Summary:
This is a re-do of https://github.com/pytorch/pytorch/issues/27064, which was reverted (b8792c0438). This was landed at the same time as other work that added new operators to the `torch` namespace so the check for whether the `torch` namespace is exhaustively checked for overridability was triggering test failures.
I've temporarily disabled that check and added an explanatory comment that the check will be re-enabled in a future PR that will be merged during a time when the commit velocity on PyTorch is lower.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30730
Differential Revision: D18813270
Pulled By: ezyang
fbshipit-source-id: 70477c4656dca8fea6e7bc59259555041fcfbf68
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29827
There are known issues for "fork tests + OMP" in Pytorch, rpc and dist autograd tests use OMP thread pools, this caused rpc fork and dist autograd fork tests to be flaky. So remove these fork tests from PyTorch repo. rpc spawn and dist autograd spawn tests are still running.
Test Plan: unit tests
Differential Revision: D18507384
fbshipit-source-id: 9e239f13850832b4b84724828537f73512f3fca9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29747
There are duplicate code for component that rely on RpcAgent. Extract them into a re-usable test fixture class.
Test Plan:
### RPC + RRef
```
buck test mode/dev-nosan //caffe2/test:rpc_fork
buck test mode/dev-nosan //caffe2/test:rpc_spawn
```
```
buck test mode/dev-nosan //caffe2/test:rpc_fork_thrift
buck test mode/dev-nosan //caffe2/test:rpc_spawn_thrift
```
### Dist Autograd
```
buck test mode/dev-nosan //caffe2/test:dist_autograd_fork
buck test mode/dev-nosan //caffe2/test:dist_autograd_spawn
```
```
buck test mode/dev-nosan //caffe2/test:dist_autograd_fork_thrift
buck test mode/dev-nosan //caffe2/test:dist_autograd_spawn_thrift
```
### Dist Optimizer
```
buck test mode/dev-nosan //caffe2/test:dist_optimizer_fork
buck test mode/dev-nosan //caffe2/test:dist_optimizer_spawn
```
```
buck test mode/dev-nosan //caffe2/test:dist_optimizer_fork_thrift
buck test mode/dev-nosan //caffe2/test:dist_optimizer_spawn_thrift
```
Differential Revision: D5689636
fbshipit-source-id: f35eea1359addaaac9bd8d00d0a5df228a236511
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27269
Remove `test_quantizer.py`, add and rewrite one of the tests in `test_quantizer`
in `test_quantization.py`
The conv test is removed for now since conv pattern is still broken, we'll add another test
later
ghstack-source-id: 92869823
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D18182916
fbshipit-source-id: 325b5d8e877228d6a513e3ddf52c974479250d42
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27251
Explicitly clean up py::objects to avoid segment faults when py::objects with CPython are cleaned up later at program exit.
See similar issues reported https://github.com/pybind/pybind11/issues/1598
and https://github.com/pybind/pybind11/issues/1493.
Our local tests also caught this segment faults if py::objects are cleaned
up at program exit. The explaination is: CPython cleans up most critical
utitlies before cleaning up PythonRpcHandler singleton, so when
PythonRpcHandler signleton cleans up py::objects and call dec_ref(), it
will crash.
The solution is to clean up py::objects earlier when Rpc agent join().
Be note that py::objects can not be cleaned up when Rpc agent is destroyed
as well, as Rpc agent is global variable and it will have same issue as
PythonRpcHandler.
close#27182
ghstack-source-id: 92035069
Test Plan: unit tests on python 3.6 and python 3.5
Differential Revision: D17727362
fbshipit-source-id: c254023f6a85acce35528ba756a4efabba9a519f
Summary:
This PR updates `test/cpp_api_parity/parity-tracker.md` to reflect our progress on C++ `torch::nn` parity. It also disables the C++ API parity test temporarily, and as the next step I will refactor the parity test to make it simpler.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28117
Differential Revision: D17957948
Pulled By: yf225
fbshipit-source-id: 1dd836c25665f57ba8efc6d1abf671a95c03eff7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27612
The file imports from torch.distributed.rpc, which won't be
initialized when running on Python 2.
