Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31116
Changelist:
- remove BUILD_NAMEDTENSOR macro
- remove torch._C._BUILD_NAMEDTENSOR
- remove all python behavior that relies on torch._C._BUILD_NAMEDTENSOR
Future:
- In the next diff, I will remove all usages of
ATen/core/EnableNamedTensor.h since that header doesn't do anything
anymore
- After that, we'll be done with the BUILD_NAMEDTENSOR removal.
Test Plan: - run CI
Differential Revision: D18934951
Pulled By: zou3519
fbshipit-source-id: 0a0df0f1f0470d0a01c495579333a2835aac9f5d
Summary:
Adds `torch.floor_divide` following the numpy's `floor_divide` api. I only implemented the out-of-place version, I can add the inplace version if requested.
Also fixes https://github.com/pytorch/pytorch/issues/27512
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30493
Differential Revision: D18896211
Pulled By: eellison
fbshipit-source-id: ee401c96ab23a62fc114ed3bb9791b8ec150ecbd
Summary:
With the CI failure caused in 8bbafa0b32d2899ef6101172d62c6049427c977b fixed (incorrect return type of the lambdas in CUDA kernels)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30521
Differential Revision: D18770151
Pulled By: ailzhang
fbshipit-source-id: 02f0fe1d5718c34d24da6dbb5884ee8b247ce39a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30193
Featuring:
- Added a NoNamesGuard::reset() function that sets NamesMode back to
what it was before the guard. This makes it so that we don't have to
create a new context to run code in an unnamed way.
- Added a diagonal(Tensor, *, Dimname outdim, Dimname dim1, Dimname dim2, int64_t offset=0)
overload. All of the non-tensor arguments are keyword only for
readability purposes; something like `tensor.diagonal("A", "B", "C")`
would be really confusing.
Test Plan: - Added new tests
Differential Revision: D18638363
Pulled By: zou3519
fbshipit-source-id: ea37b52a19535f84a69be38e95e569e88f307381
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29322
torch.equal checks if two tensors are equal in both size and values. For
named tensors, it also checks that the names are exactly equal. There is
an argument to be made for alternative semantics (check that the names
*match*), but for an API that is called "equal" I would expect it to
check equality on names as well.
Test Plan: - new tests
Differential Revision: D18453387
Pulled By: zou3519
fbshipit-source-id: d52bde4e3fdd7f331eef097a3b31d35c89c78049
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29407
Fixes https://github.com/pytorch/pytorch/issues/27753.
The bug was that random tensors print subtly differently. This causes
the "names=" tag to appear in slightly different places; sometimes it is
on the same line as the data, sometimes it is on different lines.
For this test, we wanted to know the following:
- printing a big named tensor's repr doesn't crash
- a big named tensor's repr shows the names
This PR changes the test to check those two things.
Test Plan: - run existing tests
Differential Revision: D18428657
Pulled By: zou3519
fbshipit-source-id: 6bcf247ffba010520878a175e766a496028f87d9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29199
Previously, we called `native::mean_cpu_gpu` inside `mean(Tensor, Dimname)`;
`native::mean_cpu_gpu` is not supported by autograd. This PR replaces
`native::mean_cpu_gpu` with `at::mean(Tensor, int)` so that the dimname
overload can piggyback off of autograd support for `at::mean(Tensor,
int)`.
Also added tests (those didn't exist before) for autograd support for
named tensor reduction functions.
Test Plan: - `python test/test_namedtensor.py -v`
Differential Revision: D18334617
Pulled By: zou3519
fbshipit-source-id: 1714eb3fd93714fe860f208831e8d910f01c1c78
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29129
cdist(x1, x2) does the following:
- assume x1, x2 are 2-dimensional. Then x1, x2 are each considered to be
a list of vectors.
- The operation returns a matrix that is the pairwise distance between
each vector in x1 and each vector in x2. The matrix has first dimension
size equal to the number of vectors in x1 and second dimension size equal
to the number of vectors in x2.
- cdist also supports arbitrary left-hand broadcastable batch
dimensions. In this case, x1 and x2 are each considered to be a batch
of a list of vectors.
The above leads to the following name inference rule for cdist:
- In the 2D case, propagate x1.names[-2] and x2.names[-1] (because
the final result has size (x1.size[-2], x2.size[-2]).
- in the ND case, unify all the batch dimensions together to produce the
output batch dimensions and then apply the rule for the 2D case.
