Fix some typos (eqaution_graph_view, GetValueOfRewritedOps, etc.)

paddle/cinn/adt/generate_map_expr.cc

@@ -160,7 +160,8 @@ hlir::framework::OpPatternKind GetOpPatternKind(const ::pir::Operation* node) {
   return hlir::framework::pir::CompatibleInfo::OpKind(*node);
 }
 
-bool CollectRewritedReductionOpStmts(const OpStmt& op_stmt, List<OpStmt>* ret) {
+bool CollectRewrittenReductionOpStmts(const OpStmt& op_stmt,
+                                      List<OpStmt>* ret) {
   const auto& [op, inputs, outputs] = op_stmt.tuple();
   CHECK(op.Has<const ::pir::Operation*>());
   if (GetOpPatternKind(op.Get<const ::pir::Operation*>()) ==
@@ -178,8 +179,8 @@ bool CollectRewritedReductionOpStmts(const OpStmt& op_stmt, List<OpStmt>* ret) {
   }
 }
 
-void CollectRewritedOpStmts(const OpStmt& op_stmt, List<OpStmt>* ret) {
-  if (CollectRewritedReductionOpStmts(op_stmt, ret)) {
+void CollectRewrittenOpStmts(const OpStmt& op_stmt, List<OpStmt>* ret) {
+  if (CollectRewrittenReductionOpStmts(op_stmt, ret)) {
     return;
   }
   (*ret)->emplace_back(op_stmt);
@@ -190,7 +191,7 @@ List<OpStmt> MakeOpStmts(
   List<OpStmt> ret{};
 
   VisitEachOpStmt(group, [&](const auto& op_stmt) {
-    CollectRewritedOpStmts(op_stmt, &ret);
+    CollectRewrittenOpStmts(op_stmt, &ret);
   });
 
   return ret;

paddle/cinn/adt/write_broadcast_disabled_bidirection_equation_generator.cc

@@ -101,9 +101,9 @@ Equation EraseIndexes(
 std::vector<Index> GenerateWriteBroadcastTensorIndexs(
     const std::shared_ptr<config::NaiveOpEquationContext>& ctx,
     const Equations& in_msg2out_msg_equations) {
-  const auto& eqaution_graph_view =
+  const auto& equation_graph_view =
       Graph<Variable, Equation>::New(ctx->equations())->GetGraphView();
-  GraphView graph_view = eqaution_graph_view.Merge(
+  GraphView graph_view = equation_graph_view.Merge(
       Graph<Variable, Equation>::New(in_msg2out_msg_equations)->GetGraphView());
   std::vector<Index> ret{};
   const auto& fake_op_placeholder = ctx->fake_op_placeholder();

paddle/cinn/hlir/dialect/operator/transforms/fuse_shape_ops_into_generate_shape_op_pass.cc

@@ -117,7 +117,7 @@ bool MakeGenerateShapeOpAttribute(
                                       symbol_bindings);
 }
 
-std::optional<pir::Value> GetOutOfRewritedGenerateShapeOp(
+std::optional<pir::Value> GetOutOfRewrittenGenerateShapeOp(
     pir::Value shape,
     pir::PatternRewriter* rewriter,
     const ShapeOrDataDimExprs4ValueT& ShapeOrDataDimExprs4Value) {
@@ -154,7 +154,7 @@ bool ReplaceShapeOpsToGenerateShape(
     return shape_analysis->GetShapeOrDataForValue(value);
   };
   std::optional<pir::Value> opt_generated_shape =
-      GetOutOfRewritedGenerateShapeOp(
+      GetOutOfRewrittenGenerateShapeOp(
           shape_operand, rewriter, ShapeOrDataDimExprs4Value);
   if (!opt_generated_shape.has_value()) return false;
   shape_analysis->SetShapeOrDataForValue(

paddle/cinn/hlir/dialect/operator/transforms/split_generate_shape_into_shape_ops_pass.cc

@@ -240,12 +240,12 @@ class SplitGenerateShapeIntoShapeOps
         MakeGetterTensorDim4SymbolName(op);
     if (!TensorDim4SymbolName) return std::nullopt;
     CachedDimExprToValueConverter converter{TensorDim4SymbolName, rewriter};
-    return GetValueOfRewritedOps(dim_exprs, &converter);
+    return GetValueOfRewrittenOps(dim_exprs, &converter);
   }
 
