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var.py
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482 lines (416 loc) · 15.9 KB
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import inspect
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from onnx import TensorProto
from onnx.defs import get_schema
from ..annotations import (
elem_type_int,
make_shape,
ELEMENT_TYPE,
ELEMENT_TYPE_NAME,
GRAPH_PROTO,
SHAPE_TYPE,
VAR_CONSTANT_TYPE,
)
from .model import OnnxGraph
from ._op_var import OpsVar
from ._op_vars import OpsVars
class SubDomain:
"""
Declares a domain or a piece of it (if it contains '.' in its name).
"""
def __init__(self, var: "BaseVar"):
if not isinstance(var, BaseVar):
raise TypeError(f"Unexpected type {type(var)}.")
self.parent = var
def _getclassattr_(self, name):
if not hasattr(self.__class__, name):
raise TypeError(
f"Unable to find {name!r} in class {self.__class__.__name__!r}, "
f"available {dir(self.__class__)}."
)
return getattr(self.__class__, name)
class BaseVar:
"""
Represents an input, an initializer, a node, an output,
multiple variables.
:param parent: the graph containing the Variable
"""
def __new__(cls, *args, **kwargs):
"""
If called for the first instantiation of a BaseVar, it process
all methods declared with decorator :func:`onnx_array_api.light_api.domain`
so that it can be called with a syntax `v.<domain>.<op_type>`.
"""
res = super().__new__(cls)
res.__init__(*args, **kwargs)
if getattr(cls, "__incomplete", True):
for k in dir(cls):
att = getattr(cls, k, None)
if not att:
continue
name = getattr(att, "__name__", None)
if not name or name[0] != "[":
continue
# A function with a domain name
if not inspect.isfunction(att):
raise RuntimeError(f"{cls.__name__}.{k} is not a function.")
domain, op_type = name[1:].split("]")
if "." in domain:
spl = domain.split(".", maxsplit=1)
dname = f"_{spl[0]}"
if not hasattr(cls, dname):
d = type(
f"{cls.__name__}{dname}", (SubDomain,), {"name": dname[1:]}
)
setattr(cls, dname, d)
setattr(
cls,
spl[0],
property(
lambda self, _name_=dname: _getclassattr_(self, _name_)(
self
)
),
)
else:
d = getattr(cls, dname)
suffix = spl[0]
for p in spl[1].split("."):
dname = f"_{p}"
suffix += dname
if not hasattr(d, dname):
sd = type(
f"{cls.__name__}_{suffix}",
(SubDomain,),
{"name": suffix},
)
setattr(d, dname, sd)
setattr(
d,
p,
property(
lambda self, _name_=dname: _getclassattr_(
self, _name_
)(self.parent)
),
)
d = sd
else:
d = getattr(d, dname)
elif not hasattr(cls, domain):
dname = f"_{domain}"
d = type(f"{cls.__name__}{dname}", (SubDomain,), {"name": domain})
setattr(cls, dname, d)
setattr(
cls,
domain,
property(
lambda self, _name_=dname: _getclassattr_(self, _name_)(
self
)
),
)
setattr(d, op_type, att)
setattr(cls, "__incomplete", False)
return res
def __init__(
self,
parent: OnnxGraph,
):
if not isinstance(parent, OnnxGraph):
raise RuntimeError(f"Unexpected parent type {type(parent)}.")
self.parent = parent
def make_node(
self,
op_type: str,
*inputs: List[VAR_CONSTANT_TYPE],
domain: str = "",
n_outputs: int = 1,
output_names: Optional[List[str]] = None,
**kwargs: Dict[str, Any],
) -> Union["Var", "Vars"]:
"""
Creates a node with this Var as the first input.
:param op_type: operator type
:param inputs: others inputs
:param domain: domain
:param n_outputs: number of outputs
:param output_names: output names, if not specified, outputs are given
unique names
:param kwargs: node attributes
:return: instance of :class:`onnx_array_api.light_api.Var` or
:class:`onnx_array_api.light_api.Vars`
"""
if domain in ("", "ai.onnx.ml"):
if self.parent.opset is None:
schema = get_schema(op_type, domain)
else:
schema = get_schema(op_type, self.parent.opset, domain)
if n_outputs < schema.min_output or n_outputs > schema.max_output:
raise RuntimeError(
f"Unexpected number of outputs ({n_outputs}) "
f"for node type {op_type!r}, domain={domain!r}, "
f"version={self.parent.opset}, it should be in "
f"[{schema.min_output}, {schema.max_output}]."
