Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57481
This diff introduces function name to InlinedCallStack.
Since we are using InlinedCallStack for debug information in lite
interpreter as well as delegate backends, where InlinedCallStack cannot
be constructed from model source code, we need to save function name.
In the absence of function name Function* is used to get name of the
function. This is when JIT compiles code at runtime.
When that is not possible, this diff introduces a way to obtain function
name.
Test Plan:
test_backend
test_cs_debug_info_serialization
test_backend
test_cs_debug_info_serialization
Imported from OSS
Differential Revision:
D28159097
D28159097
Reviewed By: raziel, ZolotukhinM
Pulled By: kimishpatel
fbshipit-source-id: deacaea3325e27273f92ae96cf0cd0789bbd6e72
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57441
debug info
Previous diffs did not save operator name in debug info. For delegated
backends that only idenfity op for profiling with debug handle, operator
name should be stores as well.
Furthermore to complete debug informaton also serialize function name.
Test Plan:
Existing lite interpreter and backend tests
Existing lite interpreter and backend tests
Imported from OSS
Differential Revision:
D28144581
D28144581
Reviewed By: raziel
Pulled By: kimishpatel
fbshipit-source-id: 415210f147530a53b444b07f1d6ee699a3570d99
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57501
Add an api _get_model_ops_and_info to get root operators and versioning info of a model in both cxx and python, and the input can be from a file path or buffer.
ghstack-source-id: 129620112
Test Plan: unit test.
Reviewed By: xcheng16, raziel
Differential Revision: D28162765
fbshipit-source-id: 4413c1e906b8a872e4a717d849da37347adbbea4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55462
handles and symbolicate exception callstack thrown from backend.
Objective of this diff is to achieve improve error reporting when
exceptions are raised from lowered backend. We would effectively like to
get the same model level stack trace that you would get without having
lowered some module to backend.
For example:
```
class AA(nn.Module):
def forward(self, x, y):
return x + y
class A(nn.Module):
def __init__(...):
self.AA0 = AA()
def forward(self, x, y):
return self.AA0.forward(x, y) + 3
class B(nn.Module):
def forward(self, x):
return x + 2
class C(nn.Module):
def __init__(...):
self.A0 = A()
self.B0 = B()
def forward(self, x, y):
return self.A0.forward(x, y) + self.B0.forward(x)
```
If the we then do C().forward(torch.rand((2,3)), torch.rand(14,2))) we
will likely see error stack like:
```
C++ exception with description "The following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
File "<string>", line 3, in forward
def forward(self, x, y):
return self.A0.forward(x, y) + self.B0.forward(x)
~~~~~~~~~~~~~~~ <--- HERE
File "<string>", line 3, in forward
def forward(self, x, y):
return self.AA0.forward(x, y) + 3
~~~~~~~~~~~~~~~~ <--- HERE
File "<string>", line 3, in forward
def forward(self, x, y):
return x + y
~~~~~ <--- HERE
```
We would like to see the same error stack if we lowered C.A0 to some
backend.
With this diff we get something like:
```
Module hierarchy:top(C).A0(backend_with_compiler_demoLoweredModule).AA0(AA)
Traceback of TorchScript (most recent call last):
File "<string>", line 3, in FunctionName_UNKNOWN
def forward(self, x, y):
return self.A0.forward(x, y) + self.B0.forward(x)
~~~~~~~~~~~~~~~ <--- HERE
File "<string>", line 5, in FunctionName_UNKNOWN
typed_inputs: List[Any] = [x, y, ]
if self.__backend.is_available() :
_0, = self.__backend.execute(self.__handles["forward"], typed_inputs)
~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
assert isinstance(_0, Tensor)
return _0
File "<string>", line 3, in FunctionName_UNKNOWN
def forward(self, x, y):
return self.AA0.forward(x, y) + 3
~~~~~~~~~~~~~~~~ <--- HERE
File "<string>", line 3, in FunctionName_UNKNOWN
def forward(self, x, y):
return x + y
~~~~~ <--- HERE
```
This is achieved in 3 parts:
Part 1:
A. BackendDebugInfoRecorder:
During backend lowering, in `to_backend`, before calling the preprocess
function corresponding to the backend. This will facilitate recording of
debug info (such as source range + inlined callstack) for the lowered module.
