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Author SHA1 Message Date
Jerry Zhang
0ed7fc581c [quant][graphmode][refactor] Split quantization.cpp (#37975) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37975

Test Plan:
.

Imported from OSS

Differential Revision: D21468497

fbshipit-source-id: 35cbf98a344ca6e4094d616a4040eacf017fd2de
 2020-05-08 12:24:50 -07:00
Jerry Zhang
ff9a809ccd [quant][graphmode][refactor] Remove unused code in quantization.cpp (#37974) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37974

Differential Revision: D21468498

Pulled By: jerryzh168

fbshipit-source-id: 96f34db9f98474ec8e5d33e9b7c406b1637f5de8
 2020-05-08 11:03:03 -07:00
James Reed
c1e7758b5e Back out "Revert D20229168: [quantization] Use torchbind for Linear PackedParams" (#38101) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38101

Original commit changeset: 29e8a4d3b8bf
ghstack-source-id: 103730417

Test Plan: waitforsadcastle

Differential Revision: D21471381

fbshipit-source-id: a922cdf31ba32021e7264ae1454c646c0bfd7ef4
 2020-05-08 10:53:06 -07:00
Nikita Shulga
4bc0a7f86a Revert D20229168: [quantization] Use torchbind for Linear PackedParams 
Test Plan: revert-hammer

Differential Revision:
D20229168

Original commit changeset: 3607cac9aa5b

fbshipit-source-id: 29e8a4d3b8bffd95ff6a58b46c4f1c1e23770304
 2020-05-07 19:47:45 -07:00
James Reed
eaf9b28c55 [quantization] Use torchbind for Linear PackedParams (#34140) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/34140

Test Plan: Imported from OSS

Reviewed By: ZolotukhinM

Differential Revision: D20229168

Pulled By: jamesr66a

fbshipit-source-id: 3607cac9aa5b4b044572329742baed03350491c6
 2020-05-07 19:03:44 -07:00
eellison
d5df055bbb [WIP][JIT] Add JIT backend registration API (#35833) 
Summary:
**Summary**
This commit adds `torch::jit::RegisterBackend`, an API that allows
external backends to be registered for the execution of JIT subgraphs
outside the JIT interpreter. In order to register an external backend,
one must extend the provided abstract class `PyTorchBackendInterface` and provide
two additional functions: one that creates an instance of the aforementioned subclass
of `PyTorchBackendInterface`, and another that preprocesses a `ScriptModule` so that
it can run on the backend. Then, a `ScriptModule` that can compile and execute a given
JIT subgraph using the functions provided at registration time is generated
for each registered backend.

**Testing**
This commit adds a unit test that uses a minimal test backend
to make sure that the registration endpoint and generated
`ScriptModule` work.

```
$ python test/test_jit.py TestBackends
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.183s

OK

```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35833

Differential Revision: D21231955

Pulled By: SplitInfinity

fbshipit-source-id: 452db1123d0e5d83f97fe5da8a00fdfdb50dbef9
 2020-05-07 18:15:26 -07:00
Mikhail Zolotukhin
a44824c9ed [TensorExpr] Allow to enable/disable fallback mechanism thru an envvar PYTORCH_TENSOREXPR_FALLBACK. (#37971) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37971

Test Plan: Imported from OSS

Reviewed By: protonu

Differential Revision: D21444831

Pulled By: ZolotukhinM

fbshipit-source-id: c75f58772a4730e8f40f05491f9e5afa4aa3ed30
 2020-05-07 12:20:31 -07:00
Shen Li
ee1ddcef8d Acquire GIL when constructing/destructing ConcretePyObjectHolder (#37870) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37870

Test Plan: Imported from OSS

Differential Revision: D21410785

fbshipit-source-id: 374d5f40fbdfec98262aa4c84ec4ccdc40fb2ac1
 2020-05-07 07:37:39 -07:00
Michael Suo
b53e6bfd49 [jit] normalize getMethod (#37472) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37472

Our convention is for `findX` to return an optional version and `getX`
to assert that the X is there. Fix up `getMethod` to be consistent with
this convention.

