Exported ONNX with Dynamic Shapes¶
The following script shows the exported program for many short cases and various l-plot-export-with-dynamic-shape to retrieve an ONNX model equivalent to the original model.
<<<
import inspect
import textwrap
import pandas
from onnx_diagnostic.helpers import string_type
from onnx_diagnostic.helpers.onnx_helper import pretty_onnx
from onnx_diagnostic.torch_export_patches.eval import discover, run_exporter
from onnx_diagnostic.ext_test_case import unit_test_going
cases = discover()
print()
print(":ref:`Summary <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("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 ("custom", "dynamo-ir"):
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.split("-", maxsplit=1)[-1]
if "inputs" in res:
print(f"* **inputs:** ``{string_type(res['inputs'], with_shape=True)}``")
if "dynamic_shapes" in res:
print(f"* **shapes:** ``{string_type(res['dynamic_shapes'])}``")
print()
print()
if "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()
>>>
AtenAsStrided¶
forward¶
def forward(self, x):
y = torch.as_strided(x, (2, 2, 8, 4), (128, 8, 16, 1))
return y
custom¶
FAILED
The implementation is still incorrect, x='x', shape=('batch', 2, 8, 8), size=[2, 2, 8, 4], stride=[128, 8, 16, 1], storage_offset=None
--DEBUG--
[GraphBuilder-ASE] Message starts, there are 0 initializers, 0 nodes, 1 inputs, 1 outputs.
--CONSTRAINTS--
batch = {'s77'}
s77 = {'batch'}
--SHAPE--
_dynamic_examples=
dynamic_objects=
batch = 'batch'
s77 = 's77'
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={'s77': 'batch'}
dynamic_shapes={'x': {0: Dim('batch', min=0)}}
_known_shapes={'x': ('batch', 2, 8, 8)}
_known_types={'x': 1}
_known_value_shape={}
_known_constants=[]
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
as_strided -> {output}
x -> {as_strided}
--TORCH-SHAPES--
x: ('run_node', ('', ('val', torch.float32, torch.Size([s77, 2, 8, 8])))) --- 1:4:('batch', 2, 8, 8):
as_strided: ('run_node', ('', ('val', torch.float32, torch.Size([2, 2, 8, 4])))) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions()
-- process.graph_module --
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, x: "f32[s77, 2, 8, 8]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:186 in forward, code: y = torch.as_strided(x, (2, 2, 8, 4), (128, 8, 16, 1))
as_strided: "f32[2, 2, 8, 4]" = torch.ops.aten.as_strided.default(x, [2, 2, 8, 4], [128, 8, 16, 1]); x = None
return (as_strided,)
Graph signature:
# inputs
x: USER_INPUT
# outputs
as_strided: USER_OUTPUT
Range constraints: {s77: VR[0, int_oo]}
-- process.graph_module.graph --
graph():
%x : [num_users=1] = placeholder[target=x]
%as_strided : [num_users=1] = call_function[target=torch.ops.aten.as_strided.default](args = (%x, [2, 2, 8, 4], [128, 8, 16, 1]), kwargs = {})
return (as_strided,)
-- process.progress --
node 1/3 target=aten.as_strided.default
-- 1 INPUTS
[GraphBuilder-ASE.make_tensor_input] x[1:batchx2x8x8]
-- 0 INITIALIZERS
-- 0 OUTPUTS
[GraphBuilder-ASE] Message completed, there are 0 initializers, 0 nodes, 1 inputs, 1 outputs.
dynamo-ir¶
inputs:
#1[(T1s2x2x8x8,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 2, 8, 8]
Constant(value_ints=[-1]) -> neg_1
Reshape(x, neg_1) -> self_flatten
Constant(value=[2, 2, 8, ...) -> val_0
Constant(value=[128, 8, 1...) -> val_1
Constant(value=[4]) -> rank_tensor
Constant(value_int=0) -> indices
SequenceEmpty() -> one_seq
Constant(value_int=4) -> rank_0
Loop(rank_0, , one_seq, indices, body=G1) -> one_seq_16, indices_17
CastLike(indices, indices_17) -> storage_offset_cast
Add(indices_17, 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,one_seq_1,indices_2 -> cond_out,one_seq_15,indices_13
input: name='i' type=dtype('int64') shape=None
input: name='cond_in' type=dtype('bool') shape=None
input: name='one_seq_1' type='NOTENSOR' shape=None
input: name='indices_2' type='NOTENSOR' shape=None
Constant(value_floats=[1.0]) -> tmp_14
SequenceInsert(one_seq_1, tmp_14) -> one_seq_15
Constant(value=4) -> rank_3_cast
Sub(rank_3_cast, i) -> tmp
Constant(value=1) -> int64_1_cast
Sub(tmp, int64_1_cast) -> j
Reshape(j, neg_1) -> j_tensor
Gather(val_0, j_tensor, axis=0) -> size_dim_j
Slice(val_0, j_tensor, rank_tensor) -> size_after_j
Expand(indices_2, size_after_j) -> indices_4
Gather(val_1, j_tensor, axis=0) -> stride_dim_j
Constant(value=0) -> int64_0_cast
Constant(value=1) -> int64_1_5_cast
Range(int64_0_cast, size_dim_j, int64_1_5_cast) -> tmp_6
Mul(tmp_6, stride_dim_j) -> add_value
Constant(value=0) -> int64_0_7_cast
Equal(i, int64_0_7_cast) -> cond
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
Identity(cond_in) -> cond_out
output: name='cond_out' type=dtype('bool') shape=None
output: name='one_seq_15' type='NOTENSOR' shape=None
output: name='indices_13' 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_1, 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_1, 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
AtenInterpolate¶
forward¶
def forward(self, x):
y = torch.nn.functional.interpolate(
x,
scale_factor=2.0,
mode="bilinear",
recompute_scale_factor=False,
)
return y
custom¶
inputs:
#1[(T1s2x2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 2, 3, 4]
Constant(value_floats=[1.0,1.0,2.0,2.0]) -> val_0
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]
AtenNonZero¶
forward¶
def forward(self, x):
y = torch.nonzero(x)
return y
custom¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
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]
AtenNonZeroTuple¶
forward¶
def forward(self, x):
y = torch.nonzero(x, as_tuple=True)
return y[0], y[1]
custom¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 4]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
NonZero(x) -> _onx_nonzero_x
Split(_onx_nonzero_x, num_outputs=2) -> _onx_split_nonzero_x_0, _onx_split_nonzero_x_1
Reshape(_onx_split_nonzero_x_0, init7_s1_-1) -> output_0
Reshape(_onx_split_nonzero_x_1, init7_s1_-1) -> 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=18
input: name='x' type=dtype('float32') shape=['batch', 4]
Constant(value_ints=[1]) -> unbind_axis
NonZero(x) -> val_0
Transpose(val_0, perm=[1,0]) -> nonzero
Split(nonzero, axis=1, num_outputs=2) -> unbind_split_0, unbind_split_1
Squeeze(unbind_split_0, unbind_axis) -> getitem
Squeeze(unbind_split_1, unbind_axis) -> getitem_1
output: name='getitem' type=dtype('int64') shape=['u0']
output: name='getitem_1' type=dtype('int64') shape=['u0']
AtenRollPos¶
forward¶
def forward(self, x):
return torch.roll(x, 1, -1)
custom¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 3, 4]
Constant(value_ints=[-1]) -> neg_1
Constant(value=[-1]) -> dim_tensor
Constant(value=[1]) -> shift_tensor
Constant(value=[3]) -> slice_length_3
Constant(value_ints=[0]) -> tmp_4
Slice(x, tmp_4, slice_length_3, dim_tensor) -> suffix
Size(x) -> tmp_5
Reshape(tmp_5, neg_1) -> tmp_6
Slice(x, slice_length_3, tmp_6, dim_tensor) -> prefix
Concat(prefix, suffix, axis=-1) -> roll
