torch/onnx/symbolic_opset7.py

import warnings

import torch.onnx.symbolic_opset9 as sym_opset9
from torch.onnx.symbolic_helper import _block_list_in_opset

# Note [ONNX operators that are added/updated from opset 7 to opset 8]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# New operators:
#   Expand
#
# Updated operators:
#   Min, Max, Sum, Mean: supports multidirectional broadcasting.
#   MaxPool: added optional indices output.
#   Scan

block_listed_operators = [
    "scan",
    "expand",
    "expand_as",
    "meshgrid",
    "adaptive_max_pool1d",
    "adaptive_max_pool2d",
    "adaptive_max_pool3d",
    "max_pool1d_with_indices",
    "max_pool2d_with_indices",
    "max_pool3d_with_indices",
]


# NOTE: max, min, sum, mean: broadcasting is not supported in opset 7.
# torch.max (same for torch.min) actually has two interfaces smashed together:
# torch.max(x, dim, keepdim) and torch.max(x, y)
def max(g, self, dim_or_y=None, keepdim=None):
    # torch.max(input, other)
    if keepdim is None and dim_or_y is not None:
        warnings.warn(
            "Multidirectional broadcasting is not supported in opset 7. "
            "This might cause the onnx model to be incorrect, if inputs to max operators "
            "have different shapes"
        )
    return sym_opset9.max(g, self, dim_or_y, keepdim)


def min(g, self, dim_or_y=None, keepdim=None):
    # torch.min(input, other)
    if keepdim is None and dim_or_y is not None:
        warnings.warn(
            "Multidirectional broadcasting is not supported in opset 7. "
            "This might cause the onnx model to be incorrect, if inputs to min operators "
            "have different shapes"
        )
    return sym_opset9.min(g, self, dim_or_y, keepdim)


for block_listed_op in block_listed_operators:
    vars()[block_listed_op] = _block_list_in_opset(block_listed_op)