[CPU] Enable u8 kv cache by default (openvinotoolkit#27454)

### Details:
 - *Enable u8 kv cache by default*
 - *...*

### Tickets:
 - *[152621](https://jira.devtools.intel.com/browse/CVS-152621)*

src/plugins/intel_cpu/src/config.cpp

@@ -358,6 +358,7 @@ void Config::readProperties(const ov::AnyMap& prop, const ModelType modelType) {
             }
         } else if (key == ov::hint::kv_cache_precision.name()) {
             try {
+                kvCachePrecisionSetExplicitly = true;
                 auto const prec = val.as<ov::element::Type>();
                 if (one_of(prec, ov::element::f32, ov::element::f16, ov::element::bf16, ov::element::u8)) {
                     kvCachePrecision = prec;
@@ -411,6 +412,9 @@ void Config::readProperties(const ov::AnyMap& prop, const ModelType modelType) {
         if (!fcDynamicQuantizationGroupSizeSetExplicitly) {
             fcDynamicQuantizationGroupSize = 0;
         }
+        if (!kvCachePrecisionSetExplicitly) {
+            kvCachePrecision = ov::element::f32;
+        }
     }
 
     if (!prop.empty())

src/plugins/intel_cpu/src/config.h

@@ -51,14 +51,16 @@ struct Config {
     std::string device_id = {};
     float fcSparseWeiDecompressionRate = 1.0f;
     uint64_t fcDynamicQuantizationGroupSize = 32;
-    ov::element::Type kvCachePrecision = ov::element::f16;
     bool fcDynamicQuantizationGroupSizeSetExplicitly = false;
+    bool kvCachePrecisionSetExplicitly = false;
 #if defined(OV_CPU_WITH_ACL)
     bool aclFastMath = false;
 #endif
 #if defined(OPENVINO_ARCH_X86_64)
+    ov::element::Type kvCachePrecision = ov::element::u8;
     size_t rtCacheCapacity = 5000ul;
 #else
+    ov::element::Type kvCachePrecision = ov::element::f16;
     // TODO: Executor cache may leads to incorrect behavior on oneDNN ACL primitives
     size_t rtCacheCapacity = 0ul;
 #endif

src/plugins/intel_cpu/src/memory_state.cpp

@@ -297,18 +297,19 @@ void VariableStateKVcache::set_state_impl(const ov::SoPtr<ov::ITensor>& state) {
         auto S = internal.size(3);
         auto nthr = parallel_get_max_threads();
         std::vector<PlainTensor> buffers(nthr);
+        m_scale_zp.resize<float>({L0, B, H, 2});
         parallel_for3d(B, H, L0, [&](size_t ithr, size_t b, size_t h, size_t m) {
             buffers[ithr].resize<float>({S});
-            cpu_convert(external.ptr_v(b, h, m),
+            cpu_convert(external.ptr_v(m, b, h),
                         buffers[ithr].ptr<float>(),
                         external.m_dt,
                         element::f32,
                         S);
             attn_quant_u8(buffers[ithr].ptr<float>(),
-                          internal.ptr<uint8_t>(b, h, m),
+                          internal.ptr<uint8_t>(m, b, h),
                           S,
-                          m_scale_zp.at<float>({b, h, m, size_t{0}}),
-                          m_scale_zp.at<float>({b, h, m, size_t{1}}));
+                          m_scale_zp.at<float>({m, b, h, size_t{0}}),
+                          m_scale_zp.at<float>({m, b, h, size_t{1}}));
         });
     } else {
         m_internal_mem->load(external_mem);

src/plugins/intel_cpu/tests/functional/custom/behavior/ov_executable_network/properties.cpp

@@ -194,6 +194,17 @@ TEST_F(OVClassConfigTestCPU, smoke_CpuExecNetworkCheckAccuracyModeDynamicQuantiz
     ASSERT_EQ(groupSize, 0);
 }
 
