Fix some typos (opertaor, normilized, etc.)

test/collective/README.md

@@ -6,30 +6,30 @@
     and specify the properties for the new unit test
     the properties are the following:
 * `name`: the test's name
-* `os`: The supported operator system, ignoring case. If the test run in multiple operator systems, use ";" to split systems, for example, `apple;linux` means the test runs on both Apple and Linux. The supported values are `linux`,`win32` and `apple`. If the value is empty, this means the test runs on all opertaor systems.
-* `arch`: the device's architecture. similar to `os`, multiple valuse ars splited by ";" and ignoring case. The supported architectures are `gpu`, `xpu` and `rocm`.
+* `os`: The supported operator system, ignoring case. If the test run in multiple operator systems, use ";" to split systems, for example, `apple;linux` means the test runs on both Apple and Linux. The supported values are `linux`,`win32` and `apple`. If the value is empty, this means the test runs on all operator systems.
+* `arch`: the device's architecture. similar to `os`, multiple values are splited by ";" and ignoring case. The supported architectures are `gpu`, `xpu` and `rocm`.
 * `timeout`: timeout of a unittest, whose unit is second. Blank means default.
 * `run_type`: run_type of a unittest. Supported values are `NIGHTLY`, `EXCLUSIVE`, `CINN`, `DIST`, `GPUPS`, `INFER`, `EXCLUSIVE:NIGHTLY`, `DIST:NIGHTLY`，which are case-insensitive.
 * `launcher`: the test launcher.Supported values are test_runner.py, dist_test.sh and custom scripts' name. Blank means test_runner.py.
 * `num_port`: the number of port used in a distributed unit test. Blank means automatically distributed port.
 * `run_serial`: whether in serial mode. the value can be 1 or 0.Default (empty) is 0. Blank means default.
-* `ENVS`: required environments. multiple envirenmonts are splited by ";".
+* `ENVS`: required environments. multiple environments are splited by ";".
 * `conditions`: extra required conditions for some tests. The value is a list of boolean expression in cmake programmer, splited with ";". For example, the value can be `WITH_DGC;NOT WITH_NCCL` or `WITH_NCCL;${NCCL_VERSION} VERSION_GREATER_EQUAL 2212`,The relationship between these expressions is a conjunction.
 
-### step 3. Generate CmakeLists.txt
+### step 3. Generate CMakeLists.txt
     Run the cmd:
 ```bash
         python3 ${PADDLE_ROOT}/tools/gen_ut_cmakelists.py -f ${PADDLE_ROOT}/python/paddle/fluid/tests/unittests/collective/testslist.csv
 ```
     Then the cmd generates a file named CMakeLists.txt in the same directory with the testslist.csv.
-* usgae:
+* usage:
     The command accepts --files/-f or --dirpaths/-d options, both of which accepts multiple values.
     Option -f accepts a list of testslist.csv.
     Option -d accepts a list of directory path including files named testslist.csv.
     Type `python3 ${PADDLE_ROOT}/tools/gen_ut_cmakelists.py --help` for details.
 
 * note:
-When commiting the codes, you should commit both the testslist.csv and the generated CMakeLists.txt. Once you pulled the repo, you don't need to run this command until you modify the testslists.csv file.
+When committing the codes, you should commit both the testslist.csv and the generated CMakeLists.txt. Once you pulled the repo, you don't need to run this command until you modify the testslists.csv file.
 
 ### step 4. Build and test
     Build paddle and run ctest for the new unit test

test/contrib/test_bf16_utils.py

@@ -158,7 +158,7 @@ def test_find_true_post_op_with_search_all(self):
 
         var1 = block.create_var(name="X", shape=[3], dtype='float32')
         var2 = block.create_var(name="Y", shape=[3], dtype='float32')
-        inititializer_op = startup_block._prepend_op(
+        initializer_op = startup_block._prepend_op(
             type="fill_constant",
             outputs={"Out": var1},
             attrs={"shape": var1.shape, "dtype": var1.dtype, "value": 1.0},
@@ -168,11 +168,11 @@ def test_find_true_post_op_with_search_all(self):
             type="abs", inputs={"X": [var1]}, outputs={"Out": [var2]}
         )
         result = amp.bf16.amp_utils.find_true_post_op(
-            block.ops, inititializer_op, "X", search_all=False
+            block.ops, initializer_op, "X", search_all=False
         )
         assert len(result) == 0
         result = amp.bf16.amp_utils.find_true_post_op(
-            block.ops, inititializer_op, "X", search_all=True
+            block.ops, initializer_op, "X", search_all=True
         )
         assert result == [op1]
 

