Add support for init_meta_context, materialize_module (#9920)

CHANGELOG.md

@@ -195,6 +195,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `strategy` argument to Trainer ([#8597](https://github.com/PyTorchLightning/pytorch-lightning/pull/8597))
 
 
+- Added `init_meta_context`, `materialize_module` utilities ([#9920](https://github.com/PyTorchLightning/pytorch-lightning/pull/9920))
+
+
 - Added `TPUPrecisionPlugin` ([#10020](https://github.com/PyTorchLightning/pytorch-lightning/pull/#10020))
 
 
@@ -221,6 +224,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `XLACheckpointIO` plugin ([#9972](https://github.com/PyTorchLightning/pytorch-lightning/pull/9972))
 
 
+
 ### Changed
 
 - Setting `Trainer(accelerator="ddp_cpu")` now does not spawn a subprocess if `num_processes` is kept `1` along with `num_nodes > 1` ([#9603](https://github.com/PyTorchLightning/pytorch-lightning/pull/9603)).

pytorch_lightning/plugins/training_type/deepspeed.py

@@ -426,7 +426,7 @@ def _setup_model_and_optimizer(
     def init_deepspeed(self):
         # check that `configure_gradient_clipping` hook isn't overriden since deepspeed handles
         # gradient clipping internally
-        if is_overridden("configure_gradient_clipping", self.lightning_module):
+        if is_overridden("configure_gradient_clipping", self.lightning_module, pl.LightningModule):
             rank_zero_warn(
                 "Since deepspeed handles gradient clipping internally, this hook will"
                 " be ignored. Consider setting `gradient_clip_val` and `gradient_clip_algorithm`"

pytorch_lightning/trainer/trainer.py

@@ -89,6 +89,7 @@
 from pytorch_lightning.utilities.distributed import distributed_available
 from pytorch_lightning.utilities.exceptions import ExitGracefullyException, MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
+from pytorch_lightning.utilities.meta import materialize_module
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.seed import reset_seed
 from pytorch_lightning.utilities.types import (
@@ -1349,6 +1350,7 @@ def _call_setup_hook(self) -> None:
 
     def _call_configure_sharded_model(self) -> None:
         with self.accelerator.model_sharded_context():
+            materialize_module(self.lightning_module)
             self.call_hook("configure_sharded_model")
             self.call_hook("on_configure_sharded_model")
 

pytorch_lightning/utilities/imports.py

@@ -93,6 +93,7 @@ def _compare_version(package: str, op: Callable, version: str, use_base_version:
 _OMEGACONF_AVAILABLE = _module_available("omegaconf")
 _POPTORCH_AVAILABLE = _module_available("poptorch")
 _RICH_AVAILABLE = _module_available("rich") and _compare_version("rich", operator.ge, "10.2.2")
+_TORCH_META_AVAILABLE = _compare_version("torch", operator.ge, "1.10.0.dev20210922")
 _TORCH_QUANTIZE_AVAILABLE = bool([eg for eg in torch.backends.quantized.supported_engines if eg != "none"])
 _TORCHTEXT_AVAILABLE = _module_available("torchtext")
 _TORCHVISION_AVAILABLE = _module_available("torchvision")

