fix the path change in torch v2.10 for spec dec (#863)

## What does this PR do?

**Type of change:** 
bug fix

**Overview:** 
torch v2.10 changes the path for _SDPAMerger. will need to use the new
path for import

## Usage
<!-- You can potentially add a usage example below. -->

```python
# Add a code snippet demonstrating how to use this
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **Chores**
* Updated internal import references to reflect organizational changes
in dependencies.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Ye Yu <yeyu@nvidia.com>

Author: yeyu-nvidia

.github/workflows/example_tests.yml

@@ -86,14 +86,14 @@ jobs:
       pip_install_extras: "[hf,dev-test]"
       runner: linux-amd64-gpu-h100-latest-2
 
-  ##### Speculative Decoding Example Tests (requires 25.08 image) #####
+  ##### Speculative Decoding Example Tests (requires 26.01 image) #####
   speculative-decoding-pr:
     needs: [check-file-changes, wait-checks]
     if: startsWith(github.ref, 'refs/heads/pull-request/') && needs.check-file-changes.outputs.any_changed == 'true'
     uses: ./.github/workflows/_example_tests_runner.yml
     secrets: inherit
     with:
-      docker_image: "nvcr.io/nvidia/pytorch:25.08-py3"
+      docker_image: "nvcr.io/nvidia/pytorch:26.01-py3"
       example: speculative_decoding
       pip_install_extras: "[hf,dev-test]"
       runner: linux-amd64-gpu-l4-latest-1
@@ -103,7 +103,7 @@ jobs:
     uses: ./.github/workflows/_example_tests_runner.yml
     secrets: inherit
     with:
-      docker_image: "nvcr.io/nvidia/pytorch:25.08-py3"
+      docker_image: "nvcr.io/nvidia/pytorch:26.01-py3"
       example: speculative_decoding
       pip_install_extras: "[hf,dev-test]"
       runner: linux-amd64-gpu-h100-latest-2

examples/speculative_decoding/eagle_utils.py

@@ -31,7 +31,6 @@
 from packaging.version import Version
 from PIL import Image
 from scripts.ar_validate import validate_ar
-from torch.distributed.tensor.experimental._attention import _SDPAMerger
 from torch.utils.data import Dataset
 from transformers import AutoProcessor, Trainer, TrainerCallback
 from transformers.trainer_pt_utils import LabelSmoother
@@ -581,7 +580,7 @@ def on_step_end(self, args, state, control, **kwargs):
 def get_patched_templated_ring_attn(orig_templated_attn: Callable):
     """
     Return patched version of
-    torch.distributed.tensor.experimental._attention._templated_ring_attention
+    torch.distributed.tensor.experimental._context_parallel._attention._templated_ring_attention
     to support TTT.
     """
 
@@ -630,7 +629,7 @@ def _get_sharded_ttt_msk(i, rank, size, q_len, ttt_step, dtype):
         return attn_bias
 
     def patched_templated_attn(*args, **kwargs):
-        """Patched version of torch.distributed.tensor.experimental._attention._templated_ring_attention."""
+        """Patched version of _templated_ring_attention."""
         # Get original attention op
         # Sensitive to impl of _templated_ring_attention
         original_op = args[2]
@@ -678,40 +677,35 @@ def patch_ring_attention_for_ttt():
     """Patch torch ring attention to support context parallelism for TTT."""
     # Torch Ring Attention only supports no mask or causal mask. We apply the following patches to enable TTT mask.
 
-    if not (
-        Version(torch.__version__) > Version("2.7.1")
-        and Version(torch.__version__) < Version("2.9.0")
-    ):
+    if Version(torch.__version__) < Version("2.10.0"):
         raise RuntimeError(
-            f"Context parallel TTT only supported for PyTorch 2.8.0 now. "
+            f"Context parallel TTT only supported for PyTorch >= 2.10.0. "
             f"Got {torch.__version__}. "
-            f"Please use nvcr.io/nvidia/pytorch:25.08-py3 or torch 2.8.0 or cp_size=1."
+            f"Please use torch 2.10.0 or cp_size=1."
         )
 
+    from torch.distributed.tensor.experimental._context_parallel import _attention
+
     # 1. Disable load balance, which is designed for causal mask.
     # This affect how buffers are sharded. So need to be done permanently before accelerate/hf trainer init.
-    torch.distributed.tensor.experimental._attention._cp_options.enable_load_balance = False
+    _attention._cp_options.enable_load_balance = False
 
     # 2. Patch templated ring attention for TTT mask.
-    original_templated_ring_attention = (
-        torch.distributed.tensor.experimental._attention._templated_ring_attention
-    )
-    original_templated_ring_attention_backward = (
-        torch.distributed.tensor.experimental._attention._templated_ring_attention_backward
-    )
-    torch.distributed.tensor.experimental._attention._templated_ring_attention = (
-        get_patched_templated_ring_attn(original_templated_ring_attention)
+    original_templated_ring_attention = _attention._templated_ring_attention
+    original_templated_ring_attention_backward = _attention._templated_ring_attention_backward
+    _attention._templated_ring_attention = get_patched_templated_ring_attn(
+        original_templated_ring_attention
     )
-    torch.distributed.tensor.experimental._attention._templated_ring_attention_backward = (
-        get_patched_templated_ring_attn(original_templated_ring_attention_backward)
+    _attention._templated_ring_attention_backward = get_patched_templated_ring_attn(
+        original_templated_ring_attention_backward
     )
 
     # 3. Patch merger to skip the blank shard to avoid difference in output.
-    original_sdpa_merger_step = _SDPAMerger.step
+    original_sdpa_merger_step = _attention._SDPAMerger.step
 
     def patched_sdpa_merger_step(self, out: torch.Tensor, lse: torch.Tensor, partial: bool):
         if lse.sum() <= 0:
             return
         return original_sdpa_merger_step(self, out, lse, partial)
 
-    _SDPAMerger.step = patched_sdpa_merger_step
+    _attention._SDPAMerger.step = patched_sdpa_merger_step

tests/examples/speculative_decoding/test_eagle.py

@@ -37,11 +37,8 @@ def test_llama_eagle3(tiny_llama_path, tiny_daring_anteater_path, tmp_path, eagl
     available_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
     if cp_size == 2 and available_gpus < 2:
         pytest.skip("cp_size=2 requires at least 2 GPUs, but only {} found.".format(available_gpus))
-    if cp_size == 2 and not (
-        Version(torch.__version__) > Version("2.7.0")
-        and Version(torch.__version__) < Version("2.9.0")
-    ):
-        pytest.skip("cp_size=2 requires torch 2.8.0")
+    if cp_size == 2 and not Version(torch.__version__) >= Version("2.10.0"):
+        pytest.skip("cp_size=2 requires torch 2.10.0")
     # Create an ultra-tiny EAGLE config for testing to reduce memory usage
     tiny_eagle_config = {
         "max_position_embeddings": 128,