Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53143
Meta is now an honest to goodness device type, like cpu, so you can use
device='meta' to trigger allocation of meta tensors. This way better
than empty_meta since we now have working API for most factory functions
(they don't necessarily work yet, though, because need to register Meta
versions of those functions.)
Some subtleties:
- I decided to drop the concept of CPU versus CUDA meta tensors; meta
tensors are device agnostic. It's hard to say exactly what the
correct level of abstraction here is, but in this particular case
implementation considerations trump semantic considerations: it
is way easier to have just a meta device, than to have a meta device
AND a cpu device AND a cuda device. This may limit the applicability
of meta tensors for tracing models that do explicit cpu()/cuda()
conversions (unless, perhaps, we make those operations no-ops on meta
tensors).
- I noticed that the DeviceType uppercase strings are kind of weird.
Are they really supposed to be all caps? That's weird.
- I moved the Meta dispatch key to live with the rest of the "device"
dispatch keys.
- I intentionally did NOT add a Backend for Meta. For now, I'm going to
hope meta tensors never exercise any of the Backend conversion code;
even if it does, better to fix the code to just stop converting to and
from Backend.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: samestep
Differential Revision: D26763552
Pulled By: ezyang
fbshipit-source-id: 14633b6ca738e60b921db66a763155d01795480d
Summary:
Fixes https://github.com/pytorch/pytorch/issues/52213
Nans were previously inconsistently propagated due to std::min always returning first argument if one of the args in nan
when reduction functor was called on 2 `-inf` arguments, `std::min(x,y) - std::max(x,y)` resulted in `-inf - (-inf)` = nan, even though logcumsumexp is well defined for `-inf, -inf` pair.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52947
Reviewed By: H-Huang
Differential Revision: D26718456
Pulled By: ngimel
fbshipit-source-id: a44433889da352cc959786dd15b6361a68fcfed7
Summary:
This PR adds functionality to skip a test based on CUDA version.
This way, we can be more specific when skipping a test, such as when the test only fails for a particular CUDA version.
This allows us to add back the skipped tests for CUDA 11.2 for other CUDA versions, such as 10.1 and 11.1.
I tested this locally (by using 11.0 instead of 11.2), but will run all the CI to make sure it works.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52359
Reviewed By: walterddr
Differential Revision: D26487951
Pulled By: janeyx99
fbshipit-source-id: 45c71cc6105ffd9985054880009cf68ea5ef3f6a
Summary:
Fixes https://github.com/pytorch/pytorch/issues/51719, https://github.com/pytorch/pytorch/issues/28142
**Change**
- Update `torch.Tensor.unflatten` to support users pass`-1` as the inferred size for both tensors and named tensors.
- Examples of using `-1` in the `unflatten` function are added to the docs.
- Fix the rendered issue of original `unflatten` docs by removing a blank line between its example section.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51955
Reviewed By: agolynski
Differential Revision: D26467198
Pulled By: zou3519
fbshipit-source-id: 6a3ede25561223187273796427ad0cb63f125364
Summary:
Reference: https://github.com/pytorch/pytorch/issues/50006
We should probably add aliases for these operators to be consistent with NumPy names i.e. `np.degrees` and `np.radians`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51283
Reviewed By: ngimel
Differential Revision: D26171163
Pulled By: mruberry
fbshipit-source-id: 1869604ed400820d95f6ff50a0e3cba1de1ffa84
Summary:
Adding CUDA 11.2 to Windows CI.
Disabled tests:
The following ran into `CUDA error: misaligned address` for CUDA 11.2: (issue linked below)
`test_where_scalar_valid_combination_cuda_complex128` in test_torch.py
`test_sgn_complex_cuda` in test_autograd.py
The following ran into `CUDA error: too many resources requested for launch` for CUDA 11.2: (https://github.com/pytorch/pytorch/issues/52002)
test_EmbeddingBag_per_sample_weights_and_new_offsets_cuda_int64_float64
test_EmbeddingBag_per_sample_weights_and_offsets_cuda_int64_float64
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51598
Reviewed By: mrshenli
Differential Revision: D26344965
Pulled By: janeyx99
fbshipit-source-id: 3c9a4ed16d748969e96593220ec0a9f33e1ffcef
Summary:
Toward fixing https://github.com/pytorch/pytorch/issues/47624
~Step 1: add `TORCH_WARN_MAYBE` which can either warn once or every time in c++, and add a c++ function to toggle the value.