Furthermore, I moved all of the name checking in the implementation to
occur before name inference because name inference assumes that the
shapes are valid.
Test Plan: - new test: `pytest test/test_namedtensor.py -v -k "cdist"`
Differential Revision: D18311867
Pulled By: zou3519
fbshipit-source-id: 713d7cdda93c8fe92e7f1bd7f7c5c6e20a8138e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28981
This PR adds support for calling those functions on named tensors. The
implementation is not the nicest: in the future we have plans to merge
names into TensorOptions, at which point we don't need the extra
branches that check if the tensor has names. Right now, however, these
functions are very useful to have (in particular, ones_like is used by
autograd to generate gradients).
Test Plan: - Added tests for each of these
Differential Revision: D18270937
Pulled By: zou3519
fbshipit-source-id: 720739ff0474449a960b81728345a4250becbfc3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28904
Motivation
============
Before this PR, a core problem with writing name inference rules was
that each rule needed to handle misalignment by themselves. A misaligned
name occurs when we are matching None with a non-None name, but the
non-None name already exists in the first tensor.
For example, `A` is misaligned in `Tensor[A, None] + Tensor[None, A]`.
Each op handled this in a custom way
- align_from_right (used by broadcasting) handles misalignment
- compute_matmul_outnames checks for misalignment across batch and
feature dimensions.
We can actually codify "misalignment" into something more rigorous by
folding it into the definition of `match` and eliminate special handling
of "misalignment". That is what this PR attempts to do.
Approach
============
Definition: Two names in two tensors *match* if they are equal, or if at
least one of them is a wildcard that can be *refined* to the other name.
With this new definition, to check if two names match, we need to know
about the names list that each name came from to determine if a wildcard
can successfully be *refined* to the other name.
For example, consider the following:
```
tensor: Tensor[A, None]
other: Tensor[None, A]`
```
when unifying `tensor.names[-1]` with `other.names[-1]`, we see that
`tensor.names[-1]` is None and `other.names[-1]` is A. Then we check to
see if `tensor.names[-1]` can be refined to `A`; it can't be refined if
there is already an `A` in `tensor.names`.
Enter `TensorNames`.
A TensorName represents a Dimname associated with some DimnameList
(that came from a Tensor).
`TensorNames` is a list of such TensorName objects with some helper
functions attached.
One can perform the following operations:
- unify two `TensorName` objects
- unify two `TensorNames` objects with right alignment.
Plan
============
This PR changes `compute_matmul_outnames` to use `TensorNames` to
demonstrate how they make writing name inference rules easier. In the
future I'll convert other name inference rules to use `TensorNames` as
well.
Test Plan
- run all tests
Test Plan: Imported from OSS
Differential Revision: D18270666
Pulled By: zou3519
fbshipit-source-id: 3ec96cc957747eb4cfe4ea17fd02ef3d8828a20c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28975
TensorIterator supports propagating names so we just needed to enable
them with support_named_tensor: True
Test Plan:
- really basic tests to test that each variant (outplace, inplace, out=)
supports named tensors.
Differential Revision: D18252421
Pulled By: zou3519
fbshipit-source-id: ea7fb59dcf8c708b6e45d03b9c2ba27fa6b6ce98
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27760
There's nothing special about the named tensor tests that requires that
they be run in their own CI job. In this PR we delete the
TEST_NAMEDTENSOR flag that hides named tensor tests from regular jobs.
In the future, we'll delete the named tensor CI job so that we do not
duplicate signals.
Test Plan: - wait for CI
Differential Revision: D17882262
Pulled By: zou3519
fbshipit-source-id: f90c71cb939e53b8ea23f7e2ab95a5c41b8be0e3
Summary:
One fewer legacy decorator cluttering the test suite.
Functions relying on this decorator were updated or, in the case of test_sparse, the test suite was put back on double by default.
Note: this PR is blocked on https://github.com/pytorch/pytorch/issues/27599.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27628
Differential Revision: D17896254
Pulled By: mruberry
fbshipit-source-id: 13d460301f50ef4af7a660372432108164c0de1f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27308
Currently, `tensor.align_to(*names)` has the restriction that the
`tensor` must be fully named. This doesn't need to be the case, when
using Ellipsis, we "expand the ellipsis to all unmentioned dimensions,
in the order which they appear in the original tensor".
For example, consider `tensor: Tensor[None, None, C]`.
