/usr/local/lib64/python3.6/site-packages/torch/onnx
Edit: /usr/local/lib64/python3.6/site-packages/torch/onnx/symbolic_helper.py (39439B)
import torch
import warnings
import inspect
from sys import maxsize as maxsize
from typing import Set
import torch.onnx
# This import monkey-patches graph manipulation methods on Graph, used for the
# ONNX symbolics
import torch.onnx.utils
from functools import wraps
from torch._C import OptionalType
# Note [Edit Symbolic Files]
# EDITING THIS FILE AND SYMBOLIC_OPSET
FILES? READ THIS FIRST!
#
# - These files are ONLY for ATen operators (e.g., operators that show up in the
# trace as aten::blah). If you need to special case a primitive operator,
# look at _run_symbolic_function
# - Parameter ordering does NOT necessarily match what is in VariableType.cpp;
# tensors are always first, then non-tensor arguments.
# - Parameter names must *exactly* match the names in VariableType.cpp, because
# dispatch is done with keyword arguments.
# - Looking for inplace ops? They're detected by the trailing underscore, and
# transparently dispatched to their non inplace versions in
# "run_symbolic_function". See Note [Export inplace]
#
# ----------------------------------------------------------------------------------
# A note on Tensor types
# ----------------------------------------------------------------------------------
#
# In general, we should avoid depending on the type of Tensor Values contained
# within the trace graph. However, this is sometimes unavoidable (due to ONNX
# spec requirements, etc). The TensorType object has accessors for these properties
# that return the property if it is statically known and return nullopt otherwise.
#
# In general, we should prefer to rely on the least specific information possible.
# For example, not relying on tensor properties at all is better than relying
# on the number of dimensions which is better than relying on
# concrete shapes. Doing so will make the export symbolics
# more robust to different graphs.
# ---------------------------------------------------------------------------------
# Helper functions
# ---------------------------------------------------------------------------------
# Save some builtins as locals, because we'll shadow them below
_sum = sum
def _parse_arg(value, desc, arg_name=None, node_name=None):
if desc == "none":
return value
if desc == "v" or not _is_value(value):
return value
if value.node().mustBeNone():
return None
if value.node().kind() == "onnx::Constant":
tval = value.node()["value"]
if desc == "i":
return int(tval)
elif desc == "f":
return float(tval)
elif desc == "b":
return bool(tval)
elif desc == "s":
return str(tval)
elif desc == "t":
return tval
elif desc == "is":
return [int(v) for v in tval]
elif desc == "fs":
return [float(v) for v in tval]
else:
raise RuntimeError("ONNX symbolic doesn't know to interpret Constant node")
elif value.node().kind() == "prim::ListConstruct":
if desc == "is":
for v in value.node().inputs():
if v.node().kind() != "onnx::Constant":
raise RuntimeError("Failed to export an ONNX attribute '" + v.node().kind() +
"', since it's not constant, please try to make "
"things (e.g., kernel size) static if possible")
return [int(v.node()["value"]) for v in value.node().inputs()]
else:
raise RuntimeError("ONNX symbolic doesn't know to interpret ListConstruct node")
if arg_name is None or node_name is None:
raise RuntimeError("Expected node type 'onnx::Constant', got '{}'.".format(value.node().kind()))
else:
raise RuntimeError("Expected node type 'onnx::Constant' "
"for argument '{}' of node '{}', got '{}'.".format(arg_name, node_name, value.node().kind()))
def _maybe_get_const(value, desc):
if _is_value(value) and value.node().kind() == "onnx::Constant":
return _parse_arg(value, desc)
return value
def _maybe_get_scalar(value):
value_t = _maybe_get_const(value, "t")
if isinstance(value_t, torch.Tensor) and value_t.shape == ():
return value_t
return value
def _get_const(value, desc, arg_name):
if not _is_constant(value):
raise RuntimeError("ONNX symbolic expected a constant value of the {} argument, got `{}`".format(arg_name, value))
return _parse_arg(value, desc)
def _unpack_list(list_value):
list_node = list_value.node()
assert list_node.kind() == "prim::ListConstruct"
return list(list_node.inputs())
# Check if list_value is output from prim::ListConstruct
# This is usually called before _unpack_list to ensure the list can be unpacked.
def _is_packed_list(list_value):
return _is_value(list_value) and list_value.node().kind() == "prim::ListConstruct"
def parse_args(*arg_descriptors):
"""A decorator which converts args from torch._C.Value to built-in types.
