Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19388
The old implementation forced a refcount bump when converting at::Tensor to caffe2::Tensor.
Now, it is possible to move it without a refcount bump.
Reviewed By: dzhulgakov
Differential Revision: D14986815
fbshipit-source-id: 92b4b0a6f323ed38376ffad75f960cad250ecd9b
Summary:
Currently, a TensorImpl's `is_variable_` is true if and only if the TensorImpl has AutogradMeta. This PR unifies these two concepts by removing `is_variable_` and change `is_variable()` to check existence of AutogradMeta instead.
Removing `is_variable_` is part of the work in Variable/Tensor merge.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19139
Differential Revision: D14893339
Pulled By: yf225
fbshipit-source-id: ceb5e22c3c01f79b5d21d5bdbf4a7d1bc397796a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16751
This was made more complicated by the fact that ivalue::IntList
is a thing. So I had to fix all of the sites where we referring
to IValue post facto.
The following codemods were run, in this order:
```
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntList IntArrayRef
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntArrayRef::create IntList::create
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in ivalue::IntArrayRef ivalue::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in Tag::IntArrayRef Tag::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in isIntArrayRef isIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in toIntArrayRef toIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'Shared<IntArrayRef>' 'Shared<IntList>'
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'intrusive_ptr<IntArrayRef>' 'intrusive_ptr<IntList>'
```
Some manual fixups were done afterwards; they can be reviewed separately
at https://github.com/pytorch/pytorch/pull/16752
Reviewed By: dzhulgakov
Differential Revision: D13954363
fbshipit-source-id: b5c40aacba042402155a2f5a229fa6db7992ac64
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16514
Original commit changeset: dc371697f14b
Relanding https://github.com/pytorch/pytorch/pull/15860 - the problem was that layer_norm was using at::empty which is not yet on mobile
Reviewed By: ezyang
Differential Revision: D13861480
fbshipit-source-id: e2116da32bc117175c96b9151b1beba9b31eff36
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15860
Few changes (which are harder to split in separate diffs, so together):
- make conversion explicit (as they can throw to avoid surprises)
- fix tensor legacy dispatch not initialized when tensor is created on C2 side
- add a bunch of invariants to enforce
Reviewed By: ezyang
Differential Revision: D13596031
fbshipit-source-id: d20b601e06ba47aeff2f6e8e15769840e2d46108
Summary:
This PR adds thread-local guard (`at::AutoNonVariableTypeMode`) to make sure that in VariableType.cpp the operations on baseType still dispatch to non-Variable type, even if the parameters will become Variables after the Tensor/Variable merge. We achieve this by making `legacyTensorType()` and `getType()` check the `at::AutoNonVariableTypeMode` guard to decide whether to return non-Variable type for a variable.
This is part of the VariableImpl/TensorImpl merge work: https://github.com/pytorch/pytorch/issues/13638.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15939
Reviewed By: ezyang
Differential Revision: D13640980
Pulled By: yf225
fbshipit-source-id: d12c2543822958558d7d70d36c50999a5eb8783f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16176
This makes PyTorch and Caffe2's data() method line up.
Historically, PyTorch made no distinction between tensors
with const or non-const data, and thus provided a
non-const pointer with data() member. Changing the API to
return a const-pointer would break all mutable code, whereas
changing the Caffe2 API to change a pointer doesn't break
any code, *except* for code which required an exact match
on const-ness (e.g., in template arguments). Since the latter
is less disruptive, we've opted for it here.
The few places downstream that broke due to this are fixed
in this patch.
Reviewed By: smessmer
Differential Revision: D13742916
fbshipit-source-id: baa4b4544cfdf7c1f369f4d69a1e0d5953c1bd99
Summary:
Based on offline discussion it should be less surprising to the users of existing code. Thus caffe2::Tensor is now a move-only class (as it used to be), explicit calls to UnsafeSharedInstance() are necessary to get shared_ptr behavior.
This change also identified a few places that misused the copy constructor - those are fixed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15416
Reviewed By: Yangqing
Differential Revision: D13524598
fbshipit-source-id: aea12d6dff77342606fa88ce4ddddbff266245a7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15814
Plan is to remove the APIs we want to deprecate one by one and make sure it still builds in sandcastle and ossci
Reviewed By: ezyang
Differential Revision: D12812029
fbshipit-source-id: ea0c3dd882bec95fcd4507160ebc61f598b6d040
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15967
Codemod generated with clangr shard mode, 25 files per diff,
To eliminiate partially initialized Tensor, we split the initialization of local Tensor variables into two steps, first declare un uninitialized Tensor, and
call `ReinitializeTensor` to initialize it.
motivation: https://github.com/pytorch/pytorch/pull/12407
Reviewed By: smessmer
Differential Revision: D13586735
fbshipit-source-id: eae2d79e1107a2e813ce3809e690af4706aaa9ca
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15407
Don't ask the tensor for its intrusive pointer if we just want to check if two tensors are the same.
This mirrors ATen APIs.
