def __init__(self, name: str = "main", lvl: int = logging.ERROR):
self.name = name
self.placeholders: Dict[str, PlaceholderNode] = {}
self.nodes: List[RealNode] = []
self.log = make_logger(lvl, "DeltaGraph")
# used below to silent doctest unwanted outputs
self._org_displayhook = None
def __init__(self,
tb_num_iter: int = None,
name: str = None,
lvl: int = logging.ERROR,
vcd_name: str = None,
generics: dict = None,
node_template: NodeTemplate = None,
tags: List[str] = []):
if name is None:
name = type(self).__name__
super().__init__(name, Latency(clocks=1), lvl, tags)
self.module_name = self.name
self.log = make_logger(lvl, f"{self.name}")
self.vcd_name = vcd_name
if generics is not None:
self.generics = generics
# Predefining values to satisfy linting check.
self.submodules = None
self.in_buffer = None
# None for out buffer implies there are no out-ports
self.out_buffer = None
self.specials = None
self.comb = None
self.sync = None
self.debug_signals = {}
self._dut = migen.Module()
self._dut.in_ports = {}
self._dut.out_ports = {}
self.__class__.migen_body(self=self._dut, template=self)
self.rename_port_signals()
if len(self._dut.out_ports.items()) > 0:
# Make all outputs named, as it's needed for verilog
self._outputs, self._ForkedOutput = make_forked_return(
{name: type_ for name, (_, type_)
in self._dut.out_ports.items()}
)
else:
self._outputs = Void
# define a stimulus generator
self._tb = self.tb_generator(tb_num_iter)
# set up a simulator and perform run-once elaboration
self._sim_object = None
atexit.register(self.cleanup)
# Merge with or create a new NodeTemplate
inputs = OrderedDict()
for name, (_, type_) in self._dut.in_ports.items():
inputs[name] = type_
NodeTemplate.merge_migenblock(node_template, self, inputs,
self._outputs)
def __init__(self,
out_port: OutPort,
maxsize: int = 16,
queue_interval: float = 1.0):
super().__init__(maxsize=maxsize)
self._src = out_port
self._log = make_logger(logging.WARNING, f"Queue {out_port.name}")
self._queue_interval = queue_interval
self.optional = out_port.destination.is_optional
self._type = out_port.port_type
def __init__(self,
graph,
bodies: typing.List[Body],
inputs: typing.OrderedDict[str,
typing.Union[BaseDeltaType,
Optional]] = None,
outputs: typing.OrderedDict[str, BaseDeltaType] = None,
name: typing.Optional[str] = None,
lvl: int = logging.ERROR,
is_autogenerated: bool = False):
self.graph = graph
self.graph.add_node(self) # Registering self with parent graph
self.is_autogenerated = is_autogenerated
self._body = None
self.bodies = bodies
if len(self.bodies) == 1:
self._body = bodies[0]
self.inputs = inputs if inputs is not None else typing.OrderedDict()
self.outputs = outputs if outputs is not None else typing.OrderedDict()
idx = RealNode.get_next_index()
if name is None:
# set my name to the next unique available name
self._name = ("node", idx)
else:
self._name = (name, idx)
# Ports in/out to this node
# Note that in_ports are always stored in the same order as inputs
self.in_ports: typing.List[InPort] = []
# Note that out_ports are always stored in the same order as outputs
self.out_ports: typing.List[OutPort] = []
self.log = make_logger(lvl,
f"{self.__class__.__name__} {self.full_name}")
# See MessageLog for detail
self._clock = 0
self.out_names = list(self.outputs.keys())
for out_name in self.out_names:
if out_name in dir(self):
raise NameError("Invalid out name: " + out_name +
" for node " + self.full_name)
def __init__(self,
graph,
bodies: List[Body],
inputs: OrderedDict[str, Union[BaseDeltaType,
DOptional]] = {},
outputs: BaseDeltaType = None,
name: Optional[str] = None,
lvl: int = logging.ERROR,
is_autogenerated: bool = False):
self.graph = graph
self.graph.add_node(self) # Registering self with parent graph
self.is_autogenerated = is_autogenerated
self._body = None
self.bodies = bodies
if len(self.bodies) == 1:
self._body = bodies[0]
self.inputs = inputs
self.outputs = outputs
idx = RealNode.get_next_index()
if name is None:
# set my name to the next unique available name
self._name = f"node_{idx}"
else:
self._name = f"{name}_{idx}"
# Ports in/out to this node
self.in_ports: Dict[NamespacedName, InPort] = {}
self.out_ports: List[OutPort] = []
self.log = make_logger(lvl, f"{self.__class__.__name__} {self._name}")
# See MessageLog for detail
self._clock = 0
self.fork_names = None
if isinstance(self.outputs, ForkedReturn):
self.fork_names = self.outputs.keys
for fork_name in self.fork_names:
if fork_name in dir(self):
raise NameError("Invalid fork name: " + fork_name +
" for node " + self.name)
def __init__(self,
out_port: OutPort,
maxsize: int = 16,
queue_interval: float = 1.0):
super().__init__(maxsize=maxsize)
self._src = out_port
self._log = make_logger(logging.WARNING, f"Queue {out_port.port_name}")
self._queue_interval = queue_interval