)
n_inputs = len(inputs)
if n_inputs < schema.min_input or n_inputs > schema.max_input:
raise RuntimeError(
f"Unexpected number of inputs ({n_inputs}) "
f"for node type {op_type!r}, domain={domain!r}, "
f"version={self.parent.opset}, it should be in "
f"[{schema.min_input}, {schema.max_input}]."
)
node_proto = self.parent.make_node(
op_type,
*inputs,
domain=domain,
n_outputs=n_outputs,
output_names=output_names,
**kwargs,
)
names = node_proto.output
if n_outputs is not None and len(node_proto.output) != len(names):
raise RuntimeError(
f"Expects {n_outputs} outputs but output names are {names}."
)
if len(names) == 1:
return Var(self.parent, names[0])
return Vars(self.parent, *[Var(self.parent, v) for v in names])
def vin(
self,
name: str,
elem_type: ELEMENT_TYPE = TensorProto.FLOAT,
shape: Optional[SHAPE_TYPE] = None,
) -> "Var":
"""
Declares a new input to the graph.
:param name: input name
:param elem_type: element_type
:param shape: shape
:return: instance of :class:`onnx_array_api.light_api.Var`
"""
return self.parent.vin(name, elem_type=elem_type, shape=shape)
def cst(self, value: np.ndarray, name: Optional[str] = None) -> "Var":
"""
Adds an initializer
:param value: constant tensor
:param name: input name
:return: instance of :class:`onnx_array_api.light_api.Var`
"""
c = self.parent.make_constant(value, name=name)
return Var(self.parent, c.name, elem_type=c.data_type, shape=tuple(c.dims))
def v(self, name: str) -> "Var":
"""
Retrieves another variable than this one.
:param name: name of the variable
:return: instance of :class:`onnx_array_api.light_api.Var`
"""
return self.parent.get_var(name)
def bring(self, *vars: List[Union[str, "Var"]]) -> Union["Var", "Vars"]:
"""
Creates a set of variable as an instance of
:class:`onnx_array_api.light_api.Vars`.
"""
if len(vars) == 1:
return Var(self.parent, vars[0])
return Vars(self.parent, *vars)
def vout(self, **kwargs: Dict[str, Any]) -> Union["Var", "Vars"]:
"""
This method needs to be overwritten for Var and Vars depending
on the number of variable to declare as outputs.
"""
raise RuntimeError(f"The method was not overwritten in class {type(self)}.")
def left_bring(self, *vars: List[Union[str, "Var"]]) -> "Vars":
"""
Creates a set of variables as an instance of
:class:`onnx_array_api.light_api.Vars`.
`*vars` is added to the left, `self` is added to the right.
"""
vs = [*vars, self]
return Vars(self.parent, *vs)
def right_bring(self, *vars: List[Union[str, "Var"]]) -> "Vars":
"""
Creates a set of variables as an instance of
:class:`onnx_array_api.light_api.Vars`.
`*vars` is added to the right, `self` is added to the left.
"""
vs = [self, *vars]
return Vars(self.parent, *vs)
def to_onnx(self) -> GRAPH_PROTO:
"Creates the onnx graph."
return self.parent.to_onnx()
class Var(BaseVar, OpsVar):
"""
Represents an input, an initializer, a node, an output.
:param parent: graph the variable belongs to
:param name: input name
:param elem_type: element_type
:param shape: shape
"""
def __init__(
self,
parent: OnnxGraph,
name: str,
elem_type: Optional[ELEMENT_TYPE] = 1,
shape: Optional[SHAPE_TYPE] = None,
):
BaseVar.__init__(self, parent)
self.name_ = name
self.elem_type = elem_type
self.shape = shape
@property
def name(self):
"Returns the name of the variable or the new name if it was renamed."
return self.parent.true_name(self.name_)
def __str__(self) -> str:
"usual"
s = f"{self.name}"
if self.elem_type is None:
return s
s = f"{s}:{ELEMENT_TYPE_NAME[self.elem_type]}"
if self.shape is None:
return s
return f"{s}:[{''.join(map(str, self.shape))}]"
def vout(
self,
elem_type: ELEMENT_TYPE = TensorProto.FLOAT,
shape: Optional[SHAPE_TYPE] = None,
) -> "Var":
"""
Declares a new output to the graph.
