Summary:
some people have been confused by `retain_graph` in the snippet, they thought it was an additional requirement imposed by amp.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41203
Differential Revision: D22463700
Pulled By: ngimel
fbshipit-source-id: e6fc8871be2bf0ecc1794b1c6f5ea99af922bf7e
Summary:
I ran `make linkcheck` using `sphinx.builders.linkcheck` on the documentation and noticed a few links weren't using HTTPS so I quickly updated them all.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40878
Differential Revision: D22404647
Pulled By: ngimel
fbshipit-source-id: 9c9756db59197304023fddc28f252314f6cf4af3
Summary:
Currently, a custom autograd function written with
```
torch.cuda.amp.custom_fwd(cast_inputs=dtype)
def forward(ctx, *args):
...
```
casts incoming floating-point CUDA tensors to `dtype` unconditionally, regardless of whether the function executes in an autocast-enabled region. I think I had the wrong idea there. Autocast-disabled regions should give the user control of input types. Also, `custom_fwd(cast_inputs=dtype)`-decorated functions' behavior should align with native fp32list/fp16list functions. C++-side casting wrappers have no effect when autocast is disabled, and `custom_fwd`'s casting should behave the same way.
The present PR changes `custom_fwd` so it only casts in autocast-enabled regions (also updates custom_fwd to ignore fp64 inputs, like the C++ wrappers).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36171
Differential Revision: D22179511
Pulled By: ngimel
fbshipit-source-id: 5a93d070179a43206066bce19da0a5a19ecaabbd
Summary:
Removes line mentioning `ProcessGroupRoundRobin` since we don't intend it to be used as a public API just yet. We can add this back when we officially support the API
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40380
Differential Revision: D22165556
Pulled By: rohan-varma
fbshipit-source-id: 24d0477d881dc74f2ff579de61dfd1ced2b09e75
Summary:
Make Linear layer working correct when bias is False
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38002
Differential Revision: D21509679
Pulled By: malfet
fbshipit-source-id: c7077992cf414ecc557b39e5ed1e39ef01c8b347
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37548
Moving RecordFunction from torch::autograd::profiler into at namespace
Test Plan:
CI
Imported from OSS
Differential Revision: D21315852
fbshipit-source-id: 4a4dbabf116c162f9aef0da8606590ec3f3847aa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37491
This PR modernizes RecordFunction API and adds thread local callbacks
in addition to the global ones
Changes:
- support for TLS callbacks, this is going to be the foundation of profiler and other tools
- modernize interface around simple set of functions (add|remove|has|clear)(Global|ThreadLocal)(Callback) and adding RecordFunctionCallback to easily construct callbacks to be passed
- we also add `.setShouldRun` into the callback interface to support cases when simple uniform sampling is not enough
- to properly support add/remove introduce the idea of callback handle returned by add
- internal implementation still uses SmallVector to store intermediate state (as before) - in this case these are vector of handles of callbacks that were picked to run
- to speed up runtime we keep these vectors sorted, this way we can quickly enumerate callbacks that need to be run
- added tests for new functionality
Test Plan:
BUILD_BINARY=1 USE_BLAS=MKL USE_MKLDNN=0 USE_CUDA=0 python setup.py
develop install
./build/bin/test_jit
CI
record_function_benchmark: https://gist.github.com/ilia-cher/f1e094dae47fe23e55e7672ac4dcda2f
Imported from OSS
Differential Revision: D21300448
fbshipit-source-id: 6d55c26dbf20b33d35c3f1604dcc07bb063c8c43
Summary:
xref gh-32838, gh-34032
This is a major refactor of parts of the documentation to split it up using sphinx's `autosummary` feature which will build out `autofuction` and `autoclass` stub files and link to them. The end result is that the top module pages like torch.nn.rst and torch.rst are now more like table-of-contents to the actual single-class or single-function documentations pages.
