mirror of
https://gitea.toothfairyai.com/ToothFairyAI/tf_code.git
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424 lines
14 KiB
TypeScript
424 lines
14 KiB
TypeScript
import { describe, expect, test } from "bun:test"
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import path from "path"
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import { SessionCompaction } from "../../src/session/compaction"
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import { Token } from "../../src/util/token"
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import { Instance } from "../../src/project/instance"
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import { Log } from "../../src/util/log"
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import { tmpdir } from "../fixture/fixture"
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import { Session } from "../../src/session"
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import type { Provider } from "../../src/provider/provider"
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Log.init({ print: false })
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function createModel(opts: {
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context: number
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output: number
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input?: number
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cost?: Provider.Model["cost"]
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npm?: string
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}): Provider.Model {
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return {
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id: "test-model",
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providerID: "test",
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name: "Test",
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limit: {
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context: opts.context,
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input: opts.input,
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output: opts.output,
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},
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cost: opts.cost ?? { input: 0, output: 0, cache: { read: 0, write: 0 } },
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capabilities: {
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toolcall: true,
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attachment: false,
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reasoning: false,
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temperature: true,
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input: { text: true, image: false, audio: false, video: false },
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output: { text: true, image: false, audio: false, video: false },
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},
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api: { npm: opts.npm ?? "@ai-sdk/anthropic" },
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options: {},
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} as Provider.Model
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}
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describe("session.compaction.isOverflow", () => {
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test("returns true when token count exceeds usable context", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const tokens = { input: 75_000, output: 5_000, reasoning: 0, cache: { read: 0, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
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},
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})
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})
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test("returns false when token count within usable context", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 200_000, output: 32_000 })
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const tokens = { input: 100_000, output: 10_000, reasoning: 0, cache: { read: 0, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
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},
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})
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})
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test("includes cache.read in token count", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const tokens = { input: 60_000, output: 10_000, reasoning: 0, cache: { read: 10_000, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
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},
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})
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})
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test("respects input limit for input caps", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 400_000, input: 272_000, output: 128_000 })
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const tokens = { input: 271_000, output: 1_000, reasoning: 0, cache: { read: 2_000, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
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},
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})
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})
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test("returns false when input/output are within input caps", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 400_000, input: 272_000, output: 128_000 })
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const tokens = { input: 200_000, output: 20_000, reasoning: 0, cache: { read: 10_000, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
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},
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})
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})
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test("returns false when output within limit with input caps", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 200_000, input: 120_000, output: 10_000 })
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const tokens = { input: 50_000, output: 9_999, reasoning: 0, cache: { read: 0, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
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},
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})
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})
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// ─── Bug reproduction tests ───────────────────────────────────────────
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// These tests demonstrate that when limit.input is set, isOverflow()
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// does not subtract any headroom for the next model response. This means
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// compaction only triggers AFTER we've already consumed the full input
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// budget, leaving zero room for the next API call's output tokens.
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//
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// Compare: without limit.input, usable = context - output (reserves space).
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// With limit.input, usable = limit.input (reserves nothing).
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//
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// Related issues: #10634, #8089, #11086, #12621
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// Open PRs: #6875, #12924
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test("BUG: no headroom when limit.input is set — compaction should trigger near boundary but does not", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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// Simulate Claude with prompt caching: input limit = 200K, output limit = 32K
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const model = createModel({ context: 200_000, input: 200_000, output: 32_000 })
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// We've used 198K tokens total. Only 2K under the input limit.
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// On the next turn, the full conversation (198K) becomes input,
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// plus the model needs room to generate output — this WILL overflow.
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const tokens = { input: 180_000, output: 15_000, reasoning: 0, cache: { read: 3_000, write: 0 } }
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// count = 180K + 3K + 15K = 198K
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// usable = limit.input = 200K (no output subtracted!)
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// 198K > 200K = false → no compaction triggered
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// WITHOUT limit.input: usable = 200K - 32K = 168K, and 198K > 168K = true ✓
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// WITH limit.input: usable = 200K, and 198K > 200K = false ✗
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// With 198K used and only 2K headroom, the next turn will overflow.
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// Compaction MUST trigger here.
