Files
langgraph/permchain/pregel/__init__.py
T
2023-11-15 15:30:45 +00:00

528 lines
19 KiB
Python

from __future__ import annotations
import asyncio
import concurrent.futures
from collections import defaultdict, deque
from typing import (
Any,
AsyncIterator,
Iterator,
Mapping,
Optional,
Sequence,
Type,
cast,
overload,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.globals import get_debug
from langchain.pydantic_v1 import BaseModel, Field, create_model, root_validator
from langchain.schema.runnable import (
Runnable,
RunnablePassthrough,
RunnableSerializable,
)
from langchain.schema.runnable.base import RunnableLike, coerce_to_runnable
from langchain.schema.runnable.config import (
RunnableConfig,
get_executor_for_config,
patch_config,
)
from langchain.schema.runnable.utils import (
ConfigurableFieldSpec,
get_unique_config_specs,
)
from permchain.channels.base import (
AsyncChannelsManager,
BaseChannel,
ChannelsManager,
EmptyChannelError,
create_checkpoint,
)
from permchain.checkpoint.base import BaseCheckpointAdapter, CheckpointAt
from permchain.constants import CONFIG_KEY_READ, CONFIG_KEY_SEND
from permchain.pregel.debug import print_checkpoint, print_step_start
from permchain.pregel.io import map_input, map_output
from permchain.pregel.log import logger
from permchain.pregel.read import ChannelBatch, ChannelInvoke
from permchain.pregel.reserved import ReservedChannels
from permchain.pregel.validate import validate_chains_channels
from permchain.pregel.write import ChannelWrite
class Channel:
@overload
@classmethod
def subscribe_to(cls, channels: str, key: Optional[str] = None) -> ChannelInvoke:
...
@overload
@classmethod
def subscribe_to(cls, channels: Sequence[str], key: None = None) -> ChannelInvoke:
...
@classmethod
def subscribe_to(
cls, channels: str | Sequence[str], key: Optional[str] = None
) -> ChannelInvoke:
"""Runs process.invoke() each time channels are updated,
with a dict of the channel values as input."""
if not isinstance(channels, str) and key is not None:
raise ValueError(
"Can't specify a key when subscribing to multiple channels"
)
return ChannelInvoke(
channels=cast(
Mapping[None, str] | Mapping[str, str],
{key: channels}
if isinstance(channels, str)
else {chan: chan for chan in channels},
)
)
@classmethod
def subscribe_to_each(cls, inbox: str, key: Optional[str] = None) -> ChannelBatch:
"""Runs process.batch() with the content of inbox each time it is updated."""
return ChannelBatch(channel=inbox, key=key)
@classmethod
def write_to(
cls,
*channels: str,
**kwargs: RunnableLike,
) -> ChannelWrite:
"""Writes to channels the result of the lambda, or None to skip writing."""
return ChannelWrite(
channels=(
[(c, RunnablePassthrough()) for c in channels]
+ [(k, coerce_to_runnable(v)) for k, v in kwargs.items()]
)
)
class Pregel(RunnableSerializable[dict[str, Any] | Any, dict[str, Any] | Any]):
chains: Mapping[str, ChannelInvoke | ChannelBatch]
channels: Mapping[str, BaseChannel] = Field(default_factory=dict)
output: str | Sequence[str] = "output"
input: str | Sequence[str] = "input"
step_timeout: Optional[float] = None
debug: bool = Field(default_factory=get_debug)
checkpoint: Optional[BaseCheckpointAdapter] = None
class Config:
arbitrary_types_allowed = True
@root_validator(skip_on_failure=True)
def validate_pregel(cls, values: dict[str, Any]) -> dict[str, Any]:
validate_chains_channels(
values["chains"], values["channels"], values["input"], values["output"]
)
return values
@property
def config_specs(self) -> Sequence[ConfigurableFieldSpec]:
return get_unique_config_specs(
[spec for chain in self.chains.values() for spec in chain.config_specs]
+ self.checkpoint.config_specs
if self.checkpoint is not None
else []
)
@property
def InputType(self) -> Any:
if isinstance(self.input, str):
return self.channels[self.input].UpdateType
def get_input_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
if isinstance(self.input, str):
return super().get_input_schema(config)
else:
return create_model( # type: ignore[call-overload]
"PregelInput",
**{
k: (self.channels[k].UpdateType, None)
for k in self.input or self.channels.keys()
},
)
@property
def OutputType(self) -> Any:
if isinstance(self.output, str):
return self.channels[self.output].ValueType
def get_output_schema(
self, config: Optional[RunnableConfig] = None
) -> Type[BaseModel]:
if isinstance(self.output, str):
return super().get_output_schema(config)
else:
return create_model( # type: ignore[call-overload]
"PregelOutput",
**{k: (self.channels[k].ValueType, None) for k in self.output},
)
def _transform(
self,
input: Iterator[dict[str, Any] | Any],
run_manager: CallbackManagerForChainRun,
config: RunnableConfig,
) -> Iterator[dict[str, Any] | Any]:
if config["recursion_limit"] < 1:
raise ValueError("recursion_limit must be at least 1")
processes = {**self.chains}
checkpoint = (
self.checkpoint.get(config) if self.checkpoint is not None else None
)
with ChannelsManager(
self.channels, checkpoint
) as channels, get_executor_for_config(config) as executor:
next_tasks = _apply_writes_and_prepare_next_tasks(
processes,
channels,
deque(w for c in input for w in map_input(self.input, c)),
config,
0,
)
def read(chan: str) -> Any:
try:
return channels[chan].get()
except EmptyChannelError:
return None
# Similarly to Bulk Synchronous Parallel / Pregel model
# computation proceeds in steps, while there are channel updates
# channel updates from step N are only visible in step N+1
# channels are guaranteed to be immutable for the duration of the step,
# with channel updates applied only at the transition between steps
for step in range(config["recursion_limit"]):
if self.debug:
print_step_start(step, next_tasks)
# collect all writes to channels, without applying them yet
pending_writes = deque[tuple[str, Any]]()