Along the way, I modified many of the docstrings to eliminate sphinx warnings when building. I think the only thing I changed from a non-documentation perspective is to add names to `__all__` when adding them to `globals()` in `torch.__init__.py`
I do not know the CI system: are the documentation build artifacts available after the build, so reviewers can preview before merging?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37419
Differential Revision: D21337640
Pulled By: ezyang
fbshipit-source-id: d4ad198780c3ae7a96a9f22651e00ff2d31a0c0f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37382
After adding c10::DispatchKey::Profiler the behavior of RecordFunction
observers is also controlled by the dispatch key,
this PR moves the logic outside of the profiler into the record function
Reviewed By: jamesr66a
Differential Revision: D21268320
fbshipit-source-id: 93207e3b55325d20dcc5b1e8f448ab86933321da
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37292
After adding c10::DispatchKey::Profiler the behavior of RecordFunction
observers is also controlled by the dispatch key,
this PR moves the logic outside of the profiler into the record function
Reviewed By: jamesr66a
Differential Revision: D21245094
fbshipit-source-id: 595e41b18206d2ba4cf639cb320f630907868b3f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37195
After adding c10::DispatchKey::Profiler the behavior of RecordFunction
observers is also controlled by the dispatch key,
this PR moves the logic outside of the profiler into the record function
Reviewed By: ngimel
Differential Revision: D21213786
fbshipit-source-id: e618254da74a4f1ce16c51a3869bbd75a4f561ad
Summary:
Several people have asked me about proper Amp usage with gradient accumulation. In particular, it's [unclear to people](https://github.com/NVIDIA/apex/issues/439#issuecomment-610351482) that you should only call `scaler.unscale_()` (if desired) and `scaler.update()` in iterations where you actually plan to step. This PR adds a minimal accumulation example.
I built the docs locally and it looks free from sphinx errors, at least.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36601
Differential Revision: D21082295
Pulled By: ngimel
fbshipit-source-id: b2faa6c02b9f7e1972618a0f1d5360a03f0450ac
Summary:
Full details in task: https://our.intern.facebook.com/intern/tasks/?t=64776265
With pytroch 1.5+ we remove python2 support from PyTorch. All documentation under docs/ and on the pytorch.org website needs to remove Python 2 references.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36114
Differential Revision: D20901746
Pulled By: jlin27
fbshipit-source-id: 07f8dc8e6fab0b232e5048a63079cab0c433c85f
Summary: This diff fixes the issues with current handling of debug information passed along the execution of the model. (For example, it is possible that multiple calls to the debug guard may override each other)
Test Plan: CI test/cpp/jit
Reviewed By: dzhulgakov
Differential Revision: D20602775
fbshipit-source-id: 4683957954028af81a1a0f1f12b243650230c9bb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34710
Extending RecordFunction API to support new recording scopes (such as TorchScript functions), as well as giving more flexibility to set sampling rate.
Test Plan: unit test (test_misc.cpp/testRecordFunction)
Reviewed By: gdankel, dzhulgakov
Differential Revision: D20158523
fbshipit-source-id: a9e0819d21cc06f4952d92d43246587c36137582
Summary:
## Motivation
This PR upgrades MKL-DNN from v0.20 to DNNL v1.2 and resolves https://github.com/pytorch/pytorch/issues/30300.
DNNL (Deep Neural Network Library) is the new brand of MKL-DNN, which improves performance, quality, and usability over the old version.
This PR focuses on the migration of all existing functionalities, including minor fixes, performance improvement and code clean up. It serves as the cornerstone of our future efforts to accommodate new features like OpenCL support, BF16 training, INT8 inference, etc. and to let the Pytorch community derive more benefits from the Intel Architecture.
<br>
## What's included?
Even DNNL has many breaking changes to the API, we managed to absorb most of them in ideep. This PR contains minimalist changes to the integration code in pytorch. Below is a summary of the changes:
<br>
**General:**
1. Replace op-level allocator with global-registered allocator
```
// before
ideep::sum::compute<AllocForMKLDNN>(scales, {x, y}, z);
// after
ideep::sum::compute(scales, {x, y}, z);
```
The allocator is now being registeted at `aten/src/ATen/native/mkldnn/IDeepRegistration.cpp`. Thereafter all tensors derived from the `cpu_engine` (by default) will use the c10 allocator.
```
RegisterEngineAllocator cpu_alloc(
ideep::engine::cpu_engine(),
[](size_t size) {
return c10::GetAllocator(c10::DeviceType::CPU)->raw_allocate(size);
},
[](void* p) {
c10::GetAllocator(c10::DeviceType::CPU)->raw_deallocate(p);
}
);
```
------
2. Simplify group convolution
We had such a scenario in convolution where ideep tensor shape mismatched aten tensor: when `groups > 1`, DNNL expects weights tensors to be 5-d with an extra group dimension, e.g. `goihw` instead of `oihw` in 2d conv case.
