Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35763
Adds inference function and test for ScatterAssign
Test Plan: Updated unit test
Reviewed By: yyetim, shunting1986
Differential Revision: D20501079
fbshipit-source-id: 7ec6ef0127a151250dd699c90c2b80c35cfb1fe4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35857
This fixes a lot of common ops for InferBlobShapesAndTypes as well as adds support for testing the inferred shapes and types of gradient ops.
Ops:
* Concat
* Split
* LeakyReLU
* Relu
* Prelu
* Gelu
* Elu
* Sinh, Tanh, Cosh
* Abs
* ... and a number of other simple element wise ops
Test Plan:
Added support to hypothesis test to check the shape and type of gradient ops.
Enabled it for all the ops I fixed the shape and type inference for.
buck test caffe2/caffe2/python/operator_test:
Reviewed By: pradeepd24
Differential Revision: D20806284
fbshipit-source-id: 77f796d9ff208e09e871bdbadf9a0a7c196b77f2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35507
We want to split up the SparseLengthsSumSparse op into an indirection op and the SparseLengthsSum op so that we can lower the later part. The indirection part is a plain impl now.
Test Plan:
```
for i in `seq 10`; do buck test caffe2/caffe2/python/operator_test:lengths_reducer_fused_nbit_rowwise_ops_test -- test_sparse_lengths_sum_rowwise_sparse; done
```
Reviewed By: jspark1105
Differential Revision: D20683478
fbshipit-source-id: 509effe88719d20aa0c4783bbe0ce1f183ee473c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35430
This fixes and adds tests for several commonly used operators.
There's some formatting differences due to running clang-format on one of the files.
Test Plan: buck test //caffe2/caffe2/fb/operators:hypothesis_test //caffe2/caffe2/python/operator_test:utility_ops_test //caffe2/caffe2/python/operator_test:concat_split_op_test
Reviewed By: yyetim
Differential Revision: D20657405
fbshipit-source-id: 51d86d0834003b8ac8d6acb5149ae13d7bbfc6ab
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35346
weight scale op doesn't have GPU impl. This is breaking OSS CI from D20506032. Making it cpu only
Test Plan: OSS CI
Reviewed By: ustctf
Differential Revision: D20637440
fbshipit-source-id: 9aa6cce63ce637ab7856788e5d02f527decb2a26
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34903
Reattempt of D20461609
Moving 2/4-bit SLS and row-wise 2/4-bit conversion operator to open source to be used by DLRM
Test Plan: CI
Reviewed By: jianyuh
Differential Revision: D20495304
fbshipit-source-id: 66a99677583f50fd40e29c514710c7b1a8cdbc29
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34783
Moving 2/4-bit SLS and row-wise 2/4-bit conversion operator to open source to be used by DLRM
Test Plan: CI
Reviewed By: yinghai
Differential Revision: D20461609
fbshipit-source-id: b3ef73ff10f2433afe06ffa73fe1145282d9ec4c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33431
Some elementwise operators don't have shape and type inference specified for the output tensor: `BitwiseOr`, `BitwiseAnd`, `BitwiseXor`, `Not`, `Sign`.
This change fixes this issue:
- For `Not` and `Sign` operators, the output has the same type and shape as the input, so `IdenticalTypeAndShapeOfInput` function is used to specify that.
- For bitwise operators created by `CAFFE2_SCHEMA_FOR_BINARY_BITWISE_OP` macro, the type and shape inference rules should be the same as for other binary element-wise operators, so `TensorInferenceFunction(ElementwiseOpShapeInference)` is used to specify that.
Also some tests were modified to ensure that the shape and type are inferred (`ensure_outputs_are_inferred` parameter)
Test Plan:
```
CAFFE2_ASSERT_SHAPEINFERENCE=1 buck test caffe2/caffe2/python/operator_test:elementwise_ops_test
CAFFE2_ASSERT_SHAPEINFERENCE=1 buck test caffe2/caffe2/python/operator_test:math_ops_test
```
Note that the tests have to be executed with `CAFFE2_ASSERT_SHAPEINFERENCE=1` in order to fail upon shape inference failure.