# execute tasks, and wait for one to fail or all to finish.
# each task is independent from all other concurrent tasks
done, inflight = concurrent.futures.wait(
[
executor.submit(
proc.invoke,
input,
patch_config(
config,
callbacks=run_manager.get_child(f"pregel:step:{step}"),
configurable={
# deque.extend is thread-safe
CONFIG_KEY_SEND: pending_writes.extend,
CONFIG_KEY_READ: read,
},
),
)
for proc, input, _ in next_tasks
],
return_when=concurrent.futures.FIRST_EXCEPTION,
timeout=self.step_timeout,
)
# interrupt on failure or timeout
_interrupt_or_proceed(done, inflight, step)
# apply writes to channels, decide on next step
next_tasks = _apply_writes_and_prepare_next_tasks(
processes, channels, pending_writes, config, step + 1
)
if self.debug:
print_checkpoint(step, channels)
# if any write to output channels in this step, yield current value
for output in map_output(self.output, pending_writes, channels):
yield output
# save end of step checkpoint
if (
self.checkpoint is not None
and self.checkpoint.at == CheckpointAt.END_OF_STEP
):
checkpoint = create_checkpoint(channels)
self.checkpoint.put(config, checkpoint)
# if no more tasks, we're done
if not next_tasks:
break
# save end of run checkpoint
if (
self.checkpoint is not None
and self.checkpoint.at == CheckpointAt.END_OF_RUN
):
checkpoint = create_checkpoint(channels)
self.checkpoint.put(config, checkpoint)
async def _atransform(
self,
input: AsyncIterator[dict[str, Any] | Any],
run_manager: AsyncCallbackManagerForChainRun,
config: RunnableConfig,
) -> AsyncIterator[dict[str, Any] | Any]:
if config["recursion_limit"] < 1:
raise ValueError("recursion_limit must be at least 1")
processes = {**self.chains}
checkpoint = (
await self.checkpoint.aget(config) if self.checkpoint is not None else None
)
async with AsyncChannelsManager(self.channels, checkpoint) as channels:
next_tasks = _apply_writes_and_prepare_next_tasks(
processes,
channels,
deque([w async for c in input for w in map_input(self.input, c)]),
config,
0,
)
def read(chan: str) -> Any:
try:
return channels[chan].get()
except EmptyChannelError:
return None
# Similarly to Bulk Synchronous Parallel / Pregel model
# computation proceeds in steps, while there are channel updates
# channel updates from step N are only visible in step N+1,
# channels are guaranteed to be immutable for the duration of the step,
# channel updates being applied only at the transition between steps
for step in range(config["recursion_limit"]):
if self.debug:
print_step_start(step, next_tasks)
# collect all writes to channels, without applying them yet
pending_writes = deque[tuple[str, Any]]()