`tensor.align_to(C, None, None)` is ambiguous because the user might
have wanted to switch the order of the None dimensions and there is no
way to specify that using this API. However, `tensor.align_to('C', ...)`
isn't ambiguous: we can select the two unnamed dimensions in the order
in which they appear.
To actually implement this, we write a brand-new `align_to(names,
ellipsis_idx)` function in c++ that is separate from the regular
`align_to(names)` implementation. Ideally we would support "..." as a
special name in c++ and combine the two implementations; we'll need to
support "..." in c++ in the future but that requires a bit of extra work.
In this PR, Python processees the ellipsis and then calls the correct
overload.
Test Plan: - run tests
Differential Revision: D17745179
Pulled By: zou3519
fbshipit-source-id: 9fed06d224215cfb7efecd8c002604baab3c45e6
Summary:
This PR stop common_utils.py from setting the default tensor type when it's imported. See issue https://github.com/pytorch/pytorch/issues/27355. This is a frequent source of confusion for test writers.
Many tests relied on this setting (whether they knew it or not), and this PR also updates the test suite to pass without common_utils.py setting the default tensor type. Some larger test files now set the default floating dtype themselves, however. These test files are:
- test_autograd.py
- test_distributions.py
- test_jit.py
- test_nn.py
This is still a significant improvement from today, however. First, these files set the default floating dtype much more clearly than importing it from common_utils. Second, the rest of the test suite no longer sets this globally. Third, this PR is a springboard to updating those tests, too. In particular, as tests are made generic they can be moved aways from relying on this global setting.
Notable technical changes in this PR are:
- Significant updates to test_torch.py to make it pass without setting the default floating dtype globally.
- The default_floating_dtype decorator is now defined in common_utils, a couple versions of this operator were defined in test files previously.
- test_torch-specific parts of common_utils were refactored into test_torch.
- tensor creation methods in common_utils were updated to accept an optional dtype and device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27444
Differential Revision: D17795235
Pulled By: mruberry
fbshipit-source-id: 7f77271c0c836e69f183ad9057a2c4b29f09d2e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26815
This PR adds named tensor support for:
- any, all, `bitwise_not(_)`, cumprod, cumsum, `logical_not`
In addition, it adds smoke tests for a variety of tensor attributes and
fns:
- is_shared, is_signed
- retain_grad, register_hook
Test Plan: - [namedtensor ci]
Differential Revision: D17575905
Pulled By: zou3519
fbshipit-source-id: 37bfa327e68112c5bf0f6bf1f467a527f50fa1c4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26563
This adds name inference rules for pre-existing logsumexp, mode,
kthvalue, and median ops. Also adds overloads so that they can take
`Dimname` dimensions.
There are a lot of min/max overloads. This PR adds name inference to
the following overloads for (both) min and max:
- min(Tensor, int dim)
- min(Tensor, Dimname dim)
- min(Tensor) (full reduction)
Test Plan: - new tests and [namedtensor ci]
Differential Revision: D17557050
Pulled By: zou3519
fbshipit-source-id: a099a0ef04ad90d021a38a0668fc44902e1c7171
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26543
Also adds a test for logical_xor (it already had named tensor support
but there was no test)
Test Plan: - [namedtensor ci]
Differential Revision: D17501403
Pulled By: zou3519
fbshipit-source-id: 49be15580be9fb520e25a8020164e5a599d22d40
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26558
Previously, name inference gets called after dimensions are wrapped.
This PR makes it so that name inference always wraps dimensions so that
it can be called anywhere. Ideally we would only wrap dimensions once,
but many of our operators wrap dimensions in weird places.
Wrapping dimensions in name inference is pretty inexpensive and only
happens for named tensors (name inference does not run on unnamed
tensors.)
Test Plan: - [namedtensor ci]
Differential Revision: D17557049
Pulled By: zou3519
fbshipit-source-id: 68c5636489e233dbf2588ab6ad4e379a6fe4c8ba
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26636
This PR defines a lot of dimname overloads so that when named tensor
support is added for those operators, we will not have to modify the
autogenerated TensorMethods.h, thereby avoiding potential merge
conflicts in the future.
Overloads were added for the following:
- all
- any
- argmax
- argmin
- cumsum
- cumprod
- index_copy
- kthvalue
- mode
- permute
- squeeze
- index_add
- index_fill
- scatter
- scatter_add
- index_select
- gather
- sort
- argsort
Test Plan: - [namedtensor ci]
Differential Revision: D17522984
Pulled By: zou3519
fbshipit-source-id: eca6dea819ba4e4e43b71b700d5cf09176f00061
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26648
Previously:
- `Tensor.align_to(*names)` only works on fully named tensors. In addition, the
desired ordering `names` must not have any None-names.
