deprecated GradScaler and autocast

clinicadl/interpret/gradients.py

@@ -1,7 +1,7 @@
 import abc
 
 import torch
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 
 from clinicadl.utils.exceptions import ClinicaDLArgumentError
 
@@ -28,7 +28,7 @@ def generate_gradients(
         # Forward
         input_batch = input_batch.to(self.device)
         input_batch.requires_grad = True
-        with autocast(enabled=amp):
+        with autocast("cuda", enabled=amp):
             if hasattr(self.model, "variational") and self.model.variational:
                 _, _, _, model_output = self.model(input_batch)
             else:
@@ -94,7 +94,7 @@ def generate_gradients(
         # Get last conv feature map
         feature_maps = conv_part(input_batch).detach()
         feature_maps.requires_grad = True
-        with autocast(enabled=amp):
+        with autocast("cuda", enabled=amp):
             model_output = fc_part(pre_fc_part(feature_maps))
         # Target for backprop
         one_hot_output = torch.zeros_like(model_output)

clinicadl/maps_manager/maps_manager.py

@@ -8,7 +8,7 @@
 import pandas as pd
 import torch
 import torch.distributed as dist
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 
 from clinicadl.caps_dataset.caps_dataset_utils import read_json
 from clinicadl.caps_dataset.data import (
@@ -350,7 +350,7 @@ def _compute_output_tensors(
                 data = dataset[i]
                 image = data["image"]
                 x = image.unsqueeze(0).to(model.device)
-                with autocast(enabled=self.std_amp):
+                with autocast("cuda", enabled=self.std_amp):
                     output = model(x)
                 output = output.squeeze(0).cpu().float()
                 participant_id = data["participant_id"]

clinicadl/predict/predict_manager.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import torch
 import torch.distributed as dist
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
@@ -509,7 +509,7 @@ def _compute_latent_tensors(
                 data = dataset[i]
                 image = data["image"]
                 logger.debug(f"Image for latent representation {image}")
-                with autocast(enabled=self.maps_manager.std_amp):
+                with autocast("cuda", enabled=self.maps_manager.std_amp):
                     _, latent, _ = model.module._forward(
                         image.unsqueeze(0).to(model.device)
                     )
@@ -583,7 +583,7 @@ def _compute_output_nifti(
                 data = dataset[i]
                 image = data["image"]
                 x = image.unsqueeze(0).to(model.device)
-                with autocast(enabled=self.maps_manager.std_amp):
+                with autocast("cuda", enabled=self.maps_manager.std_amp):
                     output = model(x)
                 output = output.squeeze(0).detach().cpu().float()
                 # Convert tensor to nifti image with appropriate affine

clinicadl/quality_check/t1_linear/quality_check.py

@@ -8,7 +8,7 @@
 
 import pandas as pd
 import torch
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 from torch.utils.data import DataLoader
 
 from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
@@ -153,7 +153,7 @@ def quality_check(
             inputs = data["image"]
             if computational_config.gpu:
                 inputs = inputs.cuda()
-            with autocast(enabled=computational_config.amp):
+            with autocast("cuda", enabled=computational_config.amp):
                 outputs = softmax(model(inputs))
             # We cast back to 32bits. It should be a no-op as softmax is not eligible
             # to fp16 and autocast is forbidden on CPU (output would be bf16 otherwise).

clinicadl/trainer/tasks_utils.py

@@ -12,7 +12,7 @@
     model_validator,
 )
 from torch import Tensor, nn
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 from torch.nn.functional import softmax
 from torch.nn.modules.loss import _Loss
 from torch.utils.data import DataLoader, Sampler, sampler
@@ -240,7 +240,7 @@ def test(
     with torch.no_grad():
         for i, data in enumerate(dataloader):
             # initialize the loss list to save the loss components
-            with autocast(enabled=amp):
+            with autocast("cuda", enabled=amp):
                 outputs, loss_dict = model(data, criterion, use_labels=use_labels)
 
             if i == 0: