Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63197
This solves non-determinism from using hash values in sort methods.
Changes in tests are mostly mechanical.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D30292776
Pulled By: ZolotukhinM
fbshipit-source-id: 74f57b53c3afc9d4be45715fd74781271373e055
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:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61725
Alloc/free inside a loop isn't really an optimization, and furthermore
it breaks some attempted optimization in the llvm backend: we use alloca for
small allocations, which is efficient since alloca is on the stack, but there's
no corresponding free, so we leak tons of stack. I hit this while building an
rfactor buffer inside a very deeply nested loop.
ghstack-source-id: 133627310
Test Plan:
Unit test which simulates use of a temp buffer in a deeply nested
loop.
Reviewed By: navahgar
Differential Revision: D29533364
fbshipit-source-id: c321f4cb05304cfb9146afe32edc4567b623412e
Summary:
Partial fix for https://github.com/pytorch/pytorch/issues/56157
This PR updates the `flatten` API in `LoopNest` to perform the flattening transformation in-place. After this transformation, the first loop in the input becomes the flattened loop.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56629
Reviewed By: H-Huang
Differential Revision: D28004787
Pulled By: navahgar
fbshipit-source-id: 7474ae237fae3fff0cd1c64a276a8831dc5b7db0
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
This PR includes:
* Update to the loop-carried dependence check API to correctly ignore loop-independent dependences and handle all kinds of loop-carried dependences like RAW, WAR and WAW.
* Fix for the overlap API to look only for conflicting buffer accesses where at least one of them is a Store.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56354
Reviewed By: bertmaher
Differential Revision: D27856202
Pulled By: navahgar
fbshipit-source-id: 206e4ec771fe0f7f2ccf4b11b29e35df7b9b18bc
Summary:
Partial fix for https://github.com/pytorch/pytorch/issues/56357
Changes the `fuseLoops` API to the following form:
```
static bool fuseLoops(const std::vector<For*>& loops, For** fused);
```
Also, adds a new API to check for loop-carried dependences:
```
static bool hasLoopCarriedDependence(For* loop);
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56353
Reviewed By: bertmaher
Differential Revision: D27856214
Pulled By: navahgar
fbshipit-source-id: 443557088692585657faee296602c547a00117dd
Summary:
Partially fixes https://github.com/pytorch/pytorch/issues/56157
This PR changes `normalize` API in `LoopNest` to transform the given `For` statement and not create a new one.
New API:
```
static bool normalize(For* f);
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56158
Reviewed By: agolynski
Differential Revision: D27798361
Pulled By: navahgar
fbshipit-source-id: 57626a5a367bdf94a0efbd9dc8538f5e4e410d6b
Summary:
This PR allows fusing loops whose bounds are specified as expressions that are equal.
For example:
```
for (int j = 0; j < M + N; j++) {
A[j] = 10 * j;
}
for (int k = 0; k < M + N; k++) {
B[k] = 20 * k;
}
```
`fuseLoops(j, k)` is possible since the stop bounds of the two loops are equal though they are different `Expr*` and will result in:
```
for (int j = 0; j < M + N; j++) {
A[j] = 10 * j;
B[j] = 20 * j;
}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55997
Reviewed By: bertmaher
Differential Revision: D27841270
Pulled By: navahgar
fbshipit-source-id: a64e4503b7f8f28bc0c9823225bc923177bb4c2e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56094
Now FunctionCalls are merged with Loads and vectorization for
intermediate values automatically started to work.
Fixes#53553.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D27781519
Pulled By: ZolotukhinM
fbshipit-source-id: 1ed68ca2399e9bd4598639bd6dd8f369365f0ef0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55825
The mask has never been used (in vectorization we generate an explicit
`IfThenElse` construct when we need to mask out some elements). The PR
removes it and cleans up all its traces from tests.
Differential Revision: D27717776
Test Plan: Imported from OSS
Reviewed By: navahgar
Pulled By: ZolotukhinM
fbshipit-source-id: 41d1feeea4322da75b3999d661801c2a7f82b9db
Summary:
Fixes https://github.com/pytorch/pytorch/issues/52690
This PR adds the following APIs:
```
static bool areLoopsPerfectlyNested(const std::vector<For*>& loops);
static std::vector<For*> reorder(
const std::vector<For*>& loops,
const std::vector<size_t>& permutation);
```
The first API checks if the given list of loops are perfectly nested. The second API reorders the given list of loops according to the permutation specified.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55568
Reviewed By: albanD
Differential Revision: D27689734
Pulled By: navahgar
fbshipit-source-id: dc1bffdbee068c3f401188035772b41847cbc7c6
Summary:
Partially fixes https://github.com/pytorch/pytorch/issues/55203
Fixes issues (1) and (2) in the following tests:
tests in test/cpp/tensorexpr/test_loopnest.cpp from the beginning to LoopNestReorderLongStringFull (including)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55512
Reviewed By: mrshenli
Differential Revision: D27630679
Pulled By: soulitzer
fbshipit-source-id: b581aaea4f5f54b3285f0348aa76e99779418f80
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55497
Migrating some of the NNC API's used in testing, from this issue: https://github.com/pytorch/pytorch/issues/55203
I covered the second half of `test_loopnest.cpp`, and migrated (1) and (2) in the above issue: `LoopNest::getLoopStmtsFor`, `splitWithTail`, and `splitWithMask`
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D27628625
Pulled By: bdhirsh
fbshipit-source-id: ec15efba45fae0bbb442ac3577fb9ca2f8023c2d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54998
The only reason why we couldn't use Load instead of FunctionCall was
DepTracker. Now this is gone and we finally could replace FunctionCall
with Load.
