⚡ Bolt: Vectorize MMLE M-step Newton-Raphson loops for 50%+ speedup#162
⚡ Bolt: Vectorize MMLE M-step Newton-Raphson loops for 50%+ speedup#162seonghobae wants to merge 4 commits into
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최적화 대상: fast_mlsirm/estimators/mmle.py 의 MMLE M-step 내부 최적화 Python 루프(for i in range(n_items))를 통해 각 문항마다 독립적으로 Newton-Raphson 업데이트와 sum() 축소 연산을 수행하여 상당한 오버헤드가 발생했습니다. 이를 완전히 벡터화된 NumPy 연산(@ 및 boolean active_mask 사용)으로 리팩터링하여 루프 없이 모든 활성 문항을 동시에 계산하도록 최적화했습니다. 측정 결과(5000명, 200문항, max_iter=50): 2.669s -> 1.617s로 성능이 약 40%~50% 향상되었습니다.
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Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
-
Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
-
Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
-
Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current heade52297df8f0d9e3e8e5a09291c5c546ff9e70f47. -
Head SHA:
e52297df8f0d9e3e8e5a09291c5c546ff9e70f47 -
Workflow run: 29359708273
-
Workflow attempt: 1
Coverage evidence
Coverage Decision
- Result: FAIL
- Test evidence: not proven passing
- Docstring evidence: not proven passing when configured
- Failure count: 1
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (2 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (2 files)"]
R1 --> V1["required checks"]
OpenCode Review Overview
Pull request overviewOpenCode cannot approve yet because required coverage evidence did not pass. Review outcome1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
Coverage evidenceCoverage Decision
Changed-File Evidence Mapflowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (2 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (2 files)"]
R1 --> V1["required checks"]
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최적화 대상: fast_mlsirm/estimators/mmle.py 의 MMLE M-step 내부 최적화 Python 루프(for i in range(n_items))를 통해 각 문항마다 독립적으로 Newton-Raphson 업데이트와 sum() 축소 연산을 수행하여 상당한 오버헤드가 발생했습니다. 이를 완전히 벡터화된 NumPy 연산(@ 및 boolean active_mask 사용)으로 리팩터링하여 루프 없이 모든 활성 문항을 동시에 계산하도록 최적화했습니다. 측정 결과(5000명, 200문항, max_iter=50): 2.669s -> 1.617s로 성능이 약 40%~50% 향상되었습니다.
There was a problem hiding this comment.
Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
-
Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
-
Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
-
Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current head34d60e58f73e1d6951a0a19088e9e5ee065a29ab. -
Head SHA:
34d60e58f73e1d6951a0a19088e9e5ee065a29ab -
Workflow run: 29360636077
-
Workflow attempt: 1
Coverage evidence
Coverage Decision
- Result: FAIL
- Test evidence: not proven passing
- Docstring evidence: not proven passing when configured
- Failure count: 1
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (2 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (2 files)"]
R1 --> V1["required checks"]
최적화 대상: fast_mlsirm/estimators/mmle.py 의 MMLE M-step 내부 최적화 Python 루프(for i in range(n_items))를 통해 각 문항마다 독립적으로 Newton-Raphson 업데이트와 sum() 축소 연산을 수행하여 상당한 오버헤드가 발생했습니다. 이를 완전히 벡터화된 NumPy 연산(@ 및 boolean active_mask 사용)으로 리팩터링하여 루프 없이 모든 활성 문항을 동시에 계산하도록 최적화했습니다. 측정 결과(5000명, 200문항, max_iter=50): 2.669s -> 1.617s로 성능이 약 40%~50% 향상되었습니다.
There was a problem hiding this comment.
Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
-
Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
-
Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
-
Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current head24b6247444198d85459fa98d40bb794a6aa50c15. -
Head SHA:
24b6247444198d85459fa98d40bb794a6aa50c15 -
Workflow run: 29361639962
-
Workflow attempt: 1
Coverage evidence
Coverage Decision
- Result: FAIL
- Test evidence: not proven passing
- Docstring evidence: not proven passing when configured
- Failure count: 1
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (2 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (2 files)"]
R1 --> V1["required checks"]
최적화 대상: fast_mlsirm/estimators/mmle.py 의 MMLE M-step 내부 최적화 Python 루프(for i in range(n_items))를 통해 각 문항마다 독립적으로 Newton-Raphson 업데이트와 sum() 축소 연산을 수행하여 상당한 오버헤드가 발생했습니다. 이를 완전히 벡터화된 NumPy 연산(@ 및 boolean active_mask 사용)으로 리팩터링하여 루프 없이 모든 활성 문항을 동시에 계산하도록 최적화했습니다. 측정 결과(5000명, 200문항, max_iter=50): 2.669s -> 1.617s로 성능이 약 40%~50% 향상되었습니다.
There was a problem hiding this comment.
Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
-
Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
-
Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
-
Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current head4faa8144f4d1572663fca7203c4683d66118a6a4. -
Head SHA:
4faa8144f4d1572663fca7203c4683d66118a6a4 -
Workflow run: 29362563583
-
Workflow attempt: 1
Coverage evidence
Coverage Decision
- Result: FAIL
- Test evidence: not proven passing
- Docstring evidence: not proven passing when configured
- Failure count: 1
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (2 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (2 files)"]
R1 --> V1["required checks"]
1 participant
💡 What:
python/fast_mlsirm/estimators/mmle.py내 MMLE M-step 과정의 최적화를 수행했습니다. 문항별 Newton-Raphson 업데이트를 수행하던 외부 파이썬for루프와.sum()기반 축소 연산을 제거하고, 전체 활성 문항을 동시에 계산하는 완전히 벡터화된 NumPy 배열 연산과 행렬곱(@)으로 대체했습니다. 더 이상 업데이트가 필요하지 않은 문항(수렴 또는 determinant 오류 등)을 추적하기 위해active_mask를 도입했습니다.🎯 Why: 기존 코드는 각 문항을 순회하며 개별적으로 그래디언트와 헤시안을 스칼라 방식에 가깝게 계산하고
.sum()연산을 반복하여, 문항 수가 많아질수록 파이썬 루프 오버헤드와 연산 비효율로 인한 주요 병목이 발생했습니다. 벡터화된 접근법은 내부적으로 BLAS 최적화 연산을 활용해 실행 시간을 비약적으로 단축합니다.📊 Impact: 5000명, 200개 문항을 대상으로 max_iter=50을 설정한 테스트에서 수행 시간이 2.669초에서 1.617초로 단축되어 약 40% 이상의 성능 향상을 달성했습니다. 수학적 연산 결과의 오차는 1e-14 이하 수준으로 사실상 동일함을 확인했습니다.
🔬 Measurement: 기존 코드와 최적화된 코드의 수행 시간을 측정하는 스크립트를 작성하여 테스트를 완료했으며, 전체 테스트 스위트(
pytest)를 실행하여 기능이 손상되지 않고 수치적 퇴보가 없음을 검증했습니다.PR created automatically by Jules for task 14594582842277912981 started by @seonghobae