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liubo-intelliubo-intel
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cpu causal_conv1d op
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-4
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19 files changed

+647
-4
lines changed

‎src/bindings/python/src/openvino/_pyopenvino/op/__init__.pyi‎

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@@ -183,6 +183,13 @@ class _PagedAttentionExtension(openvino._pyopenvino.Node):
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"""
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def __init__(self, arg0: collections.abc.Sequence[openvino._pyopenvino.Output]) -> None:
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...
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class _CausalConv1D(openvino._pyopenvino.Node):
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"""
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Experimental extension for CausalConv1D operation. Use with care: no backward compatibility is guaranteed in future releases.
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"""
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def __init__(self, arg0: collections.abc.Sequence[openvino._pyopenvino.Output]) -> None:
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...
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class assign(openvino._pyopenvino.Node):
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"""
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openvino.op.assign wraps ov::op::v6::Assign

‎src/bindings/python/src/openvino/op/__init__.py‎

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@@ -11,6 +11,7 @@
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from openvino._pyopenvino.op import Constant
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from openvino._pyopenvino.op import assign
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from openvino._pyopenvino.op import _PagedAttentionExtension
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from openvino._pyopenvino.op import _CausalConv1D
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from openvino._pyopenvino.op import Parameter
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from openvino._pyopenvino.op import if_op
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from openvino._pyopenvino.op import loop

‎src/bindings/python/src/openvino/op/__init__.pyi‎

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@@ -5,6 +5,7 @@ from openvino._pyopenvino.op import Constant
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from openvino._pyopenvino.op import Parameter
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from openvino._pyopenvino.op import Result
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from openvino._pyopenvino.op import _PagedAttentionExtension
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from openvino._pyopenvino.op import _CausalConv1D
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from openvino._pyopenvino.op import assign
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from openvino._pyopenvino.op import if_op
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from openvino._pyopenvino.op import loop
@@ -15,4 +16,16 @@ from openvino._pyopenvino.op import tensor_iterator
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Package: openvino.op
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Low level wrappers for the c++ api in ov::op.
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"""
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__all__: list[str] = ['Constant', 'Parameter', 'Result', 'assign', 'if_op', 'loop', 'read_value', 'tensor_iterator', 'util']
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__all__: list[str] = [
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'Constant',
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'Parameter',
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'Result',
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'_PagedAttentionExtension',
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'_CausalConv1D',
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'assign',
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'if_op',
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'loop',
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'read_value',
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'tensor_iterator',
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'util',
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]

‎src/bindings/python/src/pyopenvino/graph/ops/causal_conv1d.cpp‎

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// Copyright (C) 2018-2025 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "pyopenvino/graph/ops/causal_conv1d.hpp"
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#include "openvino/op/causal_conv1d.hpp"
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#include "pyopenvino/core/common.hpp"
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namespace py = pybind11;
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void regclass_graph_op_CausalConv1D(py::module m) {
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using ov::op::CausalConv1D;
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py::class_<CausalConv1D, std::shared_ptr<CausalConv1D>, ov::Node> cls(m, "_CausalConv1D");
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cls.doc() = "Experimental extension for CausalConv1D operation. Use with care: no backward compatibility is guaranteed in future releases.";
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cls.def(py::init<const ov::OutputVector&, const std::string&>(), py::arg("inputs"), py::arg("activation") = "silu");
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cls.def("set_activation", &CausalConv1D::set_activation);
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cls.def("get_activation", &CausalConv1D::get_activation);
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}

‎src/bindings/python/src/pyopenvino/graph/ops/causal_conv1d.hpp‎

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// Copyright (C) 2018-2025 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#pragma once
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#include <pybind11/pybind11.h>
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namespace py = pybind11;
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void regclass_graph_op_CausalConv1D(py::module m);

