f02e3947f6
Expand type checking to mypy strict files ( #165697 )
...
Expands Pyrefly type checking to check the files outlined in the mypy-strict.ini configuration file:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165697
Approved by: https://github.com/ezyang
2025-10-18 04:34:45 +00:00
deb7ebe0a3
Revert "[Reland] Use std::string_view in torchgen ( #158625 )"
...
This reverts commit 972e409829343cc2062aeee0994a9c1c735d216a.
Reverted https://github.com/pytorch/pytorch/pull/158625 on behalf of https://github.com/huydhn due to Sorry for reverting your change but it seems to break a couple of ExecuTorch tests for Vulkan backend ([comment](https://github.com/pytorch/pytorch/pull/158625#issuecomment-3287754275 ))
2025-09-13 07:52:50 +00:00
972e409829
[Reland] Use std::string_view in torchgen ( #158625 )
...
Reland of #157050 , which is incidentally closed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158625
Approved by: https://github.com/albanD
2025-09-12 08:31:54 +00:00
d5e6f42094
Revert "Use std::string_view in torchgen ( #157050 )"
...
This reverts commit 064288cbab94c9931ca2296a2b9723e864f9050a.
Reverted https://github.com/pytorch/pytorch/pull/157050 on behalf of https://github.com/jeanschmidt due to Seems to have broken internal builds, more details on D77449943. @ezyang may I count on your help to get those changes merged? ([comment](https://github.com/pytorch/pytorch/pull/157050#issuecomment-3020222668 ))
2025-06-30 18:08:54 +00:00
064288cbab
Use std::string_view in torchgen ( #157050 )
...
Let the generated code use std::sv
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157050
Approved by: https://github.com/ezyang
2025-06-27 06:36:10 +00:00
a69785b3ec
[BE] fix typos in tools/ ( #156082 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156082
Approved by: https://github.com/soulitzer
ghstack dependencies: #156079
2025-06-17 19:25:50 +00:00
8817e5ac80
Render Example: and not Example:: in docs ( #153978 )
...
Everything here is a grep except the changes in tools/autograd/load_derivatives.py which I manually corrected.
The correct notation is:
```
Example::
>>> ...
```
It is common and wrong to have:
```
Example::
>>> ...
```
In the wrong example, we get these pesky double colons:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153978
Approved by: https://github.com/soulitzer , https://github.com/malfet
2025-05-21 01:03:26 +00:00
c73a92fbf5
[BE][CI] bump ruff to 0.9.2: multiline assert statements ( #144546 )
...
Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
2025-02-27 20:46:16 +00:00
82aaf64422
[3/N] Apply py39 ruff fixes ( #142115 )
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Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142115
Approved by: https://github.com/ezyang
2024-12-11 17:50:10 +00:00
882b6af219
c10::string_view -> std::string_view in autograd ( #142354 )
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Differential Revision: D66939966
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142354
Approved by: https://github.com/Skylion007
2024-12-10 15:43:41 +00:00
267f82b860
[BE] Format .ci/ / .github/ / benchmarks/ / functorch/ / tools/ / torchgen/ with ruff format ( #132577 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132577
Approved by: https://github.com/malfet
2024-10-11 18:30:26 +00:00
28f6ae2718
[9/N] Replace c10::optional with std::optional ( #130674 )
...
Follows #130509
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130674
Approved by: https://github.com/Skylion007
2024-07-15 00:48:43 +00:00
4d7bf72d93
[BE][Easy] fix ruff rule needless-bool (SIM103) ( #130206 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130206
Approved by: https://github.com/malfet
2024-07-14 08:17:52 +00:00
973037be6a
[BE][Easy] apply autofix for ruff rules unnecessary-collection-call (C408): list() / tuple() / dict() ( #130199 )
...
