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Measure time and peak memory, einfold off and on, from Python - #85
For #79's first ask: measure peak memory next to time, einfold off and on. python/benchmarks/memory.py runs each case in a process of its own, so each peak is that case's alone. The peak is Linux's VmHWM, the process's peak resident memory, reset just before the query, so it counts what the query adds above its loaded tables. Each case runs three times, and the fastest time and largest peak are reported.
From xarray-sql's environment, on this stack (#82, #83), on an i7-8700:
case
einfold off
einfold on
contraction-16
104 ms, 106 MB
8 ms, 71 MB
contraction-64
302 ms, 171 MB
14 ms, 103 MB
contraction-256
1,359 ms, 400 MB
42 ms, 175 MB
attention-1024
1,912 ms, 630 MB
14 ms, 99 MB
attention-4096
48,905 ms, 6,059 MB
39 ms, 117 MB
gpt-loss
7,048 ms, 2,320 MB
7,272 ms, 3,079 MB
gpt-gradients
2,510 ms, 1,184 MB
1,240 ms, 587 MB
einfold takes less memory than DataFusion in every case but the GPT's loss. That's the case #79's second ask is about: EinFoldExec reads its inputs whole, where the host's joins stream their larger side.
It's a script to run by hand, not a test: it needs datafusion-python, and the GPT cases need xarray-sql and ddxdb (ddx's Python package; ddx, an XQL Systems project, differentiates SQL queries).
Adds a Python benchmark that compares query duration and peak resident-memory growth with einfold disabled and enabled. It covers contractions, causal attention, and optional xarray-sql GPT cases. Each case runs in separate subprocesses.
Changes: Memory benchmark
Layer / File(s)
Summary
Benchmark workloads python/benchmarks/memory.py
Documents benchmark usage and adds contraction, causal-attention, and optional GPT query setups. CASES maps case names to setup functions.
Query measurement python/benchmarks/memory.py
Reads Linux process-memory fields and measures elapsed time and peak-memory growth. _one runs each case three times with the selected einfold mode and returns the fastest time and largest peak increase.
Subprocess execution and reporting python/benchmarks/memory.py
Adds command-line dispatch and separate subprocess runs for each case and mode. The benchmark reports failures by return code and exits with status 1 if any subprocess fails.
sequenceDiagram
participant User
participant main
participant subprocess
participant _one
participant CaseSetup
participant QueryContext
User->>main: Select cases or use defaults
main->>subprocess: Launch case with einfold off or on
subprocess->>_one: Run one measurement
_one->>CaseSetup: Prepare query
CaseSetup->>QueryContext: Register data and provide query callable
_one->>QueryContext: Execute query three times
_one-->>subprocess: Return fastest time and largest peak
subprocess-->>main: Return JSON or failure code
main-->>User: Display measurements and failures
xqlsystems/einfold#106: Requires benchmarks to report peak memory with runtime, which this benchmark measures.
xqlsystems/einfold#49: Adds a related einfold-on/off benchmark for matrix-product gradients.
xqlsystems/einfold#86: Changes dense execution for shared batch dimensions and causal-attention folds, which the benchmark’s attention cases measure.
Merge Risk: Minimal · previous review 9740f
The benchmark documents its prerequisites and reports measurement failures. No merge-blocking issue remains.
Security architecture risk: Minimal · up to 9740f
The change is confined to a manually invoked benchmark. Its measurement state is process-local, and no material security regression was identified. Optional GPT cases execute code from the selected checkout with the invoking user's privileges; subprocess separation is not a security sandbox.
Retained concerns
No architecture-level concerns identified.
Security review details
Security Blast Radius
inferred — The visible execution scope is the invoking user's host and child-process authority. The child inherits the execution environment without an explicit privilege reduction or sandbox. External demo behavior could therefore affect resources accessible to that user, but the reviewed change does not establish a remotely supplied path or a privileged service caller.
Trust Boundaries and Controls
observed — The parent passes case arguments through an argument-vector subprocess invocation rather than a shell command. Workload dispatch uses the fixed CASES mapping. The external code path comes separately from XARRAY_SQL, so fixed case selection does not validate or sandbox the chosen checkout.
Resilience and Maintainability Implications
inferred — The harness owns process-local measurement state and query contexts rather than a durable multi-step update requiring rollback. Repetitions reuse a child-local context, while separate cases and invocations have separate processes. There is no explicit timeout or parent-interruption cleanup; unusual termination may leave temporary child work running, but no harness-owned persistent security configuration is introduced. Side effects of the external GPT code remain unverified.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
Check name
Status
Explanation
Title check
The title clearly and concisely describes the main change: a Python benchmark for comparing time and peak memory with einfold disabled and enabled.
Description check
The description clearly explains the benchmark purpose, cases, methodology, reported results, dependencies, and manual-test scope. It does not use the template headings or explicitly include a Closes …
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Description check
Explanation
The description clearly explains the benchmark purpose, cases, methodology, reported results, dependencies, and manual-test scope. It does not use the template headings or explicitly include a Closes entry, but the required information is mostly present.
