Commit Graph

60 Commits

Author SHA1 Message Date
Gabriel Ferns
254293b777 Add flag _metrics_log_runtime to disable runtime metric logging by default (#153506)
https://github.com/pytorch/pytorch/pull/152708 expanded support of `get_estimated_runtime` to many more types of `SchedulerNodes`. This caused an increase in compile time because we're always calling `get_estimated_runtime` to populate the metrics table. This PR adds a flag for this logging, which reduces the instruction count by 8%. Long term, we should probably merge metrics.py with TORCH_LOGS/tlparse (suggestion from @xmfan).

Update: added support for TORCH_LOGS for the metrics logging.

Test Plan:
mm_loop.py and many existing tests cover.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153506
Approved by: https://github.com/eellison
2025-05-22 01:02:11 +00:00
Aaron Gokaslan
3555ebb63d [BE]: Update ruff to 0.11.8 (#153249)
Fixes a ton of false negatives throughout the codebase. RUFF also properly validates NOQA comments now and most of the changes are fixing typos there or removing filewide flake8 suppressions that were also silencing ruff issues.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153249
Approved by: https://github.com/cyyever, https://github.com/albanD, https://github.com/seemethere
2025-05-12 18:30:52 +00:00
Xuehai Pan
c73a92fbf5 [BE][CI] bump ruff to 0.9.2: multiline assert statements (#144546)
Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements

> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
>     len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
>     len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
>     f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
2025-02-27 20:46:16 +00:00
Aaron Orenstein
07669ed960 PEP585 update - benchmarks tools torchgen (#145101)
This is one of a series of PRs to update us to PEP585 (changing Dict -> dict, List -> list, etc).  Most of the PRs were completely automated with RUFF as follows:

Since RUFF UP006 is considered an "unsafe" fix first we need to enable unsafe fixes:

```
--- a/tools/linter/adapters/ruff_linter.py
+++ b/tools/linter/adapters/ruff_linter.py
@@ -313,6 +313,7 @@
                     "ruff",
                     "check",
                     "--fix-only",
+                    "--unsafe-fixes",
                     "--exit-zero",
                     *([f"--config={config}"] if config else []),
                     "--stdin-filename",
```

Then we need to tell RUFF to allow UP006 (as a final PR once all of these have landed this will be made permanent):

```
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,7 +40,7 @@

 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 88
 src = ["caffe2", "torch", "torchgen", "functorch", "test"]

@@ -87,7 +87,6 @@
     "SIM116", # Disable Use a dictionary instead of consecutive `if` statements
     "SIM117",
     "SIM118",
-    "UP006", # keep-runtime-typing
     "UP007", # keep-runtime-typing
 ]
 select = [
```

