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test_tuple_construct.py
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# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import pytest
import numpy as np
from pytorch_layer_test_class import PytorchLayerTest
class TestTupleConstruct(PytorchLayerTest):
def _prepare_input(self):
return (np.random.uniform(0, 50, (1, 10)).astype(np.float32),)
def create_model(self, case):
import torch
class prim_tuple_construct_single_value(torch.nn.Module):
def forward(self, x):
return (x,)
class prim_tuple_construct(torch.nn.Module):
def forward(self, x):
return (x, x + x)
class prim_tuple_construct_with_none(torch.nn.Module):
def forward(self, x):
return (x, None, x + x, None)
class prim_tuple_construct_with_list(torch.nn.Module):
def forward(self, x):
return (x, [None, x + x], None)
class prim_tuple_construct_with_tensor_tail(torch.nn.Module):
def forward(self, x):
return ((x, x + x), x + x + x)
class prim_tuple_construct_with_list_and_tuple(torch.nn.Module):
def forward(self, x):
return (x, [None, x + x], None, (x + 1.0, x + 2.0, None))
cases = {
"single": prim_tuple_construct_single_value,
"multiple": prim_tuple_construct,
"none": prim_tuple_construct_with_none,
"list": prim_tuple_construct_with_list,
"tensor_tail": prim_tuple_construct_with_tensor_tail,
"list_and_tuple": prim_tuple_construct_with_list_and_tuple
}
ref_net = None
model = cases[case]
return model(), ref_net, "prim::TupleConstruct"
@pytest.mark.parametrize("case", ["single", "multiple", "none", "list", "tensor_tail", "list_and_tuple"])
@pytest.mark.nightly
def test_tuple_construct(self, case, ie_device, precision, ir_version):
self._test(*self.create_model(case), ie_device, precision, ir_version, use_convert_model=True)
class TestTupleConstructTupleUnpack(PytorchLayerTest):
def _prepare_input(self):
return (np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32),)
def create_model(self):
import torch
class prim_tuple_construct_tuple_unpack(torch.nn.Module):
def forward(self, x):
x1, x2, x3, x4, x5 = self.prepare_input(x)
return x1, x2, x3, x4, x5
def prepare_input(self, x):
return x, x + 2, None, x.reshape(-1), (x * 10).to(torch.int32)
ref_net = None
return prim_tuple_construct_tuple_unpack(), ref_net, ["prim::TupleConstruct", "prim::TupleUnpack"]
@pytest.mark.nightly
def test_tuple_construct_unpack(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device,
precision, ir_version, freeze_model=False, use_convert_model=True)
class TestTupleUnpackParameterSingle(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
return ((tensor_gen(), tensor_gen()), )
def create_model(self):
import torch
from typing import Tuple
class model(torch.nn.Module):
def forward(self, x: Tuple[torch.Tensor, torch.Tensor]):
x1, x2 = x
return x1, x2
return model(), None, ["prim::TupleUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestTupleUnpackParameterSingleMixed(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
# generate tensor with a different shape for easier mismatch detection in case of mixed input order
def tensor_gen_2():
return np.random.uniform(0, 50, (2, 3)).astype(np.float32)
return (tensor_gen_2(), (tensor_gen(), tensor_gen()), tensor_gen_2())
def create_model(self):
import torch
from typing import Tuple
class model(torch.nn.Module):
def forward(self, y1, x: Tuple[torch.Tensor, torch.Tensor], y2):
x1, x2 = x
return x1, x2, y1, y2
return model(), None, ["prim::TupleUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestTupleUnpackParameterNested(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
return (((tensor_gen(), tensor_gen()), (tensor_gen(), tensor_gen())), )
def create_model(self):
import torch
from typing import Tuple
class model(torch.nn.Module):
def forward(self, x: Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]):
x1, x2 = x
y1, y2 = x1
y3, y4 = x2
return y1, y2, y3, y4
return model(), None, ["prim::TupleUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestTupleUnpackParameterMultiple(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
return ((tensor_gen(), tensor_gen()), (tensor_gen(), tensor_gen()))
def create_model(self):
import torch
from typing import Tuple
class model(torch.nn.Module):
def forward(self, x: Tuple[torch.Tensor, torch.Tensor], y: Tuple[torch.Tensor, torch.Tensor]):
z1, z2 = x
z3, z4 = y
return z1, z2, z3, z4
return model(), None, ["prim::TupleUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestTupleIndex(PytorchLayerTest):
def _prepare_input(self):
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
def create_model(self):
import torch
from typing import Tuple
class model(torch.nn.Module):
def forward(self, x):
return self.some_func((x, x))
def some_func(self, x: Tuple[torch.Tensor, torch.Tensor]):
return x[1] * 2, x[0] * 3
return model(), None, "prim::TupleIndex"
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision,
ir_version, trace_model=False, freeze_model=False, use_convert_model=True)
class TestTcOutsideTuInsideIfBody(PytorchLayerTest):
def _prepare_input(self):
return (np.random.randn(1, 2, 10).astype(np.float32), np.random.randn(1, 2, 10).astype(np.float32))
def create_model(self):
import torch
from typing import Tuple
class model(torch.nn.Module):
def forward(self, x, y):
return self.some_func((x, y))
def some_func(self, x: Tuple[torch.Tensor, torch.Tensor]):
if x[0].numel() > 10:
n, m = x
return n * m
else:
n, m = x
return n - m
return model(), None, ["prim::TupleConstruct", "prim::TupleUnpack", "prim::If"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision,
ir_version, trace_model=False, freeze_model=False, use_convert_model=True)