This PR changes the empty collection factory call to Python literals:
- `list()` -> `[]`
- `tuple()` -> `()`
- `dict()` -> `{}`
The Python literals are more performant and safer. For example, the bytecode for building an empty dictionary:
```bash
$ python3 -m dis - <<EOS
import collections
d1 = {}
d2 = dict()
dict = collections.OrderedDict
d3 = dict()
EOS
```
```text
0 0 RESUME 0
1 2 LOAD_CONST 0 (0)
4 LOAD_CONST 1 (None)
6 IMPORT_NAME 0 (collections)
8 STORE_NAME 0 (collections)
3 10 BUILD_MAP 0
12 STORE_NAME 1 (d1)
4 14 PUSH_NULL
16 LOAD_NAME 2 (dict)
18 CALL 0
26 STORE_NAME 3 (d2)
6 28 LOAD_NAME 0 (collections)
30 LOAD_ATTR 8 (OrderedDict)
50 STORE_NAME 2 (dict)
7 52 PUSH_NULL
54 LOAD_NAME 2 (dict)
56 CALL 0
64 STORE_NAME 5 (d3)
66 RETURN_CONST 1 (None)
```
The dict literal `{}` only has one bytecode `BUILD_MAP`, while the factory call `dict()` has three `PUSH_NULL + LOAD_NAME + CALL`. Also, the factory call is not safe if users override the `dict` name in `locals` or `globals` (see the example of replacing with `OrderedDict` above).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130199
Approved by: https://github.com/malfet
2024-07-11 17:30:28 +00:00
8a67daf283
[BE][Easy] enable postponed annotations in tools ( #129375 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129375
Approved by: https://github.com/malfet
2024-06-29 09:23:35 +00:00
a32ce5ce34
Revert "[BE][Easy] enable postponed annotations in tools ( #129375 )"
...
This reverts commit 59eb2897f1745f513edb6c63065ffad481c4c8d0.
Reverted https://github.com/pytorch/pytorch/pull/129375 on behalf of https://github.com/huydhn due to Sorry for reverting your change but I need to revert to cleanly revert https://github.com/pytorch/pytorch/pull/129374 , please do a rebase and reland this ([comment](https://github.com/pytorch/pytorch/pull/129375#issuecomment-2197800541 ))
2024-06-29 00:44:25 +00:00
59eb2897f1
[BE][Easy] enable postponed annotations in tools ( #129375 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129375
Approved by: https://github.com/malfet
2024-06-28 15:37:54 +00:00
35ea5c6b22
[3/N][Easy] fix typo for usort config in pyproject.toml (kown -> known): sort torchgen ( #127124 )
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The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127124
Approved by: https://github.com/Skylion007
ghstack dependencies: #127122 , #127123
2024-05-25 19:20:03 +00:00
ecca533872
Use dmypy instead of mypy in lintrunner ( #118475 )
...
Signed-off-by: Edward Z. Yang <ezyang@meta.com >
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118475
Approved by: https://github.com/suo
ghstack dependencies: #118414 , #118418 , #118432 , #118467 , #118468 , #118469
2024-01-28 13:42:06 +00:00
5aac95c713
Introduce slice_inverse() op ( #117041 )
...
Introduces a new op `slice_inverse()`. This is used in the reverse view_func for slice and several other ops (e.g. `split_with_sizes`, `chunk`). It's implemented behind the scenes by a call to `as_strided()`, but it's easier for subclasses to implement the more limited `slice_inverse()` than the full `as_strided()`. This PR:
* Introduces the op itself
* Updates all relevant functional inverses to call `slice_inverse()` instead of `as_strided()` directly
* Makes codegen changes to allow `slice_scatter()` to be the copy variant for `slice_inverse()`
* Need to avoid view_copy codegen (assumes if view name ends in inverse, we don't need to gen one, which is possibly a bad assumption)
@albanD / @soulitzer / @bdhirsh: I'm most interested in your thoughts on the codegen changes and whether this is the right way to go.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117041
Approved by: https://github.com/bdhirsh
2024-01-16 23:44:54 +00:00
835c18e7ea
Avoid saving self for mean.backward ( #109935 )
...
Fixes https://github.com/pytorch/pytorch/issues/109876
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109935
Approved by: https://github.com/soulitzer
2023-09-23 11:50:54 +00:00
5814380e7b
Revert "Revert "Reland "Add forward mode AD to out-place foreach functions ( #102409 ) ( #106043 )""" ( #106320 )
...
Fixed a typo specifying the number of tensors and elements in the test having failed in slow gradcheck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106320
Approved by: https://github.com/soulitzer
2023-08-18 23:01:42 +00:00
2b427ae3a7
Revert "Reland "Add forward mode AD to out-place foreach functions ( #102409 ) ( #106043 )"
...
This reverts commit e773f28ee307e2a246a4b765f3a51117661b45ba.
