Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft: Remove more unnecessary conditional run layer calls #1713

Open
wants to merge 1 commit into
base: main
Choose a base branch
from

Conversation

chellmuth
Copy link
Collaborator

@chellmuth chellmuth commented Aug 18, 2023

This PR is a work-in-progress that I've hit a (hopefully) minor stumbling block on.

Overview

@tgrant-nv was looking at some of our shader ptx awhile ago and pointed out that a surprisingly high amount of the total instructions were just conditional layer call checks (due to all the stores and loads surrounding calls). The concern was that this was slowing down OptiX module compilation.

OSL has some basic checks in place to remove unnecessary conditional layer calls, but this patch takes things further. The high level strategy is:

  • Mark all our conditional layer calls during llvm generation
  • Run a custom llvm optimization pass that associates calls with their basic blocks
  • For each call, walk up llvm's dominator tree to find basic blocks guaranteed to have already run
  • If a guaranteed basic block already contains the same call as the current one, remove the current one

It doesn't help the compilation times for every shader, but we've seen speedups as high as 5x on some of our most expensive shaders.

Work in progress

The problem I've run into is that the optimization pass introduces quite a bit of overhead to the ptx generation. Most assets take 10-20% longer to generate ptx with this patch. For us, it's probably still a good trade to turn on for GPU (module compilation is more expensive than ptx generation), but I was hoping some more eyes could help improve things. Especially since I've never written a LLVM pass before, maybe I'm doing things particularly inefficiently.

Any feedback or ideas would be appreciated!

Tests

Checklist:

  • I have read the contribution guidelines.
  • I have previously submitted a Contributor License Agreement.
  • I have updated the documentation, if applicable.
  • I have ensured that the change is tested somewhere in the testsuite (adding new test cases if necessary).
  • My code follows the prevailing code style of this project.

@linux-foundation-easycla
Copy link

CLA Missing ID CLA Not Signed

Comment on lines +1826 to +1835
// Delete all the unnecessary stubs identified above

// WIP: This appears to be the cause of the performance regression
// (Not the dominator tree analysis above)
llvm::Value* fake = llvm::ConstantInt::get(F.getContext(), llvm::APInt(32, 1));
for (llvm::CallInst* inst : delete_queue) {
llvm::BasicBlock::iterator iterator(inst);
llvm::ReplaceInstWithValue(inst->getParent()->getInstList(), iterator, fake);
m_calls_removed++;
}
Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This appears to be where the performance hit originates from. It seems like the replacement and subsequent code-folding is pretty simple, so I'm confused why it is taking so much time compared to everything else that happens while codegen'ing a shader (+10-20%).

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Maybe the replacement causes llvm to recook something. Does the 20% vanish if you if (always_false_global) line 1833?

@tgrant-nv
Copy link
Contributor

This sounds very promising. A 10-20% increase in JIT time isn't so bad, if it increases the shader performance or even reduces the PTX compile time. We'll take a closer look and see what recommendations we can make.

@@ -1767,6 +1860,8 @@ LLVM_Util::setup_optimization_passes(int optlevel, bool target_host)

m_llvm_module_passes = new llvm::legacy::PassManager;
llvm::legacy::PassManager& mpm = (*m_llvm_module_passes);
// TODO: Add based on optlevel
mpm.add(new CheckLayerRemovalPass());
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Since the new pass is being run on the entire module, I think that means that it's running on all of the shadeops functions. Would it be possible to detect that you are processing a library function (by function name, or perhaps a function attribute), and early-exit from CheckLayerRemovalPass::runOnFunction() in that case?

That might not buy much since module pruning should remove unused library functions, but maybe it will help in some cases.

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

That seems like a responsible safe-guard to put in, I'll add that.

@chellmuth
Copy link
Collaborator Author

This sounds very promising. A 10-20% increase in JIT time isn't so bad, if it increases the shader performance or even reduces the PTX compile time. We'll take a closer look and see what recommendations we can make.

Thanks! Looking forward to your findings.

@lgritz
Copy link
Collaborator

lgritz commented Sep 14, 2023

Is this ready to be a non-draft? Looks like it might need a rebase as well.

@chellmuth
Copy link
Collaborator Author

Is this ready to be a non-draft?

It's close. It turned out the speed hit we've been seeing is because removing so many run-layer checks made the remaining ones more palatable for inlining. So LLVM's inlining pass times are growing quite a bit.

Now that it's been merged, I want to test this patch internally on #1680, since that also has inlining ramifications and I want to make sure everything interacts okay.

@AlexMWells
Copy link
Contributor

@chellmuth , as an experiment could you add a no-inline attribute to the layer functions to prevent them from being inlined? Not sure you really want to do that, but could be an interesting data point.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

None yet

5 participants