Refactor: remove past_token_len from extra_inputs

ggml/src/ggml-openvino/ggml-decoder.cpp

@@ -249,26 +249,16 @@ ov::PartialShape GgmlOvDecoder::get_graph_input_shape(const ggml_tensor* src) co
 }
 
 void GgmlOvDecoder::add_extra_inputs() {
-    int64_t past_token_len = -1;
     // attention_size not used for NPU
     int64_t attention_size = -1;
 
+    int64_t past_token_len = -1;
     for (const auto& node : m_nodes) {
         if (node->op == GGML_OP_CPY && ggml_is_contiguous(node)) {
             assert(std::string(node->view_src->name).find("cache_k") == 0);
             int64_t head_size = node->src[0]->ne[0];
             int64_t num_heads = node->src[0]->ne[1];
-            past_token_len = (int64_t)(node->src[1]->op_params[0] / node->src[1]->nb[0] / head_size / num_heads);
+            past_token_len = (int64_t) (node->src[1]->op_params[0] / node->src[1]->nb[0] / head_size / num_heads);
-
-            std::string name = "past_token_len";
-            auto param_node = std::make_shared<ov::op::v0::Parameter>(ov::element::i64, ov::Shape{1});
-            param_node->set_friendly_name(name);
-            param_node->output(0).get_tensor().set_names({name});
-            m_model_extra_inputs[name] = param_node;
-
-            auto tensor = std::make_shared<ov::Tensor>(ov::element::i64, ov::Shape{1});
-            *tensor->data<int64_t>() = past_token_len;
-            m_model_extra_input_values[name] = tensor;
             break;
         }
     }

ggml/src/ggml-openvino/openvino/op/cpy.cpp

@@ -28,12 +28,6 @@ OutputVector translate_cpy(const NodeContext& context) {
 
     auto src0 = context.get_input(0);
     auto src1 = context.get_input(1);
-    auto token_len = context.get_input("token_len");
-    auto past_token_len = context.get_input("past_token_len");
-
-    auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-    auto token_len_scalar = std::make_shared<ov::op::v0::Squeeze>(token_len, zero);
-    auto past_token_len_scalar = std::make_shared<ov::op::v0::Squeeze>(past_token_len, zero);
 
     src0 = std::make_shared<ov::op::v0::Convert>(src0, context.get_input_type(1));
     ov::Output<Node> res;
@@ -43,12 +37,6 @@ OutputVector translate_cpy(const NodeContext& context) {
         return rename_outputs_with_suffix({res}, context.get_name());
     }
 
-    auto src0_shape = context.get_input_shape(0).to_shape();
-    auto output_shape = context.get_output_shape(0).to_shape();
-
-    std::vector<size_t> input0_strides = context.get_input_stride(0);
-    std::vector<size_t> output_strides = context.get_output_stride(0);
-
     if (op_case == 1) {
         // Write K to cache_k
         auto indices = context.get_input("update_indices_k");
@@ -60,6 +48,7 @@ OutputVector translate_cpy(const NodeContext& context) {
             std::make_shared<ov::op::v1::Reshape>(src0,
                                                   ov::op::v0::Constant::create(element::i64, Shape{1}, {-1}),
                                                   false);
+        auto src0_shape = context.get_input_shape(0).to_shape();
         int64_t total_head_size = src0_shape[1];
         auto reshaped_src1 = std::make_shared<ov::op::v1::Reshape>(
             src1,

ggml/src/ggml-openvino/openvino/translate_session.cpp

@@ -72,7 +72,6 @@ void add_kv_update_indices(TensorMap& tensor_map, GgmlDecoder& ggml_model_decode
     // cache_v layout: [N, H, S] (num_heads, head_size, seq)
     // When writing to cache_v, cache should be reshaped to [N*H, S] and v-curr should be flattened
     auto inp_pos = tensor_map.at("inp_pos").get_node_shared_ptr();
-    auto past_token_len = tensor_map.at("past_token_len").get_node_shared_ptr();
     auto token_len = tensor_map.at("token_len").get_node_shared_ptr();
 
     std::shared_ptr<ov::Node> update_indices_k;
@@ -84,12 +83,8 @@ void add_kv_update_indices(TensorMap& tensor_map, GgmlDecoder& ggml_model_decode
     auto one_scalar = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {1});
     auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2});
 
-    if (ggml_model_decoder.is_static()) {
+    update_indices_k =
-        update_indices_k = past_token_len;
+        std::make_shared<ov::op::v0::Squeeze>(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {2}, {0, 1}));
-    } else {
-        update_indices_k =
-            std::make_shared<ov::op::v0::Squeeze>(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {2}, {0, 1}));
-    }
     update_indices_k = std::make_shared<ov::op::v0::Unsqueeze>(update_indices_k, one);
     update_indices_k->set_friendly_name("update_indices_k");
     tensor_map.insert({"update_indices_k", update_indices_k->output(0)});
@@ -108,14 +103,8 @@ void add_kv_update_indices(TensorMap& tensor_map, GgmlDecoder& ggml_model_decode
         std::make_shared<ov::op::v0::Concat>(ov::OutputVector{total_head_size_node, token_len, one}, 0));
 
     // 1D tensor of shape [token_len], values starting from past_token_len
-    std::shared_ptr<ov::Node> range_col;
+    auto range_col =
-    if (ggml_model_decoder.is_static()) {
+        std::make_shared<ov::op::v0::Squeeze>(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {2}, {0, 1}));
-        // aka inp_pos
-        range_col = past_token_len;
-    } else {
-        range_col =
-            std::make_shared<ov::op::v0::Squeeze>(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {2}, {0, 1}));
-    }
     auto range_col_reshaped =
         std::make_shared<ov::op::v0::Unsqueeze>(range_col, ov::op::v0::Constant::create(ov::element::i64, {2}, {0, 2}));
     auto col_indices = std::make_shared<ov::op::v3::Broadcast>(
@@ -233,10 +222,9 @@ void TranslateSession::apply_transformations(const std::shared_ptr<Model>& model
         const auto kv_param_res_names = ggml_model_decoder->get_kv_param_res_names();
         const auto kv_param_res_pairs = get_kv_param_res_pairs(model, kv_param_res_names);
         manager.register_pass<ov::pass::MakeStateful>(kv_param_res_pairs);
-
-        manager.register_pass<pass::FuseToSDPA>();
     }
 
+    manager.register_pass<pass::FuseToSDPA>();
     manager.run_passes(model);
 }