Readability part 2

Author: aobolensk

src/plugins/intel_cpu/src/.clang-tidy

@@ -49,17 +49,12 @@
 # -misc-non-private-member-variables-in-classes,
 # -misc-use-anonymous-namespace,
 # -readability-avoid-nested-conditional-operator
-# -readability-avoid-return-with-void-value
 # -readability-const-return-type
-# -readability-convert-member-functions-to-static
 # -readability-function-cognitive-complexity. Reasonable way to enforce splitting complex code into simple functions
-# -readability-inconsistent-declaration-parameter-name
 # -readability-isolate-declaration
 # -readability-magic-numbers, cppcoreguidelines-avoid-magic-numbers
 # -readability-simplify-boolean-expr
-# -readability-static-accessed-through-instance
 # -readability-suspicious-call-argument
-# -readability-use-anyofallof
 # Remove warning disablement after CI pipeline migrates to C++17 from C++20 for:
 # -modernize-use-constraints,
 # -modernize-use-std-numbers
@@ -116,19 +111,14 @@ Checks: >
   -misc-non-private-member-variables-in-classes,
   -misc-use-anonymous-namespace,
   -readability-avoid-nested-conditional-operator,
-  -readability-avoid-return-with-void-value,
   -readability-const-return-type,
-  -readability-convert-member-functions-to-static,
   -readability-function-cognitive-complexity,
   -readability-identifier-length,
-  -readability-inconsistent-declaration-parameter-name,
   -readability-isolate-declaration,
   -readability-magic-numbers,
   -readability-simplify-boolean-expr,
-  -readability-static-accessed-through-instance,
   -readability-suspicious-call-argument,
   -readability-uppercase-literal-suffix,
-  -readability-use-anyofallof,
 # Treat warnings as errors
 WarningsAsErrors: '*'
 # Use clang-format for applied fixes

src/plugins/intel_cpu/src/config.h

@@ -120,7 +120,7 @@ struct Config {
     // is reserved.
     bool DAZOn = false;
 
-    void readProperties(const ov::AnyMap& config, const ModelType modelType = ModelType::Unknown);
+    void readProperties(const ov::AnyMap& prop, const ModelType modelType = ModelType::Unknown);
 
     void updateProperties();
 

src/plugins/intel_cpu/src/cpu_memory.h

@@ -449,7 +449,7 @@ using MemoryPtr = std::shared_ptr<IMemory>;
 using MemoryCPtr = std::shared_ptr<const IMemory>;
 using StringMemoryPtr = std::shared_ptr<StringMemory>;
 
-bool mbind_move(void* data, size_t size, int numaNodeID);
+bool mbind_move(void* data, size_t size, int targetNode);
 bool mbind_move(const MemoryCPtr& mem, int numaNodeID);
 bool mbind_move(const dnnl::memory& mem, int numaNodeID);
 

src/plugins/intel_cpu/src/cpu_streams_calculation.hpp

@@ -50,7 +50,7 @@ std::vector<std::vector<int>> get_streams_info_table(
     const int input_infer_requests,
     const int model_prefer_threads,
     const std::string& input_perf_hint,
-    const std::set<ov::hint::ModelDistributionPolicy>& hint_llm_distribution_policy,
+    const std::set<ov::hint::ModelDistributionPolicy>& hint_model_distribution_policy,
     const std::vector<std::vector<int>>& proc_type_table);
 
 /**

src/plugins/intel_cpu/src/dnnl_extension_utils.h

@@ -119,7 +119,7 @@ class DnnlExtensionUtils {
         return;
     }
 
-    static bool find_implementation(dnnl::primitive_desc& desc, impl_desc_type implType);
+    static bool find_implementation(dnnl::primitive_desc& desc, impl_desc_type impl_type);
     static dnnl_primitive_desc_t clone_primitive_desc(const_dnnl_primitive_desc_t cprim_desc);
     static dnnl_memory_desc_t clone_desc(const_dnnl_memory_desc_t cdesc);
     static const char* query_pd_info(const_dnnl_primitive_desc_t pd);

src/plugins/intel_cpu/src/dnnl_postops_composer.cpp

@@ -952,9 +952,10 @@ void DnnlPostOpsComposer::appendAttrPostOpsLegacy(const FakeQuantizePostOp& post
             std::fill(binarizationThresholds.begin() + realAxisSize, binarizationThresholds.end(), 0.F);
         }
 
