torch_onnx.analyze(exported_program) will print model information, param sizes, identify unsupported nodes using the dispatcher, and generate reproduction script.

Goal: human readable messages and simple reproduction steps.

PyTorch ONNX Conversion Analysis

Model information

The model has 127398144 parameters and 0 buffers (non-trainable parameters).

Inputs:

arg199_1: TensorMetadata(shape=torch.Size([1, 12]), dtype=torch.int64, requires_grad=False, stride=(12, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})
arg200_1: TensorMetadata(shape=torch.Size([1, 12]), dtype=torch.int64, requires_grad=False, stride=(12, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})

Outputs:

slice_2778: TensorMetadata(shape=torch.Size([1, 12, 768]), dtype=torch.float32, requires_grad=False, stride=(393216, 768, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})
tanh: TensorMetadata(shape=torch.Size([1, 768]), dtype=torch.float32, requires_grad=False, stride=(768, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})

The FX graph has {model_info.fx_node_count} nodes in total. Number of FX nodes per op:

placeholder: 201
call_function: 4862
output: 1

Of the call_function nodes, the counts of operators used are:

aten.slice.Tensor: 1627
aten.view.default: 744
aten.slice_scatter.default: 528
aten.transpose.int: 420
aten.unsqueeze.default: 170
aten.select.int: 147
aten.permute.default: 144
aten.copy.default: 144
aten.t.default: 73
aten.addmm.default: 73
aten._to_copy.default: 64
aten.clone.default: 61
aten.as_strided.default: 60
aten.constant_pad_nd.default: 51
aten.select_scatter.default: 48
aten.flip.default: 48
aten.expand.default: 48
aten.full_like.default: 48
aten.where.self: 48
aten.add.Tensor: 39
aten.new_ones.default: 36
aten.native_layer_norm.default: 25
<built-in function getitem>: 25
aten.bmm.default: 24
aten.new_zeros.default: 24
aten.tril.default: 24
aten.masked_fill.Scalar: 24
aten._unsafe_view.default: 24
aten.mul.Tensor: 14
aten.ne.Scalar: 13
aten.div.Tensor: 12
aten._softmax.default: 12
aten.gelu.default: 12
aten.embedding.default: 3
aten.zeros.default: 1
aten.rsub.Scalar: 1
aten.cumsum.default: 1
aten.lt.Scalar: 1
aten.tanh.default: 1

ONNX conversion information

The model contains operators the dispatcher could not find ONNX equivalents for. This may be due to missing implementations or a bug in the dispatcher.

Errors grouped by operator:

aten.addmm.default: No decompositions defined for the real-valued input. Example node: %addmm : [num_users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%arg6_1, %view_1, %t), kwargs = {}). All nodes: [addmm, addmm_1, addmm_2, addmm_3, addmm_4, addmm_5, addmm_6, addmm_7, addmm_8, addmm_9, addmm_10, addmm_11, addmm_12, addmm_13, addmm_14, addmm_15, addmm_16, addmm_17, addmm_18, addmm_19, addmm_20, addmm_21, addmm_22, addmm_23, addmm_24, addmm_25, addmm_26, addmm_27, addmm_28, addmm_29, addmm_30, addmm_31, addmm_32, addmm_33, addmm_34, addmm_35, addmm_36, addmm_37, addmm_38, addmm_39, addmm_40, addmm_41, addmm_42, addmm_43, addmm_44, addmm_45, addmm_46, addmm_47, addmm_48, addmm_49, addmm_50, addmm_51, addmm_52, addmm_53, addmm_54, addmm_55, addmm_56, addmm_57, addmm_58, addmm_59, addmm_60, addmm_61, addmm_62, addmm_63, addmm_64, addmm_65, addmm_66, addmm_67, addmm_68, addmm_69, addmm_70, addmm_71, addmm_72]

PyTorch ONNX Conversion Error report

Error message:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes
    _handle_call_function_node_with_lowering(
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 372, in _handle_call_function_node_with_lowering
    raise RuntimeError(
RuntimeError: No ONNX function found for <OpOverload(op='aten.remainder', overload='Tensor')>. Failure message: No decompositions defined for the real-valued input

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor
    ir_model = torch_onnx.exported_program_to_ir(program)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir
    values = _add_nodes(exported_program, model, lower=lower, registry=registry)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes
    raise RuntimeError(
RuntimeError: Error when translating node %remainder : [num_users=1] = call_function[target=torch.ops.aten.remainder.Tensor](args = (%arg0_1, %arg1_1), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_utils_export_adaptor
    torch_onnx_export_adaptor(*args, **kwargs)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor
    raise OnnxConversionError(
torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96m*onnx*[0m component and attach the full error stack as well as reproduction scripts. You can run `torch_onnx.analyze()` to produce an error report after obtaining an ExportedProgram with `torch.export.export()`.

