mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
34cc77a811
Summary: Fixes https://github.com/pytorch/pytorch/issues/45215 This is a follow up PR of https://github.com/pytorch/pytorch/issues/45258 and https://github.com/pytorch/pytorch/issues/48782 Pull Request resolved: https://github.com/pytorch/pytorch/pull/48980 Reviewed By: zhangguanheng66 Differential Revision: D25399823 Pulled By: ezyang fbshipit-source-id: 798055f4abbbffecdfab0325884193c81addecec
639 lines
25 KiB
Python
639 lines
25 KiB
Python
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import torch
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import warnings
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from sys import maxsize as maxsize
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from typing import Set
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import torch.onnx
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# This import monkey-patches graph manipulation methods on Graph, used for the
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# ONNX symbolics
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import torch.onnx.utils
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from functools import wraps
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# Note [Edit Symbolic Files]
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# EDITING THIS FILE AND SYMBOLIC_OPSET<VERSION> FILES? READ THIS FIRST!
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#
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# - These files is ONLY for ATen operators (e.g., operators that show up in the
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# trace as aten::blah). If you need to special case a primitive operator,
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# look at _run_symbolic_function
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# - Parameter ordering does NOT necessarily match what is in VariableType.cpp;
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# tensors are always first, then non-tensor arguments.
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# - Parameter names must *exactly* match the names in VariableType.cpp, because
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# dispatch is done with keyword arguments.
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# - Looking for inplace ops? They're detected by the trailing underscore, and
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# transparently dispatched to their non inplace versions in
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# 'run_symbolic_function'. See Note [Export inplace]
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#
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# ----------------------------------------------------------------------------------
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# A note on Tensor types
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# ----------------------------------------------------------------------------------
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#
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# In general, we should avoid depending on the type of Tensor Values contained
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# within the trace graph. However, this is sometimes unavoidable (due to ONNX
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# spec requirements, etc). The TensorType object has accessors for these properties
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# that return the property if it is statically known and return nullopt otherwise.
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#
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# In general, we should prefer to rely on the least specific information possible.
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# For example, not relying on tensor properties at all is better than relying
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# on the number of dimensions which is better than relying on
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# concrete shapes. Doing so will make the export symbolics
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# more robust to different graphs.
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# ---------------------------------------------------------------------------------
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# Helper functions
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# ---------------------------------------------------------------------------------
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# Save some builtins as locals, because we'll shadow them below
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_sum = sum
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def _parse_arg(value, desc):
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if desc == 'none':
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return value
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if desc == 'v' or not _is_value(value):
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return value
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if value.node().mustBeNone():
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return None
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if value.node().kind() == 'onnx::Constant':
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tval = value.node()['value']
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if desc == 'i':
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return int(tval)
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elif desc == 'f':
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return float(tval)
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elif desc == 'b':
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return bool(tval)
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elif desc == 's':
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return str(tval)
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elif desc == 't':
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return tval
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elif desc == 'is':
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return [int(v) for v in tval]
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elif desc == 'fs':
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return [float(v) for v in tval]
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else:
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raise RuntimeError("ONNX symbolic doesn't know to interpret Constant node")
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elif value.node().kind() == 'prim::ListConstruct':
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if desc == 'is':
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for v in value.node().inputs():
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if v.node().kind() != 'onnx::Constant':
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raise RuntimeError("Failed to export an ONNX attribute '" + v.node().kind() +
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"', since it's not constant, please try to make "
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"things (e.g., kernel size) static if possible")
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return [int(v.node()['value']) for v in value.node().inputs()]
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else:
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raise RuntimeError("ONNX symbolic doesn't know to interpret ListConstruct node")
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raise RuntimeError("Unexpected node type: {}".format(value.node().kind()))
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def _maybe_get_const(value, desc):
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if _is_value(value) and value.node().kind() == 'onnx::Constant':
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return _parse_arg(value, desc)
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return value
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def _maybe_get_scalar(value):
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value_t = _maybe_get_const(value, 't')
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if isinstance(value_t, torch.Tensor) and value_t.shape == ():
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return value_t
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return value
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def _get_const(value, desc, arg_name):
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if _is_value(value) and value.node().kind() not in ('onnx::Constant', 'prim::Constant'):
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raise RuntimeError("ONNX symbolic expected a constant value of the {} argument, got `{}`".format(arg_name, value))
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return _parse_arg(value, desc)
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def _unpack_list(list_value):
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list_node = list_value.node()
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assert list_node.kind() == "prim::ListConstruct"
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return list(list_node.inputs())
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# Check if list_value is output from prim::ListConstruct
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# This is usually called before _unpack_list to ensure the list can be unpacked.
