Fix typo consumer, etc (#62643)

co63oc · web-flow · commit 43a90b039a96 · 2024-03-18T14:41:29.000+08:00
diff --git a/paddle/cinn/hlir/dialect/operator/transforms/cinn_group_cluster_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/cinn_group_cluster_pass.cc
@@ -840,7 +840,7 @@ std::vector<GroupClusterNode> OpMergeWithOp(cinn::dialect::GroupOp group_op) {
     if (yield_output_ops.count(op) ||
         cinn::hlir::framework::pir::CompatibleInfo::OpKind(*op) ==
             cinn::hlir::framework::kReduction) {
-      // TODO(phlrain): yield output no nedd to push into first stage output,
+      // TODO(phlrain): yield output no need to push into first stage output,
       // Update here
       if (!first_output_ops.count(op)) {
         first_stage_output.push_back(op_path[op]);
diff --git a/paddle/cinn/hlir/dialect/operator/transforms/group_merge/group_with_group_merge_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/group_merge/group_with_group_merge_pass.cc
@@ -1139,11 +1139,11 @@ class GeneralFusionMergePassHelper {
 
     while (DoGeneralRecomputeAndVerticalFusion()) {
     }
-    DoPrologueGenerateShapeOpGroupFustion();
+    DoPrologueGenerateShapeOpGroupFusion();
   }
 
-  void DoPrologueGenerateShapeOpGroupFustion() {
-    VLOG(3) << "DoPrologueGenerateShapeOpGroupFustion...!";
+  void DoPrologueGenerateShapeOpGroupFusion() {
+    VLOG(3) << "DoPrologueGenerateShapeOpGroupFusion...!";
     bool updated = false;
     for (size_t idx = 0; idx < fusion_groups_.size(); ++idx) {
       auto producer = fusion_groups_[idx];
diff --git a/paddle/cinn/hlir/framework/op_lowering_util.cc b/paddle/cinn/hlir/framework/op_lowering_util.cc
@@ -319,13 +319,13 @@ std::unordered_map<Node*, Node*> BuildVirtualConsumer(
 
     auto output_shape = GetOutputShape(t_node, shape_dict);
     if (!found && t_node != e_node && e_node) {
-      auto enode_output_shape = GetOutputShape(e_node, shape_dict);
+      auto e_node_output_shape = GetOutputShape(e_node, shape_dict);
       if (std::accumulate(output_shape.begin(),
                           output_shape.end(),
                           1,
                           std::multiplies<int>()) ==
-          std::accumulate(enode_output_shape.begin(),
-                          enode_output_shape.end(),
+          std::accumulate(e_node_output_shape.begin(),
+                          e_node_output_shape.end(),
                           1,
                           std::multiplies<int>())) {
         virtual_consumers[t_node] = e_node;
diff --git a/paddle/cinn/hlir/framework/pir/op_lowering_util.cc b/paddle/cinn/hlir/framework/pir/op_lowering_util.cc
@@ -667,7 +667,7 @@ void LoopAssignReduceWithLast(ir::IRSchedule& ir_sch,  // NOLINT
       ir_sch.Fuse(block_name, {axes[index + 1], axes[index + 1] + 1});
     }
     LoopOrderAssignReduce(ir_sch, block_name, first_axes, target, true);
-    // fuse axis before reduce to bind blockidx.
+    // fuse axis before reduce to bind block idx.
     for (int idx = 0; idx < static_cast<int>(inshape.size() - axes.size()) - 1;
          ++idx) {
       ir_sch.Fuse(block_name, {0, 1});
@@ -713,7 +713,7 @@ void LoopAssignReduceWithoutLast(ir::IRSchedule& ir_sch,  // NOLINT
                                     return left + std::to_string(right) + " ";
                                   });
 
-  VLOG(4) << "LoopAssignReduceWithoutLast: THe input shape=["
+  VLOG(4) << "LoopAssignReduceWithoutLast: The input shape=["
           << cinn::utils::Join(inshape, ", ") << "], first step reduce shape=["
           << cinn::utils::Join(shape, ", ") << "]"
           << ", axes=[" << cinn::utils::Join(axes, ", ") << "], tail=" << tail;
diff --git a/paddle/cinn/hlir/op/custom_call.cc b/paddle/cinn/hlir/op/custom_call.cc
@@ -231,11 +231,11 @@ std::vector<ir::Expr> CustomCallArgsForCublas(
 