output: name='roll' type=dtype('float32') shape=['batch', 3, 4]
AtenRollRelu¶
forward¶
def forward(self, x):
return torch.relu(torch.roll(x, -1, -1))
custom¶
inputs:
#1[(T1s2x3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 3, 4]
Constant(value_ints=[-1]) -> neg_1
Constant(value=[-1]) -> dim_tensor
Constant(value=[1]) -> slice_length_3
Constant(value_ints=[0]) -> tmp_4
Slice(x, tmp_4, slice_length_3, dim_tensor) -> suffix
Size(x) -> tmp_5
Reshape(tmp_5, neg_1) -> tmp_6
Slice(x, slice_length_3, tmp_6, dim_tensor) -> prefix
Concat(prefix, suffix, axis=-1) -> roll
Relu(roll) -> relu
output: name='relu' type=dtype('float32') shape=['batch', 3, 4]
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
custom¶
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=18
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([-0.02213002, 0.20319445, 0.44577315], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.28702256], 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=18
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([-0.12686957, -0.26009092, 0.35721844], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.50137514], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value=[1]) -> val_6
ReduceSum(lx_1, val_6, noop_with_empty_axes=0, keepdims=1) -> sum_1
Mul(lx_0, sum_1) -> mul_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, mul_1) -> add_15
output: name='add_15' type=dtype('float32') shape=['batch', 1]
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
custom¶
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=18
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([-0.04341672, -0.00942958, 0.54639035], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.29276597], 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=18
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([ 0.19770563, -0.2905411 , 0.09501803], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.18742701], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value=[1]) -> val_6
ReduceSum(lx_1, val_6, noop_with_empty_axes=0, keepdims=1) -> sum_1
Mul(lx_0, sum_1) -> mul_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, mul_1) -> add_15
output: name='add_15' type=dtype('float32') shape=['batch', 1]
FAILED
diff.1
ComplexPolar¶
forward¶
def forward(self, x, angle):
return torch.polar(x, angle)
custom¶
inputs:
#1[(T1s4x4,T1s4x4)]shapes:
dict(x:{0:Dim(batch)},angle:{0:Dim(batch)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 4]
input: name='angle' type=dtype('float32') shape=['batch', 4]
Constant(value=[-1]) -> int64_m1_1d
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
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])
custom¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 3]
Constant(value=0.0) -> scalar_tensor_default
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]
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])
custom¶
inputs:
#1[(T1s5x3,T1s5x3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='y' type=dtype('float32') shape=['batch', 3]
Constant(value=0.0) -> scalar_tensor_default
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]
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])
custom¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 3]
Constant(value=0.0) -> scalar_tensor_default
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]
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])
custom¶
inputs:
#1[(T1s1024x1024,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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)
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
Constant(value=[1, 1024]) -> init7_s2_1_10242
ConstantOfShape(init7_s2_1_10242, 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
Constant(value=[1024]) -> init7_s1_10242
Sin(x) -> sin2
Shape(x, end=1, start=0) -> x::Shape:12
Concat(x::Shape:12, init7_s1_10242, axis=0) -> _onx_concat_sym_size_int::UnSq02
ConstantOfShape(_onx_concat_sym_size_int::UnSq02, value=[1.0]) -> ones32
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=18
input: name='x' type=dtype('float32') shape=['batch', 1024]
init: name='ones_2' type=float32 shape=(1, 1024)
Constant(value=0.0) -> scalar_tensor_default
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
Constant(value=[-1]) -> val_1
Shape(x, end=1, start=0) -> val_0_2
Squeeze(val_0_2) -> sym_size_int
Reshape(sym_size_int, val_1, allowzero=0) -> val_2
Sin(x) -> sin
Constant(value=[1024]) -> val_3
Concat(val_2, val_3, axis=0) -> val_4
Constant(value=1.0) -> val_7
Expand(val_7, val_4) -> ones
Sub(sin, ones) -> sub_3_true_graph_0
output: name='sub_3_true_graph_0' type=dtype('float32') shape=['', 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]
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
custom¶
FAILED
Cond doesn't work unless it is captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 187, in _cond_op_wrapper
return cond_op(*args, **kwargs)
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
dynamo-ir¶
FAILED
Failed to export the model with torch.export. [96mThis is step 1/3[0m 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 summit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m component and attach the full error stack as well as reproduction scripts.
## Exception summary
<class 'torch._dynamo.exc.Unsupported'>: Encountered aliasing during higher order op tracing
Explanation: Higher order ops do not support aliasing. Found in cond
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://compile-graph-break-site.vercel.app/gb/GB0040
⬆️
<class 'torch._dynamo.exc.UncapturedHigherOrderOpError'>: Cond doesn't work unless it is captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/vv/this312/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py", line 187, in _cond_op_wrapper
return cond_op(*args, **kwargs)
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
(Refer to the full stack trace above for more information.)
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
custom¶
inputs:
#1[(T7s2,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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='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=['d_output_0_0']
----- 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
Constant(value=100) -> init7_s_1002
Greater(sum_122, init7_s_1002) -> 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=18
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)
Constant(value=0) -> val_0
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
Constant(value=100) -> val_0_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='NOTENSOR' shape=None
----- 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']
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
custom¶
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. [96mThis is step 1/3[0m 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 summit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m 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.)
