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[PT FE]: support mixed precision in floor divide (#16362)

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* [PT FE]: support mixed precision in floor divide

* Update floordiv.cpp
This commit is contained in:
Ekaterina Aidova 2023-03-20 14:00:26 +04:00 committed by GitHub
parent 4bf5a77ac9
commit f39684a7f8
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 35 additions and 2 deletions
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1
src/frontends/pytorch/src/op/floor_divide.cpp
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@ -18,6 +18,7 @@ OutputVector translate_floor_divide(NodeContext& context) {
num_inputs_check(context, 2, 2);
auto x = context.get_input(0);
auto y = context.get_input(1);
align_eltwise_input_types(context, x, y, true);
auto div = context.mark_node(std::make_shared<v1::Divide>(x, y, true));
return {context.mark_node(std::make_shared<v0::Floor>(div))};
};

2
src/frontends/pytorch/src/op/floordiv.cpp
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@ -21,4 +21,4 @@ OutputVector translate_floordiv(NodeContext& context) {
} // namespace op
} // namespace pytorch
} // namespace frontend
} // namespace ov
} // namespace ov

34
tests/layer_tests/pytorch_tests/test_floor_divide.py
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@ -23,7 +23,20 @@ class TestFloorDivide(PytorchLayerTest):
ref_net = None
# return aten_floor_divide(), ref_net, "aten::floor_divide"
return aten_floor_divide(), ref_net, "aten::floor_divide"
def create_model_int(self):
import torch
class aten_floor_divide(torch.nn.Module):
def __init__(self):
super(aten_floor_divide, self).__init__()
def forward(self, input_tensor, other_tensor):
return torch.floor_divide(input_tensor.to(torch.int32), other_tensor.to(torch.int64))
ref_net = None
return aten_floor_divide(), ref_net, "aten::floor_divide"
@pytest.mark.parametrize('input_tensor', ([
@ -42,4 +55,23 @@ class TestFloorDivide(PytorchLayerTest):
def test_floor_divide(self, input_tensor, other_tensor, ie_device, precision, ir_version):
self.input_tensor = input_tensor
self.other_tensor = other_tensor
self._test(*self.create_model(), ie_device, precision, ir_version, trace_model=True)
@pytest.mark.parametrize('input_tensor', ([
np.random.randint(low=0, high=10, size=5).astype(np.float32),
np.random.randint(low=1, high=10, size=(5, 5, 1)).astype(np.float32),
np.random.randint(low=1, high=10, size=(1, 1, 5, 5)).astype(np.float32),
]))
@pytest.mark.parametrize('other_tensor', ([
np.array([[2]]).astype(np.float32),
np.random.randint(low=1, high=10, size=5).astype(np.float32),
np.random.randint(low=1, high=10, size=(5, 1)).astype(np.float32),
np.random.randint(low=1, high=10, size=(1, 5)).astype(np.float32),
]))
@pytest.mark.nightly
@pytest.mark.precommit
def test_floor_divide_int(self, input_tensor, other_tensor, ie_device, precision, ir_version):
self.input_tensor = input_tensor
self.other_tensor = other_tensor
self.create_model = self.create_model_int
self._test(*self.create_model(), ie_device, precision, ir_version)