Overview of Exportability Comparison

The following script shows the exported program for many short cases to retrieve an ONNX model equivalent to the original model. Go to Bottom of the page to see a table summarizing the results.

<<<

import inspect
import textwrap
import pandas
from yobx.helpers import string_type
from yobx.helpers.onnx_helper import pretty_onnx
from yobx.torch.testing.model_eval_cases import discover, run_exporter
from yobx.ext_test_case import unit_test_going

cases = discover()
print()
print(":ref:`Summary <ledx-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} <ledx-model-case-export-{name}>`")
print()
print()

obs = []
for name, cls_model in sorted(cases.items()):
    print()
    print(f".. _ledx-model-case-export-{name}:")
    print()
    print(name)
    print("=" * len(name))
    print()
    print(f"code: :class:`yobx.torch.testing._model_eval_cases.{name}`")
    print()
    print("forward")
    print("+++++++")
    print()
    print(".. code-block:: python")
    print()
    src = inspect.getsource(cls_model.forward)
    if src:
        print(textwrap.indent(textwrap.dedent(src), "    "))
    else:
        print("    # code is missing")
    print()
    print()
    for exporter in ("yobx", "dynamo-ir", "tracing", "new-tracing"):
        expname = exporter.replace("export-", "")
        print()
        print(expname)
        print("+" * len(expname))
        print()
        res = run_exporter(exporter, cls_model, True, quiet=True)
        case_ref = f":ref:`{name} <ledx-model-case-export-{name}>`"
        expo = exporter
        if "inputs" in res:
            print(f"* **inputs:** ``{string_type(res['inputs'], with_shape=True)}``")
        if "dynamic_shapes" in res:
            print(f"* **shapes:** ``{string_type(res['dynamic_shapes'])}``")
        print()
        print()
        if "onx" in res:
            print(".. code-block:: text")
            print()
            print(textwrap.indent(pretty_onnx(res["onx"]), "    "))
            print()
            print()
            if "error" not in res:
                obs.append(dict(case=case_ref, error="", exporter=expo))
        if "error" in res:
            print("**FAILED**")
            print()
            print(".. code-block:: text")
            print()
            err = str(res["error"])
            if err:
                print(textwrap.indent(err, "    "))
            else:
                print("    # no error found for the failure")
            print()
            print()
            obs.append(dict(case=case_ref, error="FAIL", exporter=expo))

print()
print(".. _ledx-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

• ControlFlowIndirectRanks

• ControlFlowIndirectRanksCat

• ControlFlowNestCond

• ControlFlowNumelZero1

• ControlFlowNumelZero2

• ControlFlowRanks

• ControlFlowRanksType

• ControlFlowScan

• ControlFlowScan2Carried

• ControlFlowScanCDist

• ControlFlowScanCDist2

• ControlFlowScanCDistXY

• ControlFlowScanDecomposition_151564

• ControlFlowScanInplace_153705

• ControlFlowShapeCheck

• ControlFlowWhileDec

• ControlFlowWhileInc

• CreateFromShape

• CreateFromShapeThroughFunction

• CropLastDimensionWithTensorContent

• CropLastDimensionWithTensorShape

• ExportWithDimension0

• ExportWithDimension1

• ExportWithNewConstant

• ExportWithNewConstantTo

• InplaceAdd

• InplaceAdd2

• InplaceAdd_Mul

• InplaceCloneAdd_

• InplaceSetItemEllipsis_1

• InplaceSetItemEllipsis_2

• InplaceSetItemExp

• InplaceSetItemMask

• InplaceSetItemSquare

• InplaceSetItemSquareAdd

• InplaceSetItemSquareAdd2

• SignatureFloat1

• SignatureInt1

• SignatureInt2

• SignatureListFixedLength

• SignatureListFixedWithNone

• SignatureListVariableLength

• SignatureShapeAsIndex

• TypeBFloat16

• Vmap

• VmapPython

AtenAsStrided

code: yobx.torch.testing._model_eval_cases.AtenAsStrided

forward

def forward(self, x):
    y = torch.as_strided(x, (2, 2, 8, 4), (128, 8, 16, 1))
    return y

yobx

• inputs: #1[(T1s2x2x8x8,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 2, 8, 8]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1])         -- Opset.make_node.1/Shape
init: name='init7_s128_' type=int64 shape=(128,)                      -- Opset.make_node.0
init: name='init7_s4_2_2_8_4' type=int64 shape=(4,) -- array([2, 2, 8, 4])-- Opset.make_node.1/Shape
Reshape(x, init7_s1_-1) -> x::RSh-1
  Gather(x::RSh-1, init7_s128_) -> _onx_gather_x::RSh-1
    Reshape(_onx_gather_x::RSh-1, init7_s4_2_2_8_4) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 2, 8, 4]

dynamo-ir

• inputs: #1[(T1s2x2x8x8,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 2, 8, 8]
init: name='rank_tensor' type=int64 shape=(1,) -- array([4])
init: name='val_0' type=int64 shape=(4,) -- array([2, 2, 8, 4])
init: name='val_1' type=int64 shape=(4,) -- array([128,   8,  16,   1])
init: name='neg_1' type=int64 shape=(1,) -- array([-1])
init: name='indices' type=int64 shape=() -- array([0])
init: name='rank_0' type=int64 shape=() -- array([4])
init: name='int64_1_cast' type=int64 shape=() -- array([1])
init: name='tmp_14' type=float32 shape=(1,) -- array([1.], dtype=float32)
Reshape(x, neg_1) -> self_flatten
SequenceEmpty() -> one_seq
Loop(rank_0, , indices, one_seq, body=G1) -> indices_16, one_seq_17
  CastLike(indices, indices_16) -> storage_offset_cast
  Add(indices_16, storage_offset_cast) -> indices_19
  Gather(self_flatten, indices_19) -> as_strided
output: name='as_strided' type=dtype('float32') shape=[2, 2, 8, 4]
----- subgraph ---- Loop - n6_2 - att.body=G1 -- level=1 -- i,cond_in,indices_1,one_seq_2 -> cond_out,indices_13,one_seq_15
input: name='i' type=dtype('int64') shape=None
input: name='cond_in' type=dtype('bool') shape=None
input: name='indices_1' type='NOTENSOR' shape=None
input: name='one_seq_2' type='NOTENSOR' shape=None
Equal(i, indices) -> cond
Sub(rank_0, i) -> tmp
Sub(tmp, int64_1_cast) -> j
Reshape(j, neg_1) -> j_tensor
Gather(val_0, j_tensor, axis=0) -> size_dim_j
Range(indices, size_dim_j, int64_1_cast) -> tmp_6
Slice(val_0, j_tensor, rank_tensor) -> size_after_j
  Expand(indices_1, size_after_j) -> indices_4
Gather(val_1, j_tensor, axis=0) -> stride_dim_j
  Mul(tmp_6, stride_dim_j) -> add_value
If(cond, then_branch=G2, else_branch=G3) -> shape_11
  Reshape(add_value, shape_11) -> add_value_12
    Add(indices_4, add_value_12) -> indices_13
SequenceInsert(one_seq_2, tmp_14) -> one_seq_15
Identity(cond_in) -> cond_out
output: name='cond_out' type=dtype('bool') shape=None
output: name='indices_13' type='NOTENSOR' shape=None
output: name='one_seq_15' type='NOTENSOR' shape=None
----- subgraph ---- If - n20 - att.then_branch=G2 -- level=2 --  -> shape
Identity(size_dim_j) -> shape
output: name='shape' type=dtype('int64') shape=[1]
----- subgraph ---- If - n20 - att.else_branch=G3 -- level=2 --  -> shape_10
Cast(size_dim_j, to=1) -> tmp_8
ConcatFromSequence(one_seq_2, axis=0) -> ones
  Concat(tmp_8, ones, axis=0) -> shape_9
    Cast(shape_9, to=7) -> shape_10
output: name='shape_10' type=dtype('int64') shape=None
----- subgraph ---- If - n20 - att.then_branch=G2 -- level=1 --  -> shape
Identity(size_dim_j) -> shape
output: name='shape' type=dtype('int64') shape=[1]
----- subgraph ---- If - n20 - att.else_branch=G3 -- level=1 --  -> shape_10
Cast(size_dim_j, to=1) -> tmp_8
ConcatFromSequence(one_seq_2, axis=0) -> ones
  Concat(tmp_8, ones, axis=0) -> shape_9
    Cast(shape_9, to=7) -> shape_10
output: name='shape_10' type=dtype('int64') shape=None

tracing

• inputs: #1[(T1s2x2x8x8,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 2, 8, 8]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1])         -- Opset.make_node.1/Shape
init: name='init7_s128_' type=int64 shape=(128,)                      -- Opset.make_node.0
init: name='init7_s4_2_2_8_4' type=int64 shape=(4,) -- array([2, 2, 8, 4])-- Opset.make_node.1/Shape
Reshape(x, init7_s1_-1) -> x::RSh-1
  Gather(x::RSh-1, init7_s128_) -> _onx_gather_x::RSh-1
    Reshape(_onx_gather_x::RSh-1, init7_s4_2_2_8_4) -> output
output: name='output' type=dtype('float32') shape=[2, 2, 8, 4]

new-tracing

• inputs: #1[(T1s2x2x8x8,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 2, 8, 8]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1])         -- Opset.make_node.1/Shape
init: name='init7_s128_' type=int64 shape=(128,)                      -- Opset.make_node.0
init: name='init7_s4_2_2_8_4' type=int64 shape=(4,) -- array([2, 2, 8, 4])-- Opset.make_node.1/Shape
Reshape(x, init7_s1_-1) -> x::RSh-1
  Gather(x::RSh-1, init7_s128_) -> _onx_gather_x::RSh-1
    Reshape(_onx_gather_x::RSh-1, init7_s4_2_2_8_4) -> output
output: name='output' type=dtype('float32') shape=[2, 2, 8, 4]

AtenInterpolate

code: yobx.torch.testing._model_eval_cases.AtenInterpolate

forward

def forward(self, x):
    y = torch.nn.functional.interpolate(
        x, scale_factor=2.0, mode="bilinear", recompute_scale_factor=False
    )
    return y

yobx

• inputs: #1[(T1s2x2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 2, 3, 4]
init: name='init7_s2_6_8' type=int64 shape=(2,) -- array([6, 8])      -- _aten_upsample_output_size.rsize
Shape(x, end=2, start=0) -> x::Shape:2
  Concat(x::Shape:2, init7_s2_6_8, axis=0) -> _onx_concat_x::Shape:2
Resize(x, , , _onx_concat_x::Shape:2, coordinate_transformation_mode=b'pytorch_half_pixel', mode=b'linear', nearest_mode=b'floor') -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 2, 6, 8]

dynamo-ir

• inputs: #1[(T1s2x2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 2, 3, 4]
init: name='val_0' type=float32 shape=(4,) -- array([1., 1., 2., 2.], dtype=float32)
Resize(x, , val_0, keep_aspect_ratio_policy=b'stretch', antialias=0, extrapolation_value=0.00, exclude_outside=0, nearest_mode=b'floor', coordinate_transformation_mode=b'pytorch_half_pixel', cubic_coeff_a=-0.75, mode=b'linear') -> upsample_bilinear2d
output: name='upsample_bilinear2d' type=dtype('float32') shape=['batch', 2, 6, 8]

tracing

• inputs: #1[(T1s2x2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 2, 3, 4]
init: name='init7_s2_6_8' type=int64 shape=(2,) -- array([6, 8])      -- _aten_upsample_output_size.rsize
Shape(x, end=2, start=0) -> x::Shape:2
  Concat(x::Shape:2, init7_s2_6_8, axis=0) -> _onx_concat_x::Shape:2
Resize(x, , , _onx_concat_x::Shape:2, coordinate_transformation_mode=b'pytorch_half_pixel', mode=b'linear', nearest_mode=b'floor') -> output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1', 'd_output_2', 'd_output_3']

new-tracing

• inputs: #1[(T1s2x2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 2, 3, 4]
init: name='init7_s2_6_8' type=int64 shape=(2,) -- array([6, 8])      -- _aten_upsample_output_size.rsize
Shape(x, end=2, start=0) -> x::Shape:2
  Concat(x::Shape:2, init7_s2_6_8, axis=0) -> _onx_concat_x::Shape:2
Resize(x, , , _onx_concat_x::Shape:2, coordinate_transformation_mode=b'pytorch_half_pixel', mode=b'linear', nearest_mode=b'floor') -> output
output: name='output' type=dtype('float32') shape=['batch', 2, 6, 8]

AtenNonZero

code: yobx.torch.testing._model_eval_cases.AtenNonZero

forward

def forward(self, x):
    y = torch.nonzero(x)
    return y

yobx

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
NonZero(x) -> _onx_nonzero_x
  Transpose(_onx_nonzero_x, perm=[1,0]) -> output_0
output: name='output_0' type=dtype('int64') shape=['NEWDIM_nonzero', 2]

dynamo-ir

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
NonZero(x) -> val_0
  Transpose(val_0, perm=[1,0]) -> nonzero
output: name='nonzero' type=dtype('int64') shape=['u0', 2]

tracing

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
NonZero(x) -> _onx_nonzero_x
  Transpose(_onx_nonzero_x, perm=[1,0]) -> output
output: name='output' type=dtype('int64') shape=['NEWDIM_nonzero', 2]

new-tracing

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
NonZero(x) -> _onx_nonzero_x
  Transpose(_onx_nonzero_x, perm=[1,0]) -> output
output: name='output' type=dtype('int64') shape=['NEWDIM_nonzero', 2]

AtenNonZeroTuple

code: yobx.torch.testing._model_eval_cases.AtenNonZeroTuple

forward

def forward(self, x):
    y = torch.nonzero(x, as_tuple=True)
    return y[0], y[1]

yobx

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- ReshapeIsSqueezePattern.m1##ReshapeIsSqueezePattern.m1
NonZero(x) -> _onx_nonzero_x
  Split(_onx_nonzero_x, num_outputs=2) -> _onx_split_nonzero_x_0, _onx_split_nonzero_x_1
    Squeeze(_onx_split_nonzero_x_0, init7_s1_0) -> output_0
Squeeze(_onx_split_nonzero_x_1, init7_s1_0) -> output_1
output: name='output_0' type=dtype('int64') shape=['u0']
output: name='output_1' type=dtype('int64') shape=['u0']

dynamo-ir

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='val_7' type=int64 shape=(1,) -- array([1])
NonZero(x) -> val_0
  Transpose(val_0, perm=[1,0]) -> nonzero
    Split(nonzero, num_outputs=2, axis=-1) -> val_6, val_11
      Squeeze(val_6, val_7) -> getitem
Squeeze(val_11, val_7) -> getitem_1
output: name='getitem' type=dtype('int64') shape=['u0']
output: name='getitem_1' type=dtype('int64') shape=['u0']

tracing

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- ReshapeIsSqueezePattern.m1##ReshapeIsSqueezePattern.m1
NonZero(x) -> _onx_nonzero_x
  Split(_onx_nonzero_x, num_outputs=2) -> _onx_split_nonzero_x_0, _onx_split_nonzero_x_1
    Squeeze(_onx_split_nonzero_x_0, init7_s1_0) -> output_0
Squeeze(_onx_split_nonzero_x_1, init7_s1_0) -> output_1
output: name='output_0' type=dtype('int64') shape=['NEWDIM_nonzero']
output: name='output_1' type=dtype('int64') shape=['NEWDIM_nonzero']

new-tracing

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- aten_unbind_int.dim_np
NonZero(x) -> _onx_nonzero_x
  Transpose(_onx_nonzero_x, perm=[1,0]) -> nonzero_default
    Split(nonzero_default, axis=1, num_outputs=2) -> unbind_int_u0, unbind_int_u1
      Squeeze(unbind_int_u0, init7_s1_1) -> output_0
Squeeze(unbind_int_u1, init7_s1_1) -> output_1
output: name='output_0' type=dtype('int64') shape=['NEWDIM_nonzero']
output: name='output_1' type=dtype('int64') shape=['NEWDIM_nonzero']

AtenRollPos

code: yobx.torch.testing._model_eval_cases.AtenRollPos

forward

def forward(self, x):
    return torch.roll(x, 1, -1)

yobx

• inputs: #1[(T1s2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3, 4]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1])         -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_4' type=int64 shape=(1,) -- array([4])           -- Opset.make_node.1/Shape
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape
Slice(x, init7_s1_-1, init7_s1_4, init7_s1_-1) -> _onx_slice_x
Slice(x, init7_s1_0, init7_s1_-1, init7_s1_-1) -> _onx_slice_x2
  Concat(_onx_slice_x, _onx_slice_x2, axis=-1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 3, 4]

dynamo-ir

FAILED

An error occurred when running the '<onnx_ir.passes.PassManager object at 0x7e2873886ab0>' pass after the following passes: ['<onnx_ir.passes.common.inliner.InlinePass object at 0x7e28738862a0>']

tracing

• inputs: #1[(T1s2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3, 4]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1])         -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_4' type=int64 shape=(1,) -- array([4])           -- Opset.make_node.1/Shape
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape
Slice(x, init7_s1_-1, init7_s1_4, init7_s1_-1) -> _onx_slice_x
Slice(x, init7_s1_0, init7_s1_-1, init7_s1_-1) -> _onx_slice_x2
  Concat(_onx_slice_x, _onx_slice_x2, axis=-1) -> output
output: name='output' type=dtype('float32') shape=['batch', 3, 4]

new-tracing

FAILED

Could not guard on data-dependent expression Eq(12*u0, 0) (unhinted: Eq(12*u0, 0)).  (Size-like symbols: none)

consider using data-dependent friendly APIs such as guard_or_false, guard_or_true and statically_known_true.
Caused by: (_refs/__init__.py:4096 in roll)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing

For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1

AtenRollRelu

code: yobx.torch.testing._model_eval_cases.AtenRollRelu

forward

def forward(self, x):
    return torch.relu(torch.roll(x, -1, -1))

yobx

• inputs: #1[(T1s2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3, 4]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_4' type=int64 shape=(1,) -- array([4])           -- Opset.make_node.1/Shape
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1])         -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape
Slice(x, init7_s1_1, init7_s1_4, init7_s1_-1) -> _onx_slice_x
Slice(x, init7_s1_0, init7_s1_1, init7_s1_-1) -> _onx_slice_x2
  Concat(_onx_slice_x, _onx_slice_x2, axis=-1) -> _onx_concat_slice_x
    Relu(_onx_concat_slice_x) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 3, 4]

dynamo-ir

• inputs: #1[(T1s2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3, 4]
init: name='val_0' type=int64 shape=(1,) -- array([-1])
init: name='val_2' type=int64 shape=(1,) -- array([1])
init: name='val_3' type=int64 shape=(1,) -- array([0])
Size(x) -> val_5
  Reshape(val_5, val_0, allowzero=0) -> val_6
    Slice(x, val_2, val_6, val_0) -> val_7
Slice(x, val_3, val_2, val_0) -> val_4
  Concat(val_7, val_4, axis=-1) -> roll
    Relu(roll) -> relu
output: name='relu' type=dtype('float32') shape=['batch', 3, 4]

tracing

• inputs: #1[(T1s2x3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3, 4]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_4' type=int64 shape=(1,) -- array([4])           -- Opset.make_node.1/Shape
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1])         -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape
Slice(x, init7_s1_1, init7_s1_4, init7_s1_-1) -> _onx_slice_x
Slice(x, init7_s1_0, init7_s1_1, init7_s1_-1) -> _onx_slice_x2
  Concat(_onx_slice_x, _onx_slice_x2, axis=-1) -> _onx_concat_slice_x
    Relu(_onx_concat_slice_x) -> output
output: name='output' type=dtype('float32') shape=['batch', 3, 4]

new-tracing

FAILED

Could not guard on data-dependent expression Eq(12*u0, 0) (unhinted: Eq(12*u0, 0)).  (Size-like symbols: none)

consider using data-dependent friendly APIs such as guard_or_false, guard_or_true and statically_known_true.
Caused by: (_refs/__init__.py:4096 in roll)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing

For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1

BuildInIsInstance

code: yobx.torch.testing._model_eval_cases.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

yobx

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='b_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([ 0.21657902, -0.08821903, -0.48450878], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.43308964], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, b_buff) -> sub
ReduceSum(lx_1, init7_s1_1, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul
    Add(sub, mul) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

dynamo-ir

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.2733309 , -0.40132207,  0.22971864], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.5482479], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1])
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, buff) -> sub_4
ReduceSum(lx_1, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul_1
    Add(sub_4, mul_1) -> add_15
output: name='add_15' type=dtype('float32') shape=['batch', 1]

tracing

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='_traced_m2.linear.bias' type=float32 shape=(1,) -- array([0.08145382], dtype=float32)-- GraphBuilder.make_nodes/from_traced_m2.linear.bias##DynamoInterpret.get_attr.1/P(_traced_m2.linear.bias)
init: name='_traced_m2_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.get_attr.0
init: name='GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.54795367,  0.19638336, -0.24336351], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_ime___traced_m2_linear_weight::T10,init7_s2_1_3)##_sub_ime___traced_m2_linear_weight::T10/GraphBuilder.constant_folding.from/fold(_traced_m2.linear.weight)##_traced_m2.linear.weight/GraphBuilder.make_nodes/from_traced_m2.linear.weight##DynamoInterpret.get_attr.1/P(_traced_m2.linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
Gemm(x, GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10, _traced_m2.linear.bias, transB=1) -> _sub_ime___traced_m2_linear_linear
  Sigmoid(_sub_ime___traced_m2_linear_linear) -> sigmoid
    Sub(sigmoid, _traced_m2_buff) -> sub
ReduceSum(lx_1, init7_s1_1, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul
    Add(sub, mul) -> output
output: name='output' type=dtype('float32') shape=['batch', 1]

new-tracing

FAILED

'Dim' object has no attribute 'items'

BuildInLen

code: yobx.torch.testing._model_eval_cases.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

yobx

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#3[T1s8x1,T1s8x2,T1s8x3])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='b_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.39686862, -0.12332802, -0.19533376], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.32887343], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, b_buff) -> sub
ReduceSum(lx_1, init7_s1_1, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul
    Add(sub, mul) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

FAILED

diff.1

dynamo-ir

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#3[T1s8x1,T1s8x2,T1s8x3])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.17662609,  0.47379133, -0.45595127], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.17736335], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1])
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, buff) -> sub_4
ReduceSum(lx_1, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul_1
    Add(sub_4, mul_1) -> add_15
output: name='add_15' type=dtype('float32') shape=['batch', 1]

FAILED

diff.1

tracing

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#3[T1s8x1,T1s8x2,T1s8x3])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='_traced_m2.linear.bias' type=float32 shape=(1,) -- array([0.283617], dtype=float32)-- GraphBuilder.make_nodes/from_traced_m2.linear.bias##DynamoInterpret.get_attr.1/P(_traced_m2.linear.bias)
init: name='_traced_m2_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.get_attr.0
init: name='GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.40892956,  0.3099942 , -0.4227712 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_ime___traced_m2_linear_weight::T10,init7_s2_1_3)##_sub_ime___traced_m2_linear_weight::T10/GraphBuilder.constant_folding.from/fold(_traced_m2.linear.weight)##_traced_m2.linear.weight/GraphBuilder.make_nodes/from_traced_m2.linear.weight##DynamoInterpret.get_attr.1/P(_traced_m2.linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
Gemm(x, GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10, _traced_m2.linear.bias, transB=1) -> _sub_ime___traced_m2_linear_linear
  Sigmoid(_sub_ime___traced_m2_linear_linear) -> sigmoid
    Sub(sigmoid, _traced_m2_buff) -> sub
ReduceSum(lx_1, init7_s1_1, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul
    Add(sub, mul) -> output
output: name='output' type=dtype('float32') shape=['batch', 1]

FAILED

diff.1

new-tracing

FAILED

'Dim' object has no attribute 'items'

ComplexPolar

code: yobx.torch.testing._model_eval_cases.ComplexPolar

forward

def forward(self, x, angle):
    return torch.polar(x, angle)

yobx

• inputs: #1[(T1s4x4,T1s4x4)]

• shapes: dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='angle' type=dtype('float32') shape=['batch', 4]
init: name='init14_s1_' type=complex64 shape=(1,) -- array([0.+1.j], dtype=complex64)-- Opset.make_node.1/Small
Cast(x, to=14) -> x::C14
Cos(angle) -> _onx_cos_angle
  Cast(_onx_cos_angle, to=14) -> _onx_cos_angle::C14
Sin(angle) -> _onx_sin_angle
  Cast(_onx_sin_angle, to=14) -> _onx_sin_angle::C14
    Mul(_onx_sin_angle::C14, init14_s1_) -> _onx_mul_sin_angle::C14
    Add(_onx_cos_angle::C14, _onx_mul_sin_angle::C14) -> _onx_add_cos_angle::C14
  Mul(x::C14, _onx_add_cos_angle::C14) -> output_0
output: name='output_0' type=dtype('complex64') shape=['batch', 4]

FAILED

[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(complex64)' of input parameter (_onx_sin_angle::C14) of operator (Mul) in node (polar5) is invalid.

dynamo-ir

• inputs: #1[(T1s4x4,T1s4x4)]

• shapes: dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='angle' type=dtype('float32') shape=['batch', 4]
init: name='int64_m1_1d' type=int64 shape=(1,) -- array([-1])
Cos(angle) -> tmp
  Mul(x, tmp) -> tmp_0
    Unsqueeze(tmp_0, int64_m1_1d) -> real
Sin(angle) -> tmp_1
  Mul(x, tmp_1) -> tmp_2
    Unsqueeze(tmp_2, int64_m1_1d) -> imag
      Concat(real, imag, axis=-1) -> polar
output: name='polar' type=dtype('float32') shape=['batch', 4, 2]

FAILED

diff.0

tracing

• inputs: #1[(T1s4x4,T1s4x4)]

• shapes: dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='angle' type=dtype('float32') shape=['batch', 4]
init: name='init14_s1_' type=complex64 shape=(1,) -- array([0.+1.j], dtype=complex64)-- Opset.make_node.1/Small
Cast(x, to=14) -> x::C14
Cos(angle) -> _onx_cos_angle
  Cast(_onx_cos_angle, to=14) -> _onx_cos_angle::C14
Sin(angle) -> _onx_sin_angle
  Cast(_onx_sin_angle, to=14) -> _onx_sin_angle::C14
    Mul(_onx_sin_angle::C14, init14_s1_) -> _onx_mul_sin_angle::C14
    Add(_onx_cos_angle::C14, _onx_mul_sin_angle::C14) -> _onx_add_cos_angle::C14
  Mul(x::C14, _onx_add_cos_angle::C14) -> output
output: name='output' type=dtype('complex64') shape=['batch', 4]