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def _is_packed_list(list_value):
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return _is_value(list_value) and list_value.node().kind() == "prim::ListConstruct"
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def parse_args(*arg_descriptors):
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def decorator(fn):
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fn._arg_descriptors = arg_descriptors
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def wrapper(g, *args, **kwargs):
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# some args may be optional, so the length may be smaller
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assert len(arg_descriptors) >= len(args)
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args = [_parse_arg(arg, arg_desc) for arg, arg_desc in zip(args, arg_descriptors)] # type: ignore
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# only support _outputs in kwargs
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assert len(kwargs) <= 1
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if len(kwargs) == 1:
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assert '_outputs' in kwargs
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return fn(g, *args, **kwargs)
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# In Python 2 functools.wraps chokes on partially applied functions, so we need this as a workaround
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try:
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wrapper = wraps(fn)(wrapper)
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except Exception:
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pass
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return wrapper
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return decorator
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def _scalar(x):
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"""Convert a scalar tensor into a Python value."""
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assert x.numel() == 1
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return x.item()
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def _if_scalar_type_as(g, self, tensor):
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"""
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Convert self into the same type of tensor, as necessary.
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We only support implicit casting for scalars, so we never
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actually need to insert an ONNX cast operator here; just
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fix up the scalar.
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"""
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if isinstance(self, torch._C.Value):
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return self
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scalar_type = tensor.type().scalarType()
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if scalar_type:
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ty = scalar_type.lower()
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return getattr(self, ty)()
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return self
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def _is_none(x):
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return x.node().mustBeNone()
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def _is_value(x):
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return isinstance(x, torch._C.Value)
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def _is_tensor(x):
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return x.type().isSubtypeOf(torch._C.TensorType.get())
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def _is_tensor_list(x):
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return isinstance(x.type(), torch._C.ListType) and isinstance(x.type().getElementType(), torch._C.TensorType)
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def _unimplemented(op, msg):
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warnings.warn("ONNX export failed on " + op + " because " + msg + " not supported")
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def _onnx_unsupported(op_name):
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raise RuntimeError('Unsupported: ONNX export of operator {}. '
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'Please open a bug to request ONNX export support for the missing operator.'.format(op_name))
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def _onnx_opset_unsupported(op_name, current_opset, supported_opset):
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raise RuntimeError('Unsupported: ONNX export of {} in '
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'opset {}. Please try opset version {}.'.format(op_name, current_opset, supported_opset))
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def _onnx_opset_unsupported_detailed(op_name, current_opset, supported_opset, reason):
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raise RuntimeError('Unsupported: ONNX export of {} in '
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'opset {}. {}. Please try opset version {}.'.format(op_name, current_opset, reason, supported_opset))
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def _block_list_in_opset(name):
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def symbolic_fn(*args, **kwargs):
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raise RuntimeError("ONNX export failed on {}, which is not implemented for opset {}. "
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"Try exporting with other opset versions."
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.format(name, _export_onnx_opset_version))
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return symbolic_fn
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def _try_get_scalar_type(*args):
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for arg in args:
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try:
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return arg.type().scalarType()
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except RuntimeError:
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pass
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return None
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def _select_helper(g, self, dim, index, apply_reshape=True):
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index_const = _maybe_get_scalar(index)
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index_dim = index.type().dim()
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if not _is_value(index_const):
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# Index is a constant scalar. Make it a size 1 constant tensor.
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index = g.op("Constant", value_t=torch.LongTensor([index_const]))
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elif index_dim is not None and apply_reshape:
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if index_dim == 0:
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# Index is a scalar. Reshape it to a size 1 tensor.
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index = g.op("Reshape", index, g.op("Constant", value_t=torch.LongTensor([1])))
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index_scalar_type = index.type().scalarType()
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if index_scalar_type is None or index_scalar_type not in ['Long', 'Int']:
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index = g.op("Cast", index, to_i=cast_pytorch_to_onnx["Long"])
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return g.op("Gather", self, index, axis_i=dim)
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def _slice_helper(g, input, axes, starts, ends, steps=None, dynamic_slice=False):
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if _export_onnx_opset_version <= 9:
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from torch.onnx.symbolic_opset9 import _slice as _slice9
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return _slice9(g, input, axes, starts, ends)
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else:
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from torch.onnx.symbolic_opset10 import _slice as _slice10
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return _slice10(g, input, axes, starts, ends, steps, dynamic_slice)
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def _hardtanh_helper(g, input, min_val, max_val):
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if _export_onnx_opset_version <= 10:
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from torch.onnx.symbolic_opset9 import hardtanh
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return hardtanh(g, input, min_val, max_val)
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else:
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from torch.onnx.symbolic_opset11 import hardtanh # type: ignore[no-redef]
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return hardtanh(g, input, min_val, max_val)
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def _is_fp(value):
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if value:
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if isinstance(value, torch.Tensor):
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type = value.dtype
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return (type == 'torch.float32') or (type == 'torch.float64') or (type == 'torch.float16')
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else:
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type = value.type().scalarType()
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if type is None:
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warnings.warn("Type cannot be inferred, which might cause exported graph to produce incorrect results.")
