Summary:
Add some much needed documentation on the Timer callgrind output format, and expand what is shown on the website.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51664
Reviewed By: tugsbayasgalan
Differential Revision: D26246675
Pulled By: robieta
fbshipit-source-id: 7a07ff35cae07bd2da111029242a5dc8de21403c
Summary:
Uses cmake's `configure_file()` macro to generate a new `torch/csrc/api/include/torch/version.h` header with `TORCH_VERSION_{MAJOR,MINOR,PATCH}` \#defines from an input file `torch/csrc/api/include/torch/version.h.in`.
For Bazel builds, this is accomplished with `header_template_rule()`.
For Buck builds, this is accomplished with `fb_native.genrule()`.
Fixes https://github.com/pytorch/pytorch/issues/44365
<img width="1229" alt="Screen Shot 2021-01-05 at 3 19 24 PM" src="https://user-images.githubusercontent.com/75754324/103809279-3fd80380-5027-11eb-9039-fd23922cebd5.png">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50073
Reviewed By: glaringlee
Differential Revision: D25855877
Pulled By: jbschlosser
fbshipit-source-id: 6bb792718c97e2c2dbaa74b7b7b831a4f6938e49
Summary:
Notes the module is in beta and that the policy for returning optionally computed tensors may change in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51620
Reviewed By: heitorschueroff
Differential Revision: D26220254
Pulled By: mruberry
fbshipit-source-id: edf78fe448d948b43240e138d6d21b780324e41e
Summary:
There has a description error in quantization.rst, fixed it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50187
Reviewed By: mrshenli
Differential Revision: D25895294
Pulled By: soumith
fbshipit-source-id: c0b2e7ba3fadfc0977ab2d4d4e9ed4f93694cedd
Summary:
Implements `np.diff` for single order differences only:
- method and function variants for `diff` and function variant for `diff_out`
- supports out variant, but not in-place since shape changes
- adds OpInfo entry, and test in `test_torch`
- automatic autograd because we are using the `Math` dispatch
_Update: we only support Tensors for prepend and append in this PR. See discussion below and comments for more details._
Currently there is a quirk in the c++ API based on how this is implemented: it is not possible to specify scalar prepend and appends without also specifying all 4 arguments.
That is because the goal is to match NumPy's diff signature of `diff(int n=1, int dim=-1, Union[Scalar, Tensor] prepend=None, Union[Scalar, Tensor] append)=None` where all arguments are optional, positional and in the correct order.
There are a couple blockers. One is c++ ambiguity. This prevents us from simply doing `diff(int n=1, int dim=-1, Scalar? prepend=None, Tensor? append=None)` etc for all combinations of {Tensor, Scalar} x {Tensor, Scalar}.
Why not have append, prepend not have default args and then write out the whole power set of {Tensor, Scalar, omitted} x {Tensor, Scalar, omitted} you might ask. Aside from having to write 18 overloads, this is actually illegal because arguments with defaults must come after arguments without defaults. This would mean having to write `diff(prepend, append, n, dim)` which is not desired. Finally writing out the entire power set of all arguments n, dim, prepend, append is out of the question because that would actually involve 2 * 2 * 3 * 3 = 36 combinations. And if we include the out variant, that would be 72 overloads!
With this in mind, the current way this is implemented is actually to still do `diff(int n=1, int dim=-1, Scalar? prepend=None, Tensor? append=None)`. But also make use of `cpp_no_default_args`. The idea is to only have one of the 4 {Tensor, Scalar} x {Tensor, Scalar} provide default arguments for the c++ api, and add `cpp_no_default_args` for the remaining 3 overloads. With this, Python api works as expected, but some calls such as `diff(prepend=1)` won't work on c++ api.
We can optionally add 18 more overloads that cover the {dim, n, no-args} x {scalar-tensor, tensor-scalar, scalar-scalar} x {out, non-out} cases for c++ api. _[edit: counting is hard - just realized this number is still wrong. We should try to count the cases we do cover instead and subtract that from the total: (2 * 2 * 3 * 3) - (3 + 2^4) = 17. 3 comes from the 3 of 4 combinations of {tensor, scalar}^2 that we declare to be `cpp_no_default_args`, and the one remaining case that has default arguments has covers 2^4 cases. So actual count is 34 additional overloads to support all possible calls]_
_[edit: thanks to https://github.com/pytorch/pytorch/issues/50767 hacky_wrapper is no longer necessary; it is removed in the latest commit]_
hacky_wrapper was also necessary here because `Tensor?` will cause dispatch to look for the `const optional<Tensor>&` schema but also generate a `const Tensor&` declaration in Functions.h. hacky_wrapper allows us to define our function as `const Tensor&` but wraps it in optional for us, so this avoids both the errors while linking and loading.