As shown below, a lot of extra checks came with this difference in shape before. Now we've completely hidden this difference in ideep and all tensors are going to align with pytorch's definition. So we could safely remove these checks from both aten and c2 integration code.
```
// aten/src/ATen/native/mkldnn/Conv.cpp
if (w.ndims() == x.ndims() + 1) {
AT_ASSERTM(
groups > 1,
"Only group _mkldnn_conv2d weights could have been reordered to 5d");
kernel_size[0] = w.get_dim(0) * w.get_dim(1);
std::copy_n(
w.get_dims().cbegin() + 2, x.ndims() - 1, kernel_size.begin() + 1);
} else {
std::copy_n(w.get_dims().cbegin(), x.ndims(), kernel_size.begin());
}
```
------
3. Enable DNNL built-in cache
Previously, we stored DNNL jitted kernels along with intermediate buffers inside ideep using an LRU cache. Now we are switching to the newly added DNNL built-in cache, and **no longer** caching buffers in order to reduce memory footprint.
This change will be mainly reflected in lower memory usage from memory profiling results. On the code side, we removed couple of lines of `op_key_` that depended on the ideep cache before.
------
4. Use 64-bit integer to denote dimensions
We changed the type of `ideep::dims` from `vector<int32_t>` to `vector<int64_t>`. This renders ideep dims no longer compatible with 32-bit dims used by caffe2. So we use something like `{stride_.begin(), stride_.end()}` to cast parameter `stride_` into a int64 vector.
<br>
**Misc changes in each commit:**
**Commit:** change build options
Some build options were slightly changed, mainly to avoid name collisions with other projects that include DNNL as a subproject. In addition, DNNL built-in cache is enabled by option `DNNL_ENABLE_PRIMITIVE_CACHE`.
Old | New
-- | --
WITH_EXAMPLE | MKLDNN_BUILD_EXAMPLES
WITH_TEST | MKLDNN_BUILD_TESTS
MKLDNN_THREADING | MKLDNN_CPU_RUNTIME
MKLDNN_USE_MKL | N/A (not use MKL anymore)
------
**Commit:** aten reintegration
- aten/src/ATen/native/mkldnn/BinaryOps.cpp
Implement binary ops using new operation `binary` provided by DNNL
- aten/src/ATen/native/mkldnn/Conv.cpp
Clean up group convolution checks
Simplify conv backward integration
- aten/src/ATen/native/mkldnn/MKLDNNConversions.cpp
Simplify prepacking convolution weights
- test/test_mkldnn.py
Fixed an issue in conv2d unit test: it didn't check conv results between mkldnn and aten implementation before. Instead, it compared the mkldnn with mkldnn as the default cpu path will also go into mkldnn. Now we use `torch.backends.mkldnn.flags` to fix this issue
- torch/utils/mkldnn.py
Prepack weight tensor on module `__init__` to achieve better performance significantly
------
**Commit:** caffe2 reintegration
- caffe2/ideep/ideep_utils.h
Clean up unused type definitions
- caffe2/ideep/operators/adam_op.cc & caffe2/ideep/operators/momentum_sgd_op.cc
Unify tensor initialization with `ideep::tensor::init`. Obsolete `ideep::tensor::reinit`
- caffe2/ideep/operators/conv_op.cc & caffe2/ideep/operators/quantization/int8_conv_op.cc
Clean up group convolution checks
Revamp convolution API
- caffe2/ideep/operators/conv_transpose_op.cc
Clean up group convolution checks
Clean up deconv workaround code
------
**Commit:** custom allocator
- Register c10 allocator as mentioned above
<br><br>
## Performance
We tested inference on some common models based on user scenarios, and most performance numbers are either better than or on par with DNNL 0.20.
