Exported into ONNX with Static Shapes¶
The following script shows the exported program for many short cases and various way to retrieve an ONNX model equivalent to the original model. The tested scenarios are described at Tested Scenarios.
<<<
import inspect
import textwrap
import pandas
from experimental_experiment.torch_interpreter.eval import discover, run_exporter
from experimental_experiment.ext_test_case import unit_test_going
from experimental_experiment.helpers import pretty_onnx
cases = discover()
print()
print(":ref:`Summary <lo-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} <lo-model-case-export-{name}>`")
print()
obs = []
for name, cls_model in sorted(cases.items()):
print()
print(f".. _lo-model-case-export-{name}:")
print()
print(name)
print("=" * len(name))
print()
print("forward")
print("+++++++")
print()
print("::")
print()
print(
textwrap.indent(textwrap.dedent(inspect.getsource(cls_model.forward)), " ")
)
print()
for exporter in (
"custom-fallback",
"custom-dec",
"custom-tracing",
"dynamo-ir",
"script",
):
expname = exporter.replace("export-", "")
print()
print(expname)
print("+" * len(expname))
print()
res = run_exporter(exporter, cls_model, False, quiet=True)
case_ref = f":ref:`{name} <lo-model-case-export-{name}>`"
if "exported" in res:
print("::")
print()
print(textwrap.indent(pretty_onnx(res["onnx"]), " "))
print()
obs.append(dict(case=case_ref, error="", exporter=exporter))
else:
print("**FAILED**")
print()
print("::")
print()
print(textwrap.indent(str(res["error"]), " "))
print()
obs.append(dict(case=case_ref, error="FAIL", exporter=exporter))
print()
print(".. _lo-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-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 2, 8, 8]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1]) -- Opset.make_node.1/Shape
init: name='init7_s128_' type=int64 shape=(128,) -- Opset.make_node.0
init: name='init7_s4_2_2_8_4' type=int64 shape=(4,) -- array([2, 2, 8, 4])-- Opset.make_node.1/Shape
Reshape(x, init7_s1_-1) -> _reshape_x0
Gather(_reshape_x0, init7_s128_) -> _onx_gather__reshape_x00
Reshape(_onx_gather__reshape_x00, init7_s4_2_2_8_4) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 2, 8, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 2, 8, 8]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1]) -- Opset.make_node.1/Shape
init: name='init7_s128_' type=int64 shape=(128,) -- Opset.make_node.0
init: name='init7_s4_2_2_8_4' type=int64 shape=(4,) -- array([2, 2, 8, 4])-- Opset.make_node.1/Shape
Reshape(x, init7_s1_-1) -> _reshape_x0
Gather(_reshape_x0, init7_s128_) -> _onx_gather__reshape_x00
Reshape(_onx_gather__reshape_x00, init7_s4_2_2_8_4) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 2, 8, 4]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 2, 8, 8]
init: name='init7_s1_-1' type=int64 shape=(1,) -- array([-1]) -- Opset.make_node.1/Shape
init: name='init7_s128_' type=int64 shape=(128,) -- Opset.make_node.0
init: name='init7_s4_2_2_8_4' type=int64 shape=(4,) -- array([2, 2, 8, 4])-- Opset.make_node.1/Shape
Reshape(x, init7_s1_-1) -> _reshape_x0
Gather(_reshape_x0, init7_s128_) -> _onx_gather__reshape_x00
Reshape(_onx_gather__reshape_x00, init7_s4_2_2_8_4) -> output
output: name='output' type=dtype('float32') shape=[2, 2, 8, 4]
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 2, 8, 8]
Constant(value_ints=[-1]) -> neg_1
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
Constant(value_ints=[-1]) -> tmp_18
Reshape(x, tmp_18) -> self_flatten
Constant(value_int=0) -> storage_offset
CastLike(storage_offset, 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
script¶
opset: domain='' version=17
input: name='onnx::Reshape_0' type=dtype('float32') shape=[2, 2, 8, 8]
Constant(value=[-1]) -> /Constant_output_0
Reshape(onnx::Reshape_0, /Constant_output_0, allowzero=0) -> /Reshape_output_0
Constant(value=[[[[0, 1, ...) -> /Constant_1_output_0
Gather(/Reshape_output_0, /Constant_1_output_0) -> 4
output: name='4' type=dtype('float32') shape=[2, 2, 8, 4]
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-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 2, 3, 4]
init: name='_onx_concat_init7_s2_2_20' type=int64 shape=(4,) -- array([2, 2, 6, 8])-- GraphBuilder.constant_folding.from/fold(init7_s2_2_2,init7_s2_6_8)##init7_s2_2_2/_aten_upsample_output_size.batch_channel##init7_s2_6_8/_aten_upsample_output_size.rsize
Resize(x, , , _onx_concat_init7_s2_2_20, coordinate_transformation_mode=b'pytorch_half_pixel', mode=b'linear', nearest_mode=b'floor') -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 2, 6, 8]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 2, 3, 4]
init: name='_onx_concat_init7_s2_2_20' type=int64 shape=(4,) -- array([2, 2, 6, 8])-- GraphBuilder.constant_folding.from/fold(init7_s2_2_2,init7_s2_6_8)##init7_s2_2_2/_aten_upsample_output_size.batch_channel##init7_s2_6_8/_aten_upsample_output_size.rsize
Resize(x, , , _onx_concat_init7_s2_2_20, coordinate_transformation_mode=b'pytorch_half_pixel', mode=b'linear', nearest_mode=b'floor') -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 2, 6, 8]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 2, 3, 4]
init: name='_onx_concat_init7_s2_2_20' type=int64 shape=(4,) -- array([2, 2, 6, 8])-- GraphBuilder.constant_folding.from/fold(init7_s2_2_2,init7_s2_6_8)##init7_s2_2_2/_aten_upsample_output_size.batch_channel##init7_s2_6_8/_aten_upsample_output_size.rsize
Resize(x, , , _onx_concat_init7_s2_2_20, coordinate_transformation_mode=b'pytorch_half_pixel', mode=b'linear', nearest_mode=b'floor') -> output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1', 'd_output_2', 'd_output_3']
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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=[2, 2, 6, 8]
script¶
opset: domain='' version=17
input: name='x' type=dtype('float32') shape=[2, 2, 3, 4]
Constant(value=[1.0, 1.0,...) -> /Constant_output_0
Resize(x, , /Constant_output_0, coordinate_transformation_mode=b'half_pixel', cubic_coeff_a=-0.75, mode=b'linear', nearest_mode=b'floor') -> 5
output: name='5' type=dtype('float32') shape=[2, 2, 6, 8]
AtenNonZero¶
forward¶
def forward(self, x):
y = torch.nonzero(x)
return y
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
NonZero(x) -> _onx_nonzero_x0
Transpose(_onx_nonzero_x0, perm=[1,0]) -> output_0
output: name='output_0' type=dtype('int64') shape=['NEWDIM_nonzero', 2]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
NonZero(x) -> _onx_nonzero_x0
Transpose(_onx_nonzero_x0, perm=[1,0]) -> output_0
output: name='output_0' type=dtype('int64') shape=['NEWDIM_nonzero', 2]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
NonZero(x) -> _onx_nonzero_x0
Transpose(_onx_nonzero_x0, perm=[1,0]) -> output
output: name='output' type=dtype('int64') shape=['NEWDIM_nonzero', 2]
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
NonZero(x) -> val_0
Transpose(val_0, perm=[1,0]) -> nonzero
output: name='nonzero' type=dtype('int64') shape=['u0', 2]
script¶
opset: domain='' version=17
input: name='onnx::NonZero_0' type=dtype('float32') shape=[3, 4]
NonZero(onnx::NonZero_0) -> /NonZero_output_0
Transpose(/NonZero_output_0, perm=[1,0]) -> 2
output: name='2' type=dtype('int64') shape=['Transpose2_dim_0', 2]
AtenNonZeroTuple¶
forward¶
def forward(self, x):
y = torch.nonzero(x, as_tuple=True)
return y[0], y[1]
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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_x0
Split(_onx_nonzero_x0, num_outputs=2) -> _onx_split_nonzero_x00, _onx_split_nonzero_x01
Reshape(_onx_split_nonzero_x00, init7_s1_-1) -> output_0
Reshape(_onx_split_nonzero_x01, init7_s1_-1) -> output_1
output: name='output_0' type=dtype('int64') shape=['u1']
output: name='output_1' type=dtype('int64') shape=['u1']
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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_x0
Split(_onx_nonzero_x0, num_outputs=2) -> _onx_split_nonzero_x00, _onx_split_nonzero_x01
Reshape(_onx_split_nonzero_x00, init7_s1_-1) -> output_0
Reshape(_onx_split_nonzero_x01, init7_s1_-1) -> output_1
output: name='output_0' type=dtype('int64') shape=['u3']
output: name='output_1' type=dtype('int64') shape=['u3']
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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_x0
Split(_onx_nonzero_x0, num_outputs=2) -> _onx_split_nonzero_x00, _onx_split_nonzero_x01
Reshape(_onx_split_nonzero_x00, init7_s1_-1) -> output_0
Reshape(_onx_split_nonzero_x01, init7_s1_-1) -> output_1
output: name='output_0' type=dtype('int64') shape=['d_output_0_0']
output: name='output_1' type=dtype('int64') shape=['d_output_1_0']
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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']
script¶
opset: domain='' version=17
input: name='onnx::NonZero_0' type=dtype('float32') shape=[3, 4]
Constant(value=[1, 1]) -> /Constant_output_0
NonZero(onnx::NonZero_0) -> /NonZero_output_0
Transpose(/NonZero_output_0, perm=[1,0]) -> /Transpose_output_0
Split(/Transpose_output_0, /Constant_output_0, axis=1) -> /Split_output_0, /Split_output_1
Constant(value=[1]) -> /Constant_1_output_0
Squeeze(/Split_output_0, /Constant_1_output_0) -> 7
Constant(value=[1]) -> /Constant_2_output_0
Squeeze(/Split_output_1, /Constant_2_output_0) -> 9
output: name='7' type=dtype('int64') shape=['Squeeze7_dim_0']
output: name='9' type=dtype('int64') shape=['Squeeze7_dim_0']
AtenRollPos¶
forward¶
def forward(self, x):
return torch.roll(x, 1, -1)
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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_x0
Slice(x, init7_s1_0, init7_s1_-1, init7_s1_-1) -> _onx_slice_x02
Concat(_onx_slice_x0, _onx_slice_x02, axis=-1) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 3, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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_x0
Slice(x, init7_s1_0, init7_s1_-1, init7_s1_-1) -> _onx_slice_x02
Concat(_onx_slice_x0, _onx_slice_x02, axis=-1) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 3, 4]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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_x0
Slice(x, init7_s1_0, init7_s1_-1, init7_s1_-1) -> _onx_slice_x02
Concat(_onx_slice_x0, _onx_slice_x02, axis=-1) -> output
output: name='output' type=dtype('float32') shape=[2, 3, 4]
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 3, 4]
Constant(value_ints=[0]) -> tmp_4
Constant(value=[-1]) -> dim_tensor
Constant(value=[1]) -> shift_tensor
Constant(value=[3]) -> slice_length_3
Slice(x, tmp_4, slice_length_3, dim_tensor) -> suffix
Constant(value=[24]) -> 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=[2, 3, 4]
script¶
opset: domain='' version=17
input: name='onnx::Slice_0' type=dtype('float32') shape=[2, 3, 4]
Constant(value=[-1]) -> /Constant_output_0
Constant(value=[-1]) -> /Constant_1_output_0
Constant(value=[922337203...) -> /Constant_2_output_0
Slice(onnx::Slice_0, /Constant_1_output_0, /Constant_2_output_0, /Constant_output_0) -> /Slice_output_0
Constant(value=[-1]) -> /Constant_3_output_0
Constant(value=[0]) -> /Constant_4_output_0
Constant(value=[-1]) -> /Constant_5_output_0
Slice(onnx::Slice_0, /Constant_4_output_0, /Constant_5_output_0, /Constant_3_output_0) -> /Slice_1_output_0
Concat(/Slice_output_0, /Slice_1_output_0, axis=-1) -> 9
output: name='9' type=dtype('float32') shape=[2, 3, 4]
AtenRollRelu¶
forward¶
def forward(self, x):
return torch.relu(torch.roll(x, -1, -1))
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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_x0
Slice(x, init7_s1_0, init7_s1_1, init7_s1_-1) -> _onx_slice_x02
Concat(_onx_slice_x0, _onx_slice_x02, axis=-1) -> _onx_concat_slice_x00
Relu(_onx_concat_slice_x00) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 3, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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_x0
Slice(x, init7_s1_0, init7_s1_1, init7_s1_-1) -> _onx_slice_x02
Concat(_onx_slice_x0, _onx_slice_x02, axis=-1) -> _onx_concat_slice_x00
Relu(_onx_concat_slice_x00) -> output_0
output: name='output_0' type=dtype('float32') shape=[2, 3, 4]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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_x0
Slice(x, init7_s1_0, init7_s1_1, init7_s1_-1) -> _onx_slice_x02
Concat(_onx_slice_x0, _onx_slice_x02, axis=-1) -> _onx_concat_slice_x00
Relu(_onx_concat_slice_x00) -> output
output: name='output' type=dtype('float32') shape=[2, 3, 4]
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 3, 4]
Constant(value_ints=[0]) -> tmp_4
Constant(value=[-1]) -> dim_tensor
Constant(value=[-1]) -> shift_tensor
Constant(value=[1]) -> slice_length_3
Slice(x, tmp_4, slice_length_3, dim_tensor) -> suffix
Constant(value=[24]) -> 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=[2, 3, 4]
script¶
opset: domain='' version=17
input: name='onnx::Slice_0' type=dtype('float32') shape=[2, 3, 4]
Constant(value=[-1]) -> /Constant_output_0
Constant(value=[1]) -> /Constant_1_output_0
Constant(value=[922337203...) -> /Constant_2_output_0
Slice(onnx::Slice_0, /Constant_1_output_0, /Constant_2_output_0, /Constant_output_0) -> /Slice_output_0
Constant(value=[-1]) -> /Constant_3_output_0
Constant(value=[0]) -> /Constant_4_output_0
Constant(value=[1]) -> /Constant_5_output_0
Slice(onnx::Slice_0, /Constant_4_output_0, /Constant_5_output_0, /Constant_3_output_0) -> /Slice_1_output_0
Concat(/Slice_output_0, /Slice_1_output_0, axis=-1) -> /Concat_output_0
Relu(/Concat_output_0) -> 10
output: name='10' type=dtype('float32') shape=[2, 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-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.362, -0.473, -0.427], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([-0.461], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([-0.33 , 0.45 , 0.204], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.486], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-tracing¶
FAILED
Type is unknown for result 'lx', known_types={'x': 1, 'linear.weight': 1, 'linear.bias': 1, '_sub_Linear_input_1': 1, '_sub_Linear__onx_transpose_weight0': 1, '_sub_Linear__onx_matmul_input_10': 1, '_sub_Linear_linear': 1, '_sub_Linear_output': 1, 'linear': 1, 'sigmoid': 1, 'buff': 1, 'sub': 1}
--DEBUG--
[GraphBuilder-ZIU] Message starts, there are 3 initializers, 8 nodes, 2 inputs, 2 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'_sub_Linear__onx_matmul_input_10': (4, 1),
'_sub_Linear__onx_transpose_weight0': (3, 1),
'_sub_Linear_input_1': (4, 3),
'_sub_Linear_linear': (4, 1),
'_sub_Linear_output': (4, 1),
'buff': (1,),
'linear': (4, 1),
'linear.bias': (1,),
'linear.weight': (1, 3),
'sigmoid': (4, 1),
'sub': (4, 1),
'x': (4, 3)}
_known_types={'_sub_Linear__onx_matmul_input_10': 1,
'_sub_Linear__onx_transpose_weight0': 1,
'_sub_Linear_input_1': 1,
'_sub_Linear_linear': 1,
'_sub_Linear_output': 1,
'buff': 1,
'linear': 1,
'linear.bias': 1,
'linear.weight': 1,
'sigmoid': 1,
'sub': 1,
'x': 1}
_known_value_shape={}
_known_constants=['_sub_Linear__onx_transpose_weight0', 'buff', 'linear.bias', 'linear.weight']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
add -> {output}
buff -> {sub}
linear -> {sigmoid}
lx -> {add}
sigmoid -> {sub}
sub -> {add}
x -> {linear}
--TORCH-SHAPES--
x: ('run_node', (('example_value', torch.float32, torch.Size([4, 3])), '')) --- 1:2:(4, 3):
lx: ('run_node', ('', '')) --- :::
linear: ('run_node', ('', '')) --- 1:2:(4, 1):
sigmoid: ('run_node', ('', '')) --- 1:2:(4, 1):
buff: ('run_node', ('', '')) --- 1:1:(1,):
sub: ('run_node', ('', '')) --- 1:2:(4, 1):
add: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule(
(linear): Linear(in_features=3, out_features=1, bias=True)
)
def forward(self, x, lx : list_torch_Tensor):
linear = self.linear(x); x = None
sigmoid = torch.sigmoid(linear); linear = None
buff = self.buff
sub = sigmoid - buff; sigmoid = buff = None
add = sub + lx; sub = lx = None
return add
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%x : [num_users=1] = placeholder[target=x]
%lx : list | torch.Tensor [num_users=1] = placeholder[target=lx]
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %buff), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sub, %lx), kwargs = {})
return add
-- process.progress --
node 6/8 target=<built-in function add>
--
[GraphBuilder-ZIU.make_tensor_input] x[1:4x3]
[GraphBuilder-ZIU.make_tensor_input] lx[0:]
[GraphBuilder-ZIU.make_initializer] linear.weight[torch.float32:torch.float32:[-0.22660338878631592, -0.5769753456115723, -0.5719130635261536]] - SOURCE: GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)
[GraphBuilder-ZIU.make_initializer] linear.bias[torch.float32:torch.float32:[-0.4451240003108978]] - SOURCE: GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
[GraphBuilder-ZIU.make_initializer] buff[torch.float32:torch.float32:[0.5]] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-ZIU.make_node] .make_nodes [#:# ] Identity:['x']->['_sub_Linear_input_1']
[GraphBuilder-ZIU.make_node] linear [#:# ] Transpose:['linear.weight']->['_sub_Linear__onx_transpose_weight0']
[GraphBuilder-ZIU.make_node] Opset [##:# ] MatMul:['_sub_Linear_input_1', '_sub_Linear__onx_transpose_weight0']->['_sub_Linear__onx_matmul_input_10']
[GraphBuilder-ZIU.make_node] Opset2 [##:# ] Add:['_sub_Linear__onx_matmul_input_10', 'linear.bias']->['_sub_Linear_linear']
[GraphBuilder-ZIU.make_node] .output [#:# ] Identity:['_sub_Linear_linear']->['_sub_Linear_output']
[GraphBuilder-ZIU.make_node] .make_nodes2 [#:# ] Identity:['_sub_Linear_output']->['linear']
[GraphBuilder-ZIU.make_node] sigmoid [#:# ] Sigmoid:['linear']->['sigmoid']
[GraphBuilder-ZIU.make_node] sub [##:# ] Sub:['sigmoid', 'buff']->['sub']
[GraphBuilder-ZIU] Message completed, there are 3 initializers, 8 nodes, 2 inputs, 2 outputs..