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])
custom¶
inputs:
#1[(T1s5x3,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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
Constant(value=0.0) -> init1_s_22
ReduceSum(x, keepdims=0) -> sum_122
Less(sum_122, init1_s_22) -> 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=18
input: name='x' type=dtype('float32') shape=['batch', 3]
Constant(value=0.0) -> scalar_tensor_default
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
Constant(value=0.0) -> scalar_tensor_default_2
ReduceSum(x, noop_with_empty_axes=0, keepdims=0) -> sum_1_2
Less(sum_1_2, scalar_tensor_default_2) -> lt
If(lt, then_branch=G3, else_branch=G4) -> getitem_true_graph_0
output: name='getitem_true_graph_0' type='NOTENSOR' shape=None
----- 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]
ControlFlowScan¶
forward¶
def forward(self, x):
init = torch.zeros_like(x[0])
carry, out = torch.ops.higher_order.scan(
ControlFlowScan.add, [init], [x], additional_inputs=[]
)
return carry
custom¶
inputs:
#1[(T1s3x3,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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='NOTENSOR' shape=None
input: name='scan_0_x' type='NOTENSOR' shape=None
Add(init_0_zeros_like, scan_0_x) -> output_0
Identity(output_0) -> output_1
output: name='output_0' type='NOTENSOR' shape=None
output: name='output_1' type='NOTENSOR' shape=None
dynamo-ir¶
FAILED
Failed to decompose the FX graph for ONNX compatibility. [96mThis is step 2/3[0m of exporting the model to ONNX. Next steps:
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m 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 [96m*onnx*[0m 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 = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x):
x: "f32[s77, 3][3, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:386 in forward, code: init = torch.zeros_like(x[0])
select: "f32[3][1]" = torch.ops.aten.select.int(x, 0, 0)
zeros_like: "f32[3][1]" = torch.ops.aten.zeros_like.default(select, pin_memory = False); select = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:387 in forward, code: carry, out = torch.ops.higher_order.scan(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [zeros_like], [x], ()); scan_combine_graph_0 = zeros_like = x = None
getitem: "f32[3][1]" = scan[0]
getitem_1: "f32[s77, 3][3, 1]" = scan[1]; scan = getitem_1 = None
return pytree.tree_unflatten((getitem,), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, carry_1: "f32[3][1]", y_1: "f32[3][1]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:387 in forward, code: carry, out = torch.ops.higher_order.scan(
add: "f32[3][1]" = torch.ops.aten.add.Tensor(carry_1, y_1); carry_1 = y_1 = None
return [add, add]
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 387, in forward
carry, out = torch.ops.higher_order.scan(
(Refer to the full stack trace above for more information.)
ControlFlowScan2Carried¶
forward¶
def forward(self, x):
init1 = torch.zeros_like(x[0])
init2 = torch.ones_like(x[0])
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
ControlFlowScan2Carried.add,
[init1, init2],
[x, x * 2],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[],
)
return carry1, carry2, out1, out2
custom¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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='NOTENSOR' shape=None
input: name='init_1_ones_like' type='NOTENSOR' shape=None
input: name='scan_0_x' type='NOTENSOR' shape=None
input: name='scan_1_mul' type='NOTENSOR' 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='NOTENSOR' shape=None
output: name='output_1' type='NOTENSOR' shape=None
output: name='output_2' type='NOTENSOR' shape=None
output: name='output_3' type='NOTENSOR' shape=None
dynamo-ir¶
FAILED
Failed to decompose the FX graph for ONNX compatibility. [96mThis is step 2/3[0m of exporting the model to ONNX. Next steps:
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m 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 [96m*onnx*[0m 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 = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x):
x: "f32[s77, 4][4, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:404 in forward, code: init1 = torch.zeros_like(x[0])
select: "f32[4][1]" = torch.ops.aten.select.int(x, 0, 0)
zeros_like: "f32[4][1]" = torch.ops.aten.zeros_like.default(select, pin_memory = False); select = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:405 in forward, code: init2 = torch.ones_like(x[0])
select_1: "f32[4][1]" = torch.ops.aten.select.int(x, 0, 0)
ones_like: "f32[4][1]" = torch.ops.aten.ones_like.default(select_1, pin_memory = False); select_1 = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:409 in forward, code: [x, x * 2],
mul: "f32[s77, 4][4, 1]" = torch.ops.aten.mul.Tensor(x, 2)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:406 in forward, code: carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [zeros_like, ones_like], [x, mul], ()); scan_combine_graph_0 = zeros_like = ones_like = x = mul = None
getitem: "f32[4][1]" = scan[0]
getitem_1: "f32[4][1]" = scan[1]
getitem_2: "f32[s77, 4][4, 1]" = scan[2]
getitem_3: "f32[s77, 4][4, 1]" = scan[3]; scan = None
return pytree.tree_unflatten((getitem, getitem_1, getitem_2, getitem_3), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, carry1_1: "f32[4][1]", carry2_1: "f32[4][1]", y1_1: "f32[4][1]", y2_1: "f32[4][1]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:406 in forward, code: carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
add: "f32[4][1]" = torch.ops.aten.add.Tensor(carry1_1, y1_1); carry1_1 = y1_1 = None
mul: "f32[4][1]" = torch.ops.aten.mul.Tensor(carry2_1, y2_1); carry2_1 = y2_1 = None
return [add, mul, add, mul]
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 406, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
(Refer to the full stack trace above for more information.)
ControlFlowScanCDist¶
forward¶
def forward(self, x):
carry, out = torch.ops.higher_order.scan(
ControlFlowScanCDist.dist,
[x],
[x],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[],
)
return out
custom¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=['batch', 4]
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='NOTENSOR' shape=None
input: name='scan_0_x' type='NOTENSOR' shape=None
Constant(value=[1]) -> init7_s1_12
Constant(value=[1, -1]) -> init7_s2_1_-12
Reshape(scan_0_x, init7_s2_1_-12) -> reshape2
Sub(init_0_x, reshape2) -> sub2
Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12, keepdims=0) -> sum_12
Constant(value=0.5) -> init1_s_2
Pow(sum_12, init1_s_2) -> output_1
Identity(init_0_x) -> output_0
output: name='output_0' type='NOTENSOR' shape=None
output: name='output_1' type='NOTENSOR' shape=None
dynamo-ir¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 4]
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 -> 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' type=dtype('float32') shape=[4]
Constant(value=[1, -1]) -> val_1
Reshape(x_scan_combine_graph_0__subgraph_in, val_1, allowzero=1) -> view
Sub(x_scan_combine_graph_0__subgraph_in, view) -> sub_1
Mul(sub_1, sub_1) -> mul_4
Constant(value=[1]) -> val_5
ReduceSum(mul_4, val_5, noop_with_empty_axes=0, keepdims=0) -> sum_1
Constant(value=0.5) -> val_6
Pow(sum_1, val_6) -> pow_1_scan_combine_graph_0
Identity(x_scan_combine_graph_0__subgraph_in) -> clone_scan_combine_graph_0
output: name='clone_scan_combine_graph_0' type=dtype('float32') shape=['s77', 4]
output: name='pow_1_scan_combine_graph_0' type=dtype('float32') shape=['s77']
FAILED
[ONNXRuntimeError] : 1 : FAIL : Error: Duplicate definition-site for (x_scan_combine_graph_0__subgraph_in).