Test Plan: Imported from OSS
Differential Revision: D17855033
Pulled By: pietern
fbshipit-source-id: 6e6b0ca248d0512dac5a44e10e153c710cefe02c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25656
spawn multiprocessing can catch some issues that fork multiprocessing can not
catch, meanwhile fork can work properly with asan tests, but spawn
multiprocessing can not work with asan tests for some use cases right now.
so this diff adding support to launch both spawn and fork tests in
multiProcessingTestCase class, also let test_rpc and test_dist_autograd to run
both spawn and fork tests
ghstack-source-id: 91096705
Test Plan: unit tests
Reviewed By: xush6528
Differential Revision: D17086007
fbshipit-source-id: af2446e7abe948c37081cff24ed060fd87f84922
Summary:
- Makes test_indexing.py device generic
- Removes test_indexing_cuda.py
Note: a couple tests in test_indexing.py were already CPU and CUDA tests, meaning these tests were run multiple times when CUDA was available. Genericizing test_indexing.py corrects this and lets these tests be run on other device types, like XLA, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26634
Differential Revision: D17529001
Pulled By: mruberry
fbshipit-source-id: e71ba28d947749255a0aceeb7b77a42c4811439d
Summary:
expose necessary functions to python, and add round-way tests for
function schema str() and parsing functions.
We iterate over all the registered function schemas and get the string,
then parse the string. We compare the schema generated from parsing with
the original one, and make sure they are equal.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23208
ghstack-source-id: 89638026
Test Plan: buck test //caffe2/test:function_schema
Reviewed By: zrphercule
Differential Revision: D16435471
fbshipit-source-id: 6961ab096335eb88a96b132575996c24090fd4c0
Summary:
Improve handling of mixed-type tensor operations.
This PR affects the arithmetic (add, sub, mul, and div) operators implemented via TensorIterator (so dense but not sparse tensor ops).
For these operators, we will now promote to reasonable types where possible, following the rules defined in https://github.com/pytorch/pytorch/issues/9515, and error in cases where the cast would require floating point -> integral or non-boolean to boolean downcasts.
The details of the promotion rules are described here:
https://github.com/nairbv/pytorch/blob/promote_types_strict/docs/source/tensor_attributes.rst
Some specific backwards incompatible examples:
* now `int_tensor * float` will result in a float tensor, whereas previously the floating point operand was first cast to an int. Previously `torch.tensor(10) * 1.9` => `tensor(10)` because the 1.9 was downcast to `1`. Now the result will be the more intuitive `tensor(19)`
* Now `int_tensor *= float` will error, since the floating point result of this operation can't be cast into the in-place integral type result.
See more examples/detail in the original issue (https://github.com/pytorch/pytorch/issues/9515), in the above linked tensor_attributes.rst doc, or in the test_type_promotion.py tests added in this PR:
https://github.com/nairbv/pytorch/blob/promote_types_strict/test/test_type_promotion.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22273
Reviewed By: gchanan
Differential Revision: D16582230
Pulled By: nairbv
fbshipit-source-id: 4029cca891908cdbf4253e4513c617bba7306cb3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24875
As per https://github.com/pytorch/pytorch/issues/23110, each autograd pass
would be assigned a unique autograd_context_id. In this change we introduce a
DistAutogradContainer per worker which holds information for each autograd pass
currently running.
DistAutogradContainer has a map from the autograd_context_id to
DistAutogradContext (which holds all the relevant information for the autograd
pass). DistAutogradContext currently only stores the autograd_context_id and
more information would be added to it later as we build out the rest of the
framework.
The autograd_context_id is a 64 bit globally unique integer where the first 16
bits are the worker_id and next 48 bits are auto-incrementing for uniqueness.
Sample python code on how this would be used for distributed autograd:
```
import torch.distributed.autograd as dist_autograd
worker_id = 0
dist_autograd.init(worker_id)
with dist_autograd.context() as context_id:
# forward pass...
# backward pass...
# optimizer step...
```
ghstack-source-id: 89119248
Test Plan: unit tests.
Differential Revision: D16356694
fbshipit-source-id: d1a8678da0c2af611758dbb5d624d554212330ce
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25212
In eager mode, all modules need to work with input tensors that can change qparams dynamically. This issue https://github.com/pytorch/pytorch/issues/23874 will address this via FBGEMM modifications. This is a work around before that.
