Reduce allocation overhead in quantized sdpa

[kimishpatel]

Stack from ghstack (oldest at bottom):

• [Executorch][LLM] Use caching allocator for runner #15656
• [Executorch] Make module constructors uniform across #15655
• [Executorch] Use temp allocator for allocating scratch memory #15654
• [Executorch] Introduce caching cpu memory allocator #15611
• -> Reduce allocation overhead in quantized sdpa #15610
• [Executorch] Add multithreading for op_quantize #15609
• [Executorch] Add simd path for op quantize #15608
• [Executorch] parallelize op_choose_qparams #15607
• [Executorch][llm] Fix flakyness of quantized sdpa test #15653

For small models dequantizing portions of v cache causes extra alloc overhead.

Probably a better way to handle this is to dequantize entire v cache outside the model

There isnt significant perf advantage from this yet but subsequent diffs will use caching allocator where this refactor help.

Differential Revision: D85532077

[pytorch-bot]

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✅ You can merge normally! (5 Unrelated Failures)

As of commit 72292a7 with merge base 15a0fcd ():

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / test-binary-size-linux-gcc / linux-job (gh) (trunk failure)
  ##[error]The operation was canceled.
• pull / test-openvino-linux / linux-job (gh) (trunk failure)
  ##[error]The operation was canceled.
• pull / test-setup-linux-gcc / linux-job (gh) (trunk failure)
  ##[error]The operation was canceled.
• pull / unittest / windows / windows-job (gh) (trunk failure)
  backends/xnnpack/test/ops/test_conv1d.py::TestConv1d::test_qs8_conv1d_batchnorm_seq
• pull / unittest-editable / windows / windows-job (gh) (trunk failure)
  backends/xnnpack/test/ops/test_conv1d.py::TestConv1d::test_qs8_conv1d_batchnorm_seq

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