Step 2 will be to expose this to python for tests. Should I continue in this PR or should we take a different approach: add the python level exposure without changing any c++ code and then over a series of PRs change each call site to use the new macro and change the tests to make sure it is being checked?~
Step 1: add a python and c++ toggle to convert TORCH_WARN_ONCE into TORCH_WARN so the warnings can be caught in tests
Step 2: add a python-level decorator to use this toggle in tests
Step 3: (in future PRs): use the decorator to catch the warnings instead of `maybeWarnsRegex`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48560
Reviewed By: ngimel
Differential Revision: D26171175
Pulled By: mruberry
fbshipit-source-id: d83c18f131d282474a24c50f70a6eee82687158f
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51578https://github.com/pytorch/pytorch/pull/49710 introduced an edge case in which
drawing a single sample resulted in ignoring the `dtype` arg to `draw`. This
fixes this and adds a unit test to cover this behavior.
Test Plan: Unit tests
Reviewed By: danielrjiang
Differential Revision: D26204393
fbshipit-source-id: 441a44dc035002e7bbe6b662bf6d1af0e2cd88f4
Summary:
Performs the update that was suggested in https://github.com/pytorch/pytorch/issues/41489
Adjust the functionality to largely match that pf the scipy companion PR https://github.com/scipy/scipy/pull/10844/, including
- a new `draw_base2` method
- include zero as the first point in the (unscrambled) Sobol sequence
The scipy PR is also quite opinionated if the `draw` method doesn't get called with a base 2 number (for which the resulting sequence has nice properties, see the scipy PR for a comprehensive discussion of this).
Note that this update is a **breaking change** in the sense that sequences generated with the same parameters after as before will not be identical! They will have the same (better, arguably) distributional properties, but calling the engine with the same seed will result in different numbers in the sequence.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49710
Test Plan:
```
from torch.quasirandom import SobolEngine
sobol = SobolEngine(3)
sobol.draw(4)
sobol = SobolEngine(4, scramble=True)
sobol.draw(5)
sobol = SobolEngine(4, scramble=True)
sobol.draw_base2(2)
```
Reviewed By: malfet
Differential Revision: D25657233
Pulled By: Balandat
fbshipit-source-id: 9df50a14631092b176cc692b6024aa62a639ef61
Summary:
Reference: https://github.com/pytorch/pytorch/issues/33152
Changes
* Enable complex support for masked_scatter
* Enable half support for masked_scatter CPU
* Enable complex autograd support for masked_scatter CPU and masked_select (both CPU and CUDA).
**Note**:
Complex Support for masked_scatter CUDA is disabled as it depends on `masked_fill` which is yet to be ported to ATen.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51281
Reviewed By: ailzhang
Differential Revision: D26127561
Pulled By: anjali411
fbshipit-source-id: 6284926b934942213c5dfc24b5bcc8538d0231af
Summary:
Fixes https://github.com/pytorch/pytorch/issues/3307
Previously, `self.grad` was not ~cloned~ deepcopied to the returned tensor in `deepcopy`. Added a test and an implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50663
Reviewed By: heitorschueroff
Differential Revision: D26074811
Pulled By: albanD
fbshipit-source-id: 536dad36415f1d03714b4ce57453f406ad802b8c
Summary:
All these Unary operators have been an entry in OpInfo DB.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50096
Reviewed By: zhangguanheng66
Differential Revision: D25870048
Pulled By: mruberry
fbshipit-source-id: b64e06d5b9ab5a03a202cda8c22fdb7e4ae8adf8
Summary:
Based on ngimel's (Thank you!) feedback, cpu half was only accidental, so I'm removing it.
This lets us ditch the old codepath for without replacement in favour of the new, better one.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50063
Reviewed By: mruberry
Differential Revision: D25772449
Pulled By: ngimel
fbshipit-source-id: 608729c32237de4ee6d1acf7e316a6e878dac7f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49552
This PR:
1. Migrates independent autograd test for `hstack`, `dstack`, `vstack`, `movedim`, `moveaxis` from `test_autograd.py` to the new `OpInfo` based tests.
