Exported Programs with Dynamic Shapes¶
The following script shows the exported program for many short cases and various l-plot-export-with-dynamic-shape to retrieve an ONNX model equivalent to the original model.
<<<
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",
):
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,)
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,)
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,)
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)
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,)
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,)
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=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_2 : [num_users=2] = 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(s49, s10) on node 'eq'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int, %sym_size_int_2), 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(s35, s10) 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=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_5 : [num_users=4] = 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(s49, s10) on node 'eq_8'), kwargs = {})
%eq_9 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_3, %sym_size_int_5), 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(s35, s10) on node 'eq_9'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_5, %sym_size_int_5), kwargs = {})
%_assert_scalar : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s49, s10) on node 'eq'), kwargs = {})
%eq_2 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_5, %sym_size_int_5), 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(s35, s10) 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,)
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.
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=2] = placeholder[target=x]
%angle : [num_users=2] = placeholder[target=angle]
%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 = (%angle, 0), kwargs = {})
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), 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, s96) on node 'eq'), kwargs = {})
return (polar,)
nostrict¶
inputs:
#1[(T1s4x4,T1s4x4)]shapes:
dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})
graph():
%x : [num_users=2] = placeholder[target=x]
%angle : [num_users=2] = placeholder[target=angle]
%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 = (%angle, 0), kwargs = {})
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), 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(s35, s58) on node 'eq'), kwargs = {})
return (polar,)
nostrict-decall¶
inputs:
#1[(T1s4x4,T1s4x4)]shapes:
dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})
graph():
%x : [num_users=2] = placeholder[target=x]
%angle : [num_users=2] = placeholder[target=angle]
%sym_size_int_2 : [num_users=2] = 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 = (%angle, 0), kwargs = {})
%polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
%eq_2 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_2, %sym_size_int_3), kwargs = {})
%_assert_scalar_default : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_2, Runtime assertion failed for expression Eq(s35, s58) on node 'eq_2'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_2, %sym_size_int_2), kwargs = {})
%_assert_scalar : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s35, s58) on node 'eq'), 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,)
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)
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)
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,)
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 132, 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 181, 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 181, 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,)
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 132, in cond
return cond_op(pred, true_fn, false_fn, operands)
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
nostrict¶
FAILED
Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s35, 12), dtype=torch.int64), FakeTensor(..., size=(s58, s43)), 1025].
nostrict-decall¶
FAILED
Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s35, 12), dtype=torch.int64), FakeTensor(..., size=(s58, s43)), 1025].
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,)
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 = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x):
x: "f32[s35, 3][3, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:386 in forward, code: init = torch.zeros_like(x[0])
select: "f32[3][1]" = torch.ops.aten.select.int(x, 0, 0)
zeros_like: "f32[3][1]" = torch.ops.aten.zeros_like.default(select, pin_memory = False); select = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:387 in forward, code: carry, out = torch.ops.higher_order.scan(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [zeros_like], [x], ()); scan_combine_graph_0 = zeros_like = x = None
getitem: "f32[3][1]" = scan[0]
getitem_1: "f32[s35, 3][3, 1]" = scan[1]; scan = getitem_1 = None
return pytree.tree_unflatten((getitem,), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, carry_1: "f32[3][1]", y_1: "f32[3][1]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:387 in forward, code: carry, out = torch.ops.higher_order.scan(
add: "f32[3][1]" = torch.ops.aten.add.Tensor(carry_1, y_1); carry_1 = y_1 = None
return [add, add]
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 387, in forward
carry, out = torch.ops.higher_order.scan(
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 = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x):
x: "f32[s35, 4][4, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:404 in forward, code: init1 = torch.zeros_like(x[0])
select: "f32[4][1]" = torch.ops.aten.select.int(x, 0, 0)
zeros_like: "f32[4][1]" = torch.ops.aten.zeros_like.default(select, pin_memory = False); select = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:405 in forward, code: init2 = torch.ones_like(x[0])
select_1: "f32[4][1]" = torch.ops.aten.select.int(x, 0, 0)
ones_like: "f32[4][1]" = torch.ops.aten.ones_like.default(select_1, pin_memory = False); select_1 = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:409 in forward, code: [x, x * 2],
mul: "f32[s35, 4][4, 1]" = torch.ops.aten.mul.Tensor(x, 2)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:406 in forward, code: carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [zeros_like, ones_like], [x, mul], ()); scan_combine_graph_0 = zeros_like = ones_like = x = mul = None
getitem: "f32[4][1]" = scan[0]
getitem_1: "f32[4][1]" = scan[1]
getitem_2: "f32[s35, 4][4, 1]" = scan[2]
getitem_3: "f32[s35, 4][4, 1]" = scan[3]; scan = None
return pytree.tree_unflatten((getitem, getitem_1, getitem_2, getitem_3), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, carry1_1: "f32[4][1]", carry2_1: "f32[4][1]", y1_1: "f32[4][1]", y2_1: "f32[4][1]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:406 in forward, code: carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