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([0.094, 0.501, 0.467], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.211], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value=[1]) -> val_3
ReduceSum(lx_1, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
Mul(lx_0, sum_1) -> mul
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
Sigmoid(linear) -> sigmoid
Sub(sigmoid, buff) -> sub
Add(sub, mul) -> add
output: name='add' type=dtype('float32') shape=[4, 1]
script¶
opset: domain='' version=17
input: name='onnx::Gemm_0' type=dtype('float32') shape=[4, 3]
input: name='onnx::Mul_1' type=dtype('float32') shape=[4, 1]
input: name='onnx::ReduceSum_2' type=dtype('float32') shape=[4, 2]
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.239, 0.423, -0.096], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.22], dtype=float32)
Constant(value=[1]) -> onnx::ReduceSum_6
ReduceSum(onnx::ReduceSum_2, onnx::ReduceSum_6, keepdims=1) -> /ReduceSum_output_0
Mul(onnx::Mul_1, /ReduceSum_output_0) -> /Mul_output_0
Gemm(onnx::Gemm_0, linear.weight, linear.bias, alpha=1.00, beta=1.00, transB=1) -> /linear/Gemm_output_0
Sigmoid(/linear/Gemm_output_0) -> /Sigmoid_output_0
Sub(/Sigmoid_output_0, buff) -> /Sub_output_0
Add(/Sub_output_0, /Mul_output_0) -> 12
output: name='12' type=dtype('float32') shape=[4, 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-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([-0.304, -0.356, 0.509], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.56], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.476, 0.164, -0.183], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.217], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-tracing¶
FAILED
len(.) expects an integer, len needs to be replaced. You should use _len.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([0.262, 0.564, 0.54 ], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.231], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value=[1]) -> val_3
ReduceSum(lx_1, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
Mul(lx_0, sum_1) -> mul
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
Sigmoid(linear) -> sigmoid
Sub(sigmoid, buff) -> sub
Add(sub, mul) -> add
output: name='add' type=dtype('float32') shape=[4, 1]
script¶
opset: domain='' version=17
input: name='onnx::Gemm_0' type=dtype('float32') shape=[4, 3]
input: name='onnx::Mul_1' type=dtype('float32') shape=[4, 1]
input: name='onnx::ReduceSum_2' type=dtype('float32') shape=[4, 2]
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.038, 0.458, -0.463], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.551], dtype=float32)
Constant(value=[1]) -> onnx::ReduceSum_6
ReduceSum(onnx::ReduceSum_2, onnx::ReduceSum_6, keepdims=1) -> /ReduceSum_output_0
Mul(onnx::Mul_1, /ReduceSum_output_0) -> /Mul_output_0
Gemm(onnx::Gemm_0, linear.weight, linear.bias, alpha=1.00, beta=1.00, transB=1) -> /linear/Gemm_output_0
Sigmoid(/linear/Gemm_output_0) -> /Sigmoid_output_0
Sub(/Sigmoid_output_0, buff) -> /Sub_output_0
Add(/Sub_output_0, /Mul_output_0) -> 12
output: name='12' type=dtype('float32') shape=[4, 1]
ComplexPolar¶
forward¶
def forward(self, x, angle):
return torch.polar(x, angle)
custom-fallback¶
FAILED
[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(complex64)' of input parameter (_onx_cast_sin_angle00) of operator (Mul) in node (polar5) is invalid.
custom-dec¶
FAILED
[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(complex64)' of input parameter (_onx_cast_sin_angle00) of operator (Mul) in node (polar5) is invalid.
custom-tracing¶
FAILED
[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(complex64)' of input parameter (_onx_cast_sin_angle00) of operator (Mul) in node (polar5) is invalid.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
input: name='angle' type=dtype('float32') shape=[4, 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
Constant(value=[-1]) -> int64_m1_1d_3
Unsqueeze(tmp_2, int64_m1_1d_3) -> imag
Concat(real, imag, axis=-1) -> polar
output: name='polar' type=dtype('float32') shape=[4, 4, 2]
script¶
FAILED
Exporting the operator 'aten::polar' to ONNX opset version 17 is not supported
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[5, 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=[5, 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
custom-dec¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[5, 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=[5, 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
custom-tracing¶
FAILED
aten_meth_sum() missing 1 required positional argument: 'axis'
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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=[5, 3]
----- subgraph ---- If - node_If_4 - 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=[5, 3]
----- subgraph ---- If - node_If_4 - 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=[5, 3]
script¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[5, 3]
input: name='y' type=dtype('float32') shape=[5, 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=[5, 3]
output: name='output_1' type=dtype('float32') shape=[5, 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
custom-dec¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[5, 3]
input: name='y' type=dtype('float32') shape=[5, 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=[5, 3]
output: name='output_1' type=dtype('float32') shape=[5, 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
custom-tracing¶
FAILED
aten_meth_sum() missing 1 required positional argument: 'axis'
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 3]
input: name='y' type=dtype('float32') shape=[5, 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=[5, 3]
output: name='getitem_1' type=dtype('float32') shape=[5, 3]
----- subgraph ---- If - node_If_4 - att.then_branch=G1 -- level=1 -- -> sin_true_graph_0,add_true_graph_0
Cos(x) -> cos
Add(cos, y) -> add_true_graph_0
Sin(x) -> sin_true_graph_0
output: name='sin_true_graph_0' type=dtype('float32') shape=[5, 3]
output: name='add_true_graph_0' type=dtype('float32') shape=[5, 3]
----- subgraph ---- If - node_If_4 - att.else_branch=G2 -- level=1 -- -> cos_false_graph_0,add_false_graph_0
Cos(x) -> cos_false_graph_0
Sin(x) -> sin_2
Add(sin_2, y) -> add_false_graph_0
output: name='cos_false_graph_0' type=dtype('float32') shape=[5, 3]
output: name='add_false_graph_0' type=dtype('float32') shape=[5, 3]
script¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[5, 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=[5, 3]
output: name='output_1' type=dtype('float32') shape=[5, 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
custom-dec¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[5, 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=[5, 3]
output: name='output_1' type=dtype('float32') shape=[5, 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
custom-tracing¶
FAILED
aten_meth_sum() missing 1 required positional argument: 'axis'
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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=[5, 3]
output: name='getitem_1' type=dtype('float32') shape=[5, 3]
----- subgraph ---- If - node_If_4 - 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=[5, 3]
output: name='cos_true_graph_0' type=dtype('float32') shape=[5, 3]
----- subgraph ---- If - node_If_4 - 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=[5, 3]
output: name='sin_false_graph_0' type=dtype('float32') shape=[5, 3]
script¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[1024, 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=[1024, 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, 102...) -> init7_s2_1024_10242
ConstantOfShape(init7_s2_1024_10242, value=[1.0]) -> ones32
Sin(x) -> sin2
Sub(sin2, ones32) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
custom-dec¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[1024, 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=[1024, 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, 102...) -> init7_s2_1024_10242
ConstantOfShape(init7_s2_1024_10242, value=[1.0]) -> ones32
Sin(x) -> sin2
Sub(sin2, ones32) -> cond#0
output: name='cond#0' type='NOTENSOR' shape=None
custom-tracing¶
FAILED
aten_meth_sum() missing 1 required positional argument: 'axis'
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[1024, 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=[1024, 1024]
----- subgraph ---- If - node_If_4 - att.then_branch=G1 -- level=1 -- -> sub_true_graph_0
Constant(value=[1024, 102...) -> val_1
Sin(x) -> sin
Constant(value=1.0) -> val_3
Expand(val_3, val_1) -> ones
Sub(sin, ones) -> sub_true_graph_0
output: name='sub_true_graph_0' type=dtype('float32') shape=[1024, 1024]
----- subgraph ---- If - node_If_4 - att.else_branch=G2 -- level=1 -- -> add_false_graph_0
Cos(x) -> cos
Add(cos, ones_2) -> add_false_graph_0
output: name='add_false_graph_0' type=dtype('float32') shape=[1024, 1024]
script¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
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-fallback¶
FAILED
'GraphProto' object is not iterable
custom-dec¶
FAILED
'GraphProto' object is not iterable
custom-tracing¶
FAILED
aten_meth_sum() missing 1 required positional argument: 'axis'
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('int64') shape=[2]
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=[2]
----- subgraph ---- If - node_If_3 - 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_If_4 - att.then_branch=G3 -- level=2 -- -> mul_true_graph_0__true_graph_0
Cast(x, to=1) -> convert_element_type_default
Mul(convert_element_type_default, submodule.weight) -> mul_true_graph_0__true_graph_0
output: name='mul_true_graph_0__true_graph_0' type=dtype('float32') shape=[2]
----- subgraph ---- If - node_If_4 - 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=[2]
----- subgraph ---- If - node_If_4 - att.then_branch=G3 -- level=1 -- -> mul_true_graph_0__true_graph_0
Cast(x, to=1) -> convert_element_type_default
Mul(convert_element_type_default, submodule.weight) -> mul_true_graph_0__true_graph_0
output: name='mul_true_graph_0__true_graph_0' type=dtype('float32') shape=[2]
----- subgraph ---- If - node_If_4 - 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=[2]
----- subgraph ---- If - node_If_3 - att.else_branch=G2 -- level=1 -- -> sub_false_graph_0
Cast(x, to=1) -> convert_element_type_default_3
Sub(convert_element_type_default_3, weight) -> sub_false_graph_0
output: name='sub_false_graph_0' type=dtype('float32') shape=[2]
script¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
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-fallback¶
opset: domain='' version=18
input: name='input_ids' type=dtype('int64') shape=[2, 12]
input: name='image_features' type=dtype('float32') shape=[2, 16]
input: name='vocab_size' type=dtype('int64') shape=[1]
init: name='init7_s2_-1_12' type=int64 shape=(2,) -- array([-1, 12]) -- 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
init: name='init7_s1_1025' type=int64 shape=(1,) -- array([1025]) -- Opset.make_node.1/Shape
init: name='_reshape_init7_s_00' type=int64 shape=(1,) -- array([0]) -- GraphBuilder.constant_folding.from/fold(init7_s1_1,init7_s_0)##init7_s_0/shape_type_compute._cast_inputs.1(lt_Scalar)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init7_s_-10000000000' type=int64 shape=(1,) -- array([-1000000000])-- GraphBuilder.constant_folding.from/fold(init7_s1_1,init7_s_-1000000000)##init7_s_-1000000000/shape_type_compute._cast_inputs.1(gt_Scalar)##init7_s1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
Reshape(input_ids, init7_s2_-1_12) -> view
Less(view, _reshape_init7_s_00) -> lt
Greater(view, _reshape_init7_s_-10000000000) -> gt
And(lt, gt) -> and_1
NonZero(and_1) -> _onx_nonzero_and_10
Split(_onx_nonzero_and_10, num_outputs=2) -> _onx_split_nonzero_and_100, _onx_split_nonzero_and_101
Reshape(_onx_split_nonzero_and_100, init7_s1_-1) -> output_1
Reshape(_onx_split_nonzero_and_101, init7_s1_-1) -> output_2
Clip(view, init7_s1_0, init7_s1_1025) -> output_0
output: name='output_0' type=dtype('int64') shape=[2, 12]
output: name='output_1' type=dtype('int64') shape=['u2']
output: name='output_2' type=dtype('int64') shape=['u2']
custom-dec¶
opset: domain='' version=18
input: name='input_ids' type=dtype('int64') shape=[2, 12]
input: name='image_features' type=dtype('float32') shape=[2, 16]
input: name='vocab_size' type=dtype('int64') shape=[1]
init: name='init7_s2_-1_12' type=int64 shape=(2,) -- array([-1, 12]) -- 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
init: name='init7_s1_1025' type=int64 shape=(1,) -- array([1025]) -- Opset.make_node.1/Shape
init: name='_reshape_init7_s_00' type=int64 shape=(1,) -- array([0]) -- GraphBuilder.constant_folding.from/fold(init7_s1_1,init7_s_0)##init7_s_0/shape_type_compute._cast_inputs.1(lt_Scalar)##shape_type_compute._cast_inputs.1(ge)##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##Opset.make_node.1/Shape
init: name='_reshape_init7_s_-10000000000' type=int64 shape=(1,) -- array([-1000000000])-- GraphBuilder.constant_folding.from/fold(init7_s1_1,init7_s_-1000000000)##init7_s_-1000000000/shape_type_compute._cast_inputs.1(gt_Scalar)##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##Opset.make_node.1/Shape
Reshape(input_ids, init7_s2_-1_12) -> view
Less(view, _reshape_init7_s_00) -> lt
Greater(view, _reshape_init7_s_-10000000000) -> gt
And(lt, gt) -> and_1
NonZero(and_1) -> _onx_nonzero_and_10
Split(_onx_nonzero_and_10, num_outputs=2) -> _onx_split_nonzero_and_100, _onx_split_nonzero_and_101
Reshape(_onx_split_nonzero_and_100, init7_s1_-1) -> output_1
Reshape(_onx_split_nonzero_and_101, init7_s1_-1) -> output_2
Clip(view, init7_s1_0, init7_s1_1025) -> output_0
output: name='output_0' type=dtype('int64') shape=[2, 12]
output: name='output_1' type=dtype('int64') shape=['u4']
output: name='output_2' type=dtype('int64') shape=['u4']
custom-tracing¶
FAILED
val is None for node=output, output=(getitem, getitem_1, getitem_2), a='getitem', o='output_0', has_type=False, has_rank=False, has_shape=False,
meta={}
node.__dict__={}
--DEBUG--
[GraphBuilder-ZPG] Message starts, there are 2 initializers, 11 nodes, 3 inputs, 3 outputs.
