[util] Add generic torch device class (#6174)

* introduce new abstraction layer for GPU devices

* add unit test for device abstraction

* fix ruff

* convert TorchDeviceSelect into a stateless class

* move logic to select context-specific execution device into context API

* add mock hardware environments to pytest

* remove dangling mocker fixture

* fix unit test for running on non-CUDA systems

* remove unimplemented get_execution_device() call

* remove autocast precision

* Multiple changes:

1. Remove TorchDeviceSelect.get_execution_device(), as well as calls to
   context.models.get_execution_device().
2. Rename TorchDeviceSelect to TorchDevice
3. Added back the legacy public API defined in `invocation_api`, including
   choose_precision().
4. Added a config file migration script to accommodate removal of precision=autocast.

* add deprecation warnings to choose_torch_device() and choose_precision()

* fix test crash

* remove app_config argument from choose_torch_device() and choose_torch_dtype()

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -28,7 +28,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
 from invokeai.backend.util.attention import auto_detect_slice_size
-from invokeai.backend.util.devices import normalize_device
+from invokeai.backend.util.devices import TorchDevice
 
 
 @dataclass
@@ -258,7 +258,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
             mem_free = psutil.virtual_memory().free
         elif self.unet.device.type == "cuda":
-            mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device))
+            mem_free, _ = torch.cuda.mem_get_info(TorchDevice.normalize(self.unet.device))
         else:
             raise ValueError(f"unrecognized device {self.unet.device}")
         # input tensor of [1, 4, h/8, w/8]