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image-handler/image-handler10.0/venv/lib/site-packages/torch/onnx/symbolic_opset11.py

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"""This file exports ONNX ops for opset 11."""
from __future__ import annotations
import functools
import sys
import warnings
from typing import Optional, Sequence
import torch
from torch import _C
from torch._C import _onnx as _C_onnx
from torch.onnx import (
_type_utils,
errors,
symbolic_helper,
symbolic_opset10 as opset10,
symbolic_opset9 as opset9,
utils,
)
from torch.onnx._globals import GLOBALS
from torch.onnx._internal import _beartype, jit_utils, registration
# EDITING THIS FILE? READ THIS FIRST!
# see Note [Edit Symbolic Files] in README.md
__all__ = [
"add",
"append",
"arange",
"argsort",
"atleast_1d",
"atleast_2d",
"atleast_3d",
"cat",
"chunk",
"clamp_max",
"clamp_min",
"clamp",
"constant_pad_nd",
"cumsum",
"Delete",
"embedding_bag",
"embedding_renorm",
"flatten",
"gather",
"hardtanh",
"hstack",
"im2col",
"index_fill",
"index",
"index_copy",
"index_put",
"insert",
"linalg_det",
"linalg_vector_norm",
"logdet",
"masked_scatter",
"masked_select",
"mm",
"narrow",
"normal",
"pad",
"pixel_shuffle",
"pop",
"prim_constant_chunk",
"reflection_pad",
"relu6",
"remainder",
"replication_pad",
"round",
"scatter",
"select",
"size",
"sort",
"split_with_sizes",
"split",
"squeeze",
"stack",
"topk",
"unbind",
"unique_dim",
"unsqueeze",
"vstack",
]
_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=11)
def _apply_params(*args, **kwargs):
"""Returns a decorator that calls the decorated (higher-order) function with the given parameters."""
def _apply(fn):
return fn(*args, **kwargs)
return _apply
@_onnx_symbolic("aten::hardtanh")
@symbolic_helper.quantized_args(True)
@symbolic_helper.parse_args("v", "f", "f")
@_beartype.beartype
def hardtanh(g: jit_utils.GraphContext, self: _C.Value, min_val: float, max_val: float):
scalar_type = _type_utils.JitScalarType.from_value(
self, _type_utils.JitScalarType.FLOAT
)
min_val = g.op(
"Constant",
value_t=torch.tensor(min_val, dtype=scalar_type.dtype()),
)
max_val = g.op(
"Constant",
value_t=torch.tensor(max_val, dtype=scalar_type.dtype()),
)
return opset9._op_with_optional_float_cast(
g, "Clip", self, min_val, max_val, opset_before=12
)
@_onnx_symbolic("aten::clamp")
@_beartype.beartype
def clamp(g: jit_utils.GraphContext, self, min, max):
@_beartype.beartype
def _cast_if_not_none(tensor, dtype):
if tensor is not None and not symbolic_helper._is_none(tensor):
return g.op(
"Cast",
tensor,
to_i=dtype.onnx_type(),
)
else:
return tensor
scalar_type = _type_utils.JitScalarType.from_value(
self, _type_utils.JitScalarType.UNDEFINED
)
if scalar_type != _type_utils.JitScalarType.UNDEFINED:
min = _cast_if_not_none(min, scalar_type)
max = _cast_if_not_none(max, scalar_type)
if symbolic_helper._is_none(min):
return clamp_max(g, self, max)
elif symbolic_helper._is_none(max):
return clamp_min(g, self, min)
else:
if (
symbolic_helper._get_tensor_rank(min) == 0
and symbolic_helper._get_tensor_rank(max) == 0
):
return opset9._op_with_optional_float_cast(
g, "Clip", self, min, max, opset_before=12
)
else:
return clamp_max(g, clamp_min(g, self, min), max)
@_onnx_symbolic("aten::clamp_min")
@symbolic_helper.parse_args("v", "v")
@_beartype.beartype
def clamp_min(g: jit_utils.GraphContext, self, min):
min = g.op("Cast", min, to_i=_type_utils.JitScalarType.from_value(self).onnx_type())
if symbolic_helper._get_tensor_rank(min) == 0:
max = opset9.unused(g)
return opset9._op_with_optional_float_cast(
g, "Clip", self, min, max, opset_before=12
)
else:
return opset9._op_with_optional_float_cast(g, "Max", self, min, opset_before=12)
@_onnx_symbolic("aten::clamp_max")
@symbolic_helper.parse_args("v", "v")
@_beartype.beartype
def clamp_max(g: jit_utils.GraphContext, self, max):
max = g.op("Cast", max, to_i=_type_utils.JitScalarType.from_value(self).onnx_type())
if symbolic_helper._get_tensor_rank(max) == 0:
min = opset9.unused(g)
return opset9._op_with_optional_float_cast(
g, "Clip", self, min, max, opset_before=12
)
else:
return opset9._op_with_optional_float_cast(g, "Min", self, max, opset_before=12)
@_onnx_symbolic("aten::relu6")
@_beartype.beartype
def relu6(g: jit_utils.GraphContext, input):
scalar_type = _type_utils.JitScalarType.from_value(
input, _type_utils.JitScalarType.FLOAT
)
min_val = g.op(
"Constant",
value_t=torch.tensor(0, dtype=scalar_type.dtype()),
)
max_val = g.op(
"Constant",
value_t=torch.tensor(6, dtype=scalar_type.dtype()),
)
return clamp(g, input, min_val, max_val)
@_onnx_symbolic("aten::select")
# Opset 11 gather accepts negative indices
@symbolic_helper.quantized_args(True)
@symbolic_helper.parse_args("v", "i", "v")
@_beartype.beartype
def select(g: jit_utils.GraphContext, self, dim, index):
return g.op("Gather", self, index, axis_i=dim)
@_onnx_symbolic("aten::index_put")
@_beartype.beartype
def index_put(
g: jit_utils.GraphContext, self, indices_list_value, values, accumulate=False
):
if symbolic_helper._is_packed_list(indices_list_value):
indices_list = symbolic_helper._unpack_list(indices_list_value)
else:
indices_list = [indices_list_value]
if symbolic_helper.is_caffe2_aten_fallback():
args = [self] + indices_list + [values, accumulate]
return g.at("index_put", *args)
accumulate = symbolic_helper._parse_arg(accumulate, "b")
if len(indices_list) == 0:
return values
if len(indices_list) > 1:
for idx_ in range(len(indices_list)):
if symbolic_helper._is_bool(indices_list[idx_]):
indices_list[idx_] = g.op("NonZero", indices_list[idx_])
index = indices_list[0]
for ind in indices_list[1:]:
index = opset9.add(g, index, ind)
broadcast_index_shape = g.op("Shape", index)
indices_list = [
symbolic_helper._unsqueeze_helper(
g, opset9.expand(g, ind, broadcast_index_shape, None), [-1]
)
for ind in indices_list
]
index = g.op("Concat", *indices_list, axis_i=-1)
else:
# Replace index_put node with masked_scatter or masked_fill
# when inputs to the index_put node contains a single boolean input.
#
# index_put -> masked_fill
# * input index contains single tensor of Bool type (e.g.: %24 <- %23).
# * input value contains single element (e.g.: %18).
#
# Torch IR
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %16 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::to(%8, %26, %27, %11, %12, %28, %29, %15)