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return (type == 'Float') or (type == 'Double') or (type == 'Half')
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return False
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def _sort_helper(g, input, dim, decending=True, out=None):
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if out is not None:
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_unimplemented("Sort", "Out parameter is not supported")
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shape_ = g.op("Shape", input)
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dim_size_ = g.op("Gather", shape_, g.op("Constant", value_t=torch.tensor([dim], dtype=torch.int64)))
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if _export_onnx_opset_version <= 10:
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if not decending:
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_unimplemented("Sort", "Ascending is not supported")
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return g.op("TopK", input, dim_size_, axis_i=dim, outputs=2)
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else:
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return g.op("TopK", input, dim_size_, axis_i=dim, largest_i=decending, outputs=2)
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def _topk_helper(g, input, k, dim, largest=True, sorted=False, out=None):
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if out is not None:
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_unimplemented("TopK", "Out parameter is not supported")
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if not _is_value(k):
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k = g.op("Constant", value_t=torch.tensor([k], dtype=torch.int64))
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else:
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k = g.op("Reshape", k, g.op("Constant", value_t=torch.tensor([1])))
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if _export_onnx_opset_version <= 10:
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if not largest:
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_unimplemented("TopK", "Ascending is not supported")
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return g.op("TopK", input, k, axis_i=dim, outputs=2)
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else:
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return g.op("TopK", input, k, axis_i=dim, largest_i=largest, sorted_i=sorted, outputs=2)
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def _interpolate_warning(interpolate_mode):
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onnx_op = "onnx:Resize" if _export_onnx_opset_version >= 10 else "onnx:Upsample"
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warnings.warn("You are trying to export the model with " + onnx_op + " for ONNX opset version "
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"" + str(_export_onnx_opset_version) + ". "
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"This operator might cause results to not match the expected results by PyTorch.\n"
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"ONNX's Upsample/Resize operator did not match Pytorch's Interpolation until opset 11. "
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"Attributes to determine how to transform the input were added in onnx:Resize in opset 11 "
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"to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).\n"
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"We recommend using opset 11 and above for models using this operator. ")
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def _unsqueeze_helper(g, input, dim):
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from torch.onnx.symbolic_opset9 import unsqueeze
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return unsqueeze(g, input, dim)
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def _interpolate_size_to_scales(g, input, output_size, dim):
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output_size = _maybe_get_const(output_size, 'is')
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if _is_value(output_size):
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offset = 2
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offsets = g.op("Constant", value_t=torch.ones(offset, dtype=torch.float32))
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dividend = g.op("Cast", output_size, to_i=cast_pytorch_to_onnx["Float"])
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divisor = _slice_helper(g, g.op("Shape", input), axes=[0], ends=[maxsize], starts=[offset])
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divisor = g.op("Cast", divisor, to_i=cast_pytorch_to_onnx["Float"])
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scale_dims = g.op("Div", dividend, divisor)
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scales = g.op("Concat", offsets, scale_dims, axis_i=0)
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else:
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scales_constant = [1. if i < 2 else
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float(output_size[-(dim - i)]) / float(input.type().sizes()[-(dim - i)])
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for i in range(0, dim)]
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scales = g.op("Constant", value_t=torch.tensor(scales_constant, dtype=torch.float32))
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return scales
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def _interpolate_get_scales_if_available(g, scales):
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available_scales = _maybe_get_const(scales[0], 'fs') != -1 and not _is_none(scales[0])
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if not available_scales:
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return None
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offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.float32))
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scales_list = g.op("Constant", value_t=torch.tensor(_maybe_get_const(scales[0], 'fs')))
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scales = g.op("Concat", offsets, scales_list, axis_i=0)
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return scales
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def _get_interpolate_attributes(g, mode, args):
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if mode == 'nearest':
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align_corners = None
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scales = args[0:]
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else:
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align_corners = args[0]
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scales = args[1:]
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scales = _interpolate_get_scales_if_available(g, scales)
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return scales, align_corners
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def _interpolate_get_scales(g, scale_factor, dim):
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offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.float32))
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if isinstance(scale_factor.type(), torch._C.ListType) or (scale_factor.isCompleteTensor() and scale_factor.type().dim() > 0):
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return g.op("Concat", offsets, scale_factor, axis_i=0)
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else:
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scale_factor = _unsqueeze_helper(g, scale_factor, 0)
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scale_factor = g.op("Cast", scale_factor, to_i=cast_pytorch_to_onnx["Float"])