Test Plan: Imported from OSS
Reviewed By: bertmaher, pbelevich
Differential Revision: D27446412
Pulled By: ZolotukhinM
fbshipit-source-id: 9183ae5541c2618abc9026b1dc4c4c9fab085d47
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54997
DepTracker was used to automatically pull in dependent computations from
output ones. While it seems quite convenient, it's led to several
architectural issues, which are fixed in this stack.
DepTracker worked on Tensors, which is a pair of Buf and Stmt. However,
Stmt could become stale and there was no way to reliably update the
corresponding tensor. We're now using Bufs and Stmts directly and moving
away from using Tensors to avoid these problems.
Removing DepTracker allowed to unify Loads and FunctionCalls, which
essentially were duplicates of each other.
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision: D27446414
Pulled By: ZolotukhinM
fbshipit-source-id: a2a32749d5b28beed92a601da33d126c0a2cf399
Summary:
I added a helper to convert a Stmt to string and FileCheck it, so
started using it in a bunch of places. I replaced about half the current uses,
got tired, started to write a Perl script to automate it, realized that was
hard, and decided to give up for a bit. But this cleans up some of the tests a
bit, so seems easy to review and worth landing.
Test Plan: test_tensorexpr --gtest_filter=LoopNest.*
Reviewed By: navahgar
Differential Revision: D27375866
fbshipit-source-id: 15894b9089dec5cf25f340fe17e6e54546a64257
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54756
We have multiple bugs here, one relating to index flattening and the
other to computeAt.
ghstack-source-id: 125054729
Test Plan: yikes
Reviewed By: ZolotukhinM
Differential Revision: D27354082
fbshipit-source-id: 8b15bac28e3eba4629881ae0f3bd143636f65ad7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54755
As title. A step on the way to using computeAt to optimize
convolution.
ghstack-source-id: 125054730
Test Plan: new test
Reviewed By: ZolotukhinM
Differential Revision: D27353663
fbshipit-source-id: 930e09d96d1f74169bf148cd30fc195c6759a3e9
Summary:
Fixes https://github.com/pytorch/pytorch/issues/54337
This PR adds a new API to NNC to perform loop fusion.
```
static For* fuseLoops(const std::vector<For*>& loops);
```
Loop fusion is done only when all the conditions below are satisfied.
* All the loops have the same parent.
* There are no statements between these loops in their parent body.
* The start bounds are the same for all loops.
* The stop bounds are the same for all loops.
* Fusing the loops does not violate or add any dependencies.
This PR also adds an API to check for partial overlaps in `buffer_inference.h` and fixes a bug in `mem_dependency_checker.cpp`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54461
Reviewed By: bertmaher
Differential Revision: D27254888
Pulled By: navahgar
fbshipit-source-id: c21b027d738e5022e9cb88f6f72cd9e255bdb15e
Summary:
Fixes https://github.com/pytorch/pytorch/issues/53864
This PR adds the following APIs that perform loop distribution to `LoopNest`:
```
static std::vector<For*> distributeLoop(For* loop, const std::unordered_set<Stmt*>& pivots);
static std::vector<For*> distributeLoop(For* loop);
static std::vector<For*> distributeLoopOverInnerLoops(For* loop);
```
* The first method distributes the given loop over its body by splitting after every given pivot stmt.
* The second method distributes the given loop over every stmt in its body.
* The last method distributes the given loop over its body by splitting after every `For` stmt in its body.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53865
Reviewed By: mruberry
Differential Revision: D27075006
Pulled By: navahgar
fbshipit-source-id: 031746aad619fe84c109e78b53387535e7f77cef
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54094
We should be able to use 64-bit integers for loop boundaries and
buffer/tensor indexing.
ghstack-source-id: 124116846
Test Plan: New tests, disabled
Reviewed By: ZolotukhinM
Differential Revision: D27094934
fbshipit-source-id: a53de21a0ef523ea3560d5dd4707df50624896ef