For example:
@parse_args('v', 'i', 'fs')
foo(g, a, b, c):
assert isinstance(a, torch._C.Value)
assert isinstance(b, int)
assert isinstance(c, list)
assert isinstance(c[0], float)
Args:
arg_descriptors: list of str, where each element is
a string that specifies the type to convert to. Valid descriptors:
"v": no conversion, keep torch._C.Value.
"i": int
"is": list(int)
"f": float
"fs": list of float
"b": bool
"s": str
"t": torch.Tensor
"""
def decorator(fn):
fn._arg_descriptors = arg_descriptors
@wraps(fn)
def wrapper(g, *args, **kwargs):
# some args may be optional, so the length may be smaller
assert len(arg_descriptors) >= len(args)
try:
sig = inspect.signature(fn)
arg_names = list(sig.parameters.keys())[1:]
fn_name = fn.__name__
except Exception:
arg_names = [None] * len(args) # type: ignore[list-item]
fn_name = None
args = [_parse_arg(arg, arg_desc, arg_name, fn_name) # type: ignore[assignment]
for arg, arg_desc, arg_name in zip(args, arg_descriptors, arg_names)]
# only support _outputs in kwargs
assert len(kwargs) <= 1
if len(kwargs) == 1:
assert "_outputs" in kwargs
return fn(g, *args, **kwargs)
return wrapper
return decorator
def _scalar(x):
"""Convert a scalar tensor into a Python value."""
assert x.numel() == 1
return x.item()
def _if_scalar_type_as(g, self, tensor):
"""
Convert self into the same type of tensor, as necessary.
We only support implicit casting for scalars, so we never
actually need to insert an ONNX cast operator here; just
fix up the scalar.
"""
if isinstance(self, torch._C.Value):
return self
scalar_type = tensor.type().scalarType()
if scalar_type:
ty = scalar_type.lower()
return getattr(self, ty)()
return self
def _is_none(x):
return x.node().mustBeNone()
def _is_value(x):
return isinstance(x, torch._C.Value)
def _is_constant(value):
return not _is_value(value) or value.node().kind() in ('onnx::Constant', 'prim::Constant')
def _is_tensor(x):
return x.type().isSubtypeOf(torch._C.TensorType.get())
def _is_list(x):
return isinstance(x.type(), torch._C.ListType)
def _is_tensor_list(x):
return _is_list(x) and isinstance(x.type().getElementType(), torch._C.TensorType)
def _is_scalar_list(x):
"""
Check if x is a scalar list, for example: List[float], List[int].
Besides checking the type is ListType, we also check if the data type is
a valid ONNX data type.
"""
element_type = str(x.type().getElementType())
return _is_list(x) and \
element_type in scalar_name_to_pytorch.keys() and \
(scalar_name_to_pytorch[element_type] in cast_pytorch_to_onnx.keys())
def _get_tensor_rank(x):
if not _is_tensor(x) or x.type() is None:
return None
return x.type().dim()
def _get_tensor_sizes(x, allow_nonstatic=True):
if not _is_tensor(x) or x.type() is None:
return None
if allow_nonstatic:
# Each individual symbol is returned as None.
# e.g. [1, "a", "b"] -> [1, None, None]
return x.type().varyingSizes()
# returns None, if exists any symbol in sizes.
# e.g. [1, "a", "b"] -> None
return x.type().sizes()
def _get_tensor_dim_size(x, dim):
try:
sizes = _get_tensor_sizes(x)
return sizes[dim]
except Exception:
pass
return None
def _unimplemented(op, msg):
warnings.warn("ONNX export failed on " + op + " because " + msg + " not supported")
def _onnx_unsupported(op_name):
raise RuntimeError("Unsupported: ONNX export of operator {}. "
"Please feel free to request support or submit a pull request on PyTorch GitHub.".format(op_name))
def _onnx_opset_unsupported(op_name, current_opset, supported_opset):
raise RuntimeError("Unsupported: ONNX export of {} in "
"opset {}. Please try opset version {}.".format(op_name, current_opset, supported_opset))
def _onnx_opset_unsupported_detailed(op_name, current_opset, supported_opset, reason):
raise RuntimeError("Unsupported: ONNX export of {} in "
"opset {}. {}. Please try opset version {}.".format(op_name, current_opset, reason, supported_opset))
def _block_list_in_opset(name):
def symbolic_fn(*args, **kwargs):
raise RuntimeError("ONNX export failed on {}, which is not implemented for opset {}. "
"Try exporting with other opset versions."