B. Instantiate WithBackendDebugInfoRecorder with BackendDebugInfoRecorder.
This initializes thread local pointer to BackendDebugInfoRecorder.
C. generate_debug_handles:
In preprocess function, the backend will call generate_debug_handles
for each method being lowered separately. generate_debug_handles
takes `Graph` of the method being lowered and returns a map
of Node*-to-debug_handles. Backend is responsible for storing debug
handles appropriately so as to raise exception (and later profiling)
using debug handles when the exception being raised corresponds to
particular Node that was lowered.
Inside generate_debug_handles, we will query the current
BackendDebugHandleInfoRecorder, that is issuing debug handles. This debug
handle manager will issue debug handles as well as record
debug_handles-to-<source range, inlined callstack> map.
D. Back in `to_backend`, once the preprocess function is has finished
lowering the module, we will call `stopRecord` on
BackendDebugInfoRecorder. This will return the debug info map. This
debug info is then stored inside the lowered module.
Part 2:
Serialization:
During serialization for bytecode (lite interpreter), we will do two
things:
1. Extract all the source ranges that are contained inside
debug_handles-to-<source range, inlined callstack> map for lowered
module. This will be source range corresponding to debug handles,
including what is there is inlined callstack. Since we replaced original
module with lowered module, we wont be serializing code for the original
module and thus no source range. That is why the source range will have
to be stored separately. We will lump all the source ranges for all the
lowered modules in one single debug_pkl file.
2. Then we will serialize debug_handles-to-<source range, inlined
callstack> map.
Now during deserialization we will be able to reconstruct
debug_handles-to-<source range, inlined callstack> map. Given all
debug_handles are unique we would not need any module information.
Test Plan:
Tests are added in test_backend.cpp
Tests are added in test_backend.cpp
Imported from OSS
Differential Revision:
D27621330
D27621330
Reviewed By: raziel
Pulled By: kimishpatel
fbshipit-source-id: 0650ec68cda0df0a945864658cab226a97ba1890
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58428
Until now, the TP agent expected the output of a remote function to be on the same streams as the inputs. In other words, it used the lazy stream context of the inputs to synchronize the output tensors. This was true in the most common case of a synchronous remote function. However it wasn't true for async functions, for fetching RRefs, ... The more generic way is to use the CUDA events held by the Future to perform this synchronization. (These events may be on the input streams, or they may not be!).
ghstack-source-id: 129567045
Test Plan: CI
Reviewed By: mrshenli
Differential Revision: D28474982
fbshipit-source-id: c0034eb3f2a2ea525efb63a31b839bc086060e7e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58422
Similar to Future (which I tackled recently), Message is an ivalue type (a "custom class" one), and the natural way to represent it is inside an intrusive_ptr. However in the RPC code we had a mix of usages, often passing Message by value. This has undesirable consequences, as it could easily trigger a copy by accident, which I believe is why in many places we accepted _rvalue references_ to Message, in order to force the caller to move. In my experience this is non-idiomatic in C++ (normally a function signature specifies how the function consumes its arguments, and it's up to the caller to then decide whether to copy or move).
By moving to intrusive_ptr everywhere I think we eliminate and simplify many of the problems above.
In this PR I do half of the migration, by updating everything except the `toMessageImpl` methods, which will come in the next PR.
ghstack-source-id: 129567053
Test Plan: CI
Reviewed By: mrshenli
Differential Revision: D28474878
fbshipit-source-id: 5b76d45e05f6fa58c831e369c5c964d126187a6c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57397
Introduces two main classes in C++ runtime:
ScriptProfile is the implementation for enalbing and disabling interpreter
profiling in C++. This should be only used from Python, and we will add
corresponding Python API in the next diff.