Test Plan: Imported from OSS

Differential Revision: D21297543

Pulled By: suo

fbshipit-source-id: b40f56231cc8183e61bbb01fe5c0c113bcb6464d
 2020-05-06 15:22:25 -07:00
Jerry Zhang
1ad46f470f [jit] __copy__ for RecursiveScriptModule (#36830) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36830

Test Plan:
build/bin/test_jit

Imported from OSS

Differential Revision: D21431012

fbshipit-source-id: 13a1bf9744ec95ea59622226c8d8a8d55ec3f0b0
 2020-05-06 13:55:01 -07:00
Jerry Zhang
70f375becf [quant] ConvPackedParams with TorchBind (#35923) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35923

(Note: this ignores all push blocking failures!)

Test Plan:
tbd

Imported from OSS

Differential Revision: D20957089

fbshipit-source-id: 74d8bd628ccba64e902ea6ebabc2b883924050b0
 2020-05-05 20:18:36 -07:00
Jerry Zhang
9b3911c073 [quant][graphmode][refactor] rename SwapDequant and refactor code handling general ops (#37555) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37555

Test Plan:
.

Imported from OSS

Differential Revision: D21393514

fbshipit-source-id: 5bc9fa0f0be25f4c35a64acb23513f64ed07e230
 2020-05-05 11:20:15 -07:00
Mikhail Zolotukhin
7fa968b10d [TensorExpr] Add python bindings for TE fuser. (#37831) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37831

Test Plan: Imported from OSS

Reviewed By: jackm321

Differential Revision: D21404947

Pulled By: ZolotukhinM

fbshipit-source-id: 8467346d4fd8413985a33832fb3994d3ead746dc
 2020-05-05 10:58:30 -07:00
Elias Ellison
23d0441da7 [JIT] Fix GetAttr inconsistency (#37424) 
Summary:
We were previously only looking at class attributes, so that didn't include methods etc, and would silently give wrong semantics. This makes hasAttr go through the same resolution as our other attribute lookups.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37424

Differential Revision: D21282633

Pulled By: eellison

fbshipit-source-id: 8e970f365c2740d137a02331739c2ed93747b918
 2020-05-05 09:06:51 -07:00
Michael Suo
b7f258bbd3 add fmt to libtorch_python.so (#37560) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37560

Test Plan: Imported from OSS

Differential Revision: D21320059

Pulled By: suo

fbshipit-source-id: 95cfe7cf26c515fdfcb4621cc58266d838a38a3e
 2020-05-04 10:14:37 -07:00
Linbin Yu
099a84ef9b Add overload name for aten::tensor and aten::as_tensor (#37655) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37655

Add override name for aten::tensor and aten::as_tensor.
These two ops are used in NLU model, and they will included them in lite interpreter

Test Plan: verified model can be loaded correctly

Reviewed By: iseeyuan

Differential Revision: D21346142

fbshipit-source-id: 05ff4d9e0bcf7f4f9a30d95ca81aef9c3f6b0990
 2020-05-01 14:31:04 -07:00
Michael Voznesensky
91e74fd843 [JIT] Adds a code_with_constants method to module printing (#37586) 
Summary:
Closes https://github.com/pytorch/pytorch/issues/36625
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37586

Differential Revision: D21331385

Pulled By: suo

fbshipit-source-id: 752e63eac8bdd06c6719efb972cdc832ad7c1535
 2020-04-30 20:44:01 -07:00
Elias Ellison
cde1350a5d Add support for generic list constants (#36953) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36953

Add support for generic lists as a constant. generic dicts & tuples are already implemented. This is a pretty common pattern and cuts down on the number of non-tensor nodes executed in interpolate tests.

