Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19607
Explicit is better than implicit - it's pretty hard to debug where particular file is if it's not greppable.
As a follow up step - we should look whether we can just include build_variables.py in CMake directly to share setups of two build systems
Reviewed By: ezyang
Differential Revision: D15023348
fbshipit-source-id: 600ef2d1871bc28530c6a02681b284f7499904df
Summary:
We would previously have statements like
```
set_history(flatten_tensor_args( result ), grad_fn);
```
Internally, {set,rebase}_history would check grad_fn and short circuit if it is nullptr. However, this means that we are executing the expression `flatten_tensor_args( result )` and immediately throwing away the results. This was causing unnecessary allocations + overhead.
My JIT overhead benchmark script (with custom benchmark method):
```
import torch, time
torch.jit.script
def add(x, y):
return x + y
a = torch.rand([])
b = torch.rand([])
niter = 1000000
with torch.no_grad():
s = time.time()
add.__getattr__('forward').benchmark(niter, a, b)
e = time.time() - s
print('overhead per call (us)', e / niter * 1e6)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19623
Differential Revision: D15053399
Pulled By: jamesr66a
fbshipit-source-id: 8777e1a2b5c5a5bbd3a035b7247c8154c5fc4aa6
Summary:
in functional interfaces we do boolean dispatch, but all to max_pool\*d_with_indices. This change it to emit max_pool\*d op instead when it's not necessary to expose with_indices ops to different backends (for jit).
It also bind max_pool\*d to the torch namespace, which is the same behavior with avg_pool\*d
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19449
Differential Revision: D15016839
Pulled By: wanchaol
fbshipit-source-id: f77cd5f0bcd6d8534c1296d89b061023a8288a2c
Summary:
Changelog:
- Rename `potri` to `cholesky_inverse` to remain consistent with names of `cholesky` methods (`cholesky`, `cholesky_solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `cholesky_inverse` under the name `potri` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19498
Differential Revision: D15029901
Pulled By: ezyang
fbshipit-source-id: 2074286dc93d8744cdc9a45d54644fe57df3a57a
Summary:
This was actually getting pretty poor throughput with respect to memory bandwidth. I used this test to measure the memory bandwidth specifically for the AXPY call: https://gist.github.com/jamesr66a/b27ff9ecbe036eed5ec310c0a3cc53c5
And I got ~8 GB/s before this change, but ~14 GB/s after this change.
This seems to speed up the operator overall by around 1.3x (benchmark: https://gist.github.com/jamesr66a/c533817c334d0be432720ef5e54a4166):
== Before ==
time_per_iter 0.0001298875093460083
GB/s 3.082544287868467
== After ==
time_per_iter 0.00010104801654815674
GB/s 3.9623142905451076
The large difference between the local BW increase and the full-op BW increase likely indicates significant time is being spent elsewhere in the op, so I will investigate that.
EDIT: I updated this PR to include a call into caffe2/perfkernels. This is the progression:
before
time_per_iter 8.983819484710693e-05
GB/s 4.456723564864611
After no axpy
time_per_iter 7.19951868057251e-05
GB/s 5.56126065872172
AFter perfkernels
time_per_iter 5.6699180603027346e-05
GB/s 7.061548257694262
After perfkernels no grad
time_per_iter 4.388842582702637e-05
GB/s 9.122769670026413
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19329
Reviewed By: dzhulgakov
Differential Revision: D14969630
Pulled By: jamesr66a
fbshipit-source-id: 42d1015772c87bedd119e33c0aa2c8105160a738
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19282
This is largely a hack because we need to use the function schema parser from ATen/core
but aren't clear yet on how the final software architecture should look like.
- Add function schema parser files from jit to ATen/core build target.
- Also move ATen/core build target one directory up to allow this.
We only change the build targets and don't move the files yet because this is likely
not the final build set up and we want to avoid repeated interruptions
for other developers. cc zdevito
Reviewed By: dzhulgakov
Differential Revision: D14931922
fbshipit-source-id: 26462e2e7aec9e0964706138edd3d87a83b964e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19281
String<->Number conversions aren't available in the STL used in our Android environment.
This diff adds workarounds for that so that the function schema parser can be compiled for android
Reviewed By: dzhulgakov
Differential Revision: D14931649
fbshipit-source-id: d5d386f2c474d3742ed89e52dff751513142efad
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19280
We want to use the function schema parser from ATen/core, but with as little dependencies as possible.
This diff moves the function schema parser into its own file and removes some of its dependencies.
