67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
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"""
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Note [ONNX operators that are added/updated from opset 7 to opset 8]
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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New operators:
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Expand
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Updated operators:
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Min, Max, Sum, Mean: supports multidirectional broadcasting.
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MaxPool: added optional indices output.
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Scan
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"""
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import functools
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import warnings
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from torch.onnx import symbolic_helper, symbolic_opset9 as opset9
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from torch.onnx._internal import jit_utils, registration
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_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=7)
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block_listed_operators = (
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"scan",
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"expand",
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"expand_as",
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"meshgrid",
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"adaptive_max_pool1d",
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"adaptive_max_pool2d",
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"adaptive_max_pool3d",
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"max_pool1d_with_indices",
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"max_pool2d_with_indices",
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"max_pool3d_with_indices",
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)
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# NOTE: max, min, sum, mean: broadcasting is not supported in opset 7.
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# torch.max (same for torch.min) actually has two interfaces smashed together:
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# torch.max(x, dim, keepdim) and torch.max(x, y)
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@_onnx_symbolic("aten::max")
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def max(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
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# torch.max(input, other)
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if keepdim is None and dim_or_y is not None:
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warnings.warn(
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"Multidirectional broadcasting is not supported in opset 7. "
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"This might cause the onnx model to be incorrect, if inputs to max operators "
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"have different shapes"
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)
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return opset9.max(g, self, dim_or_y, keepdim)
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@_onnx_symbolic("aten::min")
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def min(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
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# torch.min(input, other)
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if keepdim is None and dim_or_y is not None:
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warnings.warn(
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"Multidirectional broadcasting is not supported in opset 7. "
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"This might cause the onnx model to be incorrect, if inputs to min operators "
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"have different shapes"
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)
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return opset9.min(g, self, dim_or_y, keepdim)
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for block_listed_op in block_listed_operators:
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_onnx_symbolic(f"aten::{block_listed_op}")(
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symbolic_helper._block_list_in_opset(block_listed_op)
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)
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