Finally running `lintrunner -a --take RUFF` will fix up the deprecated uses.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145101
Approved by: https://github.com/bobrenjc93
2025-01-18 05:05:07 +00:00
Xuehai Pan
dcc3cf7066 [BE] fix ruff rule E226: add missing whitespace around operator in f-strings (#144415)
The fixes are generated by:

```bash
ruff check --fix --preview --unsafe-fixes --select=E226 .
lintrunner -a --take "RUFF,PYFMT" --all-files
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144415
Approved by: https://github.com/huydhn, https://github.com/Skylion007
2025-01-08 21:55:00 +00:00
bobrenjc93
fcf9dc3b11 Migrate from Tuple -> tuple in benchmarks (#144259)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144259
Approved by: https://github.com/yanboliang
2025-01-07 04:09:52 +00:00
Oguz Ulgen
dc55704b48 Rename cache limit to recompile limit in configs (#143709)
This PR renames every cache_limit to recompile_limit via sed.

Old config options are maintained via Config(alias='xyz')

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143709
Approved by: https://github.com/jansel
2024-12-22 10:03:57 +00:00
William Wen
c04f0bb7b9 [dynamo] add benchmark for guard eval (#142430)
Benchmarks:
- 713.2us (3.10)
- 598.8us (3.12)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/142430
Approved by: https://github.com/jansel
ghstack dependencies: #142117
2024-12-17 18:54:27 +00:00
Xuehai Pan
267f82b860 [BE] Format .ci/ / .github/ / benchmarks/ / functorch/ / tools/ / torchgen/ with ruff format (#132577)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132577
Approved by: https://github.com/malfet
2024-10-11 18:30:26 +00:00
Oguz Ulgen
034af88c2d Add a microbechmark for cache read path (#137607)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137607
Approved by: https://github.com/jamesjwu
2024-10-10 16:36:18 +00:00
Oguz Ulgen
ae03c0cff3 Add microbenchmark for FxGraphHashDetails.debug_lines (#137506)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137506
Approved by: https://github.com/jamesjwu
2024-10-09 16:15:05 +00:00
Jason Ansel
8da9c4178c [inductor] Benchmark Halide in operatorbench.py (#136809)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136809
Approved by: https://github.com/eellison
ghstack dependencies: #136808
2024-09-28 19:26:04 +00:00
Jason Ansel
375921b755 [inductor] Improve operatorbench.py (#136808)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136808
Approved by: https://github.com/eellison
2024-09-28 06:22:02 +00:00
Yueming Hao
a71e5509bc [inductor]Add profiler to operatorbench (#135515)
Add profiling to operatorbench. The new argument `--profile` is added and the profiling trace is like the following figure.
<img width="954" alt="image" src="https://github.com/user-attachments/assets/5b00d6e3-4905-4a77-a5e9-9f62620a5fd5">

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135515
Approved by: https://github.com/shunting314
2024-09-10 02:33:30 +00:00
Nicolas Macchioni
5cb05a82b4 [BC breaking] move benchmarking + prefer inductor path (#132827)
move benchmarking out of `torch._inductor.runtime.runtime_utils` and into `torch._inductor.runtime.benchmarking`, and prefer this path over directly accessing Triton's benchmarking

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/132827
Approved by: https://github.com/eellison
2024-08-08 00:47:45 +00:00
Xuehai Pan
c0ed38e644 [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754)
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter.

You can review these PRs via:

```bash
git diff --ignore-all-space --ignore-blank-lines HEAD~1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129754
Approved by: https://github.com/ezyang
2024-07-17 14:34:42 +00:00
Peter Bell
e2e624a02f [AOTAutograd] Micro-optimize runtime_wrapper (#128188)
This moves a bunch of runtime inspection of the `output_info` for alias handling into the construction of fixed output handlers that are created during compilation and captured by the runtime wrapper.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128188
Approved by: https://github.com/bdhirsh
2024-07-04 03:53:06 +00:00
Xuehai Pan
26f4f10ac8 [5/N][Easy] fix typo for usort config in pyproject.toml (kown -> known): sort torch (#127126)
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
2024-05-27 14:49:57 +00:00
PyTorch MergeBot
55c0ab2887 Revert "[5/N][Easy] fix typo for usort config in pyproject.toml (kown -> known): sort torch (#127126)"
This reverts commit 7763c83af6.

Reverted https://github.com/pytorch/pytorch/pull/127126 on behalf of https://github.com/XuehaiPan due to Broken CI ([comment](https://github.com/pytorch/pytorch/pull/127126#issuecomment-2133044286))
2024-05-27 09:22:08 +00:00
Xuehai Pan
7763c83af6 [5/N][Easy] fix typo for usort config in pyproject.toml (kown -> known): sort torch (#127126)
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
2024-05-27 04:22:18 +00:00
eellison
d777685ef9 Script for choosing template configurations (#126560)
This adds logging that will mark any invocation of a matmul for a particular input shapes, and record every template configs performance on it. Then, we can parse that into a script which will minimize the total mm execution time given N allowed templates. And in future, other experiments..

