def fail_not_enough_args():
target = torch.ops.aten.convolution.default
kwargs = dict(
input=torch.randn(1, 3, 32, 32),
weight=torch.randn(3, 3, 3, 3),
bias=torch.randn(3),
stride=[1, 1],
padding=[0, 0],
dilation=[1, 1],
transposed=False,
output_padding=[0, 0],
# Missing groups=1,
)
build_ts_script_function(target._schema, kwargs)
def simple():
target = torch.ops.aten.addmm.default
kwargs = dict(
input=torch.randn(1, 3, 32, 32),
mat1=torch.randn(1, 3, 32, 32),
mat2=torch.randn(1, 3, 32, 32),
beta=1,
alpha=1,
)
script_fun = build_ts_script_function(target._schema, kwargs)
print(script_fun.graph)
def handle_ones_like():
target = torch.ops.aten.ones_like.default
kwargs = dict(
input=torch.randn(1, 3, 32, 32),
dtype=None,
layout=None,
device=None,
pin_memory=None,
memory_format=None,
)
script_fun = build_ts_script_function(target._schema, kwargs)
print(script_fun.graph)
def check_legal_name():
target = torch.ops.aten.native_batch_norm.default
kwargs = dict(input=torch.randn(1, 3, 32, 32),
weight=torch.randn(1, 3, 32, 32),
bias=torch.randn(1, 3, 32, 32),
running_mean=None,
running_var=None,
training=False,
momentum=1.0,
eps=1.0)
script_fun = build_ts_script_function(target._schema, kwargs)
print(script_fun.name)
def handle_nones():
target = torch.ops.aten.max_pool2d_with_indices.default
# print(target._schema)
kwargs = dict(
input=torch.randn((1, 3, 32, 32)),
kernel_size=[3, 3],
stride=None,
padding=[0, 0],
dilation=[1, 1],
ceil_mode=False,
)
script_fun = build_ts_script_function(target._schema, kwargs)
print(script_fun.graph)
def handle_optional_tensor_input():
target = torch.ops.aten.convolution.default
kwargs = dict(
input=torch.randn(1, 3, 32, 32),
weight=torch.randn(3, 3, 3, 3),
bias=torch.randn(3),
stride=[1, 1],
padding=[0, 0],
dilation=[1, 1],
transposed=False,
output_padding=[0, 0],
groups=1,
)
script_fun = build_ts_script_function(target._schema, kwargs)
print(script_fun.graph)
def simple_kwargs():
target = torch.ops.aten.convolution.default
script_fun1 = build_ts_script_function(
target._schema,
dict(
input=torch.randn(1, 3, 32, 32),
weight=torch.randn(3, 3, 3, 3),
bias=torch.randn(3),
stride=[1, 1],
padding=[0, 0],
dilation=[1, 1],
transposed=False,
output_padding=[0, 0],
groups=1,
),
)
print(script_fun1.graph)
def correctly_order_kwargs():
target = torch.ops.aten.native_batch_norm.out
input = torch.randn(2, 5, 2, 3)
running_mean = torch.randn(5)
running_var = torch.randn(5)
kwargs = dict(
input=torch.randn(2, 5, 2, 3),
weight=torch.randn(5),
bias=torch.randn(5),
running_mean=running_mean,
running_var=running_var,
training=False,
momentum=0.1,
eps=0.0001,
out=torch.empty_like(input),
save_mean=torch.empty_like(running_mean),
save_invstd=torch.empty_like(running_var),
)
script_fun = build_ts_script_function(target._schema, kwargs)
print(script_fun.graph)
