feat(pack): support React 17 compiler target

[fireairforce]

Summary

Accept reactCompiler: { target: "17" } in the TypeScript API, Rust configuration, and generated JSON Schema. The existing compiler dependency already supports React 17, but utoo's target enum previously rejected it. React 19 remains the default; React 17/18 applications must install react-compiler-runtime as a runtime dependency.

Pin the next.js submodule to merged commit 3409f1882aa5ea8b3ddc70b32ae590277a9d2bb1 from utooland/next.js#189. Its source tree is identical to the previously tested compiler commit. Add a React 17 bundler snapshot that verifies the standalone runtime import and generated memoization, while retaining the existing default React 19 snapshot.

Test Plan

• cargo fmt and cargo fmt --check
• cargo clippy --all-targets -- -D warnings --no-deps
• cargo run -p pack-schema
• cargo test -p pack-tests --test snapshot react_compiler
• tsc -p packages/pack-shared/tsconfig.json --noEmit
• tombi format --check, biome ci, and typos

[chatgpt-codex-connector]

Codex Review Summary

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Review	Status	Commit	Review trigger
📝 Code Review	✅ Completed 2026-09-08T08:53:21.187498Z	fe86d49	Draft marked ready

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[github-actions]

📊 Performance Benchmark Report (with-antd)

Utoopack Performance Report

Report ID: utoopack_performance_report_20260908_090440
Generated: 2026-09-08 09:04:40
Trace File: trace_antd.json (0.3GB, 0.82M spans)
Test Project: examples/with-antd

---

Executive Summary

Metric	Value	Assessment
Total Wall Time	6,229.4 ms	Baseline
Total Thread Work (de-duped)	18,373.7 ms	Non-overlapping busy time
Effective Parallelism	2.9x	thread_work / wall_time
Working Threads	10	Threads with actual spans
Thread Utilization	29.5%	⚠️ Suboptimal
Total Spans	820,101	All B/E + X events
Meaningful Spans (>= 10us)	205,752	(25.1% of total)
Tracing Noise (< 10us)	614,349	(74.9% of total)

---

Build Phase Timeline

Shows when each build phase is active and how much CPU it consumes.
Self-Time is the time spent exclusively in that phase (excluding children).

Phase	Spans	Inclusive (ms)	Self-Time (ms)	Wall Range (ms)
Resolve	47,250	6,348.2	1,754.3	2,949.8
Parse	8,184	1,363.6	1,014.3	5,707.2
Analyze	131,276	37,713.5	8,288.4	5,615.0
Chunk	5,733	5,652.8	950.9	2,380.5
Codegen	10,615	2,185.6	1,381.2	2,045.1
Emit	32	37.9	19.0	8.9
Other	2,662	6,210.0	3,417.7	6,229.3

---

Workload Distribution by Diagnostic Tier

Category	Spans	Inclusive (ms)	% Work	Self-Time (ms)	% Self
P0: Scheduling & Resolution	178,781	44,645.0	243.0%	10,290.9	56.0%
P1: I/O & Heavy Tasks	2,890	115.7	0.6%	96.7	0.5%
P2: Architecture (Locks/Memory)	0	0.0	0.0%	0.0	0.0%
P3: Asset Pipeline	23,091	9,239.6	50.3%	3,367.5	18.3%
P4: Bridge/Interop	0	0.0	0.0%	0.0	0.0%
Other	990	5,511.2	30.0%	3,070.6	16.7%

---

Top 20 Tasks by Self-Time

Self-time is the exclusive duration: time spent in the task itself, not in sub-tasks.
This is the most accurate indicator of where CPU cycles are actually spent.

