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()

obs = []
for name, cls_model in sorted(cases.items()):
    print()
    print(f".. _led-model-case-export-{name}:")
    print()
    print(name)
    print("=" * len(name))
    print()
    print("forward")
    print("+++++++")
    print()
    print("::")
    print()
    print(
        textwrap.indent(textwrap.dedent(inspect.getsource(cls_model.forward)), "    ")
    )
    print()
    for exporter in (
        "export-strict",
        "export-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()
        if "exported" in res:
            print("::")
            print()
            print(textwrap.indent(str(res["exported"].graph), "    "))
            print()
            obs.append(dict(case=case_ref, error="", exporter=expo))
        else:
            print("**FAILED**")
            print()
            print("::")
            print()
            print(textwrap.indent(str(res["error"]), "    "))
            print()
            obs.append(dict(case=case_ref, error="FAIL", exporter=expo))

print()
print(".. _led-summary-exported-program:")
print()
print("Summary")
print("+++++++")
print()
df = pandas.DataFrame(obs)
piv = df.pivot(index="case", columns="exporter", values="error")
print(piv.to_markdown(tablefmt="rst"))
print()

>>>

Summary

AtenAsStrided
AtenInterpolate
AtenNonZero
AtenNonZeroTuple
AtenRollPos
AtenRollRelu
BuildInIsInstance
BuildInLen
ComplexPolar
ControlFlowCond
ControlFlowCond2Inputs
ControlFlowCond2Outputs
ControlFlowCondConstant
ControlFlowCondIdentity_153832
ControlFlowCondNestedModule
ControlFlowCondNonZero
ControlFlowNestCond
ControlFlowScan
ControlFlowScan2Carried
ControlFlowScanCDist
ControlFlowScanCDist2
ControlFlowScanCDistXY
ControlFlowScanDecomposition_151564
ControlFlowScanInplace_153705
CreateFromShape
CreateFromShapeThroughFunction
CropLastDimensionWithTensorContent
CropLastDimensionWithTensorShape
InplaceAdd
InplaceAdd2
InplaceAdd_Mul
InplaceCloneAdd_
InplaceSetItemEllipsis_1
InplaceSetItemEllipsis_2
InplaceSetItemMask
InplaceSetItemSquare
InplaceSetItemSquareAdd
InplaceSetItemSquareAdd2
SignatureFloat1
SignatureInt1
SignatureInt2
SignatureListFixedLength
SignatureListFixedWithNone
SignatureListVariableLength
SignatureShapeAsIndex
TypeBFloat16

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=1] = placeholder[target=x]
    %lx_0 : [num_users=1] = placeholder[target=lx_0]
    %lx_1 : [num_users=1] = placeholder[target=lx_1]
    %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
    %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
    %linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
    %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
    %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
    return (add,)

nostrict¶

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=1] = placeholder[target=x]
    %lx_0 : [num_users=1] = placeholder[target=lx_0]
    %lx_1 : [num_users=1] = placeholder[target=lx_1]
    %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
    %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
    %linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
    %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
    %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
    return (add,)

nostrict-decall¶

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=1] = placeholder[target=x]
    %lx_0 : [num_users=1] = placeholder[target=lx_0]
    %lx_1 : [num_users=1] = placeholder[target=lx_1]
    %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
    %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
    %permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
    %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
    %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
    %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_4, %mul_1), kwargs = {})
    return (add_15,)

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=1] = placeholder[target=x]
    %angle : [num_users=1] = placeholder[target=angle]
    %polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
    return (polar,)

nostrict¶

inputs: #1[(T1s4x4,T1s4x4)]
shapes: dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})

graph():
    %x : [num_users=1] = placeholder[target=x]
    %angle : [num_users=1] = placeholder[target=angle]
    %polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
    return (polar,)

nostrict-decall¶

inputs: #1[(T1s4x4,T1s4x4)]
shapes: dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})

graph():
    %x : [num_users=1] = placeholder[target=x]
    %angle : [num_users=1] = placeholder[target=angle]
    %polar : [num_users=1] = call_function[target=torch.ops.aten.polar.default](args = (%x, %angle), kwargs = {})
    return (polar,)

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=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¶

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 380, 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 135, in cond
    return cond_op(pred, true_fn, false_fn, operands)

Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"

nostrict¶

FAILED

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 184, 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 184, 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 341, in forward
    a, b, c = torch.cond(
  File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 135, in cond
    return cond_op(pred, true_fn, false_fn, operands)

Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"

nostrict¶

FAILED

Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s35, 12), dtype=torch.int64), FakeTensor(..., size=(s58, s43)), 1025].

nostrict-decall¶

FAILED

Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s35, 12), dtype=torch.int64), FakeTensor(..., size=(s58, s43)), 1025].