Reverted https://github.com/pytorch/pytorch/pull/106043 on behalf of https://github.com/DanilBaibak due to Break slow tests ([comment](https://github.com/pytorch/pytorch/pull/106043#issuecomment-1658642734 ))
2023-07-31 15:50:36 +00:00
e773f28ee3
Reland "Add forward mode AD to out-place foreach functions ( #102409 ) ( #106043 )
...
forward-mode AD of out-of-place foreach functions, finally.
rel:
- #102409
- #105504
- #58833
- #100695
---
# Generated Foreach
```c++
::std::vector<at::Tensor> _foreach_sinh(c10::DispatchKeySet ks, at::TensorList self) {
auto self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
std::vector<bool> _any_has_forward_grad_result(self.size());
for (const auto& i : c10::irange(self.size())) {
_any_has_forward_grad_result[i] = isFwGradDefined(self[i]);
}
std::shared_ptr<ForeachSinhBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<ForeachSinhBackward0>(new ForeachSinhBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->self_ = make_saved_variable_list(self);
grad_fn->self_size_ = self.size();
}
#ifndef NDEBUG
std::vector<c10::optional<Storage>> self__storage_saved(self_.size());
for (const Tensor& tensor : self_)
self__storage_saved.push_back(
tensor.has_storage() ? c10::optional<Storage>(tensor.storage()) : c10::nullopt);
std::vector<c10::intrusive_ptr<TensorImpl>> self__impl_saved(self_.size());
for (size_t i=0; i<self_.size(); i++)
if (self_[i].defined()) self__impl_saved[i] = self_[i].getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::_foreach_sinh(ks & c10::after_autograd_keyset, self_);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__storage_saved[i].has_value() && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved[i].value().is_alias_of(self_[i].storage()));
}
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__impl_saved[i] && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved[i] == self_[i].getIntrusivePtr());
}
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
std::vector<c10::optional<at::Tensor>> result_new_fw_grad_opts(self.size(), c10::nullopt);
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
if (_any_has_forward_grad_result[i]) {
auto self_t_raw = toNonOptFwGrad(self[i]);
auto self_tensor = toNonOptTensor(self[i]);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self[i]);
result_new_fw_grad_opts[i] = (self_t.conj() * self_p.cosh().conj()).conj();
}
}
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
auto& result_new_fw_grad_opt = result_new_fw_grad_opts[i];
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result[i].defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result[i]._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
}
return result;
}
::std::vector<at::Tensor> _foreach_norm_Scalar(c10::DispatchKeySet ks, at::TensorList self, const at::Scalar & ord) {
auto self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
std::vector<bool> _any_has_forward_grad_result(self.size());
for (const auto& i : c10::irange(self.size())) {
_any_has_forward_grad_result[i] = isFwGradDefined(self[i]);
}
std::shared_ptr<ForeachNormBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<ForeachNormBackward0>(new ForeachNormBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->ord = ord;
grad_fn->self_ = make_saved_variable_list(self);
grad_fn->self_size_ = self.size();
}
#ifndef NDEBUG
std::vector<c10::optional<Storage>> self__storage_saved(self_.size());
for (const Tensor& tensor : self_)
self__storage_saved.push_back(
tensor.has_storage() ? c10::optional<Storage>(tensor.storage()) : c10::nullopt);
std::vector<c10::intrusive_ptr<TensorImpl>> self__impl_saved(self_.size());
for (size_t i=0; i<self_.size(); i++)
if (self_[i].defined()) self__impl_saved[i] = self_[i].getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::_foreach_norm(ks & c10::after_autograd_keyset, self_, ord);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__storage_saved[i].has_value() && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved[i].value().is_alias_of(self_[i].storage()));
}
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__impl_saved[i] && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved[i] == self_[i].getIntrusivePtr());
}
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
std::vector<c10::optional<at::Tensor>> result_new_fw_grad_opts(self.size(), c10::nullopt);
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
if (_any_has_forward_grad_result[i]) {
auto self_t_raw = toNonOptFwGrad(self[i]);
auto self_tensor = toNonOptTensor(self[i]);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self[i]);
result_new_fw_grad_opts[i] = norm_jvp(self_p, self_t, ord, result[i]);
}
}
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
auto& result_new_fw_grad_opt = result_new_fw_grad_opts[i];
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result[i].defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result[i]._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
}
if (grad_fn) {
grad_fn->result = result;
}
return result;
}
```
# Reference
```c++
at::Tensor sinh(c10::DispatchKeySet ks, const at::Tensor & self) {
auto& self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
[[maybe_unused]] auto _any_has_forward_grad_result = (isFwGradDefined(self));
std::shared_ptr<SinhBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<SinhBackward0>(new SinhBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->self_ = SavedVariable(self, false);