FAILED

[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(complex64)' of input parameter (_onx_sin_angle::C14) of operator (Mul) in node (polar5) is invalid.

new-tracing

E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] failed while attempting to run meta for aten.complex.default E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] Traceback (most recent call last): E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py”, line 2935, in _dispatch_impl E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] r = func(*args, **kwargs) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/_ops.py”, line 871, in __call__ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return self._op(*args, **kwargs) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/_prims_common/wrappers.py”, line 314, in _fn E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] result = fn(*args, **kwargs) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/_meta_registrations.py”, line 3573, in meta_complex E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] real.to(corresponding_complex_dtype(real.dtype)), E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/utils/_stats.py”, line 29, in wrapper E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return fn(*args, **kwargs) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py”, line 900, in __torch_dispatch__ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return handler(args) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py”, line 3423, in <lambda> E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] torch.ops.aten.size.default: lambda args: tuple( E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/_subclasses/fake_tensor.py”, line 3424, in <genexpr> E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] int(s) for s in cast(Tensor, args[0]).size() E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/__init__.py”, line 463, in __int__ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return self.node.int_() E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py”, line 472, in int_ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return self.guard_int(“”, 0) # NB: uses Python backtrace E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py”, line 522, in guard_int E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] r = self.evaluate() E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py”, line 516, in evaluate E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return self.shape_env.evaluate_sym_node(self, size_oblivious) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py”, line 7849, in evaluate_sym_node E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return self.evaluate_expr( E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py”, line 7945, in evaluate_expr E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return self._inner_evaluate_expr( E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/recording.py”, line 285, in wrapper E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return retlog(fn(*args, **kwargs)) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py”, line 7968, in _inner_evaluate_expr E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] return self._evaluate_expr( E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] ^^^^^^^^^^^^^^^^^^^^ E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py”, line 8201, in _evaluate_expr E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] raise self._make_data_dependent_error( E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: none) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] Caused by: (utils/_stats.py:29 in wrapper) E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] For more information, run with TORCH_LOGS=”dynamic” E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=”u0” E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] E0409 00:09:15.905000 15502 torch/_subclasses/fake_tensor.py:2939] For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1

FAILED

Could not extract specialized integer from data-dependent expression u0 (unhinted: u0).  (Size-like symbols: none)


Caused by: (utils/_stats.py:29 in wrapper)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing

For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1

ControlFlowCond

code: yobx.torch.testing._model_eval_cases.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])

yobx

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt_Scalar)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> cond#0
Cos(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> cond#0
Sin(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None

dynamo-ir

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='scalar_tensor_default' type=float32 shape=() -- array([0.], dtype=float32)
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Greater(sum_1, scalar_tensor_default) -> gt
    If(gt, then_branch=G1, else_branch=G2) -> getitem
output: name='getitem' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0 - att.then_branch=G1 -- level=1 --  -> sin_true_graph_0
Sin(x) -> sin_true_graph_0
output: name='sin_true_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0 - att.else_branch=G2 -- level=1 --  -> cos_false_graph_0
Cos(x) -> cos_false_graph_0
output: name='cos_false_graph_0' type=dtype('float32') shape=['batch', 3]

tracing

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output
output: name='output' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> condcc
Cos(x) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> condcc
Sin(x) -> condcc
output: name='condcc' type='NOTENSOR' shape=None

new-tracing

FAILED

invalid syntax (<eval_with_key>.386, line 7)

ControlFlowCond2Inputs

code: yobx.torch.testing._model_eval_cases.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])

yobx

• inputs: #1[(T1s5x3,T1s5x3)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='y' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt_Scalar)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0, output_1
output: name='output_0' type=dtype('float32') shape=['batch', 3]
output: name='output_1' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> cond#0,cond#1
Cos(x) -> cond#0
Sin(x) -> sin2
Add(sin2, y) -> cond#1
output: name='cond#0' type='NOTENSOR' shape=None
output: name='cond#1' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> cond#0,cond#1
Cos(x) -> cos2
Add(cos2, y) -> cond#1
Sin(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
output: name='cond#1' type='NOTENSOR' shape=None

dynamo-ir

• inputs: #1[(T1s5x3,T1s5x3)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='y' type=dtype('float32') shape=['batch', 3]
init: name='scalar_tensor_default' type=float32 shape=() -- array([0.], dtype=float32)
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Greater(sum_1, scalar_tensor_default) -> gt
    If(gt, then_branch=G1, else_branch=G2) -> getitem, getitem_1
output: name='getitem' type=dtype('float32') shape=['batch', 3]
output: name='getitem_1' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__1 - att.then_branch=G1 -- level=1 --  -> sin_true_graph_0,add_12_true_graph_0
Cos(x) -> cos
Add(cos, y) -> add_12_true_graph_0
Sin(x) -> sin_true_graph_0
output: name='sin_true_graph_0' type=dtype('float32') shape=['batch', 3]
output: name='add_12_true_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__1 - att.else_branch=G2 -- level=1 --  -> cos_false_graph_0,add_12_false_graph_0
Cos(x) -> cos_false_graph_0
Sin(x) -> sin_2
Add(sin_2, y) -> add_12_false_graph_0
output: name='cos_false_graph_0' type=dtype('float32') shape=['batch', 3]
output: name='add_12_false_graph_0' type=dtype('float32') shape=['batch', 3]

tracing

• inputs: #1[(T1s5x3,T1s5x3)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='y' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0, output_1
output: name='output_0' type=dtype('float32') shape=['batch', 3]
output: name='output_1' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> condcc#0,condcc#1
Cos(x) -> condcc#0
Sin(x) -> sin2
Add(sin2, y) -> condcc#1
output: name='condcc#0' type='NOTENSOR' shape=None
output: name='condcc#1' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> condcc#0,condcc#1
Cos(x) -> cos2
Add(cos2, y) -> condcc#1
Sin(x) -> condcc#0
output: name='condcc#0' type='NOTENSOR' shape=None
output: name='condcc#1' type='NOTENSOR' shape=None

new-tracing

FAILED

invalid syntax (<eval_with_key>.533, line 7)

ControlFlowCond2Outputs

code: yobx.torch.testing._model_eval_cases.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])

yobx

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt_Scalar)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0, output_1
output: name='output_0' type=dtype('float32') shape=['batch', 3]
output: name='output_1' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> cond#0,cond#1
Cos(x) -> cond#0
Sin(x) -> cond#1
output: name='cond#0' type='NOTENSOR' shape=None
output: name='cond#1' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> cond#0,cond#1
Cos(x) -> cond#1
Sin(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
output: name='cond#1' type='NOTENSOR' shape=None

dynamo-ir

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='scalar_tensor_default' type=float32 shape=() -- array([0.], dtype=float32)
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Greater(sum_1, scalar_tensor_default) -> gt
    If(gt, then_branch=G1, else_branch=G2) -> getitem, getitem_1
output: name='getitem' type=dtype('float32') shape=['batch', 3]
output: name='getitem_1' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__1 - att.then_branch=G1 -- level=1 --  -> sin_true_graph_0,cos_true_graph_0
Cos(x) -> cos_true_graph_0
Sin(x) -> sin_true_graph_0
output: name='sin_true_graph_0' type=dtype('float32') shape=['batch', 3]
output: name='cos_true_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__1 - att.else_branch=G2 -- level=1 --  -> cos_false_graph_0,sin_false_graph_0
Cos(x) -> cos_false_graph_0
Sin(x) -> sin_false_graph_0
output: name='cos_false_graph_0' type=dtype('float32') shape=['batch', 3]
output: name='sin_false_graph_0' type=dtype('float32') shape=['batch', 3]

tracing

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0, output_1
output: name='output_0' type=dtype('float32') shape=['batch', 3]
output: name='output_1' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> condcc#0,condcc#1
Cos(x) -> condcc#0
Sin(x) -> condcc#1
output: name='condcc#0' type='NOTENSOR' shape=None
output: name='condcc#1' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> condcc#0,condcc#1
Cos(x) -> condcc#1
Sin(x) -> condcc#0
output: name='condcc#0' type='NOTENSOR' shape=None
output: name='condcc#1' type='NOTENSOR' shape=None

new-tracing

FAILED

invalid syntax (<eval_with_key>.680, line 7)

ControlFlowCondConstant

code: yobx.torch.testing._model_eval_cases.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])

yobx

• inputs: #1[(T1s1024x1024,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 1024]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt_Scalar)
init: name='init7_s2_1_10242_cst2init' type=int64 shape=(2,) -- array([   1, 1024])-- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init7_s1_10242_cst2init' type=int64 shape=(1,) -- array([1024])-- GraphBuilderPatternOptimization.make_initializer.1/Shape
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1024]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> cond#0
ConstantOfShape(init7_s2_1_10242_cst2init, value=[1.0]) -> ones2
Cos(x) -> cos2
  Add(cos2, ones2) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> cond#0
Shape(x, end=1, start=0) -> x::Shape:12
Concat(x::Shape:12, init7_s1_10242_cst2init, axis=0) -> _onx_concat_sym_size_int_1::UnSq02
  ConstantOfShape(_onx_concat_sym_size_int_1::UnSq02, value=[1.0]) -> ones32
Sin(x) -> sin2
  Sub(sin2, ones32) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None

dynamo-ir

• inputs: #1[(T1s1024x1024,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 1024]
init: name='scalar_tensor_default' type=float32 shape=() -- array([0.], dtype=float32)
init: name='val_7' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1024])
init: name='ones_2' type=float32 shape=(1, 1024)
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Greater(sum_1, scalar_tensor_default) -> gt
    If(gt, then_branch=G1, else_branch=G2) -> getitem
output: name='getitem' type=dtype('float32') shape=['batch', 1024]
----- subgraph ---- If - node_cond__0 - att.then_branch=G1 -- level=1 --  -> sub_3_true_graph_0
Shape(x, end=1, start=0) -> val_0_2
Concat(val_0_2, val_3, axis=0) -> val_4
Expand(val_7, val_4) -> ones
Sin(x) -> sin
  Sub(sin, ones) -> sub_3_true_graph_0
output: name='sub_3_true_graph_0' type=dtype('float32') shape=['batch', 1024]
----- subgraph ---- If - node_cond__0 - att.else_branch=G2 -- level=1 --  -> add_6_false_graph_0
Cos(x) -> cos
Add(cos, ones_2) -> add_6_false_graph_0
output: name='add_6_false_graph_0' type=dtype('float32') shape=['batch', 1024]

tracing

• inputs: #1[(T1s1024x1024,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 1024]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt)
init: name='init7_s2_1_10242_cst2init' type=int64 shape=(2,) -- array([   1, 1024])-- GraphBuilderPatternOptimization.make_initializer.1/Shape
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1']
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> condcc
ConstantOfShape(init7_s2_1_10242_cst2init, value=[1.0]) -> ones2
Cos(x) -> cos2
  Add(cos2, ones2) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> condcc
Shape(x) -> size2
  ConstantOfShape(size2, value=[1.0]) -> ones32
Sin(x) -> sin2
  Sub(sin2, ones32) -> condcc
output: name='condcc' type='NOTENSOR' shape=None

new-tracing

FAILED

ones() received an invalid combination of arguments - got (TracingShape, dtype=torch.dtype), but expected one of:
 * (tuple of ints size, *, tuple of names names, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (tuple of ints size, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)

ControlFlowCondIdentity_153832

code: yobx.torch.testing._model_eval_cases.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

yobx

FAILED

This higher order operator doesn't work unless it is captured completely with torch.compile. Got graph break/error:

Encountered aliasing during higher order op tracing
  Higher Order Operator: torch.cond
  Explanation: Higher order ops do not support aliasing. Found in <bound method HigherOrderOperator.name of <torch._higher_order_ops.cond.CondOp object at 0x7e28841f4ad0>>
  Hint: Replace `return input` with `return input.clone()` to avoid aliasing.
  Hint: Consider using the debug context to change user code to avoid aliasing.
  Hint: Please open an issue.

  Developer debug context: Input-to-output aliasing detected at nodes l_args_3_0_ and l_args_3_0_ in
     graph():
        %l_args_3_0_ : torch._subclasses.fake_tensor.FakeTensor [num_users=1] = placeholder[target=l_args_3_0_]
        return (l_args_3_0_,)

 For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb0040.html

from user code:
   File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 242, in _cond_op_wrapper
    return cond_op(*args, **kwargs)
  File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 1152, in __torch_function__
    return func(*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"

dynamo-ir

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'torch._dynamo.exc.UncapturedHigherOrderOpError'>: This higher order operator doesn't work unless it is captured completely with torch.compile. Got graph break/error:

Encountered aliasing during higher order op tracing
  Higher Order Operator: torch.cond
  Explanation: Higher order ops do not support aliasing. Found in <bound method HigherOrderOperator.name of <torch._higher_order_ops.cond.CondOp object at 0x7e28841f4ad0>>
  Hint: Replace `return input` with `return input.clone()` to avoid aliasing.
  Hint: Consider using the debug context to change user code to avoid aliasing.
  Hint: Please open an issue.

  Developer debug context: Input-to-output aliasing detected at nodes l_args_3_0_ and l_args_3_0_ in
     graph():
        %l_args_3_0_ : torch._subclasses.fake_tensor.FakeTensor [num_users=1] = placeholder[target=l_args_3_0_]
        return (l_args_3_0_,)

 For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb0040.html

from user code:
   File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 242, in _cond_op_wrapper
    return cond_op(*args, **kwargs)
  File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 1152, in __torch_function__
    return func(*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"


(Refer to the full stack trace above for more information.)

tracing

FAILED

This higher order operator doesn't work unless it is captured completely with torch.compile. Got graph break/error:

Encountered aliasing during higher order op tracing
  Higher Order Operator: torch.cond
  Explanation: Higher order ops do not support aliasing. Found in <bound method HigherOrderOperator.name of <torch._higher_order_ops.cond.CondOp object at 0x7e28841f4ad0>>
  Hint: Replace `return input` with `return input.clone()` to avoid aliasing.
  Hint: Consider using the debug context to change user code to avoid aliasing.
  Hint: Please open an issue.

  Developer debug context: Input-to-output aliasing detected at nodes l_args_3_0_ and l_args_3_0_ in
     graph():
        %l_args_3_0_ : torch._subclasses.fake_tensor.FakeTensor [num_users=1] = placeholder[target=l_args_3_0_]
        return (l_args_3_0_,)

 For more details about this graph break, please visit: https://meta-pytorch.github.io/compile-graph-break-site/gb/gb0040.html

from user code:
   File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 242, 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"

new-tracing

W0409 00:09:37.735000 15502 torch/fx/experimental/symbolic_shapes.py:7980] failed during evaluate_expr(Ne(Max(1, u1), u1), hint=None, size_oblivious=False, forcing_spec=False

FAILED

vr must not be None for symbol u1

ControlFlowCondNestedModule

code: yobx.torch.testing._model_eval_cases.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

yobx

• inputs: #1[(T7s2,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions.0' version=1
opset: domain='local_functions' version=1
input: name='x' type=dtype('int64') shape=['batch']
init: name='init7_s_0' type=int64 shape=() -- array([0])              -- shape_type_compute._cast_inputs.1(gt_Scalar)
init: name='init7_s_1002_cst2init' type=int64 shape=() -- array([100])-- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='weight' type=float32 shape=(1,) -- array([42.], dtype=float32)-- DynamoInterpret.placeholder.1/P(weight)
init: name='submodule.weight' type=float32 shape=(1,) -- array([100.], dtype=float32)-- DynamoInterpret.placeholder.1/P(submodule.weight)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init7_s_0) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch']
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> cond#0
Cast(x, to=1) -> x::C12
Sub(x::C12, weight) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> cond#0
Abs(x) -> abs_12
  ReduceSum(abs_12, keepdims=0) -> sum_122
Greater(sum_122, init7_s_1002_cst2init) -> gt22
  If(gt22, else_branch=G3, then_branch=G4) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=2 --  -> cond#0
Cast(x, to=1) -> x::C132
Div(x::C132, submodule.weight) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=2 --  -> cond#0
Cast(x, to=1) -> x::C142
Mul(x::C142, submodule.weight) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=1 --  -> cond#0
Cast(x, to=1) -> x::C132
Div(x::C132, submodule.weight) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=1 --  -> cond#0
Cast(x, to=1) -> x::C142
Mul(x::C142, submodule.weight) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None

dynamo-ir

• inputs: #1[(T7s2,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('int64') shape=['batch']
init: name='weight' type=float32 shape=(1,) -- array([42.], dtype=float32)
init: name='submodule.weight' type=float32 shape=(1,) -- array([100.], dtype=float32)
init: name='val_0' type=int64 shape=() -- array([0])
init: name='val_0_2' type=int64 shape=() -- array([100])
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Greater(sum_1, val_0) -> gt
    If(gt, then_branch=G1, else_branch=G2) -> getitem
output: name='getitem' type=dtype('float32') shape=['batch']
----- subgraph ---- If - node_cond__0 - att.then_branch=G1 -- level=1 --  -> getitem_true_graph_0
Abs(x) -> abs_1
  ReduceSum(abs_1, noop_with_empty_axes=0, keepdims=0) -> sum_1_2
Greater(sum_1_2, val_0_2) -> gt_2
  If(gt_2, then_branch=G3, else_branch=G4) -> getitem_true_graph_0
output: name='getitem_true_graph_0' type=dtype('float32') shape=['batch']
----- subgraph ---- If - node_cond__0_2 - att.then_branch=G3 -- level=2 --  -> mul_1_true_graph_0__true_graph_0
Cast(x, to=1) -> convert_element_type_default
Mul(convert_element_type_default, submodule.weight) -> mul_1_true_graph_0__true_graph_0
output: name='mul_1_true_graph_0__true_graph_0' type=dtype('float32') shape=['batch']
----- subgraph ---- If - node_cond__0_2 - att.else_branch=G4 -- level=2 --  -> div_true_graph_0__false_graph_0
Cast(x, to=1) -> convert_element_type_default_2
Div(convert_element_type_default_2, submodule.weight) -> div_true_graph_0__false_graph_0
output: name='div_true_graph_0__false_graph_0' type=dtype('float32') shape=['batch']
----- subgraph ---- If - node_cond__0_2 - att.then_branch=G3 -- level=1 --  -> mul_1_true_graph_0__true_graph_0
Cast(x, to=1) -> convert_element_type_default
Mul(convert_element_type_default, submodule.weight) -> mul_1_true_graph_0__true_graph_0
output: name='mul_1_true_graph_0__true_graph_0' type=dtype('float32') shape=['batch']
----- subgraph ---- If - node_cond__0_2 - att.else_branch=G4 -- level=1 --  -> div_true_graph_0__false_graph_0
Cast(x, to=1) -> convert_element_type_default_2
Div(convert_element_type_default_2, submodule.weight) -> div_true_graph_0__false_graph_0
output: name='div_true_graph_0__false_graph_0' type=dtype('float32') shape=['batch']
----- subgraph ---- If - node_cond__0 - att.else_branch=G2 -- level=1 --  -> sub_1_false_graph_0
Cast(x, to=1) -> convert_element_type_default_3
Sub(convert_element_type_default_3, weight) -> sub_1_false_graph_0
output: name='sub_1_false_graph_0' type=dtype('float32') shape=['batch']

tracing

• inputs: #1[(T7s2,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions.0' version=1
opset: domain='local_functions' version=1
input: name='x' type=dtype('int64') shape=['batch']
init: name='init7_s_0' type=int64 shape=() -- array([0])              -- shape_type_compute._cast_inputs.1(gt)
init: name='weight2_cst2init' type=float32 shape=(1,) -- array([42.], dtype=float32)-- GraphBuilderPatternOptimization.make_initializer.1/Small
init: name='weight32_cst2init' type=float32 shape=(1,) -- array([100.], dtype=float32)-- GraphBuilderPatternOptimization.make_initializer.1/Small
init: name='init7_s_1002_cst2init' type=int64 shape=() -- array([100])-- GraphBuilderPatternOptimization.make_initializer.1/Shape
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init7_s_0) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output
output: name='output' type=dtype('float32') shape=['batch']
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> condcc
Cast(x, to=1) -> arg0::C12
Sub(arg0::C12, weight2_cst2init) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> condcc
Abs(x) -> abs_12
  ReduceSum(abs_12, keepdims=0) -> sum_122
Greater(sum_122, init7_s_1002_cst2init) -> gt22
  If(gt22, else_branch=G3, then_branch=G4) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=2 --  -> condcc
Cast(x, to=1) -> arg0::C132
Div(arg0::C132, weight32_cst2init) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=2 --  -> condcc
Cast(x, to=1) -> arg0::C142
Mul(arg0::C142, weight32_cst2init) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=1 --  -> condcc
Cast(x, to=1) -> arg0::C132
Div(arg0::C132, weight32_cst2init) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=1 --  -> condcc
Cast(x, to=1) -> arg0::C142
Mul(arg0::C142, weight32_cst2init) -> condcc
output: name='condcc' type='NOTENSOR' shape=None

new-tracing

FAILED

invalid syntax (<eval_with_key>.1250, line 7)

ControlFlowCondNonZero

code: yobx.torch.testing._model_eval_cases.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

yobx

FAILED

Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s72, 12), dtype=torch.int64), FakeTensor(..., size=(s28, s11)), 1025].

dynamo-ir

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'RuntimeError'>: Expect operands to be a tuple of possibly nested dict/list/tuple that only consists of tensor leaves, but got [FakeTensor(..., size=(s72, 12), dtype=torch.int64), FakeTensor(..., size=(s28, s11)), 1025].

(Refer to the full stack trace above for more information.)

tracing

• inputs: #2[(T7s2x12,T1s2x16,int),(T7s2x12,T1s2x0,int)]

• shapes: ({0:Dim(batch)},{0:Dim(batch),1:Dim(seq_length)},None)

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='input_ids' type=dtype('int64') shape=['batch', 12]
input: name='image_features' type=dtype('float32') shape=['batch', 'seq_length']
input: name='vocab_size' type=dtype('int64') shape=None
init: name='init7_s_0' type=int64 shape=() -- array([0])              -- shape_type_compute._cast_inputs.1(gt)
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init7_s_-1000000000::RSh12_cst2init' type=int64 shape=(1,) -- array([-1000000000])-- GraphBuilderPatternOptimization.make_initializer.1/Shape
Size(image_features) -> numel
  Greater(numel, init7_s_0) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0, output_1, output_2
output: name='output_0' type=dtype('int64') shape=['batch', 12]
output: name='output_1' type=dtype('int64') shape=['NEWDIM_nonzero']
output: name='output_2' type=dtype('int64') shape=['NEWDIM_nonzero']
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> condcc#0,condcc#1,condcc#2
Constant(value=[]) -> condcc#1
  Identity(condcc#1) -> condcc#2
Identity(input_ids) -> condcc#0
output: name='condcc#0' type='NOTENSOR' shape=None
output: name='condcc#1' type='NOTENSOR' shape=None
output: name='condcc#2' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> condcc#0,condcc#1,condcc#2
Clip(input_ids, init7_s1_02_cst2init) -> clamp_min2
Min(clamp_min2, vocab_size) -> condcc#0
Less(input_ids, init7_s1_02_cst2init) -> lt2
Greater(input_ids, init7_s_-1000000000::RSh12_cst2init) -> gt22
  And(lt2, gt22) -> and_2
    NonZero(and_2) -> _onx_nonzero_and_2
      Split(_onx_nonzero_and_2, num_outputs=2) -> _onx_split_nonzero_and__02, _onx_split_nonzero_and__12
Squeeze(_onx_split_nonzero_and__02, init7_s1_02_cst2init) -> condcc#1
Squeeze(_onx_split_nonzero_and__12, init7_s1_02_cst2init) -> condcc#2
output: name='condcc#0' type='NOTENSOR' shape=None
output: name='condcc#1' type='NOTENSOR' shape=None
output: name='condcc#2' type='NOTENSOR' shape=None

new-tracing

FAILED

invalid syntax (<eval_with_key>.1254, line 5)

ControlFlowIndirectRanks

code: yobx.torch.testing._model_eval_cases.ControlFlowIndirectRanks

forward

def forward(self, x):
    x1 = x + 1
    if x1.ndim == 2:
        return x1.clone()
    return x / x1.ndim

yobx

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape
Add(x, init1_s_::RSh1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='scalar_tensor_default' type=float32 shape=() -- array([1.], dtype=float32)
Add(x, scalar_tensor_default) -> clone
output: name='clone' type=dtype('float32') shape=['batch', 4]

tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(add)##init7_s1_1/Opset.make_node.1/Shape
Add(x, init1_s_::RSh1) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape
Add(x, init1_s_::RSh1) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