Test Plan: Imported from OSS

Differential Revision: D21160761

Pulled By: eellison

fbshipit-source-id: 1e6b7b25b7580f09067794772d44e615601c60c4
 2020-04-28 23:28:07 -07:00
Elias Ellison
c516f84525 [JIT] Add Lower Tuples Call & Run remove mutation after list unrolling (#36829) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36829

This changes the IR complexity from the previous PR for the following tests:
```
('Name', 'Ifs/Loops', 'non-tensor ops')
Before:  ('max_unpool1d', 0, 3)
After:  ('max_unpool1d', 0, 0)
Before:  ('max_unpool2d', 0, 3)
After:  ('max_unpool2d', 0, 0)
Before:  ('max_unpool3d', 0, 4)
After:  ('max_unpool3d', 0, 0)
Before:  ('adaptive_max_pool2d', 0, 3)
After:  ('adaptive_max_pool2d', 0, 0)
Before:  ('adaptive_max_pool3d', 0, 4)
After:  ('adaptive_max_pool3d', 0, 0)
Before:  ('adaptive_avg_pool2d', 0, 3)
After:  ('adaptive_avg_pool2d', 0, 0)
Before:  ('adaptive_avg_pool3d', 0, 4)
After:  ('adaptive_avg_pool3d', 0, 0)
Before:  ('upsample', 13, 68)
After:  ('upsample', 4, 28)
Before:  ('upsample', 13, 68)
After:  ('upsample', 0, 5)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 13, 67)
After:  ('interpolate', 4, 27)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 13, 67)
After:  ('interpolate', 4, 27)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 13, 67)
After:  ('interpolate', 4, 27)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 13, 67)
After:  ('interpolate', 4, 27)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 14, 59)
After:  ('interpolate', 0, 3)
Before:  ('interpolate', 13, 57)
After:  ('interpolate', 4, 21)
Before:  ('interpolate', 14, 59)
After:  ('interpolate', 0, 3)
Before:  ('interpolate', 14, 59)
After:  ('interpolate', 0, 3)
Before:  ('interpolate', 13, 57)
After:  ('interpolate', 4, 21)
Before:  ('interpolate', 14, 59)
After:  ('interpolate', 0, 3)
Before:  ('interpolate', 14, 59)
After:  ('interpolate', 0, 3)
Before:  ('interpolate', 13, 57)
After:  ('interpolate', 4, 21)
Before:  ('interpolate', 14, 59)
After:  ('interpolate', 0, 3)
Before:  ('interpolate', 13, 77)
After:  ('interpolate', 4, 33)
Before:  ('interpolate', 14, 77)
After:  ('interpolate', 0, 5)
Before:  ('interpolate', 14, 77)
After:  ('interpolate', 0, 5)
Before:  ('interpolate', 13, 77)
After:  ('interpolate', 4, 33)
Before:  ('interpolate', 14, 77)
After:  ('interpolate', 0, 5)
Before:  ('interpolate', 14, 77)
After:  ('interpolate', 0, 5)
Before:  ('interpolate', 13, 77)
After:  ('interpolate', 4, 33)
Before:  ('interpolate', 14, 77)
After:  ('interpolate', 0, 5)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 14, 68)
After:  ('interpolate', 0, 4)
Before:  ('interpolate', 15, 103)
After:  ('interpolate', 1, 23)
Before:  ('interpolate', 14, 70)
After:  ('interpolate', 0, 6)
Before:  ('interpolate', 15, 103)
After:  ('interpolate', 1, 21)
Before:  ('interpolate', 14, 70)
After:  ('interpolate', 0, 6)
Before:  ('interpolate', 15, 91)
After:  ('interpolate', 1, 13)
Before:  ('interpolate', 14, 59)
After:  ('interpolate', 0, 3)
Before:  ('interpolate', 15, 93)
After:  ('interpolate', 1, 16)
Before:  ('interpolate', 14, 61)
After:  ('interpolate', 0, 5)
Before:  ('interpolate', 15, 111)
After:  ('interpolate', 1, 28)
Before:  ('interpolate', 14, 77)
After:  ('interpolate', 0, 5)
Before:  ('interpolate', 15, 113)
After:  ('interpolate', 1, 27)
Before:  ('interpolate', 14, 79)
After:  ('interpolate', 0, 7)
Before:  ('test_nn_AdaptiveMaxPool2d_single', 0, 3)
After:  ('test_nn_AdaptiveMaxPool2d_single', 0, 0)
Before:  ('test_nn_AdaptiveMaxPool2d_tuple', 0, 3)
After:  ('test_nn_AdaptiveMaxPool2d_tuple', 0, 0)
Before:  ('test_nn_AdaptiveMaxPool3d_single', 0, 4)
After:  ('test_nn_AdaptiveMaxPool3d_single', 0, 0)
Before:  ('test_nn_AdaptiveMaxPool3d_tuple', 0, 4)
After:  ('test_nn_AdaptiveMaxPool3d_tuple', 0, 0)
Before:  ('test_nn_AdaptiveMaxPool3d_single_nonatomic', 0, 4)
After:  ('test_nn_AdaptiveMaxPool3d_single_nonatomic', 0, 0)
Before:  ('test_nn_AdaptiveMaxPool3d_tuple_nonatomic', 0, 4)
After:  ('test_nn_AdaptiveMaxPool3d_tuple_nonatomic', 0, 0)
Before:  ('test_nn_AdaptiveAvgPool2d_single', 0, 3)
After:  ('test_nn_AdaptiveAvgPool2d_single', 0, 0)
Before:  ('test_nn_AdaptiveAvgPool2d_single_1x1output', 0, 3)
After:  ('test_nn_AdaptiveAvgPool2d_single_1x1output', 0, 0)
Before:  ('test_nn_AdaptiveAvgPool2d_tuple', 0, 3)
After:  ('test_nn_AdaptiveAvgPool2d_tuple', 0, 0)
Before:  ('test_nn_AdaptiveAvgPool3d_single', 0, 4)
After:  ('test_nn_AdaptiveAvgPool3d_single', 0, 0)
Before:  ('test_nn_AdaptiveAvgPool3d_tuple', 0, 4)
After:  ('test_nn_AdaptiveAvgPool3d_tuple', 0, 0)
```