# %18 : Float(requires_grad=0, device=cpu) = prim::Constant[value={1}]()
# %23 : Bool(8, strides=[1], device=cpu) = aten::view(%16, %22)
# %24 : Tensor?[] = prim::ListConstruct(%23)
# %25 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::index_put(%mask, %24, %18, %30)
# return (%25)
#
#
# index_put -> masked_scatter
# * input index contains single tensor of Bool type (e.g.: %32 <- %31).
# * input value contains multiple elements (e.g.: %28).
#
# Torch IR
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %28 : Float(8, strides=[1], requires_grad=0, device=cpu)
# = prim::Constant[value= 1 1 1 1 1 1 1 1 [ CPUFloatType{8} ]]()
# %15 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::ne(%mask, %some_const)
# %23 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::to(%15, %34, %35, %18, %19, %36, %37, %22)
# %38 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %30 : int[] = prim::Constant[value=[-1]]()
# %31 : Bool(8, strides=[1], device=cpu) = aten::view(%23, %30)
# %32 : Tensor?[] = prim::ListConstruct(%31)
# %33 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::index_put(%mask, %32, %28, %38)
# return (%33)
index = indices_list[0]
bool_inp = index
if symbolic_helper._is_bool(bool_inp):
rank = symbolic_helper._get_tensor_rank(values)
if rank is not None and rank == 0:
return opset9.masked_fill(g, self, bool_inp, values)
mask_rank = symbolic_helper._get_tensor_rank(bool_inp)
self_rank = symbolic_helper._get_tensor_rank(self)
if (
mask_rank is not None
and self_rank is not None
and self_rank > mask_rank
):
# Unsqueeze 'bool_inp' to be broadcastable to shape of 'self'.
bool_inp = symbolic_helper._unsqueeze_helper(
g, bool_inp, list(range(mask_rank, self_rank))
)
return masked_scatter(g, self, bool_inp, values)
broadcast_index_shape = g.op("Shape", index)
index = symbolic_helper._unsqueeze_helper(g, index, [-1])
sub_data_shape = symbolic_helper._slice_helper(
g, g.op("Shape", self), axes=[0], starts=[len(indices_list)], ends=[sys.maxsize]
)
values_shape = g.op("Concat", broadcast_index_shape, sub_data_shape, axis_i=0)
# Check if values is a singular value and expand accordingly
rank = symbolic_helper._get_tensor_rank(values)
if rank is not None and rank == 0:
values = opset9.expand(g, values, values_shape, None)
values = symbolic_helper._reshape_helper(g, values, values_shape)
self_scalar_type = _type_utils.JitScalarType.from_value(
self, _type_utils.JitScalarType.UNDEFINED
)
if self_scalar_type != _type_utils.JitScalarType.UNDEFINED:
values_scalar_type = _type_utils.JitScalarType.from_value(
values, _type_utils.JitScalarType.UNDEFINED
)
if self_scalar_type != values_scalar_type:
values = g.op("Cast", values, to_i=self_scalar_type.onnx_type())
elif accumulate:
raise errors.SymbolicValueError("self does not have a valid scalar type.", self)
if accumulate:
zeros = g.op(
"ConstantOfShape",
g.op("Shape", self),
value_t=torch.tensor([0], dtype=self_scalar_type.dtype()),
)
result = g.op("ScatterND", zeros, index, values)
result = add(g, self, result)
else:
result = g.op("ScatterND", self, index, values)
return result
@_onnx_symbolic("aten::pixel_shuffle")
@symbolic_helper.parse_args("v", "i")
@_beartype.beartype
def pixel_shuffle(g: jit_utils.GraphContext, self, upscale_factor):
rank = symbolic_helper._get_tensor_rank(self)
if rank is not None and rank != 4:
return symbolic_helper._unimplemented("pixel_shuffle", "only support 4d input")
return g.op("DepthToSpace", self, blocksize_i=upscale_factor, mode_s="CRD")
@_onnx_symbolic(
"aten::upsample_nearest1d",
decorate=[_apply_params("upsample_nearest1d", 3, "nearest")],
)
@_onnx_symbolic(
"aten::upsample_nearest2d",
decorate=[_apply_params("upsample_nearest2d", 4, "nearest")],
)
@_onnx_symbolic(
"aten::upsample_nearest3d",
decorate=[_apply_params("upsample_nearest3d", 5, "nearest")],
)
@_onnx_symbolic(
"aten::upsample_linear1d",
decorate=[_apply_params("upsample_linear1d", 3, "linear")],
)
@_onnx_symbolic(
"aten::upsample_bilinear2d",
decorate=[_apply_params("upsample_bilinear2d", 4, "linear")],
)
@_onnx_symbolic(
"aten::upsample_trilinear3d",
decorate=[_apply_params("upsample_trilinear3d", 5, "linear")],
)
@_onnx_symbolic(
"aten::upsample_bicubic2d",
decorate=[_apply_params("upsample_bicubic2d", 4, "cubic")],
)
@_beartype.beartype
def _interpolate(name: str, dim: int, interpolate_mode: str):
return symbolic_helper._interpolate_helper(name, dim, interpolate_mode)
@_onnx_symbolic("aten::__interpolate")
@symbolic_helper.quantized_args(True, False, False, False, False, False, False)
@_beartype.beartype
def __interpolate(
g: jit_utils.GraphContext,
input,
size,
scale_factor,
mode,
align_corners,
recompute_scale_factor,
antialias,
):
return symbolic_helper.__interpolate_helper(
g, input, size, scale_factor, mode, align_corners, recompute_scale_factor
)
@_onnx_symbolic("aten::gather")
@symbolic_helper.parse_args("v", "i", "v", "v")
@_beartype.beartype
def gather(g: jit_utils.GraphContext, self, dim, index, sparse_grad=False):
if symbolic_helper._maybe_get_const(sparse_grad, "i"):
return symbolic_helper._unimplemented("gather", "sparse_grad == True")
if symbolic_helper.is_caffe2_aten_fallback():
return g.at("gather", self, dim, index, sparse_grad)
return g.op("GatherElements", self, index, axis_i=dim)
@_onnx_symbolic("aten::scatter")
@symbolic_helper.parse_args("v", "i", "v", "v")
@_beartype.beartype
def scatter(g: jit_utils.GraphContext, self, dim, index, src):
if symbolic_helper.is_caffe2_aten_fallback():
return g.at("scatter", self, dim, index, src, overload_name="src")
src_type = _type_utils.JitScalarType.from_value(src)
src = symbolic_helper._maybe_get_scalar(src)
if symbolic_helper._is_value(src):
return g.op("ScatterElements", self, index, src, axis_i=dim)
else:
# Check if scalar "src" has same type as self (PyTorch allows different
# type for scalar src (but not when src is tensor)). If not, insert Cast node.
if _type_utils.JitScalarType.from_value(self) != src_type:
src = g.op(
"Cast",
src,
to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
)
return g.op(
"ScatterElements", self, index, opset9.expand_as(g, src, index), axis_i=dim
)
@_onnx_symbolic("aten::cumsum")
@symbolic_helper.parse_args("v", "i", "none")
@_beartype.beartype
def cumsum(g: jit_utils.GraphContext, self, dim, dtype=None):
dim_tensor = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.int))
if dtype and dtype.node().kind() != "prim::Constant":
parsed_dtype = symbolic_helper._get_const(dtype, "i", "dtype")
cast = g.op(
"Cast", self, to_i=_type_utils.JitScalarType(parsed_dtype).onnx_type()
)
else:
cast = self
csum = g.op("CumSum", cast, dim_tensor)
return csum
@_onnx_symbolic("aten::masked_select")
@_beartype.beartype
def masked_select(g: jit_utils.GraphContext, self, mask):
index = opset9.nonzero(g, opset9.expand_as(g, mask, self))
return g.op("GatherND", self, index)
@_onnx_symbolic("aten::masked_scatter")
@_beartype.beartype
def masked_scatter(g: jit_utils.GraphContext, self, mask, source):
index = opset9.nonzero(g, opset9.expand_as(g, mask, self))
