Exported Programs with Dynamic Shapes¶
The following script shows the exported program for many short cases exported with different options. This steps happens before converting into ONNX.
<<<
import inspect
import textwrap
import pandas
from onnx_diagnostic.helpers import string_type
from onnx_diagnostic.torch_export_patches.eval import discover, run_exporter
from onnx_diagnostic.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()
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(".. code-block:: python")
print()
src = inspect.getsource(cls_model.forward)
if src:
print(textwrap.indent(textwrap.dedent(src), " "))
else:
print(" # code is missing")
print()
print()
for exporter in (
"export-strict",
"export-nostrict",
"export-nostrict-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 "inputs" in res:
print(f"* **inputs:** ``{string_type(res['inputs'], with_shape=True)}``")
if "dynamic_shapes" in res:
print(f"* **shapes:** ``{string_type(res['dynamic_shapes'])}``")
print()
print()
if "exported" in res:
print(".. code-block:: text")
print()
print(textwrap.indent(str(res["exported"].graph), " "))
print()
print()
obs.append(dict(case=case_ref, error="", exporter=expo))
else:
print("**FAILED**")
print()
print(".. code-block:: text")
print()
err = str(res["error"])
if err:
print(textwrap.indent(err, " "))
else:
print(" # no error found for the failure")
print()
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¶
inputs:
#1[(T1s2x2x8x8,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x2x8x8,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x2x8x8,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x2x8x8,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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)
tracing¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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]
%lx_0 : [num_users=2] = placeholder[target=lx_0]
%lx_1 : [num_users=2] = placeholder[target=lx_1]
%sym_size_int : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_1, 0), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%eq : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_1, %sym_size_int_2), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq, Runtime assertion failed for expression Eq(s50, s53) on node 'eq'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int, %sym_size_int_1), kwargs = {})
%_assert_scalar_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_1'), 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¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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]
%lx_0 : [num_users=2] = placeholder[target=lx_0]
%lx_1 : [num_users=2] = placeholder[target=lx_1]
%sym_size_int : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_1, 0), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%eq : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_1, %sym_size_int_2), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq, Runtime assertion failed for expression Eq(s50, s53) on node 'eq'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int, %sym_size_int_1), kwargs = {})
%_assert_scalar_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_1'), 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¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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]
%lx_0 : [num_users=2] = placeholder[target=lx_0]
%lx_1 : [num_users=2] = placeholder[target=lx_1]
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_4 : [num_users=4] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_5 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_1, 0), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
%eq_8 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_4, %sym_size_int_5), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_8, Runtime assertion failed for expression Eq(s50, s53) on node 'eq_8'), kwargs = {})
%eq_9 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_3, %sym_size_int_4), kwargs = {})
%_assert_scalar_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_9, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_9'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_4, %sym_size_int_4), kwargs = {})
%_assert_scalar : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s50, s53) on node 'eq'), kwargs = {})
%eq_2 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_4, %sym_size_int_4), kwargs = {})
%_assert_scalar_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_2, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_1'), 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 411, in __call__
- File “~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py”, line 1773, in _wrapped_call_impl
- File “~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py”, line 1784, in _call_impl
- File “<eval_with_key>.449 from ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:731 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
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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.
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.