Exported program:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "f32[4, 2, 3]", arg1_1: "f32[1, 2, 1]"):
            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:7306 in forward, code: return torch.remainder(input, other)
            remainder: "f32[4, 2, 3]" = torch.ops.aten.remainder.Tensor(arg0_1, arg1_1);  arg0_1 = arg1_1 = None
            return (remainder,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='remainder'), target=None)])
Range constraints: {}

Analysis

PyTorch ONNX Conversion Analysis

Model Information

The model has 0 parameters and 0 buffers (non-trainable parameters).

Inputs:

arg0_1: TensorMetadata(shape=torch.Size([4, 2, 3]), dtype=torch.float32, requires_grad=False, stride=(6, 3, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})
arg1_1: TensorMetadata(shape=torch.Size([1, 2, 1]), dtype=torch.float32, requires_grad=False, stride=(2, 1, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})

Outputs:

remainder: TensorMetadata(shape=torch.Size([4, 2, 3]), dtype=torch.float32, requires_grad=False, stride=(6, 3, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})

The FX graph has 4 nodes in total. Number of FX nodes per op:

placeholder: 2
call_function: 1
output: 1

Of the call_function nodes, the counts of operators used are:

aten.remainder.Tensor: 1

ONNX Conversion Information

The model contains operators the dispatcher could not find ONNX equivalents for. This may be due to missing implementations or a bug in the dispatcher.

Errors grouped by operator:

aten.remainder.Tensor: No decompositions defined for the real-valued input. Example node: %remainder : [num_users=1] = call_function[target=torch.ops.aten.remainder.Tensor](args = (%arg0_1, %arg1_1), kwargs = {}). All nodes: [remainder]

PyTorch ONNX Conversion Error report

Error message:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes
    _handle_call_function_node_with_lowering(
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload
    named_args = _get_named_fx_node_args(node)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args
    torch_schema: torch.FunctionSchema = node.target._schema
                                         ^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor
    ir_model = torch_onnx.exported_program_to_ir(program)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir
    values = _add_nodes(exported_program, model, lower=lower, registry=registry)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes
    raise RuntimeError(
RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense_1, -9223372036854775808), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_utils_export_adaptor
    torch_onnx_export_adaptor(*args, **kwargs)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor
    raise OnnxConversionError(
torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96m*onnx*[0m component and attach the full error stack as well as reproduction scripts. You can run `torch_onnx.analyze()` to produce an error report after obtaining an ExportedProgram with `torch.export.export()`.

Exported program:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "f32[10, 3, 5]", arg1_1: "f32[10, 3, 4]", arg2_1: "f32[10, 4, 5]", arg3_1: "i64[]", arg4_1: "f32[]"):
            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:7662 in forward, code: return torch.baddbmm(input, batch1, batch2, alpha=alpha, beta=beta)
            _local_scalar_dense: "Sym(f4)" = torch.ops.aten._local_scalar_dense.default(arg4_1);  arg4_1 = None
            _local_scalar_dense_1: "Sym(u4)" = torch.ops.aten._local_scalar_dense.default(arg3_1);  arg3_1 = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_export/pass_base.py:54 in _create_dummy_node_metadata, code: return NodeMetadata({"stack_trace": "".join(traceback.format_stack(limit=1))})
            ge: "Sym(u4 >= -9223372036854775808)" = _local_scalar_dense_1 >= -9223372036854775808
            scalar_tensor: "f32[]" = torch.ops.aten.scalar_tensor.default(ge);  ge = None
            _assert_async = torch.ops.aten._assert_async.msg(scalar_tensor, '_local_scalar_dense_1 is outside of inline constraint [-9223372036854775808, 9223372036854775807].');  scalar_tensor = None
            le: "Sym(u4 <= 9223372036854775807)" = _local_scalar_dense_1 <= 9223372036854775807
            scalar_tensor_1: "f32[]" = torch.ops.aten.scalar_tensor.default(le);  le = None
            _assert_async_1 = torch.ops.aten._assert_async.msg(scalar_tensor_1, '_local_scalar_dense_1 is outside of inline constraint [-9223372036854775808, 9223372036854775807].');  scalar_tensor_1 = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:7662 in forward, code: return torch.baddbmm(input, batch1, batch2, alpha=alpha, beta=beta)
            baddbmm: "f32[10, 3, 5]" = torch.ops.aten.baddbmm.default(arg0_1, arg1_1, arg2_1, beta = _local_scalar_dense, alpha = _local_scalar_dense_1);  arg0_1 = arg1_1 = arg2_1 = _local_scalar_dense = _local_scalar_dense_1 = None
            return (baddbmm,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg3_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg4_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='baddbmm'), target=None)])
Range constraints: {f0: ValueRanges(lower=-oo, upper=oo, is_bool=False), u0: ValueRanges(lower=-9223372036854775808, upper=9223372036854775807, is_bool=False), f1: ValueRanges(lower=-oo, upper=oo, is_bool=False), u1: ValueRanges(lower=-9223372036854775808, upper=9223372036854775807, is_bool=False), f2: ValueRanges(lower=-oo, upper=oo, is_bool=False), u2: ValueRanges(lower=-9223372036854775808, upper=9223372036854775807, is_bool=False), f3: ValueRanges(lower=-oo, upper=oo, is_bool=False), u3: ValueRanges(lower=-9223372036854775808, upper=9223372036854775807, is_bool=False), f4: ValueRanges(lower=-oo, upper=oo, is_bool=False), u4: ValueRanges(lower=-9223372036854775808, upper=9223372036854775807, is_bool=False)}