test/contrib/test_image_classification_fp16.py

@@ -181,7 +181,7 @@ def train_loop(main_program):
                     fetch_list=[scaled_loss, avg_cost],
                 )
                 print(
-                    'PassID {:1}, BatchID {:04}, train loss {:2.4}, scaled train closs {:2.4}'.format(
+                    'PassID {:1}, BatchID {:04}, train loss {:2.4}, scaled train loss {:2.4}'.format(
                         pass_id,
                         batch_id + 1,
                         float(loss),
@@ -272,7 +272,7 @@ def infer(use_cuda, save_dirname=None):
         ] = paddle.static.io.load_inference_model(save_dirname, exe)
 
         # The input's dimension of conv should be 4-D or 5-D.
-        # Use normilized image pixels as input data, which should be in the range [0, 1.0].
+        # Use normalized image pixels as input data, which should be in the range [0, 1.0].
         batch_size = 1
         tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")
 

test/cpp/auto_parallel/custom_op_spmd_rule_test.cc

@@ -56,7 +56,7 @@ TEST(CustomOp, Ctor) {
   std::vector<CustomSpmdInferAttrArg> attrs = {axis};
 
   auto infered_dist_attrs = forward_spmd_func(infer_inputs, attrs);
-  // list of tensor => sigle tensor
+  // list of tensor => single tensor
   EXPECT_EQ(infered_dist_attrs.first.size(), static_cast<size_t>(1));
   EXPECT_EQ(infered_dist_attrs.second.size(), static_cast<size_t>(1));
   EXPECT_TRUE(

test/cpp/auto_parallel/spmd_rule_test.cc

@@ -182,7 +182,7 @@ TEST(MatmulSPMDRule, Ctor) {
 
   check_dim_mapping(infered_dist_attrs.first[0], {-1, -1, 0, 1});
   check_dim_mapping(infered_dist_attrs.first[1],
-                    {-1, 0});  // confilct and should be changed to [-1, 0]
+                    {-1, 0});  // conflict and should be changed to [-1, 0]
   check_dim_mapping(infered_dist_attrs.second[0], {-1, -1, 1, -1});
   check_partial_dims(infered_dist_attrs.second[0], {0});
 
@@ -374,7 +374,7 @@ TEST(MatmulSPMDRuleInferBackward, Ctor) {
   auto matmul_spmd_rule =
       phi::distributed::SpmdRuleFactory::Instance().GetSpmdRule("matmul");
 
-  // TODO(zyc) update in future: propogate the partial in inferbackward
+  // TODO(zyc) update in future: propagate the partial in inferbackward
   // abmn[-1, -1, 1, -1] + partial[0] --> abmk[-1, -1, 1, -1], a1kn[-1, -1, -1,
   // -1]
   phi::distributed::InferSpmdContext ctx(
@@ -546,8 +546,8 @@ TEST(DefaultDataParallelSPMDRule, Ctor) {
   phi::distributed::DistMetaTensor out2(common::make_ddim(out2_shape),
                                         out2_dist_attr);
 
-  // 2 inputs 2 outputs, batch axis sharding is propagatd while other axes are
-  // replicatd call in vector arguments format
+  // 2 inputs 2 outputs, batch axis sharding is propagated while other axes are
+  // replicated call in vector arguments format
   auto infered_dist_attrs_st =
       phi::distributed::DefaultDataParallelInferSpmd({&x, &y}, {&out1, &out2});
   // call in variadic arguments format
@@ -677,7 +677,7 @@ TEST(ConcatRule, Ctor) {
   // test 1, inputs are aligned according to cost, and partial status is cleared
   auto inputs = build_inputs();
   auto infered_dist_attrs = phi::distributed::ConcatInferSpmd(inputs, 0);
-  // list of tensor => sigle tensor
+  // list of tensor => single tensor
   EXPECT_EQ(infered_dist_attrs.first.size(), static_cast<size_t>(1));
   EXPECT_EQ(infered_dist_attrs.second.size(), static_cast<size_t>(1));
   EXPECT_TRUE(
@@ -734,7 +734,7 @@ TEST(ConcatRule, Ctor) {
   // test 2，force replicate along concat axis
   inputs = build_inputs();
   infered_dist_attrs = phi::distributed::ConcatInferSpmd(inputs, 1);
-  // list of tensor => sigle tensor
+  // list of tensor => single tensor
   EXPECT_EQ(infered_dist_attrs.first.size(), static_cast<size_t>(1));
   EXPECT_EQ(infered_dist_attrs.second.size(), static_cast<size_t>(1));
   EXPECT_TRUE(
@@ -792,10 +792,10 @@ TEST(StackRule, Ctor) {
                                             t_dist_attr);
   };
 
