Summary:
We introduced RTTI in recent change: https://github.com/pytorch/pytorch/pull/21613
For internal mobile build we don't enable '-frtti' yet. This diff is trying to replace
RTTI with alternative approach.
According to dzhulgakov we could compare two tensors' type_id directly in most cases -
which is more strict than comparing TensorImpl subclass type as TensorImpl -> type_id
mapping is 1-to-n but it's more proper for this use case.
The only two cases where we can relax direct type comparison (for legacy reason) are:
1. CPUTensor <-> CUDATensor;
2. SparseCPUTensor <-> SparseCUDATensor;
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22773
Differential Revision: D16277696
Pulled By: ljk53
fbshipit-source-id: 043e264fbacc37b7a11af2046983c70ddb62a599
Summary:
Modify MKLDNN pooling operation to support ceil mode by adjusting the right/bottom padding accordingly. This is done similarly as in Caffe (see discussion https://github.com/pytorch/pytorch/pull/19205#discussion_r276903751).
To make this possible, I split the padding to left and right (top / bottom). This naming is confusing but actually follows mkldnn's own naming for pooling::compute(). We increase the r paddings so that it matches the ceiling mode expected output size.
Strengthened the test case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21310
Reviewed By: bddppq
Differential Revision: D15611664
Pulled By: akyrola
fbshipit-source-id: 46b40015dafef69a8fd5e7b2c261d8dbf448cd20
Summary:
As part of the Variable/Tensor merge work: https://github.com/pytorch/pytorch/issues/13638, we make the following changes in this PR:
1. Remove the `Variable::Impl` class and the `DifferentiableViewImpl` class
2. Change all `Variable.data()` call sites to either use `Variable` directly, or use `Variable.tensor_data()`
3. Remove `Variable.data()` API
3. Add `Variable.variable_data()` that matches `tensor.data` in Python API, which creates a new `Variable` that shares the same storage and tensor metadata with the original `Variable`, but with a completely new autograd history.
After this PR, Variable doesn't wrap a Tensor internally anymore, and both Variable and Tensor use the same TensorImpl class as its `impl_`. The only difference is that Variable always has AutogradMeta in its TensorImpl, but Tensor doesn't.
**Note that this PR is BC-breaking in the following use cases:**
**Use Case 1:**
Previously, `x.data = y` works even if `x` and `y` are of different TensorImpl type (e.g. `x` is a CPU dense tensor whose impl is of type TensorImpl, while `y` is a CPU sparse tensor whose impl is of type SparseTensorImpl). However, after this PR, `x.data = y` doesn't work anymore if `x` and `y` are of different TensorImpl type, because the underlying implementation `variable.set_data(tensor)` no longer works if `variable` and `tensor` have different TensorImpl type.
**Use Case 2:**
If a tensor `x`'s `grad` is sparse, accumulating dense gradients to `x` will change the tensor that `x.grad` is pointing to. This is better illustrated with the following example:
```python
params = torch.tensor([1.5, 1.5]).requires_grad_()
with torch.no_grad():
# Change gradient to a sparse tensor
params.grad = torch.sparse_coo_tensor(torch.tensor([[1, 1]]).long(), torch.tensor([1., 1.]))
grad_saved = params.grad
params.backward(torch.tensor([1.5, 1.5]))
assert id(grad_saved) == id(params.grad) # This will fail after this PR
```
The assertion in the last line will fail after this PR, because adding dense gradients to sparse gradients will change the `params.grad` tensor reference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17072
Differential Revision: D14075257
Pulled By: yf225
fbshipit-source-id: 0e681df641270dea586042dd26db59f2e76b5957
Summary:
This is a minimalist PR to add MKL-DNN tensor per discussion from Github issue: https://github.com/pytorch/pytorch/issues/16038
Ops with MKL-DNN tensor will be supported in following-up PRs to speed up imperative path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17748
Reviewed By: dzhulgakov
Differential Revision: D14614640
Pulled By: bddppq
fbshipit-source-id: c58de98e244b0c63ae11e10d752a8e8ed920c533