ratio: new / old | Latency (batch=1 4T) | Throughput (batch=64 56T)
-- | -- | --
pytorch resnet18 | 121.4% | 99.7%
pytorch resnet50 | 123.1% | 106.9%
pytorch resnext101_32x8d | 116.3% | 100.1%
pytorch resnext50_32x4d | 141.9% | 104.4%
pytorch mobilenet_v2 | 163.0% | 105.8%
caffe2 alexnet | 303.0% | 99.2%
caffe2 googlenet-v3 | 101.1% | 99.2%
caffe2 inception-v1 | 102.2% | 101.7%
caffe2 mobilenet-v1 | 356.1% | 253.7%
caffe2 resnet101 | 100.4% | 99.8%
caffe2 resnet152 | 99.8% | 99.8%
caffe2 shufflenet | 141.1% | 69.0% †
caffe2 squeezenet | 98.5% | 99.2%
caffe2 vgg16 | 136.8% | 100.6%
caffe2 googlenet-v3 int8 | 100.0% | 100.7%
caffe2 mobilenet-v1 int8 | 779.2% | 943.0%
caffe2 resnet50 int8 | 99.5% | 95.5%
_Configuration:
Platform: Skylake 8180
Latency Test: 4 threads, warmup 30, iteration 500, batch size 1
Throughput Test: 56 threads, warmup 30, iteration 200, batch size 64_
† Shufflenet is one of the few models that require temp buffers during inference. The performance degradation is an expected issue since we no longer cache any buffer in the ideep. As for the solution, we suggest users opt for caching allocator like **jemalloc** as a drop-in replacement for system allocator in such heavy workloads.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32422
Test Plan:
Perf results: https://our.intern.facebook.com/intern/fblearner/details/177790608?tab=Experiment%20Results
10% improvement for ResNext with avx512, neutral on avx2
More results: https://fb.quip.com/ob10AL0bCDXW#NNNACAUoHJP
Reviewed By: yinghai
Differential Revision: D20381325
Pulled By: dzhulgakov
fbshipit-source-id: 803b906fd89ed8b723c5fcab55039efe3e4bcb77
Summary:
We should recommend DDP instead of DP. Hope we can also cherry-pick this for 1.5
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35063
Differential Revision: D20549621
Pulled By: ngimel
fbshipit-source-id: 86b1b2134664065cc6070ea4212895f993eaf543
Summary:
Initial integration of eager autocasting, supporting out-of-place ops only for easier review.
Relevant issue/RFC: https://github.com/pytorch/pytorch/issues/25081
In-place ops and ops with user-supplied `out=...` can certainly be supported as well (my initial WIP https://github.com/pytorch/pytorch/pull/29552 handled many) but require substantially more complex special casing in the autocasting backend and tests. Support for these ops (much of which has already been written) will be broken into later PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32140
Differential Revision: D20346700
Pulled By: ezyang
fbshipit-source-id: 12d77b3917310186fbddf11c59b2794dc859131f
Summary:
- Update API calls `backward` and `optim.step` now that we require `context_id`
- Add notes to clarify purpose of distributed autograd context (this was a source of confusion in some feedback)
- Add note that details why optimizer requires context_id
- Clearly specify that we don't have SMART mode yet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34657
Differential Revision: D20427667
Pulled By: rohan-varma
fbshipit-source-id: 5f8a3539ccf648a78e9e9a0dfdfe389c678b1606
Summary:
This is a redo of https://github.com/pytorch/pytorch/pull/33791, which was reverted because it introduced a flaky test. The test was flaky and only flaky on Python3.5 because of dict order randomization.
I've fixed the issue with tests clobbering each other in b539fec and removed the override tests for `torch.nn.functional.tanh` and `torch.nn.functional.sigmoid`, which are deprecated and shouldn't be overridable in e0d7402. I also verified that no more test clobbering is happening.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34240
Differential Revision: D20252442
Pulled By: cpuhrsch
fbshipit-source-id: 069568e342a41c90e1dc76cbf85ba4aed47f24be
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:
Improves explanation of non-determinism when running on GPUs. Adds info about `torch.nn.BCELoss` operating non-deterministically on GPUs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33795
Differential Revision: D20284880
Pulled By: ngimel
fbshipit-source-id: d543959636d261a80c234150304344b19a37ba5d
Summary:
Fixes https://github.com/pytorch/pytorch/issues/33182
This adds private API functions that developers of types that implement `__torch_function__` can use to ensure full coverage of the subset of the PyTorch API that can be overrided.
