Exported Programs with Dynamic Shapes¶
The following script shows the exported program for many short cases and various way to retrieve an ONNX model equivalent to the original model. The tested scenarios are described at Tested Scenarios.
<<<
import inspect
import textwrap
import pandas
from experimental_experiment.torch_interpreter.eval import discover, run_exporter
from experimental_experiment.ext_test_case import unit_test_going
cases = discover()
print()
print(":ref:`Summary <led-summary-exported-program>`")
print()
sorted_cases = sorted(cases.items())
if unit_test_going():
sorted_cases = sorted_cases[:3]
for name, cls_model in sorted_cases:
print(f"* :ref:`{name} <led-model-case-export-{name}>`")
print()
obs = []
for name, cls_model in sorted(cases.items()):
print()
print(f".. _led-model-case-export-{name}:")
print()
print(name)
print("=" * len(name))
print()
print("forward")
print("+++++++")
print()
print("::")
print()
print(
textwrap.indent(textwrap.dedent(inspect.getsource(cls_model.forward)), " ")
)
print()
for exporter in (
"export-strict",
"export-strict-decall",
"export-nostrict",
"export-nostrict-decall",
"export-jit",
"export-jit-decall",
"export-tracing",
):
expname = exporter.replace("export-", "")
print()
print(expname)
print("+" * len(expname))
print()
res = run_exporter(exporter, cls_model, True, quiet=True)
case_ref = f":ref:`{name} <led-model-case-export-{name}>`"
expo = exporter.split("-", maxsplit=1)[-1]
if "exported" in res:
print("::")
print()
print(textwrap.indent(str(res["exported"].graph), " "))
print()
obs.append(dict(case=case_ref, error="", exporter=expo))
else:
print("**FAILED**")
print()
print("::")
print()
print(textwrap.indent(str(res["error"]), " "))
print()
obs.append(dict(case=case_ref, error="FAIL", exporter=expo))
print()
print(".. _led-summary-exported-program:")
print()
print("Summary")
print("+++++++")
print()
df = pandas.DataFrame(obs)
piv = df.pivot(index="case", columns="exporter", values="error")
print(piv.to_markdown(tablefmt="rst"))
print()
>>>
AtenAsStrided¶
forward¶
def forward(self, x):
y = torch.as_strided(x, (2, 2, 8, 4), (128, 8, 16, 1))
return y
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.ops.aten.as_strided.default](args = (%x, [2, 2, 8, 4], [128, 8, 16, 1]), kwargs = {})
return (as_strided,)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.ops.aten.as_strided.default](args = (%x, [2, 2, 8, 4], [128, 8, 16, 1]), kwargs = {})
return (as_strided,)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.ops.aten.as_strided.default](args = (%x, [2, 2, 8, 4], [128, 8, 16, 1]), kwargs = {})
return (as_strided,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.ops.aten.as_strided.default](args = (%x, [2, 2, 8, 4], [128, 8, 16, 1]), kwargs = {})
return (as_strided,)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.ops.aten.as_strided.default](args = (%x, [2, 2, 8, 4], [128, 8, 16, 1]), kwargs = {})
return (as_strided,)
jit-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.ops.aten.as_strided.default](args = (%x, [2, 2, 8, 4], [128, 8, 16, 1]), kwargs = {})
return (as_strided,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.as_strided](args = (%x, (2, 2, 8, 4), (128, 8, 16, 1)), kwargs = {})
return as_strided
AtenInterpolate¶
forward¶
def forward(self, x):
y = torch.nn.functional.interpolate(
x,
scale_factor=2.0,
mode="bilinear",
recompute_scale_factor=False,
)
return y
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%upsample_bilinear2d : [num_users=1] = call_function[target=torch.ops.aten.upsample_bilinear2d.vec](args = (%x, None, False, [2.0, 2.0]), kwargs = {})
return (upsample_bilinear2d,)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%upsample_bilinear2d : [num_users=1] = call_function[target=torch.ops.aten.upsample_bilinear2d.vec](args = (%x, None, False, [2.0, 2.0]), kwargs = {})
return (upsample_bilinear2d,)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%upsample_bilinear2d : [num_users=1] = call_function[target=torch.ops.aten.upsample_bilinear2d.vec](args = (%x, None, False, [2.0, 2.0]), kwargs = {})
return (upsample_bilinear2d,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%upsample_bilinear2d : [num_users=1] = call_function[target=torch.ops.aten.upsample_bilinear2d.vec](args = (%x, None, False, [2.0, 2.0]), kwargs = {})
return (upsample_bilinear2d,)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%upsample_bilinear2d : [num_users=1] = call_function[target=torch.ops.aten.upsample_bilinear2d.vec](args = (%x, None, False, [2.0, 2.0]), kwargs = {})
return (upsample_bilinear2d,)
jit-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%upsample_bilinear2d : [num_users=1] = call_function[target=torch.ops.aten.upsample_bilinear2d.vec](args = (%x, None, False, [2.0, 2.0]), kwargs = {})
return (upsample_bilinear2d,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%interpolate : [num_users=1] = call_function[target=torch.nn.functional.interpolate](args = (%x,), kwargs = {size: None, scale_factor: 2.0, mode: bilinear, align_corners: None, recompute_scale_factor: False, antialias: False})
return interpolate
AtenNonZero¶
forward¶
def forward(self, x):
y = torch.nonzero(x)
return y
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=2] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_1,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_1, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
return (nonzero,)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=2] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_2 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_2,), kwargs = {})
%ge_1 : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_2, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_1, Runtime assertion failed for expression u0 >= 0 on node 'ge_1'), kwargs = {})
return (nonzero,)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=2] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_1,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_1, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
return (nonzero,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=2] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_2 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_2,), kwargs = {})
%ge_1 : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_2, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_1, Runtime assertion failed for expression u0 >= 0 on node 'ge_1'), kwargs = {})
return (nonzero,)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=2] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_2 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_2,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_2, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
return (nonzero,)
jit-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=2] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_3 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_3,), kwargs = {})
%ge_1 : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_3, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_1, Runtime assertion failed for expression u0 >= 0 on node 'ge_1'), kwargs = {})
return (nonzero,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=1] = call_function[target=torch.nonzero](args = (%x,), kwargs = {})
return nonzero
AtenNonZeroTuple¶
forward¶
def forward(self, x):
y = torch.nonzero(x, as_tuple=True)
return y[0], y[1]
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero_numpy : [num_users=2] = call_function[target=torch.ops.aten.nonzero_numpy.default](args = (%x,), kwargs = {})
%getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%nonzero_numpy, 0), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%getitem_2, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_1,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_1, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%nonzero_numpy, 1), kwargs = {})
return (getitem_2, getitem_1)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=3] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_2 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_2,), kwargs = {})
%ge_2 : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_2, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_2, Runtime assertion failed for expression u0 >= 0 on node 'ge_2'), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%nonzero, 1, 0, 1), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%nonzero, 1, 1, 2), kwargs = {})
%squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dims](args = (%slice_1, [1]), kwargs = {})
%squeeze_1 : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dims](args = (%slice_2, [1]), kwargs = {})
return (squeeze, squeeze_1)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero_numpy : [num_users=2] = call_function[target=torch.ops.aten.nonzero_numpy.default](args = (%x,), kwargs = {})
%getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%nonzero_numpy, 0), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%getitem_2, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_1,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_1, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%nonzero_numpy, 1), kwargs = {})
return (getitem_2, getitem_1)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=3] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_2 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_2,), kwargs = {})
%ge_2 : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_2, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_2, Runtime assertion failed for expression u0 >= 0 on node 'ge_2'), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%nonzero, 1, 0, 1), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%nonzero, 1, 1, 2), kwargs = {})
%squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dims](args = (%slice_1, [1]), kwargs = {})