}
#ifndef NDEBUG
c10::optional<Storage> self__storage_saved =
self_.has_storage() ? c10::optional<Storage>(self_.storage()) : c10::nullopt;
c10::intrusive_ptr<TensorImpl> self__impl_saved;
if (self_.defined()) self__impl_saved = self_.getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::sinh(ks & c10::after_autograd_keyset, self_);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
if (self__storage_saved.has_value() &&
!at::impl::dispatch_mode_enabled() &&
!at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved.value().is_alias_of(self_.storage()));
if (self__impl_saved && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved == self_.getIntrusivePtr());
if (result.has_storage() && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result)) {
TORCH_INTERNAL_ASSERT(result.storage().use_count() == 1, "function: sinh");
}
if (!at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result))
TORCH_INTERNAL_ASSERT(result.use_count() <= 1, "function: sinh");
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
c10::optional<at::Tensor> result_new_fw_grad_opt = c10::nullopt;
if (_any_has_forward_grad_result && (result.defined())) {
auto self_t_raw = toNonOptFwGrad(self);
auto self_tensor = toNonOptTensor(self);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self);
result_new_fw_grad_opt = (self_t.conj() * self_p.cosh().conj()).conj();
}
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result.defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
return result;
}
at::Tensor norm_Scalar(c10::DispatchKeySet ks, const at::Tensor & self, const at::Scalar & p) {
auto& self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
[[maybe_unused]] auto _any_has_forward_grad_result = (isFwGradDefined(self));
std::shared_ptr<NormBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<NormBackward0>(new NormBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->p = p;
grad_fn->self_ = SavedVariable(self, false);
}
#ifndef NDEBUG
c10::optional<Storage> self__storage_saved =
self_.has_storage() ? c10::optional<Storage>(self_.storage()) : c10::nullopt;
c10::intrusive_ptr<TensorImpl> self__impl_saved;
if (self_.defined()) self__impl_saved = self_.getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::norm(ks & c10::after_autograd_keyset, self_, p);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
if (self__storage_saved.has_value() &&
!at::impl::dispatch_mode_enabled() &&
!at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved.value().is_alias_of(self_.storage()));
if (self__impl_saved && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved == self_.getIntrusivePtr());
if (result.has_storage() && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result)) {
TORCH_INTERNAL_ASSERT(result.storage().use_count() == 1, "function: norm_Scalar");
}
if (!at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result))
TORCH_INTERNAL_ASSERT(result.use_count() <= 1, "function: norm_Scalar");
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
throw_error_for_complex_autograd(result, "norm");
c10::optional<at::Tensor> result_new_fw_grad_opt = c10::nullopt;
if (_any_has_forward_grad_result && (result.defined())) {
auto self_t_raw = toNonOptFwGrad(self);
auto self_tensor = toNonOptTensor(self);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self);
result_new_fw_grad_opt = norm_jvp(self_p, self_t, p, result);
}
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result.defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
if (grad_fn) {
grad_fn->result_ = SavedVariable(result, true);
}
return result;
}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106043
Approved by: https://github.com/soulitzer
2023-07-27 03:13:24 +00:00
4cc1745b13
[BE] f-stringify torch/ and scripts ( #105538 )
...
This PR is a follow up on the pyupgrade series to convert more strings to use f-strings using `flynt`.
- https://docs.python.org/3/reference/lexical_analysis.html#f-strings
- https://pypi.org/project/flynt/
Command used:
```
flynt torch/ -ll 120
flynt scripts/ -ll 120
flynt tools/ -ll 120
```
and excluded `collect_env.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105538
Approved by: https://github.com/ezyang , https://github.com/malfet
2023-07-21 19:35:24 +00:00
14d87bb5ff
[BE] Enable ruff's UP rules and autoformat tools and scripts ( #105428 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105428
Approved by: https://github.com/albanD , https://github.com/soulitzer , https://github.com/malfet
2023-07-19 01:24:44 +00:00
2f95a3d0fc
[BE]: Apply ruff PERF fixes to torch ( #104917 )
...
Applies automated ruff fixes in the PERF modules and enables all automatic ones. I also updated ruff which applied some additional fixes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104917
Approved by: https://github.com/ezyang , https://github.com/albanD
2023-07-11 20:45:21 +00:00
8958f041be
Revert "Add forward mode AD to out-place foreach functions ( #102409 )"
...
This reverts commit e2ec0ba404f9fbd3c215cad4cabd7383c692cb33.
Reverted https://github.com/pytorch/pytorch/pull/102409 on behalf of https://github.com/huydhn due to Sorry for reverting your PR but it is failing some tests in trunk e799f565ebhttps://github.com/pytorch/pytorch/pull/102409#issuecomment-1615254393 ))
2023-06-30 22:46:57 +00:00
e2ec0ba404
Add forward mode AD to out-place foreach functions ( #102409 )
...