ControlFlowScanCDist2¶
forward¶
def forward(self, x):
z = torch.tensor([0], dtype=torch.float32)
y = x.clone()
out = torch.ops.higher_order.scan(
ControlFlowScanCDist2.dist,
[z],
[x],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[y],
)
return out[1]
custom¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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
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='NOTENSOR' shape=None
input: name='scan_0_x' type='NOTENSOR' shape=None
Constant(value=[1]) -> init7_s1_12
Constant(value=[1, -1]) -> init7_s2_1_-12
Reshape(scan_0_x, init7_s2_1_-12) -> reshape2
Sub(hidden_input_scan_0_clone, reshape2) -> sub2
Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12, keepdims=0) -> sum_12
Sqrt(sum_12) -> output_1
Identity(init_0_detach_) -> output_0
output: name='output_0' type='NOTENSOR' shape=None
output: name='output_1' type='NOTENSOR' shape=None
dynamo-ir¶
inputs:
#1[(T1s3x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 4]
Constant(value=[0.0]) -> clone
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]
Constant(value=[1, -1]) -> val_1
Reshape(x_scan_combine_graph_0__subgraph_in, val_1, allowzero=1) -> view
Sub(x, view) -> sub_1
Mul(sub_1, sub_1) -> mul_4
Constant(value=[1]) -> val_5
ReduceSum(mul_4, val_5, noop_with_empty_axes=0, keepdims=0) -> sum_1
Sqrt(sum_1) -> sqrt_scan_combine_graph_0
Identity(clone_scan_combine_graph_0__subgraph_in) -> clone_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']
ControlFlowScanCDistXY¶
forward¶
def forward(self, x, y):
carry, out = torch.ops.higher_order.scan(
ControlFlowScanCDistXY.dist,
[y],
[x],
# dim=0, # 01/31/2025, not supported anymore
additional_inputs=[],
)
return out
custom¶
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=18
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']
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='NOTENSOR' shape=None
input: name='scan_0_x' type='NOTENSOR' shape=None
Constant(value=[1]) -> init7_s1_12
Constant(value=[1, -1]) -> init7_s2_1_-12
Reshape(scan_0_x, init7_s2_1_-12) -> reshape2
Sub(init_0_y, reshape2) -> sub2
Mul(sub2, sub2) -> mul2
ReduceSum(mul2, init7_s1_12, keepdims=0) -> sum_12
Sqrt(sum_12) -> output_1
Identity(init_0_y) -> output_0
output: name='output_0' type='NOTENSOR' shape=None
output: name='output_1' type='NOTENSOR' 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=18
input: name='x' type=dtype('float32') shape=['x_rows', 'dim']
input: name='y' type=dtype('float32') shape=['y_rows', 'dim']
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']
Constant(value=[1, -1]) -> val_1_2
Reshape(x_scan_combine_graph_0__subgraph_in, val_1_2, allowzero=1) -> view
Sub(y_scan_combine_graph_0__subgraph_in, view) -> sub_4
Mul(sub_4, sub_4) -> mul_7
Constant(value=[1]) -> val_5
ReduceSum(mul_7, val_5, noop_with_empty_axes=0, keepdims=0) -> sum_1
Sqrt(sum_1) -> sqrt_scan_combine_graph_0
Identity(y_scan_combine_graph_0__subgraph_in) -> clone_scan_combine_graph_0
output: name='clone_scan_combine_graph_0' type=dtype('float32') shape=['s17', 's27']
output: name='sqrt_scan_combine_graph_0' type=dtype('float32') shape=['s17']
ControlFlowScanDecomposition_151564¶
forward¶
def forward(self, images, position):
return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
images, position
)
custom¶
inputs:
#1[(T1s5x6,T7s5)]shapes:
dict(images:{0:DYNAMIC,1:DYNAMIC},position:{0:DYNAMIC})
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='images' type=dtype('float32') shape=['batch', 'channel']
input: name='position' type=dtype('int64') shape=['batch_1']
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', 'channel']
----- subgraph ---- Scan - scan - att.body=G1 -- level=1 -- scan_0_images,scan_1_position -> output_0
input: name='scan_0_images' type='NOTENSOR' shape=None
input: name='scan_1_position' type='NOTENSOR' shape=None
Constant(value=[0]) -> init7_s1_02
Unsqueeze(scan_1_position, init7_s1_02) -> item::UnSq03
Slice(scan_0_images, init7_s1_02, item::UnSq03, init7_s1_02) -> slice_12
Shape(scan_0_images, end=1, start=0) -> padded_1::Shape:12
ConstantOfShape(padded_1::Shape:12, value=[0.0]) -> zeros2
Shape(zeros2) -> zeros::Shape:2
Slice(zeros2, item::UnSq03, zeros::Shape:2, init7_s1_02) -> _onx_slice_zeros2
Concat(slice_12, _onx_slice_zeros2, axis=0) -> output_0
output: name='output_0' type='NOTENSOR' shape=None
dynamo-ir¶
FAILED
Failed to decompose the FX graph for ONNX compatibility. [96mThis is step 2/3[0m of exporting the model to ONNX. Next steps:
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m 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 [96m*onnx*[0m component. Attach the error report and the pt2 model.
## Exception summary
<class 'torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode'>: Could not guard on data-dependent expression u1 < 0 (unhinted: u1 < 0). (Size-like symbols: none)
Caused by: (_decomp/decompositions.py:745 in slice_forward)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u1"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
While executing %scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [], [%images, %position], ()), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, images, position):
images: "f32[s34, s90][s90, 1]"; position: "i64[s71][1]";
images, position, = fx_pytree.tree_flatten_spec(([images, position], {}), self._in_spec)
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:568 in forward, code: return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [], [images, position], ()); scan_combine_graph_0 = images = position = None
getitem: "f32[s34, s90][s90, 1]" = scan[0]; scan = None
return pytree.tree_unflatten((getitem,), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, padded_1: "f32[s90][1]", p_1: "i64[][]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:568 in forward, code: return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
sym_size_int: "Sym(s90)" = torch.ops.aten.sym_size.int(padded_1, 0)
zeros: "f32[s90][1]" = torch.ops.aten.zeros.default([sym_size_int], device = device(type='cpu'), pin_memory = False); sym_size_int = None
item: "Sym(u0)" = torch.ops.aten.item.default(p_1); p_1 = None
slice_1: "f32[u0][1]" = torch.ops.aten.slice.Tensor(padded_1, 0, 0, item); padded_1 = None
slice_2: "f32[u0][1]" = torch.ops.aten.slice.Tensor(zeros, 0, 0, item); item = None
copy_: "f32[u0][1]" = torch.ops.aten.copy_.default(slice_2, slice_1); slice_2 = slice_1 = copy_ = None
return (zeros,)
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 568, in forward
return self.select_when_exporting(self.dummy_loop, self.dummy_loop_with_scan)(
(Refer to the full stack trace above for more information.)
