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A PyTorch model could not be compiled throgh tvmc because the shape of the input tensor could not be deduced from the model after it has been saved. We've added an --input-shape parameter to tvmc compile and tvmc tune that allows the inputs to be specified for PyTorch models.
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| def parse_input_shapes(xs): | ||
| """Turn the string from --input-shape into a list. | ||
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It would be good to have an example here, that describes the input format and expected output format, similar to what you have on test_parse_input_shapes__turn_into_list.
| "--input-shape", | ||
| type=common.parse_input_shapes, | ||
| metavar="INPUT_SHAPE,[INPUT_SHAPE]...", | ||
| help="for PyTorch, e.g. '(1,3,224,224)'", |
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Maybe clarify that it is in fact mandatory for PyTorch.
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Agree. It's confusing to see such a general option only for PyTorch. I would suggest the following changes:
- Make
--input-shapeas a general option for all frontends. If present, we skip the input shape inference. --input-shapeis optional by default. However, if users want to process a PyTorch model but don't specify--input-shape, we throw out an error in the PyTorch frontend.
| "--input-shape", | ||
| type=common.parse_input_shapes, | ||
| metavar="INPUT_SHAPE,[INPUT_SHAPE]...", | ||
| help="for PyTorch, e.g. '(1,3,224,224)'", |
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Agree. It's confusing to see such a general option only for PyTorch. I would suggest the following changes:
- Make
--input-shapeas a general option for all frontends. If present, we skip the input shape inference. --input-shapeis optional by default. However, if users want to process a PyTorch model but don't specify--input-shape, we throw out an error in the PyTorch frontend.
| # Remove white space and extract numbers | ||
| strshape = shape[1].replace(" ", "").split(",") |
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It would be safer and easier to remove all spaces in xs in the beginning of this function.
| try: | ||
| shapes.append([int(i) for i in strshape]) | ||
| except ValueError: | ||
| raise argparse.ArgumentTypeError(f"expected numbers in shape '{shape[1]}'") |
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Consider the following two input shapes:
(8):shapes=[8](8,): Value error becausestrshapewould be[8, ""].
Accordingly, I guess your intention is (8) instead of (8,). However, this is inconsistent with the Python syntax so it might confuse people. I have two proposals to deal with this:
- Use list syntax instead of tuple, so that the semantic is clear, and we can simply use JSON loader to deal with all variants (e.g., spaces):
xs = "[1,3,224,224], [32]" shapes = json.loads(xs) # [[1,3,224,224],[32]]
- Follow Python syntax to only accept
(8,)and throw an error for(8), which is treated as an integer instead of a tuple because buckets will be simplified in Python. In this case, I would suggest usingevalto deal with all variants.xs = "(1,3,224,224), (32,)" shapes = eval(xs, {}, {}) # Remember to disable all local and global symbols to isolate this expression. # shapes=[(1,3,224,224),(32,)]
Either way is fine for me, and please update the help message and make sure you have a unit test to cover corner cases.
| if input_shape: | ||
| raise TVMCException("--input-shape is not supported for {}".format(self.name())) | ||
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This is definitely too ad hoc
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| traced_model = torch.jit.load(path) | ||
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| inputs = list(traced_model.graph.inputs())[1:] |
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Is this approach not working at all? If it works for some cases, we should still use it first when --input-shape is missing.
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I looked into this and I didn't find a way to extract inputs from the model after it has been saved and loaded. I asked on the PyTorch forum as well (https://discuss.pytorch.org/t/input-size-disappears-between-torch-jit-save-and-torch-jit-load/108955) and since I received a grand total of zero responses, I suspect it is a deliberate design decision. If there was a way, it would be good to keep it, of course, but in that form it doesn't work any more.
| The underlying framework used to create the model. | ||
| If not specified, this will be inferred from the file type. | ||
| input shape : list, optional | ||
| The shape of input tensor for PyTorch models |
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ditto. make it general instead of only for PyTorch.
comaniac
added
status: need update
comaniac
added
status: suprceded
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Include the functionalities in #7366. |
3 participants
A PyTorch model could not be compiled through tvmc because the shape
of the input tensor could not be deduced from the model after it has been
saved. We've added an --input-shape parameter to tvmc compile and
tvmc tune that allows the inputs to be specified for PyTorch models.