I've refactored some of the code in the tests into a new `torch._overrides.get_overridable_functions` function. I've also changed `TENSOR_LIKE_TORCH_OVERRIDES` into `torch._overrides.get_testing_overrides` and `IGNORED_TORCH_FUNCTIONS` into `torch._overrides.get_ignored_functions`. Making these two static global variables in the tests into functions should allow rewriting their implementation to construct their return values instead of just statically defining the return value as is done here. Currently that is blocked on not being able to inspect function signatures of compiled kernels in PyTorch (see https://github.com/pytorch/pytorch/issues/28233). See the docs I've added for usage examples of these new functions. I also refactored the existing override tests to make use of these new functions, which should be a good forcing function to make sure they're kept up-to-date.
Finally, while working on this I discovered that `TestTorchFunctionOverrides.test_mean` and `TestTorchFunctionOverrides.test_mm` weren't ever being run because they were getting clobbered by the other dynamically generated override tests. I fixed that by renaming the tests and then fixing the actual test code. I've verified that all the subclassing semantics is correct and that the updated test answers are correct. I'm happy to put the fixes to the existing tests in as a separate pull request if that would be easier to review.
ping cpuhrsch since the feature request originally came from them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33791
Differential Revision: D20195053
Pulled By: cpuhrsch
fbshipit-source-id: 1585f4e405f5223932b410eae03a288dc8eb627e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33711Fixed#33480
This makes `dist_autograd.backward` and `dist_optimizer.step` functional by making the user explicitly pass in the `context_id` as opposed to relying on the confusing thread_local context_id.
This diff incorporates these API changes and all places where these functions are called.
More concretely, this code:
```
with dist_autograd.context():
# Forward pass.
dist_autograd.backward([loss.sum()])
dist_optim.step()
```
should now be written as follows:
```
with dist_autograd.context() as context_id:
# Forward pass.
dist_autograd.backward(context_id, [loss.sum()])
dist_optim.step(context_id)
```
Test Plan: Ensuring all existing dist_autograd and dist_optimizer tests pass with the new API. Also added a new test case for input checking.
Differential Revision: D20011710
fbshipit-source-id: 216e12207934a2a79c7223332b97c558d89d4d65
Summary:
Also, windows memory failures responsible for the earlier reversion have been fixed.
This PR (initially) contains 2 commits:
* a revert of the revert
* all changes to implement the original Apex scale update heuristic, squashed into a single commit for easier diff review
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33366
Differential Revision: D20099026
Pulled By: ngimel
fbshipit-source-id: 339b9b6bd5134bf055057492cd1eedb7e4461529
Summary:
This PR implements the gradient scaling API that mruberry, jjsjann123, ngimel, zdevito, gchanan and I have been discussing. Relevant issue/RFC: https://github.com/pytorch/pytorch/issues/25081.
Volume-wise, this PR is mostly documentation and tests. The Python API (found entirely in `torch/cuda/amp/amp_scaler.py`) is lightweight . The exposed functions are intended to make the implementation and control flow of gradient scaling convenient, intuitive, and performant.
The API is probably easiest to digest by looking at the documentation and examples. `docs/source/amp.rst` is the homepage for the Automatic Mixed Precision package. `docs/source/notes/amp_examples.rst` includes several examples demonstrating common but not-immediately-obvious use cases. Examples are backed by tests in `test_cuda.py` (and thankfully the tests pass :P).
Two small utility kernels have been added in `native/cuda/AmpKernels.cu` to improve performance and avoid host-device synchronizations wherever possible.
Existing optimizers, both in the wild and in Pytorch core, do not need to change to use the scaling API.
However, the API was also designed to establish a contract between user scripts and optimizers such that writers of _new_ custom optimizers have the control points they need to implement fast, optionally sync-free updates. User scripts that obey the scaling API can drop such custom optimizers in and reap performance benefits without having to change anything aside from the optimizer constructor itself. [I know what the contract with custom optimizers should be](35829f24ef/torch/cuda/amp/amp_scaler.py (L179-L184)), but I'm waiting for review on the rest of the API before I go about documenting it (it will be given a dedicated section in `docs/source/notes/amp_examples.rst`.
Currently, the gradient scaling examples do not include the auto-casting API as discussed in https://github.com/pytorch/pytorch/issues/25081. The gradient scaling API is intended to be orthogonal/modular relative to autocasting. Without auto-casting the gradient scaling API is fully use-_able_, but not terribly use-_ful_, so it's up to you guys whether you want to wait until auto-casting is ready before merging the scaling API as well.
