Use cloudpickle for parallel UDF processing (#65)

pyproject.toml

@@ -38,6 +38,7 @@ dependencies = [
   "sqlalchemy>=2",
   "multiprocess==0.70.16",
   "dill==0.3.8",
+  "cloudpickle",
   "ujson>=5.9.0",
   "pydantic>=2,<3",
   "jmespath>=1.0",

src/datachain/query/dataset.py

@@ -1,4 +1,3 @@
-import ast
 import contextlib
 import datetime
 import inspect
@@ -10,7 +9,6 @@
 import string
 import subprocess
 import sys
-import types
 from abc import ABC, abstractmethod
 from collections.abc import Generator, Iterable, Iterator, Sequence
 from copy import copy
@@ -28,9 +26,7 @@
 import attrs
 import sqlalchemy
 from attrs import frozen
-from dill import dumps, source
 from fsspec.callbacks import DEFAULT_CALLBACK, Callback, TqdmCallback
-from pydantic import BaseModel
 from sqlalchemy import Column
 from sqlalchemy.sql import func as f
 from sqlalchemy.sql.elements import ColumnClause, ColumnElement
@@ -54,7 +50,11 @@
 from datachain.progress import CombinedDownloadCallback
 from datachain.sql.functions import rand
 from datachain.storage import Storage, StorageURI
-from datachain.utils import batched, determine_processes
+from datachain.utils import (
+    batched,
+    determine_processes,
+    filtered_cloudpickle_dumps,
+)
 
 from .metrics import metrics
 from .schema import C, UDFParamSpec, normalize_param
@@ -490,7 +490,7 @@ def populate_udf_table(self, udf_table: "Table", query: Select) -> None:
             elif processes:
                 # Parallel processing (faster for more CPU-heavy UDFs)
                 udf_info = {
-                    "udf": self.udf,
+                    "udf_data": filtered_cloudpickle_dumps(self.udf),
                     "catalog_init": self.catalog.get_init_params(),
                     "id_generator_clone_params": (
                         self.catalog.id_generator.clone_params()
@@ -511,16 +511,15 @@ def populate_udf_table(self, udf_table: "Table", query: Select) -> None:
 
                 envs = dict(os.environ)
                 envs.update({"PYTHONPATH": os.getcwd()})
-                with self.process_feature_module():
-                    process_data = dumps(udf_info, recurse=True)
-                    result = subprocess.run(  # noqa: S603
-                        [datachain_exec_path, "--internal-run-udf"],
-                        input=process_data,
-                        check=False,
-                        env=envs,
-                    )
-                    if result.returncode != 0:
-                        raise RuntimeError("UDF Execution Failed!")
+                process_data = filtered_cloudpickle_dumps(udf_info)
+                result = subprocess.run(  # noqa: S603
+                    [datachain_exec_path, "--internal-run-udf"],
+                    input=process_data,
+                    check=False,
+                    env=envs,
+                )
+                if result.returncode != 0:
+                    raise RuntimeError("UDF Execution Failed!")
 
             else:
                 # Otherwise process single-threaded (faster for smaller UDFs)
@@ -569,57 +568,6 @@ def populate_udf_table(self, udf_table: "Table", query: Select) -> None:
             self.catalog.warehouse.close()
             raise
 
-    @contextlib.contextmanager
-    def process_feature_module(self):
-        # Generate a random name for the feature module
-        feature_module_name = "tmp" + _random_string(10)
-        # Create a dynamic module with the generated name
-        dynamic_module = types.ModuleType(feature_module_name)
-        # Get the import lines for the necessary objects from the main module
-        main_module = sys.modules["__main__"]
-        if getattr(main_module, "__file__", None):
-            import_lines = list(get_imports(main_module))
-        else:
-            import_lines = [
-                source.getimport(obj, alias=name)
-                for name, obj in main_module.__dict__.items()
-                if _imports(obj) and not (name.startswith("__") and name.endswith("__"))
-            ]
-
-        # Get the feature classes from the main module
-        feature_classes = {
-            name: obj
-            for name, obj in main_module.__dict__.items()
-            if _feature_predicate(obj)
-        }
-        if not feature_classes:
-            yield None
-            return
-
-        # Get the source code of the feature classes
-        feature_sources = [source.getsource(cls) for _, cls in feature_classes.items()]
-        # Set the module name for the feature classes to the generated name
-        for name, cls in feature_classes.items():
-            cls.__module__ = feature_module_name
-            setattr(dynamic_module, name, cls)
-        # Add the dynamic module to the sys.modules dictionary
-        sys.modules[feature_module_name] = dynamic_module
-        # Combine the import lines and feature sources
-        feature_file = "\n".join(import_lines) + "\n" + "\n".join(feature_sources)
-
-        # Write the module content to a .py file
-        with open(f"{feature_module_name}.py", "w") as module_file:
-            module_file.write(feature_file)
-
-        try:
-            yield feature_module_name
-        finally:
-            for cls in feature_classes.values():
-                cls.__module__ = main_module.__name__
-            os.unlink(f"{feature_module_name}.py")
-            # Remove the dynamic module from sys.modules
-            del sys.modules[feature_module_name]
-
     def create_partitions_table(self, query: Select) -> "Table":
         """
         Create temporary table with group by partitions.
@@ -1829,34 +1777,3 @@ def _random_string(length: int) -> str:
         random.choice(string.ascii_letters + string.digits)  # noqa: S311
         for i in range(length)
     )
-
-
-def _feature_predicate(obj):
-    return (
-        inspect.isclass(obj) and source.isfrommain(obj) and issubclass(obj, BaseModel)
-    )
-
-
-def _imports(obj):
-    return not source.isfrommain(obj)
-
-
-def get_imports(m):
-    root = ast.parse(inspect.getsource(m))
-
-    for node in ast.iter_child_nodes(root):
-        if isinstance(node, ast.Import):
-            module = None
-        elif isinstance(node, ast.ImportFrom):
-            module = node.module
-        else:
-            continue
-
-        for n in node.names:
-            import_script = ""
-            if module:
-                import_script += f"from {module} "
-            import_script += f"import {n.name}"
-            if n.asname:
-                import_script += f" as {n.asname}"
-            yield import_script

