requirements : bump torch to 2.11.0 (#23503)

* requirements: relax torch~=2.6.0 to torch>=2.6.0 for convert_hf_to_gguf

The ~=2.6.0 operator resolves to >=2.6.0, <2.7.0, which fails on
PyPI for platform/CPython combinations where 2.6.x is not present.
The accompanying comment already says 'PyTorch 2.6.0 or later', so
the looser >=2.6.0 matches the documented intent and unblocks
pip install -r requirements/requirements-convert_hf_to_gguf.txt.

Fixes #23408

* requirements: bump torch floor to 2.11.0 per maintainer

* requirements: pin torch to ==2.11.0 per project policy

* requirements: pin mtmd torch and torchvision to 2.11.0/0.26.0 per project policy

* requirements: suppress check_requirements pin warning on mtmd

The check_requirements script flags '==' on lines in files matched by
*/**/requirements*.txt. Append the documented suppression comment to the
pinned torch and torchvision lines (and to the s390x platform marker lines)
so the check passes while keeping the pins required by project policy.

* ty: silence Tensor/Module union check on model[0].auto_model

With torch 2.11.0 stubs, nn.Sequential.__getitem__ now returns
Tensor | Module rather than Module, so model[0].auto_model fails ty
on the SentenceTransformer code path. The runtime behavior is
unchanged because SentenceTransformer always wraps a Module at
index 0. Adding a targeted unresolved-attribute ignore keeps the
type-check green without altering behavior. A follow-up issue
tracks typing the variable explicitly.

examples/model-conversion/scripts/embedding/run-original-model.py

@@ -64,7 +64,7 @@ def load_model_and_tokenizer(model_path, use_sentence_transformers=False, device
         print("Using SentenceTransformer to apply all numbered layers")
         model = SentenceTransformer(model_path)
         tokenizer = model.tokenizer
-        config = model[0].auto_model.config
+        config = model[0].auto_model.config  # ty: ignore[unresolved-attribute]
     else:
         tokenizer = AutoTokenizer.from_pretrained(model_path)
         config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)

requirements/requirements-convert_hf_to_gguf.txt

@@ -1,8 +1,8 @@
 -r ./requirements-convert_legacy_llama.txt
 --extra-index-url https://download.pytorch.org/whl/cpu
 
-## Embedding Gemma requires PyTorch 2.6.0 or later
-torch~=2.6.0; platform_machine != "s390x"
+## Embedding Gemma requires PyTorch 2.6.0 or later, bumped to 2.11.0 for compatibility
+torch==2.11.0; platform_machine != "s390x"
 
 # torch s390x packages can only be found from nightly builds
 --extra-index-url https://download.pytorch.org/whl/nightly

tools/mtmd/requirements.txt

@@ -1,5 +1,12 @@
 -r ../../requirements/requirements-convert_legacy_llama.txt
 --extra-index-url https://download.pytorch.org/whl/cpu
 pillow~=11.3.0
-torch~=2.6.0
-torchvision~=0.21.0
+
+## Embedding Gemma requires PyTorch 2.6.0 or later, bumped to 2.11.0 for compatibility
+torch==2.11.0; platform_machine != "s390x" # check_requirements: ignore "=="
+torchvision==0.26.0; platform_machine != "s390x" # check_requirements: ignore "=="
+
+# torch s390x packages can only be found from nightly builds
+--extra-index-url https://download.pytorch.org/whl/nightly
+torch>=0.0.0.dev0; platform_machine == "s390x" # check_requirements: ignore "=="
+torchvision>=0.0.0.dev0; platform_machine == "s390x" # check_requirements: ignore "=="