add: "f32[4][1]" = torch.ops.aten.add.Tensor(carry1_1, y1_1); carry1_1 = y1_1 = None
mul: "f32[4][1]" = torch.ops.aten.mul.Tensor(carry2_1, y2_1); carry2_1 = y2_1 = None
return [add, mul, add, mul]
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 406, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
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,)
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,)
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(s43, s16) 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(s43, s16) 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(s43, s16) 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,)
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})
graph():
%images : [num_users=1] = placeholder[target=images]
%position : [num_users=1] = placeholder[target=position]
%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, [], [%images, %position], ()), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 0), kwargs = {})
return (getitem,)
nostrict-decall¶
FAILED
Could not guard on data-dependent expression u1 < 0 (unhinted: u1 < 0). (Size-like symbols: none)
Caused by: (_decomp/decompositions.py:745 in slice_forward)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u1"
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
While executing %scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [], [%images, %position], ()), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, images, position):
images: "f32[s35, s16][s16, 1]"; position: "i64[s58][1]";
images, position, = fx_pytree.tree_flatten_spec(([images, position], {}), self._in_spec)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:568 in forward, code: return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [], [images, position], ()); scan_combine_graph_0 = images = position = None
getitem: "f32[s35, s16][s16, 1]" = scan[0]; scan = None
return pytree.tree_unflatten((getitem,), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, padded_1: "f32[s16][1]", p_1: "i64[][]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:568 in forward, code: return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
sym_size_int: "Sym(s16)" = torch.ops.aten.sym_size.int(padded_1, 0)
zeros: "f32[s16][1]" = torch.ops.aten.zeros.default([sym_size_int], device = device(type='cpu'), pin_memory = False); sym_size_int = None
item: "Sym(u0)" = torch.ops.aten.item.default(p_1); p_1 = None
slice_1: "f32[u0][1]" = torch.ops.aten.slice.Tensor(padded_1, 0, 0, item); padded_1 = None
slice_2: "f32[u0][1]" = torch.ops.aten.slice.Tensor(zeros, 0, 0, item); item = None
copy_: "f32[u0][1]" = torch.ops.aten.copy_.default(slice_2, slice_1); slice_2 = slice_1 = copy_ = None
return (zeros,)
Original traceback:
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)(
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)
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,)
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,)
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:976 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:976 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()`.
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, 0, %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, 0, %sym_size_int_4), kwargs = {})
return (slice_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,)
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,)
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,)
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,)
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['args'][0][1].size()[0] = 8192 is not equal to L['args'][0][0].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['args'][0][1].size()[0] = 8192 is not equal to L['args'][0][0].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()`.
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['args'][0][1].size()[0] = 8192 is not equal to L['args'][0][0].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['args'][0][1].size()[0] = 8192 is not equal to L['args'][0][0].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()`.
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,)
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,)
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,)
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)
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
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
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,), 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,), 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,)
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=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_2 : [num_users=2] = 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(s49, s10) on node 'eq'), kwargs = {})
%eq_1 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int, %sym_size_int_2), 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(s35, s10) 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=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%lx_0, 0), kwargs = {})
%sym_size_int_5 : [num_users=4] = 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(s49, s10) on node 'eq_8'), kwargs = {})
%eq_9 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_3, %sym_size_int_5), 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(s35, s10) 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_5, %sym_size_int_5), kwargs = {})
%_assert_scalar : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%eq_1, Runtime assertion failed for expression Eq(s49, s10) on node 'eq'), kwargs = {})
%eq_2 : [num_users=1] = call_function[target=operator.eq](args = (%sym_size_int_5, %sym_size_int_5), 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(s35, s10) 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,)
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()`.
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.
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, 0, 0, 9223372036854775807), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, %sym_size_int_4), kwargs = {})
return (slice_2,)
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,), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, None, %sym_size_int_3), kwargs = {})
return (slice_2,)
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_6 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
%permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
%addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
%add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
%slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add_6,), kwargs = {})
%slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, None, %sym_size_int_6), kwargs = {})
return (slice_2,)
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,)
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
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()
Summary¶
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