%squeeze_1 : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dims](args = (%slice_2, [1]), kwargs = {})
return (squeeze, squeeze_1)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero_numpy : [num_users=2] = call_function[target=torch.ops.aten.nonzero_numpy.default](args = (%x,), kwargs = {})
%getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%nonzero_numpy, 0), kwargs = {})
%sym_size_int_2 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%getitem_2, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_2,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_2, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%nonzero_numpy, 1), kwargs = {})
return (getitem_2, getitem_1)
jit-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=3] = call_function[target=torch.ops.aten.nonzero.default](args = (%x,), kwargs = {})
%sym_size_int_3 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%nonzero, 0), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%sym_size_int_3,), kwargs = {})
%ge_2 : [num_users=1] = call_function[target=operator.ge](args = (%sym_size_int_3, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_2, Runtime assertion failed for expression u0 >= 0 on node 'ge_2'), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%nonzero, 1, 0, 1), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%nonzero, 1, 1, 2), kwargs = {})
%squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dims](args = (%slice_1, [1]), kwargs = {})
%squeeze_1 : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dims](args = (%slice_2, [1]), kwargs = {})
return (squeeze, squeeze_1)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%nonzero : [num_users=2] = call_function[target=torch.nonzero](args = (%x,), kwargs = {as_tuple: True})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%nonzero, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%nonzero, 1), kwargs = {})
return (getitem, getitem_1)
AtenRollPos¶
forward¶
def forward(self, x):
return torch.roll(x, 1, -1)
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.ops.aten.roll.default](args = (%x, [1], [-1]), kwargs = {})
return (roll,)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%arange : [num_users=1] = call_function[target=torch.ops.aten.arange.start_step](args = (0, 4), kwargs = {layout: torch.strided, device: cpu, pin_memory: False})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arange, 3), kwargs = {})
%fmod : [num_users=1] = call_function[target=torch.ops.aten.fmod.Scalar](args = (%add, 4), kwargs = {})
%index_select : [num_users=1] = call_function[target=torch.ops.aten.index_select.default](args = (%x, 2, %fmod), kwargs = {})
return (index_select,)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.ops.aten.roll.default](args = (%x, [1], [-1]), kwargs = {})
return (roll,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%arange : [num_users=1] = call_function[target=torch.ops.aten.arange.start_step](args = (0, 4), kwargs = {layout: torch.strided, device: cpu, pin_memory: False})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arange, 3), kwargs = {})
%fmod : [num_users=1] = call_function[target=torch.ops.aten.fmod.Scalar](args = (%add, 4), kwargs = {})
%index_select : [num_users=1] = call_function[target=torch.ops.aten.index_select.default](args = (%x, 2, %fmod), kwargs = {})
return (index_select,)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.ops.aten.roll.default](args = (%x, [1], [-1]), kwargs = {})
return (roll,)
jit-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_5 : [num_users=4] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 2), kwargs = {})
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sym_size_int_5, 1), kwargs = {})
%mod : [num_users=1] = call_function[target=operator.mod](args = (%sub, %sym_size_int_5), kwargs = {})
%arange : [num_users=1] = call_function[target=torch.ops.aten.arange.start_step](args = (0, %sym_size_int_5), kwargs = {layout: torch.strided, device: cpu, pin_memory: False})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arange, %mod), kwargs = {})
%fmod : [num_users=1] = call_function[target=torch.ops.aten.fmod.Scalar](args = (%add, %sym_size_int_5), kwargs = {})
%index_select : [num_users=1] = call_function[target=torch.ops.aten.index_select.default](args = (%x, 2, %fmod), kwargs = {})
return (index_select,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.roll](args = (%x, 1, -1), kwargs = {})
return roll
AtenRollRelu¶
forward¶
def forward(self, x):
return torch.relu(torch.roll(x, -1, -1))
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.ops.aten.roll.default](args = (%x, [-1], [-1]), kwargs = {})
%relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%roll,), kwargs = {})
return (relu,)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%arange : [num_users=1] = call_function[target=torch.ops.aten.arange.start_step](args = (0, 4), kwargs = {layout: torch.strided, device: cpu, pin_memory: False})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arange, 1), kwargs = {})
%fmod : [num_users=1] = call_function[target=torch.ops.aten.fmod.Scalar](args = (%add, 4), kwargs = {})
%index_select : [num_users=1] = call_function[target=torch.ops.aten.index_select.default](args = (%x, 2, %fmod), kwargs = {})
%relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%index_select,), kwargs = {})
return (relu,)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.ops.aten.roll.default](args = (%x, [-1], [-1]), kwargs = {})
%relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%roll,), kwargs = {})
return (relu,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%arange : [num_users=1] = call_function[target=torch.ops.aten.arange.start_step](args = (0, 4), kwargs = {layout: torch.strided, device: cpu, pin_memory: False})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arange, 1), kwargs = {})
%fmod : [num_users=1] = call_function[target=torch.ops.aten.fmod.Scalar](args = (%add, 4), kwargs = {})
%index_select : [num_users=1] = call_function[target=torch.ops.aten.index_select.default](args = (%x, 2, %fmod), kwargs = {})
%relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%index_select,), kwargs = {})
return (relu,)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.ops.aten.roll.default](args = (%x, [-1], [-1]), kwargs = {})
%relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%roll,), kwargs = {})
return (relu,)
jit-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_5 : [num_users=4] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 2), kwargs = {})
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sym_size_int_5, -1), kwargs = {})
%mod : [num_users=1] = call_function[target=operator.mod](args = (%sub, %sym_size_int_5), kwargs = {})
%arange : [num_users=1] = call_function[target=torch.ops.aten.arange.start_step](args = (0, %sym_size_int_5), kwargs = {layout: torch.strided, device: cpu, pin_memory: False})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arange, %mod), kwargs = {})
%fmod : [num_users=1] = call_function[target=torch.ops.aten.fmod.Scalar](args = (%add, %sym_size_int_5), kwargs = {})
%index_select : [num_users=1] = call_function[target=torch.ops.aten.index_select.default](args = (%x, 2, %fmod), kwargs = {})
%relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%index_select,), kwargs = {})
return (relu,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%roll : [num_users=1] = call_function[target=torch.roll](args = (%x, -1, -1), kwargs = {})
%relu : [num_users=1] = call_function[target=torch.relu](args = (%roll,), kwargs = {})
return relu
BuildInIsInstance¶
forward¶
def forward(self, x, lx: list | torch.Tensor):
if isinstance(lx, list):
t = lx[0] * lx[1].sum(axis=1, keepdim=True)
return torch.sigmoid(self.linear(x)) - self.buff + t
return torch.sigmoid(self.linear(x)) - self.buff + lx
strict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
return (add,)
strict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_4, %mul_1), kwargs = {})
return (add_15,)
nostrict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
return (add,)
nostrict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_4, %mul_1), kwargs = {})
return (add_15,)
jit¶
graph():
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
return (add,)
jit-decall¶
graph():
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_4, %mul_1), kwargs = {})
return (add_15,)
tracing¶
- Traceback (most recent call last):
- File “~/vv/this312/lib/python3.12/site-packages/torch/fx/graph_module.py”, line 403, in __call__
- File “~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py”, line 1767, in _wrapped_call_impl
- File “~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py”, line 1778, in _call_impl
- File “<eval_with_key>.11095 from ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:776 in forward”, line 9, in forward
- add = sub + lx; sub = lx = None
~~~~^~~~
TypeError: unsupported operand type(s) for +: ‘Tensor’ and ‘list’
- Call using an FX-traced Module, line 9 of the traced Module’s generated forward function:
sub = sigmoid - buff; sigmoid = buff = None add = sub + lx; sub = lx = None
- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <— HERE
return add
FAILED
unsupported operand type(s) for +: 'Tensor' and 'list'
BuildInLen¶
forward¶
def forward(self, x, lx: list):
t = lx[0] * lx[1].sum(axis=1, keepdim=True)
if len(lx) > 2:
t = t + lx[2].sum(axis=1, keepdim=True)
return torch.sigmoid(self.linear(x)) - self.buff + t
strict¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
strict-decall¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
nostrict¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
nostrict-decall¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
jit¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
jit-decall¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
tracing¶
FAILED
len(.) expects an integer, len needs to be replaced. You should use _len.