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¶
inputs:
#1[(T1s4x4,T1s4x4)]shapes:
dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})
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¶
inputs:
#1[(T1s4x4,T1s4x4)]shapes:
dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})
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¶
inputs:
#1[(T1s4x4,T1s4x4)]shapes:
dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})
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¶
inputs:
#1[(T1s4x4,T1s4x4)]shapes:
dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,T1s5x3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch)})
graph():
%x : [num_users=3] = placeholder[target=x]
%y : [num_users=2] = placeholder[target=y]
%sym_size_int : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_1 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 0), kwargs = {})
%sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%x,), kwargs = {})
%eq : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int, %sym_size_int_1), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq, Runtime assertion failed for expression Eq(s77, s17) on node 'eq'), 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¶
inputs:
#1[(T1s5x3,T1s5x3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,T1s5x3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch)})
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)
tracing¶
inputs:
#1[(T1s5x3,T1s5x3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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)
tracing¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s1024x1024,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s1024x1024,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s1024x1024,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#1[(T1s1024x1024,)]shapes:
dict(x:{0:Dim(batch)})
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
ControlFlowCondIdentity_153832¶
forward¶
def forward(self, x, y):
def branch_cond_then_1(x):
x = torch.abs(x) + 1
return x
def branch_cond_else_1(x):
return x # fails but succeeds with x.clone()
x = torch.cond(x.sum() > 0, branch_cond_then_1, branch_cond_else_1, [x])
return x + y
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 369, in forward
x = torch.cond(x.sum() > 0, branch_cond_then_1, branch_cond_else_1, [x])
File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 138, 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
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 "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 187, in _cond_op_wrapper
return cond_op(*args, **kwargs)
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-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 "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 187, in _cond_op_wrapper
return cond_op(*args, **kwargs)
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"
tracing¶
inputs:
#2[(T1s3x4,T1s3x4),(T1s4x5,T1s4x5)]shapes:
dict(x:{0:DYNAMIC,1:DYNAMIC},y:{0:DYNAMIC,1:DYNAMIC})
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 "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 187, in _cond_op_wrapper
return cond_op(*args, **kwargs)
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"
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¶
inputs:
#1[(T7s2,)]shapes:
dict(x:{0:Dim(batch)})
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,)
nostrict¶
inputs:
#1[(T7s2,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T7s2,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#1[(T7s2,)]shapes:
dict(x:{0:Dim(batch)})
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 328, in forward
a, b, c = torch.cond(
File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 138, 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=(s72, 12), dtype=torch.int64), FakeTensor(..., size=(s28, s11)), 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=(s72, 12), dtype=torch.int64), FakeTensor(..., size=(s28, s11)), 1025].
tracing¶
inputs:
#2[(T7s2x12,T1s2x16,int),(T7s2x12,T1s2x0,int)]shapes:
({0:Dim(batch)},{0:Dim(batch),1:Dim(seq_length)},None)
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 387, 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¶
inputs:
#1[(T1s3x3,)]shapes:
dict(x:{0:Dim(batch)})
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 = {})
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 387, in forward
carry, out = torch.ops.higher_order.scan(
Use tlparse to see full graph. (https://github.com/pytorch/tlparse?tab=readme-ov-file#tlparse-parse-structured-pt2-logs)
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 406, 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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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 = {})
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 406, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
Use tlparse to see full graph. (https://github.com/pytorch/tlparse?tab=readme-ov-file#tlparse-parse-structured-pt2-logs)
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 432, 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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 460, 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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 486, 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¶
inputs:
#2[(T1s3x4,T1s5x4),(T1s13x14,T1s15x14)]shapes:
dict(x:{0:Dim(x_rows),1:Dim(dim)},y:{0:Dim(y_rows),1:Dim(dim)})
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=2] = placeholder[target=y]
%sym_size_int_1 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
%eq : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_3, %sym_size_int_1), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq, Runtime assertion failed for expression Eq(s94, s27) on node 'eq'), kwargs = {})
%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¶
inputs:
#2[(T1s3x4,T1s5x4),(T1s13x14,T1s15x14)]shapes:
dict(x:{0:Dim(x_rows),1:Dim(dim)},y:{0:Dim(y_rows),1:Dim(dim)})
graph():
%x : [num_users=2] = placeholder[target=x]
%y : [num_users=2] = placeholder[target=y]
%sym_size_int_6 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 1), kwargs = {})
%sym_size_int_8 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%eq_5 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_8, %sym_size_int_6), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_5, Runtime assertion failed for expression Eq(s94, s27) on node 'eq_5'), kwargs = {})
%eq : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_6, %sym_size_int_6), kwargs = {})
%_assert_scalar : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq, Runtime assertion failed for expression Eq(s94, s27) on node 'eq'), 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, [%y], [%x], ()), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
return (getitem_1,)
tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
ControlFlowScanDecomposition_151564¶
forward¶
def forward(self, images, position):
return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
images, position
)
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 568, in forward
return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 556, in dummy_loop_with_scan
return 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¶
inputs:
#1[(T1s5x6,T7s5)]shapes:
dict(images:{0:DYNAMIC,1:DYNAMIC},position:{0:DYNAMIC})
FAILED
Runtime assertion failed for expression 1 < u0 on node 'gt_3'
nostrict-decall¶
inputs:
#1[(T1s5x6,T7s5)]shapes:
dict(images:{0:DYNAMIC,1:DYNAMIC},position:{0:DYNAMIC})
FAILED
Runtime assertion failed for expression 1 < u6 on node 'gt_2'
tracing¶
FAILED
zeros(): argument 'size' (position 1) must be tuple of ints, but found element of type CustomAttribute at pos 0
ControlFlowScanInplace_153705¶
forward¶
def forward(self, x, y):
def loop_body_1(z, iv, x, y):
z = z.clone()
i = iv.item()
z[i, :] = ((x[i, :] - y) ** 2).sum(dim=-1)
return [z, iv]
z = torch.empty((x.shape[0], y.shape[0]))
r = torch.ops.higher_order.scan(
loop_body_1, [z], [torch.arange(x.shape[0], dtype=torch.int64)], [x, y]
)
return r[0]
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/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 518, in forward
r = 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¶
FAILED
only integers, slices (`:`), ellipsis (`...`), None and long or byte Variables are valid indices (got SymInt)
nostrict-decall¶
FAILED
only integers, slices (`:`), ellipsis (`...`), None and long or byte Variables are valid indices (got SymInt)
tracing¶
FAILED
empty(): argument 'size' (position 1) must be tuple of ints, but found element of type CustomProxy at pos 0
CreateFromShape¶
forward¶
def forward(self, x):
y = torch.ones((x.shape[0], x.shape[1] + 1))
return y
strict¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
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¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
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¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
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,)
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¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
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¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
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¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
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,)
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
Dynamic slicing with Tensor arguments
Explanation: Creating slices with Tensor arguments is not supported. e.g. `l[:x]`, where `x` is a 1-element tensor.
Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.
Developer debug context: SliceVariable start: ConstantVariable(NoneType: None), stop: TensorVariable(), step: ConstantVariable(NoneType: None)
from user code:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 818, in forward
return x[..., : shape[0]]
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
Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: none)
Caused by: (_export/non_strict_utils.py:1066 in __torch_function__)
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 "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 818, in forward
return x[..., : shape[0]]
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
Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: none)
Caused by: (_export/non_strict_utils.py:1066 in __torch_function__)
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 "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 818, in forward
return x[..., : shape[0]]
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()`.
tracing¶
inputs:
#2[(T1s3x4x4,T7s1),(T1s6x4x4,T7s1)]shapes:
dict(x:{0:Dim(batch)},shape:{})
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¶
inputs:
#2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(crop)})
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¶
inputs:
#2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(crop)})
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, None, %sym_size_int_2), kwargs = {})
return (slice_1,)
nostrict-decall¶
inputs:
#2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(crop)})
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, None, %sym_size_int_4), kwargs = {})
return (slice_1,)
tracing¶
inputs:
#2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(crop)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
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
L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4
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
L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4
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
L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4
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()`.
tracing¶
inputs:
#1[(T7s4,T1s8192x4)]shapes:
dict(index:{0:Dim(batch)},update:{0:Dim(batch),1:DYNAMIC})
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
L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4
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
L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4
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
L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4
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()`.
tracing¶
inputs:
#1[(T7s4,T1s8192x4)]shapes:
dict(index:{0:Dim(batch)},update:{0:Dim(batch),1:DYNAMIC})
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¶
inputs:
#2[(T1s2x3x3,),(T1s3x3x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s2x3x3,),(T1s3x3x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s2x3x3,),(T1s3x3x3,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#2[(T1s2x3x3,),(T1s3x3x3,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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,)
tracing¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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)
tracing¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
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¶
FAILED
Expected input at *args[1] to be equal to 1.5, but got 2.5
nostrict¶
FAILED
Expected input at *args[1] to be equal to 1.5, but got 2.5
nostrict-decall¶
FAILED
Expected input at *args[1] to be equal to 1.5, but got 2.5
tracing¶
inputs:
#2[(T1s4x3,float),(T1s8x3,float)]shapes:
({0:Dim(batch)},None)
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¶
FAILED
Expected input at *args[1] to be equal to 1, but got 2
nostrict¶
FAILED
Expected input at *args[1] to be equal to 1, but got 2
nostrict-decall¶
FAILED
Expected input at *args[1] to be equal to 1, but got 2
tracing¶
inputs:
#2[(T1s4x3,int),(T1s8x3,int)]shapes:
({0:Dim(batch)},None)
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¶
inputs:
#1[(T1s4x3,int)]shapes:
dict(x:{0:Dim(batch)},i:None)
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,)
nostrict¶
inputs:
#1[(T1s4x3,int)]shapes:
dict(x:{0:Dim(batch)},i:None)
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,)
nostrict-decall¶
inputs:
#1[(T1s4x3,int)]shapes:
dict(x:{0:Dim(batch)},i:None)
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,)
tracing¶
inputs:
#1[(T1s4x3,int)]shapes:
dict(x:{0:Dim(batch)},i:None)
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¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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]
%lx_0 : [num_users=2] = placeholder[target=lx_0]
%lx_1 : [num_users=2] = placeholder[target=lx_1]
%sym_size_int : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_1, 0), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%eq : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_1, %sym_size_int_2), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq, Runtime assertion failed for expression Eq(s50, s53) on node 'eq'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int, %sym_size_int_1), kwargs = {})
%_assert_scalar_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_1'), 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¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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]