Pull Request resolved: https://github.com/pytorch/pytorch/pull/126560
Approved by: https://github.com/nmacchioni, https://github.com/jansel
2024-05-21 02:28:39 +00:00
Jason Ansel
7fd8870e6b [inductor] Refactor runtime files into torch._inductor.runtime (part 3) (#124557)
I am planning to make the compile_worker process not import torch so it can start up much faster.  This stack is prep for that.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124557
Approved by: https://github.com/yanboliang
ghstack dependencies: #124552, #124553
2024-04-22 18:46:24 +00:00
PyTorch MergeBot
0b90af0bf5 Revert "[inductor] Refactor runtime files into torch._inductor.runtime (part 3) (#124557)"
This reverts commit fcf28b0ad5.

Reverted https://github.com/pytorch/pytorch/pull/124557 on behalf of https://github.com/jeanschmidt due to There are internal breakages, already discussed with author and he'll FF ([comment](https://github.com/pytorch/pytorch/pull/124552#issuecomment-2070548223))
2024-04-22 18:28:05 +00:00
Jason Ansel
fcf28b0ad5 [inductor] Refactor runtime files into torch._inductor.runtime (part 3) (#124557)
I am planning to make the compile_worker process not import torch so it can start up much faster.  This stack is prep for that.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124557
Approved by: https://github.com/yanboliang
ghstack dependencies: #124552, #124553
2024-04-22 04:51:15 +00:00
Xuehai Pan
93e249969b [BE] enable ruff rule RSE and remove useless parentheses in raise statements (#124261)
Remove useless parentheses in `raise` statements if the exception type is raised with no argument.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124261
Approved by: https://github.com/albanD
2024-04-17 19:29:34 +00:00
Jason Ansel
5a10b56083 [dynamo] Small microbenchmark changes (#122032)
Used to generate numbers in #122029

Pull Request resolved: https://github.com/pytorch/pytorch/pull/122032
Approved by: https://github.com/yanboliang
2024-03-18 18:08:06 +00:00
Jason Ansel
1e9a7df8fe [dynamo] Compile time optimizations in tx.step() (#121790)
`python benchmarks/dynamo/microbenchmarks/dynamo_microbenchmarks.py`
- Before: `symbolic_convert_overhead_stress_test: 10.7s`
- After: `symbolic_convert_overhead_stress_test: 8.6s`

`tx.step()` is a small part of that benchmark, so likely the speedup in that isolated function is larger than the top line.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/121790
Approved by: https://github.com/oulgen
2024-03-15 01:01:05 +00:00
Jason Ansel
9aa3fedb75 Slightly faster FX graph iterator (#121611)
Before:
```
iterating over 100000000 FX nodes took 5.9s (16830686 nodes/s)
```

After:
```
iterating over 100000000 FX nodes took 5.0s (19937698 nodes/s)
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/121611
Approved by: https://github.com/oulgen
2024-03-11 20:00:19 +00:00
Jason Ansel
c5702a0891 [dynamo] Optimize BACKEND_MATCH guard (#118065)
As measured by `benchmarks/dynamo/microbenchmarks/overheads.py`:
- Before `22.5us`
- After `18.1us`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/118065
Approved by: https://github.com/ydwu4
2024-01-24 07:47:52 +00:00
Jason Ansel
a669319450 [inductor] Faster C++ kernel python bindings (#117500)
Calling C++ from Python via ctypes is notoriously slow.  This switches to generating our own C++ bindings directly, which is a >5x speedup on this kernel-launch-bound microbenchmark:
```python
from ctypes import c_void_p
import torch
from torch import empty
from torch._inductor.codecache import AsyncCompile
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
from torch._inductor.wrapper_benchmark import compiled_module_main

async_compile = AsyncCompile()