     if (is_infer) {
       CHECK_EQ(a_width, b_width)
-          << "The K dimension of mul shold be equal! Please check.";
+          << "The K dimension of mul should be equal! Please check.";
       trans_b = true;
     } else {
       CHECK_EQ(a_width, b_height)
-          << "The K dimension of mul shold be equal! Please check.";
+          << "The K dimension of mul should be equal! Please check.";
     }
   } else {
     LOG(FATAL) << "Unkown Matmul Setting!";
@@ -365,11 +365,11 @@ std::vector<ir::Expr> CustomCallArgsForBatchedCublas(
 
     if (is_infer) {
       CHECK_EQ(a_width, b_width)
-          << "The K dimension of mul shold be equal! Please check.";
+          << "The K dimension of mul should be equal! Please check.";
       trans_b = true;
     } else {
       CHECK_EQ(a_width, b_height)
-          << "The K dimension of mul shold be equal! Please check.";
+          << "The K dimension of mul should be equal! Please check.";
     }
   } else {
     LOG(FATAL) << "Unkown Matmul Setting!";
@@ -937,7 +937,7 @@ std::vector<ir::Expr> CustomCallArgsForMemcpy(
   return {Expr(count)};
 }
 
-bool RegisteryCustomCallArgsFunc() {
+bool RegisterCustomCallArgsFunc() {
 #ifdef CINN_WITH_CUDA
   CustomCallArgsFuncRegistry::Global().Register(
       "cinn_call_cublas",
@@ -1025,7 +1025,7 @@ bool RegisteryCustomCallArgsFunc() {
   return true;
 }
 
-static bool registry_custom_call_list_func = RegisteryCustomCallArgsFunc();
+static bool registry_custom_call_list_func = RegisterCustomCallArgsFunc();
 }  // namespace op
 }  // namespace hlir
 }  // namespace cinn
diff --git a/paddle/cinn/hlir/pass/alterlayout.cc b/paddle/cinn/hlir/pass/alterlayout.cc
@@ -139,7 +139,7 @@ std::vector<framework::shape_t> UpdateInferInfos(
 }
 
 void AlterLayoutPass(Graph* graph) {
-  // alterlayout only in X86 for it's specific layout requirements
+  // alter layout only in X86 for it's specific layout requirements
   if (graph->target_.arch == Target::Arch::X86) {
     auto store_nodes = std::get<0>(graph->topological_order());
     auto& shape_dict = graph->GetMutableAttrs<
@@ -603,7 +603,7 @@ void AlterLayoutPass(Graph* graph) {
               } else if (input_shape_size == 5 &&
                          new_input_layouts[i].size() == 4) {
                 // NCHWxc -> NCHW
-                // insert layout tranfrom
+                // insert layout transform
                 auto source = inlinks[i]->source();
                 auto src_layout = input_layouts[i];
                 layout_dict[source->id()] = src_layout;
diff --git a/paddle/cinn/hlir/pass/constant_folding_pass_test.cc b/paddle/cinn/hlir/pass/constant_folding_pass_test.cc
@@ -369,7 +369,7 @@ TEST(Constant_Folding, fold_expand_dims_to_fill_constant_2) {
 
 TEST(Constant_Folding, fold_expand_dims_to_fill_constant_3) {
   NetBuilder net_builder("fold_expand_dims_to_fill_constant_3");
-  // create model, ExpandDims axes have nagetive value
+  // create model, ExpandDims axes have negative value
   int h = 32, w = 32;
   auto A = net_builder.FillConstant<float>({h, w}, 1.0f, "A");
   auto B = net_builder.ExpandDims(A, {1, -1});
diff --git a/paddle/cinn/hlir/pass/dense_merge_pass.cc b/paddle/cinn/hlir/pass/dense_merge_pass.cc
@@ -26,7 +26,7 @@ using framework::Node;
 using framework::NodeAttr;
 
 // Dense Merge Pass: merge those gemm which has same var as input into a batched
-// cubals call op. A * B, A * C, A * D,... after A * [B, C, D,...] Using cublas
+// cublas call op. A * B, A * C, A * D,... after A * [B, C, D,...] Using cublas
 // batched gemm can avoid do concat and slice.
 