ComplexPolar¶
forward¶
def forward(self, x, angle):
return torch.polar(x, angle)
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%angle : [num_users=1] = placeholder[target=angle]
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
return (polar,)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%angle : [num_users=1] = placeholder[target=angle]
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
return (polar,)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%angle : [num_users=1] = placeholder[target=angle]
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
return (polar,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%angle : [num_users=1] = placeholder[target=angle]
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
return (polar,)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%angle : [num_users=1] = placeholder[target=angle]
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
return (polar,)
jit-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%angle : [num_users=1] = placeholder[target=angle]
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
return (polar,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%angle : [num_users=1] = placeholder[target=angle]
%polar : [num_users=1] = call_function[target=torch.polar](args = (%x, %angle), kwargs = {})
return polar
ControlFlowCond¶
forward¶
def forward(self, x):
def true_fn(x):
return torch.sin(x)
def false_fn(x):
return torch.cos(x)
return torch.cond(x.sum() > 0, true_fn, false_fn, [x])
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
jit¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
jit-decall¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_method[target=sum](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%sum_1, 0), kwargs = {})
%_cb_cond_true_fn_0 : [num_users=1] = get_attr[target=_cb_cond_true_fn_0]
%_cb_cond_false_fn_0 : [num_users=1] = get_attr[target=_cb_cond_false_fn_0]
%condcc : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_true_fn_0, %_cb_cond_false_fn_0, [%x]), kwargs = {})
return condcc
ControlFlowCond2Inputs¶
forward¶
def forward(self, x, y):
def true_fn(x, y):
return torch.sin(x), torch.cos(x) + y
def false_fn(x, y):
return torch.cos(x), torch.sin(x) + y
return torch.cond(x.sum() > 0, true_fn, false_fn, [x, y])
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %y)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %y)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %y)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %y)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
jit¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
jit-decall¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sum_1 : [num_users=1] = call_method[target=sum](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%sum_1, 0), kwargs = {})
%_cb_cond_true_fn_0 : [num_users=1] = get_attr[target=_cb_cond_true_fn_0]
%_cb_cond_false_fn_0 : [num_users=1] = get_attr[target=_cb_cond_false_fn_0]
%condcc : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_true_fn_0, %_cb_cond_false_fn_0, [%x, %y]), kwargs = {})
return condcc
ControlFlowCond2Outputs¶
forward¶
def forward(self, x):
def true_fn(x):
return torch.sin(x), torch.cos(x)
def false_fn(x):
return torch.cos(x), torch.sin(x)
return torch.cond(x.sum() > 0, true_fn, false_fn, [x])
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=2] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 1), kwargs = {})
return (getitem, getitem_1)
jit¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
jit-decall¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_method[target=sum](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%sum_1, 0), kwargs = {})
%_cb_cond_true_fn_0 : [num_users=1] = get_attr[target=_cb_cond_true_fn_0]
%_cb_cond_false_fn_0 : [num_users=1] = get_attr[target=_cb_cond_false_fn_0]
%condcc : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_true_fn_0, %_cb_cond_false_fn_0, [%x]), kwargs = {})
return condcc
ControlFlowCondConstant¶
forward¶
def forward(self, x):
def true_fn(x):
return torch.sin(x) - torch.ones(x.shape, dtype=x.dtype)
def false_fn(x):
return torch.cos(x) + torch.ones((1, 1024), dtype=x.dtype)
return torch.cond(x.sum() > 0, true_fn, false_fn, [x])
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
jit¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
jit-decall¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_method[target=sum](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%sum_1, 0), kwargs = {})
%_cb_cond_true_fn_0 : [num_users=1] = get_attr[target=_cb_cond_true_fn_0]
%_cb_cond_false_fn_0 : [num_users=1] = get_attr[target=_cb_cond_false_fn_0]
%condcc : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_true_fn_0, %_cb_cond_false_fn_0, [%x]), kwargs = {})
return condcc
ControlFlowCondNestedModule¶
forward¶
def forward(self, x):
def true_fn(x):
return self.submodule(x)
def false_fn(x):
return x - self.weight
y = torch.cond(x.sum() > 0, true_fn, false_fn, [x])
return y
strict¶
graph():
%submodule_weight : [num_users=1] = get_attr[target=submodule.weight]
%weight : [num_users=1] = get_attr[target=weight]
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %submodule_weight, %weight)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
strict-decall¶
graph():
%weight : [num_users=1] = get_attr[target=weight]
%submodule_weight : [num_users=1] = get_attr[target=submodule.weight]
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %submodule_weight, %weight)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict¶
graph():
%weight : [num_users=1] = get_attr[target=weight]
%submodule_weight : [num_users=1] = get_attr[target=submodule.weight]
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %submodule_weight, %weight)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict-decall¶
graph():
%weight : [num_users=1] = get_attr[target=weight]
%submodule_weight : [num_users=1] = get_attr[target=submodule.weight]
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x, %submodule_weight, %weight)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
jit¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
jit-decall¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_method[target=sum](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%sum_1, 0), kwargs = {})
%_cb_cond_true_fn_0 : [num_users=1] = get_attr[target=_cb_cond_true_fn_0]
%_cb_cond_false_fn_0 : [num_users=1] = get_attr[target=_cb_cond_false_fn_0]
%condcc : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_true_fn_0, %_cb_cond_false_fn_0, [%x]), kwargs = {})
return condcc
ControlFlowCondNonZero¶
forward¶
def forward(self, input_ids, image_features, vocab_size):
def then_branch(input_ids, image_features, vocab_size):
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
condition = (input_ids < 0) & (input_ids > -int(1e9))
positions = torch.nonzero(condition, as_tuple=True)
input_ids = input_ids.clamp_min(0).clamp_max(vocab_size)
return (input_ids, positions[0], positions[1])
def else_branch(input_ids, image_features, vocab_size):
r = torch.where(torch.zeros((1, 1), dtype=torch.bool))
return (input_ids, r[0], r[1])
a, b, c = torch.cond(
image_features.numel() > 0,
then_branch,
else_branch,
[input_ids, image_features, vocab_size],
)
return a, b, c
strict¶
FAILED
Cond doesn't work unless it is captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 339, in forward
a, b, c = torch.cond(
File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 135, in cond
return cond_op(pred, true_fn, false_fn, operands)
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
strict-decall¶
FAILED
Cond doesn't work unless it is captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 339, in forward
a, b, c = torch.cond(
File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 135, in cond
return cond_op(pred, true_fn, false_fn, operands)
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
nostrict¶
FAILED
Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s35, 12), dtype=torch.int64), FakeTensor(..., size=(s58, s43)), 1025].
nostrict-decall¶
FAILED
Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s35, 12), dtype=torch.int64), FakeTensor(..., size=(s58, s43)), 1025].
jit¶
FAILED
Type 'Tuple[Tensor, Tensor, int]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
jit-decall¶
FAILED
Type 'Tuple[Tensor, Tensor, int]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
tracing¶
graph():
%input_ids : [num_users=1] = placeholder[target=input_ids]
%image_features : [num_users=2] = placeholder[target=image_features]
%vocab_size : [num_users=1] = placeholder[target=vocab_size]
%numel : [num_users=1] = call_method[target=numel](args = (%image_features,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%numel, 0), kwargs = {})
%_cb_cond_then_branch_0 : [num_users=1] = get_attr[target=_cb_cond_then_branch_0]
%_cb_cond_else_branch_0 : [num_users=1] = get_attr[target=_cb_cond_else_branch_0]
%condcc : [num_users=3] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_then_branch_0, %_cb_cond_else_branch_0, [%input_ids, %image_features, %vocab_size]), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%condcc, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%condcc, 1), kwargs = {})
%getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%condcc, 2), kwargs = {})
return (getitem, getitem_1, getitem_2)
ControlFlowNestCond¶
forward¶
def forward(self, x):
def true_fn2(x):
def true_fn1(x):
return torch.sin(x)
def false_fn1(x):
return torch.cos(x)
return torch.cond(x.sum() < 0, true_fn1, false_fn1, [x])
def false_fn2(x):
return -x
return torch.cond(x.sum() > 0, true_fn2, false_fn2, [x])
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%x, []), kwargs = {})
%gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 0), kwargs = {})
%true_graph_0 : [num_users=1] = get_attr[target=true_graph_0]
%false_graph_0 : [num_users=1] = get_attr[target=false_graph_0]
%cond : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %true_graph_0, %false_graph_0, (%x,)), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%cond, 0), kwargs = {})
return (getitem,)
jit¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
jit-decall¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sum_1 : [num_users=1] = call_method[target=sum](args = (%x,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%sum_1, 0), kwargs = {})
%_cb_cond_true_fn2_0 : [num_users=1] = get_attr[target=_cb_cond_true_fn2_0]
%_cb_cond_false_fn2_0 : [num_users=1] = get_attr[target=_cb_cond_false_fn2_0]
%condcc : [num_users=1] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_true_fn2_0, %_cb_cond_false_fn2_0, [%x]), kwargs = {})
return condcc
ControlFlowScan¶
forward¶
def forward(self, x):
init = torch.zeros_like(x[0])
carry, out = torch.ops.higher_order.scan(
ControlFlowScan.add, [init], [x], additional_inputs=[]
)
return carry
strict¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 377, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
strict-decall¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 377, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%select : [num_users=1] = call_function[target=torch.ops.aten.select.int](args = (%x, 0, 0), kwargs = {})
%zeros_like : [num_users=1] = call_function[target=torch.ops.aten.zeros_like.default](args = (%select,), kwargs = {pin_memory: False})
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=2] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%zeros_like], [%x], ()), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 0), kwargs = {})
%getitem_1 : [num_users=0] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem,)
nostrict-decall¶
FAILED
scan might be aliasing the input or the output!