# NOTE: source can have more elements than needed.
# It could also have arbitrary shape.
# This is not supported by ONNX::ScatterND, so we need to flatten and slice source tensor.
source = symbolic_helper._reshape_helper(g, source, torch.LongTensor([-1]))
source = symbolic_helper._slice_helper(
g,
source,
axes=torch.LongTensor([0]),
starts=torch.LongTensor([0]),
ends=opset9.size(g, index, torch.LongTensor([0])),
)
return g.op("ScatterND", self, index, source)
@_onnx_symbolic("aten::len")
@_beartype.beartype
def _len(g: jit_utils.GraphContext, self):
if (
symbolic_helper._is_tensor_list(self)
or self.node().kind() == "onnx::SplitToSequence"
):
return g.op("SequenceLength", self)
sz_0 = size(g, self, g.op("Constant", value_t=torch.LongTensor([0])))
return symbolic_helper._squeeze_helper(g, sz_0, [0])
@_onnx_symbolic("aten::__getitem_")
@_beartype.beartype
def __getitem_(g: jit_utils.GraphContext, self, i):
if symbolic_helper._is_tensor_list(self):
# SequenceAt requires that the input be a List of Tensors
return g.op("SequenceAt", self, i)
else:
from torch.onnx.symbolic_opset9 import __getitem_ as getitem
return getitem(g, self, i)
@_onnx_symbolic("aten::_set_item")
@_beartype.beartype
def _set_item(g: jit_utils.GraphContext, tensor_list, i, v):
tensor_list = g.op("SequenceErase", tensor_list, i)
return g.op("SequenceInsert", tensor_list, v, i)
@_onnx_symbolic("aten::append")
@_beartype.beartype
def append(g: jit_utils.GraphContext, self, tensor):
return g.op("SequenceInsert", self, tensor)
@_onnx_symbolic("aten::add")
@_beartype.beartype
def add(g: jit_utils.GraphContext, self, other, alpha=None):
if symbolic_helper._is_value(self) and symbolic_helper._is_tensor_list(self):
tensor_list_node = other.node()
if tensor_list_node.kind() != "prim::ListConstruct":
return symbolic_helper._unimplemented(
"add", "does not support adding dynamic tensor list to another"
)
tensors = symbolic_helper._unpack_list(other)
l = self
for t in tensors:
l = g.op("SequenceInsert", l, t)
return l
return opset9.add(g, self, other, alpha)
@_onnx_symbolic("aten::insert")
@_beartype.beartype
def insert(g: jit_utils.GraphContext, self, pos, tensor):
return g.op("SequenceInsert", self, tensor, pos)
@_onnx_symbolic("aten::pop")
@_beartype.beartype
def pop(g: jit_utils.GraphContext, tensor_list, dim):
return g.op("SequenceErase", tensor_list, dim)
@_onnx_symbolic("aten::Delete")
@_beartype.beartype
def Delete(g: jit_utils.GraphContext, tensor_list, dim):
return g.op("SequenceErase", tensor_list, dim)
@_onnx_symbolic("aten::cat")
@symbolic_helper.quantized_args(True)
@_beartype.beartype
def cat(g: jit_utils.GraphContext, tensor_list, dim):
if symbolic_helper._is_packed_list(tensor_list):
return opset9.cat(g, tensor_list, dim)
else:
dim = symbolic_helper._get_const(dim, "i", "dim")
return g.op("ConcatFromSequence", tensor_list, axis_i=dim)
@_onnx_symbolic("aten::stack")
@_beartype.beartype
def stack(g: jit_utils.GraphContext, tensor_list, dim):
if symbolic_helper._is_packed_list(tensor_list):
return opset9.stack(g, tensor_list, dim)
else:
dim = symbolic_helper._get_const(dim, "i", "dim")
return g.op("ConcatFromSequence", tensor_list, axis_i=dim, new_axis_i=1)
@_onnx_symbolic("aten::_unique2")
@symbolic_helper.parse_args("v", "i", "i", "i")
@_beartype.beartype
def _unique2(g: jit_utils.GraphContext, self, sorted, return_inverse, return_counts):
u, indices, inverse_indices, counts = g.op(
"Unique", self, sorted_i=sorted, outputs=4
)
return u, inverse_indices, counts
@_onnx_symbolic("aten::unique_dim")
@symbolic_helper.parse_args("v", "i", "i", "i", "i")
@_beartype.beartype
def unique_dim(
g: jit_utils.GraphContext, self, dim, sorted, return_inverse, return_counts
):
u, indices, inverse_indices, counts = g.op(
"Unique", self, axis_i=dim, sorted_i=sorted, outputs=4
)
return u, inverse_indices, counts
@_onnx_symbolic("aten::topk")
@symbolic_helper.parse_args("v", "v", "i", "i", "i", "none")
@_beartype.beartype
def topk(g: jit_utils.GraphContext, self, k, dim, largest, sorted, out=None):
return symbolic_helper._topk_helper(
g, self, k, dim, largest=largest, sorted=sorted, out=out
)
@_onnx_symbolic("aten::sort")
@symbolic_helper.parse_args("v", "i", "i", "none")
@_beartype.beartype
def sort(g: jit_utils.GraphContext, self, dim, decending, out=None):
return symbolic_helper._sort_helper(g, self, dim, decending=decending, out=out)
@_onnx_symbolic("aten::argsort")
@symbolic_helper.parse_args("v", "i", "i", "none")
@_beartype.beartype
def argsort(g: jit_utils.GraphContext, self, dim, decending, out=None):
_, indices = symbolic_helper._sort_helper(
g, self, dim, decending=decending, out=out
)
return indices
@_onnx_symbolic("aten::round")
@symbolic_helper.parse_args("v", "i")
@_beartype.beartype
def round(g: jit_utils.GraphContext, self, decimals=0):
if not symbolic_helper._is_fp(self):
return self
if decimals == 0:
return g.op("Round", self)
mul = g.op("Mul", self, g.op("Constant", value_t=torch.tensor(pow(10, decimals))))
round = g.op("Round", mul)
return g.op(
"Mul", round, g.op("Constant", value_t=torch.tensor(pow(10, -1 * decimals)))
)
@_onnx_symbolic("aten::remainder")
@_beartype.beartype
def remainder(g: jit_utils.GraphContext, input, other):
if symbolic_helper._is_fp(input) or symbolic_helper._is_fp(other):
return opset9.remainder(g, input, other)
return g.op("Mod", input, other, fmod_i=0)
@_onnx_symbolic("aten::split")
@symbolic_helper.parse_args("v", "v", "i", "i")
@_beartype.beartype
def split(g: jit_utils.GraphContext, self, split_size_or_sizes, dim, _outputs=None):
if not symbolic_helper._is_split_static(split_size_or_sizes, _outputs):
split_out = g.op("SplitToSequence", self, split_size_or_sizes, axis_i=dim)
if _outputs is None:
