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masked_fill: fill value type must match tensor type #1915

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Merged
merged 4 commits into from
Jul 25, 2023
Merged

masked_fill: fill value type must match tensor type #1915

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d913390
masked_fill: fill value type must match tensor type
pcuenca Jul 15, 2023
b2e6196
Increase test coverage to int inputs, casted values
pcuenca Jul 21, 2023
cead240
Make sure results match PyTorch's
pcuenca Jul 21, 2023
eeb06fb
Use converter_input_type
pcuenca Jul 25, 2023
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4 changes: 4 additions & 0 deletions coremltools/converters/mil/frontend/torch/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@
)
from coremltools.converters.mil.mil.types import is_bool, nptype_from_builtin
from coremltools.converters.mil.mil.types.symbolic import any_symbolic, is_symbolic
from coremltools.converters.mil.mil.types.type_mapping import builtin_to_string
from coremltools.converters.mil.mil.var import ListVar, Var

from .._utils import build_einsum_mil, value_at
Expand Down Expand Up @@ -4241,6 +4242,9 @@ def masked_fill(context, node):
# cond must be bool type
mask = mb.cast(x=mask, dtype="bool")

if value.dtype != x.dtype:
value = mb.cast(x=value, dtype=builtin_to_string(x.dtype))

res = mb.select(cond=mask, a=value, b=x, name=node.name)
context.add(res)

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21 changes: 15 additions & 6 deletions coremltools/converters/mil/frontend/torch/test/test_torch_ops.py
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Expand Up @@ -7927,21 +7927,30 @@ def test_constant_pad_3d(self, compute_unit, backend):

class TestMaskedFill(TorchBaseTest):
@pytest.mark.parametrize(
"compute_unit, backend",
itertools.product(compute_units, backends),
"compute_unit, backend, dtype, value",
itertools.product(
compute_units,
backends,
[np.int32, np.float32],
[10.3, 7, 0],
),
)
def test_masked_fill(self, compute_unit, backend):
def test_masked_fill(self, compute_unit, backend, dtype, value):
SHAPE = (2, 3)
MASK = torch.bernoulli(torch.rand(SHAPE[-1])).to(torch.bool)
VALUE = 10.0

model = ModuleWrapper(torch.masked_fill, {"mask": MASK, "value": VALUE})
input_data = np.random.randint(-100, 100, SHAPE).astype(dtype)
input_data = torch.from_numpy(input_data)
model = ModuleWrapper(torch.masked_fill, {"mask": MASK, "value": value})
converter_input_type = [TensorType(shape=SHAPE, dtype=dtype)]

TorchBaseTest.run_compare_torch(
SHAPE,
input_data,
model,
backend=backend,
compute_unit=compute_unit,
input_as_shape=False,
converter_input_type=converter_input_type,
)


Expand Down