2. Migrates autograd test for `gather`, `index_select` from the method_tests to the new `OpInfo` based tests.
2. Enables complex backward for `stack, gather, index_select, index_add_` and adds tests for complex autograd for all the above mentioned ops.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25682511
Pulled By: anjali411
fbshipit-source-id: 5d8f89db4a9ec340ab99a6196987d44a23e2c6c6
Summary:
**BC-breaking Note:**
This PR updates PyTorch's digamma function to be consistent with SciPy's special.digamma function. This changes the result of the digamma function on the nonpositive integers, where the gamma function is not defined. Since the gamma function is undefined at these points, the (typical) derivative of the logarithm of the gamma function is also undefined at these points, and for negative integers this PR updates digamma to return NaN. For zero, however, it returns -inf to be consistent with SciPy.
Interestingly, SciPy made a similar change, which was noticed by at least one user: https://github.com/scipy/scipy/issues/9663#issue-396587679.
SciPy's returning of negative infinity at zero is intentional:
59347ae8b8/scipy/special/cephes/psi.c (L163)
This change is consistent with the C++ standard for the gamma function:
https://en.cppreference.com/w/cpp/numeric/math/tgamma
**PR Summary:**
Reference https://github.com/pytorch/pytorch/issues/42515
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48302
Reviewed By: ngimel
Differential Revision: D25664087
Pulled By: mruberry
fbshipit-source-id: 1168e81e218bf9fe5b849db0e07e7b22e590cf73
Summary:
**BC-Breaking Note:**
This PR updates PyTorch's angle operator to be consistent with NumPy's. Previously angle would return zero for all floating point values (including NaN). Now angle returns `pi` for negative floating point values, zero for non-negative floating point values, and propagates NaNs.
**PR Summary:**
Reference: https://github.com/pytorch/pytorch/issues/42515
TODO:
* [x] Add BC-Breaking Note (Prev all real numbers returned `0` (even `nan`)) -> Fixed to match the correct behavior of NumPy.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49163
Reviewed By: ngimel
Differential Revision: D25681758
Pulled By: mruberry
fbshipit-source-id: 54143fe6bccbae044427ff15d8daaed3596f9685
Summary:
This replaces the narrow character set APIs with the wide character set ones in `THAllocator.cpp`. This fixes the potential crashes caused by passing non-ASCII characters in `torch::from_file` on Windows.
See: https://github.com/pytorch/pytorch/issues/47422
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47905
Reviewed By: zhangguanheng66
Differential Revision: D25399146
Pulled By: ezyang
fbshipit-source-id: 0a183b65de171c48ed1718fa71e773224eaf196f
Summary:
Fixes https://github.com/pytorch/pytorch/issues/45964
Indexing operators e.g. `scatter`/`gather` use tensor restriding so the `TensorIterator` built in overlap checking needs to be disabled. This adds the missing overlap checks for these operators.
In addition, some indexing operators don't work will with `MemOverlapStatus::FULL` which is explicitly allowed by `assert_no_partial_overlap`. So, I've introduced `assert_no_overlap` that will raise an error on partial _or_ full overlap.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48651
Reviewed By: zhangguanheng66
Differential Revision: D25401047
Pulled By: ngimel
fbshipit-source-id: 53abb41ac63c4283f3f1b10a0abb037169f20b89
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48116
If you port kernels to be structured, you get Meta kernels automatically
generated for you. This is one payoff of structured kernels.
Code generation was mercifully really simple, although at risk of
"swiss cheese" syndrome: there's two new conditionals in the codegen
to tweak behavior when generating for meta keys. It's not too bad
right now but there's a risk of things getting out of hand. One
way to rationalize the logic here would be to transmit "TensorMeta-ness"
inside the TensorOptions (so tensor_from_meta can deal with it); then
the "Meta" kernel magic would literally just be generating empty
out_impls to call after all the scaffolding is done. But I didn't
do this because it seemed like it would be more annoying short term.
Also had to teach resize_ to work on meta tensors, since we use them
to implement the out kernels.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: bhosmer, ailzhang
Differential Revision: D25056640
Pulled By: ezyang
fbshipit-source-id: f8fcfa0dbb58a94d9b4196748f56e155f83b1521
Summary:
Creates multiple new test suites to have fewer tests in test_torch.py, consistent with previous test suite creation like test_unary_ufuncs.py and test_linalg.py.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47356
Reviewed By: ngimel
Differential Revision: D25202268
Pulled By: mruberry
fbshipit-source-id: 75fde3ca76545d1b32b86d432a5cb7a5ba8f5bb6
Summary:
Quiet errors from flake8. Only a couple of code changes for deprecated Python syntax from before 2.4. The rest is just adding noqa markers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48453
Reviewed By: mruberry
Differential Revision: D25181871
Pulled By: ngimel
fbshipit-source-id: f8d7298aae783b1bce2a46827b088fc390970641
Summary:
Adding Unary Ufunc Test entry for `erf` variants.
