Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12736
This updates UpsampleBilinearOp and UpsampleBilinearGradientOp to support scales to bring it inline with ResizeNearestOp https://github.com/pytorch/pytorch/pull/12720.
Reviewed By: houseroad
Differential Revision: D10416228
fbshipit-source-id: f339b7e06979c9c566afb4cee64a2d939b352957
Summary: Added 2 years ago in D3665603, never used, kill it.
Reviewed By: ezyang
Differential Revision: D10421336
fbshipit-source-id: 1b027a9ef2b71d0dd2c572cd4338bc8e046320d8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12799
Updated all non-test uses of protobuf::MessageLite::SerializeAsString to call
SerializeAsString_EnforceCheck so that the return value is checked and can
throw an exception if failing.
Most of the affected code was called from classes derived from BlobSerializeBase.
Didn't touch most tests and ENFORCE calls because they usually do checks
anyway.
Reviewed By: ezyang
Differential Revision: D10416438
fbshipit-source-id: cb842e3e26b0918829d71267a375d4dd40600d58
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12681
- Get rid of NodeMatchCriteria as a template parameter, which was too generic. So MatchNode<NodeMatchCriteria> becomes MatchNode<GraphType>, and MatchStore stores the predicate on GraphType::NodeRef.
- Similarly, get rid of NNNodeMatchCriteria
Now one can just pass in a function pointer NodeRef -> bool to NNMatchNode constructor directly like this
mg.createNode(is<Relu>)
- Merge static utilities in SubgraphMatcher class into MatchGraph class
- Rename MatchNode to MatchPredicate
Change use cases and tests to make it work
Reviewed By: ZolotukhinM
Differential Revision: D10386907
fbshipit-source-id: 43874bd154e3d7c29ce07b4b74eca8a7a9f3078a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11711
Added GPU support for spatial batch normalization. This functions by reducing values from GPUs onto a CPU and broadcasting those results back to each GPU. We have run several experiments, and found these results to be better than those without spatial bn: https://fb.quip.com/fr7HAeDliPB8
Reviewed By: enosair
Differential Revision: D9547420
fbshipit-source-id: ccbd2937efd6cfd61182fff2f098fb7c5ae8aeb1
Summary: Add a mapping for conversion -- this will help with debugging as well but is directly used by the TUI stacked on top of this
Reviewed By: duc0
Differential Revision: D10396130
fbshipit-source-id: cdd39278f0ed563bb828b1aebbbd228f486d89c8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12685
In this diff, we push the fake run of the net into the ONNXIFI transformer, because
1. We cannot do shape inference for every op
2. Since the net has been SSA rewritten, we cannot use shape info from outer workspace directly.
In addition, this diff adds input shape info when querying the `onnxBackendCompatibility` function.
Reviewed By: bddppq
Differential Revision: D10390164
fbshipit-source-id: 80475444da2170c814678ed0ed3298e28a1fba92
Summary:
Simple additions that make it vastly easier to use nomnigraph in
python
Reviewed By: duc0
Differential Revision: D10383027
fbshipit-source-id: 441a883b84d4c53cca4f9c6fcc70e58692b8f782
Summary:
This seems to be a typo that never got caught - no actual functionality changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12688
Differential Revision: D10391704
Pulled By: Yangqing
fbshipit-source-id: ce633776957628c4881956c5423bfab78294d512
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12661
Was disabled since workspace.has_mkldnn is now set to false
Reviewed By: yinghai
Differential Revision: D10383913
fbshipit-source-id: ad6dc705f0606b3711e8b450dc384ad3ebb87686
Summary:
The pytorch.org site redirects all of the http:// requests to the https:// site anyway, so the comments and error messages might as well refer directly to the https:// site. The GitHub project description should also be updated to point to https://pytorch.org
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12636
Differential Revision: D10377099
Pulled By: soumith
fbshipit-source-id: f47eaba1dd3eecc5dbe62afaf7022573dc3fd039
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12382
implement fp16-> (uint8 + scale and bias in fp32)
this is similar to fp32 rowwise quantization
we could have done scale and bias in fp16 but not too motivated since we are not saving much and those datatypes have to be converted to fp32 to process since x86 doesn't support half float operations anyways
Reviewed By: csummersea
Differential Revision: D10220463
fbshipit-source-id: 6c382026de881f03798c2e5fc43abfc80f84ea1f
Summary:
This resolves an issue where the `model.Copy` operation would
copy to the wrong GPU, such that the below `net.Sum` operation
would use an input argument for which p2p access was not enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12342
Differential Revision: D10343181
Pulled By: ezyang
fbshipit-source-id: fd2d6d0ec6c09cda2db0a9a4f8086b3560e5a3ec
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12178
Fisher GAN calls processor_util.add_mlp, which inject the layer norm through the
normalizer. We allow to use alternative impl for LayerNorn in the normalizer.
Reviewed By: Wakeupbuddy
Differential Revision: D9235528
fbshipit-source-id: 88c126c658102926613242ef84a481f6de1676ed
Summary:
1. Fix BN translator
IntelCaffe and NVCaffe fuse BN+Scale, and the "BatchNorm" op contains 5 params including (scale and bias)
2. Fix Scale translator
the translated outputs of scale have the same names with those of Conv.
All their names are output + '_w' and output + '_b'
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10056
Differential Revision: D10099205
Pulled By: yinghai
fbshipit-source-id: 73a73868e3e16c495e8b233fdb1d373d556a9537
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12390
Introduce a no op optimizer for when we don't want updates to happen, but don't want to affect downstream processes.
Reviewed By: mlappelbaum
Differential Revision: D10209812
fbshipit-source-id: 2af4ebc0fb42e78ea851c3a9f4860f3d224037b6
Summary:
Changes in this PR:
1. Intermediate Docker image is shared from build stage to test stage through ECR, in order to fix the Caffe2 flaky CUDA tests.
2. There are ~7 Caffe2 operator tests that are only flaky in `caffe2_py2_gcc4_8_ubuntu14_04_test` on CPU. Disabling those tests on that config only, which is okay to do because we are still running those tests in other test jobs.
After this PR is merged, CircleCI will be running on master automatically, and will be running on PRs if the author rebased their PR onto the newest master (which we will ask all the authors to do when we switch off Jenkins for Linux).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12389
Differential Revision: D10224267
Pulled By: yf225
fbshipit-source-id: dd1a90a425c3d13b870d3d328cb301eee2e6e2cd
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:
All usages of the `ndarray` construct have now been guarded with `USE_NUMPY`. This eliminates the requirement of NumPy while building PyTorch from source.
Fixes#11757
Reviewed By: Yangqing
Differential Revision: D10031862
Pulled By: SsnL
fbshipit-source-id: 32d84fd770a7714d544e2ca1895a3d7c75b3d712
Summary:
If block is missing control inputs when do caffe2 net execution, this PR add them back and remove the un-SSA semantics
jamesr66a
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12224
Differential Revision: D10135408
Pulled By: wanchaol
fbshipit-source-id: 746c870bde54ed4ca627167361db1b3f36cd235c
Summary:
Original commit changeset: f5614a5d2607
D9986213 is causing Multifeed Aggregator a [huge performance different](https://our.intern.facebook.com/intern/ads/analyze_canary/412951953278781781/) and is blocking aggregator push since last Friday night: https://fburl.com/feedtools/b6izvwjz
We need to land this revert ASAP to unblock aggregator push.
Reviewed By: orionr
Differential Revision: D10123245
fbshipit-source-id: d83da8e00a1250f5d09811a0a587c127e377aab2