 class DenseMergePassHelper : public FusionHelperBase {
diff --git a/paddle/cinn/hlir/pass/fusion_merge_pass.cc b/paddle/cinn/hlir/pass/fusion_merge_pass.cc
@@ -414,7 +414,7 @@ class FusionMergePassHelper : public FusionHelperBase {
     std::unordered_set<GroupPtr, Hasher, Comparator> fuse_consumers_unsafe;
     std::unordered_set<GroupPtr, Hasher, Comparator> fuse_consumers;
     for (const auto& consumer : consumers) {
-      VLOG(4) << "Check consuemr " << consumer->group_id
+      VLOG(4) << "Check consumer " << consumer->group_id
               << " can fuse to producer " << producer->group_id;
       // if can't fuse
       if (!relation.vertical_relation.count(consumer->op_pattern_kind)) {
@@ -1081,7 +1081,7 @@ class FusionMergePassHelper : public FusionHelperBase {
 
 void FusionMergePassInternal(Graph* graph) {
   if (graph->fusion_groups.size() <= 1) {
-    VLOG(3) << "Don't do Fusoin Merge Pass...!";
+    VLOG(3) << "Don't do Fusion Merge Pass...!";
     return;
   }
 
diff --git a/paddle/phi/backends/event.cc b/paddle/phi/backends/event.cc
@@ -84,7 +84,7 @@ void Event::Destroy() {
 
 void Event::Record(const stream::Stream* stream) {
   if (device_) {
-    is_recorded_ = true;  // synchronize the event during detroy
+    is_recorded_ = true;  // synchronize the event during destroy
     stream->RecordEvent(this);
   }
 }
diff --git a/paddle/phi/backends/xpu/enforce_xpu.h b/paddle/phi/backends/xpu/enforce_xpu.h
@@ -92,7 +92,7 @@ inline const char* xpuGetErrorString(int stat) {
     case XPUERR_INTERRUPTED:
       return "Execution interrupted by user";
     default:
-      return "unknown error";
+      return "Unknown error";
   }
 }
 
diff --git a/paddle/phi/kernels/gpudnn/pool_kernel.cu b/paddle/phi/kernels/gpudnn/pool_kernel.cu
@@ -142,8 +142,8 @@ void PoolRawGPUDNNKernel(const Context& ctx,
     transformed_output = *output;
   }
 
-  const T* tranformed_input_data = transformed_input.data<T>();
-  T* tranformed_output_data = ctx.template Alloc<T>(&transformed_output);
+  const T* transformed_input_data = transformed_input.data<T>();
+  T* transformed_output_data = ctx.template Alloc<T>(&transformed_output);
 
   // ------------------- cudnn descriptors ---------------------
   ScopedTensorDescriptor input_desc;
@@ -192,10 +192,10 @@ void PoolRawGPUDNNKernel(const Context& ctx,
                                     cudnn_pool_desc,
                                     &alpha,
                                     cudnn_input_desc,
-                                    tranformed_input_data,
+                                    transformed_input_data,
                                     &beta,
                                     cudnn_output_desc,
-                                    tranformed_output_data,
+                                    transformed_output_data,
                                     false,
                                     pool_workspace,
                                     pool_workernel_size_));
@@ -206,10 +206,10 @@ void PoolRawGPUDNNKernel(const Context& ctx,
                                    cudnn_pool_desc,
                                    &alpha,
                                    cudnn_input_desc,
-                                   tranformed_input_data,
+                                   transformed_input_data,
                                    &beta,
                                    cudnn_output_desc,
-                                   tranformed_output_data));
+                                   transformed_output_data));
 #endif
   // add
   if (data_format == str_NDHWC) {
diff --git a/paddle/phi/kernels/onednn/squeeze_kernel.cc b/paddle/phi/kernels/onednn/squeeze_kernel.cc
@@ -62,7 +62,7 @@ void SqueezeInferKernel(const Context& dev_ctx,
   auto x_dims_tz = x_dims.size();
   std::vector<int32_t> tmp(axes.GetData().begin(), axes.GetData().end());
 
-  // Currently there is only tranformation for tensors, while attr axes still
+  // Currently there is only transformation for tensors, while attr axes still
   // follows default dtype instead of oneDNN dtype, so here manually change it
   if ((x_dims_tz >= 3) &&
       (phi::OneDNNContext::tls().get_cur_paddle_data_layout() ==
diff --git a/paddle/phi/kernels/transfer_layout_kernel.cc b/paddle/phi/kernels/transfer_layout_kernel.cc
@@ -166,7 +166,7 @@ void TransferLayoutMKLDNN(const Context& dev_ctx,
     out->set_mem_desc(out_mem_desc);
   } else if (src_layout == DataLayout::ONEDNN &&
              dst_layout != DataLayout::ONEDNN) {
-    // Case2 - transfrom from MKLDNN OPKernel to Non-MKLDNN OPKernel
+    // Case2 - transform from MKLDNN OPKernel to Non-MKLDNN OPKernel
     // Do transform via MKLDNN lib
     funcs::TransDataLayoutFromOneDNN(
         src_layout, dst_layout, x, out, dev_ctx.GetPlace());
diff --git a/paddle/phi/kernels/xpu/take_along_axis_kernel.cc b/paddle/phi/kernels/xpu/take_along_axis_kernel.cc
@@ -33,75 +33,75 @@ void TakeAlongAxisKernel(const Context& dev_ctx,
 
   if (x.numel() == 0 || index.numel() == 0) return;
 
