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[PT FE] add support of shift operations #29057

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[PT FE] add support of shift operations #29057

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01e021d
[PT FE] add support of shift operations
Mohamed-Ashraf273 Feb 18, 2025
03f7ea1
Update op_table.cpp
Mohamed-Ashraf273 Feb 19, 2025
e38f0cc
Update bitwise.cpp
Mohamed-Ashraf273 Feb 20, 2025
68eccc2
Update op_table.cpp
Mohamed-Ashraf273 Feb 20, 2025
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36 changes: 35 additions & 1 deletion src/frontends/pytorch/src/op/bitwise.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,8 @@
#include "openvino/op/bitwise_not.hpp"
#include "openvino/op/bitwise_or.hpp"
#include "openvino/op/bitwise_xor.hpp"
#include "openvino/op/bitwise_left_shift.hpp"
#include "openvino/op/bitwise_right_shift.hpp"
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Please fix code style, sort includes

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@mvafin
thanks for your review, I've sorted them.

#include "openvino/op/convert_like.hpp"
#include "utils.hpp"

Expand Down Expand Up @@ -73,7 +75,39 @@ OutputVector translate_bitwise_xor(const NodeContext& context) {
return {xor_x};
};

OutputVector translate_bitwise_left_shift(const NodeContext& context) {
num_inputs_check(context, 2, 3);
Output<Node> x;
Output<Node> y;
std::tie(x, y) = get_inputs_with_promoted_types(context, 0, 1);
auto lshift = context.mark_node(std::make_shared<ov::op::v15::BitwiseLeftShift>(x, y))->output(0);
if (!context.input_is_none(2)) {
auto out = context.get_input(2);
if (out.get_element_type().is_dynamic() || lshift.get_element_type() != out.get_element_type()) {
lshift = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(lshift, out));
}
context.mutate_input(2, lshift);
}
return {lshift};
};

OutputVector translate_bitwise_right_shift(const NodeContext& context) {
num_inputs_check(context, 2, 3);
Output<Node> x;
Output<Node> y;
std::tie(x, y) = get_inputs_with_promoted_types(context, 0, 1);
auto rshift = context.mark_node(std::make_shared<ov::op::v15::BitwiseRightShift>(x, y))->output(0);
if (!context.input_is_none(2)) {
auto out = context.get_input(2);
if (out.get_element_type().is_dynamic() || rshift.get_element_type() != out.get_element_type()) {
rshift = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(rshift, out));
}
context.mutate_input(2, rshift);
}
return {rshift};
}

} // namespace op
} // namespace pytorch
} // namespace frontend
} // namespace ov
} // namespace ov
6 changes: 6 additions & 0 deletions src/frontends/pytorch/src/op_table.cpp
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Expand Up @@ -53,6 +53,8 @@ OP_CONVERTER(translate_bitwise_and);
OP_CONVERTER(translate_bitwise_not);
OP_CONVERTER(translate_bitwise_or);
OP_CONVERTER(translate_bitwise_xor);
OP_CONVERTER(translate_bitwise_left_shift);
OP_CONVERTER(translate_bitwise_right_shift);
OP_CONVERTER(translate_bucketize);
OP_CONVERTER(translate_cat);
OP_CONVERTER(translate_cdist);
Expand Down Expand Up @@ -343,6 +345,10 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
return {
{"aten::__and__", op::translate_bitwise_and},
{"aten::__iand__", op::inplace_op<op::translate_bitwise_and>},
{"aten::__lshift__", op::translate_bitwise_left_shift},
{"aten::__rshift__", op::translate_bitwise_right_shift},
{"aten::bitwise_left_shift", op::translate_bitwise_left_shift},
{"aten::bitwise_right_shift", op::translate_bitwise_right_shift},
{"aten::__derive_index", op::translate_derive_index},
{"aten::__getitem__", op::translate_getitem},
{"aten::__not__", op::translate_1to1_match_1_inputs<opset10::LogicalNot>},
Expand Down
108 changes: 108 additions & 0 deletions tests/layer_tests/pytorch_tests/test_shiftOperations.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,108 @@
# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import numpy as np
import pytest
import torch
from pytorch_layer_test_class import PytorchLayerTest, skip_if_export


class TestShiftOperators(PytorchLayerTest):
def _prepare_input(self, lhs_dtype, rhs_dtype, lhs_shape, rhs_shape):
choices = np.array([1, 2, 4, 8, 16, 32])
shifts = np.array([0, 1, 2, 3, 4, 5])

x = np.random.choice(choices, lhs_shape).astype(lhs_dtype)
y = np.random.choice(shifts, rhs_shape).astype(rhs_dtype)
return x, y

def create_model(self):
class aten_shift(torch.nn.Module):
def forward(self, lhs, rhs):
return lhs << rhs, lhs >> rhs

ref_net = None
return aten_shift(), ref_net, ("aten::__lshift__", "aten::__rshift__")

@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.precommit_torch_export
@pytest.mark.precommit_fx_backend
@pytest.mark.parametrize("lhs_dtype", ["int32", "int64"])
@pytest.mark.parametrize("rhs_dtype", ["int32", "int64"])
@pytest.mark.parametrize(
("lhs_shape", "rhs_shape"),
[
([2, 3], [2, 3]),
([2, 3], []),
([], [2, 3]),
([], []),
],
)
def test_shift_operators(self, lhs_dtype, rhs_dtype, lhs_shape, rhs_shape, ie_device, precision, ir_version):
self._test(
*self.create_model(),
ie_device,
precision,
ir_version,
kwargs_to_prepare_input={
"lhs_dtype": lhs_dtype,
"rhs_dtype": rhs_dtype,
"lhs_shape": lhs_shape,
"rhs_shape": rhs_shape,
},
trace_model=True,
freeze_model=False,
)


class TestBitwiseShiftFunctions(PytorchLayerTest):
def _prepare_input(self, lhs_dtype, rhs_dtype, lhs_shape, rhs_shape):
choices = np.array([1, 2, 4, 8, 16, 32])
shifts = np.array([0, 1, 2, 3, 4, 5])

x = np.random.choice(choices, lhs_shape).astype(lhs_dtype)
y = np.random.choice(shifts, rhs_shape).astype(rhs_dtype)
return x, y

def create_model(self):
class aten_bitwise_shift(torch.nn.Module):
def forward(self, lhs, rhs):
return (
torch.bitwise_left_shift(lhs, rhs),
torch.bitwise_right_shift(lhs, rhs)
)

ref_net = None
return aten_bitwise_shift(), ref_net, ("aten::bitwise_left_shift", "aten::bitwise_right_shift")

@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.precommit_torch_export
@pytest.mark.precommit_fx_backend
@pytest.mark.parametrize("lhs_dtype", ["int32", "int64"])
@pytest.mark.parametrize("rhs_dtype", ["int32", "int64"])
@pytest.mark.parametrize(
("lhs_shape", "rhs_shape"),
[
([2, 3], [2, 3]),
([2, 3], []),
([], [2, 3]),
([], []),
],
)
def test_bitwise_shift_functions(self, lhs_dtype, rhs_dtype, lhs_shape, rhs_shape, ie_device, precision, ir_version):
self._test(
*self.create_model(),
ie_device,
precision,
ir_version,
kwargs_to_prepare_input={
"lhs_dtype": lhs_dtype,
"rhs_dtype": rhs_dtype,
"lhs_shape": lhs_shape,
"rhs_shape": rhs_shape,
},
trace_model=True,
freeze_model=False,
)
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