.format(name, _export_onnx_opset_version))
return symbolic_fn
def _try_get_scalar_type(*args):
for arg in args:
try:
return arg.type().scalarType()
except RuntimeError:
pass
return None
def _select_helper(g, self, dim, index, apply_reshape=True):
index_const = _maybe_get_scalar(index)
index_dim = _get_tensor_rank(index)
if not _is_value(index_const):
# Index is a constant scalar. Make it a size 1 constant tensor.
index = g.op("Constant", value_t=torch.LongTensor([index_const]))
elif index_dim is not None and apply_reshape:
if index_dim == 0:
# Index is a scalar. Reshape it to a size 1 tensor.
index = _reshape_helper(g, index, g.op("Constant", value_t=torch.LongTensor([1])))
index_scalar_type = index.type().scalarType()
if index_scalar_type is None or index_scalar_type not in ["Long", "Int"]:
index = g.op("Cast", index, to_i=cast_pytorch_to_onnx["Long"])
return g.op("Gather", self, index, axis_i=dim)
def _slice_helper(g, input, axes, starts, ends, steps=None, dynamic_slice=False):
if _export_onnx_opset_version <= 9:
from torch.onnx.symbolic_opset9 import _slice as _slice9
return _slice9(g, input, axes, starts, ends)
else:
from torch.onnx.symbolic_opset10 import _slice as _slice10
return _slice10(g, input, axes, starts, ends, steps, dynamic_slice)
def _is_fp(value):
if value:
if isinstance(value, torch.Tensor):
type = value.dtype
return (type == "torch.float32") or (type == "torch.float64") or (type == "torch.float16")
else:
type = value.type().scalarType()
if type is None:
warnings.warn("Type cannot be inferred, which might cause exported graph to produce incorrect results.")
return (type == "Float") or (type == "Double") or (type == "Half")
return False
def _dtype_is_fp(type_value):
if type_value:
return (type_value == torch.float16) or (type_value == torch.float32) or (type_value == torch.float64)
return False
def _generate_wrapped_number(g, scalar):
"""
Create a wrapped number based on https://github.com/pytorch/pytorch/issues/9515
A Tensor is a considered a "wrapped number" if it is
auto-wrapped from a C++ or Python number type. Integer types are
wrapped as 0-dim int64 tensors and floating-point types are
wrapped as 0-dim double tensors.
The input to this function is constant value. If the data type
is a floating point type, it is converted to a 0-dim double
tensor, else it is converted to a 0-dim tensor of its original type
"""
assert not isinstance(scalar, torch.Tensor)
if isinstance(scalar, float):
return g.op("Constant", value_t=torch.tensor(scalar, dtype=torch.double))
return g.op("Constant", value_t=torch.tensor(scalar))
def _sort_helper(g, input, dim, decending=True, out=None):
if out is not None:
_unimplemented("Sort", "Out parameter is not supported")
shape_ = g.op("Shape", input)
dim_size_ = g.op("Gather", shape_, g.op("Constant", value_t=torch.tensor([dim], dtype=torch.int64)))
if _export_onnx_opset_version <= 10:
if not decending:
_unimplemented("Sort", "Ascending is not supported")
return g.op("TopK", input, dim_size_, axis_i=dim, outputs=2)
else:
return g.op("TopK", input, dim_size_, axis_i=dim, largest_i=decending, outputs=2)
def _topk_helper(g, input, k, dim, largest=True, sorted=False, out=None):
if out is not None:
_unimplemented("TopK", "Out parameter is not supported")
if not _is_value(k):
k = g.op("Constant", value_t=torch.tensor([k], dtype=torch.int64))
else:
k = _reshape_helper(g, k, g.op("Constant", value_t=torch.tensor([1])))
if _export_onnx_opset_version <= 10:
if not largest:
_unimplemented("TopK", "Ascending is not supported")
return g.op("TopK", input, k, axis_i=dim, outputs=2)
else:
return g.op("TopK", input, k, axis_i=dim, largest_i=largest, sorted_i=sorted, outputs=2)
def _interpolate_warning(interpolate_mode):
onnx_op = "onnx:Resize" if _export_onnx_opset_version >= 10 else "onnx:Upsample"
warnings.warn("You are trying to export the model with " + onnx_op + " for ONNX opset version "
"" + str(_export_onnx_opset_version) + ". "
"This operator might cause results to not match the expected results by PyTorch.\n"
"ONNX's Upsample/Resize operator did not match Pytorch's Interpolation until opset 11. "
"Attributes to determine how to transform the input were added in onnx:Resize in opset 11 "
"to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).\n"
"We recommend using opset 11 and above for models using this operator.")
