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symbolic_opset10.py
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symbolic_opset10.py
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from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from torch.nn.modules.utils import _single, _pair, _triple
import torch.onnx
# This import monkey-patches graph manipulation methods on Graph, used for the
# ONNX symbolics
import torch.onnx.utils
import torch.onnx.symbolic_helper as sym_help
from torch.onnx.symbolic_helper import parse_args, _unimplemented
import torch.onnx.symbolic_opset9
# EDITING THIS FILE? READ THIS FIRST!
# see Note [Edit Symbolic Files] in symbolic_helper.py
# This file exports ONNX ops for opset 10
# Opset 10 is supported by ONNX release 1.5.0
# release on 04/24/19
@parse_args('v', 'i', 'i', 'none')
def sort(g, self, dim, decending, out=None):
return sym_help._sort_helper(g, self, dim, decending=decending, out=out)
@parse_args('v', 'v', 'i', 'i', 'i', 'none')
def topk(g, self, k, dim, largest, sorted, out=None):
return sym_help._topk_helper(g, self, k, dim, largest=largest, sorted=sorted, out=out)
def _max_pool(name, tuple_fn, ndims, return_indices):
@parse_args('v', 'is', 'is', 'is', 'is', 'i')
def symbolic_fn(g, input, kernel_size, stride, padding, dilation, ceil_mode):
if not stride:
stride = kernel_size
kwargs = {
'kernel_shape_i': tuple_fn(kernel_size),
'pads_i': tuple_fn(padding) * 2,
'strides_i': tuple_fn(stride),
'ceil_mode_i': ceil_mode,
}
if set(tuple_fn(dilation)) != {1}:
kwargs['dilations_i'] = tuple_fn(dilation)
# easy but hacky way to get flattened indices values
# to be used to convert the indices values to non-flattened.
# In ONNX the indices are computed as a flatten 1-D tensor,
# so the values in indices are in [0, N x C x D1 x ... x Dn).
# To convert the indices to the same format used by Pytorch,
# we first execute a maxpool with a kernel and stride of 1 on the same input.
# This will result in a tensor of indices in which each index will have it's own value.
# Using this tensor as a reference, we extract the first index of each axis and subtract
# it from each index of this axis in the indices to convert.
# This step will result in a tensor were each dimension has values of indices within
# the dimension it is in.
# For more information :
# https://github.com/pytorch/pytorch/pull/16455#issuecomment-460776407
if return_indices:
r, indices = g.op("MaxPool", input, outputs=2, **kwargs)
_, flattened_indices = g.op("MaxPool", input, outputs=2,
kernel_shape_i=[1 for _ in range(ndims)],
strides_i=[1 for _ in range(ndims)])
# convert indices to have non-flattened indices values
from torch.onnx.symbolic_opset9 import sub
s = sym_help._slice_helper(g, flattened_indices, axes=[2 + i for i in range(ndims)],
starts=tuple_fn(0), ends=tuple_fn(1))
indices = sub(g, indices, s)
return r, indices
else:
r = g.op("MaxPool", input, outputs=1, **kwargs)
return r
return symbolic_fn
max_pool1d = _max_pool("max_pool1d", _single, 1, return_indices=False)
max_pool2d = _max_pool("max_pool2d", _pair, 2, return_indices=False)
max_pool3d = _max_pool("max_pool3d", _triple, 3, return_indices=False)
max_pool1d_with_indices = _max_pool("max_pool1d_with_indices", _single, 1, return_indices=True)
max_pool2d_with_indices = _max_pool("max_pool2d_with_indices", _pair, 2, return_indices=True)
max_pool3d_with_indices = _max_pool("max_pool3d_with_indices", _triple, 3, return_indices=True)
def _avg_pool(name, tuple_fn):
@parse_args('v', 'is', 'is', 'is', 'i', 'i', 'none')
def symbolic_fn(g, input, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override=None):
if not stride:
stride = kernel_size
padding = sym_help._avgpool_helper(tuple_fn, padding, kernel_size, stride, divisor_override, name)
if count_include_pad:
input = g.op("Pad", input,
pads_i=((0,) * 2 + padding) * 2,
mode_s='constant',
value_f=0.)