%lx_0 : [num_users=2] = placeholder[target=lx_0]
%lx_1 : [num_users=2] = placeholder[target=lx_1]
%sym_size_int : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_1 : [num_users=2] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_1, 0), kwargs = {})
%linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
%eq : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_1, %sym_size_int_2), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq, Runtime assertion failed for expression Eq(s50, s53) on node 'eq'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int, %sym_size_int_1), kwargs = {})
%_assert_scalar_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_1'), 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¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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]
%lx_0 : [num_users=2] = placeholder[target=lx_0]
%lx_1 : [num_users=2] = placeholder[target=lx_1]
%sym_size_int_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%x, 0), kwargs = {})
%sym_size_int_4 : [num_users=4] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_5 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_1, 0), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%eq_8 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_4, %sym_size_int_5), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_8, Runtime assertion failed for expression Eq(s50, s53) on node 'eq_8'), kwargs = {})
%eq_9 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_3, %sym_size_int_4), kwargs = {})
%_assert_scalar_default_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_9, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_9'), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_4, %sym_size_int_4), kwargs = {})
%_assert_scalar : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s50, s53) on node 'eq'), kwargs = {})
%eq_2 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_4, %sym_size_int_4), kwargs = {})
%_assert_scalar_1 : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_2, Runtime assertion failed for expression Eq(s77, s50) on node 'eq_1'), 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¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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()`.
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()`.
tracing¶
inputs:
#2[(#3[T1s4x4,T1s4x4,None],),(#3[T1s4x4,T1s4x4,T1s4x4],)]shapes:
dict(lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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.
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.
tracing¶
inputs:
#2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#3[T1s8x1,T1s8x2,T1s8x3])]shapes:
dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])
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¶
inputs:
#1[(T1s4x3,T1s4x2)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})
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_4 : [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, 1, 0, %sym_size_int_4), kwargs = {})
return (slice_1,)
nostrict¶
inputs:
#1[(T1s4x3,T1s4x2)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})
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_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, 1, None, %sym_size_int_3), kwargs = {})
return (slice_1,)
nostrict-decall¶
inputs:
#1[(T1s4x3,T1s4x2)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})
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, 1, None, %sym_size_int_5), kwargs = {})
return (slice_1,)
tracing¶
inputs:
#1[(T1s4x3,T1s4x2)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})
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¶
inputs:
#1[(T1s4x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s4x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s4x4,)]shapes:
dict(x:{0:Dim(batch)})
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¶
inputs:
#1[(T1s4x4,)]shapes:
dict(x:{0:Dim(batch)})
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
Vmap¶
forward¶
def forward(self, x, y):
f = lambda x, y: x * y + 1 # noqa: E731
return torch.vmap(f)(x, y)
strict¶
FAILED
# no error found for the failure
nostrict¶
FAILED
# no error found for the failure
nostrict-decall¶
FAILED
# no error found for the failure
tracing¶
FAILED
vmap(<lambda>, in_dims=0, ...)(<inputs>): Got in_dim=0 for an input but the input is of type <class 'experimental_experiment.torch_interpreter.tracing.CustomProxy'>. We cannot vmap over non-Tensor arguments, please use None as the respective in_dim
VmapPython¶
forward¶
def forward(self, x, y):
f = lambda x, y: x * y + 1 # noqa: E731
return patched_vmap(f)(x, y)
strict¶
FAILED
Failed to convert args/kwargs to proxy
Explanation: Missing `as_proxy()` implementation for some arg/kwarg.
Developer debug context: call_function args: ConstantVariable(bool: True) NestedUserFunctionVariable()
from user code:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 880, in forward
return patched_vmap(f)(x, y)
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/patches/patch_torch.py", line 434, in wrapped
torch._check(
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
object of type 'Node' has no len()
nostrict-decall¶
FAILED
object of type 'Node' has no len()
tracing¶
FAILED
symbolically traced variables cannot be used as inputs to control flow
Summary¶
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