Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33329
# Use case
```
torch.jit.script
def send_rpc_async(dst_worker_name, user_callable_qual_name, tensor):
# type: (str, str, Tensor) -> None
rpc._rpc_async_torchscript(
dst_worker_name, user_callable_qual_name, args=(tensor,)
)
```
# Problem
```
torch.jit.frontend.NotSupportedError: keyword-arg expansion is not supported:
File "/data/users/shihaoxu/fbsource/fbcode/buck-out/dev/gen/caffe2/test/distributed/rpc/rpc_spawn#binary,link-tree/torch/distributed/rpc/api.py", line 722
args = args if args else ()
kwargs = kwargs if kwargs else {}
fut = _invoke_rpc_torchscript(to, qualified_name, *args, **kwargs)
~~~~~~ <--- HERE
return fut
```
# Solution
Register `rpc.rpc_async(..)` as a JIT operator to handle variable-length argument list.
# Plan
This PR is the required changes to make `rpc.rpc_async(..)` a JIT prim operator, which can dynamically handle different number of arguments.
- Register "prim::rpc_async" as a `Symbol` in "interned_string.h"
- Add a if branch in "python_sugared_value.cpp" `toSugarValue(py::object, ..)` entry utility function to set up how JIT frontend convert `torch.distributed.rpc.rpc_async(..)` Python function (Python object) into a `SpecialFormValue` (IR SugaredValue).
- Add a switch case for "prim::rpc_aynsc" Symbol in "ir_emitter.cpp" and `emitApplySpecialForm(..)` to set up how JIT compiler provides inputs to the "prim::rpc_aynsc" Operator.
- Register "prim::rpc_async" as a `jit::Operator` and provide implementation in "register_distributed_ops.cpp".
Notice, since the distributed module is an optional part when building PyTorch. The code to be added in this PR should be wrapped within preprocessing maco.
```
#ifdef USE_DISTRIBUTED
new code here
#endif
```
Test Plan:
Items that need to be confirmed in the test cases
https://fb.quip.com/DCvdA9ZLjeO0
```
buck test mode/dev-nosan //caffe2/test/distributed/rpc/jit:rpc_fork
buck build mode/dev-nosan //caffe2/test/distributed/rpc/jit:rpc_fork \
\
&& buck-out/gen/caffe2/test/distributed/rpc/jit/rpc_fork\#binary.par -r test_call_python_function_remotely_from_script_not_supported
```
```
buck test mode/dev-nosan //caffe2/test/distributed/rpc/jit:rpc_spawn
```
```
buck test mode/dev-nosan //caffe2/caffe2/python/operator_test:layer_norm_op_test-2.7 -- test_layer_norm_op_jit
```
Differential Revision: D5738300
fbshipit-source-id: a4604fe762e00be062dc8232ca9790df31fb2074
Summary:
**Summary**
This commit adds an implementation of `Tensor.tolist()` to the JIT interpreter.
**Testing**
This commit adds several unit tests that test that this function works correctly for
0D, 1D, 2D and 3D tensors of type `float`, `int` and `bool`.
```
(base) meghanl-mbp:pytorch meghanl$ python test/test_jit.py TestList.test_to_list -v
Fail to import hypothesis in common_utils, tests are not derandomized
test_to_list (jit.test_list_dict.TestList)
Unit tests for Tensor.tolist() function. ... ok
----------------------------------------------------------------------
Ran 1 test in 0.329s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33472
Differential Revision: D20109738
Pulled By: SplitInfinity
fbshipit-source-id: a6e3fee5e3201d5e1f0c4ca45048488ae2bf5e33