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]
custom¶
FAILED
only integers, slices (`:`), ellipsis (`...`), None and long or byte Variables are valid indices (got SymInt)
dynamo-ir¶
FAILED
Failed to decompose the FX graph for ONNX compatibility. [96mThis is step 2/3[0m of exporting the model to ONNX. Next steps:
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m 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 [96m*onnx*[0m 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, [%empty], [%arange], (%x, %y)), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self, x, y):
x: "f32[s77, s27][s27, 1]"; y: "f32[s17, s27][s27, 1]";
x, y, = fx_pytree.tree_flatten_spec(([x, y], {}), self._in_spec)
#
sym_size_int_2: "Sym(s77)" = torch.ops.aten.sym_size.int(x, 0)
sym_size_int_3: "Sym(s27)" = torch.ops.aten.sym_size.int(x, 1)
sym_size_int_4: "Sym(s17)" = torch.ops.aten.sym_size.int(y, 0)
sym_size_int_5: "Sym(s27)" = torch.ops.aten.sym_size.int(y, 1)
eq: "Sym(True)" = sym_size_int_3 == sym_size_int_5; sym_size_int_3 = sym_size_int_5 = None
_assert_scalar_default = torch.ops.aten._assert_scalar.default(eq, "Runtime assertion failed for expression Eq(s27, s94) on node 'eq'"); eq = _assert_scalar_default = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:517 in forward, code: z = torch.empty((x.shape[0], y.shape[0]))
empty: "f32[s77, s17][s17, 1]" = torch.ops.aten.empty.memory_format([sym_size_int_2, sym_size_int_4], device = device(type='cpu'), pin_memory = False); sym_size_int_4 = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:519 in forward, code: loop_body_1, [z], [torch.arange(x.shape[0], dtype=torch.int64)], [x, y]
arange: "i64[s77][1]" = torch.ops.aten.arange.default(sym_size_int_2, dtype = torch.int64, device = device(type='cpu'), pin_memory = False); sym_size_int_2 = None
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:518 in forward, code: r = torch.ops.higher_order.scan(
scan_combine_graph_0 = self.scan_combine_graph_0
scan = torch.ops.higher_order.scan(scan_combine_graph_0, [empty], [arange], (x, y)); scan_combine_graph_0 = empty = arange = x = y = None
getitem: "f32[s77, s17][s17, 1]" = scan[0]
getitem_1: "i64[s77][1]" = scan[1]; scan = getitem_1 = None
return pytree.tree_unflatten((getitem,), self._out_spec)
class scan_combine_graph_0(torch.nn.Module):
def forward(self, z_1: "f32[s77, s17][s17, 1]", iv_1: "i64[][]", x_1: "f32[s77, s27][s27, 1]", y_1: "f32[s17, s27][s27, 1]"):
# File: ~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py:518 in forward, code: r = torch.ops.higher_order.scan(
clone: "f32[s77, s17][s17, 1]" = torch.ops.aten.clone.default(z_1); z_1 = None
select: "f32[s27][1]" = torch.ops.aten.select.int(x_1, 0, 0); x_1 = None
slice_1: "f32[s27][1]" = torch.ops.aten.slice.Tensor(select, 0, 0, 9223372036854775807); select = None
sub: "f32[s17, s27][s27, 1]" = torch.ops.aten.sub.Tensor(slice_1, y_1); slice_1 = y_1 = None
pow_1: "f32[s17, s27][s27, 1]" = torch.ops.aten.pow.Tensor_Scalar(sub, 2); sub = None
sum_1: "f32[s17][1]" = torch.ops.aten.sum.dim_IntList(pow_1, [-1]); pow_1 = None
select_1: "f32[s17][1]" = torch.ops.aten.select.int(clone, 0, 0)
slice_2: "f32[s17][1]" = torch.ops.aten.slice.Tensor(select_1, 0, 0, 9223372036854775807); select_1 = None
copy_: "f32[s17][1]" = torch.ops.aten.copy_.default(slice_2, sum_1); slice_2 = sum_1 = copy_ = None
return [clone, iv_1]
Original traceback:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 518, in forward
r = torch.ops.higher_order.scan(
(Refer to the full stack trace above for more information.)
CreateFromShape¶
forward¶
def forward(self, x):
y = torch.ones((x.shape[0], x.shape[1] + 1))
return y
custom¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='init7_s_1' type=int64 shape=() -- array([1]) -- shape_type_compute._cast_inputs.1(add)
init: name='init7_s1_0' type=int64 shape=(1,) -- array([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
Squeeze(x::Shape1:2) -> sym_size_int_3
Add(sym_size_int_3, init7_s_1) -> _onx_add_sym_size_int_3
Unsqueeze(_onx_add_sym_size_int_3, init7_s1_0) -> 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+1']
dynamo-ir¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['dx', 'dy']
Constant(value=1.0) -> val_10
Shape(x, end=1, start=0) -> val_0
Shape(x, end=2, start=1) -> val_1
Squeeze(val_1) -> sym_size_int_3
Constant(value=1) -> val_2
Add(sym_size_int_3, val_2) -> add
Constant(value=[-1]) -> val_5
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']
CreateFromShapeThroughFunction¶
forward¶
def forward(self, x):
dy1 = CreateFromShapeThroughFunction.add_one(x.shape[1])
y = torch.ones((x.shape[0], dy1))
return y
custom¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['dx', 'dy']
init: name='init7_s_1' type=int64 shape=() -- array([1]) -- shape_type_compute._cast_inputs.1(add)
init: name='init7_s1_0' type=int64 shape=(1,) -- array([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
Squeeze(x::Shape1:2) -> sym_size_int_3
Add(sym_size_int_3, init7_s_1) -> _onx_add_sym_size_int_3
Unsqueeze(_onx_add_sym_size_int_3, init7_s1_0) -> 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+1']
dynamo-ir¶
inputs:
#2[(T1s4x4,),(T1s5x5,)]shapes:
dict(x:{0:Dim(dx),1:Dim(dy)})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['dx', 'dy']
Constant(value=1.0) -> val_10
Shape(x, end=1, start=0) -> val_0
Shape(x, end=2, start=1) -> val_1
Squeeze(val_1) -> sym_size_int_3
Constant(value=1) -> val_2
Add(sym_size_int_3, val_2) -> add
Constant(value=[-1]) -> val_5
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']
CropLastDimensionWithTensorContent¶
forward¶
def forward(self, x, shape):
return x[..., : shape[0]]
custom¶
FAILED
Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: none)
Caused by: (_export/non_strict_utils.py:1054 in __torch_function__)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
The following call raised this error:
File "~/github/onnx-diagnostic/onnx_diagnostic/torch_export_patches/eval/model_cases.py", line 818, in forward
return x[..., : shape[0]]
The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.
dynamo-ir¶
inputs:
#2[(T1s3x4x4,T7s1),(T1s6x4x4,T7s1)]shapes:
dict(x:{0:Dim(batch)},shape:{})
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='shape' type=dtype('int64') shape=[1]
Constant(value_ints=[1]) -> val_14
Constant(value=[0]) -> val_7
Constant(value=[2]) -> val_10
Slice(x, val_7, val_10, val_10, val_14) -> slice_1
output: name='slice_1' type=dtype('float32') shape=['batch', 4, 2]
FAILED
diff.1
CropLastDimensionWithTensorShape¶
forward¶
def forward(self, x, y):
return x[..., : y.shape[0]]
custom¶
inputs:
#2[(T1s3x4x4,T1s2),(T1s6x4x4,T1s3)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(crop)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 4, 4]
input: name='y' type=dtype('float32') shape=['crop']
Constant(value_ints=[1]) -> val_12
Shape(y, end=1, start=0) -> val_0
Constant(value=[0]) -> val_4
Constant(value=[2]) -> val_11
Slice(x, val_4, val_0, val_11, val_12) -> slice_1
output: name='slice_1' type=dtype('float32') shape=['batch', 4, 'crop']
InplaceAdd¶
forward¶
def forward(self, x):
x += self.bias
return x
custom¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
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]
InplaceAdd2¶
forward¶
def forward(self, x):
x.add_(self.bias)
return x
custom¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
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]
InplaceAdd_Mul¶
forward¶
def forward(self, x):
x.add_(self.bias)
return x * 2
custom¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
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
Constant(value=2.0) -> scalar_tensor_default
Mul(add_3, scalar_tensor_default) -> mul_4
output: name='mul_4' type=dtype('float32') shape=['batch', 4]
InplaceCloneAdd¶
forward¶
def forward(self, x):
x = x.clone()
x.add_(self.bias)
return x
custom¶
inputs:
#2[(T1s3x4,),(T1s5x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
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]
InplaceSetItemEllipsis_1¶
forward¶
def forward(self, index, update):
copy = self.params.clone()
copy[..., index] = update
return copy
custom¶
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¶
FAILED
Failed to convert the exported program to an ONNX model. [96mThis is step 3/3[0m of exporting the model to ONNX. Next steps:
- If there is a missing ONNX function, implement it and register it to the registry.
- If there is an internal error during ONNX conversion, debug the error and summit a PR to PyTorch.
- 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 [96m*onnx*[0m component. Attach the error report and the pt2 model.
## Exception summary
<class 'TypeError'>: int() argument must be a string, a bytes-like object or a real number, not 'SymbolicDim'
⬆️
<class 'torch.onnx._internal.exporter._errors.GraphConstructionError'>: Error processing Python constants for operator '::Expand'. named_inputs={'input': SymbolicTensor(name='anonymous:124794248517968', producer=anonymous_node:124794277930208, index=0), 'shape': (SymbolicDim(s5), SymbolicDim(s32))}, named_attrs={}, opset=, op_signature=''::Expand(input: T, shape: shape) -> (T) where T=INT16 | UINT16 | INT8 | INT64 | COMPLEX64 | FLOAT | UINT8 | FLOAT16 | DOUBLE | STRING | COMPLEX128 | UINT32 | BFLOAT16 | BOOL | INT32 | UINT64, shape=INT64.
⬆️
<class 'torch.onnx._internal.exporter._errors.GraphConstructionError'>: Error calling operator 'Expand' with args (SymbolicTensor(name='anonymous:124794248517968', producer=anonymous_node:124794277930208, index=0), (SymbolicDim(s5), SymbolicDim(s32))) and kwargs {}.
⬆️
<class 'torch.onnx._internal.exporter._errors.GraphConstructionError'>: Error when calling function 'TracedOnnxFunction(<function aten_index_put at 0x717f39515620>)' with args '[SymbolicTensor(name='clone', type=Tensor(FLOAT), shape=Shape([1, 8192, 4]), producer='node_clone', index=0), [None, None, SymbolicTensor(name='index', type=Tensor(INT64), shape=Shape([SymbolicDim(s91)]))], SymbolicTensor(name='update', type=Tensor(FLOAT), shape=Shape([SymbolicDim(s5), SymbolicDim(s32)]))]' and kwargs '{}'
⬆️
<class 'torch.onnx._internal.exporter._errors.ConversionError'>: Error when translating node %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%clone, [None, None, %index], %update), kwargs = {}). See the stack trace for more information.
(Refer to the full stack trace above for more information.)
InplaceSetItemEllipsis_2¶
forward¶
def forward(self, index, update):
copy = self.params.clone()
copy[..., index] = update
return copy
custom¶
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¶
FAILED
Failed to convert the exported program to an ONNX model. [96mThis is step 3/3[0m of exporting the model to ONNX. Next steps:
- If there is a missing ONNX function, implement it and register it to the registry.
- If there is an internal error during ONNX conversion, debug the error and summit a PR to PyTorch.
- 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 [96m*onnx*[0m component. Attach the error report and the pt2 model.
## Exception summary
<class 'TypeError'>: int() argument must be a string, a bytes-like object or a real number, not 'SymbolicDim'
⬆️
<class 'torch.onnx._internal.exporter._errors.GraphConstructionError'>: Error processing Python constants for operator '::Expand'. named_inputs={'input': SymbolicTensor(name='anonymous:124791243103984', producer=anonymous_node:124794277931504, index=0), 'shape': (SymbolicDim(s5), SymbolicDim(s32))}, named_attrs={}, opset=, op_signature=''::Expand(input: T, shape: shape) -> (T) where T=INT16 | UINT16 | INT8 | INT64 | COMPLEX64 | FLOAT | UINT8 | FLOAT16 | DOUBLE | STRING | COMPLEX128 | UINT32 | BFLOAT16 | BOOL | INT32 | UINT64, shape=INT64.
⬆️
<class 'torch.onnx._internal.exporter._errors.GraphConstructionError'>: Error calling operator 'Expand' with args (SymbolicTensor(name='anonymous:124791243103984', producer=anonymous_node:124794277931504, index=0), (SymbolicDim(s5), SymbolicDim(s32))) and kwargs {}.
⬆️
<class 'torch.onnx._internal.exporter._errors.GraphConstructionError'>: Error when calling function 'TracedOnnxFunction(<function aten_index_put at 0x717f39515620>)' with args '[SymbolicTensor(name='clone', type=Tensor(FLOAT), shape=Shape([1, 8192, 6]), producer='node_clone', index=0), [None, None, SymbolicTensor(name='index', type=Tensor(INT64), shape=Shape([SymbolicDim(s91)]))], SymbolicTensor(name='update', type=Tensor(FLOAT), shape=Shape([SymbolicDim(s5), SymbolicDim(s32)]))]' and kwargs '{}'
⬆️
<class 'torch.onnx._internal.exporter._errors.ConversionError'>: Error when translating node %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put.default](args = (%clone, [None, None, %index], %update), kwargs = {}). See the stack trace for more information.
(Refer to the full stack trace above for more information.)