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scales = [scale_factor for i in range(dim - 2)]
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scale_factor = g.op("Concat", offsets, *scales, axis_i=0)
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return scale_factor
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def _interpolate_get_scales_and_mode(g, input, size, scale_factor, mode , align_corners):
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mode = _maybe_get_const(mode, 's')
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if 'linear' in mode:
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mode = 'linear'
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if 'cubic' in mode:
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mode = 'cubic'
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_interpolate_warning(mode)
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align_corners = _maybe_get_const(align_corners, 'b')
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if isinstance(align_corners, bool) and align_corners:
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return _unimplemented("interpolate", "align_corners == True")
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if not input.type().dim():
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return _unimplemented("interpolate", "missing input shape")
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dim = input.type().dim()
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if not _is_none(scale_factor):
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scale_factor = _interpolate_get_scales(g, scale_factor, dim)
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elif not _is_none(size):
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if not _is_packed_list(size):
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is_scalar = ((_maybe_get_const(size, 't').dim() == 0))
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if is_scalar:
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size = _unsqueeze_helper(g, size, 0)
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size = [size for i in range(dim - 2)]
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size = g.op("Concat", *size, axis_i=0)
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scale_factor = _interpolate_size_to_scales(g, input, size, dim)
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else:
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return _unimplemented("interpolate", "Both size and scales are None in __interpolate")
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return scale_factor, mode
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def _unbind_helper(g, self, dim, _outputs):
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if _export_onnx_opset_version <= 9:
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from torch.onnx.symbolic_opset9 import unbind
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else:
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from torch.onnx.symbolic_opset11 import unbind # type: ignore[no-redef]
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return unbind(g, self, dim, _outputs)
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def _scatter_helper(g, self, dim, index, src):
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if _export_onnx_opset_version <= 10:
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from torch.onnx.symbolic_opset9 import scatter
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else:
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# for mypy, scatter was imported two lines above
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from torch.onnx.symbolic_opset11 import scatter # type: ignore
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return scatter(g, self, dim, index, src)
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def _arange_cast_helper(g, end, start=None, step=None, dtype=None):
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def _is_all_integral(scalars):
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for scalar in scalars:
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try:
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if scalar.type().scalarType() != 'Long':
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return False
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except Exception:
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pass
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return True
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# This logic is based on torch.arange docs. If 'dtype' is provided,
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# infer input types from dtype. If not, then check if any of start, stop,
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# or step are floating point, and infer the type from get_default.
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# Otherwise, the dtype is inferred to be torch.int64.
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if dtype is None or (_is_value(dtype) and _is_none(dtype)):
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if _is_all_integral([start, end, step]):
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type = scalar_type_to_pytorch_type.index(torch.int64)
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else:
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type = scalar_type_to_pytorch_type.index(torch.get_default_dtype())
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else:
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type = dtype
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start = g.op("Cast", start, to_i=scalar_type_to_onnx[type]) if start else None
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end = g.op("Cast", end, to_i=scalar_type_to_onnx[type]) if end else None
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step = g.op("Cast", step, to_i=scalar_type_to_onnx[type]) if step else None
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return type, end, start, step
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def _size_helper(g, self, dim):
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full_shape = g.op("Shape", self)
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from torch.onnx.symbolic_opset9 import select
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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
|
|
|
|
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 = g.op("Unsqueeze", index, axes_i=[i for i in range(self_dim) if i != dim_value])
|
|
expanded_index_shape = scatter(g, g.op("Shape", self), 0,
|
|
g.op("Unsqueeze", dim, axes_i=[0]), g.op("Shape", index))
|
|
expanded_index = expand(g, unsqueezed_index, expanded_index_shape, None)
|
|
return expanded_index_shape, expanded_index
|
|
|
|
|
|
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 assert_training_mode(op_mode, op_name):
|
|
global _training_mode
|
|
op_mode = True if op_mode == 1 else False
|
|
if op_mode != _training_mode:
|
|
op_mode = "training " if op_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 training_mode
|
|
warnings.warn("ONNX export mode is set to " + training_mode +
|
|
" mode, but operator " + op_name + " is set to " +
|
|
op_mode + " mode. The model will be exported in " +
|
|
training_mode + ", as specified by the export mode.")
|
|
|
|
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
|
|
|
|
# ---------------------------------------------------------------------
|
|
# 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_master_opset = 10
|
|
_onnx_stable_opsets = [7, 8, 9, 10, 11, 12]
|
|
_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_master_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
|
|
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()
|