- `Tensor.align_to(*names)` accepted `...`, but expanded it based on
position. i.e., in `tensor.align_to('N', ..., 'C', 'H')`, `...` expand
to `*tensor.names[1:-2]`. This is wildly incorrect: see the following
concrete example.
```
tensor = tensor.refine_names('N', 'C', 'H, 'W')
tensor.align_to('W', ...) # ... expands to 'C', 'H, 'W'
```
This PR changes it so that `...` in `tensor.align_to` grabs all
unmentioned dimensions from `tensor`, in the order that they appear.
`align_to` is the only function that takes ellipsis that requires this
change. This is because all other functions (`refine_to`) require their
list of names to work in a positional manner, but `align_to` lets the
user reorder dimensions.
This does not add very much overhead to `align_to`, as shown in the
following benchmark. However, in the future, we should resolve to make
these operations faster; align_to should be as fast as view but isn't
most likely due to Python overhead.
```
[ins] In [2]: import torch
...: named = torch.randn(3, 3, 3, 3, names=('N', 'C', 'H', 'W'))
...: unnamed = torch.randn(3, 3, 3, 3)
...: %timeit unnamed[:]
...: %timeit unnamed.view(-1)
...: %timeit named.align_to(...)
...: %timeit named.align_to('N', 'C', 'H', 'W')
31 µs ± 126 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
43.8 µs ± 146 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
69.6 µs ± 142 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
66.1 µs ± 1.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
Test Plan:
- new tests [namedtensor ci]
allows the user to transpose and permute dimensions.
Differential Revision: D17528207
Pulled By: zou3519
fbshipit-source-id: 4efc70329f84058c245202d0b267d0bc5ce42069
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26548
This makes the naming more consistent with PyTorch's API. The original
concern was that `tensor.rename` might make the operation seem like it
is in-place. However, we have many "verb" APIs: `tensor.add(other)`, for
example, doesn't add other to tensor in-place, but `tensor.add_(other)`
does.
`tensor.rename_` does exactly the same place as `tensor.rename`, but
in-place.
Test Plan: - [namedtensor ci]
Differential Revision: D17502021
Pulled By: zou3519
fbshipit-source-id: 6a5b93136a820075013cd1e30fb8fc6b9d77d7d9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25582
There are a lot of min/max overloads. This PR adds name inference to
the following overloads for (both) min and max:
- min(Tensor, int dim)
- min(Tensor, Dimname dim)
- min(Tensor) (full reduction)
Test Plan: - new tests [namedtensor ci]
Differential Revision: D17521607
Pulled By: zou3519
fbshipit-source-id: 303e3cef22916dbc9da6a092d4f23e39e74c39e4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26541
`torch.pow` already supports named tensors; every one of its constituent
codepaths propagates names:
- TensorIterator propagates names
- resize_as_ and fill_ propagate names (exponent == 0 or base == 1)
- resize_as_ and copy_ propagate names (exponent == 1)
This PR adds `supports_named_tensor = True` to the pow overloads,
enabling `pow` to take named tensors.
Test Plan: - [namedtensor ci]
Differential Revision: D17501402
Pulled By: zou3519
fbshipit-source-id: 07ee91d685e55dd58bbbb3a3fc9e185de8bb7515
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26493
resize_ and resize_as_ are low level functions that are not meant to be
used as a part of the regular PyTorch user's routine. However, they are
used to implement a lot of our operations: `out=` functionality is
implemented by resizing an output to be the correct size.
To keep in line with already implemented `out=` functionality, we do the
following:
- resize_as_(self, other) propagates names according to `out=` functionality.
This means that if self doesn't have names, then we propagate
other.names. If self does have names, they must be equal to other.names.
In addition, resize_ cannot resize a named tensor to anything but the same size.
Test Plan: - [namedtensor ci]
Differential Revision: D17501404
Pulled By: zou3519
fbshipit-source-id: e396e7fba55e1419355933925226d02dccb03012
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26479
This PR doesn't delete the code for them yet because it takes some effort to
determine what to delete. I will send a followup PR fully deleting
tagged names, but this PR disables their creation.
Test Plan: - [namedtensor ci]
Differential Revision: D17484758
Pulled By: zou3519
fbshipit-source-id: 451409e36eac98ffee1b98884d0f675bb5d46c9d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26352
"named_guard: P" is the same as "supports_named_tensor: !P".
