Fix FutureWarning: Replace torch.cuda.amp.GradScaler with torch.amp.GradScaler

README.md

@@ -397,7 +397,7 @@ Few pointers to get you started:
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb) [Basic example of LR finder on
   MNIST](https://github.com/pytorch/ignite/blob/master/examples/notebooks/FastaiLRFinder_MNIST.ipynb)
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb) [Benchmark mixed precision training on Cifar100:
-  torch.cuda.amp vs nvidia/apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb)
+  torch.amp vs nvidia/apex](https://github.com/pytorch/ignite/blob/master/examples/notebooks/Cifar100_bench_amp.ipynb)
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb) [MNIST training on a single
   TPU](https://github.com/pytorch/ignite/blob/master/examples/notebooks/MNIST_on_TPU.ipynb)
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1E9zJrptnLJ_PKhmaP5Vhb6DTVRvyrKHx) [CIFAR10 Training on multiple TPUs](https://github.com/pytorch/ignite/tree/master/examples/cifar10)

docs/source/conf.py

@@ -354,7 +354,7 @@ def run(self):
     ("py:class", "torch.optim.optimizer.Optimizer"),
     ("py:class", "torch.utils.data.dataset.Dataset"),
     ("py:class", "torch.utils.data.sampler.BatchSampler"),
-    ("py:class", "torch.cuda.amp.grad_scaler.GradScaler"),
+    ("py:class", "torch.amp.grad_scaler.GradScaler"),
     ("py:class", "torch.optim.lr_scheduler._LRScheduler"),
     ("py:class", "torch.optim.lr_scheduler.LRScheduler"),
     ("py:class", "torch.utils.data.dataloader.DataLoader"),

examples/cifar10/main.py

@@ -7,8 +7,7 @@
 import torch.nn as nn
 import torch.optim as optim
 import utils
-from torch.amp import autocast
-from torch.cuda.amp import GradScaler
+from torch.amp import autocast, GradScaler
 
 import ignite
 import ignite.distributed as idist

examples/cifar100_amp_benchmark/benchmark_torch_cuda_amp.py

@@ -1,7 +1,6 @@
 import fire
 import torch
-from torch.amp import autocast
-from torch.cuda.amp import GradScaler
+from torch.amp import autocast, GradScaler
 from torch.nn import CrossEntropyLoss
 from torch.optim import SGD
 from torchvision.models import wide_resnet50_2

examples/cifar10_qat/main.py

@@ -6,8 +6,7 @@
 import torch.nn as nn
 import torch.optim as optim
 import utils
-from torch.amp import autocast
-from torch.cuda.amp import GradScaler
+from torch.amp import autocast, GradScaler
 
