Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18703
`zeroPtr` is sometimes a `std::string` tensor, so `memset` to 0 is undefined behavior.
This might be accidentally safe with `std::string` implementation that use SSO (Small String Optimization), but will crash otherwise.
Reviewed By: zheng-xq
Differential Revision: D14714458
fbshipit-source-id: 012a18464e6514d38ff791509b88ddc3fc55b2b1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16373
motivation: https://github.com/pytorch/pytorch/pull/12407
This is a manual diff.
most of the fixes should be:
```
auto* Y = Output(0);
Y->Resize(dims);
Y->raw_mutable_data(dtype);
```
-->
```
auto* Y = Output(0, dims, at::dtype(dtype));
```
But there might be other cases.
Reviewed By: dzhulgakov
Differential Revision: D13725460
fbshipit-source-id: 649a4b0e42f62cda1a60171dd9fa3e440dc9dca1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13377
* Enable junk fill for the default CPU allocator. The first diff only enables this for the tests. A second diff will change the default of zero-fill to false.
* Fix tests to use 64-bit counters that IterOp and LearningRateOp demands.
* Fix kernels that uses uninitialized memory.
Reviewed By: salexspb
Differential Revision: D10866512
fbshipit-source-id: 17860e77e63a203edf46d0da0335608f77884821
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12180
I had to fix a lot of call sites, because a lot of places assume that
you can actually get a const vector&, and if the internal representation
of sizes in a tensor is NOT a vector, it's not possible to fulfill
this API contract.
Framework changes:
- I deleted TensorImpl::dims(); caffe2::Tensor::dims() just forwards to
sizes() now.
- De-templatized SetDims; now it is an explicit list of ArrayRef and
variadic overloads. This makes implicit conversions work again,
so I don't need to explicitly list the std::vector cases too.
- As a knock-on effect, this causes Reset() to accept at::IntList as well as
const std::vector<int64_t>&
- Edited variadic overloads of SetDims to all forward to the underlying
arbitrary-dim implementation, reducing code duplication. (It's probably
marginally less efficient in the new world.)
- Replace Tensor constructor accepting const std::vector<int64_t>& with at::IntList
- Make MKLTensor accept ArrayRef along with vector in constructor and
Reset (unfortunately, no implicit conversions here, since it's templated on
index type.)
- There are a few other places, like cudnn, where I changed functions
that previously took const std::vector<int64_t>& to take at::IntList
instead.
Classification of call site changes:
- 'const std::vector<int64_t>& x_dims = x.dims()' ==>
'at::IntList x_dims = x.dims()'
- 'std::vector<int64_t> x_dims = x.dims()' ==>
'std::vector<int64_t> x_dims = x.dims().vec()' (we need a copy!)
Usually this is because we're about to mutably modify the vector
to compute some new dimension. However, it also very commonly occurs in the
form: 'x_dims_ = x.dims()' because we frequently cache sizes in operators.
- Instead of constructing std::vector<int64_t>{blah, blah}, construct an
at::IntList directly
ArrayRef changes:
- cbegin()/cend() iterators, they operate the same aas begin()/end() because
everything on ArrayRef is const.
- Moved operator<< into ArrayRef.h, so that it's always available when
working with ArrayRef. I also templated it, so it now works on an
ArrayRef of any type.
- Add operator== overload for ArrayRef, and also add variants to permit
comparison of ArrayRef with std::vector, a very common operation.
(The non-templated version of operator== can get these automatically
via implicit conversion, but with templates C++ refuses to do
any explicit conversions.)
I'm planning to audit all dims() call sites to make sure they don't
expect 'auto x = t.dims()' to give you an x whose lifetime can validly
outlive the tensor.
I opted not to do a dims() to sizes() rename, because dims() also matches
the protobufs accessor. Bad news!
Reviewed By: jerryzh168
Differential Revision: D10111759
fbshipit-source-id: a2a81dc4b92c22ad4b3b8ef4077a7e97b6479452
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9939
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: ezyang, houseroad
Differential Revision: D9024330
fbshipit-source-id: e0b8295d2dc6ebe2963383ded5af799ad17164ba
Summary:
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: xw285cornell
Differential Revision: D8121878
fbshipit-source-id: 4a5e9a677ba4ac82095df959851a054c81eccf81
Summary:
Breaking out of #8338
This fixes the build issues with pytorch on linux machines after BUILD_CAFFE2 and BUILD_ATEN are removed.
cc orionr
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9273
Reviewed By: orionr
Differential Revision: D8768869
Pulled By: mingzhe09088
fbshipit-source-id: 2730426ed1bed398eb5dc804c7348aeeb27c93d3
* Update operator documentation with markdown descriptions and interfaces
* Added rest of updated operator documentation to source files
* Commiting local changes for rebase
* fixed bracket typo in sqrt_op.cc file
* Added updated markdown documentation to remaining completed ops
Summary: The RunWithType() function of CUDA version shares a lot of code with the CPU version of the op. Merge them by pulling out the different parts of RunWithType() and putting them into a separate CPU/CUDA functions.
Reviewed By: asaadaldien
Differential Revision: D6467962
fbshipit-source-id: 83b45e697a094e959f66e898f46f06b0e2c329bc
Summary:
This is a CUDA implementation of the RemovePadding operator, modeled on akyrola's implementation for AddPadding.
There's also an incidental spelling correction: GetAddPadingGradient -> GetAddPaddingGradient.
Reviewed By: akyrola
Differential Revision: D6439594
fbshipit-source-id: b29cd0c252021c58e150b901bbaad28a3bd3cc4a
Summary:
@public
This has no functionality changes yet, only cleaning up the sequence_op file
so that the header is context-independent and I will implement the gpu parts
separately.
Reviewed By: pietern
Differential Revision: D4777140
fbshipit-source-id: 9b4aea6c36f06a64a53e235a125cd3477d54a045
Summary: Removed Def().arg() in the backward computation since they have already been included in the forward.
Differential Revision: D4563600
fbshipit-source-id: bb6ee25e7c8da99977b82963670267392893fcde
Summary: The doc for sequence ops says "pad_width" instead of "padding_width". This diff fixes it.
Differential Revision: D4277186
fbshipit-source-id: 63af6cce2fe0af0d395f78c6a6a1f41518039cf8
