Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13949
This diff adds support to fillers for `SparseLengthsWeight*` ops. It does 3 things:
1. Add the fillers for `SparseLengthsWeight*` ops
2. Add filling heuristics to consider the path of `LengthsRangeFill` -> `Gather` -> `SparseLengthsWeightedSum`, where the length input is shared by `LengthsRangeFill` and `SparseLengthsWeightedSum`. Therefore, we need to carefully bound the value of that length input so that at `Gather`, it does not index out-of-bound for the weight input of `Gather`.
3. Fix and simplify the logic of `math::RandFixedSum`, where we just keep rejecting the generated value if it violates the invariants.
Reviewed By: highker
Differential Revision: D13048216
fbshipit-source-id: bfe402e07e6421b28548047d18b298c148e0ec87
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11338
The `min_` and `max_` value of the filler is in `double` format but when we are filling a specific type of tensor, their value can exceed the type limits, resulting in crash. This diff checks the type limits first and if `min_`/`max_` is out of the limits, it will clip it.
Reviewed By: highker
Differential Revision: D9684455
fbshipit-source-id: 6da98a03c57f3296abaddc7c5cfc1c836c611eb0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11060
Adding synthetic data generation to the filler.h file (the exact distribution to be replaced later on).
Reviewed By: highker
Differential Revision: D9417594
fbshipit-source-id: 5d66dfbcb254a5961c36b7d3a081332c7372dac7
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9520
Add random data filler to predictor bench to support production nets
Reviewed By: salexspb
Differential Revision: D8712757
fbshipit-source-id: 2c732b2ba71ab210f9222adf94d08442ca71dc03
