PluginShapesOverride rt_info field is now mandatory

src/common/snippets/src/pass/insert_loops.cpp

@@ -35,11 +35,7 @@ bool ngraph::snippets::pass::InsertLoops::run_on_model(const std::shared_ptr<ov:
     const auto& body_rt_info = model->get_rt_info();
     const auto& plugin_shapes = body_rt_info.find("PluginShapesOverride");
     if (plugin_shapes == body_rt_info.end()) {
-        ioShapes.reserve(commonParams.size() + commonResults.size());
-        std::transform(commonParams.begin(), commonParams.end(), std::back_inserter(ioShapes),
-                       [](const std::shared_ptr<Node>& n) { return n->get_output_partial_shape(0); });
-        std::transform(commonResults.begin(), commonResults.end(), std::back_inserter(ioShapes),
-                       [](const std::shared_ptr<Node>& n) { return n->get_input_partial_shape(0); });
+        throw ngraph_error("InsertLoops requires PluginShapesOverride rt_info field");
     } else {
         const auto& new_shapes = plugin_shapes->second.as<std::vector<std::vector<size_t>>>();
         if (new_shapes.size() != commonResults.size() + commonParams.size())

src/plugins/intel_cpu/src/emitters/jit_snippets_emitters.cpp

@@ -134,8 +134,7 @@ KernelEmitter::KernelEmitter(dnnl::impl::cpu::x64::jit_generator* h, dnnl::impl:
     const auto& model_rt_info = model->get_rt_info();
     const auto& plugin_shapes = model_rt_info.find("PluginShapesOverride");
     if (plugin_shapes == model_rt_info.end()) {
-        for (const auto& op : io_nodes)
-            io_shapes.push_back(get_static_shape(op));
+        IE_THROW() << "JIT KernelEmitter requires plugin-overriden shapes in model rt_info";
     } else {
         const auto& new_shapes = plugin_shapes->second.as<std::vector<std::vector<size_t>>>();
         if (new_shapes.size() != num_inputs + num_outputs)

src/plugins/intel_cpu/src/emitters/jit_snippets_emitters.hpp

@@ -33,7 +33,6 @@ struct jit_snippets_call_args {
 struct jit_snippets_compile_args {
     std::vector<size_t> master_shape{};
     size_t tile_rank = 0;
-//    int64_t data_offsets[SNIPPETS_MAX_SNIPPETS_DIMS * SNIPPETS_MAX_HARNESS_DIMS] = {};
 };
 ///
 /// \brief jit_container_emitter designed to wrap Emitters that contain other Emitters (for example, KernelEmitter)

src/plugins/intel_cpu/src/nodes/subgraph.cpp

@@ -212,29 +212,6 @@ InferenceEngine::Precision Snippet::getRuntimePrecision() const {
     return getMaxPrecision(inputPrecisions);
 }
 
-void Snippet::calcJITParams(std::vector<int64_t>& offsets) const {
-    const size_t numInputs = normInputShapes.size();
-    const size_t numParams = numInputs + normOutputShapes.size();
-
-    // Note that we don't need offset for the last dim, since it's handled directly by Tile emitter
-    const size_t offset_rank = masterShape.size() - 1;
-    offsets.resize(numParams * (offset_rank), 1);
-    auto offset_calculation = [offset_rank, this](int64_t *off, const std::vector<size_t>& dims, const size_t data_size) {
-        size_t k = dims.back();
-        for (int i = offset_rank - 1; i >= 0; i--) {
-//            auto tmp = (dims[i] == masterShape[i] && masterShape[i] != 1) ? k : 0;
-            // dims could be either master_shape[i] or 1
-            auto tmp = (dims[i] != 1) ? k : 0;
-            off[i] = tmp * data_size;
-            k *= dims[i];
-        }
-    };
-    for (size_t i = 0; i < numParams; i++) {
-            offset_calculation(&offsets[i * offset_rank],
-                           i < numInputs ? normInputShapes[i] : normOutputShapes[i - numInputs],
-                           dataSize[i]);
-    }
-}
 bool Snippet::optimizeExecDomain(std::vector<VectorDims>& inputShapes, std::vector<VectorDims>& outputShapes,
                                  VectorDims &domain, size_t& TileRank) const {
     const size_t minimalConcurrency = parallel_get_max_threads();
@@ -367,7 +344,6 @@ void Snippet::createPrimitive() {
     prepareParams();
     jcp.master_shape = masterShape;
     jcp.tile_rank = tileRank;
-//    std::copy(data_offsets.begin(), data_offsets.end(), jcp.data_offsets);
     generate(&jcp);
 }
 
