Changes:
1. Bump `ruff` from 0.7.4 to 0.8.4
2. Change `%`-formatted strings to f-string
3. Change arguments with the `__`-prefix to positional-only arguments with the `/` separator in function signature.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143753
Approved by: https://github.com/Skylion007
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73875
Previously we had a few settings:
- getExecutor - which toggled between Profiling Executor and Legacy
- getGraphOptimize - if true, overrides PE/Legacy to run with simple executor (no optimizations)
and then...
- getProfilingMode - which would set PE to 0 specializtions.
The last mode is redundant with getGraphOptimize, we should just remove it and use getGraphOptimize in these cases. It would lead to potentially invalid combinations of logic - what does mean if getProfilingMode is true but getExecutor is set to false ? This would lead to a bug in specialize_autograd_zero in this case, see: https://github.com/pytorch/pytorch/blob/master/torch%2Fcsrc%2Fjit%2Fpasses%2Fspecialize_autogradzero.cpp#L93.
The tests here are failing but get fixed with the PR above it, so i'll squash for landing.
Test Plan: Imported from OSS
Reviewed By: cpuhrsch
Differential Revision: D34938130
Pulled By: eellison
fbshipit-source-id: 1a9c0ae7f6d1cfddc2ed3499a5af611053ae5e1b
(cherry picked from commit cf69ce3d155ba7d334022c42fb2cee54bb088c23)
Summary:
This PR adds an implementation for `aten::cat` in NNC without any conditionals. This version is not enabled by default.
Here is the performance of some micro benchmarks with and without conditionals. There is up to 50% improvement in performance without conditionals for some of the shapes.
aten::cat implementation in NNC **with** conditionals
```
$ python -m benchmarks.tensorexpr --device cpu --mode fwd --jit_mode trace --cpu_fusion concat
pt: concat2d2input_fwd_cpu_1_160_1_14_1: 5.44 us, SOL 0.26 GB/s, algorithmic 0.51 GB/s
pt: concat2d2input_fwd_cpu_1_580_1_174_1: 5.75 us, SOL 1.05 GB/s, algorithmic 2.10 GB/s
pt: concat2d2input_fwd_cpu_20_160_20_14_1: 6.87 us, SOL 4.05 GB/s, algorithmic 8.11 GB/s
pt: concat2d2input_fwd_cpu_20_580_20_174_1: 14.52 us, SOL 8.31 GB/s, algorithmic 16.62 GB/s
pt: concat2d2input_fwd_cpu_8_512_8_512_1: 9.58 us, SOL 6.84 GB/s, algorithmic 13.68 GB/s
```
aten::cat implementation in NNC **without** conditionals
```
$ python -m benchmarks.tensorexpr --device cpu --mode fwd --jit_mode trace --cpu_fusion --cat_wo_conditionals concat
pt: concat2d2input_fwd_cpu_1_160_1_14_1: 4.67 us, SOL 0.30 GB/s, algorithmic 0.60 GB/s
pt: concat2d2input_fwd_cpu_1_580_1_174_1: 5.65 us, SOL 1.07 GB/s, algorithmic 2.14 GB/s
pt: concat2d2input_fwd_cpu_20_160_20_14_1: 6.10 us, SOL 4.56 GB/s, algorithmic 9.12 GB/s
pt: concat2d2input_fwd_cpu_20_580_20_174_1: 7.44 us, SOL 16.22 GB/s, algorithmic 32.44 GB/s
pt: concat2d2input_fwd_cpu_8_512_8_512_1: 6.46 us, SOL 10.14 GB/s, algorithmic 20.29 GB/s
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53128
Reviewed By: bertmaher
Differential Revision: D26758613
Pulled By: navahgar
fbshipit-source-id: 00f56b7da630b42bc6e7ddd4444bae0cf3a5780a
Summary:
This is a tiny PR for two minor fixes:
1. Added `torch._C._jit_set_texpr_fuser_enabled(False)` to enable shape inference on nv fuser runs.
2. Renamed dynamic benchmark module names to avoid multiple matching. i.e. `simple_element` with `dynamic_simple_element`. I guess it'd be much easier if the pattern matching was based on `startswith`. Would be happy to update that if agreed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46778
Reviewed By: zhangguanheng66
Differential Revision: D24516911
Pulled By: bertmaher
fbshipit-source-id: 839f9a3e058f9d7aca17b2e6eb8b558e0e48e8f4
Summary:
This PR modifies `benchmarks/tensorexpr`. It follows up[ https://github.com/pytorch/pytorch/issues/44101](https://github.com/pytorch/pytorch/pull/44101) and further supports characterizing fusers with dynamic shape benchmarks. Dynamic shape condition models the use case when the input tensor shape changes in each call to the graph.
Changes include:
Added an auxiliary class `DynamicShape `that provides a simple API for enabling dynamic shapes in existing test cases, example can be found with `DynamicSimpleElementBench`
Created new bench_cls: `DynamicSimpleElementBench`, `DynamicReduce2DInnerBench`, `DynamicReduce2DOuterBench`, and `DynamicLSTM`. They are all dynamic shaped versions of existing benchmarks and examples of enabling dynamic shape with `DynamicShape`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46107
Reviewed By: glaringlee
Differential Revision: D24229400
Pulled By: bertmaher
fbshipit-source-id: 889fece5ea87d0f6f6374d31dbe11b1cd1380683
Summary:
Modified files in `benchmarks/tensorexpr` to add support for NVIDIA's Fuser for the jit compiler.
This support has some modifications besides adding an option to support the NVIDIA fuser:
* Adds FP16 Datatype support
* Fixes SOL/Algo calculations to generally use the data type instead of being fixed to 4 bytes
* Adds IR printing and kernel printing knobs
* Adds a knob `input_iter` to create ranges of inputs currently only for reductions
* Adds further reduction support for Inner and Outer dimension reductions that are compatible with the `input_iter` knob.
* Added `simple_element`, `reduce2d_inner`, and `reduce2d_outer` to isolate performance on elementwise and reduction operations in the most minimal fashion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44101
Reviewed By: ngimel
Differential Revision: D23713658
Pulled By: bertmaher
fbshipit-source-id: d6b83cfab559aefe107c23b3c0f2df9923b3adc1
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