--LOCAL FUNCTIONS--
local_functions,_cb_cond_then_branch_0(['x', 'y', 'z']) -> ['output_0', 'output_1', 'output_2']
local_functions,_cb_cond_else_branch_0(['x', 'y', 'z']) -> ['output_0', 'output_1', 'output_2']
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'_reshape_init7_s_00': (1,),
'_reshape_numel0': (1,),
'gt': (),
'image_features': (2, 16),
'init7_s1_1': (1,),
'init7_s_0': (),
'input_ids': (2, 12),
'numel': (),
'vocab_size': ()}
_known_types={'_reshape_init7_s_00': 7,
'_reshape_numel0': 7,
'gt': 9,
'image_features': 1,
'init7_s1_1': 7,
'init7_s_0': 7,
'input_ids': 7,
'numel': 7,
'vocab_size': 7}
_known_value_shape={}
_known_constants=['_reshape_init7_s_00', 'init7_s1_1', 'init7_s_0']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
_cb_cond_else_branch_0 -> {condcc}
_cb_cond_then_branch_0 -> {condcc}
condcc -> {getitem_2, getitem, getitem_1}
getitem -> {output}
getitem_1 -> {output}
getitem_2 -> {output}
gt -> {condcc}
image_features -> {condcc, numel}
input_ids -> {condcc}
numel -> {gt}
output -> set()
vocab_size -> {condcc}
--TORCH-SHAPES--
input_ids: ('run_node', (('example_value', torch.int64, torch.Size([2, 12])), '')) --- 7:2:(2, 12):
image_features: ('run_node', (('example_value', torch.float32, torch.Size([2, 16])), '')) --- 1:2:(2, 16):
vocab_size: ('run_node', (('example_value', torch.int64, torch.Size([])), '')) --- 7:0:():
numel: ('run_node', ('', '')) --- 7:0:():
gt: ('run_node', ('', '')) --- 9:0:():
_cb_cond_then_branch_0: ('run_node', ('', '')) --- :::
_cb_cond_else_branch_0: ('run_node', ('', '')) --- :::
condcc: ('run_node', ('', '')) --- :::
getitem: ('run_node', ('', '')) --- :::
getitem_1: ('run_node', ('', '')) --- :::
getitem_2: ('run_node', ('', '')) --- :::
output: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule()
def forward(self, input_ids, image_features, vocab_size):
numel = image_features.numel()
gt = numel > 0; numel = None
_cb_cond_then_branch_0 = self._cb_cond_then_branch_0
_cb_cond_else_branch_0 = self._cb_cond_else_branch_0
condcc = torch.ops.higher_order.cond(gt, _cb_cond_then_branch_0, _cb_cond_else_branch_0, [input_ids, image_features, vocab_size]); gt = _cb_cond_then_branch_0 = _cb_cond_else_branch_0 = input_ids = image_features = vocab_size = None
getitem = condcc[0]
getitem_1 = condcc[1]
getitem_2 = condcc[2]; condcc = None
return (getitem, getitem_1, getitem_2)
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%input_ids : [num_users=1] = placeholder[target=input_ids]
%image_features : [num_users=2] = placeholder[target=image_features]
%vocab_size : [num_users=1] = placeholder[target=vocab_size]
%numel : [num_users=1] = call_method[target=numel](args = (%image_features,), kwargs = {})
%gt : [num_users=1] = call_function[target=operator.gt](args = (%numel, 0), kwargs = {})
%_cb_cond_then_branch_0 : [num_users=1] = get_attr[target=_cb_cond_then_branch_0]
%_cb_cond_else_branch_0 : [num_users=1] = get_attr[target=_cb_cond_else_branch_0]
%condcc : [num_users=3] = call_function[target=torch.ops.higher_order.cond](args = (%gt, %_cb_cond_then_branch_0, %_cb_cond_else_branch_0, [%input_ids, %image_features, %vocab_size]), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%condcc, 0), kwargs = {})
%getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%condcc, 1), kwargs = {})
%getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%condcc, 2), kwargs = {})
return (getitem, getitem_1, getitem_2)
-- process.progress --
node 11/12 target=output
--
[GraphBuilder-ZPG.make_tensor_input] input_ids[7:2x12]
[GraphBuilder-ZPG.make_tensor_input] image_features[1:2x16]
[GraphBuilder-ZPG.make_tensor_input] vocab_size[7:]
[GraphBuilder-ZPG.make_initializer] init7_s_0[int64:int64:[0]] - SOURCE: shape_type_compute._cast_inputs.1(gt)
[GraphBuilder-ZPG.make_initializer] init7_s1_1[int64:int64:[1]] - SOURCE: Opset.make_node.1/Shape##Opset.make_node.1/Shape
[GraphBuilder-ZPG.make_node] meth_numel [#:# ] Size:['image_features']->['numel']
[GraphBuilder-ZPG.make_node] gt [##:# ] Reshape:['numel', 'init7_s1_1']->['_reshape_numel0']
[GraphBuilder-ZPG.make_node] gt2 [##:# ] Reshape:['init7_s_0', 'init7_s1_1']->['_reshape_init7_s_00']
[GraphBuilder-ZPG.make_node] gt3 [##:# ] Greater:['numel', 'init7_s_0']->['gt']
[GraphBuilder-ZPG.make_node] cond [#:--- ] If:['gt']->['condcc#0', 'condcc#1', 'condcc#2']
[GraphBuilder-ZPG.make_node] getitemB_tuple [-:- ] Identity:['condcc#0']->['getitem']
[GraphBuilder-ZPG.make_node] getitemB_tuple2 [-:- ] Identity:['condcc#1']->['getitem_1']
[GraphBuilder-ZPG.make_node] getitemB_tuple3 [-:- ] Identity:['condcc#2']->['getitem_2']
[GraphBuilder-ZPG.make_node] .output [-:- ] Identity:['getitem']->['output_0']
[GraphBuilder-ZPG.make_node] .output2 [-:- ] Identity:['getitem_1']->['output_1']
[GraphBuilder-ZPG.make_node] .output3 [-:- ] Identity:['getitem_2']->['output_2']
[GraphBuilder-ZPG] Message completed, there are 2 initializers, 11 nodes, 3 inputs, 3 outputs.
dynamo-ir¶
FAILED
Input mismatch, inputs[0]=(T7r2,T1r2,int) but names=['input_ids', 'image_features'], model=ControlFlowCondNonZero, export='dynamo-ir'
script¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
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-fallback¶
FAILED
'GraphProto' object is not iterable
custom-dec¶
FAILED
'GraphProto' object is not iterable
custom-tracing¶
FAILED
aten_meth_sum() missing 1 required positional argument: 'axis'
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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=[5, 3]
----- subgraph ---- If - node_If_4 - 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_If_4_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=[5, 3]
----- subgraph ---- If - node_If_4_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=[5, 3]
----- subgraph ---- If - node_If_4_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=[5, 3]
----- subgraph ---- If - node_If_4_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=[5, 3]
----- subgraph ---- If - node_If_4 - 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=[5, 3]
script¶
FAILED
Detected that you are using FX to torch.jit.trace a dynamo-optimized function. This is not supported at the moment.
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[3, 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
custom-dec¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 377, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
custom-tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
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[3, 3][3, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:376 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/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:377 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[3, 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/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:377 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/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 377, in forward
carry, out = torch.ops.higher_order.scan(
(Refer to the full stack trace above for more information.)
script¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='init7_s1_4' type=int64 shape=(1,) -- array([4]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_0' 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, _reshape_init1_s_0) -> _onx_mul_x0
Scan(zeros_like, ones_like, x, _onx_mul_x0, 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=[3, 4]
output: name='output_3' type=dtype('float32') shape=[3, 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
custom-dec¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 397, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
custom-tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
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[3, 4][4, 1]";
x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec)
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:395 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/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:396 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/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:400 in forward, code: [x, x * 2],
mul: "f32[3, 4][4, 1]" = torch.ops.aten.mul.Tensor(x, 2)
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:397 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[3, 4][4, 1]" = scan[2]
getitem_3: "f32[3, 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/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:397 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/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 397, in forward
carry1, carry2, out1, out2 = torch.ops.higher_order.scan(
(Refer to the full stack trace above for more information.)
script¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[3, 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=[3, 3]
----- 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
Sqrt(sum_12) -> output_1
Identity(init_0_x) -> output_0
output: name='output_0' type='NOTENSOR' shape=None
output: name='output_1' type='NOTENSOR' shape=None
custom-dec¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 422, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
custom-tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
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 'torch.onnx._internal.exporter._errors.DispatchError'>: No ONNX function found for <torch._higher_order_ops.scan.ScanOp object at 0x7feadabb0170>. Failure message: No decompositions registered for the real-valued input
⬆️
<class 'torch.onnx._internal.exporter._errors.ConversionError'>: Error when translating node %scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%x], [%x], ()), kwargs = {}). See the stack trace for more information.
(Refer to the full stack trace above for more information.)
script¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[3, 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=[3, 3]
----- 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
custom-dec¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 449, in forward
out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
custom-tracing¶
FAILED
(CustomProxy(clone),) can only be of (<class 'torch.Tensor'>, <class 'int'>, <class 'torch.SymInt'>) but got (<class 'experimental_experiment.torch_interpreter.tracing.CustomProxy'>,)
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 'torch.onnx._internal.exporter._errors.DispatchError'>: No ONNX function found for <torch._higher_order_ops.scan.ScanOp object at 0x7feadabb0170>. Failure message: No decompositions registered for the real-valued input
⬆️
<class 'torch.onnx._internal.exporter._errors.ConversionError'>: Error when translating node %scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%clone], [%x], (%clone_1,)), kwargs = {}). See the stack trace for more information.
(Refer to the full stack trace above for more information.)
script¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
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-fallback¶
opset: domain='' version=18
opset: domain='local_functions' version=1
input: name='x' type=dtype('float32') shape=[3, 4]
input: name='y' type=dtype('float32') shape=[5, 4]
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=[3, 5]
----- 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
custom-dec¶
FAILED
scan must be captured completely with torch.compile. Scroll up to find out what causes the graph break.
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 475, in forward
carry, out = torch.ops.higher_order.scan(
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
custom-tracing¶
FAILED
Unable to symbolically trace HigherOrderOperators
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 'torch.onnx._internal.exporter._errors.DispatchError'>: No ONNX function found for <torch._higher_order_ops.scan.ScanOp object at 0x7feadabb0170>. Failure message: No decompositions registered for the real-valued input
⬆️
<class 'torch.onnx._internal.exporter._errors.ConversionError'>: Error when translating node %scan : [num_users=1] = call_function[target=torch.ops.higher_order.scan](args = (%scan_combine_graph_0, [%y], [%x], ()), kwargs = {}). See the stack trace for more information.
(Refer to the full stack trace above for more information.)
script¶
FAILED
could not find kernel for HigherOrderOperator scan at dispatch key DispatchKey.??? (resolved from DispatchKey.???)
CreateFromShape¶
forward¶
def forward(self, x):
y = torch.ones((x.shape[0], x.shape[1] + 1))
return y
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
init: name='init7_s2_4_5' type=int64 shape=(2,) -- array([4, 5]) -- Opset.make_node.1/Shape
ConstantOfShape(init7_s2_4_5, value=[1.0]) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 5]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
init: name='init7_s2_4_5' type=int64 shape=(2,) -- array([4, 5]) -- Opset.make_node.1/Shape
ConstantOfShape(init7_s2_4_5, value=[1.0]) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 5]
custom-tracing¶
FAILED
ones(): argument 'size' (position 1) must be tuple of ints, but found element of type CustomProxy at pos 0
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
Constant(value=[[1.0, 1.0...) -> ones
output: name='ones' type=dtype('float32') shape=[4, 5]
script¶
FAILED
Input mismatch, inputs[0]=(T1r2,) but names=[], model=CreateFromShape, export='script'
CreateFromShapeThroughFunction¶
forward¶
def forward(self, x):
dy1 = CreateFromShapeThroughFunction.add_one(x.shape[1])
y = torch.ones((x.shape[0], dy1))
return y
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
init: name='init7_s2_4_5' type=int64 shape=(2,) -- array([4, 5]) -- Opset.make_node.1/Shape
ConstantOfShape(init7_s2_4_5, value=[1.0]) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 5]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
init: name='init7_s2_4_5' type=int64 shape=(2,) -- array([4, 5]) -- Opset.make_node.1/Shape
ConstantOfShape(init7_s2_4_5, value=[1.0]) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 5]
custom-tracing¶
FAILED
ones(): argument 'size' (position 1) must be tuple of ints, but found element of type CustomProxy at pos 0
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
Constant(value=[[1.0, 1.0...) -> ones
output: name='ones' type=dtype('float32') shape=[4, 5]
script¶
FAILED
Input mismatch, inputs[0]=(T1r2,) but names=[], model=CreateFromShapeThroughFunction, export='script'
CropLastDimensionWithTensorContent¶
forward¶
def forward(self, x, shape):
return x[..., : shape[0]]
custom-fallback¶
- def forward(self, arg0_1: “f32[3, 4, 4]”, arg1_1: “i64[1]”):
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:865 in forward, code: return x[…, : shape[0]]
select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None item: “Sym(u0)” = torch.ops.aten.item.default(select); select = item = None
- def forward(self, arg0_1: “f32[3, 4, 4]”, arg1_1: “i64[1]”):
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:865 in forward, code: return x[…, : shape[0]]
select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None item: “Sym(u0)” = torch.ops.aten.item.default(select); select = item = None
- def forward(self, arg0_1: “f32[3, 4, 4]”, arg1_1: “i64[1]”):
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:865 in forward, code: return x[…, : shape[0]]
select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None item: “Sym(u0)” = torch.ops.aten.item.default(select); select = item = None
- def forward(self, arg0_1: “f32[3, 4, 4]”, arg1_1: “i64[1]”):
# File: ~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py:865 in forward, code: return x[…, : shape[0]]
select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None item: “Sym(u0)” = torch.ops.aten.item.default(select); select = item = None
- def forward(self, arg0_1: “f32[s35, s16, s90]”, arg1_1: “i64[1]”):
# No stacktrace found for following nodes select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None _to_copy: “i32[]” = torch.ops.aten._to_copy.default(select, dtype = torch.int32); select = None _local_scalar_dense: “Sym(u0)” = torch.ops.aten._local_scalar_dense.default(_to_copy); _to_copy = None slice_1 = torch.ops.aten.slice.Tensor(arg0_1, 2, 0, _local_scalar_dense); arg0_1 = _local_scalar_dense = slice_1 = None
- def forward(self, arg0_1: “f32[s35, s16, s90]”, arg1_1: “i64[1]”):
# No stacktrace found for following nodes select: “i64[]” = torch.ops.aten.select.int(arg1_1, 0, 0); arg1_1 = None _to_copy: “i32[]” = torch.ops.aten._to_copy.default(select, dtype = torch.int32); select = None _local_scalar_dense: “Sym(u0)” = torch.ops.aten._local_scalar_dense.default(_to_copy); _to_copy = None slice_1 = torch.ops.aten.slice.Tensor(arg0_1, 2, 0, _local_scalar_dense); arg0_1 = _local_scalar_dense = slice_1 = None
FAILED
None of the following options [ExportOptions(strict=True, aten_as_function={'aten.scaled_dot_product_attention.default'}), ExportOptions(aten_as_function={'aten.scaled_dot_product_attention.default'}), ExportOptions(strict=True, decomposition_table='default', aten_as_function={'aten.scaled_dot_product_attention.default'}), ExportOptions(decomposition_table='default', aten_as_function={'aten.scaled_dot_product_attention.default'}), ExportOptions(dynamo=True, aten_as_function={'aten.scaled_dot_product_attention.default'}), ExportOptions(decomposition_table='default', dynamo=True, aten_as_function={'aten.scaled_dot_product_attention.default'}), ExportOptions(jit=True, aten_as_function={'aten.scaled_dot_product_attention.default'})] worked, args=(T1r3,T7r1), kwargs=None, exception=
-----
[(ExportOptions(strict=True, aten_as_function={'aten.scaled_dot_product_attention.default'}),
Unsupported('Dynamic slicing with Tensor arguments\n Explanation: Creating slices with Tensor arguments is not supported. e.g. `l[:x]`, where `x` is a 1-element tensor.\n Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.\n\n Developer debug context: SliceVariable start: ConstantVariable(NoneType: None), stop: TensorVariable(), step: ConstantVariable(NoneType: None)\n\n\nfrom user code:\n File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 865, in forward\n return x[..., : shape[0]]\n\nSet 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"\n')),
(ExportOptions(aten_as_function={'aten.scaled_dot_product_attention.default'}),
GuardOnDataDependentSymNode('Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: none)\n\nCaused by: (_export/non_strict_utils.py:973 in __torch_function__)\nFor more information, run with TORCH_LOGS="dynamic"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 865, in forward\n return x[..., : shape[0]]\n\n\nThe 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()`.')),
(ExportOptions(strict=True, decomposition_table='default', aten_as_function={'aten.scaled_dot_product_attention.default'}),
Unsupported('Dynamic slicing with Tensor arguments\n Explanation: Creating slices with Tensor arguments is not supported. e.g. `l[:x]`, where `x` is a 1-element tensor.\n Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.\n\n Developer debug context: SliceVariable start: ConstantVariable(NoneType: None), stop: TensorVariable(), step: ConstantVariable(NoneType: None)\n\n\nfrom user code:\n File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 865, in forward\n return x[..., : shape[0]]\n\nSet 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"\n')),
(ExportOptions(decomposition_table='default', aten_as_function={'aten.scaled_dot_product_attention.default'}),
GuardOnDataDependentSymNode('Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: none)\n\nCaused by: (_export/non_strict_utils.py:973 in __torch_function__)\nFor more information, run with TORCH_LOGS="dynamic"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 865, in forward\n return x[..., : shape[0]]\n\n\nThe 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()`.')),
(ExportOptions(dynamo=True, aten_as_function={'aten.scaled_dot_product_attention.default'}),
Unsupported('Dynamic slicing with Tensor arguments\n Explanation: Creating slices with Tensor arguments is not supported. e.g. `l[:x]`, where `x` is a 1-element tensor.\n Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.\n\n Developer debug context: SliceVariable start: ConstantVariable(NoneType: None), stop: TensorVariable(), step: ConstantVariable(NoneType: None)\n\n\nfrom user code:\n File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 865, in forward\n return x[..., : shape[0]]\n\nSet 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"\n')),
(ExportOptions(decomposition_table='default', dynamo=True, aten_as_function={'aten.scaled_dot_product_attention.default'}),
Unsupported('Dynamic slicing with Tensor arguments\n Explanation: Creating slices with Tensor arguments is not supported. e.g. `l[:x]`, where `x` is a 1-element tensor.\n Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.\n\n Developer debug context: SliceVariable start: ConstantVariable(NoneType: None), stop: TensorVariable(), step: ConstantVariable(NoneType: None)\n\n\nfrom user code:\n File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 865, in forward\n return x[..., : shape[0]]\n\nSet 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"\n')),
(ExportOptions(jit=True, aten_as_function={'aten.scaled_dot_product_attention.default'}),
GuardOnDataDependentSymNode('Could not guard on data-dependent expression u0 < 0 (unhinted: u0 < 0). (Size-like symbols: none)\n\nCaused by: (_decomp/decompositions.py:734 in slice_forward)\nFor more information, run with TORCH_LOGS="dynamic"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File "<string>", line 1, in <lambda>\n\n\nWhile executing %slice_tensor : [num_users=1] = call_function[target=torch.ops.aten.slice.Tensor](args = (%x, 2, 0, %_local_scalar_dense_default, 1), kwargs = {})\nGraphModule: class GraphModule(torch.nn.Module):\n def forward(self, x, shape):\n # No stacktrace found for following nodes\n select_int = torch.ops.aten.select.int(shape, 0, 0); shape = None\n _to_copy_default = torch.ops.aten._to_copy.default(select_int, dtype = torch.int32); select_int = None\n _local_scalar_dense_default = torch.ops.aten._local_scalar_dense.default(_to_copy_default); _to_copy_default = None\n slice_tensor = torch.ops.aten.slice.Tensor(x, 2, 0, _local_scalar_dense_default, 1); x = _local_scalar_dense_default = None\n return slice_tensor\n \n\nOriginal traceback:\nNone'))]
custom-dec¶
FAILED
Dynamic slicing with Tensor arguments
Explanation: Creating slices with Tensor arguments is not supported. e.g. `l[:x]`, where `x` is a 1-element tensor.
