This word appears often in class descriptions and is not consistently spelled. Update comments and some function names to use the correct spelling consistently. Facilitates searching the codebase.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155944
Approved by: https://github.com/Skylion007
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
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
This PR adds support in train_decision if one wants to learn a heuristic for ranking. The main idea is that the user has to provide a number of choices the heuristic should return. I added a way to prune the learned decision tree such that it always returns the number of choices provided by the user.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131705
Approved by: https://github.com/eellison
This PR introduces scripts that make it easier to use autoheuristic:
- `collect_data.sh`: The user can specify things like the number of GPUs to be used and the number of training samples to collect. This script will open one tmux pane per GPU and collect num_training_samples/num_gpus samples per GPU.
- `merge_data.py`: This script can be used to merge multiple training data files into a single file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133409
Approved by: https://github.com/Chillee
This PR introduces changes to AutoHeuristic that allow one to learn a heuristic as a decision tree. I used this to learn a heuristic for mixed_mm on A100 that consistenly performs better than the default choice (https://github.com/pytorch/pytorch/blob/main/torch/_inductor/kernel/mm.py#L402).
This is how the results look like:
Explanation of columns:
**wrong_max_spdup**: In the worst case, how much better would the best choice have been
**wrong_gman_spdup**: For inputs where the heuristic is wrong, how much better is the best choice on average (geomean)
**max_spdup_default**: Highest speedup achieved by the learned heuristic over the default choice
**gman_spdup_default**: Geomean speedup achived by the learned heuristic over the default choice
**max_slowdown_default**: If the default choice is better than the choice predicted by the learned heuristic, how much is it better in the worst case
**non_default_preds**: Number of times the learned heuristic predicted a choice that is not the default choice
**default_better**: Number of times the default choice is better than the choice made by the heuristic
```
set crit max_depth min_samples_leaf correct wrong unsure total wrong_max_spdup wrong_gman_spdup max_spdup_default gman_spdup_default max_slowdown_default non_default_preds default_better
train entropy 5 0.01 2376 740 323 3439 1.855386 1.063236 11.352318 3.438279 1.022164 3116 2
test entropy 5 0.01 563 183 71 817 1.622222 1.060897 10.084181 3.507741 1.017039 746 2
```
While the number of wrong predictions is high, on average the best choice is only around 6% better. What is important is that the choice predicted by the learned heuristic performs better than the default choice.
I evaluated my heuristic on gpt-fast `meta-llama/Llama-2-7b-chat-hf` with int8 weight quantization. To get the `tuned_mixed_mm` to trigger, I had to replace `F.linear()` in https://github.com/pytorch-labs/gpt-fast/blob/main/quantize.py#L355 with `torch.matmul(input, self.weight.t().to(dtype=input.dtype))` because the mixed_mm pattern does not match if there is a transpose between a cast and the matmul.
|batch size|prompt length| fallback | heuristic | speedup |
|----------|-------------|------------:|------------:|--------:|
| 1 | 7 | 75.31 tok/s | 148.83 tok/s| 1.97 |
| 1 | 11 | 75.99 tok/s | 148.15 tok/s| 1.94 |
| 4 | 7 | 103.48 tok/s | 472.00 tok/s| 4.56 |
| 4 | 11 | 103.56 tok/s | 371.36 tok/s| 3.58 |
| 8 | 7 | 201.92 tok/s | 813.44 tok/s| 4.02 |
| 8 | 11 | 201.76 tok/s | 699.36 tok/s| 3.46 |
Currently, the heuristic only applies to the following inputs:
- m <= 128, k >= 1024, n >= 1024 (For these sizes, one of the triton kernels wins in most cases, but the heuristic still has to be careful to not choose a config that performs worse than the fallback)
- k % 256 == 0 (If k is not a multiple of the block size, some choices perform extremely bad. In one case one config, that usually performs very well, was 130x slower.)