While executing %scan : [num_users=2] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%zeros_like], [%x], ()), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x):
x: "f32[s35, 3][3, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:376 in forward, code: init = torch.zeros_like(x[0])
select: "f32[3][1]" = torch.ops.aten.select.int(x, 0, 0)
zeros_like: "f32[3][1]" = torch.ops.aten.zeros_like.default(select, pin_memory = False); select = None
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:377 in forward, code: carry, out = torch.ops.higher_order.scan(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [zeros_like], [x], ()); scan_combine_graph_0 = zeros_like = x = None
getitem: "f32[3][1]" = scan[0]
getitem_1: "f32[s35, 3][3, 1]" = scan[1]; scan = getitem_1 = None
return pytree.tree_unflatten((getitem,), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, carry_1: "f32[3][1]", y_1: "f32[3][1]"):
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:377 in forward, code: carry, out = torch.ops.higher_order.scan(
add: "f32[3][1]" = torch.ops.aten.add.Tensor(carry_1, y_1); carry_1 = y_1 = None
return [add, add]
Original traceback:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 377, in forward
carry, out = torch.ops.higher_order.scan(
jit¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
jit-decall¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
ControlFlowScan2Carried¶
forward¶
def forward(self, x):
init1 = torch.zeros_like(x[0])
init2 = torch.ones_like(x[0])
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
ControlFlowScan2Carried.add,
[init1, init2],
[x, x * 2],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[],
)
return carry1, carry2, out1, out2
strict¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 397, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
strict-decall¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 397, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
nostrict¶
graph():
%x : [num_users=4] = placeholder[target=x]
%select : [num_users=1] = call_function[target=torch.ops.aten.select.int](args = (%x, 0, 0), kwargs = {})
%zeros_like : [num_users=1] = call_function[target=torch.ops.aten.zeros_like.default](args = (%select,), kwargs = {pin_memory: False})
%select_1 : [num_users=1] = call_function[target=torch.ops.aten.select.int](args = (%x, 0, 0), kwargs = {})
%ones_like : [num_users=1] = call_function[target=torch.ops.aten.ones_like.default](args = (%select_1,), kwargs = {pin_memory: False})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%x, 2), kwargs = {})
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=4] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%zeros_like, %ones_like], [%x, %mul], ()), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
%getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 2), kwargs = {})
%getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 3), kwargs = {})
return (getitem, getitem_1, getitem_2, getitem_3)
nostrict-decall¶
FAILED
scan might be aliasing the input or the output!
While executing %scan : [num_users=4] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%zeros_like, %ones_like], [%x, %mul], ()), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x):
x: "f32[s35, 4][4, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:395 in forward, code: init1 = torch.zeros_like(x[0])
select: "f32[4][1]" = torch.ops.aten.select.int(x, 0, 0)
zeros_like: "f32[4][1]" = torch.ops.aten.zeros_like.default(select, pin_memory = False); select = None
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:396 in forward, code: init2 = torch.ones_like(x[0])
select_1: "f32[4][1]" = torch.ops.aten.select.int(x, 0, 0)
ones_like: "f32[4][1]" = torch.ops.aten.ones_like.default(select_1, pin_memory = False); select_1 = None
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:400 in forward, code: [x, x * 2],
mul: "f32[s35, 4][4, 1]" = torch.ops.aten.mul.Tensor(x, 2)
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:397 in forward, code: carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [zeros_like, ones_like], [x, mul], ()); scan_combine_graph_0 = zeros_like = ones_like = x = mul = None
getitem: "f32[4][1]" = scan[0]
getitem_1: "f32[4][1]" = scan[1]
getitem_2: "f32[s35, 4][4, 1]" = scan[2]
getitem_3: "f32[s35, 4][4, 1]" = scan[3]; scan = None
return pytree.tree_unflatten((getitem, getitem_1, getitem_2, getitem_3), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, carry1_1: "f32[4][1]", carry2_1: "f32[4][1]", y1_1: "f32[4][1]", y2_1: "f32[4][1]"):
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:397 in forward, code: carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
add: "f32[4][1]" = torch.ops.aten.add.Tensor(carry1_1, y1_1); carry1_1 = y1_1 = None
mul: "f32[4][1]" = torch.ops.aten.mul.Tensor(carry2_1, y2_1); carry2_1 = y2_1 = None
return [add, mul, add, mul]
Original traceback:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 397, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
jit¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
jit-decall¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
ControlFlowScanCDist¶
forward¶
def forward(self, x):
carry, out = torch.ops.higher_order.scan(
ControlFlowScanCDist.dist,
[x],
[x],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[],
)
return out
strict¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 422, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
strict-decall¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 422, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=2] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%x], [%x], ()), kwargs = {})
%getitem : [num_users=0] = call_function[target=operator.getitem](args = (%scan, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem_1,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%x], [%x], ()), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem_1,)
jit¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
jit-decall¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
ControlFlowScanCDist2¶
forward¶
def forward(self, x):
z = torch.tensor([0], dtype=torch.float32)
y = x.clone()
out = torch.ops.higher_order.scan(
ControlFlowScanCDist2.dist,
[z],
[x],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[y],
)
return out[1]
strict¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 449, in forward
out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
strict-decall¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 449, in forward
out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
nostrict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=2] = placeholder[target=x]
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%detach_ : [num_users=1] = call_function[target=torch.ops.aten.detach_.default](args = (%lift_fresh_copy,), kwargs = {})
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x,), kwargs = {})
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=2] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%detach_], [%x], (%clone,)), kwargs = {})
%getitem : [num_users=0] = call_function[target=operator.getitem](args = (%scan, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem_1,)
nostrict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=2] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x,), kwargs = {})
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%clone], [%x], (%clone_1,)), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem_1,)
jit¶
- ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:447: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
z = torch.tensor([0], dtype=torch.float32)
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
jit-decall¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
tracing¶
FAILED
(CustomProxy(clone),) can only be of (<class 'torch.Tensor'>, <class 'int'>, <class 'torch.SymInt'>) but got (<class 'experimental_experiment.torch_interpreter.tracing.CustomProxy'>,)
ControlFlowScanCDistXY¶
forward¶
def forward(self, x, y):
carry, out = torch.ops.higher_order.scan(
ControlFlowScanCDistXY.dist,
[y],
[x],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[],
)
return out
strict¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 475, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
strict-decall¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 475, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=2] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%y], [%x], ()), kwargs = {})
%getitem : [num_users=0] = call_function[target=operator.getitem](args = (%scan, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem_1,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%y], [%x], ()), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem_1,)
jit¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
jit-decall¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
CreateFromShape¶
forward¶
def forward(self, x):
y = torch.ones((x.shape[0], x.shape[1] + 1))
return y
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%ones : [num_users=1] = call_function[target=torch.ops.aten.ones.default](args = ([%sym_size_int_2, %add],), kwargs = {device: cpu, pin_memory: False})
return (ones,)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([%sym_size_int_2, %add], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
return (full,)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%ones : [num_users=1] = call_function[target=torch.ops.aten.ones.default](args = ([%sym_size_int_2, %add],), kwargs = {device: cpu, pin_memory: False})
return (ones,)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([%sym_size_int_2, %add], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
return (full,)
jit¶
graph():
%lifted_tensor_5 : [num_users=1] = get_attr[target=lifted_tensor_5]
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%scalar_tensor : [num_users=1] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (%sym_size_int_3,), kwargs = {dtype: torch.int64})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%scalar_tensor, %lifted_tensor_5), kwargs = {})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%add,), kwargs = {dtype: torch.int32})
%_local_scalar_dense : [num_users=3] = call_function[target=torch.ops.aten._local_scalar_dense.default](args = (%_to_copy,), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%_local_scalar_dense,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
%ones : [num_users=1] = call_function[target=torch.ops.aten.ones.default](args = ([%sym_size_int_2, %_local_scalar_dense],), kwargs = {device: cpu, pin_memory: False})
return (ones,)
jit-decall¶
graph():
%lifted_tensor_5 : [num_users=1] = get_attr[target=lifted_tensor_5]
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%scalar_tensor : [num_users=1] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (%sym_size_int_3,), kwargs = {dtype: torch.int64})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%scalar_tensor, %lifted_tensor_5), kwargs = {})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%add,), kwargs = {dtype: torch.int32})
