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 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
