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[Relay] [PyTorch] Add aten::tril and aten::triu #11890

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merged 3 commits into from
Jul 1, 2022
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[Relay] [PyTorch] Add aten::tril and aten::triu #11890

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8cd20dd
add trilu
Jul 1, 2022
9f81737
update triu and tril; fix empty
Jul 1, 2022
d710b50
fix lint
Jul 1, 2022
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27 changes: 27 additions & 0 deletions python/tvm/relay/frontend/pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -319,6 +319,31 @@ def square(self, inputs, input_types):
(dtype,) = input_types
return _op.power(inputs[0], _expr.const(2, dtype))

def tril(self, inputs, input_types):
data = inputs[0]
if len(inputs) == 2:
k_value = inputs[1]
else:
k_value = 0
input_shape = self.infer_shape(data)
k1, k2 = input_shape[-2:]
k1 = k_value + 1
diag_input = _op.zeros(input_shape, dtype=input_types[0])
return _op.matrix_set_diag(data, diag_input, k=(k1, k2))

def triu(self, inputs, input_types):
data = inputs[0]
if len(inputs) == 2:
k_value = inputs[1]
else:
k_value = 0
input_shape = self.infer_shape(data)
k1, k2 = input_shape[-2:]
k1 = (k1 * -1) - 1
k2 = k_value - 1
diag_input = _op.zeros(input_shape, dtype=input_types[0])
return _op.matrix_set_diag(data, diag_input, k=(k1, k2))

def arange(self, inputs, input_types):
def _get_value(val, dtype):
# dtype is a tvm dtype
Expand Down Expand Up @@ -3328,6 +3353,8 @@ def create_convert_map(self):
"aten::sqrt": self.make_unary("sqrt"),
"aten::rsqrt": self.make_unary("rsqrt"),
"aten::square": self.square,
"aten::tril": self.tril,
"aten::triu": self.triu,
"aten::ceil": self.make_unary("ceil"),
"aten::floor": self.make_unary("floor"),
"aten::round": self.make_unary("round"),
Expand Down
81 changes: 74 additions & 7 deletions tests/python/frontend/pytorch/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,12 +199,21 @@ def visit(op):
torch.cuda.empty_cache()


def verify_model_with_input(test_func, input_data, input_dict={}):
def verify_model_with_input(
test_func,
input_data,
*,
input_dict={},
custom_convert_map={},
rtol=1e-5,
atol=1e-5,
assert_shape_only=False,
):
baseline_outputs = test_func(*input_data)
trace = torch.jit.trace(test_func, [input.clone() for input in input_data])
input_names = ["input{}".format(idx) for idx, inp in enumerate(input_data)]
input_shapes = list(zip(input_names, [inp.shape for inp in input_data]))
mod, params = relay.frontend.from_pytorch(trace, input_shapes, {})
mod, params = relay.frontend.from_pytorch(trace, input_shapes, custom_convert_map)
with tvm.transform.PassContext(opt_level=3):
for target in ["llvm", "cuda"]:
if not tvm.runtime.enabled(target):
Expand All @@ -218,7 +227,8 @@ def verify_model_with_input(test_func, input_data, input_dict={}):

compiled_output = relay_model.get_output(0).numpy()
assert_shapes_match(baseline_outputs, compiled_output)
tvm.testing.assert_allclose(baseline_outputs, compiled_output, rtol=1e-5, atol=1e-5)
if assert_shape_only == False:
tvm.testing.assert_allclose(baseline_outputs, compiled_output, rtol=rtol, atol=atol)


# Single operator tests
Expand Down Expand Up @@ -1304,7 +1314,7 @@ def test_func(input_tensor, other_tensor):

input_data = [torch.rand([2, 1, 10, 1, 10]), torch.rand([2, 1, 10, 10])]

verify_model_with_input(test_func, input_data, {"input0": input_data[0]})
verify_model_with_input(test_func, input_data, input_dict={"input0": input_data[0]})


@tvm.testing.uses_gpu
Expand Down Expand Up @@ -3423,6 +3433,64 @@ def forward(self, *args):
verify_model(Neg1().float().eval(), input_data=input_data)


@tvm.testing.uses_gpu
def test_forward_tril():
torch.set_grad_enabled(False)

def test_func(input_data):
return torch.tril(input_data)

input_data = torch.rand([3, 3]).float()
verify_model(test_func, input_data=input_data)
input_data = torch.rand([1, 3, 10, 10]).float()
verify_model(test_func, input_data=input_data)

def test_func1(input_data):
return torch.tril(input_data, 1)

input_data = torch.rand([3, 3]).float()
verify_model(test_func1, input_data=input_data)
input_data = torch.rand([1, 3, 10, 10]).float()
verify_model(test_func1, input_data=input_data)

def test_func2(input_data):
return torch.tril(input_data, -1)

input_data = torch.rand([3, 3]).float()
verify_model(test_func2, input_data=input_data)
input_data = torch.rand([1, 3, 10, 10]).float()
verify_model(test_func2, input_data=input_data)


@tvm.testing.uses_gpu
def test_forward_triu():
torch.set_grad_enabled(False)

def test_func(input_data):
return torch.triu(input_data)

input_data = torch.rand([3, 3]).float()
verify_model(test_func, input_data=input_data)
input_data = torch.rand([1, 3, 10, 10]).float()
verify_model(test_func, input_data=input_data)

def test_func1(input_data):
return torch.triu(input_data, 1)

input_data = torch.rand([3, 3]).float()
verify_model(test_func1, input_data=input_data)
input_data = torch.rand([1, 3, 10, 10]).float()
verify_model(test_func1, input_data=input_data)

def test_func2(input_data):
return torch.triu(input_data, -1)

input_data = torch.rand([3, 3]).float()
verify_model(test_func2, input_data=input_data)
input_data = torch.rand([1, 3, 10, 10]).float()
verify_model(test_func2, input_data=input_data)


@tvm.testing.uses_gpu
def test_forward_where():
torch.set_grad_enabled(False)
Expand Down Expand Up @@ -3817,15 +3885,14 @@ def test_empty():
def test_func():
return torch.empty([1, 3, 10, 10])

verify_model_with_input(test_func, [])
verify_model_with_input(test_func, [], assert_shape_only=True)


@pytest.mark.skip(reason="See https://github.com/apache/tvm/issues/11967")
def test_empty_like():
def test_func(data):
return torch.empty_like(data)

verify_model_with_input(test_func, [torch.rand([1, 3, 10, 10]).float()])
verify_model_with_input(test_func, [torch.rand([1, 3, 10, 10]).float()], assert_shape_only=True)


def test_forward_pretrained_bert_base_uncased():
Expand Down