We use scipy functions for reference implementation.
We can later update the tests once these functions will update integer input to float.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47155
Reviewed By: ngimel
Differential Revision: D25176654
Pulled By: mruberry
fbshipit-source-id: cb08efed1468b27650cec4f87a9a34e999ebd810
Summary:
The approach is to simply reuse `torch.repeat` but adding one more functionality to tile, which is to prepend 1's to reps arrays if there are more dimensions to the tensors than the reps given in input. Thus for a tensor of shape (64, 3, 24, 24) and reps of (2, 2) will become (1, 1, 2, 2), which is what NumPy does.
I've encountered some instability with the test on my end, where I could get a random failure of the test (due to, sometimes, random value of `self.dim()`, and sometimes, segfaults). I'd appreciate any feedback on the test or an explanation for this instability so I can this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47974
Reviewed By: ngimel
Differential Revision: D25148963
Pulled By: mruberry
fbshipit-source-id: bf63b72c6fe3d3998a682822e669666f7cc97c58
Summary:
Adds ldexp operator for https://github.com/pytorch/pytorch/issues/38349
I'm not entirely sure the changes to `NamedRegistrations.cpp` were needed but I saw other operators in there so I added it.
Normally the ldexp operator is used along with the frexp to construct and deconstruct floating point values. This is useful for performing operations on either the mantissa and exponent portions of floating point values.
Sleef, std math.h, and cuda support both ldexp and frexp but not for all data types. I wasn't able to figure out how to get the iterators to play nicely with a vectorized kernel so I have left this with just the normal CPU kernel for now.
This is the first operator I'm adding so please review with an eye for errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45370
Reviewed By: mruberry
Differential Revision: D24333516
Pulled By: ranman
fbshipit-source-id: 2df78088f00aa9789aae1124eda399771e120d3f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48113
Fix is simple: just treat Meta as a backend covered by AutogradOther.
This semantically makes sense, since meta kernels are just like regular
CPU/CUDA kernels, they just don't do any compute.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: zhangguanheng66
Differential Revision: D25056641
Pulled By: ezyang
fbshipit-source-id: 7b68911982352b3e0ee8616b38cd9c70bd58a740
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47023
DeviceType pretty clearly only needs 1 byte. DeviceIndex only needs 1 byte given that machines don't have anywhere near 255 GPUs in them as far as I know.
ghstack-source-id: 116901430
Test Plan: Existing tests, added assertion to catch if my assumption about DeviceIndex is incorrect
Reviewed By: dzhulgakov
Differential Revision: D24605460
fbshipit-source-id: 7c9a89027fcf8eebd623b7cdbf6302162c981cd2
Summary:
Reference https://github.com/pytorch/pytorch/issues/38349
Delegates to `torch.transpose` (not sure what is the best way to alias)
TODO:
* [x] Add test
* [x] Add documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46041
Reviewed By: gchanan
Differential Revision: D25022816
Pulled By: mruberry
fbshipit-source-id: c80223d081cef84f523ef9b23fbedeb2f8c1efc5
Summary:
Now when https://github.com/pytorch/pytorch/pull/42553 is merged we can delete a bit of code from the tests and enable some of the skipped complex tests.
Unfortunately, `test_pinverse_complex_xfailed` and `test_symeig_complex_xfailed` had bugs and it wasn't caught automatically that these tests xpass. Need to be careful next time with `unittest.expectedFailure`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47910
Reviewed By: zhangguanheng66
Differential Revision: D25052130
Pulled By: mruberry
fbshipit-source-id: 29512995c024b882f9cb78b7bede77733d5762d0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48042
Moving scalar test to a separate method so the XLA team can continue to test for the other cases without failing. Requested here https://github.com/pytorch/xla/issues/2620#issuecomment-725696108
Test Plan: Imported from OSS
Reviewed By: zhangguanheng66
Differential Revision: D25055677
Pulled By: heitorschueroff
fbshipit-source-id: 5da66bac78ea197821fee0b9b8a213ff2dc19c67
Summary:
`torch.lu_solve` now works for complex inputs both on CPU and GPU.