-  // test 1, inputs are aligned according to cost,
+  // test 1, inputs are aligned according to cost.
   auto inputs = build_inputs();
   auto infered_dist_attrs = phi::distributed::StackInferSpmd(inputs, 0);
-  // list of tensor => sigle tensor
+  // list of tensor => single tensor
   EXPECT_EQ(infered_dist_attrs.first.size(), static_cast<size_t>(1));
   EXPECT_EQ(infered_dist_attrs.second.size(), static_cast<size_t>(1));
   EXPECT_TRUE(
@@ -840,7 +840,7 @@ TEST(StackRule, Ctor) {
   // test 2，force replicate along concat axis
   inputs = build_inputs();
   infered_dist_attrs = phi::distributed::StackInferSpmd(inputs, 1);
-  // list of tensor => sigle tensor
+  // list of tensor => single tensor
   EXPECT_EQ(infered_dist_attrs.first.size(), static_cast<size_t>(1));
   EXPECT_EQ(infered_dist_attrs.second.size(), static_cast<size_t>(1));
   EXPECT_TRUE(
@@ -1432,7 +1432,7 @@ TEST(EmbeddingGradInferSpmd, Ctor) {
           << std::endl
           << std::endl;
 
-  // indices'rank is greater than 1,  x and weight is replicated, out_grad is
+  // Indices' rank is greater than 1, x and weight is replicated, out_grad is
   // sharded along axis 1
   x_dist_attr.set_dims_mapping({-1, -1});
   w_dist_attr.set_dims_mapping({-1, 1});
@@ -1464,7 +1464,7 @@ TEST(EmbeddingGradInferSpmd, Ctor) {
           << std::endl
           << std::endl;
 
-  // Indices's rank equals 1, indices and out_grad is sharded.
+  // Indices' rank equals 1, indices and out_grad is sharded.
   x_shape = {5};
   w_shape = {10, 3};
   out_grad_shape = {5, 3};

test/cpp/cinn/benchmark/test_all_ops_default.cc

@@ -29,7 +29,7 @@ namespace tests {
 using cinn::hlir::framework::AttrType;
 
 #define TEST_DEFAULT(op_name__, shape_name__, input_types_, output_types_) \
-  TEST(op_defualt, shape_name__) {                                         \
+  TEST(op_default, shape_name__) {                                         \
     std::vector<std::vector<int>> input_shapes = shapes_##shape_name__;    \
     std::string op_name = #op_name__;                                      \
     hlir::framework::NodeAttr attrs;                                       \
@@ -44,7 +44,7 @@ using cinn::hlir::framework::AttrType;
 
 #define TEST_DEFAULT1(                                                  \
     op_name__, shape_name__, input_types_, output_types_, attr_store__) \
-  TEST(op_defualt1, shape_name__) {                                     \
+  TEST(op_default1, shape_name__) {                                     \
     std::vector<std::vector<int>> input_shapes = shapes_##shape_name__; \
     std::string op_name = #op_name__;                                   \
     OpBenchmarkTester tester(op_name, input_shapes);                    \
@@ -60,7 +60,7 @@ using cinn::hlir::framework::AttrType;
   }
 