Analysis

PyTorch ONNX Conversion Analysis

Model Information

The model has 0 parameters and 0 buffers (non-trainable parameters).

Inputs:

arg0_1: None
arg1_1: None
arg2_1: None
arg3_1: None
arg4_1: None

Outputs:

baddbmm: None

The FX graph has 15 nodes in total. Number of FX nodes per op:

placeholder: 5
call_function: 9
output: 1

Of the call_function nodes, the counts of operators used are:

aten._local_scalar_dense.default: 2
aten.scalar_tensor.default: 2
aten._assert_async.msg: 2
<built-in function ge>: 1
<built-in function le>: 1
aten.baddbmm.default: 1

ONNX Conversion Information

The model contains operators the dispatcher could not find ONNX equivalents for. This may be due to missing implementations or a bug in the dispatcher.

Errors grouped by operator:

aten.scalar_tensor.default: Critical Error in dispatcher: Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes _handle_call_function_node_with_lowering( File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering onnx_function, message = _dispatching.dispatch(node, registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch overload, message = get_matching_overload( ^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload named_args = _get_named_fx_node_args(node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args torch_schema: torch.FunctionSchema = node.target._schema ^^^^^^^^^^^^^^^^^^^ AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor ir_model = torch_onnx.exported_program_to_ir(program) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir values = _add_nodes(exported_program, model, lower=lower, registry=registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes raise RuntimeError( RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense_1, -9223372036854775808), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_utils_export_adaptor torch_onnx_export_adaptor(*args, *kwargs) File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor raise OnnxConversionError( torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96monnx*[0m component and attach the full error stack as well as reproduction scripts. You can run torch_onnx.analyze() to produce an error report after obtaining an ExportedProgram with torch.export.export().

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_analysis.py", line 201, in analyze onnx_function, message = _dispatching.dispatch(node, registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch overload, message = get_matching_overload( ^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 254, in get_matching_overload arg = _get_type_from_tensor(meta_val) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 168, in _get_type_from_tensor first_tensor = next((item for item in tensor if item is not None), None) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: 'SymBool' object is not iterable . Example node: %scalar_tensor : [num_users=1] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (%ge,), kwargs = {}). All nodes: [scalar_tensor, scalar_tensor_1]
aten._assert_async.msg: No decompositions defined for the real-valued input. Example node: %_assert_async : [num_users=0] = call_function[target=torch.ops.aten._assert_async.msg](args = (%scalar_tensor, _local_scalar_dense_1 is outside of inline constraint [-9223372036854775808, 9223372036854775807].), kwargs = {}). All nodes: [_assert_async, _assert_async_1]
<built-in function le>: Critical Error in dispatcher: Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes _handle_call_function_node_with_lowering( File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering onnx_function, message = _dispatching.dispatch(node, registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch overload, message = get_matching_overload( ^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload named_args = _get_named_fx_node_args(node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args torch_schema: torch.FunctionSchema = node.target._schema ^^^^^^^^^^^^^^^^^^^ AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor ir_model = torch_onnx.exported_program_to_ir(program) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir values = _add_nodes(exported_program, model, lower=lower, registry=registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes raise RuntimeError( RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense_1, -9223372036854775808), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_utils_export_adaptor torch_onnx_export_adaptor(*args, *kwargs) File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor raise OnnxConversionError( torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96monnx*[0m component and attach the full error stack as well as reproduction scripts. You can run torch_onnx.analyze() to produce an error report after obtaining an ExportedProgram with torch.export.export().