pytorch_lightning/utilities/meta.py

@@ -0,0 +1,323 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import importlib
+import inspect
+import threading
+from contextlib import contextmanager
+from functools import partial
+from itertools import chain
+from types import ModuleType
+from typing import Callable, Dict, Generator, Iterator, List, Optional, Set, Type
+
+import torch
+from torch import nn, Tensor
+from torch.nn import Module
+from torch.nn.modules.container import ModuleDict, ModuleList, Sequential
+
+from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.imports import _TORCH_META_AVAILABLE
+
+if _TORCH_META_AVAILABLE:
+    from torch._C import _DisableTorchDispatch  # type: ignore[attr-defined]
+
+    ####################################################################
+    # BELOW: TAKEN FROM https://github.com/pytorch/pytorch/pull/66317. #
+    # TODO: Removed once merged and released on PyTorch side           #
+    ####################################################################
+
+    @contextmanager
+    def enable_python_mode(cls) -> Iterator[None]:
+        if not hasattr(cls, "__torch_dispatch__"):
+            raise ValueError("The class passed to enable_python_mode " "must have a __torch_dispatch__ classmethod")
+        if not isinstance(cls, type) or not issubclass(cls, (torch.Tensor,)):
+            raise ValueError("The argument passed to enable_python_mode " "must be the type of a Tensor subclass")
+        torch._C._enter_python_mode(cls)
+        try:
+            yield
+        finally:
+            torch._C._exit_python_mode()
+
+    _tls = threading.local()
+    _tls.in_call = False
+
+    @contextmanager
+    def _no_dispatch() -> Iterator[None]:
+        """Temporarily disables the Python dispatch mode."""
+        guard = _DisableTorchDispatch()  # noqa F841
+        try:
+            yield
+        finally:
+            del guard
+
+    def _handle_arange(func, args, kwargs):
+        kwargs["device"] = torch.device("cpu")
+        return torch.empty_like(func(*args, **kwargs), device="meta")
+
+    def _handle_tril(func, args, kwargs):
+        if args and isinstance(args[0], Tensor):
+            return torch.empty_like(args[0], device="meta")
+
+        return NotImplemented
+
+    class _MetaContext(Tensor):
+        _op_handlers: Dict[Callable, Callable] = {}
+
+        @classmethod
+        def _ensure_handlers_initialized(cls) -> None:
+            if cls._op_handlers:
+                return
+
+            cls._op_handlers.update(
+                {
+                    torch.ops.aten.arange: _handle_arange,
+                    torch.ops.aten.tril: _handle_tril,
+                }
+            )
+
+        @classmethod
+        def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+            cls._ensure_handlers_initialized()
+
+            op_handler: Optional[Callable]
+
+            try:
+                op_handler = cls._op_handlers[func]
+            except KeyError:
+                op_handler = None
+
+            with _no_dispatch():
+                if op_handler:
+                    result = op_handler(func, args, kwargs)
+                    if result is not NotImplemented:
+                        return result
+
+                if "device" in kwargs:
+                    kwargs["device"] = torch.device("meta")
+
+                return func(*args, **kwargs)
+
+    def init_meta(module_fn: Callable[..., Module], *args, **kwargs) -> Module:
+        def create_instance(module=None) -> Module:
+            if module:
+                module.__init__(*args, **kwargs)
+                return module
+            return module_fn(*args, **kwargs)
+
+        if _tls.in_call:
+            module = create_instance()
+        else:
+            _tls.in_call = True
+            try:
+                with enable_python_mode(_MetaContext):
+                    module = create_instance()
+            finally:
+                _tls.in_call = False
+
+        module.materialize = partial(create_instance, module=module)  # type: ignore[assignment]
+
+        return module
+
+    def is_meta_init() -> bool:
+        """Indicates whether the module is being instantiated by ``init_meta()``."""
+        return _tls.in_call
+
+    ####################################################################
+    # ABOVE: TAKEN FROM https://github.com/pytorch/pytorch/pull/66317. #
+    # TODO: Removed once merged and released on PyTorch side           #
+    ####################################################################
+
+else:
+
+    def init_meta(*_, **__):
+        if not _TORCH_META_AVAILABLE:
+            return MisconfigurationException("`init_meta` is supported from PyTorch 1.10.0")
+
+
+# https://stackoverflow.com/a/63851681/9201239
+def get_all_subclasses(cls: Type[nn.Module]) -> Set[nn.Module]:
+    subclass_list = []
+
+    def recurse(cl):
+        for subclass in cl.__subclasses__():
+            subclass_list.append(subclass)
+            recurse(subclass)
+
+    recurse(cls)
+
+    return set(subclass_list)
+
+
+def recursively_setattr(root_module: nn.Module, prefix: str, materialized_module: nn.Module) -> None:
+    *path, name = prefix.split(".")
+    for p in path:
+        root_module = getattr(root_module, p)
+
+    try:
+        index = int(name)
+        root_module[index] = materialized_module
+    except ValueError:
+        setattr(root_module, name, materialized_module)
+
+
+def materialize_module(root_module: nn.Module) -> nn.Module:
+    """This utility performs an in-place operation by materialize a module and its children."""
+    if not _TORCH_META_AVAILABLE:
+        return root_module
+
+    materialize_fn = getattr(root_module, "materialize", None)
+    if materialize_fn and not isinstance(root_module, (Sequential, ModuleList, ModuleDict)):