def _unsqueeze_helper(g, input, axes_i):
if _export_onnx_opset_version >= 13:
axes = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
return g.op("Unsqueeze", input, axes)
else:
return g.op("Unsqueeze", input, axes_i=axes_i)
def _squeeze_helper(g, input, axes_i):
if _export_onnx_opset_version >= 13:
axes = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
return g.op("Squeeze", input, axes)
else:
return g.op("Squeeze", input, axes_i=axes_i)
def _reducesum_helper(g, input, axes_i=None, keepdims_i=1, noop_with_empty_axes_i=0):
keepdims_i = _maybe_get_const(keepdims_i, "i")
if _export_onnx_opset_version >= 13:
if axes_i:
if not _is_value(axes_i):
axes_i = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
return g.op("ReduceSum", input, axes_i, keepdims_i=keepdims_i, noop_with_empty_axes_i=noop_with_empty_axes_i)
return g.op("ReduceSum", input, keepdims_i=keepdims_i, noop_with_empty_axes_i=noop_with_empty_axes_i)
else:
return g.op("ReduceSum", input, axes_i=axes_i, keepdims_i=keepdims_i)
def _interpolate_size_to_scales(g, input, output_size, dim):
output_size = _maybe_get_const(output_size, "is")
if _is_value(output_size):
offset = 2
offsets = g.op("Constant", value_t=torch.ones(offset, dtype=torch.float32))
dividend = g.op("Cast", output_size, to_i=cast_pytorch_to_onnx["Float"])
divisor = _slice_helper(g, g.op("Shape", input), axes=[0], ends=[maxsize], starts=[offset])
divisor = g.op("Cast", divisor, to_i=cast_pytorch_to_onnx["Float"])
scale_dims = g.op("Div", dividend, divisor)
scales = g.op("Concat", offsets, scale_dims, axis_i=0)
else:
scales_constant = [1. if i < 2 else
float(output_size[-(dim - i)]) / float(input.type().sizes()[-(dim - i)])
for i in range(0, dim)]
scales = g.op("Constant", value_t=torch.tensor(scales_constant, dtype=torch.float32))
return scales
def _interpolate_get_scales_if_available(g, scales):
available_scales = _maybe_get_const(scales[0], "fs") != -1 and not _is_none(scales[0])
if not available_scales:
return None
offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.float32))
scales_list = g.op("Constant", value_t=torch.tensor(_maybe_get_const(scales[0], "fs")))
scales = g.op("Concat", offsets, scales_list, axis_i=0)
return scales
def _get_interpolate_attributes(g, mode, args):
if mode == "nearest":
align_corners = None
scales = args[0:]
else:
align_corners = args[0]
scales = args[1:]
scales = _interpolate_get_scales_if_available(g, scales)
return scales, align_corners
def _interpolate_get_scales(g, scale_factor, dim):
offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.float32))
scale_factor_rank = _get_tensor_rank(scale_factor)
if isinstance(scale_factor.type(), torch._C.ListType) or (scale_factor_rank is not None and scale_factor_rank > 0):
return g.op("Concat", offsets, scale_factor, axis_i=0)
else:
scale_factor = _unsqueeze_helper(g, scale_factor, [0])
scale_factor = g.op("Cast", scale_factor, to_i=cast_pytorch_to_onnx["Float"])
scales = [scale_factor for i in range(dim - 2)]
scale_factor = g.op("Concat", offsets, *scales, axis_i=0)
return scale_factor
def _interpolate_get_scales_and_mode(g, input, size, scale_factor, mode , align_corners):
mode = _maybe_get_const(mode, "s")
if "linear" in mode:
mode = "linear"
if "cubic" in mode:
mode = "cubic"
_interpolate_warning(mode)
align_corners = _maybe_get_const(align_corners, "b")
if isinstance(align_corners, bool) and align_corners:
return _unimplemented("interpolate", "align_corners == True")
if not input.type().dim():
return _unimplemented("interpolate", "missing input shape")
dim = input.type().dim()
if not _is_none(scale_factor):
scale_factor = _interpolate_get_scales(g, scale_factor, dim)
elif not _is_none(size):
if not _is_packed_list(size):
is_scalar = ((_maybe_get_const(size, "t").dim() == 0))
if is_scalar:
size = _unsqueeze_helper(g, size, [0])
size = [size for i in range(dim - 2)]
size = g.op("Concat", *size, axis_i=0)
scale_factor = _interpolate_size_to_scales(g, input, size, dim)
else:
return _unimplemented("interpolate", "Both size and scales are None in __interpolate")
return scale_factor, mode
def _interpolate_helper(name, dim, interpolate_mode):