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(true)
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},
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})
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})
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test("BUG: without limit.input, same token count correctly triggers compaction", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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// Same model but without limit.input — uses context - output instead
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const model = createModel({ context: 200_000, output: 32_000 })
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// Same token usage as above
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const tokens = { input: 180_000, output: 15_000, reasoning: 0, cache: { read: 3_000, write: 0 } }
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// count = 198K
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// usable = context - output = 200K - 32K = 168K
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// 198K > 168K = true → compaction correctly triggered
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const result = await SessionCompaction.isOverflow({ tokens, model })
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expect(result).toBe(true) // ← Correct: headroom is reserved
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},
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})
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})
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test("BUG: asymmetry — limit.input model allows 30K more usage before compaction than equivalent model without it", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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// Two models with identical context/output limits, differing only in limit.input
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const withInputLimit = createModel({ context: 200_000, input: 200_000, output: 32_000 })
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const withoutInputLimit = createModel({ context: 200_000, output: 32_000 })
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// 170K total tokens — well above context-output (168K) but below input limit (200K)
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const tokens = { input: 166_000, output: 10_000, reasoning: 0, cache: { read: 5_000, write: 0 } }
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const withLimit = await SessionCompaction.isOverflow({ tokens, model: withInputLimit })
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const withoutLimit = await SessionCompaction.isOverflow({ tokens, model: withoutInputLimit })
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// Both models have identical real capacity — they should agree:
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expect(withLimit).toBe(true) // should compact (170K leaves no room for 32K output)
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expect(withoutLimit).toBe(true) // correctly compacts (170K > 168K)
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},
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})
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})
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test("returns false when model context limit is 0", async () => {
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await using tmp = await tmpdir()
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 0, output: 32_000 })
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const tokens = { input: 100_000, output: 10_000, reasoning: 0, cache: { read: 0, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
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},
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})
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})
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test("returns false when compaction.auto is disabled", async () => {
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await using tmp = await tmpdir({
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init: async (dir) => {
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await Bun.write(
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path.join(dir, "opencode.json"),
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JSON.stringify({
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compaction: { auto: false },
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}),
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)
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},
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})
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await Instance.provide({
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directory: tmp.path,
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fn: async () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const tokens = { input: 75_000, output: 5_000, reasoning: 0, cache: { read: 0, write: 0 } }
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expect(await SessionCompaction.isOverflow({ tokens, model })).toBe(false)
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},
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})
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})
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})
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describe("util.token.estimate", () => {
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test("estimates tokens from text (4 chars per token)", () => {
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const text = "x".repeat(4000)
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expect(Token.estimate(text)).toBe(1000)
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})
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test("estimates tokens from larger text", () => {
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const text = "y".repeat(20_000)
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expect(Token.estimate(text)).toBe(5000)
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})
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test("returns 0 for empty string", () => {
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expect(Token.estimate("")).toBe(0)
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})
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})
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describe("session.getUsage", () => {
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test("normalizes standard usage to token format", () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const result = Session.getUsage({
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model,
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usage: {
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inputTokens: 1000,
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outputTokens: 500,
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totalTokens: 1500,
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},
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})
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expect(result.tokens.input).toBe(1000)
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expect(result.tokens.output).toBe(500)
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expect(result.tokens.reasoning).toBe(0)
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expect(result.tokens.cache.read).toBe(0)
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expect(result.tokens.cache.write).toBe(0)
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})
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test("extracts cached tokens to cache.read", () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const result = Session.getUsage({
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model,
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usage: {
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inputTokens: 1000,
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outputTokens: 500,
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totalTokens: 1500,
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cachedInputTokens: 200,
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},
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})
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expect(result.tokens.input).toBe(800)
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expect(result.tokens.cache.read).toBe(200)
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})
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test("handles anthropic cache write metadata", () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const result = Session.getUsage({
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model,
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usage: {
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inputTokens: 1000,
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outputTokens: 500,
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totalTokens: 1500,
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},
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metadata: {
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anthropic: {
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cacheCreationInputTokens: 300,
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},
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},
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})
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expect(result.tokens.cache.write).toBe(300)
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})
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test("does not subtract cached tokens for anthropic provider", () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const result = Session.getUsage({
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model,
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usage: {
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inputTokens: 1000,
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outputTokens: 500,
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totalTokens: 1500,
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cachedInputTokens: 200,
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},
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metadata: {
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anthropic: {},
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},
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})
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expect(result.tokens.input).toBe(1000)
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expect(result.tokens.cache.read).toBe(200)
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})
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test("handles reasoning tokens", () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const result = Session.getUsage({
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model,
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usage: {
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inputTokens: 1000,
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outputTokens: 500,
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totalTokens: 1500,
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reasoningTokens: 100,
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},
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})
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expect(result.tokens.reasoning).toBe(100)
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})
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test("handles undefined optional values gracefully", () => {
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const model = createModel({ context: 100_000, output: 32_000 })
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const result = Session.getUsage({
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model,
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usage: {
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inputTokens: 0,
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outputTokens: 0,
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totalTokens: 0,
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},
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})
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expect(result.tokens.input).toBe(0)
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expect(result.tokens.output).toBe(0)
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expect(result.tokens.reasoning).toBe(0)
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expect(result.tokens.cache.read).toBe(0)
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expect(result.tokens.cache.write).toBe(0)
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expect(Number.isNaN(result.cost)).toBe(false)
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})
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test("calculates cost correctly", () => {
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const model = createModel({
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context: 100_000,
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output: 32_000,
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cost: {
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input: 3,
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output: 15,
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cache: { read: 0.3, write: 3.75 },
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},
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})
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const result = Session.getUsage({
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model,
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usage: {
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inputTokens: 1_000_000,
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outputTokens: 100_000,
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totalTokens: 1_100_000,
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},
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})
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expect(result.cost).toBe(3 + 1.5)
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})
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test.each(["@ai-sdk/anthropic", "@ai-sdk/amazon-bedrock", "@ai-sdk/google-vertex/anthropic"])(
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"computes total from components for %s models",
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(npm) => {
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const model = createModel({ context: 100_000, output: 32_000, npm })
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const usage = {
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inputTokens: 1000,
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outputTokens: 500,
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// These providers typically report total as input + output only,
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// excluding cache read/write.
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totalTokens: 1500,
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cachedInputTokens: 200,
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}
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if (npm === "@ai-sdk/amazon-bedrock") {
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const result = Session.getUsage({
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model,
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usage,
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metadata: {
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bedrock: {
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usage: {
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cacheWriteInputTokens: 300,
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},
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},
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},
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})
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expect(result.tokens.input).toBe(1000)
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expect(result.tokens.cache.read).toBe(200)
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expect(result.tokens.cache.write).toBe(300)
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expect(result.tokens.total).toBe(2000)
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return
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}
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const result = Session.getUsage({
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model,
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usage,
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metadata: {
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anthropic: {
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cacheCreationInputTokens: 300,
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},
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},
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})
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expect(result.tokens.input).toBe(1000)
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expect(result.tokens.cache.read).toBe(200)
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expect(result.tokens.cache.write).toBe(300)
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expect(result.tokens.total).toBe(2000)
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},
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)
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})
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