src/datachain/query/dispatch.py

@@ -10,7 +10,7 @@
 
 import attrs
 import multiprocess
-from dill import load
+from cloudpickle import load, loads
 from fsspec.callbacks import DEFAULT_CALLBACK, Callback
 from multiprocess import get_context
 
@@ -84,7 +84,7 @@ def put_into_queue(queue: Queue, item: Any) -> None:
 
 def udf_entrypoint() -> int:
     # Load UDF info from stdin
-    udf_info = load(stdin.buffer)  # noqa: S301
+    udf_info = load(stdin.buffer)
 
     (
         warehouse_class,
@@ -95,7 +95,7 @@ def udf_entrypoint() -> int:
 
     # Parallel processing (faster for more CPU-heavy UDFs)
     dispatch = UDFDispatcher(
-        udf_info["udf"],
+        udf_info["udf_data"],
         udf_info["catalog_init"],
         udf_info["id_generator_clone_params"],
         udf_info["metastore_clone_params"],
@@ -108,7 +108,7 @@ def udf_entrypoint() -> int:
     batching = udf_info["batching"]
     table = udf_info["table"]
     n_workers = udf_info["processes"]
-    udf = udf_info["udf"]
+    udf = loads(udf_info["udf_data"])
     if n_workers is True:
         # Use default number of CPUs (cores)
         n_workers = None
@@ -146,7 +146,7 @@ class UDFDispatcher:
 
     def __init__(
         self,
-        udf,
+        udf_data,
         catalog_init_params,
         id_generator_clone_params,
         metastore_clone_params,
@@ -155,14 +155,7 @@ def __init__(
         is_generator=False,
         buffer_size=DEFAULT_BATCH_SIZE,
     ):
-        # isinstance cannot be used here, as dill packages the entire class definition,
-        # and so these two types are not considered exactly equal,
-        # even if they have the same import path.
-        if full_module_type_path(type(udf)) != full_module_type_path(UDFFactory):
-            self.udf = udf
-        else:
-            self.udf = None
-            self.udf_factory = udf
+        self.udf_data = udf_data
         self.catalog_init_params = catalog_init_params
         (
             self.id_generator_class,
@@ -214,6 +207,15 @@ def _create_worker(self) -> "UDFWorker":
             self.catalog = Catalog(
                 id_generator, metastore, warehouse, **self.catalog_init_params
             )
+        udf = loads(self.udf_data)
+        # isinstance cannot be used here, as cloudpickle packages the entire class
+        # definition, and so these two types are not considered exactly equal,
+        # even if they have the same import path.
+        if full_module_type_path(type(udf)) != full_module_type_path(UDFFactory):
+            self.udf = udf
+        else:
+            self.udf = None
+            self.udf_factory = udf
         if not self.udf:
             self.udf = self.udf_factory()
 

src/datachain/utils.py

@@ -1,5 +1,6 @@
 import glob
 import importlib.util
+import io
 import json
 import os
 import os.path as osp
@@ -13,8 +14,10 @@
 from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
 from uuid import UUID
 
+import cloudpickle
 from dateutil import tz
 from dateutil.parser import isoparse
+from pydantic import BaseModel
 
 if TYPE_CHECKING:
     import pandas as pd
@@ -388,3 +391,39 @@ def inside_notebook() -> bool:
             return False
 
     return False
+
+
+def get_all_subclasses(cls):
+    """Return all subclasses of a given class.
+    Can return duplicates due to multiple inheritance."""
+    for subclass in cls.__subclasses__():
+        yield from get_all_subclasses(subclass)
+        yield subclass
+
+
+def filtered_cloudpickle_dumps(obj: Any) -> bytes:
+    """Equivalent to cloudpickle.dumps, but this supports Pydantic models."""
+    model_namespaces = {}
+
+    with io.BytesIO() as f:
+        pickler = cloudpickle.CloudPickler(f)
+
+        for model_class in get_all_subclasses(BaseModel):
+            # This "is not None" check is needed, because due to multiple inheritance,
+            # it is theoretically possible to get the same class twice from
+            # get_all_subclasses.
+            if model_class.__pydantic_parent_namespace__ is not None:
+                # __pydantic_parent_namespace__ can contain many unnecessary and
+                # unpickleable entities, so should be removed for serialization.
+                model_namespaces[model_class] = (
+                    model_class.__pydantic_parent_namespace__
+                )
+                model_class.__pydantic_parent_namespace__ = None
+
+        try:
+            pickler.dump(obj)
+            return f.getvalue()
+        finally:
+            for model_class, namespace in model_namespaces.items():
+                # Restore original __pydantic_parent_namespace__ locally.
+                model_class.__pydantic_parent_namespace__ = namespace