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_analysis.py", line 201, in analyze onnx_function, message = _dispatching.dispatch(node, registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch overload, message = get_matching_overload( ^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload named_args = _get_named_fx_node_args(node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args torch_schema: torch.FunctionSchema = node.target._schema ^^^^^^^^^^^^^^^^^^^ AttributeError: 'builtin_function_or_method' object has no attribute '_schema' . Example node: %le : [num_users=1] = call_function[target=operator.le](args = (%_local_scalar_dense_1, 9223372036854775807), kwargs = {}). All nodes: [le]
<built-in function ge>: Critical Error in dispatcher: Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes _handle_call_function_node_with_lowering( File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering onnx_function, message = _dispatching.dispatch(node, registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch overload, message = get_matching_overload( ^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload named_args = _get_named_fx_node_args(node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args torch_schema: torch.FunctionSchema = node.target._schema ^^^^^^^^^^^^^^^^^^^ AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor ir_model = torch_onnx.exported_program_to_ir(program) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir values = _add_nodes(exported_program, model, lower=lower, registry=registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes raise RuntimeError( RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense_1, -9223372036854775808), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_utils_export_adaptor torch_onnx_export_adaptor(*args, *kwargs) File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor raise OnnxConversionError( torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96monnx*[0m component and attach the full error stack as well as reproduction scripts. You can run torch_onnx.analyze() to produce an error report after obtaining an ExportedProgram with torch.export.export().

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_analysis.py", line 201, in analyze onnx_function, message = _dispatching.dispatch(node, registry) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch overload, message = get_matching_overload( ^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload named_args = _get_named_fx_node_args(node) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args torch_schema: torch.FunctionSchema = node.target._schema ^^^^^^^^^^^^^^^^^^^ AttributeError: 'builtin_function_or_method' object has no attribute '_schema' . Example node: %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense_1, -9223372036854775808), kwargs = {}). All nodes: [ge]

PyTorch ONNX Conversion Error report

Error message:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes
    _handle_call_function_node_with_lowering(
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload
    named_args = _get_named_fx_node_args(node)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args
    torch_schema: torch.FunctionSchema = node.target._schema
                                         ^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor
    ir_model = torch_onnx.exported_program_to_ir(program)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir
    values = _add_nodes(exported_program, model, lower=lower, registry=registry)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes
    raise RuntimeError(
RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_export_adapter_with_error_report
    torch_onnx_export_adaptor(*args, **kwargs)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor
    raise OnnxConversionError(
torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96m*onnx*[0m component and attach the full error stack as well as reproduction scripts. You can run `torch_onnx.analyze()` to produce an error report after obtaining an ExportedProgram with `torch.export.export()`.