   TensorDim4SymbolNameT MakeGetterTensorDim4SymbolName(
       cinn::dialect::GenerateShapeOp op) const {
-    std::unordered_map<std::string, TensorDim> symbol_name2tenso_dim{};
+    std::unordered_map<std::string, TensorDim> symbol_name2tensor_dim{};
     const auto& attr_map = op->attributes();
     const auto& iter = attr_map.find("symbol_bindings");
     PADDLE_ENFORCE((iter != attr_map.end()),
@@ -262,9 +262,9 @@ class SplitGenerateShapeIntoShapeOps
                                         "not be converted to symbol bindings",
                                         op->name()));
     for (const auto& symbol_binding : symbol_bindings.value()) {
-      InsertSymbolBinding(op, symbol_binding, &symbol_name2tenso_dim);
+      InsertSymbolBinding(op, symbol_binding, &symbol_name2tensor_dim);
     }
-    return [map = std::move(symbol_name2tenso_dim)](
+    return [map = std::move(symbol_name2tensor_dim)](
                const std::string& symbol_name) -> std::optional<TensorDim> {
       auto iter = map.find(symbol_name);
       if (iter == map.end()) return std::nullopt;
@@ -275,28 +275,31 @@ class SplitGenerateShapeIntoShapeOps
   void InsertSymbolBinding(
       cinn::dialect::GenerateShapeOp op,
       const cinn::dialect::GenerateShapeOp::SymbolBinding& symbol_binding,
-      std::unordered_map<std::string, TensorDim>* symbol_name2tenso_dim) const {
+      std::unordered_map<std::string, TensorDim>* symbol_name2tensor_dim)
+      const {
     return std::visit(
         [&](const auto& impl) {
-          return InsertSymbolBindingImpl(op, impl, symbol_name2tenso_dim);
+          return InsertSymbolBindingImpl(op, impl, symbol_name2tensor_dim);
         },
         symbol_binding);
   }
 
   void InsertSymbolBindingImpl(
       cinn::dialect::GenerateShapeOp op,
       const cinn::dialect::GenerateShapeOp::DataSymbolBinding& symbol_binding,
-      std::unordered_map<std::string, TensorDim>* symbol_name2tenso_dim) const {
-    (*symbol_name2tenso_dim)[symbol_binding.symbol_name] = TensorDimInData{
+      std::unordered_map<std::string, TensorDim>* symbol_name2tensor_dim)
+      const {
+    (*symbol_name2tensor_dim)[symbol_binding.symbol_name] = TensorDimInData{
         .value = op.operand_source(symbol_binding.input_tensor_idx),
         .axis = symbol_binding.input_tensor_dim_idx};
   }
 
   void InsertSymbolBindingImpl(
       cinn::dialect::GenerateShapeOp op,
       const cinn::dialect::GenerateShapeOp::ShapeSymbolBinding& symbol_binding,
-      std::unordered_map<std::string, TensorDim>* symbol_name2tenso_dim) const {
-    (*symbol_name2tenso_dim)[symbol_binding.symbol_name] = TensorDimInShape{
+      std::unordered_map<std::string, TensorDim>* symbol_name2tensor_dim)
+      const {
+    (*symbol_name2tensor_dim)[symbol_binding.symbol_name] = TensorDimInShape{
         .value = op.operand_source(symbol_binding.input_tensor_idx),
         .axis = symbol_binding.input_tensor_dim_idx};
   }
@@ -328,18 +331,18 @@ class SplitGenerateShapeIntoShapeOps
     return ret;
   }
 
-  pir::Value GetValueOfRewritedOps(
+  pir::Value GetValueOfRewrittenOps(
       const std::vector<symbol::DimExpr>& dim_exprs,
       CachedDimExprToValueConverter* converter) const {
     const std::vector<pir::Value>& values_from_dim_exprs =
-        GetValuesOfRewritedOps(dim_exprs, converter);
+        GetValuesOfRewrittenOps(dim_exprs, converter);
     if (values_from_dim_exprs.size() == 1) return values_from_dim_exprs.at(0);
     pir::Value vec =
         converter->rewriter->Build<pir::CombineOp>(values_from_dim_exprs).out();
     return converter->rewriter->Build<paddle::dialect::ConcatOp>(vec).out();
   }
 