Hint: It may be possible to write Dynamo tracing rules for this code. Please report an issue to PyTorch if you encounter this graph break often and it is causing performance issues.
Developer debug context: SliceVariable start: ConstantVariable(NoneType: None), stop: TensorVariable(), step: ConstantVariable(NoneType: None)
from user code:
File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/eval/model_cases.py", line 865, in forward
return x[..., : shape[0]]
Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4, 4]
input: name='shape' type=dtype('int64') shape=[1]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='getitem_1_axis' type=int64 shape=(1,) -- array([-1]) -- DynamoInterpreter._getitem_slice.axis.1
init: name='getitem_1_step' type=int64 shape=(1,) -- array([1]) -- DynamoInterpreter._getitem_slice.3
Gather(shape, init7_s1_0) -> _onx_gather_shape0
Slice(x, init7_s1_0, _onx_gather_shape0, getitem_1_axis, getitem_1_step) -> output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1', 'd_output_2']
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=['s35', 's16', 's90']
input: name='shape' type=dtype('int64') shape=[1]
Constant(value_ints=[1]) -> val_11
Constant(value=[0]) -> val_4
Constant(value=[2]) -> val_7
Constant(value=[2]) -> val_10
Slice(x, val_4, val_7, val_10, val_11) -> slice_1
output: name='slice_1' type=dtype('float32') shape=['s35', 's16', 2]
script¶
opset: domain='' version=17
input: name='onnx::Slice_0' type=dtype('float32') shape=[3, 4, 4]
input: name='onnx::Gather_1' type=dtype('int64') shape=[1]
Constant(value=0) -> /Constant_output_0
Gather(onnx::Gather_1, /Constant_output_0, axis=0) -> /Gather_output_0
Constant(value=[2]) -> /Constant_1_output_0
Constant(value=[0]) -> /Constant_2_output_0
Constant(value=[0]) -> /Constant_3_output_0
Unsqueeze(/Gather_output_0, /Constant_3_output_0) -> /Unsqueeze_output_0
Constant(value=[1]) -> /Constant_4_output_0
Slice(onnx::Slice_0, /Constant_2_output_0, /Unsqueeze_output_0, /Constant_1_output_0, /Constant_4_output_0) -> 14
output: name='14' type=dtype('float32') shape=[3, 4, 'Slice14_dim_2']
CropLastDimensionWithTensorShape¶
forward¶
def forward(self, x, y):
return x[..., : y.shape[0]]
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4, 4]
input: name='y' type=dtype('float32') shape=[2]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0]) -- 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
Slice(x, init7_s1_0, init7_s1_2, init7_s1_2) -> output_0
output: name='output_0' type=dtype('float32') shape=[3, 4, 2]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4, 4]
input: name='y' type=dtype('float32') shape=[2]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0]) -- 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
Slice(x, init7_s1_0, init7_s1_2, init7_s1_2) -> output_0
output: name='output_0' type=dtype('float32') shape=[3, 4, 2]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4, 4]
input: name='y' type=dtype('float32') shape=[2]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='getitem_1_axis' type=int64 shape=(1,) -- array([-1]) -- DynamoInterpreter._getitem_slice.axis.1
init: name='getitem_1_step' type=int64 shape=(1,) -- array([1]) -- DynamoInterpreter._getitem_slice.3
Shape(y) -> getattr_1
Gather(getattr_1, init7_s1_0) -> _onx_gather_getattr_10
Slice(x, init7_s1_0, _onx_gather_getattr_10, getitem_1_axis, getitem_1_step) -> output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1', 'd_output_2']
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4, 4]
input: name='y' type=dtype('float32') shape=[2]
Constant(value_ints=[1]) -> val_11
Constant(value=[0]) -> val_3
Constant(value=[2]) -> val_7
Constant(value=[2]) -> val_10
Slice(x, val_3, val_7, val_10, val_11) -> slice_1
output: name='slice_1' type=dtype('float32') shape=[3, 4, 2]
script¶
FAILED
Input mismatch, inputs[0]=(T1r3,T1r1) but names=['onnx::Slice_0'], model=CropLastDimensionWithTensorShape, export='script'
InplaceAdd¶
forward¶
def forward(self, x):
x += self.bias
return x
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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=[3, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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
Identity(output_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[3, 4]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.get_attr.0
Add(x, bias) -> output
output: name='output' type=dtype('float32') shape=[3, 4]
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
Add(x, bias) -> add
output: name='add' type=dtype('float32') shape=[3, 4]
script¶
opset: domain='' version=17
input: name='onnx::Add_0' type=dtype('float32') shape=[3, 4]
Constant(value=[[1.0, 1.0...) -> /Constant_output_0
Add(onnx::Add_0, /Constant_output_0) -> 2
output: name='2' type=dtype('float32') shape=[3, 4]
InplaceAdd2¶
forward¶
def forward(self, x):
x.add_(self.bias)
return x
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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=[3, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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
Identity(output_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[3, 4]
custom-tracing¶
FAILED
Unable to interpret method 'aten_meth_add_', args=(x, bias), kwargs={}, dispatcher=None
--DEBUG--
[GraphBuilder-TAE] Message starts, there are 1 initializers, 0 nodes, 1 inputs, 1 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'bias': (1, 4), 'x': (3, 4)}
_known_types={'bias': 1, 'x': 1}
_known_value_shape={}
_known_constants=['bias']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
add_ -> {output}
bias -> {add_}
x -> {add_}
--TORCH-SHAPES--
x: ('run_node', (('example_value', torch.float32, torch.Size([3, 4])), '')) --- 1:2:(3, 4):
bias: ('run_node', ('', '')) --- 1:2:(1, 4):
add_: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule()
def forward(self, x):
bias = self.bias
add_ = x.add_(bias); x = bias = None
return add_
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%x : [num_users=1] = placeholder[target=x]
%bias : [num_users=1] = get_attr[target=bias]
%add_ : [num_users=1] = call_method[target=add_](args = (%x, %bias), kwargs = {})
return add_
-- process.progress --
node 2/4 target=add_
--
[GraphBuilder-TAE.make_tensor_input] x[1:3x4]
[GraphBuilder-TAE.make_initializer] bias[torch.float32:torch.float32:[1.0, 1.0, 1.0, 1.0]] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-TAE] Message completed, there are 1 initializers, 0 nodes, 1 inputs, 1 outputs.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
Add(x, bias) -> add
output: name='add' type=dtype('float32') shape=[3, 4]
script¶
opset: domain='' version=17
input: name='onnx::Add_0' type=dtype('float32') shape=[3, 4]
Constant(value=[[1.0, 1.0...) -> /Constant_output_0
Add(onnx::Add_0, /Constant_output_0) -> 2
output: name='2' type=dtype('float32') shape=[3, 4]
InplaceAdd_Mul¶
forward¶
def forward(self, x):
x.add_(self.bias)
return x * 2
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='c_bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='_reshape_init1_s_0' 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_, _reshape_init1_s_0) -> output_0
output: name='output_0' type=dtype('float32') shape=[3, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='c_bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)-- DynamoInterpret.placeholder.0
init: name='_reshape_init1_s_0' 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) -> output_0
Mul(output_0, _reshape_init1_s_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[3, 4]
custom-tracing¶
FAILED
Unable to interpret method 'aten_meth_add_', args=(x, bias), kwargs={}, dispatcher=None
--DEBUG--
[GraphBuilder-LZS] Message starts, there are 1 initializers, 0 nodes, 1 inputs, 1 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'bias': (1, 4), 'x': (3, 4)}
_known_types={'bias': 1, 'x': 1}
_known_value_shape={}
_known_constants=['bias']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
add_ -> {mul}
bias -> {add_}
x -> {add_}
--TORCH-SHAPES--
x: ('run_node', (('example_value', torch.float32, torch.Size([3, 4])), '')) --- 1:2:(3, 4):
bias: ('run_node', ('', '')) --- 1:2:(1, 4):
add_: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule()
def forward(self, x):
bias = self.bias
add_ = x.add_(bias); x = bias = None
mul = add_ * 2; add_ = None
return mul
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%x : [num_users=1] = placeholder[target=x]
%bias : [num_users=1] = get_attr[target=bias]
%add_ : [num_users=1] = call_method[target=add_](args = (%x, %bias), kwargs = {})
%mul : [num_users=1] = call_function[target=operator.mul](args = (%add_, 2), kwargs = {})
return mul
-- process.progress --
node 2/5 target=add_
--
[GraphBuilder-LZS.make_tensor_input] x[1:3x4]
[GraphBuilder-LZS.make_initializer] bias[torch.float32:torch.float32:[1.0, 1.0, 1.0, 1.0]] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-LZS] Message completed, there are 1 initializers, 0 nodes, 1 inputs, 1 outputs.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
Add(x, bias) -> add
Constant(value=2.0) -> scalar_tensor_default
Mul(add, scalar_tensor_default) -> mul
output: name='mul' type=dtype('float32') shape=[3, 4]
script¶
opset: domain='' version=17
input: name='onnx::Add_0' type=dtype('float32') shape=[3, 4]
Constant(value=[[1.0, 1.0...) -> /Constant_output_0
Add(onnx::Add_0, /Constant_output_0) -> /Add_output_0
Constant(value=2.0) -> /Constant_1_output_0
Mul(/Add_output_0, /Constant_1_output_0) -> 4
output: name='4' type=dtype('float32') shape=[3, 4]
InplaceCloneAdd¶
forward¶
def forward(self, x):
x = x.clone()
x.add_(self.bias)
return x
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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=[3, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 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=[3, 4]
custom-tracing¶
FAILED
Unable to interpret method 'aten_meth_add_', args=(clone, bias), kwargs={}, dispatcher=None
--DEBUG--
[GraphBuilder-THG] Message starts, there are 1 initializers, 1 nodes, 1 inputs, 1 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'bias': (1, 4), 'clone': (3, 4), 'x': (3, 4)}
_known_types={'bias': 1, 'clone': 1, 'x': 1}
_known_value_shape={}
_known_constants=['bias']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
add_ -> {output}
bias -> {add_}
clone -> {add_}
x -> {clone}
--TORCH-SHAPES--
x: ('run_node', (('example_value', torch.float32, torch.Size([3, 4])), '')) --- 1:2:(3, 4):
clone: ('run_node', ('', '')) --- 1:2:(3, 4):
bias: ('run_node', ('', '')) --- 1:2:(1, 4):
add_: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule()
def forward(self, x):
clone = x.clone(); x = None
bias = self.bias
add_ = clone.add_(bias); clone = bias = None
return add_
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%x : [num_users=1] = placeholder[target=x]
%clone : [num_users=1] = call_method[target=clone](args = (%x,), kwargs = {})
%bias : [num_users=1] = get_attr[target=bias]
%add_ : [num_users=1] = call_method[target=add_](args = (%clone, %bias), kwargs = {})
return add_
-- process.progress --
node 3/5 target=add_
--
[GraphBuilder-THG.make_tensor_input] x[1:3x4]
[GraphBuilder-THG.make_initializer] bias[torch.float32:torch.float32:[1.0, 1.0, 1.0, 1.0]] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-THG.make_node] .clone [#:# ] Identity:['x']->['clone']
[GraphBuilder-THG] Message completed, there are 1 initializers, 1 nodes, 1 inputs, 1 outputs.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[3, 4]
init: name='bias' type=float32 shape=(1, 4) -- array([1., 1., 1., 1.], dtype=float32)
Add(x, bias) -> add
output: name='add' type=dtype('float32') shape=[3, 4]
script¶
opset: domain='' version=17
input: name='onnx::Add_0' type=dtype('float32') shape=[3, 4]
Constant(value=[[1.0, 1.0...) -> /Constant_output_0
Add(onnx::Add_0, /Constant_output_0) -> 2
output: name='2' type=dtype('float32') shape=[3, 4]
InplaceSetItemEllipsis_1¶
forward¶
def forward(self, index, update):
copy = self.params.clone()
copy[..., index] = update
return copy
custom-fallback¶
opset: domain='' version=18
input: name='index' type=dtype('int64') shape=[4]
input: name='update' type=dtype('float32') shape=[8192, 4]
init: name='init7_s3_1_1_-1' type=int64 shape=(3,) -- array([ 1, 1, -1])-- Opset.make_node.1/Shape
init: name='init7_s3_1_8192_4' type=int64 shape=(3,) -- array([ 1, 8192, 4])-- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_32768' type=int64 shape=(1,) -- array([32768]) -- 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##Opset.make_node.1/Shape
init: name='init7_s32768_' type=int64 shape=(32768,) -- Opset.make_node.0
init: name='_reshape_init7_s1_00' type=int64 shape=(1, 1, 1) -- array([0])-- GraphBuilder.constant_folding.from/fold(init7_s1_0,init7_s3_-1_1_1)##init7_s1_0/Opset.make_node.1/Shape##init7_s3_-1_1_1/Opset.make_node.1/Shape
init: name='_reshape_init7_s8192_0' type=int64 shape=(1, 8192, 1) -- GraphBuilder.constant_folding.from/fold(init7_s3_1_-1_1,init7_s8192_)##init7_s8192_/Opset.make_node.0##init7_s3_1_-1_1/Opset.make_node.1/Shape
init: name='_reshape_clone0' type=float32 shape=(32768,) -- GraphBuilder.constant_folding.from/fold(c_params,init7_s1_-1)##c_params/DynamoInterpret.placeholder.0##init7_s1_-1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
Expand(update, init7_s3_1_8192_4) -> _onx_expand_update0