def symbolic_fn(g, input, output_size, *args):
scales, align_corners = _get_interpolate_attributes(g, interpolate_mode, args)
align_corners = _maybe_get_scalar(align_corners)
coordinate_transformation_mode = "asymmetric" if interpolate_mode == "nearest" \
else "align_corners" if align_corners else "pytorch_half_pixel"
if scales is None:
input_size = g.op("Shape", input)
input_size_beg = _slice_helper(g, input_size, axes=[0], ends=[2], starts=[0])
output_size = g.op("Cast", output_size, to_i=cast_pytorch_to_onnx["Long"])
output_size = g.op("Concat", input_size_beg, output_size, axis_i=0)
if _export_onnx_opset_version >= 13:
empty_roi = _optional_input_placeholder_tensor(g)
empty_scales = _optional_input_placeholder_tensor(g)
else:
empty_roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
empty_scales = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
return g.op("Resize",
input,
empty_roi,
empty_scales,
output_size,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=interpolate_mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
else:
if _export_onnx_opset_version >= 13:
empty_roi = _optional_input_placeholder_tensor(g)
else:
empty_roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
return g.op("Resize",
input,
empty_roi,
scales,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=interpolate_mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
return symbolic_fn
def __interpolate_helper(g, input, size, scale_factor, mode, align_corners, recompute_scale_factor):
mode = _maybe_get_const(mode, "s")
if "linear" in mode:
mode = "linear"
if "cubic" in mode:
mode = "cubic"
align_corners = _maybe_get_const(align_corners, "b")
align_corners = False if not isinstance(align_corners, bool) else align_corners
coordinate_transformation_mode = "asymmetric" if mode == "nearest" \
else "align_corners" if align_corners else "pytorch_half_pixel"
if not _is_none(size) :
input_size = g.op("Shape", input)
input_size = _slice_helper(g, input_size, axes=[0], ends=[2], starts=[0])
# in some cases size is not a packed list but size is a scalar
# We need to also verify that (_maybe_get_const(size, "t").dim() == 0)
# but this information is not always available. Try to get the dim,
# and if not assume that it is not a scalar.
try:
is_scalar = not _is_packed_list(size) and ((_maybe_get_const(size, "t").dim() == 0))
except AttributeError:
is_scalar = not _is_packed_list(size)
if not is_scalar:
warnings.warn("Cannot verify if the output_size is a scalar "
"while exporting interpolate. Assuming that it is not a scalar.")
if is_scalar:
rank = _get_tensor_rank(input)
if rank is None:
return _unimplemented("interpolate (with a scalar output_size)",
"missing input shape (try giving an array of output_size values)")
size = _unsqueeze_helper(g, size, [0])
size = [size for i in range(rank - 2)]
size = g.op("Concat", *size, axis_i=0)
size = g.op("Cast", size, to_i=cast_pytorch_to_onnx["Long"])
size = g.op("Concat", input_size, size, axis_i=0)
if _export_onnx_opset_version >= 13:
empty_roi = _optional_input_placeholder_tensor(g)
empty_scales = _optional_input_placeholder_tensor(g)
else:
empty_roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
empty_scales = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
return g.op("Resize",
input,
empty_roi,
empty_scales,
size,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=mode, # nearest, linear, or cubic
nearest_mode_s="floor")
else: # if not _is_none(scales)
rank = _get_tensor_rank(input)
if rank is None:
return _unimplemented("interpolate (with scales)", "missing input shape")
if _export_onnx_opset_version >= 13:
empty_roi = _optional_input_placeholder_tensor(g)
else:
empty_roi = g.op("Constant", value_t=torch.tensor([], dtype=torch.float32))
scales = _interpolate_get_scales(g, scale_factor, rank)
return g.op("Resize",
input,
empty_roi,
scales,
coordinate_transformation_mode_s=coordinate_transformation_mode,
cubic_coeff_a_f=-0.75, # only valid when mode="cubic"
mode_s=mode, # nearest, linear, or cubic
nearest_mode_s="floor") # only valid when mode="nearest"
def _unbind_helper(g, self, dim, _outputs):
if _export_onnx_opset_version < 11:
