CUDA delegate: fuse int4 kernel for better performance

[mergennachin]

[pytorch-bot[bot]]

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:test_tube: See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/15813

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:x: 4 New Failures

As of commit 3d749b5f98175be40d7156c8f8849ec348e789e9 with merge base da6306f4863f7eb16c27337cd8a42aa9d4ac4be7 ():

NEW FAILURES - The following jobs have failed:

• Lint / lintrunner / linux-job (gh)
• pull / test-moshi-linux / linux-job (gh) RuntimeError: Command docker exec -t 9907ddb62d97ec7acd7082a1c66e47d548d803a5f431bd36ae431972bb439c5b /exec failed with exit code 1
• Test CUDA Builds / test-model-cuda-e2e (openai, whisper-large-v3-turbo, quantized-int4-tile-packed) / linux-job (gh) RuntimeError: Command docker exec -t 44e8dc9bb0bd25aeec97801ad7169c81e416b57da216ece37849b4564ac3aa94 /exec failed with exit code 1
• Test CUDA Builds / test-model-cuda-e2e (openai, whisper-small, quantized-int4-tile-packed) / linux-job (gh) RuntimeError: Command docker exec -t 91c62d8959f3759f41e3a1955ed27434b683a2f04050b290fe7bd7ffb6a70d26 /exec failed with exit code 1

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[desertfire]

Is it possible to make this a part of Inductor freezing optimization? I think it will benefit all models beyond ET use cases.