Reshape(_onx_expand_update0, init7_s1_-1) -> _reshape_expand_update00
Reshape(index, init7_s3_1_1_-1) -> _reshape_index0
Mul(_reshape_init7_s1_00, init7_s1_32768) -> _onx_mul__reshape_init7_s1_000
Add(_onx_mul__reshape_init7_s1_000, _reshape_index0) -> add-_onx_mul__reshape_init7_s1_000
Mul(_reshape_init7_s8192_0, init7_s1_4) -> _onx_mul__reshape_init7_s8192_00
Add(add-_onx_mul__reshape_init7_s1_000, _onx_mul__reshape_init7_s8192_00) -> _onx_add_add_mul__reshape_init7_s1_00000
Reshape(_onx_add_add_mul__reshape_init7_s1_00000, init7_s1_-1) -> _reshape_add_add_mul__reshape_init7_s1_000000
GatherElements(init7_s32768_, _reshape_add_add_mul__reshape_init7_s1_000000) -> _onx_gatherelements_init7_s32768_0
ScatterElements(_reshape_clone0, _onx_gatherelements_init7_s32768_0, _reshape_expand_update00) -> _onx_scatterelements__reshape_clone00
Reshape(_onx_scatterelements__reshape_clone00, init7_s3_1_8192_4) -> output_0
output: name='output_0' type=dtype('float32') shape=[1, 8192, 4]
custom-dec¶
opset: domain='' version=18
input: name='index' type=dtype('int64') shape=[4]
input: name='update' type=dtype('float32') shape=[8192, 4]
init: name='init7_s3_1_1_-1' type=int64 shape=(3,) -- array([ 1, 1, -1])-- Opset.make_node.1/Shape
init: name='init7_s3_1_8192_4' type=int64 shape=(3,) -- array([ 1, 8192, 4])-- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='init7_s1_32768' type=int64 shape=(1,) -- array([32768]) -- 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##Opset.make_node.1/Shape
init: name='init7_s32768_' type=int64 shape=(32768,) -- Opset.make_node.0
init: name='_reshape_init7_s1_00' type=int64 shape=(1, 1, 1) -- array([0])-- GraphBuilder.constant_folding.from/fold(init7_s1_0,init7_s3_-1_1_1)##init7_s1_0/Opset.make_node.1/Shape##init7_s3_-1_1_1/Opset.make_node.1/Shape
init: name='_reshape_init7_s8192_0' type=int64 shape=(1, 8192, 1) -- GraphBuilder.constant_folding.from/fold(init7_s3_1_-1_1,init7_s8192_)##init7_s8192_/Opset.make_node.0##init7_s3_1_-1_1/Opset.make_node.1/Shape
init: name='_reshape_clone0' type=float32 shape=(32768,) -- GraphBuilder.constant_folding.from/fold(c_params,init7_s1_-1)##c_params/DynamoInterpret.placeholder.0##init7_s1_-1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
Expand(update, init7_s3_1_8192_4) -> _onx_expand_update0
Reshape(_onx_expand_update0, init7_s1_-1) -> _reshape_expand_update00
Reshape(index, init7_s3_1_1_-1) -> _reshape_index0
Mul(_reshape_init7_s1_00, init7_s1_32768) -> _onx_mul__reshape_init7_s1_000
Add(_onx_mul__reshape_init7_s1_000, _reshape_index0) -> add-_onx_mul__reshape_init7_s1_000
Mul(_reshape_init7_s8192_0, init7_s1_4) -> _onx_mul__reshape_init7_s8192_00
Add(add-_onx_mul__reshape_init7_s1_000, _onx_mul__reshape_init7_s8192_00) -> _onx_add_add_mul__reshape_init7_s1_00000
Reshape(_onx_add_add_mul__reshape_init7_s1_00000, init7_s1_-1) -> _reshape_add_add_mul__reshape_init7_s1_000000
GatherElements(init7_s32768_, _reshape_add_add_mul__reshape_init7_s1_000000) -> _onx_gatherelements_init7_s32768_0
ScatterElements(_reshape_clone0, _onx_gatherelements_init7_s32768_0, _reshape_expand_update00) -> _onx_scatterelements__reshape_clone00
Reshape(_onx_scatterelements__reshape_clone00, init7_s3_1_8192_4) -> output_0
output: name='output_0' type=dtype('float32') shape=[1, 8192, 4]
custom-tracing¶
FAILED
setitem is not implemented when indices=(Ellipsis, 'index') and rank is unknown or not equal to the number of indices
--DEBUG--
[GraphBuilder-UMY] Message starts, there are 1 initializers, 0 nodes, 2 inputs, 2 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'_tensor_constant0': (1, 8192, 4), 'index': (4,), 'update': (8192, 4)}
_known_types={'_tensor_constant0': 1, 'index': 7, 'update': 1}
_known_value_shape={}
_known_constants=['_tensor_constant0']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
_tensor_constant0 -> {setitem}
index -> {setitem}
setitem -> {output}
update -> {setitem}
--TORCH-SHAPES--
index: ('run_node', (('example_value', torch.int64, torch.Size([4])), '')) --- 7:1:(4,):
update: ('run_node', (('example_value', torch.float32, torch.Size([8192, 4])), '')) --- 1:2:(8192, 4):
_tensor_constant0: ('run_node', ('', '')) --- 1:3:(1, 8192, 4):
setitem: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule()
def forward(self, index, update):
_tensor_constant0 = self._tensor_constant0
_tensor_constant0[(Ellipsis, index)] = update; setitem = _tensor_constant0; _tensor_constant0 = index = update = None
return setitem
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%_tensor_constant0 : [num_users=1] = get_attr[target=_tensor_constant0]
%setitem : [num_users=1] = call_function[target=operator.setitem](args = (%_tensor_constant0, (Ellipsis, %index), %update), kwargs = {})
return setitem
-- process.progress --
node 3/5 target=<built-in function setitem>
--
[GraphBuilder-UMY.make_tensor_input] index[7:4]
[GraphBuilder-UMY.make_tensor_input] update[1:8192x4]
[GraphBuilder-UMY.make_initializer] _tensor_constant0[torch.float32:torch.float32] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-UMY] Message completed, there are 1 initializers, 0 nodes, 2 inputs, 2 outputs.
dynamo-ir¶
opset: domain='' version=18
input: name='index' type=dtype('int64') shape=[4]
input: name='update' type=dtype('float32') shape=[8192, 4]
init: name='params' type=float32 shape=(1, 8192, 4)
init: name='val_6' type=int64 shape=(8192, 4)
init: name='val_12' type=int64 shape=(8192,)
Constant(value=[1]) -> val_9
Constant(value=[8192, 4]) -> val_5
Constant(value=[-1]) -> val_7
Reshape(val_6, val_7, allowzero=0) -> val_8
Unsqueeze(val_8, val_9) -> val_10
Constant(value=[8192, 1]) -> val_13
Reshape(val_12, val_13, allowzero=0) -> val_14
Expand(val_14, val_5) -> val_15
Reshape(val_15, val_7, allowzero=0) -> val_16
Unsqueeze(val_16, val_9) -> val_17
Constant(value=[1, 4]) -> val_19
Reshape(index, val_19) -> val_20
Expand(val_20, val_5) -> val_21
Reshape(val_21, val_7, allowzero=0) -> val_22
Unsqueeze(val_22, val_9) -> val_23
Concat(val_10, val_17, val_23, axis=1) -> val_24
Reshape(update, val_7, allowzero=0) -> val_25
ScatterND(params, val_24, val_25, reduction=b'none') -> index_put
output: name='index_put' type=dtype('float32') shape=[1, 8192, 4]
script¶
FAILED
[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. In Node, ("/Add", Add, "", -1) : ("","",) -> ("/Add_output_0",) , Error Node (/Add)'s input 0 is marked single but has an empty string in the graph
InplaceSetItemEllipsis_2¶
forward¶
def forward(self, index, update):
copy = self.params.clone()
copy[..., index] = update
return copy
custom-fallback¶
opset: domain='' version=18
input: name='index' type=dtype('int64') shape=[4]
input: name='update' type=dtype('float32') shape=[8192, 4]
init: name='init7_s3_1_1_-1' type=int64 shape=(3,) -- array([ 1, 1, -1])-- Opset.make_node.1/Shape
init: name='init7_s3_1_8192_4' type=int64 shape=(3,) -- array([ 1, 8192, 4])-- Opset.make_node.1/Shape
init: name='init7_s1_49152' type=int64 shape=(1,) -- array([49152]) -- Opset.make_node.1/Shape
init: name='init7_s1_6' type=int64 shape=(1,) -- array([6]) -- 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_s49152_' type=int64 shape=(49152,) -- Opset.make_node.0
init: name='init7_s3_1_8192_6' type=int64 shape=(3,) -- array([ 1, 8192, 6])-- Opset.make_node.1/Shape
init: name='_reshape_init7_s1_00' type=int64 shape=(1, 1, 1) -- array([0])-- GraphBuilder.constant_folding.from/fold(init7_s1_0,init7_s3_-1_1_1)##init7_s1_0/Opset.make_node.1/Shape##init7_s3_-1_1_1/Opset.make_node.1/Shape
init: name='_reshape_init7_s8192_0' type=int64 shape=(1, 8192, 1) -- GraphBuilder.constant_folding.from/fold(init7_s3_1_-1_1,init7_s8192_)##init7_s8192_/Opset.make_node.0##init7_s3_1_-1_1/Opset.make_node.1/Shape
init: name='_reshape_clone0' type=float32 shape=(49152,) -- GraphBuilder.constant_folding.from/fold(c_params,init7_s1_-1)##c_params/DynamoInterpret.placeholder.0##init7_s1_-1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
Expand(update, init7_s3_1_8192_4) -> _onx_expand_update0
Reshape(_onx_expand_update0, init7_s1_-1) -> _reshape_expand_update00
Reshape(index, init7_s3_1_1_-1) -> _reshape_index0
Mul(_reshape_init7_s1_00, init7_s1_49152) -> _onx_mul__reshape_init7_s1_000
Add(_onx_mul__reshape_init7_s1_000, _reshape_index0) -> add-_onx_mul__reshape_init7_s1_000
Mul(_reshape_init7_s8192_0, init7_s1_6) -> _onx_mul__reshape_init7_s8192_00
Add(add-_onx_mul__reshape_init7_s1_000, _onx_mul__reshape_init7_s8192_00) -> _onx_add_add_mul__reshape_init7_s1_00000
Reshape(_onx_add_add_mul__reshape_init7_s1_00000, init7_s1_-1) -> _reshape_add_add_mul__reshape_init7_s1_000000
GatherElements(init7_s49152_, _reshape_add_add_mul__reshape_init7_s1_000000) -> _onx_gatherelements_init7_s49152_0
ScatterElements(_reshape_clone0, _onx_gatherelements_init7_s49152_0, _reshape_expand_update00) -> _onx_scatterelements__reshape_clone00
Reshape(_onx_scatterelements__reshape_clone00, init7_s3_1_8192_6) -> output_0
output: name='output_0' type=dtype('float32') shape=[1, 8192, 6]
custom-dec¶
opset: domain='' version=18
input: name='index' type=dtype('int64') shape=[4]
input: name='update' type=dtype('float32') shape=[8192, 4]
init: name='init7_s3_1_1_-1' type=int64 shape=(3,) -- array([ 1, 1, -1])-- Opset.make_node.1/Shape
init: name='init7_s3_1_8192_4' type=int64 shape=(3,) -- array([ 1, 8192, 4])-- Opset.make_node.1/Shape
init: name='init7_s1_49152' type=int64 shape=(1,) -- array([49152]) -- Opset.make_node.1/Shape
init: name='init7_s1_6' type=int64 shape=(1,) -- array([6]) -- 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_s49152_' type=int64 shape=(49152,) -- Opset.make_node.0
init: name='init7_s3_1_8192_6' type=int64 shape=(3,) -- array([ 1, 8192, 6])-- Opset.make_node.1/Shape
init: name='_reshape_init7_s1_00' type=int64 shape=(1, 1, 1) -- array([0])-- GraphBuilder.constant_folding.from/fold(init7_s1_0,init7_s3_-1_1_1)##init7_s1_0/Opset.make_node.1/Shape##init7_s3_-1_1_1/Opset.make_node.1/Shape
init: name='_reshape_init7_s8192_0' type=int64 shape=(1, 8192, 1) -- GraphBuilder.constant_folding.from/fold(init7_s3_1_-1_1,init7_s8192_)##init7_s8192_/Opset.make_node.0##init7_s3_1_-1_1/Opset.make_node.1/Shape
init: name='_reshape_clone0' type=float32 shape=(49152,) -- GraphBuilder.constant_folding.from/fold(c_params,init7_s1_-1)##c_params/DynamoInterpret.placeholder.0##init7_s1_-1/Opset.make_node.1/Shape##Opset.make_node.1/Shape##Opset.make_node.1/Shape
Expand(update, init7_s3_1_8192_4) -> _onx_expand_update0
Reshape(_onx_expand_update0, init7_s1_-1) -> _reshape_expand_update00
Reshape(index, init7_s3_1_1_-1) -> _reshape_index0
Mul(_reshape_init7_s1_00, init7_s1_49152) -> _onx_mul__reshape_init7_s1_000
Add(_onx_mul__reshape_init7_s1_000, _reshape_index0) -> add-_onx_mul__reshape_init7_s1_000
Mul(_reshape_init7_s8192_0, init7_s1_6) -> _onx_mul__reshape_init7_s8192_00
Add(add-_onx_mul__reshape_init7_s1_000, _onx_mul__reshape_init7_s8192_00) -> _onx_add_add_mul__reshape_init7_s1_00000
Reshape(_onx_add_add_mul__reshape_init7_s1_00000, init7_s1_-1) -> _reshape_add_add_mul__reshape_init7_s1_000000
GatherElements(init7_s49152_, _reshape_add_add_mul__reshape_init7_s1_000000) -> _onx_gatherelements_init7_s49152_0
ScatterElements(_reshape_clone0, _onx_gatherelements_init7_s49152_0, _reshape_expand_update00) -> _onx_scatterelements__reshape_clone00
Reshape(_onx_scatterelements__reshape_clone00, init7_s3_1_8192_6) -> output_0
output: name='output_0' type=dtype('float32') shape=[1, 8192, 6]
custom-tracing¶
FAILED
setitem is not implemented when indices=(Ellipsis, 'index') and rank is unknown or not equal to the number of indices
--DEBUG--
[GraphBuilder-SRG] Message starts, there are 1 initializers, 0 nodes, 2 inputs, 2 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'_tensor_constant0': (1, 8192, 6), 'index': (4,), 'update': (8192, 4)}
_known_types={'_tensor_constant0': 1, 'index': 7, 'update': 1}
_known_value_shape={}
_known_constants=['_tensor_constant0']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
_tensor_constant0 -> {setitem}
index -> {setitem}
setitem -> {output}
update -> {setitem}
--TORCH-SHAPES--
index: ('run_node', (('example_value', torch.int64, torch.Size([4])), '')) --- 7:1:(4,):
update: ('run_node', (('example_value', torch.float32, torch.Size([8192, 4])), '')) --- 1:2:(8192, 4):
_tensor_constant0: ('run_node', ('', '')) --- 1:3:(1, 8192, 6):
setitem: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule()
def forward(self, index, update):
_tensor_constant0 = self._tensor_constant0
_tensor_constant0[(Ellipsis, index)] = update; setitem = _tensor_constant0; _tensor_constant0 = index = update = None
return setitem
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%index : [num_users=1] = placeholder[target=index]
%update : [num_users=1] = placeholder[target=update]
%_tensor_constant0 : [num_users=1] = get_attr[target=_tensor_constant0]
%setitem : [num_users=1] = call_function[target=operator.setitem](args = (%_tensor_constant0, (Ellipsis, %index), %update), kwargs = {})
return setitem
-- process.progress --
node 3/5 target=<built-in function setitem>
--
[GraphBuilder-SRG.make_tensor_input] index[7:4]
[GraphBuilder-SRG.make_tensor_input] update[1:8192x4]
[GraphBuilder-SRG.make_initializer] _tensor_constant0[torch.float32:torch.float32] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-SRG] Message completed, there are 1 initializers, 0 nodes, 2 inputs, 2 outputs.