Full details: Docstring Coverage
Explanation
Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 6 functions across 1 files.
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
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🪭 Claude (einfold): This is #91's commit, moved onto `main` so that it
can merge ahead of the stack it was part of. #91 is closed in its favor.
Answers #79's second ask, and opens its third, as design v18.
**The goal.** For models trained in SQL, such as the GPT in the ML demos
of xarray-sql, an XQL Systems project for querying xarray datasets with
SQL (its `benchmarks/ml/02_nanogpt.py`), memory limits model size as
much as time does. Principle 9, "never more memory than the host", says
how einfold's operators keep to it. They reserve what they read, and
what their kernels allocate, from the host's memory pool, before
allocating it. Where the pool can't give it, they run the host's own
plan, whose operators can spill to disk. Every benchmark measures peak
memory next to time.
**What changes in the design.**
- **Section 4** adds principle 9.
- **Section 10.2** gains a bullet on memory: what `EinFoldExec` does
(#89, #90), and what comes next. The positional fold should stream its
larger operand in row blocks, so that its peak stays below the host's
even without a pool to enforce it. That needs each dimension's extent
before reading, and #80's dictionaries answer it: the dictionary's
length, when it is the whole axis. Exact statistics or a first pass over
the keys also give it.
- **Section 15** reports peak memory next to time, which
`python/benchmarks/memory.py` (#85) does for the Python extension.
- **Section 14** opens #79's third ask, dense chains of folds: handing
the dense form from one einfold operator to the next, so the joined
intermediates never exist as rows. It lists what is open: the form
handed on, how a host operator between two folds reads it, and when a
chain is worth keeping dense.
- **`lessons.md`** records what running einfold under the GPT taught
(#77, #79, #81):
- profile through the real bridge;
- report statistics, and cache them;
- plan a fallback only if it runs;
- batch dimensions are common.
**Where the stack stands on #79**, at its size (128 channels, context
64, batch 12, one layer), through datafusion-python on an Intel i7-8700:
| | DataFusion alone | einfold, before #86 | einfold, with #86–#90 |
|---|---:|---:|---:|
| the loss | 108 s, 8.9 GB peak | 42 s, 11.8 GB | 13.6 s, 6.9 GB |
| the gradients | 36.5 s, 9.8 GB | | 4.2 s, 1.8 GB |
The peak is above the loaded tables. So the GPT already keeps to the
principle at this size. Streaming in row blocks would make that hold by
construction, not only under a pool.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **Documentation**
* Updated the design guidance to cover host-managed memory use, fallback
behavior, peak-memory benchmarks, and current limitations around
whole-input processing.
* Added lessons on measuring end-to-end performance, reporting row-count
statistics, delaying fallback planning until execution, and supporting
shared batch dimensions.
* Clarified that success benchmarks compare speed while keeping memory
use within the host’s plan.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @python/benchmarks/memory.py:
- Around line 163-165: Update the subprocess failure handling in main: write
each failed process’s stderr to standard error and track whether any measurement
failed. After processing all cases, exit with status 1 if a failure occurred;
preserve the existing table output and successful measurement handling.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix review comments
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Inline comments:
Review comments at @python/benchmarks/memory.py:
- Line 12: Update the benchmark usage instructions to include NumPy among the
prerequisites, so the documented commands account for the import in the
benchmark.
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python/benchmarks/memory.py
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For #79, which asks for peak memory to be measured next to time wherever
einfold measures speed. `python/benchmarks/memory.py` runs each case in a
process of its own, einfold off and on, and reports its time and its peak
resident memory above its loaded tables: #79's contraction at three sizes,
causal attention, and, given an xarray-sql checkout, the loss and gradients
of xarray-sql's GPT demo.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
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🪭 Claude (einfold)
For #79's first ask: measure peak memory next to time, einfold off and on.
python/benchmarks/memory.pyruns each case in a process of its own, so each peak is that case's alone. The peak is Linux's VmHWM, the process's peak resident memory, reset just before the query, so it counts what the query adds above its loaded tables. Each case runs three times, and the fastest time and largest peak are reported.The cases:
x(s, p, k) · w(k, e), at s = 16, 64 and 256;XARRAY_SQLpointing at an xarray-sql checkout, the loss and the gradients of its GPT demo (benchmarks/ml/02_nanogpt.py, in Add an ml extra for gradients in SQL, and two ML demos xarray-sql#269), at batch 8, 64 channels, context 32.From xarray-sql's environment, on this stack (#82, #83), on an i7-8700:
einfold takes less memory than DataFusion in every case but the GPT's loss. That's the case #79's second ask is about:
EinFoldExecreads its inputs whole, where the host's joins stream their larger side.It's a script to run by hand, not a test: it needs datafusion-python, and the GPT cases need xarray-sql and ddxdb (ddx's Python package; ddx, an XQL Systems project, differentiates SQL queries).
🤖 Generated with Claude Code
https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS