fix(inference): tokenize endpoint sent wrong field — every token estimate was 0

LlamaCppTokenizer posted {"tokens":[text]} but llama.cpp's /tokenize expects
{"content":text}; the server silently answered {"tokens":[]}, so countTokens
returned 0 for every string. Every context entry carried tokenEstimate=0 and
the whole budget/trim pipeline was blind — live QA saw a 34k-token prompt
sail through a 16384 stage budget (three raw file_read results of plan docs),
crater t/s, degrade output, and fail the stage on validation 3x.

Also guard estimateTokens: a zero count for non-blank content falls back to
the chars/4 heuristic so a lying tokenizer can never blind budgeting again.
This commit is contained in:
2026-06-11 22:50:53 +04:00
parent e503a28db2
commit 35e921c6d1
4 changed files with 39 additions and 3 deletions
@@ -1542,7 +1542,13 @@ abstract class SessionOrchestrator(
protected open suspend fun estimateTokens(content: String): Int { protected open suspend fun estimateTokens(content: String): Int {
val t = tokenizer val t = tokenizer
if (t != null) { if (t != null) {
return runCatching { t.countTokens(content) }.getOrElse { fallbackTokenEstimate(content) } // A zero count for non-blank content means the tokenizer endpoint is lying
// (e.g. a malformed request silently answered with an empty token list) —
// trust the heuristic instead, or every budget check goes blind.
return runCatching { t.countTokens(content) }
.getOrElse { fallbackTokenEstimate(content) }
.takeIf { it > 0 || content.isBlank() }
?: fallbackTokenEstimate(content)
} }
return fallbackTokenEstimate(content) return fallbackTokenEstimate(content)
} }
@@ -49,7 +49,7 @@ data class Usage(
) )
@Serializable @Serializable
data class TokenizeRequest(val tokens: List<String>) data class TokenizeRequest(val content: String)
@Serializable @Serializable
data class TokenizeResponse(val tokens: List<Int>) data class TokenizeResponse(val tokens: List<Int>)
@@ -19,7 +19,7 @@ class LlamaCppTokenizer(
httpClient.post("$baseUrl/tokenize") { httpClient.post("$baseUrl/tokenize") {
contentType(ContentType.Application.Json) contentType(ContentType.Application.Json)
accept(ContentType.Application.Json) accept(ContentType.Application.Json)
setBody(TokenizeRequest(tokens = listOf(text))) setBody(TokenizeRequest(content = text))
}.body<TokenizeResponse>().tokens.map { Token(it) } }.body<TokenizeResponse>().tokens.map { Token(it) }
override suspend fun countTokens(text: String): Int = tokenize(text).size override suspend fun countTokens(text: String): Int = tokenize(text).size
@@ -31,6 +31,36 @@ class DefaultContextPackBuilderTest {
tokenEstimate = tokens tokenEstimate = tokens
) )
private fun typedEntry(id: String, layer: ContextLayer, tokens: Int, sourceType: String) = ContextEntry(
id = ContextEntryId(id),
layer = layer,
content = "content-$id",
sourceType = sourceType,
sourceId = id,
tokenEstimate = tokens
)
@Test
fun `oversized tool results from a stage tool loop are trimmed to budget`() {
// Live repro (2026-06-11): analyst stage with three file_read results of ~5.6k/11.9k/10.7k
// tokens sailed through a 16384 budget untrimmed and produced a 34k-token prompt.
val entries = listOf(
typedEntry("sys", ContextLayer.L0, 3500, "systemPrompt"),
typedEntry("task", ContextLayer.L1, 150, "agentPrompt"),
typedEntry("call1", ContextLayer.L2, 40, "assistantToolCall"),
typedEntry("res1", ContextLayer.L2, 5600, "toolResult"),
typedEntry("call2", ContextLayer.L2, 40, "assistantToolCall"),
typedEntry("res2", ContextLayer.L2, 11900, "toolResult"),
typedEntry("call3", ContextLayer.L2, 40, "assistantToolCall"),
typedEntry("res3", ContextLayer.L2, 10700, "toolResult"),
)
val pack = builder.build(packId, sessionId, stageId, entries, TokenBudget(limit = 16384))
assertTrue(
pack.budgetUsed <= 16384,
"budgetUsed ${pack.budgetUsed} must not exceed the 16384 stage budget",
)
}
@Test @Test
fun `build groups entries by layer`() { fun `build groups entries by layer`() {
val entries = listOf( val entries = listOf(