InplaceSetItemMask¶
forward¶
def forward(self, x):
mask = x.to(bool)
x[mask] = 2
return x
custom¶
inputs:
#2[(T1s2x3x3,),(T1s3x3x3,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
input: name='x' type=dtype('float32') shape=['batch', 3, 3]
Cast(x, to=9) -> _to_copy
Constant(value=2.0) -> clone
Where(_to_copy, clone, x) -> index_put
output: name='index_put' type=dtype('float32') shape=['batch', 3, 3]
InplaceSetItemSquare¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x
custom¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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##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
init: name='init7_s2_-1_1' type=int64 shape=(2,) -- array([-1, 1]) -- Opset.make_node.1/Shape##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) -> x::Shape:
Gather(x::Shape:, init7_s1_0) -> _onx_gather_x::Shape:
Range(init7_s1_0, _onx_gather_x::Shape:, init7_s1_1) -> _onx_range_init7_s1_0
Slice(_onx_range_init7_s1_0, init7_s1_0, init7_s1_2, init7_s1_0, init7_s1_1) -> _onx_slice_range_init7_s1_0
Reshape(_onx_slice_range_init7_s1_0, init7_s2_-1_1) -> _onx_slice_range_init7_s1_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_s1_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=18
input: name='x' type=dtype('float32') shape=['batch', 5]
Constant(value=0) -> val_0
Constant(value=1) -> val_19
Constant(value=[0]) -> val_26
Constant(value=[2]) -> val_29
Constant(value_ints=[1]) -> val_33
Slice(x, val_26, val_29, val_26, val_33) -> slice_3
Transpose(slice_3, perm=[1,0]) -> val_45
Constant(value=[-1]) -> val_42
Constant(value=[[0], [1],...) -> val_43
Constant(value=[[1.0, 1.0...) -> val_44
ScatterND(val_45, val_43, val_44, reduction=b'none') -> val_46
Transpose(val_46, perm=[1,0]) -> slice_scatter
Constant(value_ints=[0]) -> val_47
Shape(x, start=0) -> val_48
Gather(val_48, val_0, axis=0) -> val_49
Range(val_0, val_49, val_19) -> val_50
Constant(value=[1]) -> val_53
Slice(val_50, val_26, val_29, val_47, val_53) -> val_54
Unsqueeze(val_54, val_42) -> val_55
ScatterND(x, val_55, slice_scatter, reduction=b'none') -> slice_scatter_1
output: name='slice_scatter_1' type=dtype('float32') shape=['batch', 5]
InplaceSetItemSquareAdd¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x + 2
custom¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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##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
init: name='init7_s2_-1_1' type=int64 shape=(2,) -- array([-1, 1]) -- Opset.make_node.1/Shape##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
Shape(x) -> x::Shape:
Gather(x::Shape:, init7_s1_0) -> _onx_gather_x::Shape:
Range(init7_s1_0, _onx_gather_x::Shape:, init7_s1_1) -> _onx_range_init7_s1_0
Slice(_onx_range_init7_s1_0, init7_s1_0, init7_s1_2, init7_s1_0, init7_s1_1) -> _onx_slice_range_init7_s1_0
Reshape(_onx_slice_range_init7_s1_0, init7_s2_-1_1) -> _onx_slice_range_init7_s1_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_s1_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=18
input: name='x' type=dtype('float32') shape=['batch', 5]
Constant(value=0) -> val_0
Constant(value=1) -> val_19
Constant(value=[0]) -> val_26
Constant(value=[2]) -> val_29
Constant(value_ints=[1]) -> val_33
Slice(x, val_26, val_29, val_26, val_33) -> slice_3
Transpose(slice_3, perm=[1,0]) -> val_45
Constant(value=[-1]) -> val_42
Constant(value=[[0], [1],...) -> val_43
Constant(value=[[1.0, 1.0...) -> val_44
ScatterND(val_45, val_43, val_44, reduction=b'none') -> val_46
Transpose(val_46, perm=[1,0]) -> slice_scatter
Constant(value_ints=[0]) -> val_47
Shape(x, start=0) -> val_48
Gather(val_48, val_0, axis=0) -> val_49
Range(val_0, val_49, val_19) -> val_50
Constant(value=[1]) -> val_53
Slice(val_50, val_26, val_29, val_47, val_53) -> val_54
Unsqueeze(val_54, val_42) -> val_55
ScatterND(x, val_55, slice_scatter, reduction=b'none') -> slice_scatter_1
Constant(value=2.0) -> scalar_tensor_default
Add(slice_scatter_1, scalar_tensor_default) -> add
output: name='add' type=dtype('float32') shape=['batch', 5]
InplaceSetItemSquareAdd2¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x + 2, x + 3
custom¶
inputs:
#2[(T1s5x5,),(T1s7x5,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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##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##Opset.make_node.1/Shape
init: name='init7_s2_-1_1' type=int64 shape=(2,) -- array([-1, 1]) -- Opset.make_node.1/Shape##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##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##Opset.make_node.1/Shape
Shape(x) -> x::Shape:
Gather(x::Shape:, init7_s1_0) -> _onx_gather_x::Shape:
Range(init7_s1_0, _onx_gather_x::Shape:, init7_s1_1) -> _onx_range_init7_s1_0
Slice(_onx_range_init7_s1_0, init7_s1_0, init7_s1_2, init7_s1_0, init7_s1_1) -> _onx_slice_range_init7_s1_0
Reshape(_onx_slice_range_init7_s1_0, init7_s2_-1_1) -> _onx_slice_range_init7_s1_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_s1_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=18
input: name='x' type=dtype('float32') shape=['batch', 5]
Constant(value=0) -> val_0
Constant(value=1) -> val_19
Constant(value=[0]) -> val_26
Constant(value=[2]) -> val_29
Constant(value_ints=[1]) -> val_33
Slice(x, val_26, val_29, val_26, val_33) -> slice_3
Transpose(slice_3, perm=[1,0]) -> val_45
Constant(value=[-1]) -> val_42
Constant(value=[[0], [1],...) -> val_43
Constant(value=[[1.0, 1.0...) -> val_44
ScatterND(val_45, val_43, val_44, reduction=b'none') -> val_46
Transpose(val_46, perm=[1,0]) -> slice_scatter
Constant(value_ints=[0]) -> val_47
Shape(x, start=0) -> val_48
Gather(val_48, val_0, axis=0) -> val_49
Range(val_0, val_49, val_19) -> val_50
Constant(value=[1]) -> val_53
Slice(val_50, val_26, val_29, val_47, val_53) -> val_54
Unsqueeze(val_54, val_42) -> val_55
ScatterND(x, val_55, slice_scatter, reduction=b'none') -> slice_scatter_1
Constant(value=2.0) -> scalar_tensor_default
Add(slice_scatter_1, scalar_tensor_default) -> add
Constant(value=3.0) -> scalar_tensor_default_1
Add(slice_scatter_1, scalar_tensor_default_1) -> add_4
output: name='add' type=dtype('float32') shape=['batch', 5]
output: name='add_4' type=dtype('float32') shape=['batch', 5]
SignatureFloat1¶
forward¶
def forward(self, x, alpha: float = 2.0):
return torch.sigmoid(self.linear(x)) - self.buff * alpha
custom¶
inputs:
#2[(T1s4x3,float),(T1s8x3,float)]shapes:
({0:Dim(batch)},None)
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='alpha' type=dtype('float32') shape=None
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/
init: name='GemmTransposePattern--p_linear_weight::T10' type=float32 shape=(1, 3) -- array([-0.43953928, -0.4258589 , -0.5343343 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.03914045], 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
diff.1
dynamo-ir¶
inputs:
#2[(T1s4x3,float),(T1s8x3,float)]shapes:
({0:Dim(batch)},None)
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['s77', 3]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([ 0.00109956, 0.30786404, -0.00813827], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.5752803], dtype=float32)