Exported program:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "f32[6, 4, 3]", arg1_1: "i64[]", arg2_1: "i64[]"):
            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:5743 in forward, code: split_list: List[Tensor] = input.split(split_sizes)
            _local_scalar_dense: "Sym(u8)" = torch.ops.aten._local_scalar_dense.default(arg1_1);  arg1_1 = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_export/pass_base.py:54 in _create_dummy_node_metadata, code: return NodeMetadata({"stack_trace": "".join(traceback.format_stack(limit=1))})
            ge: "Sym(u8 >= 0)" = _local_scalar_dense >= 0
            scalar_tensor: "f32[]" = torch.ops.aten.scalar_tensor.default(ge);  ge = None
            _assert_async = torch.ops.aten._assert_async.msg(scalar_tensor, '_local_scalar_dense is outside of inline constraint [0, 6].');  scalar_tensor = None
            le: "Sym(u8 <= 6)" = _local_scalar_dense <= 6
            scalar_tensor_1: "f32[]" = torch.ops.aten.scalar_tensor.default(le);  le = None
            _assert_async_1 = torch.ops.aten._assert_async.msg(scalar_tensor_1, '_local_scalar_dense is outside of inline constraint [0, 6].');  scalar_tensor_1 = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:5743 in forward, code: split_list: List[Tensor] = input.split(split_sizes)
            _local_scalar_dense_1: "Sym(u9)" = torch.ops.aten._local_scalar_dense.default(arg2_1);  arg2_1 = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_export/pass_base.py:54 in _create_dummy_node_metadata, code: return NodeMetadata({"stack_trace": "".join(traceback.format_stack(limit=1))})
            ge_1: "Sym(u9 >= 0)" = _local_scalar_dense_1 >= 0
            scalar_tensor_2: "f32[]" = torch.ops.aten.scalar_tensor.default(ge_1);  ge_1 = None
            _assert_async_2 = torch.ops.aten._assert_async.msg(scalar_tensor_2, '_local_scalar_dense_1 is outside of inline constraint [0, 6].');  scalar_tensor_2 = None
            le_1: "Sym(u9 <= 6)" = _local_scalar_dense_1 <= 6
            scalar_tensor_3: "f32[]" = torch.ops.aten.scalar_tensor.default(le_1);  le_1 = None
            _assert_async_3 = torch.ops.aten._assert_async.msg(scalar_tensor_3, '_local_scalar_dense_1 is outside of inline constraint [0, 6].');  scalar_tensor_3 = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:5743 in forward, code: split_list: List[Tensor] = input.split(split_sizes)
            split_with_sizes = torch.ops.aten.split_with_sizes.default(arg0_1, [_local_scalar_dense, _local_scalar_dense_1]);  arg0_1 = _local_scalar_dense = _local_scalar_dense_1 = None
            getitem: "f32[u8, 4, 3]" = split_with_sizes[0]
            getitem_1: "f32[u9, 4, 3]" = split_with_sizes[1];  split_with_sizes = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_export/passes/add_runtime_assertions_for_constraints_pass.py:153 in call, code: node.meta["stack_trace"] = "".join(traceback.format_stack(limit=1))
            sym_size: "Sym(u8)" = torch.ops.aten.sym_size.int(getitem, 0)
            sym_constrain_range = torch.ops.aten.sym_constrain_range.default(sym_size, min = 0, max = 6)
            mul: "Sym(-u8)" = -1 * sym_size
            le_2: "Sym(-u8 <= 0)" = mul <= 0;  mul = None
            _assert_scalar = torch.ops.aten._assert_scalar.default(le_2, 'Deferred runtime assertion failed -u0 <= 0');  le_2 = None
            sym_size_1: "Sym(u9)" = torch.ops.aten.sym_size.int(getitem_1, 0)
            sym_constrain_range_1 = torch.ops.aten.sym_constrain_range.default(sym_size_1, min = 0, max = 6)
            mul_1: "Sym(-u9)" = -1 * sym_size_1
            le_3: "Sym(-u9 <= 0)" = mul_1 <= 0;  mul_1 = None
            _assert_scalar_1 = torch.ops.aten._assert_scalar.default(le_3, 'Deferred runtime assertion failed -u1 <= 0');  le_3 = None
            add: "Sym(u8 + u9)" = sym_size + sym_size_1;  sym_size = sym_size_1 = None
            eq: "Sym(Eq(u8 + u9, 6))" = add == 6;  add = None
            _assert_scalar_2 = torch.ops.aten._assert_scalar.default(eq, 'Deferred runtime assertion failed Eq(u0 + u1, 6)');  eq = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:5747 in forward, code: return torch.cat(out, dim=0)
            cat: "f32[u8 + u9, 4, 3]" = torch.ops.aten.cat.default([getitem, getitem_1]);  getitem = getitem_1 = None
            return (cat,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg0_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg1_1'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='cat'), target=None)])
Range constraints: {u0: ValueRanges(lower=0, upper=6, is_bool=False), u1: ValueRanges(lower=0, upper=6, is_bool=False), u2: ValueRanges(lower=0, upper=6, is_bool=False), u3: ValueRanges(lower=0, upper=6, is_bool=False), u4: ValueRanges(lower=0, upper=6, is_bool=False), u5: ValueRanges(lower=0, upper=6, is_bool=False), u6: ValueRanges(lower=0, upper=6, is_bool=False), u7: ValueRanges(lower=0, upper=6, is_bool=False), u8: ValueRanges(lower=0, upper=6, is_bool=False), u9: ValueRanges(lower=0, upper=6, is_bool=False)}

Analysis

PyTorch ONNX Conversion Analysis

Model Information

The model has 0 parameters and 0 buffers (non-trainable parameters).

Inputs:

arg0_1: None
arg1_1: None
arg2_1: None

Outputs:

cat: None

The FX graph has 35 nodes in total. Number of FX nodes per op:

placeholder: 3
call_function: 31
output: 1

Of the call_function nodes, the counts of operators used are:

aten.scalar_tensor.default: 4
aten._assert_async.msg: 4
<built-in function le>: 4
aten._assert_scalar.default: 3
aten._local_scalar_dense.default: 2
<built-in function ge>: 2
<built-in function getitem>: 2
aten.sym_size.int: 2
aten.sym_constrain_range.default: 2
<built-in function mul>: 2
aten.split_with_sizes.default: 1
<built-in function add>: 1
<built-in function eq>: 1
aten.cat.default: 1

ONNX Conversion Information

The model contains operators the dispatcher could not find ONNX equivalents for. This may be due to missing implementations or a bug in the dispatcher.

