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[Relay][Bugfix] Fix the wrong implementation about operator Threshold in oneflow #15715

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merged 6 commits into from
Sep 12, 2023
Merged

[Relay][Bugfix] Fix the wrong implementation about operator Threshold in oneflow #15715

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fb976c4
[Relay][BugFix] fix the wrong implementation of Threshold in OneFlow
jikechao Sep 9, 2023
2a57a38
Update test_forward.py
jikechao Sep 9, 2023
0118bbf
Update oneflow.py
jikechao Sep 9, 2023
e1e1923
Update test_forward.py
jikechao Sep 11, 2023
4098b84
Update oneflow.py
jikechao Sep 11, 2023
127fc55
Update test_forward.py
jikechao Sep 11, 2023
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13 changes: 8 additions & 5 deletions python/tvm/relay/frontend/oneflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1025,15 +1025,17 @@ def _impl_v1(cls, inputs, attrs, params):
return out


class ThresholdedRelu(OneFlowOpConverter):
"""Operator converter for ThresholdedRelu."""
class Threshold(OneFlowOpConverter):
"""Operator converter for Threshold."""

@classmethod
def _impl_v1(cls, inputs, attrs, params):
alpha = float(attrs.get("alpha", 1.0))
alpha_tensor = _op.full_like(inputs[0], fill_value=_expr.const(alpha))
threshold = float(attrs.get("threshold_val", 1.0))
threshold_tensor = _op.full_like(inputs[0], fill_value=_expr.const(alpha))
value = float(attrs.get("value"))
value_tensor = _op.full_like(inputs[0], fill_value=_expr.const(value))
mask = _op.greater(inputs[0], alpha_tensor).astype("float32")
return inputs[0] * mask
return _op.where(mask, inputs[0], value_tensor)


class Elu(OneFlowOpConverter):
Expand Down Expand Up @@ -1422,6 +1424,7 @@ def get_convert_map():
"relu": Renamer("relu"),
"leaky_relu": Renamer("leaky_relu"),
"prelu": PReLU.get_converter(),
"threshold": Threshold.get_converter(),
"selu": Selu.get_converter(),
"silu": Silu.get_converter(),
"gelu": Gelu.get_converter(),
Expand Down
11 changes: 11 additions & 0 deletions tests/python/frontend/oneflow/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -702,6 +702,15 @@ def forward(self, x):
x = x.softmax(dim=-1)
return x

class Threshold(flow.nn.Module):
def __init__(self):
super().__init__()
self.active = flow.nn.Threshold()

def forward(self, x):
x = self.active(x)
return x

if os.path.exists(MODEL_HOME):
rmdir(MODEL_HOME)

Expand All @@ -718,6 +727,7 @@ def forward(self, x):
model11 = GELU().eval()
model12 = HardTanh().eval()
model13 = TensorSoftmax().eval()
model14 = Threshold().eval()

for device in ["llvm"]:
verify_activation(model1, device=device)
Expand All @@ -737,6 +747,7 @@ def forward(self, x):
device=device,
inputs=flow.tensor(np.random.rand(1, 12, 197, 197).astype(np.float32)),
)
verify_activation(model14, device=device)


@tvm.testing.uses_gpu
Expand Down