The major difference from in-place support is that some out-place functions have their derivatives spelled out in derivatives.yaml, which requires some changes in `load_derivatives.py` and some handlings in various places due to the others whose derivatives are generated by `torchgen`.
rel:
- #58833
- #100695
---
# Generated Foreach
```c++
::std::vector<at::Tensor> _foreach_sinh(c10::DispatchKeySet ks, at::TensorList self) {
auto self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
std::vector<bool> _any_has_forward_grad_result(self.size());
for (const auto& i : c10::irange(self.size())) {
_any_has_forward_grad_result[i] = isFwGradDefined(self[i]);
}
std::shared_ptr<ForeachSinhBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<ForeachSinhBackward0>(new ForeachSinhBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->self_ = make_saved_variable_list(self);
grad_fn->self_size_ = self.size();
}
#ifndef NDEBUG
std::vector<c10::optional<Storage>> self__storage_saved(self_.size());
for (const Tensor& tensor : self_)
self__storage_saved.push_back(
tensor.has_storage() ? c10::optional<Storage>(tensor.storage()) : c10::nullopt);
std::vector<c10::intrusive_ptr<TensorImpl>> self__impl_saved(self_.size());
for (size_t i=0; i<self_.size(); i++)
if (self_[i].defined()) self__impl_saved[i] = self_[i].getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::_foreach_sinh(ks & c10::after_autograd_keyset, self_);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__storage_saved[i].has_value() && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved[i].value().is_alias_of(self_[i].storage()));
}
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__impl_saved[i] && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved[i] == self_[i].getIntrusivePtr());
}
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
std::vector<c10::optional<at::Tensor>> result_new_fw_grad_opts(self.size(), c10::nullopt);
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
if (_any_has_forward_grad_result[i]) {
auto self_t_raw = toNonOptFwGrad(self[i]);
auto self_tensor = toNonOptTensor(self[i]);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self[i]);
result_new_fw_grad_opts[i] = (self_t.conj() * self_p.cosh().conj()).conj();
}
}
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
auto& result_new_fw_grad_opt = result_new_fw_grad_opts[i];
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result[i].defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result[i]._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
}
return result;
}
::std::vector<at::Tensor> _foreach_norm_Scalar(c10::DispatchKeySet ks, at::TensorList self, const at::Scalar & ord) {
auto self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
std::vector<bool> _any_has_forward_grad_result(self.size());
for (const auto& i : c10::irange(self.size())) {
_any_has_forward_grad_result[i] = isFwGradDefined(self[i]);
}
std::shared_ptr<ForeachNormBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<ForeachNormBackward0>(new ForeachNormBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->ord = ord;
grad_fn->self_ = make_saved_variable_list(self);
grad_fn->self_size_ = self.size();
}
#ifndef NDEBUG
std::vector<c10::optional<Storage>> self__storage_saved(self_.size());
for (const Tensor& tensor : self_)
self__storage_saved.push_back(
tensor.has_storage() ? c10::optional<Storage>(tensor.storage()) : c10::nullopt);
std::vector<c10::intrusive_ptr<TensorImpl>> self__impl_saved(self_.size());
for (size_t i=0; i<self_.size(); i++)
if (self_[i].defined()) self__impl_saved[i] = self_[i].getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::_foreach_norm(ks & c10::after_autograd_keyset, self_, ord);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__storage_saved[i].has_value() && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved[i].value().is_alias_of(self_[i].storage()));
}
for (size_t i=0; i<self_.size() && !at::impl::dispatch_mode_enabled(); i++) {
if (self__impl_saved[i] && !at::impl::tensorlist_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved[i] == self_[i].getIntrusivePtr());
}
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
std::vector<c10::optional<at::Tensor>> result_new_fw_grad_opts(self.size(), c10::nullopt);
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
if (_any_has_forward_grad_result[i]) {
auto self_t_raw = toNonOptFwGrad(self[i]);
auto self_tensor = toNonOptTensor(self[i]);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self[i]);
result_new_fw_grad_opts[i] = norm_jvp(self_p, self_t, ord, result[i]);
}
}
for (const auto& i : c10::irange(result_new_fw_grad_opts.size())) {
auto& result_new_fw_grad_opt = result_new_fw_grad_opts[i];
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result[i].defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result[i]._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
