[GPU] Fix beam search accuracy/functional errors

src/plugins/intel_gpu/src/graph/impls/ocl/scaled_dot_product_attention.cpp

@@ -53,15 +53,31 @@ struct scaled_dot_product_attention_impl : multi_stage_primitive<scaled_dot_prod
 
 protected:
     std::vector<layout> get_internal_buffer_layouts_impl() const override {
-        // TODO: current implementation is supposed to have the same kernel version for both indirect/default paths,
-        // considering this, we may assume that both indirect/default kernels have absolutely the same intermediate
-        // buffers number and its' sizes (since update_dispatch_data is called for both kernels too), and
-        // do not double memory allocations during reallocate_if_needed() function call
+        // Look for the first sdpa_opt kernel entry. Currently, it can be used as default sdpa, indirect sdpa, or for both default
+        // and indirect cases. All of sdpa_opt kernels use the same internal buffers, so we can find the first sdpa_opt and
+        // use its` internal buffers configuration. The following scenarios are possible:
+        // 1) _kernels_data[0] - micro_sdpa (default)
+        //   => internal buffers are not needed
+        // 2) _kernels_data[0] - sdpa_opt (default)
+        //   => use internal buffers from [0] kernel
+        // 2) _kernels_data[0] - sdpa_opt (default)
+        //    _kernels_data[1] - sdpa_opt (indirect)
+        //   => use internal buffers from [0] kernel
+        // 3) _kernels_data[0] - micro_sdpa (default)
+        //    _kernels_data[1] - sdpa_opt (indirect)
+        //   => use internal buffers from [1] kernel
+        size_t kernel_idx = _kernels_data.size();
+        if (_kernels_data.size() >= 1 && !_kernels_data[0].internalBufferSizes.empty()) {
+            kernel_idx = 0;
+        } else if (_kernels_data.size() >= 2 && !_kernels_data[1].internalBufferSizes.empty()) {
+            kernel_idx = 1;
+        }
+
         std::vector<layout> layouts;
-        if (_kernels_data.size() > 0 && !_kernels_data[0].internalBufferSizes.empty()) {
-            auto dtype = from_data_type(_kernels_data[0].internalBufferDataType);
+        if (kernel_idx < _kernels_data.size()) {
+            auto dtype = from_data_type(_kernels_data[kernel_idx].internalBufferDataType);
             const auto bpp = data_type_traits::size_of(dtype);
-            for (auto size : _kernels_data[0].internalBufferSizes) {
+            for (auto size : _kernels_data[kernel_idx].internalBufferSizes) {
                 layout inbuf_layout = {dtype, format::bfyx, // simple linear format (flattern to x channel)
                                         {1, 1, 1, (tensor::value_type)(size / bpp)}};
                 layouts.push_back(inbuf_layout);
@@ -300,7 +316,7 @@ struct scaled_dot_product_attention_impl : multi_stage_primitive<scaled_dot_prod
         }
 
         if (indirect && has_indirect_inputs(impl_param)) {
-            params.beam_table.SetDynamicShapeOffset(get_beam_table_id(desc));
+            params.beam_table.SetDynamicShapeOffset(in_offsets_map.at(get_beam_table_id(desc)));
         }
 
         return params;

src/plugins/intel_gpu/src/plugin/multi_tensor_variable_state.cpp

@@ -87,10 +87,10 @@ static void rearrange_cache(cldnn::memory::ptr kv_in_mem, cldnn::memory::ptr bt_
         for (size_t f = 0; f < kv_shape[1]; f++) {
             for (size_t y = 0; y < kv_shape[2]; y++) {
                 for (size_t x = 0; x < kv_shape[3]; x++) {
-                    size_t b_kv = bt_in_ptr[b* kv_shape[concat_axis] + y];
+                    auto out_idx = std::vector<int>{static_cast<int>(b), static_cast<int>(f), static_cast<int>(y), static_cast<int>(x)};
 
+                    size_t b_kv = bt_in_ptr[b * kv_shape[concat_axis] + out_idx[concat_axis]]; // bt_idx = b * total_seq_len + seq_len_idx
                     auto in_idx = std::vector<int>{static_cast<int>(b_kv), static_cast<int>(f), static_cast<int>(y), static_cast<int>(x)};
-                    auto out_idx = std::vector<int>{static_cast<int>(b), static_cast<int>(f), static_cast<int>(y), static_cast<int>(x)};
 
                     cldnn::tensor in(cldnn::format::bfyx, in_idx, 0);
                     cldnn::tensor out(cldnn::format::bfyx, out_idx, 0);

src/plugins/intel_gpu/src/runtime/ocl/ocl_stream.cpp

@@ -124,7 +124,7 @@ void set_arguments_impl(ocl_kernel_type& kernel,
                     switch (scalar.t) {
                         case scalar_t::UINT8:
                             status = kernel.setArg(i, scalar.v.u8);
-                            GPU_DEBUG_TRACE_DETAIL << "kernel: " << kernel.get() << " set scalar " << i << " (u8): " << scalar.v.u8 << "\n";
+                            GPU_DEBUG_TRACE_DETAIL << "kernel: " << kernel.get() << " set scalar " << i << " (u8): " << static_cast<int>(scalar.v.u8) << "\n";
                             break;
                         case scalar_t::UINT16:
                             status = kernel.setArg(i, scalar.v.u16);
@@ -140,7 +140,7 @@ void set_arguments_impl(ocl_kernel_type& kernel,
                             break;
                         case scalar_t::INT8:
                             status = kernel.setArg(i, scalar.v.s8);
-                            GPU_DEBUG_TRACE_DETAIL << "kernel: " << kernel.get() << " set scalar " << i << " (s8): " << scalar.v.s8 << "\n";
+                            GPU_DEBUG_TRACE_DETAIL << "kernel: " << kernel.get() << " set scalar " << i << " (s8): " << static_cast<int>(scalar.v.s8) << "\n";
                             break;
                         case scalar_t::INT16:
                             status = kernel.setArg(i, scalar.v.s16);

src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/kv_cache_sdpa.cpp

@@ -343,6 +343,10 @@ std::vector<Params> get_test_params() {
     p.push_back({with_rearrange, with_mask, !with_scale, !causal, !compressed, 1, ov::element::Type_t::f16, 10, 4, 1, {0, 2, 1, 3}});
     p.push_back({!with_rearrange, with_mask, !with_scale, !causal, !compressed, 1, ov::element::Type_t::f16, 10, 4, 1, {0, 2, 1, 3}});
 
+    // Beam search
+    p.push_back({with_rearrange, !with_mask, !with_scale, !causal, !compressed, 2, ov::element::Type_t::f16, 10, 4, 1, {0, 1, 2, 3}});
+    p.push_back({with_rearrange, !with_mask, !with_scale, !causal, !compressed, 4, ov::element::Type_t::f16, 5, 16, 1, {0, 2, 1, 3}});
+
     // Compressed
     p.push_back({with_rearrange, with_mask, !with_scale, !causal, compressed, 1, ov::element::Type_t::f16, 10, 1, 1, {0, 1, 2, 3}});
     p.push_back({with_rearrange, with_mask, !with_scale, !causal, compressed, 1, ov::element::Type_t::f16, 10, 4, 1, {0, 2, 1, 3}});