Test Plan: Imported from OSS

Differential Revision: D21160758

Pulled By: eellison

fbshipit-source-id: 68ccbf3af74398e8dbad7e6bedb639635dafdb2e
 2020-04-28 23:28:02 -07:00
Jerry Zhang
6fa76b8a0c [jit] __deepcopy__ for RecursiveScriptModule (#32684) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32684

Previously we have `clone` and `clone_instance`, where `clone` will clone both type
and value, and `clone_instance` only clone the value, both of them are shallow copies.
We need to re-evaluate whether we should expose them as a user facing API.
I think we should hide `clone`, but `clone_instance` might be useful as well, especially
when we are copying a model with very large weights, people might just want to do shallow copy.

This PR adds a `deepcopy` that might be useful as a user API, which deep copies the values, including
Tensor, but we didn't deepcopy `Blob`, `Capsule`, `Future` or `PyObject`.
For more discussions please see the following issue.

fixes: https://github.com/pytorch/pytorch/issues/32519

Test Plan: Imported from OSS

Differential Revision: D21220756

fbshipit-source-id: 476bf11fe82c08fac36e7457879a09f545ffdc5e
 2020-04-28 18:47:11 -07:00
Nikolay Korovaiko
a80a438e37 correctly set and restore states in te tests (#37210) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37210

Differential Revision: D21238634

Pulled By: Krovatkin

fbshipit-source-id: 6462239753399c10c871baa5d5fdff5465cf2544
 2020-04-24 20:16:51 -07:00
Xiang Gao
3880f14b64 Canonicalize includes in torch, and add tests for it (#36303) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36303

Test Plan: Imported from OSS

Differential Revision: D20943003

Pulled By: ezyang

fbshipit-source-id: 81fcbaccc1a7eec422bd8347d196bb66a5467884
 2020-04-23 08:09:21 -07:00
David Reiss
63e5058c88 Fix naming of "strides" method in TensorType (#36727) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36727

Looks like this was renamed by accident in 0cbd7fa46f

Test Plan:
Unit test.
Lint.