%_local_scalar_dense : [num_users=3] = call_function[target=torch.ops.aten._local_scalar_dense.default](args = (%_to_copy,), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%_local_scalar_dense,), kwargs = {})
%ge_3 : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_3, Runtime assertion failed for expression u0 >= 0 on node 'ge_3'), kwargs = {})
%full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([%sym_size_int_2, %_local_scalar_dense], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
return (full,)
tracing¶
FAILED
ones(): argument 'size' (position 1) must be tuple of ints, but found element of type CustomProxy at pos 0
CreateFromShapeThroughFunction¶
forward¶
def forward(self, x):
dy1 = CreateFromShapeThroughFunction.add_one(x.shape[1])
y = torch.ones((x.shape[0], dy1))
return y
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%ones : [num_users=1] = call_function[target=torch.ops.aten.ones.default](args = ([%sym_size_int_2, %add],), kwargs = {device: cpu, pin_memory: False})
return (ones,)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([%sym_size_int_2, %add], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
return (full,)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%ones : [num_users=1] = call_function[target=torch.ops.aten.ones.default](args = ([%sym_size_int_2, %add],), kwargs = {device: cpu, pin_memory: False})
return (ones,)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sym_size_int_3, 1), kwargs = {})
%full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([%sym_size_int_2, %add], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
return (full,)
jit¶
graph():
%lifted_tensor_6 : [num_users=1] = get_attr[target=lifted_tensor_6]
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%scalar_tensor : [num_users=1] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (%sym_size_int_3,), kwargs = {dtype: torch.int64})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%scalar_tensor, %lifted_tensor_6), kwargs = {})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%add,), kwargs = {dtype: torch.int32})
%_local_scalar_dense : [num_users=3] = call_function[target=torch.ops.aten._local_scalar_dense.default](args = (%_to_copy,), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%_local_scalar_dense,), kwargs = {})
%ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge, Runtime assertion failed for expression u0 >= 0 on node 'ge'), kwargs = {})
%ones : [num_users=1] = call_function[target=torch.ops.aten.ones.default](args = ([%sym_size_int_2, %_local_scalar_dense],), kwargs = {device: cpu, pin_memory: False})
return (ones,)
jit-decall¶
graph():
%lifted_tensor_6 : [num_users=1] = get_attr[target=lifted_tensor_6]
%x : [num_users=2] = placeholder[target=x]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%scalar_tensor : [num_users=1] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (%sym_size_int_3,), kwargs = {dtype: torch.int64})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%scalar_tensor, %lifted_tensor_6), kwargs = {})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%add,), kwargs = {dtype: torch.int32})
%_local_scalar_dense : [num_users=3] = call_function[target=torch.ops.aten._local_scalar_dense.default](args = (%_to_copy,), kwargs = {})
%sym_constrain_range_for_size_default : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range_for_size.default](args = (%_local_scalar_dense,), kwargs = {})
%ge_3 : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%ge_3, Runtime assertion failed for expression u0 >= 0 on node 'ge_3'), kwargs = {})
%full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([%sym_size_int_2, %_local_scalar_dense], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
return (full,)
tracing¶
FAILED
ones(): argument 'size' (position 1) must be tuple of ints, but found element of type CustomProxy at pos 0
CropLastDimensionWithTensorContent¶
forward¶
def forward(self, x, shape):
return x[..., : shape[0]]
strict¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['x', 'shape'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
strict-decall¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['x', 'shape'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['x', 'shape'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict-decall¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['x', 'shape'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
jit¶
- def forward(self, arg0_1: “f32[s35, s16, s90]”, arg1_1: “i64[1]”):
# No stacktrace found for following nodes select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None _to_copy: “i32[]” = torch.ops.aten._to_copy.default(select, dtype = torch.int32); select = None _local_scalar_dense: “Sym(u0)” = torch.ops.aten._local_scalar_dense.default(_to_copy); _to_copy = None slice_1 = torch.ops.aten.slice.Tensor(arg0_1, 2, 0, _local_scalar_dense); arg0_1 = _local_scalar_dense = slice_1 = None
- def forward(self, arg0_1: “f32[s35, s16, s90]”, arg1_1: “i64[1]”):
# No stacktrace found for following nodes select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None _to_copy: “i32[]” = torch.ops.aten._to_copy.default(select, dtype = torch.int32); select = None _local_scalar_dense: “Sym(u0)” = torch.ops.aten._local_scalar_dense.default(_to_copy); _to_copy = None slice_1 = torch.ops.aten.slice.Tensor(arg0_1, 2, 0, _local_scalar_dense); arg0_1 = _local_scalar_dense = slice_1 = None
FAILED
Could not guard on data-dependent expression u0 < 0 (unhinted: u0 < 0). (Size-like symbols: none)
Caused by: (_decomp/decompositions.py:734 in slice_forward)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
The following call raised this error:
File "<string>", line 1, in <lambda>
While executing %slice_tensor : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %_local_scalar_dense_default, 1), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x, shape):
# No stacktrace found for following nodes
select_int = torch.ops.aten.select.int(shape, 0, 0); shape = None
_to_copy_default = torch.ops.aten._to_copy.default(select_int, dtype = torch.int32); select_int = None
_local_scalar_dense_default = torch.ops.aten._local_scalar_dense.default(_to_copy_default); _to_copy_default = None
slice_tensor = torch.ops.aten.slice.Tensor(x, 2, 0, _local_scalar_dense_default, 1); x = _local_scalar_dense_default = None
return slice_tensor
Original traceback:
None
jit-decall¶
- def forward(self, arg0_1: “f32[s35, s16, s90]”, arg1_1: “i64[1]”):
# No stacktrace found for following nodes select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None _to_copy: “i32[]” = torch.ops.aten._to_copy.default(select, dtype = torch.int32); select = None _local_scalar_dense: “Sym(u0)” = torch.ops.aten._local_scalar_dense.default(_to_copy); _to_copy = None slice_1 = torch.ops.aten.slice.Tensor(arg0_1, 2, 0, _local_scalar_dense); arg0_1 = _local_scalar_dense = slice_1 = None
- def forward(self, arg0_1: “f32[s35, s16, s90]”, arg1_1: “i64[1]”):
# No stacktrace found for following nodes select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None _to_copy: “i32[]” = torch.ops.aten._to_copy.default(select, dtype = torch.int32); select = None _local_scalar_dense: “Sym(u0)” = torch.ops.aten._local_scalar_dense.default(_to_copy); _to_copy = None slice_1 = torch.ops.aten.slice.Tensor(arg0_1, 2, 0, _local_scalar_dense); arg0_1 = _local_scalar_dense = slice_1 = None
FAILED
Could not guard on data-dependent expression u0 < 0 (unhinted: u0 < 0). (Size-like symbols: none)
Caused by: (_decomp/decompositions.py:734 in slice_forward)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
The following call raised this error:
File "<string>", line 1, in <lambda>
While executing %slice_tensor : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %_local_scalar_dense_default, 1), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x, shape):
# No stacktrace found for following nodes
select_int = torch.ops.aten.select.int(shape, 0, 0); shape = None
_to_copy_default = torch.ops.aten._to_copy.default(select_int, dtype = torch.int32); select_int = None
_local_scalar_dense_default = torch.ops.aten._local_scalar_dense.default(_to_copy_default); _to_copy_default = None
slice_tensor = torch.ops.aten.slice.Tensor(x, 2, 0, _local_scalar_dense_default, 1); x = _local_scalar_dense_default = None
return slice_tensor
Original traceback:
None
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%shape : [num_users=1] = placeholder[target=shape]
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%shape, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%x, (Ellipsis, slice(None, getitem, None))), kwargs = {})
return getitem_1
CropLastDimensionWithTensorShape¶
forward¶
def forward(self, x, y):
return x[..., : y.shape[0]]
strict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 0), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %sym_size_int_2), kwargs = {})
return (slice_1,)
strict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_4 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 0), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %sym_size_int_4), kwargs = {})
return (slice_1,)
nostrict¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 0), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %sym_size_int_2), kwargs = {})
return (slice_1,)
nostrict-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_4 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 0), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %sym_size_int_4), kwargs = {})
return (slice_1,)
jit¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_4 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 0), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %sym_size_int_4), kwargs = {})
return (slice_1,)
jit-decall¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_7 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 0), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %sym_size_int_7), kwargs = {})
return (slice_1,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%getattr_1 : [num_users=1] = call_function[target=builtins.getattr](args = (%y, shape), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%getattr_1, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%x, (Ellipsis, slice(None, getitem, None))), kwargs = {})
return getitem_1
InplaceAdd¶
forward¶
def forward(self, x):
x += self.bias
return x
strict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %bias), kwargs = {})
return (add_,)
strict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %bias), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (copy__default,)
nostrict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %bias), kwargs = {})
return (add_,)
nostrict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %bias), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (copy__default,)
jit¶
graph():
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %lifted_tensor_3), kwargs = {})
return (add_,)
jit-decall¶
graph():
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %lifted_tensor_3), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (copy__default,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%bias : [num_users=1] = get_attr[target=bias]
%add : [num_users=1] = call_function[target=operator.add](args = (%x, %bias), kwargs = {})
