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

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* Add support for aten::numpy_t and aten::feature_dropout

* Update tests/layer_tests/pytorch_tests/test_transpose.py

Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>

---------

Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>
This commit is contained in:
Maxim Vafin 2023-10-03 11:52:29 +02:00 committed by Alexander Nesterov
parent 08bc3b2d7c
commit 7a6c5d0d41
2 changed files with 14 additions and 7 deletions
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2
src/frontends/pytorch/src/op_table.cpp
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@ -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},
@ -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},

19
tests/layer_tests/pytorch_tests/test_transpose.py
View File

@ -57,12 +57,14 @@ class TestTSmall(PytorchLayerTest):
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):
def __init__(self, mode):
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
@ -70,18 +72,21 @@ class TestTSmall(PytorchLayerTest):
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,