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[PT FE] Add support for aten::numpy_T and aten::feature_dropout #20136

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merged 6 commits into from
Oct 3, 2023
Merged

[PT FE] Add support for aten::numpy_T and aten::feature_dropout #20136

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f176fe4
Add support for aten::numpy_t and aten::feature_dropout
mvafin Sep 28, 2023
0a3d191
Merge branch 'master' into mvafin/pt_fe/transpose_dropout
mvafin Sep 28, 2023
0b3a5b8
Merge branch 'master' into mvafin/pt_fe/transpose_dropout
mvafin Sep 29, 2023
ed3b8ba
Update tests/layer_tests/pytorch_tests/test_transpose.py
mvafin Oct 2, 2023
29a5b60
Merge branch 'master' into mvafin/pt_fe/transpose_dropout
mvafin Oct 2, 2023
09ca47e
Merge branch 'master' into mvafin/pt_fe/transpose_dropout
mvafin Oct 2, 2023
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2 changes: 2 additions & 0 deletions src/frontends/pytorch/src/op_table.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -301,6 +301,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
{"aten::eye", op::translate_eye},
{"aten::fake_quantize_per_channel_affine", op::translate_fake_quantize_per_channel_affine},
{"aten::fake_quantize_per_tensor_affine", op::translate_fake_quantize_per_tensor_affine},
{"aten::feature_dropout", op::skip_node},
{"aten::fill_", op::inplace_op<op::translate_fill_>},
{"aten::flatten", op::quantizable_op<op::translate_flatten>},
{"aten::flip", op::translate_flip},
Expand Down Expand Up @@ -384,6 +385,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
{"aten::nonzero", op::translate_nonzero},
{"aten::norm", op::translate_norm},
{"aten::numel", op::translate_numel},
{"aten::numpy_T", op::translate_t},
{"aten::one_hot", op::translate_one_hot},
{"aten::ones", op::translate_ones},
{"aten::ones_like", op::translate_ones_like},
Expand Down
17 changes: 11 additions & 6 deletions tests/layer_tests/pytorch_tests/test_transpose.py
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Expand Up @@ -57,31 +57,36 @@ def _prepare_input(self, num_dims=2, input_dtype="float32"):
return (np.array(num_dims).astype(input_dtype),)
return (np.random.randn(*shape[:num_dims]).astype(input_dtype),)

def create_model(self, num_dims=2, inplace=False):
def create_model(self, mode):
class aten_transpose(torch.nn.Module):
def __init__(self, inplace):
super(aten_transpose, self).__init__()
if inplace:
if mode == "inplace":
self.forward = self.forward_inplace
elif mode == "numpy":
self.forward = self.forward_numpy_t

def forward(self, x):
return x.t(), x

def forward_inplace(self, x):
return x.t_(), x

def forward_numpy_t(self, x):
return x.T, x

ref_net = None

return aten_transpose(inplace), ref_net, "aten::t" if not inplace else "aten::t_"
return aten_transpose(mode), ref_net, "aten::t_" if mode == "inplace" else ("aten::numpy_T" if mode == "numpy" else "aten::t")

@pytest.mark.parametrize("num_dims", [0, 1, 2])
@pytest.mark.parametrize("input_dtype", ["float32", "int32"])
@pytest.mark.parametrize("inplace", [True, False])
@pytest.mark.parametrize("mode", [None, "inplace", "numpy"])
@pytest.mark.nightly
@pytest.mark.precommit
def test_t_small(self, num_dims, input_dtype, inplace, ie_device, precision, ir_version):
def test_t_small(self, num_dims, input_dtype, mode, ie_device, precision, ir_version):
self._test(
*self.create_model(num_dims, inplace),
*self.create_model(mode),
ie_device,
precision,
ir_version,
Expand Down