server/executors/one_by_one.py

from .. import ops
from .. import workspace
import orjson
import pandas as pd
import pydantic
import traceback
import inspect
import typing


class Context(ops.BaseConfig):
    """Passed to operation functions as "_ctx" if they have such a parameter."""

    node: workspace.WorkspaceNode
    last_result: typing.Any = None


class Output(ops.BaseConfig):
    """Return this to send values to specific outputs of a node."""

    output_handle: str
    value: dict


def df_to_list(df):
    return df.to_dict(orient="records")


def has_ctx(op):
    sig = inspect.signature(op.func)
    return "_ctx" in sig.parameters


CACHES = {}


def register(env: str, cache: bool = True):
    """Registers the one-by-one executor."""
    if cache:
        CACHES[env] = {}
        cache = CACHES[env]
    else:
        cache = None
    ops.EXECUTORS[env] = lambda ws: execute(ws, ops.CATALOGS[env], cache=cache)


def get_stages(ws, catalog):
    """Inputs on top/bottom are batch inputs. We decompose the graph into a DAG of components along these edges."""
    nodes = {n.id: n for n in ws.nodes}
    batch_inputs = {}
    inputs = {}
    for edge in ws.edges:
        inputs.setdefault(edge.target, []).append(edge.source)
        node = nodes[edge.target]
        op = catalog[node.data.title]
        i = op.inputs[edge.targetHandle]
        if i.position in "top or bottom":
            batch_inputs.setdefault(edge.target, []).append(edge.source)
    stages = []
    for bt, bss in batch_inputs.items():
        upstream = set(bss)
        new = set(bss)
        while new:
            n = new.pop()
            for i in inputs.get(n, []):
                if i not in upstream:
                    upstream.add(i)
                    new.add(i)
        stages.append(upstream)
    stages.sort(key=lambda s: len(s))
    stages.append(set(nodes))
    return stages


def _default_serializer(obj):
    if isinstance(obj, pydantic.BaseModel):
        return obj.dict()
    return {"__nonserializable__": id(obj)}


def make_cache_key(obj):
    return orjson.dumps(obj, default=_default_serializer)


EXECUTOR_OUTPUT_CACHE = {}


async def await_if_needed(obj):
    if inspect.isawaitable(obj):
        return await obj
    return obj


async def execute(ws, catalog, cache=None):
    nodes = {n.id: n for n in ws.nodes}
    contexts = {n.id: Context(node=n) for n in ws.nodes}
    edges = {n.id: [] for n in ws.nodes}
    for e in ws.edges:
        edges[e.source].append(e)
    tasks = {}
    NO_INPUT = object()  # Marker for initial tasks.
    for node in ws.nodes:
        node.data.error = None
        op = catalog.get(node.data.title)
        if op is None:
            node.data.error = f'Operation "{node.data.title}" not found.'
            continue
        # Start tasks for nodes that have no non-batch inputs.
        if all([i.position in "top or bottom" for i in op.inputs.values()]):
            tasks[node.id] = [NO_INPUT]
    batch_inputs = {}
    # Run the rest until we run out of tasks.
    stages = get_stages(ws, catalog)
    for stage in stages:
        next_stage = {}
        while tasks:
            n, ts = tasks.popitem()
            if n not in stage:
                next_stage.setdefault(n, []).extend(ts)
                continue
            node = nodes[n]
            data = node.data
            op = catalog[data.title]
            params = {**data.params}
            if has_ctx(op):
                params["_ctx"] = contexts[node.id]
            results = []
            for task in ts:
                try:
                    inputs = [
                        batch_inputs[(n, i.name)]
                        if i.position in "top or bottom"
                        else task
                        for i in op.inputs.values()
                    ]
                    if cache is not None:
                        key = make_cache_key((inputs, params))
                        if key not in cache:
                            cache[key] = await await_if_needed(op(*inputs, **params))
                        result = cache[key]
                    else:
                        result = await await_if_needed(op(*inputs, **params))
                except Exception as e:
                    traceback.print_exc()
                    data.error = str(e)
                    break
                contexts[node.id].last_result = result
                # Returned lists and DataFrames are considered multiple tasks.
                if isinstance(result, pd.DataFrame):
                    result = df_to_list(result)
                elif not isinstance(result, list):
                    result = [result]
                results.extend(result)
            else:  # Finished all tasks without errors.
                if (
                    op.type == "visualization"
                    or op.type == "table_view"
                    or op.type == "image"
                ):
                    data.display = results[0]
                for edge in edges[node.id]:
                    t = nodes[edge.target]
                    op = catalog[t.data.title]
                    i = op.inputs[edge.targetHandle]
                    if i.position in "top or bottom":
                        batch_inputs.setdefault(
                            (edge.target, edge.targetHandle), []
                        ).extend(results)
                    else:
                        tasks.setdefault(edge.target, []).extend(results)
        tasks = next_stage
    return contexts