-  const auto& index_type = index.dtype();
-  bool index_type_match =
-      index_type == DataType::INT32 || index_type == DataType::INT64;
-  PADDLE_ENFORCE_EQ(index_type_match,
+  const auto& index_dtype = index.dtype();
+  bool index_dtype_match =
+      index_dtype == DataType::INT32 || index_dtype == DataType::INT64;
+  PADDLE_ENFORCE_EQ(index_dtype_match,
                     true,
                     errors::InvalidArgument(
                         "Input(Index) holds the wrong type, it holds %s, but "
                         "desires to be %s or %s",
-                        DataTypeToString(index_type),
+                        DataTypeToString(index_dtype),
                         DataTypeToString(DataType::INT32),
                         DataTypeToString(DataType::INT64)));
 
-  std::vector<int64_t> xshape(x.dims().size());
+  std::vector<int64_t> x_shape(x.dims().size());
   for (int i = 0; i < x.dims().size(); ++i) {
-    xshape[i] = x.dims()[i];
+    x_shape[i] = x.dims()[i];
   }
-  std::vector<int64_t> idxshape(index.dims().size());
+  std::vector<int64_t> index_shape(index.dims().size());
   for (int i = 0; i < index.dims().size(); ++i) {
-    idxshape[i] = index.dims()[i];
+    index_shape[i] = index.dims()[i];
   }
 
-  if (xshape.size() <= 1 && idxshape.size() <= 1) {
-    for (int i = xshape.size(); i < 2; ++i) {
-      xshape.push_back(1);
-      idxshape.push_back(1);
+  if (x_shape.size() <= 1 && index_shape.size() <= 1) {
+    for (int i = x_shape.size(); i < 2; ++i) {
+      x_shape.push_back(1);
+      index_shape.push_back(1);
     }
   }
 
   using XPUType = typename XPUTypeTrait<T>::Type;
   int r = XPU_SUCCESS;
 #ifndef PADDLE_WITH_XPU_PLUGIN
-  if (index_type == DataType::INT32) {
+  if (index_dtype == DataType::INT32) {
     r = xpu::gather_element<XPUType, int>(
         dev_ctx.x_context(),
         reinterpret_cast<const XPUType*>(x.data<T>()),
         index.data<int>(),
         reinterpret_cast<XPUType*>(out->data<T>()),
-        xshape,
-        idxshape,
+        x_shape,
+        index_shape,
         axis);
   } else {
     r = xpu::gather_element<XPUType, int64_t>(
         dev_ctx.x_context(),
         reinterpret_cast<const XPUType*>(x.data<T>()),
         index.data<int64_t>(),
         reinterpret_cast<XPUType*>(out->data<T>()),
-        xshape,
-        idxshape,
+        x_shape,
+        index_shape,
         axis);
   }
   PADDLE_ENFORCE_XDNN_SUCCESS(r, "gather_element");
 #else
-  if (index_type == DataType::INT32) {
+  if (index_dtype == DataType::INT32) {
     r = xpu::plugin::take_along_axis<XPUType, int>(
         dev_ctx.x_context(),
         reinterpret_cast<const XPUType*>(x.data<T>()),
         index.data<int>(),
         reinterpret_cast<XPUType*>(out->data<T>()),
-        xshape,
-        idxshape,
+        x_shape,
+        index_shape,
         axis);
   } else {
     r = xpu::plugin::take_along_axis<XPUType, int64_t>(
         dev_ctx.x_context(),
         reinterpret_cast<const XPUType*>(x.data<T>()),
         index.data<int64_t>(),
         reinterpret_cast<XPUType*>(out->data<T>()),
-        xshape,
-        idxshape,
+        x_shape,
+        index_shape,
         axis);
   }
   PADDLE_ENFORCE_XDNN_SUCCESS(r, "take_along_axis");
diff --git a/paddle/pir/include/core/interface_support.h b/paddle/pir/include/core/interface_support.h
@@ -26,31 +26,31 @@ class ConstructInterfacesOrTraits {
   /// Construct method for interfaces.
   static void interface(InterfaceSet &interface_set) {  // NOLINT
     (void)std::initializer_list<int>{
-        0, (ConstrctInterface<Args>(interface_set), 0)...};
+        0, (ConstructInterface<Args>(interface_set), 0)...};
   }
 
