[Misc] Add standalone safetensors retention diagnostic (#7145)

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

benchmarks/host_weight_runtime/README.md

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+# Host weight dependency memory diagnostic
+
+`safetensors_retention.py` isolates repeated CPU `get_tensor()` calls from
+access to one cached tensor view. It imports neither vLLM nor HWR and uses a
+synthetic float32 payload controlled by `--tensor-elements` (default: 64).
+Tensor size can affect retention: a negative result for 16 elements does not
+rule out growth for 64 elements. The selected size is recorded in the JSON
+arguments and used for both file creation and the correctness check.
+HWR itself calls `get_tensor()` when
+acquiring a lease, then exposes cached views; this probe does not measure HWR
+requests or repeated lease acquisition.
+
+Run one mode per fresh process in the same Python environment and CPU affinity:
+
+```bash
+git rev-parse HEAD > revision.txt
+git status --short > working-tree.txt
+git diff > working-tree.patch
+# Select an allowed CPU from your process affinity; 0 is only an example.
+CUDA_VISIBLE_DEVICES='' taskset -c 0 timeout 120s python \
+  benchmarks/host_weight_runtime/safetensors_retention.py \
+  --mode get_tensor --iterations 10000 --sample-every 5000 > feasibility.json
+```
+
+For a size comparison, run 16 and 64 elements twice each in fresh processes,
+with cached reuse as a control at both sizes:
+
+```bash
+for repetition in 1 2; do
+  for elements in 16 64; do
+    for mode in get_tensor reuse; do
+      CUDA_VISIBLE_DEVICES='' taskset -c 0 timeout 120s python \
+        benchmarks/host_weight_runtime/safetensors_retention.py \
+        --mode "$mode" --tensor-elements "$elements" \
+        > "$mode-$elements-$repetition.json"
+    done
+  done
+done
+```
+
+The diagnostic sets Torch to one CPU thread, warms up before the baseline,
+and reports preparation separately from timed loop work. Checkpoints run GC
+and report Linux private/anonymous memory, RSS, and open descriptors. It checks
+that transient tensor objects and the final cached view are released. Linux
+`/proc/self/smaps_rollup` must be readable; invalid arguments and missing proc
+support fail explicitly. Keep raw JSON and the repository snapshot with results.
+
+Compare the final loop sample with the warmed baseline, then inspect the closed
+sample. Private-memory growth after Python tensor objects disappear is evidence
+of retention in the dependency/allocator path, not proof of a leak's root cause.
+RSS can include file-backed pages; it is not a private-memory metric. Samples
+also include small diagnostic bookkeeping allocations. Do not assert a portable
+memory threshold or turn this synthetic loop into a per-request service estimate.
+
+Do not drop shared caches, trim allocators, or add serving-time GC to make the
+numbers look smaller. A dependency replacement or version change requires its
+own reproducible comparison and correctness/lifecycle validation.