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new d1399f3 [Torch] Support hard_swish op (#7174)
d1399f3 is described below
commit d1399f378e37e9e4d9bfadc5cdae57fdc5bcaf7f
Author: Xuxue1 <[email protected]>
AuthorDate: Tue Dec 29 22:08:17 2020 +0800
[Torch] Support hard_swish op (#7174)
* imp_hardswish
* format
* fix
* hard_swish_inplace test case
---
python/tvm/relay/frontend/pytorch.py | 11 +++++++++++
tests/python/frontend/pytorch/test_forward.py | 14 +++++++++++---
2 files changed, 22 insertions(+), 3 deletions(-)
diff --git a/python/tvm/relay/frontend/pytorch.py
b/python/tvm/relay/frontend/pytorch.py
index 94ee928..8e69739 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -790,6 +790,15 @@ class PyTorchOpConverter:
data = inputs[0]
return _op.log(_op.tensor.sigmoid(data))
+ def hard_swish(self, inputs, input_types):
+ data = inputs[0]
+ dtype = input_types[0]
+
+ def _relu6(input_tensor):
+ return _op.tensor.clip(input_tensor, 0.0, 6.0)
+
+ return data * _relu6(data + _expr.const(3.0, dtype=dtype)) /
_expr.const(6.0, dtype=dtype)
+
def adaptive_avg_pool_2d(self, inputs, input_types):
data = inputs[0]
output_size = inputs[1]
@@ -2266,6 +2275,8 @@ class PyTorchOpConverter:
"aten::bincount": self.bincount,
"aten::scatter_add": self.scatter_add,
"aten::__not__": self.logical_not,
+ "aten::hardswish_": self.hard_swish,
+ "aten::hardswish": self.hard_swish,
}
def update_convert_map(self, custom_map):
diff --git a/tests/python/frontend/pytorch/test_forward.py
b/tests/python/frontend/pytorch/test_forward.py
index 04f08b9..f76c697 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -181,14 +181,14 @@ def verify_model(model_name, input_data=[],
custom_convert_map={}, rtol=1e-5, at
baseline_input = [inp.cuda() for inp in baseline_input]
with torch.no_grad():
- baseline_outputs = baseline_model(*baseline_input)
+ baseline_outputs = baseline_model(*[input.clone() for input in
baseline_input])
if isinstance(baseline_outputs, tuple):
baseline_outputs = tuple(out.cpu().numpy() for out in baseline_outputs)
else:
baseline_outputs = (baseline_outputs.cpu().numpy(),)
- trace = torch.jit.trace(baseline_model, baseline_input)
+ trace = torch.jit.trace(baseline_model, [input.clone() for input in
baseline_input])
if isinstance(baseline_model, torch.nn.Module):
trace = trace.float().eval()
@@ -200,7 +200,7 @@ def verify_model(model_name, input_data=[],
custom_convert_map={}, rtol=1e-5, at
input_names = ["input{}".format(idx) for idx, inp in
enumerate(baseline_input)]
input_shapes = list(zip(input_names, [inp.shape for inp in
baseline_input]))
mod, params = relay.frontend.from_pytorch(trace, input_shapes,
custom_convert_map)
- compiled_input = dict(zip(input_names, [inp.cpu().numpy() for inp in
baseline_input]))
+ compiled_input = dict(zip(input_names, [inp.clone().cpu().numpy() for inp
in baseline_input]))
with tvm.transform.PassContext(opt_level=3):
for target, ctx in tvm.testing.enabled_targets():
@@ -3437,6 +3437,13 @@ def test_bincount():
verify_trace_model(test_fn, [inp, weights], targets)
+def test_hard_swish():
+ examples = [torch.rand(8).float(), torch.rand(8, 10).float(),
torch.rand(1, 1, 10).float()]
+ for input in examples:
+ verify_model(torch.nn.Hardswish().eval(), input_data=input)
+ verify_model(torch.nn.Hardswish(inplace=True).eval(), input_data=input)
+
+
if __name__ == "__main__":
# some structural tests
test_forward_traced_function()
@@ -3603,3 +3610,4 @@ if __name__ == "__main__":
# Test convert torch script(jit) with specific inputs' types
test_convert_torch_script_with_input_types()
+ test_hard_swish()