return split_out
# Convert to multiple slice nodes iff number of splits and number of outputs are statically known.
if (
symbolic_helper._is_packed_list(split_size_or_sizes)
and len(symbolic_helper._unpack_list(split_size_or_sizes)) == _outputs
):
split_sizes = [
symbolic_helper._unsqueeze_helper(g, v, [0])
for v in symbolic_helper._unpack_list(split_size_or_sizes)
]
start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
axis = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
res = []
for i in range(_outputs):
end = g.op(
"Add", start, split_sizes[i]
) # split_sizes is a list of same length as _outputs
res.append(g.op("Slice", self, start, end, axis))
start = end
return res
return [
g.op(
"SequenceAt",
split_out,
g.op("Constant", value_t=torch.tensor([i], dtype=torch.long)),
)
for i in range(_outputs)
]
else:
return opset9.split(g, self, split_size_or_sizes, dim, _outputs)
@_onnx_symbolic("aten::split_with_sizes")
@symbolic_helper.parse_args("v", "v", "i", "i")
@_beartype.beartype
def split_with_sizes(g: jit_utils.GraphContext, self, split_sizes, dim, _outputs=None):
return split(g, self, split_sizes, dim, _outputs)
@_onnx_symbolic("aten::unbind")
@symbolic_helper.parse_args("v", "i", "i")
@_beartype.beartype
def unbind(g: jit_utils.GraphContext, self, dim=0, _outputs=None):
if _outputs is None:
return g.op(
"SplitToSequence",
self,
g.op("Constant", value_t=torch.tensor(1, dtype=torch.long)),
axis_i=dim,
keepdims_i=0,
)
else:
return opset9.unbind(g, self, dim, _outputs)
@_beartype.beartype
def _prepare_onnx_paddings(g: jit_utils.GraphContext, input, pad):
"""Generate paddings in ONNX order based on pad in pytorch.
Args:
input: the input tensor.
pad: the paddings in pytorch.
The order is dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, ..., dim_m_begin, dim_m_end,
where m is in range [0, n].
"""
if (
not symbolic_helper._is_packed_list(pad)
and symbolic_helper._is_list(pad)
and symbolic_helper._is_scalar_list(pad)
):
pad = g.op("ConcatFromSequence", pad, axis_i=0, new_axis_i=1)
# The desired order of paddings is
# dim_0_begin, dim_1_begin, ... , dim_0_end, ..., dim_n_end.
# n is the dimension of input.
# Assume zero-dimensions in the beginning, pad the "pad" sequence with zeros in the beginning
pad_len = opset9.size(g, pad, g.op("Constant", value_t=torch.tensor([0])))
# Set extension = [0] * (dim * 2 - len(pad))
rank = symbolic_helper._get_tensor_rank(input)
if rank is None:
rank = g.op("Size", g.op("Shape", input))
else:
rank = g.op("Constant", value_t=torch.tensor(rank, dtype=torch.int64))
extension = g.op(
"Sub",
g.op("Mul", rank, g.op("Constant", value_t=torch.tensor(2, dtype=torch.int64))),
pad_len,
)
# Concat pad with extension: paddings = [dim_n_begin, dim_n_end, dim_n-1_begin, dim_n-1_end, 0, 0, ... ]
# Currently ONNX only supports int64 type for Pad
pad = g.op("Cast", pad, to_i=_C_onnx.TensorProtoDataType.INT64)
paddings = g.op(
"Concat",
pad,
g.op(
"ConstantOfShape", extension, value_t=torch.tensor([0], dtype=torch.int64)
),
axis_i=0,
)
# Reshape and reverse order and collate first beginnings and then ends
# paddings = [[..., 0, dim_n-1_begin, dim_n_begin],
# [..., 0, dim_n-1_end, dim_n_end]]
# Reshape back to 1-D paddings = [..., 0, dim_n - 1_begin, dim_n_begin, ..., 0, dim_n - 1_end, dim_n_end]
paddings = symbolic_helper._reshape_helper(
g, paddings, g.op("Constant", value_t=torch.tensor([-1, 2]))
)
paddings = g.op("Transpose", opset10.flip(g, paddings, [0]), perm_i=[1, 0])
paddings = symbolic_helper._reshape_helper(
g, paddings, g.op("Constant", value_t=torch.tensor([-1]))
)
padding_c = g.op("Cast", paddings, to_i=_C_onnx.TensorProtoDataType.INT64)
return padding_c
@_onnx_symbolic("aten::constant_pad_nd")
@_beartype.beartype
def constant_pad_nd(g: jit_utils.GraphContext, input, padding, value=None):
mode = "constant"
value = symbolic_helper._maybe_get_scalar(value)
value = symbolic_helper._if_scalar_type_as(value, input)
pad = _prepare_onnx_paddings(g, input, padding)
return g.op("Pad", input, pad, value, mode_s=mode)
@_onnx_symbolic("aten::reflection_pad1d")
@_onnx_symbolic("aten::reflection_pad2d")
@_onnx_symbolic("aten::reflection_pad3d")
@_beartype.beartype
def reflection_pad(g: jit_utils.GraphContext, input, padding):
mode = "reflect"
paddings = _prepare_onnx_paddings(g, input, padding)
return g.op("Pad", input, paddings, mode_s=mode)
@_onnx_symbolic("aten::replication_pad1d")
@_onnx_symbolic("aten::replication_pad2d")
@_onnx_symbolic("aten::replication_pad3d")
@_beartype.beartype
def replication_pad(g: jit_utils.GraphContext, input, padding):
mode = "edge"
paddings = _prepare_onnx_paddings(g, input, padding)
return g.op("Pad", input, paddings, mode_s=mode)
@_onnx_symbolic("aten::pad")
@_beartype.beartype
def pad(
g: jit_utils.GraphContext,
input: _C.Value,
pad: _C.Value,
mode: _C.Value,
value: _C.Value,
):
mode = symbolic_helper._parse_arg(mode, "s")
if mode == "replicate":
return replication_pad(g, input, pad)
elif mode == "reflect":
return reflection_pad(g, input, pad)
elif mode == "constant":
return constant_pad_nd(g, input, pad, value)
elif mode == "circular":
return opset9._pad_circular(g, input, pad)
else:
raise errors.SymbolicValueError(f"Unrecognized padding mode {mode}", input)
@_onnx_symbolic("aten::linalg_det")
@_beartype.beartype
def linalg_det(g: jit_utils.GraphContext, self):
return g.op("Det", self)
@_onnx_symbolic("aten::logdet")
@_beartype.beartype
def logdet(g: jit_utils.GraphContext, input):
return opset9.log(g, linalg_det(g, input))
@_onnx_symbolic("aten::arange")
@_beartype.beartype
def arange(g: jit_utils.GraphContext, *args):
def _get_arange_dtype(dtype):
dtype = symbolic_helper._maybe_get_const(dtype, "i")
return dtype
if len(args) == 2 and all(isinstance(val, int) for val in args):
# aten::arange(Scalar start, Scalar end)
dtype = torch.int64
# Start index.
start = g.op(
"Constant",
value_t=torch.tensor(args[0], dtype=dtype),
)
# End (exclusive) index.
end = g.op(
"Constant",
value_t=torch.tensor(args[1], dtype=dtype),
)