-        return ops.append_binarization(dnnl::algorithm::binarization_depthwise,
-                                       reinterpret_cast<const float*>(binarizationThresholds.data()),
-                                       reinterpret_cast<const float*>(binarizationOutputMask.data()));
+        ops.append_binarization(dnnl::algorithm::binarization_depthwise,
+                                reinterpret_cast<const float*>(binarizationThresholds.data()),
+                                reinterpret_cast<const float*>(binarizationOutputMask.data()));
+        return;
     }
 
     dnnl::algorithm alg = postOp.type() == FakeQuantizePostOp::Type::quantization_only

src/plugins/intel_cpu/src/emitters/plugin/x64/debug_capabilities.hpp

@@ -77,7 +77,7 @@ class RegPrinter {
     template <typename PRC_T, size_t vlen>
     static void print_vmm_prc(const char* name, const char* ori_name, PRC_T* ptr);
     template <typename T>
-    static void print_reg_prc(const char* name, const char* ori_name, T* val);
+    static void print_reg_prc(const char* name, const char* ori_name, T* ptr);
     static void preamble(jit_generator& h);
     static void postamble(jit_generator& h);
     template <typename T>

src/plugins/intel_cpu/src/emitters/plugin/x64/jit_load_store_emitters.hpp

@@ -165,13 +165,13 @@ class jit_store_emitter : public jit_emitter {
     void store_bytes(const Xbyak::Reg64& reg, int offset, int store_size) const;
 
     template <typename Vmm>
-    void store_dword_to_byte_extension(const Xbyak::Reg64& reg, int offset, bool is_signed, int store_size) const;
+    void store_dword_to_byte_extension(const Xbyak::Reg64& reg, int offset, bool is_signed, int store_num) const;
 
     template <typename Vmm>
     void store_dword_to_word_extension(const Xbyak::Reg64& reg,
                                        int offset,
                                        ov::element::Type precision,
-                                       int store_size) const;
+                                       int store_num) const;
 
     void register_table_entries() override;
 

src/plugins/intel_cpu/src/emitters/snippets/x64/jit_fill_emitter.hpp

@@ -34,9 +34,9 @@ class jit_fill_emitter : public jit_emitter {
     template <dnnl::impl::cpu::x64::cpu_isa_t isa>
     void emit_isa(const std::vector<size_t>& in, const std::vector<size_t>& out) const;
     template <typename Vmm>
-    void fill_full(const Vmm& vmm_dst) const;
+    void fill_full(const Vmm& dst_vmm) const;
     template <typename Vmm>
-    void fill_tail(const Vmm& vmm_src, const Vmm& vmm_dst) const;
+    void fill_tail(const Vmm& src_vmm, const Vmm& dst_vmm) const;
 
     bool is_full_reg() const {
         return offset == 0;

src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.cpp

@@ -90,7 +90,7 @@ std::shared_ptr<BrgemmCompiledKernel> BrgemmKernelExecutor::compile_kernel(const
 void BrgemmKernelExecutor::update_config(const ov::snippets::lowered::ExpressionPtr& expr,
                                          const ov::snippets::lowered::LinearIRCPtr& linear_ir,
                                          BrgemmKernelConfig& config) const {
-    return BrgemmBaseKernelExecutor::update_config(expr, linear_ir, config);
+    BrgemmBaseKernelExecutor::update_config(expr, linear_ir, config);
 }
 
 void BrgemmKernelExecutor::execute(const BrgemmKernelExecutor* executor, call_args* args) {

src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm_amx.cpp

@@ -247,7 +247,7 @@ void BrgemmAMXKernelExecutor::create_brgemm_copy_a_kernel(
 void BrgemmAMXKernelExecutor::update_config(const ov::snippets::lowered::ExpressionPtr& expr,
                                             const ov::snippets::lowered::LinearIRCPtr& linear_ir,
                                             BrgemmAMXKernelConfig& config) const {
-    return BrgemmBaseKernelExecutor::update_config(expr, linear_ir, config);
+    BrgemmBaseKernelExecutor::update_config(expr, linear_ir, config);
 }
 