return add
InplaceAdd2¶
forward¶
def forward(self, x):
x.add_(self.bias)
return x
strict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %bias), kwargs = {})
return (add_,)
strict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %bias), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (copy__default,)
nostrict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %bias), kwargs = {})
return (add_,)
nostrict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %bias), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (copy__default,)
jit¶
graph():
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %lifted_tensor_3), kwargs = {})
return (add_,)
jit-decall¶
graph():
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %lifted_tensor_3), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (copy__default,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%bias : [num_users=1] = get_attr[target=bias]
%add_ : [num_users=1] = call_method[target=add_](args = (%x, %bias), kwargs = {})
return add_
InplaceAdd_Mul¶
forward¶
def forward(self, x):
x.add_(self.bias)
return x * 2
strict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %bias), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_, 2), kwargs = {})
return (mul,)
strict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %bias), kwargs = {})
%mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, 2), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (mul_4,)
nostrict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x, %bias), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_, 2), kwargs = {})
return (mul,)
nostrict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=2] = placeholder[target=x]
%add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %bias), kwargs = {})
%mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, 2), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %add_3), kwargs = {})
return (mul_4,)
jit¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%lifted_tensor_4 : [num_users=1] = get_attr[target=lifted_tensor_4]
%x_1 : [num_users=1] = placeholder[target=x_1]
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%x_1, %lifted_tensor_4), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_, %lifted_tensor_2), kwargs = {})
return (mul,)
jit-decall¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%lifted_tensor_4 : [num_users=1] = get_attr[target=lifted_tensor_4]
%x_1 : [num_users=2] = placeholder[target=x_1]
%add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%x_1, %lifted_tensor_4), kwargs = {})
%mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, %lifted_tensor_2), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x_1, %add_3), kwargs = {})
return (mul_4,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%bias : [num_users=1] = get_attr[target=bias]
%add_ : [num_users=1] = call_method[target=add_](args = (%x, %bias), kwargs = {})
%mul : [num_users=1] = call_function[target=operator.mul](args = (%add_, 2), kwargs = {})
return mul
InplaceCloneAdd¶
forward¶
def forward(self, x):
x = x.clone()
x.add_(self.bias)
return x
strict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x,), kwargs = {})
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%clone, %bias), kwargs = {})
return (add_,)
strict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%clone, %bias), kwargs = {})
return (add_6,)
nostrict¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x,), kwargs = {})
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%clone, %bias), kwargs = {})
return (add_,)
nostrict-decall¶
graph():
%bias : [num_users=1] = get_attr[target=bias]
%x : [num_users=1] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%clone, %bias), kwargs = {})
return (add_6,)
jit¶
graph():
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x_1 : [num_users=1] = placeholder[target=x_1]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x_1,), kwargs = {})
%add_ : [num_users=1] = call_function[target=torch.ops.aten.add_.Tensor](args = (%clone, %lifted_tensor_3), kwargs = {})
return (add_,)
jit-decall¶
graph():
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x_1 : [num_users=1] = placeholder[target=x_1]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%x_1,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%clone, %lifted_tensor_3), kwargs = {})
return (add_6,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%clone : [num_users=1] = call_method[target=clone](args = (%x,), kwargs = {})
%bias : [num_users=1] = get_attr[target=bias]
%add_ : [num_users=1] = call_method[target=add_](args = (%clone, %bias), kwargs = {})
return add_
InplaceSetItemEllipsis_1¶
forward¶
def forward(self, index, update):
copy = self.params.clone()
copy[..., index] = update
return copy
strict¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
strict-decall¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict-decall¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
jit¶
graph():
%lifted_tensor_5 : [num_users=1] = get_attr[target=lifted_tensor_5]
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_5,), kwargs = {})
%index_put_ : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%clone, [None, None, %index], %update), kwargs = {})
return (index_put_,)
jit-decall¶
graph():
%lifted_tensor_5 : [num_users=1] = get_attr[target=lifted_tensor_5]
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_5,), kwargs = {})
%index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%clone, [None, None, %index], %update), kwargs = {})
return (index_put,)
tracing¶
graph():
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%_tensor_constant0 : [num_users=1] = get_attr[target=_tensor_constant0]
%setitem : [num_users=1] = call_function[target=operator.setitem](args = (%_tensor_constant0, (Ellipsis, %index), %update), kwargs = {})
return setitem
InplaceSetItemEllipsis_2¶
forward¶
def forward(self, index, update):
copy = self.params.clone()
copy[..., index] = update
return copy
strict¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
strict-decall¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict-decall¶
FAILED
When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
jit¶
graph():
%lifted_tensor_5 : [num_users=1] = get_attr[target=lifted_tensor_5]
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_5,), kwargs = {})
%index_put_ : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%clone, [None, None, %index], %update), kwargs = {})
return (index_put_,)
jit-decall¶
graph():
%lifted_tensor_5 : [num_users=1] = get_attr[target=lifted_tensor_5]
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_5,), kwargs = {})
%index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%clone, [None, None, %index], %update), kwargs = {})
return (index_put,)
tracing¶
graph():
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%_tensor_constant0 : [num_users=1] = get_attr[target=_tensor_constant0]
%setitem : [num_users=1] = call_function[target=operator.setitem](args = (%_tensor_constant0, (Ellipsis, %index), %update), kwargs = {})
return setitem
InplaceSetItemMask¶
forward¶
def forward(self, x):
mask = x.to(bool)
x[mask] = 2
return x
strict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=3] = placeholder[target=x]
%_assert_tensor_metadata_default : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x,), kwargs = {dtype: torch.float32, device: cpu, layout: torch.strided})
%to : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%x, torch.bool), kwargs = {})
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%index_put_ : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%x, [%to], %lift_fresh_copy), kwargs = {})
return (index_put_,)
strict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%_assert_tensor_metadata : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x, None, None, torch.float32), kwargs = {device: cpu, layout: torch.strided})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%x,), kwargs = {dtype: torch.bool})
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%x, [%_to_copy], %clone), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %index_put), kwargs = {})
return (copy__default,)
nostrict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=3] = placeholder[target=x]
%_assert_tensor_metadata_default : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x,), kwargs = {dtype: torch.float32, device: cpu, layout: torch.strided})
%to : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%x, torch.bool), kwargs = {})
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%index_put_ : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%x, [%to], %lift_fresh_copy), kwargs = {})
return (index_put_,)
nostrict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%_assert_tensor_metadata : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x, None, None, torch.float32), kwargs = {device: cpu, layout: torch.strided})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%x,), kwargs = {dtype: torch.bool})
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%x, [%_to_copy], %clone), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %index_put), kwargs = {})
return (copy__default,)
jit¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%x : [num_users=3] = placeholder[target=x]
%_assert_tensor_metadata_default : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x,), kwargs = {dtype: torch.float32, device: cpu, layout: torch.strided})
%to : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%x, torch.bool), kwargs = {})
%index_put_ : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%x, [%to], %lifted_tensor_2), kwargs = {})
return (index_put_,)
jit-decall¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%x : [num_users=4] = placeholder[target=x]
%_assert_tensor_metadata : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x, None, None, torch.float32), kwargs = {device: cpu, layout: torch.strided})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%x,), kwargs = {dtype: torch.bool})
%index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%x, [%_to_copy], %lifted_tensor_2), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %index_put), kwargs = {})
return (copy__default,)
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%to : [num_users=1] = call_method[target=to](args = (%x, torch.bool), kwargs = {})
%setitem : [num_users=1] = call_function[target=operator.setitem](args = (%x, %to, 2), kwargs = {})
return setitem
InplaceSetItemSquare¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x
strict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=2] = placeholder[target=x]
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lift_fresh_copy), kwargs = {})
return (x,)
strict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %clone), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (copy__default,)
nostrict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=2] = placeholder[target=x]
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lift_fresh_copy), kwargs = {})
return (x,)
nostrict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %clone), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (copy__default,)