src = '''
#include "/tmp/torchinductor_jansel/gb/cgbau5vlj6cetmcjbjbtw6x4rrivaln6f45s5d72gy2bfx5foz3k.h"
extern "C" void kernel(const float* in_ptr0,
                       float* out_ptr0)
{
    {
        auto tmp0 = in_ptr0[static_cast<long>(0L)];
        auto tmp1 = static_cast<float>(1.0);
        auto tmp2 = decltype(tmp0)(tmp0 + tmp1);
        out_ptr0[static_cast<long>(0L)] = tmp2;
    }
}
'''

cpp_fused_add_ctypes = async_compile.cpp(src)
cpp_fused_add_cpython = async_compile.cpp_pybinding(["const float*", "float*"], src)

async_compile.wait(globals())
del async_compile

def call(arg0_1):
    buf0 = empty((1,), device='cpu', dtype=torch.float32)
    if use_ctypes:
        for _ in range(100):
            cpp_fused_add_ctypes(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
    else:
        for _ in range(100):
            cpp_fused_add_cpython(arg0_1, buf0)
    del arg0_1
    return (buf0,)

def benchmark_compiled_module(times=1000, repeat=100):
    arg0_1 = rand_strided((1,), (1,), device='cpu', dtype=torch.float32)
    return print_performance(lambda: call(arg0_1), times=times, repeat=repeat)

print("old ctypes bindings: ", end='')
use_ctypes = True
compiled_module_main('None', benchmark_compiled_module)
print("new bindings:        ", end='')
use_ctypes = False
compiled_module_main('None', benchmark_compiled_module)
```
Output:
```
old ctypes bindings: 0.000073
new bindings:        0.000013
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/117500
Approved by: https://github.com/desertfire
2024-01-18 16:20:12 +00:00
Nikita Shulga
a1afd1b195 Revert "[inductor] Faster C++ kernel python bindings (#117500)"
It should have never been landed, but was landed again, thanks to
ghstack grafting/ungrafting see discussion on https://github.com/pytorch/pytorch/pull/116910

This reverts commit e457b6fb18.
2024-01-17 17:06:32 -08:00
titaiwangms
e457b6fb18 [inductor] Faster C++ kernel python bindings (#117500)
Calling C++ from Python via ctypes is notoriously slow.  This switches to generating our own C++ bindings directly, which is a >5x speedup on this kernel-launch-bound microbenchmark:
```python
from ctypes import c_void_p
import torch
from torch import empty
from torch._inductor.codecache import AsyncCompile
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
from torch._inductor.wrapper_benchmark import compiled_module_main

async_compile = AsyncCompile()

src = '''
#include "/tmp/torchinductor_jansel/gb/cgbau5vlj6cetmcjbjbtw6x4rrivaln6f45s5d72gy2bfx5foz3k.h"
extern "C" void kernel(const float* in_ptr0,
                       float* out_ptr0)
{
    {
        auto tmp0 = in_ptr0[static_cast<long>(0L)];
        auto tmp1 = static_cast<float>(1.0);
        auto tmp2 = decltype(tmp0)(tmp0 + tmp1);
        out_ptr0[static_cast<long>(0L)] = tmp2;
    }
}
'''

cpp_fused_add_ctypes = async_compile.cpp(src)
cpp_fused_add_cpython = async_compile.cpp_pybinding(["const float*", "float*"], src)

async_compile.wait(globals())
del async_compile

def call(arg0_1):
    buf0 = empty((1,), device='cpu', dtype=torch.float32)
    if use_ctypes:
        for _ in range(100):
            cpp_fused_add_ctypes(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
    else:
        for _ in range(100):
            cpp_fused_add_cpython(arg0_1, buf0)
    del arg0_1
    return (buf0,)

def benchmark_compiled_module(times=1000, repeat=100):
    arg0_1 = rand_strided((1,), (1,), device='cpu', dtype=torch.float32)
    return print_performance(lambda: call(arg0_1), times=times, repeat=repeat)

print("old ctypes bindings: ", end='')
use_ctypes = True
compiled_module_main('None', benchmark_compiled_module)
print("new bindings:        ", end='')
use_ctypes = False
compiled_module_main('None', benchmark_compiled_module)
```
Output:
```
old ctypes bindings: 0.000073
new bindings:        0.000013
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/117500
Approved by: https://github.com/desertfire
ghstack dependencies: #117409, #116667, #117591
2024-01-17 23:03:15 +00:00
PyTorch MergeBot
da6abaeeac Revert "[inductor] Faster C++ kernel python bindings (#117500)"
This reverts commit bb0fd1bd3c.

Reverted https://github.com/pytorch/pytorch/pull/117500 on behalf of https://github.com/PaliC due to breaking internal discussed with author offline ([comment](https://github.com/pytorch/pytorch/pull/117500#issuecomment-1896516512))
2024-01-17 19:34:26 +00:00
titaiwangms
bb0fd1bd3c [inductor] Faster C++ kernel python bindings (#117500)
Calling C++ from Python via ctypes is notoriously slow.  This switches to generating our own C++ bindings directly, which is a >5x speedup on this kernel-launch-bound microbenchmark:
```python
from ctypes import c_void_p
import torch
from torch import empty
from torch._inductor.codecache import AsyncCompile
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
from torch._inductor.wrapper_benchmark import compiled_module_main

