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# mypy: allow-untyped-defs
"""This file exports ONNX ops for opset 18.
Note [ONNX Operators that are added/updated in opset 18]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-18-of-the-default-onnx-operator-set
New operators:
BitwiseAnd
CenterCropPad
Col2Im
Mish
OptionalGetElement
OptionalHasElement
Pad
Resize
ScatterElements
ScatterND
Split
"""
import functools
from collections.abc import Sequence
from typing import Optional
import torch
from torch import _C
from torch.onnx import _type_utils, symbolic_helper, symbolic_opset9 as opset9
from torch.onnx._internal import jit_utils, registration
# EDITING THIS FILE? READ THIS FIRST!
# see Note [Edit Symbolic Files] in symbolic_helper.py
__all__ = [
"col2im",
]
_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=18)
@_onnx_symbolic("aten::__and_")
@_onnx_symbolic("aten::bitwise_and")
def __and_(g: jit_utils.GraphContext, self, other):
# do type promotion (scalars don't seem to apply)
args = [self, other]
# type promotion doesn't happen with torch.bitwise_and(tensor, scalar)
prom_args = [arg for arg in args if symbolic_helper._get_tensor_rank(arg)]
if len(prom_args) == 0:
prom_args = args
promotion_jit_type = symbolic_helper._type_promote_from_values(*prom_args)
self = symbolic_helper._maybe_cast_to_type(g, self, promotion_jit_type)
other = symbolic_helper._maybe_cast_to_type(g, other, promotion_jit_type)
if promotion_jit_type == _type_utils.JitScalarType.BOOL:
return g.op("And", self, other)
return g.op("BitwiseAnd", self, other)
@_onnx_symbolic("aten::col2im")
@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is")
def col2im(
g,
input: _C.Value,
output_size: _C.Value,
kernel_size: _C.Value,
dilation: Sequence[int],
padding: Sequence[int],
stride: Sequence[int],
):
# convert [i0, i1, ..., in] into [i0, i0, i1, i1, ..., in, in]
adjusted_padding: list[int] = []
for pad in padding:
adjusted_padding.extend(pad for _ in range(2))
num_dimensional_axis = symbolic_helper._get_tensor_sizes(output_size)[0]
if not adjusted_padding:
adjusted_padding = [0, 0] * num_dimensional_axis
if not dilation:
dilation = [1] * num_dimensional_axis
if not stride:
stride = [1] * num_dimensional_axis
return g.op(
"Col2Im",
input,
output_size,
kernel_size,
dilations_i=dilation,
pads_i=adjusted_padding,
strides_i=stride,
)
@_onnx_symbolic(
"aten::mean", decorate=[symbolic_helper._apply_params("ReduceMean", "mean")]
)
@_onnx_symbolic(
"aten::prod",
decorate=[
symbolic_helper._apply_params(
"ReduceProd", "prod", allow_multi_dim_support=False
)
],
)
def _reduce_with_dtype(onnx_op: str, name: str, allow_multi_dim_support: bool = True):
return symbolic_helper._reduce_with_dtype_helper(
onnx_op, name, allow_multi_dim_support
)
@_onnx_symbolic("aten::native_layer_norm")
@symbolic_helper.quantized_args(True, False, False, False)
@symbolic_helper.parse_args("v", "is", "v", "v", "f")
def _native_layer_norm(
g: jit_utils.GraphContext,
input: _C.Value,
normalized_shape: Sequence[int],
weight: _C.Value,
bias: _C.Value,
eps: float,
) -> tuple[_C.Value, _C.Value, _C.Value]:
return opset9.native_layer_norm(g, input, normalized_shape, weight, bias, eps)
@_onnx_symbolic("aten::glu")
@symbolic_helper.parse_args("v", "i")
def _glu(g: jit_utils.GraphContext, input, dim):
dim_size = symbolic_helper._get_tensor_dim_size(input, dim)
if dim_size is not None:
assert dim_size % 2 == 0
first, second = g.op("Split", input, axis_i=dim, num_outputs_i=2, outputs=2)
return g.op("Mul", first, g.op("Sigmoid", second))
@_onnx_symbolic("aten::max")