### Todo
- [ ] How do I get c10 registered status for my two custom kernels? They're very simple.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26512
Differential Revision: D19859905
Pulled By: mruberry
fbshipit-source-id: bb8ae6966214718dfee11345db824389e4286923
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33083
Added more recommendations, some notes and warning
Test Plan: cd docs ; make html
Differential Revision: D19829133
Pulled By: ilia-cher
fbshipit-source-id: b9fbd89f5875b3ce35cc42ba75a3b44bb132c506
Summary:
"in_features" and "out_features" are not defined. Possibly a typo. They should be "input_features" and "output_features" instead
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31682
Differential Revision: D19251685
Pulled By: zou3519
fbshipit-source-id: ac9e524e792a1853a16e8876d76b908495d8f35e
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/30330
This is now possible due to previous changes made in `gloo` and `ProcessGroupGloo`. We `abort` the listener thread that is waiting for a message, and join all other threads. The API is changed so that the previous `wait_all_workers` does not destroy the agent, and this is now done in a new `shutdown` method. All callsites are updated appropriately.
ghstack-source-id: 94673884
ghstack-source-id: 94673884
Test Plan: Unit tests pass.
Reviewed By: mrshenli
Differential Revision: D18661775
fbshipit-source-id: 5aaa7c14603e18253394224994f6cd43234301c2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30208
Adds default arg for init_method so users don't have to pass this in,
and moves it to `RpcBackendOptions` struct. Removes `init_method` arg from rpc.init_rpc. Also fixes some docs.
ghstack-source-id: 94500475
Test Plan: Unit tests pass.
Reviewed By: mrshenli
Differential Revision: D18630074
fbshipit-source-id: 04b7dd7ec96f4c4da311b71d250233f1f262135a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30020
This is now possible due to previous changes made in `gloo` and `ProcessGroupGloo`. We `abort` the listener thread that is waiting for a message, and join all other threads. The destructor calls this same `localShutdown` method, but we ensure this is not called multiple times.
ghstack-source-id: 94415336
Test Plan: Unit tests pass.
Differential Revision: D5578006
fbshipit-source-id: 6258879fb44c9fca97fdfad64468c1488c16ac02
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30240
Get rid of the following warning when build docs:
```
/Users/shenli/Project/pytorch/docs/source/notes/rref.rst:184: WARNING: Error in "code" directive:
maximum 1 argument(s) allowed, 6 supplied.
.. code::
import torch
import torch.distributed.rpc as rpc
# on worker A
rref = rpc.remote('B', torch.add, args=(torch.ones(2), 1))
# say the rref has RRefId 100 and ForkId 1
rref.to_here()
```
Test Plan: Imported from OSS
Differential Revision: D18640016
Pulled By: mrshenli
fbshipit-source-id: d527827f01183411d4b4c73e0a976bdd7fccbf49
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30066
This commit adds design reasoning and walks through four scenarios
for RRef.
Test Plan: Imported from OSS
Reviewed By: rohan-varma
Differential Revision: D18595094
Pulled By: mrshenli
fbshipit-source-id: 134102901ce515a44a2e7cd013b62143a6158120
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30050
Renames this API to wait_all_workers as discussed.
ghstack-source-id: 94273005
Test Plan: Unit tests pass
Differential Revision: D18581466
fbshipit-source-id: 4ff5d5fb2d528f17252d5b5f30c3047d2efb92bf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29927
With the docs page now up, we can update the links in the design doc
to point to the docs page.
ghstack-source-id: 94055423
Test Plan: waitforbuildbot
Differential Revision: D18541878
fbshipit-source-id: f44702d9a8296ccc0a5d58d56c3b6dc8a822b520
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29175
Updates our docs to include a design doc for distributed autograd.
Currently, this doc only covers the FAST mode algorithm. The Smart mode
algorithm section just refers to the original RFC.
There is a section for Distributed Optimizer that we can complete once we've
finalized the API for the same.
ghstack-source-id: 93701129
Test Plan: look at docs.
Differential Revision: D18318949
fbshipit-source-id: 670ea1b6bb84692f07facee26946bbc6ce8c650c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27850
Many of these are real problems in the documentation (i.e., link or
bullet point doesn't display correctly).
Test Plan: - built and viewed the documentation for each change locally.