Differential Revision: D21076697

Pulled By: dreiss

fbshipit-source-id: dbd18cb41c7b26479984a7a7b12ad41a4c5b7658
 2020-04-16 17:07:27 -07:00
Elias Ellison
9cbeb0faed [JIT] Dont optimize shape peepholes on inline (#36404) 
Summary:
With https://github.com/pytorch/pytorch/pull/35562, we are running peephole optimization on inlining to reduce the number of nodes that are copied.

The tracer encodes the sizes in the graph like:
```
graph(%0 : Double(7)):
  %1 : Function = prim::Constant[name="tensor_size"]()
  %2 : Tensor = prim::CallFunction(%1, %0)
  return (%2)
```

however people would like to reuse the graph with different shapes so running size invalidations would invalidate that. long term it might be better for the tracer to not include shape information but there are downstream users of that.

Separates out FuseAddMM from peephole so that now there is a single `disable_size_optimizations` parameter, and onnx explicitly invokes fuseaddmm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36404

Differential Revision: D20968974

Pulled By: eellison

fbshipit-source-id: 56f8f1699e3b0adeeccdfd5a67bb975fd41a2913
 2020-04-15 17:49:48 -07:00
Negin Raoof
f99a28f515 [ONNX] Adding a pass to replace interpolate function with aten::__interpolate (#35744) 
Summary:
Since aten;:__interpolate is removed in https://github.com/pytorch/pytorch/pull/34514, we need a pass replace interpolate function with aten::__interpolate for ONNX export.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35744

Reviewed By: hl475

Differential Revision: D20907041

Pulled By: houseroad

fbshipit-source-id: f2d2cdfec47389245c50f538267124eedf682adf
 2020-04-14 23:16:22 -07:00
Wanchao Liang
999d7f6ab2 [jit] tracer flag to guard risky behaivors (#36277) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36277

This PR introduce a flag to the tracer that guard the risky behaviors
like adding list/dict as output of the tracer. Currently to ensure not
BC breaking user, we throw warning if the tracer output is list, and
will throw error when the tracer output is dict to enforce using this
flag (next PR)

Test Plan: Imported from OSS

Differential Revision: D20998157

Pulled By: wanchaol

fbshipit-source-id: 0d2c55f1a263a48b1b92dd6ad54407815e0a6f72
 2020-04-13 22:35:03 -07:00
Mikhail Zolotukhin
765bf8f03d Remove duplicate bindings from torch/csrc/jit/python/init.cpp. (#36492) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/36492

Test Plan: Imported from OSS

Differential Revision: D20995235

Pulled By: ZolotukhinM

fbshipit-source-id: 6afa3a956e57c2fb94bb29d332177be73a2bac2a
 2020-04-13 12:28:32 -07:00
Mike Ruberry
62f9312abd Revert D20783298: Fix naming of "strides" method in TensorType 
Test Plan: revert-hammer

Differential Revision:
D20783298

Original commit changeset: 8fcc146284af

fbshipit-source-id: 30e3cb6d7a30d82048534d4d2e794b7e08ae01bb
 2020-04-09 04:24:43 -07:00
David Reiss
16980e455f Fix naming of "strides" method in TensorType (#35170) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35170

Looks like this was renamed by accident in 0cbd7fa46f

Test Plan:
Unit test.

Imported from OSS

Differential Revision: D20783298

fbshipit-source-id: 8fcc146284af022ec1afe8d651baf6721b190ad3
 2020-04-08 15:59:28 -07:00
David Reiss
645d57ea01 Expose JIT Module's "register_attribute" to Python (#35630) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35630

Prefix underscored for now because the semantics of this method can be
confusing.  It adds a new attribute to the *type*, which can be shared
by several objects.