}
if (grad_fn) {
grad_fn->result = result;
}
return result;
}
```
# Reference
```c++
at::Tensor sinh(c10::DispatchKeySet ks, const at::Tensor & self) {
auto& self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
[[maybe_unused]] auto _any_has_forward_grad_result = (isFwGradDefined(self));
std::shared_ptr<SinhBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<SinhBackward0>(new SinhBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->self_ = SavedVariable(self, false);
}
#ifndef NDEBUG
c10::optional<Storage> self__storage_saved =
self_.has_storage() ? c10::optional<Storage>(self_.storage()) : c10::nullopt;
c10::intrusive_ptr<TensorImpl> self__impl_saved;
if (self_.defined()) self__impl_saved = self_.getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::sinh(ks & c10::after_autograd_keyset, self_);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
if (self__storage_saved.has_value() &&
!at::impl::dispatch_mode_enabled() &&
!at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved.value().is_alias_of(self_.storage()));
if (self__impl_saved && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved == self_.getIntrusivePtr());
if (result.has_storage() && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result)) {
TORCH_INTERNAL_ASSERT(result.storage().use_count() == 1, "function: sinh");
}
if (!at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result))
TORCH_INTERNAL_ASSERT(result.use_count() <= 1, "function: sinh");
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
c10::optional<at::Tensor> result_new_fw_grad_opt = c10::nullopt;
if (_any_has_forward_grad_result && (result.defined())) {
auto self_t_raw = toNonOptFwGrad(self);
auto self_tensor = toNonOptTensor(self);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self);
result_new_fw_grad_opt = (self_t.conj() * self_p.cosh().conj()).conj();
}
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result.defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
return result;
}
at::Tensor norm_Scalar(c10::DispatchKeySet ks, const at::Tensor & self, const at::Scalar & p) {
auto& self_ = unpack(self, "self", 0);
[[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self );
[[maybe_unused]] auto _any_has_forward_grad_result = (isFwGradDefined(self));
std::shared_ptr<NormBackward0> grad_fn;
if (_any_requires_grad) {
grad_fn = std::shared_ptr<NormBackward0>(new NormBackward0(), deleteNode);
grad_fn->set_next_edges(collect_next_edges( self ));
grad_fn->p = p;
grad_fn->self_ = SavedVariable(self, false);
}
#ifndef NDEBUG
c10::optional<Storage> self__storage_saved =
self_.has_storage() ? c10::optional<Storage>(self_.storage()) : c10::nullopt;
c10::intrusive_ptr<TensorImpl> self__impl_saved;
if (self_.defined()) self__impl_saved = self_.getIntrusivePtr();
#endif
auto _tmp = ([&]() {
at::AutoDispatchBelowADInplaceOrView guard;
return at::redispatch::norm(ks & c10::after_autograd_keyset, self_, p);
})();
auto result = std::move(_tmp);
#ifndef NDEBUG
if (self__storage_saved.has_value() &&
!at::impl::dispatch_mode_enabled() &&
!at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__storage_saved.value().is_alias_of(self_.storage()));
if (self__impl_saved && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(self_))
TORCH_INTERNAL_ASSERT(self__impl_saved == self_.getIntrusivePtr());
if (result.has_storage() && !at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result)) {
TORCH_INTERNAL_ASSERT(result.storage().use_count() == 1, "function: norm_Scalar");
}
if (!at::impl::dispatch_mode_enabled() && !at::impl::tensor_has_dispatch(result))
TORCH_INTERNAL_ASSERT(result.use_count() <= 1, "function: norm_Scalar");
#endif
if (grad_fn) {
set_history(flatten_tensor_args( result ), grad_fn);
}
throw_error_for_complex_autograd(result, "norm");
c10::optional<at::Tensor> result_new_fw_grad_opt = c10::nullopt;
if (_any_has_forward_grad_result && (result.defined())) {
auto self_t_raw = toNonOptFwGrad(self);
auto self_tensor = toNonOptTensor(self);
auto self_t = (self_t_raw.defined() || !self_tensor.defined())
? self_t_raw : at::_efficientzerotensor(self_tensor.sizes(), self_tensor.options());
auto self_p = toNonOptPrimal(self);
result_new_fw_grad_opt = norm_jvp(self_p, self_t, p, result);
}
if (result_new_fw_grad_opt.has_value() && result_new_fw_grad_opt.value().defined() && result.defined()) {
// The hardcoded 0 here will need to be updated once we support multiple levels.
result._set_fw_grad(result_new_fw_grad_opt.value(), /* level */ 0, /* is_inplace_op */ false);
}
if (grad_fn) {
grad_fn->result_ = SavedVariable(result, true);
}
return result;
}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102409
Approved by: https://github.com/soulitzer
2023-06-30 04:51:43 +00:00
4135295a76
Excise yaml dependency in torchgen.model ( #100203 )
...