- mat1 not transposed
- mat2 transposed (In some cases, it was hard for the learned heuristic to detect some cases where it
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131613
Approved by: https://github.com/eellison
This PR introduces changes to AutoHeuristic that allow one to learn a heuristic as a decision tree. I used this to learn a heuristic for mixed_mm on A100 that consistenly performs better than the default choice (https://github.com/pytorch/pytorch/blob/main/torch/_inductor/kernel/mm.py#L402).
This is how the results look like:
Explanation of columns:
**wrong_max_spdup**: In the worst case, how much better would the best choice have been
**wrong_gman_spdup**: For inputs where the heuristic is wrong, how much better is the best choice on average (geomean)
**max_spdup_default**: Highest speedup achieved by the learned heuristic over the default choice
**gman_spdup_default**: Geomean speedup achived by the learned heuristic over the default choice
**max_slowdown_default**: If the default choice is better than the choice predicted by the learned heuristic, how much is it better in the worst case
**non_default_preds**: Number of times the learned heuristic predicted a choice that is not the default choice
**default_better**: Number of times the default choice is better than the choice made by the heuristic
```
set crit max_depth min_samples_leaf correct wrong unsure total wrong_max_spdup wrong_gman_spdup max_spdup_default gman_spdup_default max_slowdown_default non_default_preds default_better
train entropy 5 0.01 2376 740 323 3439 1.855386 1.063236 11.352318 3.438279 1.022164 3116 2
test entropy 5 0.01 563 183 71 817 1.622222 1.060897 10.084181 3.507741 1.017039 746 2
```
While the number of wrong predictions is high, on average the best choice is only around 6% better. What is important is that the choice predicted by the learned heuristic performs better than the default choice.
I evaluated my heuristic on gpt-fast `meta-llama/Llama-2-7b-chat-hf` with int8 weight quantization. To get the `tuned_mixed_mm` to trigger, I had to replace `F.linear()` in https://github.com/pytorch-labs/gpt-fast/blob/main/quantize.py#L355 with `torch.matmul(input, self.weight.t().to(dtype=input.dtype))` because the mixed_mm pattern does not match if there is a transpose between a cast and the matmul.
|batch size|prompt length| fallback | heuristic | speedup |
|----------|-------------|------------:|------------:|--------:|
| 1 | 7 | 75.31 tok/s | 148.83 tok/s| 1.97 |
| 1 | 11 | 75.99 tok/s | 148.15 tok/s| 1.94 |
| 4 | 7 | 103.48 tok/s | 472.00 tok/s| 4.56 |
| 4 | 11 | 103.56 tok/s | 371.36 tok/s| 3.58 |
| 8 | 7 | 201.92 tok/s | 813.44 tok/s| 4.02 |
| 8 | 11 | 201.76 tok/s | 699.36 tok/s| 3.46 |
Currently, the heuristic only applies to the following inputs:
- m <= 128, k >= 1024, n >= 1024 (For these sizes, one of the triton kernels wins in most cases, but the heuristic still has to be careful to not choose a config that performs worse than the fallback)
- k % 256 == 0 (If k is not a multiple of the block size, some choices perform extremely bad. In one case one config, that usually performs very well, was 130x slower.)
- mat1 not transposed
- mat2 transposed (In some cases, it was hard for the learned heuristic to detect some cases where it
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131613
Approved by: https://github.com/eellison
ghstack dependencies: #131610, #131611
This PR introduces a script that can be used to collect data for mixed_mm to learn a heuristic with AutoHeuristic. This PR also includes the following things:
Move pad_mm related AutoHeuristic files into subdirectory
Introduce an interface benchmark_runner.py that can be subclassed to introduce new scripts to run benchmarks in order to collect data with AutoHeuristic (see gen_data_pad_mm.py and gen_data_mixed_mm.py).
The idea behind the interface is that, in the end, it hopefully makes it easier to collect data for new optimizations, and thus makes it easier to learn a heuristic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131611
Approved by: https://github.com/eellison
ghstack dependencies: #131610