Test Plan:
Next diff in stack uses it, and has unit tests.

Imported from OSS

Differential Revision: D20904253

fbshipit-source-id: dcbf60eacf0e0e075c19238165aa33954aa73b5f
 2020-04-08 13:09:28 -07:00
Kimish Patel
d559a47933 Enable relu fusion with prepacked linear/conv. (#35705) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35705

Introduces a pass for relu fusion.

Test Plan:
python test/test_xnnpack_integration.py

Imported from OSS

Differential Revision: D20746592

fbshipit-source-id: 6c22f60a20e9121618c85077b9b58fb8d4082b3b
 2020-04-03 15:38:45 -07:00
Mikhail Zolotukhin
af5121f62a Invoke TensorExpr fuser pass from a graph executor. (#35913) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35913

The pass itself is still disabled by default, but with this change we
don't need to register it as a custom pass anymore. It allows us to
control its behavior with env variables more easily.

Test Plan: Imported from OSS

Reviewed By: suo

Differential Revision: D20827189

Pulled By: ZolotukhinM

fbshipit-source-id: e74d90b5e46422e7ab7bc40974a805220da50fbc
 2020-04-03 12:20:26 -07:00
davidriazati
6e13a7787b [jit] Fix type comparisons segfault (#35929) 
Summary:
Pybind will convert `None`s to `nullptr`s, so this adds a check to make
sure those don't get into the actual type comparison logic. Fixes #35778
](https://our.intern.facebook.com/intern/diff/20831278/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35929

Pulled By: driazati

Differential Revision: D20831278

fbshipit-source-id: 5800050e5eec280072afde58141ad00c1e8db8e2
 2020-04-03 11:33:48 -07:00
Elias Ellison
2595c62208 [JIT] Better error on default params error (#35888) 
Summary:
Someone messaged me abt this when a better error msg would have solved their problem
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35888

Differential Revision: D20819538

Pulled By: eellison

fbshipit-source-id: 95d124bfd162e1747dcdf7a981703a279a5dfaa6
 2020-04-02 15:31:22 -07:00
Christian Sarofeen
6d24f8fe21 Infrastructure for a new CUDA Fuser (#34785) 
Summary:
**Summary:** This PR contains the infrastructure of a new CUDA fuser. This CUDA fuser is based on many of the same principles of TensorExpressions and Halide, however the implementation is ground up. The fusion pass itself is similar to the default CUDA fuser, however, it has undergone some refactoring and is using the new code generation infrastructure. For those who are interested in how the code generation in this PR works, I would recommend reviewing _test/cpp/jit/test_gpu_fusion.cpp_ as well as the long comment section at the beginning of _torch/csrc/jit/codegen/cuda/transform_replay.h_  One of the largest differences between our approach and that of TVM/Halide, is the concept of "TensorView". TensorView from a high level should be thought of similarly to how we think of working with Tensors in PyTorch. It's an N-D object which can undergo transformations that change its dimensionality. Dimensionality changes are done through the operations split/merge/reorder/computeAt. These transformations are similar to split/fuse/reorder/compute_at of TVM, they modify how a tensor is iterated over to generate GPU code. Interestingly, in our scheme these transformations are applied to tensors and only impact how that tensor is generated.

**Warning:** This PR is purposefully not feature complete with the current fuser. We wanted to separate out the infrastructure from the fusion capabilities. Once in, smaller incremental PRs will be submitted to expand capabilities of the fuser.