Also changed the error message to be more understandable to users.
Test Plan:
- `TEST_NAMEDTENSOR=1 pytest test/test_namedtensor.py -v`
- [namedtensor ci]
Differential Revision: D17426234
Pulled By: zou3519
fbshipit-source-id: 4cab780e6e29e184e79cdd3690f41df9ebb2ecb5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26350
Python 3 lets us use `...` to perform indexing. Semantically, `...`
means "the rest of the unspecified dimensions". For example, while
indexing, one can do (for 5D `tensor`) `tensor[0, 0, ..., 0]` and
the `...` is expanded into `tensor[0, 0, :, :, 0]`.
Previously, we were using '*' to represent a similar behavior in names.
For example, `tensor.refine_names` supports things like the following:
```
x = torch.randn(2, 3, 4, 5, 6)
x_out = x.refine_names('*', 'H', 'W') # refine only the last two
dimensions
```
This PR changes it so that named tensor API functions recognize `'...'`
(in Python 2 and Python 3) and `...` (in Python 3 exclusively) instead
of `'*'`.
Test Plan: - [namedtensor ci]
Differential Revision: D17424666
Pulled By: zou3519
fbshipit-source-id: 003182879fd38ced3fea051217572a457cdaf7cf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25658
This unflattens `dim` according to the shape specified in `namedshape`.
`namedshape` may be either an OrderedDict or an iterable of (name, size)
tuples.
Future:
- It is possible to make it take a dict in Python >= 3.6 because those are
ordered by default, but I'll leave that task for the future.
Test Plan: - new tests [namedtensor ci]
Differential Revision: D17192655
Pulled By: zou3519
fbshipit-source-id: fd9bd2f462c23a4df1c23d66f2aa95076ff1b160
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26106
Previously, in the named tensors build, an operator is marked as
non-traceable if ANY of its overloads are named tensor overloads. This
breaks the tracer for things like torch.full (has a names= overload for
named tensor) and tensor.sum (has a Dimname overload for named tensor).
This PR fixes the problem by putting the "no tracer support" logic into
the location where the tracer attempts to construct a graph by adding a
Dimname/DimnameList argument to a node.
Test Plan:
- new test in test_jit.py to check if torch.full is traceable
- new test in test_namedtensor.py to check what happens when someone
tries to trace a function that uses named tensor APIs.
- [namedtensor ci]
Differential Revision: D17353452
Pulled By: zou3519
fbshipit-source-id: b0b843c8357ffe54baee6e8df86db914f0b1ece4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25843
`tensor.align_to(*names)` permutes the dimensions of `tensor` and adds
additional 1-sized dimensions such that the output tensor has dimensions
in the same order as `names`. All dimensions of `tensor` must be
present in `names`, in addition, this function requires that all dims of
`tensor` be named.
`tensor.align_as(other)` is equivalent to
`tensor.align_to(*other.names)`.
I'm planning on changing `torch.align_tensors(*tensors)` to align closer
to these semantics because there didn't seem to be a clear use case for the old
semantics that preserve unnamed dimensions. That will come in a future
change.
Test Plan: - new tests [namedtensor ci]
Differential Revision: D17255549
Pulled By: zou3519
fbshipit-source-id: 1e437ad81e9359b4d5bd0e7e64c3a1be441fc3e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25842
`tensor.refine_names(*names)` takes `tensor` and attempts to name its
dimensions `names` out-of-place. If a dimension `i` already had a name,
then it cannot be changed (so tensor.names[i] must equal names[i]);
if the original dimension did not have a name, then the new name
(names[i]) can be anything.
`tensor.refine_names(*names)` also accepts a glob '*' that greedily selects
names from `tensor`. Here are some examples:
- `Tensor[None].refine_names('N') -> Tensor[N]`
- `Tensor[N].refine_names('N') -> Tensor[N]`
- `Tensor[N].refine_names('D') -> Error!`
- `Tensor[N].refine_names(None) -> Error!`
- `Tensor[None, None].refine_names('*', D) -> Tensor[None, D]`
Test Plan: - new tests [namedtensor ci]
Differential Revision: D17255548
Pulled By: zou3519
fbshipit-source-id: fdbdb3a12f24fbe37ce1e53ed09dc8a42589d928
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26050
Throws a warning once when someone attempts to attach names to a tensor.
This is guaranteed to happen at the callsite `set_named_tensor_meta`.