Differential Revision: D17908123
Pulled By: zou3519
fbshipit-source-id: 65c92a352c89b90fb6b508c388b0874233a3817a
Summary:
Adds comprehensive memory instrumentation to the CUDA caching memory allocator.
# Counters
Added comprehensive instrumentation for the following stats:
- Allocation requests (`allocation`)
- Allocated memory (`allocated_bytes`)
- Reserved segments from cudaMalloc (`segment`)
- Reserved memory (`reserved_bytes`)
- Active memory blocks (`active`)
- Active memory (`active_bytes`)
- Inactive, non-releasable blocks (`inactive_split`)
- Inactive, non-releasable memory (`inactive_split_bytes`)
- Number of failed cudaMalloc calls that result in a cache flush and retry (`cuda_malloc_retries`)
- Number of OOMs (`num_ooms`)
Except for the last two, these stats are segmented between all memory, large blocks, and small blocks. Along with the current value of each stat, historical counts of allocs/frees as well as peak usage are tracked by the allocator.
# Snapshots
Added the capability to get a "memory snapshot" – that is, to generate a complete dump of the allocator block/segment state.
# Implementation: major changes
- Added `torch.cuda.memory_stats()` (and associated C++ changes) which returns all instrumented stats as a dictionary.
- Added `torch.cuda.snapshot()` (and associated C++ changes) which returns a complete dump of the allocator block/segment state as a list of segments.
- Added memory summary generator in `torch.cuda.memory_summary()` for ease of client access to the instrumentation stats. Potentially useful to dump when catching OOMs. Sample output here: https://pastebin.com/uKZjtupq
# Implementation: minor changes
- Add error-checking helper functions for Python dicts and lists in `torch/csrc/utils/`.
- Existing memory management functions in `torch.cuda` moved from `__init__.py` to `memory.py` and star-imported to the main CUDA module.
- Add various helper functions to `torch.cuda` to return individual items from `torch.cuda.memory_stats()`.
- `torch.cuda.reset_max_memory_cached()` and `torch.cuda.reset_max_memory_allocated()` are deprecated in favor of `reset_peak_stats`. It's a bit difficult to think of a case where only one of those stats should be reset, and IMO this makes the peak stats collectively more consistent.
- `torch.cuda.memory_cached()` and `torch.cuda.max_memory_cached()` are deprecated in favor of `*memory_reserved()`.
- Style (add access modifiers in the allocator class, random nit fixes, etc.)
# Testing
- Added consistency check for stats in `test_cuda.py`. This verifies that the data from `memory_stats()` is faithful to the data from `snapshot()`.
- Ran on various basic workflows (toy example, CIFAR)
# Performance
Running the following speed benchmark: https://pastebin.com/UNndQg50
- Before this PR: 45.98 microseconds per tensor creation
- After this PR: 46.65 microseconds per tensor creation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27361
Differential Revision: D17758747
Pulled By: jma127
fbshipit-source-id: 5a84e82d696c40c505646b9a1b4e0c3bba38aeb6
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23417
Test Plan:
cd docs; make html
Imported from OSS
Differential Revision: D16523781
Pulled By: ilia-cher
fbshipit-source-id: d6c09e8a85d39e6185bbdc4b312fea44fcdfff06
Summary:
Covering fleet-wide profiling, api logging, etc.
It's my first time writing rst, so suggestions are definitely welcomed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23010
Differential Revision: D16456721
Pulled By: dzhulgakov
fbshipit-source-id: 3d3018f41499d04db0dca865bb3a9652d8cdf90a
Summary:
This is a modified version of https://github.com/pytorch/pytorch/pull/14705 since commit structure for that PR is quite messy.
1. Add `IterableDataset`.
3. So we have 2 data loader mods: `Iterable` and `Map`.
1. `Iterable` if the `dataset` is an instance of `IterableDataset`
2. `Map` o.w.
3. Add better support for non-batch loading (i.e., `batch_size=None` and `batch_sampler=None`). This is useful in doing things like bulk loading.
3. Refactor `DataLoaderIter` into two classes, `_SingleProcessDataLoaderIter` and `_MultiProcessingDataLoaderIter`. Rename some methods to be more generic, e.g., `get_batch` -> `get_data`.
4. Add `torch.utils.data.get_worker_info` which returns worker information in a worker proc (e.g., worker id, dataset obj copy, etc.) and can be used in `IterableDataset.__iter__` and `worker_init_fn` to do per-worker configuration.