   /// Construct method for traits.
   static TypeId *trait(TypeId *p_trait) {
     (void)std::initializer_list<int>{
-        0, (PlacementConstrctTrait<Args>(p_trait), 0)...};
+        0, (PlacementConstructTrait<Args>(p_trait), 0)...};
     return p_trait;
   }
 
  private:
   /// Placement new interface.
   template <typename T>
-  static void ConstrctInterface(InterfaceSet &interface_set) {  // NOLINT
+  static void ConstructInterface(InterfaceSet &interface_set) {  // NOLINT
     InterfaceValue val =
         InterfaceValue::Get<T, typename T::template Model<ConcreteT>>();
-    auto suceess = interface_set.insert(std::move(val)).second;
-    IR_ENFORCE(suceess,
+    auto success = interface_set.insert(std::move(val)).second;
+    IR_ENFORCE(success,
                "Interface: id[%u] is already registered. inset failed",
                TypeId::get<T>());
   }
 
   /// Placement new trait.
   template <typename T>
-  static void PlacementConstrctTrait(pir::TypeId *&p_trait) {  // NOLINT
+  static void PlacementConstructTrait(pir::TypeId *&p_trait) {  // NOLINT
     *p_trait = TypeId::get<T>();
     ++p_trait;
   }
diff --git a/paddle/pir/include/core/ir_context.h b/paddle/pir/include/core/ir_context.h
@@ -118,12 +118,12 @@ class IR_API IrContext {
                       void (*verify_region)(Operation *));
 
   ///
-  /// \brief Get registered operaiton infomation.
+  /// \brief Get registered operation infomation.
   ///
   OpInfo GetRegisteredOpInfo(const std::string &name);
 
   ///
-  /// \brief Get registered operaiton infomation map.
+  /// \brief Get registered operation infomation map.
   ///
   const OpInfoMap &registered_op_info_map();
 
diff --git a/paddle/pir/include/core/operation.h b/paddle/pir/include/core/operation.h
@@ -34,7 +34,7 @@ class OpResult;
 
 namespace detail {
 class OpResultImpl;
-class OpOperendImpl;
+class OpOperandImpl;
 }  // namespace detail
 
 class CloneOptions {
diff --git a/paddle/pir/include/dialect/shape/utils/shape_or_data_expr.h b/paddle/pir/include/dialect/shape/utils/shape_or_data_expr.h
diff --git a/paddle/pir/src/core/block_operand_impl.h b/paddle/pir/src/core/block_operand_impl.h
diff --git a/paddle/pir/src/core/op_operand_impl.h b/paddle/pir/src/core/op_operand_impl.h
diff --git a/paddle/pir/src/core/parser/ir_parser.cc b/paddle/pir/src/core/parser/ir_parser.cc
diff --git a/paddle/pir/src/core/parser/lexer.cc b/paddle/pir/src/core/parser/lexer.cc

Original file line number	Diff line number	Diff line change
`@@ -1139,11 +1139,11 @@ class GeneralFusionMergePassHelper {`
`1139`	`1139`
`1140`	`1140`	`while (DoGeneralRecomputeAndVerticalFusion()) {`
`1141`	`1141`	`}`
`1142`		`- DoPrologueGenerateShapeOpGroupFustion();`
	`1142`	`+ DoPrologueGenerateShapeOpGroupFusion();`
`1143`	`1143`	`}`
`1144`	`1144`
`1145`		`- void DoPrologueGenerateShapeOpGroupFustion() {`
`1146`		`- VLOG(3) << "DoPrologueGenerateShapeOpGroupFustion...!";`
	`1145`	`+ void DoPrologueGenerateShapeOpGroupFusion() {`
	`1146`	`+ VLOG(3) << "DoPrologueGenerateShapeOpGroupFusion...!";`
`1147`	`1147`	`bool updated = false;`
`1148`	`1148`	`for (size_t idx = 0; idx < fusion_groups_.size(); ++idx) {`
`1149`	`1149`	`auto producer = fusion_groups_[idx];`
Original file line number	Diff line number	Diff line change
`@@ -84,7 +84,7 @@ void Event::Destroy() {`
`84`	`84`
`85`	`85`	`void Event::Record(const stream::Stream* stream) {`
`86`	`86`	`if (device_) {`
`87`		`- is_recorded_ = true; // synchronize the event during detroy`
	`87`	`+ is_recorded_ = true; // synchronize the event during destroy`
`88`	`88`	`stream->RecordEvent(this);`
`89`	`89`	`}`
`90`	`90`	`}`