Errors grouped by operator:

aten.sym_size.int: No decompositions registered for the real-valued input. Example node: %sym_size : [num_users=3] = call_function[target=torch.ops.aten.sym_size.int](args = (%getitem, 0), kwargs = {}). All nodes: [sym_size, sym_size_1]
aten.sym_constrain_range.default: No decompositions registered for the real-valued input. Example node: %sym_constrain_range : [num_users=0] = call_function[target=torch.ops.aten.sym_constrain_range.default](args = (%sym_size,), kwargs = {min: 0, max: 6}). All nodes: [sym_constrain_range, sym_constrain_range_1]

aten.scalar_tensor.default: Critical Error in dispatcher:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes
    _handle_call_function_node_with_lowering(
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload
    named_args = _get_named_fx_node_args(node)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args
    torch_schema: torch.FunctionSchema = node.target._schema
                                         ^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor
    ir_model = torch_onnx.exported_program_to_ir(program)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir
    values = _add_nodes(exported_program, model, lower=lower, registry=registry)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes
    raise RuntimeError(
RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_export_adapter_with_error_report
    torch_onnx_export_adaptor(*args, **kwargs)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor
    raise OnnxConversionError(
torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96m*onnx*[0m component and attach the full error stack as well as reproduction scripts. You can run `torch_onnx.analyze()` to produce an error report after obtaining an ExportedProgram with `torch.export.export()`.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_analysis.py", line 201, in analyze
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 254, in get_matching_overload
    arg = _get_type_from_tensor(meta_val)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 168, in _get_type_from_tensor
    first_tensor = next((item for item in tensor if item is not None), None)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: 'SymBool' object is not iterable

. Example node: %scalar_tensor : [num_users=1] = call_function[target=torch.ops.aten.scalar_tensor.default](args = (%ge,), kwargs = {}). All nodes: [scalar_tensor, scalar_tensor_1, scalar_tensor_2, scalar_tensor_3]

aten._assert_scalar.default: No decompositions registered for the real-valued input. Example node: %_assert_scalar : [num_users=0] = call_function[target=torch.ops.aten._assert_scalar.default](args = (%le_2, Deferred runtime assertion failed -u0 <= 0), kwargs = {}). All nodes: [_assert_scalar, _assert_scalar_1, _assert_scalar_2]
aten._assert_async.msg: No decompositions registered for the real-valued input. Example node: %_assert_async : [num_users=0] = call_function[target=torch.ops.aten._assert_async.msg](args = (%scalar_tensor, _local_scalar_dense is outside of inline constraint [0, 6].), kwargs = {}). All nodes: [_assert_async, _assert_async_1, _assert_async_2, _assert_async_3]

<built-in function le>: Critical Error in dispatcher:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes
    _handle_call_function_node_with_lowering(
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload
    named_args = _get_named_fx_node_args(node)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args
    torch_schema: torch.FunctionSchema = node.target._schema
                                         ^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor
    ir_model = torch_onnx.exported_program_to_ir(program)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir
    values = _add_nodes(exported_program, model, lower=lower, registry=registry)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes
    raise RuntimeError(
RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_export_adapter_with_error_report
    torch_onnx_export_adaptor(*args, **kwargs)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor
    raise OnnxConversionError(
torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96m*onnx*[0m component and attach the full error stack as well as reproduction scripts. You can run `torch_onnx.analyze()` to produce an error report after obtaining an ExportedProgram with `torch.export.export()`.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_analysis.py", line 201, in analyze
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload
    named_args = _get_named_fx_node_args(node)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args
    torch_schema: torch.FunctionSchema = node.target._schema
                                         ^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

. Example node: %le : [num_users=1] = call_function[target=operator.le](args = (%_local_scalar_dense, 6), kwargs = {}). All nodes: [le, le_1, le_2, le_3]