Test Plan: - run tests [namedtensor ci]
Differential Revision: D17331634
Pulled By: zou3519
fbshipit-source-id: 44f5e5c95acd9c7ba543c1210a3b1314aab348f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25948
Previously, test/test_namedtensor.py is skipped if pytorch was not
compiled with BUILD_NAMEDTENSOR. Now, we skip test/test_namedtensor.py
if pytorch was not compiled with BUILD_NAMEDTENSOR or if
TEST_NAMEDTENSOR is not set.
This is done in preparation for turning on BUILD_NAMEDTENSOR=1 permanently;
at that point we will use TEST_NAMEDTENSOR to differentiate between the
named tensor ci and the regular ci.
Test Plan:
- [namedtensor ci] (and check that the named tensor tests are actually
running).
Differential Revision: D17300132
Pulled By: zou3519
fbshipit-source-id: 928f71f4d50445680b6ae1aa54b8857bc92e4d08
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25424
Test Plan
- new tests [namedtensor ci]
Test Plan: Imported from OSS
Differential Revision: D17120399
Pulled By: zou3519
fbshipit-source-id: 93d7944f2ec4c5a7256f505323b879af706131df
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25672
There are three overloads:
1) flatten(tensor, int start_dim, int end_dim, Dimname out_dim)
2) flatten(tensor, Dimname start_dim, Dimname end_dim, Dimname out_dim)
3) flatten(tensor, DimnameList dims, Dimname out_dim)
`flatten` joins all the dimensions between start_dim and end_dim into
one dimension. The name of the output dimension is specified by
`out_dim`.
In the case where flatten takes a list of `dims` to flatten, all the
dimensions in `dims` must be in consecutive order.
Test Plan: - new tests [namedtensor ci]
Differential Revision: D17192656
Pulled By: zou3519
fbshipit-source-id: 55d2b23358bd77cbef299f66701a8da8cd194f4f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25711
This function renames the dimensions of a tensor out-of-place. Because
of that, I think `tensor.renamed(...)` is a clearer name: `view_names`
has the connotation that we can use names to `view` our tensors with a
"different shape", but what this function really does is let us rename a
tensor no matter the previous names.
`tensor.names_`, the in-place version of this, is unchanged for now.
However, we might delete this or not advertise it if it has no use case
and also because its naming is a little inconsistent with `tensor.renamed`.
Test Plan: - [namedtensor ci]
Differential Revision: D17206515
Pulled By: zou3519
fbshipit-source-id: 67053951fcc8130c84566b5ebbdce35ef619c90d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25673
We recently moved new_empty into ATen. new_empty doesn't support named
tensors (in fact, it was hackily supporting named tensors before). This
fixes the named tensor test by changing all uses of `new_empty` to
`empty`.
Named tensor support for `new_empty` will come eventually, but it might
be a little tricky.
Test Plan: - [namedtensor ci]
Differential Revision: D17206043
Pulled By: zou3519
fbshipit-source-id: 1697bd1d63e7cb344f3d459a29af0fcb9696ea49
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25604
In this initial version:
- autograd ignores all names.
- tensor.grad is unnamed, unless the user manually assigns to it.
- if a grad tensor has any names, perhaps the user was hoping for some
alignment-checking behavior that named tensor offers for other ops. We
raise a warning in this case.
Future: do some more extensive checking to see if this actually works in
all cases.
Test Plan:
- [namedtensor ci]
- Check a warning is raised if a grad tensor has names.
- Check tensor.grad field is unnamed.
- Check that we can perform backward on an op that doesn't explictly
support names in backward. `sigmoid` is one such op.
Differential Revision: D17171788
Pulled By: zou3519
fbshipit-source-id: 64837fde94d8269610b6d3539ac025516dbe1df4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25569
Test Plan
- new tests [namedtensor ci]
Test Plan: Imported from OSS
Differential Revision: D17159121
Pulled By: zou3519
fbshipit-source-id: c68bdb543155488aa3634f908bd576e5c30c8d77
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25564
There are a number of ops that get called while printing tensors
depending on how large the tensors are. This PR makes it so that before
we attempt to format tensor data for printing, we drop the names of the
tensor (if there are any). This is easier than supporting named tensors
for all of those ops (which should happen eventually).
Test Plan: - new test [namedtensor ci]
Differential Revision: D17158872
Pulled By: zou3519
fbshipit-source-id: 282023837645b8cb16a4d93896a843dd598fc738
