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"""
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
"""
import functools
import warnings
from torch.onnx import symbolic_helper, symbolic_opset9 as opset9
from torch.onnx._internal import jit_utils, registration
_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=7)
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)
@_onnx_symbolic("aten::max")
def max(g: jit_utils.GraphContext, 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 opset9.max(g, self, dim_or_y, keepdim)
@_onnx_symbolic("aten::min")
def min(g: jit_utils.GraphContext, 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 opset9.min(g, self, dim_or_y, keepdim)
for block_listed_op in block_listed_operators:
_onnx_symbolic(f"aten::{block_listed_op}")(
symbolic_helper._block_list_in_opset(block_listed_op)
)
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