Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10439
Update Im2Col related to make preparation for group conv in NHWC order.
Reviewed By: houseroad
Differential Revision: D9285344
fbshipit-source-id: 1377b0243acb880d2ad9cf73084529a787dcb97d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10528
adding 2 features to core and model_helper
- reroute_tensor which supports op insertion on net level
- model_helper complete net and cut net used for full graph analysis
Differential Revision: D9330345
fbshipit-source-id: 56341d3f500e72069ee306e20266c8590ae7985a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10395
Order switch ops (NCHW2NHWC and NHWC2NCHW) were only supporting 2D images.
This diff generalizes them to 1D and 3D, and also add a unit test we didn't have.
Reviewed By: protonu
Differential Revision: D9261177
fbshipit-source-id: 56e7ec54c9a8fb71781ac1336f3f28cf024b4bda
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10390
Fixed a bug in box_with_nms_limit where it may produce more bounding boxes than specified.
* The original code first finds the threshold for the boxes at the 'detectons_per_im' position, and filters out boxes lower than the threshold.
* In some cases that there are multiple boxes have the same threshold, the op will return more boxes than 'detectons_per_im'.
Reviewed By: wat3rBro
Differential Revision: D9252726
fbshipit-source-id: 63f40829bcd275cb181692bc7547c384cee01499
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10389
Added some unit test for box_with_nms_limit_op.
Reviewed By: wat3rBro
Differential Revision: D9237860
fbshipit-source-id: 2d65744bd387314071b68d2a0c934289fc64a731
Summary:
AffineChannel is being used by public Detectron models, e.g. Mask-RCNN and Faster-RCNN. This PR folds this op into convolution the same way as BN to speed up inference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10293
Differential Revision: D9276789
Pulled By: yinghai
fbshipit-source-id: fbf6dd2c1be05f5713f760752e7245b1320a122b
Summary:
This operator implements b (1/2/4/8) bit stochastic quantization of a floating
matrix in a row-wise fashion. 8/b floating values are concatenated to a byte
and returned in uint8 tensor. PR: https://github.com/pytorch/pytorch/pull/8629
Reviewed By: harouwu
Differential Revision: D8493264
fbshipit-source-id: 01f64066568a1e5a2b87c6d2134bd31cdf119c02
Summary:
This was used as a convenient way for us to convert c1 models. Now that conversion is more or less done, we should probably require any users who need to convert c1 models to explicitly install c1. This PR removes the explicit c1 proto (which was copied from c1) in favor of explicit installation.
Note that caffe_translator would still work properly, only difference is that now users need to install c1 separately.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10380
Differential Revision: D9267981
Pulled By: Yangqing
fbshipit-source-id: a6ce5d9463e6567976da83f2d08b2c3d94d14390
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10197
Support generic feature in DPER2
For now since we only have one generic type 1, we are directly adding the parsed feature record to embedding feature.
For new feature types with specific structure, there should also be corresponding coding changes expected.
Reviewed By: itomatik
Differential Revision: D8788177
fbshipit-source-id: 9aaa6f35ece382acb4072ec5e57061bb0727f184
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9905
This diff improves lars operator in Caffe2 by applying clipping to the computed learning rate
Reviewed By: pjh5
Differential Revision: D9020606
fbshipit-source-id: b579f1d628113c09366feac9406002f1ef4bd54f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10035
This is an initial diff which refactors some of the components in the Seq2SeqModelCaffe2EnsembleDecoder class.
Reviewed By: jmp84
Differential Revision: D9026372
fbshipit-source-id: 449635208f24494209ae2fb78a19fca872970ea8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10044
The test was subtly broken! This transform wasn't writing to the correct blob and the test did not catch that because it was looking at the old version.
thanks @[100022211048576:kerenzhou] for catching this
Reviewed By: Jokeren
Differential Revision: D9075520
fbshipit-source-id: c31ff0afcd78dd2dc7ffc240e2e89eeda87f1fb4
Summary:
The goal of this PR is to update the hip files to reflect relevant changes in cuda source files.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9826
Differential Revision: D9032840
Pulled By: bddppq
fbshipit-source-id: 504e55c46308eebfee3c9a7beea1f294fe03470f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9747
Currently the ctc_greedy_decoder op initializes the `merge_repeated` argument only if it has been provided by the user. Change to initialize in all cases.
Reviewed By: houseroad
Differential Revision: D8963635
fbshipit-source-id: 18955c7c26a77d9d7f5137e4dec085252ffabfeb
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/pytorch/pytorch/pull/9581
Mostly to simplify code. Should also improve performance but order switch ops
don't take much time anyway.
Reviewed By: viswanathgs
Differential Revision: D8909766
fbshipit-source-id: 17a302d5bf4aba2755d88223fc01a41fd72c5919
Summary:
And let Gemm conversion to inspect the input `C` to try converting to FC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9870
Reviewed By: houseroad
Differential Revision: D9013198
Pulled By: bddppq
fbshipit-source-id: b4c509cfccca238262e1c406b004e66cef256321
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/9643
Current map interface assumes float data type, which is not always correct.
Reviewed By: kennyhorror
Differential Revision: D8455784
fbshipit-source-id: b94a31267760f7f97c15aa4b03008affc347fd10
Summary:
Not in the same format. Skip at the moment.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9751
Reviewed By: yinghai
Differential Revision: D8965636
Pulled By: houseroad
fbshipit-source-id: 81d39c2f5625c14c0e1ee11408b5f7267b53798f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9667
MKL-DNN doesn't support 64-bit integger (cfee61bf81/include/mkldnn_types.h (L62-L75)). So force converting from `TensorCPU<long>` to `s32` Ideep tensor will cause memory issue. This diff gives an alternative solution, where we just fall through to TensorCPU. The reasoning is that since MKL-DNN doesn't support 64 bit integer tensor, downstream ops have to be in CPUConext. So there is no reason force converting to ideep tensor and back.
Reviewed By: pjh5
Differential Revision: D8943544
fbshipit-source-id: f514903cda27e34b8887271c9df56c8220895116