InstructionSpan is a utility class to instrument execution of each single
instruction. A start timestamp is recorded in the consturctor, and an end
timestamp is recorded in the destructor. During destruction, this will send
runtime data to all enabled ScriptProfile instances.
Test Plan:
build/bin/test_jit --gtest_filter='ScriptProfileTest.Basic'
Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D28133579
fbshipit-source-id: e7e30e96151367022793ab3ad323f01c51ad4a3b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58060
Generic way to check if Operator belongs to predefined map, and if so via public method(s) access to map value. In general value can be anything for example Operator's schema.
Test Plan: buck test caffe2/test/cpp/jit:jit -- OperatorMap
Reviewed By: Krovatkin
Differential Revision: D28357933
fbshipit-source-id: ba3248cf06c07f16aebafccb7ae71c1245afb083
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58510
In some case that I don't fully understand we're getting a stride that is:
```
{2:1, 1:1, 0:*}
```
(in this debug output, M:N means stride index M, stride value N). This shape
should be considered incomplete, since we don't actually know the values of the
stride, but VaryingShape::isComplete considers it complete because it only
checks the presence of elements in the vector, not whether those elements are
themselves complete.
ghstack-source-id: 129279583
Test Plan:
new unit test in test/cpp/jit
To see the failure in the context of a real model:
```
./fblearner/predictor/loadgen/download-requests.sh 272478342_0 10 ~/local/requests/272478342_0.recordio
buck-out/gen/fblearner/predictor/loadgen/replay_model_requests --model_id=272478342_0 --replay_record_source=recordio:/data/users/bertrand/requests/272478342_0.recordio --remote_port=9119 --output_file=/data/users/bertrand/responses/272478342_0_actual.recordio --output_type=recordio
buck-out/gen/fblearner/predictor/loadgen/replay_model_requests --model_id=272478342_0 --replay_record_source=recordio:/data/users/bertrand/requests/272478342_0.recordio --remote_port=9119 --output_file=/data/users/bertrand/responses/272478342_0_actual.recordio --output_type=recordio
```
Reviewed By: Krovatkin
Differential Revision: D28520062
fbshipit-source-id: 3ca900337d86480a40fbd90349a698cbb2fa5f11
Summary:
Freezing exists as a pass which partially evaluates your model and applies generic optimizations which should speed it up. Optimize for inference is a counterpart to these optimizations which runs build & server specific optimizations. The interaction with existing `optimize_frozen_module` is not great, I guess we could just deprecate the API entirely? it was never officially released but just existed to document the `optimize_numerics` keyword.
Eventually, I would like to add a way of adding example inputs but I didnt add that here because they are not being used at all yet. I also have not yet included a way to blacklist individual optimizations, and would like to wait until we move this to Beta and have a little more clarity on how everything will fit together. I also think blacklisting will be an uncommon use case for the current optimizations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58193
Reviewed By: bertmaher, navahgar
Differential Revision: D28443714
Pulled By: eellison
fbshipit-source-id: b032355bb2585720a6d2f00c89d0d9a7ef60e649
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58202
This unit test was testing the wrong target. It should test the sampler under jit::mobile. This diff fixes it.
Test Plan: run unit tests
Reviewed By: shreyanb98
Differential Revision: D28384839
fbshipit-source-id: 35cc63be2e73ca9b1a7d30d6f67fffcfe5021fa2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58205
It's worthing moving train related files into their own folder since we are adding more code under the mobile directory.
This diff does that.
Test Plan: run unit tests and ci
Reviewed By: iseeyuan
Differential Revision: D28402432
fbshipit-source-id: cd76a1c4f8ff06508cdc3aad8a169fbf34bb4995
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58201
Add light version of RandomSampler which can be used torch mobile.