dynamo-ir¶
opset: domain='' version=18
input: name='index' type=dtype('int64') shape=[4]
input: name='update' type=dtype('float32') shape=[8192, 4]
init: name='params' type=float32 shape=(1, 8192, 6)
init: name='val_6' type=int64 shape=(8192, 4)
init: name='val_12' type=int64 shape=(8192,)
Constant(value=[1]) -> val_9
Constant(value=[8192, 4]) -> val_5
Constant(value=[-1]) -> val_7
Reshape(val_6, val_7, allowzero=0) -> val_8
Unsqueeze(val_8, val_9) -> val_10
Constant(value=[8192, 1]) -> val_13
Reshape(val_12, val_13, allowzero=0) -> val_14
Expand(val_14, val_5) -> val_15
Reshape(val_15, val_7, allowzero=0) -> val_16
Unsqueeze(val_16, val_9) -> val_17
Constant(value=[1, 4]) -> val_19
Reshape(index, val_19) -> val_20
Expand(val_20, val_5) -> val_21
Reshape(val_21, val_7, allowzero=0) -> val_22
Unsqueeze(val_22, val_9) -> val_23
Concat(val_10, val_17, val_23, axis=1) -> val_24
Reshape(update, val_7, allowzero=0) -> val_25
ScatterND(params, val_24, val_25, reduction=b'none') -> index_put
output: name='index_put' type=dtype('float32') shape=[1, 8192, 6]
script¶
FAILED
[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. In Node, ("/Add", Add, "", -1) : ("","",) -> ("/Add_output_0",) , Error Node (/Add)'s input 0 is marked single but has an empty string in the graph
InplaceSetItemMask¶
forward¶
def forward(self, x):
mask = x.to(bool)
x[mask] = 2
return x
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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=[2, 3, 3]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 3, 3]
init: name='c_lifted_tensor_0' type=float32 shape=() -- array([2.], dtype=float32)-- DynamoInterpret.placeholder.0
Cast(x, to=9) -> _to_copy
Where(_to_copy, c_lifted_tensor_0, x) -> output_0
Identity(output_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[2, 3, 3]
custom-tracing¶
FAILED
Unexpected type <class 'int'> for name.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[2, 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=[2, 3, 3]
script¶
opset: domain='' version=17
input: name='onnx::Cast_0' type=dtype('float32') shape=[2, 3, 3]
Cast(onnx::Cast_0, to=9) -> /Cast_output_0
Cast(/Cast_output_0, to=9) -> /Cast_1_output_0
Constant(value=2.0) -> /Constant_output_0
Where(/Cast_1_output_0, /Constant_output_0, onnx::Cast_0) -> 4
output: name='4' type=dtype('float32') shape=[2, 3, 3]
InplaceSetItemSquare¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init7_s3_0_1_20' 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='_onx_transpose_fill0' type=float32 shape=(3, 2) -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='_reshape_init7_s2_0_10' type=int64 shape=(2, 1) -- array([0, 1])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s2_0_1)##init7_s2_0_1/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
Transpose(slice_3, perm=[1,0]) -> _onx_transpose_slice_30
ScatterND(_onx_transpose_slice_30, _reshape_init7_s3_0_1_20, _onx_transpose_fill0) -> _onx_scatternd_transpose_slice_300
Transpose(_onx_scatternd_transpose_slice_300, perm=[1,0]) -> slice_scatter
ScatterND(x, _reshape_init7_s2_0_10, slice_scatter) -> output_0
Identity(output_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[5, 5]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init7_s3_0_1_20' 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='_onx_transpose_fill0' type=float32 shape=(3, 2) -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='_reshape_init7_s2_0_10' type=int64 shape=(2, 1) -- array([0, 1])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s2_0_1)##init7_s2_0_1/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
Transpose(slice_3, perm=[1,0]) -> _onx_transpose_slice_30
ScatterND(_onx_transpose_slice_30, _reshape_init7_s3_0_1_20, _onx_transpose_fill0) -> _onx_scatternd_transpose_slice_300
Transpose(_onx_scatternd_transpose_slice_300, perm=[1,0]) -> slice_scatter
ScatterND(x, _reshape_init7_s2_0_10, slice_scatter) -> output_0
Identity(output_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[5, 5]
custom-tracing¶
FAILED
Unexpected type <class 'int'> for name.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 5]
Constant(value=[[0], [1],...) -> val_43
Constant(value=[0]) -> val_26
Constant(value=[2]) -> val_29
Constant(value=[0]) -> val_32
Constant(value_ints=[1]) -> val_33
Slice(x, val_26, val_29, val_32, val_33) -> slice_3
Transpose(slice_3, perm=[1,0]) -> val_45
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=[[0], [1]]) -> val_55
ScatterND(x, val_55, slice_scatter, reduction=b'none') -> slice_scatter_1
output: name='slice_scatter_1' type=dtype('float32') shape=[5, 5]
script¶
opset: domain='' version=17
input: name='onnx::Slice_0' type=dtype('float32') shape=[5, 5]
Constant(value=[0]) -> /Constant_output_0
Constant(value=[0]) -> /Constant_1_output_0
Constant(value=[2]) -> /Constant_2_output_0
Constant(value=[1]) -> /Constant_3_output_0
Slice(onnx::Slice_0, /Constant_1_output_0, /Constant_2_output_0, /Constant_output_0, /Constant_3_output_0) -> /Slice_output_0
Constant(value=[1]) -> /Constant_4_output_0
Constant(value=[0]) -> /Constant_5_output_0
Constant(value=[3]) -> /Constant_6_output_0
Constant(value=[1]) -> /Constant_7_output_0
Slice(/Slice_output_0, /Constant_5_output_0, /Constant_6_output_0, /Constant_4_output_0, /Constant_7_output_0) -> /Slice_1_output_0
Shape(/Slice_1_output_0) -> /Shape_output_0
Constant(value=[[1.0]]) -> /Constant_8_output_0
Expand(/Constant_8_output_0, /Shape_output_0) -> /Expand_output_0
Constant(value=[0, 1, 2]) -> /Constant_9_output_0
Constant(value=[[0], [1]]) -> onnx::Expand_27
Constant(value=[2, 3]) -> /Constant_10_output_0
Constant(value=[2]) -> /Constant_11_output_0
ConstantOfShape(/Constant_11_output_0, value=[1]) -> /ConstantOfShape_output_0
Constant(value=-1) -> /Constant_12_output_0
Mul(/ConstantOfShape_output_0, /Constant_12_output_0) -> /Mul_output_0
Equal(/Constant_10_output_0, /Mul_output_0) -> /Equal_output_0
Where(/Equal_output_0, /ConstantOfShape_output_0, /Constant_10_output_0) -> /Where_output_0
Expand(onnx::Expand_27, /Where_output_0) -> /Expand_1_output_0
Constant(value=[-1]) -> /Constant_13_output_0
Unsqueeze(/Expand_1_output_0, /Constant_13_output_0) -> /Unsqueeze_output_0
Constant(value=[2]) -> /Constant_14_output_0
ConstantOfShape(/Constant_14_output_0, value=[1]) -> /ConstantOfShape_1_output_0
Constant(value=-1) -> /Constant_15_output_0
Mul(/ConstantOfShape_1_output_0, /Constant_15_output_0) -> /Mul_1_output_0
Equal(/Constant_10_output_0, /Mul_1_output_0) -> /Equal_1_output_0
Where(/Equal_1_output_0, /ConstantOfShape_1_output_0, /Constant_10_output_0) -> /Where_1_output_0
Expand(/Constant_9_output_0, /Where_1_output_0) -> /Expand_2_output_0
Constant(value=[-1]) -> /Constant_16_output_0
Unsqueeze(/Expand_2_output_0, /Constant_16_output_0) -> /Unsqueeze_1_output_0
Concat(/Unsqueeze_output_0, /Unsqueeze_1_output_0, axis=-1) -> /Concat_output_0
Shape(onnx::Slice_0) -> /Shape_1_output_0
Constant(value=[0]) -> /Constant_17_output_0
Constant(value=[2]) -> /Constant_18_output_0
Constant(value=[922337203...) -> /Constant_19_output_0
Slice(/Shape_1_output_0, /Constant_18_output_0, /Constant_19_output_0, /Constant_17_output_0) -> /Slice_2_output_0
Concat(/Constant_10_output_0, /Slice_2_output_0, axis=0) -> /Concat_1_output_0
Reshape(/Expand_output_0, /Concat_1_output_0, allowzero=0) -> /Reshape_output_0
ScatterND(onnx::Slice_0, /Concat_output_0, /Reshape_output_0) -> 58
output: name='58' type=dtype('float32') shape=[5, 5]
InplaceSetItemSquareAdd¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x + 2
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init7_s3_0_1_20' 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='_onx_transpose_fill0' type=float32 shape=(3, 2) -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='_reshape_init7_s2_0_10' type=int64 shape=(2, 1) -- array([0, 1])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s2_0_1)##init7_s2_0_1/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_0' 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
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
Transpose(slice_3, perm=[1,0]) -> _onx_transpose_slice_30
ScatterND(_onx_transpose_slice_30, _reshape_init7_s3_0_1_20, _onx_transpose_fill0) -> _onx_scatternd_transpose_slice_300
Transpose(_onx_scatternd_transpose_slice_300, perm=[1,0]) -> slice_scatter
ScatterND(x, _reshape_init7_s2_0_10, slice_scatter) -> output_0
Add(output_0, _reshape_init1_s_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[5, 5]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init7_s3_0_1_20' 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='_onx_transpose_fill0' type=float32 shape=(3, 2) -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='_reshape_init7_s2_0_10' type=int64 shape=(2, 1) -- array([0, 1])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s2_0_1)##init7_s2_0_1/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_0' 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
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
Transpose(slice_3, perm=[1,0]) -> _onx_transpose_slice_30
ScatterND(_onx_transpose_slice_30, _reshape_init7_s3_0_1_20, _onx_transpose_fill0) -> _onx_scatternd_transpose_slice_300
Transpose(_onx_scatternd_transpose_slice_300, perm=[1,0]) -> slice_scatter
ScatterND(x, _reshape_init7_s2_0_10, slice_scatter) -> output_0
Add(output_0, _reshape_init1_s_0) -> output_1
output: name='output_1' type=dtype('float32') shape=[5, 5]
custom-tracing¶
FAILED
Unexpected type <class 'int'> for name.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 5]
Constant(value=[[0], [1],...) -> val_43
Constant(value=[0]) -> val_26
Constant(value=[2]) -> val_29
Constant(value=[0]) -> val_32
Constant(value_ints=[1]) -> val_33
Slice(x, val_26, val_29, val_32, val_33) -> slice_3
Transpose(slice_3, perm=[1,0]) -> val_45
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=[[0], [1]]) -> 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=[5, 5]
script¶
opset: domain='' version=17
input: name='onnx::Slice_0' type=dtype('float32') shape=[5, 5]
Constant(value=[0]) -> /Constant_output_0
Constant(value=[0]) -> /Constant_1_output_0
Constant(value=[2]) -> /Constant_2_output_0
Constant(value=[1]) -> /Constant_3_output_0
Slice(onnx::Slice_0, /Constant_1_output_0, /Constant_2_output_0, /Constant_output_0, /Constant_3_output_0) -> /Slice_output_0
Constant(value=[1]) -> /Constant_4_output_0
Constant(value=[0]) -> /Constant_5_output_0
Constant(value=[3]) -> /Constant_6_output_0
Constant(value=[1]) -> /Constant_7_output_0
Slice(/Slice_output_0, /Constant_5_output_0, /Constant_6_output_0, /Constant_4_output_0, /Constant_7_output_0) -> /Slice_1_output_0
Shape(/Slice_1_output_0) -> /Shape_output_0
Constant(value=[[1.0]]) -> /Constant_8_output_0
Expand(/Constant_8_output_0, /Shape_output_0) -> /Expand_output_0
Constant(value=[0, 1, 2]) -> /Constant_9_output_0
Constant(value=[[0], [1]]) -> onnx::Expand_27
Constant(value=[2, 3]) -> /Constant_10_output_0
Constant(value=[2]) -> /Constant_11_output_0
ConstantOfShape(/Constant_11_output_0, value=[1]) -> /ConstantOfShape_output_0
Constant(value=-1) -> /Constant_12_output_0
Mul(/ConstantOfShape_output_0, /Constant_12_output_0) -> /Mul_output_0
Equal(/Constant_10_output_0, /Mul_output_0) -> /Equal_output_0
Where(/Equal_output_0, /ConstantOfShape_output_0, /Constant_10_output_0) -> /Where_output_0
Expand(onnx::Expand_27, /Where_output_0) -> /Expand_1_output_0
Constant(value=[-1]) -> /Constant_13_output_0
Unsqueeze(/Expand_1_output_0, /Constant_13_output_0) -> /Unsqueeze_output_0
Constant(value=[2]) -> /Constant_14_output_0
ConstantOfShape(/Constant_14_output_0, value=[1]) -> /ConstantOfShape_1_output_0
Constant(value=-1) -> /Constant_15_output_0
Mul(/ConstantOfShape_1_output_0, /Constant_15_output_0) -> /Mul_1_output_0
Equal(/Constant_10_output_0, /Mul_1_output_0) -> /Equal_1_output_0
Where(/Equal_1_output_0, /ConstantOfShape_1_output_0, /Constant_10_output_0) -> /Where_1_output_0
Expand(/Constant_9_output_0, /Where_1_output_0) -> /Expand_2_output_0
Constant(value=[-1]) -> /Constant_16_output_0
Unsqueeze(/Expand_2_output_0, /Constant_16_output_0) -> /Unsqueeze_1_output_0
Concat(/Unsqueeze_output_0, /Unsqueeze_1_output_0, axis=-1) -> /Concat_output_0
Shape(onnx::Slice_0) -> /Shape_1_output_0
Constant(value=[0]) -> /Constant_17_output_0
Constant(value=[2]) -> /Constant_18_output_0
Constant(value=[922337203...) -> /Constant_19_output_0
Slice(/Shape_1_output_0, /Constant_18_output_0, /Constant_19_output_0, /Constant_17_output_0) -> /Slice_2_output_0
Concat(/Constant_10_output_0, /Slice_2_output_0, axis=0) -> /Concat_1_output_0
Reshape(/Expand_output_0, /Concat_1_output_0, allowzero=0) -> /Reshape_output_0
ScatterND(onnx::Slice_0, /Concat_output_0, /Reshape_output_0) -> /ScatterND_output_0
Constant(value=2.0) -> /Constant_20_output_0
Add(/ScatterND_output_0, /Constant_20_output_0) -> 60
output: name='60' type=dtype('float32') shape=[5, 5]
InplaceSetItemSquareAdd2¶
forward¶
def forward(self, x):
x[:2, :3] = 1
return x + 2, x + 3
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init7_s3_0_1_20' 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='_onx_transpose_fill0' type=float32 shape=(3, 2) -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='_reshape_init7_s2_0_10' type=int64 shape=(2, 1) -- array([0, 1])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s2_0_1)##init7_s2_0_1/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_0' 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
init: name='_reshape_init1_s_20' 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
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
Transpose(slice_3, perm=[1,0]) -> _onx_transpose_slice_30
ScatterND(_onx_transpose_slice_30, _reshape_init7_s3_0_1_20, _onx_transpose_fill0) -> _onx_scatternd_transpose_slice_300
Transpose(_onx_scatternd_transpose_slice_300, perm=[1,0]) -> slice_scatter
ScatterND(x, _reshape_init7_s2_0_10, slice_scatter) -> output_0
Add(output_0, _reshape_init1_s_0) -> output_1
Add(output_0, _reshape_init1_s_20) -> output_2
output: name='output_1' type=dtype('float32') shape=[5, 5]
output: name='output_2' type=dtype('float32') shape=[5, 5]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 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
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init7_s3_0_1_20' 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='_onx_transpose_fill0' type=float32 shape=(3, 2) -- GraphBuilder.constant_folding.from/fold(fill)##fill/
init: name='_reshape_init7_s2_0_10' type=int64 shape=(2, 1) -- array([0, 1])-- GraphBuilder.constant_folding.from/fold(init7_s2_-1_1,init7_s2_0_1)##init7_s2_0_1/Opset.make_node.1/Shape##init7_s2_-1_1/Opset.make_node.1/Shape##Opset.make_node.1/Shape
init: name='_reshape_init1_s_0' 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
init: name='_reshape_init1_s_20' 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
Slice(x, init7_s1_0, init7_s1_2, init7_s1_0) -> slice_3
Transpose(slice_3, perm=[1,0]) -> _onx_transpose_slice_30
ScatterND(_onx_transpose_slice_30, _reshape_init7_s3_0_1_20, _onx_transpose_fill0) -> _onx_scatternd_transpose_slice_300
Transpose(_onx_scatternd_transpose_slice_300, perm=[1,0]) -> slice_scatter
ScatterND(x, _reshape_init7_s2_0_10, slice_scatter) -> output_0
Add(output_0, _reshape_init1_s_0) -> output_1
Add(output_0, _reshape_init1_s_20) -> output_2
output: name='output_1' type=dtype('float32') shape=[5, 5]
output: name='output_2' type=dtype('float32') shape=[5, 5]
custom-tracing¶
FAILED
Unexpected type <class 'int'> for name.