:param elem_type: element_type
:param shape: shape
:return: instance of :class:`onnx_array_api.light_api.Var`
If the checker fails, try `shape=[]`.
"""
output = self.parent.make_output(self.name, elem_type=elem_type, shape=shape)
return Var(
self.parent,
output,
elem_type=output.type.tensor_type.elem_type,
shape=make_shape(output.type.tensor_type.shape),
)
def rename(self, new_name: str) -> "Var":
"Renames a variable."
self.parent.rename(self.name, new_name)
return self
def to(self, to: ELEMENT_TYPE) -> "Var":
"Casts a tensor into another element type."
return self.Cast(to=elem_type_int(to))
def astype(self, to: ELEMENT_TYPE) -> "Var":
"Casts a tensor into another element type."
return self.Cast(to=elem_type_int(to))
def reshape(self, new_shape: VAR_CONSTANT_TYPE) -> "Var":
"Reshapes a variable."
if isinstance(new_shape, tuple):
cst = self.cst(np.array(new_shape, dtype=np.int64))
return self.bring(self, cst).Reshape()
return self.bring(self, new_shape).Reshape()
def __add__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Add()
def __eq__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Equal()
def __float__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Cast(to=TensorProto.FLOAT)
def __gt__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Greater()
def __ge__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).GreaterOrEqual()
def __int__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Cast(to=TensorProto.INT64)
def __lt__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Less()
def __le__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).LessOrEqual()
def __matmul__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).MatMul()
def __mod__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Mod()
def __mul__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Mul()
def __ne__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Equal().Not()
def __neg__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.Neg()
def __pow__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Pow()
def __sub__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Sub()
def __truediv__(self, var: VAR_CONSTANT_TYPE) -> "Var":
"Intuitive."
return self.bring(self, var).Div()
class Vars(BaseVar, OpsVars):
"""
Represents multiple Var.
:param parent: graph the variable belongs to
:param vars: list of names or variables
"""
def __init__(self, parent, *vars: List[Union[str, Var]]):
BaseVar.__init__(self, parent)
self.vars_ = []
for v in vars:
if isinstance(v, str):
var = self.parent.get_var(v)
else:
var = v
self.vars_.append(var)
def __len__(self):
"Returns the number of variables."
return len(self.vars_)
def _check_nin(self, n_inputs):
if len(self) != n_inputs:
raise RuntimeError(f"Expecting {n_inputs} inputs not {len(self)}.")
return self
def rename(self, *new_names: List[str]) -> "Vars":
"Renames variables."
if len(new_names) != len(self):
raise ValueError(
f"Vars has {len(self)} elements but the method "
f"received {len(new_names)} names."
)
new_vars = []
for var, name in zip(self.vars_, new_names):
new_vars.append(var.rename(name))
return Vars(self.parent, *new_names)
def vout(
self,
*elem_type_shape: List[
Union[ELEMENT_TYPE, Tuple[ELEMENT_TYPE, Optional[SHAPE_TYPE]]]
],
) -> "Vars":
"""
Declares a new output to the graph.
:param elem_type_shape: list of tuple(element_type, shape)
:return: instance of :class:`onnx_array_api.light_api.Vars`
If the checker fails, try `shape=[]`.
"""
vars = []
for i, v in enumerate(self.vars_):
if i < len(elem_type_shape):
if isinstance(elem_type_shape[i]) or len(elem_type_shape[i]) < 2:
elem_type = elem_type_shape[i][0]
shape = None
else:
elem_type, shape = elem_type_shape[i]
else:
elem_type = TensorProto.FLOAT
shape = None
vars.append(v.vout(elem_type=elem_type, shape=shape))
return Vars(self.parent, *vars)