I moved the existing tests to `test_linalg.py` and modified them to test complex dtypes, but I didn't modify/improve the body of the tests.
Ref. https://github.com/pytorch/pytorch/issues/33152
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46862
Reviewed By: nikithamalgifb
Differential Revision: D24543682
Pulled By: anjali411
fbshipit-source-id: 165bde39ef95cafebf976c5ba4b487297efe8433
Summary:
Fixed test:
- `test_is_nonzero`, this is asserting exact match, which is flaky when `TORCH_SHOW_CPP_STACKTRACES=1`, I changed this to non-exact assert
- `test_pinverse` TF32
- `test_symeig` TF32
- `test_triangular_solve_batched_many_batches_cpu_float64` precision on CPU BLAS
- `test_qr` TF32, as well as the tensor factory forgets a `dtype=dtype`
- `test_lu` TF32
- `ConvTranspose2d` TF32
- `Conv3d_1x1x1_no_bias` TF32
- `Transformer*` TF32
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46941
Reviewed By: heitorschueroff
Differential Revision: D24852725
Pulled By: mruberry
fbshipit-source-id: ccd4740cc643476178d81059d1c78da34e5082ed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42553
Ports `torch.bmm` and `torch.baddbmm` from TH to ATen, as well as adds support for complex dtypes. Also removes dead TH code for Level 2 functions.
Closes#24539
Test Plan: Imported from OSS
Reviewed By: ansley
Differential Revision: D24893511
Pulled By: anjali411
fbshipit-source-id: 0eba3f2aec99c48b3018a5264ee7789279cfab58
Summary:
`torch.triangular_solve` now works for complex inputs on GPU.
I moved the existing tests to `test_linalg.py` and modified them to test complex and float32 dtypes.
Ref. https://github.com/pytorch/pytorch/issues/33152
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46916
Reviewed By: navahgar, agolynski
Differential Revision: D24706647
Pulled By: anjali411
fbshipit-source-id: fe780eac93d2ae1b2549539bb385e5fac25213b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46398
This PR makes torch.einsum compatible with numpy.einsum except for the sublist input option as requested here https://github.com/pytorch/pytorch/issues/21412. It also fixed 2 performance issues linked below and adds a check for reducing to torch.dot instead of torch.bmm which is faster in some cases.
fixes#45854, #37628, #30194, #15671fixes#41467 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer
a = torch.randn(10000, 100, 101, device='cuda')
b = torch.randn(10000, 101, 3, device='cuda')
c = torch.randn(10000, 100, 1, device='cuda')
d = torch.randn(10000, 100, 1, 3, device='cuda')
print(Timer(
stmt='torch.einsum("bij,bjf->bif", a, b)',
globals={'a': a, 'b': b}
).blocked_autorange())
print()
print(Timer(
stmt='torch.einsum("bic,bicf->bif", c, d)',
globals={'c': c, 'd': d}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413850>
torch.einsum("bij,bjf->bif", a, b)
Median: 4.53 ms
IQR: 0.00 ms (4.53 to 4.53)
45 measurements, 1 runs per measurement, 1 thread
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413700>
torch.einsum("bic,bicf->bif", c, d)
Median: 63.86 us
IQR: 1.52 us (63.22 to 64.73)
4 measurements, 1000 runs per measurement, 1 thread
```
fixes#32591 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer
a = torch.rand(1, 1, 16, 2, 16, 2, 16, 2, 2, 2, 2, device="cuda")
b = torch.rand(729, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, device="cuda")
print(Timer(
stmt='(a * b).sum(dim = (-3, -2, -1))',
globals={'a': a, 'b': b}
).blocked_autorange())
print()
print(Timer(
stmt='torch.einsum("...ijk, ...ijk -> ...", a, b)',
globals={'a': a, 'b': b}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de28850>
(a * b).sum(dim = (-3, -2, -1))
Median: 17.86 ms
2 measurements, 10 runs per measurement, 1 thread
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de286a0>
torch.einsum("...ijk, ...ijk -> ...", a, b)
Median: 296.11 us
IQR: 1.38 us (295.42 to 296.81)
662 measurements, 1 runs per measurement, 1 thread
```
TODO
- [x] add support for ellipsis broadcasting
- [x] fix corner case issues with sumproduct_pair
- [x] update docs and add more comments
- [x] add tests for error cases
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D24860367
Pulled By: heitorschueroff
fbshipit-source-id: 31110ee598fd598a43acccf07929b67daee160f9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45092
Adding two operators
1. at::float_to_half -> Converts FP32 tensor to FP16 tensor
2. at::half_to_float -> Converts FP16 tensor to FP32 tensor.