jit¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%x : [num_users=2] = placeholder[target=x]
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lifted_tensor_2), kwargs = {})
return (x,)
jit-decall¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%x : [num_users=4] = placeholder[target=x]
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %lifted_tensor_2), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%copy__default : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (copy__default,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%setitem : [num_users=1] = call_function[target=operator.setitem](args = (%x, (slice(None, 2, None), slice(None, 3, None)), 1), kwargs = {})
return setitem
InplaceSetItemSquareAdd¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x + 2
strict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=2] = placeholder[target=x]
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lift_fresh_copy), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 2), kwargs = {})
return (add,)
strict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %clone), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, 2), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (add,)
nostrict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=2] = placeholder[target=x]
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lift_fresh_copy), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 2), kwargs = {})
return (add,)
nostrict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %clone), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, 2), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (add,)
jit¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x : [num_users=2] = placeholder[target=x]
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lifted_tensor_3), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %lifted_tensor_2), kwargs = {})
return (add,)
jit-decall¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%x : [num_users=4] = placeholder[target=x]
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %lifted_tensor_3), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, %lifted_tensor_2), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (add_12,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%setitem : [num_users=1] = call_function[target=operator.setitem](args = (%x, (slice(None, 2, None), slice(None, 3, None)), 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%setitem, 2), kwargs = {})
return add
InplaceSetItemSquareAdd2¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x + 2, x + 3
strict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=3] = placeholder[target=x]
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lift_fresh_copy), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 2), kwargs = {})
%add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 3), kwargs = {})
return (add, add_1)
strict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %clone), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, 2), kwargs = {})
%add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, 3), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (add, add_4)
nostrict¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=3] = placeholder[target=x]
%lift_fresh_copy : [num_users=1] = call_function[target=torch.ops.aten.lift_fresh_copy.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lift_fresh_copy), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 2), kwargs = {})
%add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 3), kwargs = {})
return (add, add_1)
nostrict-decall¶
graph():
%lifted_tensor_0 : [num_users=1] = get_attr[target=lifted_tensor_0]
%x : [num_users=4] = placeholder[target=x]
%clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%lifted_tensor_0,), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %clone), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, 2), kwargs = {})
%add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, 3), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (add, add_4)
jit¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%lifted_tensor_4 : [num_users=1] = get_attr[target=lifted_tensor_4]
%x : [num_users=3] = placeholder[target=x]
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%fill_ : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_2, %lifted_tensor_4), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %lifted_tensor_3), kwargs = {})
%add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, %lifted_tensor_2), kwargs = {})
return (add, add_1)
jit-decall¶
graph():
%lifted_tensor_2 : [num_users=1] = get_attr[target=lifted_tensor_2]
%lifted_tensor_3 : [num_users=1] = get_attr[target=lifted_tensor_3]
%lifted_tensor_4 : [num_users=1] = get_attr[target=lifted_tensor_4]
%x : [num_users=4] = placeholder[target=x]
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, 3), kwargs = {})
%copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_2, %lifted_tensor_4), kwargs = {})
%slice_3 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 2), kwargs = {})
%slice_scatter : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_3, %copy, 1, 0, 3), kwargs = {})
%slice_scatter_1 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%x, %slice_scatter, 0, 0, 2), kwargs = {})
%add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, %lifted_tensor_3), kwargs = {})
%add_16 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_scatter_1, %lifted_tensor_2), kwargs = {})
%copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%x, %slice_scatter_1), kwargs = {})
return (add_12, add_16)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%setitem : [num_users=2] = call_function[target=operator.setitem](args = (%x, (slice(None, 2, None), slice(None, 3, None)), 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%setitem, 2), kwargs = {})
%add_1 : [num_users=1] = call_function[target=operator.add](args = (%setitem, 3), kwargs = {})
return (add, add_1)
SignatureFloat1¶
forward¶
def forward(self, x, alpha: float = 2.0):
return torch.sigmoid(self.linear(x)) - self.buff * alpha
strict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%alpha : [num_users=0] = placeholder[target=alpha]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%buff, 1.5), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %mul), kwargs = {})
return (sub,)
strict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%alpha : [num_users=0] = placeholder[target=alpha]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%buff, 1.5), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %mul_2), kwargs = {})
return (sub_2,)
nostrict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%alpha : [num_users=0] = placeholder[target=alpha]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%buff, 1.5), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %mul), kwargs = {})
return (sub,)
nostrict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%alpha : [num_users=0] = placeholder[target=alpha]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%buff, 1.5), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %mul_2), kwargs = {})
return (sub_2,)
jit¶
FAILED
Type 'Tuple[Tensor, float]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
jit-decall¶
FAILED
Type 'Tuple[Tensor, float]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%alpha : float [num_users=1] = placeholder[target=alpha](default=2.0)
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%mul : [num_users=1] = call_method[target=mul](args = (%buff, %alpha), kwargs = {})
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %mul), kwargs = {})
return sub
SignatureInt1¶
forward¶
def forward(self, x, i: int = 2):
return torch.sigmoid(self.linear(x)) - self.buff + x[:, i : i + 1]
strict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 9223372036854775807), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 1, 2), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %slice_2), kwargs = {})
return (add,)
strict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 9223372036854775807), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 1, 2), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %slice_2), kwargs = {})
return (add_15,)
nostrict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x,), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 1, 2), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %slice_2), kwargs = {})
return (add,)
nostrict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x,), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 1, 2), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %slice_2), kwargs = {})
return (add_15,)
jit¶
FAILED
Type 'Tuple[Tensor, int]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
jit-decall¶
FAILED
Type 'Tuple[Tensor, int]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%i : int [num_users=2] = placeholder[target=i](default=2)
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %buff), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%i, 1), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%x, (slice(None, None, None), slice(i, add, None))), kwargs = {})
%add_1 : [num_users=1] = call_function[target=operator.add](args = (%sub, %getitem), kwargs = {})
return add_1
SignatureInt2¶
forward¶
def forward(self, x, i: int = 2):
return torch.sigmoid(self.linear(x)) - self.buff + x[:, i]
strict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 9223372036854775807), kwargs = {})
%select : [num_users=1] = call_function[target=torch.ops.aten.select.int](args = (%slice_1, 1, 1), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %select), kwargs = {})
return (add,)
strict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 0, 0, 9223372036854775807), kwargs = {})
%select : [num_users=1] = call_function[target=torch.ops.aten.select.int](args = (%slice_1, 1, 1), kwargs = {})
%add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %select), kwargs = {})
return (add_14,)
nostrict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x,), kwargs = {})
%select : [num_users=1] = call_function[target=torch.ops.aten.select.int](args = (%slice_1, 1, 1), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %select), kwargs = {})
return (add,)
nostrict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%i : [num_users=0] = placeholder[target=i]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x,), kwargs = {})
%select : [num_users=1] = call_function[target=torch.ops.aten.select.int](args = (%slice_1, 1, 1), kwargs = {})
%add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %select), kwargs = {})
return (add_14,)
jit¶
FAILED
Type 'Tuple[Tensor, int]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
jit-decall¶
FAILED
Type 'Tuple[Tensor, int]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%i : int [num_users=1] = placeholder[target=i](default=2)
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %buff), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%x, (slice(None, None, None), %i)), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sub, %getitem), kwargs = {})
return add
SignatureListFixedLength¶
forward¶