ControlFlowIndirectRanksCat

code: yobx.torch.testing._model_eval_cases.ControlFlowIndirectRanksCat

forward

def forward(self, x, y):
    x1 = x + 1
    y1 = y + 2
    cat = torch.cat([x1, y1], dim=1)
    if cat.ndim == 2:
        return cat.clone()
    return cat / cat.ndim

yobx

• inputs: #2[(T1s3x4,T1s3x4),(T1s5x4,T1s5x2)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='y' type=dtype('float32') shape=['batch', 'seq']
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s_2::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_2,init7_s1_1)##init1_s_2/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
Add(x, init1_s_::RSh1) -> add
Add(y, init1_s_2::RSh1) -> add_1
  Concat(add, add_1, axis=1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 'seq+4']

dynamo-ir

• inputs: #2[(T1s3x4,T1s3x4),(T1s5x4,T1s5x2)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='y' type=dtype('float32') shape=['batch', 'seq']
init: name='scalar_tensor_default' type=float32 shape=() -- array([1.], dtype=float32)
init: name='scalar_tensor_default_1' type=float32 shape=() -- array([2.], dtype=float32)
Add(x, scalar_tensor_default) -> add
Add(y, scalar_tensor_default_1) -> add_4
  Concat(add, add_4, axis=1) -> clone
output: name='clone' type=dtype('float32') shape=['batch', 'seq + 4']

tracing

• inputs: #2[(T1s3x4,T1s3x4),(T1s5x4,T1s5x2)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='y' type=dtype('float32') shape=['batch', 'seq']
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(add)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s_2::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_2,init7_s1_1)##init1_s_2/shape_type_compute._cast_inputs.1(add)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
Add(x, init1_s_::RSh1) -> _onx_add_x
Add(y, init1_s_2::RSh1) -> _onx_add_y
  Concat(_onx_add_x, _onx_add_y, axis=1) -> output
output: name='output' type=dtype('float32') shape=['batch', 'seq+4']

new-tracing

FAILED

# no error found for the failure

ControlFlowNestCond

code: yobx.torch.testing._model_eval_cases.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])

yobx

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions.0' version=1
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt_Scalar)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> cond#0
Neg(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> cond#0
ReduceSum(x, keepdims=0) -> sum_122
Less(sum_122, init1_s_) -> lt2
  If(lt2, else_branch=G3, then_branch=G4) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=2 --  -> cond#0
Cos(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=2 --  -> cond#0
Sin(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=1 --  -> cond#0
Cos(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=1 --  -> cond#0
Sin(x) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None

dynamo-ir

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='scalar_tensor_default' type=float32 shape=() -- array([0.], dtype=float32)
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Greater(sum_1, scalar_tensor_default) -> gt
    If(gt, then_branch=G1, else_branch=G2) -> getitem
output: name='getitem' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0 - att.then_branch=G1 -- level=1 --  -> getitem_true_graph_0
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1_2
Less(sum_1_2, scalar_tensor_default) -> lt
  If(lt, then_branch=G3, else_branch=G4) -> getitem_true_graph_0
output: name='getitem_true_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0_2 - att.then_branch=G3 -- level=2 --  -> sin_true_graph_0__true_graph_0
Sin(x) -> sin_true_graph_0__true_graph_0
output: name='sin_true_graph_0__true_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0_2 - att.else_branch=G4 -- level=2 --  -> cos_true_graph_0__false_graph_0
Cos(x) -> cos_true_graph_0__false_graph_0
output: name='cos_true_graph_0__false_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0_2 - att.then_branch=G3 -- level=1 --  -> sin_true_graph_0__true_graph_0
Sin(x) -> sin_true_graph_0__true_graph_0
output: name='sin_true_graph_0__true_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0_2 - att.else_branch=G4 -- level=1 --  -> cos_true_graph_0__false_graph_0
Cos(x) -> cos_true_graph_0__false_graph_0
output: name='cos_true_graph_0__false_graph_0' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - node_cond__0 - att.else_branch=G2 -- level=1 --  -> neg_false_graph_0
Neg(x) -> neg_false_graph_0
output: name='neg_false_graph_0' type=dtype('float32') shape=['batch', 3]

tracing

• inputs: #1[(T1s5x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions.0' version=1
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- shape_type_compute._cast_inputs.1(gt)
ReduceSum(x, keepdims=0) -> sum_1
  Greater(sum_1, init1_s_) -> gt
    If(gt, else_branch=G1, then_branch=G2) -> output
output: name='output' type=dtype('float32') shape=['batch', 3]
----- subgraph ---- If - cond - att.else_branch=G1 -- level=1 --  -> condcc
Neg(x) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond - att.then_branch=G2 -- level=1 --  -> condcc
ReduceSum(x, keepdims=0) -> sum_122
Less(sum_122, init1_s_) -> lt2
  If(lt2, else_branch=G3, then_branch=G4) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=2 --  -> condcc
Cos(x) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=2 --  -> condcc
Sin(x) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.else_branch=G3 -- level=1 --  -> condcc
Cos(x) -> condcc
output: name='condcc' type='NOTENSOR' shape=None
----- subgraph ---- If - cond22 - att.then_branch=G4 -- level=1 --  -> condcc
Sin(x) -> condcc
output: name='condcc' type='NOTENSOR' shape=None

new-tracing

FAILED

invalid syntax (<eval_with_key>.1634, line 7)

ControlFlowNumelZero1

code: yobx.torch.testing._model_eval_cases.ControlFlowNumelZero1

forward

def forward(self, x):
    def empty_cache(x):
        return x.shape[-2]

    size = (empty_cache(x), 1)
    return torch.full(size, fill_value=2)

yobx

• inputs: #3[(T1s3x2x2x5,),(T1s3x2x1x5,),(T1s3x2x0x5,)]

• shapes: dict(x:{0:DYNAMIC,2:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 2, 'D0_1', 5]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- GraphBuilder.make_shape_from_results.conc
Shape(x, end=3, start=2) -> x::Shape2:3
  Concat(x::Shape2:3, init7_s1_1, axis=0) -> _onx_concat_sym_size_int_2::UnSq0
    ConstantOfShape(_onx_concat_sym_size_int_2::UnSq0, value=[2]) -> output_0
output: name='output_0' type=dtype('int64') shape=['D0_1', 1]

dynamo-ir

• inputs: #3[(T1s3x2x2x5,),(T1s3x2x1x5,),(T1s3x2x0x5,)]

• shapes: dict(x:{0:DYNAMIC,2:DYNAMIC})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s77', 2, 's53', 5]
init: name='val_2' type=float32 shape=() -- array([2.], dtype=float32)
init: name='val_5' type=int64 shape=(1,) -- array([1])
Shape(x, end=3, start=2) -> val_0
  Concat(val_0, val_5, axis=0) -> val_6
    Expand(val_2, val_6) -> full
output: name='full' type=dtype('float32') shape=['s53', 1]

tracing

• inputs: #3[(T1s3x2x2x5,),(T1s3x2x1x5,),(T1s3x2x0x5,)]

• shapes: dict(x:{0:DYNAMIC,2:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 2, 'D0_1', 5]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##GraphBuilder.make_shape_from_results.conc
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape
Shape(x) -> size
  Gather(size, init7_s1_2) -> _onx_gather_size3
    Concat(_onx_gather_size3, init7_s1_1, axis=0) -> _onx_concat_getitem_2::UnSq0
      ConstantOfShape(_onx_concat_getitem_2::UnSq0, value=[2]) -> output
output: name='output' type=dtype('int64') shape=['D0_1', 1]

new-tracing

FAILED

full(): argument 'size' (position 1) must be tuple of ints, but found element of type TracingInt at pos 0

ControlFlowNumelZero2

code: yobx.torch.testing._model_eval_cases.ControlFlowNumelZero2

forward

def forward(self, x):
    def empty_cache(x):
        if x.numel() == 0:
            return 0
        return x.shape[-2]

    size = (empty_cache(x), 1)
    return torch.full(size, fill_value=2)

yobx

• inputs: #3[(T1s3x2x2x5,),(T1s3x2x1x5,),(T1s3x2x0x5,)]

• shapes: dict(x:{0:DYNAMIC,2:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 2, 'D0_1', 5]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- GraphBuilder.make_shape_from_results.conc
Shape(x, end=3, start=2) -> x::Shape2:3
  Concat(x::Shape2:3, init7_s1_1, axis=0) -> _onx_concat_sym_size_int_2::UnSq0
    ConstantOfShape(_onx_concat_sym_size_int_2::UnSq0, value=[2]) -> output_0
output: name='output_0' type=dtype('int64') shape=['D0_1', 1]

dynamo-ir

• inputs: #3[(T1s3x2x2x5,),(T1s3x2x1x5,),(T1s3x2x0x5,)]

• shapes: dict(x:{0:DYNAMIC,2:DYNAMIC})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s77', 2, 's53', 5]
init: name='val_2' type=float32 shape=() -- array([2.], dtype=float32)
init: name='val_5' type=int64 shape=(1,) -- array([1])
Shape(x, end=3, start=2) -> val_0
  Concat(val_0, val_5, axis=0) -> val_6
    Expand(val_2, val_6) -> full
output: name='full' type=dtype('float32') shape=['s53', 1]

tracing

• inputs: #3[(T1s3x2x2x5,),(T1s3x2x1x5,),(T1s3x2x0x5,)]

• shapes: dict(x:{0:DYNAMIC,2:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 2, 'D0_1', 5]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##GraphBuilder.make_shape_from_results.conc
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape
Shape(x) -> size
  Gather(size, init7_s1_2) -> _onx_gather_size3
    Concat(_onx_gather_size3, init7_s1_1, axis=0) -> _onx_concat_getitem_2::UnSq0
      ConstantOfShape(_onx_concat_getitem_2::UnSq0, value=[2]) -> output
output: name='output' type=dtype('int64') shape=['D0_1', 1]

new-tracing

FAILED

Function ControlFlowNumelZero2() returned a real torch.Tensor. All tensor outputs must be TracingTensor instances produced during tracing.

ControlFlowRanks

code: yobx.torch.testing._model_eval_cases.ControlFlowRanks

forward

def forward(self, x):
    if x.ndim == 2:
        return x.clone()
    return x / x.ndim

yobx

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> clone
output: name='clone' type=dtype('float32') shape=['batch', 4]

tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

ControlFlowRanksType

code: yobx.torch.testing._model_eval_cases.ControlFlowRanksType

forward

def forward(self, x=None):
    if (
        x is not None
        and (x.dtype == torch.float32 or x.dtype == torch.float16)
        and x.ndim == 2
    ):
        return x.clone()
    torch._check(x is not None)
    return (x / x.ndim).to(torch.float32)  # type: ignore

yobx

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> clone
output: name='clone' type=dtype('float32') shape=['batch', 4]

tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Identity(x) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

ControlFlowScan

code: yobx.torch.testing._model_eval_cases.ControlFlowScan

forward

def forward(self, x):
    def add(carry: torch.Tensor, y: torch.Tensor):
        next_carry = carry + y
        return [next_carry, next_carry]

    init = torch.zeros_like(x[0])
    carry, _out = torch.ops.higher_order.scan(add, [init], [x], additional_inputs=[])
    return carry

yobx

• inputs: #1[(T1s3x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init7_s1_3' type=int64 shape=(1,) -- array([3])           -- Opset.make_node.1/Shape
ConstantOfShape(init7_s1_3, value=[0.0]) -> zeros_like
  Scan(zeros_like, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> output_0, scan#1
output: name='output_0' type=dtype('float32') shape=[3]
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_zeros_like,scan_0_x -> output_0,output_1
input: name='init_0_zeros_like' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Add(init_0_zeros_like, scan_0_x) -> output_0
  Identity(output_0) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

dynamo-ir

FAILED

Failed to decompose the FX graph for ONNX compatibility. This is step 2/3 of exporting the model to ONNX. Next steps:
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.
- Create an error report with `torch.onnx.export(..., report=True)`, and save the ExportedProgram as a pt2 file. Create an issue in the PyTorch GitHub repository against the *onnx* component. Attach the error report and the pt2 model.

## Exception summary

<class 'RuntimeError'>: scan might be aliasing the input or the output!

While executing %scan : [num_users=2] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%zeros_like], [%x], ()), kwargs = {})
Original traceback:
File "~/github/yet-another-onnx-builder/yobx/torch/testing/_model_eval_cases.py", line 601, in forward
    carry, _out = torch.ops.higher_order.scan(add, [init], [x], additional_inputs=[])
Use tlparse to see full graph. (https://github.com/pytorch/tlparse?tab=readme-ov-file#tlparse-parse-structured-pt2-logs)

(Refer to the full stack trace above for more information.)

tracing

• inputs: #1[(T1s3x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='init7_s1_3' type=int64 shape=(1,) -- array([3])           -- Opset.make_node.1/Shape
ConstantOfShape(init7_s1_3, value=[0.0]) -> zeros_like
  Scan(zeros_like, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> output, scancc#1
output: name='output' type=dtype('float32') shape=[3]
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_zeros_like,scan_0_x -> output_0,output_1
input: name='init_0_zeros_like' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Add(init_0_zeros_like, scan_0_x) -> output_0
  Identity(output_0) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

new-tracing

FAILED

index 0 is out of bounds for dimension 0 with size 0

ControlFlowScan2Carried

code: yobx.torch.testing._model_eval_cases.ControlFlowScan2Carried

forward

def forward(self, x):
    def add(carry1: torch.Tensor, carry2: torch.Tensor, y1: torch.Tensor, y2: torch.Tensor):
        next_carry1 = carry1 + y1
        next_carry2 = carry2 * y2
        return [next_carry1, next_carry2, next_carry1, next_carry2]

    init1 = torch.zeros_like(x[0])
    init2 = torch.ones_like(x[0])
    carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
        add,
        [init1, init2],
        [x, x * 2],
        # dim=0,  # 01/31/2025, not supported anymore
        additional_inputs=[],
    )
    return carry1, carry2, out1, out2

yobx

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_4' type=int64 shape=(1,) -- array([4])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape
ConstantOfShape(init7_s1_4, value=[1.0]) -> ones_like
ConstantOfShape(init7_s1_4, value=[0.0]) -> zeros_like
Mul(x, init1_s_::RSh1) -> _onx_mul_x
  Scan(zeros_like, ones_like, x, _onx_mul_x, body=G1, num_scan_inputs=2, scan_input_directions=[0,0], scan_output_axes=[0,0], scan_output_directions=[0,0]) -> output_0, output_1, output_2, output_3
output: name='output_0' type=dtype('float32') shape=[4]
output: name='output_1' type=dtype('float32') shape=[4]
output: name='output_2' type=dtype('float32') shape=['batch', 4]
output: name='output_3' type=dtype('float32') shape=['batch', 4]
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_zeros_like,init_1_ones_like,scan_0_x,scan_1_mul -> output_0,output_1,output_2,output_3
input: name='init_0_zeros_like' type=dtype('float32') shape=None
input: name='init_1_ones_like' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
input: name='scan_1_mul' type=dtype('float32') shape=None
Add(init_0_zeros_like, scan_0_x) -> output_0
  Identity(output_0) -> output_2
Mul(init_1_ones_like, scan_1_mul) -> output_1
  Identity(output_1) -> output_3
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None
output: name='output_2' type=dtype('float32') shape=None
output: name='output_3' type=dtype('float32') shape=None

dynamo-ir

FAILED

Failed to decompose the FX graph for ONNX compatibility. This is step 2/3 of exporting the model to ONNX. Next steps:
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.
- Create an error report with `torch.onnx.export(..., report=True)`, and save the ExportedProgram as a pt2 file. Create an issue in the PyTorch GitHub repository against the *onnx* component. Attach the error report and the pt2 model.

## Exception summary

<class 'RuntimeError'>: scan might be aliasing the input or the output!

While executing %scan : [num_users=4] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%zeros_like, %ones_like], [%x, %mul], ()), kwargs = {})
Original traceback:
File "~/github/yet-another-onnx-builder/yobx/torch/testing/_model_eval_cases.py", line 617, in forward
    carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
Use tlparse to see full graph. (https://github.com/pytorch/tlparse?tab=readme-ov-file#tlparse-parse-structured-pt2-logs)

(Refer to the full stack trace above for more information.)

tracing

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_4' type=int64 shape=(1,) -- array([4])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(mul)##init7_s1_1/Opset.make_node.1/Shape
ConstantOfShape(init7_s1_4, value=[1.0]) -> ones_like
ConstantOfShape(init7_s1_4, value=[0.0]) -> zeros_like
Mul(x, init1_s_::RSh1) -> _onx_mul_x
  Scan(zeros_like, ones_like, x, _onx_mul_x, body=G1, num_scan_inputs=2, scan_input_directions=[0,0], scan_output_axes=[0,0], scan_output_directions=[0,0]) -> output_0, output_1, output_2, output_3
output: name='output_0' type=dtype('float32') shape=[4]
output: name='output_1' type=dtype('float32') shape=[4]
output: name='output_2' type=dtype('float32') shape=['d_output_2_0', 'd_output_2_1']
output: name='output_3' type=dtype('float32') shape=['d_output_3_0', 'd_output_3_1']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_zeros_like,init_1_ones_like,scan_0_x,scan_1_mul -> output_0,output_1,output_2,output_3
input: name='init_0_zeros_like' type=dtype('float32') shape=None
input: name='init_1_ones_like' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
input: name='scan_1_mul' type=dtype('float32') shape=None
Add(init_0_zeros_like, scan_0_x) -> output_0
  Identity(output_0) -> output_2
Mul(init_1_ones_like, scan_1_mul) -> output_1
  Identity(output_1) -> output_3
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None
output: name='output_2' type=dtype('float32') shape=None
output: name='output_3' type=dtype('float32') shape=None

new-tracing

FAILED

index 0 is out of bounds for dimension 0 with size 0

ControlFlowScanCDist

code: yobx.torch.testing._model_eval_cases.ControlFlowScanCDist

forward

def forward(self, x):
    def dist(carry: torch.Tensor, x: torch.Tensor):
        sub = carry - x.reshape((1, -1))
        sq = sub * sub
        rd = sq.sum(dim=1) ** 0.5
        # clone --> UnsupportedAliasMutationException:
        # Combine_fn might be aliasing the input!
        return [carry.clone(), rd]

    _carry, out = torch.ops.higher_order.scan(
        dist,
        [x],
        [x],
        # dim=0,  # 01/31/2025, not supported anymore
        additional_inputs=[],
    )
    return out

yobx

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_12_cst2init' type=int64 shape=(1,) -- array([1]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init1_s_2_cst2init' type=float32 shape=() -- array([0.5], dtype=float32)-- GraphBuilderPatternOptimization.make_initializer.1/Small
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
Scan(x, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> scan#0, output_0
output: name='output_0' type=dtype('float32') shape=['batch', 'batch']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_x,scan_0_x -> output_0,output_1
input: name='init_0_x' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Identity(init_0_x) -> output_0
Unsqueeze(scan_0_x, init7_s1_02_cst2init) -> reshape2
  Sub(init_0_x, reshape2) -> sub2
    Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12_cst2init, keepdims=0) -> sum_12
Pow(sum_12, init1_s_2_cst2init) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

dynamo-ir

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='val_3' type=int64 shape=(2,) -- array([ 1, -1])
init: name='val_4' type=int64 shape=(1,) -- array([1])
init: name='val_5' type=float32 shape=() -- array([0.5], dtype=float32)
Scan(x, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_directions=[0]) -> scan__0, getitem_1
output: name='getitem_1' type=dtype('float32') shape=['batch', 'batch']
----- subgraph ---- Scan - node_scan__1 - att.body=G1 -- level=1 -- x_scan_combine_graph_0__subgraph_in,x_scan_combine_graph_0__subgraph_in_1 -> clone_scan_combine_graph_0,pow_1_scan_combine_graph_0
input: name='x_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=['s77', 4]
input: name='x_scan_combine_graph_0__subgraph_in_1' type=dtype('float32') shape=[4]
Identity(x_scan_combine_graph_0__subgraph_in) -> clone_scan_combine_graph_0
Reshape(x_scan_combine_graph_0__subgraph_in_1, val_3, allowzero=1) -> view
  Sub(x_scan_combine_graph_0__subgraph_in, view) -> sub_1
    Mul(sub_1, sub_1) -> mul_4
ReduceSum(mul_4, val_4, noop_with_empty_axes=0, keepdims=0) -> sum_1
Pow(sum_1, val_5) -> pow_1_scan_combine_graph_0
output: name='clone_scan_combine_graph_0' type=dtype('float32') shape=['batch', 4]
output: name='pow_1_scan_combine_graph_0' type=dtype('float32') shape=['batch']

tracing

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_12_cst2init' type=int64 shape=(1,) -- array([1]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
Scan(x, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> scancc#0, output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_x,scan_0_x -> output_0,output_1
input: name='init_0_x' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Identity(init_0_x) -> output_0
Unsqueeze(scan_0_x, init7_s1_02_cst2init) -> reshape2
  Sub(init_0_x, reshape2) -> sub2
    Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12_cst2init, keepdims=0) -> sum_12
  Sqrt(sum_12) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

new-tracing

FAILED

There was no rule registered for HOP scan and subclass <class 'yobx.torch.new_tracing.tensor.TracingTensor'>. We recommend filing an issue.

ControlFlowScanCDist2

code: yobx.torch.testing._model_eval_cases.ControlFlowScanCDist2

forward

def forward(self, x):
    def dist(unused: torch.Tensor, x: torch.Tensor, samex: torch.Tensor):
        sub = samex - x.reshape((1, -1))
        sq = sub * sub
        rd = torch.sqrt(sq.sum(dim=1))
        # clone --> UnsupportedAliasMutationException:
        # Combine_fn might be aliasing the input!
        return [unused.clone(), rd]

    z = torch.tensor([0], dtype=torch.float32)
    y = x.clone()
    out = torch.ops.higher_order.scan(
        dist,
        [z],
        [x],
        # dim=0,  # 01/31/2025, not supported anymore
        additional_inputs=[y],
    )
    return out[1]

yobx

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='c_lifted_tensor_0' type=float32 shape=(1,) -- array([0.], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s1_12_cst2init' type=int64 shape=(1,) -- array([1]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
Identity(x) -> hidden_input_scan_0_clone
Scan(c_lifted_tensor_0, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> scan#0, output_0
output: name='output_0' type=dtype('float32') shape=['batch', 'batch']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_detach_,scan_0_x -> output_0,output_1
input: name='init_0_detach_' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Identity(init_0_detach_) -> output_0
Unsqueeze(scan_0_x, init7_s1_02_cst2init) -> reshape2
Sub(hidden_input_scan_0_clone, reshape2) -> sub2
  Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12_cst2init, keepdims=0) -> sum_12
  Sqrt(sum_12) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

dynamo-ir

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='clone' type=float32 shape=(1,) -- array([0.], dtype=float32)
init: name='val_3' type=int64 shape=(2,) -- array([ 1, -1])
init: name='val_4' type=int64 shape=(1,) -- array([1])
Scan(clone, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_directions=[0]) -> scan__0, getitem_1
output: name='getitem_1' type=dtype('float32') shape=['batch', 'batch']
----- subgraph ---- Scan - node_scan__1 - att.body=G1 -- level=1 -- clone_scan_combine_graph_0__subgraph_in,x_scan_combine_graph_0__subgraph_in -> clone_scan_combine_graph_0,sqrt_scan_combine_graph_0
input: name='clone_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=[1]
input: name='x_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=[4]
Identity(clone_scan_combine_graph_0__subgraph_in) -> clone_scan_combine_graph_0
Reshape(x_scan_combine_graph_0__subgraph_in, val_3, allowzero=1) -> view
Sub(x, view) -> sub_1
  Mul(sub_1, sub_1) -> mul_4
ReduceSum(mul_4, val_4, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Sqrt(sum_1) -> sqrt_scan_combine_graph_0
output: name='clone_scan_combine_graph_0' type=dtype('float32') shape=[1]
output: name='sqrt_scan_combine_graph_0' type=dtype('float32') shape=['batch']

tracing

• inputs: #1[(T1s3x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='cst' type=float32 shape=(1,) -- array([0.], dtype=float32)-- _process_arg
init: name='init7_s1_12_cst2init' type=int64 shape=(1,) -- array([1]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
Identity(x) -> hidden_input_scan_0_clone
Scan(cst, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> scancc#0, output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_cst,scan_0_x -> output_0,output_1
input: name='init_0_cst' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Identity(init_0_cst) -> output_0
Unsqueeze(scan_0_x, init7_s1_02_cst2init) -> reshape2
Sub(hidden_input_scan_0_clone, reshape2) -> sub2
  Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12_cst2init, keepdims=0) -> sum_12
  Sqrt(sum_12) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

new-tracing

FAILED

There was no rule registered for HOP scan and subclass <class 'yobx.torch.new_tracing.tensor.TracingTensor'>. We recommend filing an issue.

ControlFlowScanCDistXY

code: yobx.torch.testing._model_eval_cases.ControlFlowScanCDistXY

forward

def forward(self, x, y):
    def dist(y: torch.Tensor, scanned_x: torch.Tensor):
        sub = y - scanned_x.reshape((1, -1))
        sq = sub * sub
        rd = torch.sqrt(sq.sum(dim=1))
        # clone --> UnsupportedAliasMutationException:
        # Combine_fn might be aliasing the input!
        return [y.clone(), rd]

    _carry, out = torch.ops.higher_order.scan(
        dist,
        [y],
        [x],
        # dim=0,  # 01/31/2025, not supported anymore
        additional_inputs=[],
    )
    return out

yobx

• inputs: #2[(T1s3x4,T1s5x4),(T1s13x14,T1s15x14)]

• shapes: dict(x:{0:Dim(x_rows),1:Dim(dim)},y:{0:Dim(y_rows),1:Dim(dim)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['x_rows', 'dim']
input: name='y' type=dtype('float32') shape=['y_rows', 'dim']
init: name='init7_s1_12_cst2init' type=int64 shape=(1,) -- array([1]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
Scan(y, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> scan#0, output_0
output: name='output_0' type=dtype('float32') shape=['x_rows', 'y_rows']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_y,scan_0_x -> output_0,output_1
input: name='init_0_y' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Identity(init_0_y) -> output_0
Unsqueeze(scan_0_x, init7_s1_02_cst2init) -> reshape2
  Sub(init_0_y, reshape2) -> sub2
    Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12_cst2init, keepdims=0) -> sum_12
  Sqrt(sum_12) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

dynamo-ir

• inputs: #2[(T1s3x4,T1s5x4),(T1s13x14,T1s15x14)]

• shapes: dict(x:{0:Dim(x_rows),1:Dim(dim)},y:{0:Dim(y_rows),1:Dim(dim)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['x_rows', 'dim']
input: name='y' type=dtype('float32') shape=['y_rows', 'dim']
init: name='val_5' type=int64 shape=(2,) -- array([ 1, -1])
init: name='val_6' type=int64 shape=(1,) -- array([1])
Scan(y, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_directions=[0]) -> scan__0, getitem_1
output: name='getitem_1' type=dtype('float32') shape=['x_rows', 'y_rows']
----- subgraph ---- Scan - node_scan__1 - att.body=G1 -- level=1 -- y_scan_combine_graph_0__subgraph_in,x_scan_combine_graph_0__subgraph_in -> clone_scan_combine_graph_0,sqrt_scan_combine_graph_0
input: name='y_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=['s17', 's27']
input: name='x_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=['s27']
Identity(y_scan_combine_graph_0__subgraph_in) -> clone_scan_combine_graph_0
Reshape(x_scan_combine_graph_0__subgraph_in, val_5, allowzero=1) -> view
  Sub(y_scan_combine_graph_0__subgraph_in, view) -> sub_4
    Mul(sub_4, sub_4) -> mul_7
ReduceSum(mul_7, val_6, noop_with_empty_axes=0, keepdims=0) -> sum_1
  Sqrt(sum_1) -> sqrt_scan_combine_graph_0
output: name='clone_scan_combine_graph_0' type=dtype('float32') shape=['y_rows', 'dim']
output: name='sqrt_scan_combine_graph_0' type=dtype('float32') shape=['y_rows']

tracing

• inputs: #2[(T1s3x4,T1s5x4),(T1s13x14,T1s15x14)]

• shapes: dict(x:{0:Dim(x_rows),1:Dim(dim)},y:{0:Dim(y_rows),1:Dim(dim)})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['x_rows', 'dim']
input: name='y' type=dtype('float32') shape=['y_rows', 'dim']
init: name='init7_s1_12_cst2init' type=int64 shape=(1,) -- array([1]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
Scan(y, x, body=G1, num_scan_inputs=1, scan_input_directions=[0], scan_output_axes=[0], scan_output_directions=[0]) -> scancc#0, output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- init_0_y,scan_0_x -> output_0,output_1
input: name='init_0_y' type=dtype('float32') shape=None
input: name='scan_0_x' type=dtype('float32') shape=None
Identity(init_0_y) -> output_0
Unsqueeze(scan_0_x, init7_s1_02_cst2init) -> reshape2
  Sub(init_0_y, reshape2) -> sub2
    Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12_cst2init, keepdims=0) -> sum_12
  Sqrt(sum_12) -> output_1
output: name='output_0' type=dtype('float32') shape=None
output: name='output_1' type=dtype('float32') shape=None

new-tracing

FAILED

There was no rule registered for HOP scan and subclass <class 'yobx.torch.new_tracing.tensor.TracingTensor'>. We recommend filing an issue.

