cpu memory optimization rebased to main #3868
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Make sure to add a link to the resource_management page in index.rst
| This shifts one model copy from GPU to CPU memory. | ||
| As a result, peak GPU memory usage decreases to about **1×** | ||
| the model size, while CPU memory usage increases by roughly **1×**. |
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This is a bit confusing, can we say increases to roughly **2x** model size
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We cannot say that because it depends on what users choose other CPU memory optimization options. Can we say "one more copy of the model will occupy the CPU memory" to make it more clear?
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| @needs_refit # type: ignore[misc] | ||
| def _insert_engine_to_cache(self, hash_val: str, serialized_engine: bytes) -> None: | ||
| def _insert_engine_to_cache(self, hash_val: str, engine: trt.ICudaEngine) -> None: |
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@zewenli98 when do these calls run? will this conflict with the goal of keeping mem usage under 3x?
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should we do caching in a post processing step?
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Like we can give the cache entry as one of the Interpreter Result fields
py/torch_tensorrt/dynamo/partitioning/_adjacency_partitioner.py
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| settings: CompilationSettings = CompilationSettings(), | ||
| arg_inputs: Optional[Sequence[Input]] = None, | ||
| kwarg_inputs: Optional[dict[str, Any]] = None, | ||
| engine_cache: Optional[BaseEngineCache] = None, |
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We need to move the cache insert here
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Description
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