Repeats #106429 for scatter_reduce so that the backward will pass for PT2. The .item() call is only needed to make double-backward work, which isn't supported anyway for PT2; so an easy fix is to just skip the .item() call if we know we won't need double-backward.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107353
Approved by: https://github.com/eellison
1. add a python meta registration, to fix an issue with the forward pass. The problem was that previously, the C++ meta registration calls [numel()](7b14a14e27/aten/src/ATen/native/TensorAdvancedIndexing.cpp (L329)) which fails (LMK if it's better to fix the C++ implementation to not do this check)
2. Modify the backward to fix an issue in the backward. The backward is not a custom op - it's a custom manual backward implementation. In particular, there's some situations that don't support double backward; the check for whether double backward is allowed requires a .item() call. To fix the meta/fake tensor case, this PR will avoid setting the double backward error only if `GradMode::is_enabled()` - which shouldn't be turned on in PT2.
3. Update skips.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106429
Approved by: https://github.com/zou3519
### Description
This PR is to fix#99413, which shows the limitation of double backward using oneDNN in LSTM.
This PR does not implement double backward function itself, because that is pretty hard to spell out. Instead, it implements mkldnn_rnn_layer_backward using differentiable operations, so that double backward can be done automatically.
During backward process, it needs to use gates and hidden states between cells during one layer. However, these middle variables are stored in the `workspace`, and it is hard to figure them out. Therefore, in backward, we need re-calculate them first.
Corresponding UT has been added based on the failing case in # 99413. The UT with gradcheck and gradgradcheck which is added in https://github.com/pytorch/pytorch/pull/26660 cannot test LSTM using oneDNN, because UT only supports `double` datatype, while oneDNN does not support it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100627
Approved by: https://github.com/jgong5, https://github.com/soulitzer
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### <samp>🤖 Generated by Copilot at 8bb6158</samp>
This pull request adds forward and backward AD support for the `logcumsumexp` operator in functorch, a library for composable function transformations. It implements a forward-mode formula and a decomposition in `derivatives.yaml`, a C++ function for computing directional derivatives in `FunctionsManual.cpp`, and updates the tests and metadata in `test_ops.py` and `common_methods_invocations.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100629
Approved by: https://github.com/soulitzer
This PR introduces **-Wmissing-prototypes** of clang-tidy to prevent further coding errors such as the one fixed by PR #96714.
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### <samp>🤖 Generated by Copilot at fd2cf2a</samp>
This pull request makes several internal functions static to improve performance and avoid name clashes. It also fixes some typos, formatting, and missing includes in various files. It adds a new .clang-tidy check to warn about missing prototypes for non-static functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96805
Approved by: https://github.com/malfet, https://github.com/albanD
The higher order derivatives calculations of `max_pool2d` require indices provided, but `mps_max_pool2d` kernel doesn't calculate it. If we calculate indices during back propagations afterwards, that would be expensive and unnecessary since users can directly call `max_pool2d` with `return_indices=True`, which calculates `indices` along.
This PR adds a warning for it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98582
Approved by: https://github.com/soulitzer
This is needed for the HSTU model.
Details:
* ~~NT `chunk` now calls into NT `split_with_sizes` since the latter is more general~~ (removed; they're totally separate)
* Throws for backward
* Only operates over the last dim (`dim=-1`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97446
Approved by: https://github.com/cpuhrsch
# Summary
There exists an optimization within the scaled_dot_product_efficieint bacwkard attention path to, under the right conditions, output grad_q, grad_k, grad_v all as aliases of the same storage. This was done to optimize for the hot path where mha does packed linear_projection -> chunk -> (view stuff) -> sdpa. The thought was that chunk-> would be able to "trivially" cat inputs to chunk.backward(). However upon closer inspection chunk.backward will call ` cat` irregardless of the inputs so this is not being utilized.
I validated this by profiling on main and then this branch and the traces produced the same both with `split.backward()` calling into cat.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96880
Approved by: https://github.com/cpuhrsch
Fixes internal linking problem after `DECLARE_DISPATCH` was introduced in SparseTensorUtils.cpp, but implemented inside the native library.
Also, fix `sign-unsigned` compare in `_flatten_indices_impl`
Followups:
Move code declared/implemented in `SparseTensorUtils.*` to `at::native` namespace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96696
Approved by: https://github.com/albanD
follow-up https://github.com/pytorch/pytorch/pull/93901.
Unexpected numerical mismatches observed in some foreach functions' backward result seemed to be caused by the wrong order of `IndexRangeGenerator::range` call.
This pr has `args_with_derivatives` have the same or similar order of `foreach_native_function.func.arguments.flat_non_out`
---
what the current master generates for `_foreach_mul.List`:
```cpp
variable_list ForeachMulBackward0List::apply(variable_list&& grads) {
std::lock_guard<std::mutex> lock(mutex_);
TORCH_CHECK(!other_released_, ERR_BACKWARD_TWICE);
TORCH_CHECK(!self_released_, ERR_BACKWARD_TWICE);
IndexRangeGenerator gen;
auto other_ix = gen.range(other_size_);
auto self_ix = gen.range(self_size_);
variable_list grad_inputs(gen.size());
auto other = unpack_list(other_);
auto self = unpack_list(self_);
if (task_should_compute_output({ other_ix })) {
std::vector<Tensor> grad_result;
grad_result.reserve(grads.size());
for (const auto & i : c10::irange(grads.size())) {
grad_result.emplace_back(mul_tensor_backward(grads[i], self[i], other[i].scalar_type()));
}
copy_range(grad_inputs, other_ix, grad_result);
}
if (task_should_compute_output({ self_ix })) {
std::vector<Tensor> grad_result;
grad_result.reserve(grads.size());
for (const auto & i : c10::irange(grads.size())) {
grad_result.emplace_back(mul_tensor_backward(grads[i], other[i], self[i].scalar_type()));
}
copy_range(grad_inputs, self_ix, grad_result);
}
return grad_inputs;
}
```
with this PR the generated backward is
```cpp
variable_list ForeachMulBackward0List::apply(variable_list&& grads) {
std::lock_guard<std::mutex> lock(mutex_);
TORCH_CHECK(!self_released_, ERR_BACKWARD_TWICE);
TORCH_CHECK(!other_released_, ERR_BACKWARD_TWICE);
IndexRangeGenerator gen;
auto self_ix = gen.range(self_size_); <----- diff
auto other_ix = gen.range(other_size_); <----- diff
variable_list grad_inputs(gen.size());
auto self = unpack_list(self_);
auto other = unpack_list(other_);
if (task_should_compute_output({ other_ix })) {
std::vector<Tensor> grad_result;
grad_result.reserve(grads.size());
for (const auto & i : c10::irange(grads.size())) {
grad_result.emplace_back(mul_tensor_backward(grads[i], self[i], other[i].scalar_type()));
}
copy_range(grad_inputs, other_ix, grad_result);
}
if (task_should_compute_output({ self_ix })) {
std::vector<Tensor> grad_result;
grad_result.reserve(grads.size());
for (const auto & i : c10::irange(grads.size())) {
grad_result.emplace_back(mul_tensor_backward(grads[i], other[i], self[i].scalar_type()));
}
copy_range(grad_inputs, self_ix, grad_result);
}
return grad_inputs;
}
```
The change is to fix the order of `self_ix` and `other_ix`.[](url)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95263
Approved by: https://github.com/soulitzer