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[5, 5]
Constant(value=[[0], [1],...) -> val_43
Constant(value=[0]) -> val_26
Constant(value=[2]) -> val_29
Constant(value=[0]) -> val_32
Constant(value_ints=[1]) -> val_33
Slice(x, val_26, val_29, val_32, val_33) -> slice_3
Transpose(slice_3, perm=[1,0]) -> val_45
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=[[0], [1]]) -> 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_1
output: name='add' type=dtype('float32') shape=[5, 5]
output: name='add_1' type=dtype('float32') shape=[5, 5]
script¶
opset: domain='' version=17
input: name='onnx::Slice_0' type=dtype('float32') shape=[5, 5]
Constant(value=[0]) -> /Constant_output_0
Constant(value=[0]) -> /Constant_1_output_0
Constant(value=[2]) -> /Constant_2_output_0
Constant(value=[1]) -> /Constant_3_output_0
Slice(onnx::Slice_0, /Constant_1_output_0, /Constant_2_output_0, /Constant_output_0, /Constant_3_output_0) -> /Slice_output_0
Constant(value=[1]) -> /Constant_4_output_0
Constant(value=[0]) -> /Constant_5_output_0
Constant(value=[3]) -> /Constant_6_output_0
Constant(value=[1]) -> /Constant_7_output_0
Slice(/Slice_output_0, /Constant_5_output_0, /Constant_6_output_0, /Constant_4_output_0, /Constant_7_output_0) -> /Slice_1_output_0
Shape(/Slice_1_output_0) -> /Shape_output_0
Constant(value=[[1.0]]) -> /Constant_8_output_0
Expand(/Constant_8_output_0, /Shape_output_0) -> /Expand_output_0
Constant(value=[0, 1, 2]) -> /Constant_9_output_0
Constant(value=[[0], [1]]) -> onnx::Expand_27
Constant(value=[2, 3]) -> /Constant_10_output_0
Constant(value=[2]) -> /Constant_11_output_0
ConstantOfShape(/Constant_11_output_0, value=[1]) -> /ConstantOfShape_output_0
Constant(value=-1) -> /Constant_12_output_0
Mul(/ConstantOfShape_output_0, /Constant_12_output_0) -> /Mul_output_0
Equal(/Constant_10_output_0, /Mul_output_0) -> /Equal_output_0
Where(/Equal_output_0, /ConstantOfShape_output_0, /Constant_10_output_0) -> /Where_output_0
Expand(onnx::Expand_27, /Where_output_0) -> /Expand_1_output_0
Constant(value=[-1]) -> /Constant_13_output_0
Unsqueeze(/Expand_1_output_0, /Constant_13_output_0) -> /Unsqueeze_output_0
Constant(value=[2]) -> /Constant_14_output_0
ConstantOfShape(/Constant_14_output_0, value=[1]) -> /ConstantOfShape_1_output_0
Constant(value=-1) -> /Constant_15_output_0
Mul(/ConstantOfShape_1_output_0, /Constant_15_output_0) -> /Mul_1_output_0
Equal(/Constant_10_output_0, /Mul_1_output_0) -> /Equal_1_output_0
Where(/Equal_1_output_0, /ConstantOfShape_1_output_0, /Constant_10_output_0) -> /Where_1_output_0
Expand(/Constant_9_output_0, /Where_1_output_0) -> /Expand_2_output_0
Constant(value=[-1]) -> /Constant_16_output_0
Unsqueeze(/Expand_2_output_0, /Constant_16_output_0) -> /Unsqueeze_1_output_0
Concat(/Unsqueeze_output_0, /Unsqueeze_1_output_0, axis=-1) -> /Concat_output_0
Shape(onnx::Slice_0) -> /Shape_1_output_0
Constant(value=[0]) -> /Constant_17_output_0
Constant(value=[2]) -> /Constant_18_output_0
Constant(value=[922337203...) -> /Constant_19_output_0
Slice(/Shape_1_output_0, /Constant_18_output_0, /Constant_19_output_0, /Constant_17_output_0) -> /Slice_2_output_0
Concat(/Constant_10_output_0, /Slice_2_output_0, axis=0) -> /Concat_1_output_0
Reshape(/Expand_output_0, /Concat_1_output_0, allowzero=0) -> /Reshape_output_0
ScatterND(onnx::Slice_0, /Concat_output_0, /Reshape_output_0) -> /ScatterND_output_0
Constant(value=2.0) -> /Constant_20_output_0
Add(/ScatterND_output_0, /Constant_20_output_0) -> 60
Constant(value=3.0) -> /Constant_21_output_0
Add(/ScatterND_output_0, /Constant_21_output_0) -> 62
output: name='60' type=dtype('float32') shape=[5, 5]
output: name='62' type=dtype('float32') shape=[5, 5]
SignatureFloat1¶
forward¶
def forward(self, x, alpha: float = 2.0):
return torch.sigmoid(self.linear(x)) - self.buff * alpha
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='alpha' type=dtype('float32') shape=[1]
init: name='mul' type=float32 shape=(1,) -- array([0.75], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_mul_b_buff0)##_onx_mul_b_buff0/
init: name='GemmTransposePattern--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([-0.301, 0.225, -0.31 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([-0.321], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, linear.bias, transB=1) -> linear
Sigmoid(linear) -> sigmoid
Sub(sigmoid, mul) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 1]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='alpha' type=dtype('float32') shape=[1]
init: name='mul' type=float32 shape=(1,) -- array([0.75], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_mul_b_buff0)##_onx_mul_b_buff0/
init: name='GemmTransposePattern--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.113, 0.007, -0.153], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([-0.142], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, linear.bias, transB=1) -> linear
Sigmoid(linear) -> sigmoid
Sub(sigmoid, mul) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 1]
custom-tracing¶
FAILED
Unable to interpret method 'aten_meth_mul', args=(buff, alpha), kwargs={}, dispatcher=None
--DEBUG--
[GraphBuilder-HXG] Message starts, there are 3 initializers, 7 nodes, 2 inputs, 2 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'_sub_Linear__onx_matmul_input_10': (4, 1),
'_sub_Linear__onx_transpose_weight0': (3, 1),
'_sub_Linear_input_1': (4, 3),
'_sub_Linear_linear': (4, 1),
'_sub_Linear_output': (4, 1),
'alpha': (),
'buff': (1,),
'linear': (4, 1),
'linear.bias': (1,),
'linear.weight': (1, 3),
'sigmoid': (4, 1),
'x': (4, 3)}
_known_types={'_sub_Linear__onx_matmul_input_10': 1,
'_sub_Linear__onx_transpose_weight0': 1,
'_sub_Linear_input_1': 1,
'_sub_Linear_linear': 1,
'_sub_Linear_output': 1,
'alpha': 1,
'buff': 1,
'linear': 1,
'linear.bias': 1,
'linear.weight': 1,
'sigmoid': 1,
'x': 1}
_known_value_shape={}
_known_constants=['_sub_Linear__onx_transpose_weight0', 'buff', 'linear.bias', 'linear.weight']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
alpha -> {mul}
buff -> {mul}
linear -> {sigmoid}
mul -> {sub}
sigmoid -> {sub}
x -> {linear}
--TORCH-SHAPES--
x: ('run_node', (('example_value', torch.float32, torch.Size([4, 3])), '')) --- 1:2:(4, 3):
alpha: ('run_node', (('example_value', torch.float32, torch.Size([])), '')) --- 1:0:():
linear: ('run_node', ('', '')) --- 1:2:(4, 1):
sigmoid: ('run_node', ('', '')) --- 1:2:(4, 1):
buff: ('run_node', ('', '')) --- 1:1:(1,):
mul: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule(
(linear): Linear(in_features=3, out_features=1, bias=True)
)
def forward(self, x, alpha : float = 2.0):
linear = self.linear(x); x = None
sigmoid = torch.sigmoid(linear); linear = None
buff = self.buff
mul = buff.mul(alpha); buff = alpha = None
sub = sigmoid - mul; sigmoid = mul = None
return sub
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%x : [num_users=1] = placeholder[target=x]
%alpha : float [num_users=1] = placeholder[target=alpha](default=2.0)
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%mul : [num_users=1] = call_method[target=mul](args = (%buff, %alpha), kwargs = {})
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %mul), kwargs = {})
return sub
-- process.progress --
node 5/8 target=mul
--
[GraphBuilder-HXG.make_tensor_input] x[1:4x3]
[GraphBuilder-HXG.make_tensor_input] alpha[1:]
[GraphBuilder-HXG.make_initializer] linear.weight[torch.float32:torch.float32:[0.41223570704460144, 0.5759496092796326, -0.40965598821640015]] - SOURCE: GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)
[GraphBuilder-HXG.make_initializer] linear.bias[torch.float32:torch.float32:[-0.11819536238908768]] - SOURCE: GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
[GraphBuilder-HXG.make_initializer] buff[torch.float32:torch.float32:[0.5]] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-HXG.make_node] .make_nodes [#:# ] Identity:['x']->['_sub_Linear_input_1']
[GraphBuilder-HXG.make_node] linear [#:# ] Transpose:['linear.weight']->['_sub_Linear__onx_transpose_weight0']
[GraphBuilder-HXG.make_node] Opset [##:# ] MatMul:['_sub_Linear_input_1', '_sub_Linear__onx_transpose_weight0']->['_sub_Linear__onx_matmul_input_10']
[GraphBuilder-HXG.make_node] Opset2 [##:# ] Add:['_sub_Linear__onx_matmul_input_10', 'linear.bias']->['_sub_Linear_linear']
[GraphBuilder-HXG.make_node] .output [#:# ] Identity:['_sub_Linear_linear']->['_sub_Linear_output']
[GraphBuilder-HXG.make_node] .make_nodes2 [#:# ] Identity:['_sub_Linear_output']->['linear']
[GraphBuilder-HXG.make_node] sigmoid [#:# ] Sigmoid:['linear']->['sigmoid']
[GraphBuilder-HXG] Message completed, there are 3 initializers, 7 nodes, 2 inputs, 2 outputs.
dynamo-ir¶
FAILED
Input mismatch, inputs[0]=(T1r2,float) but names=['x'], model=SignatureFloat1, export='dynamo-ir'
script¶
FAILED
[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(float)) , expected: (tensor(double))
SignatureInt1¶
forward¶
def forward(self, x, i: int = 2):
return torch.sigmoid(self.linear(x)) - self.buff + x[:, i : i + 1]
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='i' type=dtype('int64') shape=[1]
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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([-0.041, 0.264, 0.188], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([-0.545], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='i' type=dtype('int64') shape=[1]
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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.453, -0.148, 0.174], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([-0.253], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-tracing¶
FAILED
[ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Concat node. Name:'_getitem_slicenSD' Status Message: /home/xadupre/github/onnxruntime/onnxruntime/core/providers/cpu/tensor/concat.cc:139 onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const InlinedTensorsVector&, onnxruntime::Prepare&) const input_rank == reference_rank was false. Ranks of input data are different, cannot concatenate them. expected rank: 1 got: 2
dynamo-ir¶
FAILED
Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt1, export='dynamo-ir'
script¶
FAILED
[ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running Slice node. Name:'/Slice' Status Message: slice.cc:195 FillVectorsFromInput Starts must be a 1-D array
SignatureInt2¶
forward¶
def forward(self, x, i: int = 2):
return torch.sigmoid(self.linear(x)) - self.buff + x[:, i]
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='i' type=dtype('int64') shape=[1]
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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([-0.498, 0.444, -0.566], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([-0.27], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gather(x, init7_s_1, axis=1) -> select
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='i' type=dtype('int64') shape=[1]
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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.499, 0.084, -0.347], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.424], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gather(x, init7_s_1, axis=1) -> select
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 4]
custom-tracing¶
FAILED
One index is given as an integer i but this requires to append a node 'Squeeze' after this one and this is not yet implemented. You can replace the integer by `i:i+1`
--DEBUG--
[GraphBuilder-SIY] Message starts, there are 4 initializers, 8 nodes, 2 inputs, 2 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'_sub_Linear__onx_matmul_input_10': (4, 1),
'_sub_Linear__onx_transpose_weight0': (3, 1),
'_sub_Linear_input_1': (4, 3),
'_sub_Linear_linear': (4, 1),
'_sub_Linear_output': (4, 1),
'buff': (1,),
'getitem_axis': (2,),
'i': (),
'linear': (4, 1),
'linear.bias': (1,),
'linear.weight': (1, 3),
'sigmoid': (4, 1),
'sub': (4, 1),
'x': (4, 3)}
_known_types={'_sub_Linear__onx_matmul_input_10': 1,
'_sub_Linear__onx_transpose_weight0': 1,
'_sub_Linear_input_1': 1,
'_sub_Linear_linear': 1,
'_sub_Linear_output': 1,
'buff': 1,
'getitem_axis': 7,
'i': 7,
'linear': 1,
'linear.bias': 1,
'linear.weight': 1,
'sigmoid': 1,
'sub': 1,
'x': 1}
_known_value_shape={}
_known_constants=['_sub_Linear__onx_transpose_weight0',
'buff',
'getitem_axis',
'linear.bias',
'linear.weight']
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
buff -> {sub}
getitem -> {add}
i -> {getitem}
linear -> {sigmoid}
sigmoid -> {sub}
sub -> {add}
x -> {linear, getitem}
--TORCH-SHAPES--
x: ('run_node', (('example_value', torch.float32, torch.Size([4, 3])), '')) --- 1:2:(4, 3):
i: ('run_node', (('example_value', torch.int64, torch.Size([])), '')) --- 7:0:():
linear: ('run_node', ('', '')) --- 1:2:(4, 1):
sigmoid: ('run_node', ('', '')) --- 1:2:(4, 1):
buff: ('run_node', ('', '')) --- 1:1:(1,):
sub: ('run_node', ('', '')) --- 1:2:(4, 1):
getitem: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule(
(linear): Linear(in_features=3, out_features=1, bias=True)
)
def forward(self, x, i : int = 2):
linear = self.linear(x)
sigmoid = torch.sigmoid(linear); linear = None
buff = self.buff
sub = sigmoid - buff; sigmoid = buff = None
getitem = x[(slice(None, None, None), i)]; x = i = None
add = sub + getitem; sub = getitem = None
return add
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%x : [num_users=2] = placeholder[target=x]
%i : int [num_users=1] = placeholder[target=i](default=2)
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %buff), kwargs = {})
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%x, (slice(None, None, None), %i)), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sub, %getitem), kwargs = {})
return add
-- process.progress --
node 6/9 target=<built-in function getitem>
--
[GraphBuilder-SIY.make_tensor_input] x[1:4x3]
[GraphBuilder-SIY.make_tensor_input] i[7:]
[GraphBuilder-SIY.make_initializer] linear.weight[torch.float32:torch.float32:[-0.1787819117307663, 0.4661900997161865, 0.10162513703107834]] - SOURCE: GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)
[GraphBuilder-SIY.make_initializer] linear.bias[torch.float32:torch.float32:[-0.5469270944595337]] - SOURCE: GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
[GraphBuilder-SIY.make_initializer] buff[torch.float32:torch.float32:[0.5]] - SOURCE: DynamoInterpret.get_attr.0
[GraphBuilder-SIY.make_initializer] getitem_axis[int64:int64:[0, 1]] - SOURCE: DynamoInterpreter._getitem_slice.axis.1
[GraphBuilder-SIY.make_node] .make_nodes [#:# ] Identity:['x']->['_sub_Linear_input_1']
[GraphBuilder-SIY.make_node] linear [#:# ] Transpose:['linear.weight']->['_sub_Linear__onx_transpose_weight0']
[GraphBuilder-SIY.make_node] Opset [##:# ] MatMul:['_sub_Linear_input_1', '_sub_Linear__onx_transpose_weight0']->['_sub_Linear__onx_matmul_input_10']
[GraphBuilder-SIY.make_node] Opset2 [##:# ] Add:['_sub_Linear__onx_matmul_input_10', 'linear.bias']->['_sub_Linear_linear']
[GraphBuilder-SIY.make_node] .output [#:# ] Identity:['_sub_Linear_linear']->['_sub_Linear_output']
[GraphBuilder-SIY.make_node] .make_nodes2 [#:# ] Identity:['_sub_Linear_output']->['linear']
[GraphBuilder-SIY.make_node] sigmoid [#:# ] Sigmoid:['linear']->['sigmoid']
[GraphBuilder-SIY.make_node] sub [##:# ] Sub:['sigmoid', 'buff']->['sub']
[GraphBuilder-SIY] Message completed, there are 4 initializers, 8 nodes, 2 inputs, 2 outputs.