from torch.onnx.symbolic_opset9 import unbind
elif _export_onnx_opset_version <= 12:
from torch.onnx.symbolic_opset11 import unbind # type: ignore[no-redef]
else:
from torch.onnx.symbolic_opset13 import unbind # type: ignore[no-redef]
return unbind(g, self, dim, _outputs)
def _scatter_helper(g, self, dim, index, src):
if _export_onnx_opset_version <= 10:
from torch.onnx.symbolic_opset9 import scatter
else:
# for mypy, scatter was imported two lines above
from torch.onnx.symbolic_opset11 import scatter # type: ignore[no-redef]
return scatter(g, self, dim, index, src)
def _repeat_interleave_split_helper(g, self, reps, dim):
if _export_onnx_opset_version <= 12:
return g.op("Split", self, split_i=[1] * reps, axis_i=dim, outputs=reps)
else:
from torch.onnx.symbolic_opset13 import split
repeats = g.op("Constant", value_t=torch.tensor([1] * reps))
return split(g, self, repeats, dim, _outputs=reps)
def _arange_cast_helper(g, end, start=None, step=None, dtype=None):
def _is_all_integral(scalars):
for scalar in scalars:
try:
if scalar.type().scalarType() != "Long":
return False
except Exception:
pass
return True
# This logic is based on torch.arange docs. If "dtype" is provided,
# infer input types from dtype. If not, then check if any of start, stop,
# or step are floating point, and infer the type from get_default.
# Otherwise, the dtype is inferred to be torch.int64.
if dtype is None or (_is_value(dtype) and _is_none(dtype)):
if _is_all_integral([start, end, step]):
type = scalar_type_to_pytorch_type.index(torch.int64)
else:
type = scalar_type_to_pytorch_type.index(torch.get_default_dtype())
else:
type = dtype
start = g.op("Cast", start, to_i=scalar_type_to_onnx[type]) if start else None
end = g.op("Cast", end, to_i=scalar_type_to_onnx[type]) if end else None
step = g.op("Cast", step, to_i=scalar_type_to_onnx[type]) if step else None
return type, end, start, step
def _arange_helper(g, *args):
if _export_onnx_opset_version <= 10:
from torch.onnx.symbolic_opset9 import arange
else:
from torch.onnx.symbolic_opset11 import arange # type: ignore[no-redef]
return arange(g, *args)
def _size_helper(g, self, dim):
full_shape = g.op("Shape", self)
from torch.onnx.symbolic_opset9 import select
return select(g, full_shape, g.op("Constant", value_t=torch.tensor([0])), dim)
def _index_fill_reshape_helper(g, self, dim, index):
# 1. reshape index => [1, ..., 1, dim, 1, ..., 1]
# 2. expand index => [..., dim, ...], same shape as self except for dim.
# 3. expand value as well.
# 4. apply onnx::scatter.
from torch.onnx.symbolic_opset9 import expand
if _export_onnx_opset_version <= 10:
from torch.onnx.symbolic_opset9 import scatter
else:
# for mypy, scatter was imported two lines above
from torch.onnx.symbolic_opset11 import scatter # type: ignore[no-redef]
if self.type().dim() is None:
return _unimplemented("index_fill", "input rank not accesible")
self_dim = self.type().dim()
dim_value = _parse_arg(dim, "i")
unsqueezed_index = _unsqueeze_helper(g, index, [i for i in range(self_dim) if i != dim_value])
expanded_index_shape = scatter(g, g.op("Shape", self), 0,
_unsqueeze_helper(g, dim, [0]), g.op("Shape", index))
expanded_index = expand(g, unsqueezed_index, expanded_index_shape, None)
return expanded_index_shape, expanded_index
# When using reshape helper (opset_version >= 14), if reshape has -1,
# allowzero cannot be set to 1
def _reshape_helper(g, input, shape, allowzero=0):
shape = _maybe_get_const(shape, "is")
if not _is_value(shape):
shape = g.op("Constant", value_t=torch.LongTensor(shape))
if _export_onnx_opset_version <= 13:
return g.op("Reshape", input, shape)
else:
warnings.warn("allowzero=0 by default. In order to honor zero value in shape use allowzero=1")
return g.op("Reshape", input, shape, allowzero_i=allowzero)
def _batchnorm_helper(g, input, weight, bias, running_mean, running_var):
from torch.onnx.symbolic_opset9 import _var_mean
batch_size = _get_tensor_dim_size(input, 0)
channel_size = _get_tensor_dim_size(input, 1)
if weight is None or _is_none(weight):
if channel_size is None:
raise RuntimeError("Unsupported: ONNX export of batch_norm for unknown "
"channel size.")