def forward(self, x, lx: list):
return (
torch.sigmoid(self.linear(x)) - self.buff + lx[0] * lx[1].sum(axis=1, keepdim=True)
)
strict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
return (add,)
strict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %mul_4), kwargs = {})
return (add_15,)
nostrict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
return (add,)
nostrict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %mul_4), kwargs = {})
return (add_15,)
jit¶
graph():
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
return (add,)
jit-decall¶
graph():
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%buff : [num_users=1] = get_attr[target=buff]
%x : [num_users=1] = placeholder[target=x]
%lx_0 : [num_users=1] = placeholder[target=lx_0]
%lx_1 : [num_users=1] = placeholder[target=lx_1]
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
%add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %mul_4), kwargs = {})
return (add_15,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%lx : list [num_users=2] = placeholder[target=lx]
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %buff), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%lx, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%lx, 1), kwargs = {})
%sum_1 : [num_users=1] = call_method[target=sum](args = (%getitem_1,), kwargs = {axis: 1, keepdim: True})
%mul : [num_users=1] = call_function[target=operator.mul](args = (%getitem, %sum_1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sub, %mul), kwargs = {})
return add
SignatureListFixedWithNone¶
forward¶
def forward(self, lx):
x = lx[0]
if lx[1] is not None:
x += lx[1]
if lx[2] is not None:
x += lx[2]
return x
strict¶
FAILED
Detected mismatch between the structure of `inputs` and `dynamic_shapes`: `inputs['lx']` has 3 elements, but `dynamic_shapes['lx']` has 2 elements
For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
strict-decall¶
FAILED
Detected mismatch between the structure of `inputs` and `dynamic_shapes`: `inputs['lx']` has 3 elements, but `dynamic_shapes['lx']` has 2 elements
For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict¶
FAILED
Detected mismatch between the structure of `inputs` and `dynamic_shapes`: `inputs['lx']` has 3 elements, but `dynamic_shapes['lx']` has 2 elements
For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
nostrict-decall¶
FAILED
Detected mismatch between the structure of `inputs` and `dynamic_shapes`: `inputs['lx']` has 3 elements, but `dynamic_shapes['lx']` has 2 elements
For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
jit¶
FAILED
Type 'Tuple[List[Optional[Tensor]]]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
jit-decall¶
FAILED
Type 'Tuple[List[Optional[Tensor]]]' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and Tuples of Tensors can be traced
tracing¶
FAILED
Unable to clone type <class 'NoneType'>, x=None into numpy
SignatureListVariableLength¶
forward¶
def forward(self, x, lx: list):
t = torch.cat(lx, dim=1).sum(axis=1, keepdim=True)
return torch.sigmoid(self.linear(x)) - self.buff + t
strict¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
strict-decall¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
nostrict¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
nostrict-decall¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
jit¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
jit-decall¶
FAILED
Trying to flatten user inputs with exported input tree spec:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*])]),
TreeSpec(dict, [], [])])
but actually got inputs with tree spec of:
TreeSpec(tuple, None, [TreeSpec(tuple, None, [*,
TreeSpec(list, None, [*,
*,
*])]),
TreeSpec(dict, [], [])]).
Please check that the inputs have the same number and type of args and kwargs as the ones you used when tracing.
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%lx : list [num_users=1] = placeholder[target=lx]
%cat : [num_users=1] = call_function[target=torch.cat](args = (%lx, 1), kwargs = {})
%sum_1 : [num_users=1] = call_method[target=sum](args = (%cat,), kwargs = {axis: 1, keepdim: True})
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %buff), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sub, %sum_1), kwargs = {})
return add
SignatureShapeAsIndex¶
forward¶
def forward(self, x, y):
t = torch.sigmoid(self.linear(x)) + x
return t[:, : y.shape[1]]
strict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add, 0, 0, 9223372036854775807), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, %sym_size_int_3), kwargs = {})
return (slice_2,)
strict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_5 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add_6, 0, 0, 9223372036854775807), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, %sym_size_int_5), kwargs = {})
return (slice_2,)
nostrict¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=0] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add,), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, None, %sym_size_int_2), kwargs = {})
return (slice_2,)
nostrict-decall¶
graph():
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%buff : [num_users=0] = get_attr[target=buff]
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_5 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add_6,), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, None, %sym_size_int_5), kwargs = {})
return (slice_2,)
jit¶
graph():
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
%add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add, 0, 0, 9223372036854775807), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, %sym_size_int_3), kwargs = {})
return (slice_2,)
jit-decall¶
graph():
%linear_bias : [num_users=1] = get_attr[target=linear.bias]
%linear_weight : [num_users=1] = get_attr[target=linear.weight]
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%sym_size_int_6 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add_6, 0, 0, 9223372036854775807), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, %sym_size_int_6), kwargs = {})
return (slice_2,)
tracing¶
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=1] = placeholder[target=y]
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sigmoid, %x), kwargs = {})
%getattr_1 : [num_users=1] = call_function[target=builtins.getattr](args = (%y, shape), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%getattr_1, 1), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%add, (slice(None, None, None), slice(None, getitem, None))), kwargs = {})
return getitem_1
TypeBFloat16¶
forward¶
def forward(self, x):
xb = x.to(torch.bfloat16)
return (xb + xb).to(torch.float32)
strict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%_assert_tensor_metadata_default : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x,), kwargs = {dtype: torch.float32, device: cpu, layout: torch.strided})
%to : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%x, torch.bfloat16), kwargs = {})
%add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%to, %to), kwargs = {})
%_assert_tensor_metadata_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%add,), kwargs = {dtype: torch.bfloat16, device: cpu, layout: torch.strided})
%to_1 : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%add, torch.float32), kwargs = {})
return (to_1,)
strict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%_assert_tensor_metadata : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x, None, None, torch.float32), kwargs = {device: cpu, layout: torch.strided})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%x,), kwargs = {dtype: torch.bfloat16})
%add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_to_copy, %_to_copy), kwargs = {})
%_assert_tensor_metadata_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%add_3, None, None, torch.bfloat16), kwargs = {device: cpu, layout: torch.strided})
%_to_copy_1 : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%add_3,), kwargs = {dtype: torch.float32})
return (_to_copy_1,)
nostrict¶
graph():
%x : [num_users=2] = placeholder[target=x]
%_assert_tensor_metadata_default : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x,), kwargs = {dtype: torch.float32, device: cpu, layout: torch.strided})
%to : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%x, torch.bfloat16), kwargs = {})
%add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%to, %to), kwargs = {})
%_assert_tensor_metadata_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%add,), kwargs = {dtype: torch.bfloat16, device: cpu, layout: torch.strided})
%to_1 : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%add, torch.float32), kwargs = {})
return (to_1,)
nostrict-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%_assert_tensor_metadata : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x, None, None, torch.float32), kwargs = {device: cpu, layout: torch.strided})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%x,), kwargs = {dtype: torch.bfloat16})
%add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_to_copy, %_to_copy), kwargs = {})
%_assert_tensor_metadata_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%add_3, None, None, torch.bfloat16), kwargs = {device: cpu, layout: torch.strided})
%_to_copy_1 : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%add_3,), kwargs = {dtype: torch.float32})
return (_to_copy_1,)
jit¶
graph():
%x : [num_users=2] = placeholder[target=x]
%_assert_tensor_metadata_default : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x,), kwargs = {dtype: torch.float32, device: cpu, layout: torch.strided})
%to : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%x, torch.bfloat16), kwargs = {})
%add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%to, %to), kwargs = {})
%_assert_tensor_metadata_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%add,), kwargs = {dtype: torch.bfloat16, device: cpu, layout: torch.strided})
%to_1 : [num_users=1] = call_function[target=torch.ops.aten.to.dtype](args = (%add, torch.float32), kwargs = {})
return (to_1,)
jit-decall¶
graph():
%x : [num_users=2] = placeholder[target=x]
%_assert_tensor_metadata : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%x, None, None, torch.float32), kwargs = {device: cpu, layout: torch.strided})
%_to_copy : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%x,), kwargs = {dtype: torch.bfloat16})
%add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_to_copy, %_to_copy), kwargs = {})
%_assert_tensor_metadata_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_tensor_metadata.default](args = (%add_3, None, None, torch.bfloat16), kwargs = {device: cpu, layout: torch.strided})
%_to_copy_1 : [num_users=1] = call_function[target=torch.ops.aten._to_copy.default](args = (%add_3,), kwargs = {dtype: torch.float32})
return (_to_copy_1,)
tracing¶
graph():
%x : [num_users=1] = placeholder[target=x]
%to : [num_users=1] = call_method[target=to](args = (%x, torch.bfloat16), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%to, %to), kwargs = {})
%to_1 : [num_users=1] = call_method[target=to](args = (%add, torch.float32), kwargs = {})
return to_1
Summary¶
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