ControlFlowScanDecomposition_151564

code: yobx.torch.testing._model_eval_cases.ControlFlowScanDecomposition_151564

forward

def forward(self, images, position):
    def dummy_loop(padded: torch.Tensor, pos: torch.Tensor):
        copy = torch.zeros(padded.shape)
        for i in range(pos.shape[0]):
            p = pos[i]
            copy[i, :p] = padded[i, :p]
        return copy

    def dummy_loop_with_scan(padded: torch.Tensor, pos: torch.Tensor):
        def pad_row(padded, p):
            row = torch.zeros((padded.shape[0],))
            torch._check(p.item() > 0)
            torch._check(p.item() < padded.shape[0])
            # this check is not always true, we add it anyway to make this dimension >= 2
            # and avoid raising an exception about dynamic dimension in {0, 1}
            if torch.compiler.is_exporting():
                torch._check(p.item() > 1)
            row[: p.item()] = padded[: p.item()]
            return (row,)

        return torch.ops.higher_order.scan(pad_row, [], [padded, pos], [])

    def select_when_exporting(f, f_scan):
        return f_scan if torch.compiler.is_exporting() else f

    return select_when_exporting(dummy_loop, dummy_loop_with_scan)(images, position)

yobx

• inputs: #1[(T1s5x6,T7s5)]

• shapes: dict(images:{0:DYNAMIC,1:DYNAMIC},position:{0:DYNAMIC})

opset: domain='' version=21
opset: domain='aten' version=1
opset: domain='local_functions' version=1
input: name='images' type=dtype('float32') shape=['batch_2', 'channel_1']
input: name='position' type=dtype('int64') shape=['batch_3']
init: name='init7_s1_02_cst2init' type=int64 shape=(1,) -- array([0]) -- GraphBuilderPatternOptimization.make_initializer.1/Shape
Scan(images, position, body=G1, num_scan_inputs=2, scan_input_directions=[0,0], scan_output_axes=[0], scan_output_directions=[0]) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch_2', 'channel_1']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- scan_0_images,scan_1_position -> output_0
input: name='scan_0_images' type=dtype('float32') shape=None
input: name='scan_1_position' type=dtype('int64') shape=None
Shape(scan_0_images, end=1, start=0) -> padded_1::Shape:12
  ConstantOfShape(padded_1::Shape:12, value=[0.0]) -> zeros2
Unsqueeze(scan_1_position, init7_s1_02_cst2init) -> item::UnSq02
Slice(scan_0_images, init7_s1_02_cst2init, item::UnSq02, init7_s1_02_cst2init) -> slice_12
  aten_setitem[aten](zeros2, scan_1_position, slice_12) -> output_0
output: name='output_0' type=dtype('float32') shape=None
----- function name=aten_setitem domain=aten
----- doc_string: -- function_options=FunctionOptions(export_as_function=...
opset: domain='' version=21
input: 'zeros'
input: 'item'
input: 'slice_1'
Constant(value=[0]) -> init7_s1_0
  Unsqueeze(item, init7_s1_0) -> item::UnSq0
Shape(zeros) -> zeros::Shape:
  Slice(zeros, item::UnSq0, zeros::Shape:, init7_s1_0) -> _onx_slice_zeros
    Concat(slice_1, _onx_slice_zeros, axis=0) -> setitem
output: name='setitem' type=? shape=?

dynamo-ir

• inputs: #1[(T1s5x6,T7s5)]

• shapes: dict(images:{0:DYNAMIC,1:DYNAMIC},position:{0:DYNAMIC})

opset: domain='' version=20
input: name='images' type=dtype('float32') shape=['s34', 's90']
input: name='position' type=dtype('int64') shape=['s71']
init: name='val_13' type=int64 shape=(1,) -- array([0])
init: name='val_37' type=int64 shape=(1,) -- array([1])
init: name='val_1' type=float32 shape=() -- array([0.], dtype=float32)
init: name='val_5' type=int64 shape=(1,) -- array([-1])
init: name='val_7' type=int64 shape=() -- array([0])
init: name='val_10' type=int64 shape=() -- array([1])
Scan(images, position, body=G1, num_scan_inputs=2, scan_input_directions=[0,0], scan_output_directions=[0]) -> getitem
output: name='getitem' type=dtype('float32') shape=['s34', 's90']
----- subgraph ---- Scan - node_scan__0 - att.body=G1 -- level=1 -- images_scan_combine_graph_0__subgraph_in,position_scan_combine_graph_0__subgraph_in -> slice_scatter_scan_combine_graph_0
input: name='images_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=['s90']
input: name='position_scan_combine_graph_0__subgraph_in' type=dtype('int64') shape=None
Reshape(position_scan_combine_graph_0__subgraph_in, val_5, allowzero=0) -> val_6
Gather(val_6, val_7, axis=0) -> val_8
Reshape(val_8, val_5, allowzero=0) -> val_16
Slice(images_scan_combine_graph_0__subgraph_in, val_13, val_16, val_13, val_37) -> copy
Shape(images_scan_combine_graph_0__subgraph_in, end=1, start=0) -> val_0
Expand(val_1, val_0) -> zeros
  Shape(zeros, start=0) -> val_32
Gather(val_32, val_7, axis=0) -> val_33
Range(val_7, val_33, val_10) -> val_34
Unsqueeze(val_8, val_13) -> val_36
Slice(val_34, val_13, val_36, val_13, val_37) -> val_38
Unsqueeze(val_38, val_5) -> val_39
  ScatterND(zeros, val_39, copy, reduction=b'none') -> slice_scatter_scan_combine_graph_0
output: name='slice_scatter_scan_combine_graph_0' type=dtype('float32') shape=['s90']

tracing

FAILED

'CustomProxyInt' object cannot be interpreted as an integer

new-tracing

FAILED

zeros(): argument 'size' (position 1) must be tuple of ints, not TracingShape

ControlFlowScanInplace_153705

code: yobx.torch.testing._model_eval_cases.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]

yobx

FAILED

only integers, slices (`:`), ellipsis (`...`), None and long or byte Variables are valid indices (got SymInt)

dynamo-ir

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'IndexError'>: only integers, slices (`:`), ellipsis (`...`), None and long or byte Variables are valid indices (got SymInt)

(Refer to the full stack trace above for more information.)

tracing

FAILED

Unable to interpret function <class 'builtin_function_or_method'>: <built-in method empty of type object at 0x7e28f4744240>, searched for ['transformers_empty', '_VariableFunctionsClass_empty', 'empty'] and attributes ['__qualname__', '__name__'], args=((getitem, getitem_2),), kwargs={}, dispatcher=None
--DEBUG--
-- to print the exported program: PRINT_EXPORTED_PROGRAM=1

[GraphBuilder-SII] Message starts, there are 2 initializers, 10 nodes, 2 inputs, 2 outputs.
input_names=['x', 'y']
output_names=[]
--CONSTRAINTS--
    DYN0 = {'s26'}
    DYN1 = {'s49'}
    DYN2 = {'s93'}
    DYN3 = {'s70'}
    s26 = {'DYN0'}
    s49 = {'DYN1'}
    s70 = {'DYN3'}
    s93 = {'DYN2'}
--SHAPE--
_dynamic_examples=
dynamic_objects=
   DYN0 = 'DYN0'
   DYN1 = 'DYN1'
   DYN2 = 'DYN2'
   DYN3 = 'DYN3'
   s26 = 's26'
   s49 = 's49'
   s70 = 's70'
   s93 = 's93'
dynamic_objects_rev=
   'DYN0' = <class 'list'>
     tuple
       'DYN0'
       ERR**: <class 'torch.SymInt'>:'DYN0'
   'DYN1' = <class 'list'>
     tuple
       'DYN1'
       ERR**: <class 'torch.SymInt'>:'DYN1'
   'DYN2' = <class 'list'>
     tuple
       'DYN2'
       ERR**: <class 'torch.SymInt'>:'DYN2'
   'DYN3' = <class 'list'>
     tuple
       'DYN3'
       ERR**: <class 'torch.SymInt'>:'DYN3'
dynamic_dimensions_source={'DYN0': [{'axis': 0, 'input_name': 'x'}],
 'DYN1': [{'axis': 1, 'input_name': 'x'}],
 'DYN2': [{'axis': 0, 'input_name': 'y'}],
 'DYN3': [{'axis': 1, 'input_name': 'y'}]}
dynamic_dimensions_source_flat=['x', 'y']
output_dynamic_dimensions_source_flat=None
dynamic_alias={'s26': 'DYN0', 's49': 'DYN1', 's70': 'DYN3', 's93': 'DYN2'}
dynamic_shapes={'x': {0: Dim('DYN0', min=0), 1: Dim('DYN1', min=0)},
 'y': {0: Dim('DYN2', min=0), 1: Dim('DYN3', min=0)}}
_known_shapes={'_onx_gather_size': (1,),
 '_onx_gather_size2': (1,),
 '_onx_gather_size_1': (1,),
 '_onx_gather_size_12': (1,),
 'getitem': (),
 'getitem_1': (),
 'getitem_2': (),
 'getitem_3': (),
 'init7_s1_0': (1,),
 'init7_s1_1': (1,),
 'size': (2,),
 'size_1': (2,),
 'x': ('DYN0', 'DYN1'),
 'y': ('DYN2', 'DYN3')}
_known_types={'_onx_gather_size': 7,
 '_onx_gather_size2': 7,
 '_onx_gather_size_1': 7,
 '_onx_gather_size_12': 7,
 'getitem': 7,
 'getitem_1': 7,
 'getitem_2': 7,
 'getitem_3': 7,
 'init7_s1_0': 7,
 'init7_s1_1': 7,
 'size': 7,
 'size_1': 7,
 'x': 1,
 'y': 1}
_known_devices={'_onx_gather_size': -1,
 '_onx_gather_size2': -1,
 '_onx_gather_size_1': -1,
 '_onx_gather_size_12': -1,
 'getitem': -1,
 'getitem_1': -1,
 'getitem_2': -1,
 'getitem_3': -1,
 'size': -1,
 'size_1': -1,
 'x': -1,
 'y': -1}
_context=[]
_known_value_shape={'_onx_gather_size': ('DYN0',),
 '_onx_gather_size2': ('DYN1',),
 '_onx_gather_size_1': ('DYN2',),
 '_onx_gather_size_12': ('DYN3',),
 'getitem': 'DYN0',
 'getitem_1': 'DYN1',
 'getitem_2': 'DYN2',
 'getitem_3': 'DYN3',
 'init7_s1_0': (0,),
 'init7_s1_1': (1,),
 'size': ('DYN0', 'DYN1'),
 'size_1': ('DYN2', 'DYN3')}
_known_constants=['init7_s1_0', 'init7_s1_1']
_known_ranks (with no shape)={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
    empty -> {scancc}
    getitem -> {empty}
    getitem_1 -> set()
    getitem_2 -> {empty}
    getitem_3 -> set()
    size -> {getitem_1, getitem}
    size_1 -> {getitem_3, getitem_2}
    x -> {size_2, size, scancc}
    y -> {size_1, scancc}
--TORCH-SHAPES--
    x: ('run_node', (('example_value', torch.float32, torch.Size([3, 4])), ('val', torch.float32, torch.Size([s26, s49])))) --- 1:2:('DYN0', 'DYN1'):
    y: ('run_node', (('example_value', torch.float32, torch.Size([5, 4])), ('val', torch.float32, torch.Size([s93, s70])))) --- 1:2:('DYN2', 'DYN3'):
    size: ('run_node', ('', '')) --- 7:1:(2,):
    getitem: ('run_node', ('', '')) --- 7:0:():
    getitem_1: ('run_node', ('', '')) --- 7:0:():
    size_1: ('run_node', ('', '')) --- 7:1:(2,):
    getitem_2: ('run_node', ('', '')) --- 7:0:():
    getitem_3: ('run_node', ('', '')) --- 7:0:():
    empty: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export
export_options=ExportOptions(tracing=TracingMode.TRACING, aten_as_function=('aten.histc.default', 'aten.index_copy.default', 'aten.index_put.default', 'aten._grouped_mm.default', 'aten.setitem', <built-in function setitem>))
function_options=None
-- process.graph_module --
ControlFlowScanInplace_153705()



def forward(self, x, y):
    size = x.size()
    getitem = size[0]
    getitem_1 = size[1];  size = getitem_1 = None
    size_1 = y.size()
    getitem_2 = size_1[0]
    getitem_3 = size_1[1];  size_1 = getitem_3 = None
    empty = torch.empty((getitem, getitem_2));  getitem = getitem_2 = None
    size_2 = x.size()
    getitem_4 = size_2[0]
    getitem_5 = size_2[1];  size_2 = getitem_5 = None
    arange = torch.arange(getitem_4, dtype = torch.int64);  getitem_4 = None
    _cb_scan_loop_body_1_0 = self._cb_scan_loop_body_1_0
    scancc = torch.ops.higher_order.scan(_cb_scan_loop_body_1_0, [empty], [arange], [x, y]);  _cb_scan_loop_body_1_0 = empty = arange = x = y = None
    getitem_6 = scancc[0];  scancc = None
    return getitem_6

# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
    %x : [num_users=3] = placeholder[target=x]
    %y : [num_users=2] = placeholder[target=y]
    %size : [num_users=2] = call_method[target=size](args = (%x,), kwargs = {})
    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%size, 0), kwargs = {})
    %getitem_1 : [num_users=0] = call_function[target=operator.getitem](args = (%size, 1), kwargs = {})
    %size_1 : [num_users=2] = call_method[target=size](args = (%y,), kwargs = {})
    %getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%size_1, 0), kwargs = {})
    %getitem_3 : [num_users=0] = call_function[target=operator.getitem](args = (%size_1, 1), kwargs = {})
    %empty : [num_users=1] = call_function[target=torch.empty](args = ((%getitem, %getitem_2),), kwargs = {})
    %size_2 : [num_users=2] = call_method[target=size](args = (%x,), kwargs = {})
    %getitem_4 : [num_users=1] = call_function[target=operator.getitem](args = (%size_2, 0), kwargs = {})
    %getitem_5 : [num_users=0] = call_function[target=operator.getitem](args = (%size_2, 1), kwargs = {})
    %arange : [num_users=1] = call_function[target=torch.arange](args = (%getitem_4,), kwargs = {dtype: torch.int64})
    %_cb_scan_loop_body_1_0 : [num_users=1] = get_attr[target=_cb_scan_loop_body_1_0]
    %scancc : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%_cb_scan_loop_body_1_0, [%empty], [%arange], [%x, %y]), kwargs = {})
    %getitem_6 : [num_users=1] = call_function[target=operator.getitem](args = (%scancc, 0), kwargs = {})
    return getitem_6
-- process.inputs_to_remove --
set()
-- process.progress --
node 8/17 target=<built-in method empty of type object at 0x7e28f4744240>
-- 2 INPUTS
[GraphBuilder-SII.1.make_tensor_input] x[1:DYN0xDYN1]
[GraphBuilder-SII.1.make_tensor_input] y[1:DYN2xDYN3]
-- 2 INITIALIZERS
[GraphBuilder-SII.1.make_initializer] init7_s1_0[int64:int64:[0]] - SOURCE: Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
[GraphBuilder-SII.1.make_initializer] init7_s1_1[int64:int64:[1]] - SOURCE: Opset.make_node.1/Shape##Opset.make_node.1/Shape
[GraphBuilder-SII.4.make_node] .sizeA          [@:@   ] Shape:['x']->['size']
[GraphBuilder-SII.4.make_node] getitemB_index  [@#:@  ] Gather:['size', 'init7_s1_0']->['_onx_gather_size']
[GraphBuilder-SII.4.make_node] getitemB_index2 [@#:@  ] Squeeze:['_onx_gather_size', 'init7_s1_0']->['getitem']
[GraphBuilder-SII.4.make_node] getitemB_index3 [@#:@  ] Gather:['size', 'init7_s1_1']->['_onx_gather_size2']
[GraphBuilder-SII.4.make_node] getitemB_index4 [@#:@  ] Squeeze:['_onx_gather_size2', 'init7_s1_0']->['getitem_1']
[GraphBuilder-SII.4.make_node] .sizeA2         [@:@   ] Shape:['y']->['size_1']
[GraphBuilder-SII.4.make_node] getitemB_index5 [@#:@  ] Gather:['size_1', 'init7_s1_0']->['_onx_gather_size_1']
[GraphBuilder-SII.4.make_node] getitemB_index6 [@#:@  ] Squeeze:['_onx_gather_size_1', 'init7_s1_0']->['getitem_2']
[GraphBuilder-SII.4.make_node] getitemB_index7 [@#:@  ] Gather:['size_1', 'init7_s1_1']->['_onx_gather_size_12']
[GraphBuilder-SII.4.make_node] getitemB_index8 [@#:@  ] Squeeze:['_onx_gather_size_12', 'init7_s1_0']->['getitem_3']
-- 0 OUTPUTS
[GraphBuilder-SII] Message completed, there are 2 initializers, 10 nodes, 2 inputs, 2 outputs., 

new-tracing

FAILED

empty(): argument 'size' (position 1) must be tuple of ints, but found element of type TracingInt at pos 0

ControlFlowShapeCheck

code: yobx.torch.testing._model_eval_cases.ControlFlowShapeCheck

forward

def forward(self, x, y):
    x1 = x + 1
    y1 = y + 2
    cat = torch.cat([x1, y1], dim=1)
    torch._check(cat.shape[0] > 0, "batch size must be positive")
    if cat.shape[0] > 2:
        return cat / cat.shape[0]
    return cat / cat.ndim

yobx

FAILED

Constraints violated (batch)! For more information, run with TORCH_LOGS="+dynamic".
  - Not all values of batch = L['x'].size()[0] in the specified range satisfy the generated guard 3 <= L['x'].size()[0] and L['x'].size()[0] <= IntInfinity()
Suggested fixes:
  batch = Dim('batch', min=3)

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()`.

dynamo-ir

• inputs: #2[(T1s3x4,T1s3x4),(T1s5x4,T1s5x2)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='y' type=dtype('float32') shape=['batch', 'seq']
init: name='scalar_tensor_default' type=float32 shape=() -- array([1.], dtype=float32)
init: name='scalar_tensor_default_1' type=float32 shape=() -- array([2.], dtype=float32)
Add(x, scalar_tensor_default) -> add
Shape(y, end=1, start=0) -> val_1
  Squeeze(val_1) -> sym_size_int_5
    Cast(sym_size_int_5, to=1) -> scalar_tensor_default_2
Add(y, scalar_tensor_default_1) -> add_4
  Concat(add, add_4, axis=1) -> cat
    Div(cat, scalar_tensor_default_2) -> div
output: name='div' type=dtype('float32') shape=['batch', 'seq + 4']

tracing

FAILED

symbolically traced variables cannot be used as inputs to control flow

new-tracing

FAILED

# no error found for the failure

ControlFlowWhileDec

code: yobx.torch.testing._model_eval_cases.ControlFlowWhileDec

forward

def forward(self, ci, a, b):
    def cond_fn(i, x, y):
        return i > 0

    def body_fn(i, x, y):
        return i - 1, x + y, y - x

    return torch._higher_order_ops.while_loop(cond_fn, body_fn, [ci, a, b])

yobx

FAILED

Found the following conflicts between user-specified ranges and inferred ranges from model tracing:
- Received user-specified dim hint Dim.DYNAMIC(min=None, max=None), but tracing inferred a static shape of 3 for dimension inputs['a'].shape[1].

dynamo-ir

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'ValueError'>: Found the following conflicts between user-specified ranges and inferred ranges from model tracing:
- Received user-specified dim hint Dim.DYNAMIC(min=None, max=None), but tracing inferred a static shape of 3 for dimension inputs['a'].shape[1].

(Refer to the full stack trace above for more information.)

tracing

FAILED

[CustomProxy(ci), CustomProxy(a), CustomProxy(b)] can only be of (<class 'torch.Tensor'>, <class 'int'>, <class 'torch.SymInt'>) but got (<class 'yobx.torch.tracing.CustomProxy'>, <class 'yobx.torch.tracing.CustomProxy'>, <class 'yobx.torch.tracing.CustomProxy'>)

new-tracing

FAILED

from user code:
   File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py", line 249, in _while_loop_op_wrapper
    return while_loop_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"

ControlFlowWhileInc

code: yobx.torch.testing._model_eval_cases.ControlFlowWhileInc

forward

def forward(self, ci, a, b):
    def cond_fn(i, x, y):
        return i < x.size(0)

    def body_fn(i, x, y):
        return i + 1, x + y, y - x

    return torch._higher_order_ops.while_loop(cond_fn, body_fn, [ci, a, b])

yobx

FAILED

Found the following conflicts between user-specified ranges and inferred ranges from model tracing:
- Received user-specified dim hint Dim.DYNAMIC(min=None, max=None), but tracing inferred a static shape of 3 for dimension inputs['a'].shape[1].

dynamo-ir

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'ValueError'>: Found the following conflicts between user-specified ranges and inferred ranges from model tracing:
- Received user-specified dim hint Dim.DYNAMIC(min=None, max=None), but tracing inferred a static shape of 3 for dimension inputs['a'].shape[1].

(Refer to the full stack trace above for more information.)

tracing

FAILED

[CustomProxy(ci), CustomProxy(a), CustomProxy(b)] can only be of (<class 'torch.Tensor'>, <class 'int'>, <class 'torch.SymInt'>) but got (<class 'yobx.torch.tracing.CustomProxy'>, <class 'yobx.torch.tracing.CustomProxy'>, <class 'yobx.torch.tracing.CustomProxy'>)

new-tracing

FAILED

from user code:
   File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py", line 249, in _while_loop_op_wrapper
    return while_loop_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"

CreateFromShape

code: yobx.torch.testing._model_eval_cases.CreateFromShape

forward

def forward(self, x):
    y = torch.ones((x.shape[0], x.shape[1] + 1))
    return y

yobx

• inputs: #2[(T1s4x4,),(T1s5x5,)]

• shapes: dict(x:{0:Dim(dx),1:Dim(dy)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='SqueezeBinaryUnsqueezePattern_init7_s_1' type=int64 shape=(1,) -- array([1])-- GraphBuilder.constant_folding.from/fold(init7_s1_0,init7_s_1)##init7_s_1/shape_type_compute._cast_inputs.1(add)##init7_s1_0/Opset.make_node.1/Shape##Opset.make_node.1/Shape
Shape(x, end=1, start=0) -> x::Shape:1
Shape(x, end=2, start=1) -> x::Shape1:2
  Add(x::Shape1:2, SqueezeBinaryUnsqueezePattern_init7_s_1) -> add::UnSq0
  Concat(x::Shape:1, add::UnSq0, axis=0) -> _onx_concat_sym_size_int_2::UnSq0
    ConstantOfShape(_onx_concat_sym_size_int_2::UnSq0, value=[1.0]) -> output_0
output: name='output_0' type=dtype('float32') shape=['dx', 'dy']

dynamo-ir

• inputs: #2[(T1s4x4,),(T1s5x5,)]

• shapes: dict(x:{0:Dim(dx),1:Dim(dy)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='val_10' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_2' type=int64 shape=() -- array([1])
init: name='val_5' type=int64 shape=(1,) -- array([-1])
Shape(x, end=1, start=0) -> val_0
Shape(x, end=2, start=1) -> val_1
  Squeeze(val_1) -> sym_size_int_3
    Add(sym_size_int_3, val_2) -> add
      Reshape(add, val_5, allowzero=0) -> val_6
  Concat(val_0, val_6, axis=0) -> val_7
    Expand(val_10, val_7) -> ones
output: name='ones' type=dtype('float32') shape=['dx', 'dy + 1']

tracing

• inputs: #2[(T1s4x4,),(T1s5x5,)]

• shapes: dict(x:{0:Dim(dx),1:Dim(dy)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- shape_type_compute._cast_inputs.1(add)
Shape(x) -> size
  Split(size, axis=0, num_outputs=2) -> _onx_gather_size, _onx_gather_size_12
    Squeeze(_onx_gather_size_12, init7_s1_0) -> getitem_3
      Add(getitem_3, init7_s_1) -> _onx_add_getitem_3
        Unsqueeze(_onx_add_getitem_3, init7_s1_0) -> add::UnSq0
    Concat(_onx_gather_size, add::UnSq0, axis=0) -> _onx_concat_getitem::UnSq0
      ConstantOfShape(_onx_concat_getitem::UnSq0, value=[1.0]) -> output
output: name='output' type=dtype('float32') shape=['dx', 'add']

new-tracing

FAILED

ones(): argument 'size' (position 1) must be tuple of ints, but found element of type TracingInt at pos 0

CreateFromShapeThroughFunction

code: yobx.torch.testing._model_eval_cases.CreateFromShapeThroughFunction

forward

def forward(self, x):
    def add_one(dim):
        return dim + 1

    dy1 = add_one(x.shape[1])
    y = torch.ones((x.shape[0], dy1))
    return y

yobx

• inputs: #1[(T1s4x4,)]

• shapes: dict(x:{0:Dim(dx),1:Dim(dy)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='SqueezeBinaryUnsqueezePattern_init7_s_1' type=int64 shape=(1,) -- array([1])-- GraphBuilder.constant_folding.from/fold(init7_s1_0,init7_s_1)##init7_s_1/shape_type_compute._cast_inputs.1(add)##init7_s1_0/Opset.make_node.1/Shape##Opset.make_node.1/Shape
Shape(x, end=1, start=0) -> x::Shape:1
Shape(x, end=2, start=1) -> x::Shape1:2
  Add(x::Shape1:2, SqueezeBinaryUnsqueezePattern_init7_s_1) -> add::UnSq0
  Concat(x::Shape:1, add::UnSq0, axis=0) -> _onx_concat_sym_size_int_2::UnSq0
    ConstantOfShape(_onx_concat_sym_size_int_2::UnSq0, value=[1.0]) -> output_0
output: name='output_0' type=dtype('float32') shape=['dx', 'dy']

dynamo-ir

• inputs: #1[(T1s4x4,)]

• shapes: dict(x:{0:Dim(dx),1:Dim(dy)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='val_10' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_2' type=int64 shape=() -- array([1])
init: name='val_5' type=int64 shape=(1,) -- array([-1])
Shape(x, end=1, start=0) -> val_0
Shape(x, end=2, start=1) -> val_1
  Squeeze(val_1) -> sym_size_int_3
    Add(sym_size_int_3, val_2) -> add
      Reshape(add, val_5, allowzero=0) -> val_6
  Concat(val_0, val_6, axis=0) -> val_7
    Expand(val_10, val_7) -> ones
output: name='ones' type=dtype('float32') shape=['dx', 'dy + 1']

tracing

• inputs: #1[(T1s4x4,)]