async_compile = AsyncCompile()

src = '''
#include "/tmp/torchinductor_jansel/gb/cgbau5vlj6cetmcjbjbtw6x4rrivaln6f45s5d72gy2bfx5foz3k.h"
extern "C" void kernel(const float* in_ptr0,
                       float* out_ptr0)
{
    {
        auto tmp0 = in_ptr0[static_cast<long>(0L)];
        auto tmp1 = static_cast<float>(1.0);
        auto tmp2 = decltype(tmp0)(tmp0 + tmp1);
        out_ptr0[static_cast<long>(0L)] = tmp2;
    }
}
'''

cpp_fused_add_ctypes = async_compile.cpp(src)
cpp_fused_add_cpython = async_compile.cpp_pybinding(["const float*", "float*"], src)

async_compile.wait(globals())
del async_compile

def call(arg0_1):
    buf0 = empty((1,), device='cpu', dtype=torch.float32)
    if use_ctypes:
        for _ in range(100):
            cpp_fused_add_ctypes(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
    else:
        for _ in range(100):
            cpp_fused_add_cpython(arg0_1, buf0)
    del arg0_1
    return (buf0,)

def benchmark_compiled_module(times=1000, repeat=100):
    arg0_1 = rand_strided((1,), (1,), device='cpu', dtype=torch.float32)
    return print_performance(lambda: call(arg0_1), times=times, repeat=repeat)

print("old ctypes bindings: ", end='')
use_ctypes = True
compiled_module_main('None', benchmark_compiled_module)
print("new bindings:        ", end='')
use_ctypes = False
compiled_module_main('None', benchmark_compiled_module)
```
Output:
```
old ctypes bindings: 0.000073
new bindings:        0.000013
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/117500
Approved by: https://github.com/desertfire
ghstack dependencies: #117409, #116667, #117591
2024-01-17 19:12:24 +00:00
PyTorch MergeBot
9da01affd3 Revert "[inductor] Faster C++ kernel python bindings (#117500)"
This reverts commit 3a52147cc5.