 #define TEST_DEFAULT_INT(op_name__, shape_name__, input_types_, output_types_) \
-  TEST(op_defualt, shape_name__) {                                             \
+  TEST(op_default, shape_name__) {                                             \
     std::vector<std::vector<int>> input_shapes = shapes_##shape_name__;        \
     std::string op_name = #op_name__;                                          \
     hlir::framework::NodeAttr attrs;                                           \

test/cpp/cinn/benchmark/test_matmul.cc

@@ -73,9 +73,9 @@ std::vector<ir::Tensor> MatmulBlockTester::CreateSpecificStrategy(
   auto k1 = Var(input_shapes_[0][1], "k1");
   CHECK_EQ(input_shapes_.size(), 2U) << "matmul's input shape should be 2.\n";
   CHECK_EQ(input_shapes_[0].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[1].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[0][1], input_shapes_[1][0])
       << "matmul's reduce axis shape should be same\n";
   auto C = Compute(
@@ -109,9 +109,9 @@ std::vector<ir::Tensor> MatmulVectorizeTester::CreateSpecificStrategy(
   auto k1 = Var(input_shapes_[0][1], "k1");
   CHECK_EQ(input_shapes_.size(), 2U) << "matmul's input shape should be 2.\n";
   CHECK_EQ(input_shapes_[0].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[1].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[0][1], input_shapes_[1][0])
       << "matmul's reduce axis shape should be same\n";
   auto C = Compute(
@@ -146,9 +146,9 @@ std::vector<ir::Tensor> MatmulLoopPermutationTester::CreateSpecificStrategy(
   auto k1 = Var(input_shapes_[0][1], "k1");
   CHECK_EQ(input_shapes_.size(), 2U) << "matmul's input shape should be 2.\n";
   CHECK_EQ(input_shapes_[0].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[1].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[0][1], input_shapes_[1][0])
       << "matmul's reduce axis shape should be same\n";
   auto C = Compute(
@@ -183,9 +183,9 @@ std::vector<ir::Tensor> MatmulArrayPackingTester::CreateSpecificStrategy(
   std::vector<ir::Tensor> outs;
   CHECK_EQ(input_shapes_.size(), 2U) << "matmul's input shape should be 2.\n";
   CHECK_EQ(input_shapes_[0].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[1].size(), 2U)
-      << "matmul's input teosor's shape should be 2.\n";
+      << "matmul's input tensor's shape should be 2.\n";
   CHECK_EQ(input_shapes_[0][1], input_shapes_[1][0])
       << "matmul's reduce axis shape should be same\n";
 

test/cpp/cinn/benchmark/test_utils.cc

@@ -39,9 +39,9 @@ void OpBenchmarkTester::TestOp(const std::string& test_name,
                                const hlir::framework::NodeAttr& attrs,
                                const std::vector<Type>& input_types,
                                const std::vector<Type>& out_types,
-                               bool use_default_stragegy) {
+                               bool use_default_strategy) {
   auto module =
-      CreateCinnModule(input_tensors, attrs, out_types, use_default_stragegy);
+      CreateCinnModule(input_tensors, attrs, out_types, use_default_strategy);
   auto engine = CreateExecutionEngine(module);
   auto test_func_ptr =
       reinterpret_cast<void (*)(void**, int32_t)>(engine->Lookup(op_name_));
@@ -68,15 +68,15 @@ Module OpBenchmarkTester::CreateCinnModule(
     const std::vector<Tensor>& input_tensors,
     const hlir::framework::NodeAttr& attrs,
     const std::vector<Type>& out_types,
-    bool use_default_stragegy) {
+    bool use_default_strategy) {
   std::vector<Tensor> outs;
   std::vector<Tensor> rets;
   poly::StageMap stages;
   CHECK(!out_types.empty());
   rets = input_tensors;
   Module::Builder builder("module_" + op_name_, target_);
 
-  if (use_default_stragegy) {
+  if (use_default_strategy) {
     auto strategy =
         hlir::framework::Operator::GetAttrs<hlir::framework::StrategyFunction>(
             "CINNStrategy");

test/cpp/cinn/benchmark/test_utils.h

@@ -47,14 +47,14 @@ class OpBenchmarkTester {
               const hlir::framework::NodeAttr &attrs,
               const std::vector<Type> &input_types,
               const std::vector<Type> &out_types,
-              bool use_default_stragegy = true);
+              bool use_default_strategy = true);
 
   virtual Module CreateCinnModule(const std::vector<ir::Tensor> &input_tensors,
                                   const hlir::framework::NodeAttr &attrs,
                                   const std::vector<Type> &out_types,
-                                  bool use_default_stragegy = true);
+                                  bool use_default_strategy = true);
 
