Fix FutureWarning: Replace torch.cuda.amp.GradScaler with torch.amp.GradScaler (#3458)

## Plan to Fix FutureWarning for torch.cuda.amp.GradScaler

- [x] Update ignite/engine/__init__.py:
- [x] Change import from `torch.cuda.amp.GradScaler` to
`torch.amp.GradScaler`
- [x] Update type hints from `"torch.cuda.amp.GradScaler"` to
`"torch.amp.GradScaler"`
- [x] Update documentation example from
`torch.cuda.amp.GradScaler(2**10)` to `torch.amp.GradScaler('cuda',
2**10)`
  - [x] Update docstring reference from `torch.cuda.amp` to `torch.amp`
- [x] Collapse imports: combine `from torch.amp import autocast` and
`from torch.amp import GradScaler`
- [x] Remove explicit 'cuda' parameter from GradScaler instantiations
(device is auto-detected)
- [x] Keep PyTorch version requirements at >= 1.12.0 (when torch.amp was
introduced)
- [x] Update tests/ignite/engine/test_create_supervised.py:
- [x] Update all type hints from `"torch.cuda.amp.GradScaler"` to
`"torch.amp.GradScaler"`
- [x] Update test instantiations from `torch.cuda.amp.GradScaler` to
`torch.amp.GradScaler`
  - [x] Remove 'cuda' parameter from test GradScaler instantiations
- [x] Update example files:
  - [x] examples/cifar10/main.py - removed 'cuda' parameter
  - [x] examples/cifar10_qat/main.py - removed 'cuda' parameter
- [x] examples/cifar100_amp_benchmark/benchmark_torch_cuda_amp.py -
removed 'cuda' parameter
  - [x] examples/transformers/main.py - removed 'cuda' parameter
- [x] examples/references/segmentation/pascal_voc2012/main.py - removed
'cuda' parameter
- [x] examples/references/classification/imagenet/main.py - removed
'cuda' parameter
- [x] examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb - reverted
to original state
- [x] Update documentation and notebooks:
- [x] README.md - updated benchmark description from torch.cuda.amp to
torch.amp
- [x] docs/source/conf.py - updated type hint reference from
torch.cuda.amp.grad_scaler to torch.amp.grad_scaler
- [x] examples/notebooks/CycleGAN_with_torch_cuda_amp.ipynb - updated
GradScaler references and collapsed imports
- [x] Fix code style issues:
- [x] Added newline at end of CycleGAN notebook to satisfy pre-commit
hooks
- [x] Run linters and tests to validate changes
  - [x] Python syntax check passed
  - [x] Module import test passed
- [x] GradScaler instantiation tests passed (without explicit device
parameter)
  - [x] Fixed CI test failures
  - [x] Verified no remaining torch.cuda.amp.GradScaler references
  - [x] Code style checks passing

## Summary

Successfully replaced all occurrences of the deprecated
`torch.cuda.amp.GradScaler` with `torch.amp.GradScaler`:

- **Changed 11 files** (1 core module, 1 test file, 6 example scripts, 1
README, 1 config, 1 notebook)
- **Collapsed imports** as requested: `from torch.amp import autocast,
GradScaler`
- **Updated all type hints** from `"torch.cuda.amp.GradScaler"` to
`"torch.amp.GradScaler"`
- **Simplified instantiations** to use `GradScaler(enabled=...)` without
explicit device parameter (auto-detected)
- **Maintained version requirements** at PyTorch >= 1.12.0 (when
torch.amp was introduced)
- **Updated documentation and notebooks** to reflect the new API
- **Fixed code style issues** to pass pre-commit checks

The changes have been tested and validated:
- ✓ No syntax errors
- ✓ Module imports successfully
- ✓ No deprecation warnings
- ✓ GradScaler works correctly with simplified API (device
auto-detection)
- ✓ Fixed test failures by removing 'cuda' parameter from test
instantiations
- ✓ All torch.cuda.amp.GradScaler references replaced with
torch.amp.GradScaler
- ✓ Code style checks passing

<!-- START COPILOT CODING AGENT SUFFIX -->



<details>

<summary>Original prompt</summary>

> 
> ----
> 
> *This section details on the original issue you should resolve*
> 
> <issue_title>Fix FutureWarning: `torch.cuda.amp.GradScaler(args...)`
is deprecated. Please use `torch.amp.GradScaler('cuda', args...)`
instead.</issue_title>
> <issue_description></issue_description>
> 
> <agent_instructions>Change the code where used
`torch.cuda.amp.GradScaler` into `torch.amp.GradScaler`. Make sure to
collapse imports like:
> ```python
> from torch.amp import autocast
> from torch.amp import GradScaler
> ```
> into
> ```python
> from torch.amp import autocast, GradScaler
> ```</agent_instructions>
> 
> ## Comments on the Issue (you are @copilot in this section)
> 
> <comments>
> <comment_new><author>@vfdev-5</author><body>
> @keelobytes go ahead. Thanks!</body></comment_new>
> <comment_new><author>@vfdev-5</author><body>
> @keelobytes any progress on this ticket?</body></comment_new>
> <comment_new><author>@vfdev-5</author><body>
> Send a PR if you can work on this issue</body></comment_new>
> </comments>
> 


</details>

Fixes #3435

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---

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---------

Co-authored-by: copilot-swe-agent[bot] <[email protected]>
Co-authored-by: vfdev-5 <[email protected]>
Co-authored-by: vfdev <[email protected]>

Author: Copilot

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.