Copy of #126089, with some additional fixes & tests
Partial fix for #125635: previously, the deepcopy implementation would group together any tensors with any aliasing relationship and assign them to the same tensor. This was sort of good if you have two tensors `b = a.detach()`, because then if you deepcopy `list = [a, b]` to `list2 = list.deepcopy()`, then writes to `list2[0]` will also modify `list2[1]`. But for the most part, it's bad; (1) if you have `b = a.as_strided((4, 4), (16, 1), 16)`, then it'll make `b == a` in the deepcopied implementation, which is completely wrong; and (2) even if you have `b = a.detach()`, these are still initially two different tensors which become the same tensor after the old deepcopy implementation.
The new implementation only groups together tensors that have the same identity. This is a partial fix, but it's more reasonable. What changes:
* (becomes more correct): different views of the same base tensor will no longer all become equal after deepcopying
* (still kind of wrong): views won't actually alias each other after deepcopying.
* (arguably a minor regression): equivalent views of the same tensor will no longer be copied to the same tensor - so they won't alias.
BC breaking: C++ deepcopy interface changes from accepting `IValue::HashAliasedIValueMap memo` to accepting `IValue::HashIdentityIValueMap memo`. If there are objections, we can keep the old API. However, it seems likely that users generally won't try to deepcopy from C++.
Differential Revision: [D57406306](https://our.internmc.facebook.com/intern/diff/D57406306)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126126
Approved by: https://github.com/ezyang
Recently we made it possible to serialize ExportedPrograms with fake parameters/buffers/etc.
The serialization regime was kind of whacky; basically we serialized a stub and reassembled the FakeTensor using metadata that we had stashed elsewhere in the Graph state.
This was bad for a few reasons:
- Storing the metadata separately from the actual serialized object caused situations where you could have one but not the other. An example case is if you had a FakeTensor contained inside a TorchBind object—there was no obviously place to store the metadata for this. This actually happens—TensorQueue in fbgemm does this.
- It created an annoying cycle: we had to deserialize the Graph's tensor metadata in order to deserialize (potentially faked) constants, but we need constants in order to deserialize the Graph.
This fixes all that. The basic idea is to patch the reducer function for FakeTensor at serialization time, and serialize a copy of the FakeTensor metadata. We already are policing BC for the TensorMeta schema struct so it's not a net increase in the BC surface.
As a bonus, I fixed a weird bug with torchbind tracing where we were accidentally reinterpreting a torch.ScriptObject as a torch.ScriptModule (which was the root cause of some weird behavior @bahuang was seeing last week).
Differential Revision: [D53601251](https://our.internmc.facebook.com/intern/diff/D53601251/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119531
Approved by: https://github.com/zhxchen17
Summary:
GraphFunction internally stores the optimized graph after generating it and then it is passed into the executor which makes a copy of it. So we store the optimized graph effectively twice.
This diff allows to set a flag to not store the optimized graph inside the GraphFunction.
The code is NoP right now until the flag is enabled.
Test Plan:
I ran SL with this on raas with good memory saving on raas server. From command line:
exmaple model run
```
buck run mode/opt-clang sigrid/predictor/client/localnet:run_model -- --model_id_to_load=953556500 --model_snapshot_to_load=362
I1207 11:04:58.657143 3556226 SigridPredictorLocalModelFactory.cpp:32] Memory usage for 953556500_362 is 255646 Kb
```
then with flag enabled:
```
buck run mode/opt-clang sigrid/predictor/client/localnet:run_model -- --model_id_to_load=953556500 --model_snapshot_to_load=362 --torch_jit_do_not_store_optimized_graph=true
I1207 11:06:25.245779 3577383 SigridPredictorLocalModelFactory.cpp:32] Memory usage for 953556500_362 is 165167 Kb
```
So collective with this flag and the flag from D51950418
```
buck run mode/opt-clang sigrid/predictor/client/localnet:run_model -- --model_id_to_load=953556500 --model_snapshot_to_load=362 --torch_jit_do_not_store_optimized_graph=true --torch_jit_enable_profiling_graph_executor=false
I1207 11:09:17.502743 3592345 SigridPredictorLocalModelFactory.cpp:32] Memory usage for 953556500_362 is 114848 Kb
```
Differential Revision: D51931895
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115381
Approved by: https://github.com/malfet
Summary: Registering param/buffer will write into a vector inside Object, need to maintain thread safety if we have threads reading from the vector and writing to the vector at the same time.
Test Plan: CI
Differential Revision: D49882601
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110488
Approved by: https://github.com/davidberard98
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
Summary:
Before we copy a meta merge, and use it as a skeleton to do d2d merge replication. However some models like prospector has CPU op LongIndex which takes quite long time to load. That makes the meta merge copy expensive.
Modify jit::Module::deepcopy() to allow device copy. It simplifies user code and removes all unnecessary copies like tempfile, meta merge
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106521
Approved by: https://github.com/davidberard98
