Merge pull request #2308 from huggingface/device_amp_cleanup

Cleanup some amp related behaviour to better support different (non-cuda) devices

Author: rwightman

benchmark.py

@@ -32,13 +32,6 @@
 except ImportError:
     pass
 
-has_native_amp = False
-try:
-    if getattr(torch.cuda.amp, 'autocast') is not None:
-        has_native_amp = True
-except AttributeError:
-    pass
-
 try:
     from deepspeed.profiling.flops_profiler import get_model_profile
     has_deepspeed_profiling = True
@@ -242,7 +235,7 @@ def __init__(
         self.amp_dtype, self.model_dtype, self.data_dtype = resolve_precision(precision)
         self.channels_last = kwargs.pop('channels_last', False)
         if self.amp_dtype is not None:
-            self.amp_autocast = partial(torch.cuda.amp.autocast, dtype=self.amp_dtype)
+            self.amp_autocast = partial(torch.amp.autocast, device_type=device, dtype=self.amp_dtype)
         else:
             self.amp_autocast = suppress
 

inference.py

@@ -28,13 +28,6 @@
 except ImportError:
     has_apex = False
 
-has_native_amp = False
-try:
-    if getattr(torch.cuda.amp, 'autocast') is not None:
-        has_native_amp = True
-except AttributeError:
-    pass
-
 try:
     from functorch.compile import memory_efficient_fusion
     has_functorch = True
@@ -170,7 +163,6 @@ def main():
     # resolve AMP arguments based on PyTorch / Apex availability
     amp_autocast = suppress
     if args.amp:
-        assert has_native_amp, 'Please update PyTorch to a version with native AMP (or use APEX).'
         assert args.amp_dtype in ('float16', 'bfloat16')
         amp_dtype = torch.bfloat16 if args.amp_dtype == 'bfloat16' else torch.float16
         amp_autocast = partial(torch.autocast, device_type=device.type, dtype=amp_dtype)

timm/data/loader.py

@@ -113,13 +113,17 @@ def __init__(
             )
         else:
             self.random_erasing = None
-        self.is_cuda = torch.cuda.is_available() and device.type == 'cuda'
+        self.is_cuda = device.type == 'cuda' and torch.cuda.is_available()
+        self.is_npu = device.type == 'npu' and torch.npu.is_available()
 
     def __iter__(self):
         first = True
         if self.is_cuda:
             stream = torch.cuda.Stream()
             stream_context = partial(torch.cuda.stream, stream=stream)
+        elif self.is_npu:
+            stream = torch.npu.Stream()
+            stream_context = partial(torch.npu.stream, stream=stream)
         else:
             stream = None
             stream_context = suppress
@@ -139,7 +143,10 @@ def __iter__(self):
                 first = False
 
             if stream is not None:
-                torch.cuda.current_stream().wait_stream(stream)
+                if self.is_cuda:
+                    torch.cuda.current_stream().wait_stream(stream)
+                elif self.is_npu:
+                    torch.npu.current_stream().wait_stream(stream)
 
             input = next_input
             target = next_target

timm/layers/fast_norm.py

@@ -28,6 +28,30 @@
 _USE_FAST_NORM = False  # defaulting to False for now
 
 
+def get_autocast_dtype(device: str = 'cuda'):
+    try:
+        return torch.get_autocast_dtype(device)
+    except (AttributeError, TypeError):
+        # dispatch to older device specific fns, only covering cuda/cpu devices here
+        if device == 'cpu':
+            return torch.get_autocast_cpu_dtype()
+        else:
+            assert device == 'cuda'
+            return torch.get_autocast_gpu_dtype()
+
+
+def is_autocast_enabled(device: str = 'cuda'):
+    try:
+        return torch.is_autocast_enabled(device)
+    except TypeError:
+        # dispatch to older device specific fns, only covering cuda/cpu devices here
+        if device == 'cpu':
+            return torch.is_autocast_cpu_enabled()
+        else:
+            assert device == 'cuda'
+            return torch.is_autocast_enabled()  # defaults cuda (only cuda on older pytorch)
+
+
 def is_fast_norm():
     return _USE_FAST_NORM
 
@@ -48,14 +72,14 @@ def fast_group_norm(
         # currently cannot use is_autocast_enabled within torchscript
         return F.group_norm(x, num_groups, weight, bias, eps)
 
-    if torch.is_autocast_enabled():
+    if is_autocast_enabled(x.device.type):
         # normally native AMP casts GN inputs to float32
         # here we use the low precision autocast dtype
         # FIXME what to do re CPU autocast?
-        dt = torch.get_autocast_gpu_dtype()
+        dt = get_autocast_dtype(x.device.type)
         x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
 
-    with torch.cuda.amp.autocast(enabled=False):
+    with torch.amp.autocast(device_type=x.device.type, enabled=False):
         return F.group_norm(x, num_groups, weight, bias, eps)
 
 
@@ -73,14 +97,14 @@ def fast_layer_norm(
     if has_apex:
         return fused_layer_norm_affine(x, weight, bias, normalized_shape, eps)
 
-    if torch.is_autocast_enabled():
+    if is_autocast_enabled(x.device.type):
         # normally native AMP casts LN inputs to float32
         # apex LN does not, this is behaving like Apex
-        dt = torch.get_autocast_gpu_dtype()
+        dt = get_autocast_dtype(x.device.type)
         # FIXME what to do re CPU autocast?
         x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
 