-  std::vector<pir::Value> GetValuesOfRewritedOps(
+  std::vector<pir::Value> GetValuesOfRewrittenOps(
       const std::vector<symbol::DimExpr>& dim_exprs,
       CachedDimExprToValueConverter* converter) const {
     std::vector<pir::Value> ret;

python/paddle/distributed/fleet/launch.py

@@ -116,7 +116,7 @@ def _parse_args():
         type=str,
         default=os.environ.get('PADDLE_DISTRI_BACKEND', 'auto'),
         help="Specifize the backend, can be gloo|nccl|bkcl|auto|heter. "
-        "Default value is auto which perfers nccl or bkcl.",
+        "Default value is auto which prefers nccl or bkcl.",
     )
     base_group.add_argument(
         "--nproc_per_node",
@@ -329,7 +329,7 @@ def get_cluster_info(args):
         )
     trainers_num = cloud_utils.get_trainers_num()
     logger.debug(
-        "parsed from args trainerss_num:{} mode:{} devices:{}".format(
+        "parsed from args trainers_num:{} mode:{} devices:{}".format(
             trainers_num, device_mode, devices_per_proc
         )
     )
@@ -346,7 +346,7 @@ def get_cluster_info(args):
     if args.enable_auto_mapping:
         assert (
             args.cluster_topo_path is not None
-        ), "The cluster topology must be provied when enabling auto mapping."
+        ), "The cluster topology must be provided when enabling auto mapping."
         rank_mapping_path = args.rank_mapping_path or os.getenv(
             "PADDLE_RANK_MAPPING_PATH"
         )

python/paddle/distributed/fleet/launch_utils.py

@@ -138,7 +138,7 @@ def __str__(self):
         return f"{self.endpoint}"
 
     def __eq__(self, j):
-        return self.endpint == j.endpoint
+        return self.endpoint == j.endpoint
 
     def __ne__(self, j):
         return not self == j
@@ -1082,7 +1082,7 @@ def get_mapped_cluster_from_args_with_rank_mapping(args, device_mode):
 
     assert (
         len(node_ranks[node_rank]) <= gpus_num
-    ), "number of ranks mapped to one node should not exceed the avaiable ones."
+    ), "number of ranks mapped to one node should not exceed the available ones."
     assert len(node_ranks) == len(
         node_ips
     ), "ranks length should be equal to ips length."
@@ -1164,7 +1164,7 @@ def get_role_endpoints(self, args):
             if args.servers:
                 assert (
                     len(args.servers.split(",")) == self.server_num
-                ), "The server_num and servers doesn't match. Expect servers endpoints num epual to server_num, but received servers enpoint num: {} and server_num {}".format(
+                ), "The server_num and servers doesn't match. Expect servers endpoints num equal to server_num, but received servers endpoint num: {} and server_num {}".format(
                     len(args.servers.split(",")), self.server_num
                 )
                 self.server_endpoints = args.servers
@@ -1186,7 +1186,7 @@ def get_role_endpoints(self, args):
             if args.workers:
                 assert (
                     len(args.workers.split(",")) == self.worker_num
-                ), "The worker_num and workers doesn't match. Expect workers endpoints num epual to worker_num, but received workers enpoint num: {} and worker_num {}".format(
+                ), "The worker_num and workers doesn't match. Expect workers endpoints num equal to worker_num, but received workers endpoint num: {} and worker_num {}".format(
                     len(args.workers.split(",")), self.worker_num
                 )
 
@@ -1238,7 +1238,7 @@ def get_role_endpoints(self, args):
             if args.coordinators:
                 assert (
                     len(args.coordinators.split(",")) == self.coordinator_num
-                ), "The coordinator_num and coordinators doesn't match. Expect coordinators endpoints num epual to coordinator_num, but received coordinator enpoint num: {} and coordinator_num {}".format(
+                ), "The coordinator_num and coordinators doesn't match. Expect coordinators endpoints num equal to coordinator_num, but received coordinator endpoint num: {} and coordinator_num {}".format(
                     len(args.coordinators.split(",")), self.coordinator_num
                 )
 
@@ -1271,7 +1271,7 @@ def get_role_endpoints(self, args):
                 if args.heter_workers:
                     assert len(args.heter_workers.split(";")) == len(
                         self.stage_heter_trainer_num
-                    ), "The stage_num and heter_workers doesn't match. Expect heter_workers endpoints stage num epual to heter_worker_num stage, but received heter_workers enpoint stage num: {} and heter_worker_num stage {}".format(
+                    ), "The stage_num and heter_workers doesn't match. Expect heter_workers endpoints stage num equal to heter_worker_num stage, but received heter_workers endpoint stage num: {} and heter_worker_num stage {}".format(
                         len(args.heter_workers.split(";")),
                         len(self.stage_heter_trainer_num),
                     )
@@ -1537,7 +1537,7 @@ def start_ps(self):
         pod = cluster.pods[self.node_rank]
         self.gloo_rendezvous_dir = tempfile.mkdtemp()
 