Test Plan: run unit test
Reviewed By: iseeyuan
Differential Revision: D28364467
fbshipit-source-id: 3148129fa56533f5f4b76b63b60e8778eeaf815f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56845
Handle forward/backward compatibility caused by added default arguments in mobile. As an example,
In older version, operator aten::foo's schema is
```
foo(Tensor a, Tensor b) -> Tensor
```
In the new version, the schema is updated to
```
foo(Tensor a, Tensor b, int groups=1) -> Tensor
```
## Model file
Serialize the number of specified arguments to each operator into the bytecode operator table. Before the operator table contains operator name and overload name:
```
('operators', (('aten::foo', ''),))
```
Now the number of specified arguments is added:
```
# bytecode version 6
('operators', (('aten::foo', '', 2),))
```
where "2" means the number of specified arguments.
Since there's bytecode schema change, the bytecode version number is bumped. This PR is to be landed after #56002 , where the version number is bumped from 4 to 5. This PR bumps the version number from 5 to 6.
## Runtime and backward compatibility
When the operator is found (either jit or c10), we have the OperatorHandle, where the operator schema can be accessed by
```
op.value().schema().arguments()
```
Adaptation is implemented to handle backward compatibility. For the example above, the new runtime holds the updated schema:
```
foo(Tensor a, Tensor b, int groups=1) -> Tensor
```
Whereas the model file carries
```
(('aten::foo', ''), 2)
```
We can implement a wrapper around the original function pointer to push the default argument to the stack.
## Deliver time and forward compatibility
At model delivery time, two checks can be done:
### Operator check
Two APIs to be provided:
* Runtime: An API to get a runtime’s ops and their schemas (i.e. the # of args). D27920185(WIP)
* Model: An API to get a model’s ops and their schema requirements (i.e. the # of args required).
The APIs can be used to check
* runtime.ops() is a superset of model.ops()
* for each op in model.ops() validate their schemas are compatible with those in runtime.ops() -- i.e. the # args required in a model op are <= # args in the runtime op.
Note that only root ops in the model needs to be checked here. For transient ops it's not necessary. For example, if a root op, "aten::root" calls "aten::foo", it's "aten::root"'s responsibility to adapt to "aten::foo"'s change, or "aten::root" itself needs to be updated too.
### Bytecode version backport (PR coming)
When delivering a model with bytecode v6, if the runtime only works with bytecode v5 and lower, backport is needed.
* The number of arguments is removed from the operator table
* The bytecode version is changed from 6 to 5
Note that this backport is a pure format change, it does not guarantee the backported model always runs in old runtime. The operator check mentioned before should be done first, before it’s back ported to v5.
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision: D27986544
Pulled By: iseeyuan
fbshipit-source-id: 143e19d4798cfb96b65095538dd648eead4e3fda
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57570
Move runtime ops compatibility api to OSS and introduce schema information
ghstack-source-id: 128789159
Test Plan: unit test and manually ran it for a runtime with all (non custom) ops, and the bixray models unittest {P412728176}
Reviewed By: raziel
Differential Revision: D28203104
fbshipit-source-id: 432a7d0247bccfb2e1ce90e8d41f81596efa3d67
Summary:
Fixes https://github.com/pytorch/pytorch/issues/56608
- Adds binding to the `c10::InferenceMode` RAII class in `torch._C._autograd.InferenceMode` through pybind. Also binds the `torch.is_inference_mode` function.
- Adds context manager `torch.inference_mode` to manage an instance of `c10::InferenceMode` (global). Implemented in `torch.autograd.grad_mode.py` to reuse the `_DecoratorContextManager` class.