 void BrgemmAMXKernelExecutor::configure_tiles_if_needed(amx_tile_config_t* config,

src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm_copy_b.hpp

@@ -112,8 +112,8 @@ struct BrgemmCopyBKernelConfig : public snippets::KernelExecutorBase::GenericCon
 
 private:
     struct StaticParams {
-        StaticParams(const element::Type& src_dt,
-                     const element::Type& wei_dt,
+        StaticParams(const element::Type& src_type,
+                     const element::Type& wei_type,
                      dnnl::impl::cpu::x64::cpu_isa_t isa,
                      bool is_with_comp,
                      bool is_transposed_B,

src/plugins/intel_cpu/src/graph.cpp

@@ -405,12 +405,10 @@ void Graph::Activate() {
 void Graph::Configure([[maybe_unused]] bool optimize) {
     OPENVINO_ASSERT(status == Status::NotReady, "Invalid graph status");
 
-    GraphOptimizer optimizer;
-
     SortTopologically();
     InitNodes();
 
-    optimizer.ApplyCommonGraphOptimizations(*this);
+    ov::intel_cpu::GraphOptimizer::ApplyCommonGraphOptimizations(*this);
 
     SortTopologically();
 
@@ -429,7 +427,7 @@ void Graph::Configure([[maybe_unused]] bool optimize) {
 
     ResolveComplexInplaceConflicts();
 
-    optimizer.ApplyImplSpecificGraphOptimizations(*this);
+    ov::intel_cpu::GraphOptimizer::ApplyImplSpecificGraphOptimizations(*this);
 
     SortTopologically();
 

src/plugins/intel_cpu/src/graph.h

@@ -310,7 +310,7 @@ class Graph {
 
     bool graphHasDynamicInput = false;
 
-    void Replicate(const std::shared_ptr<const ov::Model>& subgraph,
+    void Replicate(const std::shared_ptr<const ov::Model>& model,
                    const std::vector<node::Input::InputConfig>& inputConfigs = {},
                    const std::vector<node::Input::OutputConfig>& outputConfigs = {});
 

src/plugins/intel_cpu/src/graph_optimizer.cpp

@@ -1650,11 +1650,11 @@ void GraphOptimizer::FuseConvolutionSumAndConvolutionSumActivation(Graph& graph)
             }
 
             if (fuseCandidate->getAlgorithm() == Algorithm::EltwiseAdd) {
-                for (auto& fusedNode : binConv->fusedWith) {
-                    const auto eltwise = std::dynamic_pointer_cast<Eltwise>(fusedNode);
-                    if (eltwise && eltwise->isSpecialConvolutionAddFusing()) {
-                        return false;
-                    }
+                if (!std::all_of(binConv->fusedWith.begin(), binConv->fusedWith.end(), [](const NodePtr& fusedNode) {
+                        const auto eltwise = std::dynamic_pointer_cast<Eltwise>(fusedNode);
+                        return !(eltwise && eltwise->isSpecialConvolutionAddFusing());
+                    })) {
+                    return false;
                 }
                 return true;
             }
@@ -1672,12 +1672,10 @@ void GraphOptimizer::FuseConvolutionSumAndConvolutionSumActivation(Graph& graph)
         }
 
         auto checkFusedWithSum = [](Convolution* conv) -> bool {
-            for (const auto& node : conv->getFusedWith()) {
+            return std::any_of(conv->getFusedWith().begin(), conv->getFusedWith().end(), [](const NodePtr& node) {
                 const auto eltwise = std::dynamic_pointer_cast<Eltwise>(node);
-                if (eltwise && eltwise->isSpecialConvolutionAddFusing()) {
-                    return true;
-                }
-            }
+                return eltwise && eltwise->isSpecialConvolutionAddFusing();
+            });
             return false;
         };
 