Reverted https://github.com/pytorch/pytorch/pull/117500 on behalf of https://github.com/PaliC due to breaking internal discussed with author offline ([comment](https://github.com/pytorch/pytorch/pull/117500#issuecomment-1896426304))
2024-01-17 18:42:39 +00:00
Jason Ansel
3a52147cc5 [inductor] Faster C++ kernel python bindings (#117500)
Calling C++ from Python via ctypes is notoriously slow.  This switches to generating our own C++ bindings directly, which is a >5x speedup on this kernel-launch-bound microbenchmark:
```python
from ctypes import c_void_p
import torch
from torch import empty
from torch._inductor.codecache import AsyncCompile
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
from torch._inductor.wrapper_benchmark import compiled_module_main

async_compile = AsyncCompile()

src = '''
#include "/tmp/torchinductor_jansel/gb/cgbau5vlj6cetmcjbjbtw6x4rrivaln6f45s5d72gy2bfx5foz3k.h"
extern "C" void kernel(const float* in_ptr0,
                       float* out_ptr0)
{
    {
        auto tmp0 = in_ptr0[static_cast<long>(0L)];
        auto tmp1 = static_cast<float>(1.0);
        auto tmp2 = decltype(tmp0)(tmp0 + tmp1);
        out_ptr0[static_cast<long>(0L)] = tmp2;
    }
}
'''

cpp_fused_add_ctypes = async_compile.cpp(src)
cpp_fused_add_cpython = async_compile.cpp_pybinding(["const float*", "float*"], src)

async_compile.wait(globals())
del async_compile

def call(arg0_1):
    buf0 = empty((1,), device='cpu', dtype=torch.float32)
    if use_ctypes:
        for _ in range(100):
            cpp_fused_add_ctypes(c_void_p(arg0_1.data_ptr()), c_void_p(buf0.data_ptr()))
    else:
        for _ in range(100):
            cpp_fused_add_cpython(arg0_1, buf0)
    del arg0_1
    return (buf0,)

def benchmark_compiled_module(times=1000, repeat=100):
    arg0_1 = rand_strided((1,), (1,), device='cpu', dtype=torch.float32)
    return print_performance(lambda: call(arg0_1), times=times, repeat=repeat)

print("old ctypes bindings: ", end='')
use_ctypes = True
compiled_module_main('None', benchmark_compiled_module)
print("new bindings:        ", end='')
use_ctypes = False
compiled_module_main('None', benchmark_compiled_module)
```
Output:
```
old ctypes bindings: 0.000073
new bindings:        0.000013
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/117500
Approved by: https://github.com/desertfire
2024-01-16 22:30:04 +00:00
Aaron Gokaslan
d9f2cf9974 [BE]: Enable ruff rule PIE800 - unnecessary nested dict expansion (#113880)
Adds an additional list which removes unnecessary dict literal unpacking, also applies the fixes.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/113880
Approved by: https://github.com/albanD
2023-11-16 22:34:38 +00:00
Peter Bell
bbd5b935e4 Use pytree.tree_leaves everywhere (#112324)
This changes all the instances I could find of `tree_flatten(...)[0]` or
`x, _ = tree_flatten` to use `tree_leaves`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/112324
Approved by: https://github.com/lezcano
ghstack dependencies: #112327, #112323
2023-10-30 03:39:04 +00:00
Justin Chu
5ef023b05a [BE] Enable ruff's UP rules and autoformat benchmarks/ (#105429)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105429
Approved by: https://github.com/malfet
2023-07-19 04:46:37 +00:00
Aaron Gokaslan
2f95a3d0fc [BE]: Apply ruff PERF fixes to torch (#104917)
Applies automated ruff fixes in the PERF modules and enables all automatic ones. I also updated ruff which applied some additional fixes.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/104917
Approved by: https://github.com/ezyang, https://github.com/albanD
2023-07-11 20:45:21 +00:00
Elias Ellison
2baadc2ade Small operatorbench improvements (#103110)
- Don't copy inputs in cudagraphs wrapping, since the copies will distorts timing and triton do_bench will clear cache anyway
- Don't skip op if there is a fallback, since we have both fallbacks and lowerings for some ops
- Add option for channels last

Pull Request resolved: https://github.com/pytorch/pytorch/pull/103110
Approved by: https://github.com/desertfire