-        # 3. subproces start
+        # 3. subprocess start
         self.procs = {
             "worker": [],
             "coordinator": [],

python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py

@@ -59,7 +59,7 @@ def get_action(is_dp, shard_split_param=False):
     return HOOK_ACTION.REDUCE
 
 
-# assume only the first stage and last stage need data, and data consumption is ordred
+# assume only the first stage and last stage need data, and data consumption is ordered
 # to be replaced by real micro dataset from reader
 class FakeMicroDataset:
     def __init__(
@@ -198,7 +198,7 @@ def __init__(self, layers, hcg, strategy):
             "pp_configs"
         ].delay_scale_loss
         # TODO(PP Dev): support dp_comm_overlap without use_main_grad training.
-        # This combination will trigger inplace check error during `reshape_` in funct `_split_tensors`.
+        # This combination will trigger inplace check error during `reshape_` in function `_split_tensors`.
         self._dp_comm_overlap = self._strategy.hybrid_configs[
             "pp_configs"
         ].dp_comm_overlap

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py

@@ -342,7 +342,7 @@ def _update_opt_status(self):
         # func 1
         self._integration_params()
 
-    # Segement helpers
+    # Segment helpers
 
     def _segment_params(self):
         """

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage2.py

@@ -623,7 +623,7 @@ def _build_grad_storages(self):
         for dtype in self._grad_storages.keys():
             for dst_rank, grad_storage in self._grad_storages[dtype].items():
                 if self._offload or dst_rank != self._rank:
-                    grad_storage.manumal_relase()
+                    grad_storage.manual_release()
                     grad_storage.rebuild()
 
     def _rank_buffer_size(self, buffer_max_size, model_size):

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py

@@ -323,10 +323,10 @@ def _clear_gradients(self):
                 tmp_var._share_buffer_to(param)
                 del tmp_var
             for grad_storage in self._grad_storages.values():
-                grad_storage.manumal_relase()
+                grad_storage.manual_release()
                 grad_storage.rebuild()
 
-    # Update param memery slice
+    # Update param memory slice
     def _update_params_slice(self):
         update_list = self._update_params()
 
@@ -385,7 +385,7 @@ def _handle_unslice_params(self):
         buffer_size[Type.fp32.value] = 0
         buffer_size[Type.fp16.value] = 0
         for param in self._unslice_params:
-            # Updata optimizer master weights
+            # Update optimizer master weights
             if (
                 param.dtype == Type.fp16.value or param.dtype == Type.bf16.value
             ) and not self._offload:
@@ -537,7 +537,7 @@ def _param_storage(self, param, buffer_size):
             )
         param.status = "part"
 
-        # Updata optimizer master weights
+        # Update optimizer master weights
         if (
             param.trainable
             and (

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_storage.py

@@ -294,7 +294,7 @@ def add_grad(self, param, align):
         self._param_ids.append(id(param))
 
     @paddle.autograd.no_grad()
-    def manumal_relase(self):
+    def manual_release(self):
         """
         Release the buffer from InternalStorage. The InternalStorage will need to be rebuilt before use.
         """

python/paddle/distributed/fleet/model.py

@@ -34,7 +34,7 @@ def distributed_model(model):
     Return distributed data parallel model (Only work in dygraph mode)
 
     Args:
-        model (Layer): the user-defind model which inherits Layer.
+        model (Layer): the user-defined model which inherits Layer.
 
     Returns:
         distributed data parallel model which inherits Layer.

python/paddle/distributed/sharding/group_sharded.py

@@ -115,7 +115,7 @@ def group_sharded_parallel(
         ]
         or device in paddle.device.get_all_custom_device_type()
     ), "group_sharded_parallel only support gpu, xpu and custom_device now"
-    # check optition type
+    # check option type
     assert isinstance(
         model, paddle.nn.Layer
     ), "The model must be the instance of paddle.nn.Layer."

python/paddle/distributed/utils/launch_utils.py

@@ -89,7 +89,7 @@ def get_gpus(selected_gpus):
                 for x in selected_gpus.split(',')
             ]
             logger.info(
-                f"Change selected_gpus into reletive values. --ips:{selected_gpus} "
+                f"Change selected_gpus into relative values. --ips:{selected_gpus} "
                 f"will change into relative_ips:{gpus} according to your "
                 f"CUDA_VISIBLE_DEVICES:{cuda_visible_devices_list}"
             )
@@ -200,7 +200,7 @@ def __str__(self):
         return f"{self.endpoint}"
 
     def __eq__(self, j):
-        return self.endpint == j.endpoint
+        return self.endpoint == j.endpoint
 
     def __ne__(self, j):
         return not self == j