5. Add `ChainDataset`, which is the analog of `ConcatDataset` for `IterableDataset`.
7. Import torch.utils.data in `torch/__init__.py`
9. data loader examples and documentations
10. Use `get_worker_info` to detect whether we are in a worker process in `default_collate`
Closes https://github.com/pytorch/pytorch/issues/17909, https://github.com/pytorch/pytorch/issues/18096, https://github.com/pytorch/pytorch/issues/19946, and some of https://github.com/pytorch/pytorch/issues/13023
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19228
Reviewed By: bddppq
Differential Revision: D15058152
fbshipit-source-id: 9e081a901a071d7e4502b88054a34b450ab5ddde
Summary:
include atomicAdd commentary as this is less well known
There is some discussion in #12207
Unfortunately, I cannot seem to get the ..include working in `_tensor_docs.py` and `_torch_docs.py`. I could use a hint for that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12217
Differential Revision: D10419739
Pulled By: SsnL
fbshipit-source-id: eecd04fb7486bd9c6ee64cd34859d61a0a97ec4e
Summary:
goldsborough Modify the docs to match the changes made in #4999
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12158
Differential Revision: D10103964
Pulled By: SsnL
fbshipit-source-id: 1b8692da86aca1a52e8d2e6cea76a5ad1f71e058
Summary:
This adds a Note on making experiments reproducible.
It also adds Instructions for building the Documentation to `README.md`. Please ping if I missed any requirements.
I'm not sure what to do about the submodule changes. Please advise.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11329
Differential Revision: D9784939
Pulled By: ezyang
fbshipit-source-id: 5c5acbe343d1fffb15bdcb84c6d8d925c2ffcc5e
Summary:
Commits:
1. In extension doc, get rid of all references of `Variable` s (Closes#6947 )
+ also add minor improvements
+ also added a section with links to cpp extension :) goldsborough
+ removed mentions of `autograd.Function.requires_grad` as it's not used anywhere and hardcoded to `return_Py_True`.
2. Fix several sphinx warnings
3. Change `*` in equations in `module/conv.py` to `\times`
4. Fix docs for `Fold` and `Unfold`.
+ Added better shape check for `Fold` (it previously may give bogus result when there are not enough blocks). Added test for the checks.
5. Fix doc saying `trtrs` not available for CUDA (#9247 )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9239
Reviewed By: soumith
Differential Revision: D8762492
Pulled By: SsnL
fbshipit-source-id: 13cd91128981a94493d5efdf250c40465f84346a
This PR enables users to print extra information of their subclassed nn.Module.
Now I simply insert the user-defined string at the ending of module name, which should be discussed in this PR.
Before this PR, users should redefine the __repr__ and copy&paste the source code from Module.
* Add support for extra information on Module
* Rewrite the repr method of Module
* Fix flake8
* Change the __repr__ to get_extra_repr in Linear
* Fix extra new-line for empty line
* Add test for __repr__ method
* Fix bug of block string indent
* Add indent for multi-line repr test.
* Address review comments
* Update tutorial for creating nn.Module
* Fix flake8, add extra_repr of bilinear
* Refactor DropoutNd
* Change to extra_repr in some Modules
* Fix flake8
* Refactor padding modules
* Refactor pooling module
* Fix typo
* Change to extra_repr
* Fix bug for GroupNorm
* Fix bug for LayerNorm
* Deprecate ctx.saved_variables via python warning.
Advises replacing saved_variables with saved_tensors.
Also replaces all instances of ctx.saved_variables with ctx.saved_tensors in the
codebase.
Test by running:
```
import torch
from torch.autograd import Function
class MyFunction(Function):
@staticmethod
def forward(ctx, tensor1, tensor2):
ctx.save_for_backward(tensor1, tensor2)
return tensor1 + tensor2
@staticmethod
def backward(ctx, grad_output):
var1, var2 = ctx.saved_variables
return (grad_output, grad_output)
x = torch.randn((3, 3), requires_grad=True)
y = torch.randn((3, 3), requires_grad=True)
model = MyFunction()
model.apply(x, y).sum().backward()
```
and assert the warning shows up.
* Address comments
* Add deprecation test for saved_variables
* Add a FAQ, for now just 'out of memory' advice.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* Updates based on comments.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* minor copyedit