2023-06-07 22:04:59 +00:00
xndcn
bebb8b7c1e [inductor] use native fetch_add function for trivial types (#101931)
floating-point is supported by std::atomic::fetch_add since C++20.
However, this code path is not activated yet because cpp_flags in codecache.py is hard-coded to "-std=c++17"

Pull Request resolved: https://github.com/pytorch/pytorch/pull/101931
Approved by: https://github.com/jgong5, https://github.com/EikanWang, https://github.com/jansel
2023-06-01 03:47:56 +00:00
lezcano
8b4e28d65d Fix microbenchmarks (#101065)
As per title

Pull Request resolved: https://github.com/pytorch/pytorch/pull/101065
Approved by: https://github.com/jansel
2023-05-11 09:14:22 +00:00
Shunting Zhang
68bc0fc012 [inductor] a script to benchmark the perf impact from tensor layout (#99583)
Follow up on Jason's idea of tensor layout tuning. Add a script to show the perf impact of layout to convolution (will add more cases like batch/layer norm, reduction to the scripts).

For convolution, a quick test shows using channels last layout, we get 1.4x speedup for convolution:
```
baseline 4.509183883666992 test 3.178528070449829 speedup 1.419x
```

The speedup definitely also depends on input/weight shapes. E.g., change input channel from 3 in the test to 8, we see speedup to be 2.1x

The trace shows cudnn calls different kernels when input layout changes to channels last.

<img width="997" alt="Screenshot 2023-04-19 at 5 27 54 PM" src="https://user-images.githubusercontent.com/52589240/233228656-4bdcac0a-7633-416a-82e1-17d8dc8ea9a6.png">

Pull Request resolved: https://github.com/pytorch/pytorch/pull/99583
Approved by: https://github.com/jansel
2023-04-20 06:26:10 +00:00
PyTorch MergeBot
629377ea8b Revert "Replace _dynamo.config with an object instead of module (#96455)"
This reverts commit 420104a886.

Reverted https://github.com/pytorch/pytorch/pull/96455 on behalf of https://github.com/jansel due to BC breaking, was landed prematurely
2023-04-12 15:06:14 +00:00
Han Qi
420104a886 Replace _dynamo.config with an object instead of module (#96455)
Summary:
    Replace _dynamo.config with an object instead of module

    Current usage patterns of setting and reading fields on config will work
    unchanged.

    Only changes needed going forward:
    1. import torch._dynamo.config will not work. However, just doing
       import torch._dynamo is sufficient to access dynamo config
       as torch._dynamo.config.

    2. Files inside of _dynamo folder need to access config via
       from torch._dynamo.config_util import config instead of
       from torch._dynamo import config. Because _dynamo/__init__.py
       imports some of the files so it would be circular import.

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96455
Approved by: https://github.com/williamwen42
2023-04-11 21:23:32 +00:00
Edward Z. Yang
9a8f71f23e Convert logging f-strings to use % format (#98697)
Codemod done with
https://gist.github.com/ezyang/2e8b0463cdc6be278478495b23ff0530 with
assistance from ChatGPT.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/98697
Approved by: https://github.com/voznesenskym
2023-04-10 12:19:31 +00:00
Jason Ansel
9370f253e3 [inductor] Rewrite convolution triton templates (#95556)
Fixes #95775

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95556
Approved by: https://github.com/Chillee, https://github.com/ngimel
2023-03-22 18:12:23 +00:00
BowenBao
60a68477a6 Bump black version to 23.1.0 (#96578)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96578
Approved by: https://github.com/ezyang
2023-03-15 06:27:59 +00:00
Aaron Gokaslan
3d82d8d0ed [BE] Enable more flake8-comprehensions checks (#94601)
I applied some flake8 fixes and enabled checking for them in the linter. I also enabled some checks for my previous comprehensions PR.

This is a follow up to #94323 where I enable the flake8 checkers for the fixes I made and fix a few more of them.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94601
Approved by: https://github.com/ezyang
2023-02-10 23:40:29 +00:00