# Step size from start to end indexes.
delta_default = g.op(
"Constant",
value_t=torch.tensor(1, dtype=dtype),
)
return g.op("Range", start, end, delta_default)
elif len(args) == 2 or len(args) == 5:
if len(args) == 2:
# aten::arange(Scalar end, Tensor out)
dtype = None
else:
# aten::arange(Scalar end, ScalarType dtype, Layout, Device, bool pin_memory)
dtype = _get_arange_dtype(args[1])
type_, end, start, step = symbolic_helper._arange_cast_helper(
g, end=args[0], dtype=dtype
)
start_default = g.op(
"Constant",
value_t=torch.tensor(0, dtype=type_.dtype()),
)
delta_default = g.op(
"Constant",
value_t=torch.tensor(1, dtype=type_.dtype()),
)
return g.op("Range", start_default, end, delta_default)
elif len(args) == 4 or len(args) == 7:
if len(args) == 4:
# aten::arange(Scalar start, Scalar end, Scalar step, Tensor out)
dtype = None
else:
# aten::arange(Scalar start, Scalar end, Scalar step, ScalarType dtype, Layout, Device, bool pin_memory)
dtype = _get_arange_dtype(args[3])
_, end, start, step = symbolic_helper._arange_cast_helper(
g, start=args[0], end=args[1], step=args[2], dtype=dtype
)
return g.op("Range", start, end, step)
elif len(args) == 6:
# aten::arange(Scalar start, Scalar end, ScalarType dtype, Layout, Device, bool pin_memory)
dtype = _get_arange_dtype(args[2])
type_, end, start, step = symbolic_helper._arange_cast_helper(
g, start=args[0], end=args[1], dtype=dtype
)
delta_default = g.op(
"Constant",
value_t=torch.tensor(1, dtype=type_.dtype()),
)
return g.op("Range", start, end, delta_default)
else:
return symbolic_helper._unimplemented(
"aten::arange", f"with {len(args)} arguments"
)
@_onnx_symbolic("aten::_dim_arange")
@symbolic_helper.parse_args("v", "i")
@_beartype.beartype
def _dim_arange(g: jit_utils.GraphContext, like, dim):
like_shape = g.op("Shape", like)
stop = g.op(
"Gather", like_shape, g.op("Constant", value_t=torch.tensor(dim)), axis_i=0
)
if symbolic_helper.is_caffe2_aten_fallback():
return g.op("_caffe2::Range", stop)
return arange(g, stop, 4, None, None, None)
@_onnx_symbolic("aten::size")
@symbolic_helper.quantized_args(True, quantize_output=False)
@_beartype.beartype
def size(g: jit_utils.GraphContext, self, dim=None):
if dim is None:
return g.op("Shape", self)
return symbolic_helper._size_helper(g, self, dim)
@_onnx_symbolic("aten::squeeze")
@_beartype.beartype
def squeeze(g: jit_utils.GraphContext, self, dim=None):
if dim is None:
return g.op("Squeeze", self)
# dim as a tensor
if not symbolic_helper._is_constant(dim):
return symbolic_helper._squeeze_helper(g, self, [dim])
dim = symbolic_helper._get_const(dim, "i", "dim")
input_rank = symbolic_helper._get_tensor_rank(self)
adjusted_dim = dim
if input_rank is not None and dim < 0:
adjusted_dim += input_rank
dim_size = symbolic_helper._get_tensor_dim_size(self, adjusted_dim)
if (dim < 0 and input_rank is None) or dim_size is None:
# If onnx shape inference is not on, export always as dynamic.
# Because we cannot tell if observed static shape is also static at runtime.
# create "cond" node (condition is shape[i]==1)
dim_constant = g.op("Constant", value_t=torch.tensor([dim]))
size = symbolic_helper._size_helper(g, self, dim_constant)
const_one = g.op("Constant", value_t=torch.ones(1, dtype=torch.int64))
cond = g.op("Equal", size, const_one)
# create the "If" node and add the "then" and "else" blocks to it.
if_op, (if_context, else_context), _ = jit_utils.add_op_with_blocks(
g, "If", cond, n_blocks=2
)
squeeze_ = symbolic_helper._squeeze_helper(if_context, self, [dim])
utils._add_output_to_block(if_context.block, squeeze_)
identity_ = else_context.op("Identity", self)
utils._add_output_to_block(else_context.block, identity_)
return if_op
# For static input shape
dim = adjusted_dim
if dim_size > 1:
warnings.warn(
"This model contains a squeeze operation on dimension "
+ str(dim)
+ ". The size of "
+ "this dimension in the given input is "
+ str(dim_size)
+ ". The model will "
+ "be exported without the squeeze node. If the model is intended to be used with dynamic "
+ "input shapes, please export with dynamic_axes argument."
)
return self
return symbolic_helper._squeeze_helper(g, self, [dim])
@_onnx_symbolic("aten::unsqueeze")
@_beartype.beartype
def unsqueeze(g: jit_utils.GraphContext, self, dim):
if symbolic_helper._is_constant(dim):
dim = symbolic_helper._get_const(dim, "i", "dim")
return symbolic_helper._unsqueeze_helper(g, self, [dim])
@_onnx_symbolic("aten::mm")
@_beartype.beartype
def mm(g: jit_utils.GraphContext, self, other):
return g.op("Gemm", self, other, beta_f=0.0, alpha_f=1.0)
@_onnx_symbolic("aten::index")
@_beartype.beartype
def index(g: jit_utils.GraphContext, self, index):
if symbolic_helper.is_caffe2_aten_fallback():
return g.at("index", self, index, overload_name="Tensor")
if symbolic_helper._is_packed_list(index):
indices = symbolic_helper._unpack_list(index)
else:
indices = [index]
# Handle single mask index.
if len(indices) == 1:
index = indices[0]
if not symbolic_helper._is_none(index) and (
symbolic_helper._is_bool(index)
or _type_utils.JitScalarType.from_value(index)
== _type_utils.JitScalarType.UINT8
):
index = opset9.nonzero(g, index)
return g.op("GatherND", self, index)
return opset9.index(g, self, index)
@_onnx_symbolic("aten::index_fill")
@_beartype.beartype
def index_fill(g: jit_utils.GraphContext, self, dim, index, value):
dim_value = symbolic_helper._parse_arg(dim, "i")
if symbolic_helper.is_caffe2_aten_fallback():
return g.at(
"index_fill",
self,
index,
value,
overload_name="int_Scalar",
dim_i=dim_value,
)
expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper(
g, self, dim, index
)
value = symbolic_helper._maybe_get_scalar(value)
value = symbolic_helper._if_scalar_type_as(value, self)
expanded_value = opset9.expand(g, value, expanded_index_shape, None)
return scatter(g, self, dim, expanded_index, expanded_value)