ghstack-source-id: 89118038
Test Plan:
buck test caffe2/test:quantized -- 'test_conv_api \(test_quantized_nn_mods\.ModuleAPITest\)' --print-passing-details
Summary (total time 65.86s):
PASS: 1
FAIL: 0
SKIP: 0
FATAL: 0
TIMEOUT: 0
OMIT: 0
Differential Revision: D17064471
fbshipit-source-id: 3c192442b19bf2d9d88d4e52de6c24dc134a846f
Summary:
This PR adds test harness for checking Python / C++ API parity for `torch.nn.Module` subclasses. Under the hood, we use JIT tracing to transfer `nn.Module` state from Python to C++, so that we can test initialization / forward / backward on Python / C++ modules with the same parameters and buffers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23852
Differential Revision: D16830204
Pulled By: yf225
fbshipit-source-id: 9b5298c0e8cd30e341a9f026e6f05604a82d6002
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24989
This fixes the cases where a type annotated with optional cannot
be conditionally assigned to none:
```
x : Optional[int] = 4
if ...:
x = None
```
Test Plan: Imported from OSS
Differential Revision: D16949314
Pulled By: zdevito
fbshipit-source-id: 7f63d88b30a3f5b024c2a539aa74967c9202af00
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24448
The setting `--durations=10` was hard-coded, which is annoying as I
don't necessarily care. A good alternative to get the same behavior is:
```
python run_tests.py --pytest -- --durations=10
```
Test Plan: Imported from OSS
Differential Revision: D16876380
Pulled By: suo
fbshipit-source-id: 1e14d366db45b6b9bf4a4ab1633b0f6ece29f6bc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24201
It turns out that the `run_test` script uses a blacklist of "exclude" tests and tests if the test name [starts with](https://github.com/pytorch/pytorch/blob/master/test/run_test.py#L342) the given blacklist item. `nn` was passed as a blacklist item in CI, and that meant that not only was test_nn skipped, but also test_nn_quantized. This renames the test to avoid this situation, and imo puts it in a better position lexicographically next to the other quantization tests.
Test Plan: Imported from OSS
Differential Revision: D16772820
Pulled By: jamesr66a
fbshipit-source-id: 4cde0729b48ae3e36fcedab9c98197831af82dde
Summary:
Features:
* sync and async RPC for builtin operators
* RpcAgent API
* ProcessGroupAgent implementation
Goal:
* have a minimum working and testable RPC implementation
* make sure the RpcAgent API is sufficient for future ThriftAgent and TensorPipeAgent implementation
* For tensor pipe implementation, it might allocate multiple underlying communication channels with different types, and might also use streaming serialization/deserialization for large tensors. To support this requirement, the current implementation only convert a BuiltinOp into a Message which contains a byte vector and a tensor table. It is up to the RpcAgent implementation to determine how it would like to serialize a Message object.
* For ThriftAgent, as Thrift has it own request/response matching solution, the Message.id is no longer necessary. Hence the id can be dropped during serialization. All it needs to do is to pass the response Message object to the Future returned by send(...).
* support blocking and non-blocking RequestCallback
* blocking means the callback won't return before sending out the response
* non-blocking can be achieved by enqueue the `(from, request, RpcAgent&)` tuple and use a different thread to process them. That is why there is an `RpcAgent&` arg in the param list.
We are not exporting this diff until we finalize distributed autograd design and publish the API review publicly.
https://fb.quip.com/FabTAZKVgQpf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23228
ghstack-source-id: 87816717
Reviewed By: zhaojuanmao
Differential Revision: D15194693
fbshipit-source-id: 7adb600796613cde6073db6c227451b89940ecaf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23858
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23718
Changes:
- Enable tests for quantization test files in `run_tests.py`
- Remove `__future__` imports from `torch/nn/qat/modules/__init__.py`, since `unicode_literals` messes up imports on python2 because the elements in `__all__` will be Unicode and not string
- Skip PostTrainingQuantTests if the build doesn't have FBGEMM (only a small subset of targets in tests) or if testing under UBSAN (the suppression file doesn't seem to work)
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D16639467
Pulled By: jamesr66a
fbshipit-source-id: 532766797c216976dd7e07d751f768ff8e0fc207
Summary:
This is achieved by using `cuDevicePrimaryCtxGetState` as a way to check whether a primary context exists on a device. It is not too slow, from this benchmark of a single call to it on CUDA 10.1, Titan Xp, driver 415.27:
```
---------------------------------------------------------------------
Benchmark Time CPU Iterations
---------------------------------------------------------------------
BM_cuDevicePrimaryCtxGetState 301 ns 301 ns 2319746
```
Commits:
1. Add `CUDAHooks::getDeviceWithPrimaryContext` which returns a device index with primary context (if exists).
Link `c10/cuda` against `libcuda` for device API calls.
2. Use `getDeviceWithPrimaryContext` to check primary context in `pin_memory`.
Fix `OptionalDeviceGuard` doc.
3. Refactor `test_cuda_primary_ctx.py` to support multiple tests.
Add test for this in that file.