_[edit: rewrote the above to improve clarity and correct the fact that we actually need 18 more overloads (26 total), not 18 in total to complete the c++ api]_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50569
Reviewed By: H-Huang
Differential Revision: D26176105
Pulled By: soulitzer
fbshipit-source-id: cd8e77cc2de1117c876cd71c29b312887daca33f
Summary:
A tiny PR to update the links in the lefthand navbar under Libraries. The canonical link for vision and text is `https://pytorch.org/vision/stable` and `https://pytorch.org/text/stable` respectively. The link without the `/stable` works via a redirect, this is cleaner.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51103
Reviewed By: izdeby
Differential Revision: D26079760
Pulled By: heitorschueroff
fbshipit-source-id: df1fa64d7895831f4e6242445bae02c1faa5e4dc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50791
Add a dedicated pipeline parallelism doc page explaining the APIs and
the overall value of the module.
ghstack-source-id: 120257168
Test Plan:
1) View locally
2) waitforbuildbot
Reviewed By: rohan-varma
Differential Revision: D25967981
fbshipit-source-id: b607b788703173a5fa4e3526471140506171632b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50649
**Summary**
Tutorials, documentation and comments are not consistent with the file
extension they use for JIT archives. This commit modifies certain
instances of `*.pth` in `torch.jit.save` calls with `*.pt`.
**Test Plan**
Continuous integration.
**Fixes**
This commit fixes#49660.
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision: D25961628
Pulled By: SplitInfinity
fbshipit-source-id: a40c97954adc7c255569fcec1f389aa78f026d47
Summary:
This PR adds `torch.linalg.slogdet`.
Changes compared to the original torch.slogdet:
- Complex input now works as in NumPy
- Added out= variant (allocates temporary and makes a copy for now)
- Updated `slogdet_backward` to work with complex input
Ref. https://github.com/pytorch/pytorch/issues/42666
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49194
Reviewed By: VitalyFedyunin
Differential Revision: D25916959
Pulled By: mruberry
fbshipit-source-id: cf9be8c5c044870200dcce38be48cd0d10e61a48
Summary:
This PR adds `torch.linalg.pinv`.
Changes compared to the original `torch.pinverse`:
* New kwarg "hermitian": with `hermitian=True` eigendecomposition is used instead of singular value decomposition.
* `rcond` argument can now be a `Tensor` of appropriate shape to apply matrix-wise clipping of singular values.
* Added `out=` variant (allocates temporary and makes a copy for now)
Ref. https://github.com/pytorch/pytorch/issues/42666
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48399
Reviewed By: zhangguanheng66
Differential Revision: D25869572
Pulled By: mruberry
fbshipit-source-id: 0f330a91d24ba4e4375f648a448b27594e00dead
Summary:
This PR adds `torch.linalg.inv` for NumPy compatibility.
`linalg_inv_out` uses in-place operations on provided `result` tensor.
I modified `apply_inverse` to accept tensor of Int instead of std::vector, that way we can write a function similar to `linalg_inv_out` but removing the error checks and device memory synchronization.
I fixed `lda` (leading dimension parameter which is max(1, n)) in many places to handle 0x0 matrices correctly.
Zero batch dimensions are also working and tested.
Ref https://github.com/pytorch/pytorch/issues/42666
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48261
Reviewed By: gchanan
Differential Revision: D25849590
Pulled By: mruberry
fbshipit-source-id: cfee6f1daf7daccbe4612ec68f94db328f327651
Summary:
BC-breaking note:
This PR changes the behavior of the any and all functions to always return a bool tensor. Previously these functions were only defined on bool and uint8 tensors, and when called on uint8 tensors they would also return a uint8 tensor. (When called on a bool tensor they would return a bool tensor.)
PR summary:
https://github.com/pytorch/pytorch/pull/44790#issuecomment-725596687
Fixes 2 and 3
Also Fixes https://github.com/pytorch/pytorch/issues/48352
Changes
* Output dtype is always `bool` (consistent with numpy) **BC Breaking (Previously used to match the input dtype**)
* Uses vectorized version for all dtypes on CPU
* Enables test for complex
* Update doc for `torch.all` and `torch.any`
TODO
* [x] Update docs
* [x] Benchmark
* [x] Raise issue on XLA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47878
Reviewed By: albanD
Differential Revision: D25714324
Pulled By: mruberry
fbshipit-source-id: a87345f725297524242d69402dfe53060521ea5d
Summary:
This is related to https://github.com/pytorch/pytorch/issues/42666 .
I am opening this PR to have the opportunity to discuss things.