dynamo-ir¶
FAILED
Input mismatch, inputs[0]=(T1r2,int) but names=['x'], model=SignatureInt2, export='dynamo-ir'
script¶
opset: domain='' version=17
input: name='onnx::Gemm_0' type=dtype('float32') shape=[4, 3]
input: name='onnx::Gather_1' type=dtype('int64') shape=None
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.139, 0.497, 0.077], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.16], dtype=float32)
Gather(onnx::Gemm_0, onnx::Gather_1, axis=1) -> /Gather_output_0
Gemm(onnx::Gemm_0, linear.weight, linear.bias, alpha=1.00, beta=1.00, transB=1) -> /linear/Gemm_output_0
Sigmoid(/linear/Gemm_output_0) -> /Sigmoid_output_0
Sub(/Sigmoid_output_0, buff) -> /Sub_output_0
Add(/Sub_output_0, /Gather_output_0) -> 9
output: name='9' type=dtype('float32') shape=[4, 4]
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-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.308, -0.055, -0.001], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.513], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([0.038, 0.001, 0.366], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.194], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx' type='NOTENSOR' shape=None
init: name='linear.bias' type=float32 shape=(1,) -- array([0.366], dtype=float32)-- GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)-- DynamoInterpret.get_attr.0
init: name='init7_s_0' type=int64 shape=() -- array([0]) -- DynamoInterpreter.getitem.1
init: name='init7_s_1' type=int64 shape=() -- array([1]) -- DynamoInterpreter.getitem.1
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1]) -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--_sub_Linear__onx_transpose_weight0' type=float32 shape=(1, 3) -- array([0.019, 0.364, 0.445], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_Linear__onx_transpose_weight0,init7_s2_1_-1)##_sub_Linear__onx_transpose_weight0/GraphBuilder.constant_folding.from/fold(linear.weight)##linear.weight/GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)##init7_s2_1_-1/TransposeEqualReshapePattern.apply.new_shape
Gemm(x, GemmTransposePattern--_sub_Linear__onx_transpose_weight0, linear.bias, transB=1) -> _sub_Linear_linear
Sigmoid(_sub_Linear_linear) -> sigmoid
Sub(sigmoid, buff) -> sub
SequenceAt(lx, init7_s_0) -> getitem
SequenceAt(lx, init7_s_1) -> getitem_1
ReduceSum(getitem_1, init7_s1_1, keepdims=1) -> sum_1
Mul(getitem, sum_1) -> mul
Add(sub, mul) -> output
output: name='output' type=dtype('float32') shape=[4, 1]
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.142, -0.439, -0.412], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.125], dtype=float32)
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
Constant(value=[1]) -> val_3
ReduceSum(lx_1, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
Mul(lx_0, sum_1) -> mul
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
Sigmoid(linear) -> sigmoid
Sub(sigmoid, buff) -> sub
Add(sub, mul) -> add
output: name='add' type=dtype('float32') shape=[4, 1]
script¶
opset: domain='' version=17
input: name='onnx::Gemm_0' type=dtype('float32') shape=[4, 3]
input: name='onnx::Mul_1' type=dtype('float32') shape=[4, 1]
input: name='onnx::ReduceSum_2' type=dtype('float32') shape=[4, 2]
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.467, -0.355, 0.536], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.187], dtype=float32)
Constant(value=[1]) -> onnx::ReduceSum_9
ReduceSum(onnx::ReduceSum_2, onnx::ReduceSum_9, keepdims=1) -> /ReduceSum_output_0
Mul(onnx::Mul_1, /ReduceSum_output_0) -> /Mul_output_0
Gemm(onnx::Gemm_0, linear.weight, linear.bias, alpha=1.00, beta=1.00, transB=1) -> /linear/Gemm_output_0
Sigmoid(/linear/Gemm_output_0) -> /Sigmoid_output_0
Sub(/Sigmoid_output_0, buff) -> /Sub_output_0
Add(/Sub_output_0, /Mul_output_0) -> 12
output: name='12' type=dtype('float32') shape=[4, 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-fallback¶
FAILED
Input mismatch, inputs[0]=(#3[T1r2,T1r2,None],) but names=['lx_0', 'lx_1'], model=SignatureListFixedWithNone, export='custom-fallback'
custom-dec¶
FAILED
Input mismatch, inputs[0]=(#3[T1r2,T1r2,None],) but names=['lx_0', 'lx_1'], model=SignatureListFixedWithNone, export='custom-dec'
custom-tracing¶
FAILED
Unable to create an input 'lx' with type #3[T1r2,T1r2,None]
--DEBUG--
[GraphBuilder-KDC] Message starts, there are 0 initializers, 0 nodes, 0 inputs, 0 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={}
_known_types={}
_known_value_shape={}
_known_constants=[]
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
lx -> {getitem_2, getitem_4, getitem_1, getitem_3, getitem}
--TORCH-SHAPES--
lx: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule()
def forward(self, lx):
getitem = lx[0]
getitem_1 = lx[1]; getitem_1 = None
getitem_2 = lx[1]
add = getitem + getitem_2; getitem = getitem_2 = None
getitem_3 = lx[2]; getitem_3 = None
getitem_4 = lx[2]; lx = None
add_1 = add + getitem_4; add = getitem_4 = None
return add_1
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%lx : [num_users=5] = placeholder[target=lx]
%getitem : [num_users=1] = call_function[target=operator.getitem](args = (%lx, 0), kwargs = {})
%getitem_1 : [num_users=0] = call_function[target=operator.getitem](args = (%lx, 1), kwargs = {})
%getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%lx, 1), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%getitem, %getitem_2), kwargs = {})
%getitem_3 : [num_users=0] = call_function[target=operator.getitem](args = (%lx, 2), kwargs = {})
%getitem_4 : [num_users=1] = call_function[target=operator.getitem](args = (%lx, 2), kwargs = {})
%add_1 : [num_users=1] = call_function[target=operator.add](args = (%add, %getitem_4), kwargs = {})
return add_1
-- process.progress --
node 0/9 target=lx
--
[GraphBuilder-KDC] Message completed, there are 0 initializers, 0 nodes, 0 inputs, 0 outputs.
dynamo-ir¶
FAILED
Input mismatch, inputs[0]=(#3[T1r2,T1r2,None],) but names=['lx_0', 'lx_1'], model=SignatureListFixedWithNone, export='dynamo-ir'
script¶
FAILED
Input mismatch, inputs[0]=(#3[T1r2,T1r2,None],) but names=['onnx::Add_0', 'onnx::Add_1'], model=SignatureListFixedWithNone, export='script'
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-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([-0.061, -0.335, 0.526], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.494], 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--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 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--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.154, 0.037, -0.227], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.397], 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--_onx_transpose_p_linear_weight0, 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=[4, 1]
custom-tracing¶
FAILED
Type is unknown for result 'l', known_types={'x': 1}
--DEBUG--
[GraphBuilder-GBG] Message starts, there are 0 initializers, 0 nodes, 2 inputs, 2 outputs.
--SHAPE--
_dynamic_examples=
dynamic_objects=
dynamic_objects_rev=
dynamic_dimensions_source={}
dynamic_dimensions_source_flat=None
output_dynamic_dimensions_source_flat=None
dynamic_alias={}
dynamic_shapes=None
_known_shapes={'x': (4, 3)}
_known_types={'x': 1}
_known_value_shape={}
_known_constants=[]
_known_ranks={}
--PARAMETERS--
_parameter_renaming=
--TORCH-USERS--
cat -> {sum_1}
lx -> {cat}
x -> {linear}
--TORCH-SHAPES--
x: ('run_node', (('example_value', torch.float32, torch.Size([4, 3])), '')) --- 1:2:(4, 3):
lx: ('run_node', ('', '')) --- :::
cat: ('run_node', ('', '')) --- :::
--ONNX--
-- EXEPATH --
export-export_options=ExportOptions(strict=True, tracing=True, aten_as_function={'aten.scaled_dot_product_attention.default'})
-- process.graph_module --
GraphModule(
(linear): Linear(in_features=3, out_features=1, bias=True)
)
def forward(self, x, lx : list):
cat = torch.cat(lx, 1); lx = None
sum_1 = cat.sum(axis = 1, keepdim = True); cat = None
linear = self.linear(x); x = None
sigmoid = torch.sigmoid(linear); linear = None
buff = self.buff
sub = sigmoid - buff; sigmoid = buff = None
add = sub + sum_1; sub = sum_1 = None
return add
# To see more debug info, please use `graph_module.print_readable()`
-- process.graph_module.graph --
graph():
%x : [num_users=1] = placeholder[target=x]
%lx : list [num_users=1] = placeholder[target=lx]
%cat : [num_users=1] = call_function[target=torch.cat](args = (%lx, 1), kwargs = {})
%sum_1 : [num_users=1] = call_method[target=sum](args = (%cat,), kwargs = {axis: 1, keepdim: True})
%linear : [num_users=1] = call_module[target=linear](args = (%x,), kwargs = {})
%sigmoid : [num_users=1] = call_function[target=torch.sigmoid](args = (%linear,), kwargs = {})
%buff : [num_users=1] = get_attr[target=buff]
%sub : [num_users=1] = call_function[target=operator.sub](args = (%sigmoid, %buff), kwargs = {})
%add : [num_users=1] = call_function[target=operator.add](args = (%sub, %sum_1), kwargs = {})
return add
-- process.progress --
node 2/10 target=<built-in method cat of type object at 0x7febd8cf5320>
--
[GraphBuilder-GBG.make_tensor_input] x[1:4x3]
[GraphBuilder-GBG.make_tensor_input] lx[0:]
[GraphBuilder-GBG] Message completed, there are 0 initializers, 0 nodes, 2 inputs, 2 outputs..
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='lx_0' type=dtype('float32') shape=[4, 1]
input: name='lx_1' type=dtype('float32') shape=[4, 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.295, -0.251, 0.408], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.571], 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_3
ReduceSum(cat, val_3, noop_with_empty_axes=0, keepdims=1) -> sum_1
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
Sigmoid(linear) -> sigmoid
Sub(sigmoid, buff) -> sub
Add(sub, sum_1) -> add
output: name='add' type=dtype('float32') shape=[4, 1]
script¶
opset: domain='' version=17
input: name='onnx::Gemm_0' type=dtype('float32') shape=[4, 3]
input: name='onnx::Concat_1' type=dtype('float32') shape=[4, 1]
input: name='onnx::Concat_2' type=dtype('float32') shape=[4, 2]
init: name='buff' type=float32 shape=(1,) -- array([0.5], dtype=float32)
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.346, 0.391, -0.332], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([0.327], dtype=float32)
Concat(onnx::Concat_1, onnx::Concat_2, axis=1) -> /Concat_output_0
Constant(value=[1]) -> onnx::ReduceSum_7
ReduceSum(/Concat_output_0, onnx::ReduceSum_7, keepdims=1) -> /ReduceSum_output_0
Gemm(onnx::Gemm_0, linear.weight, linear.bias, alpha=1.00, beta=1.00, transB=1) -> /linear/Gemm_output_0
Sigmoid(/linear/Gemm_output_0) -> /Sigmoid_output_0
Sub(/Sigmoid_output_0, buff) -> /Sub_output_0
Add(/Sub_output_0, /ReduceSum_output_0) -> 12
output: name='12' type=dtype('float32') shape=[4, 1]
SignatureShapeAsIndex¶
forward¶
def forward(self, x, y):
t = torch.sigmoid(self.linear(x)) + x
return t[:, : y.shape[1]]
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='y' type=dtype('float32') shape=[4, 2]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0]) -- Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1]) -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([-0.494, 0.393, -0.464], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.323], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, linear.bias, transB=1) -> linear
Sigmoid(linear) -> sigmoid
Add(sigmoid, x) -> add
Slice(add, init7_s1_0, init7_s1_2, init7_s1_1) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 2]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='y' type=dtype('float32') shape=[4, 2]
init: name='init7_s1_0' type=int64 shape=(1,) -- array([0]) -- Opset.make_node.1/Shape
init: name='init7_s1_2' type=int64 shape=(1,) -- array([2]) -- Opset.make_node.1/Shape
init: name='init7_s1_1' type=int64 shape=(1,) -- array([1]) -- Opset.make_node.1/Shape
init: name='GemmTransposePattern--_onx_transpose_p_linear_weight0' type=float32 shape=(1, 3) -- array([ 0.094, -0.416, -0.57 ], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_onx_transpose_p_linear_weight0,init7_s2_1_-1)##_onx_transpose_p_linear_weight0/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([0.219], dtype=float32)-- DynamoInterpret.placeholder.1/P(linear.bias)
Gemm(x, GemmTransposePattern--_onx_transpose_p_linear_weight0, linear.bias, transB=1) -> linear
Sigmoid(linear) -> sigmoid
Add(sigmoid, x) -> add
Slice(add, init7_s1_0, init7_s1_2, init7_s1_1) -> output_0
output: name='output_0' type=dtype('float32') shape=[4, 2]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='y' type=dtype('float32') shape=[4, 2]
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.299], dtype=float32)-- GraphBuilder.make_nodes/fromlinear.bias##DynamoInterpret.get_attr.1/P(linear.bias)
init: name='getitem_1_axis' type=int64 shape=(2,) -- array([0, 1]) -- DynamoInterpreter._getitem_slice.axis.1
init: name='getitem_1_start' type=int64 shape=(2,) -- array([0, 0]) -- DynamoInterpreter._getitem_slice.2
init: name='getitem_1_step' type=int64 shape=(2,) -- array([1, 1]) -- DynamoInterpreter._getitem_slice.3
init: name='_onx_concat_init7_s1_40' type=int64 shape=(2,) -- array([4, 2])-- GraphBuilder.constant_folding.from/fold(_onx_unsqueeze_getitem0,init7_s1_4)##init7_s1_4/Opset.make_node.1/Shape##_onx_unsqueeze_getitem0/
init: name='GemmTransposePattern--_sub_Linear__onx_transpose_weight0' type=float32 shape=(1, 3) -- array([-0.432, -0.468, 0.165], dtype=float32)-- GraphBuilder.constant_folding.from/fold(_sub_Linear__onx_transpose_weight0,init7_s2_1_-1)##_sub_Linear__onx_transpose_weight0/GraphBuilder.constant_folding.from/fold(linear.weight)##linear.weight/GraphBuilder.make_nodes/fromlinear.weight##DynamoInterpret.get_attr.1/P(linear.weight)##init7_s2_1_-1/TransposeEqualReshapePattern.apply.new_shape
Gemm(x, GemmTransposePattern--_sub_Linear__onx_transpose_weight0, linear.bias, transB=1) -> _sub_Linear_linear
Sigmoid(_sub_Linear_linear) -> sigmoid
Add(sigmoid, x) -> add
Slice(add, getitem_1_start, _onx_concat_init7_s1_40, getitem_1_axis, getitem_1_step) -> output
output: name='output' type=dtype('float32') shape=['d_output_0', 'd_output_1']
dynamo-ir¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 3]
input: name='y' type=dtype('float32') shape=[4, 2]
init: name='linear.weight' type=float32 shape=(1, 3) -- array([-0.576, 0.269, 0.155], dtype=float32)
init: name='linear.bias' type=float32 shape=(1,) -- array([-0.117], dtype=float32)
Constant(value_ints=[0]) -> val_7
Gemm(x, linear.weight, linear.bias, beta=1.00, transB=1, alpha=1.00, transA=0) -> linear
Sigmoid(linear) -> sigmoid
Add(sigmoid, x) -> add
Constant(value=[2]) -> val_11
Constant(value=[1]) -> val_15
Constant(value_ints=[1]) -> val_16
Slice(add, val_7, val_11, val_15, val_16) -> slice_2
output: name='slice_2' type=dtype('float32') shape=[4, 2]
script¶
FAILED
Input mismatch, inputs[0]=(T1r2,T1r2) but names=['onnx::Gemm_0'], model=SignatureShapeAsIndex, export='script'
TypeBFloat16¶
forward¶
def forward(self, x):
xb = x.to(torch.bfloat16)
return (xb + xb).to(torch.float32)
custom-fallback¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 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=[4, 4]
custom-dec¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 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=[4, 4]
custom-tracing¶
opset: domain='' version=18
input: name='x' type=dtype('float32') shape=[4, 4]
Add(x, x) -> add-x
Cast(add-x, to=16) -> add
Cast(add, to=1) -> output
output: name='output' type=dtype('float32') shape=[4, 4]
dynamo-ir¶
FAILED
[ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for Add(14) node with name 'node_Add_1'
script¶
FAILED
[ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for Add(14) node with name '/Add'
Summary¶
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