 import ignite
 import ignite.distributed as idist

examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb

@@ -875,10 +875,10 @@
     "As suggested, we divide the objective by 2 while optimizing D, which slows down the rate at which D learns, relative to the rate of G. \n",
     "\n",
     "According to the paper:\n",
-    "- generator A is trained  minimize $\\text{mean}_{x \\in A}[(D_B(G(x)) − 1)^2]$ and cycle loss $\\text{mean}_{x \\in A}\\left[ |F(G(x)) - x|_1 \\right]$\n",
-    "- generator B is trained  minimize $\\text{mean}_{y \\in B}[(D_A(F(y)) − 1)^2]$ and cycle loss $\\text{mean}_{y \\in B}\\left[ |G(F(y)) - y|_1 \\right]$\n",
-    "- discriminators A is trained to minimize $\\text{mean}_{x \\in A}[(D_A(x) − 1)^2] + \\text{mean}_{y \\in B}[D_A(F(y))^2]$.\n",
-    "- discriminator B is trained to minimize $\\text{mean}_{y \\in B}[(D_B(y) − 1)^2] + \\text{mean}_{x \\in A}[D_B(G(x))^2]$."
+    "- generator A is trained  minimize $\\text{mean}_{x \\in A}[(D_B(G(x)) \u2212 1)^2]$ and cycle loss $\\text{mean}_{x \\in A}\\left[ |F(G(x)) - x|_1 \\right]$\n",
+    "- generator B is trained  minimize $\\text{mean}_{y \\in B}[(D_A(F(y)) \u2212 1)^2]$ and cycle loss $\\text{mean}_{y \\in B}\\left[ |G(F(y)) - y|_1 \\right]$\n",
+    "- discriminators A is trained to minimize $\\text{mean}_{x \\in A}[(D_A(x) \u2212 1)^2] + \\text{mean}_{y \\in B}[D_A(F(y))^2]$.\n",
+    "- discriminator B is trained to minimize $\\text{mean}_{y \\in B}[(D_B(y) \u2212 1)^2] + \\text{mean}_{x \\in A}[D_B(G(x))^2]$."
    ]
   },
   {
@@ -887,7 +887,7 @@
     "id": "JE8dLeEfIl_Z"
    },
    "source": [
-    "We will use [`torch.amp.autocast`](https://pytorch.org/docs/master/amp.html#torch.amp.autocast) and [`torch.cuda.amp.GradScaler`](https://pytorch.org/docs/master/amp.html#torch.cuda.amp.GradScaler) to perform automatic mixed precision training. Our code follows a [typical mixed precision training example](https://pytorch.org/docs/master/notes/amp_examples.html#typical-mixed-precision-training)."
+    "We will use [`torch.amp.autocast`](https://pytorch.org/docs/master/amp.html#torch.amp.autocast) and [`torch.amp.GradScaler`](https://pytorch.org/docs/master/amp.html#torch.amp.GradScaler) to perform automatic mixed precision training. Our code follows a [typical mixed precision training example](https://pytorch.org/docs/master/notes/amp_examples.html#typical-mixed-precision-training)."
    ]
   },
   {
@@ -896,8 +896,7 @@
     "id": "vrJls4p-FRcA"
    },
    "source": [
-    "from torch.cuda.amp import GradScaler\n",
-    "from torch.amp import autocast\n",
+    "from torch.amp import autocast, GradScaler\n",
     "\n",
     "from ignite.utils import convert_tensor\n",
     "import torch.nn.functional as F\n",
@@ -924,7 +923,7 @@
     "\n",
     "\n",
     "def compute_loss_discriminator(decision_real, decision_fake):\n",
-    "    # loss = mean (D_b(y) − 1)^2 + mean D_b(G(x))^2    \n",
+    "    # loss = mean (D_b(y) \u2212 1)^2 + mean D_b(G(x))^2    \n",
     "    loss = F.mse_loss(decision_fake, torch.zeros_like(decision_fake))\n",
     "    loss += F.mse_loss(decision_real, torch.ones_like(decision_real))\n",
     "    return loss\n",
@@ -954,10 +953,10 @@
     "        decision_fake_b = discriminator_B(fake_b)\n",
     "\n",
     "        # Compute loss for generators and update generators\n",
-    "        # loss_a2b = GAN loss: mean (D_b(G(x)) − 1)^2 + Forward cycle loss: || F(G(x)) - x ||_1    \n",
+    "        # loss_a2b = GAN loss: mean (D_b(G(x)) \u2212 1)^2 + Forward cycle loss: || F(G(x)) - x ||_1    \n",
     "        loss_a2b = compute_loss_generator(decision_fake_b, real_a, rec_a, lambda_value)    \n",
     "\n",
-    "        # loss_b2a = GAN loss: mean (D_a(F(x)) − 1)^2 + Backward cycle loss: || G(F(y)) - y ||_1\n",
+    "        # loss_b2a = GAN loss: mean (D_a(F(x)) \u2212 1)^2 + Backward cycle loss: || G(F(y)) - y ||_1\n",
     "        loss_b2a = compute_loss_generator(decision_fake_a, real_b, rec_b, lambda_value)\n",
     "\n",
     "        # total generators loss:\n",
@@ -977,10 +976,10 @@
     "        decision_real_a, decision_fake_a = discriminator_forward_pass(discriminator_A, real_a, fake_a.detach(), fake_a_buffer)    \n",
     "        decision_real_b, decision_fake_b = discriminator_forward_pass(discriminator_B, real_b, fake_b.detach(), fake_b_buffer)    \n",
     "        # Compute loss for discriminators and update discriminators\n",
-    "        # loss_a = mean (D_a(y) − 1)^2 + mean D_a(F(x))^2\n",
+    "        # loss_a = mean (D_a(y) \u2212 1)^2 + mean D_a(F(x))^2\n",
     "        loss_a = compute_loss_discriminator(decision_real_a, decision_fake_a)\n",
     "\n",
-    "        # loss_b = mean (D_b(y) − 1)^2 + mean D_b(G(x))^2\n",
+    "        # loss_b = mean (D_b(y) \u2212 1)^2 + mean D_b(G(x))^2\n",
     "        loss_b = compute_loss_discriminator(decision_real_b, decision_fake_b)\n",
     "      \n",
     "        # total discriminators loss:\n",