self.optional = out_port.destination.is_optional
if out_port.port_name.n_index is not None:
self._index = out_port.port_name.n_index
else:
self._index = None
self._type = out_port.port_type
if isinstance(self._type, ForkedReturn):
self._type = self._type.elem_dict[self._index]
def __init__(self,
graph: DeltaGraph,
lvl: int = logging.ERROR,
msg_lvl: int = logging.ERROR,
switchinterval: float = None,
queue_size: int = 16,
queue_interval: float = 1.0):
self.log = make_logger(lvl, "DeltaPySimulator")
self.msg_log = MessageLog(msg_lvl)
self.set_excepthook()
# speed optimization
if switchinterval is not None:
sys.setswitchinterval(switchinterval)
self.queue_size = queue_size
self.queue_interval = queue_interval
# the graph
self.graph = graph
self.graph.do_automatic_splitting()
self.graph.check()
self.add_message_log()
# i/o queues
self.in_queues: Dict[str, Dict[str, DeltaQueue]] = {
node.name: {}
for node in self.graph.nodes
}
self.out_queues: Dict[str, Dict[str, DeltaQueue]] = {
node.name: {}
for node in self.graph.nodes
}
for node in self.graph.nodes:
self._create_io_queues(node)
# Signal to stop child threads
self.sig_stop = threading.Event()
for node in self.graph.nodes:
node.set_communications(self)
# child threads for node's workers
self.threads: Dict[str, threading.Thread] = {}
self.running = False
from ._node_classes.latency import Latency
from ._node_classes.node_bodies import Body
from ._node_classes.real_nodes import (PythonNode,
as_node,
get_func_inputs_outputs,
inputs_as_delta_types,
outputs_as_delta_types)
from ._body_templates import (BodyTemplate,
FuncBodyTemplate,
InteractiveBodyTemplate,
MethodBodyTemplate)
if TYPE_CHECKING:
from ._delta_graph import DeltaGraph
log = make_logger(logging.WARNING, "Node Templates")
class NodeTemplate():
"""NodeTemplates are recipes for how to create a specific node.
They have a list of :py:class:`BodyTemplate`s that represent how to
construct a number of different bodies for the node. Each
``BodyTemplate`` has a call method to allow creation of the
node. This node will be given bodies from all valid ``BodyTemplates``.
Parameters
----------
name : str
Default name for the nodes created using this node template
inputs : Union[List[Tuple[str, Type]],
OrderedDict[str, Type]],
from deltalanguage.logging import make_logger
from .._decorators import get_func_inputs_outputs
from .._node_templates import NodeTemplate
from .abstract_node import ForkedNode, ProxyNode
from .node_bodies import PyInteractiveBody
from .real_nodes import as_node, PythonNode
if TYPE_CHECKING:
from .._node_templates import InteractiveBodyTemplate
from .._delta_graph import DeltaGraph
from .abstract_node import AbstractNode
from .port_classes import InPort
log = make_logger(logging.WARNING, "PROXY NODES")
class PlaceholderNode(ProxyNode):
"""Node class to represent nodes that have yet to be specified.
These nodes have to be specified in the context of :py:class:`DeltaGraph`,
their main use case is creation of cyclic dependencies in the graph,
i.e. loops.
Use :py:class:`placeholder_node_factory` for construction in the
context of :py:class:`DeltaGraph`.
Parameters
----------
graph : DeltaGraph
def __init__(self, name=None, lvl: int = logging.ERROR):
self.name = name
self.placeholders: Dict[str, PlaceholderNode] = {}
self.nodes: List[RealNode] = []
self.log = make_logger(lvl, "DeltaGraph")
"""Classes to represent different node bodies a Deltaflow node could represent.
"""
from abc import ABC, abstractmethod
from typing import Callable, List
import dill
import dis
import logging
from deltalanguage.logging import make_logger
from deltalanguage.data_types import DeltaIOError
from .latency import Latency
log = make_logger(logging.WARNING, "Bodies")
class Body(ABC):
def __init__(self,
latency: Latency = Latency(time=300),
tags: List[str] = None):
"""
Parameters
----------
latency : Latency
Estimated time this body takes to run
tags : List[str]
List of strings to be added as access tags.
"""
tags = tags if tags is not None else []
"""
from abc import ABC, abstractmethod
import logging
import struct
from typing import (Any, Dict, Iterable, List, NamedTuple, Tuple, Type, Union)
import attr
import numpy as np
from deltalanguage.logging import make_logger
from ._exceptions import DeltaTypeError
from ._special import DSize, Void
logger = make_logger(logging.WARNING, "Data Types")
class BaseDeltaType(ABC):
"""The base Deltaflow type, of which other types are derived.
Attributes
----------
size : DSize
Full size of the data type. There are several cases:
* for primitive types it's the number of bits
* for compound types it's the sum of all componenets' sizes
* for :py:class:`DUnion` that is the the size of the largest element
PLUS 1 byte that stores which particular data type is encoded
"""
