Revert "Add support for init_meta_context, materialize_module (#9920)"

This reverts commit 454e93b.

CHANGELOG.md

@@ -195,9 +195,6 @@ 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))
 
 
@@ -224,7 +221,6 @@ 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, pl.LightningModule):
+        if is_overridden("configure_gradient_clipping", self.lightning_module):
             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

@@ -89,7 +89,6 @@
 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 (
@@ -1350,7 +1349,6 @@ 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

@@ -93,7 +93,6 @@ 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")