@_onnx_symbolic("aten::index_copy")
@_beartype.beartype
def index_copy(g: jit_utils.GraphContext, self, dim, index, source):
dim_value = symbolic_helper._parse_arg(dim, "i")
if symbolic_helper.is_caffe2_aten_fallback():
return g.at("index_copy", self, index, source, dim_i=dim_value)
expanded_index_shape, expanded_index = symbolic_helper._index_fill_reshape_helper(
g, self, dim, index
)
return scatter(g, self, dim, expanded_index, source)
@_onnx_symbolic("aten::__rshift_")
@_beartype.beartype
def __rshift_(g: jit_utils.GraphContext, self, other):
# make sure to cast other to self's type
# (when self is long, make sure that other is not float)
if _type_utils.JitScalarType.from_value(
other, _type_utils.JitScalarType.UNDEFINED
) != _type_utils.JitScalarType.from_value(self):
other = g.op(
"Cast",
other,
to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
)
if (
_type_utils.JitScalarType.from_value(self, _type_utils.JitScalarType.UNDEFINED)
== _type_utils.JitScalarType.UINT8
):
return g.op("BitShift", self, other, direction_s="RIGHT")
two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32))
# exponent (same type as self) has to be float or double in onnx::Pow
if not symbolic_helper._is_fp(self):
other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.FLOAT)
two_pow = g.op("Pow", two, other)
two_pow = g.op(
"Cast",
two_pow,
to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
)
rshift = g.op("Div", self, two_pow)
return rshift
@_onnx_symbolic("aten::__lshift_")
@_beartype.beartype
def __lshift_(g: jit_utils.GraphContext, self, other):
# make sure to cast other to self's type
# (when self is long, make sure that other is not float)
if _type_utils.JitScalarType.from_value(
other, _type_utils.JitScalarType.UNDEFINED
) != _type_utils.JitScalarType.from_value(self):
other = g.op(
"Cast",
other,
to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
)
if (
_type_utils.JitScalarType.from_value(self, _type_utils.JitScalarType.UNDEFINED)
== _type_utils.JitScalarType.UINT8
):
return g.op("BitShift", self, other, direction_s="LEFT")
two = g.op("Constant", value_t=torch.tensor(2, dtype=torch.float32))
# exponent (same type as self) has to be float or double in onnx::Pow
if not symbolic_helper._is_fp(self):
other = g.op("Cast", other, to_i=_C_onnx.TensorProtoDataType.FLOAT)
two_pow = g.op("Pow", two, other)
two_pow = g.op(
"Cast",
two_pow,
to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
)
lshift = g.op("Mul", self, two_pow)
return lshift
@_beartype.beartype
def _get_im2col_indices_along_dim(
g: jit_utils.GraphContext, input_d, kernel_size_d, dilation_d, padding_d, stride_d
):
# Input is always 4-D (N, C, H, W)
# Calculate indices of sliding blocks along spatial dimension
# Slide kernel over input each dim d:
# each dimension d ranges from 0 to input[d]+2xpadding[d]-dilation[d]x(kernel_size[d]-1)
# with steps = stride
blocks_d = g.op(
"Add", input_d, g.op("Constant", value_t=torch.tensor(padding_d * 2))
)
blocks_d = g.op(
"Sub",
blocks_d,
g.op("Constant", value_t=torch.tensor(dilation_d * (kernel_size_d - 1))),
)
# Stride kernel over input and find starting indices along dim d
blocks_d_indices = g.op(
"Range",
g.op("Constant", value_t=torch.tensor(0)),
blocks_d,
g.op("Constant", value_t=torch.tensor(stride_d)),
)
# Apply dilation on kernel and find its indices along dim d
kernel_grid = torch.arange(0, kernel_size_d * dilation_d, dilation_d)
kernel_grid = g.op("Constant", value_t=kernel_grid.unsqueeze(0))
# Broadcast and add kernel staring positions (indices) with
# kernel_grid along dim d, to get block indices along dim d
blocks_d_indices = symbolic_helper._unsqueeze_helper(
g, blocks_d_indices, [0]
) # Reshape to [1, -1]
kernel_mask = symbolic_helper._reshape_helper(
g, kernel_grid, g.op("Constant", value_t=torch.tensor([-1, 1]))
)
block_mask = g.op("Add", blocks_d_indices, kernel_mask)
return block_mask
@_beartype.beartype
def _get_im2col_padded_input(g: jit_utils.GraphContext, input, padding_h, padding_w):
# Input is always 4-D tensor (N, C, H, W)
# Padding tensor has the following format: (padding_h, padding_w)
# Reshape the padding to follow ONNX format: (dim1_begin, dim2_begin,...,dim1_end, dim2_end,...)
pad = g.op("Constant", value_t=torch.LongTensor([0, 0, padding_h, padding_w] * 2))
return g.op("Pad", input, pad)
@_beartype.beartype
def _get_im2col_output_shape(g: jit_utils.GraphContext, input, kernel_h, kernel_w):
batch_dim = size(g, input, g.op("Constant", value_t=torch.tensor(0)))
channel_dim = size(g, input, g.op("Constant", value_t=torch.tensor(1)))
channel_unfolded = g.op(
"Mul", channel_dim, g.op("Constant", value_t=torch.tensor(kernel_h * kernel_w))
)
return g.op(
"Concat",
symbolic_helper._unsqueeze_helper(g, batch_dim, [0]),
symbolic_helper._unsqueeze_helper(g, channel_unfolded, [0]),
g.op("Constant", value_t=torch.tensor([-1])),
axis_i=0,
)
@_onnx_symbolic("aten::im2col")
@symbolic_helper.parse_args("v", "is", "is", "is", "is")
@_beartype.beartype
def im2col(g: jit_utils.GraphContext, input, kernel_size, dilation, padding, stride):
# Input is always 4-D tensor (N, C, H, W)
# All other args are int[2]
input_h = size(g, input, g.op("Constant", value_t=torch.tensor(2)))
input_w = size(g, input, g.op("Constant", value_t=torch.tensor(3)))
stride_h, stride_w = stride[0], stride[1]
padding_h, padding_w = padding[0], padding[1]
dilation_h, dilation_w = dilation[0], dilation[1]
kernel_h, kernel_w = kernel_size[0], kernel_size[1]
blocks_row_indices = _get_im2col_indices_along_dim(
g, input_h, kernel_h, dilation_h, padding_h, stride_h
)
blocks_col_indices = _get_im2col_indices_along_dim(
g, input_w, kernel_w, dilation_w, padding_w, stride_w
)
output_shape = _get_im2col_output_shape(g, input, kernel_h, kernel_w)
padded_input = _get_im2col_padded_input(g, input, padding_h, padding_w)