@@ -3186,10 +3184,10 @@ void GraphOptimizer::RemoveConvertMemoryOutput(Graph& graph) {
         }
 
         auto&& childEdges = node->getChildEdgesAtPort(0);
-        for (auto&& edge : childEdges) {
-            if (Type::MemoryOutput != edge->getChild()->getType()) {
-                return false;
-            }
+        if (!std::all_of(childEdges.begin(), childEdges.end(), [](const auto& edge) {
+                return Type::MemoryOutput == edge->getChild()->getType();
+            })) {
+            return false;
         }
 
         return true;

src/plugins/intel_cpu/src/graph_optimizer.h

@@ -17,7 +17,7 @@ class GraphOptimizer {
 
 public:
     static void ApplyCommonGraphOptimizations(Graph& graph);
-    void ApplyImplSpecificGraphOptimizations(Graph& graph);
+    static void ApplyImplSpecificGraphOptimizations(Graph& graph);
     static void ShareReorders(Graph& graph);
 
 private:
@@ -49,8 +49,8 @@ class GraphOptimizer {
     static void FuseEltwiseAndSimple(Graph& graph);
     static void FusePerformedAsScaleShiftAndFakeQuantize(Graph& graph);
     static void FuseClampAndFakeQuantize(Graph& graph);
-    void MergeTransposeAndReorder(Graph& graph);
-    void MergeReorderAndTranspose(Graph& graph);
+    static void MergeTransposeAndReorder(Graph& graph);
+    static void MergeReorderAndTranspose(Graph& graph);
     static void reshapeRnnSeq(Graph& graph);
     static void RemoveSameConvert(Graph& graph);
     static void RemoveMemoryInputConvert(Graph& graph);

src/plugins/intel_cpu/src/node.cpp

@@ -234,6 +234,7 @@ void Node::remove() {
 }
 
 bool Node::isEdgesEmpty(const std::vector<EdgeWeakPtr>& edges) {
+    // NOLINTNEXTLINE(readability-use-anyofallof)
     for (const auto& edge : edges) {
         if (edge.lock()) {
             return false;
@@ -371,7 +372,8 @@ void Node::selectPreferPrimitiveDescriptorWithShape(const std::vector<impl_desc_
                                                     bool ignoreConstInputs) {
     // Filter out dynamic shape.
     if (isDynamic) {
-        return selectPreferPrimitiveDescriptor(priority, ignoreConstInputs);
+        selectPreferPrimitiveDescriptor(priority, ignoreConstInputs);
+        return;
     }
 
     auto estimateReorderOverhead = [&](const ov::intel_cpu::NodeDesc& supportedPrimitiveDesc,
@@ -835,9 +837,10 @@ void Node::updateDynamicParams() {
 
 void Node::execute(const dnnl::stream& strm, int numaId) {
     if (isDynamicNode()) {
-        return executeDynamic(strm, numaId);
+        executeDynamic(strm, numaId);
+    } else {
+        executeStatic(strm, numaId);
     }
-    return executeStatic(strm, numaId);
 }
 
 void Node::executeStatic(const dnnl::stream& strm, int numaId) {
@@ -1218,7 +1221,7 @@ void Node::toNumaNode(int numaNodeID) {
         return;
     }
 
-    return toNumaNodeImpl(numaNodeID);
+    toNumaNodeImpl(numaNodeID);
 }
 
 void Node::toNumaNodeImpl(int numaNodeID) {
@@ -1521,13 +1524,9 @@ void Node::appendPostOpArgs([[maybe_unused]] const dnnl::primitive_attr& attr,
 }
 
 bool Node::isFusedWith(Type fusedNodeType) const {
-    for (const auto& fusedNode : fusedWith) {
-        if (fusedNode->type == fusedNodeType) {
-            return true;
-        }
-    }
-
-    return false;
+    return std::any_of(fusedWith.begin(), fusedWith.end(), [fusedNodeType](const NodePtr& fusedNode) {
+        return fusedNode->type == fusedNodeType;
+    });
 }
 