Reviewed By: dzhulgakov
Differential Revision: D13520389
fbshipit-source-id: 681317f36f480ab60e532bb08a073f98f39770fd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14819
This is a minimal wrapper for a c10::TensorImpl,
maybe destined for greatness later when we move caffe2::Tensor or at::Tensor into c10.
Reviewed By: dzhulgakov
Differential Revision: D13348039
fbshipit-source-id: 874f515358e94f35dc7a4c3e55b35fde59c51ff1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15418
Previously we are using Resize + ShareData.
Instead, we'll create a function on Tensor that clones itself with same storage.
Suppose we want `t` to `ShareData` with `t0`, Previous:
```
Tensor t(dims, CPU);
t.Resize(t0.sizes());
t.ShareData(t0);
```
Now:
```
Tensor t = t0.Alias();
```
Reviewed By: dzhulgakov
Differential Revision: D13507609
fbshipit-source-id: 6e4275d02f4c3356cbce91127f1b01111dc86b9f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14858
This diff doesn't change logic but just takes the existing code and moves it to caffe2::Tensor
Reviewed By: ezyang
Differential Revision: D13365817
fbshipit-source-id: bc73b27a793602cb14200dcdf357aa63233da43c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14656
This diff doesn't move it yet, but prepares it to be moved, i.e. removes all access to class internals.
dzhulgakov: Please comment on if you think it still makes sense to land this even though it's not blocking anymore since we're going to move at::CopyBytes anyhow.
ezyang: There's some changes in the implementation, especially handling undefined dest tensors. Please review carefully.
Reviewed By: ezyang
Differential Revision: D13287688
fbshipit-source-id: 17800ca8a79ab1633f23be58d96f99a160d8ed24
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14269
Removes reference to Context proper and instead adds a bool argument for async copy (the same as `copy_`)
For CopyFrom - I haven't tweaked all callsites yet. Instead I rely on a terrible hack that pointer to context is implicitly converted to bool when passed, haha :) It's not a good code and I propose to fix it in a follow up diff (maybe using clangr tooling).
Reviewed By: ezyang
Differential Revision: D13117981
fbshipit-source-id: 7cb1dc2ba6a4c50ac26614f45ab8318ea96e3138
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13629
Previously we have a Tensor which has a initialized storage(therefore a known device_type) and
then we'll call CopyFrom on it to initialize the sizes and data.
We want to eliminate partially initialized Tensor by replacing the pattern of calling CopyFrom with a partially initialized Tensor with either splitting that to undefined Tensor + initialization API(1)(3) or combine all the initialization in the same step(2).
1. member variable initialization + CopyFrom
Previously we have a tensor that is initialized with device_type, and then use CopyFrom to populate the content, now we remove the partial initialization by make the original member variable an undefined Tensor and use ReinitializeFrom to copy from another Tensor.
2. Output + CopyFrom
Previously, we first get a tensor with device_type, and then CopyFrom another Tensor,
We changed it two combining these two operations into OperatorBase::OutputTensor.
3. Output + custom functions
Example can be found in TransformGPU function.
In this case we move the part that initializes the tensor outside of the function, and do that explicitly outside so that we could reuse the Output functions to make a fully initialized Tensor.
Note that to keep the original semantics, both of the APIs has a caching effect based on device_type, which means we only create a Tensor object when device_type does not match or the Tensor is undefined, otherwise, we will reuse the original Tensor object.
Reviewed By: dzhulgakov
Differential Revision: D12848855
fbshipit-source-id: 37bb4ddc1698ebea533b73006eeb1218faa8ddf8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13134
For tensor, we plan to do the following renaming:
```
* t.ndim() → t.dim()
* t.size() → t.numel()
* dims() → t.sizes()
* t.meta() → t.dtype()
* t.dim(d) → t.size(d)
```
This diff adds new APIs in caffe2::Tensor so we can start codemod,
we'll remove old API after the codemod
Reviewed By: ezyang
Differential Revision: D10856028
fbshipit-source-id: 1638997e234d7b3113ef8be65a16246f902273c7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12656
I originally wanted to do this in two steps, but deleting the Storage-only
constructor also changes the default numel state (which breaks tests),
so easiest to do it all in one go.)
- I still need a way to compute the correct TensorTypeId for all of the
Caffe2 constructors; rather than hard-code it, I wrote a function
in at::detail::computeTensorTypeId() to do this calculation. Maybe
this function could be used more widely, but for now, it's used
by Caffe2 only.
- Added a pile more TensorTypeId for all of Caffe2's supported DeviceTypes
- Because I still can't put arbitrary TypeMeta in TensorOptions, the
TensorTypeId() calculation doesn't respect dtype. For now, this is
not a problem, but this might block work to split non-POD dtypes
into their own TensorTypeId.
Reviewed By: li-roy
Differential Revision: D10380678
fbshipit-source-id: 10c5d12020596fc9f27d5579adffad00513af363
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12729
This may have a dependency on D10380678 if size_from_dim(0)
was required because numel() used to return -1 in some cases.
This is no longer true.
Reviewed By: li-roy, dzhulgakov
Differential Revision: D10415069
fbshipit-source-id: 39f46f56249ecaf3533f62a0205b3a45d519d789