These operators internally use the kernel provided by FBGeMM. Both C2 and PT will use the same FBGeMM kernel underneath.
Test Plan:
buck test //caffe2/test:torch -- .*test_half_tensor.*
Run benchmark locally using
```
buck run //caffe2/benchmarks/operator_benchmark/pt:tensor_to_test
```
AI Bench results are pending. I expect that not to finish as we have large queue with jobs pending for 2+ days.
Benchmark for 512x512 tensor with FbGeMM implementation
```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short
# Benchmarking PyTorch: FloatToHalfTensorConversionBenchmark
# Mode: Eager
# Name: FloatToHalfTensorConversionBenchmark_M512_N512_cpu
# Input: M: 512, N: 512, device: cpu
Forward Execution Time (us) : 1246.332
# Benchmarking PyTorch: HalfToFloatTensorConversionBenchmark
# Mode: Eager
# Name: HalfToFloatTensorConversionBenchmark_M512_N512_cpu
# Input: M: 512, N: 512, device: cpu
Forward Execution Time (us) : 1734.304
```
Benchmark for 512x512 tensor trunk with no FbGeMM integration.
```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short
# Benchmarking PyTorch: FloatToHalfTensorConversionBenchmark
# Mode: Eager
# Name: FloatToHalfTensorConversionBenchmark_M512_N512_cpu
# Input: M: 512, N: 512, device: cpu
Forward Execution Time (us) : 169045.724
# Benchmarking PyTorch: HalfToFloatTensorConversionBenchmark
# Mode: Eager
# Name: HalfToFloatTensorConversionBenchmark_M512_N512_cpu
# Input: M: 512, N: 512, device: cpu
Forward Execution Time (us) : 152382.494
```
Reviewed By: ngimel
Differential Revision: D23824869
fbshipit-source-id: ef044459b6c8c6e5ddded72080204c6a0ab4582c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47305
Fixes https://github.com/pytorch/pytorch/issues/47127.
Ideally this would just use diag and sum (as the CUDA implementation does), but that seems to have performance problems, which I'll link in the github PR.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D24729627
Pulled By: gchanan
fbshipit-source-id: 151b786b53e7b958f0929c803dbf8e95981c6884
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47474
After enabling GPU/Re, some issues were specific to those runs
Test Plan:
```
buck test -c test.external_runner=tpx mode/opt //caffe2/test:torch_cuda -- --use-remote-execution --force-tpx --run-disabled
```
Reviewed By: malfet, janeyx99
Differential Revision: D24771578
fbshipit-source-id: 1ada79dae12c8cb6f795a0d261c60f038eee2dfb
Summary:
`torch.inverse` now works for complex inputs on GPU.
Test cases with complex matrices are xfailed for now. For example, batched matmul does not work with complex yet.
Ref. https://github.com/pytorch/pytorch/issues/33152
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45034
Reviewed By: zou3519
Differential Revision: D24730264
Pulled By: anjali411
fbshipit-source-id: b9c94ec463012913c117278a884adeee96ea02aa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46758
It's in general helpful to support int32 indices and offsets, especially when such tensors are large and need to be transferred to accelerator backends. Since it may not be very useful to support the combination of int32 indices and int64 offsets, here we enforce that these two must have the same type.
Test Plan: unit tests
Reviewed By: ngimel
Differential Revision: D24470808
fbshipit-source-id: 94b8a1d0b7fc9fe3d128247aa042c04d7c227f0b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47126
Context
-------
This PR is a rebase of shihongzhi's https://github.com/pytorch/pytorch/pull/35360.
I forgot to merge it back when it was submitted so I rebased it and ran new benchmarks on it.
Benchmarks
----------
TL;DR: The op has more overhead than the TH version but for larger shapes the overhead disappears.