# torch.max (same for torch.min) actually has two interfaces smashed together:
# torch.max(x, dim, keepdim) and torch.max(x, y)
# TODO(justinchuby): Support multiple quantized args in output
def max(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
return symbolic_helper._max_helper(g, self, dim_or_y, keepdim)
@_onnx_symbolic("aten::maximum")
@symbolic_helper.quantized_args(True, True)
def maximum(g: jit_utils.GraphContext, input, other):
return max(g, input, dim_or_y=other)
@_onnx_symbolic("aten::min")
# TODO(justinchuby): Support multiple quantized args in output
def min(g: jit_utils.GraphContext, self, dim_or_y=None, keepdim=None):
return symbolic_helper._min_helper(g, self, dim_or_y, keepdim)
@_onnx_symbolic("aten::minimum")
@symbolic_helper.quantized_args(True, True)
def minimum(g: jit_utils.GraphContext, input, other):
return min(g, input, dim_or_y=other)
@_onnx_symbolic("aten::amax")
@symbolic_helper.quantized_args(True)
@symbolic_helper.parse_args("v", "is", "i")
def amax(g: jit_utils.GraphContext, self, dim, keepdim):
axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
return g.op("ReduceMax", self, axes, keepdims_i=keepdim)
@_onnx_symbolic("aten::amin")
@symbolic_helper.quantized_args(True)
@symbolic_helper.parse_args("v", "is", "i")
def amin(g: jit_utils.GraphContext, self, dim, keepdim):
axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
return g.op("ReduceMin", self, axes, keepdims_i=keepdim)
@_onnx_symbolic("aten::aminmax")
@symbolic_helper.quantized_args(True)
@symbolic_helper.parse_args("v", "v", "i")
def aminmax(g: jit_utils.GraphContext, self, dim, keepdim):
if not symbolic_helper._is_none(dim):
dim = symbolic_helper._get_const(dim, "i", "dim")
axes = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
return g.op("ReduceMin", self, axes, keepdims_i=keepdim), g.op(
"ReduceMax", self, axes, keepdims_i=keepdim
)
else:
return g.op("ReduceMin", self, keepdims_i=keepdim), g.op(
"ReduceMax", self, keepdims_i=keepdim
)
@_onnx_symbolic("aten::var_mean")
def _var_mean(g: jit_utils.GraphContext, input, *args):
if len(args) == 1:
return symbolic_helper._var_mean_helper(g, input, None, args[0], None)
else:
return symbolic_helper._var_mean_helper(g, input, *args)
@_onnx_symbolic("aten::logsumexp")
@symbolic_helper.parse_args("v", "is", "i")
def _logsumexp(g: jit_utils.GraphContext, input, dim, keepdim):
if dim is None:
return g.op("ReduceLogSumExp", input, keepdims_i=0)
else:
axes = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
return g.op("ReduceLogSumExp", input, axes, keepdims_i=keepdim)
@_onnx_symbolic("aten::linalg_matrix_norm")
@symbolic_helper.parse_args("v", "v", "is", "b", "v")
def _linalg_matrix_norm(
g: jit_utils.GraphContext,
self: torch._C.Value,
ord: torch._C.Value,
dim: list[int],
keepdim: bool,
dtype: torch._C.Value,
):
return opset9.linalg_matrix_norm(g, self, ord, dim, keepdim, dtype)
@_onnx_symbolic("aten::embedding_bag")
@symbolic_helper.parse_args("v", "v", "v", "i", "i", "i", "v", "i", "i")
def embedding_bag(
g: jit_utils.GraphContext,
embedding_matrix,
indices,
offsets,
scale_grad_by_freq,
mode,
sparse,
per_sample_weights,
include_last_offset,
padding_idx,
):
return symbolic_helper._embedding_bag_helper(
g,
embedding_matrix,
indices,
offsets,
scale_grad_by_freq,
mode,
sparse,
per_sample_weights,
include_last_offset,
padding_idx,
)
@_onnx_symbolic("aten::linalg_vector_norm")
@symbolic_helper.parse_args("v", "f", "is", "b", "v")
def linalg_vector_norm(
g: jit_utils.GraphContext,
self: torch._C.Value,
ord: float,
dim: Optional[Sequence[int]],
keepdim: bool,
dtype: torch._C.Value,
):
return symbolic_helper._linalg_vector_norm_helper(g, self, ord, dim, keepdim, dtype)
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