• shapes: dict(x:{0:Dim(dx),1:Dim(dy)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- shape_type_compute._cast_inputs.1(add)
Shape(x) -> size
  Split(size, axis=0, num_outputs=2) -> _onx_gather_size_1, _onx_gather_size2
    Squeeze(_onx_gather_size2, init7_s1_0) -> getitem_1
      Add(getitem_1, init7_s_1) -> _onx_add_getitem_1
        Unsqueeze(_onx_add_getitem_1, init7_s1_0) -> add::UnSq0
    Concat(_onx_gather_size_1, add::UnSq0, axis=0) -> _onx_concat_getitem_2::UnSq0
      ConstantOfShape(_onx_concat_getitem_2::UnSq0, value=[1.0]) -> output
output: name='output' type=dtype('float32') shape=['dx', 'add']

new-tracing

FAILED

ones(): argument 'size' (position 1) must be tuple of ints, but found element of type TracingInt at pos 0

CropLastDimensionWithTensorContent

code: yobx.torch.testing._model_eval_cases.CropLastDimensionWithTensorContent

forward

def forward(self, x, shape):
    return x[..., : shape.item()]

yobx

• inputs: #2[(T1s3x4x4,T7s1),(T1s6x4x4,T7s1)]

• shapes: dict(x:{0:Dim(batch)},shape:{})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='shape' type=dtype('int64') shape=[1]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape
Slice(x, init7_s1_0, shape, init7_s1_2) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4, 'item']

dynamo-ir

• inputs: #2[(T1s3x4x4,T7s1),(T1s6x4x4,T7s1)]

• shapes: dict(x:{0:Dim(batch)},shape:{})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='shape' type=dtype('int64') shape=[1]
init: name='val_11' type=int64 shape=(1,) -- array([2])
init: name='val_0' type=int64 shape=(1,) -- array([-1])
init: name='val_2' type=int64 shape=() -- array([0])
init: name='val_4' type=int64 shape=(1,) -- array([0])
init: name='val_12' type=int64 shape=(1,) -- array([1])
Reshape(shape, val_0, allowzero=0) -> val_1
  Gather(val_1, val_2, axis=0) -> val_3
    Reshape(val_3, val_0, allowzero=0) -> val_7
      Slice(x, val_4, val_7, val_11, val_12) -> slice_1
output: name='slice_1' type=dtype('float32') shape=['batch', 4, 'u1']

tracing

• inputs: #2[(T1s3x4x4,T7s1),(T1s6x4x4,T7s1)]

• shapes: dict(x:{0:Dim(batch)},shape:{})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='shape' type=dtype('int64') shape=[1]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='getitem_axis' type=int64 shape=(1,) -- array([-1])        -- DynamoInterpreter._getitem_slice.axis.1
init: name='getitem_step' type=int64 shape=(1,) -- array([1])         -- DynamoInterpreter._getitem_slice.3
Slice(x, init7_s1_0, shape, getitem_axis, getitem_step) -> output
output: name='output' type=dtype('float32') shape=['batch', 4, 'item']

new-tracing

FAILED

Cannot determine if it is a constant argument SymInt

CropLastDimensionWithTensorShape

code: yobx.torch.testing._model_eval_cases.CropLastDimensionWithTensorShape

forward

def forward(self, x, y):
    return x[..., : y.shape[0]]

yobx

• inputs: #2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(crop)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='y' type=dtype('float32') shape=['crop']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape
Shape(y, end=1, start=0) -> y::Shape:1
  Slice(x, init7_s1_0, y::Shape:1, init7_s1_2) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4, 'crop']

dynamo-ir

• inputs: #2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(crop)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='y' type=dtype('float32') shape=['crop']
init: name='val_8' type=int64 shape=(1,) -- array([2])
init: name='val_1' type=int64 shape=(1,) -- array([0])
init: name='val_9' type=int64 shape=(1,) -- array([1])
Shape(y, end=1, start=0) -> val_0
  Slice(x, val_1, val_0, val_8, val_9) -> slice_1
output: name='slice_1' type=dtype('float32') shape=['batch', 4, 'crop']

tracing

• inputs: #2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(crop)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='y' type=dtype('float32') shape=['crop']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='getitem_1_axis' type=int64 shape=(1,) -- array([-1])      -- DynamoInterpreter._getitem_slice.axis.1
init: name='getitem_1_step' type=int64 shape=(1,) -- array([1])       -- DynamoInterpreter._getitem_slice.3
Shape(y) -> size
  Gather(size, init7_s1_0) -> _onx_gather_size
    Slice(x, init7_s1_0, _onx_gather_size, getitem_1_axis, getitem_1_step) -> output
output: name='output' type=dtype('float32') shape=['batch', 4, 'crop']

new-tracing

FAILED

TracingInt('crop') has no concrete integer value; pass a concrete int or check .value

ExportWithDimension0

code: yobx.torch.testing._model_eval_cases.ExportWithDimension0

forward

def forward(self, x):
    return x @ torch.arange(x.shape[1], dtype=torch.float32).reshape((-1, 1))

yobx

• inputs: #1[(T1s0x3,)]

• shapes: dict(x:{0:DYNAMIC,1:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 'channel_1']
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- Opset.make_node.1/Small
init: name='init1_s_2' type=float32 shape=() -- array([1.], dtype=float32)-- Opset.make_node.1/Small
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- ReshapeIsSqueezePattern.m1
Shape(x, end=2, start=1) -> x::Shape1:2
  Squeeze(x::Shape1:2) -> sym_size_int_2
    Cast(sym_size_int_2, to=1) -> sym_size_int_2::C1
      Range(init1_s_, sym_size_int_2::C1, init1_s_2) -> arange
        Unsqueeze(arange, init7_s1_1) -> reshape
          MatMul(x, reshape) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch_1', 1]

dynamo-ir

• inputs: #1[(T1s0x3,)]

• shapes: dict(x:{0:DYNAMIC,1:DYNAMIC})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s77', 's27']
init: name='val_3' type=float32 shape=() -- array([0.], dtype=float32)
init: name='val_5' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_9' type=int64 shape=(2,) -- array([-1,  1])
Shape(x, end=2, start=1) -> val_0
  Squeeze(val_0) -> sym_size_int_5
    Cast(sym_size_int_5, to=1) -> val_1
      Range(val_3, val_1, val_5) -> arange
        Reshape(arange, val_9, allowzero=1) -> view
          MatMul(x, view) -> matmul
output: name='matmul' type=dtype('float32') shape=['s77', 1]

tracing

• inputs: #1[(T1s0x3,)]

• shapes: dict(x:{0:DYNAMIC,1:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 'channel_1']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##ReshapeIsSqueezePattern.m1
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- Opset.make_node.1/Small
init: name='init1_s_2' type=float32 shape=() -- array([1.], dtype=float32)-- Opset.make_node.1/Small
Shape(x) -> size
  Gather(size, init7_s1_1) -> _onx_gather_size2
    Squeeze(_onx_gather_size2, init7_s1_0) -> getitem_1
      Cast(getitem_1, to=1) -> getitem_1::C1
        Range(init1_s_, getitem_1::C1, init1_s_2) -> arange
          Unsqueeze(arange, init7_s1_1) -> reshape
            MatMul(x, reshape) -> output
output: name='output' type=dtype('float32') shape=['batch_1', 1]

new-tracing

FAILED

arange() received an invalid combination of arguments - got (TracingInt, dtype=torch.dtype), but expected one of:
 * (Number end, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (Number start, Number end, *, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (Number start, Number end, Number step = 1, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)

ExportWithDimension1

code: yobx.torch.testing._model_eval_cases.ExportWithDimension1

forward

def forward(self, x):
    return x @ torch.arange(x.shape[1], dtype=torch.float32).reshape((-1, 1))

yobx

• inputs: #1[(T1s1x3,)]

• shapes: dict(x:{0:DYNAMIC,1:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 'channel_1']
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- Opset.make_node.1/Small
init: name='init1_s_2' type=float32 shape=() -- array([1.], dtype=float32)-- Opset.make_node.1/Small
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- ReshapeIsSqueezePattern.m1
Shape(x, end=2, start=1) -> x::Shape1:2
  Squeeze(x::Shape1:2) -> sym_size_int_2
    Cast(sym_size_int_2, to=1) -> sym_size_int_2::C1
      Range(init1_s_, sym_size_int_2::C1, init1_s_2) -> arange
        Unsqueeze(arange, init7_s1_1) -> reshape
          MatMul(x, reshape) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch_1', 1]

dynamo-ir

• inputs: #1[(T1s1x3,)]

• shapes: dict(x:{0:DYNAMIC,1:DYNAMIC})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s77', 's27']
init: name='val_3' type=float32 shape=() -- array([0.], dtype=float32)
init: name='val_5' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_9' type=int64 shape=(2,) -- array([-1,  1])
Shape(x, end=2, start=1) -> val_0
  Squeeze(val_0) -> sym_size_int_5
    Cast(sym_size_int_5, to=1) -> val_1
      Range(val_3, val_1, val_5) -> arange
        Reshape(arange, val_9, allowzero=1) -> view
          MatMul(x, view) -> matmul
output: name='matmul' type=dtype('float32') shape=['s77', 1]

tracing

• inputs: #1[(T1s1x3,)]

• shapes: dict(x:{0:DYNAMIC,1:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_1', 'channel_1']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##ReshapeIsSqueezePattern.m1
init: name='init1_s_' type=float32 shape=() -- array([0.], dtype=float32)-- Opset.make_node.1/Small
init: name='init1_s_2' type=float32 shape=() -- array([1.], dtype=float32)-- Opset.make_node.1/Small
Shape(x) -> size
  Gather(size, init7_s1_1) -> _onx_gather_size2
    Squeeze(_onx_gather_size2, init7_s1_0) -> getitem_1
      Cast(getitem_1, to=1) -> getitem_1::C1
        Range(init1_s_, getitem_1::C1, init1_s_2) -> arange
          Unsqueeze(arange, init7_s1_1) -> reshape
            MatMul(x, reshape) -> output
output: name='output' type=dtype('float32') shape=['batch_1', 1]

new-tracing

FAILED

arange() received an invalid combination of arguments - got (TracingInt, dtype=torch.dtype), but expected one of:
 * (Number end, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (Number start, Number end, *, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (Number start, Number end, Number step = 1, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)

ExportWithNewConstant

code: yobx.torch.testing._model_eval_cases.ExportWithNewConstant

forward

def forward(self, x):
    new_shape = (x.shape[0], 1)
    ones = torch.ones(new_shape, dtype=x.dtype, device=x.device)
    return torch.cat([x, ones], dim=1)

yobx

• inputs: #2[(T1s4x4,),(T1s5x6,)]

• shapes: dict(x:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 'seq']
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- GraphBuilder.make_shape_from_results.conc
Shape(x, end=1, start=0) -> x::Shape:1
  Concat(x::Shape:1, init7_s1_1, axis=0) -> _onx_concat_sym_size_int_1::UnSq0
    ConstantOfShape(_onx_concat_sym_size_int_1::UnSq0, value=[1.0]) -> ones
      Concat(x, ones, axis=1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 'seq+1']

dynamo-ir

• inputs: #2[(T1s4x4,),(T1s5x6,)]

• shapes: dict(x:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 'seq']
init: name='val_7' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1])
Shape(x, end=1, start=0) -> val_0
  Concat(val_0, val_3, axis=0) -> val_4
    Expand(val_7, val_4) -> ones
      Concat(x, ones, axis=1) -> cat
output: name='cat' type=dtype('float32') shape=['batch', 'seq + 1']

tracing

• inputs: #2[(T1s4x4,),(T1s5x6,)]

• shapes: dict(x:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 'seq']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##GraphBuilder.make_shape_from_results.conc
Shape(x) -> size
  Gather(size, init7_s1_0) -> _onx_gather_size
    Concat(_onx_gather_size, init7_s1_1, axis=0) -> _onx_concat_getitem::UnSq0
      ConstantOfShape(_onx_concat_getitem::UnSq0, value=[1.0]) -> ones
        Concat(x, ones, axis=1) -> output
output: name='output' type=dtype('float32') shape=['batch', 'seq+1']

new-tracing

FAILED

ones() received an invalid combination of arguments - got (tuple, device=torch.device, dtype=torch.dtype), but expected one of:
 * (tuple of ints size, *, tuple of names names, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (tuple of ints size, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)

ExportWithNewConstantTo

code: yobx.torch.testing._model_eval_cases.ExportWithNewConstantTo

forward

def forward(self, x):
    new_shape = (x.shape[0], 1)
    ones = torch.ones(new_shape, dtype=x.dtype)
    return torch.cat([x, ones.to(x.device)], dim=1)

yobx

• inputs: #2[(T1s4x4,),(T1s5x6,)]

• shapes: dict(x:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 'seq']
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- GraphBuilder.make_shape_from_results.conc
Shape(x, end=1, start=0) -> x::Shape:1
  Concat(x::Shape:1, init7_s1_1, axis=0) -> _onx_concat_sym_size_int_1::UnSq0
    ConstantOfShape(_onx_concat_sym_size_int_1::UnSq0, value=[1.0]) -> ones
      Concat(x, ones, axis=1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 'seq+1']

dynamo-ir

• inputs: #2[(T1s4x4,),(T1s5x6,)]

• shapes: dict(x:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 'seq']
init: name='val_7' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1])
Shape(x, end=1, start=0) -> val_0
  Concat(val_0, val_3, axis=0) -> val_4
    Expand(val_7, val_4) -> ones
      Concat(x, ones, axis=1) -> cat
output: name='cat' type=dtype('float32') shape=['batch', 'seq + 1']

tracing

• inputs: #2[(T1s4x4,),(T1s5x6,)]

• shapes: dict(x:{0:Dim(batch),1:Dim(seq)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 'seq']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##GraphBuilder.make_shape_from_results.conc
Shape(x) -> size
  Gather(size, init7_s1_0) -> _onx_gather_size
    Concat(_onx_gather_size, init7_s1_1, axis=0) -> _onx_concat_getitem::UnSq0
      ConstantOfShape(_onx_concat_getitem::UnSq0, value=[1.0]) -> ones
        Concat(x, ones, axis=1) -> output
output: name='output' type=dtype('float32') shape=['batch', 'seq+1']

new-tracing

FAILED

ones() received an invalid combination of arguments - got (tuple, dtype=torch.dtype), but expected one of:
 * (tuple of ints size, *, tuple of names names, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (tuple of ints size, *, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)

InplaceAdd

code: yobx.torch.testing._model_eval_cases.InplaceAdd

forward

def forward(self, x):
    x += self.bias
    return x

yobx

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='c_bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.placeholder.0
Add(x, c_bias) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
Add(x, bias) -> add_3
output: name='add_3' type=dtype('float32') shape=['batch', 4]

tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.get_attr.0
Add(x, bias) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

FAILED

'DynamoInterpreter' object has no attribute 'TracingInt'

InplaceAdd2

code: yobx.torch.testing._model_eval_cases.InplaceAdd2

forward

def forward(self, x):
    x.add_(self.bias)
    return x

yobx

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='c_bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.placeholder.0
Add(x, c_bias) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
Add(x, bias) -> add_3
output: name='add_3' type=dtype('float32') shape=['batch', 4]

tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.get_attr.0
Add(x, bias) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

FAILED

'DynamoInterpreter' object has no attribute 'TracingInt'

InplaceAdd_Mul

code: yobx.torch.testing._model_eval_cases.InplaceAdd_Mul

forward

def forward(self, x):
    x.add_(self.bias)
    return x * 2

yobx

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='c_bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape
Add(x, c_bias) -> add_
  Mul(add_, init1_s_::RSh1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
init: name='scalar_tensor_default' type=float32 shape=() -- array([2.], dtype=float32)
Add(x, bias) -> add_3
  Mul(add_3, scalar_tensor_default) -> mul_4
output: name='mul_4' type=dtype('float32') shape=['batch', 4]

tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.get_attr.0
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(mul)##init7_s1_1/Opset.make_node.1/Shape
Add(x, bias) -> add_
  Mul(add_, init1_s_::RSh1) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

FAILED

'DynamoInterpreter' object has no attribute 'TracingInt'

InplaceCloneAdd

code: yobx.torch.testing._model_eval_cases.InplaceCloneAdd_

forward

def forward(self, x):
    x = x.clone()
    x.add_(self.bias)
    return x

yobx

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='c_bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.placeholder.0
Add(x, c_bias) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
Add(x, bias) -> add_6
output: name='add_6' type=dtype('float32') shape=['batch', 4]

tracing

• inputs: #2[(T1s3x4,),(T1s5x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.get_attr.0
Add(x, bias) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

FAILED

'DynamoInterpreter' object has no attribute 'TracingInt'

InplaceSetItemEllipsis_1

code: yobx.torch.testing._model_eval_cases.InplaceSetItemEllipsis_1

forward

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

yobx

FAILED

L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4

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

dynamo-ir

E0409 00:10:36.503000 15502 torch/_guards.py:370] Error while creating guard: E0409 00:10:36.503000 15502 torch/_guards.py:370] Name: ‘’ E0409 00:10:36.503000 15502 torch/_guards.py:370] Source: shape_env E0409 00:10:36.503000 15502 torch/_guards.py:370] Create Function: SHAPE_ENV E0409 00:10:36.503000 15502 torch/_guards.py:370] Guard Types: None E0409 00:10:36.503000 15502 torch/_guards.py:370] Code List: None E0409 00:10:36.503000 15502 torch/_guards.py:370] Object Weakref: None E0409 00:10:36.503000 15502 torch/_guards.py:370] Guarded Class Weakref: None E0409 00:10:36.503000 15502 torch/_guards.py:370] Traceback (most recent call last): E0409 00:10:36.503000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/_guards.py”, line 368, in create E0409 00:10:36.503000 15502 torch/_guards.py:370] return self.create_fn(builder, self) E0409 00:10:36.503000 15502 torch/_guards.py:370] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:10:36.503000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/guards.py”, line 3112, in SHAPE_ENV E0409 00:10:36.503000 15502 torch/_guards.py:370] python_code_parts, verbose_code_parts = _get_code_parts( E0409 00:10:36.503000 15502 torch/_guards.py:370] ^^^^^^^^^^^^^^^^ E0409 00:10:36.503000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/guards.py”, line 3085, in _get_code_parts E0409 00:10:36.503000 15502 torch/_guards.py:370] return output_graph.shape_env.produce_guards_verbose( E0409 00:10:36.503000 15502 torch/_guards.py:370] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:10:36.503000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py”, line 5842, in produce_guards_verbose E0409 00:10:36.503000 15502 torch/_guards.py:370] raise ConstraintViolationError( E0409 00:10:36.503000 15502 torch/_guards.py:370] torch.fx.experimental.symbolic_shapes.ConstraintViolationError: L[‘flat_args’][1].size()[0] = 8192 is not equal to L[‘flat_args’][0].size()[0] = 4 E0409 00:10:36.562000 15502 torch/_guards.py:372] Created at: E0409 00:10:36.562000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py”, line 832, in trace_frame E0409 00:10:36.562000 15502 torch/_guards.py:372] tracer = InstructionTranslator( E0409 00:10:36.562000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py”, line 4790, in __init__ E0409 00:10:36.562000 15502 torch/_guards.py:372] output=OutputGraph( E0409 00:10:36.562000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/output_graph.py”, line 705, in __init__ E0409 00:10:36.562000 15502 torch/_guards.py:372] self.init_ambient_guards() E0409 00:10:36.562000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/output_graph.py”, line 990, in init_ambient_guards E0409 00:10:36.562000 15502 torch/_guards.py:372] self.guards.add(ShapeEnvSource().make_guard(GuardBuilder.SHAPE_ENV))

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'torch._dynamo.exc.UserError'>: L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4

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

(Refer to the full stack trace above for more information.)

tracing

• inputs: #1[(T7s4,T1s8192x4)]

• shapes: dict(index:{0:Dim(batch)},update:{0:Dim(batch),1:DYNAMIC})

opset: domain='' version=21
opset: domain='aten' version=1
input: name='index' type=dtype('int64') shape=['batch']
input: name='update' type=dtype('float32') shape=['batch', 'channel_1']
init: name='_tensor_constant0' type=float32 shape=(1, 8192, 4)        -- DynamoInterpret.get_attr.0
aten_setitem[aten](_tensor_constant0, index, update) -> output
output: name='output' type=dtype('float32') shape=[1, 8192, 4]
----- function name=aten_setitem domain=aten
----- doc_string: -- function_options=FunctionOptions(export_as_function=...
opset: domain='' version=21
input: '_tensor_constant0'
input: 'index'
input: 'update'
Constant(value=[1, 1, -1]) -> init7_s3_1_1_-1
  Reshape(index, init7_s3_1_1_-1) -> index::RSh1x1x-1
Shape(_tensor_constant0, end=2, start=0) -> _tensor_constant0::Shape:2
Shape(index) -> index::Shape:
  Concat(_tensor_constant0::Shape:2, index::Shape:, axis=0) -> _onx_concat__tensor_constant0::Shape:2
    Expand(index::RSh1x1x-1, _onx_concat__tensor_constant0::Shape:2) -> _onx_expand_index::RSh1x1x-1
Expand(update, _onx_concat__tensor_constant0::Shape:2) -> _onx_expand_update
  ScatterElements(_tensor_constant0, _onx_expand_index::RSh1x1x-1, _onx_expand_update, axis=2) -> setitem
output: name='setitem' type=? shape=?

new-tracing

FAILED

Cannot determine if it is a constant argument None

InplaceSetItemEllipsis_2

code: yobx.torch.testing._model_eval_cases.InplaceSetItemEllipsis_2

forward

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

yobx

FAILED

L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4

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

dynamo-ir

E0409 00:10:38.090000 15502 torch/_guards.py:370] Error while creating guard: E0409 00:10:38.090000 15502 torch/_guards.py:370] Name: ‘’ E0409 00:10:38.090000 15502 torch/_guards.py:370] Source: shape_env E0409 00:10:38.090000 15502 torch/_guards.py:370] Create Function: SHAPE_ENV E0409 00:10:38.090000 15502 torch/_guards.py:370] Guard Types: None E0409 00:10:38.090000 15502 torch/_guards.py:370] Code List: None E0409 00:10:38.090000 15502 torch/_guards.py:370] Object Weakref: None E0409 00:10:38.090000 15502 torch/_guards.py:370] Guarded Class Weakref: None E0409 00:10:38.090000 15502 torch/_guards.py:370] Traceback (most recent call last): E0409 00:10:38.090000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/_guards.py”, line 368, in create E0409 00:10:38.090000 15502 torch/_guards.py:370] return self.create_fn(builder, self) E0409 00:10:38.090000 15502 torch/_guards.py:370] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:10:38.090000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/guards.py”, line 3112, in SHAPE_ENV E0409 00:10:38.090000 15502 torch/_guards.py:370] python_code_parts, verbose_code_parts = _get_code_parts( E0409 00:10:38.090000 15502 torch/_guards.py:370] ^^^^^^^^^^^^^^^^ E0409 00:10:38.090000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/guards.py”, line 3085, in _get_code_parts E0409 00:10:38.090000 15502 torch/_guards.py:370] return output_graph.shape_env.produce_guards_verbose( E0409 00:10:38.090000 15502 torch/_guards.py:370] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ E0409 00:10:38.090000 15502 torch/_guards.py:370] File “~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py”, line 5842, in produce_guards_verbose E0409 00:10:38.090000 15502 torch/_guards.py:370] raise ConstraintViolationError( E0409 00:10:38.090000 15502 torch/_guards.py:370] torch.fx.experimental.symbolic_shapes.ConstraintViolationError: L[‘flat_args’][1].size()[0] = 8192 is not equal to L[‘flat_args’][0].size()[0] = 4 E0409 00:10:38.091000 15502 torch/_guards.py:372] Created at: E0409 00:10:38.091000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py”, line 832, in trace_frame E0409 00:10:38.091000 15502 torch/_guards.py:372] tracer = InstructionTranslator( E0409 00:10:38.091000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/symbolic_convert.py”, line 4790, in __init__ E0409 00:10:38.091000 15502 torch/_guards.py:372] output=OutputGraph( E0409 00:10:38.091000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/output_graph.py”, line 705, in __init__ E0409 00:10:38.091000 15502 torch/_guards.py:372] self.init_ambient_guards() E0409 00:10:38.091000 15502 torch/_guards.py:372] File “~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/output_graph.py”, line 990, in init_ambient_guards E0409 00:10:38.091000 15502 torch/_guards.py:372] self.guards.add(ShapeEnvSource().make_guard(GuardBuilder.SHAPE_ENV))

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'torch._dynamo.exc.UserError'>: L['update'].size()[0] = 8192 is not equal to L['index'].size()[0] = 4

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

(Refer to the full stack trace above for more information.)

tracing

• inputs: #1[(T7s4,T1s8192x4)]

• shapes: dict(index:{0:Dim(batch)},update:{0:Dim(batch),1:DYNAMIC})

opset: domain='' version=21
opset: domain='aten' version=1
input: name='index' type=dtype('int64') shape=['batch']
input: name='update' type=dtype('float32') shape=['batch', 'channel_1']
init: name='_tensor_constant0' type=float32 shape=(1, 8192, 6)        -- DynamoInterpret.get_attr.0
aten_setitem[aten](_tensor_constant0, index, update) -> output
output: name='output' type=dtype('float32') shape=[1, 8192, 6]
----- function name=aten_setitem domain=aten
----- doc_string: -- function_options=FunctionOptions(export_as_function=...
opset: domain='' version=21
input: '_tensor_constant0'
input: 'index'
input: 'update'
Constant(value=[1, 1, -1]) -> init7_s3_1_1_-1
  Reshape(index, init7_s3_1_1_-1) -> index::RSh1x1x-1
Shape(_tensor_constant0, end=2, start=0) -> _tensor_constant0::Shape:2
Shape(index) -> index::Shape:
  Concat(_tensor_constant0::Shape:2, index::Shape:, axis=0) -> _onx_concat__tensor_constant0::Shape:2
    Expand(index::RSh1x1x-1, _onx_concat__tensor_constant0::Shape:2) -> _onx_expand_index::RSh1x1x-1
Expand(update, _onx_concat__tensor_constant0::Shape:2) -> _onx_expand_update
  ScatterElements(_tensor_constant0, _onx_expand_index::RSh1x1x-1, _onx_expand_update, axis=2) -> setitem
output: name='setitem' type=? shape=?