```
import torch
shapes = [
[1, 1],
[100, 100],
[1000, 1000],
[10000, 10000],
[100000, 100000],
]
for shape in shapes:
x = torch.ones(shape)
%timeit x.trace()
Before:
1.83 µs ± 42.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.98 µs ± 48.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
3.19 µs ± 10.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
85.2 µs ± 700 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
1.23 ms ± 4.34 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
After:
2.16 µs ± 325 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
2.08 µs ± 275 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
4.45 µs ± 19.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
81.8 µs ± 766 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
1.27 ms ± 6.75 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
Future work
-----------
Things that can be done after this PR:
- add complex tensor support
- Fix the type promotion discrepancy between CPU and CUDA
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D24683259
Pulled By: zou3519
fbshipit-source-id: f92b566ad0d58b72663ab64899d209c96edb78eb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47125
We didn't actually have any tests for torch.trace. The tests expose a
discrepancy between the behavior of torch.trace on CPU and CUDA that
I'll file an issue for.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D24683260
Pulled By: zou3519
fbshipit-source-id: 71dd3af62bc98c6b9b0ba2bf2923cb6d44daa640
Summary:
Related https://github.com/pytorch/pytorch/issues/38349
This PR implements `column_stack` as the composite ops of `torch.reshape` and `torch.hstack`, and makes `row_stack` as the alias of `torch.vstack`.
Todo
- [x] docs
- [x] alias pattern for `row_stack`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46313
Reviewed By: ngimel
Differential Revision: D24585471
Pulled By: mruberry
fbshipit-source-id: 62fc0ffd43d051dc3ecf386a3e9c0b89086c1d1c
Summary:
Fixes https://github.com/pytorch/pytorch/issues/41768
The fault was that a NULL `tau` would get passed to LAPACK function. This PR fixes that by checking whether the `tau` contains 0 elements at the beginning of the function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46700
Reviewed By: albanD
Differential Revision: D24616427
Pulled By: mruberry
fbshipit-source-id: 92e8f1489b113c0ceeca6e54dea8b810a51a63c3
Summary:
Looks like this op is never tested for the support of different dtypes?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45155
Reviewed By: zou3519
Differential Revision: D24438839
Pulled By: ngimel
fbshipit-source-id: 103ff609e11811a0705d04520c2b97c456b623ef
Summary:
Follow-up of https://github.com/pytorch/pytorch/issues/46461 with a similar goal
Makes them more readable and possibly faster. Care has to be taken because `map` applies the function immediately while `(x for x in xs)` is a generator expression which gets evaluated later. This is a benefit in some cases where it is not required to actually create the list of values in memory (e.g. when passing to `tuple` or `extend` or `join`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46462
Reviewed By: zou3519
Differential Revision: D24422343
Pulled By: ezyang
fbshipit-source-id: 252e33499c92ac0b15238f2df32681dbbda2b237
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46046
*_like functions are used in pytorch to create a new tensor with the same shape of the input tensor. But we don’t always preserve the layout permutation of the tensor. Current behavior is that, for a dense and non-overlapping tensor, its layout permutation is preserved. For eg. passing a channel last contiguous tensor t with ‘shape/stride’ (2, 4, 3, 2)/(24, 1, 8, 4) to empty_like(t) function will create a new tensor with exactly the same ‘shape/stride’ as the input tensor t. However, if the input tensor is non-dense or has overlap, we simply create a contiguous tensor based on input tensor’s shape, so the tensor layout permutation is lost.