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 399, 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:398 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:399 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:399 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 399, 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 418, 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:416 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:417 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:421 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:418 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:418 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 418, 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 444, 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 472, 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 499, 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=1] = placeholder[target=x]
    %y : [num_users=1] = placeholder[target=y]
    %scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
    %scan : [num_users=2] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%y], [%x], ()), kwargs = {})
    %getitem : [num_users=0] = call_function[target=operator.getitem](args = (%scan, 0), kwargs = {})
    %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
    return (getitem_1,)

nostrict-decall¶

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=1] = placeholder[target=x]
    %y : [num_users=1] = placeholder[target=y]
    %scan_combine_graph_0 : [num_users=1] = get_attr[target=scan_combine_graph_0]
    %scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%y], [%x], ()), kwargs = {})
    %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%scan, 1), kwargs = {})
    return (getitem_1,)

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 577, 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 565, 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:734 in slice_forward)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="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:577 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:577 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 577, 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 529, 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

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['x', 'shape'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

nostrict¶

FAILED

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['x', 'shape'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

nostrict-decall¶

FAILED

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['x', 'shape'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

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

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

nostrict¶

FAILED

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

nostrict-decall¶

FAILED

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

InplaceSetItemEllipsis_2¶

forward¶

def forward(self, index, update):
    copy = self.params.clone()
    copy[..., index] = update
    return copy

strict¶

FAILED

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

nostrict¶

FAILED

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

nostrict-decall¶

FAILED

When `dynamic_shapes` is specified as a dict, its top-level keys must be the arg names ['index', 'update'] of `inputs`, but here they are ['x']. Alternatively, you could also ignore arg names entirely and specify `dynamic_shapes` as a list/tuple matching `inputs`. For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation

The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.

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=1] = placeholder[target=x]
    %lx_0 : [num_users=1] = placeholder[target=lx_0]
    %lx_1 : [num_users=1] = placeholder[target=lx_1]
    %linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
    %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
    %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
    %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
    %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
    return (add,)

nostrict¶

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=1] = placeholder[target=x]
    %lx_0 : [num_users=1] = placeholder[target=lx_0]
    %lx_1 : [num_users=1] = placeholder[target=lx_1]
    %linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
    %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
    %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
    %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
    %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %mul), kwargs = {})
    return (add,)

nostrict-decall¶

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=1] = placeholder[target=x]
    %lx_0 : [num_users=1] = placeholder[target=lx_0]
    %lx_1 : [num_users=1] = placeholder[target=lx_1]
    %permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
    %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
    %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %buff), kwargs = {})
    %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lx_1, [1], True), kwargs = {})
    %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%lx_0, %sum_1), kwargs = {})
    %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %mul_4), kwargs = {})
    return (add_15,)

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_3 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
    %linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
    %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
    %slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add, 0, 0, 9223372036854775807), kwargs = {})
    %slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, 0, %sym_size_int_3), kwargs = {})
    return (slice_2,)

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_2 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
    %linear : [num_users=1] = call_function[target=torch.ops.aten.linear.default](args = (%x, %linear_weight, %linear_bias), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%linear,), kwargs = {})
    %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
    %slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add,), kwargs = {})
    %slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, None, %sym_size_int_2), kwargs = {})
    return (slice_2,)

nostrict-decall¶

inputs: #1[(T1s4x3,T1s4x2)]
shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})

graph():
    %linear_weight : [num_users=1] = get_attr[target=linear.weight]
    %linear_bias : [num_users=1] = get_attr[target=linear.bias]
    %buff : [num_users=0] = get_attr[target=buff]
    %x : [num_users=2] = placeholder[target=x]
    %y : [num_users=1] = placeholder[target=y]
    %sym_size_int_5 : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%y, 1), kwargs = {})
    %permute : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%linear_weight, [1, 0]), kwargs = {})
    %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%linear_bias, %x, %permute), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
    %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %x), kwargs = {})
    %slice_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%add_6,), kwargs = {})
    %slice_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_1, 1, None, %sym_size_int_5), kwargs = {})
    return (slice_2,)

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,)

Summary¶

case	nostrict	nostrict-decall	strict
AtenAsStrided
AtenInterpolate
AtenNonZero
AtenNonZeroTuple
AtenRollPos
AtenRollRelu
BuildInIsInstance
BuildInLen	FAIL	FAIL	FAIL
ComplexPolar
ControlFlowCond
ControlFlowCond2Inputs
ControlFlowCond2Outputs
ControlFlowCondConstant
ControlFlowCondIdentity_153832	FAIL	FAIL	FAIL
ControlFlowCondNestedModule
ControlFlowCondNonZero	FAIL	FAIL	FAIL
ControlFlowNestCond
ControlFlowScan		FAIL	FAIL
ControlFlowScan2Carried		FAIL	FAIL
ControlFlowScanCDist			FAIL
ControlFlowScanCDist2			FAIL
ControlFlowScanCDistXY			FAIL
ControlFlowScanDecomposition_151564		FAIL	FAIL
ControlFlowScanInplace_153705	FAIL	FAIL	FAIL
CreateFromShape
CreateFromShapeThroughFunction
CropLastDimensionWithTensorContent	FAIL	FAIL	FAIL
CropLastDimensionWithTensorShape
InplaceAdd
InplaceAdd2
InplaceAdd_Mul
InplaceCloneAdd_
InplaceSetItemEllipsis_1	FAIL	FAIL	FAIL
InplaceSetItemEllipsis_2	FAIL	FAIL	FAIL
InplaceSetItemMask
InplaceSetItemSquare
InplaceSetItemSquareAdd
InplaceSetItemSquareAdd2
SignatureFloat1	FAIL	FAIL	FAIL
SignatureInt1	FAIL	FAIL	FAIL
SignatureInt2
SignatureListFixedLength
SignatureListFixedWithNone	FAIL	FAIL	FAIL
SignatureListVariableLength	FAIL	FAIL	FAIL
SignatureShapeAsIndex
TypeBFloat16