# For a 4D matrix of size (1, 1, 3, 3) as below with kernel_size=2, stride=1, and dilation=1
# [[[[1., 2., 3.,],
# [4., 5., 6.,],
# [7., 8., 9.,]]]]
# First gather indices along rows (dim=2) with blocks_row_indices = [[0,1], [1,2]] to get:
# [[[[[1., 2., 3.],
# [4., 5., 6.]],
# [[4., 5., 6.],
# [7., 8., 9.]]]]]
# And then gather along cols (dim=4) with blocks_row_indices = [[0,1], [1,2]] to get:
# [[[[[[1., 2.],
# [4., 5.]],
# [[2., 3.],
# [5., 6]]],
# [[[4., 5.],
# [7., 8.]],
# [[5., 6.],
# [8., 9.]]]]]]
# Transpose dims 3 (depth) and 4 (rows), and then reshape to output shape (1, 1, 4, 4) to get:
# [[[1., 2., 4., 5.],
# [2., 3., 5., 6.],
# [4., 5., 7., 8.],
# [5., 6., 8., 9.]]]
output = g.op("Gather", padded_input, blocks_row_indices, axis_i=2)
output = g.op("Gather", output, blocks_col_indices, axis_i=4)
output = g.op("Transpose", output, perm_i=[0, 1, 2, 4, 3, 5])
return symbolic_helper._reshape_helper(g, output, output_shape)
@_onnx_symbolic("aten::narrow")
@_beartype.beartype
def narrow(g: jit_utils.GraphContext, input, dim, start, length):
end = g.op("Add", start, length)
return symbolic_helper._slice_helper(g, input, axes=dim, starts=start, ends=end)
@_onnx_symbolic("aten::flatten")
@symbolic_helper.quantized_args(True, False, False)
@symbolic_helper.parse_args("v", "i", "i")
@_beartype.beartype
def flatten(g: jit_utils.GraphContext, input, start_dim, end_dim):
dim = symbolic_helper._get_tensor_rank(input)
if dim == 1:
return input
# use ONNX's Flatten operator for cases where the output shape is 2D
if start_dim == 1:
if end_dim == -1 or (dim is not None and end_dim == dim - 1):
return g.op("Flatten", input, axis_i=start_dim)
elif start_dim == 0:
if end_dim == -2 or (dim is not None and end_dim == dim - 2):
return g.op("Flatten", input, axis_i=end_dim + 1)
if dim is None:
return symbolic_helper._unimplemented(
"dim",
"ONNX and PyTorch use different strategies to split the input. "
"Input rank must be known at export time.",
)
# if end_dim is negative add dim
if end_dim < 0:
end_dim = dim + end_dim
return symbolic_helper._flatten_helper(g, input, start_dim, end_dim, dim)
@_onnx_symbolic("aten::linalg_vector_norm")
@symbolic_helper.parse_args("v", "f", "is", "b", "v")
@_beartype.beartype
def linalg_vector_norm(
g: jit_utils.GraphContext,
self,
ord,
dim: Optional[Sequence[int]],
keepdim: bool,
dtype,
):
if ord == 0:
if dim is None:
self = symbolic_helper._reshape_helper(
g, self, g.op("Constant", value_t=torch.tensor([-1], dtype=torch.int64))
)
keepdim = False
cond_op = g.op(
"Not", g.op("Equal", self, g.op("Constant", value_t=torch.LongTensor([0])))
)
cond_op = g.op(
"Cast",
cond_op,
to_i=_type_utils.JitScalarType.from_value(self).onnx_type(),
)
return symbolic_helper._reducesum_helper(
g, cond_op, axes_i=dim, keepdims_i=keepdim
)
else:
return opset9.linalg_vector_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")
@_beartype.beartype
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,
):
if scale_grad_by_freq and GLOBALS.export_training:
return symbolic_helper._onnx_unsupported(
"embedding_bag with scale_grad_by_freq for training mode"
)
if padding_idx is not None and padding_idx >= 0:
raise RuntimeError("embedding_bag with padding_idx")
loop_condition = g.op("Constant", value_t=torch.tensor(1))
loop_condition = g.op("Cast", loop_condition, to_i=_C_onnx.TensorProtoDataType.BOOL)
zero = g.op("Constant", value_t=torch.tensor([0]))
indices_len = symbolic_helper._unsqueeze_helper(
g,
symbolic_helper._size_helper(
g, indices, g.op("Constant", value_t=torch.tensor(0))
),
[0],
)
if not include_last_offset:
offsets = [offsets, indices_len]
offsets = g.op("Concat", *offsets, axis_i=0)
# Offsets holds the starting index position of each bag. So we create a list of the indices slices (determined by
# offsets) and gather those indices in indices_row. Then we use this subset of indices to gather from embeddings.
# The embeddings output is a loop scan output, so we can avoid creating a sequence and inserting elements in.
offsets_starts = symbolic_helper._slice_helper(
g, offsets, axes=[0], starts=[0], ends=[sys.maxsize], steps=[1]
)
offsets_ends = symbolic_helper._slice_helper(
g, offsets, axes=[0], starts=[1], ends=[sys.maxsize], steps=[1]
)
loop_len = symbolic_helper._size_helper(
g, offsets_ends, g.op("Constant", value_t=torch.tensor(0))
)
loop, (loop_context,), _ = jit_utils.add_op_with_blocks(
g, "Loop", loop_len, loop_condition, n_blocks=1
)
loop_block = loop_context.block
# FIXME(justinchuby): We need to handle what happens when we call b.op on a node return
block_input_iter = utils._add_input_to_block(loop_block)
cond = utils._add_input_to_block(loop_block)
indices_start = loop_context.op(
"Gather", offsets_starts, block_input_iter, axis_i=0
)
indices_end = loop_context.op("Gather", offsets_ends, block_input_iter, axis_i=0)
indices_start = symbolic_helper._unsqueeze_helper(loop_context, indices_start, [0])
indices_end = symbolic_helper._unsqueeze_helper(loop_context, indices_end, [0])
indices_row = loop_context.op("Slice", indices, indices_start, indices_end, zero)
embeddings = loop_context.op("Gather", embedding_matrix, indices_row, axis_i=0)
if not symbolic_helper._is_none(per_sample_weights):
per_sample_weights_row = loop_context.op(
"Slice", per_sample_weights, indices_start, indices_end, zero
)
per_sample_weights_row = symbolic_helper._unsqueeze_helper(
loop_context, per_sample_weights_row, [1]
)
embeddings = loop_context.op("Mul", embeddings, per_sample_weights_row)
if mode == 0:
embeddings = symbolic_helper._reducesum_helper(
loop_context, embeddings, axes_i=[0], keepdims_i=0
)
elif mode == 1:
embeddings = loop_context.op("ReduceMean", embeddings, axes_i=[0], keepdims_i=0)
else:
embeddings = loop_context.op("ReduceMax", embeddings, axes_i=[0], keepdims_i=0)
cond_out = loop_context.op(
"Cast", loop_condition, to_i=_C_onnx.TensorProtoDataType.BOOL
)
utils._add_output_to_block(loop_block, cond_out)
utils._add_output_to_block(loop_block, embeddings)
# aten::embedding_bag returns a tuple of 4 elements: output, offset2bag, bag_size, max_indices.
# But the last three outputs are not used in torch.nn.EmbeddingBag or torch.nn.functional.embedding_bag.
return loop.node().output(), None, None, None
@_onnx_symbolic("aten::embedding_renorm")
@symbolic_helper.parse_args("v", "v", "f", "f")
@_beartype.beartype
def embedding_renorm(g: jit_utils.GraphContext, weight, indices, max_norm, norm_type):
unique_indices = g.op("Unique", indices)
partial_weight = g.op("Gather", weight, unique_indices)
norm_i = int(norm_type)
if norm_i == 1:
norm_type = "ReduceL1"
elif norm_i == 2:
norm_type = "ReduceL2"
else:
raise errors.SymbolicValueError(
f"Unsupported: ONNX export of embedding_renorm with norm: {norm_i}. "
"Only 1. and 2. are supported.",
weight,
)
partial_weight_norm = g.op(norm_type, partial_weight, axes_i=[1], keepdims_i=1)
# https://github.com/pytorch/pytorch/blob/0a07488ed2c47765e337e290bd138c0e6e459cbd/aten/src/ATen/native/Embedding.cpp#L177
# Add 1e-7 to prevent division by zero.
partial_weight_norm_ = g.op(
"Add", partial_weight_norm, g.op("Constant", value_t=torch.tensor(1e-7))
)
max_norm = torch.tensor(max_norm)
scales = g.op("Div", max_norm, partial_weight_norm_)
partial_weight_renorm = g.op("Mul", partial_weight, scales)
partial_weight_renorm = g.op(
"Where",
g.op("Greater", partial_weight_norm, max_norm),
partial_weight_renorm,
partial_weight,
)
return g.op(
"ScatterND",
weight,
symbolic_helper._unsqueeze_helper(g, unique_indices, [1]),
partial_weight_renorm,
)
@_onnx_symbolic("aten::chunk")
@_beartype.beartype
def chunk(g: jit_utils.GraphContext, self, chunks, dim):
# Calculate chunk size for dynamic chunk
dim_size = g.op("Gather", g.op("Shape", self), dim, axis_i=0)
chunk_size_s = g.op(
"Sub", chunks, g.op("Constant", value_t=torch.tensor([1], dtype=torch.long))
)
chunk_size = g.op("Div", g.op("Add", dim_size, chunk_size_s), chunks)
# Create splits vector
chunk_vec = [
opset9.expand(g, chunk_size, chunk_size_s, None),
g.op("Sub", dim_size, g.op("Mul", chunk_size, chunk_size_s)),
]
chunk_vec = g.op("Concat", *chunk_vec, axis_i=0)
return split(g, self, chunk_vec, dim)
@_onnx_symbolic("aten::normal")
@_beartype.beartype
def normal(
g: jit_utils.GraphContext,
mean,
std,
sizes=None,
generator=None,
dtype=None,
layout=None,
device=None,
pin_memory=None,
):
