[Enhance] Enable full precision training on Ascend NPU. (#3085)

## Motivation

We will support full precision training on the next generation Ascend
NPU, so there is no need to enable mixed precision by default.

## Modification

Determine whether the current chip supports full precision training, and
automatically enable mixed precision.

## BC-breaking (Optional)

Not affected.

## Use cases (Optional)

We have verified the correctness on the Ascend NPU.

mmseg/apis/train.py

@@ -15,7 +15,7 @@
 from mmseg.core import DistEvalHook, EvalHook, build_optimizer
 from mmseg.datasets import build_dataloader, build_dataset
 from mmseg.utils import (build_ddp, build_dp, find_latest_checkpoint,
-                         get_root_logger)
+                         get_root_logger, is_npu_support_full_precision)
 
 
 def init_random_seed(seed=None, device='cuda'):
@@ -136,7 +136,7 @@ def train_segmentor(model,
             logger=logger,
             meta=meta))
 
-    if cfg.device == 'npu':
+    if cfg.device == 'npu' and not is_npu_support_full_precision():
         optimiter_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
         cfg.optimizer_config = optimiter_config if \
             not cfg.optimizer_config else cfg.optimizer_config

mmseg/utils/__init__.py

@@ -3,9 +3,11 @@
 from .logger import get_root_logger
 from .misc import find_latest_checkpoint
 from .set_env import setup_multi_processes
-from .util_distribution import build_ddp, build_dp, get_device
+from .util_distribution import (build_ddp, build_dp, get_device,
+                                is_npu_support_full_precision)
 
 __all__ = [
     'get_root_logger', 'collect_env', 'find_latest_checkpoint',
-    'setup_multi_processes', 'build_ddp', 'build_dp', 'get_device'
+    'setup_multi_processes', 'build_ddp', 'build_dp', 'get_device',
+    'is_npu_support_full_precision'
 ]

mmseg/utils/util_distribution.py

@@ -94,6 +94,15 @@ def is_npu_available():
     return hasattr(torch, 'npu') and torch.npu.is_available()
 
 
+def is_npu_support_full_precision() -> bool:
+    """Returns True if npu devices support full precision training."""
+    if not is_npu_available():
+        return False
+    import torch_npu.npu.utils as npu_utils
+    version_of_support_full_precision = 220
+    return npu_utils.get_soc_version() >= version_of_support_full_precision
+
+
 def get_device():
     """Returns an available device, cpu, npu, cuda or mlu."""
     is_device_available = {