examples/references/classification/imagenet/main.py

@@ -6,8 +6,7 @@
 import torch
 
 try:
-    from torch.amp import autocast
-    from torch.cuda.amp import GradScaler
+    from torch.amp import autocast, GradScaler
 except ImportError:
     raise RuntimeError("Please, use recent PyTorch version, e.g. >=1.12.0")
 

examples/references/segmentation/pascal_voc2012/main.py

@@ -6,8 +6,7 @@
 import torch
 
 try:
-    from torch.amp import autocast
-    from torch.cuda.amp import GradScaler
+    from torch.amp import autocast, GradScaler
 except ImportError:
     raise RuntimeError("Please, use recent PyTorch version, e.g. >=1.12.0")
 

examples/transformers/main.py

@@ -7,8 +7,7 @@
 import torch.nn as nn
 import torch.optim as optim
 import utils
-from torch.amp import autocast
-from torch.cuda.amp import GradScaler
+from torch.amp import autocast, GradScaler
 
 import ignite
 import ignite.distributed as idist

ignite/engine/__init__.py

@@ -133,11 +133,11 @@ def supervised_training_step_amp(
     prepare_batch: Callable = _prepare_batch,
     model_transform: Callable[[Any], Any] = lambda output: output,
     output_transform: Callable[[Any, Any, Any, torch.Tensor], Any] = lambda x, y, y_pred, loss: loss.item(),
-    scaler: Optional["torch.cuda.amp.GradScaler"] = None,
+    scaler: Optional["torch.amp.GradScaler"] = None,
     gradient_accumulation_steps: int = 1,
     model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x),
 ) -> Callable:
-    """Factory function for supervised training using ``torch.cuda.amp``.
+    """Factory function for supervised training using ``torch.amp``.
 
     Args:
         model: the model to train.
@@ -170,7 +170,7 @@ def supervised_training_step_amp(
             model = ...
             optimizer = ...
             loss_fn = ...
-            scaler = torch.cuda.amp.GradScaler(2**10)
+            scaler = torch.amp.GradScaler('cuda', 2**10)
 
             update_fn = supervised_training_step_amp(model, optimizer, loss_fn, 'cuda', scaler=scaler)
             trainer = Engine(update_fn)
@@ -185,7 +185,7 @@ def supervised_training_step_amp(
     """
 
     try:
-        from torch.amp import autocast
+        from torch.amp import autocast, GradScaler
     except ImportError:
         raise ImportError("Please install torch>=1.12.0 to use amp_mode='amp'.")
 
@@ -393,8 +393,8 @@ def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[to
 
 
 def _check_arg(
-    on_tpu: bool, on_mps: bool, amp_mode: Optional[str], scaler: Optional[Union[bool, "torch.cuda.amp.GradScaler"]]
-) -> Tuple[Optional[str], Optional["torch.cuda.amp.GradScaler"]]:
+    on_tpu: bool, on_mps: bool, amp_mode: Optional[str], scaler: Optional[Union[bool, "torch.amp.GradScaler"]]
+) -> Tuple[Optional[str], Optional["torch.amp.GradScaler"]]:
     """Checking tpu, mps, amp and GradScaler instance combinations."""
     if on_mps and amp_mode:
         raise ValueError("amp_mode cannot be used with mps device. Consider using amp_mode=None or device='cuda'.")
@@ -410,7 +410,7 @@ def _check_arg(
             raise ValueError(f"scaler argument is {scaler}, but amp_mode is {amp_mode}. Consider using amp_mode='amp'.")
         elif amp_mode == "amp" and isinstance(scaler, bool):
             try:
-                from torch.cuda.amp import GradScaler
+                from torch.amp import GradScaler
             except ImportError:
                 raise ImportError("Please install torch>=1.6.0 to use scaler argument.")
             scaler = GradScaler(enabled=True)
@@ -434,7 +434,7 @@ def create_supervised_trainer(
     output_transform: Callable[[Any, Any, Any, torch.Tensor], Any] = lambda x, y, y_pred, loss: loss.item(),
     deterministic: bool = False,
     amp_mode: Optional[str] = None,
-    scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False,
+    scaler: Union[bool, "torch.amp.GradScaler"] = False,
     gradient_accumulation_steps: int = 1,
     model_fn: Callable[[torch.nn.Module, Any], Any] = lambda model, x: model(x),
 ) -> Engine:
@@ -459,7 +459,7 @@ def create_supervised_trainer(
             :class:`~ignite.engine.deterministic.DeterministicEngine`, otherwise :class:`~ignite.engine.engine.Engine`
             (default: False).
         amp_mode: can be ``amp`` or ``apex``, model and optimizer will be casted to float16 using
-            `torch.cuda.amp <https://pytorch.org/docs/stable/amp.html>`_ for ``amp`` and
+            `torch.amp <https://pytorch.org/docs/stable/amp.html>`_ for ``amp`` and
             using `apex <https://nvidia.github.io/apex>`_ for ``apex``. (default: None)
         scaler: GradScaler instance for gradient scaling if `torch>=1.6.0`
             and ``amp_mode`` is ``amp``. If ``amp_mode`` is ``apex``, this argument will be ignored.