weight_value = torch.tensor([1.] * channel_size).type(
"torch." + input.type().scalarType() + "Tensor")
weight = g.op("Constant", value_t=weight_value)
if bias is None or _is_none(bias):
if channel_size is None:
raise RuntimeError("Unsupported: ONNX export of batch_norm for unknown "
"channel size.")
bias_value = torch.tensor([0.] * channel_size).type(
"torch." + input.type().scalarType() + "Tensor")
bias = g.op("Constant", value_t=bias_value)
# If track_running_stats is set to False batch statistics are instead used during evaluation time
if running_mean is None or _is_none(running_mean) or running_var is None or _is_none(running_var):
assert batch_size is not None and channel_size is not None
reshape_in = _reshape_helper(g, input,
g.op("Constant", value_t=torch.tensor([batch_size, channel_size, -1],
dtype=torch.int64)))
trans_in = g.op("Transpose", reshape_in, perm_i=[0, 2, 1])
running_var, running_mean = _var_mean(g, trans_in,
g.op("Constant", value_t=torch.tensor([0, 1], dtype=torch.int64)),
False, False)
return weight, bias, running_mean, running_var
def _avgpool_helper(tuple_fn, padding, kernel_size, stride, divisor_override, name):
if divisor_override and divisor_override.node().kind() != "prim::Constant":
return _unimplemented(name, "divisor_override")
if not stride:
stride = kernel_size
padding = tuple(tuple_fn(padding))
return padding
def check_training_mode(op_train_mode, op_name):
global _training_mode
op_train_mode = True if op_train_mode == 1 else False
if _training_mode is not None and op_train_mode != _training_mode:
op_mode = "training " if op_train_mode else "inference"
training_mode = "training " if _training_mode else "inference"
# setting the model mode could result in op_mode != _training_mode
# if the model is a FuncModule. In this case we warn the user of
# the state and export depending on op_mode
# This is to support use-cases of fixing certain layer weights
# in training.
warnings.warn("ONNX export mode is set to " + training_mode +
" mode, but operator " + op_name + " is set to " +
op_mode + " mode. The operators will be exported in " +
op_mode + ", as specified by the functional operator.")
def _flatten_helper(g, input, start_dim, end_dim, dim):
input_size = g.op("Shape", input)
slice1 = _slice_helper(g, input_size, axes=[0], starts=[0], ends=[start_dim])
slices = [slice1, g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long))]
if end_dim < dim - 1:
slice3 = _slice_helper(g, input_size, axes=[0], starts=[end_dim + 1], ends=[dim])
slices = [slice1, g.op("Constant", value_t=torch.tensor([-1], dtype=torch.long)), slice3]
final_shape = g.op("Concat", *slices, axis_i=0)
from torch.onnx.symbolic_opset9 import _reshape_from_tensor
return _reshape_from_tensor(g, input, final_shape)
def _is_split_static(split_size_or_sizes, _outputs):
if _outputs is None:
return False
if _is_value(split_size_or_sizes) and split_size_or_sizes.node().kind() != "onnx::Constant":
return False
return True
def _optional_input_placeholder_tensor(g):
n = g.op("prim::Constant")
n.setType(OptionalType.ofTensor())
return n
def _handle_reduce_dim_none(g, self, op_name):
rank = _get_tensor_rank(self)
if rank is not None and any([_get_tensor_dim_size(self, i) == 0 for i in range(rank)]):
# If input tensor is empty, according to ONNX ReduceSum definition,
# set keepdims=1 so that the resulted tensor has the same rank as the input.
return g.op(op_name, self, keepdims_i=1)
return g.op(op_name, self, keepdims_i=0)
# ---------------------------------------------------------------------
# ONNX operator version
# ---------------------------------------------------------------------
# READ ME BEFORE EDITING _default_onnx_opset_version:
#
# The variable below controls which ONNX operator set version we are
# targeting. THIS VARIABLE HAS SEMANTIC EFFECT! Say a breaking
# change occurred in version 8. As long as this variable < 8, you can
# export models targeting the old behavior. However, if you bump
# this variable to 8 or later, the breaking change will take into effect:
# you MUST adjust any symbolic affected by breaking changes. The ONNX
# spec publishes a *comprehensive* list of BC-breaking changes for every
# operator revision at:
#
# https://github.com/onnx/onnx/blob/master/docs/Changelog.md
#
# Please be sure to go through and check all of our implementations here before
# increasing this number. This includes symbolic definitions NOT in this
# file, so grep for "OpName" (with quotes)
#
# Besides, opset_version can be specified in the invocation of export()
# and export_to_pretty_string(), and _export_onnx_opset_version will be set
# and the symbolic functions should check it to determine the behavior
# of the exporter.
_default_onnx_opset_version = 9
_onnx_main_opset = 14
_onnx_stable_opsets = [7, 8, 9, 10, 11, 12, 13]
_export_onnx_opset_version = _default_onnx_opset_version
def _set_opset_version(opset_version):
global _export_onnx_opset_version
if opset_version == _default_onnx_opset_version:
_export_onnx_opset_version = opset_version
return
if opset_version in _onnx_stable_opsets + [_onnx_main_opset]:
_export_onnx_opset_version = opset_version
return
raise ValueError("Unsupported ONNX opset version: " + str(opset_version))
_operator_export_type = None
def _set_operator_export_type(operator_export_type):
global _operator_export_type
_operator_export_type = operator_export_type
_training_mode = None
def _set_training_mode(training_mode):
global _training_mode
_training_mode = training_mode
_onnx_shape_inference = False
# This function is for debug use only.
# onnx_shape_inference = True by default.
def _set_onnx_shape_inference(onnx_shape_inference):
global _onnx_shape_inference
_onnx_shape_inference = onnx_shape_inference
# Metaprogram symbolics for each ATen native specialized cast operator.
# For e.g. we specify a function named `_cast_uint8_t` that instantiates an
# ONNX cast node with `to` attribute "UINT8"
#
# TODO: remove these once we support Type's in the JIT IR and we can once again
# use the unified toType operator
cast_pytorch_to_onnx = {
"Byte": torch.onnx.TensorProtoDataType.UINT8,
"Char": torch.onnx.TensorProtoDataType.INT8,
"Double": torch.onnx.TensorProtoDataType.DOUBLE,
"Float": torch.onnx.TensorProtoDataType.FLOAT,
"Half": torch.onnx.TensorProtoDataType.FLOAT16,
"Int": torch.onnx.TensorProtoDataType.INT32,
"Long": torch.onnx.TensorProtoDataType.INT64,
"Short": torch.onnx.TensorProtoDataType.INT16,
"Bool": torch.onnx.TensorProtoDataType.BOOL,
"ComplexFloat": torch.onnx.TensorProtoDataType.COMPLEX64,
"ComplexDouble": torch.onnx.TensorProtoDataType.COMPLEX128,
"Undefined": torch.onnx.TensorProtoDataType.UNDEFINED,
}
scalar_name_to_pytorch = {
"uint8_t": "Byte",
"int8_t": "Char",
"double": "Double",
"float": "Float",
"half": "Half",
"int": "Int",
"int64_t": "Long",
"int16_t": "Short",
"bool": "Bool",
"complex64": "ComplexFloat",
"complex128": "ComplexDouble"
}
# This indicates each scalar type's corresponding
# torch type. Related source:
# https://github.com/pytorch/pytorch/blob/da7468853ae322252270bbb58032668bd21b7457/c10/core/ScalarType.h
scalar_type_to_pytorch_type = [
torch.uint8, # 0
torch.int8, # 1
torch.short, # 2
torch.int, # 3
torch.int64, # 4
torch.half, # 5
torch.float, # 6
torch.double, # 7
torch.complex32, # 8
torch.complex64, # 9
torch.complex128, # 10
torch.bool, # 11
]
def _cast_func_template(to_i, g, input, non_blocking):
return g.op("Cast", input, to_i=to_i)
scalar_type_to_onnx = [
cast_pytorch_to_onnx["Byte"],
cast_pytorch_to_onnx["Char"],
cast_pytorch_to_onnx["Short"],
cast_pytorch_to_onnx["Int"],
cast_pytorch_to_onnx["Long"],
cast_pytorch_to_onnx["Half"],
cast_pytorch_to_onnx["Float"],
cast_pytorch_to_onnx["Double"],
cast_pytorch_to_onnx["Undefined"],
cast_pytorch_to_onnx["ComplexFloat"],
cast_pytorch_to_onnx["ComplexDouble"],
cast_pytorch_to_onnx["Bool"],
]
# Global set to store the list of quantized operators in the network.
# This is currently only used in the conversion of quantized ops from PT -> C2 via ONNX.
_quantized_ops: Set[int] = set()