The problem:
- The new CustomOp API depends on torchgen.model
- torchgen.model imports `yaml`
- `yaml` is not a PyTorch runtime dependency
To unblock myself, because I'm not sure how long it'll take to
convince people yaml should be a PyTorch runtime dependency
(unless one of you wants to approve #100166 ), this PR removes the
yaml dependency from torchgen.model.
It does so by splitting torchgen.utils (the offender) into
torchgen.utils (no yaml) and torchgen.yaml (which uses yaml).
Test Plan:
- CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100203
Approved by: https://github.com/ezyang , https://github.com/Skylion007
2023-04-28 13:45:39 +00:00
e2a3817dfd
[BE] Enable C419 rule for any all shortcircuiting ( #99890 )
...
Apparently https://github.com/pytorch/pytorch/pull/78142 made torch.JIT allow for simple generator expressions which allows us to enable rules that replace unnecessary list comprehensions with generators in any/all. This was originally part of #99280 but I split it off into this PR so that it can be easily reverted should anything break.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99890
Approved by: https://github.com/justinchuby , https://github.com/kit1980 , https://github.com/malfet
2023-04-25 15:02:13 +00:00
60a68477a6
Bump black version to 23.1.0 ( #96578 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96578
Approved by: https://github.com/ezyang
2023-03-15 06:27:59 +00:00
3870a9e28d
to_sparse_XXX: backward support ( #90281 )
...
As per title. Fixes https://github.com/pytorch/pytorch/issues/85226
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90281
Approved by: https://github.com/cpuhrsch , https://github.com/soulitzer
2022-12-14 09:05:17 +00:00
4801397b6e
ban .sizes() and .strides() calls in derivatives.yaml ( #86611 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86611
Approved by: https://github.com/wconstab , https://github.com/albanD
2022-10-19 15:59:28 +00:00
55663b7f81
Reland 3 of Symintify getitem and add the required helper functions ( #86207 ) ( #86487 )
...
Note that this might not cover every use of the function (we know it doesn't)
But this is enough to get few models passing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86487
Approved by: https://github.com/ezyang
2022-10-10 15:54:28 +00:00
33f0e98a49
Re-land*4 "SymIntify cat and narrow" ( #86468 )
...
This re-lands https://github.com/pytorch/pytorch/pull/86289 but with more wrappers.
Contains implicit inclusion of <ATen/native/NonSymbolicBC.h> in internal usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86468
Approved by: https://github.com/albanD
2022-10-08 07:17:37 +00:00
65b408074f
Revert "Relandx3 "SymIntify cat and narrow" ( #86289 )"
...
This reverts commit a00f8489df5586178d7b5f83928bf8049ce32f24.
Reverted https://github.com/pytorch/pytorch/pull/86289 on behalf of https://github.com/malfet due to @seemether unlanded the rest of the stack and it will fail intern import anyway
2022-10-07 16:29:27 +00:00
5b69b87d5a
Revert "Symintify getitem and add the required helper functions ( #86207 )"
...
This reverts commit fd5085c445c3f1a4c90e55154cf26fe30f52a0ab.
Reverted https://github.com/pytorch/pytorch/pull/86207 on behalf of https://github.com/seemethere due to Fails internal tests, see: https://www.internalfb.com/intern/sandcastle/job/22517998926071860/insights
2022-10-07 16:10:30 +00:00
a00f8489df
Relandx3 "SymIntify cat and narrow" ( #86289 )
...
This reverts commit fc94a2115b31dfe7a0d8f28eb4f5ed532c4f0792.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86289
Approved by: https://github.com/wconstab
2022-10-07 14:04:10 +00:00
2110c89443
Revert "Revert "Revert "SymIntify cat and narrow ( #86191 )"" ( #86289 )"
...
This reverts commit e778fbf5197638d6196c5d5acf6f9588a1e83368.
Reverted https://github.com/pytorch/pytorch/pull/86289 on behalf of https://github.com/seemethere due to Fails internal tests see: https://www.internalfb.com/intern/sandcastle/job/27021598552487548/
2022-10-07 05:20:36 +00:00
fd5085c445
Symintify getitem and add the required helper functions ( #86207 )
...
Note that this might not cover every use of the function (we know it doesn't)
But this is enough to get few models passing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86207
Approved by: https://github.com/ezyang , https://github.com/Chillee , https://github.com/bdhirsh
2022-10-06 04:46:19 +00:00
168ba066e3
Revert "Symintify getitem and add the required helper functions ( #86207 )"
...
This reverts commit 17addb307ee9a4d12ad6918e90358a9a47a4f12b.