@@ -453,8 +429,6 @@ void Snippet::prepareParams() {
     }
     exec_domain = masterShape;
 
-    // todo: probably better to pass a call_args instance
-    calcJITParams(data_offsets);
     auto initStartMemoryOffsets = [this]() {
         const auto config = getSelectedPrimitiveDescriptor()->getConfig();
         const size_t numInputs = inputShapes.size();
@@ -488,30 +462,12 @@ void Snippet::prepareParams() {
         dim = 1;
     }
 
-    // Note: if exec domain is updated (dimensions are collapsed) then we need to communicate updated shapes
-    if (execDomainIsUpdated) {
-        auto& body_rt_info = snippet->get_body()->get_rt_info();
-        std::vector<std::vector<size_t>> new_shapes;
-        std::copy(normInputShapes.begin(), normInputShapes.end(), std::back_inserter(new_shapes));
-        std::copy(normOutputShapes.begin(), normOutputShapes.end(), std::back_inserter(new_shapes));
-        body_rt_info["PluginShapesOverride"] = new_shapes;
-        snippet->set_master_shape(ov::PartialShape(masterShape));
-    }
-//    snippet->set_overriden_shapes(new_shapes);
-    // todo: tileRank should be determined by snippets based on body shapes and operation semantics, not by plugin
+    auto& body_rt_info = snippet->get_body()->get_rt_info();
+    std::vector<std::vector<size_t>> new_shapes(normInputShapes);
+    std::copy(normOutputShapes.begin(), normOutputShapes.end(), std::back_inserter(new_shapes));
+    body_rt_info["PluginShapesOverride"] = new_shapes;
+    snippet->set_master_shape(ov::PartialShape(masterShape));
     snippet->tileRank = tileRank;
-    /*
-    snippet->set_master_shape(PartialShape(scheduler_work_amounts));
-    // If the snippets has domain sensitive ops (e.g. Transpose) then domain optimizations are not performed
-    // so need only update Parameter and Result shapes so Loops will be appropriately inserted in the snippets::pass::InsertLoops
-    if (snippet->has_domain_sensitive_ops()) {
-        for (const auto& s : get_shapes_for_snippet(normOutputShapes, tileRank))
-            new_shapes.push_back(s);
-        snippet->set_overriden_shapes(new_shapes);
-    } else {
-        snippet->reshape_body(new_shapes);
-    }
-    */
 }
 
 bool Snippet::needPrepareParams() const {

src/plugins/intel_cpu/src/nodes/subgraph.h

@@ -61,7 +61,6 @@ class Snippet : public Node {
     ov::PartialShape canonicalizeBody();
     // returns true if exec domain was modified
     bool optimizeExecDomain(std::vector<VectorDims>&, std::vector<VectorDims>&, VectorDims&, size_t&) const;
-    void calcJITParams(std::vector<int64_t>& offsets) const;
 
     void generate(const jit_snippets_compile_args*);
     void updateSrcDstPtrs(jit_snippets_call_args&) const;
@@ -96,7 +95,6 @@ class Snippet : public Node {
     std::vector<MemoryPtr> dstMemPtrs = {};
     std::vector<size_t> dataSize = {};
 
-    std::vector<int64_t> data_offsets;
     // this is needed for fast shape inference of blocking-invariant prepended shapes
     std::vector<bool> inputShapeIsBlocked = {}; // we need this info to shape-infer mixed layouts
     std::vector<bool> outputShapeIsBlocked = {}; // we need this info to shape-infer mixed layouts