Reviewed By: dzhulgakov
Differential Revision: D14931651
fbshipit-source-id: c2d787202795ff034da8cba255b9f007e69b4aea
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19082
When you have just one line of deletions, just as with additions, there is no count printed.
Without this fix, we ignore all globs with single-line deletions when selecting which lines were changed.
When all the changes in the file were single-line, this meant no line-filtering at all!
Differential Revision: D14860426
fbshipit-source-id: c60e9d84f9520871fc0c08fa8c772c227d06fa27
Summary:
Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary.
Supported functions:
```python
torch.rand_like(t, pin_memory=True)
torch.randn_like(t, pin_memory=True)
torch.empty_like(t, pin_memory=True)
torch.full_like(t, 4, pin_memory=True)
torch.zeros_like(t, pin_memory=True)
torch.ones_like(t, pin_memory=True)
torch.tensor([10,11], pin_memory=True)
torch.randn(3, 5, pin_memory=True)
torch.rand(3, pin_memory=True)
torch.zeros(3, pin_memory=True)
torch.randperm(3, pin_memory=True)
torch.empty(6, pin_memory=True)
torch.ones(6, pin_memory=True)
torch.eye(6, pin_memory=True)
torch.arange(3, 5, pin_memory=True)
```
Part of the bigger: `Remove Storage` plan.
Now compatible with both torch scripts:
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"), pin_memory=False)`
and
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"))`
Same checked for all similar functions `rand_like`, `empty_like` and others
It is fixed version of #18455
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18952
Differential Revision: D14801792
Pulled By: VitalyFedyunin
fbshipit-source-id: 8dbc61078ff7a637d0ecdb95d4e98f704d5450ba
Summary:
Add input information into generated RecordFunction calls in
VariableType wrappers, JIT operators and a few more locations
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18717
Differential Revision: D14729156
Pulled By: ilia-cher
fbshipit-source-id: 811ac4cbfd85af5c389ef030a7e82ef454afadec
Summary:
The derivative of the Cholesky decomposition was previously a triangular matrix.
Changelog:
- Modify the derivative of Cholesky from a triangular matrix to symmetric matrix
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19116
Differential Revision: D14935470
Pulled By: ezyang
fbshipit-source-id: 1c1c76b478c6b99e4e16624682842cb632e8e8b9
Summary:
This PR propagates where we use first-class modules objects into the compiler. This creates a transitionary state where:
* compiler.cpp creates Graphs where `self` is a Module class and attributes/parameters/buffers/submodules are looked up with `prim::GetAttr`
* GraphExecutor still runs "lowered graphs" where the self object has been removed by a compiler pass `lower_first_class_method`.
* Tracing still creates "lowered graphs", and a pass "lift_lowered_method" creates a first-class method graph for things.
* This PR separates out Method and Function. A script::Function is a pure Graph with no `self` bound. Similar to Python, a script::Method is just a bound `self` and its underlying `script::Function`.
* This PR also separates CompilationUnit from Module. A CompilationUnit is just a list of named script::Functions. Class's have a CompilationUnit holding the class methods, and Modules also have a CompilationUnit holding their Methods. This avoids the weird circular case Module --has a-> Class -> has a -> Module ...
Details:
* In this transitionary state, we maintain two copies of a Graph, first-class module and lowered. Th first-class one has a self argument that is the module's class type. The lowered one is the lowered graph that uses the initial_ivalues inputs.
* When defining lowered methods using `_defined_lowered` we immediately create the first-class equivalent. The reverse is done lazily, creating lowered_methods on demand from the class.
* The two way conversions will be deleted in a future PR when the executor itself runs first-class objects. However this requires more changes to (1) the traces, (2) the python bindings, and (3) the onnx export pass and would make this PR way to large.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19167
Differential Revision: D14891966
Pulled By: zdevito
fbshipit-source-id: 0b5f03118aa65448a15c7a7818e64089ec93d7ea
Summary:
Changelog:
- Rename `btrisolve` to `lu_solve` to remain consistent with names of solve methods (`cholesky_solve`, `triangular_solve`, `solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lu_solve` under the name `btrisolve` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18726
Differential Revision: D14726237
Pulled By: zou3519
fbshipit-source-id: bf25f6c79062183a4153015e0ec7ebab2c8b986b
Summary:
Partial fix of: https://github.com/pytorch/pytorch/issues/394
- `gels` and `triangular_solve` now returns namedtuple
- refactor test for namedtuple API for better coverage and maintainability
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17195
Differential Revision: D14851875
Pulled By: ezyang
fbshipit-source-id: 9b2cba95564269d2c3a15324ba48751d68ed623c