Constant(value=[0.75]) -> mul_2
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'
SignatureInt1¶
forward¶
def forward(self, x, i: int = 2):
return torch.sigmoid(self.linear(x)) - self.buff + x[:, i : i + 1]
custom¶
inputs:
#2[(T1s4x3,int),(T1s8x3,int)]shapes:
({0:Dim(batch)},None)
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='i' type=dtype('int64') shape=None
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([-0.3806404 , 0.08264066, -0.34595048], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.04519193], 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_2
Add(sub, slice_2) -> output_0
output: name='output_0' type=dtype('float32') shape=['batch', 1]
FAILED
diff.1
dynamo-ir¶
inputs:
#2[(T1s4x3,int),(T1s8x3,int)]shapes:
({0:Dim(batch)},None)
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['s77', 3]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([0.23367907, 0.19212429, 0.1634195 ], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.10709677], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value_ints=[1]) -> val_18
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
Constant(value=[1]) -> val_10
Constant(value=[2]) -> val_14
Slice(x, val_10, val_14, val_10, val_18) -> slice_2
Add(sub_2, slice_2) -> add_15
output: name='add_15' type=dtype('float32') shape=['s77', 1]
FAILED
Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt1, export='dynamo-ir'
SignatureInt2¶
forward¶
def forward(self, x, i: int = 2):
return torch.sigmoid(self.linear(x)) - self.buff + x[:, i]
custom¶
inputs:
#1[(T1s4x3,int)]shapes:
dict(x:{0:Dim(batch)},i:None)
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['batch', 3]
input: name='i' type=dtype('int64') shape=None
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([ 0.18782002, 0.4895845 , -0.21890567], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.29688284], 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']
dynamo-ir¶
inputs:
#1[(T1s4x3,int)]shapes:
dict(x:{0:Dim(batch)},i:None)
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['s77', 3]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([ 0.06624848, -0.33821517, 0.34434238], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.42984465], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value=1) -> val_7
Gather(x, val_7, 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'
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)
)
custom¶
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=18
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([0.3891831 , 0.09377435, 0.03149043], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([0.2391392], 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=18
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([-0.5071991 , -0.45073202, 0.02424338], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.45949516], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value=[1]) -> val_6
ReduceSum(lx_1, val_6, noop_with_empty_axes=0, keepdims=1) -> sum_1
Mul(lx_0, sum_1) -> mul_4
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, mul_4) -> add_15
output: name='add_15' type=dtype('float32') shape=['batch', 1]
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
custom¶
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. [96mThis is step 1/3[0m 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 summit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m 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.)
SignatureListVariableLength¶
forward¶
def forward(self, x, lx: list):
t = torch.cat(lx, dim=1).sum(axis=1, keepdim=True)
return torch.sigmoid(self.linear(x)) - self.buff + t
custom¶
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=18
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([0.37471053, 0.13973135, 0.42098212], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.38351223], 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=18
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([ 0.0339137 , -0.19899122, 0.19272114], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.4467994], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Concat(lx_0, lx_1, axis=1) -> cat
Constant(value=[1]) -> val_6
ReduceSum(cat, val_6, 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
SignatureShapeAsIndex¶
forward¶
def forward(self, x, y):
t = torch.sigmoid(self.linear(x)) + x
return t[:, : y.shape[1]]
custom¶
inputs:
#1[(T1s4x3,T1s4x2)]shapes:
dict(x:{0:Dim(batch)},y:{0:Dim(batch),1:Dim(length)})
opset: domain='' version=18
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([ 0.55131036, 0.36992598, -0.06646762], dtype=float32)-- GraphBuilder.constant_folding.from/fold(init7_s2_1_-1,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_-1/TransposeEqualReshapePattern.apply.new_shape
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.48297313], 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=18
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([-0.2249915, 0.5258373, -0.5751655], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.41185388], dtype=float32)
Constant(value_ints=[1]) -> val_16
Shape(y, end=2, start=1) -> val_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
Constant(value_ints=[0]) -> val_8
Constant(value=[1]) -> val_15
Slice(add_6, val_8, val_0, val_15, val_16) -> slice_2
output: name='slice_2' type=dtype('float32') shape=['batch', 'length']
TypeBFloat16¶
forward¶
def forward(self, x):
xb = x.to(torch.bfloat16)
return (xb + xb).to(torch.float32)
custom¶
inputs:
#1[(T1s4x4,)]shapes:
dict(x:{0:Dim(batch)})
opset: domain='' version=18
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=18
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'
Vmap¶
forward¶
def forward(self, x, y):
f = lambda x, y: x * y + 1 # noqa: E731
return torch.vmap(f)(x, y)
custom¶
FAILED
# no error found for the failure
dynamo-ir¶
FAILED
Failed to export the model with torch.export. [96mThis is step 1/3[0m 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 summit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m component and attach the full error stack as well as reproduction scripts.
## Exception summary
<class 'AssertionError'>:
(Refer to the full stack trace above for more information.)
VmapPython¶
forward¶
def forward(self, x, y):
f = lambda x, y: x * y + 1 # noqa: E731
return patched_vmap(f)(x, y)
custom¶
FAILED
object of type 'Node' has no len()
dynamo-ir¶
FAILED
Failed to export the model with torch.export. [96mThis is step 1/3[0m 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 summit a PR to PyTorch.
- Create an issue in the PyTorch GitHub repository against the [96m*torch.export*[0m component and attach the full error stack as well as reproduction scripts.
## Exception summary
<class 'TypeError'>: object of type 'Node' has no len()
(Refer to the full stack trace above for more information.)
Summary¶
case |
custom |
ir |
|---|---|---|
FAIL |
||
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
||
FAIL |
||
FAIL |
||
FAIL |
||
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
||
FAIL |
FAIL |
|
FAIL |
FAIL |
|
FAIL |
||
FAIL |
FAIL |
|
FAIL |
FAIL |