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Revert "Revert "Qualcomm AI Engine Direct - heap profiling at runtime… (pytorch#19710)
… with HTP backend" (pytorch#19705)"
This reverts commit 576ed77 which reverted the original heap
profiling feature (pytorch#19224). The internal CI test failures that caused
the original revert have been fixed by adding @unittest.skip overrides
for test_qnn_backend_runtime_option_heap_profile in the simulator test
classes.
Original commit changeset: 576ed77
3. Does not support graphs with partitions (partial delegation).
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4. Does not support LLM models.
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5. Does not support graphs with multiple methods.
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## ExecuTorch QNN HTP Heap Profiling
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Measures DSP memory usage when using context binary models on the HTP backend.
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### Introduction
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DSP heap profiling is available for `QnnContext_createFromBinary` use-cases. It captures total DSP heap usage at two checkpoints:
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-**Before the first context is created** (`before_context_created`)
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-**After the last context is freed** (`after_context_freed`)
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The difference between the two values represents heap consumed during context execution. The value after freeing is typically equal to or greater than before creation.
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