I noticed that when `SymInt` was introduced, `jit_type_base.h` was
added as an include to the `Operator.h` template which is supposed to
be kept extremely clean and only use forward declarations. Also,
that forward declarations for `OptionalArrayRef` were missing.
So, I've refactored the forward declarations into
`ATen/core/ATen_fwd.h` and cleaned up some of the `c10`
headers that were masking these missing declarations. I've also
re-generated the pre-compiled header so `SymInt` is included.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76576
Approved by: https://github.com/albanD
Summary:
The `Buf` uses `std::vector<ExprHandle>` to represent its strides. The `ExprHandle` could be an immediate value or a mathematical expression with variables involved both for the static shape and dynamic shape. So it is hard to directly deduce the channels-last contiguous layout based on the numerical calculation. Hence, the utility functions of this PR are based on the pattern match to check whether the `Buf` is channels-last contiguous.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75938
Reviewed By: cpuhrsch
Differential Revision: D35724091
Pulled By: ZolotukhinM
fbshipit-source-id: f79ae21749d0aad8601f0434b52df88602ff09bf
(cherry picked from commit 3712bbbe4bea57c5c1abe1eafde4b8778e13e0c4)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72389
This is an NFC change that just prepares the code for the upcoming
deletion of `DimArg` class. This change makes `Compute` and `Reduce`
APIs to use `ExprHandle` everywhere.
There should be no observable behavior change from this PR.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D34030295
Pulled By: ZolotukhinM
fbshipit-source-id: 3fd035b6a6bd0a07ccfa92e118819478ae85412a
(cherry picked from commit 1b0a4b6fac)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64887
BufHandle has exactly the same functionality and should be used instead.
Differential Revision:
D30889483
D30889483
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 365fe8e396731b88920535a3de96bd3301aaa3f3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64077
We were assuming kernel dimensions fit in 32 bits (the old fuser made
this assumption too), but we should be able to support 64.
ghstack-source-id: 136933272
Test Plan: unit tests; new IR level test with huge sizes
Reviewed By: ZolotukhinM
Differential Revision: D30596689
fbshipit-source-id: 23b7e393a2ebaecb0c391a6b1f0c4b05a98bcc94
Summary:
Adds a C++ codegen backend to NNC to generate C++ for CPU instead of generating LLVM IR.
Tensors are represented as blobs of float. Vector operations are devectorized/unrolled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62869
Test Plan:
https://github.com/pytorch/pytorch/tree/mvz-nnc-aot-prototype makes it able to AOT compile the whole MobileNetV3 model into binary code through LLVM codegen in NNC.
I forked that branch to https://github.com/cheng-chang/pytorch/tree/cc-aot-cpp, merged this PR into it, and modified `fancy_compile` to compile MobileNetV3 into C++ through
```
import torch
m = torch.jit.load('mobnet.pt')
m.eval()
f = torch.jit.freeze(m)
torch._C._fancy_compile(f.graph, [1, 3, 224, 224])
```
The generated C++ file `mobnet.cc` can be found at https://gist.github.com/cheng-chang/e2830cc6920b39204ebf368035b2bcec.
I manually compiled the generated C++ through `g++ -o mobnet -std=c++14 -L./build/lib -ltorch_cpu -ltorch mobnet.cc`, and it succeeded.
Reviewed By: ZolotukhinM
Differential Revision: D30149482
Pulled By: cheng-chang
fbshipit-source-id: e77b189f0353e37cd309423a48a513e668d07675
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63587
Now that there is no classes using KernelArena for memory management we
can remove it.
Differential Revision:
D30429115
D30429115
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 375f6f9294d27790645eeb7cb5a8e87047a57544
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63216
Currently there are three classes managed by KernelArena: Expr, Stmt,
and Tensor (and derived classes). KernelArena has been a long standing
painpoint for NNC devs and we're moving away from that memory management
model to ref-count based memory model (using shared_ptr). This commit
switches Expr and Stmt to shared_ptr and is the biggest change in this
transition. Later commits will detach Tensor from KernelArena and kill
the arena + scope altogether.