- Adds some tests based on those linked in the issue + several more for just the context manager
Issues/todos (not necessarily for this PR):
- Improve short inference mode description
- Small example
- Improved testing since there is no direct way of checking TLS/dispatch keys
-
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58045
Reviewed By: agolynski
Differential Revision: D28390595
Pulled By: soulitzer
fbshipit-source-id: ae98fa036c6a2cf7f56e0fd4c352ff804904752c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49352
In this PR, we replace all definitions of slice to take None parameters for the start, end, and step. This will simplify the compiler logic
Test Plan:
test_jit test cases
Imported from OSS
Reviewed By: jamesr66a, nikithamalgifb
Differential Revision: D25929903
fbshipit-source-id: 5bfc6bad514a8aafbef2dacc706f95f867fe85f1
Summary:
This PR adds a new pass in JIT that optimizes `aten::cat` ops.
Specifically, here are optimizations performed:
* Eliminate redundant in `cat` inputs by performing cse on the list of inputs.
- This includes eliminating fully redundant `cat` ops when all the inputs are the same as well the case when "all but one" of the inputs have already been concatenated.
* Expand `cat` into multiple copies and eliminate redundancies.
- This also includes eliminating redundancies in the underlying buffers used for `cat`.
These optimizations are not enabled in any compilation flow at this point.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55474
Reviewed By: albanD
Differential Revision: D27624511
Pulled By: navahgar
fbshipit-source-id: d509289fafc23e73b02f64a90219148896817339
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57792
There are two problems when using CUDA RPC with distributed autograd
and distributed optimizer:
1) In local autograd engine, all autograd functions/nodes, including
AccumualteGrad will use the forward stream for backward computation.
But distributed autograd skips AccumulateGrad autograd function/node
and directly calls into `AccumulateGrad::accumulateGrad`. As the
result, it will use the default stream to accumulate gradients
instead of the forward stream. This commit changes that and uses the
forward stream to accumulate gradients, matching forward behavior.
2) Distributed optimizer and distributed autograd backward are
separate RPC calls, and CUDA streams are not synchronized across
different RPC calls. As a result, distributed optimizer might
consume gradients before they are ready. This commit uses CUDA
events to record the completion of gradient computation, and use
those events to block current streams when getGradients() are called.
Test Plan: Imported from OSS
Reviewed By: pritamdamania87
Differential Revision: D28274876
Pulled By: mrshenli
fbshipit-source-id: 22e607152324ae918084066cde8c5dbb418bba7c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57879
_save_data() and _load_data() were designed as a protocol of data serialization of trainer client. As confirmed with kwanmacher and dreiss , they are not used. In addition, there's no plan to use them in Federated Learning flow. Remove them for now.
Test Plan: Imported from OSS
Reviewed By: kwanmacher
Differential Revision: D28306682
Pulled By: iseeyuan
fbshipit-source-id: 1b993ce4d78e372ae9b83bcbe496a196f9269d47
Summary:
Add an api to backport a model vn to model vi. It accept an input model (file or buffer) and output a model (file or buffer) with an expected bytecode version.
In this change, the input is a model and it can come from a file or buffer. The output is a model and can be either file path or buffer.
When backport fails, function return false with a warning message :
```
/Users/chenlai/pytorch/cmake-build-debug/bin/test_jit --gtest_filter=LiteInterpreterTest.BackPortByteCodeModelV4:LiteInterpreterTest/*.BackPortByteCodeModelV4:*/LiteInterpreterTest.BackPortByteCodeModelV4/*:*/LiteInterpreterTest/*.BackPortByteCodeModelV4 --gtest_color=no
Testing started at 2:32 PM ...
CUDA not available. Disabling CUDA and MultiCUDA tests
[W backport.cpp:419] Warning: Backport doesn't support backport to version3 (function _backport_for_mobile_impl)
Process finished with exit code 0
```
## Test
1. Run both `caffe2/test/cpp/jit/test_lite_interpreter.cpp` and `caffe2/test/mobile/test_bytecode.py`.
2. Run all prod models with backport api.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56802
ghstack-source-id: 128425510
Test Plan: CI
Reviewed By: raziel, iseeyuan
Differential Revision: D27844651
fbshipit-source-id: 8a803cf6c76433ee0a3049b1a5570585d569f8d6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57635
Note: this PR looks massive, but it's just one simple change, codemodded many times.