-  // should define specific stragey if not use default schedule
+  // should define specific strategy if not use default schedule
   virtual std::vector<ir::Tensor> CreateSpecificStrategy(
       const std::vector<ir::Tensor> &inputs, poly::StageMap *stages) {
     CINN_NOT_IMPLEMENTED

test/cpp/cinn/test02_matmul_case.cc

@@ -172,7 +172,7 @@ TEST(test02, basic) {
 
   // Currently, the execution of a LoweredFunc is scheduled by the outer
   // framework, so no need to Call inside another LoweredFunc.
-  // TODO(Superjomn) Fixit latter.
+  // TODO(Superjomn) Fix it later.
   // TEST_FUNC(matmul_main);
 
 #define TEST_LLVM_MATMUL(test_name, TARGET)                                \

test/cpp/eager/task_tests/generated_test.cc

@@ -58,7 +58,7 @@ TEST(Generated, Sigmoid) {
   eager_test::CompareTensorWithValue<float>(output_tensor, 0.5);
 
   std::vector<paddle::Tensor> target_tensors = {output_tensor};
-  VLOG(6) << "Runing Backward";
+  VLOG(6) << "Running Backward";
   Backward(target_tensors, {});
 
   VLOG(6) << "Finish Backward";

test/cpp/eager/task_tests/grad_test.cc

@@ -65,7 +65,7 @@ TEST(Grad, SingleNodeEmptyGrad) {
     // Set grad in/out meta
     node0_ptr->SetDefaultGradInOutMeta();
 
-    // Output_tensor set GradNode、OutRank、StopGradient propertis
+    // Output_tensor set GradNode、OutRank、StopGradient properties
     AutogradMeta* auto_grad_meta = EagerUtils::autograd_meta(&output_tensor);
     auto_grad_meta->SetGradNode(
         std::dynamic_pointer_cast<GradNodeBase>(node0_ptr));
@@ -80,7 +80,7 @@ TEST(Grad, SingleNodeEmptyGrad) {
     auto acc_node_ptr =
         std::make_shared<egr::GradNodeAccumulation>(auto_grad_meta1);
 
-    // input tensor set GradNode、OutRank、StopGradient propertis
+    // input tensor set GradNode、OutRank、StopGradient properties
     auto_grad_meta1->SetGradNode(
         std::dynamic_pointer_cast<GradNodeBase>(acc_node_ptr));
     auto_grad_meta1->SetSingleOutRankWithSlot(0, 0);

test/cpp/eager/task_tests/hook_test_intermidiate.cc

@@ -95,7 +95,7 @@ void test_sigmoid(bool is_remove_gradient_hook) {
   VLOG(6) << "Register ReduceHook for Tensor";
   egr_utils_api::RegisterReduceHookForTensor(tensor, reduce_hook);
 
-  VLOG(6) << "Runing Forward";
+  VLOG(6) << "Running Forward";
   auto output_tensor = sigmoid_dygraph_function(tensor, {});
   VLOG(6) << "Finish Forward";
 
@@ -108,7 +108,7 @@ void test_sigmoid(bool is_remove_gradient_hook) {
     grad_node_tmp->RemoveGradientHook(hook_id);
   }
 
-  VLOG(6) << "Runing Backward";
+  VLOG(6) << "Running Backward";
   Backward(target_tensors, {});
   VLOG(6) << "Finish Backward";
 
@@ -290,7 +290,7 @@ void test_backward_final_hooks() {
   VLOG(6) << "Register Backward Final Hook";
   egr_utils_api::RegisterBackwardFinalHook(backward_final_hook);
 
-  VLOG(6) << "Runing Forward";
+  VLOG(6) << "Running Forward";
   auto output_tensor = matmul_v2_dygraph_function(
       X, Y, {{"trans_x", false}, {"trans_y", false}});
   auto res = sigmoid_dygraph_function(output_tensor, {});
@@ -300,7 +300,7 @@ void test_backward_final_hooks() {
 
   std::vector<paddle::Tensor> target_tensors = {output_tensor};
 
-  VLOG(6) << "Runing Backward";
+  VLOG(6) << "Running Backward";
   Backward(target_tensors, {});
   VLOG(6) << "Finish Backward";
   eager_test::CompareTensorWithValue<float>(X, 100.0);

test/cpp/fluid/benchmark/op_tester.cc

@@ -181,7 +181,7 @@ void OpTester::CreateInputVarDesc() {
     PADDLE_ENFORCE_NOT_NULL(
         input,
         platform::errors::NotFound(
-            "The input %s of operator %s is not correctlly provided.",
+            "The input %s of operator %s is not correctly provided.",
             name,
             config_.op_type));