This PR preserves the layout permutation for non-dense or overlapping tensor. The strides propagation rule that used in this PR is exactly the same as what is being used in TensorIterator. The behavior changes are listed below:
| code | old | new |
|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|------------------------------------------------------|
| #strided tensors<br>a=torch.randn(2,3,8)[:,:,::2].permute(2,0,1)<br>print(a.stride())<br>print(a.exp().stride())<br>print((a+a).stride())<br>out = torch.empty(0)<br>torch.add(a,a,out=out)<br>print(out.stride()) | (2, 24, 8) <br>(6, 3, 1) <br>(1, 12, 4) <br>(6, 3, 1) | (2, 24, 8)<br>(1, 12, 4)<br>(1, 12, 4)<br>(1, 12, 4) |
| #memory dense tensors<br>a=torch.randn(3,1,1).as_strided((3,1,1), (1,3,3))<br>print(a.stride(), (a+torch.randn(1)).stride())<br>a=torch.randn(2,3,4).permute(2,0,1)<br>print(a.stride())<br>print(a.exp().stride())<br>print((a+a).stride())<br>out = torch.empty(0)<br>torch.add(a,a,out=out)<br>print(out.stride()) | (1, 3, 3) (1, 1, 1)<br>(1, 12, 4)<br>(6, 3, 1)<br>(1, 12, 4)<br>(6, 3, 1) | (1, 3, 3) (1, 3, 3)<br>(1, 12, 4)<br>(1, 12, 4)<br>(1, 12, 4)<br>(1, 12, 4) |
This is to solve the non-dense tensor layout problem in #45505
TODO:
- [x] Fix all the BC broken test cases in pytorch
- [ ] Investigate if any fb internal tests are broken
This change will cover all kinds of non-dense tensors.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D24288970
Pulled By: glaringlee
fbshipit-source-id: 320fd4e0d1a810a12abfb1441472298c983a368d
Summary:
As per title. LU decomposition is used for computing determinants, and I need this functionality to implement the matrix square root. Next PR on my list is to enable `torch.det` on CUDA with complex input.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45898
Reviewed By: heitorschueroff
Differential Revision: D24306951
Pulled By: anjali411
fbshipit-source-id: 168f578fe65ae1b978617a66741aa27e72b2172b
Summary:
Fixes https://github.com/pytorch/pytorch/issues/46037
I now isolated the special case to be only between cuda tensor bases and cpu tensor exponents. My previous fix was not a complete fix--it fixed some stuff but broke others. The current fix is a more complete fix:
```
In [1]: import torch
In [2]: a=torch.randn(3)
In [3]: b=torch.tensor(2, device="cuda")
In [4]: torch.pow(a,b) #should not work and throws exception now!
In [5]: a=torch.tensor(3, device="cuda")
In [6]: b=torch.tensor(2)
In [7]: torch.pow(a,b) #should work, and now does
In [8]: a=torch.randn(3, device="cuda")
In [9]: torch.pow(a,b) # yeah, that one is fixed and still works
```
To add a test case to reflect the change, I had to modify the existing setup a little bit. I think it is an improvement but would appreciate any tips on how to make it better!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46320
Reviewed By: malfet
Differential Revision: D24306610
Pulled By: janeyx99
fbshipit-source-id: cc74c61373d1adc2892a7a31226f38895b83066a
Summary:
This PR adds support for complex-valued input for `torch.pinverse`.
Fixed cuda SVD implementation to return singular values with real dtype.
Fixes https://github.com/pytorch/pytorch/issues/45385.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45819
Reviewed By: heitorschueroff
Differential Revision: D24306539
Pulled By: anjali411
fbshipit-source-id: 2fe19bc630de528e0643132689e1bc5ffeaa162a
Summary:
Fixes https://github.com/pytorch/pytorch/issues/46037
I'm not sure this is the most performant solution, but this works:
torch.pow(cuda_tensor, 5) should work and worked before.
torch.pow(cuda_tensor, torch.tensor(5)), should work **and works now!**
torch.pow(cuda_tensor, torch.tensor((5,))), should NOT work and complain the tensors are on different devices and indeed continues to complain.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46185
Reviewed By: glaringlee, malfet
Differential Revision: D24257687
Pulled By: janeyx99
fbshipit-source-id: 2daf235d62ec5886d7c153da05445c2ec71dec98
Summary:
* Removes incorrect statement that "the vector norm will be applied to the last dimension".
* More clearly describe each different combination of `p`, `ord`, and input size.
* Moves norm tests from `test/test_torch.py` to `test/test_linalg.py`
* Adds test ensuring that `p='fro'` and `p=2` give same results for mutually valid inputs
Fixes https://github.com/pytorch/pytorch/issues/41388
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42696
Reviewed By: bwasti
Differential Revision: D23876862
Pulled By: mruberry
fbshipit-source-id: 36f33ccb6706d5fe13f6acf3de8ae14d7fbdff85
Summary:
`TCPStoreTest.test_numkeys_delkeys` takes 5+ min (mostly in idle wait for socket timeout)
`TestDataLoader.test_proper_exit` and `TestDataLoaderPersistentWorkers.test_proper_exit` take 2.5 min each
`TestXNNPACKConv1dTransformPass.test_conv1d_with_relu_fc` takes 2 min to finish
Add option to skip reporting test classes that run for less than a second to `print_test_stats.py` and speed up `TestTorchDeviceTypeCUDA.test_matmul_45724_cuda`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46068
Reviewed By: mruberry
Differential Revision: D24208660
Pulled By: malfet
fbshipit-source-id: 780e0d8be4f0cf69ea28de79e423291a1f3349b7