Differential Revision:
D30353195
D30353195
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 9575225ada3d0fb65087ae40435f3dfea4792cae
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63195
This helps us to later switch from using KernelArena with raw pointers
to shared pointers without having to change all our source files at
once.
The changes are mechanical and should not affect any functionality.
With this PR, we're changing the following:
* `Add*` --> `AddPtr`
* `new Add(...)` --> `alloc<Add>(...)`
* `dynamic_cast<Add*>` --> `to<Add>`
* `static_cast<Add*>` --> `static_to<Add>`
Due to some complications with args forwarding, some places became more
verbose, e.g.:
* `new Block({})` --> `new Block(std::vector<ExprPtr>())`
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D30292779
Pulled By: ZolotukhinM
fbshipit-source-id: 150301c7d2df56b608b035827b6a9a87f5e2d9e9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62336
This PR was generated by removing `const` for all types of nodes in NNC IR, and fixing compilation errors that were the result of this change.
This is the first step in making all NNC mutations in-place.
Test Plan: Imported from OSS
Reviewed By: iramazanli
Differential Revision: D30049829
Pulled By: navahgar
fbshipit-source-id: ed14e2d2ca0559ffc0b92ac371f405579c85dd63
Summary:
This PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
Summary:
A few more quality of life improvements for NNC's python bindings:
- Use standard `torch.dtype`s (rather than `te.Dtype`)
- Make names optional (they don't seem to matter)
- Make shapes optional
- A few implicit conversions to make code cleaner
Followup to https://github.com/pytorch/pytorch/issues/59920
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60058
Reviewed By: bertmaher
Differential Revision: D29151953
Pulled By: jansel
fbshipit-source-id: c8286e329eb4ee3921ca0786e17248cf6a898bd8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55001
The enum is only used for precedence computation thus we only need to
enum node-types for which we know the precedence priority.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D27446410
Pulled By: ZolotukhinM
fbshipit-source-id: 217dd63c4fd086155030ebf0c3e1772605109f7b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50993
This is the first step to make 'Tensor` a thin wrapper over 'Buf' and
'Stmt', which will be finished in subsequent PRs. This change also
allows to remove 'buf_initializers_' from 'LoopNest', making it "less
stateful".
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D26038224
Pulled By: ZolotukhinM
fbshipit-source-id: f418816e54c62f291fa45812901487394e9b95b5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49357
This is a follow-up fix for PR #48679, where the previous PR
adds support for integer inputs to aten::abs by promoting integers to
float and then demote the result back to integers. This PR supports
integer inputs to aten::abs more efficiently in the SimpleIREvaluator
by allowing implementing integer inputs for kAbs (renamed from kFabs).
- Rename kFabs to kAbs
- Add support for integer input to kAbs in SimpleIREvalator (note that:
llvm_codegen and cuda_codegen already supports integer inputs to kAbs)
Test Plan:
- `PYTORCH_TENSOREXPR_DONT_USE_LLVM=1 python test/test_jit_fuser_te.py
TestTEFuser.test_unary_ops`
- `python test/test_jit_fuser_te.py TestTEFuser.test_unary_ops`
Imported from OSS
Reviewed By: eellison
Differential Revision: D25545791
fbshipit-source-id: e52f51a352d149f66ce8341fb3beb479be08a230
Summary:
This is a fast log implementations
benchmark:
```
buck run mode/opt //caffe2/benchmarks/cpp/tensorexpr:tensorexpr_bench -c 'fbcode.caffe2_gpu_type=none'
```
Test Plan: buck test mode/no-gpu //caffe2/test/cpp/tensorexpr:tensorexpr -- *.fastLogFloat
Reviewed By: bertmaher
Differential Revision: D25445815
fbshipit-source-id: 20696eacd12a55e797f606f4a6dbbd94c9652888