# execute tasks, and wait for one to fail or all to finish.
# each task is independent from all other concurrent tasks
done, inflight = await asyncio.wait(
[
asyncio.create_task(
proc.ainvoke(
input,
patch_config(
config,
callbacks=run_manager.get_child(
f"pregel:step:{step}"
),
configurable={
# deque.extend is thread-safe
CONFIG_KEY_SEND: pending_writes.extend,
CONFIG_KEY_READ: read,
},
),
)
)
for proc, input, _ in next_tasks
],
return_when=asyncio.FIRST_EXCEPTION,
timeout=self.step_timeout,
)
# interrupt on failure or timeout
_interrupt_or_proceed(done, inflight, step)
# apply writes to channels, decide on next step
next_tasks = _apply_writes_and_prepare_next_tasks(
processes, channels, pending_writes, config, step + 1
)
if self.debug:
print_checkpoint(step, channels)
# if any write to output channels in this step, yield current value
for output in map_output(self.output, pending_writes, channels):
yield output
# save end of step checkpoint
if (
self.checkpoint is not None
and self.checkpoint.at == CheckpointAt.END_OF_STEP
):
checkpoint = create_checkpoint(channels)
await self.checkpoint.aput(config, checkpoint)
# if no more tasks, we're done
if not next_tasks:
break
# save end of run checkpoint
if (
self.checkpoint is not None
and self.checkpoint.at == CheckpointAt.END_OF_RUN
):
checkpoint = create_checkpoint(channels)
await self.checkpoint.aput(config, checkpoint)
def invoke(
self,
input: dict[str, Any] | Any,
config: RunnableConfig | None = None,
**kwargs: Any,
) -> dict[str, Any] | Any:
latest: dict[str, Any] | Any = None
for chunk in self.stream(input, config, **kwargs):
latest = chunk
return latest
def stream(
self,
input: dict[str, Any] | Any,
config: RunnableConfig | None = None,
**kwargs: Any,
) -> Iterator[dict[str, Any] | Any]:
return self.transform(iter([input]), config, **kwargs)
def transform(
self,
input: Iterator[dict[str, Any] | Any],
config: RunnableConfig | None = None,
**kwargs: Any | None,
) -> Iterator[dict[str, Any] | Any]:
return self._transform_stream_with_config(
input, self._transform, config, **kwargs
)
async def ainvoke(
self,
input: dict[str, Any] | Any,
config: RunnableConfig | None = None,
**kwargs: Any,
) -> dict[str, Any] | Any:
latest: dict[str, Any] | Any = None
async for chunk in self.astream(input, config, **kwargs):
latest = chunk
return latest
async def astream(
self,
input: dict[str, Any] | Any,
config: RunnableConfig | None = None,
**kwargs: Any,
) -> AsyncIterator[dict[str, Any] | Any]:
async def input_stream() -> AsyncIterator[dict[str, Any] | Any]:
yield input
async for chunk in self.atransform(input_stream(), config, **kwargs):
yield chunk
async def atransform(
self,
input: AsyncIterator[dict[str, Any] | Any],
config: RunnableConfig | None = None,
**kwargs: Any | None,
) -> AsyncIterator[dict[str, Any] | Any]:
async for chunk in self._atransform_stream_with_config(
input, self._atransform, config, **kwargs
):
yield chunk
def _interrupt_or_proceed(
done: set[concurrent.futures.Future[Any]] | set[asyncio.Task[Any]],
inflight: set[concurrent.futures.Future[Any]] | set[asyncio.Task[Any]],
step: int,
) -> None:
while done:
# if any task failed
if exc := done.pop().exception():
# cancel all pending tasks
while inflight:
inflight.pop().cancel()
# raise the exception
raise exc
# TODO this is where retry of an entire step would happen
if inflight:
# if we got here means we timed out
while inflight:
# cancel all pending tasks
inflight.pop().cancel()
# raise timeout error
raise TimeoutError(f"Timed out at step {step}")
def _apply_writes_and_prepare_next_tasks(
processes: Mapping[str, ChannelInvoke | ChannelBatch],
channels: Mapping[str, BaseChannel],
pending_writes: Sequence[tuple[str, Any]],
config: RunnableConfig,
for_step: int,
) -> list[tuple[Runnable, Any, str]]:
pending_writes_by_channel: dict[str, list[Any]] = defaultdict(list)
# Group writes by channel
for chan, val in pending_writes:
if chan in [c.value for c in ReservedChannels]:
raise ValueError(f"Can't write to reserved channel {chan}")
pending_writes_by_channel[chan].append(val)
# Update reserved channels
pending_writes_by_channel[ReservedChannels.is_last_step] = [
for_step + 1 == config["recursion_limit"]
]
updated_channels: set[str] = set()
# Apply writes to channels
for chan, vals in pending_writes_by_channel.items():
if chan in channels:
channels[chan].update(vals)
updated_channels.add(chan)
else:
logger.warning(f"Skipping write for channel {chan} which has no readers")
# Channels that weren't updated in this step are notified of a new step
for chan in channels:
if chan not in updated_channels:
channels[chan].update([])
tasks: list[tuple[Runnable, Any, str]] = []
# Check if any processes should be run in next step
# If so, prepare the values to be passed to them
for name, proc in processes.items():
if isinstance(proc, ChannelInvoke):
# If any of the channels read by this process were updated
if any(chan in updated_channels for chan in proc.channels.values()):
# If all channels read by this process have been initialized
try:
val = {k: channels[chan].get() for k, chan in proc.channels.items()}
except EmptyChannelError:
continue
# Processes that subscribe to a single keyless channel get
# the value directly, instead of a dict
if list(proc.channels.keys()) == [None]:
tasks.append((proc, val[None], name))
else:
tasks.append((proc, val, name))
elif isinstance(proc, ChannelBatch):
# If the channel read by this process was updated
if proc.channel in updated_channels:
# Here we don't catch EmptyChannelError because the channel
# must be intialized if the previous `if` condition is true
val = channels[proc.channel].get()
if proc.key is not None:
val = [{proc.key: v} for v in val]
tasks.append((proc, val, name))
return tasks