new-tracing

FAILED

Cannot determine if it is a constant argument None

InplaceSetItemExp

code: yobx.torch.testing._model_eval_cases.InplaceSetItemExp

forward

def forward(self, x):
    K_33 = x.clone()
    torch.exp_(K_33[2:-2, 2:-2, :-1])
    return K_33

yobx

FAILED

Constraints violated (batch)! For more information, run with TORCH_LOGS="+dynamic".
  - Not all values of batch = L['x'].size()[0] in the specified range satisfy the generated guard 6 <= L['x'].size()[0] and L['x'].size()[0] <= IntInfinity()
Suggested fixes:
  batch = Dim('batch', min=6)

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()`.

dynamo-ir

• inputs: #2[(T1s7x9x11,),(T1s8x9x11,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 9, 11]
init: name='val_4' type=int64 shape=(1,) -- array([2])
init: name='val_8' type=int64 shape=(1,) -- array([-2])
init: name='val_12' type=int64 shape=(1,) -- array([0])
init: name='val_24' type=int64 shape=(1,) -- array([1])
init: name='val_32' type=int64 shape=(1,) -- array([-1])
init: name='val_1' type=int64 shape=() -- array([2])
init: name='val_9' type=int64 shape=() -- array([0])
init: name='val_21' type=int64 shape=() -- array([1])
Shape(x, start=0) -> val_86
  Gather(val_86, val_9, axis=0) -> val_87
    Range(val_9, val_87, val_21) -> val_88
      Slice(val_88, val_4, val_8, val_12, val_24) -> val_92
        Unsqueeze(val_92, val_32) -> val_93
Slice(x, val_4, val_8, val_12, val_24) -> slice_1
  Slice(slice_1, val_4, val_8, val_24, val_24) -> slice_2
    Slice(slice_2, val_12, val_32, val_4, val_24) -> slice_3
      Exp(slice_3) -> exp
        Transpose(exp, perm=[2,1,0]) -> val_70
    Shape(slice_2, start=0) -> val_61
      Gather(val_61, val_1, axis=0) -> val_62
        Range(val_9, val_62, val_21) -> val_63
          Slice(val_63, val_12, val_32, val_12, val_24) -> val_67
            Unsqueeze(val_67, val_32) -> val_69
    Transpose(slice_2, perm=[2,1,0]) -> val_71
      ScatterND(val_71, val_69, val_70, reduction=b'none') -> val_72
        Transpose(val_72, perm=[1,2,0]) -> val_82
  Shape(slice_1, start=0) -> val_74
    Gather(val_74, val_21, axis=0) -> val_75
      Range(val_9, val_75, val_21) -> val_76
        Slice(val_76, val_4, val_8, val_12, val_24) -> val_80
          Unsqueeze(val_80, val_32) -> val_81
  Transpose(slice_1, perm=[1,0,2]) -> val_83
    ScatterND(val_83, val_81, val_82, reduction=b'none') -> val_84
      Transpose(val_84, perm=[1,0,2]) -> slice_scatter_1
        ScatterND(x, val_93, slice_scatter_1, reduction=b'none') -> slice_scatter_2
output: name='slice_scatter_2' type=dtype('float32') shape=['batch', 9, 11]

tracing

• inputs: #2[(T1s7x9x11,),(T1s8x9x11,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 9, 11]
init: name='init7_s3_2_2_0' type=int64 shape=(3,) -- array([2, 2, 0]) -- Opset.make_node.1/Shape
init: name='init7_s3_-2_-2_-1' type=int64 shape=(3,) -- array([-2, -2, -1])-- Opset.make_node.1/Shape
init: name='init7_s3_0_1_2' type=int64 shape=(3,) -- array([0, 1, 2]) -- Opset.make_node.1/Shape
init: name='init7_s6_' type=int64 shape=(6,)                          -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s_' type=float32 shape=() -- array([1.], dtype=float32)-- Opset.make_node.1/Small
Slice(x, init7_s3_2_2_0, init7_s3_-2_-2_-1, init7_s3_0_1_2) -> _onx_slice_clone
  Exp(_onx_slice_clone) -> _onx_exp_slice_clone
    Shape(_onx_exp_slice_clone) -> _onx_exp_slice_clone::Shape:
      ConstantOfShape(_onx_exp_slice_clone::Shape:, value=[0.0]) -> _onx_constantofshape_exp_slice_clone::Shape:
        Pad(_onx_constantofshape_exp_slice_clone::Shape:, init7_s6_, init1_s_) -> clone_mask
          Mul(x, clone_mask) -> _onx_mul_clone
    Pad(_onx_exp_slice_clone, init7_s6_) -> _onx_exp_slice_clone_padded
      Add(_onx_mul_clone, _onx_exp_slice_clone_padded) -> output
output: name='output' type=dtype('float32') shape=['batch', 9, 11]

new-tracing

FAILED

Unable to interpret function <class 'torch._ops.OpOverload'>: <OpOverload(op='aten.exp_', overload='default')>, searched for ['aten_exp_', 'exp__default'] and attributes ['__qualname__', '__name__'], args=(slice_tensor_2,), kwargs={}, dispatcher=None
--DEBUG--
-- to print the exported program: PRINT_EXPORTED_PROGRAM=1

[GraphBuilder-TKQ] Message starts, there are 5 initializers, 4 nodes, 1 inputs, 1 outputs.
input_names=['x']
output_names=[]
--CONSTRAINTS--
    batch-4 = {'u1'}
    u1 = {'batch-4'}
--SHAPE--
_dynamic_examples=
dynamic_objects=
   batch = 'batch'
   batch-4 = 'batch-4'
   u1 = TracingInt('u1')
dynamic_objects_rev=
   'batch' = <class 'list'>
     tuple
       'batch'
       ERR**: <class 'torch.SymInt'>:'batch'
dynamic_dimensions_source={'batch': [{'axis': 0, 'input_name': 'x'}]}
dynamic_dimensions_source_flat=['x']
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes={'x': {0: Dim('batch', min=0)}}
_known_shapes={'clone_default': ('batch', 9, 11),
 'init7_s1_-1': (1,),
 'init7_s1_-2': (1,),
 'init7_s1_0': (1,),
 'init7_s1_1': (1,),
 'init7_s1_2': (1,),
 'slice_tensor': ('batch-4', 9, 11),
 'slice_tensor_1': ('batch-4', 5, 11),
 'slice_tensor_2': ('batch-4', 5, 10),
 'x': ('batch', 9, 11)}
_known_types={'clone_default': 1,
 'init7_s1_-1': 7,
 'init7_s1_-2': 7,
 'init7_s1_0': 7,
 'init7_s1_1': 7,
 'init7_s1_2': 7,
 'slice_tensor': 1,
 'slice_tensor_1': 1,
 'slice_tensor_2': 1,
 'x': 1}
_known_devices={'clone_default': -1,
 'slice_tensor': -1,
 'slice_tensor_1': -1,
 'slice_tensor_2': -1,
 'x': -1}
_context=[]
_known_value_shape={'init7_s1_-1': (-1,),
 'init7_s1_-2': (-2,),
 'init7_s1_0': (0,),
 'init7_s1_1': (1,),
 'init7_s1_2': (2,)}
_known_constants=['init7_s1_-1', 'init7_s1_-2', 'init7_s1_0', 'init7_s1_1', 'init7_s1_2']
_known_ranks (with no shape)={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
    clone_default -> {slice_tensor, output}
    exp__default -> set()
    slice_tensor -> {slice_tensor_1}
    slice_tensor_1 -> {slice_tensor_2}
    slice_tensor_2 -> {exp__default}
    x -> {clone_default}
--TORCH-SHAPES--
    x: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 9, 11])))) --- 1:3:('batch', 9, 11):
    clone_default: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 9, 11])))) --- 1:3:('batch', 9, 11):
    slice_tensor: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('u1'), 9, 11])))) --- 1:3:('batch-4', 9, 11):
    slice_tensor_1: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('u1'), 5, 11])))) --- 1:3:('batch-4', 5, 11):
    slice_tensor_2: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('u1'), 5, 10])))) --- 1:3:('batch-4', 5, 10):
    exp__default: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('u1'), 5, 10])))) --- :::
--ONNX--
-- EXEPATH --
export
export_options=ExportOptions(tracing=TracingMode.NEW_TRACING, aten_as_function=('aten.histc.default', 'aten.index_copy.default', 'aten.index_put.default', 'aten._grouped_mm.default', 'aten.setitem', <built-in function setitem>))
function_options=None
-- process.graph_module --
InplaceSetItemExp()



def forward(self, x):
    clone_default = torch.ops.aten.clone.default(x);  x = None
    slice_tensor = torch.ops.aten.slice.Tensor(clone_default, 0, 2, -2)
    slice_tensor_1 = torch.ops.aten.slice.Tensor(slice_tensor, 1, 2, -2);  slice_tensor = None
    slice_tensor_2 = torch.ops.aten.slice.Tensor(slice_tensor_1, 2, 0, -1);  slice_tensor_1 = None
    exp__default = torch.ops.aten.exp_.default(slice_tensor_2);  slice_tensor_2 = exp__default = None
    return clone_default

# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
    %x : [num_users=1] = placeholder[target=x]
    %clone_default : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%x,), kwargs = {})
    %slice_tensor : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%clone_default, 0, 2, -2), kwargs = {})
    %slice_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_tensor, 1, 2, -2), kwargs = {})
    %slice_tensor_2 : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%slice_tensor_1, 2, 0, -1), kwargs = {})
    %exp__default : [num_users=0] = call_function[target=torch.ops.aten.exp_.default](args = (%slice_tensor_2,), kwargs = {})
    return clone_default
-- process.inputs_to_remove --
set()
-- process.progress --
node 5/7 target=aten.exp_.default
-- 1 INPUTS
[GraphBuilder-TKQ.1.make_tensor_input] x[1:batchx9x11]
-- 5 INITIALIZERS
[GraphBuilder-TKQ.1.make_initializer] init7_s1_2[int64:int64:[2]] - SOURCE: Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
[GraphBuilder-TKQ.1.make_initializer] init7_s1_-2[int64:int64:[-2]] - SOURCE: Opset.make_node.1/Shape##Opset.make_node.1/Shape
[GraphBuilder-TKQ.1.make_initializer] init7_s1_0[int64:int64:[0]] - SOURCE: Opset.make_node.1/Shape##Opset.make_node.1/Shape
[GraphBuilder-TKQ.1.make_initializer] init7_s1_1[int64:int64:[1]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-TKQ.1.make_initializer] init7_s1_-1[int64:int64:[-1]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-TKQ.4.make_node] clone           [@:@   ] Identity:['x']->['clone_default']
[GraphBuilder-TKQ.4.make_node] slice_Tensor    [@###:@] Slice:['clone_default', 'init7_s1_2', 'init7_s1_-2', 'init7_s1_0']->['slice_tensor']
[GraphBuilder-TKQ.4.make_node] slice_Tensor2   [@###:@] Slice:['slice_tensor', 'init7_s1_2', 'init7_s1_-2', 'init7_s1_1']->['slice_tensor_1']
[GraphBuilder-TKQ.4.make_node] slice_Tensor3   [@###:@] Slice:['slice_tensor_1', 'init7_s1_0', 'init7_s1_-1', 'init7_s1_2']->['slice_tensor_2']
-- 0 OUTPUTS
[GraphBuilder-TKQ] Message completed, there are 5 initializers, 4 nodes, 1 inputs, 1 outputs., 

InplaceSetItemMask

code: yobx.torch.testing._model_eval_cases.InplaceSetItemMask

forward

def forward(self, x):
    mask = x.to(bool)
    x[mask] = 2
    return x

yobx

• inputs: #2[(T1s2x3x3,),(T1s3x3x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3, 3]
init: name='c_lifted_tensor_0' type=float32 shape=() -- array([2.], dtype=float32)-- DynamoInterpret.placeholder.0
Cast(x, to=9) -> to
  Where(to, c_lifted_tensor_0, x) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 3, 3]

dynamo-ir

• inputs: #2[(T1s2x3x3,),(T1s3x3x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3, 3]
init: name='clone' type=float32 shape=() -- array([2.], dtype=float32)
Cast(x, to=9) -> _to_copy
  Where(_to_copy, clone, x) -> index_put
output: name='index_put' type=dtype('float32') shape=['batch', 3, 3]

tracing

• inputs: #2[(T1s2x3x3,),(T1s3x3x3,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='aten' version=1
input: name='x' type=dtype('float32') shape=['batch', 3, 3]
Cast(x, to=9) -> to
  aten_setitem[aten](x, to) -> output
output: name='output' type=dtype('float32') shape=['batch', 3, 3]
----- function name=aten_setitem domain=aten
----- doc_string: -- function_options=FunctionOptions(export_as_function=...
opset: domain='' version=21
input: 'x'
input: 'to'
Constant(value=2.0) -> init1_s_
  Where(to, init1_s_, x) -> setitem
output: name='setitem' type=? shape=?

new-tracing

FAILED

Cannot determine if it is a constant argument dtype(torch.bool)

InplaceSetItemSquare

code: yobx.torch.testing._model_eval_cases.InplaceSetItemSquare

forward

def forward(self, x):
    x[:2, :3] = 1
    return x

yobx

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##ReshapeIsSqueezePattern.m1
init: name='init7_s_0' type=int64 shape=() -- array([0])              -- Opset.make_node.1/Shape
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- Opset.make_node.1/Shape
init: name='init7_s3_0_1_2::RSh-1x1' type=int64 shape=(3, 1) -- array([0, 1, 2])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s3_0_1_2)##init7_s3_0_1_2/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='fill::T10' type=float32 shape=(3, 2)                      -- GraphBuilder.constant_folding.from/fold(fill)##fill/
Shape(x, end=1, start=0) -> x::Shape:1
  Squeeze(x::Shape:1) -> x::Shape:1::Sq
    Range(init7_s_0, x::Shape:1::Sq, init7_s_1) -> _onx_range_init7_s_0
      Slice(_onx_range_init7_s_0, init7_s1_0, init7_s1_2, init7_s1_0, init7_s1_1) -> _onx_slice_range_init7_s_0
        Unsqueeze(_onx_slice_range_init7_s_0, init7_s1_1) -> _onx_slice_range_init7_s_0::RSh-1x1
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
  Transpose(slice_3, perm=[1,0]) -> slice_3::T10
    ScatterND(slice_3::T10, init7_s3_0_1_2::RSh-1x1, fill::T10) -> _onx_scatternd_slice_3::T10
      Transpose(_onx_scatternd_slice_3::T10, perm=[1,0]) -> slice_scatter
        ScatterND(x, _onx_slice_range_init7_s_0::RSh-1x1, slice_scatter) -> output_0
          Identity(output_0) -> output_1
output: name='output_1' type=dtype('float32') shape=['batch', 5]

dynamo-ir

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='val_3' type=int64 shape=(1,) -- array([0])
init: name='val_7' type=int64 shape=(1,) -- array([2])
init: name='val_18' type=int64 shape=(1,) -- array([3])
init: name='val_22' type=int64 shape=(1,) -- array([1])
init: name='value_0' type=float32 shape=() -- array([1.], dtype=float32)
init: name='val_0' type=int64 shape=() -- array([0])
init: name='val_19' type=int64 shape=() -- array([1])
init: name='val_42' type=int64 shape=(1,) -- array([-1])
Shape(x, start=0) -> val_48
  Gather(val_48, val_0, axis=0) -> val_49
    Range(val_0, val_49, val_19) -> val_50
      Slice(val_50, val_3, val_7, val_3, val_22) -> val_54
        Unsqueeze(val_54, val_42) -> val_55
Slice(x, val_3, val_7, val_3, val_22) -> slice_1
  Slice(slice_1, val_3, val_18, val_22, val_22) -> slice_2
    Shape(slice_2) -> shape
      Expand(value_0, shape) -> fill
        Transpose(fill, perm=[1,0]) -> val_44
  Shape(slice_1, start=0) -> val_35
    Gather(val_35, val_19, axis=0) -> val_36
      Range(val_0, val_36, val_19) -> val_37
        Slice(val_37, val_3, val_18, val_3, val_22) -> val_41
          Unsqueeze(val_41, val_42) -> val_43
  Transpose(slice_1, perm=[1,0]) -> val_45
    ScatterND(val_45, val_43, val_44, reduction=b'none') -> val_46
      Transpose(val_46, perm=[1,0]) -> slice_scatter
        ScatterND(x, val_55, slice_scatter, reduction=b'none') -> slice_scatter_1
output: name='slice_scatter_1' type=dtype('float32') shape=['batch', 5]

tracing

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='aten' version=1
input: name='x' type=dtype('float32') shape=['batch', 5]
aten_setitem[aten](x) -> output
output: name='output' type=dtype('float32') shape=['batch', 5]
----- function name=aten_setitem domain=aten
----- doc_string: -- function_options=FunctionOptions(export_as_function=...
opset: domain='' version=21
input: 'x'
Constant(value=1.0) -> setitem_val
Constant(value=[0]) -> init7_s1_0
  Concat(init7_s1_0, init7_s1_0, axis=0) -> _onx_concat_init7_s1_0
Constant(value=2) -> init7_s_2
Constant(value=3) -> init7_s_3
Shape(x, end=1, start=0) -> x::Shape:1
  Squeeze(x::Shape:1, init7_s1_0) -> x::Shape:1::Sq0
  Sub(x::Shape:1::Sq0, init7_s_2) -> _onx_sub_x::Shape:1::Sq0
  Unsqueeze(_onx_sub_x::Shape:1::Sq0, init7_s1_0) -> _onx_sub_x::Shape:1::Sq0::UnSq0
Shape(x, end=2, start=1) -> x::Shape1:2
  Squeeze(x::Shape1:2, init7_s1_0) -> x::Shape1:2::Sq0
  Sub(x::Shape1:2::Sq0, init7_s_3) -> _onx_sub_x::Shape1:2::Sq0
  Unsqueeze(_onx_sub_x::Shape1:2::Sq0, init7_s1_0) -> _onx_sub_x::Shape1:2::Sq0::UnSq0
    Concat(_onx_sub_x::Shape:1::Sq0::UnSq0, _onx_sub_x::Shape1:2::Sq0::UnSq0, axis=0) -> _onx_concat_sub_x::Shape:1::Sq0::UnSq0
    Add(_onx_concat_init7_s1_0, _onx_concat_sub_x::Shape:1::Sq0::UnSq0) -> _onx_add_concat_init7_s1_0
Shape(x) -> x::Shape:
  Sub(x::Shape:, _onx_add_concat_init7_s1_0) -> _onx_sub_x::Shape:
  Expand(setitem_val, _onx_sub_x::Shape:) -> _onx_expand_setitem_val
    Shape(_onx_expand_setitem_val) -> _onx_expand_setitem_val::Shape:
      ConstantOfShape(_onx_expand_setitem_val::Shape:, value=[0.0]) -> _onx_constantofshape_expand_setitem_val::Shape:
  Unsqueeze(_onx_sub_x::Shape:1::Sq0, init7_s1_0) -> _onx_sub_x::Shape:1::Sq0::UnSq02
Unsqueeze(_onx_sub_x::Shape1:2::Sq0, init7_s1_0) -> _onx_sub_x::Shape1:2::Sq0::UnSq02
  Concat(init7_s1_0, init7_s1_0, _onx_sub_x::Shape:1::Sq0::UnSq02, _onx_sub_x::Shape1:2::Sq0::UnSq02, axis=0) -> _onx_concat_init7_s1_02
    Pad(_onx_expand_setitem_val, _onx_concat_init7_s1_02) -> _onx_expand_setitem_val_padded
  Pad(_onx_constantofshape_expand_setitem_val::Shape:, _onx_concat_init7_s1_02, setitem_val) -> x_mask
    Mul(x, x_mask) -> _onx_mul_x
      Add(_onx_mul_x, _onx_expand_setitem_val_padded) -> setitem
output: name='setitem' type=? shape=?

new-tracing

FAILED

Unable to interpret function <class 'torch._ops.OpOverload'>: <OpOverload(op='aten.fill_', overload='Tensor')>, searched for ['aten_fill__Tensor', 'fill__Tensor'] and attributes ['__qualname__', '__name__'], args=(slice_tensor, param_1), kwargs={}, dispatcher=None
--DEBUG--
-- to print the exported program: PRINT_EXPORTED_PROGRAM=1

[GraphBuilder-KHK] Message starts, there are 4 initializers, 1 nodes, 1 inputs, 1 outputs.
input_names=['x']
output_names=[]
--SHAPE--
_dynamic_examples=
dynamic_objects=
   batch = 'batch'
dynamic_objects_rev=
   'batch' = <class 'list'>
     tuple
       'batch'
       ERR**: <class 'torch.SymInt'>:'batch'
dynamic_dimensions_source={'batch': [{'axis': 0, 'input_name': 'x'}]}
dynamic_dimensions_source_flat=['x']
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes={'x': {0: Dim('batch', min=0)}}
_known_shapes={'init7_s1_0': (1,),
 'init7_s1_1': (1,),
 'init7_s1_3': (1,),
 'param_1': (),
 'slice_tensor': ('batch', 3),
 'x': ('batch', 5)}
_known_types={'init7_s1_0': 7,
 'init7_s1_1': 7,
 'init7_s1_3': 7,
 'param_1': 1,
 'slice_tensor': 1,
 'x': 1}
_known_devices={'slice_tensor': -1, 'x': -1}
_context=[]
_known_value_shape={'init7_s1_0': (0,), 'init7_s1_1': (1,), 'init7_s1_3': (3,)}
_known_constants=['init7_s1_0', 'init7_s1_1', 'init7_s1_3', 'param_1']
_known_ranks (with no shape)={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
    fill__tensor -> set()
    param_1 -> {fill__tensor}
    slice_tensor -> {fill__tensor}
    x -> {output, slice_tensor}
--TORCH-SHAPES--
    x: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 5])))) --- 1:2:('batch', 5):
    slice_tensor: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 3])))) --- 1:2:('batch', 3):
    param_1: ('run_node', ('', ('val', torch.float32, TracingShape([])))) --- 1:0:():
    fill__tensor: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 3])))) --- :::
--ONNX--
-- EXEPATH --
export
export_options=ExportOptions(tracing=TracingMode.NEW_TRACING, aten_as_function=('aten.histc.default', 'aten.index_copy.default', 'aten.index_put.default', 'aten._grouped_mm.default', 'aten.setitem', <built-in function setitem>))
function_options=None
-- process.graph_module --
InplaceSetItemSquare()



def forward(self, x, param_1):
    slice_tensor = torch.ops.aten.slice.Tensor(x, 1, 0, 3)
    fill__tensor = torch.ops.aten.fill_.Tensor(slice_tensor, param_1);  slice_tensor = param_1 = fill__tensor = None
    return x

# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
    %x : [num_users=2] = placeholder[target=x]
    %slice_tensor : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 1, 0, 3), kwargs = {})
    %param_1 : [num_users=1] = placeholder[target=param_1]
    %fill__tensor : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_tensor, %param_1), kwargs = {})
    return x
-- process.inputs_to_remove --
set()
-- process.progress --
node 3/5 target=aten.fill_.Tensor
-- 1 INPUTS
[GraphBuilder-KHK.1.make_tensor_input] x[1:batchx5]
-- 4 INITIALIZERS
[GraphBuilder-KHK.1.make_initializer] init7_s1_0[int64:int64:[0]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-KHK.1.make_initializer] init7_s1_3[int64:int64:[3]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-KHK.1.make_initializer] init7_s1_1[int64:int64:[1]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-KHK.1.make_initializer] param_1[torch.float32:torch.float32:[1.0]] - SOURCE: DynamoInterpret.placeholder.0
[GraphBuilder-KHK.4.make_node] slice_Tensor    [@###:@] Slice:['x', 'init7_s1_0', 'init7_s1_3', 'init7_s1_1']->['slice_tensor']
-- 0 OUTPUTS
[GraphBuilder-KHK] Message completed, there are 4 initializers, 1 nodes, 1 inputs, 1 outputs., 

