Update benchmarks#208
Merged
Merged
Conversation
Matches the Python sample's fast paths: flush_to_zero on every op and rounding<approx> on the reciprocal. SiLU is bandwidth-bound but the approximate divide on the SFU shaves a few percent off the MoE pipeline. Bench (RTX 5080, 4096³ MoE config): 18.8 → 19.2 TFLOPS.
Softmax's chunked kernel was looping over `Int32(0):num_chunks - Int32(1)` where `num_chunks` was Int64 (because the kernel takes `n_elems::Int`). That widens the inner ForOp to `tile<i64>` and forces a `trunci` per iteration in the offset arithmetic. Casting `num_chunks` to Int32 keeps the loop in i32 throughout, matching cuTile Python's IR shape. Bench (RTX 5080, 4096² Float32 chunked): 1587 → 1601 GB/s.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
No description provided.