**Short term goals:**

Parity with current CUDA fuser (including performance):
- Dynamic shapes (no recompilation)
- Implicit handling of braodcast (broadcasted tensors are treated as tensors of the braodcasted size in the generated code)
- Dropout

**Mid-term goals:**

- Transposes fused with pointwise operations where transpose involves only 2 axes (across the fused operation).
- 1-D reductions fused with pointwise operations
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34785

Reviewed By: ZolotukhinM

Differential Revision: D20650977

Pulled By: soumith

fbshipit-source-id: ee39c95a880e1b9822e874ed4cc180971572bf63
 2020-04-02 09:22:42 -07:00
Michael Suo
866d9d4e6a [jit] Fix name collision on load (#35720) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35720

When modules are saved, all relevant types are serialized according to
their qualified name with a compilation unit. Since qualified names are
guaranteed to be unique within a compilation unit, this normally works
fine.

On load, all types are registered in a compilation unit owned by the
script::Module. Type names are not unique across compilation units, so
if you load two modules with colliding type names, make them submodules
of yet another module, and save that module, there is the potential of a
name collision. See the added tests for examples if that description is
confusing.

The solution is to unique type names when serializing code by mangling
them if we detect a name collision.

Test Plan: Imported from OSS

Differential Revision: D20749423

Pulled By: suo

fbshipit-source-id: a8827ff1d4a89f3e7964dbbb49b4381863da3e6a
 2020-04-01 00:02:38 -07:00
Michael Suo
06dcb70905 [jit] Fix Type equality in some cases (#35719) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35719

Test Plan: Imported from OSS

Differential Revision: D20749422

Pulled By: suo

fbshipit-source-id: 09b697766c1eb3e56f4cf8acc7e854b0981d7991
 2020-03-31 22:29:12 -07:00
Supriya Rao
a090de380c [quant][graph] Add quant fusion for dynamic quantization (#35586) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35586

This pass fuses the choose_qparams-quant-dequant sequence
Fusion for weight tensor is the same as static quant.

Test Plan:
python test/test_quantize_script.py

Imported from OSS

Differential Revision: D20755680

fbshipit-source-id: b7443770642b6e6fa0fa9da8a44637e9b2d4df70
 2020-03-30 23:34:56 -07:00
Supriya Rao
1f7ee7b6b7 [quant][graph] Add pass to insert quant dequant for dynamic quantization (#35448) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35448

Add _choose_qparams_per_tensor which returns scale and zero_point similar to the dynamic quantization in the operator

Test Plan:
python test/test_quantize_script.py

Imported from OSS

Differential Revision: D20755679

fbshipit-source-id: c9066d8f1bb3e331809be26c4be806faafc9b981
 2020-03-30 23:33:32 -07:00
Jerry Zhang
6fc2403951 [quant][graphmode] qconfig_dict support None (#35336) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35336

Test Plan:
python test/test_quantization.py

Imported from OSS

Differential Revision: D20655302

fbshipit-source-id: b453f3240ac487aa29629953b4d71274dbbc25fc
 2020-03-29 12:47:47 -07:00
Mikhail Zolotukhin
cd00bbc23f clang-format. (#35605) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35605

Test Plan: Imported from OSS

Reviewed By: orionr

Differential Revision: D20720486

Pulled By: ZolotukhinM

fbshipit-source-id: f081a9fb6ef84fdce3b8f071d5e251e267854a18
 2020-03-28 11:45:06 -07:00
davidriazati
f27403d761 [jit] Fix named tuple resolution (#35409) 
Summary:
Fixes #29035

Previously we were missing a case for namedtuples in our Python value resolution logic, so they were just getting resolved as regular Python values, hence the `OSError`s in the linked issue
](https://our.intern.facebook.com/intern/diff/20653496/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35409

Pulled By: driazati

Differential Revision: D20653496

fbshipit-source-id: b5db1a11e918175aa02fda92993d233695417c56
 2020-03-27 17:07:26 -07:00
Nikolay Korovaiko
9e22d15f14 Enable tensorexpr cpp tests in CI. try #2 (#35454) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/35454