tests/ignite/engine/test_create_supervised.py

@@ -48,7 +48,7 @@ def _default_create_supervised_trainer(
     trainer_device: Optional[str] = None,
     trace: bool = False,
     amp_mode: str = None,
-    scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False,
+    scaler: Union[bool, "torch.amp.GradScaler"] = False,
     with_model_transform: bool = False,
     with_model_fn: bool = False,
 ):
@@ -104,7 +104,7 @@ def _test_create_supervised_trainer(
     trainer_device: Optional[str] = None,
     trace: bool = False,
     amp_mode: str = None,
-    scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False,
+    scaler: Union[bool, "torch.amp.GradScaler"] = False,
     with_model_transform: bool = False,
     with_model_fn: bool = False,
 ):
@@ -170,18 +170,18 @@ def _():
 @pytest.mark.skipif(Version(torch.__version__) < Version("1.12.0"), reason="Skip if < 1.12.0")
 def test_create_supervised_training_scalar_assignment():
     with mock.patch("ignite.engine._check_arg") as check_arg_mock:
-        check_arg_mock.return_value = None, torch.cuda.amp.GradScaler(enabled=False)
+        check_arg_mock.return_value = None, torch.amp.GradScaler(enabled=False)
         trainer, _ = _default_create_supervised_trainer(model_device="cpu", trainer_device="cpu", scaler=True)
         assert hasattr(trainer.state, "scaler")
-        assert isinstance(trainer.state.scaler, torch.cuda.amp.GradScaler)
+        assert isinstance(trainer.state.scaler, torch.amp.GradScaler)
 
 
 def _test_create_mocked_supervised_trainer(
     model_device: Optional[str] = None,
     trainer_device: Optional[str] = None,
     trace: bool = False,
     amp_mode: str = None,
-    scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False,
+    scaler: Union[bool, "torch.amp.GradScaler"] = False,
 ):
     with mock.patch("ignite.engine.supervised_training_step_amp") as training_step_amp_mock:
         with mock.patch("ignite.engine.supervised_training_step_apex") as training_step_apex_mock:
@@ -462,7 +462,7 @@ def test_create_supervised_trainer_amp_error(mock_torch_cuda_amp_module):
 
 @pytest.mark.skipif(Version(torch.__version__) < Version("1.12.0"), reason="Skip if < 1.12.0")
 def test_create_supervised_trainer_scaler_not_amp():
-    scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available())
+    scaler = torch.amp.GradScaler(enabled=torch.cuda.is_available())
 
     with pytest.raises(ValueError, match=f"scaler argument is {scaler}, but amp_mode is None."):
         _test_create_supervised_trainer(amp_mode=None, scaler=scaler)
@@ -540,7 +540,7 @@ def test_create_supervised_trainer_on_cuda_amp_scaler():
     _test_create_mocked_supervised_trainer(
         model_device=model_device, trainer_device=trainer_device, amp_mode="amp", scaler=True
     )
-    scaler = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available())
+    scaler = torch.amp.GradScaler(enabled=torch.cuda.is_available())
     _test_create_supervised_trainer(
         gradient_accumulation_steps=1,
         model_device=model_device,