InplaceSetItemSquareAdd

code: yobx.torch.testing._model_eval_cases.InplaceSetItemSquareAdd

forward

def forward(self, x):
    x[:2, :3] = 1
    return x + 2

yobx

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##ReshapeIsSqueezePattern.m1
init: name='init7_s_0' type=int64 shape=() -- array([0])              -- Opset.make_node.1/Shape
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- Opset.make_node.1/Shape
init: name='init7_s3_0_1_2::RSh-1x1' type=int64 shape=(3, 1) -- array([0, 1, 2])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s3_0_1_2)##init7_s3_0_1_2/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='fill::T10' type=float32 shape=(3, 2)                      -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
Shape(x, end=1, start=0) -> x::Shape:1
  Squeeze(x::Shape:1) -> x::Shape:1::Sq
    Range(init7_s_0, x::Shape:1::Sq, init7_s_1) -> _onx_range_init7_s_0
      Slice(_onx_range_init7_s_0, init7_s1_0, init7_s1_2, init7_s1_0, init7_s1_1) -> _onx_slice_range_init7_s_0
        Unsqueeze(_onx_slice_range_init7_s_0, init7_s1_1) -> _onx_slice_range_init7_s_0::RSh-1x1
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
  Transpose(slice_3, perm=[1,0]) -> slice_3::T10
    ScatterND(slice_3::T10, init7_s3_0_1_2::RSh-1x1, fill::T10) -> _onx_scatternd_slice_3::T10
      Transpose(_onx_scatternd_slice_3::T10, perm=[1,0]) -> slice_scatter
        ScatterND(x, _onx_slice_range_init7_s_0::RSh-1x1, slice_scatter) -> output_0
          Add(output_0, init1_s_::RSh1) -> output_1
output: name='output_1' type=dtype('float32') shape=['batch', 5]

dynamo-ir

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='val_3' type=int64 shape=(1,) -- array([0])
init: name='val_7' type=int64 shape=(1,) -- array([2])
init: name='val_18' type=int64 shape=(1,) -- array([3])
init: name='val_22' type=int64 shape=(1,) -- array([1])
init: name='value_0' type=float32 shape=() -- array([1.], dtype=float32)
init: name='scalar_tensor_default' type=float32 shape=() -- array([2.], dtype=float32)
init: name='val_0' type=int64 shape=() -- array([0])
init: name='val_19' type=int64 shape=() -- array([1])
init: name='val_42' type=int64 shape=(1,) -- array([-1])
Shape(x, start=0) -> val_48
  Gather(val_48, val_0, axis=0) -> val_49
    Range(val_0, val_49, val_19) -> val_50
      Slice(val_50, val_3, val_7, val_3, val_22) -> val_54
        Unsqueeze(val_54, val_42) -> val_55
Slice(x, val_3, val_7, val_3, val_22) -> slice_1
  Slice(slice_1, val_3, val_18, val_22, val_22) -> slice_2
    Shape(slice_2) -> shape
      Expand(value_0, shape) -> fill
        Transpose(fill, perm=[1,0]) -> val_44
  Shape(slice_1, start=0) -> val_35
    Gather(val_35, val_19, axis=0) -> val_36
      Range(val_0, val_36, val_19) -> val_37
        Slice(val_37, val_3, val_18, val_3, val_22) -> val_41
          Unsqueeze(val_41, val_42) -> val_43
  Transpose(slice_1, perm=[1,0]) -> val_45
    ScatterND(val_45, val_43, val_44, reduction=b'none') -> val_46
      Transpose(val_46, perm=[1,0]) -> slice_scatter
        ScatterND(x, val_55, slice_scatter, reduction=b'none') -> slice_scatter_1
          Add(slice_scatter_1, scalar_tensor_default) -> add_12
output: name='add_12' type=dtype('float32') shape=['batch', 5]

tracing

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='aten' version=1
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(add)##init7_s1_1/Opset.make_node.1/Shape
aten_setitem[aten](x) -> setitem
  Add(setitem, init1_s_::RSh1) -> output
output: name='output' type=dtype('float32') shape=['batch', 5]
----- function name=aten_setitem domain=aten
----- doc_string: -- function_options=FunctionOptions(export_as_function=...
opset: domain='' version=21
input: 'x'
Constant(value=1.0) -> setitem_val
Constant(value=[0]) -> init7_s1_0
  Concat(init7_s1_0, init7_s1_0, axis=0) -> _onx_concat_init7_s1_0
Constant(value=2) -> init7_s_2
Constant(value=3) -> init7_s_3
Shape(x, end=1, start=0) -> x::Shape:1
  Squeeze(x::Shape:1, init7_s1_0) -> x::Shape:1::Sq0
  Sub(x::Shape:1::Sq0, init7_s_2) -> _onx_sub_x::Shape:1::Sq0
  Unsqueeze(_onx_sub_x::Shape:1::Sq0, init7_s1_0) -> _onx_sub_x::Shape:1::Sq0::UnSq0
Shape(x, end=2, start=1) -> x::Shape1:2
  Squeeze(x::Shape1:2, init7_s1_0) -> x::Shape1:2::Sq0
  Sub(x::Shape1:2::Sq0, init7_s_3) -> _onx_sub_x::Shape1:2::Sq0
  Unsqueeze(_onx_sub_x::Shape1:2::Sq0, init7_s1_0) -> _onx_sub_x::Shape1:2::Sq0::UnSq0
    Concat(_onx_sub_x::Shape:1::Sq0::UnSq0, _onx_sub_x::Shape1:2::Sq0::UnSq0, axis=0) -> _onx_concat_sub_x::Shape:1::Sq0::UnSq0
    Add(_onx_concat_init7_s1_0, _onx_concat_sub_x::Shape:1::Sq0::UnSq0) -> _onx_add_concat_init7_s1_0
Shape(x) -> x::Shape:
  Sub(x::Shape:, _onx_add_concat_init7_s1_0) -> _onx_sub_x::Shape:
  Expand(setitem_val, _onx_sub_x::Shape:) -> _onx_expand_setitem_val
    Shape(_onx_expand_setitem_val) -> _onx_expand_setitem_val::Shape:
      ConstantOfShape(_onx_expand_setitem_val::Shape:, value=[0.0]) -> _onx_constantofshape_expand_setitem_val::Shape:
  Unsqueeze(_onx_sub_x::Shape:1::Sq0, init7_s1_0) -> _onx_sub_x::Shape:1::Sq0::UnSq02
Unsqueeze(_onx_sub_x::Shape1:2::Sq0, init7_s1_0) -> _onx_sub_x::Shape1:2::Sq0::UnSq02
  Concat(init7_s1_0, init7_s1_0, _onx_sub_x::Shape:1::Sq0::UnSq02, _onx_sub_x::Shape1:2::Sq0::UnSq02, axis=0) -> _onx_concat_init7_s1_02
    Pad(_onx_expand_setitem_val, _onx_concat_init7_s1_02) -> _onx_expand_setitem_val_padded
  Pad(_onx_constantofshape_expand_setitem_val::Shape:, _onx_concat_init7_s1_02, setitem_val) -> x_mask
    Mul(x, x_mask) -> _onx_mul_x
      Add(_onx_mul_x, _onx_expand_setitem_val_padded) -> setitem
output: name='setitem' type=? shape=?

new-tracing

FAILED

Unable to interpret function <class 'torch._ops.OpOverload'>: <OpOverload(op='aten.fill_', overload='Tensor')>, searched for ['aten_fill__Tensor', 'fill__Tensor'] and attributes ['__qualname__', '__name__'], args=(slice_tensor, param_1), kwargs={}, dispatcher=None
--DEBUG--
-- to print the exported program: PRINT_EXPORTED_PROGRAM=1

[GraphBuilder-HSA] Message starts, there are 4 initializers, 1 nodes, 1 inputs, 1 outputs.
input_names=['x']
output_names=[]
--SHAPE--
_dynamic_examples=
dynamic_objects=
   batch = 'batch'
dynamic_objects_rev=
   'batch' = <class 'list'>
     tuple
       'batch'
       ERR**: <class 'torch.SymInt'>:'batch'
dynamic_dimensions_source={'batch': [{'axis': 0, 'input_name': 'x'}]}
dynamic_dimensions_source_flat=['x']
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes={'x': {0: Dim('batch', min=0)}}
_known_shapes={'init7_s1_0': (1,),
 'init7_s1_1': (1,),
 'init7_s1_3': (1,),
 'param_1': (),
 'slice_tensor': ('batch', 3),
 'x': ('batch', 5)}
_known_types={'init7_s1_0': 7,
 'init7_s1_1': 7,
 'init7_s1_3': 7,
 'param_1': 1,
 'slice_tensor': 1,
 'x': 1}
_known_devices={'slice_tensor': -1, 'x': -1}
_context=[]
_known_value_shape={'init7_s1_0': (0,), 'init7_s1_1': (1,), 'init7_s1_3': (3,)}
_known_constants=['init7_s1_0', 'init7_s1_1', 'init7_s1_3', 'param_1']
_known_ranks (with no shape)={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
    fill__tensor -> set()
    param_1 -> {fill__tensor}
    slice_tensor -> {fill__tensor}
    x -> {add_tensor, slice_tensor}
--TORCH-SHAPES--
    x: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 5])))) --- 1:2:('batch', 5):
    slice_tensor: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 3])))) --- 1:2:('batch', 3):
    param_1: ('run_node', ('', ('val', torch.float32, TracingShape([])))) --- 1:0:():
    fill__tensor: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 3])))) --- :::
--ONNX--
-- EXEPATH --
export
export_options=ExportOptions(tracing=TracingMode.NEW_TRACING, aten_as_function=('aten.histc.default', 'aten.index_copy.default', 'aten.index_put.default', 'aten._grouped_mm.default', 'aten.setitem', <built-in function setitem>))
function_options=None
-- process.graph_module --
InplaceSetItemSquareAdd()



def forward(self, x, param_1):
    slice_tensor = torch.ops.aten.slice.Tensor(x, 1, 0, 3)
    fill__tensor = torch.ops.aten.fill_.Tensor(slice_tensor, param_1);  slice_tensor = param_1 = fill__tensor = None
    add_tensor = torch.ops.aten.add.Tensor(x, 2);  x = None
    return add_tensor

# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
    %x : [num_users=2] = placeholder[target=x]
    %slice_tensor : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 1, 0, 3), kwargs = {})
    %param_1 : [num_users=1] = placeholder[target=param_1]
    %fill__tensor : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_tensor, %param_1), kwargs = {})
    %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 2), kwargs = {})
    return add_tensor
-- process.inputs_to_remove --
set()
-- process.progress --
node 3/6 target=aten.fill_.Tensor
-- 1 INPUTS
[GraphBuilder-HSA.1.make_tensor_input] x[1:batchx5]
-- 4 INITIALIZERS
[GraphBuilder-HSA.1.make_initializer] init7_s1_0[int64:int64:[0]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-HSA.1.make_initializer] init7_s1_3[int64:int64:[3]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-HSA.1.make_initializer] init7_s1_1[int64:int64:[1]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-HSA.1.make_initializer] param_1[torch.float32:torch.float32:[1.0]] - SOURCE: DynamoInterpret.placeholder.0
[GraphBuilder-HSA.4.make_node] slice_Tensor    [@###:@] Slice:['x', 'init7_s1_0', 'init7_s1_3', 'init7_s1_1']->['slice_tensor']
-- 0 OUTPUTS
[GraphBuilder-HSA] Message completed, there are 4 initializers, 1 nodes, 1 inputs, 1 outputs., 

InplaceSetItemSquareAdd2

code: yobx.torch.testing._model_eval_cases.InplaceSetItemSquareAdd2

forward

def forward(self, x):
    x[:2, :3] = 1
    return x + 2, x + 3

yobx

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##ReshapeIsSqueezePattern.m1
init: name='init7_s_0' type=int64 shape=() -- array([0])              -- Opset.make_node.1/Shape
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- Opset.make_node.1/Shape
init: name='init7_s3_0_1_2::RSh-1x1' type=int64 shape=(3, 1) -- array([0, 1, 2])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s3_0_1_2)##init7_s3_0_1_2/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='fill::T10' type=float32 shape=(3, 2)                      -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s_2::RSh1' type=float32 shape=(1,) -- array([3.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_2,init7_s1_1)##init1_s_2/shape_type_compute._cast_inputs.0##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
Shape(x, end=1, start=0) -> x::Shape:1
  Squeeze(x::Shape:1) -> x::Shape:1::Sq
    Range(init7_s_0, x::Shape:1::Sq, init7_s_1) -> _onx_range_init7_s_0
      Slice(_onx_range_init7_s_0, init7_s1_0, init7_s1_2, init7_s1_0, init7_s1_1) -> _onx_slice_range_init7_s_0
        Unsqueeze(_onx_slice_range_init7_s_0, init7_s1_1) -> _onx_slice_range_init7_s_0::RSh-1x1
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
  Transpose(slice_3, perm=[1,0]) -> slice_3::T10
    ScatterND(slice_3::T10, init7_s3_0_1_2::RSh-1x1, fill::T10) -> _onx_scatternd_slice_3::T10
      Transpose(_onx_scatternd_slice_3::T10, perm=[1,0]) -> slice_scatter
        ScatterND(x, _onx_slice_range_init7_s_0::RSh-1x1, slice_scatter) -> output_0
          Add(output_0, init1_s_::RSh1) -> output_1
Add(output_0, init1_s_2::RSh1) -> output_2
output: name='output_1' type=dtype('float32') shape=['batch', 5]
output: name='output_2' type=dtype('float32') shape=['batch', 5]

dynamo-ir

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='val_3' type=int64 shape=(1,) -- array([0])
init: name='val_7' type=int64 shape=(1,) -- array([2])
init: name='val_18' type=int64 shape=(1,) -- array([3])
init: name='val_22' type=int64 shape=(1,) -- array([1])
init: name='value_0' type=float32 shape=() -- array([1.], dtype=float32)
init: name='scalar_tensor_default' type=float32 shape=() -- array([2.], dtype=float32)
init: name='scalar_tensor_default_1' type=float32 shape=() -- array([3.], dtype=float32)
init: name='val_0' type=int64 shape=() -- array([0])
init: name='val_19' type=int64 shape=() -- array([1])
init: name='val_42' type=int64 shape=(1,) -- array([-1])
Shape(x, start=0) -> val_48
  Gather(val_48, val_0, axis=0) -> val_49
    Range(val_0, val_49, val_19) -> val_50
      Slice(val_50, val_3, val_7, val_3, val_22) -> val_54
        Unsqueeze(val_54, val_42) -> val_55
Slice(x, val_3, val_7, val_3, val_22) -> slice_1
  Slice(slice_1, val_3, val_18, val_22, val_22) -> slice_2
    Shape(slice_2) -> shape
      Expand(value_0, shape) -> fill
        Transpose(fill, perm=[1,0]) -> val_44
  Shape(slice_1, start=0) -> val_35
    Gather(val_35, val_19, axis=0) -> val_36
      Range(val_0, val_36, val_19) -> val_37
        Slice(val_37, val_3, val_18, val_3, val_22) -> val_41
          Unsqueeze(val_41, val_42) -> val_43
  Transpose(slice_1, perm=[1,0]) -> val_45
    ScatterND(val_45, val_43, val_44, reduction=b'none') -> val_46
      Transpose(val_46, perm=[1,0]) -> slice_scatter
        ScatterND(x, val_55, slice_scatter, reduction=b'none') -> slice_scatter_1
          Add(slice_scatter_1, scalar_tensor_default) -> add_12
Add(slice_scatter_1, scalar_tensor_default_1) -> add_16
output: name='add_12' type=dtype('float32') shape=['batch', 5]
output: name='add_16' type=dtype('float32') shape=['batch', 5]

tracing

• inputs: #2[(T1s5x5,),(T1s7x5,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
opset: domain='aten' version=1
input: name='x' type=dtype('float32') shape=['batch', 5]
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([2.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(add)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init1_s_2::RSh1' type=float32 shape=(1,) -- array([3.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_2,init7_s1_1)##init1_s_2/shape_type_compute._cast_inputs.1(add)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
aten_setitem[aten](x) -> setitem
  Add(setitem, init1_s_::RSh1) -> output_0
Add(setitem, init1_s_2::RSh1) -> output_1
output: name='output_0' type=dtype('float32') shape=['batch', 5]
output: name='output_1' type=dtype('float32') shape=['batch', 5]
----- function name=aten_setitem domain=aten
----- doc_string: -- function_options=FunctionOptions(export_as_function=...
opset: domain='' version=21
input: 'x'
Constant(value=1.0) -> setitem_val
Constant(value=[0]) -> init7_s1_0
  Concat(init7_s1_0, init7_s1_0, axis=0) -> _onx_concat_init7_s1_0
Constant(value=2) -> init7_s_2
Constant(value=3) -> init7_s_3
Shape(x, end=1, start=0) -> x::Shape:1
  Squeeze(x::Shape:1, init7_s1_0) -> x::Shape:1::Sq0
  Sub(x::Shape:1::Sq0, init7_s_2) -> _onx_sub_x::Shape:1::Sq0
  Unsqueeze(_onx_sub_x::Shape:1::Sq0, init7_s1_0) -> _onx_sub_x::Shape:1::Sq0::UnSq0
Shape(x, end=2, start=1) -> x::Shape1:2
  Squeeze(x::Shape1:2, init7_s1_0) -> x::Shape1:2::Sq0
  Sub(x::Shape1:2::Sq0, init7_s_3) -> _onx_sub_x::Shape1:2::Sq0
  Unsqueeze(_onx_sub_x::Shape1:2::Sq0, init7_s1_0) -> _onx_sub_x::Shape1:2::Sq0::UnSq0
    Concat(_onx_sub_x::Shape:1::Sq0::UnSq0, _onx_sub_x::Shape1:2::Sq0::UnSq0, axis=0) -> _onx_concat_sub_x::Shape:1::Sq0::UnSq0
    Add(_onx_concat_init7_s1_0, _onx_concat_sub_x::Shape:1::Sq0::UnSq0) -> _onx_add_concat_init7_s1_0
Shape(x) -> x::Shape:
  Sub(x::Shape:, _onx_add_concat_init7_s1_0) -> _onx_sub_x::Shape:
  Expand(setitem_val, _onx_sub_x::Shape:) -> _onx_expand_setitem_val
    Shape(_onx_expand_setitem_val) -> _onx_expand_setitem_val::Shape:
      ConstantOfShape(_onx_expand_setitem_val::Shape:, value=[0.0]) -> _onx_constantofshape_expand_setitem_val::Shape:
  Unsqueeze(_onx_sub_x::Shape:1::Sq0, init7_s1_0) -> _onx_sub_x::Shape:1::Sq0::UnSq02
Unsqueeze(_onx_sub_x::Shape1:2::Sq0, init7_s1_0) -> _onx_sub_x::Shape1:2::Sq0::UnSq02
  Concat(init7_s1_0, init7_s1_0, _onx_sub_x::Shape:1::Sq0::UnSq02, _onx_sub_x::Shape1:2::Sq0::UnSq02, axis=0) -> _onx_concat_init7_s1_02
    Pad(_onx_expand_setitem_val, _onx_concat_init7_s1_02) -> _onx_expand_setitem_val_padded
  Pad(_onx_constantofshape_expand_setitem_val::Shape:, _onx_concat_init7_s1_02, setitem_val) -> x_mask
    Mul(x, x_mask) -> _onx_mul_x
      Add(_onx_mul_x, _onx_expand_setitem_val_padded) -> setitem
output: name='setitem' type=? shape=?

new-tracing

FAILED

Unable to interpret function <class 'torch._ops.OpOverload'>: <OpOverload(op='aten.fill_', overload='Tensor')>, searched for ['aten_fill__Tensor', 'fill__Tensor'] and attributes ['__qualname__', '__name__'], args=(slice_tensor, param_1), kwargs={}, dispatcher=None
--DEBUG--
-- to print the exported program: PRINT_EXPORTED_PROGRAM=1

[GraphBuilder-RAE] Message starts, there are 4 initializers, 1 nodes, 1 inputs, 1 outputs.
input_names=['x']
output_names=[]
--SHAPE--
_dynamic_examples=
dynamic_objects=
   batch = 'batch'
dynamic_objects_rev=
   'batch' = <class 'list'>
     tuple
       'batch'
       ERR**: <class 'torch.SymInt'>:'batch'
dynamic_dimensions_source={'batch': [{'axis': 0, 'input_name': 'x'}]}
dynamic_dimensions_source_flat=['x']
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes={'x': {0: Dim('batch', min=0)}}
_known_shapes={'init7_s1_0': (1,),
 'init7_s1_1': (1,),
 'init7_s1_3': (1,),
 'param_1': (),
 'slice_tensor': ('batch', 3),
 'x': ('batch', 5)}
_known_types={'init7_s1_0': 7,
 'init7_s1_1': 7,
 'init7_s1_3': 7,
 'param_1': 1,
 'slice_tensor': 1,
 'x': 1}
_known_devices={'slice_tensor': -1, 'x': -1}
_context=[]
_known_value_shape={'init7_s1_0': (0,), 'init7_s1_1': (1,), 'init7_s1_3': (3,)}
_known_constants=['init7_s1_0', 'init7_s1_1', 'init7_s1_3', 'param_1']
_known_ranks (with no shape)={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
    fill__tensor -> set()
    param_1 -> {fill__tensor}
    slice_tensor -> {fill__tensor}
    x -> {add_tensor_1, slice_tensor, add_tensor}
--TORCH-SHAPES--
    x: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 5])))) --- 1:2:('batch', 5):
    slice_tensor: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 3])))) --- 1:2:('batch', 3):
    param_1: ('run_node', ('', ('val', torch.float32, TracingShape([])))) --- 1:0:():
    fill__tensor: ('run_node', ('', ('val', torch.float32, TracingShape([TracingInt('batch'), 3])))) --- :::
--ONNX--
-- EXEPATH --
export
export_options=ExportOptions(tracing=TracingMode.NEW_TRACING, aten_as_function=('aten.histc.default', 'aten.index_copy.default', 'aten.index_put.default', 'aten._grouped_mm.default', 'aten.setitem', <built-in function setitem>))
function_options=None
-- process.graph_module --
InplaceSetItemSquareAdd2()



def forward(self, x, param_1):
    slice_tensor = torch.ops.aten.slice.Tensor(x, 1, 0, 3)
    fill__tensor = torch.ops.aten.fill_.Tensor(slice_tensor, param_1);  slice_tensor = param_1 = fill__tensor = None
    add_tensor = torch.ops.aten.add.Tensor(x, 2)
    add_tensor_1 = torch.ops.aten.add.Tensor(x, 3);  x = None
    return (add_tensor, add_tensor_1)

# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
    %x : [num_users=3] = placeholder[target=x]
    %slice_tensor : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 1, 0, 3), kwargs = {})
    %param_1 : [num_users=1] = placeholder[target=param_1]
    %fill__tensor : [num_users=0] = call_function[target=torch.ops.aten.fill_.Tensor](args = (%slice_tensor, %param_1), kwargs = {})
    %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 2), kwargs = {})
    %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x, 3), kwargs = {})
    return (add_tensor, add_tensor_1)
-- process.inputs_to_remove --
set()
-- process.progress --
node 3/7 target=aten.fill_.Tensor
-- 1 INPUTS
[GraphBuilder-RAE.1.make_tensor_input] x[1:batchx5]
-- 4 INITIALIZERS
[GraphBuilder-RAE.1.make_initializer] init7_s1_0[int64:int64:[0]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-RAE.1.make_initializer] init7_s1_3[int64:int64:[3]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-RAE.1.make_initializer] init7_s1_1[int64:int64:[1]] - SOURCE: Opset.make_node.1/Shape
[GraphBuilder-RAE.1.make_initializer] param_1[torch.float32:torch.float32:[1.0]] - SOURCE: DynamoInterpret.placeholder.0
[GraphBuilder-RAE.4.make_node] slice_Tensor    [@###:@] Slice:['x', 'init7_s1_0', 'init7_s1_3', 'init7_s1_1']->['slice_tensor']
-- 0 OUTPUTS
[GraphBuilder-RAE] Message completed, there are 4 initializers, 1 nodes, 1 inputs, 1 outputs., 

SignatureFloat1

code: yobx.torch.testing._model_eval_cases.SignatureFloat1

forward

def forward(self, x, alpha: float = 2.0):
    return torch.sigmoid(self.linear(x)) - self.buff * alpha

yobx

• inputs: #2[(T1s4x3,float),(T1s8x3,float)]

• shapes: ({0:Dim(batch)},None)

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='mul' type=float32 shape=(1,) -- array([0.75], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_mul_b_buff)##_onx_mul_b_buff/GraphBuilder.constant_folding.from/fold(b_buff,init1_s_::RSh1)##b_buff/DynamoInterpret.placeholder.0##init1_s_::RSh1/GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(mul_Tensor)##init7_s1_1/Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.51618   ,  0.09904081, -0.01243608], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.28516936], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, mul) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

FAILED

Input mismatch, inputs[0]=(T1r2,float) but names=['x'], model=SignatureFloat1, export='yobx'

dynamo-ir

• inputs: #2[(T1s4x3,float),(T1s8x3,float)]

• shapes: ({0:Dim(batch)},None)

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s77', 3]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([ 0.37671953, -0.2158258 ,  0.30445737], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.45429665], dtype=float32)
init: name='mul_2' type=float32 shape=(1,) -- array([0.75], dtype=float32)
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, mul_2) -> sub_2
output: name='sub_2' type=dtype('float32') shape=['s77', 1]

FAILED

Input mismatch, inputs[0]=(T1r2,float) but names=['x'], model=SignatureFloat1, export='dynamo-ir'

tracing

FAILED

Unable to interpret method 'aten_meth_mul', args=(buff, alpha), kwargs={}, dispatcher=None
--DEBUG--
-- to print the exported program: PRINT_EXPORTED_PROGRAM=1

[GraphBuilder-NKI] Message starts, there are 3 initializers, 7 nodes, 2 inputs, 2 outputs.
input_names=['x', 'alpha']
output_names=[]
--CONSTRAINTS--
    batch = {'s26'}
    s26 = {'batch'}
--SHAPE--
_dynamic_examples=
dynamic_objects=
   batch = WrapSym(batch)
dynamic_objects_rev=
   'batch' = <class 'list'>
     tuple
       'batch'
       ERR**: <class 'torch.SymInt'>:'batch'
dynamic_dimensions_source={'batch': [{'axis': 0, 'input_name': 'x'}]}
dynamic_dimensions_source_flat=[0]
output_dynamic_dimensions_source_flat=None
dynamic_alias={'s26': 'batch'}
dynamic_shapes=({0: Dim('batch', min=1, max=1024)}, None)
_known_shapes={'_sub_ime__linear__onx_matmul_input_1': ('batch', 1),
 '_sub_ime__linear_input_1': ('batch', 3),
 '_sub_ime__linear_linear': ('batch', 1),
 '_sub_ime__linear_output': ('batch', 1),
 '_sub_ime__linear_weight::T10': (3, 1),
 'alpha': (),
 'buff': (1,),
 'linear': ('batch', 1),
 'linear.bias': (1,),
 'linear.weight': (1, 3),
 'sigmoid': ('batch', 1),
 'x': ('batch', 3)}
_known_types={'_sub_ime__linear__onx_matmul_input_1': 1,
 '_sub_ime__linear_input_1': 1,
 '_sub_ime__linear_linear': 1,
 '_sub_ime__linear_output': 1,
 '_sub_ime__linear_weight::T10': 1,
 'alpha': 1,
 'buff': 1,
 'linear': 1,
 'linear.bias': 1,
 'linear.weight': 1,
 'sigmoid': 1,
 'x': 1}
_known_devices={'_sub_ime__linear_input_1': -1, 'alpha': -1, 'x': -1}
_context=[]
_known_value_shape={}
_known_constants=['_sub_ime__linear_weight::T10', 'buff', 'linear.bias', 'linear.weight']
_known_ranks (with no shape)={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
    alpha -> {mul}
    buff -> {mul}
    linear -> {sigmoid}
    mul -> {sub}
    sigmoid -> {sub}
    x -> {linear}
--TORCH-SHAPES--
    x: ('run_node', (('example_value', torch.float32, torch.Size([4, 3])), ('val', torch.float32, torch.Size([s26, 3])))) --- 1:2:('batch', 3):
    alpha: ('run_node', (('example_value', torch.float32, torch.Size([])), ('val', torch.float32, torch.Size([])))) --- 1:0:():
    linear: ('run_node', ('', '')) --- 1:2:('batch', 1):
    sigmoid: ('run_node', ('', '')) --- 1:2:('batch', 1):
    buff: ('run_node', ('', '')) --- 1:1:(1,):
    mul: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export
export_options=ExportOptions(tracing=TracingMode.TRACING, aten_as_function=('aten.histc.default', 'aten.index_copy.default', 'aten.index_put.default', 'aten._grouped_mm.default', 'aten.setitem', <built-in function setitem>))
function_options=None
-- process.graph_module --
SignatureFloat1(
  (linear): Linear(in_features=3, out_features=1, bias=True)
)



def forward(self, x, alpha : float = 2.0):
    linear = self.linear(x);  x = None
    sigmoid = torch.sigmoid(linear);  linear = None
    buff = self.buff
    mul = buff.mul(alpha);  buff = alpha = None
    sub = sigmoid - mul;  sigmoid = mul = None
    return sub

# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
    %x : [num_users=1] = placeholder[target=x]
    %alpha : float [num_users=1] = placeholder[target=alpha](default=2.0)
    %linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
    %sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
    %buff : [num_users=1] = get_attr[target=buff]
    %mul : [num_users=1] = call_method[target=mul](args = (%buff, %alpha), kwargs = {})
    %sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %mul), kwargs = {})
    return sub
-- process.inputs_to_remove --
set()
-- process.progress --
node 5/8 target=mul
-- 2 INPUTS
[GraphBuilder-NKI.1.make_tensor_input] x[1:batchx3]
[GraphBuilder-NKI.1.make_tensor_input] alpha[1:]
-- 3 INITIALIZERS
[GraphBuilder-NKI.1.make_initializer] linear.weight[torch.float32:torch.float32:[0.03782457485795021, -0.1351017951965332, -0.06911272555589676]] - SOURCE: GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)
[GraphBuilder-NKI.1.make_initializer] linear.bias[torch.float32:torch.float32:[-0.10022220015525818]] - SOURCE: GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
[GraphBuilder-NKI.1.make_initializer] buff[torch.float32:torch.float32:[0.5]] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-NKI.4.make_node] .make_nodes     [@:@   ] Identity:['x']->['_sub_ime__linear_input_1']
[GraphBuilder-NKI.4.make_node] linear          [#:#   ] Transpose:['linear.weight']->['_sub_ime__linear_weight::T10']
[GraphBuilder-NKI.4.make_node] Opset           [@#:#  ] MatMul:['_sub_ime__linear_input_1', '_sub_ime__linear_weight::T10']->['_sub_ime__linear__onx_matmul_input_1']
[GraphBuilder-NKI.4.make_node] Opset2          [##:#  ] Add:['_sub_ime__linear__onx_matmul_input_1', 'linear.bias']->['_sub_ime__linear_linear']
[GraphBuilder-NKI.4.make_node] .output         [#:#   ] Identity:['_sub_ime__linear_linear']->['_sub_ime__linear_output']
[GraphBuilder-NKI.4.make_node] .make_nodes2    [#:#   ] Identity:['_sub_ime__linear_output']->['linear']
[GraphBuilder-NKI.4.make_node] sigmoid         [#:#   ] Sigmoid:['linear']->['sigmoid']
-- 0 OUTPUTS
[GraphBuilder-NKI] Message completed, there are 3 initializers, 7 nodes, 2 inputs, 2 outputs.

new-tracing

FAILED

Unable to convert TracingInt('batch') into string

SignatureInt1

code: yobx.torch.testing._model_eval_cases.SignatureInt1

forward

def forward(self, x, i: int = 2):
    return torch.sigmoid(self.linear(x)) - self.buff + x[:, i : i + 1]

yobx

• inputs: #2[(T1s4x3,int),(T1s8x3,int)]

• shapes: ({0:Dim(batch)},None)

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='b_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.33427685,  0.38108557, -0.5341409 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.0069776], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, b_buff) -> sub
Slice(x, init7_s1_1, init7_s1_2, init7_s1_1) -> slice_1
  Add(sub, slice_1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

FAILED

Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt1, export='yobx'

dynamo-ir

• inputs: #2[(T1s4x3,int),(T1s8x3,int)]

• shapes: ({0:Dim(batch)},None)

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s77', 3]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.32234368,  0.20540713, -0.42376068], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.45084313], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1])
init: name='val_7' type=int64 shape=(1,) -- array([2])
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, buff) -> sub_2
Slice(x, val_3, val_7, val_3, val_3) -> slice_1
  Add(sub_2, slice_1) -> add_12
output: name='add_12' type=dtype('float32') shape=['s77', 1]

FAILED

Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt1, export='dynamo-ir'

tracing

FAILED

[ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Concat node. Name:'_getitem_slicenSD' Status Message: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc:139 onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const InlinedTensorsVector&, onnxruntime::Prepare&) const input_rank == reference_rank was false. Ranks of input data are different, cannot concatenate them. expected rank: 1 got: 2

new-tracing

FAILED

Unable to convert TracingInt('batch') into string

SignatureInt2

code: yobx.torch.testing._model_eval_cases.SignatureInt2

forward

def forward(self, x, i: int = 2):
    return torch.sigmoid(self.linear(x)) - self.buff + x[:, i]

yobx

• inputs: #1[(T1s4x3,int)]

• shapes: dict(x:{0:Dim(batch)},i:None)

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='b_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.29279187,  0.1419851 ,  0.39510235], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.1290936], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gather(x, init7_s_1, axis=1) -> select
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, b_buff) -> sub
  Add(sub, select) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 'batch']

FAILED

Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt2, export='yobx'

dynamo-ir

• inputs: #1[(T1s4x3,int)]

• shapes: dict(x:{0:Dim(batch)},i:None)

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s77', 3]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([ 0.21152173, -0.01215376,  0.3124781 ], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.37264183], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='val_12' type=int64 shape=() -- array([1])
Gather(x, val_12, axis=1) -> select
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, buff) -> sub_2
  Add(sub_2, select) -> add_14
output: name='add_14' type=dtype('float32') shape=['s77', 's77']

FAILED

Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt2, export='dynamo-ir'

tracing

• inputs: #1[(T1s4x3,int)]

• shapes: dict(x:{0:Dim(batch)},i:None)

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='i' type=dtype('int64') shape=None
init: name='linear.bias' type=float32 shape=(1,) -- array([0.53036976], dtype=float32)-- GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.get_attr.0
init: name='getitem_axis' type=int64 shape=(2,) -- array([0, 1])      -- DynamoInterpreter._getitem_slice.axis.1
init: name='getitem_axis_0' type=int64 shape=(1,) -- array([0])       -- DynamoInterpreter._getitem_slice.axis.2##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- DynamoInterpreter._getitem_slice.int_end
init: name='getitem_step' type=int64 shape=(2,) -- array([1, 1])      -- DynamoInterpreter._getitem_slice.3
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--_sub_ime__linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.39079183,  0.3826622 , -0.3570949 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_ime__linear_weight::T10,init7_s2_1_3)##_sub_ime__linear_weight::T10/GraphBuilder.constant_folding.from/fold(linear.weight)##linear.weight/GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
Add(i, init7_s_1) -> getitem_slice_end_1
  Unsqueeze(getitem_slice_end_1, getitem_axis_0) -> getitem_slice_end_1::UnSq0
Shape(x) -> getitem_shape
  GatherElements(getitem_shape, getitem_axis_0) -> getitem_end
    Concat(getitem_end, getitem_slice_end_1::UnSq0, axis=0) -> _onx_concat_getitem_end
Gemm(x, GemmTransposePattern--_sub_ime__linear_weight::T10, linear.bias, transB=1) -> _sub_ime__linear_linear
  Sigmoid(_sub_ime__linear_linear) -> sigmoid
    Sub(sigmoid, buff) -> sub
Unsqueeze(i, getitem_axis_0) -> i::UnSq0
  Concat(getitem_axis_0, i::UnSq0, axis=0) -> _onx_concat_getitem_axis_0
    Slice(x, _onx_concat_getitem_axis_0, _onx_concat_getitem_end, getitem_axis, getitem_step) -> getitem_sliced
      Squeeze(getitem_sliced, init7_s1_1) -> getitem
      Add(sub, getitem) -> output
output: name='output' type=dtype('float32') shape=['batch', 'NEWDIM_slice']

FAILED

[ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Concat node. Name:'_getitem_slicenSD' Status Message: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc:139 onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const InlinedTensorsVector&, onnxruntime::Prepare&) const input_rank == reference_rank was false. Ranks of input data are different, cannot concatenate them. expected rank: 1 got: 2

new-tracing

• inputs: #1[(T1s4x3,int)]

• shapes: dict(x:{0:Dim(batch)},i:None)

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s_1' type=int64 shape=() -- array([1])              -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--param_1' type=float32 shape=(1, 3) -- array([ 0.19034088, -0.14372618,  0.5627756 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,param_1)##param_1/DynamoInterpret.placeholder.0##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.19902611], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gather(x, init7_s_1, axis=1) -> select_int
Gemm(x, GemmTransposePattern--param_1, linear.bias, transB=1) -> addmm_default
  Sigmoid(addmm_default) -> sigmoid_default
    Sub(sigmoid_default, buff) -> sub_tensor
  Add(sub_tensor, select_int) -> output
output: name='output' type=dtype('float32') shape=['batch', 'batch']

FAILED

Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt2, export='new-tracing'

SignatureListFixedLength

code: yobx.torch.testing._model_eval_cases.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)

yobx

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='b_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([ 0.46800545, -0.1644351 , -0.00810799], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.39386934], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, b_buff) -> sub
ReduceSum(lx_1, init7_s1_1, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul
    Add(sub, mul) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

dynamo-ir

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([ 0.18170342, -0.2575322 , -0.29987118], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.26654318], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1])
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, buff) -> sub_2
ReduceSum(lx_1, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul_4
    Add(sub_2, mul_4) -> add_15
output: name='add_15' type=dtype('float32') shape=['batch', 1]

tracing

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#2[T1s8x1,T1s8x2])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='_traced_m2.linear.bias' type=float32 shape=(1,) -- array([-0.2663221], dtype=float32)-- GraphBuilder.make_nodes/from_traced_m2.linear.bias##DynamoInterpret.get_attr.1/P(_traced_m2.linear.bias)
init: name='_traced_m2_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.get_attr.0
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.34504578, -0.10411504, -0.2340521 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_ime___traced_m2_linear_weight::T10,init7_s2_1_3)##_sub_ime___traced_m2_linear_weight::T10/GraphBuilder.constant_folding.from/fold(_traced_m2.linear.weight)##_traced_m2.linear.weight/GraphBuilder.make_nodes/from_traced_m2.linear.weight##DynamoInterpret.get_attr.1/P(_traced_m2.linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
Gemm(x, GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10, _traced_m2.linear.bias, transB=1) -> _sub_ime___traced_m2_linear_linear
  Sigmoid(_sub_ime___traced_m2_linear_linear) -> sigmoid
    Sub(sigmoid, _traced_m2_buff) -> sub
ReduceSum(lx_1, init7_s1_1, keepdims=1) -> sum_1
  Mul(lx_0, sum_1) -> mul
    Add(sub, mul) -> output
output: name='output' type=dtype('float32') shape=['batch', 1]

new-tracing

FAILED

'Dim' object has no attribute 'items'

SignatureListFixedWithNone

code: yobx.torch.testing._model_eval_cases.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

yobx

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()`.

dynamo-ir

FAILED

Failed to export the model with torch.export. This is step 1/3 of exporting the model to ONNX. Next steps:
- Modify the model code for `torch.export.export` to succeed. Refer to https://pytorch.org/docs/stable/generated/exportdb/index.html for more information.
- Debug `torch.export.export` and submit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the *torch.export* component and attach the full error stack as well as reproduction scripts.

## Exception summary

<class 'torch._dynamo.exc.UserError'>: 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()`.

(Refer to the full stack trace above for more information.)

tracing

FAILED

Length mismatch between x (len=3) and dynamic_shapes (len=2); dynamic_shapes must have one entry per element of x, or be None to use no dynamic dimensions, dynamic_shapes=[{0: Dim('batch', min=0)}, {0: Dim('batch', min=0)}]

new-tracing

FAILED

Length mismatch between arg (3) and the dynamic_shapes (2), name='lx'

SignatureListVariableLength

code: yobx.torch.testing._model_eval_cases.SignatureListVariableLength

forward

def forward(self, x, lx: list):
    t = torch.cat(lx, dim=1).sum(dim=1, keepdim=True)
    return torch.sigmoid(self.linear(x)) - self.buff + t

yobx

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#3[T1s8x1,T1s8x2,T1s8x3])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='b_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([ 0.07056267, -0.48215383, -0.01945801], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.31997368], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Concat(lx_0, lx_1, axis=1) -> cat
  ReduceSum(cat, init7_s1_1, keepdims=1) -> sum_1
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, b_buff) -> sub
    Add(sub, sum_1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]

FAILED

diff.1

dynamo-ir

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#3[T1s8x1,T1s8x2,T1s8x3])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([ 0.1020776 , -0.04105373, -0.31296235], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.12330545], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='val_3' type=int64 shape=(1,) -- array([1])
Concat(lx_0, lx_1, axis=1) -> cat
  ReduceSum(cat, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Sub(sigmoid, buff) -> sub_4
    Add(sub_4, sum_1) -> add_15
output: name='add_15' type=dtype('float32') shape=['batch', 1]

FAILED

diff.1

tracing

• inputs: #2[(T1s4x3,#2[T1s4x1,T1s4x2]),(T1s8x3,#3[T1s8x1,T1s8x2,T1s8x3])]

• shapes: dict(x:{0:Dim(batch)},lx:#2[{0:Dim(batch)},{0:Dim(batch)}])

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='lx_0' type=dtype('float32') shape=['batch', 1]
input: name='lx_1' type=dtype('float32') shape=['batch', 2]
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='_traced_m2.linear.bias' type=float32 shape=(1,) -- array([-0.38783452], dtype=float32)-- GraphBuilder.make_nodes/from_traced_m2.linear.bias##DynamoInterpret.get_attr.1/P(_traced_m2.linear.bias)
init: name='_traced_m2_buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.get_attr.0
init: name='GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10' type=float32 shape=(1, 3) -- array([ 0.3161527 ,  0.33454782, -0.29694074], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_ime___traced_m2_linear_weight::T10,init7_s2_1_3)##_sub_ime___traced_m2_linear_weight::T10/GraphBuilder.constant_folding.from/fold(_traced_m2.linear.weight)##_traced_m2.linear.weight/GraphBuilder.make_nodes/from_traced_m2.linear.weight##DynamoInterpret.get_attr.1/P(_traced_m2.linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
Concat(lx_0, lx_1, axis=1) -> cat
  ReduceSum(cat, init7_s1_1, keepdims=1) -> sum_1
Gemm(x, GemmTransposePattern--_sub_ime___traced_m2_linear_weight::T10, _traced_m2.linear.bias, transB=1) -> _sub_ime___traced_m2_linear_linear
  Sigmoid(_sub_ime___traced_m2_linear_linear) -> sigmoid
    Sub(sigmoid, _traced_m2_buff) -> sub
    Add(sub, sum_1) -> output
output: name='output' type=dtype('float32') shape=['batch', 1]

FAILED

diff.1

new-tracing

FAILED

'Dim' object has no attribute 'items'

SignatureShapeAsIndex

code: yobx.torch.testing._model_eval_cases.SignatureShapeAsIndex

forward

def forward(self, x, y):
    t = torch.sigmoid(self.linear(x)) + x
    return t[:, : y.shape[1]]

yobx

• inputs: #1[(T1s4x3,T1s4x2)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='y' type=dtype('float32') shape=['batch', 'length']
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([ 0.269283  , -0.04220374,  0.06395845], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_3,p_linear_weight::T10)##p_linear_weight::T10/GraphBuilder.constant_folding.from/fold(p_linear_weight)##p_linear_weight/DynamoInterpret.placeholder.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.05627105], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--p_linear_weight::T10, linear.bias, transB=1) -> linear
  Sigmoid(linear) -> sigmoid
    Add(sigmoid, x) -> add
Shape(y, end=2, start=1) -> y::Shape1:2
  Slice(add, init7_s1_0, y::Shape1:2, init7_s1_1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 'length']

dynamo-ir

• inputs: #1[(T1s4x3,T1s4x2)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='y' type=dtype('float32') shape=['batch', 'length']
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.11689394,  0.10962067,  0.47619072], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.07751294], dtype=float32)
init: name='val_8' type=int64 shape=(1,) -- array([1])
init: name='val_1' type=int64 shape=(1,) -- array([0])
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
  Sigmoid(linear) -> sigmoid
    Add(sigmoid, x) -> add_6
Shape(y, end=2, start=1) -> val_0
  Slice(add_6, val_1, val_0, val_8, val_8) -> slice_1
output: name='slice_1' type=dtype('float32') shape=['batch', 'length']

tracing

• inputs: #1[(T1s4x3,T1s4x2)]

• shapes: dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='y' type=dtype('float32') shape=['batch', 'length']
init: name='linear.bias' type=float32 shape=(1,) -- array([0.37225145], dtype=float32)-- GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0])           -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1])           -- Opset.make_node.1/Shape
init: name='getitem_2_axis' type=int64 shape=(2,) -- array([0, 1])    -- DynamoInterpreter._getitem_slice.axis.1
init: name='getitem_2_start' type=int64 shape=(2,) -- array([0, 0])   -- DynamoInterpreter._getitem_slice.2
init: name='getitem_2_step' type=int64 shape=(2,) -- array([1, 1])    -- DynamoInterpreter._getitem_slice.3
init: name='GemmTransposePattern--_sub_ime__linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.21513638, -0.32068107,  0.3226202 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_ime__linear_weight::T10,init7_s2_1_3)##_sub_ime__linear_weight::T10/GraphBuilder.constant_folding.from/fold(linear.weight)##linear.weight/GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)##init7_s2_1_3/TransposeEqualReshapePattern.apply.new_shape
Gemm(x, GemmTransposePattern--_sub_ime__linear_weight::T10, linear.bias, transB=1) -> _sub_ime__linear_linear
  Sigmoid(_sub_ime__linear_linear) -> sigmoid
    Add(sigmoid, x) -> add
      Shape(add) -> getitem_2_shape
        GatherElements(getitem_2_shape, init7_s1_0) -> getitem_2_end
Shape(y) -> size
  Gather(size, init7_s1_1) -> _onx_gather_size2
    Concat(getitem_2_end, _onx_gather_size2, axis=0) -> _onx_concat_getitem_2_end
      Slice(add, getitem_2_start, _onx_concat_getitem_2_end, getitem_2_axis, getitem_2_step) -> output
output: name='output' type=dtype('float32') shape=['NEWDIM_slice', 'NEWDIM_slice1']

new-tracing

FAILED

TracingInt('length') has no concrete integer value; pass a concrete int or check .value

TypeBFloat16

code: yobx.torch.testing._model_eval_cases.TypeBFloat16

forward

def forward(self, x):
    xb = x.to(torch.bfloat16)
    return (xb + xb).to(torch.float32)

yobx

• inputs: #1[(T1s4x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Add(x, x) -> add-x
  Cast(add-x, to=16) -> add
    Cast(add, to=1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 4]

dynamo-ir

• inputs: #1[(T1s4x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['batch', 4]
Cast(x, to=16) -> _to_copy
  Add(_to_copy, _to_copy) -> add_3
    Cast(add_3, to=1) -> _to_copy_1
output: name='_to_copy_1' type=dtype('float32') shape=['batch', 4]

FAILED

[ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for Add(14) node with name 'node_add_3'

tracing

• inputs: #1[(T1s4x4,)]

• shapes: dict(x:{0:Dim(batch)})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch', 4]
Add(x, x) -> add-x
  Cast(add-x, to=16) -> add
    Cast(add, to=1) -> output
output: name='output' type=dtype('float32') shape=['batch', 4]

new-tracing

FAILED

Cannot determine if it is a constant argument dtype(torch.bfloat16)

Vmap

code: yobx.torch.testing._model_eval_cases.Vmap

forward

def forward(self, x, y):
    f = lambda x, y: x * y + 1  # noqa: E731
    return torch.vmap(f)(x, y)

yobx

• inputs: #1[(T1s3,T1s3)]

• shapes: dict(x:{0:DYNAMIC},y:{0:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_2']
input: name='y' type=dtype('float32') shape=['batch_3']
init: name='init1_s_' type=float32 shape=() -- array([1.], dtype=float32)-- shape_type_compute._cast_inputs.0
Shape(y, end=1, start=0) -> y::Shape:1
Squeeze(x) -> clone_default::Sq
Squeeze(y) -> clone_default_1::Sq
  Mul(clone_default::Sq, clone_default_1::Sq) -> mul
    Add(mul, init1_s_) -> add
  Expand(add, y::Shape:1) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch_3']

dynamo-ir

• inputs: #1[(T1s3,T1s3)]

• shapes: dict(x:{0:DYNAMIC},y:{0:DYNAMIC})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s17']
input: name='y' type=dtype('float32') shape=['s17']
init: name='_to_copy' type=float32 shape=() -- array([1.], dtype=float32)
Mul(x, y) -> mul
  Add(mul, _to_copy) -> add_2
output: name='add_2' type=dtype('float32') shape=['s17']

tracing

• inputs: #1[(T1s3,T1s3)]

• shapes: dict(x:{0:DYNAMIC},y:{0:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_2']
input: name='y' type=dtype('float32') shape=['batch_3']
init: name='init1_s_::RSh1' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init1_s_,init7_s1_1)##init1_s_/shape_type_compute._cast_inputs.1(add)##init7_s1_1/Opset.make_node.1/Shape
Mul(x, y) -> mul
  Add(mul, init1_s_::RSh1) -> output
output: name='output' type=dtype('float32') shape=['DYN0^DYN1']

new-tracing

• inputs: #1[(T1s3,T1s3)]

• shapes: dict(x:{0:DYNAMIC},y:{0:DYNAMIC})

opset: domain='' version=21
input: name='x' type=dtype('float32') shape=['batch_2']
input: name='y' type=dtype('float32') shape=['batch_3']
init: name='param_1::RSh1' type=float32 shape=(1,) -- array([1.], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s1_1,param_1)##param_1/DynamoInterpret.placeholder.0##init7_s1_1/Opset.make_node.1/Shape
Mul(x, y) -> mul_tensor
  Add(mul_tensor, param_1::RSh1) -> output
output: name='output' type=dtype('float32') shape=['batch_2']

VmapPython

code: yobx.torch.testing._model_eval_cases.VmapPython

forward

def forward(self, x, y):
    f = lambda x, y: x * y + 1  # noqa: E731
    return patched_vmap(f)(x, y)

yobx

• inputs: #1[(T1s3,T1s3)]

• shapes: dict(x:{0:DYNAMIC},y:{0:DYNAMIC})

opset: domain='' version=21
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch_2']
input: name='y' type=dtype('float32') shape=['batch_3']
init: name='init1_s_2_cst2init' type=float32 shape=() -- array([1.], dtype=float32)-- GraphBuilderPatternOptimization.make_initializer.1/Small
Scan(x, y, body=G1, num_scan_inputs=2, scan_input_directions=[0,0], scan_output_axes=[0], scan_output_directions=[0]) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch_3']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- scan_0_movedim,scan_1_movedim_1 -> output_0
input: name='scan_0_movedim' type=dtype('float32') shape=None
input: name='scan_1_movedim_1' type=dtype('float32') shape=None
Mul(scan_0_movedim, scan_1_movedim_1) -> mul2
Add(mul2, init1_s_2_cst2init) -> output_0
output: name='output_0' type=dtype('float32') shape=None

dynamo-ir

• inputs: #1[(T1s3,T1s3)]

• shapes: dict(x:{0:DYNAMIC},y:{0:DYNAMIC})

opset: domain='' version=20
input: name='x' type=dtype('float32') shape=['s17']
input: name='y' type=dtype('float32') shape=['s17']
init: name='scalar_tensor_default' type=float32 shape=() -- array([1.], dtype=float32)
Scan(x, y, body=G1, num_scan_inputs=2, scan_input_directions=[0,0], scan_output_directions=[0]) -> getitem
output: name='getitem' type=dtype('float32') shape=['s17']
----- subgraph ---- Scan - node_scan__0 - att.body=G1 -- level=1 -- permute_scan_combine_graph_0__subgraph_in,permute_1_scan_combine_graph_0__subgraph_in -> add_scan_combine_graph_0
input: name='permute_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=None
input: name='permute_1_scan_combine_graph_0__subgraph_in' type=dtype('float32') shape=None
Mul(permute_scan_combine_graph_0__subgraph_in, permute_1_scan_combine_graph_0__subgraph_in) -> mul
Add(mul, scalar_tensor_default) -> add_scan_combine_graph_0
output: name='add_scan_combine_graph_0' type=dtype('float32') shape=None

tracing

FAILED

symbolically traced variables cannot be used as inputs to control flow

new-tracing

FAILED

list index out of range

Summary

case	dynamo-ir	new-tracing	tracing	yobx
AtenAsStrided
AtenInterpolate
AtenNonZero
AtenNonZeroTuple
AtenRollPos	FAIL	FAIL
AtenRollRelu		FAIL
BuildInIsInstance		FAIL
BuildInLen	FAIL	FAIL	FAIL	FAIL
ComplexPolar	FAIL	FAIL	FAIL	FAIL
ControlFlowCond		FAIL
ControlFlowCond2Inputs		FAIL
ControlFlowCond2Outputs		FAIL
ControlFlowCondConstant		FAIL
ControlFlowCondIdentity_153832	FAIL	FAIL	FAIL	FAIL
ControlFlowCondNestedModule		FAIL
ControlFlowCondNonZero	FAIL	FAIL		FAIL
ControlFlowIndirectRanks
ControlFlowIndirectRanksCat		FAIL
ControlFlowNestCond		FAIL
ControlFlowNumelZero1		FAIL
ControlFlowNumelZero2		FAIL
ControlFlowRanks
ControlFlowRanksType
ControlFlowScan	FAIL	FAIL
ControlFlowScan2Carried	FAIL	FAIL
ControlFlowScanCDist		FAIL
ControlFlowScanCDist2		FAIL
ControlFlowScanCDistXY		FAIL
ControlFlowScanDecomposition_151564		FAIL	FAIL
ControlFlowScanInplace_153705	FAIL	FAIL	FAIL	FAIL
ControlFlowShapeCheck		FAIL	FAIL	FAIL
ControlFlowWhileDec	FAIL	FAIL	FAIL	FAIL
ControlFlowWhileInc	FAIL	FAIL	FAIL	FAIL
CreateFromShape		FAIL
CreateFromShapeThroughFunction		FAIL
CropLastDimensionWithTensorContent		FAIL
CropLastDimensionWithTensorShape		FAIL
ExportWithDimension0		FAIL
ExportWithDimension1		FAIL
ExportWithNewConstant		FAIL
ExportWithNewConstantTo		FAIL
InplaceAdd		FAIL
InplaceAdd2		FAIL
InplaceAdd_Mul		FAIL
InplaceCloneAdd_		FAIL
InplaceSetItemEllipsis_1	FAIL	FAIL		FAIL
InplaceSetItemEllipsis_2	FAIL	FAIL		FAIL
InplaceSetItemExp		FAIL		FAIL
InplaceSetItemMask		FAIL
InplaceSetItemSquare		FAIL
InplaceSetItemSquareAdd		FAIL
InplaceSetItemSquareAdd2		FAIL
SignatureFloat1	FAIL	FAIL	FAIL	FAIL
SignatureInt1	FAIL	FAIL	FAIL	FAIL
SignatureInt2	FAIL	FAIL	FAIL	FAIL
SignatureListFixedLength		FAIL
SignatureListFixedWithNone	FAIL	FAIL	FAIL	FAIL
SignatureListVariableLength	FAIL	FAIL	FAIL	FAIL
SignatureShapeAsIndex		FAIL
TypeBFloat16	FAIL	FAIL
Vmap
VmapPython		FAIL	FAIL

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