Reviewed By: idning
Differential Revision: D19880164
fbshipit-source-id: 5d7902e045d79e5669e5e98dfb13a39711294939
Summary:
For both the Caffe2 and PyTorch backends, enable 3D convolutions through MIOpen.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33067
Reviewed By: BIT-silence
Differential Revision: D19880495
Pulled By: bddppq
fbshipit-source-id: 8f6f970910654c1c5aa871b48a04c1054875691c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32271
Use the 2-stage EmbeddingSpMDM interface in D19425982 to reduce the overhead of code cache lookup and lock contention.
Fix an issue in sparse_lengths_sum_benchmarks generating empty indices when average length is small like 1.
Test Plan: CI
Reviewed By: dskhudia
Differential Revision: D19425987
fbshipit-source-id: d5c5f0d46e0072403901809c31d516fa0f4b9b31
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32448
Using binary search to compute the value for the given quantile among the input tensors.
Test Plan: Newly added unittests;
Reviewed By: jspark1105
Differential Revision: D19487604
fbshipit-source-id: 0dc6627b78d1310ac35b3f1d53b89cc89a697ece
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32475
As title
Test Plan: CI
Reviewed By: houseroad
Differential Revision: D19508778
fbshipit-source-id: fd9ad63607535980505d155f3e3c3b7c6b95daf7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31612
Count the number recent update on rows. Exponential decay is applied on the counter with decay rate r, such that
r^{counter_halflife} = 0.5;
If counter_halflife is nonpositive, this operator is turned off.
Test Plan: added unittest
Reviewed By: chocjy
Differential Revision: D19217921
fbshipit-source-id: 96d850123e339212cc0e0ef352ea8a1b1bf61dfa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24341
ConvTransposeOp doesn't crash for zero-batch, but it doesn't modify the output blob. This leads to buggy behaviour especially when running the same network twice using different input, or backprop during training.
Seems `ConvTransposeUnpoolBase<Context>::GetOutputSize` works for zero-batch, so I remove the check for `input.numel() > 0`, and reshape the output blob before returning.
For CudnnConvTransposeGradientOp, it's a bit verbose to set `dfilter` and `dbias`, it's a seems the Cudnn can handle it, so simply remove the `X.numel() == 0` branch.
Test Plan: buck test mode/dev-nosan caffe2/caffe2/python/operator_test:conv_transpose_test -- --run-disabled
Reviewed By: BIT-silence
Differential Revision: D16807606
fbshipit-source-id: 0d72c5bd8f2e03c34465e7b530cca548d9bdd5e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19705
Optimizing for a case when there's a consecutive dims that are not broadcasted followed by another consecutive dims that are broadcasted.
For example, MulGradient(["dC", "A", "B"], ["dA", "dB"], broadcast=True, axis=0) where A.shape == dC.shape == [9508, 80] and B.shape == [80] .
Test Plan:
In SKL T6,
Running mul_gradient_benchmark without this optimization
Operator #0 (dA, MulGradient) 11.9119 ms/iter
After this optimization,
Operator #0 (dA, MulGradient) 0.672759 ms/iter
Need to land D15291800 before to fix the unit test error
Reviewed By: dmudiger
Differential Revision: D15075415
fbshipit-source-id: 0f97be17cf8f1dacbafa34cd637fb8bc1c5e5387
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29167
As titled.
This fix is crucial as multi_channel splitting would create history that has no items (i.e., D == 0), which leads to flow failure.
Test Plan:
Unittest
flow test:
before fix: f148783160
after fix: f149082299
buck test mode/dev-nosan caffe2/caffe2/python/operator_test:softmax_ops_test
Reviewed By: xianjiec
Differential Revision: D18296081
fbshipit-source-id: e0bb2dc2c4e5b465e213f31e5c5ced3a7e1fd574
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26227
In the previous implementation of composite lr, the lr_scale for each sub policy will be rewritten by the last lr_scale.
Due to another bug in unittest (where policy_lr_scale being the same for all sub policies), this bug was not detected by unittest...
Fix: add an additional field in CompositeLearningRateItem so that we store lr_scale values for all sub policies
If fix unittest, the error in previous implementation:
https://fburl.com/testinfra/ikdbnmey
With the fix,
https://fburl.com/testinfra/m694ehl1
Test Plan:
unittest
buck test caffe2/caffe2/python/operator_test:learning_rate_op_test -- test_composite_learning_rate_op
Reviewed By: chocjy, alex1o1o7cloud
Differential Revision: D17380363
fbshipit-source-id: 161e9cb71bb2ea7f0734a3361e270616057a08e4