+TEST_F(OVClassConfigTestCPU, smoke_CpuExecNetworkCheckAccuracyModeKVCachePrecision) {
+    ov::Core core;
+
+    ASSERT_NO_THROW(core.set_property(deviceName, ov::hint::execution_mode(ov::hint::ExecutionMode::ACCURACY)));
+    ov::CompiledModel compiledModel = core.compile_model(model, deviceName);
+
+    auto kv_cache_precision_value = ov::element::undefined;
+    ASSERT_NO_THROW(kv_cache_precision_value = compiledModel.get_property(ov::hint::kv_cache_precision));
+    ASSERT_EQ(kv_cache_precision_value, ov::element::f32);
+}
+
 const auto bf16_if_can_be_emulated = ov::with_cpu_x86_avx512_core() ? ov::element::bf16 : ov::element::f32;
 
 TEST_F(OVClassConfigTestCPU, smoke_CpuExecNetworkCheckExecutionModeIsAvailableInCoreAndModel) {

src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/common/concat_transpose_sdp_transpose.cpp

@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/core/type/float16.hpp"
 #include "openvino/opsets/opset13.hpp"
 #include "openvino/pass/manager.hpp"
 #include "transformations/op_conversions/scaled_dot_product_attention_decomposition.hpp"
@@ -207,6 +208,10 @@ class ConcatSDPTransposeTestBase : public testing::WithParamInterface<ConcatSDPT
                 ov::Tensor t{ov::element::f32, shape};
                 strided_iota(static_cast<float*>(t.data()), t.get_size(), val, 0.1f);
                 inputs.insert({param, t});
+            } else if (param->get_element_type() == ov::element::f16) {
+                ov::Tensor t{ov::element::f16, shape};
+                strided_iota(static_cast<ov::float16*>(t.data()), t.get_size(), val, 0.1f);
+                inputs.insert({param, t});
             } else {
                 ASSERT_TRUE(param->get_element_type() == ov::element::bf16);
                 ov::Tensor t{ov::element::bf16, shape};
@@ -365,6 +370,15 @@ class ConcatSDPTransposeTestSetState : public ConcatSDPTransposeTestBase {
     }
     std::vector<ov::Tensor> run_test(std::shared_ptr<ov::Model> model) {
         function = model;
+        // on spr, all kvccache precision will be covered and all paths for get/set_state will be tested
+        auto input_type = model->get_parameters()[0]->get_element_type();
+        if (input_type == ov::element::f32) {
+            configuration[ov::hint::kv_cache_precision.name()] = "f32";
+        } else if (input_type == ov::element::bf16) {
+            configuration[ov::hint::kv_cache_precision.name()] = "bf16";
+        } else {
+            configuration[ov::hint::kv_cache_precision.name()] = "u8";
+        }
         prepare();
         std::vector<ov::Tensor> outputs;
         // case 1: initialization + pastkv reaches limitation, remove some state
@@ -407,6 +421,15 @@ class ConcatSDPTransposeTestSetState : public ConcatSDPTransposeTestBase {
 
 TEST_P(ConcatSDPTransposeTestSetState, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    ElementType inType;
+    InputShapeAndTransposeOrder inputShapeAndOrders;
+    bool hasShapeOf;
+    std::tie(inType, inputShapeAndOrders, hasShapeOf) = this->GetParam();
+
+    // skip bf16 test on avx512 platform
+    if (inType == ElementType::bf16 && !ov::with_cpu_x86_bfloat16())
+        GTEST_SKIP();
+
     auto actualOutputs = run_test(function);
     CheckNumberOfNodesWithType(compiledModel, "ScaledDotProductAttention", 1);
     CheckNumberOfNodesWithType(compiledModel, "Concatenation", 0);
@@ -438,7 +461,7 @@ const std::vector<InputShapeAndTransposeOrder> inputShapeAndReordersSetState = {
 
 INSTANTIATE_TEST_SUITE_P(smoke_ConcatSDPTransposeTestSetState,
                          ConcatSDPTransposeTestSetState,
-                         ::testing::Combine(::testing::Values(ElementType::f32),
+                         ::testing::Combine(::testing::Values(ElementType::f32, ElementType::bf16, ElementType::f16),
                                             ::testing::ValuesIn(inputShapeAndReordersSetState),
                                             ::testing::Values(false)),
                          ConcatSDPTransposeTest::getTestCaseName);