🐞Describing the bug
I’m trying to create a coreml package from a traced model, but I get the following error when calling coremltools.convert:
ValueError: Op "135" (op_type: slice_by_index) Input x="130" expects tensor or scalar of dtype from type domain ['fp16', 'fp32', 'int32', 'bool'] but got tensor[1,2,2049,440,complex64]
Stack Trace
Converting PyTorch Frontend ==> MIL Ops: 5%| | 91/1722 [00:00<00:00, 3237.72 o
Traceback (most recent call last):
File "/Users/andreascuderi/nTrack/trunk/n-Track_EX/Scripts/./hybrid_demucs_tracer.py", line 72, in
mlpackage_obj = ct.convert(
^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/_converters_entry.py", line 574, in convert
mlmodel = mil_convert(
^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 188, in mil_convert
return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 212, in _mil_convert
proto, mil_program = mil_convert_to_proto(
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 286, in mil_convert_to_proto
prog = frontend_converter(model, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/converter.py", line 108, in call
return load(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 80, in load
return _perform_torch_convert(converter, debug)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 99, in _perform_torch_convert
prog = converter.convert()
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 519, in convert
convert_nodes(self.context, self.graph)
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 88, in convert_nodes
add_op(context, node)
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 4209, in _slice
res = mb.slice_by_index(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/mil/ops/registry.py", line 182, in add_op
return cls._add_op(op_cls_to_add, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/mil/builder.py", line 168, in _add_op
new_op = op_cls(**kwargs)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/mil/operation.py", line 190, in init
self._validate_and_set_inputs(input_kv)
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/mil/operation.py", line 503, in _validate_and_set_inputs
self.input_spec.validate_inputs(self.name, self.op_type, input_kvs)
File "/usr/local/lib/python3.11/site-packages/coremltools/converters/mil/mil/input_type.py", line 163, in validate_inputs
raise ValueError(msg.format(name, var.name, input_type.type_str,
ValueError: Op "138" (op_type: slice_by_index) Input x="133" expects tensor or scalar of dtype from type domain ['fp16', 'fp32', 'int32', 'bool'] but got tensor[1,2,2049,44,complex64]
To Reproduce
input = torch.rand(1, 2, 40000)
traced_module = torch.jit.trace(model, input)
ct.convert(traced_module, convert_to="neuralnetwork”, inputs=[ct.TensorType(shape=input.shape)])
System environment (please complete the following information):
- coremltools version: 7.1
- macOS 13.2.1
- PyTorch version: 2.1.0
Additional context
- Add anything else about the problem here that you want to share.
It looks like your PyTorch model has an inplace slice operation on a tensor of complex numbers. Given that the Core ML Framework does not support complex numbers, I don't think coremltools is going to be able to support converting this model.
I encourage you to use the Feedback Assistant tool to submit a feature request for the Core ML Framework to support complex numbers.
@TobyRoseman thanks for the answer. According to this post #1539, coremltools supports complex numbers ops since version 6.2. Does the problem strictly depend on the slice op?
@junpeiz We managed to support some complex algebra by decomposing the real and the imaginary parts. Is it possible to do the same for slice_by_index?
I guess not, since
- Eventually, we need the model to have real output?
slice_by_index would give complex output, violating 1
@junpeiz We managed to support some complex algebra by decomposing the real and the imaginary parts. Is it possible to do the same for slice_by_index?
I guess not, since
- Eventually, we need the model to have real output?
slice_by_index would give complex output, violating 1
It's possible to do the same for slice_by_index, where the real and imaginary part are sliced individually. The real output requirement is for the whole model, which means as long as the complex output of slice_by_index is comsumed by some following ops which produce a real output, it should be fine.
apple