padding = (0,) * len(padding)
output = g.op("AveragePool", input,
kernel_shape_i=tuple_fn(kernel_size),
strides_i=tuple_fn(stride),
pads_i=padding * 2,
ceil_mode_i=ceil_mode)
return output
return symbolic_fn
avg_pool1d = _avg_pool('avg_pool1d', _single)
avg_pool2d = _avg_pool('avg_pool2d', _pair)
avg_pool3d = _avg_pool('avg_pool3d', _triple)
def _interpolate(name, dim, interpolate_mode):
def symbolic_fn(g, input, output_size, *args):
scales, align_corners = sym_help._get_interpolate_attributes(g, interpolate_mode, args)
sym_help._interpolate_warning(interpolate_mode)
align_corners = sym_help._maybe_get_scalar(align_corners)
if align_corners:
return _unimplemented(name, "align_corners == True")
if scales is None:
scales = sym_help._interpolate_size_to_scales(g, input, output_size, dim)
return g.op("Resize", input, scales, mode_s=interpolate_mode)
return symbolic_fn
upsample_nearest1d = _interpolate('upsample_nearest1d', 3, "nearest")
upsample_nearest2d = _interpolate('upsample_nearest2d', 4, "nearest")
upsample_nearest3d = _interpolate('upsample_nearest3d', 5, "nearest")
upsample_linear1d = _interpolate('upsample_linear1d', 3, "linear")
upsample_bilinear2d = _interpolate('upsample_bilinear2d', 4, "linear")
upsample_trilinear3d = _interpolate('upsample_trilinear3d', 5, "linear")
def __interpolate(g, input, size, scale_factor, mode , align_corners, recompute_scale_factor):
scales, mode = sym_help._interpolate_get_scales_and_mode(g, input, size, scale_factor,
mode , align_corners)
return g.op("Resize", input, scales, mode_s=mode)
def _slice(g, input, axes, starts, ends, steps=None, dynamic_slice=False):
if dynamic_slice:
starts = g.op("Unsqueeze", starts, axes_i=[0])
ends = g.op("Unsqueeze", ends, axes_i=[0])
axes = g.op("Unsqueeze", axes, axes_i=[0])
else:
assert len(starts) == len(ends)
assert len(starts) == len(axes)
assert steps is None or len(starts) == len(steps)
if len(starts) == 1 and starts[0] == 0 and ends[0] == 9223372036854775807 \
and (steps is None or (len(steps) == 1 and steps[0] == 1)):
return input
axes = g.op("Constant", value_t=torch.tensor(axes))
starts = g.op("Constant", value_t=torch.tensor(starts))
ends = g.op("Constant", value_t=torch.tensor(ends))
if steps is None:
return g.op("Slice", input, starts, ends, axes)
steps = g.op("Constant", value_t=torch.tensor(steps))
return g.op("Slice", input, starts, ends, axes, steps)
@parse_args('v', 'v', 'v', 'v', 'i')
def slice(g, self, dim, start, end, step):
if (start.node().kind() != 'onnx::Constant' or
end.node().kind() != 'onnx::Constant' or dim.node().kind() != 'onnx::Constant'):
dynamic_slice = True
else:
start = [sym_help._parse_arg(start, 'i')]
end = [sym_help._parse_arg(end, 'i')]
dim = [sym_help._parse_arg(dim, 'i')]
dynamic_slice = False
return sym_help._slice_helper(g, self, axes=dim, starts=start, ends=end, steps=[step], dynamic_slice=dynamic_slice)
@parse_args('v', 'is')
def flip(g, input, dims):
return sym_help._slice_helper(g, input, axes=dims,
starts=[-1] * len(dims),
ends=[-9223372036854775807] * len(dims),
steps=[-1] * len(dims))
def fmod(g, input, other):
return g.op("Mod", input, other, fmod_i=1)