[cuda backend] share AOTI weights by FQN across methods

[Gasoonjia]

Summary

Currently we store weight in method granularity: if two methods do not share identical weight we have to keep two weight copies, even if they may just one tensor different.

This PR stores the weight in tensor granularity: we store the weight pickle format and use FQN as the key for each tensor. With this PR weight can be shared across different methods even if they are partially overlapped.

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21823

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❌ 3 New Failures, 1 Unrelated Failure

As of commit b7d877f with merge base e60faa2 ():

NEW FAILURES - The following jobs have failed:

• pull / test-arduino-library / run / arduino-library (gh)
  RuntimeError: Command docker exec -t 54925bc434bb8674930cf055420980eb5441e9d13b0fd90eb75e492b15963c37 /exec failed with exit code 1
• Test CUDA Builds / export-model-cuda-artifact (SocialLocalMobile, Qwen3.5-35B-A3B-HQQ-INT4, quantized-int4-tile-packed) / linux-job (gh)
  RuntimeError: Command bash /__w/_temp/exec_script failed with exit code 1
• Test CUDA Builds / unittest-cuda / linux-job (gh)
  backends/cuda/tests/test_chunk_gated_delta_rule.py::TestChunkGatedDeltaRule::test_e2e_cpp_runner

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• pull / unittest / windows / windows-job (gh) (detected as infra flaky with no log or failing log classifier)

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