‎src/bindings/python/src/pyopenvino/pyopenvino.cpp‎

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@@ -57,6 +57,7 @@
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#include "pyopenvino/graph/ops/if.hpp"
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#include "pyopenvino/graph/ops/loop.hpp"
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#include "pyopenvino/graph/ops/paged_attention_extension.hpp"
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#include "pyopenvino/graph/ops/causal_conv1d.hpp"
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#include "pyopenvino/graph/ops/parameter.hpp"
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#include "pyopenvino/graph/ops/read_value.hpp"
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#include "pyopenvino/graph/ops/result.hpp"
@@ -242,6 +243,7 @@ PYBIND11_MODULE(_pyopenvino, m) {
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regclass_graph_op_Assign(m_op);
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regclass_graph_op_Constant(m_op);
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regclass_graph_op_PagedAttentionExtension(m_op);
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regclass_graph_op_CausalConv1D(m_op);
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regclass_graph_op_Parameter(m_op);
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regclass_graph_op_ReadValue(m_op);
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regclass_graph_op_Result(m_op);

‎src/core/dev_api/openvino/op/causal_conv1d.hpp‎

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// Copyright (C) 2018-2025 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#pragma once
5+
6+
#include "openvino/op/op.hpp"
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8+
namespace ov {
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namespace op {
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// Experimental op mirroring the CPU plugin implementation. Backward compatibility is not guaranteed.
12+
class OPENVINO_API CausalConv1D : public ov::op::Op {
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public:
14+
OPENVINO_OP("CausalConv1D");
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CausalConv1D() = default;
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CausalConv1D(const ov::OutputVector& args, const std::string& activation = "silu");
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void validate_and_infer_types() override;
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std::shared_ptr<ov::Node> clone_with_new_inputs(const ov::OutputVector& new_args) const override;
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bool visit_attributes(ov::AttributeVisitor& visitor) override;
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void set_activation(const std::string& activation);
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const std::string& get_activation() const;
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private:
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std::string m_activation = "silu";
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};
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} // namespace op
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} // namespace ov