# If you can sample from a given distribution with mean 0 and variance 1, then you can easily sample from a
# scale-location transformation of that distribution, which has mean μ and variance σ's square. If x is a sample
# from a mean 0 and variance 1 distribution then
# σx+μ
# is a sample with mean μ and variance σ's square.
if sizes is not None and not symbolic_helper._is_none(sizes):
mean = opset9.expand(g, mean, sizes, None)
result = opset9.mul(g, std, g.op("RandomNormalLike", mean))
return add(g, result, mean)
@_onnx_symbolic("aten::atleast_1d")
@_beartype.beartype
def atleast_1d(g: jit_utils.GraphContext, self: torch._C.Value):
# NOTE: If it's 0D, reshape to 1D
# NOTE: self could be a packed list or a tensor
if symbolic_helper._is_value(self) and symbolic_helper._is_packed_list(self):
tensor_list = symbolic_helper._unpack_list(self)
new_tensor_list = []
for tensor in tensor_list:
new_tensor = tensor
tensor_rank = symbolic_helper._get_tensor_rank(tensor)
if tensor_rank == 0:
new_tensor = symbolic_helper._reshape_helper(
g, new_tensor, g.op("Constant", value_t=torch.tensor([1]))
)
new_tensor_list.append(new_tensor)
return g.op("SequenceConstruct", *new_tensor_list)
tensor_rank = symbolic_helper._get_tensor_rank(self)
if tensor_rank == 0:
self = symbolic_helper._reshape_helper(
g, self, g.op("Constant", value_t=torch.tensor([1]))
)
return self
@_onnx_symbolic("aten::atleast_2d")
@_beartype.beartype
def atleast_2d(g: jit_utils.GraphContext, self: torch._C.Value):
# NOTE: If it's 0D, reshape to 2D
# If it's 1D, unsqueeze to 2D
# NOTE: self could be a packed list or a tensor
if symbolic_helper._is_value(self) and symbolic_helper._is_packed_list(self):
tensor_list = symbolic_helper._unpack_list(self)
new_tensor_list = []
for tensor in tensor_list:
new_tensor = tensor
tensor_rank = symbolic_helper._get_tensor_rank(tensor)
if tensor_rank == 0:
new_tensor = symbolic_helper._reshape_helper(
g, new_tensor, g.op("Constant", value_t=torch.tensor([1, 1]))
)
elif tensor_rank == 1:
new_tensor = symbolic_helper._unsqueeze_helper(
g, new_tensor, axes_i=[0]
)
new_tensor_list.append(new_tensor)
return g.op("SequenceConstruct", *new_tensor_list)
tensor_rank = symbolic_helper._get_tensor_rank(self)
if tensor_rank == 0:
self = symbolic_helper._reshape_helper(
g, self, g.op("Constant", value_t=torch.tensor([1, 1]))
)
elif tensor_rank == 1:
self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[0])
return self
@_onnx_symbolic("aten::atleast_3d")
@_beartype.beartype
def atleast_3d(g: jit_utils.GraphContext, self: torch._C.Value):
# NOTE: If it's 0D, reshape to 3D
# If it's 1D, unsqueeze to 3D
# If it's 2D, unsqueeze to 3D
# NOTE: self could be a packed list or a tensor
if symbolic_helper._is_value(self) and symbolic_helper._is_packed_list(self):
tensor_list = symbolic_helper._unpack_list(self)
new_tensor_list = []
for tensor in tensor_list:
new_tensor = tensor
tensor_rank = symbolic_helper._get_tensor_rank(tensor)
if tensor_rank == 0:
new_tensor = symbolic_helper._reshape_helper(
g, new_tensor, g.op("Constant", value_t=torch.tensor([1, 1, 1]))
)
elif tensor_rank == 1:
new_tensor = symbolic_helper._unsqueeze_helper(
g, new_tensor, axes_i=[0]
)
new_tensor = symbolic_helper._unsqueeze_helper(
g, new_tensor, axes_i=[-1]
)
elif tensor_rank == 2:
new_tensor = symbolic_helper._unsqueeze_helper(
g, new_tensor, axes_i=[-1]
)
new_tensor_list.append(new_tensor)
return g.op("SequenceConstruct", *new_tensor_list)
tensor_rank = symbolic_helper._get_tensor_rank(self)
if tensor_rank == 0:
self = symbolic_helper._reshape_helper(
g, self, g.op("Constant", value_t=torch.tensor([1, 1, 1]))
)
elif tensor_rank == 1:
self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[0])
self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[-1])
elif tensor_rank == 2:
self = symbolic_helper._unsqueeze_helper(g, self, axes_i=[-1])
return self
@_onnx_symbolic("prim::ConstantChunk")
@_beartype.beartype
def prim_constant_chunk(g: jit_utils.GraphContext, self, chunks, dim):
input_shape = g.op("Shape", self)
axis = g.op("Constant", value_t=torch.tensor([dim], dtype=torch.long))
input_shape_dim = g.op("Gather", input_shape, axis, axis_i=0)
start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
chunk_size = g.op("Constant", value_t=torch.tensor([chunks], dtype=torch.long))
chunk_size_minus_1 = g.op(
"Constant", value_t=torch.tensor([chunks - 1], dtype=torch.long)
)
input_shape_dim_shift = g.op("Add", input_shape_dim, chunk_size_minus_1)
chunk_dim = g.op("Div", input_shape_dim_shift, chunk_size)
res = []
for i in range(chunks):
index = g.op("Constant", value_t=torch.tensor([i + 1], dtype=torch.long))
end = g.op("Mul", chunk_dim, index)
res.append(g.op("Slice", self, start, end, axis))
start = end
return res
@_onnx_symbolic("aten::hstack")
@_beartype.beartype
def hstack(g: jit_utils.GraphContext, tensor_list: _C.Value):
tensor_list = atleast_1d(g, tensor_list)
first_tensor = g.op(
"SequenceAt",
tensor_list,
g.op("Constant", value_t=torch.tensor(0, dtype=torch.long)),
)
first_tensor_shape = g.op("Shape", first_tensor)
first_tensor_dim = g.op("Size", first_tensor_shape)
const_one = g.op("Constant", value_t=torch.tensor(1, dtype=torch.long))
equal_to_one = g.op("Equal", first_tensor_dim, const_one)
(
if_op_greater,
(if_context_equal, else_context_equal),
_,
) = jit_utils.add_op_with_blocks(g, "If", equal_to_one, n_blocks=2, outputs=1)
result_if = if_context_equal.op(
"ConcatFromSequence", tensor_list, axis_i=0, new_axis_i=0
)
utils._add_output_to_block(if_context_equal.block, result_if)
result_else = else_context_equal.op(
"ConcatFromSequence", tensor_list, axis_i=1, new_axis_i=0
)
utils._add_output_to_block(else_context_equal.block, result_else)
result = if_op_greater.node().output()
return result
@_onnx_symbolic("aten::vstack")
@_beartype.beartype
def vstack(g: jit_utils.GraphContext, tensor_list: _C.Value):
tensor_list = atleast_2d(g, tensor_list)
return g.op("ConcatFromSequence", tensor_list, axis_i=0, new_axis_i=0)