-    with torch.cuda.amp.autocast(enabled=False):
+    with torch.amp.autocast(device_type=x.device.type, enabled=False):
         return F.layer_norm(x, normalized_shape, weight, bias, eps)
 
 

timm/utils/cuda.py

@@ -46,8 +46,11 @@ def load_state_dict(self, state_dict):
 class NativeScaler:
     state_dict_key = "amp_scaler"
 
-    def __init__(self):
-        self._scaler = torch.cuda.amp.GradScaler()
+    def __init__(self, device='cuda'):
+        try:
+            self._scaler = torch.amp.GradScaler(device=device)
+        except (AttributeError, TypeError) as e:
+            self._scaler = torch.cuda.amp.GradScaler()
 
     def __call__(
             self,

timm/utils/distributed.py

@@ -116,6 +116,7 @@ def init_distributed_device_so(
             "xpu": "ccl",
             "hpu": "hccl",
             "cuda": "nccl",
+            "npu": "hccl",
         }
         dist_backend = dist_backends.get(device_type, 'gloo')
     dist_url = dist_url or 'env://'
@@ -159,6 +160,8 @@ def init_distributed_device_so(
 
     if device_type == 'cuda':
         assert torch.cuda.is_available(), f'CUDA is not available but {device} was specified.'
+    if device_type == 'npu':
+        assert torch.npu.is_available(), f'Ascend NPU is not available but {device} was specified.'
 
     if distributed and device != 'cpu':
         # Ignore manually specified device index in distributed mode and

train.py

@@ -48,12 +48,6 @@
 except ImportError:
     has_apex = False
 
-has_native_amp = False
-try:
-    if getattr(torch.cuda.amp, 'autocast') is not None:
-        has_native_amp = True
-except AttributeError:
-    pass
 
 try:
     import wandb
@@ -442,7 +436,6 @@ def main():
             use_amp = 'apex'
             assert args.amp_dtype == 'float16'
         else:
-            assert has_native_amp, 'Please update PyTorch to a version with native AMP (or use APEX).'
             use_amp = 'native'
             assert args.amp_dtype in ('float16', 'bfloat16')
         if args.amp_dtype == 'bfloat16':
@@ -572,15 +565,10 @@ def main():
         if utils.is_primary(args):
             _logger.info('Using NVIDIA APEX AMP. Training in mixed precision.')
     elif use_amp == 'native':
-        try:
-            amp_autocast = partial(torch.autocast, device_type=device.type, dtype=amp_dtype)
-        except (AttributeError, TypeError):
-            # fallback to CUDA only AMP for PyTorch < 1.10
-            assert device.type == 'cuda'
-            amp_autocast = torch.cuda.amp.autocast
-        if device.type == 'cuda' and amp_dtype == torch.float16:
+        amp_autocast = partial(torch.autocast, device_type=device.type, dtype=amp_dtype)
+        if device.type in ('cuda',) and amp_dtype == torch.float16:
             # loss scaler only used for float16 (half) dtype, bfloat16 does not need it
-            loss_scaler = NativeScaler()
+            loss_scaler = NativeScaler(device=device.type)
         if utils.is_primary(args):
             _logger.info('Using native Torch AMP. Training in mixed precision.')
     else:
@@ -1054,8 +1042,11 @@ def _backward(_loss):
         if model_ema is not None:
             model_ema.update(model, step=num_updates)
 
-        if args.synchronize_step and device.type == 'cuda':
-            torch.cuda.synchronize()
+        if args.synchronize_step:
+            if device.type == 'cuda':
+                torch.cuda.synchronize()
+            elif device.type == 'npu':
+                torch.npu.synchronize()
         time_now = time.time()
         update_time_m.update(time.time() - update_start_time)
         update_start_time = time_now
@@ -1155,6 +1146,8 @@ def validate(
 
             if device.type == 'cuda':
                 torch.cuda.synchronize()
+            elif device.type == "npu":
+                torch.npu.synchronize()
 
             losses_m.update(reduced_loss.item(), input.size(0))
             top1_m.update(acc1.item(), output.size(0))

validate.py

@@ -34,13 +34,6 @@
 except ImportError:
     has_apex = False
 
-has_native_amp = False
-try:
-    if getattr(torch.cuda.amp, 'autocast') is not None:
-        has_native_amp = True
-except AttributeError:
-    pass
-
 try:
     from functorch.compile import memory_efficient_fusion
     has_functorch = True
@@ -183,7 +176,6 @@ def validate(args):
             use_amp = 'apex'
             _logger.info('Validating in mixed precision with NVIDIA APEX AMP.')
         else:
-            assert has_native_amp, 'Please update PyTorch to a version with native AMP (or use APEX).'
             assert args.amp_dtype in ('float16', 'bfloat16')
             use_amp = 'native'
             amp_dtype = torch.bfloat16 if args.amp_dtype == 'bfloat16' else torch.float16
@@ -395,8 +387,10 @@ def _try_run(args, initial_batch_size):
     while batch_size:
         args.batch_size = batch_size * args.num_gpu  # multiply by num-gpu for DataParallel case
         try:
-            if torch.cuda.is_available() and 'cuda' in args.device:
+            if 'cuda' in args.device and torch.cuda.is_available():
                 torch.cuda.empty_cache()
+            elif "npu" in args.device and torch.npu.is_available():
+                torch.npu.empty_cache()
             results = validate(args)
             return results
         except RuntimeError as e: