        CHECK(in_shape.size() > 1);
+        CHECK_GT(in_shape.size(), 1);
         // [NHWC]->[C], only the last dim kept
         bool all_preceding_dim_reduced = true;
         for (auto i = 0; i < in_shape.size() - 1; ++i) {
@@ -122,7 +122,7 @@ class ReduceSplitPass {
             in_shape.begin(), in_shape.end(), 1, std::multiplies<int>());
         int reduce_numel = std::accumulate(
             in_shape.begin(), in_shape.end() - 1, 1, std::multiplies<int>());
-        CHECK(reduce_numel > 0);
+        CHECK_GT(reduce_numel, 0);
         // if the numel is not large enough, it is no need to split
         // if loop times is too large with reduce optimize
         int size = std::accumulate(

paddle/cinn/hlir/pe/nn.cc

@@ -1414,8 +1414,8 @@ std::vector<Tensor> Pool1d(const Tensor &tensor,
 std::vector<Tensor> GlobalPool2d(const Tensor &tensor,
                                  const std::string &pool_type,
                                  const std::string &output_name) {
-  // TODO 1. check warp shuffle is supported!
-  // TODO 2. using `cub` with NVRTC
+  // TODO(hp03): 1. check warp shuffle is supported!
+  // TODO(hp03): 2. using `cub` with NVRTC
   Expr extend = tensor->shape[2] * tensor->shape[3];
   if (pool_type == "max") {
     auto temp = Compute(

paddle/cinn/ir/schedule_desc_test.cc

@@ -293,7 +293,8 @@ TEST_F(TestScheduleDesc, StepKind_GetBlock) {
   CheckTracingOutputs({block_b}, trace);
   CheckTracingOutputs({block_b}, ir_sch.GetTraceDesc());
 }
-// TODO: fix in future, as fix split var name, this case some problem.
+// TODO(SunNy820828449): fix in future, as fix split var name, this case some
+// problem.
 /*
 TEST_F(TestScheduleDesc, StepKind_Split) {
   lowered_funcs                         = LowerCompute({32, 32, 32}, target);

paddle/cinn/optim/vectorize_loops.cc

@@ -730,7 +730,7 @@ struct VectorizeLoops_ : public IRMutator<Expr *> {
     if (forloop->is_vectorized()) {
       Context::info_rgt().Get<int>("vectorized_forloop_count")++;
 
-      CHECK(forloop->vectorize_info().factor > 0);
+      CHECK_GT(forloop->vectorize_info().factor, 0);
 
       CHECK(is_zero(forloop->min));
       Expr for_extent = common::AutoSimplify(forloop->extent);

paddle/cinn/runtime/tiny_runtime.cc

@@ -58,10 +58,10 @@ void *load_program(const char *paramfile) {
   fclose(f);
 
   if (std::string(buf, buf + 4) != "CINN") {
-    // TODO LOG fatal
+    // TODO(hp03): LOG fatal
     return nullptr;
   }
-  // TODO check param file version
+  // TODO(hp03): check param file version
   ctx->major_v = *(int *)(buf + 4);
   ctx->minor_v = *(int *)(buf + 8);
 

paddle/cinn/utils/random_engine.h

@@ -109,7 +109,7 @@ double SampleUniformDouble(double min,
 template <typename T>
 int SampleDiscreteFromDistribution(const std::vector<T>& weights,
                                    LinearRandomEngine::StateType* rand_seed) {
-  CHECK(weights.size() > 0);
+  CHECK_GT(weights.size(), 0);
   LinearRandomEngine engine(rand_seed);
   std::discrete_distribution<int> dist(weights.begin(), weights.end());
   return dist(engine);

test/cpp/cinn/concrete_program_builder.h

@@ -27,7 +27,7 @@ class BiasBnReLUBuilder : public ProgramBuilder {
   BiasBnReLUBuilder() : ProgramBuilder("bias_bn_relu_builder") {}
   frontend::Program Build(const std::vector<VariableInfo>& inputs_varinfo,
                           const utils::AttributeMap& attrs = {}) {
-    CHECK(inputs_varinfo.size() == 4);
+    CHECK_EQ(inputs_varinfo.size(), 4);
     auto conv_output = builder_.CreateInput(
         inputs_varinfo[0].type, inputs_varinfo[0].shape, inputs_varinfo[0].id);
     auto bias = builder_.CreateInput(
@@ -55,7 +55,7 @@ class ExpTwoConsumersOpBuilder : public ProgramBuilder {
   ExpTwoConsumersOpBuilder() : ProgramBuilder("exp_two_consumers_builder") {}
   frontend::Program Build(const std::vector<VariableInfo>& inputs_varinfo,
                           const utils::AttributeMap& attrs = {}) {
-    CHECK(inputs_varinfo.size() == 1);
+    CHECK_EQ(inputs_varinfo.size(), 1);
     auto x = builder_.CreateInput(
         inputs_varinfo[0].type, inputs_varinfo[0].shape, inputs_varinfo[0].id);
     auto exp_x = builder_.Exp(x);
@@ -76,7 +76,7 @@ class GatherAddSubBuilder : public ProgramBuilder {
   GatherAddSubBuilder() : ProgramBuilder("gather_add_sub_builder") {}
   frontend::Program Build(const std::vector<VariableInfo>& inputs_varinfo,
                           const utils::AttributeMap& attrs = {}) {
-    CHECK(inputs_varinfo.size() == 2);
+    CHECK_EQ(inputs_varinfo.size(), 2);
     auto x = builder_.CreateInput(
         inputs_varinfo[0].type, inputs_varinfo[0].shape, inputs_varinfo[0].id);
     auto y = builder_.CreateInput(
@@ -102,7 +102,7 @@ class FillConstantAddBuilder : public ProgramBuilder {
   FillConstantAddBuilder() : ProgramBuilder("fill_constant_add_builder") {}
   frontend::Program Build(const std::vector<VariableInfo>& inputs_varinfo,
                           const utils::AttributeMap& attrs = {}) {
-    CHECK(inputs_varinfo.size() == 1);
+    CHECK_EQ(inputs_varinfo.size(), 1);
     auto x = builder_.CreateInput(
         inputs_varinfo[0].type, inputs_varinfo[0].shape, inputs_varinfo[0].id);
     auto fill_constant =