 dnnl::memory::format_tag Node::getWeightsFormatTagByDims(const VectorDims& dims) {
@@ -2155,6 +2154,7 @@ void Node::resolveInPlaceDirection() {
             } else if (parentInPlaceDirection == InplaceDirectionType::DOWN) {
                 // search if siblings already have downstream direction
                 auto downstreamPeers = [&] {
+                    // NOLINTNEXTLINE(readability-use-anyofallof)
                     for (auto& peerEdge : pParent->getChildEdgesAtPort(pEdge->getInputNum())) {
                         auto* peerNode = peerEdge->getChild().get();
                         if (peerNode == this) {
@@ -2234,8 +2234,8 @@ void Node::resolveInPlaceDirection() {
                                 } else {
                                     auto result = inPlaceDirection(pChild, PortType::INPUT, edge->getOutputNum());
                                     if (InplaceDirectionType::CYCLIC == result) {
-                                        return searchReferencingOutput(pChild,
-                                                                       pChild->inPlaceInputPort(edge->getOutputNum()));
+                                        searchReferencingOutput(pChild, pChild->inPlaceInputPort(edge->getOutputNum()));
+                                        return;
                                     }
                                 }
                             }

src/plugins/intel_cpu/src/node.h

@@ -527,7 +527,7 @@ class Node {
     void updateShapes();
     void updateDynamicParams();
     void executeDynamic(const dnnl::stream& strm, int numaId = -1);
-    virtual void redefineOutputMemory(const std::vector<VectorDims>& newShapes);
+    virtual void redefineOutputMemory(const std::vector<VectorDims>& newOutputShapes);
     void redefineOutputMemory(const size_t port, const VectorDims& new_output_shape) const;
     bool outputShapeDataDependency() const;
 
@@ -766,8 +766,8 @@ class Node {
 
     int curNumaNode = -1;
 
-    void toNumaNode(int numaID);
-    virtual void toNumaNodeImpl(int numaID);
+    void toNumaNode(int numaNodeID);
+    virtual void toNumaNodeImpl(int numaNodeID);
 
     std::string primitivesPriority;
     std::vector<impl_desc_type> customImplPriorities;
@@ -781,8 +781,8 @@ class Node {
     Node(const std::shared_ptr<ov::Node>& op, GraphContext::CPtr ctx, const ShapeInferFactory& shapeInferFactory);
 
     Node(const std::string& type,
-         std::vector<Shape> inputShapes,
-         std::vector<Shape> outputShapes,
+         std::vector<Shape> inShapes,
+         std::vector<Shape> outShapes,
          std::vector<ov::element::Type> originalInputPrecisions,
          std::vector<ov::element::Type> originalOutputPrecisions,
          const std::string& name,
@@ -864,10 +864,10 @@ class Node {
 
     bool inputShapesModified() const;
     virtual bool needShapeInfer() const;
-    std::vector<VectorDims> shapeInferGeneric(const std::vector<Shape>& inputDims) const;
+    std::vector<VectorDims> shapeInferGeneric(const std::vector<Shape>& shapes) const;
     virtual IShapeInfer::Result shapeInfer() const;
 
-    void execute(const dnnl::stream& stream, int numaId);
+    void execute(const dnnl::stream& strm, int numaId);
     virtual void execute(const dnnl::stream& strm) = 0;
     // TODO [DS] : make pure after all nodes support dynamic shapes
     virtual void executeDynamicImpl(const dnnl::stream& strm) {

src/plugins/intel_cpu/src/nodes/common/tile_broadcast_utils.h

@@ -35,8 +35,8 @@ class TileBroadcastCommon {
                                                VectorDims& optimizedSrcStrides);
     static void broadcastScalar(const char* srcData, char* dstData, size_t elt_cnt, size_t data_size);
 
-    static bool canBeExecutedInBlockedLayout(VectorDims srcDims, VectorDims repeats, const size_t elemsInBlock);
-    static bool canBeExecutedInNSPCLayout(VectorDims srcDims, VectorDims repeats);
+    static bool canBeExecutedInBlockedLayout(VectorDims srcBlockedDims, VectorDims repeats, const size_t elemsInBlock);
+    static bool canBeExecutedInNSPCLayout(VectorDims srcBlockedDims, VectorDims repeats);
 
     struct {
         VectorDims dims;

src/plugins/intel_cpu/src/nodes/conv.cpp

@@ -698,7 +698,8 @@ void Convolution::prepareParams() {
 
 void Convolution::redefineOutputMemory(const std::vector<VectorDims>& newOutputShapes) {
     if (!withSum) {  // fast path
-        return Node::redefineOutputMemory(newOutputShapes);
+        Node::redefineOutputMemory(newOutputShapes);
+        return;
     }
 
     const size_t sumPortNum = getParentEdges().size() - 1;