Reverted https://github.com/pytorch/pytorch/pull/86207 on behalf of https://github.com/malfet due to Broke lint, by double-registering `meta_index_put`, but no CI was run during the outage
2022-10-05 22:42:56 +00:00
17addb307e
Symintify getitem and add the required helper functions ( #86207 )
...
Note that this might not cover every use of the function (we know it doesn't)
But this is enough to get few models passing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86207
Approved by: https://github.com/ezyang
2022-10-05 21:19:00 +00:00
e778fbf519
Revert "Revert "SymIntify cat and narrow ( #86191 )"" ( #86289 )
...
This reverts commit fc94a2115b31dfe7a0d8f28eb4f5ed532c4f0792.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86289
Approved by: https://github.com/wconstab
2022-10-05 20:51:28 +00:00
fc94a2115b
Revert "SymIntify cat and narrow ( #86191 )"
...
This reverts commit 63d8d4f6ec5c973ad7b8669cd39ee9b550e5f55b.
Reverted https://github.com/pytorch/pytorch/pull/86191 on behalf of https://github.com/seemethere due to Fails internal tests, see [D40106464](https://www.internalfb.com/diff/D40106464 )
2022-10-05 17:19:55 +00:00
63d8d4f6ec
SymIntify cat and narrow ( #86191 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86191
Approved by: https://github.com/ezyang
2022-10-05 14:46:55 +00:00
84a06d7193
Enable convolution_backward with bias and symints ( #85970 )
...
Originally by Krovatkin from https://github.com/pytorch/pytorch/pull/85816
Signed-off-by: Edward Z. Yang <ezyang@fb.com >
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85970
Approved by: https://github.com/albanD
2022-09-30 21:21:11 +00:00
15c52ffc4f
Disallow auto_element_wise for in-place and fix some in-place gradients ( #85634 )
...
Fixes https://github.com/pytorch/pytorch/issues/85535
Also fixes the backward and forward gradients of `nn.functional.threshold`. The issue was that in-place gradients weren't tested because the in-place variants were not properly registered to the OpInfo.
Perhaps an alternative to this to make auto_element_wise smart enough to actually handle the in-places cases (we have 4 cases total now where we manually copy_ after doing auto_element_wise), but that requires a few more changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85634
Approved by: https://github.com/albanD
2022-09-27 15:35:24 +00:00
4a2d2e5e40
Change API type Tensor[] for structured kernels. ( #73350 )
...
Partially fixes : #66328
This PR:
- adds support for `ITensorList` to the dispatcher for:
- computing the dispatch key
- boxing and unboxing `ITensorList`
- modified the codegen for structured kernels:
- codegen APIs use `ITensorList` instead of `ArrayRef<Tensor>`
**Changes summary:**
- Signature changes due to the different APIs:
- dispatcher API (e.g. `BatchingRegistrations.cpp`)
- C++ API (e.g. `TensorShape.cpp`)
- Miscelaneous functions used by codegen'd functions (e.g. `FunctionalTensorWrapper.*`)
- Dispatcher changes for handling `ITensorList` correctly (e.g. `DispatchKeyExtractor.h`)
- Signature changes of `at::cat` due to the need of `const` inside `TensorBody.h`
- Forward declarations of `ITensorList` (e.g. `MethodOperators.h`)
- Codegen changes, special casing structured kernels (e.g. `gen.py`)
**Short description of structured kernels special casing:**
I introduced, mainly, 5 types of changes to the codegen for generating code depending on
whether the kernel is structured or not:
1. Added a `structured_type_override` flag to the `argument_type` function definition of
the affected APIs (mainly the dispatcher and C++ APIs).
- `api/cpp.py`, `api/dispatcher.py`, `api/native.py`
2. Added a `structured_type_override` member to the signature
classes (e.g. `CppSignature`), since `FunctionSchema` doesn't really know whether the
function is structured or not
- `api/types.py`
3. Added a `part_of_structured_group` to `NativeFunction` class, which is just a
convenient function to forward to `structured_type_override` wherever needed
- `model.py`
4. Appropriately changed the rest of the codegen, whenever it used either the signature
classes or the `arguments` function directly
5. Added a check for `const ITensorList&` type wherever there was a check for `TensorList`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73350
Approved by: https://github.com/bdhirsh
2022-09-26 21:46:38 +00:00
6a18616296
Support for sym_strides() in backwards formulas ( #85210 )
...
Signed-off-by: Edward Z. Yang <ezyang@fb.com >
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85210
Approved by: https://github.com/Chillee , https://github.com/voznesenskym
2022-09-19 18:05:09 +00:00
