Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43647
Nothing fancy, just a basic implementation of the graph executor without using stack machine.
Reviewed By: bwasti
Differential Revision: D23208413
fbshipit-source-id: e483bb6ad7ba8591bbe1767e669654d82f42c356
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43182
We should avoid using `deepcopy` on the module because it involves copying the weights.
Comparing the implementation of `c10::ivalue::Object::copy()` vs `c10::ivalue::Object::deepcopy()`, the only difference is `deepcopy` copies the attributes (slots) while `copy` does not.
Reviewed By: bwasti
Differential Revision: D23171770
fbshipit-source-id: 3cd711c6a2a19ea31d1ac1ab2703a0248b5a4ef3
Summary:
This PR whitelists and simplifies graphs to help with development later on. Key to note in this PR is the use of both a pattern substitution and the registration of custom operators. This will likely be one of the main optimization types done in this folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43024
Reviewed By: hlu1
Differential Revision: D23114262
Pulled By: bwasti
fbshipit-source-id: e25aa3564dcc8a2b48cfd1561b3ee2a4780ae462
Summary:
The premise of this approach is that a small subset of neural networks are well represented by a data flow graph. The README contains more information.
The name is subject to change, but I thought it was a cute reference to fire.
suo let me know if you'd prefer this in a different spot. Since it lowers a JIT'd module directly I assumed the JIT folder would be appropriate. There is no exposed Python interface yet (but is mocked up in `test_accelerant.py`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42753
Reviewed By: zou3519
Differential Revision: D23043771
Pulled By: bwasti
fbshipit-source-id: 5353731e3aae31c08b5b49820815da98113eb551