Self (ms)	Inclusive (ms)	Count	Avg Self (us)	P95 Self (ms)	Max Self (ms)	% Work	Task Name	Top Caller
4,148.0	23,357.5	90,585	45.8	0.1	9.5	22.6%	module	module (60%)
2,002.1	2,126.4	2,276	879.7	2.8	213.2	10.9%	analyze ecmascript module	module (76%)
1,938.4	2,038.8	20	96920.9	302.6	485.0	10.5%	save snapshot	persist (5%)
1,261.8	11,131.3	29,233	43.2	0.1	6.0	6.9%	process module	process module (80%)
1,153.6	3,102.1	25,591	45.1	0.1	5.3	6.3%	internal resolving	internal resolving (76%)
946.1	1,295.4	6,014	157.3	0.6	60.4	5.1%	parse ecmascript	parse ecmascript (65%)
821.5	5,254.8	3,983	206.2	0.2	97.2	4.5%	chunking	chunking (44%)
776.7	844.5	8,366	92.8	0.4	4.7	4.2%	precompute code generation	generate merged code (42%)
731.3	838.5	6,572	111.3	0.4	129.1	4.0%	compute async module info	compute async module info (55%)
636.1	2,065.1	731	870.1	1.9	216.7	3.5%	generate merged code	chunking (60%)
591.1	3,236.6	20,971	28.2	0.0	4.8	3.2%	resolving	module (57%)
414.5	414.5	329	1259.9	1.1	245.4	2.3%	generate source map	code generation (83%)
367.3	783.0	129	2847.1	4.7	217.9	2.0%	emit code	emit code (53%)
314.2	541.6	1,354	232.1	0.1	171.9	1.7%	write all entrypoints to disk	write all entrypoints to disk (16%)
190.0	926.6	1,920	99.0	0.2	38.6	1.0%	code generation	code generation (84%)
124.8	392.6	1,666	74.9	0.1	10.7	0.7%	compute async chunks	compute async chunks (47%)
104.1	104.1	9	11571.0	58.8	95.3	0.6%	blocking	save snapshot (56%)
89.4	105.9	729	122.6	0.0	31.9	0.5%	compute binding usage info	compute binding usage info (49%)
68.2	68.2	2,168	31.5	0.0	3.0	0.4%	read file	parse ecmascript (91%)
42.6	75.1	1,863	22.9	0.0	15.7	0.2%	collect mergeable modules	collect mergeable modules (100%)

---

Critical Path Analysis

The longest sequential dependency chains that determine wall-clock time.
Focus on reducing the depth of these chains to improve parallelism.

Rank	Self-Time (ms)	Depth	Path
1	489.1	3	persist → save snapshot → blocking
2	438.1	6	chunking → generate merged code → emit code → emit code → emit code → read file
3	282.3	4	chunking → generate merged code → emit code → generate source map
4	213.6	7	module → module → process module → process module → process module → process module → analyze ecmascript module
5	192.5	4	chunking → generate merged code → emit code → generate source map

---

Batching Candidates

High-volume tasks dominated by a single parent. If the parent can batch them,
it drastically reduces scheduler overhead.

Task Name	Count	Top Caller (Attribution)	Avg Self	P95 Self	Total Self
process module	29,233	process module (80%)	43.2 us	0.07 ms	1,261.8 ms
internal resolving	25,591	internal resolving (76%)	45.1 us	0.08 ms	1,153.6 ms

---

Duration Distribution

Range	Count	Percentage
<10us	614,349	74.9%
10us-100us	134,081	16.3%
100us-1ms	61,723	7.5%
1ms-10ms	9,732	1.2%
10ms-100ms	178	0.0%
>100ms	38	0.0%

---

Action Items

1. [P0] Focus on tasks with the highest Self-Time — these are where CPU cycles are actually spent.
2. [P0] Use Batching Candidates to identify callers that should use try_join or reduce #[turbo_tasks::function] granularity.
3. [P1] Check Build Phase Timeline for phases with disproportionate wall range vs. self-time (= serialization).
4. [P1] Inspect P95 Self (ms) for heavy monolith tasks. Focus on long-tail outliers, not averages.
5. [P1] Review Critical Paths — reducing the longest chain depth directly improves wall-clock time.
6. [P2] If Thread Utilization < 60%, investigate scheduling gaps (lock contention or deep dependency chains).

Report generated by Utoopack Performance Analysis Agent