mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
a34c28e0d2
This is a proof-of-concept of how we could serialize a guard and deserialize it back from the bytes. The main behavioral change introduced in this diff is on CheckFunctionManager: ``` check_fn_manager = CheckFunctionManager(code, output_graph, guards_serialization_mode="save") guards_state: bytes = check_fn_manager.guards_state ``` Once `guards_serialization_mode` is set to `save`, CheckFunctionManager will return an addtional `bytes` object called `guards_state` which should contain all the information needed for deserializing guards later. When we load back guards state, we will set `guards_serialization_mode` is set to `load`: ``` output_graph_state = pickle.loads(guards_state) check_fn_manager = CheckFunctionManager(code, output_graph_state, guards_serialization_mode="load") ``` # TENSOR_MATCH Since we have many types of guards to support, we will break the work into small diffs instead of a single diff to support every guards. We kick off the work from TENSOR_MATCH from this diff. # Testing For each type of guard we will test it like the following: 1. Use guard_filter_fn to select 1 type of guard each time. 2. Call InstructionTranslator directly on an example function to get OutputGraph and CheckFunctionManager (reference guard manager) 3. Serialize->deserialize the output graph state and re-build the guards with a new CheckFunctionManager (loaded guard manager) 4. Throw a set of example inputs to both reference and loaded guard manager to see if their behavior match. Pull Request resolved: https://github.com/pytorch/pytorch/pull/151318 Approved by: https://github.com/jansel, https://github.com/anijain2305 |
||
|---|---|---|
| .. | ||
| __init__.py | ||
| _fake_tensor_utils.py | ||
| fake_impls.py | ||
| fake_tensor.py | ||
| fake_utils.py | ||
| functional_tensor.py | ||
| meta_utils.py | ||
| schema_check_mode.py | ||