‎src/core/src/op/causal_conv1d.cpp‎

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// Copyright (C) 2018-2025 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "openvino/op/causal_conv1d.hpp"
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#include <algorithm>
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#include <array>
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#include <initializer_list>
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#include "dimension_util.hpp"
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#include "itt.hpp"
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#include "openvino/core/validation_util.hpp"
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#include "openvino/core/attribute_visitor.hpp"
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namespace {
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inline void input_check(const ov::Node* node,
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size_t idx,
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const std::string_view input_name,
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std::initializer_list<ov::Rank>&& allowed_ranks,
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const std::vector<ov::element::Type>& allowed_types) {
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using namespace ov;
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using namespace ov::util;
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using namespace ov::element;
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const auto& rank = node->get_input_partial_shape(idx).rank();
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const auto& tp = node->get_input_element_type(idx);
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auto rank_check = [&](const Rank& r) {
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return r.is_dynamic() || allowed_ranks.size() == 0 || is_rank_compatible_any_of(r.get_length(), allowed_ranks);
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};
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auto type_check = [&](const Type& t) {
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if (allowed_types.empty()) {
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return true;
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}
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auto it = std::find(allowed_types.begin(), allowed_types.end(), tp);
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return t.is_dynamic() || it != allowed_types.end();
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};
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NODE_VALIDATION_CHECK(node,
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rank_check(rank),
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"Rank of `",
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input_name,
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"` input should be in [dynamic, ",
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join(allowed_ranks),
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"] list, but it is ",
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rank,
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".");
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NODE_VALIDATION_CHECK(node,
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type_check(tp),
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"Element type of `",
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input_name,
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"` input should be in [dynamic, ",
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join(allowed_types),
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"] list, but it is ",
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tp,
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".");
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}
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} // namespace
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namespace ov {
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namespace op {
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CausalConv1D::CausalConv1D(const ov::OutputVector& args, const std::string& activation)
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: ov::op::Op(args), m_activation(activation) {
70+
constructor_validate_and_infer_types();
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}
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void CausalConv1D::validate_and_infer_types() {
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OV_OP_SCOPE(CausalConv1D_validate_and_infer_types);
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NODE_VALIDATION_CHECK(this,
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get_input_size() == 3 || get_input_size() == 4,
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"CausalConv1D expects 3 or 4 inputs, but it has ",
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get_input_size());
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// hidden_states: (batch, hidden_size, seq_len)
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input_check(this, 0, "hidden_states", {3}, {});
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// conv_state: (batch, hidden_size, kernel)
84+
input_check(this, 1, "conv_state", {3}, {});
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// weight: (hidden_size, kernel)
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input_check(this, 2, "weight", {2}, {});
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if (get_input_size() == 4) {
89+
// bias: (hidden_size)
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input_check(this, 3, "bias", {1}, {});
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}
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const auto hidden_ps = get_input_partial_shape(0);
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const auto state_ps = get_input_partial_shape(1);
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const auto weight_ps = get_input_partial_shape(2);
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if (hidden_ps.rank().is_static() && state_ps.rank().is_static()) {
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NODE_VALIDATION_CHECK(this,
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hidden_ps[1].compatible(state_ps[1]),
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"Hidden size of hidden_states (",
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hidden_ps[1],
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") and conv_state (",
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state_ps[1],
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") inputs must match.");
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}
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if (hidden_ps.rank().is_static() && weight_ps.rank().is_static()) {
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NODE_VALIDATION_CHECK(this,
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hidden_ps[1].compatible(weight_ps[0]),
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"Hidden size of hidden_states (",
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hidden_ps[1],
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") and weight (",
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weight_ps[0],
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") inputs must match.");
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}
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if (state_ps.rank().is_static() && weight_ps.rank().is_static()) {
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NODE_VALIDATION_CHECK(this,
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state_ps[2].compatible(weight_ps[1]),
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"Kernel length of conv_state (",
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state_ps[2],
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") and weight (",
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weight_ps[1],
122+
") inputs must match.");
123+
}
124+
if (get_input_size() == 4) {
125+
const auto bias_ps = get_input_partial_shape(3);
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if (bias_ps.rank().is_static() && weight_ps.rank().is_static()) {
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NODE_VALIDATION_CHECK(this,
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bias_ps[0].compatible(weight_ps[0]),
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"Bias length (",
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bias_ps[0],
131+
") must match hidden size (",
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weight_ps[0],
133+
").");
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}
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}
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set_output_type(0, get_input_element_type(0), get_input_partial_shape(0));
138+
set_output_type(1, get_input_element_type(1), get_input_partial_shape(1));
139+
}
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std::shared_ptr<ov::Node> CausalConv1D::clone_with_new_inputs(const ov::OutputVector& new_args) const {
142+
return std::make_shared<CausalConv1D>(new_args, m_activation);
143+
}
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bool CausalConv1D::visit_attributes(ov::AttributeVisitor& visitor) {
146+
visitor.on_attribute("activation", m_activation);
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return true;
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}
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void CausalConv1D::set_activation(const std::string& activation) {
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m_activation = activation;
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}
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const std::string& CausalConv1D::get_activation() const {
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return m_activation;
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}
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} // namespace op
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} // namespace ov

‎src/plugins/intel_cpu/src/cpu_types.cpp‎

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@@ -267,7 +267,8 @@ static const TypeToNameMap& get_type_to_name_tbl() {
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{"SearchSorted", Type::SearchSorted},
268268
{"LoraSubgraph", Type::LoRA},
269269
{"BatchGatherMatmul", Type::GatherMatmul},
270-
{"BatchGatherMatmulCompressed", Type::GatherMatmul}};
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{"BatchGatherMatmulCompressed", Type::GatherMatmul},
271+
{"CausalConv1D", Type::CausalConv1D}};
271272
return type_to_name_tbl;
272273
}
273274

@@ -403,6 +404,7 @@ std::string NameFromType(const Type type) {
403404
CASE(SegmentMax);
404405
CASE(LoRA);
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CASE(GatherMatmul);
407+
CASE(CausalConv1D);
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CASE(Unknown);
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}
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#undef CASE

‎src/plugins/intel_cpu/src/cpu_types.h‎

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@@ -138,7 +138,8 @@ enum class Type : uint8_t {
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SearchSorted,
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SegmentMax,
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LoRA,
141-
GatherMatmul
141+
GatherMatmul,
142+
CausalConv1D
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};
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enum class Algorithm : uint8_t {

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