Fixes https://github.com/pytorch/pytorch/issues/21081.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22229
Differential Revision: D16170194
Pulled By: zou3519
fbshipit-source-id: 485a45f211b7844c9e69c63f3b3b75194a796c5d
Summary:
Ops on a Process Group (pg) instance will hit an error when input/output tensors are created on a different process, because, pg calls `recordStream` on `CUDACachingAllocator` which only knows tensors created within the same process.
The proposed solution is to add a `suppressError` arg (suggestions for better names?) to `recordStream`. See comments in code for arguments.
CC pichuang1984
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21449
Differential Revision: D15689736
Pulled By: mrshenli
fbshipit-source-id: e7fc81b167868f8666536067eaa7ae2c8584d88e
Summary:
Now you can run `python test/run_tests --jit` to run all jit tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21161
Differential Revision: D15563912
Pulled By: eellison
fbshipit-source-id: 4bb0285cda4168b72a3dc4bba471485566a59873
Summary:
Resubmit #20698 which got messed up.
Idea is that when PyTorch is used in a custom build environment (e.g. Facebook), it's useful to track usage of various APIs centrally. This PR introduces a simple very lightweight mechanism to do so - only first invocation of a trigger point would be logged. This is significantly more lightweight than #18235 and thus we can allow to put logging in e.g. TensorImpl.
Also adds an initial list of trigger points. Trigger points are added in such a way that no static initialization triggers them, i.e. just linking with libtorch.so will not cause any logging. Further suggestions of what to log are welcomed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20745
Differential Revision: D15429196
Pulled By: dzhulgakov
fbshipit-source-id: a5e41a709a65b7ebccc6b95f93854e583cf20aca
Summary:
This PR adds TensorBoard logging support natively within PyTorch. It is based on the tensorboardX code developed by lanpa and relies on changes inside the tensorflow/tensorboard repo landing at https://github.com/tensorflow/tensorboard/pull/2065.
With these changes users can simply `pip install tensorboard; pip install torch` and then log PyTorch data directly to the TensorBoard protobuf format using
```
import torch
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
s1 = torch.rand(1)
writer.add_scalar('data/scalar1', s1[0], 0)
writer.close()
```
Design:
- `EventFileWriter` and `RecordWriter` from tensorboardX now live in tensorflow/tensorboard
- `SummaryWriter` and PyTorch-specific conversion from tensors, nn modules, etc. now live in pytorch/pytorch. We also support Caffe2 blobs and nets.
Action items:
- [x] `from torch.utils.tensorboard import SummaryWriter`
- [x] rename functions
- [x] unittests
- [x] move actual writing function to tensorflow/tensorboard in https://github.com/tensorflow/tensorboard/pull/2065
Review:
- Please review for PyTorch standard formatting, code usage, etc.
- Please verify unittest usage is correct and executing in CI
Any significant changes made here will likely be synced back to github.com/lanpa/tensorboardX/ in the future.
cc orionr, ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16196
Differential Revision: D15062901
Pulled By: orionr
fbshipit-source-id: 3812eb6aa07a2811979c5c7b70810261f9ea169e
Summary:
This should have been fixed in newest ROCm version.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19436
Reviewed By: ezyang
Differential Revision: D15004685
Pulled By: bddppq
fbshipit-source-id: 19fd4cca94c914dc54aabfbb4e62b328aa348a35
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19091
Implements a basic quantized ReLU (uint8). This is a temporary solution before using the `QTensor` type instead of the tuple.
Reviewed By: dzhulgakov
Differential Revision: D14565413
fbshipit-source-id: 7d53cf5628cf9ec135603d6a1fb7c79cd9383019
Summary:
This is a minimalist PR to add MKL-DNN tensor per discussion from Github issue: https://github.com/pytorch/pytorch/issues/16038
Ops with MKL-DNN tensor will be supported in following-up PRs to speed up imperative path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17748
Reviewed By: dzhulgakov
Differential Revision: D14614640
Pulled By: bddppq
fbshipit-source-id: c58de98e244b0c63ae11e10d752a8e8ed920c533
Summary:
Start of breaking up test_jit.py
New files will have the format test_jit_* so they are easily grepable but remain in the same directory so we don't have to go through multiple sources for imports.
I am adding a test that's expected to fail to be sure it's running.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18590
Reviewed By: wanchaol
Differential Revision: D14677094
Pulled By: eellison
fbshipit-source-id: 9782c6aa9525bb6f332fc75cfff004c83a417522