Differential Revision: D20665160

Pulled By: Krovatkin

fbshipit-source-id: e04cbe92b2ee5a3288f3c4e5c83533bfea85bf85
 2020-03-27 12:09:55 -07:00
Bram Wasti
a3e10d2a17 Expose enablement of TensorExpr fuser as env variable (#35341) 
Summary:
This commit allows one to use an environment variable to enable the fuser in torch/csrc/jit/tensorexpr/

```
PYTORCH_TENSOREXPR=1 python benchmark.py
```

This commit also changes the registration to happen by default, removing the requirement for the python exposed "_jit_register_tensorexpr_fuser"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35341

Reviewed By: ZolotukhinM

Differential Revision: D20676348

Pulled By: bwasti

fbshipit-source-id: 4c997cdc310e7567c03905ebff72b3e8a4c2f464
 2020-03-26 14:31:57 -07:00
Meghan Lele
6384c2d81b [JIT] clang-format JIT code (#35115) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115

This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.

Testing:
Ran the script, CI.

Test Plan: Imported from OSS

Reviewed By: eellison

Differential Revision: D20568523

Pulled By: SplitInfinity

fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
 2020-03-26 11:24:51 -07:00
Suraj Menon
aa01a95c6d Revert D20630760: [pytorch][PR] Enable NNC tests vol. i. add test_tensorexpr.py tests [WIP] 
Test Plan: revert-hammer

Differential Revision:
D20630760

Original commit changeset: 7d2f27aca6b1

fbshipit-source-id: 28ac92b3390651a4a67061d6ebf208515b9b9463
 2020-03-25 20:34:46 -07:00
Kimish Patel
dc2c4d02f9 Add a wrapper to wrap all optimization for mobile. (#35227) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35227

This wraps.
1. Conv BN folding (not mobile specific)
2. insert XNNPACK conv2d/Linear ops
3. Remove prepacking ops.

Test Plan: Imported from OSS

Differential Revision: D20603562

fbshipit-source-id: ff373af7112c070ec6198bac51845282e09ff1f8
 2020-03-25 19:21:14 -07:00
Nikolay Korovaiko
f3a5081bd4 Enable NNC tests vol. i. add test_tensorexpr.py tests [WIP] (#34897) 
Summary:
This  PR add tensorexpr cpp tests to test_jit.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34897

Differential Revision: D20630760

Pulled By: Krovatkin

fbshipit-source-id: 7d2f27aca6b1e23e3ffed1c765d8f590688118e3
 2020-03-25 17:23:48 -07:00
Elias Ellison
aab4beb87f [JIT] Pass To Safely Remove Aten Inplace Ops (#33186) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33186

This helps create larger functional graphs. It has the potential to increase memory use, so in order to land this on by default we would probably also do a reuse of buffers pass.

This is currently O(n * | Removed Nodes | ) because we have to rebuild the alias Db each time we make a change. This pass is critical to creating functional graphs, so this might be a compelling use case to build incremental updates to alias Db.

Test Plan: Imported from OSS

Differential Revision: D20603189

Pulled By: eellison

fbshipit-source-id: 105db52bf38e02188ca6df6d36294466d3309a0a
 2020-03-24 23:45:58 -07:00
Elias Ellison
5b2f8cef08 [JIT] Functional Graph Pass (#33020) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33020

This is a pass to create functional blocks. The other PRs in the stack help avoid some of the limitations that are are often found in graphs. It's possible that this would work well with a graph that is frozen. Follow up work items that will help this pass:

- We don't currently have any capacity in alias analysis to tell whether a Value that came from the wildcard set "re-escapes" back into the wildcard set.
- More comments on the semantics of the graph and correctness conditions
- We could consider using dynamic dag if the perf of this is a limitation.
- potential make Functional Graphs Functional Blocks instead, so that we do not repeatedly copy constants, also to make IR read easier.

Test Plan: Imported from OSS

Differential Revision: D20603188

Pulled By: eellison

fbshipit-source-id: 6822a6e65f4cc2676f8f6445fe8aa1cb858ebeeb
 2020-03-24 23:44:18 -07:00