In many cases, a callback needs to access the value/error produced by the parent future. In Python this was easy because the callback was invoked with the parent future as argument, and could thus inspect it. In C++ the callbacks didn't take any arguments, thus in many cases we worked around this by capturing the future in its own callback. This is risky (leads to reference cycle and thus memory leak) and must be done carefully (spoiler: sometimes we weren't).
ghstack-source-id: 128296580
Test Plan: CI
Reviewed By: wanchaol
Differential Revision: D28178783
fbshipit-source-id: 6de02c4568be42123372edc008f630d5ddae0081
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57634
`wrapPropagateTLSState` was restricting its argument to be an argument-less function, and I need to relax this for later work.
Also, it was requiring its argument to be converted to `std::function`, and also returned a `std::function`. Each creation of a `std::function` could cause a heap allocation. It's not particularly expensive, but here we can easily avoid it by having `wrapPropagateTLSState` directly operate on generic callables (thus, possibly, raw lambdas).
ghstack-source-id: 128295264
Test Plan: CI
Reviewed By: ilia-cher
Differential Revision: D28178782
fbshipit-source-id: d657f5751514974518606dd4fc4175e805dcb90a
Summary:
Fixes a bug introduced by https://github.com/pytorch/pytorch/issues/57057
cc ailzhang while writing the tests, I realized that for these functions, we don't properly set the CreationMeta in no grad mode and Inference mode. Added a todo there.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57669
Reviewed By: soulitzer
Differential Revision: D28231005
Pulled By: albanD
fbshipit-source-id: 08a68d23ded87027476914bc87f3a0537f01fc33
Summary:
Add an api `_get_bytecode_version` to get version number given a bytecode model in both cxx and python, and the input can be both from file path and buffer.
## Test
CI (new added unit test will run as part of `pytorch_core-buck`)
1. run test_lite_interpreter.cpp
2. `python test/mobile/test_bytecode.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56801
ghstack-source-id: 128169647
Test Plan:
CI (new added unit test will run as part of `pytorch_core-buck`)
1. run test_lite_interpreter.cpp
2. `python test/mobile/test_bytecode.py`
Reviewed By: iseeyuan
Differential Revision: D27961417
fbshipit-source-id: f786cc9573d855feecff0b4fe8e5363e25f5728c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57560
The new methods allow to peak into bufferArgs which describe parameters
that codegen expects. This description includes info whether a given
parameter is a scalar var or a buffer and in case it's a buffer allows
to get the corresponding `Buf*` pointer from which we could get the
expected sizes.
Relanding #57074 which was reverted because I forgot to guard a new
test with `ifdef LLVM`.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D28199048
Pulled By: ZolotukhinM
fbshipit-source-id: 636e838e7e242a3c63e97ec453b8fae9b6380231
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57552
This method uses `CodeGen::call_raw` instead of `CodeGen::call`.
Relanding #57328 (the entire stack) which was reverted because I forgot
to guard a new test with `ifdef LLVM`.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D28195047
Pulled By: ZolotukhinM
fbshipit-source-id: bcfd3cb5b4f33a149b7549515ffd705e2c4f208f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57551
The new method allows to pass input and output arguments by `void*`
pointers instead of CallArgs. That helps to reduce the invocation
overhead. Currently this is only supported in LLVM codegen.
Relanding #55113 (the entire stack) which was reverted because I forgot
to guard a new test with `ifdef LLVM`.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D28195049
Pulled By: ZolotukhinM
fbshipit-source-id: 035b77ae996dbbcd542b4b0e4c011b41e8d7828b
