new shape infer for lsm_cell, changes from review

Signed-off-by: Michal Miotk <[email protected]>

src/plugins/intel_gpu/src/graph/impls/onednn/lstm_seq_onednn.hpp

@@ -3,6 +3,7 @@
 //
 
 #include "lstm_seq_inst.h"
+#include "reshape_inst.h"
 #include "intel_gpu/runtime/utils.hpp"
 #include "impls/registry/implementation_manager.hpp"
 #include "transformations/utils/utils.hpp"
@@ -20,7 +21,8 @@ struct LSTMSeqImplementationManager : public ImplementationManager {
 
     bool validate_impl(const program_node& node) const override {
         assert(node.is_type<lstm_seq>());
-        return node.get_input_layout(0).format == cldnn::format::bfyx || node.get_input_layout(0).format == cldnn::format::fbyx;
+        return node.get_input_layout(0).format == cldnn::format::bfyx || node.get_input_layout(0).format == cldnn::format::fbyx \
+            || node.get_input_layout(0).format == cldnn::format::ybfx;
     }
 
     in_out_fmts_t query_formats(const program_node& node) const override {

src/plugins/intel_gpu/src/graph/layout_optimizer.cpp

@@ -1088,7 +1088,7 @@ format layout_optimizer::get_expected_format(quantize_node const& node) {
         return all_users_gemm;
     };
 
-    auto use_onednn_impls = contains_onednn_impls_optimization_attribute(&node);
+    auto use_onednn_impls = has_all_enabled_onednn_impls_optimization_attribute();
 
     if (use_onednn_impls) {
         expected = format::any;
@@ -1445,8 +1445,6 @@ bool layout_optimizer::has_all_enabled_onednn_impls_optimization_attribute() {
         is_enabled_onednn_for<pooling>() && is_enabled_onednn_for<reduce>() && is_enabled_onednn_for<reorder>();
 }
 
-
-
 void layout_optimizer::set_value_onednn(primitive_type_id p_type, bool val) {
     _optimization_attributes.onednn_impls[p_type] = val;
 }

src/plugins/intel_gpu/src/kernel_selector/cl_kernels/lstm_cell_and_seq_bfyx.cl

@@ -149,11 +149,11 @@ KERNEL(lstm_cell_and_seq_bfyx)(
                 }
 
                 unroll_for(uint j=block_num*VEC_SIZE;j<INPUT_SIZE;++j) { //leftovers
-                        if (dir == 1) {
-                            input_result += x[GET_IN0_IDX(b, real_seq_length-1-i, j)]*W[GET_IN3_IDX(dir, weight_idx, j)];
-                        } else {
-                            input_result += x[GET_IN0_IDX(b, i, j)]*W[GET_IN3_IDX(dir, weight_idx, j)];
-                        }
+                    if (dir == 1) {
+                        input_result += x[GET_IN0_IDX(b, real_seq_length-1-i, j)]*W[GET_IN3_IDX(dir, weight_idx, j)];
+                    } else {
+                        input_result += x[GET_IN0_IDX(b, i, j)]*W[GET_IN3_IDX(dir, weight_idx, j)];
+                    }
                 }
                 gate_output[k] = hidden_result + input_result + TO_ACCUMULATOR_TYPE(B[GET_IN5_IDX(dir, weight_idx)]);
                 switch(k){

src/plugins/intel_gpu/src/plugin/ops/rnn.cpp

@@ -80,23 +80,6 @@ static void CreateLSTMCellOp(ProgramBuilder& p, const std::shared_ptr<ov::op::v4
                         cldnn::input_info(), "",  layerName + "_md_write.1", clip, false, activations, \
                         activation_params, cldnn::lstm_weights_order::fizo, ov::op::RecurrentSequenceDirection::FORWARD, cldnn::padding(), \
                         static_cast<int>(op->get_output_size())));
-    } else {
-        auto mutable_precision_first = op->get_output_element_type(1);
-        cldnn::layout outLayout = cldnn::layout(
-                cldnn::element_type_to_data_type(mutable_precision_first),
-                cldnn::format::get_default_format(op->get_output_shape(1).size()),
-                tensor_from_dims(op->get_output_shape(1)));
-
-        cldnn::memory::ptr shared_memory = p.get_engine().allocate_memory(outLayout);
-        const cldnn::primitive_id mutable_id_1 = layerName + "_md_write.1";
-        const cldnn::mutable_data mutable_prim_1{mutable_id_1, shared_memory};
-        p.add_primitive(*op, mutable_prim_1);
-
-        p.add_primitive(*op, cldnn::lstm_cell(layerName+".out0", inputs[0], inputs[1], inputs[2], inputs[3], inputs[4], inputs[5], \
-                        cldnn::input_info(), "",  layerName + "_md_write.1", clip, false, activations, \
-                        activation_params, cldnn::lstm_weights_order::fizo, ov::op::RecurrentSequenceDirection::FORWARD, cldnn::padding(), 1));
-
-        p.add_primitive(*op, cldnn::mutable_data(layerName + ".out1", {cldnn::input_info(layerName + ".out0")}, shared_memory));
     }
 }
 

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

@@ -189,9 +189,6 @@ std::shared_ptr<ov::ICompiledModel> Plugin::compile_model(const std::shared_ptr<
 
     ExecutionConfig config = m_configs_map.at(device_id);
     config.set_user_property(orig_config);
-    if (context->get_engine().get_device_info().supports_immad) {
-        config.set_property(ov::intel_gpu::use_onednn(true));
-    }
     config.apply_user_properties(context->get_engine().get_device_info());
 
     set_cache_info(model, config);
@@ -213,9 +210,6 @@ std::shared_ptr<ov::ICompiledModel> Plugin::compile_model(const std::shared_ptr<
 
     ExecutionConfig config = m_configs_map.at(device_id);
     config.set_user_property(orig_config);
-    if (context_impl->get_engine().get_device_info().supports_immad) {
-        config.set_property(ov::intel_gpu::use_onednn(true));
-    }
     config.apply_user_properties(context_impl->get_engine().get_device_info());
 
     set_cache_info(model, config);

src/plugins/intel_gpu/src/plugin/program_builder.cpp

@@ -356,7 +356,7 @@ bool ProgramBuilder::requires_new_shape_infer(const std::shared_ptr<ov::Node>& o
             return true;
     }
 
-    if (ov::is_type<ov::op::v5::LSTMSequence>(op)) {
+    if (ov::is_type<ov::op::v5::LSTMSequence>(op) || ov::is_type<ov::op::v4::LSTMCell>(op)) {
         return true;
     }
     // When input node has dynamic shape with 4 dimension, this function return false