<built-in function ge>: Critical Error in dispatcher:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 490, in _add_nodes
    _handle_call_function_node_with_lowering(
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 368, in _handle_call_function_node_with_lowering
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload
    named_args = _get_named_fx_node_args(node)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args
    torch_schema: torch.FunctionSchema = node.target._schema
                                         ^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 137, in torch_onnx_export_adaptor
    ir_model = torch_onnx.exported_program_to_ir(program)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 626, in exported_program_to_ir
    values = _add_nodes(exported_program, model, lower=lower, registry=registry)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_core.py", line 501, in _add_nodes
    raise RuntimeError(
RuntimeError: Error when translating node %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {}). See the stack trace for more information.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_export_adapter_with_error_report
    torch_onnx_export_adaptor(*args, **kwargs)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor
    raise OnnxConversionError(
torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96m*onnx*[0m component and attach the full error stack as well as reproduction scripts. You can run `torch_onnx.analyze()` to produce an error report after obtaining an ExportedProgram with `torch.export.export()`.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_analysis.py", line 201, in analyze
    onnx_function, message = _dispatching.dispatch(node, registry)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 323, in dispatch
    overload, message = get_matching_overload(
                        ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 208, in get_matching_overload
    named_args = _get_named_fx_node_args(node)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_dispatching.py", line 186, in _get_named_fx_node_args
    torch_schema: torch.FunctionSchema = node.target._schema
                                         ^^^^^^^^^^^^^^^^^^^
AttributeError: 'builtin_function_or_method' object has no attribute '_schema'

. Example node: %ge : [num_users=1] = call_function[target=operator.ge](args = (%_local_scalar_dense, 0), kwargs = {}). All nodes: [ge, ge_1]

<built-in function eq>: No decompositions registered for the real-valued input. Example node: %eq : [num_users=1] = call_function[target=operator.eq](args = (%add, 6), kwargs = {}). All nodes: [eq]

PyTorch ONNX Conversion Error report

Error message:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 156, in torch_onnx_export_adaptor
    onnx.checker.check_model(proto, full_check=True)
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/onnx/checker.py", line 179, in check_model
    C.check_model(
onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_native_batch_norm_inference_onnx, node name: node__aten_native_batch_norm_inference_onnx_2): [ShapeInferenceError] Inferred shape and existing shape differ in dimension 0: (33) vs (0)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 180, in _torch_onnx_export_adapter_with_error_report
    torch_onnx_export_adaptor(*args, **kwargs)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 160, in torch_onnx_export_adaptor
    raise OnnxConversionError(
torch_onnx._patch.OnnxConversionError: Failed to convert the exported program to an ONNX model. [96mThis is step 2/2[0m of exporting the model to ONNX. Please create an issue in the PyTorch GitHub repository against the [96m*onnx*[0m component and attach the full error stack as well as reproduction scripts. You can run `torch_onnx.analyze()` to produce an error report after obtaining an ExportedProgram with `torch.export.export()`.

Exported program:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: "f32[33, 16, 3]", arg1_1: "f32[33]", arg2_1: "f32[33]", arg3_1: "f32[33]", arg4_1: "f32[33]", arg5_1: "f32[33]", arg6_1: "i64[]", arg7_1: "f32[20, 16, 50]"):
            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:128 in forward, code: out = self.conv(x)
            convolution: "f32[20, 33, 24]" = torch.ops.aten.convolution.default(arg7_1, arg0_1, arg1_1, [2], [0], [1], False, [0], 1);  arg7_1 = arg0_1 = arg1_1 = None

            # File: /Users/justinc/Documents/GitHub/torch-onnx/tests/pytorch_test.py:129 in forward, code: return self.bn(out)
            _native_batch_norm_legit_no_training = torch.ops.aten._native_batch_norm_legit_no_training.default(convolution, arg2_1, arg3_1, arg4_1, arg5_1, 0.1, 1e-05);  convolution = arg2_1 = arg3_1 = arg4_1 = arg5_1 = None
            getitem: "f32[20, 33, 24]" = _native_batch_norm_legit_no_training[0];  _native_batch_norm_legit_no_training = None
            return (getitem,)

Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg0_1'), target='conv.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg1_1'), target='conv.bias', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg2_1'), target='bn.weight', persistent=None), InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg3_1'), target='bn.bias', persistent=None), InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='arg4_1'), target='bn.running_mean', persistent=True), InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='arg5_1'), target='bn.running_var', persistent=True), InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='arg6_1'), target='bn.num_batches_tracked', persistent=True), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg7_1'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}

Analysis

PyTorch ONNX Conversion Analysis

Model Information

The model has 1683 parameters and 67 buffers (non-trainable parameters).

Inputs:

arg7_1: TensorMetadata(shape=torch.Size([20, 16, 50]), dtype=torch.float32, requires_grad=True, stride=(800, 50, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})

Outputs:

getitem: TensorMetadata(shape=torch.Size([20, 33, 24]), dtype=torch.float32, requires_grad=False, stride=(792, 24, 1), memory_format=torch.contiguous_format, is_quantized=False, qparams={})

The FX graph has 12 nodes in total. Number of FX nodes per op:

placeholder: 8
call_function: 3
output: 1

Of the call_function nodes, the counts of operators used are:

aten.convolution.default: 1
aten._native_batch_norm_legit_no_training.default: 1
<built-in function getitem>: 1

ONNX Conversion Information

All operators in the model have ONNX equivalents.

