The semantics of ignored modes previously had edge cases, this eliminates these by in essence filtering any ignored modes out of both the ref stack and the current torch function mode stack. This is purely to fix complexity in #135422.  The ignored modes handling will be removed in a future PR after #135422 lands, since we will then trace through DeviceContexts vs inserting them into the graph which needed these extra workarounds for correctness.

Pull Request resolved: #135444
Approved by: https://github.com/anijain2305, https://github.com/williamwen42
ghstack dependencies: #134732, #133137, #135443

This reverts commit 160c228.

Reverted #135443 on behalf of https://github.com/clee2000 due to something in this stack broke functorch/test_control_flow.py::TestControlFlow::test_scan_simple_graph [GH job link](https://github.com/pytorch/pytorch/actions/runs/10804912306/job/29980571390) [HUD commit link](https://hud.pytorch.org/pytorch/pytorch/commit/444b52ff40cf4afce7bc3fdcf021a88eab3b954c), newly added test yesterday ([comment](#135443 (comment)))

In preparation for tracing through DeviceContext (https://github.com/pytorch/pytorch/blob/defb515306fc53ec62e92937a5a76fa5cbc05b84/torch/utils/_device.py#L66)
This PR adds support for calling the setattr of thread local objects. These objects have a slots impl, and since this doesn't appear to have any side effects, we call this setattr impl when replaying mutations, since calling `object.__setattr__` on these objects results in a type error.

Pull Request resolved: pytorch#135443
Approved by: https://github.com/anijain2305
ghstack dependencies: pytorch#134732, pytorch#133137

The semantics of ignored modes previously had edge cases, this eliminates these by in essence filtering any ignored modes out of both the ref stack and the current torch function mode stack. This is purely to fix complexity in pytorch#135422.  The ignored modes handling will be removed in a future PR after pytorch#135422 lands, since we will then trace through DeviceContexts vs inserting them into the graph which needed these extra workarounds for correctness.

Pull Request resolved: pytorch#135444
Approved by: https://github.com/anijain2305, https://github.com/williamwen42
ghstack dependencies: pytorch#134732, pytorch#133137, pytorch#135443

This reverts commit 160c228.

Reverted pytorch#135443 on behalf of https://github.com/clee2000 due to something in this stack broke functorch/test_control_flow.py::TestControlFlow::test_scan_simple_graph [GH job link](https://github.com/pytorch/pytorch/actions/runs/10804912306/job/29980571390) [HUD commit link](https://hud.pytorch.org/pytorch/pytorch/commit/444b52ff40cf4afce7bc3fdcf021a88eab3b954c), newly added test yesterday ([comment](pytorch#135443 (comment)))

In preparation for tracing through DeviceContext (https://github.com/pytorch/pytorch/blob/defb515306fc53ec62e92937a5a76fa5cbc05b84/torch/utils/_device.py#L66)
This PR adds support for calling the setattr of thread local objects. These objects have a slots impl, and since this doesn't appear to have any side effects, we call this setattr impl when replaying mutations, since calling `object.__setattr__` on these objects results in a type error.

Pull Request resolved: pytorch#135443
Approved by: https://github.com/anijain2305
ghstack dependencies: pytorch#134732, pytorch#133137

The semantics of ignored modes previously had edge cases, this eliminates these by in essence filtering any ignored modes out of both the ref stack and the current torch function mode stack. This is purely to fix complexity in pytorch#135422.  The ignored modes handling will be removed in a future PR after pytorch#135422 lands, since we will then trace through DeviceContexts vs inserting them into the graph which needed these extra workarounds for correctness.

Pull Request resolved: pytorch#135444
Approved by: https://github.com/anijain2305, https://github.com/williamwen42
ghstack dependencies: pytorch#134732, pytorch#133137, pytorch#135443

This PR implements tracing of with contexts with TorchFunction modes which have the default enter/exit behavior (ie pushing/popping the mode)

Typically the bytecode for a context manager looks like this during a graph break:
1. graph call
2. enter context
3. unsupported code
4. exit context
5. resume call

resume fn structure:
1. enter context
2. jump
...
3. exit context

The issue with torch function modes is that side effects will replay any mutations to the torch function stack performed during tracing. So, we do not need to enter and exit around the unsupported code in the original function (doing so would result in a duplicate torch function mode entry during execution of the unsupported code), and we don't need to enter again in the resume function (the mode that was pushed from the side effects bytecode would still be on the stack).

So for torch function modes the structure of our output code is this:

1. graph call
2. mutate tf mode stack to replay mutations
4. unsupported code
5. on exception restore stack
6. resume function

Then our resume fn looks like this:

1. no-op enter torch function mode
2. jump
3.  exit tf mode

To implement the no-op enter of the torch function mode I added torch function mode in polyfill which no-op enters, but normally exits. This is needed because we still want to trace the with context in the resume function, and exit properly (the exit instructions will still be in the function, so we need to generate instructions to set up the context).

Separately from the bytecode, dynamo also tracks contexts on the block stack, which is how the SETUP_* instructions are implemented. Naturally at a graph break, we exit these block stacks to properly reset the contexts entirely, so that we can re-enter around the unsupported code soundly. However once again, in the torch function mode case, in the event of a graph we do not want to perform any exit side effects because we want to preserve the state of the mode stack as is so that we will properly update the stack with bytecode mentioned in the first section. If we exited here, dynamo would pop the mode off of the symbolic stack, and not update the true python torch function mode stack with the suffix bytecode. All in all, for torch function modes we enter exactly once, update the global torch function mode stack with side effects bytecode, re-read this stack when compiling the resume function, and exit exactly once in the resume function. This matches the semantics of eager exactly.

Pull Request resolved: pytorch#135422
Approved by: https://github.com/williamwen42
ghstack dependencies: pytorch#134732, pytorch#133137, pytorch#135443, pytorch#135444

Pull Request resolved: pytorch#135502
Approved by: https://github.com/anijain2305
ghstack dependencies: pytorch#134732, pytorch#133137, pytorch#135443, pytorch#135444, pytorch#135422