+        return materialize_fn()
+
+    for name, child in root_module.named_children():
+        materialize_fn = getattr(child, "materialize", None)
+        if not materialize_fn or isinstance(child, (Sequential, ModuleList, ModuleDict)):
+            materialize_module(child)
+        else:
+            setattr(child, name, materialize_fn())
+    return root_module
+
+
+# cache subclasses to optimize the search when resetting the meta device later on.
+__STORAGE_META__ = {}
+
+__CREATED_MODULES__ = set()
+
+
+def _unset_meta_device(from_created: bool = False) -> None:
+    """Replace all meta module by their original version."""
+    if not _TORCH_META_AVAILABLE:
+        raise MisconfigurationException("`init_meta` is supported from PyTorch 1.10.0")
+
+    if from_created:
+        values = [__STORAGE_META__[key] for key in __CREATED_MODULES__]
+    else:
+        values = __STORAGE_META__.values()
+
+    for mods, subclass, _ in values:
+        for mod in mods:
+            setattr(mod, subclass.__name__, subclass)
+
+
+def _set_meta_device_populated(from_created: bool = False) -> None:
+    """Replace all meta module by their original version."""
+    if not _TORCH_META_AVAILABLE:
+        raise MisconfigurationException("`init_meta` is supported from PyTorch 1.10.0")
+
+    if from_created:
+        values = [__STORAGE_META__[key] for key in __CREATED_MODULES__]
+    else:
+        values = __STORAGE_META__.values()
+
+    for mods, subclass, meta_class in values:
+        for mod in mods:
+            setattr(mod, subclass.__name__, meta_class)
+
+
+def _set_meta_device() -> None:
+    """Replace all torch.nn.Module by their meta replacement."""
+
+    if not _TORCH_META_AVAILABLE:
+        raise MisconfigurationException("`init_meta` is supported from PyTorch 1.10.0")
+
+    # Author note: This can be optimized further by searching all subclasses at once.
+    # Its time complexity is O(n*m) where n is the number of all subclasses if there's no multiple inheritance
+    # and m the number of all subclasses belonging to its subclass module.
+
+    for subclass in get_all_subclasses(torch.nn.modules.module.Module):
+
+        if isinstance(subclass, (Sequential, ModuleList, ModuleDict)):
+            continue
+
+        # if a subclass has already been stored, we should use the cache
+        if str(subclass) in __STORAGE_META__:
+            # reset the class import package to its rightfull state.
+            mods, subclass, meta_class = __STORAGE_META__[subclass]
+            for mod in mods:
+                setattr(mod, subclass.__name__, meta_class)
+            continue
+
+        # Create a class subclassing current `subclass` overriding its new method.
+        # this will enable use to use `torch.distributed.nn.utils.init_meta` to create a `meta`
+        # version of the current subclass module
+        class _MetaClass(subclass):
+            @classmethod
+            @contextmanager
+            def instantiation_context(cls, materialize: bool):
+                _unset_meta_device(from_created=True)
+                yield
+                _set_meta_device_populated(from_created=True)
+
+            @classmethod
+            def materialize(cls, materialize_fn: Callable):
+                with cls.instantiation_context(materialize=True):
+                    obj = materialize_fn()
+                return obj
+
+            @staticmethod
+            def add_subclasses(subclass):
+                """This is used to unrol the instantion tree while creating the modules."""
+                __CREATED_MODULES__.add(subclass)
+                if subclass.__bases__[0] != torch.nn.modules.module.Module:
+                    _MetaClass.add_subclasses(subclass.__bases__[0])
+
+            def __new__(cls, *args, **kwargs):
+                subclass = cls.__bases__[0]
+                cls.add_subclasses(subclass)
+                with cls.instantiation_context(materialize=False):
+                    obj = init_meta(subclass, *args, **kwargs)
+
+                obj.materialize = partial(cls.materialize, materialize_fn=obj.materialize)
+                return obj
+
+        def search(mod: ModuleType) -> List[ModuleType]:
+            out = []
+            for _, obj in inspect.getmembers(mod):
+                if obj == subclass:
+                    out.append(mod)
+            return out
+
+        submodules = subclass.__module__.split(".")
+        mod = importlib.import_module(submodules[0])
+
+        # nn.Module class can be imported at different level and they all need to be mocked.
+        # Example: torch.nn.Linear is actually torch.nn.modules.linear.Linear
+        # Therefore, torch.nn.Linear, torch.nn.modules.Linear, torch.nn.modules.linear.Linear
+        # needs to be replaced by the torch.nn.linear.modules.Linear _MetaClass
+        out = []
+        out.append(search(mod))
+        for name in submodules[1:]:
+            mod = getattr(mod, name)
+            out.append(search(mod))
+
+        # drop empty module
+        mods = [mod for mod in chain(*out) if mod]
+
+        # store the modules search so it doesn't have to be performed again for this class
+        __STORAGE_META__[subclass] = (mods, subclass, _MetaClass)
+
+        # replace all subclass by its meta form
+        for mod in mods:
+            setattr(mod, subclass.__name__, _MetaClass)
+
+
+@contextmanager
+def init_meta_context() -> Generator:
+    rank_zero_warn(
+        "Be aware this feature is highly experimental and there are a number of weird edge cases "
+        "where it can internal assert and/or crash. A more stable version is to be expected from PyTorch 1.11."
+    )
+    _set_meta_device()
+    yield
+    _unset_meta_device()