PyTorch ONNX Conversion Error report

torch.export.export error

Error message:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 1190, in wrap_fake_exception
    return fn()
           ^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/builder.py", line 1881, in <lambda>
    lambda: tx.fake_mode.from_tensor(
            ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_subclasses/fake_tensor.py", line 1666, in from_tensor
    return self.fake_tensor_converter(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_subclasses/fake_tensor.py", line 349, in __call__
    return self.from_real_tensor(
           ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_subclasses/fake_tensor.py", line 286, in from_real_tensor
    raise UnsupportedFakeTensorException("quantized nyi in meta tensors")
torch._subclasses.fake_tensor.UnsupportedFakeTensorException: quantized nyi in meta tensors

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 125, in torch_onnx_export_adaptor
    program = torch.export.export(
              ^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/export/__init__.py", line 174, in export
    return _export(
           ^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/export/_trace.py", line 635, in wrapper
    raise e
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/export/_trace.py", line 618, in wrapper
    ep = fn(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/export/exported_program.py", line 83, in wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/export/_trace.py", line 860, in _export
    gm_torch_level = _export_to_torch_ir(
                     ^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/export/_trace.py", line 347, in _export_to_torch_ir
    gm_torch_level, _ = torch._dynamo.export(
                        ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 1311, in inner
    result_traced = opt_f(*args, **kwargs)
                    ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state, skip=1)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
    return _compile(
           ^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.11/3.11.8/Frameworks/Python.framework/Versions/3.11/lib/python3.11/contextlib.py", line 81, in inner
    return func(*args, **kwds)
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
        ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
    out_code = transform_code_object(code, transform)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
    transformations(instructions, code_options)
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 482, in transform
    tracer = InstructionTranslator(
             ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 2115, in __init__
    self.symbolic_locals = VariableTracker.apply(
                           ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/base.py", line 217, in apply
    result = {
             ^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/base.py", line 218, in <dictcomp>
    k: cls.apply(fn, v, cache, skip_fn) for k, v in list(value.items())
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/base.py", line 203, in apply
    result = fn(update_object_dict(value))
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 2116, in <lambda>
    lambda x: x.realize(), self.symbolic_locals
              ^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/lazy.py", line 58, in realize
    self._cache.realize(self.parents_tracker)
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/lazy.py", line 24, in realize
    self.vt = VariableBuilder(tx, self.source)(self.value)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/builder.py", line 269, in __call__
    vt = self._wrap(value)
         ^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/builder.py", line 402, in _wrap
    return type_dispatch(self, value)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/builder.py", line 1073, in wrap_tensor
    tensor_variable = wrap_fx_proxy(
                      ^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/builder.py", line 1330, in wrap_fx_proxy
    return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/builder.py", line 1440, in wrap_fx_proxy_cls
    example_value = wrap_to_fake_tensor_and_record(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/variables/builder.py", line 1880, in wrap_to_fake_tensor_and_record
    fake_e = wrap_fake_exception(
             ^^^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 1196, in wrap_fake_exception
    raise unimplemented(msg) from e
          ^^^^^^^^^^^^^^^^^^
  File "/Users/justinc/Documents/GitHub/torch-onnx/venv/lib/python3.11/site-packages/torch/_dynamo/exc.py", line 190, in unimplemented
    raise Unsupported(msg)
torch._dynamo.exc.Unsupported: Unsupported: quantized nyi in meta tensors with fake tensor propagation.

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 182, in _torch_onnx_export_adapter_with_error_report
    torch_onnx_export_adaptor(*args, **kwargs, check=True)
  File "/Users/justinc/Documents/GitHub/torch-onnx/src/torch_onnx/_patch.py", line 129, in torch_onnx_export_adaptor
    raise TorchExportError(
torch_onnx._patch.TorchExportError: Failed to export the model with torch.export. [96mThis is step 1/2[0m of exporting the model to ONNX. Please 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.

justinchuby / torch-onnx

Analysis interface #22

PyTorch ONNX Conversion Analysis

Model information

ONNX conversion information

PyTorch ONNX Conversion Error report

Analysis

Model Information

ONNX Conversion Information

PyTorch ONNX Conversion Error report

Analysis

Model Information

ONNX Conversion Information

PyTorch ONNX Conversion Error report

Analysis

Model Information

ONNX Conversion Information

PyTorch ONNX Conversion Error report

Analysis

Model Information

ONNX Conversion Information

PyTorch ONNX Conversion Error report