⚡ Thunderbolt: Softmax — 8x Unrolling Optimization

[bugparty]

💡 What: Aggressive 8x unrolling across all map-reduce phases of the AVX2 softmax kernel (softmax_v6), processing 64 elements per iteration.

🎯 Why: Complex mathematical sequences like the _mm256_max_ps and multi-instruction exp256 approximations exhibit FMA latencies that can bottleneck 4x unrolled implementations. 8x unrolling effectively hides this latency by maintaining 8 independent accumulator chains without causing YMM register spilling.

🏗️ How: 8x unrolling across the max, exp/sum, and normalization loops explicitly utilizing 8 unique YMM registers per section while keeping standard scalar fallbacks for the remainder.

📊 Impact: Showed up to ~5.56 GFLOP/s vs ~5.19 GFLOP/s (softmax_v5) on N=16384 arrays in fixed-memory benchmarking, confirming transition toward a throughput-bound behavior.

🖥️ Tested on: Intel(R) Xeon(R) Processor @ 2.30GHz, Linux, GCC 13.3.0.

🔬 How to reproduce:

1. cd build && make -j$(nproc)
2. ./ml_kernels/ml_kernel_bench --filter "softmax_v6"

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PR created automatically by Jules for task 1136076901946106662 started by @bugparty

Summary by CodeRabbit

• New Features

  • Added the softmax_v6 AVX2 softmax implementation with enhanced parallel processing for improved throughput on large inputs.
  • Added benchmark coverage for comparing softmax_v6 performance.

• Bug Fixes

  • Added correctness validation to ensure accurate outputs and normalized results across larger input sizes.

• Documentation

  • Documented performance guidance favoring 8-way unrolling when system resources allow.

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[coderabbitai]

📝 Walkthrough

Walkthrough

Adds the AVX2 softmax_v6 kernel with 8-way unrolling across reduction, exponentiation, summation, and normalization phases, then registers it for benchmarking and adds correctness tests for larger inputs.

Changes

Softmax v6

Layer / File(s)	Summary
Implement 8-way unrolled softmax ml_kernels/include/ml_kernels/softmax.h, .jules/thunderbolt.md	Adds softmax_v6 with 64-element SIMD blocks, unrolled reductions and normalization, scalar tail handling, and related optimization notes.
Register and validate the kernel ml_kernels/src/kernel_bench.cpp, ml_kernels/src/test_naive_ops.cpp	Registers the softmax_v6 benchmark and compares its 72-element output with softmax_naive, including sum validation.

Estimated code review effort: 4 (Complex) | ~45 minutes

Possibly related PRs

• bugparty/cpu_math_kernels_pri#31: Introduced the exp256_ps_v2 core reused by this kernel.
• bugparty/cpu_math_kernels_pri#77: Contains overlapping softmax_v6 implementation, benchmark, and test changes.
• bugparty/cpu_math_kernels_pri#82: Contains the corresponding 8-way unrolled softmax implementation and integrations.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely summarizes the main change: an AVX2 softmax optimization using 8x unrolling.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

✨ Finishing Touches

📝 Generate docstrings

• Create stacked PR
• Commit on current branch

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Commit unit tests in branch thunderbolt-softmax-8x-unroll-1136076901946106662

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[coderabbitai]

🧹 Nitpick comments (1)

ml_kernels/src/test_naive_ops.cpp (1)

184-214: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Test doesn't exercise softmax_v6's scalar tail path.

Input length is 72 (=64+8), so n % 8 == 0 and n % 64 == 8; the single-element scalar tail loops in softmax_v6 (max/sum/normalize) never execute for this test. Consider using a length not divisible by 8 (e.g. drop or add one element) to cover the scalar fallback path.

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@ml_kernels/src/test_naive_ops.cpp` around lines 184 - 214, Update
test_softmax_v6 so its input length is not divisible by 8, ensuring softmax_v6
executes its scalar tail handling in the max, sum, and normalization passes.
Adjust the test data or add a single element while preserving the existing
naive-versus-v6 comparison and sum-to-one assertions.

🤖 Prompt for all review comments with AI agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Nitpick comments:
In `@ml_kernels/src/test_naive_ops.cpp`:
- Around line 184-214: Update test_softmax_v6 so its input length is not
divisible by 8, ensuring softmax_v6 executes its scalar tail handling in the
max, sum, and normalization passes. Adjust the test data or add a single element
while preserving the existing naive-versus-v6 comparison and sum-to-one
assertions.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: defaults

Review profile: CHILL

Plan: Pro

Run ID: 772ea217-f65d-4003-ae8d-cd11b814c483

📥 Commits

Reviewing files that changed from the base of the PR and between acca01e and 69a4c3d.

📒 Files selected for processing (4)

• .jules/thunderbolt.md
• ml_kernels/include/ml_kernels/softmax.h
• ml_kernels/src/kernel_bench.cpp
• ml_kernels/src/test_naive_ops.cpp