| from __future__ import absolute_import, division, print_function, unicode_literals |
| |
| import torch |
| from torch._C import ListType |
| import warnings |
| from sys import maxsize as maxsize |
| |
| 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 |
| |
| |
| # Note [Edit Symbolic Files] |
| # EDITING THIS FILE AND SYMBOLIC_OPSET<VERSION> FILES? READ THIS FIRST! |
| # |
| # - These files is 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): |
| 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] |
| 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") |
| |
| raise RuntimeError("Unexpected node type: {}".format(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 _is_value(value) and value.node().kind() not in ('onnx::Constant', 'prim::Constant'): |
| 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): |
| def decorator(fn): |
| fn._arg_descriptors = arg_descriptors |
| |
| def wrapper(g, *args): |
| # some args may be optional, so the length may be smaller |
| assert len(arg_descriptors) >= len(args) |
| args = [_parse_arg(arg, arg_desc) for arg, arg_desc in zip(args, arg_descriptors)] |
| return fn(g, *args) |
| # In Python 2 functools.wraps chokes on partially applied functions, so we need this as a workaround |
| try: |
| wrapper = wraps(fn)(wrapper) |
| except Exception: |
| pass |
| 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_tensor_list(x): |
| return x.type().isSubtypeOf(ListType.ofTensors()) |
| |
| |
| 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 open a bug to request ONNX export support for the missing operator.'.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 _black_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 _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 |
| return _slice(g, input, axes, starts, ends) |
| else: |
| from torch.onnx.symbolic_opset10 import _slice |
| return _slice(g, input, axes, starts, ends, steps, dynamic_slice) |
| |
| |
| def _is_fp(value): |
| if value: |
| type = value.type().scalarType() |
| return (type == 'Float') or (type == 'Double') or (type == 'Half') |
| return False |
| |
| |
| 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 = g.op("Reshape", 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, dim): |
| from torch.onnx.symbolic_opset9 import unsqueeze |
| return unsqueeze(g, input, dim) |
| |
| 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], 'f') != -1 and not _is_none(scales[0]) |
| |
| if not available_scales: |
| return None |
| |
| scales_list = [] |
| for scale in scales: |
| unsqueezed_scale = _unsqueeze_helper(g, scale, 0) |
| # ONNX only supports float for the scales. double -> float. |
| unsqueezed_scale = g.op("Cast", unsqueezed_scale, |
| to_i=cast_pytorch_to_onnx["Float"]) |
| scales_list.append(unsqueezed_scale) |
| offsets = g.op("Constant", value_t=torch.ones(2, dtype=torch.float32)) |
| 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)) |
| if isinstance(scale_factor.type(), torch._C.ListType): |
| 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("Both size and scales are None in __interpolate") |
| return scale_factor, mode |
| |
| |
| def _scatter_helper(g, self, dim, index, src): |
| if _export_onnx_opset_version <= 10: |
| from torch.onnx.symbolic_opset9 import scatter |
| else: |
| from torch.onnx.symbolic_opset11 import scatter |
| return scatter(g, self, dim, index, src) |
| |
| |
| 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 _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 _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: |
| from torch.onnx.symbolic_opset11 import scatter |
| |
| 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.") |
| |
| # --------------------------------------------------------------------- |
| # 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 |
| |
| # 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() |