First, we need to consider the differences between `torch.svd` and `numpy.linalg.svd`:
1. `torch.svd` takes `some=True`, while `numpy.linalg.svd` takes `full_matrices=True`, which is effectively the opposite (and with the opposite default, too!)
2. `torch.svd` returns `(U, S, V)`, while `numpy.linalg.svd` returns `(U, S, VT)` (i.e., V transposed).
3. `torch.svd` always returns a 3-tuple; `numpy.linalg.svd` returns only `S` in case `compute_uv==False`
4. `numpy.linalg.svd` also takes an optional `hermitian=False` argument.
I think that the plan is to eventually deprecate `torch.svd` in favor of `torch.linalg.svd`, so this PR does the following:
1. Rename/adapt the old `svd` C++ functions into `linalg_svd`: in particular, now `linalg_svd` takes `full_matrices` and returns `VT`
2. Re-implement the old C++ interface on top of the new (by negating `full_matrices` and transposing `VT`).
3. The C++ version of `linalg_svd` *always* returns a 3-tuple (we can't do anything else). So, there is a python wrapper which manually calls `torch._C._linalg.linalg_svd` to tweak the return value in case `compute_uv==False`.
Currently, `linalg_svd_backward` is broken because it has not been adapted yet after the `V ==> VT` change, but before continuing and spending more time on it I wanted to make sure that the general approach is fine.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45562
Reviewed By: H-Huang
Differential Revision: D25803557
Pulled By: mruberry
fbshipit-source-id: 4966f314a0ba2ee391bab5cda4563e16275ce91f
Summary:
Uses the Python standard library 2to3 script to convert a number of Python 2 files to Python 3. This facilitates code maintenance such as dropping unused imports in D25500422.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49351
Test Plan: Standard sandcastle tests
Reviewed By: xush6528
Differential Revision: D25499576
fbshipit-source-id: 0c44718ac734771ce0758b1cb30676cc3d76ac10
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49902
Adds a common errors section, and details the two errors
we see often on the discuss forums, with recommended solutions.
Test Plan: build the docs on Mac OS, the new section renders correctly.
Reviewed By: supriyar
Differential Revision: D25718195
Pulled By: vkuzo
fbshipit-source-id: c5ef2b24831d18d57bbafdb82d26d8fbf3a90781
Summary:
I am opening this PR early to have a place to discuss design issues.
The biggest difference between `torch.qr` and `numpy.linalg.qr` is that the former `torch.qr` takes a boolean parameter `some=True`, while the latter takes a string parameter `mode='reduced'` which can be one of the following:
`reduced`
this is completely equivalent to `some=True`, and both are the default.
`complete`
this is completely equivalent to `some=False`.
`r`
this returns only `r` instead of a tuple `(r, q)`. We have already decided that we don't want different return types depending on the parameters, so I propose to return `(r, empty_tensor)` instead. I **think** that in this mode it will be impossible to implement the backward pass, so we should raise an appropriate error in that case.
`raw`
in this mode, it returns `(h, tau)` instead of `(q, r)`. Internally, `h` and `tau` are obtained by calling lapack's `dgeqrf` and are later used to compute the actual values of `(q, r)`. The numpy docs suggest that these might be useful to call other lapack functions, but at the moment none of them is exposed by numpy and I don't know how often it is used in the real world.
I suppose the implementing the backward pass need attention to: the most straightforward solution is to use `(h, tau)` to compute `(q, r)` and then use the normal logic for `qr_backward`, but there might be faster alternatives.
`full`, `f`
alias for `reduced`, deprecated since numpy 1.8.0
`economic`, `e`
similar to `raw but it returns only `h` instead of `(h, tau). Deprecated since numpy 1.8.0
To summarize:
* `reduce`, `complete` and `r` are straightforward to implement.
* `raw` needs a bit of extra care, but I don't know how much high priority it is: since it is used rarely, we might want to not support it right now and maybe implement it in the future?
* I think we should just leave `full` and `economic` out, and possibly add a note to the docs explaining what you need to use instead
/cc mruberry
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47764
Reviewed By: ngimel
Differential Revision: D25708870
Pulled By: mruberry
fbshipit-source-id: c25c70a23a02ec4322430d636542041e766ebe1b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49803
Per "https://fb.workplace.com/groups/e/permalink/3320810064641820/" we can no longer use the terms "whitelist" and "blacklist", and editing any file containing them results in a critical error signal. Let's embrace the change.
This diff changes "blacklist" to "blocklist" in a number of non-interface contexts (interfaces would require more extensive testing and might interfere with reading stored data, so those are deferred until later).
Test Plan: Sandcastle
Reviewed By: vkuzo
Differential Revision: D25686924
fbshipit-source-id: 117de2ca43a0ea21b6e465cf5082e605e42adbf6
